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322 lines
9.4 KiB
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322 lines
9.4 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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"# MongoDB Atlas\n",
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"\n",
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"[MongoDB Atlas](https://www.mongodb.com/) is a document database that can be \n",
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"used as a vector databse.\n",
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"\n",
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"In the walkthrough, we'll demo the `SelfQueryRetriever` with a `MongoDB Atlas` vector store."
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]
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},
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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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"## Creating a MongoDB Atlas vectorstore\n",
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"First we'll want to create a MongoDB Atlas VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
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"\n",
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"NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `pymongo` package."
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet lark pymongo"
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]
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},
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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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"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
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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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"\n",
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"OPENAI_API_KEY = \"Use your OpenAI key\"\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
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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_community.vectorstores import MongoDBAtlasVectorSearch\n",
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"from langchain_core.documents import Document\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"from pymongo import MongoClient\n",
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"\n",
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"CONNECTION_STRING = \"Use your MongoDB Atlas connection string\"\n",
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"DB_NAME = \"Name of your MongoDB Atlas database\"\n",
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"COLLECTION_NAME = \"Name of your collection in the database\"\n",
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"INDEX_NAME = \"Name of a search index defined on the collection\"\n",
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"\n",
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"MongoClient = MongoClient(CONNECTION_STRING)\n",
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"collection = MongoClient[DB_NAME][COLLECTION_NAME]\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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"docs = [\n",
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" Document(\n",
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" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
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" metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
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" metadata={\"year\": 2010, \"genre\": \"thriller\", \"rating\": 8.2},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
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" metadata={\"year\": 2019, \"rating\": 8.3, \"genre\": \"drama\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
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" metadata={\"year\": 1979, \"rating\": 9.9, \"genre\": \"science fiction\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
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" metadata={\"year\": 2006, \"genre\": \"thriller\", \"rating\": 9.0},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Toys come alive and have a blast doing so\",\n",
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" metadata={\"year\": 1995, \"genre\": \"animated\", \"rating\": 9.3},\n",
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" ),\n",
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"]\n",
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"\n",
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"vectorstore = MongoDBAtlasVectorSearch.from_documents(\n",
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" docs,\n",
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" embeddings,\n",
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" collection=collection,\n",
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" index_name=INDEX_NAME,\n",
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")"
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]
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},
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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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"Now, let's create a vector search index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/field-types/knn-vector) to get more details on how to define an Atlas Vector Search index.\n",
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"You can name the index `{COLLECTION_NAME}` and create the index on the namespace `{DB_NAME}.{COLLECTION_NAME}`. Finally, write the following definition in the JSON editor on MongoDB Atlas:\n",
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"\n",
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"```json\n",
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"{\n",
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" \"mappings\": {\n",
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" \"dynamic\": true,\n",
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" \"fields\": {\n",
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" \"embedding\": {\n",
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" \"dimensions\": 1536,\n",
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" \"similarity\": \"cosine\",\n",
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" \"type\": \"knnVector\"\n",
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" },\n",
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" \"genre\": {\n",
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" \"type\": \"token\"\n",
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" },\n",
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" \"ratings\": {\n",
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" \"type\": \"number\"\n",
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" },\n",
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" \"year\": {\n",
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" \"type\": \"number\"\n",
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" }\n",
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" }\n",
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" }\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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Creating our self-querying retriever\n",
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"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."
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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.query_constructor.base import AttributeInfo\n",
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"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
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"from langchain_openai import OpenAI\n",
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"\n",
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"metadata_field_info = [\n",
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" AttributeInfo(\n",
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" name=\"genre\",\n",
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" description=\"The genre of the movie\",\n",
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" type=\"string\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"year\",\n",
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" description=\"The year the movie was released\",\n",
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" type=\"integer\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
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" ),\n",
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"]\n",
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"document_content_description = \"Brief summary of a movie\""
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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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"llm = OpenAI(temperature=0)\n",
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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
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")"
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]
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},
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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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"## Testing it out\n",
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"And now we can try actually using our 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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"# This example only specifies a relevant query\n",
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"retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
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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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"# This example specifies a filter\n",
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"retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")"
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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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"# This example only specifies a query and a filter\n",
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"retriever.get_relevant_documents(\n",
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" \"I want to watch a movie about toys rated higher than 9\"\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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"# This example specifies a composite filter\n",
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"retriever.get_relevant_documents(\n",
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" \"What's a highly rated (above or equal 9) thriller film?\"\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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"# This example specifies a query and composite filter\n",
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"retriever.get_relevant_documents(\n",
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" \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n",
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" and preferably has a lot of action\"\n",
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")"
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]
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},
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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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"## Filter k\n",
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"\n",
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"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
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"\n",
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"We can do this by passing `enable_limit=True` to the constructor."
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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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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm,\n",
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" vectorstore,\n",
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" document_content_description,\n",
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" metadata_field_info,\n",
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" verbose=True,\n",
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" enable_limit=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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"# This example only specifies a relevant query\n",
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"retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")"
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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": ".venv",
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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": "3.11.5"
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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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