diff --git a/apps/chatbot-kickstarter/README.md b/apps/chatbot-kickstarter/README.md deleted file mode 100644 index 5a7d4aec..00000000 --- a/apps/chatbot-kickstarter/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Powering your products with ChatGPT and your own data - -The Chatbot Kickstarter is a starter repo to get you used to building a basic Chatbot using the ChatGPT API and your own knowledge base. The flow you're taken through was originally presented with [these slides](https://drive.google.com/file/d/1dB-RQhZC_Q1iAsHkNNdkqtxxXqYODFYy/view?usp=share_link), which may come in useful to refer to. - -This repo contains one notebook and two basic Streamlit apps: -- `powering_your_products_with_chatgpt_and_your_data.ipynb`: A notebook containing a step by step process of tokenising, chunking and embedding your data in a vector database, and building simple Q&A and Chatbot functionality on top. -- `search.py`: A Streamlit app providing simple Q&A via a search bar to query your knowledge base. -- `chat.py`: A Streamlit app providing a simple Chatbot via a search bar to query your knowledge base. - -To run either version of the app, please follow the instructions in the respective README.md files in the subdirectories. - -## How it works - -The notebook is the best place to start, and is broadly laid out as follows: -- **Lay the foundations:** - - Set up the vector database to accept vectors and data - - Load the dataset, chunk the data up for embedding and store in the vector database -- **Make it a product:** - - Add a retrieval step where users provide queries and we return the most relevant entries - - Summarise search results with GPT-3 - - Test out this basic Q&A app in Streamlit -- **Build your moat:** - - Create an Assistant class to manage context and interact with our bot - - Use the Chatbot to answer questions using semantic search context - - Test out this basic Chatbot app in Streamlit - -Once you've run the notebook and tried the two Streamlit apps, you should be in a position to strip out any useful snippets and start your own Q&A or Chat application. - -## Limitations - -- This app uses Redis as a vector database, but there are many other options highlighted `../examples/vector_databases` depending on your need. -- This is a simple starting point - if you hit issues deploying your use case you may need to tune (non-exhaustive list): - - The prompt and parameters for the model for it to answer accurately - - Your search to return more relevant results - - Your chunking/embedding approach to store the most relevant content effectively for retrieval diff --git a/apps/chatbot-kickstarter/chat.py b/apps/chatbot-kickstarter/chat.py deleted file mode 100644 index 582d29d2..00000000 --- a/apps/chatbot-kickstarter/chat.py +++ /dev/null @@ -1,83 +0,0 @@ -import streamlit as st -from streamlit_chat import message - -from database import get_redis_connection -from chatbot import RetrievalAssistant, Message - -# Initialise database - -## Initialise Redis connection -redis_client = get_redis_connection() - -# Set instruction - -# System prompt requiring Question and Year to be extracted from the user -system_prompt = ''' -You are a helpful Formula 1 knowledge base assistant. You need to capture a Question and Year from each customer. -The Question is their query on Formula 1, and the Year is the year of the applicable Formula 1 season. -Think about this step by step: -- The user will ask a Question -- You will ask them for the Year if their question didn't include a Year -- Once you have the Year, say "searching for answers". - -Example: - -User: I'd like to know the cost cap for a power unit - -Assistant: Certainly, what year would you like this for? - -User: 2023 please. - -Assistant: Searching for answers. -''' - -### CHATBOT APP - -st.set_page_config( - page_title="Streamlit Chat - Demo", - page_icon=":robot:" -) - -st.title('Formula 1 Chatbot') -st.subheader("Help us help you learn about Formula 1") - -if 'generated' not in st.session_state: - st.session_state['generated'] = [] - -if 'past' not in st.session_state: - st.session_state['past'] = [] - -def query(question): - response = st.session_state['chat'].ask_assistant(question) - return response - -prompt = st.text_input(f"What do you want to know: ", key="input") - -if st.button('Submit', key='generationSubmit'): - - # Initialization - if 'chat' not in st.session_state: - st.session_state['chat'] = RetrievalAssistant() - messages = [] - system_message = Message('system',system_prompt) - messages.append(system_message.message()) - else: - messages = [] - - - user_message = Message('user',prompt) - messages.append(user_message.message()) - - response = query(messages) - - # Debugging step to print the whole response - #st.write(response) - - st.session_state.past.append(prompt) - st.session_state.generated.append(response['content']) - -if st.session_state['generated']: - - for i in range(len(st.session_state['generated'])-1, -1, -1): - message(st.session_state["generated"][i], key=str(i)) - message(st.session_state['past'][i], is_user=True, key=str(i) + '_user') diff --git a/apps/chatbot-kickstarter/chatbot.py b/apps/chatbot-kickstarter/chatbot.py deleted file mode 100644 index 46c49e4b..00000000 --- a/apps/chatbot-kickstarter/chatbot.py +++ /dev/null @@ -1,111 +0,0 @@ -import openai -from termcolor import colored -import streamlit as st - -from database import get_redis_connection, get_redis_results - -from config import CHAT_MODEL, COMPLETIONS_MODEL, INDEX_NAME - -redis_client = get_redis_connection() - -# A basic class to create a message as a dict for chat -class Message: - - def __init__(self, role,content): - self.role = role - self.content = content - - def message(self): - return { - "role": self.role, - "content": self.content - } - - -# New Assistant class to add a vector database call to its responses -class RetrievalAssistant: - - def __init__(self): - self.conversation_history = [] - - def _get_assistant_response(self, prompt): - try: - completion = openai.ChatCompletion.create( - model=CHAT_MODEL, - messages=prompt, - temperature=0.1 - ) - - response_message = Message( - completion['choices'][0]['message']['role'], - completion['choices'][0]['message']['content'] - ) - return response_message.message() - - except Exception as e: - - return f'Request failed with exception {e}' - - # The function to retrieve Redis search results - - def _get_search_results(self,prompt): - latest_question = prompt - search_content = get_redis_results( - redis_client,latest_question, - INDEX_NAME - )['result'][0] - - return search_content - - def ask_assistant(self, next_user_prompt): - [self.conversation_history.append(x) for x in next_user_prompt] - assistant_response = self._get_assistant_response(self.conversation_history) - - # Answer normally unless the trigger sequence is used "searching_for_answers" - if 'searching for answers' in assistant_response['content'].lower(): - question_extract = openai.Completion.create( - model = COMPLETIONS_MODEL, - prompt=f''' - Extract the user's latest question and the year for that question from this - conversation: {self.conversation_history}. Extract it as a sentence stating the Question and Year" - ''' - ) - search_result = self._get_search_results(question_extract['choices'][0]['text']) - - # We insert an extra system prompt here to give fresh context to the Chatbot on how to use the Redis results - # In this instance we add it to the conversation history, but in production it may be better to hide - self.conversation_history.insert( - -1,{ - "role": 'system', - "content": f''' - Answer the user's question using this content: {search_result}. - If you cannot answer the question, say 'Sorry, I don't know the answer to this one' - ''' - } - ) - - assistant_response = self._get_assistant_response( - self.conversation_history - ) - - self.conversation_history.append(assistant_response) - return assistant_response - else: - self.conversation_history.append(assistant_response) - return assistant_response - - def pretty_print_conversation_history( - self, - colorize_assistant_replies=True): - - for entry in self.conversation_history: - if entry['role']=='system': - pass - else: - prefix = entry['role'] - content = entry['content'] - if colorize_assistant_replies and entry['role'] == 'assistant': - output = colored(f"{prefix}:\n{content}, green") - else: - output = colored(f"{prefix}:\n{content}") - print(output) diff --git a/apps/chatbot-kickstarter/config.py b/apps/chatbot-kickstarter/config.py deleted file mode 100644 index 16ce3d4b..00000000 --- a/apps/chatbot-kickstarter/config.py +++ /dev/null @@ -1,7 +0,0 @@ -COMPLETIONS_MODEL = "text-davinci-003" -EMBEDDINGS_MODEL = "text-embedding-ada-002" -CHAT_MODEL = 'gpt-3.5-turbo' -TEXT_EMBEDDING_CHUNK_SIZE=300 -VECTOR_FIELD_NAME='content_vector' -PREFIX = "sportsdoc" -INDEX_NAME = "f1-index" diff --git a/apps/chatbot-kickstarter/data/FIA Practice Directions - Competitor's Staff Registration System.pdf b/apps/chatbot-kickstarter/data/FIA Practice Directions - Competitor's Staff Registration System.pdf deleted file mode 100644 index 95122dff..00000000 Binary files a/apps/chatbot-kickstarter/data/FIA Practice Directions - Competitor's Staff Registration System.pdf and /dev/null differ diff --git a/apps/chatbot-kickstarter/data/fia_2022_formula_1_sporting_regulations_-_issue_9_-_2022-10-19_0.pdf b/apps/chatbot-kickstarter/data/fia_2022_formula_1_sporting_regulations_-_issue_9_-_2022-10-19_0.pdf deleted file mode 100644 index 133790b7..00000000 Binary files a/apps/chatbot-kickstarter/data/fia_2022_formula_1_sporting_regulations_-_issue_9_-_2022-10-19_0.pdf and /dev/null differ diff --git a/apps/chatbot-kickstarter/data/fia_2023_formula_1_technical_regulations_-_issue_4_-_2022-12-07.pdf b/apps/chatbot-kickstarter/data/fia_2023_formula_1_technical_regulations_-_issue_4_-_2022-12-07.pdf deleted file mode 100644 index 9b2da4a7..00000000 Binary files a/apps/chatbot-kickstarter/data/fia_2023_formula_1_technical_regulations_-_issue_4_-_2022-12-07.pdf and /dev/null differ diff --git a/apps/chatbot-kickstarter/data/fia_f1_power_unit_financial_regulations_issue_1_-_2022-08-16.pdf b/apps/chatbot-kickstarter/data/fia_f1_power_unit_financial_regulations_issue_1_-_2022-08-16.pdf deleted file mode 100644 index 19f2083c..00000000 Binary files a/apps/chatbot-kickstarter/data/fia_f1_power_unit_financial_regulations_issue_1_-_2022-08-16.pdf and /dev/null differ diff --git a/apps/chatbot-kickstarter/data/fia_formula_1_financial_regulations_iss.13.pdf b/apps/chatbot-kickstarter/data/fia_formula_1_financial_regulations_iss.13.pdf deleted file mode 100644 index e74ae78b..00000000 Binary files a/apps/chatbot-kickstarter/data/fia_formula_1_financial_regulations_iss.13.pdf and /dev/null differ diff --git a/apps/chatbot-kickstarter/database.py b/apps/chatbot-kickstarter/database.py deleted file mode 100644 index 4ccbe71a..00000000 --- a/apps/chatbot-kickstarter/database.py +++ /dev/null @@ -1,82 +0,0 @@ -import pandas as pd -import numpy as np -import openai -from redis import Redis -from redis.commands.search.field import VectorField -from redis.commands.search.field import TextField, NumericField -from redis.commands.search.query import Query - -from config import EMBEDDINGS_MODEL, PREFIX, VECTOR_FIELD_NAME - -# Get a Redis connection -def get_redis_connection(host='localhost',port='6379',db=0): - - r = Redis(host=host, port=port, db=db,decode_responses=False) - return r - -# Create a Redis index to hold our data -def create_hnsw_index (redis_conn,vector_field_name,vector_dimensions=1536, distance_metric='COSINE'): - redis_conn.ft().create_index([ - VectorField(vector_field_name, "HNSW", {"TYPE": "FLOAT32", "DIM": vector_dimensions, "DISTANCE_METRIC": distance_metric}), - TextField("filename"), - TextField("text_chunk"), - NumericField("file_chunk_index") - ]) - -# Create a Redis pipeline to load all the vectors and their metadata -def load_vectors(client:Redis, input_list, vector_field_name): - p = client.pipeline(transaction=False) - for text in input_list: - #hash key - key=f"{PREFIX}:{text['id']}" - - #hash values - item_metadata = text['metadata'] - # - item_keywords_vector = np.array(text['vector'],dtype= 'float32').tobytes() - item_metadata[vector_field_name]=item_keywords_vector - - # HSET - p.hset(key,mapping=item_metadata) - - p.execute() - -# Make query to Redis -def query_redis(redis_conn,query,index_name, top_k=2): - - - - ## Creates embedding vector from user query - embedded_query = np.array(openai.Embedding.create( - input=query, - model=EMBEDDINGS_MODEL, - )["data"][0]['embedding'], dtype=np.float32).tobytes() - - #prepare the query - q = Query(f'*=>[KNN {top_k} @{VECTOR_FIELD_NAME} $vec_param AS vector_score]').sort_by('vector_score').paging(0,top_k).return_fields('vector_score','filename','text_chunk','text_chunk_index').dialect(2) - params_dict = {"vec_param": embedded_query} - - - #Execute the query - results = redis_conn.ft(index_name).search(q, query_params = params_dict) - - return results - -# Get mapped documents from Weaviate results -def get_redis_results(redis_conn,query,index_name): - - # Get most relevant documents from Redis - query_result = query_redis(redis_conn,query,index_name) - - # Extract info into a list - query_result_list = [] - for i, result in enumerate(query_result.docs): - result_order = i - text = result.text_chunk - score = result.vector_score - query_result_list.append((result_order,text,score)) - - # Display result as a DataFrame for ease of us - result_df = pd.DataFrame(query_result_list) - result_df.columns = ['id','result','certainty'] - return result_df \ No newline at end of file diff --git a/apps/chatbot-kickstarter/powering_your_products_with_chatgpt_and_your_data.ipynb b/apps/chatbot-kickstarter/powering_your_products_with_chatgpt_and_your_data.ipynb deleted file mode 100644 index 5088bd0d..00000000 --- a/apps/chatbot-kickstarter/powering_your_products_with_chatgpt_and_your_data.ipynb +++ /dev/null @@ -1,1043 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "63785634", - "metadata": {}, - "source": [ - "# Power your products with ChatGPT and your own data\n", - "\n", - "This is a walkthrough taking readers through how to build starter Q&A and Chatbot applications using the ChatGPT API and their own data. \n", - "\n", - "It is laid out in these sections:\n", - "- **Setup:** \n", - " - Initiate variables and source the data\n", - "- **Lay the foundations:**\n", - " - Set up the vector database to accept vectors and data\n", - " - Load the dataset, chunk the data up for embedding and store in the vector database\n", - "- **Make it a product:**\n", - " - Add a retrieval step where users provide queries and we return the most relevant entries\n", - " - Summarise search results with GPT-3\n", - " - Test out this basic Q&A app in Streamlit\n", - "- **Build your moat:**\n", - " - Create an Assistant class to manage context and interact with our bot\n", - " - Use the Chatbot to answer questions using semantic search context\n", - " - Test out this basic Chatbot app in Streamlit\n", - " \n", - "Upon completion, you have the building blocks to create your own production chatbot or Q&A application using OpenAI APIs and a vector database.\n", - "\n", - "This notebook was originally presented with [these slides](https://drive.google.com/file/d/1dB-RQhZC_Q1iAsHkNNdkqtxxXqYODFYy/view?usp=share_link), which provide visual context for this journey." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "59f08ea7", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "id": "13649895", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "First we'll setup our libraries and environment variables" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7590fbfc", - "metadata": {}, - "outputs": [], - "source": [ - "import openai\n", - "import os\n", - "import requests\n", - "import numpy as np\n", - "import pandas as pd\n", - "from typing import Iterator\n", - "import tiktoken\n", - "import textract\n", - "from numpy import array, average\n", - "\n", - "from database import get_redis_connection\n", - "\n", - "# Set our default models and chunking size\n", - "from config import COMPLETIONS_MODEL, EMBEDDINGS_MODEL, CHAT_MODEL, TEXT_EMBEDDING_CHUNK_SIZE, VECTOR_FIELD_NAME\n", - "\n", - "# Ignore unclosed SSL socket warnings - optional in case you get these errors\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(action=\"ignore\", message=\"unclosed\", category=ImportWarning)\n", - "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "760efc1e", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_colwidth', 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3f90817d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[\"FIA Practice Directions - Competitor's Staff Registration System.pdf\",\n", - " 'fia_2022_formula_1_sporting_regulations_-_issue_9_-_2022-10-19_0.pdf',\n", - " 'fia_2023_formula_1_technical_regulations_-_issue_4_-_2022-12-07.pdf',\n", - " 'fia_f1_power_unit_financial_regulations_issue_1_-_2022-08-16.pdf',\n", - " 'fia_formula_1_financial_regulations_iss.13.pdf']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_dir = os.path.join(os.curdir,'data')\n", - "pdf_files = sorted([x for x in os.listdir(data_dir) if 'DS_Store' not in x])\n", - "pdf_files" - ] - }, - { - "cell_type": "markdown", - "id": "5dc4018c", - "metadata": {}, - "source": [ - "## Laying the foundations" - ] - }, - { - "cell_type": "markdown", - "id": "632b82ed", - "metadata": {}, - "source": [ - "### Storage\n", - "\n", - "We're going to use Redis as our database for both document contents and the vector embeddings. You will need the full Redis Stack to enable use of Redisearch, which is the module that allows semantic search - more detail is in the [docs for Redis Stack](https://redis.io/docs/stack/get-started/install/docker/).\n", - "\n", - "To set this up locally, you will need to install Docker and then run the following command: ```docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest```.\n", - "\n", - "The code used here draws heavily on [this repo](https://github.com/RedisAI/vecsim-demo).\n", - "\n", - "After setting up the Docker instance of Redis Stack, you can follow the below instructions to initiate a Redis connection and create a Hierarchical Navigable Small World (HNSW) index for semantic search." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "17d6b886", - "metadata": {}, - "outputs": [], - "source": [ - "# Setup Redis\n", - "from redis import Redis\n", - "from redis.commands.search.query import Query\n", - "from redis.commands.search.field import (\n", - " TextField,\n", - " VectorField,\n", - " NumericField\n", - ")\n", - "from redis.commands.search.indexDefinition import (\n", - " IndexDefinition,\n", - " IndexType\n", - ")\n", - "\n", - "redis_client = get_redis_connection()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4f3d3e6b", - "metadata": {}, - "outputs": [], - "source": [ - "# Constants\n", - "VECTOR_DIM = 1536 #len(data['title_vector'][0]) # length of the vectors\n", - "#VECTOR_NUMBER = len(data) # initial number of vectors\n", - "PREFIX = \"sportsdoc\" # prefix for the document keys\n", - "DISTANCE_METRIC = \"COSINE\" # distance metric for the vectors (ex. COSINE, IP, L2)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d3c352ca", - "metadata": {}, - "outputs": [], - "source": [ - "# Create search index\n", - "\n", - "# Index\n", - "INDEX_NAME = \"f1-index\" # name of the search index\n", - "VECTOR_FIELD_NAME = 'content_vector'\n", - "\n", - "# Define RediSearch fields for each of the columns in the dataset\n", - "# This is where you should add any additional metadata you want to capture\n", - "filename = TextField(\"filename\")\n", - "text_chunk = TextField(\"text_chunk\")\n", - "file_chunk_index = NumericField(\"file_chunk_index\")\n", - "\n", - "# define RediSearch vector fields to use HNSW index\n", - "\n", - "text_embedding = VectorField(VECTOR_FIELD_NAME,\n", - " \"HNSW\", {\n", - " \"TYPE\": \"FLOAT32\",\n", - " \"DIM\": VECTOR_DIM,\n", - " \"DISTANCE_METRIC\": DISTANCE_METRIC\n", - " }\n", - ")\n", - "# Add all our field objects to a list to be created as an index\n", - "fields = [filename,text_chunk,file_chunk_index,text_embedding]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a6c78b7e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "redis_client.ping()" - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "id": "cf3ad41f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not there yet. Creating\n" - ] - } - ], - "source": [ - "# Optional step to drop the index if it already exists\n", - "#redis_client.ft(INDEX_NAME).dropindex()\n", - "\n", - "# Check if index exists\n", - "try:\n", - " redis_client.ft(INDEX_NAME).info()\n", - " print(\"Index already exists\")\n", - "except Exception as e:\n", - " print(e)\n", - " # Create RediSearch Index\n", - " print('Not there yet. Creating')\n", - " redis_client.ft(INDEX_NAME).create_index(\n", - " fields = fields,\n", - " definition = IndexDefinition(prefix=[PREFIX], index_type=IndexType.HASH)\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "f74ebeb5", - "metadata": {}, - "source": [ - "### Ingestion\n", - "\n", - "We'll load up our PDFs and do the following\n", - "- Initiate our tokenizer\n", - "- Run a processing pipeline to:\n", - " - Mine the text from each PDF\n", - " - Split them into chunks and embed them\n", - " - Store them in Redis" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ed23bf9d", - "metadata": {}, - "outputs": [], - "source": [ - "# The transformers.py file contains all of the transforming functions, including ones to chunk, embed and load data\n", - "# For more details, check the file and work through each function individually\n", - "from transformers import handle_file_string" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "31f299f6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "./data/FIA Practice Directions - Competitor's Staff Registration System.pdf\n", - "./data/fia_2022_formula_1_sporting_regulations_-_issue_9_-_2022-10-19_0.pdf\n", - "./data/fia_2023_formula_1_technical_regulations_-_issue_4_-_2022-12-07.pdf\n", - "./data/fia_f1_power_unit_financial_regulations_issue_1_-_2022-08-16.pdf\n", - "./data/fia_formula_1_financial_regulations_iss.13.pdf\n", - "CPU times: user 3.16 s, sys: 372 ms, total: 3.54 s\n", - "Wall time: 45.8 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# This step takes about 5 minutes\n", - "\n", - "# Initialise tokenizer\n", - "tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n", - "\n", - "# Process each PDF file and prepare for embedding\n", - "for pdf_file in pdf_files:\n", - " \n", - " pdf_path = os.path.join(data_dir,pdf_file)\n", - " print(pdf_path)\n", - " \n", - " # Extract the raw text from each PDF using textract\n", - " text = textract.process(pdf_path, method='pdfminer')\n", - " \n", - " # Chunk each document, embed the contents and load to Redis\n", - " handle_file_string((pdf_file,text.decode(\"utf-8\")),tokenizer,redis_client,VECTOR_FIELD_NAME,INDEX_NAME)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "22aff597", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'829'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check that our docs have been inserted\n", - "redis_client.ft(INDEX_NAME).info()['num_docs']" - ] - }, - { - "cell_type": "markdown", - "id": "6b12cb6e", - "metadata": {}, - "source": [ - "## Make it a product\n", - "\n", - "Now we can test that our search works as intended by:\n", - "- Querying our data in Redis using semantic search and verifying results\n", - "- Adding a step to pass the results to GPT-3 for summarisation" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e921ac96", - "metadata": {}, - "outputs": [], - "source": [ - "from database import get_redis_results" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "cb9dfacf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 5.85 ms, sys: 2.61 ms, total: 8.45 ms\n", - "Wall time: 240 ms\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
idresultcertainty
00The IT will, therefore, be competent to establish the existence, or not, of a breach of the FIA regulations and to impose any sanction upon the person and Competitor concerned (see the process governed by the FIA Judicial and Disciplinary Rules). The President of the FIA, in its capacity as prosecuting authority, will ask, in respect of every disciplinary procedure: - - for the imposition of a suspension upon Competitor’s Staff Certificate of Registration holders who have contravened the FIA Code of Good Standing or the withdrawal of the Competitor’s Staff Certificate of Registration (any withdrawal can only be imposed for the remaining period of the current season of the FIA Formula One World Championship) and that these same people not be fined. The person and/or Competitor sanctioned may bring an appeal before the ICA against the IT’s decision. ********* The FIA will inform the relevant Competitor of any proceedings instigated against any member of its staff. It is the responsibility to the relevant Competitor to send the IT a written request to be heard, and if granted, it shall be permitted to submit written observations. The FIA undertakes to support before the IT and/or the ICA any request from the Competitor to intervene as a third party within the framework of a disciplinary procedure. The right to deprive any duly registered member of a Competitor’s staff of access to the Reserved Areas at events forming part of the FIA Formula One World Championship is subject to the procedure set forth in the FIA Judicial and Disciplinary Rules. The Stewards during the course of an Event or otherwise will have no authority to suspend or withdraw a Competitor’s Staff Certificate of Registration for any breach or alleged breach of the FIA Code of Good Standing.0.205749571323
11The following sets out examples of the type of behaviours which might constitute an infringement of the FIA Code of Good Standing (non-exhaustive list of examples) in relation to a person who is subject to the Code of Good Standing: - - - - giving instructions to a driver or other member of a Competitor’s staff with the intention or with the likely result of causing an accident, collision or crash or a race to be stopped or suspended any action which is likely to endanger or materially compromise the safety of any driver, other members of the Competitor’s staff, other participants in a race, Officials or any spectators or other members of the public who attend an event giving instructions to make any changes to a car in breach of any safety requirements or regulations giving instructions to tamper with or adversely affect the set-up or performance of the car of any other Competitor 4 / 5 \f", - "FIA Legal Department Practice Directions - Competitor’s Staff Registration System 17 March 2011 - - giving instructions to a driver or otherwise taking any action by which the result or course of a race may be influenced or affected for the purpose of profiting or assisting someone to profit through betting on the outcome of a race or any part of a race or being convicted of a criminal offence (other than a driving offence) which carries a maximum prison sentence of five years. VII. AMENDMENTS TO THE COMPETITOR’S STAFF REGISTRATION SYSTEM The FIA will not make any amendments with regard to the Competitor’s Staff Registration System, either to the International Sporting Code or to the Practice Directions, prior consultation with the Competitors entered in the FIA Formula One World Championship and adequate opportunity to provide input on the proposed amendments.0.206525266171
\n", - "
" - ], - "text/plain": [ - " id \\\n", - "0 0 \n", - "1 1 \n", - "\n", - " result \\\n", - "0 The IT will, therefore, be competent to establish the existence, or not, of a breach of the FIA regulations and to impose any sanction upon the person and Competitor concerned (see the process governed by the FIA Judicial and Disciplinary Rules). The President of the FIA, in its capacity as prosecuting authority, will ask, in respect of every disciplinary procedure: - - for the imposition of a suspension upon Competitor’s Staff Certificate of Registration holders who have contravened the FIA Code of Good Standing or the withdrawal of the Competitor’s Staff Certificate of Registration (any withdrawal can only be imposed for the remaining period of the current season of the FIA Formula One World Championship) and that these same people not be fined. The person and/or Competitor sanctioned may bring an appeal before the ICA against the IT’s decision. ********* The FIA will inform the relevant Competitor of any proceedings instigated against any member of its staff. It is the responsibility to the relevant Competitor to send the IT a written request to be heard, and if granted, it shall be permitted to submit written observations. The FIA undertakes to support before the IT and/or the ICA any request from the Competitor to intervene as a third party within the framework of a disciplinary procedure. The right to deprive any duly registered member of a Competitor’s staff of access to the Reserved Areas at events forming part of the FIA Formula One World Championship is subject to the procedure set forth in the FIA Judicial and Disciplinary Rules. The Stewards during the course of an Event or otherwise will have no authority to suspend or withdraw a Competitor’s Staff Certificate of Registration for any breach or alleged breach of the FIA Code of Good Standing. \n", - "1 The following sets out examples of the type of behaviours which might constitute an infringement of the FIA Code of Good Standing (non-exhaustive list of examples) in relation to a person who is subject to the Code of Good Standing: - - - - giving instructions to a driver or other member of a Competitor’s staff with the intention or with the likely result of causing an accident, collision or crash or a race to be stopped or suspended any action which is likely to endanger or materially compromise the safety of any driver, other members of the Competitor’s staff, other participants in a race, Officials or any spectators or other members of the public who attend an event giving instructions to make any changes to a car in breach of any safety requirements or regulations giving instructions to tamper with or adversely affect the set-up or performance of the car of any other Competitor 4 / 5 \n", - "FIA Legal Department Practice Directions - Competitor’s Staff Registration System 17 March 2011 - - giving instructions to a driver or otherwise taking any action by which the result or course of a race may be influenced or affected for the purpose of profiting or assisting someone to profit through betting on the outcome of a race or any part of a race or being convicted of a criminal offence (other than a driving offence) which carries a maximum prison sentence of five years. VII. AMENDMENTS TO THE COMPETITOR’S STAFF REGISTRATION SYSTEM The FIA will not make any amendments with regard to the Competitor’s Staff Registration System, either to the International Sporting Code or to the Practice Directions, prior consultation with the Competitors entered in the FIA Formula One World Championship and adequate opportunity to provide input on the proposed amendments. \n", - "\n", - " certainty \n", - "0 0.205749571323 \n", - "1 0.206525266171 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "\n", - "f1_query='what are the criteria for disqualification'\n", - "\n", - "result_df = get_redis_results(redis_client,f1_query,index_name=INDEX_NAME)\n", - "result_df.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "51340903", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "- Breach of FIA regulations can lead to disqualification\n", - "- Imposed sanctions may include suspension of Competitor's Staff Certificate of Registration\n", - "- Competitor's Staff Certificate of Registration may also be withdrawn for remaining duration of the season\n", - "- Competitor has right to appeal the IT's decision\n", - "- Competitor must make written request to be heard\n", - "- FIA will support Competitor's request\n", - "- Stewards have no authority to suspend or withdraw Competitor's Staff Certificate of Registration for any breach or alleged breach of the FIA Code of Good Standing\n" - ] - } - ], - "source": [ - "# Build a prompt to provide the original query, the result and ask to summarise for the user\n", - "summary_prompt = '''Summarise this result in a bulleted list to answer the search query a customer has sent.\n", - "Search query: SEARCH_QUERY_HERE\n", - "Search result: SEARCH_RESULT_HERE\n", - "Summary:\n", - "'''\n", - "summary_prepped = summary_prompt.replace('SEARCH_QUERY_HERE',f1_query).replace('SEARCH_RESULT_HERE',result_df['result'][0])\n", - "summary = openai.Completion.create(engine=COMPLETIONS_MODEL,prompt=summary_prepped,max_tokens=500)\n", - "# Response provided by GPT-3\n", - "print(summary['choices'][0]['text'])" - ] - }, - { - "cell_type": "markdown", - "id": "d008ff23", - "metadata": {}, - "source": [ - "### Search\n", - "\n", - "Now that we've got our knowledge embedded and stored in Redis, we can now create an internal search application. Its not sophisticated but it'll get the job done for us.\n", - "\n", - "In the directory containing this app, execute ```streamlit run search.py```. This will open up a Streamlit app in your browser where you can ask questions of your embedded data.\n", - "\n", - "__Example Questions__:\n", - "- what is the cost cap for a power unit in 2023\n", - "- what should competitors include on their application form" - ] - }, - { - "cell_type": "markdown", - "id": "dd12b31e", - "metadata": {}, - "source": [ - "## Build your moat\n", - "\n", - "The Q&A was useful, but fairly limited in the complexity of interaction we can have - if the user asks a sub-optimal question, there is no assistance from the system to prompt them for more info or conversation to lead them down the right path.\n", - "\n", - "For the next step we'll make a Chatbot using the Chat Completions endpoint, which will:\n", - "- Be given instructions on how it should act and what the goals of its users are\n", - "- Be supplied some required information that it needs to collect\n", - "- Go back and forth with the customer until it has populated that information\n", - "- Say a trigger word that will kick off semantic search and summarisation of the response\n", - "\n", - "For more details on our Chat Completions endpoint and how to interact with it, please check out the docs [here](https://platform.openai.com/docs/guides/chat)." - ] - }, - { - "cell_type": "markdown", - "id": "34135886", - "metadata": {}, - "source": [ - "### Framework\n", - "\n", - "This section outlines a basic framework for working with the API and storing context of previous conversation \"turns\". Once this is established, we'll extend it to use our retrieval endpoint." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "45c0acc8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "assistant: \n", - "\n", - "As an AI language model developed by OpenAI, I can help you in several ways. Here are some examples:\n", - "\n", - "\n", - "- Answer questions: You can ask me any questions you have, and I'll do my best to provide accurate and helpful answers. I can help you with anything from math problems to historical facts to recommendations for restaurants or movies.\n", - "\n", - "- Assist in writing: Whether you need help with grammar, sentence structure, or word choice, I can assist you in writing. I can also help you generate ideas for writing assignments and provide writing prompts for inspiration.\n", - "\n", - "- Provide information: If you need information on a specific topic or want to learn more about a particular subject, I can provide you with useful information and resources.\n", - "\n", - "- Give suggestions: If you're unsure about something or need advice, I can give you suggestions and recommendations based on my knowledge and experience.\n", - "\n", - "- Chat and entertain: If you're feeling bored or lonely, I can chat with you and try to entertain you with jokes, stories, or interesting facts.