mirror of https://github.com/hwchase17/langchain
[OpenSearch] Add Self Query Retriever Support to OpenSearch (#11184)
### Description Add Self Query Retriever Support to OpenSearch ### Maintainers @rlancemartin, @eyurtsev, @navneet1v ### Twitter Handle @OpenSearchProj Signed-off-by: Naveen Tatikonda <navtat@amazon.com>pull/11198/head
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "13afcae7",
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"metadata": {},
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"source": [
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"# OpenSearch\n",
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"\n",
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"> [OpenSearch](https://opensearch.org/) is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. `OpenSearch` is a distributed search and analytics engine based on `Apache Lucene`.\n",
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"\n",
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"In this notebook, we'll demo the `SelfQueryRetriever` with an `OpenSearch` 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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"id": "68e75fb9",
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"metadata": {},
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"source": [
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"## Creating an OpenSearch vector store\n",
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"\n",
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"First, we'll want to create an `OpenSearch` vector store 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 `opensearch-py` 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": null,
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"outputs": [],
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"source": [
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"!pip install lark opensearch-py"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\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": 3,
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"id": "cb4a5787",
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"metadata": {
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"tags": []
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},
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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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"from langchain.schema import Document\n",
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.vectorstores import OpenSearchVectorSearch\n",
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
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"\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": 8,
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"id": "bcbe04d9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"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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" \"rating\": 9.9,\n",
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" \"director\": \"Andrei Tarkovsky\",\n",
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" \"genre\": \"science fiction\",\n",
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" },\n",
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" ),\n",
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"]\n",
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"vectorstore = OpenSearchVectorSearch.from_documents(\n",
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" docs, embeddings, index_name=\"opensearch-self-query-demo\", opensearch_url=\"http://localhost:9200\"\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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"id": "5ecaab6d",
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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": 9,
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"id": "86e34dbf",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
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"from langchain.chains.query_constructor.base import AttributeInfo\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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"id": "ea9df8d4",
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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": 10,
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"id": "38a126e9",
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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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"query='dinosaur' filter=None limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
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" 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",
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" 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'})]"
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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 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": 11,
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"id": "60bf0074-e65e-4558-a4f2-8190f3e4e2f9",
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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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"query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[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'}),\n",
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" 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})]"
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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 only specifies a filter\n",
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"retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")\n"
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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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"id": "b19d4da0",
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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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"query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[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})]"
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]
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},
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"execution_count": 12,
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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.get_relevant_documents(\"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": 13,
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"id": "a59f946b-78a1-4d3e-9942-63834c7d7589",
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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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"query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.CONTAIN: 'contain'>, attribute='genre', value='science fiction')]) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[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'})]"
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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 specifies a composite filter\n",
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"retriever.get_relevant_documents(\"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": "markdown",
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"id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
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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": 14,
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"id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"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": 15,
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"id": "2758d229-4f97-499c-819f-888acaf8ee10",
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"metadata": {
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"tags": []
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},
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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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"query='dinosaur' filter=None limit=2\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
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]
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},
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"execution_count": 15,
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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.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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"cell_type": "markdown",
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"id": "61a10294",
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"metadata": {},
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"source": [
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"## Complex queries in Action!\n",
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"We've tried out some simple queries, but what about more complex ones? Let's try out a few more complex queries that utilize the full power of OpenSearch."
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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": 16,
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"id": "e460da93",
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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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"query='animated toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Operation(operator=<Operator.OR: 'or'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated'), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='comedy')]), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='year', value=1990)]) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
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]
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},
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"execution_count": 16,
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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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"retriever.get_relevant_documents(\"what animated or comedy movies have been released in the last 30 years about animated toys?\")"
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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": 17,
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"id": "0851fc42",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'acknowledged': True}"
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]
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},
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"execution_count": 17,
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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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"vectorstore.client.indices.delete(index=\"opensearch-self-query-demo\")\n"
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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": "3.9.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,84 @@
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from typing import Dict, Tuple, Union
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from langchain.chains.query_constructor.ir 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 OpenSearchTranslator(Visitor):
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"""Translate `OpenSearch` internal query domain-specific
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language elements to valid filters."""
