mirror of
https://github.com/hwchase17/langchain
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9cbefcc56c
### Description This PR includes the following changes: - Adds AOSS (Amazon OpenSearch Service Serverless) support to OpenSearch. Please refer to the documentation on how to use it. - While creating an index, AOSS only supports Approximate Search with `nmslib` and `faiss` engines. During Search, only Approximate Search and Script Scoring (on doc values) are supported. - This PR also adds support to `efficient_filter` which can be used with `faiss` and `lucene` engines. - The `lucene_filter` is deprecated. Instead please use the `efficient_filter` for the lucene engine. Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
436 lines
12 KiB
Plaintext
436 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "683953b3",
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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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"\n",
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"This notebook shows how to use functionality related to the `OpenSearch` database.\n",
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"\n",
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"To run, you should have an OpenSearch instance up and running: [see here for an easy Docker installation](https://hub.docker.com/r/opensearchproject/opensearch).\n",
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"\n",
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"`similarity_search` by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for\n",
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"large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting.\n",
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"Check [this](https://opensearch.org/docs/latest/search-plugins/knn/index/) for more details."
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]
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},
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{
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"cell_type": "markdown",
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"id": "94963977-9dfc-48b7-872a-53f2947f46c6",
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"metadata": {},
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"source": [
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"## Installation\n",
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"Install the Python client."
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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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"id": "6e606066-9386-4427-8a87-1b93f435c57e",
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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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"!pip install opensearch-py"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b1fa637e-4fbf-4d5a-9188-2cad826a193e",
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"metadata": {},
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"source": [
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"We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "28e5455e-322d-4010-9e3b-491d522ef5db",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import getpass\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": null,
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"id": "aac9563e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import OpenSearchVectorSearch\n",
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"from langchain.document_loaders import TextLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3c3999a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "01a9a035",
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"metadata": {},
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"source": [
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"## similarity_search using Approximate k-NN\n",
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"\n",
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"`similarity_search` using `Approximate k-NN` Search with Custom Parameters"
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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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"id": "803fe12b",
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"metadata": {},
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"outputs": [],
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"source": [
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"docsearch = OpenSearchVectorSearch.from_documents(\n",
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" docs, embeddings, opensearch_url=\"http://localhost:9200\"\n",
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")\n",
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"\n",
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"# If using the default Docker installation, use this instantiation instead:\n",
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"# docsearch = OpenSearchVectorSearch.from_documents(\n",
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"# docs,\n",
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"# embeddings,\n",
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"# opensearch_url=\"https://localhost:9200\",\n",
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"# http_auth=(\"admin\", \"admin\"),\n",
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"# use_ssl = False,\n",
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"# verify_certs = False,\n",
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"# ssl_assert_hostname = False,\n",
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"# ssl_show_warn = False,\n",
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"# )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "db3fa309",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(query, k=10)"
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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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"id": "c160d5bb",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "96215c90",
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"metadata": {},
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"outputs": [],
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"source": [
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"docsearch = OpenSearchVectorSearch.from_documents(\n",
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" docs,\n",
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" embeddings,\n",
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" opensearch_url=\"http://localhost:9200\",\n",
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" engine=\"faiss\",\n",
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" space_type=\"innerproduct\",\n",
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" ef_construction=256,\n",
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" m=48,\n",
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")\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62a7cea0",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d0cd877",
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"metadata": {},
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"source": [
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"## similarity_search using Script Scoring\n",
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"\n",
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"`similarity_search` using `Script Scoring` with Custom Parameters"
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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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"id": "0a8e3c0e",
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"metadata": {},
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"outputs": [],
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"source": [
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"docsearch = OpenSearchVectorSearch.from_documents(\n",
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" docs, embeddings, opensearch_url=\"http://localhost:9200\", is_appx_search=False\n",
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")\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(\n",
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" \"What did the president say about Ketanji Brown Jackson\",\n",
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" k=1,\n",
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" search_type=\"script_scoring\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "92bc40db",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a4af96cc",
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"metadata": {},
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"source": [
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"## similarity_search using Painless Scripting\n",
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"\n",
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"`similarity_search` using `Painless Scripting` with Custom Parameters"
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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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"id": "6d9f436e",
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"metadata": {},
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"outputs": [],
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"source": [
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"docsearch = OpenSearchVectorSearch.from_documents(\n",
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" docs, embeddings, opensearch_url=\"http://localhost:9200\", is_appx_search=False\n",
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")\n",
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"filter = {\"bool\": {\"filter\": {\"term\": {\"text\": \"smuggling\"}}}}\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(\n",
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" \"What did the president say about Ketanji Brown Jackson\",\n",
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" search_type=\"painless_scripting\",\n",
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" space_type=\"cosineSimilarity\",\n",
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" pre_filter=filter,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8ca50bce",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4f8fb0d0",
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"metadata": {},
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"source": [
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"## Maximum marginal relevance search (MMR)\n",
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"If you’d like to look up for some similar documents, but you’d also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents."
