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add neo4j query constructor for self query (#25288)
- [x] **PR title - community: add neo4j query constructor for self query** - [x] **PR message** - **Description:** adding a Neo4jTranslator so that the Neo4j vector database can use SelfQueryRetriever - **Issue:** this issue had been raised before in #19748 - **Dependencies:** none. - **Twitter handle:** @moyi_dang - p.s. I have not added the query constructor in BUILTIN_TRANSLATORS in this PR, I want to make changes to only one package at a time. - [x] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Chester Curme <chester.curme@gmail.com>
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{
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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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"# Neo4j\n",
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"\n",
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">[Neo4j](https://neo4j.com/docs/) is a graph database that stores nodes and relationships, that also supports native vector search.\n",
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"\n",
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"In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `Neo4j` 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 Neo4j vector store\n",
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"First we'll want to create a Neo4j vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies."
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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": 1,
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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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"Requirement already satisfied: neo4j in /Users/moyi/git/langchain/env/lib/python3.11/site-packages (5.24.0)\n",
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"Requirement already satisfied: pytz in /Users/moyi/git/langchain/env/lib/python3.11/site-packages (from neo4j) (2024.1)\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install --upgrade neo4j"
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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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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"OpenAI API Key: ········\n"
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]
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}
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],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"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": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdin",
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"output_type": "stream",
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"text": [
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"Neo4j URL: ········\n",
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"Neo4j User Name: ········\n",
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"Neo4j Password: ········\n"
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]
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}
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],
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"source": [
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"# To run this notebook, you can set up a free neo4j account on neo4j.com and input the following information.\n",
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"# (If you are having trouble connecting to the database, try using neo4j+ssc: instead of neo4j+s)\n",
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"\n",
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"os.environ[\"NEO4J_URI\"] = getpass.getpass(\"Neo4j URL:\")\n",
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"os.environ[\"NEO4J_USERNAME\"] = getpass.getpass(\"Neo4j User Name:\")\n",
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"os.environ[\"NEO4J_PASSWORD\"] = getpass.getpass(\"Neo4j Password:\")"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.vectorstores import Neo4jVector\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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"\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": 5,
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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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"Received notification from DBMS server: {severity: WARNING} {code: Neo.ClientNotification.Statement.FeatureDeprecationWarning} {category: DEPRECATION} {title: This feature is deprecated and will be removed in future versions.} {description: CALL subquery without a variable scope clause is now deprecated. Use CALL (row) { ... }} {position: line: 1, column: 21, offset: 20} for query: \"UNWIND $data AS row CALL { WITH row MERGE (c:`Chunk` {id: row.id}) WITH c, row CALL db.create.setNodeVectorProperty(c, 'embedding', row.embedding) SET c.`text` = row.text SET c += row.metadata } IN TRANSACTIONS OF 1000 ROWS \"\n"
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]
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}
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],
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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\": \"science fiction\"},\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, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\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, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\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, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\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\"},\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={\n",
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" \"year\": 1979,\n",
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" \"director\": \"Andrei Tarkovsky\",\n",
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" \"genre\": \"science fiction\",\n",
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" \"rating\": 9.9,\n",
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" },\n",
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" ),\n",
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"]\n",
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"vectorstore = Neo4jVector.from_documents(docs, embeddings)"
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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": 6,
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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 or list[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=\"director\",\n",
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" description=\"The name of the movie director\",\n",
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" type=\"string\",\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\"\n",
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(metadata={'genre': 'science fiction', 'year': 1993, 'rating': 7.7}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
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" Document(metadata={'genre': 'animated', 'year': 1995}, page_content='Toys come alive and have a blast doing so'),\n",
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" Document(metadata={'genre': 'science fiction', 'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky'}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
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" Document(metadata={'year': 2006, 'rating': 8.6, 'director': 'Satoshi Kon'}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a relevant query\n",
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"retriever.invoke(\"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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(metadata={'genre': 'science fiction', 'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky'}, page_content='Three men walk into the Zone, three men walk out of the Zone'),\n",