\n" - ] - } - ], - "source": [ - "# A basic example of how to interact with our ChatCompletion endpoint\n", - "# It requires a list of \"messages\", consisting of a \"role\" (one of system, user or assistant) and \"content\"\n", - "question = 'How can you help me'\n", - "\n", - "\n", - "completion = openai.ChatCompletion.create(\n", - " model=\"gpt-3.5-turbo\",\n", - " messages=[\n", - " {\"role\": \"user\", \"content\": question}\n", - " ]\n", - ")\n", - "print(f\"{completion['choices'][0]['message']['role']}: {completion['choices'][0]['message']['content']}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "23e4fc55", - "metadata": {}, - "outputs": [], - "source": [ - "from termcolor import colored\n", - "\n", - "# A basic class to create a message as a dict for chat\n", - "class Message:\n", - " \n", - " \n", - " def __init__(self,role,content):\n", - " \n", - " self.role = role\n", - " self.content = content\n", - " \n", - " def message(self):\n", - " \n", - " return {\"role\": self.role,\"content\": self.content}\n", - " \n", - "# Our assistant class we'll use to converse with the bot\n", - "class Assistant:\n", - " \n", - " def __init__(self):\n", - " self.conversation_history = []\n", - "\n", - " def _get_assistant_response(self, prompt):\n", - " \n", - " try:\n", - " completion = openai.ChatCompletion.create(\n", - " model=\"gpt-3.5-turbo\",\n", - " messages=prompt\n", - " )\n", - " \n", - " response_message = Message(completion['choices'][0]['message']['role'],completion['choices'][0]['message']['content'])\n", - " return response_message.message()\n", - " \n", - " except Exception as e:\n", - " \n", - " return f'Request failed with exception {e}'\n", - "\n", - " def ask_assistant(self, next_user_prompt, colorize_assistant_replies=True):\n", - " [self.conversation_history.append(x) for x in next_user_prompt]\n", - " assistant_response = self._get_assistant_response(self.conversation_history)\n", - " self.conversation_history.append(assistant_response)\n", - " return assistant_response\n", - " \n", - " \n", - " def pretty_print_conversation_history(self, colorize_assistant_replies=True):\n", - " for entry in self.conversation_history:\n", - " if entry['role'] == 'system':\n", - " pass\n", - " else:\n", - " prefix = entry['role']\n", - " content = entry['content']\n", - " output = colored(prefix +':\\n' + content, 'green') if colorize_assistant_replies and entry['role'] == 'assistant' else prefix +':\\n' + content\n", - " print(output)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e18c88b4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'role': 'system',\n", - " 'content': 'You are a helpful business assistant who has innovative ideas'},\n", - " {'role': 'user', 'content': 'What can you do to help me'}]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Initiate our Assistant class\n", - "conversation = Assistant()\n", - "\n", - "# Create a list to hold our messages and insert both a system message to guide behaviour and our first user question\n", - "messages = []\n", - "system_message = Message('system','You are a helpful business assistant who has innovative ideas')\n", - "user_message = Message('user','What can you do to help me')\n", - "messages.append(system_message.message())\n", - "messages.append(user_message.message())\n", - "messages" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "377243c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "As a business assistant with innovative ideas, I can:\n", - "\n", - "1. Conduct market research: I can carry out thorough research on your target market, analyze consumer behavior, and gather insights to help you make informed decisions about your business.\n", - "\n", - "2. Develop a strategic plan: I can help you come up with a comprehensive business plan that includes a clear vision, goals, and actionable steps to help you achieve your business objectives.\n", - "\n", - "3. Create a strong online presence: I can help you create a compelling website, develop a social media strategy, and create engaging online content to help you connect with your customers.\n", - "\n", - "4. Improve customer service: I can help you identify areas where your customer service needs improvement and help you develop strategies for how to provide excellent service to your customers.\n", - "\n", - "5. Optimize your operations: I can help you streamline your business processes, identify inefficiencies, and create systems that will improve productivity, reduce costs, and increase profitability.\n", - "\n", - "6. Explore new revenue streams: I can help you identify new opportunities for revenue generation, such as introducing new products or services, or expanding into new markets.\n", - "\n", - "7. Develop innovative marketing: I can help you come up with innovative marketing strategies that will help you stand out from competitors and resonate with your target audience.\n" - ] - } - ], - "source": [ - "# Get back a response from the Chatbot to our question\n", - "response_message = conversation.ask_assistant(messages)\n", - "print(response_message['content'])" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f364c3b5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Developing a strategic plan is one of the most critical aspects of running a successful business. A strategic plan outlines a clear vision for your business and provides a roadmap for achieving your goals. As a business assistant, I can help you develop a comprehensive strategic plan that includes:\n", - "\n", - "1. A clear and concise vision statement that defines what you want to achieve with your business.\n", - "\n", - "2. An analysis of your competition and the market environment to identify opportunities and threats.\n", - "\n", - "3. A SWOT analysis to identify your business's strengths, weaknesses, opportunities, and threats.\n", - "\n", - "4. A clear set of objectives and goals that are specific, measurable, attainable, relevant, and time-bound.\n", - "\n", - "5. An action plan that outlines the steps you need to take to achieve your goals.\n", - "\n", - "6. A timeline that details when each objective will be completed.\n", - "\n", - "7. A monitoring and evaluation plan to keep track of your progress and identify areas that need improvement.\n", - "\n", - "By working together, we can develop a strategic plan that aligns with your long-term goals and helps you grow your business.\n" - ] - } - ], - "source": [ - "next_question = 'Tell me more about option 2'\n", - "\n", - "# Initiate a fresh messages list and insert our next question\n", - "messages = []\n", - "user_message = Message('user',next_question)\n", - "messages.append(user_message.message())\n", - "response_message = conversation.ask_assistant(messages)\n", - "print(response_message['content'])" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "f62842a1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "user:\n", - "What can you do to help me\n", - "\u001b[32massistant:\n", - "As a business assistant with innovative ideas, I can:\n", - "\n", - "1. Conduct market research: I can carry out thorough research on your target market, analyze consumer behavior, and gather insights to help you make informed decisions about your business.\n", - "\n", - "2. Develop a strategic plan: I can help you come up with a comprehensive business plan that includes a clear vision, goals, and actionable steps to help you achieve your business objectives.\n", - "\n", - "3. Create a strong online presence: I can help you create a compelling website, develop a social media strategy, and create engaging online content to help you connect with your customers.\n", - "\n", - "4. Improve customer service: I can help you identify areas where your customer service needs improvement and help you develop strategies for how to provide excellent service to your customers.\n", - "\n", - "5. Optimize your operations: I can help you streamline your business processes, identify inefficiencies, and create systems that will improve productivity, reduce costs, and increase profitability.\n", - "\n", - "6. Explore new revenue streams: I can help you identify new opportunities for revenue generation, such as introducing new products or services, or expanding into new markets.\n", - "\n", - "7. Develop innovative marketing: I can help you come up with innovative marketing strategies that will help you stand out from competitors and resonate with your target audience.\u001b[0m\n", - "user:\n", - "Tell me more about option 2\n", - "\u001b[32massistant:\n", - "Developing a strategic plan is one of the most critical aspects of running a successful business. A strategic plan outlines a clear vision for your business and provides a roadmap for achieving your goals. As a business assistant, I can help you develop a comprehensive strategic plan that includes:\n", - "\n", - "1. A clear and concise vision statement that defines what you want to achieve with your business.\n", - "\n", - "2. An analysis of your competition and the market environment to identify opportunities and threats.\n", - "\n", - "3. A SWOT analysis to identify your business's strengths, weaknesses, opportunities, and threats.\n", - "\n", - "4. A clear set of objectives and goals that are specific, measurable, attainable, relevant, and time-bound.\n", - "\n", - "5. An action plan that outlines the steps you need to take to achieve your goals.\n", - "\n", - "6. A timeline that details when each objective will be completed.\n", - "\n", - "7. A monitoring and evaluation plan to keep track of your progress and identify areas that need improvement.\n", - "\n", - "By working together, we can develop a strategic plan that aligns with your long-term goals and helps you grow your business.\u001b[0m\n" - ] - } - ], - "source": [ - "# Print out a log of our conversation so far\n", - "\n", - "conversation.pretty_print_conversation_history()" - ] - }, - { - "cell_type": "markdown", - "id": "f18d5b54", - "metadata": {}, - "source": [ - "### Knowledge retrieval\n", - "\n", - "Now we'll extend the class to call a downstream service when a stop sequence is spoken by the Chatbot.\n", - "\n", - "The main changes are:\n", - "- The system message is more comprehensive, giving criteria for the Chatbot to advance the conversation\n", - "- Adding an explicit stop sequence for it to use when it has the info it needs\n", - "- Extending the class with a function ```_get_search_results``` which sources Redis results" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "8a0cef87", - "metadata": {}, - "outputs": [], - "source": [ - "# Updated system prompt requiring Question and Year to be extracted from the user\n", - "system_prompt = '''\n", - "You are a helpful Formula 1 knowledge base assistant. You need to capture a Question and Year from each customer.\n", - "The Question is their query on Formula 1, and the Year is the year of the applicable Formula 1 season.\n", - "If they haven't provided the Year, ask them for it again.\n", - "Once you have the Year, say \"searching for answers\".\n", - "\n", - "Example 1:\n", - "\n", - "User: I'd like to know the cost cap for a power unit\n", - "\n", - "Assistant: Certainly, what year would you like this for?\n", - "\n", - "User: 2023 please.\n", - "\n", - "Assistant: Searching for answers.\n", - "'''\n", - "\n", - "# New Assistant class to add a vector database call to its responses\n", - "class RetrievalAssistant:\n", - " \n", - " def __init__(self):\n", - " self.conversation_history = [] \n", - "\n", - " def _get_assistant_response(self, prompt):\n", - " \n", - " try:\n", - " completion = openai.ChatCompletion.create(\n", - " model=CHAT_MODEL,\n", - " messages=prompt,\n", - " temperature=0.1\n", - " )\n", - " \n", - " response_message = Message(completion['choices'][0]['message']['role'],completion['choices'][0]['message']['content'])\n", - " return response_message.message()\n", - " \n", - " except Exception as e:\n", - " \n", - " return f'Request failed with exception {e}'\n", - " \n", - " # The function to retrieve Redis search results\n", - " def _get_search_results(self,prompt):\n", - " latest_question = prompt\n", - " search_content = get_redis_results(redis_client,latest_question,INDEX_NAME)['result'][0]\n", - " return search_content\n", - " \n", - "\n", - " def ask_assistant(self, next_user_prompt):\n", - " [self.conversation_history.append(x) for x in next_user_prompt]\n", - " assistant_response = self._get_assistant_response(self.conversation_history)\n", - " \n", - " # Answer normally unless the trigger sequence is used \"searching_for_answers\"\n", - " if 'searching for answers' in assistant_response['content'].lower():\n", - " question_extract = openai.Completion.create(model=COMPLETIONS_MODEL,prompt=f\"Extract the user's latest question and the year for that question from this conversation: {self.conversation_history}. Extract it as a sentence stating the Question and Year\")\n", - " search_result = self._get_search_results(question_extract['choices'][0]['text'])\n", - " \n", - " # We insert an extra system prompt here to give fresh context to the Chatbot on how to use the Redis results\n", - " # In this instance we add it to the conversation history, but in production it may be better to hide\n", - " self.conversation_history.insert(-1,{\"role\": 'system',\"content\": f\"Answer the user's question using this content: {search_result}. If you cannot answer the question, say 'Sorry, I don't know the answer to this one'\"})\n", - " #[self.conversation_history.append(x) for x in next_user_prompt]\n", - " \n", - " assistant_response = self._get_assistant_response(self.conversation_history)\n", - " print(next_user_prompt)\n", - " print(assistant_response)\n", - " self.conversation_history.append(assistant_response)\n", - " return assistant_response\n", - " else:\n", - " self.conversation_history.append(assistant_response)\n", - " return assistant_response\n", - " \n", - " \n", - " def pretty_print_conversation_history(self, colorize_assistant_replies=True):\n", - " for entry in self.conversation_history:\n", - " if entry['role'] == 'system':\n", - " pass\n", - " else:\n", - " prefix = entry['role']\n", - " content = entry['content']\n", - " output = colored(prefix +':\\n' + content, 'green') if colorize_assistant_replies and entry['role'] == 'assistant' else prefix +':\\n' + content\n", - " #prefix = entry['role']\n", - " print(output)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "101d502c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'role': 'assistant',\n", - " 'content': 'Sure, what year would you like this information for?'}" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "conversation = RetrievalAssistant()\n", - "messages = []\n", - "system_message = Message('system',system_prompt)\n", - "user_message = Message('user','How can a competitor be disqualified from competition')\n", - "messages.append(system_message.message())\n", - "messages.append(user_message.message())\n", - "response_message = conversation.ask_assistant(messages)\n", - "response_message" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "702eb4fc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'role': 'user', 'content': 'For 2023 please.'}]\n", - "{'role': 'assistant', 'content': 'According to the FIA Sporting Regulations for the 2023 Formula One season, a competitor can be disqualified from the competition if they breach the FIA regulations. The FIA will investigate and establish the existence of the breach and impose any sanction upon the person and competitor concerned. The President of the FIA, in its capacity as prosecuting authority, will ask for the imposition of a suspension upon Competitor’s Staff Certificate of Registration holders who have contravened the FIA Code of Good Standing or the withdrawal of the Competitor’s Staff Certificate of Registration. The person and/or competitor sanctioned may bring an appeal before the ICA against the IT’s decision.'}\n" - ] - } - ], - "source": [ - "messages = []\n", - "user_message = Message('user','For 2023 please.')\n", - "messages.append(user_message.message())\n", - "response_message = conversation.ask_assistant(messages)\n", - "#response_message" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "e2f2c812", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "user:\n", - "How can a competitor be disqualified from competition\n", - "\u001b[32massistant:\n", - "Sure, what year would you like this information for?\u001b[0m\n", - "user:\n", - "For 2023 please.\n", - "\u001b[32massistant:\n", - "According to the FIA Sporting Regulations for the 2023 Formula One season, a competitor can be disqualified from the competition if they breach the FIA regulations. The FIA will investigate and establish the existence of the breach and impose any sanction upon the person and competitor concerned. The President of the FIA, in its capacity as prosecuting authority, will ask for the imposition of a suspension upon Competitor’s Staff Certificate of Registration holders who have contravened the FIA Code of Good Standing or the withdrawal of the Competitor’s Staff Certificate of Registration. The person and/or competitor sanctioned may bring an appeal before the ICA against the IT’s decision.\u001b[0m\n" - ] - } - ], - "source": [ - "conversation.pretty_print_conversation_history()" - ] - }, - { - "cell_type": "markdown", - "id": "a9f9ef37", - "metadata": {}, - "source": [ - "### Chatbot\n", - "\n", - "Now we'll put all this into action with a real (basic) Chatbot.\n", - "\n", - "In the directory containing this app, execute ```streamlit run chat.py```. This will open up a Streamlit app in your browser where you can ask questions of your embedded data. \n", - "\n", - "__Example Questions__:\n", - "- what is the cost cap for a power unit in 2023\n", - "- what should competitors include on their application form\n", - "- how can a competitor be disqualified" - ] - }, - { - "cell_type": "markdown", - "id": "b8e6c4ca", - "metadata": {}, - "source": [ - "### Consolidation\n", - "\n", - "Over the course of this notebook you have:\n", - "- Laid the foundations of your product by embedding our knowledge base\n", - "- Created a Q&A application to serve basic use cases\n", - "- Extended this to be an interactive Chatbot\n", - "\n", - "These are the foundational building blocks of any Q&A or Chat application using our APIs - these are your starting point, and we look forward to seeing what you build with them!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "oaibook", - "language": "python", - "name": "oaibook" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/apps/chatbot-kickstarter/requirements.txt b/apps/chatbot-kickstarter/requirements.txt deleted file mode 100644 index a6239c77..00000000 --- a/apps/chatbot-kickstarter/requirements.txt +++ /dev/null @@ -1,12 +0,0 @@ -numpy==1.24.2 -openai==0.27.1 -pandas==1.5.3 -redis==4.5.4 -requests==2.31.0 -streamlit==1.20.0 -streamlit_chat==0.0.2.2 -termcolor==2.2.0 -jupyter -ipykernel -textract -tiktoken \ No newline at end of file diff --git a/apps/chatbot-kickstarter/search.py b/apps/chatbot-kickstarter/search.py deleted file mode 100644 index 05fadb62..00000000 --- a/apps/chatbot-kickstarter/search.py +++ /dev/null @@ -1,39 +0,0 @@ -import streamlit as st -import openai - -from database import get_redis_connection, get_redis_results -from config import INDEX_NAME, COMPLETIONS_MODEL - -# initialise Redis connection - -client = get_redis_connection() - -### SEARCH APP - -st.set_page_config( - page_title="Streamlit Search - Demo", - page_icon=":robot:" -) - -st.title('Formula 1 Search') -st.subheader("Search for any Formula 1 rule questions you have") - -prompt = st.text_input("Enter your search here","", key="input") - -if st.button('Submit', key='generationSubmit'): - result_df = get_redis_results(client,prompt,INDEX_NAME) - - # Build a prompt to provide the original query, the result and ask to summarise for the user - summary_prompt = '''Summarise this result in a bulleted list to answer the search query a customer has sent. - Search query: SEARCH_QUERY_HERE - Search result: SEARCH_RESULT_HERE - Summary: - ''' - summary_prepped = summary_prompt.replace('SEARCH_QUERY_HERE',prompt).replace('SEARCH_RESULT_HERE',result_df['result'][0]) - summary = openai.Completion.create(engine=COMPLETIONS_MODEL,prompt=summary_prepped,max_tokens=500) - - # Response provided by GPT-3 - st.write(summary['choices'][0]['text']) - - # Option to display raw table instead of summary from GPT-3 - #st.table(result_df) \ No newline at end of file diff --git a/apps/chatbot-kickstarter/transformers.py b/apps/chatbot-kickstarter/transformers.py deleted file mode 100644 index e23de7a0..00000000 --- a/apps/chatbot-kickstarter/transformers.py +++ /dev/null @@ -1,131 +0,0 @@ -from typing import Iterator -from numpy import array, average -import openai -import pandas as pd -import numpy as np - -from config import TEXT_EMBEDDING_CHUNK_SIZE, EMBEDDINGS_MODEL -from database import load_vectors - -def get_col_average_from_list_of_lists(list_of_lists): - """Return the average of each column in a list of lists.""" - if len(list_of_lists) == 1: - return list_of_lists[0] - else: - list_of_lists_array = array(list_of_lists) - average_embedding = average(list_of_lists_array, axis=0) - return average_embedding.tolist() - -# Create embeddings for a text using a tokenizer and an OpenAI engine - - -def create_embeddings_for_text(text, tokenizer): - """Return a list of tuples (text_chunk, embedding) and an average embedding for a text.""" - token_chunks = list(chunks(text, TEXT_EMBEDDING_CHUNK_SIZE, tokenizer)) - text_chunks = [tokenizer.decode(chunk) for chunk in token_chunks] - - embeddings_response = get_embeddings(text_chunks, EMBEDDINGS_MODEL) - embeddings = [embedding["embedding"] for embedding in embeddings_response] - text_embeddings = list(zip(text_chunks, embeddings)) - - average_embedding = get_col_average_from_list_of_lists(embeddings) - - return (text_embeddings, average_embedding) - -def get_embeddings(text_array, engine): - return openai.Engine(id=engine).embeddings(input=text_array)["data"] - -# Split a text into smaller chunks of size n, preferably ending at the end of a sentence -def chunks(text, n, tokenizer): - """Yield successive n-sized chunks from text.""" - tokens = tokenizer.encode(text) - i = 0 - while i < len(tokens): - # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens - j = min(i + int(1.5 * n), len(tokens)) - while j > i + int(0.5 * n): - # Decode the tokens and check for full stop or newline - chunk = tokenizer.decode(tokens[i:j]) - if chunk.endswith(".") or chunk.endswith("\n"): - break - j -= 1 - # If no end of sentence found, use n tokens as the chunk size - if j == i + int(0.5 * n): - j = min(i + n, len(tokens)) - yield tokens[i:j] - i = j - -def get_unique_id_for_file_chunk(filename, chunk_index): - return str(filename+"-!"+str(chunk_index)) - -def handle_file_string(file, tokenizer, redis_conn, text_embedding_field, index_name): - """ - Handle a file string by cleaning it up, creating embeddings, and uploading them to Redis. - - Args: - file (tuple): A tuple containing the filename and file body string. - tokenizer: The tokenizer object to use for encoding and decoding text. - redis_conn: The Redis connection object. - text_embedding_field (str): The field in Redis where the text embeddings will be stored. - index_name: The name of the index or identifier for the embeddings. - - Returns: - None - - Raises: - Exception: If there is an error creating embeddings or uploading to Redis. - - """ - filename = file[0] - file_body_string = file[1] - - # Clean up the file string by replacing newlines, double spaces, and semi-colons - clean_file_body_string = file_body_string.replace(" ", " ").replace("\n", "; ").replace(';', ' ') - - # Add the filename to the text to embed - text_to_embed = "Filename is: {}; {}".format(filename, clean_file_body_string) - - try: - # Create embeddings for the text - text_embeddings, average_embedding = create_embeddings_for_text(text_to_embed, tokenizer) - # print("[handle_file_string] Created embedding for {}".format(filename)) - except Exception as e: - print("[handle_file_string] Error creating embedding: {}".format(e)) - - # Get the vectors array of triples: file_chunk_id, embedding, metadata for each embedding - # Metadata is a dict with keys: filename, file_chunk_index - vectors = [] - for i, (text_chunk, embedding) in enumerate(text_embeddings): - id = get_unique_id_for_file_chunk(filename, i) - vectors.append({'id': id, "vector": embedding, 'metadata': {"filename": filename, - "text_chunk": text_chunk, - "file_chunk_index": i}}) - - try: - # Load vectors into Redis - load_vectors(redis_conn, vectors, text_embedding_field) - except Exception as e: - print(f'Ran into a problem uploading to Redis: {e}') - - -# Make a class to generate batches for insertion -class BatchGenerator: - - - def __init__(self, batch_size: int = 10) -> None: - self.batch_size = batch_size - - # Makes chunks out of an input DataFrame - def to_batches(self, df: pd.DataFrame) -> Iterator[pd.DataFrame]: - splits = self.splits_num(df.shape[0]) - if splits <= 1: - yield df - else: - for chunk in np.array_split(df, splits): - yield chunk - - # Determines how many chunks DataFrame contains - def splits_num(self, elements: int) -> int: - return round(elements / self.batch_size) - - __call__ = to_batches diff --git a/apps/embeddings-playground/README.md b/apps/embeddings-playground/README.md deleted file mode 100644 index 4bb03eac..00000000 --- a/apps/embeddings-playground/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# Embeddings Playground - -[`embeddings_playground.py`](embeddings_playground.py) is a single-page streamlit app for experimenting with OpenAI embeddings. - -## Installation - -Before running, install required dependencies with: - -`pip install -r apps/embeddings-playground/requirements.txt` - -(You may need to change the path to match your local path.) - -Verify installation of streamlit with `streamlit hello`. - -## Usage - -Run the script with: - -`streamlit run apps/embeddings-playground/embeddings_playground.py` - -(Again, you may need to change the path to match your local path.) - -In the app, first select your choice of: -- distance metric (we recommend cosine) -- embedding model (we recommend `text-embedding-ada-002` for most use cases, as of May 2023) - -Then, enter a variable number of strings to compare. Click `rank` to see: -- the ranked list of strings, sorted by distance from the first string -- a heatmap showing the distance between each pair of strings - -## Example - -Here's an example distance matrix for 8 example strings related to `The sky is blue`: - -![example distance matrix](example_distance_matrix.png) - -From these distance pairs, you can see: -- embeddings measure topical similarity more than logical similarity (e.g., `The sky is blue` is very close to `The sky is not blue`) -- punctuation affects embeddings (e.g., `"THE. SKY. IS. BLUE!"` is only third closest to `The sky is blue`) -- within-language pairs are stronger than across-language pairs (e.g., `El cielo as azul` is closer to `El cielo es rojo` than to `The sky is blue`) - -Experiment with your own strings to see what you can learn. \ No newline at end of file diff --git a/apps/embeddings-playground/embeddings_playground.py b/apps/embeddings-playground/embeddings_playground.py deleted file mode 100644 index 86890287..00000000 --- a/apps/embeddings-playground/embeddings_playground.py +++ /dev/null @@ -1,178 +0,0 @@ -""" -EMBEDDINGS PLAYGROUND - -This is a single-page streamlit app for experimenting with OpenAI embeddings. - -Before running, install required dependencies with: - -`pip install -r apps/embeddings-playground/requirements.txt` - -You may need to change the path to match your local path. - -Verify installation of streamlit with `streamlit hello`. - -Run this script with: - -`streamlit run apps/embeddings-playground/embeddings_playground.py` - -Again, you may need to change the path to match your local path. -""" - -# IMPORTS -import altair as alt -import openai -import os -import pandas as pd -from scipy import spatial -import streamlit as st -from tenacity import ( - retry, - stop_after_attempt, - wait_random_exponential, -) - -# FUNCTIONS - -# get embeddings -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@st.cache_data -def embedding_from_string(input: str, model: str) -> list: - response = openai.Embedding.create(input=input, model=model) - embedding = response["data"][0]["embedding"] - return embedding - - -# plot distance matrix -def plot_distance_matrix(strings: list, engine: str, distance: str): - # create dataframe of embedding distances - df = pd.DataFrame({"string": strings, "index": range(len(strings))}) - df["embedding"] = df["string"].apply(lambda string: embedding_from_string(string, engine)) - df["string"] = df.apply(lambda row: f"({row['index'] + 1}) {row['string']}", axis=1) - df["dummy_key"] = 0 - df = pd.merge(df, df, on="dummy_key", suffixes=("_1", "_2")).drop("dummy_key", axis=1) - df = df[df["string_1"] != df["string_2"]] # filter out diagonal (always 0) - df["distance"] = df.apply( - lambda row: distance_metrics[distance](row["embedding_1"], row["embedding_2"]), - axis=1, - ) - df["label"] = df["distance"].apply(lambda d: f"{d:.2f}") - - # set chart params - text_size = 32 - label_size = 16 - pixels_per_string = 80 # aka row height & column width (perpendicular to text) - max_label_width = 256 # in pixels, not characters, I think? - chart_width = ( - 50 - + min(max_label_width, max(df["string_1"].apply(len) * label_size/2)) - + len(strings) * pixels_per_string - ) - - # extract chart parameters from data - color_min = df["distance"].min() - color_max = 1.5 * df["distance"].max() - x_order = df["string_1"].values - ranked = False - if ranked: - ranked_df = df[(df["string_1"] == f"(1) {strings[0]}")].sort_values(by="distance") - y_order = ranked_df["string_2"].values - else: - y_order = x_order - - # create chart - boxes = ( - alt.Chart(df, title=f"{engine}") - .mark_rect() - .encode( - x=alt.X("string_1", title=None, sort=x_order), - y=alt.Y("string_2", title=None, sort=y_order), - color=alt.Color("distance:Q", title=f"{distance} distance", scale=alt.Scale(domain=[color_min,color_max], scheme="darkblue", reverse=True)), - ) - ) - - labels = ( - boxes.mark_text(align="center", baseline="middle", fontSize=text_size) - .encode(text="label") - .configure_axis(labelLimit=max_label_width, labelFontSize=label_size) - .properties(width=chart_width, height=chart_width) - ) - - st.altair_chart(labels) # note: layered plots are not supported in streamlit :( - - -# PAGE - -st.title("OpenAI Embeddings Playground") - -# get API key -try: - openai.api_key = os.getenv("OPENAI_API_KEY") - st.write(f"API key sucessfully retrieved: {openai.api_key[:3]}...{openai.api_key[-4:]}") -except: - st.header("Enter API Key") - openai.api_key = st.text_input("API key") - -# select distance metric -st.header("Select distance metric") -distance_metrics = { - "cosine": spatial.distance.cosine, - "L1 (cityblock)": spatial.distance.cityblock, - "L2 (euclidean)": spatial.distance.euclidean, - "Linf (chebyshev)": spatial.distance.chebyshev, - #'correlation': spatial.distance.correlation, # not sure this makes sense for individual vectors - looks like cosine -} -distance_metric_options = list(distance_metrics.keys()) -distance = st.radio("Distance metric", distance_metric_options) - -# select models -st.header("Select models") -models = [ - "text-embedding-ada-002", - "text-similarity-ada-001", - "text-similarity-babbage-001", - "text-similarity-curie-001", - "text-similarity-davinci-001", -] -prechecked_models = [ - "text-embedding-ada-002" -] -model_values = [st.checkbox(model, key=model, value=(model in prechecked_models)) for model in models] - -# enter strings -st.header("Enter strings") -strings = [] -if "num_boxes" not in st.session_state: - st.session_state.num_boxes = 5 -if st.session_state.num_boxes > 2: - if st.button("Remove last text box"): - st.session_state.num_boxes -= 1 -if st.button("Add new text box"): - st.session_state.num_boxes += 1 -for i in range(st.session_state.num_boxes): - string = st.text_input(f"String {i+1}") - strings.append(string) - -# rank strings -st.header("Rank strings by relatedness") -if st.button("Rank"): - # display a dataframe comparing rankings to string #1 - st.subheader("Rankings") - ranked_strings = {} - for model, value in zip(models, model_values): - if value: - query_embedding = embedding_from_string(strings[0], model) - df = pd.DataFrame({"string": strings}) - df[model] = df["string"].apply(lambda string: embedding_from_string(string, model)) - df["distance"] = df[model].apply( - lambda embedding: distance_metrics[distance](query_embedding, embedding) - ) - df = df.sort_values(by="distance") - ranked_strings[model] = df["string"].values - df = pd.DataFrame(ranked_strings) - st.dataframe(df) - - # display charts of all the pairwise distances between strings - st.subheader("Distance matrices") - for model, value in zip(models, model_values): - if value: - plot_distance_matrix(strings, model, distance) diff --git a/apps/embeddings-playground/example_distance_matrix.png b/apps/embeddings-playground/example_distance_matrix.png deleted file mode 100644 index 741d0d7c..00000000 Binary files a/apps/embeddings-playground/example_distance_matrix.png and /dev/null differ diff --git a/apps/embeddings-playground/requirements.txt b/apps/embeddings-playground/requirements.txt deleted file mode 100644 index c2326224..00000000 --- a/apps/embeddings-playground/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -altair -openai -pandas -scipy -streamlit -tenacity \ No newline at end of file diff --git a/apps/enterprise-knowledge-retrieval/README.md b/apps/enterprise-knowledge-retrieval/README.md deleted file mode 100644 index 5417e927..00000000 --- a/apps/enterprise-knowledge-retrieval/README.md +++ /dev/null @@ -1,36 +0,0 @@ -# Enterprise Knowledge Retrieval - -This app is a deep dive on Enterprise Knowledge Retrieval, which aims to take some unstructured text documents and create a usable knowledge base application with it. - -This repo contains a notebook and a basic Streamlit app: -- `enterprise_knowledge_retrieval.ipynb`: A notebook containing a step by step process of tokenising, chunking and embedding your data in a vector database, building a chat agent on top and running a basic evaluation of its performance. -- `chatbot.py`: A Streamlit app providing simple Q&A via a search bar to query your knowledge base. - -To run the app, please follow the instructions below in the ```App``` section - -## Notebook - -The notebook is the best place to start, and takes you through an end-to-end workflow for setting up and evaluating a simple back-end knowledge retrieval service: -- **Setup:** Initiate variables and connect to a vector database. -- **Storage:** Configure the database, prepare our data and store embeddings and metadata for retrieval. -- **Search:** Extract relevant documents back out with a basic search function and use an LLM to summarise results into a concise reply. -- **Answer:** Add a more sophisticated agent which will process the user's query and maintain a memory for follow-up questions. -- **Evaluate:** Take question/answer pairs using our service, evaluate and plot them to scope out remedial action - -Once you've run the notebook through to the Search stage, you should have what you need to set up and run the app. - -## App - -We've rolled in a basic Streamlit app that you can interact with to test your retrieval service using either standard semantic search or [HyDE](https://arxiv.org/abs/2212.10496) retrievals. - -To use it: -- Ensure you followed the Setup and Storage steps from the notebook to populate a vector database with searchable content. -- Set up a virtual environment with pip by running ```virtualenv venv``` (ensure ```virtualenv``` is installed). -- Activate the environment by running ```source venv/bin/activate```. -- Install requirements by running ```pip install -r requirements.txt```. -- Run ```streamlit run chatbot.py``` to fire up the Streamlit app in your browser. - -## Limitations - -- This app uses Redis as a vector database, but there are many other options highlighted `../examples/vector_databases` depending on your need. -- We introduce many areas you may optimize in the notebook, but we'll deep dive on these in subsequent cookbooks. \ No newline at end of file diff --git a/apps/enterprise-knowledge-retrieval/assistant.py b/apps/enterprise-knowledge-retrieval/assistant.py deleted file mode 100644 index 01795cdb..00000000 --- a/apps/enterprise-knowledge-retrieval/assistant.py +++ /dev/null @@ -1,183 +0,0 @@ -from langchain.agents import ( - Tool, - AgentExecutor, - LLMSingleActionAgent, - AgentOutputParser, -) -from langchain.prompts import BaseChatPromptTemplate -from langchain import SerpAPIWrapper, LLMChain -from langchain.chat_models import ChatOpenAI -from typing import List, Union -from langchain.schema import AgentAction, AgentFinish, HumanMessage -from langchain.memory import ConversationBufferWindowMemory -import openai -import re -import streamlit as st - -from database import get_redis_results, get_redis_connection -from config import RETRIEVAL_PROMPT, CHAT_MODEL, INDEX_NAME, SYSTEM_PROMPT - - -redis_client = get_redis_connection() - - -def answer_user_question(query): - - results = get_redis_results(redis_client, query, INDEX_NAME) - - results.to_csv("results.csv") - - search_content = "" - for x, y in results.head(3).iterrows(): - search_content += y["title"] + "\n" + y["result"] + "\n\n" - - retrieval_prepped = RETRIEVAL_PROMPT.format( - SEARCH_QUERY_HERE=query, SEARCH_CONTENT_HERE=search_content - ) - - retrieval = openai.ChatCompletion.create( - model=CHAT_MODEL, - messages=[{"role": "user", "content": retrieval_prepped}], - max_tokens=500, - ) - - # Response provided by GPT-3.5 - return retrieval["choices"][0]["message"]["content"] - - -def answer_question_hyde(query): - - hyde_prompt = """You are OracleGPT, an helpful expert who answers user questions to the best of their ability. - Provide a confident answer to their question. If you don't know the answer, make the best guess you can based on the context of the question. - - User question: {USER_QUESTION_HERE} - - Answer:""" - - hypothetical_answer = openai.ChatCompletion.create( - model=CHAT_MODEL, - messages=[ - { - "role": "user", - "content": hyde_prompt.format(USER_QUESTION_HERE=query), - } - ], - )["choices"][0]["message"]["content"] - # st.write(hypothetical_answer) - results = get_redis_results(redis_client, hypothetical_answer, INDEX_NAME) - - results.to_csv("results.csv") - - search_content = "" - for x, y in results.head(3).iterrows(): - search_content += y["title"] + "\n" + y["result"] + "\n\n" - - retrieval_prepped = RETRIEVAL_PROMPT.replace("SEARCH_QUERY_HERE", query).replace( - "SEARCH_CONTENT_HERE", search_content - ) - retrieval = openai.ChatCompletion.create( - model=CHAT_MODEL, - messages=[{"role": "user", "content": retrieval_prepped}], - max_tokens=500, - ) - - return retrieval["choices"][0]["message"]["content"] - - -# Set up a prompt template -class CustomPromptTemplate(BaseChatPromptTemplate): - # The template to use - template: str - # The list of tools available - tools: List[Tool] - - def format_messages(self, **kwargs) -> str: - # Get the intermediate steps (AgentAction, Observation tuples) - # Format them in a particular way - intermediate_steps = kwargs.pop("intermediate_steps") - thoughts = "" - for action, observation in intermediate_steps: - thoughts += action.log - thoughts += f"\nObservation: {observation}\nThought: " - # Set the agent_scratchpad variable to that value - kwargs["agent_scratchpad"] = thoughts - # Create a tools variable from the list of tools provided - kwargs["tools"] = "\n".join( - [f"{tool.name}: {tool.description}" for tool in self.tools] - ) - # Create a list of tool names for the tools provided - kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools]) - formatted = self.template.format(**kwargs) - return [HumanMessage(content=formatted)] - - -class CustomOutputParser(AgentOutputParser): - def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]: - # Check if agent should finish - if "Final Answer:" in llm_output: - return AgentFinish( - # Return values is generally always a dictionary with a single `output` key - # It is not recommended to try anything else at the moment :) - return_values={"output": llm_output.split("Final Answer:")[-1].strip()}, - log=llm_output, - ) - # Parse out the action and action input - regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)" - match = re.search(regex, llm_output, re.DOTALL) - if not match: - raise ValueError(f"Could not parse LLM output: `{llm_output}`") - action = match.group(1).strip() - action_input = match.group(2) - # Return the action and action input - return AgentAction( - tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output - ) - - -def initiate_agent(tools): - prompt = CustomPromptTemplate( - template=SYSTEM_PROMPT, - tools=tools, - # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically - # The history template includes "history" as an input variable so we can interpolate it into the prompt - input_variables=["input", "intermediate_steps", "history"], - ) - - # Initiate the memory with k=2 to keep the last two turns - # Provide the memory to the agent - memory = ConversationBufferWindowMemory(k=2) - - output_parser = CustomOutputParser() - - llm = ChatOpenAI(temperature=0) - - # LLM chain consisting of the LLM and a prompt - llm_chain = LLMChain(llm=llm, prompt=prompt) - - tool_names = [tool.name for tool in tools] - agent = LLMSingleActionAgent( - llm_chain=llm_chain, - output_parser=output_parser, - stop=["\nObservation:"], - allowed_tools=tool_names, - ) - - agent_executor = AgentExecutor.from_agent_and_tools( - agent=agent, tools=tools, verbose=True, memory=memory - ) - - return agent_executor - - -def ask_gpt(query): - response = openai.ChatCompletion.create( - model=CHAT_MODEL, - messages=[ - { - "role": "user", - "content": "Please answer my question.