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allowed_comparators = [
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Comparator.EQ,
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Comparator.LT,
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Comparator.LTE,
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Comparator.GT,
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Comparator.GTE,
|
||||
Comparator.CONTAIN,
|
||||
Comparator.LIKE,
|
||||
]
|
||||
"""Subset of allowed logical comparators."""
|
||||
|
||||
allowed_operators = [Operator.AND, Operator.OR, Operator.NOT]
|
||||
"""Subset of allowed logical operators."""
|
||||
|
||||
def _format_func(self, func: Union[Operator, Comparator]) -> str:
|
||||
self._validate_func(func)
|
||||
comp_operator_map = {
|
||||
Comparator.EQ: "term",
|
||||
Comparator.LT: "lt",
|
||||
Comparator.LTE: "lte",
|
||||
Comparator.GT: "gt",
|
||||
Comparator.GTE: "gte",
|
||||
Comparator.CONTAIN: "match",
|
||||
Comparator.LIKE: "fuzzy",
|
||||
Operator.AND: "must",
|
||||
Operator.OR: "should",
|
||||
Operator.NOT: "must_not",
|
||||
}
|
||||
return comp_operator_map[func]
|
||||
|
||||
def visit_operation(self, operation: Operation) -> Dict:
|
||||
args = [arg.accept(self) for arg in operation.arguments]
|
||||
|
||||
return {"bool": {self._format_func(operation.operator): args}}
|
||||
|
||||
def visit_comparison(self, comparison: Comparison) -> Dict:
|
||||
field = f"metadata.{comparison.attribute}"
|
||||
|
||||
if comparison.comparator in [
|
||||
Comparator.LT,
|
||||
Comparator.LTE,
|
||||
Comparator.GT,
|
||||
Comparator.GTE,
|
||||
]:
|
||||
return {
|
||||
"range": {
|
||||
field: {self._format_func(comparison.comparator): comparison.value}
|
||||
}
|
||||
}
|
||||
|
||||
if comparison.comparator == Comparator.LIKE:
|
||||
return {
|
||||
self._format_func(comparison.comparator): {
|
||||
field: {"value": comparison.value}
|
||||
}
|
||||
}
|
||||
field = f"{field}.keyword" if isinstance(comparison.value, str) else field
|
||||
|
||||
return {self._format_func(comparison.comparator): {field: 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 +1,87 @@
|
||||
from langchain.chains.query_constructor.ir import (
|
||||
Comparator,
|
||||
Comparison,
|
||||
Operation,
|
||||
Operator,
|
||||
StructuredQuery,
|
||||
)
|
||||
from langchain.retrievers.self_query.opensearch import OpenSearchTranslator
|
||||
|
||||
DEFAULT_TRANSLATOR = OpenSearchTranslator()
|
||||
|
||||
|
||||
def test_visit_comparison() -> None:
|
||||
comp = Comparison(comparator=Comparator.EQ, attribute="foo", value="10")
|
||||
expected = {"term": {"metadata.foo.keyword": "10"}}
|
||||
actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
|
||||
assert expected == actual
|
||||
|
||||
|
||||
def test_visit_operation() -> None:
|
||||
op = Operation(
|
||||
operator=Operator.AND,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.GTE, attribute="bar", value=5),
|
||||
Comparison(comparator=Comparator.LT, attribute="bar", value=10),
|
||||
Comparison(comparator=Comparator.EQ, attribute="baz", value="abcd"),
|
||||
],
|
||||
)
|
||||
expected = {
|
||||
"bool": {
|
||||
"must": [
|
||||
{"range": {"metadata.bar": {"gte": 5}}},
|
||||
{"range": {"metadata.bar": {"lt": 10}}},
|
||||
{"term": {"metadata.baz.keyword": "abcd"}},
|
||||
]
|
||||
}
|
||||
}
|
||||
actual = DEFAULT_TRANSLATOR.visit_operation(op)
|
||||
assert expected == actual
|
||||
|
||||
|
||||
def test_visit_structured_query() -> None:
|
||||
query = "What is the capital of France?"
|
||||
operation = Operation(
|
||||
operator=Operator.AND,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.EQ, attribute="foo", value="20"),
|
||||
Operation(
|
||||
operator=Operator.OR,
|
||||
arguments=[
|
||||
Comparison(comparator=Comparator.LTE, attribute="bar", value=7),
|
||||
Comparison(
|
||||
comparator=Comparator.LIKE, attribute="baz", value="abc"
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
)
|
||||
structured_query = StructuredQuery(query=query, filter=operation, limit=None)
|
||||
expected = (
|
||||
query,
|
||||
{
|
||||
"filter": {
|
||||
"bool": {
|
||||
"must": [
|
||||
{"term": {"metadata.foo.keyword": "20"}},
|
||||
{
|
||||
"bool": {
|
||||
"should": [
|
||||
{"range": {"metadata.bar": {"lte": 7}}},
|
||||
{
|
||||
"fuzzy": {
|
||||
"metadata.baz": {
|
||||
"value": "abc",
|
||||
}
|
||||
}
|
||||
},
|
||||
]
|
||||
}
|
||||
},
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
)
|
||||
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
|
||||
assert expected == actual
|
Loading…
Reference in New Issue