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" Document(metadata={'year': 2006, 'rating': 8.6, 'director': 'Satoshi Kon'}, page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea')]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a filter\n",
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"retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(metadata={'year': 2019, 'rating': 8.3, 'director': 'Greta Gerwig'}, page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them')]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example specifies a query and a filter\n",
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"retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(metadata={'year': 2006, 'rating': 8.6, 'director': 'Satoshi Kon'}, 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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" Document(metadata={'genre': 'science fiction', 'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky'}, page_content='Three men walk into the Zone, three men walk out of the Zone')]"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example specifies a composite filter\n",
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"retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
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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": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(metadata={'genre': 'animated', 'year': 1995}, page_content='Toys come alive and have a blast doing so')]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example specifies a query and composite filter\n",
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"retriever.invoke(\n",
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" \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\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": 12,
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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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" enable_limit=True,\n",
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" 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": "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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"data": {
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"text/plain": [
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"[Document(metadata={'genre': 'science fiction', 'year': 1993, 'rating': 7.7}, page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose'),\n",
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" Document(metadata={'genre': 'animated', 'year': 1995}, page_content='Toys come alive and have a blast doing so')]"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This example only specifies a relevant query\n",
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"retriever.invoke(\"what are two 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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},
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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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}
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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": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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from typing import Dict, Tuple, Union
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from langchain_core.structured_query import (
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Comparator,
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Comparison,
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Operation,
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Operator,
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StructuredQuery,
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Visitor,
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)
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class Neo4jTranslator(Visitor):
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"""Translate `Neo4j` internal query language elements to valid filters."""
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allowed_operators = [Operator.AND, Operator.OR]
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"""Subset of allowed logical operators."""
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allowed_comparators = [
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Comparator.EQ,
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Comparator.NE,
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Comparator.GTE,
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Comparator.LTE,
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Comparator.LT,
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Comparator.GT,
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]
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def _format_func(self, func: Union[Operator, Comparator]) -> str:
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self._validate_func(func)
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map_dict = {
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Operator.AND: "$and",
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Operator.OR: "$or",
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Comparator.EQ: "$eq",
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Comparator.NE: "$ne",
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Comparator.GTE: "$gte",
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Comparator.LTE: "$lte",
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Comparator.LT: "$lt",
|
||||
Comparator.GT: "$gt",
|
||||
}
|
||||
return map_dict[func]
|
||||
|
||||
def visit_operation(self, operation: Operation) -> Dict:
|
||||
args = [arg.accept(self) for arg in operation.arguments]
|
||||
return {self._format_func(operation.operator): args}
|
||||
|
||||
def visit_comparison(self, comparison: Comparison) -> Dict:
|
||||
return {
|
||||
comparison.attribute: {
|
||||
self._format_func(comparison.comparator): comparison.value
|
||||
}
|
||||
}
|
||||
|
||||
def visit_structured_query(
|
||||
self, structured_query: StructuredQuery
|
||||
) -> Tuple[str, dict]:
|
||||
if structured_query.filter is None:
|
||||
kwargs = {}
|
||||
else:
|
||||
kwargs = {"filter": structured_query.filter.accept(self)}
|
||||
return structured_query.query, kwargs
|
@ -0,0 +1,90 @@
|
||||
from typing import Dict, Tuple
|
||||
|
||||
from langchain_core.structured_query import (
|
||||
Comparator,
|
||||
Comparison,
|
||||
Operation,
|
||||
Operator,
|
||||
StructuredQuery,
|
||||
)
|
||||
|
||||
from langchain_community.query_constructors.neo4j import Neo4jTranslator
|
||||
|
||||
DEFAULT_TRANSLATOR = Neo4jTranslator()
|
||||
|
||||
|
||||
def test_visit_comparison() -> None:
|
||||
comp = Comparison(comparator=Comparator.LT, attribute="foo", value=["1", "2"])
|
||||
expected = {"foo": {"$lt": ["1", "2"]}}
|
||||
actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
|
||||
assert expected == actual
|
||||
|
||||
|
||||
def test_visit_operation() -> None:
|
||||
op = Operation(
|
||||
operator=Operator.AND,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
|
||||
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
|
||||
Comparison(comparator=Comparator.LT, attribute="abc", value=["1", "2"]),
|
||||
],
|
||||
)
|
||||
expected = {
|
||||
"$and": [
|
||||
{"foo": {"$lt": 2}},
|
||||
{"bar": {"$eq": "baz"}},
|
||||
{"abc": {"$lt": ["1", "2"]}},
|
||||
]
|
||||
}
|
||||
actual = DEFAULT_TRANSLATOR.visit_operation(op)
|
||||
assert expected == actual
|
||||
|
||||
|
||||
def test_visit_structured_query() -> None:
|
||||
query = "What is the capital of France?"
