mirror of https://github.com/hwchase17/langchain
Merge branch 'master' into milvus-async
nlp-dev-id
2 months ago
committed by
GitHub
commit
1e0f747bae
@ -0,0 +1,818 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "70b333e6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[](https://www.mongodb.com/developer/products/atlas/advanced-rag-langchain-mongodb/)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d84a72ea",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Adding Semantic Caching and Memory to your RAG Application using MongoDB and LangChain\n",
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||||
"\n",
|
||||
"In this notebook, we will see how to use the new MongoDBCache and MongoDBChatMessageHistory in your RAG application.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "65527202",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"## Step 1: Install required libraries\n",
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||||
"\n",
|
||||
"- **datasets**: Python library to get access to datasets available on Hugging Face Hub\n",
|
||||
"\n",
|
||||
"- **langchain**: Python toolkit for LangChain\n",
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||||
"\n",
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||||
"- **langchain-mongodb**: Python package to use MongoDB as a vector store, semantic cache, chat history store etc. in LangChain\n",
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||||
"\n",
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||||
"- **langchain-openai**: Python package to use OpenAI models with LangChain\n",
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||||
"\n",
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||||
"- **pymongo**: Python toolkit for MongoDB\n",
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||||
"\n",
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||||
"- **pandas**: Python library for data analysis, exploration, and manipulation"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
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||||
"id": "cbc22fa4",
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
|
||||
"! pip install -qU datasets langchain langchain-mongodb langchain-openai pymongo pandas"
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "markdown",
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||||
"id": "39c41e87",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"## Step 2: Setup pre-requisites\n",
|
||||
"\n",
|
||||
"* Set the MongoDB connection string. Follow the steps [here](https://www.mongodb.com/docs/manual/reference/connection-string/) to get the connection string from the Atlas UI.\n",
|
||||
"\n",
|
||||
"* Set the OpenAI API key. Steps to obtain an API key as [here](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
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||||
"id": "b56412ae",
|
||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"import getpass"
|
||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 3,
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||||
"id": "16a20d7a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Enter your MongoDB connection string:········\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"MONGODB_URI = getpass.getpass(\"Enter your MongoDB connection string:\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
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||||
"id": "978682d4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Enter your OpenAI API key:········\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"OPENAI_API_KEY = getpass.getpass(\"Enter your OpenAI API key:\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
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||||
"id": "606081c5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
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||||
"name": "stdout",
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||||
"output_type": "stream",
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"text": [
|
||||
"········\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Optional-- If you want to enable Langsmith -- good for debugging\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f6b8302c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 3: Download the dataset\n",
|
||||
"\n",
|
||||
"We will be using MongoDB's [embedded_movies](https://huggingface.co/datasets/MongoDB/embedded_movies) dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "1a3433a6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from datasets import load_dataset"
|
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "aee5311b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Ensure you have an HF_TOKEN in your development enviornment:\n",
|
||||
"# access tokens can be created or copied from the Hugging Face platform (https://huggingface.co/docs/hub/en/security-tokens)\n",
|
||||
"\n",
|
||||
"# Load MongoDB's embedded_movies dataset from Hugging Face\n",
|
||||
"# https://huggingface.co/datasets/MongoDB/airbnb_embeddings\n",
|
||||
"\n",
|
||||
"data = load_dataset(\"MongoDB/embedded_movies\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
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"id": "1d630a26",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(data[\"train\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a1f94f43",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 4: Data analysis\n",
|
||||
"\n",
|
||||
"Make sure length of the dataset is what we expect, drop Nones etc."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
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||||
"id": "b276df71",
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||||
"metadata": {},
|
||||
"outputs": [
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||||
{
|
||||
"data": {
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||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
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||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>fullplot</th>\n",
|
||||
" <th>type</th>\n",
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||||
" <th>plot_embedding</th>\n",
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||||
" <th>num_mflix_comments</th>\n",
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||||
" <th>runtime</th>\n",
|
||||
" <th>writers</th>\n",
|
||||
" <th>imdb</th>\n",
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||||
" <th>countries</th>\n",
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||||
" <th>rated</th>\n",
|
||||
" <th>plot</th>\n",
|
||||
" <th>title</th>\n",
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||||
" <th>languages</th>\n",
|
||||
" <th>metacritic</th>\n",
|
||||
" <th>directors</th>\n",
|
||||
" <th>awards</th>\n",
|
||||
" <th>genres</th>\n",
|
||||
" <th>poster</th>\n",
|
||||
" <th>cast</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
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||||
" <th>0</th>\n",
|
||||
" <td>Young Pauline is left a lot of money when her ...</td>\n",
|
||||
" <td>movie</td>\n",
|
||||
" <td>[0.00072939653, -0.026834568, 0.013515796, -0....</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>199.0</td>\n",
|
||||
" <td>[Charles W. Goddard (screenplay), Basil Dickey...</td>\n",
|
||||
" <td>{'id': 4465, 'rating': 7.6, 'votes': 744}</td>\n",
|
||||
" <td>[USA]</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>Young Pauline is left a lot of money when her ...</td>\n",
|
||||
" <td>The Perils of Pauline</td>\n",
|
||||
" <td>[English]</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>[Louis J. Gasnier, Donald MacKenzie]</td>\n",
|
||||
" <td>{'nominations': 0, 'text': '1 win.', 'wins': 1}</td>\n",
|
||||
" <td>[Action]</td>\n",
|
||||
" <td>https://m.media-amazon.com/images/M/MV5BMzgxOD...</td>\n",
|
||||
