mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
720 lines
19 KiB
Plaintext
720 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Activeloop's Deep Lake\n",
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"\n",
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">[Activeloop's Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n",
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"\n",
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"This notebook showcases basic functionality related to `Activeloop's Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks. \n",
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"\n",
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"For more information, please see the Deep Lake [documentation](https://docs.activeloop.ai) or [api reference](https://docs.deeplake.ai)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install openai 'deeplake[enterprise]' tiktoken"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import DeepLake"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
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"activeloop_token = getpass.getpass(\"activeloop token:\")\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create a dataset locally at `./deeplake/`, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so `dataset` and `vector store` are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, [adjust the path accordingly](https://docs.activeloop.ai/storage-and-credentials/storage-options)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"db = DeepLake(\n",
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" dataset_path=\"./my_deeplake/\", embedding_function=embeddings, overwrite=True\n",
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")\n",
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"db.add_documents(docs)\n",
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"# or shorter\n",
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"# db = DeepLake.from_documents(docs, dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True)\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = db.similarity_search(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Later, you can reload the dataset without recomputing embeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"db = DeepLake(\n",
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" dataset_path=\"./my_deeplake/\", embedding_function=embeddings, read_only=True\n",
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")\n",
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"docs = db.similarity_search(query)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Deep Lake, for now, is single writer and multiple reader. Setting `read_only=True` helps to avoid acquiring the writer lock."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Retrieval Question/Answering"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import RetrievalQA\n",
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"from langchain.llms import OpenAIChat\n",
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"\n",
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"qa = RetrievalQA.from_chain_type(\n",
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" llm=OpenAIChat(model=\"gpt-3.5-turbo\"),\n",
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" chain_type=\"stuff\",\n",
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" retriever=db.as_retriever(),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"qa.run(query)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Attribute based filtering in metadata"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's create another vector store containing metadata with the year the documents were created."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"\n",
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"for d in docs:\n",
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" d.metadata[\"year\"] = random.randint(2012, 2014)\n",
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"\n",
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"db = DeepLake.from_documents(\n",
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" docs, embeddings, dataset_path=\"./my_deeplake/\", overwrite=True\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"db.similarity_search(\n",
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" \"What did the president say about Ketanji Brown Jackson\",\n",
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" filter={\"metadata\": {\"year\": 2013}},\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Choosing distance function\n",
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"Distance function `L2` for Euclidean, `L1` for Nuclear, `Max` l-infinity distance, `cos` for cosine similarity, `dot` for dot product "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"db.similarity_search(\n",
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" \"What did the president say about Ketanji Brown Jackson?\", distance_metric=\"cos\"\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Maximal Marginal relevance\n",
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"Using maximal marginal relevance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"db.max_marginal_relevance_search(\n",
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" \"What did the president say about Ketanji Brown Jackson?\"\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Delete dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": []
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}
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],
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"source": [
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"db.delete_dataset()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"and if delete fails you can also force delete"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": []
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}
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],
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"source": [
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"DeepLake.force_delete_by_path(\"./my_deeplake\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory\n",
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"By default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the [corresponding path and credentials when creating the vector store](https://docs.activeloop.ai/storage-and-credentials/storage-options). Some paths require registration with Activeloop and creation of an API token that can be [retrieved here](https://app.activeloop.ai/)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Embed and store the texts\n",
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"username = \"<username>\" # your username on app.activeloop.ai\n",
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"dataset_path = f\"hub://{username}/langchain_testing_python\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
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"\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embedding = OpenAIEmbeddings()\n",
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"db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings, overwrite=True)\n",
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"db.add_documents(docs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = db.similarity_search(query)\n",
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"print(docs[0].page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### `tensor_db` execution option "
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In order to utilize Deep Lake's Managed Tensor Database, 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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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Embed and store the texts\n",
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"username = \"adilkhan\" # your username on app.activeloop.ai\n",
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"dataset_path = f\"hub://{username}/langchain_testing\"\n",
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"\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embedding = OpenAIEmbeddings()\n",