|
||||
structured_query = StructuredQuery(
|
||||
query=query,
|
||||
filter=None,
|
||||
)
|
||||
expected: Tuple[str, Dict] = (query, {})
|
||||
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
|
||||
assert expected == actual
|
||||
|
||||
comp = Comparison(comparator=Comparator.LT, attribute="foo", value=["1", "2"])
|
||||
expected = (
|
||||
query,
|
||||
{"filter": {"foo": {"$lt": ["1", "2"]}}},
|
||||
)
|
||||
structured_query = StructuredQuery(
|
||||
query=query,
|
||||
filter=comp,
|
||||
)
|
||||
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
|
||||
assert expected == actual
|
||||
|
||||
op = Operation(
|
||||
operator=Operator.AND,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.LT, attribute="foo", value=2),
|
||||
Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"),
|
||||
Comparison(comparator=Comparator.LT, attribute="abc", value=["1", "2"]),
|
||||
],
|
||||
)
|
||||
structured_query = StructuredQuery(
|
||||
query=query,
|
||||
filter=op,
|
||||
)
|
||||
expected = (
|
||||
query,
|
||||
{
|
||||
"filter": {
|
||||
"$and": [
|
||||
{"foo": {"$lt": 2}},
|
||||
{"bar": {"$eq": "baz"}},
|
||||
{"abc": {"$lt": ["1", "2"]}},
|
||||
]
|
||||
}
|
||||
},
|
||||
)
|
||||
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
|
||||
assert expected == actual
|
@ -48,6 +48,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
|
||||
MongoDBAtlasTranslator,
|
||||
)
|
||||
from langchain_community.query_constructors.myscale import MyScaleTranslator
|
||||
from langchain_community.query_constructors.neo4j import Neo4jTranslator
|
||||
from langchain_community.query_constructors.opensearch import OpenSearchTranslator
|
||||
from langchain_community.query_constructors.pgvector import PGVectorTranslator
|
||||
from langchain_community.query_constructors.pinecone import PineconeTranslator
|
||||
@ -70,6 +71,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
|
||||
Dingo,
|
||||
Milvus,
|
||||
MyScale,
|
||||
Neo4jVector,
|
||||
OpenSearchVectorSearch,
|
||||
PGVector,
|
||||
Qdrant,
|
||||
@ -111,6 +113,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
|
||||
TimescaleVector: TimescaleVectorTranslator,
|
||||
OpenSearchVectorSearch: OpenSearchTranslator,
|
||||
CommunityMongoDBAtlasVectorSearch: MongoDBAtlasTranslator,
|
||||
Neo4jVector: Neo4jTranslator,
|
||||
}
|
||||
if isinstance(vectorstore, DatabricksVectorSearch):
|
||||
return DatabricksVectorSearchTranslator()
|
||||
|
Loading…
Reference in New Issue
Block a user