\nQuestion: {}".format(query), - } - ], - temperature=0, - ) - return response["choices"][0]["message"]["content"] diff --git a/apps/enterprise-knowledge-retrieval/chatbot.py b/apps/enterprise-knowledge-retrieval/chatbot.py deleted file mode 100644 index bec51be8..00000000 --- a/apps/enterprise-knowledge-retrieval/chatbot.py +++ /dev/null @@ -1,86 +0,0 @@ -from langchain.agents import Tool -import pandas as pd -import streamlit as st -from streamlit_chat import message - -from database import get_redis_connection -from assistant import ( - answer_user_question, - initiate_agent, - answer_question_hyde, - ask_gpt, -) - -# Initialise database - -## Initialise Redis connection -redis_client = get_redis_connection() - - -### CHATBOT APP - -# --- GENERAL SETTINGS --- -PAGE_TITLE: str = "Knowledge Retrieval Bot" -PAGE_ICON: str = "🤖" - -st.set_page_config(page_title=PAGE_TITLE, page_icon=PAGE_ICON) - -st.title("Wiki Chatbot") -st.subheader("Learn things - random things!") - -# Using object notation -add_selectbox = st.sidebar.selectbox( - "What kind of search?", ("Standard vector search", "HyDE") -) - -# Define which tools the agent can use to answer user queries -tools = [ - Tool( - name="Search", - func=answer_user_question - if add_selectbox == "Standard vector search" - else answer_question_hyde, - description="Useful for when you need to answer general knowledge questions. Input should be a fully formed question.", - ), - Tool( - name="Ask", - func=ask_gpt, - description="Useful if the question is not general knowledge. Input should be a fully formed question.", - ), -] - -if "generated" not in st.session_state: - st.session_state["generated"] = [] - -if "past" not in st.session_state: - st.session_state["past"] = [] - - -def query(question): - response = st.session_state["chat"].ask_assistant(question) - return response - - -prompt = st.text_input("What do you want to know: ", "", key="input") - -if st.button("Submit", key="generationSubmit"): - with st.spinner("Thinking..."): - # Initialization - if "agent" not in st.session_state: - st.session_state["agent"] = initiate_agent(tools) - - response = st.session_state["agent"].run(prompt) - - st.session_state.past.append(prompt) - st.session_state.generated.append(response) - -if len(st.session_state["generated"]) > 0: - for i in range(len(st.session_state["generated"]) - 1, -1, -1): - message(st.session_state["generated"][i], key=str(i)) - message(st.session_state["past"][i], is_user=True, key=str(i) + "_user") - - with st.expander("See search results"): - - results = list(pd.read_csv("results.csv")["result"]) - - st.write(results) diff --git a/apps/enterprise-knowledge-retrieval/config.py b/apps/enterprise-knowledge-retrieval/config.py deleted file mode 100644 index a464f95b..00000000 --- a/apps/enterprise-knowledge-retrieval/config.py +++ /dev/null @@ -1,46 +0,0 @@ -REDIS_HOST = "localhost" -REDIS_PORT = "6379" -REDIS_DB = "0" -INDEX_NAME = "wiki-index" -VECTOR_FIELD_NAME = "content_vector" -CHAT_MODEL = "gpt-3.5-turbo" -EMBEDDINGS_MODEL = "text-embedding-ada-002" -# Set up the base template -SYSTEM_PROMPT = """You are WikiGPT, a helpful bot who has access to a database of Wikipedia data to answer questions. -Accept the first answer that you are provided for the user. -You have access to the following tools:: - -{tools} - -Use the following format: - -Question: the input question you must answer -Thought: you should always think about what to do -Action: the action to take, should be one of [{tool_names}] -Action Input: the input to the action -Observation: the result of the action -... (this Thought/Action/Action Input/Observation can repeat N times) -Thought: I now know the final answer -Final Answer: the final answer to the original input question - -Begin! Remember to give detailed, informative answers - -Previous conversation history: -{history} - -New question: {input} -{agent_scratchpad}""" -# Build a prompt to provide the original query, the result and ask to summarise for the user -RETRIEVAL_PROMPT = """Use the content to answer the search query the customer has sent. Provide the source for your answer. -If you can't answer the user's question, say "Sorry, I am unable to answer the question with the content". Do not guess. - -Search query: - -{SEARCH_QUERY_HERE} - -Content: - -{SEARCH_CONTENT_HERE} - -Answer: -""" diff --git a/apps/enterprise-knowledge-retrieval/database.py b/apps/enterprise-knowledge-retrieval/database.py deleted file mode 100644 index 58914a67..00000000 --- a/apps/enterprise-knowledge-retrieval/database.py +++ /dev/null @@ -1,72 +0,0 @@ -import ast -from math import isnan -import numpy as np -import pandas as pd -import openai -from redis import Redis as r -from redis.commands.search.query import Query - -from config import ( - REDIS_DB, - REDIS_HOST, - REDIS_PORT, - VECTOR_FIELD_NAME, - EMBEDDINGS_MODEL, - INDEX_NAME, -) - - -def get_redis_connection(): - redis_client = r( - host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=False - ) - return redis_client - - -# Make query to Redis -def query_redis(redis_conn, query, index_name, top_k=5): - - ## Creates embedding vector from user query - embedded_query = np.array( - openai.Embedding.create(input=query, model=EMBEDDINGS_MODEL,)["data"][ - 0 - ]["embedding"], - dtype=np.float32, - ).tobytes() - - # prepare the query - q = ( - Query(f"*=>[KNN {top_k} @{VECTOR_FIELD_NAME} $vec_param AS vector_score]") - .sort_by("vector_score") - .paging(0, top_k) - .return_fields("vector_score", "url", "title", "content", "text_chunk_index") - .dialect(2) - ) - params_dict = {"vec_param": embedded_query} - - # Execute the query - results = redis_conn.ft(index_name).search(q, query_params=params_dict) - - return results - - -# Get mapped documents from Redis results -def get_redis_results(redis_conn, query, index_name): - - # Get most relevant documents from Redis - query_result = query_redis(redis_conn, query, index_name) - - # Extract info into a list - query_result_list = [] - for i, result in enumerate(query_result.docs): - result_order = i - url = result.url - title = result.title - text = result.content - score = result.vector_score - query_result_list.append((result_order, url, title, text, score)) - - # Display result as a DataFrame for ease of us - result_df = pd.DataFrame(query_result_list) - result_df.columns = ["id", "url", "title", "result", "certainty"] - return result_df diff --git a/apps/enterprise-knowledge-retrieval/enterprise_knowledge_retrieval.ipynb b/apps/enterprise-knowledge-retrieval/enterprise_knowledge_retrieval.ipynb deleted file mode 100644 index 727285bb..00000000 --- a/apps/enterprise-knowledge-retrieval/enterprise_knowledge_retrieval.ipynb +++ /dev/null @@ -1,2209 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "685d4507", - "metadata": {}, - "source": [ - "# Enterprise Knowledge Retrieval\n", - "\n", - "This notebook contains an end-to-end workflow to set up an Enterprise Knowledge Retrieval solution from scratch.\n", - "\n", - "### Problem Statement\n", - "\n", - "LLMs have great conversational ability but their knowledge is general and often out of date. Relevant knowledge often exists, but is kept in disparate datestores that are hard to surface with current search solutions.\n", - "\n", - "\n", - "### Objective\n", - "\n", - "We want to deliver an outstanding user experience where the user is presented with the right knowledge when they need it in a clear and conversational way. To accomplish this we need an LLM-powered solution that knows our organizational context and data, that can retrieve the right knowledge when the user needs it. \n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "8eab9aae", - "metadata": {}, - "source": [ - "## Solution\n", - "\n", - "![title](img/enterprise_knowledge_retrieval.png)\n", - "\n", - "We'll build a knowledge retrieval solution that will embed a corpus of knowledge (in our case a database of Wikipedia manuals) and use it to answer user questions.\n", - "\n", - "### Learning Path\n", - "\n", - "#### Walkthrough\n", - "\n", - "You can follow on to this solution walkthrough through either the video recorded here, or the text walkthrough below. We'll build out the solution in the following stages:\n", - "- **Setup:** Initiate variables and connect to a vector database.\n", - "- **Storage:** Configure the database, prepare our data and store embeddings and metadata for retrieval.\n", - "- **Search:** Extract relevant documents back out with a basic search function and use an LLM to summarise results into a concise reply.\n", - "- **Answer:** Add a more sophisticated agent which will process the user's query and maintain a memory for follow-up questions.\n", - "- **Evaluate:** Take a sample evaluated question/answer pairs using our service and plot them to scope out remedial action." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ae9b1412", - "metadata": {}, - "source": [ - "## Walkthrough" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4e85be52", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ab1a0a6a", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Import libraries and set up a connection to a Redis vector database for our knowledge base.\n", - "\n", - "You can substitute Redis for any other vectorstore or database - there are a [selection](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html) that are supported by Langchain natively, while other connectors will need to be developed yourself." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c79535f1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: redis in /opt/homebrew/lib/python3.11/site-packages (4.5.5)\r\n" - ] - } - ], - "source": [ - "!pip install redis\n", - "!pip install openai\n", - "!pip install tiktoken\n", - "!pip install wget" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "cd8e3d30", - "metadata": {}, - "outputs": [], - "source": [ - "from ast import literal_eval\n", - "import concurrent\n", - "import openai\n", - "import os\n", - "import numpy as np\n", - "from numpy import array, average\n", - "import pandas as pd\n", - "from tenacity import retry, wait_random_exponential, stop_after_attempt\n", - "import tiktoken\n", - "from tqdm import tqdm\n", - "from typing import List, Iterator\n", - "import wget\n", - "\n", - "# Redis imports\n", - "from redis import Redis as r\n", - "from redis.commands.search.query import Query\n", - "from redis.commands.search.field import (\n", - " TextField,\n", - " VectorField,\n", - " NumericField\n", - ")\n", - "from redis.commands.search.indexDefinition import (\n", - " IndexDefinition,\n", - " IndexType\n", - ")\n", - "\n", - "# Langchain imports\n", - "from langchain.embeddings import OpenAIEmbeddings\n", - "from langchain.chains import RetrievalQA\n", - "\n", - "CHAT_MODEL = \"gpt-3.5-turbo\"" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "53641bc5", - "metadata": {}, - "outputs": [], - "source": [ - "pd.set_option('display.max_colwidth', 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6fbde85b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - " 0% [ ] 0 / 4470649\r\n", - " 0% [ ] 8192 / 4470649\r\n", - " 0% [ ] 16384 / 4470649\r\n", - " 0% [ ] 24576 / 4470649\r\n", - " 0% [ ] 32768 / 4470649\r\n", - " 0% [ ] 40960 / 4470649\r\n", - " 1% [ ] 49152 / 4470649\r\n", - " 1% [ ] 57344 / 4470649\r\n", - " 1% [. ] 65536 / 4470649\r\n", - " 1% [. ] 73728 / 4470649\r\n", - " 1% [. ] 81920 / 4470649\r\n", - " 2% [. ] 90112 / 4470649\r\n", - " 2% [. ] 98304 / 4470649\r\n", - " 2% [. ] 106496 / 4470649\r\n", - " 2% [. ] 114688 / 4470649\r\n", - " 2% [.. ] 122880 / 4470649\r\n", - " 2% [.. ] 131072 / 4470649\r\n", - " 3% [.. ] 139264 / 4470649\r\n", - " 3% [.. ] 147456 / 4470649\r\n", - " 3% [.. ] 155648 / 4470649\r\n", - " 3% [.. ] 163840 / 4470649\r\n", - " 3% [.. ] 172032 / 4470649\r\n", - " 4% [.. ] 180224 / 4470649\r\n", - 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Unnamed: 0idurltitletext
08783661https://simple.wikipedia.org/wiki/PhotonPhotonPhotons (from Greek φως, meaning light), in many atomic models in physics, are particles which transmit light. In other words, light is carried over space by photons. Photon is an elementary particle that is its own antiparticle. In quantum mechanics each photon has a characteristic quantum of energy that depends on frequency: A photon associated with light at a higher frequency will have more energy (and be associated with light at a shorter wavelength).\\n\\nPhotons have a rest mass of 0 (zero). However, Einstein's theory of relativity says that they do have a certain amount of momentum. Before the photon got its name, Einstein revived the proposal that light is separate pieces of energy (particles). These particles came to be known as photons. \\n\\nA photon is usually given the symbol γ (gamma),\\n\\nProperties \\n\\nPhotons are fundamental particles. Although they can be created and destroyed, their lifetime is infinite.\\n\\nIn a vacuum, all photons move at the speed of light, c, which is equal to 299,792,458 meters (approximately 300,000 kilometers) per second.\\n\\nA photon has a given frequency, which determines its color. Radio technology makes great use of frequency. Beyond the visible range, frequency is less discussed, for example it is little used in distinguishing between X-Ray photons and infrared. Frequency is equivalent to the quantum energy of the photon, as related by the Planck constant equation,\\n\\n,\\n\\nwhere is the photon's energy, is the Plank constant, and is the frequency of the light associated with the photon. This frequency, , is typically measured in cycles per second, or equivalently, in Hz. The quantum energy of different photons is often used in cameras, and other machines that use visible and higher than visible radiation. This because these photons are energetic enough to ionize atoms. \\n\\nAnother property of a photon is its wavelength. The frequency , wavelength , and speed of light are related by the equation,\\n\\n,\\n\\nwhere (lambda) is the wavelength, or length of the wave (typically measured in meters.)\\n\\nAnother important property of a photon is its polarity. If you saw a giant photon coming straight at you, it could appear as a swath whipping vertically, horizontally, or somewhere in between. Polarized sunglasses stop photons swinging up and down from passing. This is how they reduce glare as light bouncing off of surfaces tend to fly that way. Liquid crystal displays also use polarity to control which light passes through. Some animals can see light polarization. \\n\\nFinally, a photon has a property called spin. Spin is related to light's circular polarization.\\n\\nPhoton interactions with matter\\nLight is often created or absorbed when an electron gains or loses energy. This energy can be in the form of heat, kinetic energy, or other form. For example, an incandescent light bulb uses heat. The increase of energy can push an electron up one level in a shell called a \"valence\". This makes it unstable, and like everything, it wants to be in the lowest energy state. (If being in the lowest energy state is confusing, pick up a pencil and drop it. Once on the ground, the pencil will be in a lower energy state). When the electron drops back down to a lower energy state, it needs to release the energy that hit it, and it must obey the conservation of energy (energy can neither be created nor destroyed). Electrons release this energy as photons, and at higher intensities, this photon can be seen as visible light.\\n\\nPhotons and the electromagnetic force\\nIn particle physics, photons are responsible for electromagnetic force. Electromagnetism is an idea that combines electricity with magnetism. One common way that we experience electromagnetism in our daily lives is light, which is caused by electromagnetism. Electromagnetism is also responsible for charge, which is the reason that you can not push your hand through a table. Since photons are the force-carrying particle of electromagnetism, they are also gauge bosons. Some matter–called dark matter–is not believed to be affected by electromagnetism. This would mean that dark matter does not have a charge, and does not give off light.\\n\\nRelated pages\\n Particle physics\\n\\nBasic physics ideas\\nElectromagnetism\\nLight\\nElementary particles
124257796https://simple.wikipedia.org/wiki/Thomas%20DolbyThomas DolbyThomas Dolby (born Thomas Morgan Robertson; 14 October 1958) is a British musican and computer designer. He is probably most famous for his 1982 hit, \"She Blinded me with Science\".\\n\\nHe married actress Kathleen Beller in 1988. The couple have three children together.\\n\\nDiscography\\n\\nSingles\\n\\nA Track did not chart in North America until 1983, after the success of \"She Blinded Me With Science\".\\n\\nAlbums\\n\\nStudio albums\\n\\nEPs\\n\\nReferences\\n\\nEnglish musicians\\nLiving people\\n1958 births\\nNew wave musicians\\nWarner Bros. Records artists
21805967912https://simple.wikipedia.org/wiki/EmbroideryEmbroideryEmbroidery is the art of decorating fabric or other materials with designs stitched in strands of thread or yarn using a needle. Embroidery may also incorporate other materials such as metal strips, pearls, beads, quills, and sequins. Sewing machines can be used to create machine embroidery.\\n\\nQualifications \\nCity and Guilds qualification in Embroidery allows embroiderers to become recognized for their skill. This qualification also gives them the credibility to teach. For example, the notable textiles artist, Kathleen Laurel Sage, began her teaching career by getting the City and Guilds Embroidery 1 and 2 qualifications. She has now gone on to write a book on the subject.\\n\\nReferences\\n\\nOther websites\\n The Crimson Thread of Kinship at the National Museum of Australia\\n\\nNeedlework
31204544309https://simple.wikipedia.org/wiki/Consecutive%20integerConsecutive integerConsecutive numbers are numbers that follow each other in order. They have a difference of 1 between every two numbers. In a set of consecutive numbers, the mean and the median are equal. \\n\\nIf n is a number, then the next numbers will be n+1 and n+2. \\n\\nExamples \\n\\nConsecutive numbers that follow each other in order:\\n\\n 1, 2, 3, 4, 5\\n -3, −2, −1, 0, 1, 2, 3, 4\\n 6, 7, 8, 9, 10, 11, 12, 13\\n\\nConsecutive even numbers \\nConsecutive even numbers are even numbers that follow each other. They have a difference of 2 between every two numbers.\\n\\nIf n is an even integer, then n, n+2, n+4 and n+6 will be consecutive even numbers.\\n\\nFor example - 2,4,6,8,10,12,14,18 etc.\\n\\nConsecutive odd numbers\\nConsecutive odd numbers are odd numbers that follow each other. Like consecutive odd numbers, they have a difference of 2 between every two numbers.\\n\\nIf n is an odd integer, then n, n+2, n+4 and n+6 will be consecutive odd numbers.\\n\\nExamples\\n\\n3, 5, 7, 9, 11, 13, etc.\\n\\n−23, −21, −19, −17, −15, -13, -11\\n\\nIntegers
41147741741https://simple.wikipedia.org/wiki/German%20EmpireGerman EmpireThe German Empire (\"Deutsches Reich\" or \"Deutsches Kaiserreich\" in the German language) is the name for a group of German countries from January 18, 1871 to November 9, 1918. This is from the Unification of Germany when Wilhelm I of Prussia was made German Kaiser to when the third Emperor Wilhelm II was removed from power at the end of the First World War. In the 1920s, German nationalists started to call it the \"Second Reich\".\\n\\nThe name of Germany was \"Deutsches Reich\" until 1945. \"Reich\" can mean many things, empire, kingdom, state, \"richness\" or \"wealth\". Most members of the Empire were previously members of the North German Confederation. \\n\\nAt different times, there were three groups of smaller countries, each group was later called a \"Reich\" by some Germans. The first was the Holy Roman Empire. The second was the German Empire. The third was the Third Reich.\\n\\nThe words \"Second Reich\" were used for the German Empire by Arthur Moeller van den Bruck, a nationalist writer in the 1920s. He was trying to make a link with the earlier Holy Roman Empire which had once been very strong. Germany had lost First World War and was suffering big problems. van den Bruck wanted to start a \"Third Reich\" to unite the country. These words were later used by the Nazis to make themselves appear stronger.\\n\\nStates in the Empire\\n\\nRelated pages\\n Germany\\n Holy Roman Empire\\n Nazi Germany, or \"Drittes Reich\"\\n\\n1870s establishments in Germany\\n \\nStates and territories disestablished in the 20th century\\nStates and territories established in the 19th century\\n1871 establishments in Europe\\n1918 disestablishments in Germany
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" - ], - "text/plain": [ - " Unnamed: 0 id url \\\n", - "0 878 3661 https://simple.wikipedia.org/wiki/Photon \n", - "1 2425 7796 https://simple.wikipedia.org/wiki/Thomas%20Dolby \n", - "2 18059 67912 https://simple.wikipedia.org/wiki/Embroidery \n", - "3 12045 44309 https://simple.wikipedia.org/wiki/Consecutive%20integer \n", - "4 11477 41741 https://simple.wikipedia.org/wiki/German%20Empire \n", - "\n", - " title \\\n", - "0 Photon \n", - "1 Thomas Dolby \n", - "2 Embroidery \n", - "3 Consecutive integer \n", - "4 German Empire \n", - "\n", - " text \n", - "0 Photons (from Greek φως, meaning light), in many atomic models in physics, are particles which transmit light. In other words, light is carried over space by photons. Photon is an elementary particle that is its own antiparticle. In quantum mechanics each photon has a characteristic quantum of energy that depends on frequency: A photon associated with light at a higher frequency will have more energy (and be associated with light at a shorter wavelength).\\n\\nPhotons have a rest mass of 0 (zero). However, Einstein's theory of relativity says that they do have a certain amount of momentum. Before the photon got its name, Einstein revived the proposal that light is separate pieces of energy (particles). These particles came to be known as photons. \\n\\nA photon is usually given the symbol γ (gamma),\\n\\nProperties \\n\\nPhotons are fundamental particles. Although they can be created and destroyed, their lifetime is infinite.\\n\\nIn a vacuum, all photons move at the speed of light, c, which is equal to 299,792,458 meters (approximately 300,000 kilometers) per second.\\n\\nA photon has a given frequency, which determines its color. Radio technology makes great use of frequency. Beyond the visible range, frequency is less discussed, for example it is little used in distinguishing between X-Ray photons and infrared. Frequency is equivalent to the quantum energy of the photon, as related by the Planck constant equation,\\n\\n,\\n\\nwhere is the photon's energy, is the Plank constant, and is the frequency of the light associated with the photon. This frequency, , is typically measured in cycles per second, or equivalently, in Hz. The quantum energy of different photons is often used in cameras, and other machines that use visible and higher than visible radiation. This because these photons are energetic enough to ionize atoms. \\n\\nAnother property of a photon is its wavelength. The frequency , wavelength , and speed of light are related by the equation,\\n\\n,\\n\\nwhere (lambda) is the wavelength, or length of the wave (typically measured in meters.)\\n\\nAnother important property of a photon is its polarity. If you saw a giant photon coming straight at you, it could appear as a swath whipping vertically, horizontally, or somewhere in between. Polarized sunglasses stop photons swinging up and down from passing. This is how they reduce glare as light bouncing off of surfaces tend to fly that way. Liquid crystal displays also use polarity to control which light passes through. Some animals can see light polarization. \\n\\nFinally, a photon has a property called spin. Spin is related to light's circular polarization.\\n\\nPhoton interactions with matter\\nLight is often created or absorbed when an electron gains or loses energy. This energy can be in the form of heat, kinetic energy, or other form. For example, an incandescent light bulb uses heat. The increase of energy can push an electron up one level in a shell called a \"valence\". This makes it unstable, and like everything, it wants to be in the lowest energy state. (If being in the lowest energy state is confusing, pick up a pencil and drop it. Once on the ground, the pencil will be in a lower energy state). When the electron drops back down to a lower energy state, it needs to release the energy that hit it, and it must obey the conservation of energy (energy can neither be created nor destroyed). Electrons release this energy as photons, and at higher intensities, this photon can be seen as visible light.\\n\\nPhotons and the electromagnetic force\\nIn particle physics, photons are responsible for electromagnetic force. Electromagnetism is an idea that combines electricity with magnetism. One common way that we experience electromagnetism in our daily lives is light, which is caused by electromagnetism. Electromagnetism is also responsible for charge, which is the reason that you can not push your hand through a table. Since photons are the force-carrying particle of electromagnetism, they are also gauge bosons. Some matter–called dark matter–is not believed to be affected by electromagnetism. This would mean that dark matter does not have a charge, and does not give off light.\\n\\nRelated pages\\n Particle physics\\n\\nBasic physics ideas\\nElectromagnetism\\nLight\\nElementary particles \n", - "1 Thomas Dolby (born Thomas Morgan Robertson; 14 October 1958) is a British musican and computer designer. He is probably most famous for his 1982 hit, \"She Blinded me with Science\".\\n\\nHe married actress Kathleen Beller in 1988. The couple have three children together.\\n\\nDiscography\\n\\nSingles\\n\\nA Track did not chart in North America until 1983, after the success of \"She Blinded Me With Science\".\\n\\nAlbums\\n\\nStudio albums\\n\\nEPs\\n\\nReferences\\n\\nEnglish musicians\\nLiving people\\n1958 births\\nNew wave musicians\\nWarner Bros. Records artists \n", - "2 Embroidery is the art of decorating fabric or other materials with designs stitched in strands of thread or yarn using a needle. Embroidery may also incorporate other materials such as metal strips, pearls, beads, quills, and sequins. Sewing machines can be used to create machine embroidery.\\n\\nQualifications \\nCity and Guilds qualification in Embroidery allows embroiderers to become recognized for their skill. This qualification also gives them the credibility to teach. For example, the notable textiles artist, Kathleen Laurel Sage, began her teaching career by getting the City and Guilds Embroidery 1 and 2 qualifications. She has now gone on to write a book on the subject.\\n\\nReferences\\n\\nOther websites\\n The Crimson Thread of Kinship at the National Museum of Australia\\n\\nNeedlework \n", - "3 Consecutive numbers are numbers that follow each other in order. They have a difference of 1 between every two numbers. In a set of consecutive numbers, the mean and the median are equal. \\n\\nIf n is a number, then the next numbers will be n+1 and n+2. \\n\\nExamples \\n\\nConsecutive numbers that follow each other in order:\\n\\n 1, 2, 3, 4, 5\\n -3, −2, −1, 0, 1, 2, 3, 4\\n 6, 7, 8, 9, 10, 11, 12, 13\\n\\nConsecutive even numbers \\nConsecutive even numbers are even numbers that follow each other. They have a difference of 2 between every two numbers.\\n\\nIf n is an even integer, then n, n+2, n+4 and n+6 will be consecutive even numbers.\\n\\nFor example - 2,4,6,8,10,12,14,18 etc.\\n\\nConsecutive odd numbers\\nConsecutive odd numbers are odd numbers that follow each other. Like consecutive odd numbers, they have a difference of 2 between every two numbers.\\n\\nIf n is an odd integer, then n, n+2, n+4 and n+6 will be consecutive odd numbers.\\n\\nExamples\\n\\n3, 5, 7, 9, 11, 13, etc.\\n\\n−23, −21, −19, −17, −15, -13, -11\\n\\nIntegers \n", - "4 The German Empire (\"Deutsches Reich\" or \"Deutsches Kaiserreich\" in the German language) is the name for a group of German countries from January 18, 1871 to November 9, 1918. This is from the Unification of Germany when Wilhelm I of Prussia was made German Kaiser to when the third Emperor Wilhelm II was removed from power at the end of the First World War. In the 1920s, German nationalists started to call it the \"Second Reich\".\\n\\nThe name of Germany was \"Deutsches Reich\" until 1945. \"Reich\" can mean many things, empire, kingdom, state, \"richness\" or \"wealth\". Most members of the Empire were previously members of the North German Confederation. \\n\\nAt different times, there were three groups of smaller countries, each group was later called a \"Reich\" by some Germans. The first was the Holy Roman Empire. The second was the German Empire. The third was the Third Reich.\\n\\nThe words \"Second Reich\" were used for the German Empire by Arthur Moeller van den Bruck, a nationalist writer in the 1920s. He was trying to make a link with the earlier Holy Roman Empire which had once been very strong. Germany had lost First World War and was suffering big problems. van den Bruck wanted to start a \"Third Reich\" to unite the country. These words were later used by the Nazis to make themselves appear stronger.\\n\\nStates in the Empire\\n\\nRelated pages\\n Germany\\n Holy Roman Empire\\n Nazi Germany, or \"Drittes Reich\"\\n\\n1870s establishments in Germany\\n \\nStates and territories disestablished in the 20th century\\nStates and territories established in the 19th century\\n1871 establishments in Europe\\n1918 disestablishments in Germany " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "article_df = pd.read_csv('./wikipedia_articles_2000.csv')\n", - "article_df.head()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ee8240c5", - "metadata": {}, - "source": [ - "## Storage\n", - "\n", - "We'll initialise our vector database first. Which database you choose and how you store data in it is a key decision point, and we've collated a few principles to aid your decision here:\n", - "\n", - "#### How much data to store\n", - "How much metadata do you want to include in the index. Metadata can be used to filter your queries or to bring back more information upon retrieval for your application to use, but larger indices will be slower so there is a trade-off.\n", - "\n", - "There are two common design patterns here:\n", - "- **All-in-one:** Store your metadata with the vector embeddings so you perform semantic search and retrieval on the same database. This is easier to setup and run, but can run into scaling issues when your index grows.\n", - "- **Vectors only:** Store just the embeddings and any IDs/references needed to locate the metadata that goes with the vector in a different database or location. In this pattern the vector database is only used to locate the most relevant IDs, then those are looked up from a different database. This can be more scalable if your vector database is going to be extremely large, or if you have large volumes of metadata with each vector.\n", - "\n", - "#### Which vector database to use\n", - "\n", - "The vector database market is wide and varied, so we won't recommend one over the other. For a few options you can review [this cookbook](./vector_databases/Using_vector_databases_for_embeddings_search.ipynb) and the sub-folders, which have examples supplied by many of the vector database providers in the market. \n", - "\n", - "We're going to use Redis as our database for both document contents and the vector embeddings. You will need the full Redis Stack to enable use of Redisearch, which is the module that allows semantic search - more detail is in the [docs for Redis Stack](https://redis.io/docs/stack/get-started/install/docker/).\n", - "\n", - "To set this up locally, you will need to:\n", - "- Install an appropriate version of [Docker](https://docs.docker.com/desktop/) for your OS\n", - "- Ensure Docker is running i.e. by running ```docker run hello-world```\n", - "- Run the following command: ```docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest```.\n", - "\n", - "The code used here draws heavily on [this repo](https://github.com/RedisAI/vecsim-demo).