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"db = DeepLake(\n",
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" dataset_path=dataset_path,\n",
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" embedding_function=embeddings,\n",
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" overwrite=True,\n",
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" runtime={\"tensor_db\": True},\n",
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")\n",
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"db.add_documents(docs)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### TQL Search"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Furthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"search_id = db.vectorstore.dataset.id[0].numpy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = db.similarity_search(\n",
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" query=None,\n",
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" tql_query=f\"SELECT * WHERE id == '{search_id[0]}'\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"docs"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Creating vector stores on AWS S3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 82,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"s3://hub-2.0-datasets-n/langchain_test loaded successfully.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00\n",
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"\\"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
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"\n",
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" tensor htype shape dtype compression\n",
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" ------- ------- ------- ------- ------- \n",
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" embedding generic (4, 1536) float32 None \n",
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" ids text (4, 1) str None \n",
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" metadata json (4, 1) str None \n",
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" text text (4, 1) str None \n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" \r"
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]
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}
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],
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"source": [
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"dataset_path = f\"s3://BUCKET/langchain_test\" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n",
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"\n",
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"embedding = OpenAIEmbeddings()\n",
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"db = DeepLake.from_documents(\n",
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" docs,\n",
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" dataset_path=dataset_path,\n",
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" embedding=embeddings,\n",
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" overwrite=True,\n",
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" creds={\n",
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" \"aws_access_key_id\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n",
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" \"aws_secret_access_key\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n",
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" \"aws_session_token\": os.environ[\"AWS_SESSION_TOKEN\"], # Optional\n",
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" },\n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Deep Lake API\n",
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"you can access the Deep Lake dataset at `db.vectorstore`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n",
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"\n",
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" tensor htype shape dtype compression\n",
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" ------- ------- ------- ------- ------- \n",
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" embedding embedding (42, 1536) float32 None \n",
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" id text (42, 1) str None \n",
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" metadata json (42, 1) str None \n",
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" text text (42, 1) str None \n"
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]
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}
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],
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"source": [
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"# get structure of the dataset\n",
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"db.vectorstore.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
|
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"source": [
|
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"# get embeddings numpy array\n",
|
|
"embeds = db.vectorstore.dataset.embedding.numpy()"
|
|
]
|
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},
|
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{
|
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"attachments": {},
|
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"cell_type": "markdown",
|
|
"metadata": {},
|
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"source": [
|
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"### Transfer local dataset to cloud\n",
|
|
"Copy already created dataset to the cloud. You can also transfer from cloud to local."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 73,
|
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"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
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"text": [
|
|
"Copying dataset: 100%|██████████| 56/56 [00:38<00:00\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
|
|
"Your Deep Lake dataset has been successfully created!\n",
|
|
"The dataset is private so make sure you are logged in!\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])"
|
|
]
|
|
},
|
|
"execution_count": 73,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import deeplake\n",
|
|
"\n",
|
|
"username = \"davitbun\" # your username on app.activeloop.ai\n",
|
|
"source = f\"hub://{username}/langchain_test\" # could be local, s3, gcs, etc.\n",
|
|
"destination = f\"hub://{username}/langchain_test_copy\" # could be local, s3, gcs, etc.\n",
|
|
"\n",
|
|
"deeplake.deepcopy(src=source, dest=destination, overwrite=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 76,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" \r"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"hub://davitbun/langchain_test_copy loaded successfully.\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
|
|
"\n",
|
|
" tensor htype shape dtype compression\n",
|
|
" ------- ------- ------- ------- ------- \n",
|
|
" embedding generic (4, 1536) float32 None \n",
|
|
" ids text (4, 1) str None \n",
|
|
" metadata json (4, 1) str None \n",
|
|
" text text (4, 1) str None \n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00\n",
|
|
"-"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
|
|
"\n",
|
|
" tensor htype shape dtype compression\n",
|
|
" ------- ------- ------- ------- ------- \n",
|
|
" embedding generic (8, 1536) float32 None \n",
|
|
" ids text (8, 1) str None \n",
|
|
" metadata json (8, 1) str None \n",
|
|
" text text (8, 1) str None \n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" \r"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['ad42f3fe-e188-11ed-b66d-41c5f7b85421',\n",
|
|
" 'ad42f3ff-e188-11ed-b66d-41c5f7b85421',\n",
|
|
" 'ad42f400-e188-11ed-b66d-41c5f7b85421',\n",
|
|
" 'ad42f401-e188-11ed-b66d-41c5f7b85421']"
|
|
]
|
|
},
|
|
"execution_count": 76,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"db = DeepLake(dataset_path=destination, embedding_function=embeddings)\n",
|
|
"db.add_documents(docs)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.9.6 ('langchain_venv': venv)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.6"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "0b0bacaffd430edc3085253ee7ee1bcda9f76a5e66b369dda8ba68baa6d14ba7"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|