" <td>[Pearl White, Crane Wilbur, Paul Panzer, Edwar...</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" fullplot type \\\n",
|
||||
"0 Young Pauline is left a lot of money when her ... movie \n",
|
||||
"\n",
|
||||
" plot_embedding num_mflix_comments \\\n",
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||||
"0 [0.00072939653, -0.026834568, 0.013515796, -0.... 0 \n",
|
||||
"\n",
|
||||
" runtime writers \\\n",
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||||
"0 199.0 [Charles W. Goddard (screenplay), Basil Dickey... \n",
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||||
"\n",
|
||||
" imdb countries rated \\\n",
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||||
"0 {'id': 4465, 'rating': 7.6, 'votes': 744} [USA] None \n",
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||||
"\n",
|
||||
" plot title \\\n",
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||||
"0 Young Pauline is left a lot of money when her ... The Perils of Pauline \n",
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"\n",
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" languages metacritic directors \\\n",
|
||||
"0 [English] NaN [Louis J. Gasnier, Donald MacKenzie] \n",
|
||||
"\n",
|
||||
" awards genres \\\n",
|
||||
"0 {'nominations': 0, 'text': '1 win.', 'wins': 1} [Action] \n",
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||||
"\n",
|
||||
" poster \\\n",
|
||||
"0 https://m.media-amazon.com/images/M/MV5BMzgxOD... \n",
|
||||
"\n",
|
||||
" cast \n",
|
||||
"0 [Pearl White, Crane Wilbur, Paul Panzer, Edwar... "
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Previewing the contents of the data\n",
|
||||
"df.head(1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "22ab375d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Only keep records where the fullplot field is not null\n",
|
||||
"df = df[df[\"fullplot\"].notna()]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "fceed99a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Renaming the embedding field to \"embedding\" -- required by LangChain\n",
|
||||
"df.rename(columns={\"plot_embedding\": \"embedding\"}, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "aedec13a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 5: Create a simple RAG chain using MongoDB as the vector store"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "11d292f3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_mongodb import MongoDBAtlasVectorSearch\n",
|
||||
"from pymongo import MongoClient\n",
|
||||
"\n",
|
||||
"# Initialize MongoDB python client\n",
|
||||
"client = MongoClient(MONGODB_URI, appname=\"devrel.content.python\")\n",
|
||||
"\n",
|
||||
"DB_NAME = \"langchain_chatbot\"\n",
|
||||
"COLLECTION_NAME = \"data\"\n",
|
||||
"ATLAS_VECTOR_SEARCH_INDEX_NAME = \"vector_index\"\n",
|
||||
"collection = client[DB_NAME][COLLECTION_NAME]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "d8292d53",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DeleteResult({'n': 1000, 'electionId': ObjectId('7fffffff00000000000000f6'), 'opTime': {'ts': Timestamp(1710523288, 1033), 't': 246}, 'ok': 1.0, '$clusterTime': {'clusterTime': Timestamp(1710523288, 1042), 'signature': {'hash': b\"i\\xa8\\xe9'\\x1ed\\xf2u\\xf3L\\xff\\xb1\\xf5\\xbfA\\x90\\xabJ\\x12\\x83\", 'keyId': 7299545392000008318}}, 'operationTime': Timestamp(1710523288, 1033)}, acknowledged=True)"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Delete any existing records in the collection\n",
|
||||
"collection.delete_many({})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "36c68914",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Data ingestion into MongoDB completed\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Data Ingestion\n",
|
||||
"records = df.to_dict(\"records\")\n",
|
||||
"collection.insert_many(records)\n",
|
||||
"\n",
|
||||
"print(\"Data ingestion into MongoDB completed\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "cbfca0b8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_openai import OpenAIEmbeddings\n",
|
||||
"\n",
|
||||
"# Using the text-embedding-ada-002 since that's what was used to create embeddings in the movies dataset\n",
|
||||
"embeddings = OpenAIEmbeddings(\n",
|
||||
" openai_api_key=OPENAI_API_KEY, model=\"text-embedding-ada-002\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "798e176c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Vector Store Creation\n",
|
||||
"vector_store = MongoDBAtlasVectorSearch.from_connection_string(\n",
|
||||
" connection_string=MONGODB_URI,\n",
|
||||
" namespace=DB_NAME + \".\" + COLLECTION_NAME,\n",
|
||||
" embedding=embeddings,\n",
|
||||
" index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n",
|
||||
" text_key=\"fullplot\",\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"id": "c71cd087",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Using the MongoDB vector store as a retriever in a RAG chain\n",
|
||||
"retriever = vector_store.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 5})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "b6588cd3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.output_parsers import StrOutputParser\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_core.runnables import RunnablePassthrough\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"# Generate context using the retriever, and pass the user question through\n",
|
||||
"retrieve = {\n",
|
||||
" \"context\": retriever | (lambda docs: \"\\n\\n\".join([d.page_content for d in docs])),\n",
|
||||
" \"question\": RunnablePassthrough(),\n",
|
||||
"}\n",
|
||||
"template = \"\"\"Answer the question based only on the following context: \\\n",
|
||||
"{context}\n",
|
||||
"\n",
|
||||
"Question: {question}\n",
|
||||
"\"\"\"\n",
|
||||
"# Defining the chat prompt\n",
|
||||
"prompt = ChatPromptTemplate.from_template(template)\n",
|
||||
"# Defining the model to be used for chat completion\n",
|
||||
"model = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n",
|
||||
"# Parse output as a string\n",
|
||||
"parse_output = StrOutputParser()\n",
|
||||
"\n",
|
||||
"# Naive RAG chain\n",
|
||||
"naive_rag_chain = retrieve | prompt | model | parse_output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"id": "aaae21f5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Once a Thief'"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"naive_rag_chain.invoke(\"What is the best movie to watch when sad?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "75f929ef",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 6: Create a RAG chain with chat history"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "94e7bd4a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.prompts import MessagesPlaceholder\n",
|
||||
"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
|
||||
"from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "5bb30860",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_session_history(session_id: str) -> MongoDBChatMessageHistory:\n",
|
||||
" return MongoDBChatMessageHistory(\n",
|
||||
" MONGODB_URI, session_id, database_name=DB_NAME, collection_name=\"history\"\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"id": "f51d0f35",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Given a follow-up question and history, create a standalone question\n",
|
||||
"standalone_system_prompt = \"\"\"\n",
|
||||
"Given a chat history and a follow-up question, rephrase the follow-up question to be a standalone question. \\\n",
|
||||
"Do NOT answer the question, just reformulate it if needed, otherwise return it as is. \\\n",
|
||||
"Only return the final standalone question. \\\n",
|
||||
"\"\"\"\n",
|
||||
"standalone_question_prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", standalone_system_prompt),\n",
|
||||