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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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"id": "ba85e092",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10, lambda_param=0.5)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "73264864",
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"metadata": {},
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"source": [
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"## Using a preexisting OpenSearch instance\n",
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"\n",
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"It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present."
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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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"id": "82a23440",
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"metadata": {},
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"outputs": [],
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"source": [
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"# this is just an example, you would need to change these values to point to another opensearch instance\n",
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"docsearch = OpenSearchVectorSearch(\n",
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" index_name=\"index-*\",\n",
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" embedding_function=embeddings,\n",
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" opensearch_url=\"http://localhost:9200\",\n",
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")\n",
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"\n",
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"# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadata\n",
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"docs = docsearch.similarity_search(\n",
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" \"Who was asking about getting lunch today?\",\n",
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" search_type=\"script_scoring\",\n",
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" space_type=\"cosinesimil\",\n",
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" vector_field=\"message_embedding\",\n",
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" text_field=\"message\",\n",
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" metadata_field=\"message_metadata\",\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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"source": [
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"## Using AOSS (Amazon OpenSearch Service Serverless)"
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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": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# This is just an example to show how to use AOSS with faiss engine and efficient_filter, you need to set proper values.\n",
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"\n",
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"service = 'aoss' # must set the service as 'aoss'\n",
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"region = 'us-east-2'\n",
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"credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
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"awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
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"\n",
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"docsearch = OpenSearchVectorSearch.from_documents(\n",
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" docs,\n",
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" embeddings,\n",
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" opensearch_url=\"host url\",\n",
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" http_auth=awsauth,\n",
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" timeout = 300,\n",
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" use_ssl = True,\n",
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" verify_certs = True,\n",
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" connection_class = RequestsHttpConnection,\n",
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" index_name=\"test-index-using-aoss\",\n",
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" engine=\"faiss\",\n",
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")\n",
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"\n",
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"docs = docsearch.similarity_search(\n",
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" \"What is feature selection\",\n",
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" efficient_filter=filter,\n",
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" k=200,\n",
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")"
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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": "markdown",
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"source": [
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"## Using AOS (Amazon OpenSearch Service)"
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],
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"metadata": {
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"collapsed": false
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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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"# This is just an example to show how to use AOS , you need to set proper values.\n",
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"\n",
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"service = 'es' # must set the service as 'es'\n",
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"region = 'us-east-2'\n",
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"credentials = boto3.Session(aws_access_key_id='xxxxxx',aws_secret_access_key='xxxxx').get_credentials()\n",
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"awsauth = AWS4Auth('xxxxx', 'xxxxxx', region,service, session_token=credentials.token)\n",
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"\n",
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"docsearch = OpenSearchVectorSearch.from_documents(\n",
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" docs,\n",
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" embeddings,\n",
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" opensearch_url=\"host url\",\n",
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" http_auth=awsauth,\n",
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" timeout = 300,\n",
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" use_ssl = True,\n",
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" verify_certs = True,\n",
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" connection_class = RequestsHttpConnection,\n",
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" index_name=\"test-index\",\n",
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")\n",
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"\n",
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"docs = docsearch.similarity_search(\n",
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" \"What is feature selection\",\n",
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" k=200,\n",
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")"
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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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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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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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} |