\n", - "\n", - "After setting up the Docker instance of Redis Stack, you can follow the below instructions to initiate a Redis connection and create a Hierarchical Navigable Small World (HNSW) index for semantic search." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "fecba6de", - "metadata": {}, - "outputs": [], - "source": [ - "# Setup Redis\n", - "\n", - "\n", - "REDIS_HOST = 'localhost'\n", - "REDIS_PORT = '6379'\n", - "REDIS_DB = '0'\n", - "\n", - "redis_client = r(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB,decode_responses=False)\n", - "\n", - "\n", - "# Constants\n", - "VECTOR_DIM = 1536 # length of the vectors\n", - "PREFIX = \"wiki\" # prefix for the document keys\n", - "DISTANCE_METRIC = \"COSINE\" # distance metric for the vectors (ex. COSINE, IP, L2)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4cb5247d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Create search index\n", - "\n", - "# Index\n", - "INDEX_NAME = \"wiki-index\" # name of the search index\n", - "VECTOR_FIELD_NAME = 'content_vector'\n", - "\n", - "# Define RediSearch fields for each of the columns in the dataset\n", - "# This is where you should add any additional metadata you want to capture\n", - "id = TextField(\"id\")\n", - "url = TextField(\"url\")\n", - "title = TextField(\"title\")\n", - "text_chunk = TextField(\"content\")\n", - "file_chunk_index = NumericField(\"file_chunk_index\")\n", - "\n", - "# define RediSearch vector fields to use HNSW index\n", - "\n", - "text_embedding = VectorField(VECTOR_FIELD_NAME,\n", - " \"HNSW\", {\n", - " \"TYPE\": \"FLOAT32\",\n", - " \"DIM\": VECTOR_DIM,\n", - " \"DISTANCE_METRIC\": DISTANCE_METRIC\n", - " }\n", - ")\n", - "# Add all our field objects to a list to be created as an index\n", - "fields = [url,title,text_chunk,file_chunk_index,text_embedding]\n", - "\n", - "redis_client.ping()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "33c07f00", - "metadata": {}, - "source": [ - "Optional step to drop the index if it already exists\n", - "\n", - "```redis_client.ft(INDEX_NAME).dropindex()```\n", - "\n", - "If you want to clear the whole DB use:\n", - "\n", - "```redis_client.flushall()```" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "08f30b56", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Unknown Index name\n", - "Not there yet. Creating\n" - ] - } - ], - "source": [ - "# Check if index exists\n", - "try:\n", - " redis_client.ft(INDEX_NAME).info()\n", - " print(\"Index already exists\")\n", - "except Exception as e:\n", - " print(e)\n", - " # Create RediSearch Index\n", - " print('Not there yet. Creating')\n", - " redis_client.ft(INDEX_NAME).create_index(\n", - " fields = fields,\n", - " definition = IndexDefinition(prefix=[PREFIX], index_type=IndexType.HASH)\n", - " )" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7684b539", - "metadata": {}, - "source": [ - "### Data preparation\n", - "\n", - "The next step is to prepare your data. There are a few decisions to keep in mind here:\n", - "\n", - "#### Chunking your data\n", - "\n", - "In this context, \"chunking\" means cutting up the text into reasonable sizes so that the content will fit into the context length of the language model you choose. If your data is small enough or your LLM has a large enough context limit then you can proceed with no chunking, but in many cases you'll need to chunk your data. I'll share two main design patterns here:\n", - "- **Token-based:** Chunking your data based on some common token threshold i.e. 300, 500, 1000 depending on your use case. This approach works best with a grid-search evaluation to decide the optimal chunking logic over a set of evaluation questions. Variables to consider are whether chunks have overlaps, and whether you extend or truncate a section to keep full sentences and paragraphs together.\n", - "- **Deterministic:** Deterministic chunking uses some common delimiter, like a page break, paragraph end, section header etc. to chunk. This can work well if you have data of reasonable uniform structure, or if you can use GPT to help annotate the data first so you can guarantee common delimiters. However, it can be difficult to handle your chunks when you stuff them into the prompt given you need to cater for many different lengths of content, so consider that in your application design.\n", - "\n", - "#### Which vectors should you store\n", - "\n", - "It is critical to think through the user experience you're building towards because this will inform both the number and content of your vectors. Here are two example use cases that show how these can pan out:\n", - "- **Tool Manual Knowledge Base:** We have a database of manuals that our customers want to search over. For this use case, we want a vector to allow the user to identify the right manual, before searching a different set of vectors to interrogate the content of the manual to avoid any cross-pollination of similar content between different manuals. \n", - " - **Title Vector:** Could include title, author name, brand and abstract.\n", - " - **Content Vector:** Includes content only.\n", - "- **Investor Reports:** We have a database of investor reports that contain financial information about public companies. I want relevant snippets pulled out and summarised so I can decide how to invest. In this instance we want one set of content vectors, so that the retrieval can pull multiple entries on a company or industry, and summarise them to form a composite analysis.\n", - " - **Content Vector:** Includes content only, or content supplemented by other features that improve search quality such as author, industry etc.\n", - " \n", - "For this walkthrough we'll go with 1000 token-based chunking of text content with no overlap, and embed them with the article title included as a prefix." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "948225f7", - "metadata": {}, - "outputs": [], - "source": [ - "# We'll use 1000 token chunks with some intelligence to not split at the end of a sentence\n", - "TEXT_EMBEDDING_CHUNK_SIZE = 1000\n", - "EMBEDDINGS_MODEL = \"text-embedding-ada-002\"" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "31004582", - "metadata": {}, - "outputs": [], - "source": [ - "## Chunking Logic\n", - "\n", - "# Split a text into smaller chunks of size n, preferably ending at the end of a sentence\n", - "def chunks(text, n, tokenizer):\n", - " tokens = tokenizer.encode(text)\n", - " \"\"\"Yield successive n-sized chunks from text.\"\"\"\n", - " i = 0\n", - " while i < len(tokens):\n", - " # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens\n", - " j = min(i + int(1.5 * n), len(tokens))\n", - " while j > i + int(0.5 * n):\n", - " # Decode the tokens and check for full stop or newline\n", - " chunk = tokenizer.decode(tokens[i:j])\n", - " if chunk.endswith(\".\") or chunk.endswith(\"\\n\"):\n", - " break\n", - " j -= 1\n", - " # If no end of sentence found, use n tokens as the chunk size\n", - " if j == i + int(0.5 * n):\n", - " j = min(i + n, len(tokens))\n", - " yield tokens[i:j]\n", - " i = j\n", - " \n", - "def get_unique_id_for_file_chunk(title, chunk_index):\n", - " return str(title+\"-!\"+str(chunk_index))\n", - "\n", - "def chunk_text(x,text_list):\n", - " url = x['url']\n", - " title = x['title']\n", - " file_body_string = x['text']\n", - " \n", - " \"\"\"Return a list of tuples (text_chunk, embedding) for a text.\"\"\"\n", - " token_chunks = list(chunks(file_body_string, TEXT_EMBEDDING_CHUNK_SIZE, tokenizer))\n", - " text_chunks = [f'Title: {title};\\n'+ tokenizer.decode(chunk) for chunk in token_chunks]\n", - " \n", - " #embeddings_response = openai.Embedding.create(input=text_chunks, model=EMBEDDINGS_MODEL)\n", - "\n", - " #embeddings = [embedding[\"embedding\"] for embedding in embeddings_response['data']]\n", - " #text_embeddings = list(zip(text_chunks, embeddings))\n", - "\n", - " # Get the vectors array of triples: file_chunk_id, embedding, metadata for each embedding\n", - " # Metadata is a dict with keys: filename, file_chunk_index\n", - " \n", - " for i, text_chunk in enumerate(text_chunks):\n", - " id = get_unique_id_for_file_chunk(title, i)\n", - " text_list.append(({'id': id\n", - " , 'metadata': {\"url\": x['url']\n", - " ,\"title\": title\n", - " , \"content\": text_chunk\n", - " , \"file_chunk_index\": i}}))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4f4cf064", - "metadata": {}, - "outputs": [], - "source": [ - "## Batch Embedding Logic\n", - "\n", - "# Simple function to take in a list of text objects and return them as a list of embeddings\n", - "@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(10))\n", - "def get_embeddings(input: List):\n", - " response = openai.Embedding.create(\n", - " input=input,\n", - " model=EMBEDDINGS_MODEL,\n", - " )[\"data\"]\n", - " return [data[\"embedding\"] for data in response]\n", - "\n", - "def batchify(iterable, n=1):\n", - " l = len(iterable)\n", - " for ndx in range(0, l, n):\n", - " yield iterable[ndx : min(ndx + n, l)]\n", - "\n", - "# Function for batching and parallel processing the embeddings\n", - "def embed_corpus(\n", - " corpus: List[str],\n", - " batch_size=64,\n", - " num_workers=8,\n", - " max_context_len=8191,\n", - "):\n", - "\n", - " # Encode the corpus, truncating to max_context_len\n", - " encoding = tiktoken.get_encoding(\"cl100k_base\")\n", - " encoded_corpus = [\n", - " encoded_article[:max_context_len] for encoded_article in encoding.encode_batch(corpus)\n", - " ]\n", - "\n", - " # Calculate corpus statistics: the number of inputs, the total number of tokens, and the estimated cost to embed\n", - " num_tokens = sum(len(article) for article in encoded_corpus)\n", - " cost_to_embed_tokens = num_tokens / 1_000 * 0.0004\n", - " print(\n", - " f\"num_articles={len(encoded_corpus)}, num_tokens={num_tokens}, est_embedding_cost={cost_to_embed_tokens:.2f} USD\"\n", - " )\n", - "\n", - " # Embed the corpus\n", - " with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:\n", - " \n", - " futures = [\n", - " executor.submit(get_embeddings, text_batch)\n", - " for text_batch in batchify(encoded_corpus, batch_size)\n", - " ]\n", - "\n", - " with tqdm(total=len(encoded_corpus)) as pbar:\n", - " for _ in concurrent.futures.as_completed(futures):\n", - " pbar.update(batch_size)\n", - "\n", - " embeddings = []\n", - " for future in futures:\n", - " data = future.result()\n", - " embeddings.extend(data)\n", - "\n", - " return embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "dfeff174", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.04 s, sys: 131 ms, total: 1.17 s\n", - "Wall time: 1.2 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# Initialise tokenizer\n", - "tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n", - "\n", - "# List to hold vectors\n", - "text_list = []\n", - "\n", - "# Process each PDF file and prepare for embedding\n", - "x = article_df.apply(lambda x: chunk_text(x, text_list),axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "6f259e2d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'Photon-!0',\n", - " 'metadata': {'url': 'https://simple.wikipedia.org/wiki/Photon',\n", - " 'title': 'Photon',\n", - " 'content': 'Title: Photon;\\nPhotons (from Greek φως, meaning light), in many atomic models in physics, are particles which transmit light. In other words, light is carried over space by photons. Photon is an elementary particle that is its own antiparticle. In quantum mechanics each photon has a characteristic quantum of energy that depends on frequency: A photon associated with light at a higher frequency will have more energy (and be associated with light at a shorter wavelength).\\n\\nPhotons have a rest mass of 0 (zero). However, Einstein\\'s theory of relativity says that they do have a certain amount of momentum. Before the photon got its name, Einstein revived the proposal that light is separate pieces of energy (particles). These particles came to be known as photons. \\n\\nA photon is usually given the symbol γ (gamma),\\n\\nProperties \\n\\nPhotons are fundamental particles. Although they can be created and destroyed, their lifetime is infinite.\\n\\nIn a vacuum, all photons move at the speed of light, c, which is equal to 299,792,458 meters (approximately 300,000 kilometers) per second.\\n\\nA photon has a given frequency, which determines its color. Radio technology makes great use of frequency. Beyond the visible range, frequency is less discussed, for example it is little used in distinguishing between X-Ray photons and infrared. Frequency is equivalent to the quantum energy of the photon, as related by the Planck constant equation,\\n\\n,\\n\\nwhere is the photon\\'s energy, is the Plank constant, and is the frequency of the light associated with the photon. This frequency, , is typically measured in cycles per second, or equivalently, in Hz. The quantum energy of different photons is often used in cameras, and other machines that use visible and higher than visible radiation. This because these photons are energetic enough to ionize atoms. \\n\\nAnother property of a photon is its wavelength. The frequency , wavelength , and speed of light are related by the equation,\\n\\n,\\n\\nwhere (lambda) is the wavelength, or length of the wave (typically measured in meters.)\\n\\nAnother important property of a photon is its polarity. If you saw a giant photon coming straight at you, it could appear as a swath whipping vertically, horizontally, or somewhere in between. Polarized sunglasses stop photons swinging up and down from passing. This is how they reduce glare as light bouncing off of surfaces tend to fly that way. Liquid crystal displays also use polarity to control which light passes through. Some animals can see light polarization. \\n\\nFinally, a photon has a property called spin. Spin is related to light\\'s circular polarization.\\n\\nPhoton interactions with matter\\nLight is often created or absorbed when an electron gains or loses energy. This energy can be in the form of heat, kinetic energy, or other form. For example, an incandescent light bulb uses heat. The increase of energy can push an electron up one level in a shell called a \"valence\". This makes it unstable, and like everything, it wants to be in the lowest energy state. (If being in the lowest energy state is confusing, pick up a pencil and drop it. Once on the ground, the pencil will be in a lower energy state). When the electron drops back down to a lower energy state, it needs to release the energy that hit it, and it must obey the conservation of energy (energy can neither be created nor destroyed). Electrons release this energy as photons, and at higher intensities, this photon can be seen as visible light.\\n\\nPhotons and the electromagnetic force\\nIn particle physics, photons are responsible for electromagnetic force. Electromagnetism is an idea that combines electricity with magnetism. One common way that we experience electromagnetism in our daily lives is light, which is caused by electromagnetism. Electromagnetism is also responsible for charge, which is the reason that you can not push your hand through a table. Since photons are the force-carrying particle of electromagnetism, they are also gauge bosons. Some matter–called dark matter–is not believed to be affected by electromagnetism. This would mean that dark matter does not have a charge, and does not give off light.\\n\\nRelated pages\\n Particle physics\\n\\nBasic physics ideas\\nElectromagnetism\\nLight\\n',\n", - " 'file_chunk_index': 0}}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "text_list[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "4fd503a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num_articles=2693, num_tokens=1046988, est_embedding_cost=0.42 USD\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2752it [00:10, 271.48it/s] \n" - ] - } - ], - "source": [ - "# Batch embed our chunked text - this will cost you about $0.50\n", - "embeddings = embed_corpus([text[\"metadata\"]['content'] for text in text_list])" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "49f75d78", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': 'Photon-!0',\n", - " 'metadata': {'url': 'https://simple.wikipedia.org/wiki/Photon',\n", - " 'title': 'Photon',\n", - " 'content': 'Title: Photon;\\nPhotons (from Greek φως, meaning light), in many atomic models in physics, are particles which transmit light. In other words, light is carried over space by photons. Photon is an elementary particle that is its own antiparticle. In quantum mechanics each photon has a characteristic quantum of energy that depends on frequency: A photon associated with light at a higher frequency will have more energy (and be associated with light at a shorter wavelength).\\n\\nPhotons have a rest mass of 0 (zero). However, Einstein\\'s theory of relativity says that they do have a certain amount of momentum. Before the photon got its name, Einstein revived the proposal that light is separate pieces of energy (particles). These particles came to be known as photons. \\n\\nA photon is usually given the symbol γ (gamma),\\n\\nProperties \\n\\nPhotons are fundamental particles. Although they can be created and destroyed, their lifetime is infinite.\\n\\nIn a vacuum, all photons move at the speed of light, c, which is equal to 299,792,458 meters (approximately 300,000 kilometers) per second.\\n\\nA photon has a given frequency, which determines its color. Radio technology makes great use of frequency. Beyond the visible range, frequency is less discussed, for example it is little used in distinguishing between X-Ray photons and infrared. Frequency is equivalent to the quantum energy of the photon, as related by the Planck constant equation,\\n\\n,\\n\\nwhere is the photon\\'s energy, is the Plank constant, and is the frequency of the light associated with the photon. This frequency, , is typically measured in cycles per second, or equivalently, in Hz. The quantum energy of different photons is often used in cameras, and other machines that use visible and higher than visible radiation. This because these photons are energetic enough to ionize atoms. \\n\\nAnother property of a photon is its wavelength. The frequency , wavelength , and speed of light are related by the equation,\\n\\n,\\n\\nwhere (lambda) is the wavelength, or length of the wave (typically measured in meters.)\\n\\nAnother important property of a photon is its polarity. If you saw a giant photon coming straight at you, it could appear as a swath whipping vertically, horizontally, or somewhere in between. Polarized sunglasses stop photons swinging up and down from passing. This is how they reduce glare as light bouncing off of surfaces tend to fly that way. Liquid crystal displays also use polarity to control which light passes through. Some animals can see light polarization. \\n\\nFinally, a photon has a property called spin. Spin is related to light\\'s circular polarization.\\n\\nPhoton interactions with matter\\nLight is often created or absorbed when an electron gains or loses energy. This energy can be in the form of heat, kinetic energy, or other form. For example, an incandescent light bulb uses heat. The increase of energy can push an electron up one level in a shell called a \"valence\". This makes it unstable, and like everything, it wants to be in the lowest energy state. (If being in the lowest energy state is confusing, pick up a pencil and drop it. Once on the ground, the pencil will be in a lower energy state). When the electron drops back down to a lower energy state, it needs to release the energy that hit it, and it must obey the conservation of energy (energy can neither be created nor destroyed). Electrons release this energy as photons, and at higher intensities, this photon can be seen as visible light.\\n\\nPhotons and the electromagnetic force\\nIn particle physics, photons are responsible for electromagnetic force. Electromagnetism is an idea that combines electricity with magnetism. One common way that we experience electromagnetism in our daily lives is light, which is caused by electromagnetism. Electromagnetism is also responsible for charge, which is the reason that you can not push your hand through a table. Since photons are the force-carrying particle of electromagnetism, they are also gauge bosons. Some matter–called dark matter–is not believed to be affected by electromagnetism. This would mean that dark matter does not have a charge, and does not give off light.\\n\\nRelated pages\\n Particle physics\\n\\nBasic physics ideas\\nElectromagnetism\\nLight\\n',\n", - " 'file_chunk_index': 0},\n", - " 'embedding': [0.006123070605099201,\n", - " 0.0076594278216362,\n", - " -0.012744419276714325,\n", - " ...]}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Join up embeddings with our original list\n", - "embeddings_list = [{\"embedding\": v} for v in embeddings]\n", - "for i,x in enumerate(embeddings_list):\n", - " text_list[i].update(x)\n", - "text_list[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "932818e9", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Redis pipeline to load all the vectors and their metadata\n", - "def load_vectors(client:r, input_list, vector_field_name):\n", - " p = client.pipeline(transaction=False)\n", - " for text in input_list: \n", - " #hash key\n", - " key=f\"{PREFIX}:{text['id']}\"\n", - " \n", - " #hash values\n", - " item_metadata = text['metadata']\n", - " #\n", - " item_keywords_vector = np.array(text['embedding'],dtype= 'float32').tobytes()\n", - " item_metadata[vector_field_name]=item_keywords_vector\n", - " \n", - " # HSET\n", - " p.hset(key,mapping=item_metadata)\n", - " \n", - " p.execute()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "b218a207", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████| 27/27 [00:07<00:00, 3.40it/s]\n" - ] - } - ], - "source": [ - "batch_size = 100 # how many vectors we insert at once\n", - "\n", - "for i in tqdm(range(0, len(text_list), batch_size)):\n", - " # find end of batch\n", - " i_end = min(len(text_list), i+batch_size)\n", - " meta_batch = text_list[i:i_end]\n", - " \n", - " load_vectors(redis_client,meta_batch,vector_field_name=VECTOR_FIELD_NAME)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d3466f7d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2693'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "redis_client.ft(INDEX_NAME).info()['num_docs']" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "43839177", - "metadata": {}, - "source": [ - "### Search\n", - "\n", - "We can now use our knowledge base to bring back search results. This is one of the areas of highest friction in enterprise knowledge retrieval use cases, with the most common being that the system is not retrieving what you intuitively think are the most relevant documents. There are a few ways of tackling this - I'll share a few options here, as well as some resources to take your research further:\n", - "\n", - "#### Vector search, keyword search or a hybrid\n", - "\n", - "Despite the strong capabilities out of the box that vector search gives, search is still not a solved problem, and there are well proven [Lucene-based](https://en.wikipedia.org/wiki/Apache_Lucene) search solutions such Elasticsearch and Solr that use methods that work well for certain use cases, as well as the sparse vector methods of traditional NLP such as [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf). If your retrieval is poor, the answer may be one of these in particular, or a combination:\n", - "- **Vector search:** Converts your text into vector embeddings which can be searched using KNN, SVM or some other model to return the most relevant results. This is the approach we take in this workbook, using a RediSearch vector DB which employs a KNN search under the hood.\n", - "- **Keyword search:** This method uses any keyword-based search approach to return a score - it could use Elasticsearch/Solr out-of-the-box, or a TF-IDF approach like BM25.\n", - "- **Hybrid search:** This last approach is a mix of the two, where you produce both a vector search and keyword search result, before using an ```alpha``` between 0 and 1 to weight the outputs. There is a great example of this explained by the Weaviate team [here](https://weaviate.io/blog/hybrid-search-explained).\n", - "\n", - "#### Hypothetical Document Embeddings (HyDE)\n", - "\n", - "This is a novel approach from [this paper](https://arxiv.org/abs/2212.10496), which states that a hypothetical answer to a question is more semantically similar to the real answer than the question is. In practice this means that your search would use GPT to generate a hypothetical answer, then embed that and use it for search. I've seen success with this both as a pure search, and as a retry step if the initial retrieval fails to retrieve relevant content. A simple example implementation is here:\n", - "```\n", - "def answer_question_hyde(question,prompt):\n", - " \n", - " hyde_prompt = '''You are OracleGPT, an helpful expert who answers user questions to the best of their ability.\n", - " Provide a confident answer to their question. If you don't know the answer, make the best guess you can based on the context of the question.\n", - "\n", - " User question: USER_QUESTION_HERE\n", - " \n", - " Answer:'''\n", - " \n", - " hypothetical_answer = openai.Completion.create(model=COMPLETIONS_MODEL,prompt=hyde_prompt.replace('USER_QUESTION_HERE',question))['choices'][0]['text']\n", - " \n", - " search_results = get_redis_results(redis_client,hypothetical_answer)\n", - " \n", - " return search_results\n", - "```\n", - "\n", - "#### Fine-tuning embeddings\n", - "\n", - "This next approach leverages the learning you gain from real question/answer pairs that your users will generate during the evaluation approach. It works by:\n", - "- Creating a dataset of positive (and optionally negative) question and answer pairs. Positive examples would be a correct retrieval to a question, while negative would be poor retrievals.\n", - "- Calculating the embeddings for both questions and answers and the cosine similarity between them.\n", - "- Train a model to optimize the embeddings matrix and test retrieval, picking the best one.\n", - "- Perform a matrix multiplication of the base Ada embeddings by this new best matrix, creating a new fine-tuned embedding to do for retrieval.\n", - "\n", - "There is a great walkthrough of both the approach and the code to perform it in [this cookbook](./Customizing_embeddings.ipynb).\n", - "\n", - "#### Reranking\n", - "\n", - "One other well-proven method from traditional search solutions that can be applied to any of the above approaches is reranking, where we over-fetch our search results, and then deterministically rerank based on a modifier or set of modifiers.\n", - "\n", - "An example is investor reports again - it is highly likely that if we have 3 reports on Apple, we'll want to make our investment decisions based on the latest one. In this instance a ```recency``` modifier could be applied to the vector scores to sort them, giving us the latest one on the top even if it is not the most semantically similar to our search question. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "9b2fdc7a", - "metadata": {}, - "source": [ - "For this walkthrough we'll stick with a basic semantic search bringing back the top 5 chunks for a user question, and providing a summarised response using GPT." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "89da0c45", - "metadata": {}, - "outputs": [], - "source": [ - "# Make query to Redis\n", - "def query_redis(redis_conn,query,index_name, top_k=5):\n", - " \n", - " \n", - "\n", - " ## Creates embedding vector from user query\n", - " embedded_query = np.array(openai.Embedding.create(\n", - " input=query,\n", - " model=EMBEDDINGS_MODEL,\n", - " )[\"data\"][0]['embedding'], dtype=np.float32).tobytes()\n", - "\n", - " #prepare the query\n", - " q = Query(f'*=>[KNN {top_k} @{VECTOR_FIELD_NAME} $vec_param AS vector_score]').sort_by('vector_score').paging(0,top_k).return_fields('vector_score','url','title','content','text_chunk_index').dialect(2) \n", - " params_dict = {\"vec_param\": embedded_query}\n", - "\n", - " \n", - " #Execute the query\n", - " results = redis_conn.ft(index_name).search(q, query_params = params_dict)\n", - " \n", - " return results\n", - "\n", - "# Get mapped documents from Redis results\n", - "def get_redis_results(redis_conn,query,index_name):\n", - " \n", - " # Get most relevant documents from Redis\n", - " query_result = query_redis(redis_conn,query,index_name)\n", - " \n", - " # Extract info into a list\n", - " query_result_list = []\n", - " for i, result in enumerate(query_result.docs):\n", - " result_order = i\n", - " url = result.url\n", - " title = result.title\n", - " text = result.content\n", - " score = result.vector_score\n", - " query_result_list.append((result_order,url,title,text,score))\n", - " \n", - " # Display result as a DataFrame for ease of us\n", - " result_df = pd.DataFrame(query_result_list)\n", - " result_df.columns = ['id','url','title','result','certainty']\n", - " return result_df" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f0161a54", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.1 ms, sys: 2.35 ms, total: 9.45 ms\n", - "Wall time: 495 ms\n" - ] - }, - { - "data": { - "text/html": [ - "
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idurltitleresultcertainty
00https://simple.wikipedia.org/wiki/Thomas%20DolbyThomas DolbyTitle: Thomas Dolby;\\nThomas Dolby (born Thomas Morgan Robertson; 14 October 1958) is a British musican and computer designer. He is probably most famous for his 1982 hit, \"She Blinded me with Science\".\\n\\nHe married actress Kathleen Beller in 1988. The couple have three children together.\\n\\nDiscography\\n\\nSingles\\n\\nA Track did not chart in North America until 1983, after the success of \"She Blinded Me With Science\".\\n\\nAlbums\\n\\nStudio albums\\n\\nEPs\\n\\nReferences\\n\\nEnglish musicians\\nLiving people\\n1958 births\\nNew wave musicians\\nWarner Bros. Records artists0.132723689079
11https://simple.wikipedia.org/wiki/SynthesizerSynthesizerTitle: Synthesizer;\\nAudio technology0.223129153252
\n", - "
" - ], - "text/plain": [ - " id url title \\\n", - "0 0 https://simple.wikipedia.org/wiki/Thomas%20Dolby Thomas Dolby \n", - "1 1 https://simple.wikipedia.org/wiki/Synthesizer Synthesizer \n", - "\n", - " result \\\n", - "0 Title: Thomas Dolby;\\nThomas Dolby (born Thomas Morgan Robertson; 14 October 1958) is a British musican and computer designer. He is probably most famous for his 1982 hit, \"She Blinded me with Science\".\\n\\nHe married actress Kathleen Beller in 1988. The couple have three children together.\\n\\nDiscography\\n\\nSingles\\n\\nA Track did not chart in North America until 1983, after the success of \"She Blinded Me With Science\".\\n\\nAlbums\\n\\nStudio albums\\n\\nEPs\\n\\nReferences\\n\\nEnglish musicians\\nLiving people\\n1958 births\\nNew wave musicians\\nWarner Bros. Records artists \n", - "1 Title: Synthesizer;\\nAudio technology \n", - "\n", - " certainty \n", - "0 0.132723689079 \n", - "1 0.223129153252 " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "\n", - "wiki_query='What is Thomas Dolby known for?'\n", - "\n", - "result_df = get_redis_results(redis_client,wiki_query,index_name=INDEX_NAME)\n", - "result_df.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "48d136b0", - "metadata": {}, - "outputs": [], - "source": [ - "# Build a prompt to provide the original query, the result and ask to summarise for the user\n", - "retrieval_prompt = '''Use the content to answer the search query the customer has sent.\n", - "If you can't answer the user's question, say \"Sorry, I am unable to answer the question with the content\". Do not guess.\n", - "\n", - "Search query: \n", - "\n", - "SEARCH_QUERY_HERE\n", - "\n", - "Content: \n", - "\n", - "SEARCH_CONTENT_HERE\n", - "\n", - "Answer:\n", - "'''\n", - "\n", - "def answer_user_question(query):\n", - " \n", - " results = get_redis_results(redis_client,query,INDEX_NAME)\n", - " \n", - " retrieval_prepped = retrieval_prompt.replace('SEARCH_QUERY_HERE',query).replace('SEARCH_CONTENT_HERE',results['result'][0])\n", - " retrieval = openai.ChatCompletion.create(model=CHAT_MODEL,messages=[{'role':\"user\",'content': retrieval_prepped}],max_tokens=500)\n", - " \n", - " # Response provided by GPT-3.5\n", - " return retrieval['choices'][0]['message']['content']" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "06f6e6ed", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thomas Dolby is known for his music, particularly his 1982 hit \"She Blinded Me With Science\". He is also a computer designer.\n" - ] - } - ], - "source": [ - "print(answer_user_question(wiki_query))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "70e9b239", - "metadata": {}, - "source": [ - "### Answer\n", - "\n", - "We've now created a knowledge base that can answer user questions on Wikipedia. However, the user experience could be better, and this is where the Answer layer comes in, where an LLM Agent is used to interact with the user.\n", - "\n", - "There are different level of complexity in building a knowledge retrieval experience leveraging an LLM; there is an experience vs. effort trade-off to consider when selecting the right type of interaction. There are many patterns, but I'll highlight a few of the most common here:\n", - "\n", - "#### Choosing the user experience and architecture\n", - "\n", - "There are different level of complexity in building a knowledge retrieval experience leveraging an LLM; there is an experience vs. effort trade-off to consider when selecting the right type of interaction. There are many patterns, but I'll highlight a few of the most common here:\n", - "- **Q&A:** Your classic search engine use case, where the user inputs a question and your LLM gives them an answer either using its knowledge or, much more commonly, using a knowledge base that you prepare using the steps we've covered already. This simple use case assumes no memory of past queries is required, and no ability to clarify with the human or ask for more information.\n", - "- **Chat:** I think of Chat as being Q&A + memory - this is a slightly more sophisticated interaction where the LLM remembers what was previously asked and can delve deeper on something already covered.\n", - "- **Agent:** The most sophisticated is what LangChain calls an Agent, they leverage large language models to process and produce human-like results through a variety of tools, and will chain queries together dynamically until it has an answer that the LLM feels is appropriate to answer the user's question. However, for every \"turn\" you allow between Agent and user you increase the risks of loss of context, hallucination, or parsing errors, so be clear about the exact requirements your users have before embarking on building the Answer layer.\n", - "\n", - "Q&A use cases are the simplest to implement, while Agents can give the most sophisticated user experience - in this notebook we'll build an Agent with memory and a single Tool to give an appreciation for the flexibilty prompt chaining gives you in getting a more complete answer for your users.\n", - "\n", - "#### Ensuring reliability\n", - "\n", - "The more complexity you add, the more chance your LLM will fail to respond correctly, or a response will come back in the wrong format and break your Answer pipeline. We'll share a few methods our customers have used elsewhere to help \"channel\" the Agent down a more deterministic path, and to deal with issues when they do crop up:\n", - "- **Prompt chaining:** Prompting the model to take a step-by-step approach and think aloud using a scratchpad has been proven to deliver more consistent results. It also means that as a developer you can break up one complex prompt into many simpler, more deterministic prompts, with the output of one prompt becoming the input for the next. This approach is known as Chain-of-Thought (CoT) reasoning - I'd suggest digging deeper as this is a dynamic new area of research, with a few of the key papers referenced here:\n", - " - Chain of thought prompting [paper](https://arxiv.org/abs/2201.11903)\n", - " - Self-reflecting agent [paper](https://arxiv.org/abs/2303.11366)\n", - "- **Self-referencing:** You can return references for the LLM's answer through either your application logic, or by prompt engineering it to return references. I would generally suggest doing it in your application logic, although if you have multiple chunks then a hybrid approach where you ask the LLM to return the key of the chunk it used could be advisable. I view this as a UX opportunity, where for many search use cases giving the \"raw\" output of the chunks retrieved as well as the summarised answer can give the user the best of both worlds, but please go with whatever is most appropriate for your users.