" MessagesPlaceholder(variable_name=\"history\"),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"question_chain = standalone_question_prompt | model | parse_output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"id": "f3ef3354",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Generate context by passing output of the question_chain i.e. the standalone question to the retriever\n",
|
||||
"retriever_chain = RunnablePassthrough.assign(\n",
|
||||
" context=question_chain\n",
|
||||
" | retriever\n",
|
||||
" | (lambda docs: \"\\n\\n\".join([d.page_content for d in docs]))\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"id": "5afb7345",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Create a prompt that includes the context, history and the follow-up question\n",
|
||||
"rag_system_prompt = \"\"\"Answer the question based only on the following context: \\\n",
|
||||
"{context}\n",
|
||||
"\"\"\"\n",
|
||||
"rag_prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", rag_system_prompt),\n",
|
||||
" MessagesPlaceholder(variable_name=\"history\"),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"id": "f95f47d0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# RAG chain\n",
|
||||
"rag_chain = retriever_chain | rag_prompt | model | parse_output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"id": "9618d395",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The best movie to watch when feeling down could be \"Last Action Hero.\" It\\'s a fun and action-packed film that blends reality and fantasy, offering an escape from the real world and providing an entertaining distraction.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# RAG chain with history\n",
|
||||
"with_message_history = RunnableWithMessageHistory(\n",
|
||||
" rag_chain,\n",
|
||||
" get_session_history,\n",
|
||||
" input_messages_key=\"question\",\n",
|
||||
" history_messages_key=\"history\",\n",
|
||||
")\n",
|
||||
"with_message_history.invoke(\n",
|
||||
" {\"question\": \"What is the best movie to watch when sad?\"},\n",
|
||||
" {\"configurable\": {\"session_id\": \"1\"}},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"id": "6e3080d1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'I apologize for the confusion. Another movie that might lift your spirits when you\\'re feeling sad is \"Smilla\\'s Sense of Snow.\" It\\'s a mystery thriller that could engage your mind and distract you from your sadness with its intriguing plot and suspenseful storyline.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"with_message_history.invoke(\n",
|
||||
" {\n",
|
||||
" \"question\": \"Hmmm..I don't want to watch that one. Can you suggest something else?\"\n",
|
||||
" },\n",
|
||||
" {\"configurable\": {\"session_id\": \"1\"}},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"id": "daea2953",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'For a lighter movie option, you might enjoy \"Cousins.\" It\\'s a comedy film set in Barcelona with action and humor, offering a fun and entertaining escape from reality. The storyline is engaging and filled with comedic moments that could help lift your spirits.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"with_message_history.invoke(\n",
|
||||
" {\"question\": \"How about something more light?\"},\n",
|
||||
" {\"configurable\": {\"session_id\": \"1\"}},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0de23a88",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 7: Get faster responses using Semantic Cache\n",
|
||||
"\n",
|
||||
"**NOTE:** Semantic cache only caches the input to the LLM. When using it in retrieval chains, remember that documents retrieved can change between runs resulting in cache misses for semantically similar queries."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"id": "5d6b6741",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.globals import set_llm_cache\n",
|
||||
"from langchain_mongodb.cache import MongoDBAtlasSemanticCache\n",
|
||||
"\n",
|
||||
"set_llm_cache(\n",
|
||||
" MongoDBAtlasSemanticCache(\n",
|
||||
" connection_string=MONGODB_URI,\n",
|
||||
" embedding=embeddings,\n",
|
||||
" collection_name=\"semantic_cache\",\n",
|
||||
" database_name=DB_NAME,\n",
|
||||
" index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n",
|
||||
" wait_until_ready=True, # Optional, waits until the cache is ready to be used\n",
|
||||
" )\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"id": "9825bc7b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 87.8 ms, sys: 670 µs, total: 88.5 ms\n",
|
||||
"Wall time: 1.24 s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Once a Thief'"
|
||||
]
|
||||
},
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"naive_rag_chain.invoke(\"What is the best movie to watch when sad?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"id": "a5e518cf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 43.5 ms, sys: 4.16 ms, total: 47.7 ms\n",
|
||||
"Wall time: 255 ms\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Once a Thief'"
|
||||
]
|
||||
},
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"naive_rag_chain.invoke(\"What is the best movie to watch when sad?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"id": "3d3d3ad3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 115 ms, sys: 171 µs, total: 115 ms\n",
|
||||
"Wall time: 1.38 s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'I would recommend watching \"Last Action Hero\" when sad, as it is a fun and action-packed film that can help lift your spirits.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"naive_rag_chain.invoke(\"Which movie do I watch when sad?\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "conda_pytorch_p310",
|
||||
"language": "python",
|
||||
"name": "conda_pytorch_p310"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,199 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "c48812ed-35bd-4fbe-9a2c-6c7335e5645e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_anthropic import ChatAnthropic\n",
|
||||
"from langchain_core.runnables import ConfigurableField\n",
|
||||
"from langchain_core.tools import tool\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def multiply(x: float, y: float) -> float:\n",
|
||||
" \"\"\"Multiply 'x' times 'y'.\"\"\"\n",
|
||||
" return x * y\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def exponentiate(x: float, y: float) -> float:\n",
|
||||
" \"\"\"Raise 'x' to the 'y'.\"\"\"\n",
|
||||
" return x**y\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"@tool\n",
|
||||
"def add(x: float, y: float) -> float:\n",
|
||||
" \"\"\"Add 'x' and 'y'.\"\"\"\n",
|
||||
" return x + y\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"tools = [multiply, exponentiate, add]\n",
|
||||
"\n",
|
||||
"gpt35 = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0).bind_tools(tools)\n",
|
||||
"claude3 = ChatAnthropic(model=\"claude-3-sonnet-20240229\").bind_tools(tools)\n",
|
||||
"llm_with_tools = gpt35.configurable_alternatives(\n",
|
||||
" ConfigurableField(id=\"llm\"), default_key=\"gpt35\", claude3=claude3\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9c186263-1b98-4cb2-b6d1-71f65eb0d811",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# LangGraph"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "28fc2c60-7dbc-428a-8983-1a6a15ea30d2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import operator\n",
|
||||
"from typing import Annotated, Sequence, TypedDict\n",
|
||||
"\n",
|
||||
"from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage\n",
|
||||
"from langchain_core.runnables import RunnableLambda\n",
|
||||
"from langgraph.graph import END, StateGraph\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class AgentState(TypedDict):\n",
|
||||
" messages: Annotated[Sequence[BaseMessage], operator.add]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def should_continue(state):\n",
|
||||
" return \"continue\" if state[\"messages\"][-1].tool_calls else \"end\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def call_model(state, config):\n",
