mirror of
https://github.com/hwchase17/langchain
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b4914888a7
### Features include - Metadata based embedding search - Choice of distance metric function (`L2` for Euclidean, `L1` for Nuclear, `max` L-infinity distance, `cos` for cosine similarity, 'dot' for dot product. Defaults to `L2` - Returning scores - Max Marginal Relevance Search - Deleting samples from the dataset ### Notes - Added numerous tests, let me know if you would like to shorten them or make smarter --------- Co-authored-by: Davit Buniatyan <d@activeloop.ai>
267 lines
6.5 KiB
Plaintext
267 lines
6.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Deep Lake\n",
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"\n",
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"This notebook showcases basic functionality related to Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a fully fledged serverless data lake with version control, query engine and streaming dataloader to deep learning frameworks. \n",
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"\n",
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"For more information, please see the Deep Lake [documentation](docs.activeloop.ai) or [api reference](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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"!python3 -m pip install openai 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": null,
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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\n",
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"from langchain.document_loaders import TextLoader"
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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 os\n",
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"os.environ['OPENAI_API_KEY'] = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'"
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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.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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"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.from_documents(docs, embeddings)\n",
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"\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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"### 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(llm=OpenAIChat(model='gpt-3.5-turbo'), chain_type='stuff', retriever=db.as_retriever())"
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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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"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(docs, 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.similarity_search('What did the president say about Ketanji Brown Jackson', filter={'year': 2013})"
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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 distnace, `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('What did the president say about Ketanji Brown Jackson?', distance_metric='cos')"
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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('What did the president say about Ketanji Brown Jackson?')"
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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 local\n",
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"By default deep lake datasets are stored in memory, in case you want to persist locally or to any object storage you can simply provide path to the dataset. You can retrieve token from [app.activeloop.ai](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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!activeloop login -t <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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"dataset_path = \"hub://{username}/{dataset_name}\" # 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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"embedding = OpenAIEmbeddings()\n",
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"vectordb = DeepLake.from_documents(documents=docs, embedding=embedding, dataset_path=dataset_path)"
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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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"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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"vectordb.ds.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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = vectordb.ds.embedding.numpy()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.0"
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},
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"vscode": {
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"interpreter": {
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"hash": "7b14174bb6f9d4680b62ac2a6390e1ce94fbfabf172a10844870451d539c58d6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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