\n", - "- **Discriminator models:** The best control for unwanted outputs is undoubtably through preventing it from happening with prompt engineering, prompt chaining and retrieval. However, when all these fail then a discriminator model is a useful detective control. This is a classifier trained on past unwanted outputs, that flags the Agent's response to the user as Safe or Not, enabling you to perform some business logic to either retry, pass to a human, or say it doesn't know. \n", - " - There is an example in our [Help Center](https://help.openai.com/en/articles/5528730-fine-tuning-a-classifier-to-improve-truthfulness).\n", - "\n", - "This is a dynamic topic that has still not consolidated to a clear design that works best above all others, so for ease of implementation we will use LangChain, which supplies a framework with implementations for most of the concepts we've discussed above.\n", - "\n", - "We'll create an Agent with access to our knowledge base, give it a prompt template and a custom parser for extracting the answers, set up a prompt chain and then let it answer our Wikipedia questions.\n", - "\n", - "Our work here draws heavily on LangChain's great documentation, in particular [this guide](https://python.langchain.com/en/latest/modules/agents/agents/custom_llm_chat_agent.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "0ccca3da", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser\n", - "from langchain.prompts import BaseChatPromptTemplate\n", - "from langchain import SerpAPIWrapper, LLMChain\n", - "from langchain.chat_models import ChatOpenAI\n", - "from typing import List, Union\n", - "from langchain.schema import AgentAction, AgentFinish, HumanMessage\n", - "from langchain.memory import ConversationBufferWindowMemory\n", - "import re" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "63455a8e", - "metadata": {}, - "outputs": [], - "source": [ - "def ask_gpt(query):\n", - " response = openai.ChatCompletion.create(model=CHAT_MODEL,messages=[{\"role\":\"user\",\"content\":\"Please answer my question.\\nQuestion: {}\".format(query)}],temperature=0)\n", - " return response['choices'][0]['message']['content']" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "68a0b8dd", - "metadata": {}, - "outputs": [], - "source": [ - "# Define which tools the agent can use to answer user queries\n", - "tools = [\n", - " Tool(\n", - " name = \"Search\",\n", - " func=answer_user_question,\n", - " description=\"Useful for when you need to answer general knowledge questions. Input should be a fully formed question.\"\n", - " ),\n", - " Tool(\n", - " name = \"Knowledge\",\n", - " func = ask_gpt,\n", - " description = \"Useful for any other questions. Input should be a fully formed question.\"\n", - " )\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "39e101ee", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up the base template\n", - "template = \"\"\"You are WikiGPT, a helpful bot who answers question using your tools or your own knowledge.\n", - "You have access to the following tools::\n", - "\n", - "{tools}\n", - "\n", - "Use the following format:\n", - "\n", - "Question: the input question you must answer\n", - "Thought: you should always think about what to do\n", - "Action: the action to take, should be one of [{tool_names}]\n", - "Action Input: the input to the action\n", - "Observation: the result of the action\n", - "... (this Thought/Action/Action Input/Observation can repeat N times)\n", - "Thought: I now know the final answer\n", - "Final Answer: the final answer to the original input question\n", - "\n", - "Begin!\n", - "\n", - "Previous conversation history:\n", - "{history}\n", - "\n", - "New question: {input}\n", - "{agent_scratchpad}\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "a2b9f271", - "metadata": {}, - "outputs": [], - "source": [ - "# Set up a prompt template\n", - "class CustomPromptTemplate(BaseChatPromptTemplate):\n", - " # The template to use\n", - " template: str\n", - " # The list of tools available\n", - " tools: List[Tool]\n", - " \n", - " def format_messages(self, **kwargs) -> str:\n", - " # Get the intermediate steps (AgentAction, Observation tuples)\n", - " # Format them in a particular way\n", - " intermediate_steps = kwargs.pop(\"intermediate_steps\")\n", - " thoughts = \"\"\n", - " for action, observation in intermediate_steps:\n", - " thoughts += action.log\n", - " thoughts += f\"\\nObservation: {observation}\\nThought: \"\n", - " # Set the agent_scratchpad variable to that value\n", - " kwargs[\"agent_scratchpad\"] = thoughts\n", - " # Create a tools variable from the list of tools provided\n", - " kwargs[\"tools\"] = \"\\n\".join([f\"{tool.name}: {tool.description}\" for tool in self.tools])\n", - " # Create a list of tool names for the tools provided\n", - " kwargs[\"tool_names\"] = \", \".join([tool.name for tool in self.tools])\n", - " formatted = self.template.format(**kwargs)\n", - " return [HumanMessage(content=formatted)]\n", - " \n", - " \n", - "class CustomOutputParser(AgentOutputParser):\n", - " \n", - " def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n", - " # Check if agent should finish\n", - " if \"Final Answer:\" in llm_output:\n", - " return AgentFinish(\n", - " # Return values is generally always a dictionary with a single `output` key\n", - " # It is not recommended to try anything else at the moment :)\n", - " return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n", - " log=llm_output,\n", - " )\n", - " # Parse out the action and action input\n", - " regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n", - " match = re.search(regex, llm_output, re.DOTALL)\n", - " if not match:\n", - " raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n", - " action = match.group(1).strip()\n", - " action_input = match.group(2)\n", - " # Return the action and action input\n", - " return AgentAction(tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "454c3ca9", - "metadata": {}, - "outputs": [], - "source": [ - "prompt = CustomPromptTemplate(\n", - " template=template,\n", - " tools=tools,\n", - " # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n", - " # The history template includes \"history\" as an input variable so we can interpolate it into the prompt\n", - " input_variables=[\"input\", \"intermediate_steps\", \"history\"]\n", - ")\n", - "\n", - "# Initiate the memory with k=2 to keep the last two turns\n", - "# Provide the memory to the agent\n", - "memory = ConversationBufferWindowMemory(k=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "34de07d2", - "metadata": {}, - "outputs": [], - "source": [ - "output_parser = CustomOutputParser()\n", - "\n", - "llm = ChatOpenAI(temperature=0)\n", - "\n", - "# LLM chain consisting of the LLM and a prompt\n", - "llm_chain = LLMChain(llm=llm, prompt=prompt)\n", - "\n", - "tool_names = [tool.name for tool in tools]\n", - "agent = LLMSingleActionAgent(\n", - " llm_chain=llm_chain, \n", - " output_parser=output_parser,\n", - " stop=[\"\\nObservation:\"], \n", - " allowed_tools=tool_names\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "d3603d58", - "metadata": {}, - "outputs": [], - "source": [ - "agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "6bfb594b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3mThought: I'm not sure who Thomas Dolby is, I should probably search for more information.\n", - "Action: Search\n", - "Action Input: \"What is Thomas Dolby known for?\"\u001b[0m\n", - "\n", - "Observation:\u001b[36;1m\u001b[1;3mThomas Dolby is known for being a British musician and computer designer, and his 1982 hit \"She Blinded Me With Science\".\u001b[0m\u001b[32;1m\u001b[1;3mNow that I know who Thomas Dolby is, I can answer the question.\n", - "Final Answer: Thomas Dolby is known for being a British musician and computer designer, and his 1982 hit \"She Blinded Me With Science\".\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "'Thomas Dolby is known for being a British musician and computer designer, and his 1982 hit \"She Blinded Me With Science\".'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agent_executor.run(wiki_query)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "ba65b7e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3mThought: This is a simple math question.\n", - "Action: Knowledge\n", - "Action Input: What is the sum of 5 and 5?\u001b[0m\n", - "\n", - "Observation:\u001b[33;1m\u001b[1;3mThe sum of 5 and 5 is 10.\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer.\n", - "Final Answer: The sum of 5 and 5 is 10.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "'The sum of 5 and 5 is 10.'" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agent_executor.run('What is 5 + 5')" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f19f7a7e", - "metadata": {}, - "source": [ - "### Evaluation\n", - "\n", - "Last comes the not-so-fun bit that will make the difference between nifty prototype and production application - the process of evaluating and tuning your results. \n", - "\n", - "The key takeaway here is to make a framework that saves the results of each evaluation, as well as the parameters. Evaluation can be a difficult task that takes significant resources, so it is best to start prepared to handle multiple iterations. Some useful principles we've seen successful deployments use are:\n", - "- **Assign clear product ownership and metrics:** Ensure you have a team aligned from the start to annotate the outputs and determine whether they're bad or good. This may seem an obvious step, but too often the focus is on the engineering challenge of successfully retrieving content rather than the product challenge of providing retrieval results that are useful.\n", - "- **Log everything:** Store all requests and responses to and from your LLM and retrieval service if you can, it builds a great base for fine-tuning both the embeddings and any fine-tuned models or few-shot LLMs in future.\n", - "- **Use GPT-4 as a labeller:** When running evaluations, it can help to use GPT-4 as a gatekeeper for human annotation. Human annotation is costly and time-consuming, so doing an initial evaluation run with GPT-4 can help set a quality bar that needs to be met to justify human labeling. At this stage I would not suggest using GPT-4 as your only labeler, but it can certainly ease the burden.\n", - " - This approach is outlined further in [this paper](https://arxiv.org/abs/2108.13487).\n", - "\n", - "We'll use these principles to make a quick evaluation framework where we will:\n", - "- Use GPT-4 to make a list of hypothetical questions on our topic\n", - "- Ask our Agent the questions and save question/answer tuples\n", - " - These two above steps simulate the actual users interacting with your application\n", - "- Get GPT-4 to evaluate whether the answers correctly respond to the questions\n", - "- Look at our results to measure how well the Agent answered the questions\n", - "- Plan remedial action" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "b8314f7b", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "\n", - "# Build a prompt to provide the original query, the result and ask to summarise for the user\n", - "evaluation_question_prompt = '''You are a helpful Wikipedia assistant who will generate a list of 10 creative general knowledge questions in markdown format.\n", - "\n", - "Example:\n", - "- Explain how photons work\n", - "- What is Thomas Dolby known for?\n", - "- What are some key events of the 20th century?\n", - "\n", - "Begin!\n", - "'''\n", - "\n", - "try:\n", - " # We'll use our model to generate 10 hypothetical questions to evaluate\n", - " question = openai.ChatCompletion.create(model=CHAT_MODEL\n", - " ,messages=[{\"role\":\"user\",\"content\":evaluation_question_prompt}]\n", - " ,temperature=0.9)\n", - " evaluation_questions = question['choices'][0]['message']['content']\n", - "except Exception as e:\n", - " print(e)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "3e0ee344", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['1. What is the difference between weather and climate?', '2. Who designed the Eiffel Tower?', '3. What is the capital of Australia?', '4. What is the chemical symbol for gold?', '5. Who invented the telephone?', '6. What is the largest organ in the human body?', '7. Which famous artist painted the Mona Lisa?', '8. What is the highest mountain in Africa?', '9. What famous building was destroyed during the September 11th attacks?', '10. Who wrote the novel \"To Kill a Mockingbird\"?']\n" - ] - } - ], - "source": [ - "cleaned_questions = evaluation_questions.split('\\n')\n", - "print(cleaned_questions)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "4446041c", - "metadata": {}, - "outputs": [], - "source": [ - "# We'll use our agent to answer the generated questions to simulate users interacting with the system\n", - "question_answer_pairs = []\n", - "\n", - "for question in cleaned_questions:\n", - " memory = ConversationBufferWindowMemory(k=2)\n", - " \n", - " agent_executor = AgentExecutor.from_agent_and_tools(agent=agent\n", - " , tools=tools\n", - " , verbose=False\n", - " ,memory=memory)\n", - " try:\n", - " \n", - " answer = agent_executor.run(question)\n", - " except Exception as e:\n", - " print(question)\n", - " print(e)\n", - " answer = 'Unable to answer question'\n", - " question_answer_pairs.append((question,answer))\n", - " time.sleep(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "b8ea3f6a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(10,\n", - " [('1. What is the difference between weather and climate?',\n", - " 'Weather refers to short-term atmospheric conditions in a specific area, while climate refers to long-term patterns and trends of weather in a particular region over a period of time.'),\n", - " ('2. Who designed the Eiffel Tower?',\n", - " 'Gustave Eiffel designed the Eiffel Tower.'),\n", - " ('3. What is the capital of Australia?',\n", - " 'The capital of Australia is Canberra.'),\n", - " ('4. What is the chemical symbol for gold?',\n", - " 'The chemical symbol for gold is Au.'),\n", - " ('5. Who invented the telephone?',\n", - " 'Alexander Graham Bell invented the telephone.')])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(question_answer_pairs), question_answer_pairs[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "1cf3c9e1", - "metadata": {}, - "outputs": [], - "source": [ - "# Build a prompt to provide the original query, the result and ask to evaluate for the user\n", - "gpt_evaluator_system = '''You are WikiGPT, a helpful Wikipedia expert.\n", - "You will be presented with general knowledge questions our users have asked.\n", - "\n", - "Think about this step by step:\n", - "- You need to decide whether the answer adequately answers the question\n", - "- If it answers the question, you will say \"Correct\"\n", - "- If it doesn't answer the question, you will say one of the following:\n", - " - If it couldn't answer at all, you will say \"Unable to answer\"\n", - " - If the answer was provided but was incorrect, you will say \"Incorrect\" \n", - "- If none of these rules are met, say \"Unable to evaluate\"\n", - "\n", - "Evaluation can only be \"Correct\", \"Incorrect\", \"Unable to answer\", and \"Unable to evaluate\"\n", - "\n", - "Example 1:\n", - "\n", - "Question: What is the cost cap for the 2023 season of Formula 1?\n", - "\n", - "Answer: The cost cap for 2023 is 95m USD.\n", - "\n", - "Evaluation: Correct\n", - "\n", - "Example 2:\n", - "\n", - "Question: What is Thomas Dolby known for?\n", - "\n", - "Answer: Inventing electricity\n", - "\n", - "Evaluation: Incorrect\n", - "\n", - "Begin!'''\n", - "\n", - "# We'll provide our evaluator the questions and answers we've generated and get it to evaluate them as one of our four evaluation categories.\n", - "gpt_evaluator_message = '''\n", - "Question: {question}\n", - "\n", - "Answer: {answer}\n", - "\n", - "Evaluation:'''" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "8a351793", - "metadata": {}, - "outputs": [], - "source": [ - "evaluation_output = []" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "3b286e5f", - "metadata": {}, - "outputs": [], - "source": [ - "for pair in question_answer_pairs:\n", - " \n", - " message = gpt_evaluator_message.format(question=pair[0]\n", - " ,answer=pair[1])\n", - " evaluation = openai.ChatCompletion.create(model=CHAT_MODEL\n", - " ,messages=[{\"role\":\"system\",\"content\":gpt_evaluator_system}\n", - " ,{\"role\":\"user\",\"content\":message}]\n", - " ,temperature=0)\n", - " \n", - " evaluation_output.append((pair[0]\n", - " ,pair[1]\n", - " ,evaluation['choices'][0]['message']['content']))" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "f18678e0", - "metadata": {}, - "outputs": [], - "source": [ - "# We'll smooth the results for a simpler evaluation matrix\n", - "# In a real scenario we would take time and tune our prompt/add few shot examples to ensure consistent output from the evaluation step\n", - "def collate_results(x):\n", - " text = x.lower()\n", - " \n", - " if 'incorrect' in text:\n", - " return 'incorrect'\n", - " elif 'correct' in text:\n", - " return 'correct'\n", - " else: \n", - " return 'unable to answer'" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "cd29fc90", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "correct 10\n", - "Name: evaluation, dtype: int64" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eval_df = pd.DataFrame(evaluation_output)\n", - "eval_df.columns = ['question','answer','evaluation']\n", - "# Replacing all the \"unable to evaluates\" with \"unable to answer\"\n", - "eval_df['evaluation'] = eval_df['evaluation'].apply(lambda x: collate_results(x))\n", - "eval_df.evaluation.value_counts()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "566053d6", - "metadata": {}, - "source": [ - "#### Analysis\n", - "\n", - "Depending on how GPT did here you may have actually gotten some good responses, but in all likelihood in the real world you'll end up with incorrect or unable to answer results, and will need to tune your search, LLM or another aspect of the pipeline.\n", - "\n", - "Your remediation plan could be as follows:\n", - "- **Incorrect answers:** Either prompt engineering to help the model work out how to answer better (maybe even a bigger model like GPT-4), or search optimisation to return more relevant chunks. Chunking/embedding changes may help this as well - larger chunks may give more context, allowing the model to formulate a better answer.\n", - "- **Unable to answer:** This is either a retrieval problem, or the data doesn't exist in our knowledge base. We can prompt engineer to classify questions that are \"out-of-bounds\" and give the user a stock reply, or we can tune our search so the relevant data is returned.\n", - "\n", - "This is the framework we'll build on to get our knowledge retrieval solution to production - again, log everything and store each run down to a question level so you can track regressions and iterate towards your production solution." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "5016fcb8", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "This concludes our Enterprise Knowledge Retrieval walkthrough. We hope you've found it useful, and that you're now in a position to build enterprise knowledge retrieval solutions, and have a few tricks to start you down the road of putting them into production." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "pchain_env", - "language": "python", - "name": "pchain_env" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/apps/enterprise-knowledge-retrieval/img/enterprise_knowledge_retrieval.png b/apps/enterprise-knowledge-retrieval/img/enterprise_knowledge_retrieval.png deleted file mode 100644 index cbb86b22..00000000 Binary files a/apps/enterprise-knowledge-retrieval/img/enterprise_knowledge_retrieval.png and /dev/null differ diff --git a/apps/enterprise-knowledge-retrieval/requirements.txt b/apps/enterprise-knowledge-retrieval/requirements.txt deleted file mode 100644 index 1e58edba..00000000 --- a/apps/enterprise-knowledge-retrieval/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -langchain==0.0.312 -numpy==1.24.2 -openai==0.27.4 -pandas==2.0.0 -redis==4.5.4 -streamlit==1.22.0 -streamlit_chat==0.0.2.2 diff --git a/apps/file-q-and-a/README.md b/apps/file-q-and-a/README.md deleted file mode 100644 index a4d2e944..00000000 --- a/apps/file-q-and-a/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# File Q&A - -File Q&A is a [Next.js](https://nextjs.org/) app that lets you find answers in your files using OpenAI APIs. You can upload files and ask questions related to their content, and the app will use embeddings and GPT to generate answers from the most relevant files. - -This repo contains two versions of the app: - -- `/nextjs`: A standalone Next.js app that stores embeddings locally in the browser. You will need an OpenAI API key to use this app. Read more in its [README](./nextjs/README.md). -- `/nextjs-with-flask-server`: A Next.js app that uses a Flask server as a proxy to access the OpenAI APIs, and Pinecone as a vector database to store embeddings. You will need an OpenAI API key and a Pinecone API key to use this app. Read more in its [README](./nextjs-with-flask-server/README.md). - -To run either version of the app, please follow the instructions in the respective README.md files in the subdirectories. - -## How it works - -When a file is uploaded, text is extracted from the file. This text is then split into shorter text chunks, and an embedding is created for each text chunk. When the user asks a question, an embedding is created for the question, and a similarity search is performed to find the file chunk embeddings that are most similar to the question (i.e. have highest cosine similarities with the question embedding). An API call is then made to the completions endpoint, with the question and the most relevant file chunks are included in the prompt. The generative model then gives the answer to the question found in the file chunks, if the answer can be found in the extracts. - -## Limitations - -The app may sometimes generate answers that are not in the files, or hallucinate about the existence of files that are not uploaded. diff --git a/apps/file-q-and-a/nextjs-with-flask-server/README.md b/apps/file-q-and-a/nextjs-with-flask-server/README.md deleted file mode 100644 index dcf0f2ef..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/README.md +++ /dev/null @@ -1,47 +0,0 @@ -# File Q&A with Next.js and Flask - -File Q&A is a web app that lets you find answers in your files. You can upload files and ask questions related to their content, and the app will use embeddings and GPT to generate answers from the most relevant files. \ - -## Requirements - -To run the app, you need: - -- An OpenAI API key. You can create a new API key [here](https://beta.openai.com/account/api-keys). -- A Pinecone API key and index name. You can create a new account and index [here](https://www.pinecone.io/). -- Python 3.7 or higher and pipenv for the Flask server. -- Node.js and npm for the Next.js client. - -## Set-Up and Development - -### Server - -Fill out the config.yaml file with your Pinecone API key, index name and environment. - -Run the Flask server: - -``` -cd server -bash script/start "" -``` - -### Client - -Navigate to the client directory and install Node dependencies: - -``` -cd client -npm install -``` - -Run the Next.js client: - -``` -cd client -npm run dev -``` - -Open [http://localhost:3000](http://localhost:3000) with your browser to see the app. - -## Limitations - -The app may sometimes generate answers that are not in the files, or hallucinate about the existence of files that are not uploaded. diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/.eslintrc.json b/apps/file-q-and-a/nextjs-with-flask-server/client/.eslintrc.json deleted file mode 100644 index bffb357a..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/.eslintrc.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "extends": "next/core-web-vitals" -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/.gitignore b/apps/file-q-and-a/nextjs-with-flask-server/client/.gitignore deleted file mode 100644 index c87c9b39..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/.gitignore +++ /dev/null @@ -1,36 +0,0 @@ -# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. - -# dependencies -/node_modules -/.pnp -.pnp.js - -# testing -/coverage - -# next.js -/.next/ -/out/ - -# production -/build - -# misc -.DS_Store -*.pem - -# debug -npm-debug.log* -yarn-debug.log* -yarn-error.log* -.pnpm-debug.log* - -# local env files -.env*.local - -# vercel -.vercel - -# typescript -*.tsbuildinfo -next-env.d.ts diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/next.config.js b/apps/file-q-and-a/nextjs-with-flask-server/client/next.config.js deleted file mode 100644 index a843cbee..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/next.config.js +++ /dev/null @@ -1,6 +0,0 @@ -/** @type {import('next').NextConfig} */ -const nextConfig = { - reactStrictMode: true, -} - -module.exports = nextConfig diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/package-lock.json b/apps/file-q-and-a/nextjs-with-flask-server/client/package-lock.json deleted file mode 100644 index 64652020..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/package-lock.json +++ /dev/null @@ -1,8959 +0,0 @@ -{ - "name": "file-q-and-a", - "version": "0.1.0", - "lockfileVersion": 2, - "requires": true, - "packages": { - "": { - "name": "file-q-and-a", - "version": "0.1.0", - "dependencies": { - "@headlessui/react": "^1.7.7", - "@heroicons/react": "^2.0.13", - "@next/font": "13.1.2", - "@tailwindcss/line-clamp": "^0.4.2", - "@tailwindcss/typography": "^0.5.9", - "@types/formidable": "^2.0.5", - "@types/lodash": "^4.14.191", - "@types/node": "18.11.18", - "@types/pdf-parse": "^1.1.1", - "@types/react": "18.0.27", - "@types/react-dom": "18.0.10", - "axios": "^1.2.3", - "clsx": "^1.2.1", - "eslint": "8.32.0", - "eslint-config-next": "13.1.2", - 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}, - "yallist": { - "version": "4.0.0", - "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz", - "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==" - }, - "yaml": { - "version": "1.10.2", - "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz", - "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==" - }, - "yocto-queue": { - "version": "0.1.0", - "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", - "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==" - } - } -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/package.json b/apps/file-q-and-a/nextjs-with-flask-server/client/package.json deleted file mode 100644 index 7541143e..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/package.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "name": "file-q-and-a", - "version": "0.1.0", - "private": true, - "scripts": { - "dev": "next dev", - "build": "next build", - "start": "next start", - "lint": "next lint" - }, - "dependencies": { - "@headlessui/react": "^1.7.7", - "@heroicons/react": "^2.0.13", - "@next/font": "13.1.2", - "@tailwindcss/line-clamp": "^0.4.2", - "@tailwindcss/typography": "^0.5.9", - "@types/formidable": "^2.0.5", - "@types/lodash": "^4.14.191", - "@types/node": "18.11.18", - "@types/pdf-parse": "^1.1.1", - "@types/react": "18.0.27", - "@types/react-dom": "18.0.10", - "axios": "^1.2.3", - "clsx": "^1.2.1", - "eslint": "8.32.0", - "eslint-config-next": "13.1.2", - "formidable": "^2.1.1", - "lodash": "^4.17.21", - "mammoth": "^1.5.1", - "next": "13.1.2", - "node-html-markdown": "^1.3.0", - "openai": "^3.1.0", - "pdf-parse": "^1.1.1", - "react": "18.2.0", - "react-dom": "18.2.0", - "react-markdown": "^8.0.5", - "typescript": "4.9.4" - }, - "devDependencies": { - "autoprefixer": "^10.4.13", - "postcss": "^8.4.31", - "tailwindcss": "^3.2.4" - } -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/postcss.config.js b/apps/file-q-and-a/nextjs-with-flask-server/client/postcss.config.js deleted file mode 100644 index 12a703d9..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -module.exports = { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -}; diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/public/favicon.ico b/apps/file-q-and-a/nextjs-with-flask-server/client/public/favicon.ico deleted file mode 100644 index b6c3abcb..00000000 Binary files a/apps/file-q-and-a/nextjs-with-flask-server/client/public/favicon.ico and /dev/null differ diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/public/openai-horizontal.svg b/apps/file-q-and-a/nextjs-with-flask-server/client/public/openai-horizontal.svg deleted file mode 100644 index 0d31b2d4..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/public/openai-horizontal.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/public/openai.svg b/apps/file-q-and-a/nextjs-with-flask-server/client/public/openai.svg deleted file mode 100644 index fe51f0e5..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/public/openai.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/File.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/File.tsx deleted file mode 100644 index a402e04c..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/File.tsx +++ /dev/null @@ -1,77 +0,0 @@ -import { useState, useCallback, memo } from "react"; -import { Transition } from "@headlessui/react"; -import { - MagnifyingGlassMinusIcon, - MagnifyingGlassPlusIcon, - ArrowTopRightOnSquareIcon, -} from "@heroicons/react/24/outline"; - -import { FileLite } from "../types/file"; - -type FileProps = { - file: FileLite; - showScore?: boolean; -}; - -function File(props: FileProps) { - const [expanded, setExpanded] = useState(false); - - const handleExpand = useCallback(() => { - setExpanded((prev) => !prev); - }, []); - - return ( -
-
-
{props.file.name}
- -
- {props.showScore && props.file.score && ( -
- {props.file.score.toFixed(2)} -
- )} - -
- {expanded ? ( - - ) : ( - - )} -
- - e.stopPropagation()} // prevent the click event from bubbling up to the list item - > - - -
-
- -
- -
-
-
- ); -} - -export default memo(File); diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileQandAArea.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileQandAArea.tsx deleted file mode 100644 index 6b183aa9..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileQandAArea.tsx +++ /dev/null @@ -1,147 +0,0 @@ -import React, { memo, useCallback, useRef, useState } from "react"; -import { Transition } from "@headlessui/react"; -import axios from "axios"; -import ReactMarkdown from "react-markdown"; - -import FileViewerList from "./FileViewerList"; -import LoadingText from "./LoadingText"; -import { isFileNameInString } from "../services/utils"; -import { FileChunk, FileLite } from "../types/file"; -import { SERVER_ADDRESS } from "../types/constants"; - -type FileQandAAreaProps = { - files: FileLite[]; -}; - -function FileQandAArea(props: FileQandAAreaProps) { - const searchBarRef = useRef(null); - const [answerError, setAnswerError] = useState(""); - const [searchResultsLoading, setSearchResultsLoading] = - useState(false); - const [answer, setAnswer] = useState(""); - - const handleSearch = useCallback(async () => { - if (searchResultsLoading) { - return; - } - - const question = (searchBarRef?.current as any)?.value ?? ""; - setAnswer(""); - - if (!question) { - setAnswerError("Please ask a question."); - return; - } - if (props.files.length === 0) { - setAnswerError("Please upload files before asking a question."); - return; - } - - setSearchResultsLoading(true); - setAnswerError(""); - - let results: FileChunk[] = []; - - try { - const answerResponse = await axios.post( - `${SERVER_ADDRESS}/answer_question`, - { - question, - } - ); - - if (answerResponse.status === 200) { - setAnswer(answerResponse.data.answer); - } else { - setAnswerError("Sorry, something went wrong!"); - } - } catch (err: any) { - setAnswerError("Sorry, something went wrong!"); - } - - setSearchResultsLoading(false); - }, [props.files, searchResultsLoading]); - - const handleEnterInSearchBar = useCallback( - async (event: React.SyntheticEvent) => { - if ((event as any).key === "Enter") { - await handleSearch(); - } - }, - [handleSearch] - ); - - return ( -
-
- Ask a question based on the content of your files: -
-
- -
- {searchResultsLoading ? ( - - ) : ( - "Ask question" - )} -
-
-
- {answerError &&
{answerError}
} - - {/* answer from files */} - {answer && ( -
- - {answer} - -
- )} - - - isFileNameInString(file.name, answer) - ).length > 0 - } - enter="transition duration-600 ease-out" - enterFrom="transform opacity-0" - enterTo="transform opacity-100" - leave="transition duration-125 ease-out" - leaveFrom="transform opacity-100" - leaveTo="transform opacity-0" - className="mb-8" - > - - isFileNameInString(file.name, answer) - )} - title="Sources" - listExpanded={true} - /> - -
-
-
- ); -} - -export default memo(FileQandAArea); diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileUploadArea.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileUploadArea.tsx deleted file mode 100644 index 8ee502e9..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileUploadArea.tsx +++ /dev/null @@ -1,195 +0,0 @@ -import React, { - Dispatch, - SetStateAction, - useCallback, - useState, - memo, - useRef, -} from "react"; -import axios from "axios"; -import { ArrowUpTrayIcon } from "@heroicons/react/24/outline"; -import { compact } from "lodash"; - -import LoadingText from "./LoadingText"; -import { FileLite } from "../types/file"; -import FileViewerList from "./FileViewerList"; -import { SERVER_ADDRESS } from "../types/constants"; - -type FileUploadAreaProps = { - handleSetFiles: Dispatch>; - maxNumFiles: number; - maxFileSizeMB: number; -}; - -function FileUploadArea(props: FileUploadAreaProps) { - const handleSetFiles = props.handleSetFiles; - - const [files, setFiles] = useState([]); - const [loading, setLoading] = useState(false); - const [error, setError] = useState(""); - const [dragOver, setDragOver] = useState(false); - const dropzoneRef = useRef(null); - - const handleFileChange = useCallback( - async (selectedFiles: FileList | null) => { - if (selectedFiles && selectedFiles.length > 0) { - setError(""); - - if (files.length + selectedFiles.length > props.maxNumFiles) { - setError(`You can only upload up to ${props.maxNumFiles} files.`); - if (dropzoneRef.current) { - (dropzoneRef.current as any).value = ""; - } - return; - } - - setLoading(true); - - const uploadedFiles = await Promise.all( - Array.from(selectedFiles).map(async (file) => { - // Check the file type - if ( - file.type.match( - /(text\/plain|application\/(pdf|msword|vnd\.openxmlformats-officedocument\.wordprocessingml\.document))/ - ) && // AND file isnt too big - file.size < props.maxFileSizeMB * 1024 * 1024 - ) { - // Check if the file name already exists in the files state - if (files.find((f) => f.name === file.name)) { - return null; // skip this file - } - - const formData = new FormData(); - formData.append("file", file); - - try { - const processFileResponse = await axios.post( - `${SERVER_ADDRESS}/process_file`, - formData, - { - headers: { - "Content-Type": "multipart/form-data", - }, - } - ); - - if ( - processFileResponse.status === 200 && - processFileResponse.data.success - ) { - const fileObject: FileLite = { - name: file.name, - url: URL.createObjectURL(file), - expanded: false, - }; - console.log(fileObject); - - return fileObject; - } else { - console.log("Error processing file"); - return null; - } - } catch (err: any) { - console.log(`error processing file: ${err}`); - return null; - } - } else { - alert( - `Invalid file type or size. Only TXT, PD or DOCX are allowed, up to ${props.maxFileSizeMB}MB.` - ); - return null; // Skip this file - } - }) - ); - - // Filter out any null values from the uploadedFiles array - const validFiles = compact(uploadedFiles); - - // Set the files state with the valid files and the existing files - setFiles((prevFiles) => [...prevFiles, ...validFiles]); - handleSetFiles((prevFiles) => [...prevFiles, ...validFiles]); - - setLoading(false); - } - }, - [files, handleSetFiles, props.maxFileSizeMB, props.maxNumFiles] - ); - - const handleDragEnter = useCallback((event: React.DragEvent) => { - event.preventDefault(); - setDragOver(true); - }, []); - - const handleDragOver = useCallback((event: React.DragEvent) => { - event.preventDefault(); - }, []); - - const handleDragLeave = useCallback((event: React.DragEvent) => { - event.preventDefault(); - setDragOver(false); - }, []); - - const handleDrop = useCallback( - (event: React.DragEvent) => { - event.preventDefault(); - setDragOver(false); - const droppedFiles = event.dataTransfer.files; - handleFileChange(droppedFiles); - }, - [handleFileChange] - ); - - return ( -
- - - {error && ( -
-

{error}

-
- )} - - -