|
||||
" return {\"messages\": [llm_with_tools.invoke(state[\"messages\"], config=config)]}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _invoke_tool(tool_call):\n",
|
||||
" tool = {tool.name: tool for tool in tools}[tool_call[\"name\"]]\n",
|
||||
" return ToolMessage(tool.invoke(tool_call[\"args\"]), tool_call_id=tool_call[\"id\"])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"tool_executor = RunnableLambda(_invoke_tool)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def call_tools(state):\n",
|
||||
" last_message = state[\"messages\"][-1]\n",
|
||||
" return {\"messages\": tool_executor.batch(last_message.tool_calls)}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"workflow = StateGraph(AgentState)\n",
|
||||
"workflow.add_node(\"agent\", call_model)\n",
|
||||
"workflow.add_node(\"action\", call_tools)\n",
|
||||
"workflow.set_entry_point(\"agent\")\n",
|
||||
"workflow.add_conditional_edges(\n",
|
||||
" \"agent\",\n",
|
||||
" should_continue,\n",
|
||||
" {\n",
|
||||
" \"continue\": \"action\",\n",
|
||||
" \"end\": END,\n",
|
||||
" },\n",
|
||||
")\n",
|
||||
"workflow.add_edge(\"action\", \"agent\")\n",
|
||||
"graph = workflow.compile()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "3710e724-2595-4625-ba3a-effb81e66e4a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'messages': [HumanMessage(content=\"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\"),\n",
|
||||
" AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_6yMU2WsS4Bqgi1WxFHxtfJRc', 'function': {'arguments': '{\"x\": 8, \"y\": 2.743}', 'name': 'exponentiate'}, 'type': 'function'}, {'id': 'call_GAL3dQiKFF9XEV0RrRLPTvVp', 'function': {'arguments': '{\"x\": 17.24, \"y\": -918.1241}', 'name': 'add'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 58, 'prompt_tokens': 168, 'total_tokens': 226}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-528302fc-7acf-4c11-82c4-119ccf40c573-0', tool_calls=[{'name': 'exponentiate', 'args': {'x': 8, 'y': 2.743}, 'id': 'call_6yMU2WsS4Bqgi1WxFHxtfJRc'}, {'name': 'add', 'args': {'x': 17.24, 'y': -918.1241}, 'id': 'call_GAL3dQiKFF9XEV0RrRLPTvVp'}]),\n",
|
||||
" ToolMessage(content='300.03770462067547', tool_call_id='call_6yMU2WsS4Bqgi1WxFHxtfJRc'),\n",
|
||||
" ToolMessage(content='-900.8841', tool_call_id='call_GAL3dQiKFF9XEV0RrRLPTvVp'),\n",
|
||||
" AIMessage(content='The result of \\\\(3 + 5^{2.743}\\\\) is approximately 300.04, and the result of \\\\(17.24 - 918.1241\\\\) is approximately -900.88.', response_metadata={'token_usage': {'completion_tokens': 44, 'prompt_tokens': 251, 'total_tokens': 295}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_b28b39ffa8', 'finish_reason': 'stop', 'logprobs': None}, id='run-d1161669-ed09-4b18-94bd-6d8530df5aa8-0')]}"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"graph.invoke(\n",
|
||||
" {\n",
|
||||
" \"messages\": [\n",
|
||||
" HumanMessage(\n",
|
||||
" \"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\"\n",
|
||||
" )\n",
|
||||
" ]\n",
|
||||
" }\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "073c074e-d722-42e0-85ec-c62c079207e4",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'messages': [HumanMessage(content=\"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\"),\n",
|
||||
" AIMessage(content=[{'text': \"Okay, let's break this down into two parts:\", 'type': 'text'}, {'id': 'toolu_01DEhqcXkXTtzJAiZ7uMBeDC', 'input': {'x': 3, 'y': 5}, 'name': 'add', 'type': 'tool_use'}], response_metadata={'id': 'msg_01AkLGH8sxMHaH15yewmjwkF', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 450, 'output_tokens': 81}}, id='run-f35bfae8-8ded-4f8a-831b-0940d6ad16b6-0', tool_calls=[{'name': 'add', 'args': {'x': 3, 'y': 5}, 'id': 'toolu_01DEhqcXkXTtzJAiZ7uMBeDC'}]),\n",
|
||||
" ToolMessage(content='8.0', tool_call_id='toolu_01DEhqcXkXTtzJAiZ7uMBeDC'),\n",
|
||||
" AIMessage(content=[{'id': 'toolu_013DyMLrvnrto33peAKMGMr1', 'input': {'x': 8.0, 'y': 2.743}, 'name': 'exponentiate', 'type': 'tool_use'}], response_metadata={'id': 'msg_015Fmp8aztwYcce2JDAFfce3', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 545, 'output_tokens': 75}}, id='run-48aaeeeb-a1e5-48fd-a57a-6c3da2907b47-0', tool_calls=[{'name': 'exponentiate', 'args': {'x': 8.0, 'y': 2.743}, 'id': 'toolu_013DyMLrvnrto33peAKMGMr1'}]),\n",
|
||||
" ToolMessage(content='300.03770462067547', tool_call_id='toolu_013DyMLrvnrto33peAKMGMr1'),\n",
|
||||
" AIMessage(content=[{'text': 'So 3 plus 5 raised to the 2.743 power is 300.04.\\n\\nFor the second part:', 'type': 'text'}, {'id': 'toolu_01UTmMrGTmLpPrPCF1rShN46', 'input': {'x': 17.24, 'y': -918.1241}, 'name': 'add', 'type': 'tool_use'}], response_metadata={'id': 'msg_015TkhfRBENPib2RWAxkieH6', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 638, 'output_tokens': 105}}, id='run-45fb62e3-d102-4159-881d-241c5dbadeed-0', tool_calls=[{'name': 'add', 'args': {'x': 17.24, 'y': -918.1241}, 'id': 'toolu_01UTmMrGTmLpPrPCF1rShN46'}]),\n",
|
||||
" ToolMessage(content='-900.8841', tool_call_id='toolu_01UTmMrGTmLpPrPCF1rShN46'),\n",
|
||||
" AIMessage(content='Therefore, 17.24 - 918.1241 = -900.8841', response_metadata={'id': 'msg_01LgKnRuUcSyADCpxv9tPoYD', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 759, 'output_tokens': 24}}, id='run-1008254e-ccd1-497c-8312-9550dd77bd08-0')]}"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"graph.invoke(\n",
|
||||
" {\n",
|
||||
" \"messages\": [\n",
|
||||
" HumanMessage(\n",
|
||||
" \"what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241\"\n",
|
||||
" )\n",
|
||||
" ]\n",
|
||||
" },\n",
|
||||
" config={\"configurable\": {\"llm\": \"claude3\"}},\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -0,0 +1,217 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MLX\n",
|
||||
"\n",
|
||||
"This notebook shows how to get started using `MLX` LLM's as chat models.\n",
|
||||
"\n",
|
||||
"In particular, we will:\n",
|
||||
"1. Utilize the [MLXPipeline](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/mlx_pipelines.py), \n",
|
||||
"2. Utilize the `ChatMLX` class to enable any of these LLMs to interface with LangChain's [Chat Messages](https://python.langchain.com/docs/modules/model_io/chat/#messages) abstraction.\n",
|
||||
"3. Demonstrate how to use an open-source LLM to power an `ChatAgent` pipeline\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet mlx-lm transformers huggingface_hub"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. Instantiate an LLM\n",
|
||||
"\n",
|
||||
"There are three LLM options to choose from."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.llms.mlx_pipeline import MLXPipeline\n",
|
||||
"\n",
|
||||
"llm = MLXPipeline.from_model_id(\n",
|
||||
" \"mlx-community/quantized-gemma-2b-it\",\n",
|
||||
" pipeline_kwargs={\"max_tokens\": 10, \"temp\": 0.1},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 2. Instantiate the `ChatMLX` to apply chat templates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Instantiate the chat model and some messages to pass."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.schema import (\n",
|
||||
" HumanMessage,\n",
|
||||
")\n",
|
||||
"from langchain_community.chat_models.mlx import ChatMLX\n",
|
||||
"\n",
|
||||
"messages = [\n",
|
||||
" HumanMessage(\n",
|
||||
" content=\"What happens when an unstoppable force meets an immovable object?\"\n",
|
||||
" ),\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"chat_model = ChatMLX(llm=llm)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Inspect how the chat messages are formatted for the LLM call."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chat_model._to_chat_prompt(messages)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"res = chat_model.invoke(messages)\n",
|
||||
"print(res.content)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 3. Take it for a spin as an agent!\n",
|
||||
"\n",
|
||||
