langchain/docs/modules/indexes/retrievers/examples/chroma_self_query_retriever.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13afcae7",
   "metadata": {},
   "source": [
    "# Self-querying retriever with Chroma\n",
    "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Chroma vector store. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e75fb9",
   "metadata": {},
   "source": [
    "## Creating a Pinecone index\n",
    "First we'll want to create a Chroma VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
    "\n",
    "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63a8af5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install lark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb4a5787",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema import Document\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores import Chroma\n",
    "\n",
    "embeddings = OpenAIEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcbe04d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using embedded DuckDB without persistence: data will be transient\n"
     ]
    }
   ],
   "source": [
    "docs = [\n",
    "    Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"}),\n",
    "    Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2}),\n",
    "    Document(page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\", metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6}),\n",
    "    Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3}),\n",
    "    Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\"}),\n",
    "    Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"director\": \"Andrei Tarkovsky\", \"genre\": \"science fiction\", \"rating\": 9.9})\n",
    "]\n",
    "vectorstore = Chroma.from_documents(\n",
    "    docs, embeddings\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ecaab6d",
   "metadata": {},
   "source": [
    "# Creating our self-querying retriever\n",
    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86e34dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import OpenAI\n",
    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
    "from langchain.chains.query_constructor.base import AttributeInfo\n",
    "\n",
    "metadata_field_info=[\n",
    "    AttributeInfo(\n",
    "        name=\"genre\",\n",
    "        description=\"The genre of the movie\", \n",
    "        type=\"string or list[string]\", \n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"year\",\n",
    "        description=\"The year the movie was released\", \n",
    "        type=\"integer\", \n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"director\",\n",
    "        description=\"The name of the movie director\", \n",
    "        type=\"string\", \n",
    "    ),\n",
    "    AttributeInfo(\n",
    "        name=\"rating\",\n",
    "        description=\"A 1-10 rating for the movie\",\n",
    "        type=\"float\"\n",
    "    ),\n",
    "]\n",
    "document_content_description = \"Brief summary of a movie\"\n",
    "llm = OpenAI(temperature=0)\n",
    "retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_field_info, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9df8d4",
   "metadata": {},
   "source": [
    "# Testing it out\n",
    "And now we can try actually using our retriever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38a126e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query='dinosaur' filter=None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),\n",
       " Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2})]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example only specifies a relevant query\n",
    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc3f1e6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6}),\n",
       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example only specifies a filter\n",
    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b19d4da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example specifies a query and a filter\n",
    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f900e40e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction'), Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5)])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example specifies a composite filter\n",
    "retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12a51522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This example specifies a query and composite filter\n",
    "retriever.get_relevant_documents(\"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\")"
   ]
  }
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