- ); -} - -export default memo(FileUploadArea); diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileViewerList.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileViewerList.tsx deleted file mode 100644 index 04ebe416..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/FileViewerList.tsx +++ /dev/null @@ -1,73 +0,0 @@ -import React, { memo, useCallback, useState } from "react"; -import { ChevronUpIcon } from "@heroicons/react/24/outline"; -import clsx from "clsx"; -import { Transition } from "@headlessui/react"; - -import File from "./File"; -import { FileLite } from "../types/file"; - -type FileViewerListProps = { - files: FileLite[]; - title: string; - listExpanded?: boolean; - showScores?: boolean; -}; - -function FileViewerList(props: FileViewerListProps) { - const [listExpanded, setListExpanded] = useState(props.listExpanded ?? false); - - const handleListExpand = useCallback(() => { - setListExpanded((prev) => !prev); - }, []); - - return ( -
- {props.files.length > 0 && ( -
-
-
- {props.title} -
- {props.files.length} -
-
-
- -
-
- - -
- {props.files.map((file) => ( - - ))} -
-
-
- )} -
- ); -} - -export default memo(FileViewerList); diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/LoadingSpinner.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/LoadingSpinner.tsx deleted file mode 100644 index 74d77ee4..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/LoadingSpinner.tsx +++ /dev/null @@ -1,33 +0,0 @@ -import clsx from "clsx"; - -type Props = { - className?: string; - size?: number; -}; - -export default function LoadingSpinner(props: Props) { - const size = props.size || 5; - return ( -
- -
- ); -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/LoadingText.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/LoadingText.tsx deleted file mode 100644 index 021b26af..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/components/LoadingText.tsx +++ /dev/null @@ -1,18 +0,0 @@ -import React, { memo } from "react"; - -import LoadingSpinner from "./LoadingSpinner"; - -type LoadingTextProps = { - text: string; -}; - -function LoadingText(props: LoadingTextProps) { - return ( -
- - {props.text &&
{props.text}
} -
- ); -} - -export default memo(LoadingText); diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/_app.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/_app.tsx deleted file mode 100644 index a7a790fb..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/_app.tsx +++ /dev/null @@ -1,6 +0,0 @@ -import "@/styles/globals.css"; -import type { AppProps } from "next/app"; - -export default function App({ Component, pageProps }: AppProps) { - return ; -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/_document.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/_document.tsx deleted file mode 100644 index b2fff8b4..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/_document.tsx +++ /dev/null @@ -1,13 +0,0 @@ -import { Html, Head, Main, NextScript } from "next/document"; - -export default function Document() { - return ( - - - -
- - - - ); -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/index.tsx b/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/index.tsx deleted file mode 100644 index c94ef584..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/pages/index.tsx +++ /dev/null @@ -1,35 +0,0 @@ -import Head from "next/head"; -import { useState } from "react"; - -import FileQandAArea from "../components/FileQandAArea"; -import { FileLite } from "../types/file"; -import FileUploadArea from "../components/FileUploadArea"; - -export default function FileQandA() { - const [files, setFiles] = useState([]); - - return ( -
- - File Q&A - -
-

File Q&A

- -
- To search for answers from the content in your files, upload them here - and we will use OpenAI embeddings and GPT to find answers from the - relevant documents. -
- - - - -
-
- ); -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/services/utils.ts b/apps/file-q-and-a/nextjs-with-flask-server/client/src/services/utils.ts deleted file mode 100644 index 843a4865..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/services/utils.ts +++ /dev/null @@ -1,19 +0,0 @@ -// A function that takes a file name and a string and returns true if the file name is contained in the string -// after removing punctuation and whitespace from both -export const isFileNameInString = (fileName: string, str: string) => { - // Check if the input string is null or undefined - if (!str) { - return false; - } - - // Convert both to lowercase and remove punctuation and whitespace - const normalizedFileName = fileName - .toLowerCase() - .replace(/[.,/#!$%^&*;:{}=-_~()\s]/g, ""); - const normalizedStr = str - .toLowerCase() - .replace(/[.,/#!$%^&*;:{}=-_~()\s]/g, ""); - - // Return true if the normalized file name is included in the normalized string - return normalizedStr.includes(normalizedFileName); -}; diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/styles/globals.css b/apps/file-q-and-a/nextjs-with-flask-server/client/src/styles/globals.css deleted file mode 100644 index 50087802..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/styles/globals.css +++ /dev/null @@ -1,5 +0,0 @@ -@import "./preflight.css"; - -@tailwind base; -@tailwind components; -@tailwind utilities; diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/styles/preflight.css b/apps/file-q-and-a/nextjs-with-flask-server/client/src/styles/preflight.css deleted file mode 100644 index 3632940f..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/styles/preflight.css +++ /dev/null @@ -1,368 +0,0 @@ -/* Using a custom preflight to fix conflicts with Ant Design */ -/* Original: https://unpkg.com/tailwindcss@3.2.4/src/css/preflight.css */ - -/* -1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4) -2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116) -*/ - -*, -::before, -::after { - box-sizing: border-box; /* 1 */ - border-width: 0; /* 2 */ - border-style: solid; /* 2 */ - border-color: theme("borderColor.DEFAULT"); /* 2 */ -} - -::before, -::after { - --tw-content: ""; -} - -/* -1. Use a consistent sensible line-height in all browsers. -2. Prevent adjustments of font size after orientation changes in iOS. -3. Use a more readable tab size. -4. Use the user's configured `sans` font-family by default. -5. Use the user's configured `sans` font-feature-settings by default. -*/ - -html { - line-height: 1.5; /* 1 */ - -webkit-text-size-adjust: 100%; /* 2 */ - -moz-tab-size: 4; /* 3 */ - tab-size: 4; /* 3 */ - font-family: theme("fontFamily.sans"); /* 4 */ -} - -/* -1. Remove the margin in all browsers. -2. Inherit line-height from `html` so users can set them as a class directly on the `html` element. -*/ - -body { - margin: 0; /* 1 */ - line-height: inherit; /* 2 */ -} - -/* -1. Add the correct height in Firefox. -2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655) -3. Ensure horizontal rules are visible by default. -*/ - -hr { - height: 0; /* 1 */ - color: inherit; /* 2 */ - border-top-width: 1px; /* 3 */ -} - -/* -Add the correct text decoration in Chrome, Edge, and Safari. -*/ - -abbr:where([title]) { - text-decoration: underline dotted; -} - -/* -Remove the default font size and weight for headings. -*/ - -h1, -h2, -h3, -h4, -h5, -h6 { - font-size: inherit; - font-weight: inherit; -} - -/* -Reset links to optimize for opt-in styling instead of opt-out. -*/ - -a { - color: inherit; - text-decoration: inherit; -} - -/* -Add the correct font weight in Edge and Safari. -*/ - -b, -strong { - font-weight: bolder; -} - -/* -1. Use the user's configured `mono` font family by default. -2. Correct the odd `em` font sizing in all browsers. -*/ - -code, -kbd, -samp, -pre { - font-family: theme("fontFamily.mono"); /* 1 */ - font-size: 1em; /* 2 */ -} - -/* -Add the correct font size in all browsers. -*/ - -small { - font-size: 80%; -} - -/* -Prevent `sub` and `sup` elements from affecting the line height in all browsers. -*/ - -sub, -sup { - font-size: 75%; - line-height: 0; - position: relative; - vertical-align: baseline; -} - -sub { - bottom: -0.25em; -} - -sup { - top: -0.5em; -} - -/* -1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297) -2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016) -3. Remove gaps between table borders by default. -*/ - -table { - text-indent: 0; /* 1 */ - border-color: inherit; /* 2 */ - border-collapse: collapse; /* 3 */ -} - -/* -1. Change the font styles in all browsers. -2. Remove the margin in Firefox and Safari. -3. Remove default padding in all browsers. -*/ - -button, -input, -optgroup, -select, -textarea { - font-family: inherit; /* 1 */ - font-size: 100%; /* 1 */ - font-weight: inherit; /* 1 */ - line-height: inherit; /* 1 */ - color: inherit; /* 1 */ - margin: 0; /* 2 */ - padding: 0; /* 3 */ -} - -/* -Remove the inheritance of text transform in Edge and Firefox. -*/ - -button, -select { - text-transform: none; -} - -/* -1. Correct the inability to style clickable types in iOS and Safari. -2. Remove default button styles. -*/ - -button, -[type="button"], -[type="reset"], -[type="submit"] { - -webkit-appearance: button; /* 1 */ - background-image: none; /* 2 */ -} - -/* -Use the modern Firefox focus style for all focusable elements. -*/ - -:-moz-focusring { - outline: auto; -} - -/* -Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737) -*/ - -:-moz-ui-invalid { - box-shadow: none; -} - -/* -Add the correct vertical alignment in Chrome and Firefox. -*/ - -progress { - vertical-align: baseline; -} - -/* -Correct the cursor style of increment and decrement buttons in Safari. -*/ - -::-webkit-inner-spin-button, -::-webkit-outer-spin-button { - height: auto; -} - -/* -1. Correct the odd appearance in Chrome and Safari. -2. Correct the outline style in Safari. -*/ - -[type="search"] { - -webkit-appearance: textfield; /* 1 */ - outline-offset: -2px; /* 2 */ -} - -/* -Remove the inner padding in Chrome and Safari on macOS. -*/ - -::-webkit-search-decoration { - -webkit-appearance: none; -} - -/* -1. Correct the inability to style clickable types in iOS and Safari. -2. Change font properties to `inherit` in Safari. -*/ - -::-webkit-file-upload-button { - -webkit-appearance: button; /* 1 */ - font: inherit; /* 2 */ -} - -/* -Add the correct display in Chrome and Safari. -*/ - -summary { - display: list-item; -} - -/* -Removes the default spacing and border for appropriate elements. -*/ - -blockquote, -dl, -dd, -h1, -h2, -h3, -h4, -h5, -h6, -hr, -figure, -p, -pre { - margin: 0; -} - -fieldset { - margin: 0; - padding: 0; -} - -legend { - padding: 0; -} - -ol, -ul, -menu { - list-style: none; - margin: 0; - padding: 0; -} - -/* -Prevent resizing textareas horizontally by default. -*/ - -textarea { - resize: vertical; -} - -/* -1. Reset the default placeholder opacity in Firefox. (https://github.com/tailwindlabs/tailwindcss/issues/3300) -2. Set the default placeholder color to the user's configured gray 400 color. -*/ - -input::placeholder, -textarea::placeholder { - opacity: 1; /* 1 */ - color: theme("colors.gray.400"); /* 2 */ -} - -/* -Set the default cursor for buttons. -*/ - -button, -[role="button"] { - cursor: pointer; -} - -/* -Make sure disabled buttons don't get the pointer cursor. -*/ -:disabled { - cursor: default; -} - -/* -1. Make replaced elements `display: block` by default. (https://github.com/mozdevs/cssremedy/issues/14) -2. Add `vertical-align: middle` to align replaced elements more sensibly by default. (https://github.com/jensimmons/cssremedy/issues/14#issuecomment-634934210) - This can trigger a poorly considered lint error in some tools but is included by design. -*/ - -img, -svg, -video, -canvas, -audio, -iframe, -embed, -object { - display: block; /* 1 */ - vertical-align: middle; /* 2 */ -} - -/* -Constrain images and videos to the parent width and preserve their intrinsic aspect ratio. (https://github.com/mozdevs/cssremedy/issues/14) -*/ - -img, -video { - max-width: 100%; - height: auto; -} - -/* Make elements with the HTML hidden attribute stay hidden by default */ -[hidden] { - display: none; -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/types/constants.ts b/apps/file-q-and-a/nextjs-with-flask-server/client/src/types/constants.ts deleted file mode 100644 index 273a3c22..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/types/constants.ts +++ /dev/null @@ -1 +0,0 @@ -export const SERVER_ADDRESS = "http://localhost:8080"; diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/src/types/file.ts b/apps/file-q-and-a/nextjs-with-flask-server/client/src/types/file.ts deleted file mode 100644 index b87d0ed6..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/src/types/file.ts +++ /dev/null @@ -1,21 +0,0 @@ -export interface FileLite { - expanded?: boolean; - name: string; - url?: string; - type?: string; - score?: number; - size?: number; - embedding?: number[]; // The file embedding -- or mean embedding if there are multiple embeddings for the file - chunks?: TextEmbedding[]; // The chunks of text and their embeddings - extractedText?: string; // The extracted text from the file -} - -export interface FileChunk extends TextEmbedding { - filename: string; - score?: number; -} - -export interface TextEmbedding { - text: string; - embedding: number[]; -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/tailwind.config.js b/apps/file-q-and-a/nextjs-with-flask-server/client/tailwind.config.js deleted file mode 100644 index 6cf79462..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/tailwind.config.js +++ /dev/null @@ -1,28 +0,0 @@ -const { fontFamily } = require("tailwindcss/defaultTheme"); - -/** @type {import('tailwindcss').Config} */ -module.exports = { - content: [ - "./app/**/*.{js,ts,jsx,tsx}", - "./src/**/*.{js,ts,jsx,tsx}", - "./pages/**/*.{js,ts,jsx,tsx}", - "./components/**/*.{js,ts,jsx,tsx}", - ], - corePlugins: { - preflight: false, - }, - theme: { - extend: { - }, - }, - keyframes: { - blink: { - "0%, 100%": { opacity: 1 }, - "50%": { opacity: 0 }, - }, - }, - plugins: [ - require("@tailwindcss/line-clamp"), - require("@tailwindcss/typography"), - ], -}; diff --git a/apps/file-q-and-a/nextjs-with-flask-server/client/tsconfig.json b/apps/file-q-and-a/nextjs-with-flask-server/client/tsconfig.json deleted file mode 100644 index 2159bf45..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/client/tsconfig.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "compilerOptions": { - "target": "es5", - "lib": ["dom", "dom.iterable", "esnext"], - "allowJs": true, - "skipLibCheck": true, - "strict": true, - "forceConsistentCasingInFileNames": true, - "noEmit": true, - "esModuleInterop": true, - "module": "esnext", - "moduleResolution": "node", - "resolveJsonModule": true, - "isolatedModules": true, - "jsx": "preserve", - "incremental": true, - "baseUrl": ".", - "paths": { - "@/*": ["./src/*"] - } - }, - "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx"], - "exclude": ["node_modules"] -} diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/answer_question.py b/apps/file-q-and-a/nextjs-with-flask-server/server/answer_question.py deleted file mode 100644 index 6d466ec7..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/answer_question.py +++ /dev/null @@ -1,80 +0,0 @@ -from utils import get_embedding -from flask import jsonify -from config import * -from flask import current_app - -import openai - -from config import * - -TOP_K = 10 - - -def get_answer_from_files(question, session_id, pinecone_index): - logging.info(f"Getting answer for question: {question}") - - search_query_embedding = get_embedding(question, EMBEDDINGS_MODEL) - - try: - query_response = pinecone_index.query( - namespace=session_id, - top_k=TOP_K, - include_values=False, - include_metadata=True, - vector=search_query_embedding, - ) - logging.info( - f"[get_answer_from_files] received query response from Pinecone: {query_response}") - - files_string = "" - file_text_dict = current_app.config["file_text_dict"] - - for i in range(len(query_response.matches)): - result = query_response.matches[i] - file_chunk_id = result.id - score = result.score - filename = result.metadata["filename"] - file_text = file_text_dict.get(file_chunk_id) - file_string = f"###\n\"{filename}\"\n{file_text}\n" - if score < COSINE_SIM_THRESHOLD and i > 0: - logging.info( - f"[get_answer_from_files] score {score} is below threshold {COSINE_SIM_THRESHOLD} and i is {i}, breaking") - break - files_string += file_string - - # Note: this is not the proper way to use the ChatGPT conversational format, but it works for now - messages = [ - { - "role": "system", - "content": f"Given a question, try to answer it using the content of the file extracts below, and if you cannot answer, or find " \ - f"a relevant file, just output \"I couldn't find the answer to that question in your files.\".\n\n" \ - f"If the answer is not contained in the files or if there are no file extracts, respond with \"I couldn't find the answer " \ - f"to that question in your files.\" If the question is not actually a question, respond with \"That's not a valid question.\"\n\n" \ - f"In the cases where you can find the answer, first give the answer. Then explain how you found the answer from the source or sources, " \ - f"and use the exact filenames of the source files you mention. Do not make up the names of any other files other than those mentioned "\ - f"in the files context. Give the answer in markdown format." \ - f"Use the following format:\n\nQuestion: \n\nFiles:\n<###\n\"filename 1\"\nfile text>\n<###\n\"filename 2\"\nfile text>...\n\n"\ - f"Answer: \n\n" \ - f"Question: {question}\n\n" \ - f"Files:\n{files_string}\n" \ - f"Answer:" - }, - ] - - response = openai.ChatCompletion.create( - messages=messages, - model=GENERATIVE_MODEL, - max_tokens=1000, - temperature=0, - ) - - choices = response["choices"] # type: ignore - answer = choices[0].message.content.strip() - - logging.info(f"[get_answer_from_files] answer: {answer}") - - return jsonify({"answer": answer}) - - except Exception as e: - logging.info(f"[get_answer_from_files] error: {e}") - return str(e) diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/app.py b/apps/file-q-and-a/nextjs-with-flask-server/server/app.py deleted file mode 100644 index 0dd2660c..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/app.py +++ /dev/null @@ -1,100 +0,0 @@ -from __future__ import print_function -from config import * - -import tiktoken -import pinecone -import uuid -import sys -import logging - -from flask import Flask, jsonify -from flask_cors import CORS, cross_origin -from flask import request - -from handle_file import handle_file -from answer_question import get_answer_from_files - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[ - logging.FileHandler("debug.log"), - logging.StreamHandler(sys.stdout) - ] -) - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[ - logging.FileHandler("debug.log"), - logging.StreamHandler(sys.stdout) - ] -) - -def load_pinecone_index() -> pinecone.Index: - """ - Load index from Pinecone, raise error if the index can't be found. - """ - pinecone.init( - api_key=PINECONE_API_KEY, - environment=PINECONE_ENV, - ) - index_name = PINECONE_INDEX - if not index_name in pinecone.list_indexes(): - print(pinecone.list_indexes()) - raise KeyError(f"Index '{index_name}' does not exist.") - index = pinecone.Index(index_name) - - return index - -def create_app(): - pinecone_index = load_pinecone_index() - tokenizer = tiktoken.get_encoding("gpt2") - session_id = str(uuid.uuid4().hex) - app = Flask(__name__) - app.pinecone_index = pinecone_index - app.tokenizer = tokenizer - app.session_id = session_id - # log session id - logging.info(f"session_id: {session_id}") - app.config["file_text_dict"] = {} - CORS(app, supports_credentials=True) - - return app - -app = create_app() - -@app.route(f"/process_file", methods=["POST"]) -@cross_origin(supports_credentials=True) -def process_file(): - try: - file = request.files['file'] - logging.info(str(file)) - handle_file( - file, app.session_id, app.pinecone_index, app.tokenizer) - return jsonify({"success": True}) - except Exception as e: - logging.error(str(e)) - return jsonify({"success": False}) - -@app.route(f"/answer_question", methods=["POST"]) -@cross_origin(supports_credentials=True) -def answer_question(): - try: - params = request.get_json() - question = params["question"] - - answer_question_response = get_answer_from_files( - question, app.session_id, app.pinecone_index) - return answer_question_response - except Exception as e: - return str(e) - -@app.route("/healthcheck", methods=["GET"]) -@cross_origin(supports_credentials=True) -def healthcheck(): - return "OK" - -if __name__ == "__main__": - app.run(debug=True, port=SERVER_PORT, threaded=True) diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/config.py b/apps/file-q-and-a/nextjs-with-flask-server/server/config.py deleted file mode 100644 index 438b04e4..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/config.py +++ /dev/null @@ -1,35 +0,0 @@ -from pathlib import Path -import logging -import sys -from pprint import pformat -import yaml - -# Load config items from config.yaml. -# Use Path.resolve() to get the absolute path of the parent directory -yaml_dir = Path(__file__).resolve().parent -yaml_path = yaml_dir / "config.yaml" # Use Path / operator to join paths - -def load_yaml_config(path): - """Load a yaml file and return a dictionary of its contents.""" - try: - with open(path, "r") as stream: - return yaml.safe_load(stream) - except yaml.YAMLError as exc: - logging.error(f"Failed to load {path}: {exc}") - return None - -# Load the config and update the global variables -yaml_config = load_yaml_config(yaml_path) -if yaml_config is not None: - logging.info(f"Loaded config from {yaml_path}:") - logging.info(pformat(yaml_config)) - globals().update(yaml_config) -else: - logging.error(f"Could not load config from {yaml_path}.") - sys.exit(1) # Exit the program if the config is invalid - -# Set a default value for SERVER_PORT if not specified in the config -SERVER_PORT = yaml_config.get("SERVER_PORT", None) - -# Use Path.resolve() to get the absolute path of the current directory -SERVER_DIR = Path(__file__).resolve().parent diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/config.yaml b/apps/file-q-and-a/nextjs-with-flask-server/server/config.yaml deleted file mode 100644 index 1af0568c..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/config.yaml +++ /dev/null @@ -1,18 +0,0 @@ -# ----- PINECONE CONFIG ----- -PINECONE_API_KEY: "" -PINECONE_INDEX: "" # dimensions: 1536, metric: cosine similarity -PINECONE_ENV: "" - -# ----- SERVER PORT ---- -SERVER_PORT: "8080" - -# ---- OPENAI CONFIG ----- -EMBEDDINGS_MODEL: "text-embedding-ada-002" -GENERATIVE_MODEL: "gpt-3.5-turbo" # use gpt-4 for better results -EMBEDDING_DIMENSIONS: 1536 -TEXT_EMBEDDING_CHUNK_SIZE: 200 -# This is the minimum cosine similarity score that a file must have with the search query to be considered relevant -# This is an arbitrary value, and you should vary/ remove this depending on the diversity of your dataset -COSINE_SIM_THRESHOLD: 0.7 -MAX_TEXTS_TO_EMBED_BATCH_SIZE: 100 -MAX_PINECONE_VECTORS_TO_UPSERT_PATCH_SIZE: 100 diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/handle_file.py b/apps/file-q-and-a/nextjs-with-flask-server/server/handle_file.py deleted file mode 100644 index bf6cbb3f..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/handle_file.py +++ /dev/null @@ -1,168 +0,0 @@ -import logging -import sys -import docx2txt - -from PyPDF2 import PdfReader -from numpy import array, average -from flask import current_app -from config import * - -from utils import get_embeddings, get_pinecone_id_for_file_chunk - -# Set up logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[ - logging.FileHandler("debug.log"), - logging.StreamHandler(sys.stdout) - ] -) - -# Handle a file by extracting its text, creating embeddings, and upserting them to Pinecone -def handle_file(file, session_id, pinecone_index, tokenizer): - """Handle a file by extracting its text, creating embeddings, and upserting them to Pinecone.""" - filename = file.filename - logging.info("[handle_file] Handling file: {}".format(filename)) - - # Get the file text dict from the current app config - file_text_dict = current_app.config["file_text_dict"] - - # Extract text from the file - try: - extracted_text = extract_text_from_file(file) - except ValueError as e: - logging.error( - "[handle_file] Error extracting text from file: {}".format(e)) - raise e - - # Save extracted text to file text dict - file_text_dict[filename] = extracted_text - - # Handle the extracted text as a string - return handle_file_string(filename, session_id, extracted_text, pinecone_index, tokenizer, file_text_dict) - -# Extract text from a file based on its mimetype -def extract_text_from_file(file): - """Return the text content of a file.""" - if file.mimetype == "application/pdf": - # Extract text from pdf using PyPDF2 - reader = PdfReader(file) - extracted_text = "" - for page in reader.pages: - extracted_text += page.extract_text() - elif file.mimetype == "text/plain": - # Read text from plain text file - extracted_text = file.read().decode("utf-8") - file.close() - elif file.mimetype == "application/vnd.openxmlformats-officedocument.wordprocessingml.document": - # Extract text from docx using docx2txt - extracted_text = docx2txt.process(file) - else: - # Unsupported file type - raise ValueError("Unsupported file type: {}".format(file.mimetype)) - - return extracted_text - -# Handle a file string by creating embeddings and upserting them to Pinecone -def handle_file_string(filename, session_id, file_body_string, pinecone_index, tokenizer, file_text_dict): - """Handle a file string by creating embeddings and upserting them to Pinecone.""" - logging.info("[handle_file_string] Starting...") - - # Clean up the file string by replacing newlines and double spaces - clean_file_body_string = file_body_string.replace( - "\n", "; ").replace(" ", " ") - # Add the filename to the text to embed - text_to_embed = "Filename is: {}; {}".format( - filename, clean_file_body_string) - - # Create embeddings for the text - try: - text_embeddings, average_embedding = create_embeddings_for_text( - text_to_embed, tokenizer) - logging.info( - "[handle_file_string] Created embedding for {}".format(filename)) - except Exception as e: - logging.error( - "[handle_file_string] Error creating embedding: {}".format(e)) - raise e - - # Get the vectors array of triples: file_chunk_id, embedding, metadata for each embedding - # Metadata is a dict with keys: filename, file_chunk_index - vectors = [] - for i, (text_chunk, embedding) in enumerate(text_embeddings): - id = get_pinecone_id_for_file_chunk(session_id, filename, i) - file_text_dict[id] = text_chunk - vectors.append( - (id, embedding, {"filename": filename, "file_chunk_index": i})) - - logging.info( - "[handle_file_string] Text chunk {}: {}".format(i, text_chunk)) - - # Split the vectors array into smaller batches of max length 2000 - batch_size = MAX_PINECONE_VECTORS_TO_UPSERT_PATCH_SIZE - batches = [vectors[i:i+batch_size] for i in range(0, len(vectors), batch_size)] - - # Upsert each batch to Pinecone - for batch in batches: - try: - pinecone_index.upsert( - vectors=batch, namespace=session_id) - - logging.info( - "[handle_file_string] Upserted batch of embeddings for {}".format(filename)) - except Exception as e: - logging.error( - "[handle_file_string] Error upserting batch of embeddings to Pinecone: {}".format(e)) - raise e - -# Compute the column-wise average of a list of lists -def get_col_average_from_list_of_lists(list_of_lists): - """Return the average of each column in a list of lists.""" - if len(list_of_lists) == 1: - return list_of_lists[0] - else: - list_of_lists_array = array(list_of_lists) - average_embedding = average(list_of_lists_array, axis=0) - return average_embedding.tolist() - -# Create embeddings for a text using a tokenizer and an OpenAI engine -def create_embeddings_for_text(text, tokenizer): - """Return a list of tuples (text_chunk, embedding) and an average embedding for a text.""" - token_chunks = list(chunks(text, TEXT_EMBEDDING_CHUNK_SIZE, tokenizer)) - text_chunks = [tokenizer.decode(chunk) for chunk in token_chunks] - - # Split text_chunks into shorter arrays of max length 10 - text_chunks_arrays = [text_chunks[i:i+MAX_TEXTS_TO_EMBED_BATCH_SIZE] for i in range(0, len(text_chunks), MAX_TEXTS_TO_EMBED_BATCH_SIZE)] - - # Call get_embeddings for each shorter array and combine the results - embeddings = [] - for text_chunks_array in text_chunks_arrays: - embeddings_response = get_embeddings(text_chunks_array, EMBEDDINGS_MODEL) - embeddings.extend([embedding["embedding"] for embedding in embeddings_response]) - - text_embeddings = list(zip(text_chunks, embeddings)) - - average_embedding = get_col_average_from_list_of_lists(embeddings) - - return (text_embeddings, average_embedding) - -# Split a text into smaller chunks of size n, preferably ending at the end of a sentence -def chunks(text, n, tokenizer): - tokens = tokenizer.encode(text) - """Yield successive n-sized chunks from text.""" - i = 0 - while i < len(tokens): - # Find the nearest end of sentence within a range of 0.5 * n and 1.5 * n tokens - j = min(i + int(1.5 * n), len(tokens)) - while j > i + int(0.5 * n): - # Decode the tokens and check for full stop or newline - chunk = tokenizer.decode(tokens[i:j]) - if chunk.endswith(".") or chunk.endswith("\n"): - break - j -= 1 - # If no end of sentence found, use n tokens as the chunk size - if j == i + int(0.5 * n): - j = min(i + n, len(tokens)) - yield tokens[i:j] - i = j diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/requirements.txt b/apps/file-q-and-a/nextjs-with-flask-server/server/requirements.txt deleted file mode 100644 index a1f56b0d..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -Flask-Cors>=3.0.10 -openai>=0.27.2 -pinecone-client>=2.0.13 -PyPDF2>=2.10.4 -numpy>=1.23.2 -scikit-learn>=1.1.2 -docx2txt>=0.8 -flask>=1.1.4 -jinja2>=3.0.1 -PyYAML>=6.0 -tiktoken>=0.1.2 \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/script/start b/apps/file-q-and-a/nextjs-with-flask-server/server/script/start deleted file mode 100644 index 29b2369e..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/script/start +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -set -e - -echo "Starting Python server..." - -pip install virtualenv -python -m virtualenv venv -source venv/bin/activate -pip install -r requirements.txt -OPENAI_API_KEY=$1 python app.py diff --git a/apps/file-q-and-a/nextjs-with-flask-server/server/utils.py b/apps/file-q-and-a/nextjs-with-flask-server/server/utils.py deleted file mode 100644 index b50025ab..00000000 --- a/apps/file-q-and-a/nextjs-with-flask-server/server/utils.py +++ /dev/null @@ -1,38 +0,0 @@ -import openai -import logging -import sys -import time - -from config import * - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[ - logging.FileHandler("debug.log"), - logging.StreamHandler(sys.stdout) - ] -) - -def get_pinecone_id_for_file_chunk(session_id, filename, chunk_index): - return str(session_id+"-!"+filename+"-!"+str(chunk_index)) - -def get_embedding(text, engine): - return openai.Engine(id=engine).embeddings(input=[text])["data"][0]["embedding"] - -def get_embeddings(text_array, engine): - # Parameters for exponential backoff - max_retries = 5 # Maximum number of retries - base_delay = 1 # Base delay in seconds - factor = 2 # Factor to multiply the delay by after each retry - while True: - try: - return openai.Engine(id=engine).embeddings(input=text_array)["data"] - except Exception as e: - if max_retries > 0: - logging.info(f"Request failed. Retrying in {base_delay} seconds.") - time.sleep(base_delay) - max_retries -= 1 - base_delay *= factor - else: - raise e \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs/.devcontainer/Dockerfile b/apps/file-q-and-a/nextjs/.devcontainer/Dockerfile deleted file mode 100644 index 4a63dfac..00000000 --- a/apps/file-q-and-a/nextjs/.devcontainer/Dockerfile +++ /dev/null @@ -1,2 +0,0 @@ -ARG VARIANT=16-bullseye -FROM mcr.microsoft.com/vscode/devcontainers/typescript-node:${VARIANT} \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs/.devcontainer/devcontainer.json b/apps/file-q-and-a/nextjs/.devcontainer/devcontainer.json deleted file mode 100644 index 6edb7f75..00000000 --- a/apps/file-q-and-a/nextjs/.devcontainer/devcontainer.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "name": "openai-cookbook file-q-and-a", - "dockerFile": "Dockerfile", - "forwardPorts": [3000] -} \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs/.env.local.example b/apps/file-q-and-a/nextjs/.env.local.example deleted file mode 100644 index f73e84d4..00000000 --- a/apps/file-q-and-a/nextjs/.env.local.example +++ /dev/null @@ -1,4 +0,0 @@ -# create a copy of this file named .env.local - -# Your own API key for OpenAI -OPENAI_API_KEY='sk-......' \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs/.eslintrc.json b/apps/file-q-and-a/nextjs/.eslintrc.json deleted file mode 100644 index bffb357a..00000000 --- a/apps/file-q-and-a/nextjs/.eslintrc.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "extends": "next/core-web-vitals" -} diff --git a/apps/file-q-and-a/nextjs/.gitignore b/apps/file-q-and-a/nextjs/.gitignore deleted file mode 100644 index c87c9b39..00000000 --- a/apps/file-q-and-a/nextjs/.gitignore +++ /dev/null @@ -1,36 +0,0 @@ -# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. - -# dependencies -/node_modules -/.pnp -.pnp.js - -# testing -/coverage - -# next.js -/.next/ -/out/ - -# production -/build - -# misc -.DS_Store -*.pem - -# debug -npm-debug.log* -yarn-debug.log* -yarn-error.log* -.pnpm-debug.log* - -# local env files -.env*.local - -# vercel -.vercel - -# typescript -*.tsbuildinfo -next-env.d.ts diff --git a/apps/file-q-and-a/nextjs/README.md b/apps/file-q-and-a/nextjs/README.md deleted file mode 100644 index 0a0703ac..00000000 --- a/apps/file-q-and-a/nextjs/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# File Q&A - -File Q&A is a [Next.js](https://nextjs.org/) app that lets you find answers in your files using OpenAI APIs. You can upload files and ask questions related to their content, and the app will use embeddings and GPT to generate answers from the most relevant files. - -## Requirements - -To run the app, you need an OpenAI API key. You can create a new API key [here](https://beta.openai.com/account/api-keys). - -## Set Up - -If you don't have Node.js and npm already, install them from [https://nodejs.org/en/download/](https://nodejs.org/en/download/). - -Otherwise you can run the project inside a [Dev Container](https://code.visualstudio.com/docs/devcontainers/containers). If you are using VS Code, open the Command Pallette (ctrl+shift+p on Windows / cmd+shift+p on Mac), and run the command `Dev Containers: Open Folder in Container`. Then select the folder `apps/file-q-and-a/nextjs`, and VS Code will take care of booting a container with Node.js and npm ready to go. You may need to install the [Dev Containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) extension. - -In your terminal, navigate to the `nextjs` directory of this example app, and then install dependencies: - -``` -npm install -``` - -Copy the .env.local.example file into a .env.local file and fill out the OpenAI API key field. - -## Development - -Run the development server: - -``` -npm run dev -``` - -Open [http://localhost:3000](http://localhost:3000) with your browser to see the app. - -## Deployment - -You can deploy the app on [Vercel](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme), the platform from the creators of Next.js. Check out the [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details. - -## Limitations - -Uploaded files and generated embeddings don't persist on browser refresh. If you want to store more embeddings, we recommend using a vector database (e.g. Pinecone, Weaviate, Milvus, Qdrant, Redis, FAISS, etc.). The `nextjs-with-flask-server` version of this demo uses a Pinecone vector database. - -The app may sometimes generate answers that are not in the files, or hallucinate about the existence of files that are not uploaded. diff --git a/apps/file-q-and-a/nextjs/next.config.js b/apps/file-q-and-a/nextjs/next.config.js deleted file mode 100644 index a843cbee..00000000 --- a/apps/file-q-and-a/nextjs/next.config.js +++ /dev/null @@ -1,6 +0,0 @@ -/** @type {import('next').NextConfig} */ -const nextConfig = { - reactStrictMode: true, -} - -module.exports = nextConfig diff --git a/apps/file-q-and-a/nextjs/package-lock.json b/apps/file-q-and-a/nextjs/package-lock.json deleted file mode 100644 index e65d7824..00000000 --- a/apps/file-q-and-a/nextjs/package-lock.json +++ /dev/null @@ -1,8959 +0,0 @@ -{ - "name": "file-q-and-a", - "version": "0.1.0", - "lockfileVersion": 2, - "requires": true, - "packages": { - "": { - "name": "file-q-and-a", - "version": "0.1.0", - "dependencies": { - "@headlessui/react": "^1.7.7", - 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"build": "next build", - "start": "next start", - "lint": "next lint" - }, - "dependencies": { - "@headlessui/react": "^1.7.7", - "@heroicons/react": "^2.0.13", - "@next/font": "13.1.2", - "@tailwindcss/line-clamp": "^0.4.2", - "@tailwindcss/typography": "^0.5.9", - "@types/formidable": "^2.0.5", - "@types/lodash": "^4.14.191", - "@types/node": "18.11.18", - "@types/pdf-parse": "^1.1.1", - "@types/react": "18.0.27", - "@types/react-dom": "18.0.10", - "axios": "^1.2.3", - "clsx": "^1.2.1", - "eslint": "8.32.0", - "eslint-config-next": "13.1.2", - "formidable": "^2.1.1", - "lodash": "^4.17.21", - "mammoth": "^1.5.1", - "next": "13.1.2", - "node-html-markdown": "^1.3.0", - "openai": "^3.2.1", - "pdf-parse": "^1.1.1", - "react": "18.2.0", - "react-dom": "18.2.0", - "react-markdown": "^8.0.5", - "typescript": "4.9.4" - }, - "devDependencies": { - "autoprefixer": "^10.4.13", - "postcss": "^8.4.31", - "tailwindcss": "^3.2.4" - } -} diff --git a/apps/file-q-and-a/nextjs/postcss.config.js b/apps/file-q-and-a/nextjs/postcss.config.js deleted file mode 100644 index 12a703d9..00000000 --- a/apps/file-q-and-a/nextjs/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -module.exports = { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -}; diff --git a/apps/file-q-and-a/nextjs/public/favicon.ico b/apps/file-q-and-a/nextjs/public/favicon.ico deleted file mode 100644 index b6c3abcb..00000000 Binary files a/apps/file-q-and-a/nextjs/public/favicon.ico and /dev/null differ diff --git a/apps/file-q-and-a/nextjs/public/openai-horizontal.svg b/apps/file-q-and-a/nextjs/public/openai-horizontal.svg deleted file mode 100644 index 0d31b2d4..00000000 --- a/apps/file-q-and-a/nextjs/public/openai-horizontal.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs/public/openai.svg b/apps/file-q-and-a/nextjs/public/openai.svg deleted file mode 100644 index fe51f0e5..00000000 --- a/apps/file-q-and-a/nextjs/public/openai.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/apps/file-q-and-a/nextjs/src/components/File.tsx b/apps/file-q-and-a/nextjs/src/components/File.tsx deleted file mode 100644 index a402e04c..00000000 --- a/apps/file-q-and-a/nextjs/src/components/File.tsx +++ /dev/null @@ -1,77 +0,0 @@ -import { useState, useCallback, memo } from "react"; -import { Transition } from "@headlessui/react"; -import { - MagnifyingGlassMinusIcon, - MagnifyingGlassPlusIcon, - ArrowTopRightOnSquareIcon, -} from "@heroicons/react/24/outline"; - -import { FileLite } from "../types/file"; - -type FileProps = { - file: FileLite; - showScore?: boolean; -}; - -function File(props: FileProps) { - const [expanded, setExpanded] = useState(false); - - const handleExpand = useCallback(() => { - setExpanded((prev) => !prev); - }, []); - - return ( -