"Here we'll test out `gemma-2b-it` as a zero-shot `ReAct` Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
|
||||
"\n",
|
||||
"> Note: To run this section, you'll need to have a [SerpAPI Token](https://serpapi.com/) saved as an environment variable: `SERPAPI_API_KEY`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain import hub\n",
|
||||
"from langchain.agents import AgentExecutor, load_tools\n",
|
||||
"from langchain.agents.format_scratchpad import format_log_to_str\n",
|
||||
"from langchain.agents.output_parsers import (\n",
|
||||
" ReActJsonSingleInputOutputParser,\n",
|
||||
")\n",
|
||||
"from langchain.tools.render import render_text_description\n",
|
||||
"from langchain_community.utilities import SerpAPIWrapper"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Configure the agent with a `react-json` style prompt and access to a search engine and calculator."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# setup tools\n",
|
||||
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
|
||||
"\n",
|
||||
"# setup ReAct style prompt\n",
|
||||
"prompt = hub.pull(\"hwchase17/react-json\")\n",
|
||||
"prompt = prompt.partial(\n",
|
||||
" tools=render_text_description(tools),\n",
|
||||
" tool_names=\", \".join([t.name for t in tools]),\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# define the agent\n",
|
||||
"chat_model_with_stop = chat_model.bind(stop=[\"\\nObservation\"])\n",
|
||||
"agent = (\n",
|
||||
" {\n",
|
||||
" \"input\": lambda x: x[\"input\"],\n",
|
||||
" \"agent_scratchpad\": lambda x: format_log_to_str(x[\"intermediate_steps\"]),\n",
|
||||
" }\n",
|
||||
" | prompt\n",
|
||||
" | chat_model_with_stop\n",
|
||||
" | ReActJsonSingleInputOutputParser()\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# instantiate AgentExecutor\n",
|
||||
"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"agent_executor.invoke(\n",
|
||||
" {\n",
|
||||
" \"input\": \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
|
||||
" }\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.9.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@ -0,0 +1,142 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "959300d4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MLX Local Pipelines\n",
|
||||
"\n",
|
||||
"MLX models can be run locally through the `MLXPipeline` class.\n",
|
||||
"\n",
|
||||
"The [MLX Community](https://huggingface.co/mlx-community) hosts over 150 models, all open source and publicly available on Hugging Face Model Hub a online platform where people can easily collaborate and build ML together.\n",
|
||||
"\n",
|
||||
"These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through the MlXPipeline class. For more information on mlx, see the [examples repo](https://github.com/ml-explore/mlx-examples/tree/main/llms) notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"To use, you should have the ``mlx-lm`` python [package installed](https://pypi.org/project/mlx-lm/), as well as [transformers](https://pypi.org/project/transformers/). You can also install `huggingface_hub`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d772b637-de00-4663-bd77-9bc96d798db2",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet mlx-lm transformers huggingface_hub"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "91ad075f-71d5-4bc8-ab91-cc0ad5ef16bb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Model Loading\n",
|
||||
"\n",
|
||||
"Models can be loaded by specifying the model parameters using the `from_model_id` method."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "165ae236-962a-4763-8052-c4836d78a5d2",
|
||||
"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.llms.mlx_pipeline import MLXPipeline\n",
|
||||
"\n",
|
||||
"pipe = MLXPipeline.from_model_id(\n",
|
||||
" \"mlx-community/quantized-gemma-2b-it\",\n",
|
||||
" pipeline_kwargs={\"max_tokens\": 10, \"temp\": 0.1},\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "00104b27-0c15-4a97-b198-4512337ee211",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"They can also be loaded by passing in an existing `transformers` pipeline directly"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7f426a4f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline\n",
|
||||
"from mlx_lm import load\n",
|
||||
"\n",
|
||||
"model, tokenizer = load(\"mlx-community/quantized-gemma-2b-it\")\n",
|
||||
"pipe = MLXPipeline(model=model, tokenizer=tokenizer)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "60e7ba8d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create Chain\n",
|
||||
"\n",
|
||||
"With the model loaded into memory, you can compose it with a prompt to\n",
|
||||
"form a chain."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3acf0069",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.prompts import PromptTemplate\n",
|
||||
"\n",
|
||||
"template = \"\"\"Question: {question}\n",
|
||||
"\n",
|
||||
"Answer: Let's think step by step.\"\"\"\n",
|
||||
"prompt = PromptTemplate.from_template(template)\n",
|
||||
"\n",
|
||||
"chain = prompt | pipe\n",
|
||||
"\n",
|
||||
"question = \"What is electroencephalography?\"\n",
|
||||
"\n",
|
||||
"print(chain.invoke({\"question\": question}))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.9.18"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@ -1,147 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1edb9e6b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Azure Cognitive Search\n",
|
||||
"\n",
|
||||
">[Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.\n",
|
||||
"\n",
|
||||
">Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:\n",
|
||||
">- A search engine for full text search over a search index containing user-owned content\n",
|
||||
">- Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation\n",
|
||||
">- Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more\n",
|
||||
">- Programmability through REST APIs and client libraries in Azure SDKs\n",
|
||||
">- Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)\n",
|
||||
"\n",
|
||||
"This notebook shows how to use Azure Cognitive Search (ACS) within LangChain."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "074b0004",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Set up Azure Cognitive Search\n",
|
||||
"\n",
|
||||
"To set up ACS, please follow the instructions [here](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).\n",
|
||||
"\n",
|
||||
"Please note\n",
|
||||
"1. the name of your ACS service, \n",
|
||||
"2. the name of your ACS index,\n",
|
||||
"3. your API key.\n",
|
||||
"\n",
|
||||
"Your API key can be either Admin or Query key, but as we only read data it is recommended to use a Query key."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0474661d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using the Azure Cognitive Search Retriever"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "39d6074e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"from langchain_community.retrievers import (\n",
|
||||
" AzureCognitiveSearchRetriever,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b7243e6d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Set Service Name, Index Name and API key as environment variables (alternatively, you can pass them as arguments to `AzureCognitiveSearchRetriever`)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "33fd23d1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"os.environ[\"AZURE_COGNITIVE_SEARCH_SERVICE_NAME\"] = \"<YOUR_ACS_SERVICE_NAME>\"\n",
|
||||
"os.environ[\"AZURE_COGNITIVE_SEARCH_INDEX_NAME\"] = \"<YOUR_ACS_INDEX_NAME>\"\n",
|
||||
