2023-04-18 03:56:42 +00:00
{
"cells": [
{
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"attachments": {},
2023-04-18 03:56:42 +00:00
"cell_type": "markdown",
"metadata": {},
"source": [
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"# Question answering over a group chat messages using Activeloop's DeepLake\n",
"In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT4 to semantically search and ask questions over a group chat.\n",
2023-04-18 03:56:42 +00:00
"\n",
2023-04-18 15:59:56 +00:00
"View a working demo [here](https://twitter.com/thisissukh_/status/1647223328363679745)"
2023-04-18 03:56:42 +00:00
]
},
{
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"attachments": {},
2023-04-18 03:56:42 +00:00
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Install required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Added deeplake use case examples of the new features (#6528)
<!--
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"!python3 -m pip install --upgrade langchain 'deeplake[enterprise]' openai tiktoken"
2023-04-18 03:56:42 +00:00
]
},
{
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"attachments": {},
2023-04-18 03:56:42 +00:00
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Add API keys"
]
},
{
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"attachments": {},
2023-04-18 03:56:42 +00:00
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import getpass\n",
"from langchain.document_loaders import PyPDFLoader, TextLoader\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import (\n",
" RecursiveCharacterTextSplitter,\n",
" CharacterTextSplitter,\n",
")\n",
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"from langchain.vectorstores import DeepLake\n",
"from langchain.chains import ConversationalRetrievalChain, RetrievalQA\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.llms import OpenAI\n",
"\n",
2023-06-16 18:52:56 +00:00
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
Fix `make docs_build` and related scripts (#7276)
**Description: a description of the change**
Fixed `make docs_build` and related scripts which caused errors. There
are several changes.
First, I made the build of the documentation and the API Reference into
two separate commands. This is because it takes less time to build. The
commands for documents are `make docs_build`, `make docs_clean`, and
`make docs_linkcheck`. The commands for API Reference are `make
api_docs_build`, `api_docs_clean`, and `api_docs_linkcheck`.
It looked like `docs/.local_build.sh` could be used to build the
documentation, so I used that. Since `.local_build.sh` was also building
API Rerefence internally, I removed that process. `.local_build.sh` also
added some Bash options to stop in error or so. Futher more added `cd
"${SCRIPT_DIR}"` at the beginning so that the script will work no matter
which directory it is executed in.
`docs/api_reference/api_reference.rst` is removed, because which is
generated by `docs/api_reference/create_api_rst.py`, and added it to
.gitignore.
Finally, the description of CONTRIBUTING.md was modified.
**Issue: the issue # it fixes (if applicable)**
https://github.com/hwchase17/langchain/issues/6413
**Dependencies: any dependencies required for this change**
`nbdoc` was missing in group docs so it was added. I installed it with
the `poetry add --group docs nbdoc` command. I am concerned if any
modifications are needed to poetry.lock. I would greatly appreciate it
if you could pay close attention to this file during the review.
**Tag maintainer**
- General / Misc / if you don't know who to tag: @baskaryan
If this PR needs any additional changes, I'll be happy to make them!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-12 02:05:14 +00:00
"activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token\n",
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"os.environ[\"ACTIVELOOP_ORG\"] = getpass.getpass(\"Activeloop Org:\")\n",
2023-04-18 03:56:42 +00:00
"\n",
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"org_id = os.environ[\"ACTIVELOOP_ORG\"]\n",
2023-04-18 03:56:42 +00:00
"embeddings = OpenAIEmbeddings()\n",
"\n",
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"dataset_path = \"hub://\" + org_id + \"/data\""
2023-04-18 03:56:42 +00:00
]
},
{
Added deeplake use case examples of the new features (#6528)
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"attachments": {},
2023-04-18 03:56:42 +00:00
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## 2. Create sample data"
]
},
{
Added deeplake use case examples of the new features (#6528)
<!--
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
"attachments": {},
2023-04-18 03:56:42 +00:00
"cell_type": "markdown",
"metadata": {},
"source": [
"You can generate a sample group chat conversation using ChatGPT with this prompt:\n",
"\n",
"```\n",
"Generate a group chat conversation with three friends talking about their day, referencing real places and fictional names. Make it funny and as detailed as possible.\n",
"```\n",
"\n",
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"I've already generated such a chat in `messages.txt`. We can keep it simple and use this for our example.\n",
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"\n",
"## 3. Ingest chat embeddings\n",
"\n",
"We load the messages in the text file, chunk and upload to ActiveLoop Vector store."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(\"messages.txt\") as f:\n",
" state_of_the_union = f.read()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"pages = text_splitter.split_text(state_of_the_union)\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
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"texts = text_splitter.create_documents(pages)\n",
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"\n",
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"print(texts)\n",
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"\n",
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"dataset_path = \"hub://\" + org + \"/data\"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"db = DeepLake.from_documents(\n",
" texts, embeddings, dataset_path=dataset_path, overwrite=True\n",
")"
2023-04-18 03:56:42 +00:00
]
},
{
Added deeplake use case examples of the new features (#6528)
<!--
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1. Added use cases of the new features
2. Done some code refactoring
---------
Co-authored-by: Ivo Stranic <istranic@gmail.com>
2023-07-10 14:04:29 +00:00
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"`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
]
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"# with open(\"messages.txt\") as f:\n",
"# state_of_the_union = f.read()\n",
"# text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"# pages = text_splitter.split_text(state_of_the_union)\n",
"\n",
"# text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
"# texts = text_splitter.create_documents(pages)\n",
"\n",
"# print(texts)\n",
"\n",
"# dataset_path = \"hub://\" + org + \"/data\"\n",
"# embeddings = OpenAIEmbeddings()\n",
"# db = DeepLake.from_documents(\n",
"# texts, embeddings, dataset_path=dataset_path, overwrite=True, runtime=\"tensor_db\"\n",
"# )"
]
},
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2023-04-18 03:56:42 +00:00
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"## 4. Ask questions\n",
"\n",
"Now we can ask a question and get an answer back with a semantic search:"
]
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"db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)\n",
"\n",
"retriever = db.as_retriever()\n",
2023-06-16 18:52:56 +00:00
"retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
"retriever.search_kwargs[\"k\"] = 4\n",
2023-04-18 03:56:42 +00:00
"\n",
2023-06-16 18:52:56 +00:00
"qa = RetrievalQA.from_chain_type(\n",
" llm=OpenAI(), chain_type=\"stuff\", retriever=retriever, return_source_documents=False\n",
")\n",
2023-04-18 03:56:42 +00:00
"\n",
"# What was the restaurant the group was talking about called?\n",
"query = input(\"Enter query:\")\n",
"\n",
"# The Hungry Lobster\n",
"ans = qa({\"query\": query})\n",
"\n",
"print(ans)"
]
},
{
2023-04-18 15:59:56 +00:00
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2023-04-18 03:56:42 +00:00
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2023-04-18 15:59:56 +00:00
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2023-04-18 03:56:42 +00:00
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