-
-
{props.file.name}
- -
- {props.showScore && props.file.score && ( -
- {props.file.score.toFixed(2)} -
- )} - -
- {expanded ? ( - - ) : ( - - )} -
- - e.stopPropagation()} // prevent the click event from bubbling up to the list item - > - - -
-
- -
- -
-
-
- ); -} - -export default memo(File); diff --git a/apps/file-q-and-a/nextjs/src/components/FileQandAArea.tsx b/apps/file-q-and-a/nextjs/src/components/FileQandAArea.tsx deleted file mode 100644 index 85799698..00000000 --- a/apps/file-q-and-a/nextjs/src/components/FileQandAArea.tsx +++ /dev/null @@ -1,179 +0,0 @@ -import React, { memo, useCallback, useRef, useState } from "react"; -import { Transition } from "@headlessui/react"; -import axios from "axios"; -import ReactMarkdown from "react-markdown"; - -import FileViewerList from "./FileViewerList"; -import LoadingText from "./LoadingText"; -import { isFileNameInString } from "../services/utils"; -import { FileChunk, FileLite } from "../types/file"; - -type FileQandAAreaProps = { - files: FileLite[]; -}; - -function FileQandAArea(props: FileQandAAreaProps) { - const questionRef = useRef(null); - const [hasAskedQuestion, setHasAskedQuestion] = useState(false); - const [answerError, setAnswerError] = useState(""); - const [answerLoading, setAnswerLoading] = useState(false); - const [answer, setAnswer] = useState(""); - const [answerDone, setAnswerDone] = useState(false); - - const handleSearch = useCallback(async () => { - if (answerLoading) { - return; - } - - const question = (questionRef?.current as any)?.value ?? ""; - setAnswer(""); - setAnswerDone(false); - - if (!question) { - setAnswerError("Please ask a question."); - return; - } - if (props.files.length === 0) { - setAnswerError("Please upload files before asking a question."); - return; - } - - setAnswerLoading(true); - setAnswerError(""); - - let results: FileChunk[] = []; - - try { - const searchResultsResponse = await axios.post( - "/api/search-file-chunks", - { - searchQuery: question, - files: props.files, - maxResults: 10, - } - ); - - if (searchResultsResponse.status === 200) { - results = searchResultsResponse.data.searchResults; - } else { - setAnswerError("Sorry, something went wrong!"); - } - } catch (err: any) { - setAnswerError("Sorry, something went wrong!"); - } - - setHasAskedQuestion(true); - - const res = await fetch("/api/get-answer-from-files", { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - question, - fileChunks: results, - }), - }); - - if (res.status === 500) { - setAnswerError("Internal server error. Please try again later."); - setAnswerLoading(false); - return; - } - - const reader = res.body!.getReader(); - - while (true) { - const { done, value } = await reader.read(); - if (done) { - setAnswerDone(true); - break; - } - setAnswer((prev) => prev + new TextDecoder().decode(value)); - } - - setAnswerLoading(false); - }, [props.files, answerLoading]); - - const handleEnterInSearchBar = useCallback( - async (event: React.SyntheticEvent) => { - if ((event as any).key === "Enter") { - await handleSearch(); - } - }, - [handleSearch] - ); - - return ( -
-
- Ask a question based on the content of your files: -
-
- -
- {answerLoading ? ( - - ) : ( - "Ask question" - )} -
-
-
- {answerError &&
{answerError}
} - - {answer && ( -
- - {`${answer}${answerDone ? "" : " |"}`} - -
- )} - - - isFileNameInString(file.name, answer) - ).length > 0 - } - enter="transition duration-600 ease-out" - enterFrom="transform opacity-0" - enterTo="transform opacity-100" - leave="transition duration-125 ease-out" - leaveFrom="transform opacity-100" - leaveTo="transform opacity-0" - className="mb-8" - > - - isFileNameInString(file.name, answer) - )} - title="Sources" - listExpanded={true} - /> - -
-
-
- ); -} - -export default memo(FileQandAArea); diff --git a/apps/file-q-and-a/nextjs/src/components/FileUploadArea.tsx b/apps/file-q-and-a/nextjs/src/components/FileUploadArea.tsx deleted file mode 100644 index d41bb94b..00000000 --- a/apps/file-q-and-a/nextjs/src/components/FileUploadArea.tsx +++ /dev/null @@ -1,201 +0,0 @@ -import React, { - Dispatch, - SetStateAction, - useCallback, - useState, - memo, - useRef, -} from "react"; -import axios from "axios"; -import { ArrowUpTrayIcon } from "@heroicons/react/24/outline"; -import { compact } from "lodash"; - -import LoadingText from "./LoadingText"; -import { FileLite } from "../types/file"; -import FileViewerList from "./FileViewerList"; - -type FileUploadAreaProps = { - handleSetFiles: Dispatch>; - maxNumFiles: number; - maxFileSizeMB: number; -}; - -function FileUploadArea(props: FileUploadAreaProps) { - const handleSetFiles = props.handleSetFiles; - - const [files, setFiles] = useState([]); - const [loading, setLoading] = useState(false); - const [error, setError] = useState(""); - const [dragOver, setDragOver] = useState(false); - const dropzoneRef = useRef(null); - - const handleFileChange = useCallback( - async (selectedFiles: FileList | null) => { - if (selectedFiles && selectedFiles.length > 0) { - setError(""); - - if (files.length + selectedFiles.length > props.maxNumFiles) { - setError(`You can only upload up to ${props.maxNumFiles} files.`); - if (dropzoneRef.current) { - (dropzoneRef.current as any).value = ""; - } - return; - } - - setLoading(true); - - const uploadedFiles = await Promise.all( - Array.from(selectedFiles).map(async (file) => { - // Check the file type - if ( - file.type.match( - /(text\/plain|application\/(pdf|msword|vnd\.openxmlformats-officedocument\.wordprocessingml\.document)|text\/(markdown|x-markdown))/ - ) && // AND file isn't too big - file.size < props.maxFileSizeMB * 1024 * 1024 - ) { - // Check if the file name already exists in the files state - if (files.find((f) => f.name === file.name)) { - return null; // Skip this file - } - - const formData = new FormData(); - formData.append("file", file); - formData.append("filename", file.name); - - try { - const processFileResponse = await axios.post( - "/api/process-file", - formData, - { - headers: { - "Content-Type": "multipart/form-data", - }, - } - ); - - if (processFileResponse.status === 200) { - const text = processFileResponse.data.text; - const meanEmbedding = processFileResponse.data.meanEmbedding; - const chunks = processFileResponse.data.chunks; - - const fileObject: FileLite = { - name: file.name, - url: URL.createObjectURL(file), - type: file.type, - size: file.size, - expanded: false, - embedding: meanEmbedding, - chunks, - extractedText: text, - }; - console.log(fileObject); - - return fileObject; - } else { - console.log("Error creating file embedding"); - return null; - } - } catch (err: any) { - console.log(`Error creating file embedding: ${err}`); - return null; - } - } else { - alert( - `Invalid file type or size. Only TXT, PDF, DOCX or MD are allowed, up to ${props.maxFileSizeMB}MB.` - ); - return null; // Skip this file - } - }) - ); - - // Filter out any null values from the uploadedFiles array - const validFiles = compact(uploadedFiles); - - // Set the files state with the valid files and the existing files - setFiles((prevFiles) => [...prevFiles, ...validFiles]); - handleSetFiles((prevFiles) => [...prevFiles, ...validFiles]); - - setLoading(false); - } - }, - [files, handleSetFiles, props.maxFileSizeMB, props.maxNumFiles] - ); - - const handleDragEnter = useCallback((event: React.DragEvent) => { - event.preventDefault(); - setDragOver(true); - }, []); - - const handleDragOver = useCallback((event: React.DragEvent) => { - event.preventDefault(); - }, []); - - const handleDragLeave = useCallback((event: React.DragEvent) => { - event.preventDefault(); - setDragOver(false); - }, []); - - const handleDrop = useCallback( - (event: React.DragEvent) => { - event.preventDefault(); - setDragOver(false); - const droppedFiles = event.dataTransfer.files; - handleFileChange(droppedFiles); - }, - [handleFileChange] - ); - - return ( -
- - - {error && ( -
-

{error}

-
- )} - - -
- ); -} - -export default memo(FileUploadArea); diff --git a/apps/file-q-and-a/nextjs/src/components/FileViewerList.tsx b/apps/file-q-and-a/nextjs/src/components/FileViewerList.tsx deleted file mode 100644 index 04ebe416..00000000 --- a/apps/file-q-and-a/nextjs/src/components/FileViewerList.tsx +++ /dev/null @@ -1,73 +0,0 @@ -import React, { memo, useCallback, useState } from "react"; -import { ChevronUpIcon } from "@heroicons/react/24/outline"; -import clsx from "clsx"; -import { Transition } from "@headlessui/react"; - -import File from "./File"; -import { FileLite } from "../types/file"; - -type FileViewerListProps = { - files: FileLite[]; - title: string; - listExpanded?: boolean; - showScores?: boolean; -}; - -function FileViewerList(props: FileViewerListProps) { - const [listExpanded, setListExpanded] = useState(props.listExpanded ?? false); - - const handleListExpand = useCallback(() => { - setListExpanded((prev) => !prev); - }, []); - - return ( -
- {props.files.length > 0 && ( -
-
-
- {props.title} -
- {props.files.length} -
-
-
- -
-
- - -
- {props.files.map((file) => ( - - ))} -
-
-
- )} -
- ); -} - -export default memo(FileViewerList); diff --git a/apps/file-q-and-a/nextjs/src/components/LoadingSpinner.tsx b/apps/file-q-and-a/nextjs/src/components/LoadingSpinner.tsx deleted file mode 100644 index 74d77ee4..00000000 --- a/apps/file-q-and-a/nextjs/src/components/LoadingSpinner.tsx +++ /dev/null @@ -1,33 +0,0 @@ -import clsx from "clsx"; - -type Props = { - className?: string; - size?: number; -}; - -export default function LoadingSpinner(props: Props) { - const size = props.size || 5; - return ( -
- -
- ); -} diff --git a/apps/file-q-and-a/nextjs/src/components/LoadingText.tsx b/apps/file-q-and-a/nextjs/src/components/LoadingText.tsx deleted file mode 100644 index 021b26af..00000000 --- a/apps/file-q-and-a/nextjs/src/components/LoadingText.tsx +++ /dev/null @@ -1,18 +0,0 @@ -import React, { memo } from "react"; - -import LoadingSpinner from "./LoadingSpinner"; - -type LoadingTextProps = { - text: string; -}; - -function LoadingText(props: LoadingTextProps) { - return ( -
- - {props.text &&
{props.text}
} -
- ); -} - -export default memo(LoadingText); diff --git a/apps/file-q-and-a/nextjs/src/pages/_app.tsx b/apps/file-q-and-a/nextjs/src/pages/_app.tsx deleted file mode 100644 index a7a790fb..00000000 --- a/apps/file-q-and-a/nextjs/src/pages/_app.tsx +++ /dev/null @@ -1,6 +0,0 @@ -import "@/styles/globals.css"; -import type { AppProps } from "next/app"; - -export default function App({ Component, pageProps }: AppProps) { - return ; -} diff --git a/apps/file-q-and-a/nextjs/src/pages/_document.tsx b/apps/file-q-and-a/nextjs/src/pages/_document.tsx deleted file mode 100644 index b2fff8b4..00000000 --- a/apps/file-q-and-a/nextjs/src/pages/_document.tsx +++ /dev/null @@ -1,13 +0,0 @@ -import { Html, Head, Main, NextScript } from "next/document"; - -export default function Document() { - return ( - - - -
- - - - ); -} diff --git a/apps/file-q-and-a/nextjs/src/pages/api/get-answer-from-files.ts b/apps/file-q-and-a/nextjs/src/pages/api/get-answer-from-files.ts deleted file mode 100644 index ee9a8f68..00000000 --- a/apps/file-q-and-a/nextjs/src/pages/api/get-answer-from-files.ts +++ /dev/null @@ -1,74 +0,0 @@ -import type { NextApiRequest, NextApiResponse } from "next"; - -import { completionStream } from "../../services/openai"; -import { FileChunk } from "../../types/file"; - -type Data = { - answer?: string; - error?: string; -}; - -const MAX_FILES_LENGTH = 2000 * 3; - -export default async function handler( - req: NextApiRequest, - res: NextApiResponse -) { - // Only accept POST requests - if (req.method !== "POST") { - res.status(405).json({ error: "Method not allowed" }); - return; - } - - const fileChunks = req.body.fileChunks as FileChunk[]; - - const question = req.body.question as string; - - if (!Array.isArray(fileChunks)) { - res.status(400).json({ error: "fileChunks must be an array" }); - return; - } - - if (!question) { - res.status(400).json({ error: "question must be a string" }); - return; - } - - try { - const filesString = fileChunks - .map((fileChunk) => `###\n\"${fileChunk.filename}\"\n${fileChunk.text}`) - .join("\n") - .slice(0, MAX_FILES_LENGTH); - - const prompt = - `Given a question, try to answer it using the content of the file extracts below, and if you cannot answer, or find a relevant file, just output \"I couldn't find the answer to that question in your files.\".\n\n` + - `If the answer is not contained in the files or if there are no file extracts, respond with \"I couldn't find the answer to that question in your files.\" If the question is not actually a question, respond with \"That's not a valid question.\"\n\n` + - `In the cases where you can find the answer, first give the answer. Then explain how you found the answer from the source or sources, and use the exact filenames of the source files you mention. Do not make up the names of any other files other than those mentioned in the files context. Give the answer in markdown format.` + - `Use the following format:\n\nQuestion: \n\nFiles:\n<###\n\"filename 1\"\nfile text>\n<###\n\"filename 2\"\nfile text>...\n\nAnswer: \n\n` + - `Question: ${question}\n\n` + - `Files:\n${filesString}\n\n` + - `Answer:`; - - const stream = completionStream({ - prompt, - }); - - // Set the response headers for streaming - res.writeHead(200, { - "Content-Type": "text/event-stream", - "Cache-Control": "no-cache, no-transform", - Connection: "keep-alive", - }); - - // Write the data from the stream to the response - for await (const data of stream) { - res.write(data); - } - - // End the response when the stream is done - res.end(); - } catch (error) { - console.error(error); - res.status(500).json({ error: "Something went wrong" }); - } -} diff --git a/apps/file-q-and-a/nextjs/src/pages/api/process-file.ts b/apps/file-q-and-a/nextjs/src/pages/api/process-file.ts deleted file mode 100644 index 2b0d2280..00000000 --- a/apps/file-q-and-a/nextjs/src/pages/api/process-file.ts +++ /dev/null @@ -1,69 +0,0 @@ -import type { NextApiRequest, NextApiResponse } from "next"; -import formidable, { Fields, Files } from "formidable"; // to handle file uploads - -import { TextEmbedding } from "../../types/file"; -import extractTextFromFile from "../../services/extractTextFromFile"; -import { createEmbeddings } from "../../services/createEmbeddings"; - -// Disable the default body parser to handle file uploads -export const config = { api: { bodyParser: false } }; - -type Data = { - text?: string; - meanEmbedding?: number[]; - chunks?: TextEmbedding[]; - error?: string; -}; - -// This function receives a file as a multipart form and returns the text extracted fom the file and the OpenAI embedding for that text -export default async function handler( - req: NextApiRequest, - res: NextApiResponse -) { - if (req.method !== "POST") { - res.status(405).json({ error: "Method not allowed" }); - return; - } - - // Create a formidable instance to parse the request as a multipart form - const options = { - maxFileSize: 30 * 1024 * 1024 // Set the max file size to 30MB - }; - const form = formidable(options); - - try { - const { fields, files } = await new Promise<{ - fields: Fields; - files: Files; - }>((resolve, reject) => { - form.parse(req, (err, fields, files) => { - if (err) { - reject(err); - } else { - resolve({ fields, files } as { fields: Fields; files: Files }); - } - }); - }); - const file = files.file; - if (!file || Array.isArray(file) || file.size === 0) { - res.status(400).json({ error: "Invalid or missing file" }); - return; - } - - const text = await extractTextFromFile({ - filepath: file.filepath, - filetype: file.mimetype ?? "", - }); - - const { meanEmbedding, chunks } = await createEmbeddings({ - text, - }); - - res.status(200).json({ text, meanEmbedding, chunks }); - } catch (error: any) { - res.status(500).json({ error: error.message }); - } finally { - // Always send a response, even if it is empty - res.end(); - } -} diff --git a/apps/file-q-and-a/nextjs/src/pages/api/search-file-chunks.ts b/apps/file-q-and-a/nextjs/src/pages/api/search-file-chunks.ts deleted file mode 100644 index 9831fd70..00000000 --- a/apps/file-q-and-a/nextjs/src/pages/api/search-file-chunks.ts +++ /dev/null @@ -1,59 +0,0 @@ -import type { NextApiRequest, NextApiResponse } from "next"; - -import { searchFileChunks } from "../../services/searchFileChunks"; -import { FileChunk, FileLite } from "../../types/file"; - -type Data = { - searchResults?: FileChunk[]; - error?: string; -}; - -export const config = { - api: { - bodyParser: { - sizeLimit: "30mb", - }, - }, -}; - -export default async function handler( - req: NextApiRequest, - res: NextApiResponse -) { - try { - const searchQuery = req.body.searchQuery as string; - - const files = req.body.files as FileLite[]; - - const maxResults = req.body.maxResults as number; - - if (!searchQuery) { - res.status(400).json({ error: "searchQuery must be a string" }); - return; - } - - if (!Array.isArray(files) || files.length === 0) { - res.status(400).json({ error: "files must be a non-empty array" }); - return; - } - - if (!maxResults || maxResults < 1) { - res - .status(400) - .json({ error: "maxResults must be a number greater than 0" }); - return; - } - - const searchResults = await searchFileChunks({ - searchQuery, - files, - maxResults, - }); - - res.status(200).json({ searchResults }); - } catch (error) { - console.error(error); - - res.status(500).json({ error: "Something went wrong" }); - } -} diff --git a/apps/file-q-and-a/nextjs/src/pages/index.tsx b/apps/file-q-and-a/nextjs/src/pages/index.tsx deleted file mode 100644 index c94ef584..00000000 --- a/apps/file-q-and-a/nextjs/src/pages/index.tsx +++ /dev/null @@ -1,35 +0,0 @@ -import Head from "next/head"; -import { useState } from "react"; - -import FileQandAArea from "../components/FileQandAArea"; -import { FileLite } from "../types/file"; -import FileUploadArea from "../components/FileUploadArea"; - -export default function FileQandA() { - const [files, setFiles] = useState([]); - - return ( -
- - File Q&A - -
-

File Q&A

- -
- To search for answers from the content in your files, upload them here - and we will use OpenAI embeddings and GPT to find answers from the - relevant documents. -
- - - - -
-
- ); -} diff --git a/apps/file-q-and-a/nextjs/src/services/chunkText.ts b/apps/file-q-and-a/nextjs/src/services/chunkText.ts deleted file mode 100644 index 1ae48355..00000000 --- a/apps/file-q-and-a/nextjs/src/services/chunkText.ts +++ /dev/null @@ -1,74 +0,0 @@ -// A function that splits a text into smaller pieces of roughly equal length -// The pieces are delimited by sentences and try to avoid breaking words or punctuation -// This can be useful for processing long texts with natural language models that have a limited input size -export function chunkText({ - text, // The input text to be split - // The desired maximum length of each piece in characters - // This uses 4 characters as an approximation of the average token length - // since there isn't a good JS tokenizer at the moment - maxCharLength = 250 * 4, -}: { - text: string; - maxCharLength?: number; -}): string[] { - // Create an empty array to store the pieces - const chunks: string[] = []; - - // Create a variable to hold the current piece - let currentChunk = ""; - - // Remove any newline characters from the text and split it by periods - // This assumes that periods mark the end of sentences, which may not be true for some languages - const sentences = text.replace(/\n/g, " ").split(/([.])/); - - for (const sentence of sentences) { - // Remove any extra whitespace from the beginning and end of the sentence - const trimmedSentence = sentence.trim(); - - // If the sentence is empty, skip it - if (!trimmedSentence) continue; - - // Check if adding the sentence to the current piece would make it too long, too short, or just right - // This uses a tolerance range of 50% of the maximum length to allow some flexibility - // If the piece is too long, save it and start a new one - // If the piece is too short, add the sentence and continue - // If the piece is just right, save it and start a new one - const chunkLength = currentChunk.length + trimmedSentence.length + 1; - const lowerBound = maxCharLength - maxCharLength * 0.5; - const upperBound = maxCharLength + maxCharLength * 0.5; - - if ( - chunkLength >= lowerBound && - chunkLength <= upperBound && - currentChunk - ) { - // The piece is just right, so we save it and start a new one - // We remove any periods or spaces from the beginning of the piece and trim any whitespace - currentChunk = currentChunk.replace(/^[. ]+/, "").trim(); - // We only push the piece if it is not empty - if (currentChunk) chunks.push(currentChunk); - // Reset the current piece - currentChunk = ""; - } else if (chunkLength > upperBound) { - // The piece is too long, so save it and start a new one with the sentence - // Remove any periods or spaces from the beginning of the piece and trim any whitespace - currentChunk = currentChunk.replace(/^[. ]+/, "").trim(); - // We only push the piece if it is not empty - if (currentChunk) chunks.push(currentChunk); - // Set the current piece to the sentence - currentChunk = trimmedSentence; - } else { - // The piece is too short, so add the sentence and continue - // Add a space before the sentence unless it is a period - currentChunk += `${trimmedSentence === "." ? "" : " "}${trimmedSentence}`; - } - } - - // If there is any remaining piece, save it - if (currentChunk) { - chunks.push(currentChunk); - } - - // Return the array of pieces - return chunks; -} diff --git a/apps/file-q-and-a/nextjs/src/services/createEmbeddings.ts b/apps/file-q-and-a/nextjs/src/services/createEmbeddings.ts deleted file mode 100644 index b59233cc..00000000 --- a/apps/file-q-and-a/nextjs/src/services/createEmbeddings.ts +++ /dev/null @@ -1,54 +0,0 @@ -import { TextEmbedding } from "../types/file"; -import { getEmbeddingsForText } from "./getEmbeddingsForText"; - -export type Embeddings = { - meanEmbedding: number[]; - chunks: TextEmbedding[]; -}; - -export async function createEmbeddings({ - text, - maxCharLength, -}: { - text: string; - maxCharLength?: number; -}): Promise { - try { - const textEmbeddings = await getEmbeddingsForText({ - text, - maxCharLength, - }); - - // If there are 0 or 1 embeddings, the mean embedding is the same as the embedding - if (textEmbeddings.length <= 1) { - return { - meanEmbedding: textEmbeddings[0]?.embedding ?? [], - chunks: textEmbeddings, - }; - } - - // If there are multiple embeddings, calculate their average - const embeddingLength = textEmbeddings[0].embedding.length; - const meanEmbedding = []; - for (let i = 0; i < embeddingLength; i++) { - // Sum up the values at the same index of each embedding - let sum = 0; - for (const textEmbedding of textEmbeddings) { - sum += textEmbedding.embedding[i]; - } - // Divide by the number of embeddings to get the mean - meanEmbedding.push(sum / textEmbeddings.length); - } - - return { - meanEmbedding, - chunks: textEmbeddings, - }; - } catch (error: any) { - console.log("Error: ", error); - return { - meanEmbedding: [], - chunks: [], - }; - } -} diff --git a/apps/file-q-and-a/nextjs/src/services/extractTextFromFile.ts b/apps/file-q-and-a/nextjs/src/services/extractTextFromFile.ts deleted file mode 100644 index eb8edaa1..00000000 --- a/apps/file-q-and-a/nextjs/src/services/extractTextFromFile.ts +++ /dev/null @@ -1,45 +0,0 @@ -import fs from "fs"; -import mammoth from "mammoth"; -import pdfParse from "pdf-parse"; -import { NodeHtmlMarkdown } from "node-html-markdown"; - -export default async function extractTextFromFile({ - filepath, - filetype, -}: { - filepath: string; - filetype: string; -}): Promise { - const buffer: Buffer = await new Promise((resolve, reject) => { - const fileStream = fs.createReadStream(filepath); - const chunks: any[] = []; - fileStream.on("data", (chunk) => { - chunks.push(chunk); - }); - fileStream.on("error", (error) => { - reject(error); - }); - fileStream.on("end", () => { - resolve(Buffer.concat(chunks)); - }); - }); - - // Handle different file types using different modules - switch (filetype) { - case "application/pdf": - const pdfData = await pdfParse(buffer); - return pdfData.text; - case "application/vnd.openxmlformats-officedocument.wordprocessingml.document": // i.e. docx file - const docxResult = await mammoth.extractRawText({ path: filepath }); - return docxResult.value; - case "text/markdown": - case "text/csv": - case "text/html": - const html = buffer.toString(); - return NodeHtmlMarkdown.translate(html); - case "text/plain": - return buffer.toString(); - default: - throw new Error("Unsupported file type"); - } -} diff --git a/apps/file-q-and-a/nextjs/src/services/getEmbeddingsForText.ts b/apps/file-q-and-a/nextjs/src/services/getEmbeddingsForText.ts deleted file mode 100644 index c9a1ed72..00000000 --- a/apps/file-q-and-a/nextjs/src/services/getEmbeddingsForText.ts +++ /dev/null @@ -1,42 +0,0 @@ -import { TextEmbedding } from "../types/file"; -import { chunkText } from "./chunkText"; -import { embedding } from "./openai"; - -// There isn't a good JS tokenizer at the moment, so we are using this approximation of 4 characters per token instead. This might break for some languages. -const MAX_CHAR_LENGTH = 250 * 4; - -// This function takes a text and returns an array of embeddings for each chunk of the text -// The text is split into chunks of a given maximum character length -// The embeddings are computed in batches of a given size -export async function getEmbeddingsForText({ - text, - maxCharLength = MAX_CHAR_LENGTH, - batchSize = 20, -}: { - text: string; - maxCharLength?: number; - batchSize?: number; -}): Promise { - const textChunks = chunkText({ text, maxCharLength }); - - const batches = []; - for (let i = 0; i < textChunks.length; i += batchSize) { - batches.push(textChunks.slice(i, i + batchSize)); - } - - try { - const batchPromises = batches.map((batch) => embedding({ input: batch })); - - const embeddings = (await Promise.all(batchPromises)).flat(); - - const textEmbeddings = embeddings.map((embedding, index) => ({ - embedding, - text: textChunks[index], - })); - - return textEmbeddings; - } catch (error: any) { - console.log("Error: ", error); - return []; - } -} diff --git a/apps/file-q-and-a/nextjs/src/services/openai.ts b/apps/file-q-and-a/nextjs/src/services/openai.ts deleted file mode 100644 index 3d6c6c80..00000000 --- a/apps/file-q-and-a/nextjs/src/services/openai.ts +++ /dev/null @@ -1,150 +0,0 @@ -import { IncomingMessage } from "http"; -import { - ChatCompletionRequestMessageRoleEnum, - Configuration, - CreateChatCompletionResponse, - CreateCompletionRequest, - OpenAIApi, -} from "openai"; - -// This file contains utility functions for interacting with the OpenAI API - -if (!process.env.OPENAI_API_KEY) { - throw new Error("Missing OPENAI_API_KEY environment variable"); -} - -const configuration = new Configuration({ - apiKey: process.env.OPENAI_API_KEY, -}); -export const openai = new OpenAIApi(configuration); - -type CompletionOptions = Partial & { - prompt: string; - fallback?: string; -}; - -type EmbeddingOptions = { - input: string | string[]; - model?: string; -}; - -export async function completion({ - prompt, - fallback, - max_tokens, - temperature = 0, - model = "gpt-3.5-turbo", // use gpt-4 for better results -}: CompletionOptions) { - try { - // Note: this is not the proper way to use the ChatGPT conversational format, but it works for now - const messages = [ - { - role: ChatCompletionRequestMessageRoleEnum.System, - content: prompt ?? "", - }, - ]; - - const result = await openai.createChatCompletion({ - model, - messages, - temperature, - max_tokens: max_tokens ?? 800, - }); - - if (!result.data.choices[0].message) { - throw new Error("No text returned from completions endpoint"); - } - return result.data.choices[0].message.content; - } catch (error) { - if (fallback) return fallback; - else throw error; - } -} - -export async function* completionStream({ - prompt, - fallback, - max_tokens = 800, - temperature = 0, - model = "gpt-3.5-turbo", // use gpt-4 for better results -}: CompletionOptions) { - try { - // Note: this is not the proper way to use the ChatGPT conversational format, but it works for now - const messages = [ - { - role: ChatCompletionRequestMessageRoleEnum.System, - content: prompt ?? "", - }, - ]; - - const result = await openai.createChatCompletion( - { - model, - messages, - temperature, - max_tokens: max_tokens ?? 800, - stream: true, - }, - { - responseType: "stream", - } - ); - const stream = result.data as any as IncomingMessage; - - let buffer = ""; - const textDecoder = new TextDecoder(); - - for await (const chunk of stream) { - buffer += textDecoder.decode(chunk, { stream: true }); - const lines = buffer.split("\n"); - - // Check if the last line is complete - if (buffer.endsWith("\n")) { - buffer = ""; - } else { - buffer = lines.pop() || ""; - } - - for (const line of lines) { - const message = line.trim().split("data: ")[1]; - if (message === "[DONE]") { - break; - } - - // Check if the message is not undefined and a valid JSON string - if (message) { - try { - const data = JSON.parse(message) as CreateChatCompletionResponse; - // @ts-ignore - if (data.choices[0].delta?.content) { - // @ts-ignore - yield data.choices[0].delta?.content; - } - } catch (error) { - console.error("Error parsing JSON message:", error); - } - } - } - } - } catch (error) { - if (fallback) yield fallback; - else throw error; - } -} - -export async function embedding({ - input, - model = "text-embedding-ada-002", -}: EmbeddingOptions): Promise { - const result = await openai.createEmbedding({ - model, - input, - }); - - if (!result.data.data[0].embedding) { - throw new Error("No embedding returned from the completions endpoint"); - } - - // Otherwise, return the embeddings - return result.data.data.map((d) => d.embedding); -} diff --git a/apps/file-q-and-a/nextjs/src/services/searchFileChunks.ts b/apps/file-q-and-a/nextjs/src/services/searchFileChunks.ts deleted file mode 100644 index 9f8239e7..00000000 --- a/apps/file-q-and-a/nextjs/src/services/searchFileChunks.ts +++ /dev/null @@ -1,53 +0,0 @@ -import { FileLite, FileChunk } from "../types/file"; -import { embedding } from "./openai"; - -// This is the minimum cosine similarity score that a file must have with the search query to be considered relevant -// This is an arbitrary value, and you should vary/ remove this depending on the diversity of your dataset -const COSINE_SIM_THRESHOLD = 0.72; - -// This function takes a search query and a list of files, and returns the chunks of text that are most semantically similar to the query -export async function searchFileChunks({ - searchQuery, - files, - maxResults, -}: { - searchQuery: string; - files: FileLite[]; - maxResults: number; -}): Promise { - // Get the search query embedding - const searchQueryEmbeddingResponse = await embedding({ - input: searchQuery, - }); - - // Get the first element in the embedding array - const searchQueryEmbedding = - searchQueryEmbeddingResponse.length > 0 - ? searchQueryEmbeddingResponse[0] - : []; - - // Rank the chunks by their cosine similarity to the search query (using dot product since the embeddings are normalized) and return this - const rankedChunks = files - // Map each file to an array of chunks with the file name and score - .flatMap((file) => - file.chunks - ? file.chunks.map((chunk) => { - // Calculate the dot product between the chunk embedding and the search query embedding - const dotProduct = chunk.embedding.reduce( - (sum, val, i) => sum + val * searchQueryEmbedding[i], - 0 - ); - // Assign the dot product as the score for the chunk - return { ...chunk, filename: file.name, score: dotProduct }; - }) - : [] - ) - // Sort the chunks by their scores in descending order - .sort((a, b) => b.score - a.score) - // Filter the chunks by their score above the threshold - .filter((chunk) => chunk.score > COSINE_SIM_THRESHOLD) - // Take the first maxResults chunks - .slice(0, maxResults); - - return rankedChunks; -} diff --git a/apps/file-q-and-a/nextjs/src/services/utils.ts b/apps/file-q-and-a/nextjs/src/services/utils.ts deleted file mode 100644 index 843a4865..00000000 --- a/apps/file-q-and-a/nextjs/src/services/utils.ts +++ /dev/null @@ -1,19 +0,0 @@ -// A function that takes a file name and a string and returns true if the file name is contained in the string -// after removing punctuation and whitespace from both -export const isFileNameInString = (fileName: string, str: string) => { - // Check if the input string is null or undefined - if (!str) { - return false; - } - - // Convert both to lowercase and remove punctuation and whitespace - const normalizedFileName = fileName - .toLowerCase() - .replace(/[.,/#!$%^&*;:{}=-_~()\s]/g, ""); - const normalizedStr = str - .toLowerCase() - .replace(/[.,/#!