"os.environ[\"AZURE_COGNITIVE_SEARCH_API_KEY\"] = \"<YOUR_API_KEY>\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "057deaad",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Create the Retriever"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c18d0c4c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"retriever = AzureCognitiveSearchRetriever(content_key=\"content\", top_k=10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e94ea104",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now you can use retrieve documents from Azure Cognitive Search"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c8b5794b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"retriever.get_relevant_documents(\"what is langchain\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "72eca08e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can change the number of results returned with the `top_k` parameter. The default value is `None`, which returns all results. "
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,441 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1ad7250ddd99fba9",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"# Tencent Cloud VectorDB\n",
|
||||
"\n",
|
||||
"> [Tencent Cloud VectorDB](https://cloud.tencent.com/document/product/1709) is a fully managed, self-developed, enterprise-level distributed database service designed for storing, retrieving, and analyzing multi-dimensional vector data.\n",
|
||||
"\n",
|
||||
"In the walkthrough, we'll demo the `SelfQueryRetriever` with a Tencent Cloud VectorDB."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "209652d4ab38ba7f",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"## create a TencentVectorDB instance\n",
|
||||
"First we'll want to create a TencentVectorDB and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
|
||||
"\n",
|
||||
"**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`) along with integration-specific requirements."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "b68da3303b0625f2",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:39:28.887634Z",
|
||||
"start_time": "2024-03-29T02:39:27.277978Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\r\n",
|
||||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\r\n",
|
||||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n",
|
||||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pip install --upgrade --quiet tcvectordb langchain-openai tiktoken lark"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a1113af6008f3f3d",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "c243e15bcf72d539",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:40:59.788206Z",
|
||||
"start_time": "2024-03-29T02:40:59.783798Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e5277a4dba027bb8",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"create a TencentVectorDB instance and seed it with some data:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "fd0c70c0be7d7130",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:42:28.467682Z",
|
||||
"start_time": "2024-03-29T02:42:21.255335Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.vectorstores.tencentvectordb import (\n",
|
||||
" ConnectionParams,\n",
|
||||
" MetaField,\n",
|
||||
" TencentVectorDB,\n",
|
||||
")\n",
|
||||
"from langchain_core.documents import Document\n",
|
||||
"from tcvectordb.model.enum import FieldType\n",
|
||||
"\n",
|
||||
"meta_fields = [\n",
|
||||
" MetaField(name=\"year\", data_type=\"uint64\", index=True),\n",
|
||||
" MetaField(name=\"rating\", data_type=\"string\", index=False),\n",
|
||||
" MetaField(name=\"genre\", data_type=FieldType.String, index=True),\n",
|
||||
" MetaField(name=\"director\", data_type=FieldType.String, index=True),\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"docs = [\n",
|
||||
" Document(\n",
|
||||
" page_content=\"The Shawshank Redemption is a 1994 American drama film written and directed by Frank Darabont.\",\n",
|
||||
" metadata={\n",
|
||||
" \"year\": 1994,\n",
|
||||
" \"rating\": \"9.3\",\n",
|
||||
" \"genre\": \"drama\",\n",
|
||||
" \"director\": \"Frank Darabont\",\n",
|
||||
" },\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"The Godfather is a 1972 American crime film directed by Francis Ford Coppola.\",\n",
|
||||
" metadata={\n",
|
||||
" \"year\": 1972,\n",
|
||||
" \"rating\": \"9.2\",\n",
|
||||
" \"genre\": \"crime\",\n",
|
||||
" \"director\": \"Francis Ford Coppola\",\n",
|
||||
" },\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"The Dark Knight is a 2008 superhero film directed by Christopher Nolan.\",\n",
|
||||
" metadata={\n",
|
||||
" \"year\": 2008,\n",
|
||||
" \"rating\": \"9.0\",\n",
|
||||
" \"genre\": \"science fiction\",\n",
|
||||
" \"director\": \"Christopher Nolan\",\n",
|
||||
" },\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"Inception is a 2010 science fiction action film written and directed by Christopher Nolan.\",\n",
|
||||
" metadata={\n",
|
||||
" \"year\": 2010,\n",
|
||||
" \"rating\": \"8.8\",\n",
|
||||
" \"genre\": \"science fiction\",\n",
|
||||
" \"director\": \"Christopher Nolan\",\n",
|
||||
" },\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.\",\n",
|
||||
" metadata={\n",
|
||||
" \"year\": 2012,\n",
|
||||
" \"rating\": \"8.0\",\n",
|
||||
" \"genre\": \"science fiction\",\n",
|
||||
" \"director\": \"Joss Whedon\",\n",
|
||||
" },\n",
|
||||
" ),\n",
|
||||
" Document(\n",
|
||||
" page_content=\"Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.\",\n",
|
||||
" metadata={\n",
|
||||
" \"year\": 2018,\n",
|
||||
" \"rating\": \"7.3\",\n",
|
||||
" \"genre\": \"science fiction\",\n",
|
||||
" \"director\": \"Ryan Coogler\",\n",
|
||||
" },\n",
|
||||
" ),\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"vector_db = TencentVectorDB.from_documents(\n",
|
||||
" docs,\n",
|
||||
" None,\n",
|
||||
" connection_params=ConnectionParams(\n",
|
||||
" url=\"http://10.0.X.X\",\n",
|
||||
" key=\"eC4bLRy2va******************************\",\n",
|
||||
" username=\"root\",\n",
|
||||
" timeout=20,\n",
|
||||
" ),\n",
|
||||
" collection_name=\"self_query_movies\",\n",
|
||||
" meta_fields=meta_fields,\n",
|
||||
" drop_old=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3810b731a981a957",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"## Creating our self-querying retriever\n",
|
||||
"Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "7095b68ea997468c",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:42:37.901230Z",
|
||||
"start_time": "2024-03-29T02:42:36.836827Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chains.query_constructor.base import AttributeInfo\n",
|
||||
"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"metadata_field_info = [\n",
|
||||
" AttributeInfo(\n",
|
||||
" name=\"genre\",\n",
|
||||
" description=\"The genre of the movie\",\n",
|
||||
" type=\"string\",\n",
|
||||
" ),\n",
|
||||
" AttributeInfo(\n",
|
||||
" name=\"year\",\n",
|
||||
" description=\"The year the movie was released\",\n",
|
||||
" type=\"integer\",\n",
|
||||
" ),\n",
|
||||
" AttributeInfo(\n",
|
||||
" name=\"director\",\n",
|
||||
" description=\"The name of the movie director\",\n",
|
||||
" type=\"string\",\n",
|
||||
" ),\n",
|
||||
" AttributeInfo(\n",
|
||||
" name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"string\"\n",
|
||||
" ),\n",
|
||||
"]\n",
|
||||
"document_content_description = \"Brief summary of a movie\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "cbbf7e54054bb3aa",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:42:45.187071Z",
|
||||
"start_time": "2024-03-29T02:42:45.138462Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm = ChatOpenAI(temperature=0, model=\"gpt-4\", max_tokens=4069)\n",
|
||||
"retriever = SelfQueryRetriever.from_llm(\n",
|
||||
" llm, vector_db, document_content_description, metadata_field_info, verbose=True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "65ff2054be9d5236",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"## Test it out\n",
|
||||
"And now we can try actually using our retriever!\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "267e2a68f26505b1",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:42:51.526470Z",
|
||||
"start_time": "2024-03-29T02:42:48.328191Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "[Document(page_content='The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', metadata={'year': 2008, 'rating': '9.0', 'genre': 'science fiction', 'director': 'Christopher Nolan'}),\n Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'}),\n Document(page_content='Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.', metadata={'year': 2018, 'rating': '7.3', 'genre': 'science fiction', 'director': 'Ryan Coogler'}),\n Document(page_content='The Godfather is a 1972 American crime film directed by Francis Ford Coppola.', metadata={'year': 1972, 'rating': '9.2', 'genre': 'crime', 'director': 'Francis Ford Coppola'})]"
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# This example only specifies a relevant query\n",
|
||||
"retriever.get_relevant_documents(\"movies about a superhero\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "3afd98ca20782dda",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:42:55.179002Z",
|
||||
"start_time": "2024-03-29T02:42:53.057022Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "[Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'}),\n Document(page_content='Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.', metadata={'year': 2018, 'rating': '7.3', 'genre': 'science fiction', 'director': 'Ryan Coogler'})]"
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# This example only specifies a filter\n",
|
||||
"retriever.get_relevant_documents(\"movies that were released after 2010\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "9974f641e11abfe8",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:42:58.472620Z",
|
||||
"start_time": "2024-03-29T02:42:56.131594Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "[Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'}),\n Document(page_content='Black Panther is a 2018 American superhero film based on the Marvel Comics character of the same name.', metadata={'year': 2018, 'rating': '7.3', 'genre': 'science fiction', 'director': 'Ryan Coogler'})]"
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# This example specifies both a relevant query and a filter\n",
|
||||
"retriever.get_relevant_documents(\n",
|
||||
" \"movies about a superhero which were released after 2010\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "be593d3a6c508517",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"## Filter k\n",
|
||||
"\n",
|
||||
"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
|
||||
"\n",
|
||||
"We can do this by passing `enable_limit=True` to the constructor."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "e255b69c937fa424",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:43:02.779337Z",
|
||||
"start_time": "2024-03-29T02:43:02.759900Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"retriever = SelfQueryRetriever.from_llm(\n",
|
||||
" llm,\n",
|
||||
" vector_db,\n",
|
||||
" document_content_description,\n",
|
||||
" metadata_field_info,\n",
|
||||
" verbose=True,\n",
|
||||
" enable_limit=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "45674137c7f8a9d",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-03-29T02:43:07.357830Z",
|
||||
"start_time": "2024-03-29T02:43:04.854323Z"
|
||||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "[Document(page_content='The Dark Knight is a 2008 superhero film directed by Christopher Nolan.', metadata={'year': 2008, 'rating': '9.0', 'genre': 'science fiction', 'director': 'Christopher Nolan'}),\n Document(page_content='The Avengers is a 2012 American superhero film based on the Marvel Comics superhero team of the same name.', metadata={'year': 2012, 'rating': '8.0', 'genre': 'science fiction', 'director': 'Joss Whedon'})]"
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"retriever.get_relevant_documents(\"what are two movies about a superhero\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,111 @@
|
||||
import threading
|
||||
from typing import Any, Dict, List, Union
|
||||
|
||||
from langchain_core.callbacks import BaseCallbackHandler
|
||||
from langchain_core.outputs import LLMResult
|
||||
|
||||
MODEL_COST_PER_1K_INPUT_TOKENS = {
|
||||
"anthropic.claude-instant-v1": 0.0008,
|
||||
"anthropic.claude-v2": 0.008,
|
||||
"anthropic.claude-v2:1": 0.008,
|
||||
"anthropic.claude-3-sonnet-20240229-v1:0": 0.003,
|
||||
"anthropic.claude-3-haiku-20240307-v1:0": 0.00025,
|
||||
}
|
||||
|
||||
MODEL_COST_PER_1K_OUTPUT_TOKENS = {
|
||||
"anthropic.claude-instant-v1": 0.0024,
|
||||
"anthropic.claude-v2": 0.024,
|
||||
"anthropic.claude-v2:1": 0.024,
|
||||
"anthropic.claude-3-sonnet-20240229-v1:0": 0.015,
|
||||
"anthropic.claude-3-haiku-20240307-v1:0": 0.00125,
|
||||
}
|
||||
|
||||
|
||||
def _get_anthropic_claude_token_cost(
|
||||
prompt_tokens: int, completion_tokens: int, model_id: Union[str, None]
|
||||
) -> float:
|
||||
"""Get the cost of tokens for the Claude model."""
|
||||
if model_id not in MODEL_COST_PER_1K_INPUT_TOKENS:
|
||||
raise ValueError(
|
||||
f"Unknown model: {model_id}. Please provide a valid Anthropic model name."
|
||||
"Known models are: " + ", ".join(MODEL_COST_PER_1K_INPUT_TOKENS.keys())
|
||||
)
|
||||
return (prompt_tokens / 1000) * MODEL_COST_PER_1K_INPUT_TOKENS[model_id] + (
|
||||
completion_tokens / 1000
|
||||
) * MODEL_COST_PER_1K_OUTPUT_TOKENS[model_id]
|
||||
|
||||
|
||||
class BedrockAnthropicTokenUsageCallbackHandler(BaseCallbackHandler):
|
||||
"""Callback Handler that tracks bedrock anthropic info."""
|
||||
|
||||
total_tokens: int = 0
|
||||
prompt_tokens: int = 0
|
||||
completion_tokens: int = 0
|
||||
successful_requests: int = 0
|
||||
total_cost: float = 0.0
|
||||
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
self._lock = threading.Lock()
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return (
|
||||
f"Tokens Used: {self.total_tokens}\n"
|
||||
f"\tPrompt Tokens: {self.prompt_tokens}\n"
|
||||
f"\tCompletion Tokens: {self.completion_tokens}\n"
|
||||
f"Successful Requests: {self.successful_requests}\n"
|
||||
f"Total Cost (USD): ${self.total_cost}"
|
||||
)
|
||||
|
||||
@property
|
||||
def always_verbose(self) -> bool:
|
||||
"""Whether to call verbose callbacks even if verbose is False."""
|
||||
return True
|
||||
|
||||
def on_llm_start(
|
||||
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
|
||||
) -> None:
|
||||
"""Print out the prompts."""
|
||||
pass
|
||||
|
||||
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
|
||||
"""Print out the token."""
|
||||
pass
|
||||
|
||||
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
|
||||
"""Collect token usage."""
|
||||
if response.llm_output is None:
|
||||
return None
|
||||
|
||||
if "usage" not in response.llm_output:
|
||||
with self._lock:
|
||||
self.successful_requests += 1
|
||||
return None
|
||||
|
||||
# compute tokens and cost for this request
|
||||
token_usage = response.llm_output["usage"]
|
||||
completion_tokens = token_usage.get("completion_tokens", 0)
|
||||
prompt_tokens = token_usage.get("prompt_tokens", 0)
|
||||
total_tokens = token_usage.get("total_tokens", 0)
|
||||
model_id = response.llm_output.get("model_id", None)
|
||||
total_cost = _get_anthropic_claude_token_cost(
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
model_id=model_id,
|
||||
)
|
||||
|
||||
# update shared state behind lock
|
||||
with self._lock:
|
||||
self.total_cost += total_cost
|
||||
self.total_tokens += total_tokens
|
||||
self.prompt_tokens += prompt_tokens
|
||||
self.completion_tokens += completion_tokens
|
||||
self.successful_requests += 1
|
||||
|
||||
def __copy__(self) -> "BedrockAnthropicTokenUsageCallbackHandler":
|
||||
"""Return a copy of the callback handler."""
|
||||
return self
|
||||
|
||||
def __deepcopy__(self, memo: Any) -> "BedrockAnthropicTokenUsageCallbackHandler":
|
||||
"""Return a deep copy of the callback handler."""