$%^&*;:{}=-_~()\s]/g, ""); - - // Return true if the normalized file name is included in the normalized string - return normalizedStr.includes(normalizedFileName); -}; diff --git a/apps/file-q-and-a/nextjs/src/styles/globals.css b/apps/file-q-and-a/nextjs/src/styles/globals.css deleted file mode 100644 index 50087802..00000000 --- a/apps/file-q-and-a/nextjs/src/styles/globals.css +++ /dev/null @@ -1,5 +0,0 @@ -@import "./preflight.css"; - -@tailwind base; -@tailwind components; -@tailwind utilities; diff --git a/apps/file-q-and-a/nextjs/src/styles/preflight.css b/apps/file-q-and-a/nextjs/src/styles/preflight.css deleted file mode 100644 index 3632940f..00000000 --- a/apps/file-q-and-a/nextjs/src/styles/preflight.css +++ /dev/null @@ -1,368 +0,0 @@ -/* Using a custom preflight to fix conflicts with Ant Design */ -/* Original: https://unpkg.com/tailwindcss@3.2.4/src/css/preflight.css */ - -/* -1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4) -2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116) -*/ - -*, -::before, -::after { - box-sizing: border-box; /* 1 */ - border-width: 0; /* 2 */ - border-style: solid; /* 2 */ - border-color: theme("borderColor.DEFAULT"); /* 2 */ -} - -::before, -::after { - --tw-content: ""; -} - -/* -1. Use a consistent sensible line-height in all browsers. -2. Prevent adjustments of font size after orientation changes in iOS. -3. Use a more readable tab size. -4. Use the user's configured `sans` font-family by default. -5. Use the user's configured `sans` font-feature-settings by default. -*/ - -html { - line-height: 1.5; /* 1 */ - -webkit-text-size-adjust: 100%; /* 2 */ - -moz-tab-size: 4; /* 3 */ - tab-size: 4; /* 3 */ - font-family: theme("fontFamily.sans"); /* 4 */ -} - -/* -1. Remove the margin in all browsers. -2. Inherit line-height from `html` so users can set them as a class directly on the `html` element. -*/ - -body { - margin: 0; /* 1 */ - line-height: inherit; /* 2 */ -} - -/* -1. Add the correct height in Firefox. -2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655) -3. Ensure horizontal rules are visible by default. -*/ - -hr { - height: 0; /* 1 */ - color: inherit; /* 2 */ - border-top-width: 1px; /* 3 */ -} - -/* -Add the correct text decoration in Chrome, Edge, and Safari. -*/ - -abbr:where([title]) { - text-decoration: underline dotted; -} - -/* -Remove the default font size and weight for headings. -*/ - -h1, -h2, -h3, -h4, -h5, -h6 { - font-size: inherit; - font-weight: inherit; -} - -/* -Reset links to optimize for opt-in styling instead of opt-out. -*/ - -a { - color: inherit; - text-decoration: inherit; -} - -/* -Add the correct font weight in Edge and Safari. -*/ - -b, -strong { - font-weight: bolder; -} - -/* -1. Use the user's configured `mono` font family by default. -2. Correct the odd `em` font sizing in all browsers. -*/ - -code, -kbd, -samp, -pre { - font-family: theme("fontFamily.mono"); /* 1 */ - font-size: 1em; /* 2 */ -} - -/* -Add the correct font size in all browsers. -*/ - -small { - font-size: 80%; -} - -/* -Prevent `sub` and `sup` elements from affecting the line height in all browsers. -*/ - -sub, -sup { - font-size: 75%; - line-height: 0; - position: relative; - vertical-align: baseline; -} - -sub { - bottom: -0.25em; -} - -sup { - top: -0.5em; -} - -/* -1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297) -2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016) -3. Remove gaps between table borders by default. -*/ - -table { - text-indent: 0; /* 1 */ - border-color: inherit; /* 2 */ - border-collapse: collapse; /* 3 */ -} - -/* -1. Change the font styles in all browsers. -2. Remove the margin in Firefox and Safari. -3. Remove default padding in all browsers. -*/ - -button, -input, -optgroup, -select, -textarea { - font-family: inherit; /* 1 */ - font-size: 100%; /* 1 */ - font-weight: inherit; /* 1 */ - line-height: inherit; /* 1 */ - color: inherit; /* 1 */ - margin: 0; /* 2 */ - padding: 0; /* 3 */ -} - -/* -Remove the inheritance of text transform in Edge and Firefox. -*/ - -button, -select { - text-transform: none; -} - -/* -1. Correct the inability to style clickable types in iOS and Safari. -2. Remove default button styles. -*/ - -button, -[type="button"], -[type="reset"], -[type="submit"] { - -webkit-appearance: button; /* 1 */ - background-image: none; /* 2 */ -} - -/* -Use the modern Firefox focus style for all focusable elements. -*/ - -:-moz-focusring { - outline: auto; -} - -/* -Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737) -*/ - -:-moz-ui-invalid { - box-shadow: none; -} - -/* -Add the correct vertical alignment in Chrome and Firefox. -*/ - -progress { - vertical-align: baseline; -} - -/* -Correct the cursor style of increment and decrement buttons in Safari. -*/ - -::-webkit-inner-spin-button, -::-webkit-outer-spin-button { - height: auto; -} - -/* -1. Correct the odd appearance in Chrome and Safari. -2. Correct the outline style in Safari. -*/ - -[type="search"] { - -webkit-appearance: textfield; /* 1 */ - outline-offset: -2px; /* 2 */ -} - -/* -Remove the inner padding in Chrome and Safari on macOS. -*/ - -::-webkit-search-decoration { - -webkit-appearance: none; -} - -/* -1. Correct the inability to style clickable types in iOS and Safari. -2. Change font properties to `inherit` in Safari. -*/ - -::-webkit-file-upload-button { - -webkit-appearance: button; /* 1 */ - font: inherit; /* 2 */ -} - -/* -Add the correct display in Chrome and Safari. -*/ - -summary { - display: list-item; -} - -/* -Removes the default spacing and border for appropriate elements. -*/ - -blockquote, -dl, -dd, -h1, -h2, -h3, -h4, -h5, -h6, -hr, -figure, -p, -pre { - margin: 0; -} - -fieldset { - margin: 0; - padding: 0; -} - -legend { - padding: 0; -} - -ol, -ul, -menu { - list-style: none; - margin: 0; - padding: 0; -} - -/* -Prevent resizing textareas horizontally by default. -*/ - -textarea { - resize: vertical; -} - -/* -1. Reset the default placeholder opacity in Firefox. (https://github.com/tailwindlabs/tailwindcss/issues/3300) -2. Set the default placeholder color to the user's configured gray 400 color. -*/ - -input::placeholder, -textarea::placeholder { - opacity: 1; /* 1 */ - color: theme("colors.gray.400"); /* 2 */ -} - -/* -Set the default cursor for buttons. -*/ - -button, -[role="button"] { - cursor: pointer; -} - -/* -Make sure disabled buttons don't get the pointer cursor. -*/ -:disabled { - cursor: default; -} - -/* -1. Make replaced elements `display: block` by default. (https://github.com/mozdevs/cssremedy/issues/14) -2. Add `vertical-align: middle` to align replaced elements more sensibly by default. (https://github.com/jensimmons/cssremedy/issues/14#issuecomment-634934210) - This can trigger a poorly considered lint error in some tools but is included by design. -*/ - -img, -svg, -video, -canvas, -audio, -iframe, -embed, -object { - display: block; /* 1 */ - vertical-align: middle; /* 2 */ -} - -/* -Constrain images and videos to the parent width and preserve their intrinsic aspect ratio. (https://github.com/mozdevs/cssremedy/issues/14) -*/ - -img, -video { - max-width: 100%; - height: auto; -} - -/* Make elements with the HTML hidden attribute stay hidden by default */ -[hidden] { - display: none; -} diff --git a/apps/file-q-and-a/nextjs/src/types/file.ts b/apps/file-q-and-a/nextjs/src/types/file.ts deleted file mode 100644 index b87d0ed6..00000000 --- a/apps/file-q-and-a/nextjs/src/types/file.ts +++ /dev/null @@ -1,21 +0,0 @@ -export interface FileLite { - expanded?: boolean; - name: string; - url?: string; - type?: string; - score?: number; - size?: number; - embedding?: number[]; // The file embedding -- or mean embedding if there are multiple embeddings for the file - chunks?: TextEmbedding[]; // The chunks of text and their embeddings - extractedText?: string; // The extracted text from the file -} - -export interface FileChunk extends TextEmbedding { - filename: string; - score?: number; -} - -export interface TextEmbedding { - text: string; - embedding: number[]; -} diff --git a/apps/file-q-and-a/nextjs/tailwind.config.js b/apps/file-q-and-a/nextjs/tailwind.config.js deleted file mode 100644 index 6cf79462..00000000 --- a/apps/file-q-and-a/nextjs/tailwind.config.js +++ /dev/null @@ -1,28 +0,0 @@ -const { fontFamily } = require("tailwindcss/defaultTheme"); - -/** @type {import('tailwindcss').Config} */ -module.exports = { - content: [ - "./app/**/*.{js,ts,jsx,tsx}", - "./src/**/*.{js,ts,jsx,tsx}", - "./pages/**/*.{js,ts,jsx,tsx}", - "./components/**/*.{js,ts,jsx,tsx}", - ], - corePlugins: { - preflight: false, - }, - theme: { - extend: { - }, - }, - keyframes: { - blink: { - "0%, 100%": { opacity: 1 }, - "50%": { opacity: 0 }, - }, - }, - plugins: [ - require("@tailwindcss/line-clamp"), - require("@tailwindcss/typography"), - ], -}; diff --git a/apps/file-q-and-a/nextjs/tsconfig.json b/apps/file-q-and-a/nextjs/tsconfig.json deleted file mode 100644 index 2159bf45..00000000 --- a/apps/file-q-and-a/nextjs/tsconfig.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "compilerOptions": { - "target": "es5", - "lib": ["dom", "dom.iterable", "esnext"], - "allowJs": true, - "skipLibCheck": true, - "strict": true, - "forceConsistentCasingInFileNames": true, - "noEmit": true, - "esModuleInterop": true, - "module": "esnext", - "moduleResolution": "node", - "resolveJsonModule": true, - "isolatedModules": true, - "jsx": "preserve", - "incremental": true, - "baseUrl": ".", - "paths": { - "@/*": ["./src/*"] - } - }, - "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx"], - "exclude": ["node_modules"] -} diff --git a/apps/web-crawl-q-and-a/requirements.txt b/apps/web-crawl-q-and-a/requirements.txt deleted file mode 100644 index a14f085f..00000000 --- a/apps/web-crawl-q-and-a/requirements.txt +++ /dev/null @@ -1,79 +0,0 @@ -aiohttp==3.8.5 -aiosignal==1.3.1 -appnope==0.1.3 -asttokens==2.2.1 -async-timeout==4.0.2 -attrs==22.2.0 -backcall==0.2.0 -beautifulsoup4==4.11.1 -blobfile==2.0.1 -bs4==0.0.1 -certifi==2023.7.22 -charset-normalizer==2.1.1 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being required\n", - "HTTP Error 403: Forbidden\n", - "https://openai.com/api/login\n", - "Unable to parse page https://openai.com/api/login due to JavaScript being required\n", - "HTTP Error 403: Forbidden\n", - "https://openai.com/newsroom\n", - "https://openai.com/api/policies\n", - "https://openai.com/api/pricing\n", - "https://openai.com/contact-sales\n", - "https://openai.com/api/pricing/#faq-fine-tuning-pricing-calculation\n", - "https://openai.com/blog/tags/events\n", - "https://openai.com/blog/chatgpt\n" - ] - } - ], - "source": [ - "import requests\n", - "import re\n", - "import urllib.request\n", - "from bs4 import BeautifulSoup\n", - "from collections import deque\n", - "from html.parser import HTMLParser\n", - "from urllib.parse import urlparse\n", - "import os\n", - "\n", - "# Regex pattern to match a URL\n", - "HTTP_URL_PATTERN = r'^http[s]*://.+'\n", - "\n", - "# Define root domain to crawl\n", - "domain = \"openai.com\"\n", - "full_url = \"https://openai.com/\"\n", - "\n", - "# Create a class to parse the HTML and get the hyperlinks\n", - "class HyperlinkParser(HTMLParser):\n", - " def __init__(self):\n", - " super().__init__()\n", - " # Create a list to store the hyperlinks\n", - " self.hyperlinks = []\n", - "\n", - " # Override the HTMLParser's handle_starttag method to get the hyperlinks\n", - " def handle_starttag(self, tag, attrs):\n", - " attrs = dict(attrs)\n", - "\n", - " # If the tag is an anchor tag and it has an href attribute, add the href attribute to the list of hyperlinks\n", - " if tag == \"a\" and \"href\" in attrs:\n", - " self.hyperlinks.append(attrs[\"href\"])\n", - "\n", - "# Function to get the hyperlinks from a URL\n", - "def get_hyperlinks(url):\n", - " \n", - " # Try to open the URL and read the HTML\n", - " try:\n", - " # Open the URL and read the HTML\n", - " with urllib.request.urlopen(url) as response:\n", - "\n", - " # If the response is not HTML, return an empty list\n", - " if not response.info().get('Content-Type').startswith(\"text/html\"):\n", - " return []\n", - " \n", - " # Decode the HTML\n", - " html = response.read().decode('utf-8')\n", - " except Exception as e:\n", - " print(e)\n", - " return []\n", - "\n", - " # Create the HTML Parser and then Parse the HTML to get hyperlinks\n", - " parser = HyperlinkParser()\n", - " parser.feed(html)\n", - "\n", - " return parser.hyperlinks\n", - "\n", - "# Function to get the hyperlinks from a URL that are within the same domain\n", - "def get_domain_hyperlinks(local_domain, url):\n", - " clean_links = []\n", - " for link in set(get_hyperlinks(url)):\n", - " clean_link = None\n", - "\n", - " # If the link is a URL, check if it is within the same domain\n", - " if re.search(HTTP_URL_PATTERN, link):\n", - " # Parse the URL and check if the domain is the same\n", - " url_obj = urlparse(link)\n", - " if url_obj.netloc == local_domain:\n", - " clean_link = link\n", - "\n", - " # If the link is not a URL, check if it is a relative link\n", - " else:\n", - " if link.startswith(\"/\"):\n", - " link = link[1:]\n", - " elif link.startswith(\"#\") or link.startswith(\"mailto:\"):\n", - " continue\n", - " clean_link = \"https://\" + local_domain + \"/\" + link\n", - "\n", - " if clean_link is not None:\n", - " if clean_link.endswith(\"/\"):\n", - " clean_link = clean_link[:-1]\n", - " clean_links.append(clean_link)\n", - "\n", - " # Return the list of hyperlinks that are within the same domain\n", - " return list(set(clean_links))\n", - "\n", - "\n", - "def crawl(url):\n", - " # Parse the URL and get the domain\n", - " local_domain = urlparse(url).netloc\n", - "\n", - " # Create a queue to store the URLs to crawl\n", - " queue = deque([url])\n", - "\n", - " # Create a set to store the URLs that have already been seen (no duplicates)\n", - " seen = set([url])\n", - "\n", - " # Create a directory to store the text files\n", - " if not os.path.exists(\"text/\"):\n", - " os.mkdir(\"text/\")\n", - "\n", - " if not os.path.exists(\"text/\"+local_domain+\"/\"):\n", - " os.mkdir(\"text/\" + local_domain + \"/\")\n", - "\n", - " # Create a directory to store the csv files\n", - " if not os.path.exists(\"processed\"):\n", - " os.mkdir(\"processed\")\n", - "\n", - " # While the queue is not empty, continue crawling\n", - " while queue:\n", - "\n", - " # Get the next URL from the queue\n", - " url = queue.pop()\n", - " print(url) # for debugging and to see the progress\n", - "\n", - " # Save text from the url to a .txt file\n", - " with open('text/'+local_domain+'/'+url[8:].replace(\"/\", \"_\") + \".txt\", \"w\") as f:\n", - "\n", - " # Get the text from the URL using BeautifulSoup\n", - " soup = BeautifulSoup(requests.get(url).text, \"html.parser\")\n", - "\n", - " # Get the text but remove the tags\n", - " text = soup.get_text()\n", - "\n", - " # If the crawler gets to a page that requires JavaScript, it will stop the crawl\n", - " if (\"You need to enable JavaScript to run this app.\" in text):\n", - " print(\"Unable to parse page \" + url + \" due to JavaScript being required\")\n", - " \n", - " # Otherwise, write the text to the file in the text directory\n", - " f.write(text)\n", - "\n", - " # Get the hyperlinks from the URL and add them to the queue\n", - " for link in get_domain_hyperlinks(local_domain, url):\n", - " if link not in seen:\n", - " queue.append(link)\n", - " seen.add(link)\n", - "\n", - "crawl(full_url)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "def remove_newlines(serie):\n", - " serie = serie.str.replace('\\n', ' ')\n", - " serie = serie.str.replace('\\\\n', ' ')\n", - " serie = serie.str.replace(' ', ' ')\n", - " serie = serie.str.replace(' ', ' ')\n", - " return serie" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# Create a list to store the text files\n", - "texts=[]\n", - "\n", - "# Get all the text files in the text directory\n", - "for file in os.listdir(\"text/\" + domain + \"/\"):\n", - "\n", - " # Open the file and read the text\n", - " with open(\"text/\" + domain + \"/\" + file, \"r\") as f:\n", - " text = f.read()\n", - "\n", - " # Omit the first 11 lines and the last 4 lines, then replace -, _, and #update with spaces.\n", - " texts.append((file[11:-4].replace('-',' ').replace('_', ' ').replace('#update',''), text))\n", - "\n", - "# Create a dataframe from the list of texts\n", - "df = pd.DataFrame(texts, columns = ['fname', 'text'])\n", - "\n", - "# Set the text column to be the raw text with the newlines removed\n", - "df['text'] = df.fname + \". \" + remove_newlines(df.text)\n", - "df.to_csv('processed/scraped.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import tiktoken\n", - "\n", - "# Load the cl100k_base tokenizer which is designed to work with the ada-002 model\n", - "tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n", - "\n", - "df = pd.read_csv('processed/scraped.csv', index_col=0)\n", - "df.columns = ['title', 'text']\n", - "\n", - "# Tokenize the text and save the number of tokens to a new column\n", - "df['n_tokens'] = df.text.apply(lambda x: len(tokenizer.encode(x)))\n", - "\n", - "# Visualize the distribution of the number of tokens per row using a histogram\n", - "df.n_tokens.hist()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "max_tokens = 500\n", - "\n", - "# Function to split the text into chunks of a maximum number of tokens\n", - "def split_into_many(text, max_tokens = max_tokens):\n", - "\n", - " # Split the text into sentences\n", - " sentences = text.split('. ')\n", - "\n", - " # Get the number of tokens for each sentence\n", - " n_tokens = [len(tokenizer.encode(\" \" + sentence)) for sentence in sentences]\n", - " \n", - " chunks = []\n", - " tokens_so_far = 0\n", - " chunk = []\n", - "\n", - " # Loop through the sentences and tokens joined together in a tuple\n", - " for sentence, token in zip(sentences, n_tokens):\n", - "\n", - " # If the number of tokens so far plus the number of tokens in the current sentence is greater \n", - " # than the max number of tokens, then add the chunk to the list of chunks and reset\n", - " # the chunk and tokens so far\n", - " if tokens_so_far + token > max_tokens:\n", - " chunks.append(\". \".join(chunk) + \".\")\n", - " chunk = []\n", - " tokens_so_far = 0\n", - "\n", - " # If the number of tokens in the current sentence is greater than the max number of \n", - " # tokens, go to the next sentence\n", - " if token > max_tokens:\n", - " continue\n", - "\n", - " # Otherwise, add the sentence to the chunk and add the number of tokens to the total\n", - " chunk.append(sentence)\n", - " tokens_so_far += token + 1\n", - "\n", - " # Add the last chunk to the list of chunks\n", - " if chunk:\n", - " chunks.append(\". \".join(chunk) + \".\")\n", - "\n", - " return chunks\n", - " \n", - "\n", - "shortened = []\n", - "\n", - "# Loop through the dataframe\n", - "for row in df.iterrows():\n", - "\n", - " # If the text is None, go to the next row\n", - " if row[1]['text'] is None:\n", - " continue\n", - "\n", - " # If the number of tokens is greater than the max number of tokens, split the text into chunks\n", - " if row[1]['n_tokens'] > max_tokens:\n", - " shortened += split_into_many(row[1]['text'])\n", - " \n", - " # Otherwise, add the text to the list of shortened texts\n", - " else:\n", - " shortened.append( row[1]['text'] )" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df = pd.DataFrame(shortened, columns = ['text'])\n", - "df['n_tokens'] = df.text.apply(lambda x: len(tokenizer.encode(x)))\n", - "df.n_tokens.hist()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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textn_tokensembeddings
0blog authors maddie. Maddie Hall - OpenAI ...175[-0.012958061881363392, -0.006103983614593744,...
1blog authors tom. Tom Brown - OpenAI ...228[-0.0053874170407652855, -0.009962032549083233...
2blog openai scholars 2019 final projects. Op...492[0.0019150723237544298, -0.0070442273281514645...
3In this project, I used curiosity-driven explo...478[-0.0067560747265815735, 0.0004431474662851542...
4Results revealed that the optimal RL policies ...499[-0.012868616729974747, 0.0029640409629791975,...
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textn_tokensembeddings
0blog authors maddie. Maddie Hall - OpenAI ...175[-0.012958061881363392, -0.006103983614593744,...
1blog authors tom. Tom Brown - OpenAI ...228[-0.0053874170407652855, -0.009962032549083233...
2blog openai scholars 2019 final projects. Op...492[0.0019150723237544298, -0.0070442273281514645...
3In this project, I used curiosity-driven explo...478[-0.0067560747265815735, 0.0004431474662851542...
4Results revealed that the optimal RL policies ...499[-0.012868616729974747, 0.0029640409629791975,...
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" - ], - "text/plain": [ - " text n_tokens \\\n", - "0 blog authors maddie. Maddie Hall - OpenAI ... 175 \n", - "1 blog authors tom. Tom Brown - OpenAI ... 228 \n", - "2 blog openai scholars 2019 final projects. Op... 492 \n", - "3 In this project, I used curiosity-driven explo... 478 \n", - "4 Results revealed that the optimal RL policies ... 499 \n", - "\n", - " embeddings \n", - "0 [-0.012958061881363392, -0.006103983614593744,... \n", - "1 [-0.0053874170407652855, -0.009962032549083233... \n", - "2 [0.0019150723237544298, -0.0070442273281514645... \n", - "3 [-0.0067560747265815735, 0.0004431474662851542... \n", - "4 [-0.012868616729974747, 0.0029640409629791975,... " - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from ast import literal_eval\n", - "from openai.embeddings_utils import distances_from_embeddings, cosine_similarity\n", - "\n", - "df=pd.read_csv('processed/embeddings.csv', index_col=0)\n", - "df['embeddings'] = df['embeddings'].apply(literal_eval).apply(np.array)\n", - "\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'No, you are not allowed to publish model outputs to Twitter without a human review. You must manually review each generation before sharing or while streaming, and indicate that the content is AI-generated in a way no user could reasonably miss or misunderstand.'" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def create_context(\n", - " question, df, max_len=1800, size=\"ada\"\n", - "):\n", - " \"\"\"\n", - " Create a context for a question by finding the most similar context from the dataframe\n", - " \"\"\"\n", - "\n", - " # Get the embeddings for the question\n", - " q_embeddings = openai.Embedding.create(input=question, engine='text-embedding-ada-002')['data'][0]['embedding']\n", - "\n", - " # Get the distances from the embeddings\n", - " df['distances'] = distances_from_embeddings(q_embeddings, df['embeddings'].values, distance_metric='cosine')\n", - "\n", - "\n", - " returns = []\n", - " cur_len = 0\n", - "\n", - " # Sort by distance and add the text to the context until the context is too long\n", - " for i, row in df.sort_values('distances', ascending=True).iterrows():\n", - " \n", - " # Add the length of the text to the current length\n", - " cur_len += row['n_tokens'] + 4\n", - " \n", - " # If the context is too long, break\n", - " if cur_len > max_len:\n", - " break\n", - " \n", - " # Else add it to the text that is being returned\n", - " returns.append(row[\"text\"])\n", - "\n", - " # Return the context\n", - " return \"\\n\\n###\\n\\n\".join(returns)\n", - "\n", - "def answer_question(\n", - " df,\n", - " model=\"text-davinci-003\",\n", - " question=\"Am I allowed to publish model outputs to Twitter, without a human review?\",\n", - " max_len=1800,\n", - " size=\"ada\",\n", - " debug=False,\n", - " max_tokens=150,\n", - " stop_sequence=None\n", - "):\n", - " \"\"\"\n", - " Answer a question based on the most similar context from the dataframe texts\n", - " \"\"\"\n", - " context = create_context(\n", - " question,\n", - " df,\n", - " max_len=max_len,\n", - " size=size,\n", - " )\n", - " # If debug, print the raw model response\n", - " if debug:\n", - " print(\"Context:\\n\" + context)\n", - " print(\"\\n\\n\")\n", - "\n", - " try:\n", - " # Create a completions using the question and context\n", - " response = openai.Completion.create(\n", - " prompt=f\"Answer the question based on the context below, and if the question can't be answered based on the context, say \\\"I don't know\\\"\\n\\nContext: {context}\\n\\n---\\n\\nQuestion: {question}\\nAnswer:\",\n", - " temperature=0,\n", - " max_tokens=max_tokens,\n", - " top_p=1,\n", - " frequency_penalty=0,\n", - " presence_penalty=0,\n", - " stop=stop_sequence,\n", - " model=model,\n", - " )\n", - " return response[\"choices\"][0][\"text\"].strip()\n", - " except Exception as e:\n", - " print(e)\n", - " return \"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"I don't know.\"" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "answer_question(df, question=\"What day is it?\", debug=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'The newest embeddings model is text-embedding-ada-002.'" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "answer_question(df, question=\"What is our newest embeddings model?\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "05f34a34d73b71652304030c1097be3a5720ea2447153dd6542d145a26b73181" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/apps/web-crawl-q-and-a/web-qa.py b/apps/web-crawl-q-and-a/web-qa.py deleted file mode 100644 index 68eceeab..00000000 --- a/apps/web-crawl-q-and-a/web-qa.py +++ /dev/null @@ -1,399 +0,0 @@ -################################################################################ -### Step 1 -################################################################################ - -import requests -import re -import urllib.request -from bs4 import BeautifulSoup -from collections import deque -from html.parser import HTMLParser -from urllib.parse import urlparse -import os -import pandas as pd -import tiktoken -import openai -import numpy as np -from openai.embeddings_utils import distances_from_embeddings, cosine_similarity -from ast import literal_eval - -# Regex pattern to match a URL -HTTP_URL_PATTERN = r'^http[s]{0,1}://.+$' - -# Define OpenAI api_key -# openai.api_key = '' - -# Define root domain to crawl -domain = "openai.com" -full_url = "https://openai.com/" - -# Create a class to parse the HTML and get the hyperlinks -class HyperlinkParser(HTMLParser): - def __init__(self): - super().__init__() - # Create a list to store the hyperlinks - self.hyperlinks = [] - - # Override the HTMLParser's handle_starttag method to get the hyperlinks - def handle_starttag(self, tag, attrs): - attrs = dict(attrs) - - # If the tag is an anchor tag and it has an href attribute, add the href attribute to the list of hyperlinks - if tag == "a" and "href" in attrs: - self.hyperlinks.append(attrs["href"]) - -################################################################################ -### Step 2 -################################################################################ - -# Function to get the hyperlinks from a URL -def get_hyperlinks(url): - - # Try to open the URL and read the HTML - try: - # Open the URL and read the HTML - with urllib.request.urlopen(url) as response: - - # If the response is not HTML, return an empty list - if not response.info().get('Content-Type').startswith("text/html"): - return [] - - # Decode the HTML - html = response.read().decode('utf-8') - except Exception as e: - print(e) - return [] - - # Create the HTML Parser and then Parse the HTML to get hyperlinks - parser = HyperlinkParser() - parser.feed(html) - - return parser.hyperlinks - -################################################################################ -### Step 3 -################################################################################ - -# Function to get the hyperlinks from a URL that are within the same domain -def get_domain_hyperlinks(local_domain, url): - clean_links = [] - for link in set(get_hyperlinks(url)): - clean_link = None - - # If the link is a URL, check if it is within the same domain - if re.search(HTTP_URL_PATTERN, link): - # Parse the URL and check if the domain is the same - url_obj = urlparse(link) - if url_obj.netloc == local_domain: - clean_link = link - - # If the link is not a URL, check if it is a relative link - else: - if link.startswith("/"): - link = link[1:] - elif ( - link.startswith("#") - or link.startswith("mailto:") - or link.startswith("tel:") - ): - continue - clean_link = "https://" + local_domain + "/" + link - - if clean_link is not None: - if clean_link.endswith("/"): - clean_link = clean_link[:-1] - clean_links.append(clean_link) - - # Return the list of hyperlinks that are within the same domain - return list(set(clean_links)) - - -################################################################################ -### Step 4 -################################################################################ - -def crawl(url): - # Parse the URL and get the domain - local_domain = urlparse(url).netloc - - # Create a queue to store the URLs to crawl - queue = deque([url]) - - # Create a set to store the URLs that have already been seen (no duplicates) - seen = set([url]) - - # Create a directory to store the text files - if not os.path.exists("text/"): - os.mkdir("text/") - - if not os.path.exists("text/"+local_domain+"/"): - os.mkdir("text/" + local_domain + "/") - - # Create a directory to store the csv files - if not os.path.exists("processed"): - os.mkdir("processed") - - # While the queue is not empty, continue crawling - while queue: - - # Get the next URL from the queue - url = queue.pop() - print(url) # for debugging and to see the progress - - # Try extracting the text from the link, if failed proceed with the next item in the queue - try: - # Save text from the url to a .txt file - with open('text/'+local_domain+'/'+url[8:].replace("/", "_") + ".txt", "w", encoding="UTF-8") as f: - - # Get the text from the URL using BeautifulSoup - soup = BeautifulSoup(requests.get(url).text, "html.parser") - - # Get the text but remove the tags - text = soup.get_text() - - # If the crawler gets to a page that requires JavaScript, it will stop the crawl - if ("You need to enable JavaScript to run this app." in text): - print("Unable to parse page " + url + " due to JavaScript being required") - - # Otherwise, write the text to the file in the text directory - f.write(text) - except Exception as e: - print("Unable to parse page " + url) - - # Get the hyperlinks from the URL and add them to the queue - for link in get_domain_hyperlinks(local_domain, url): - if link not in seen: - queue.append(link) - seen.add(link) - -crawl(full_url) - -################################################################################ -### Step 5 -################################################################################ - -def remove_newlines(serie): - serie = serie.str.replace('\n', ' ') - serie = serie.str.replace('\\n', ' ') - serie = serie.str.replace(' ', ' ') - serie = serie.str.replace(' ', ' ') - return serie - - -################################################################################ -### Step 6 -################################################################################ - -# Create a list to store the text files -texts=[] - -# Get all the text files in the text directory -for file in os.listdir("text/" + domain + "/"): - - # Open the file and read the text - with open("text/" + domain + "/" + file, "r", encoding="UTF-8") as f: - text = f.read() - - # Omit the first 11 lines and the last 4 lines, then replace -, _, and #update with spaces. - texts.append((file[11:-4].replace('-',' ').replace('_', ' ').replace('#update',''), text)) - -# Create a dataframe from the list of texts -df = pd.DataFrame(texts, columns = ['fname', 'text']) - -# Set the text column to be the raw text with the newlines removed -df['text'] = df.fname + ". " + remove_newlines(df.text) -df.to_csv('processed/scraped.csv') -df.head() - -################################################################################ -### Step 7 -################################################################################ - -# Load the cl100k_base tokenizer which is designed to work with the ada-002 model -tokenizer = tiktoken.get_encoding("cl100k_base") - -df = pd.read_csv('processed/scraped.csv', index_col=0) -df.columns = ['title', 'text'] - -# Tokenize the text and save the number of tokens to a new column -df['n_tokens'] = df.text.apply(lambda x: len(tokenizer.encode(x))) - -# Visualize the distribution of the number of tokens per row using a histogram -df.n_tokens.hist() - -################################################################################ -### Step 8 -################################################################################ - -max_tokens = 500 - -# Function to split the text into chunks of a maximum number of tokens -def split_into_many(text, max_tokens = max_tokens): - - # Split the text into sentences - sentences = text.split('. ') - - # Get the number of tokens for each sentence - n_tokens = [len(tokenizer.encode(" " + sentence)) for sentence in sentences] - - chunks = [] - tokens_so_far = 0 - chunk = [] - - # Loop through the sentences and tokens joined together in a tuple - for sentence, token in zip(sentences, n_tokens): - - # If the number of tokens so far plus the number of tokens in the current sentence is greater - # than the max number of tokens, then add the chunk to the list of chunks and reset - # the chunk and tokens so far - if tokens_so_far + token > max_tokens: - chunks.append(". ".join(chunk) + ".") - chunk = [] - tokens_so_far = 0 - - # If the number of tokens in the current sentence is greater than the max number of - # tokens, go to the next sentence - if token > max_tokens: - continue - - # Otherwise, add the sentence to the chunk and add the number of tokens to the total - chunk.append(sentence) - tokens_so_far += token + 1 - - # Add the last chunk to the list of chunks - if chunk: - chunks.append(". ".join(chunk) + ".") - - return chunks - - -shortened = [] - -# Loop through the dataframe -for row in df.iterrows(): - - # If the text is None, go to the next row - if row[1]['text'] is None: - continue - - # If the number of tokens is greater than the max number of tokens, split the text into chunks - if row[1]['n_tokens'] > max_tokens: - shortened += split_into_many(row[1]['text']) - - # Otherwise, add the text to the list of shortened texts - else: - shortened.append( row[1]['text'] ) - -################################################################################ -### Step 9 -################################################################################ - -df = pd.DataFrame(shortened, columns = ['text']) -df['n_tokens'] = df.text.apply(lambda x: len(tokenizer.encode(x))) -df.n_tokens.hist() - -################################################################################ -### Step 10 -################################################################################ - -# Note that you may run into rate limit issues depending on how many files you try to embed -# Please check out our rate limit guide to learn more on how to handle this: https://platform.openai.com/docs/guides/rate-limits - -df['embeddings'] = df.text.apply(lambda x: openai.Embedding.create(input=x, engine='text-embedding-ada-002')['data'][0]['embedding']) -df.to_csv('processed/embeddings.csv') -df.head() - -################################################################################ -### Step 11 -################################################################################ - -df=pd.read_csv('processed/embeddings.csv', index_col=0) -df['embeddings'] = df['embeddings'].apply(literal_eval).apply(np.array) - -df.head() - -################################################################################ -### Step 12 -################################################################################ - -def create_context( - question, df, max_len=1800, size="ada" -): - """ - Create a context for a question by finding the most similar context from the dataframe - """ - - # Get the embeddings for the question - q_embeddings = openai.Embedding.create(input=question, engine='text-embedding-ada-002')['data'][0]['embedding'] - - # Get the distances from the embeddings - df['distances'] = distances_from_embeddings(q_embeddings, df['embeddings'].values, distance_metric='cosine') - - - returns = [] - cur_len = 0 - - # Sort by distance and add the text to the context until the context is too long - for i, row in df.sort_values('distances', ascending=True).iterrows(): - - # Add the length of the text to the current length - cur_len += row['n_tokens'] + 4 - - # If the context is too long, break - if cur_len > max_len: - break - - # Else add it to the text that is being returned - returns.append(row["text"]) - - # Return the context - return "\n\n###\n\n".join(returns) - -def answer_question( - df, - model="text-davinci-003", - question="Am I allowed to publish model outputs to Twitter, without a human review?", - max_len=1800, - size="ada", - debug=False, - max_tokens=150, - stop_sequence=None -): - """ - Answer a question based on the most similar context from the dataframe texts - """ - context = create_context( - question, - df, - max_len=max_len, - size=size, - ) - # If debug, print the raw model response - if debug: - print("Context:\n" + context) - print("\n\n") - - try: - # Create a completions using the questin and context - response = openai.Completion.create( - prompt=f"Answer the question based on the context below, and if the question can't be answered based on the context, say \"I don't know\"\n\nContext: {context}\n\n---\n\nQuestion: {question}\nAnswer:", - temperature=0, - max_tokens=max_tokens, - top_p=1, - frequency_penalty=0, - presence_penalty=0, - stop=stop_sequence, - model=model, - ) - return response["choices"][0]["text"].strip() - except Exception as e: - print(e) - return "" - -################################################################################ -### Step 13 -################################################################################ - -print(answer_question(df, question="What day is it?", debug=False)) - -print(answer_question(df, question="What is our newest embeddings model?"))