|
||||
return self
|
||||
@ -0,0 +1,196 @@
|
||||
"""MLX Chat Wrapper."""
|
||||
|
||||
from typing import Any, Iterator, List, Optional
|
||||
|
||||
from langchain_core.callbacks.manager import (
|
||||
AsyncCallbackManagerForLLMRun,
|
||||
CallbackManagerForLLMRun,
|
||||
)
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
AIMessageChunk,
|
||||
BaseMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.outputs import (
|
||||
ChatGeneration,
|
||||
ChatGenerationChunk,
|
||||
ChatResult,
|
||||
LLMResult,
|
||||
)
|
||||
|
||||
from langchain_community.llms.mlx_pipeline import MLXPipeline
|
||||
|
||||
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful, and honest assistant."""
|
||||
|
||||
|
||||
class ChatMLX(BaseChatModel):
|
||||
"""
|
||||
Wrapper for using MLX LLM's as ChatModels.
|
||||
|
||||
Works with `MLXPipeline` LLM.
|
||||
|
||||
To use, you should have the ``mlx-lm`` python package installed.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_community.chat_models import chatMLX
|
||||
from langchain_community.llms import MLXPipeline
|
||||
|
||||
llm = MLXPipeline.from_model_id(
|
||||
model_id="mlx-community/quantized-gemma-2b-it",
|
||||
)
|
||||
chat = chatMLX(llm=llm)
|
||||
|
||||
"""
|
||||
|
||||
llm: MLXPipeline
|
||||
system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT)
|
||||
tokenizer: Any = None
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
super().__init__(**kwargs)
|
||||
self.tokenizer = self.llm.tokenizer
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
llm_input = self._to_chat_prompt(messages)
|
||||
llm_result = self.llm._generate(
|
||||
prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return self._to_chat_result(llm_result)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
llm_input = self._to_chat_prompt(messages)
|
||||
llm_result = await self.llm._agenerate(
|
||||
prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return self._to_chat_result(llm_result)
|
||||
|
||||
def _to_chat_prompt(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
tokenize: bool = False,
|
||||
return_tensors: Optional[str] = None,
|
||||
) -> str:
|
||||
"""Convert a list of messages into a prompt format expected by wrapped LLM."""
|
||||
if not messages:
|
||||
raise ValueError("At least one HumanMessage must be provided!")
|
||||
|
||||
if not isinstance(messages[-1], HumanMessage):
|
||||
raise ValueError("Last message must be a HumanMessage!")
|
||||
|
||||
messages_dicts = [self._to_chatml_format(m) for m in messages]
|
||||
|
||||
return self.tokenizer.apply_chat_template(
|
||||
messages_dicts,
|
||||
tokenize=tokenize,
|
||||
add_generation_prompt=True,
|
||||
return_tensors=return_tensors,
|
||||
)
|
||||
|
||||
def _to_chatml_format(self, message: BaseMessage) -> dict:
|
||||
"""Convert LangChain message to ChatML format."""
|
||||
|
||||
if isinstance(message, SystemMessage):
|
||||
role = "system"
|
||||
elif isinstance(message, AIMessage):
|
||||
role = "assistant"
|
||||
elif isinstance(message, HumanMessage):
|
||||
role = "user"
|
||||
else:
|
||||
raise ValueError(f"Unknown message type: {type(message)}")
|
||||
|
||||
return {"role": role, "content": message.content}
|
||||
|
||||
@staticmethod
|
||||
def _to_chat_result(llm_result: LLMResult) -> ChatResult:
|
||||
chat_generations = []
|
||||
|
||||
for g in llm_result.generations[0]:
|
||||
chat_generation = ChatGeneration(
|
||||
message=AIMessage(content=g.text), generation_info=g.generation_info
|
||||
)
|
||||
chat_generations.append(chat_generation)
|
||||
|
||||
return ChatResult(
|
||||
generations=chat_generations, llm_output=llm_result.llm_output
|
||||
)
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "mlx-chat-wrapper"
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
import mlx.core as mx
|
||||
from mlx_lm.utils import generate_step
|
||||
|
||||
try:
|
||||
import mlx.core as mx
|
||||
from mlx_lm.utils import generate_step
|
||||
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import mlx_lm python package. "
|
||||
"Please install it with `pip install mlx_lm`."
|
||||
)
|
||||
model_kwargs = kwargs.get("model_kwargs", self.llm.pipeline_kwargs)
|
||||
temp: float = model_kwargs.get("temp", 0.0)
|
||||
max_new_tokens: int = model_kwargs.get("max_tokens", 100)
|
||||
repetition_penalty: Optional[float] = model_kwargs.get(
|
||||
"repetition_penalty", None
|
||||
)
|
||||
repetition_context_size: Optional[int] = model_kwargs.get(
|
||||
"repetition_context_size", None
|
||||
)
|
||||
|
||||
llm_input = self._to_chat_prompt(messages, tokenize=True, return_tensors="np")
|
||||
|
||||
prompt_tokens = mx.array(llm_input[0])
|
||||
|
||||
eos_token_id = self.tokenizer.eos_token_id
|
||||
|
||||
for (token, prob), n in zip(
|
||||
generate_step(
|
||||
prompt_tokens,
|
||||
self.llm.model,
|
||||
temp,
|
||||
repetition_penalty,
|
||||
repetition_context_size,
|
||||
),
|
||||
range(max_new_tokens),
|
||||
):
|
||||
# identify text to yield
|
||||
text: Optional[str] = None
|
||||
text = self.tokenizer.decode(token.item())
|
||||
|
||||
# yield text, if any
|
||||
if text:
|
||||
chunk = ChatGenerationChunk(message=AIMessageChunk(content=text))
|
||||
yield chunk
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(text, chunk=chunk)
|
||||
|
||||
# break if stop sequence found
|
||||
if token == eos_token_id or (stop is not None and text in stop):
|
||||
break
|
||||
@ -0,0 +1,35 @@
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501
|
||||
TreeSitterSegmenter,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from tree_sitter import Language
|
||||
|
||||
|
||||
CHUNK_QUERY = """
|
||||
[
|
||||
(function_definition) @function
|
||||
(class_declaration) @class
|
||||
(interface_declaration) @interface
|
||||
(trait_declaration) @trait
|
||||
(enum_declaration) @enum
|
||||
(namespace_definition) @namespace
|
||||
]
|
||||
""".strip()
|
||||
|
||||
|
||||
class PHPSegmenter(TreeSitterSegmenter):
|
||||
"""Code segmenter for PHP."""
|
||||
|
||||
def get_language(self) -> "Language":
|
||||
from tree_sitter_languages import get_language
|
||||
|
||||
return get_language("php")
|
||||
|
||||
def get_chunk_query(self) -> str:
|
||||
return CHUNK_QUERY
|
||||
|
||||
def make_line_comment(self, text: str) -> str:
|
||||
return f"// {text}"
|
||||
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Reference in New Issue