mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
e1f4f9ac3e
Added missed pages for `integrations/providers` from `vectorstores`. Updated several `vectorstores` notebooks.
304 lines
9.3 KiB
Plaintext
304 lines
9.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Meilisearch\n",
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"\n",
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"> [Meilisearch](https://meilisearch.com) is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. \n",
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">\n",
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"> You can [self-host Meilisearch](https://www.meilisearch.com/docs/learn/getting_started/installation#local-installation) or run on [Meilisearch Cloud](https://www.meilisearch.com/pricing).\n",
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"\n",
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"Meilisearch v1.3 supports vector search. This page guides you through integrating Meilisearch as a vector store and using it to perform vector search."
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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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"## Setup\n",
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"\n",
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"### Launching a Meilisearch instance\n",
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"\n",
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"You will need a running Meilisearch instance to use as your vector store. You can run [Meilisearch in local](https://www.meilisearch.com/docs/learn/getting_started/installation#local-installation) or create a [Meilisearch Cloud](https://cloud.meilisearch.com/) account.\n",
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"\n",
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"As of Meilisearch v1.3, vector storage is an experimental feature. After launching your Meilisearch instance, you need to **enable vector storage**. For self-hosted Meilisearch, read the docs on [enabling experimental features](https://www.meilisearch.com/docs/learn/experimental/vector-search). On **Meilisearch Cloud**, enable _Vector Store_ via your project _Settings_ page.\n",
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"\n",
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"You should now have a running Meilisearch instance with vector storage enabled. 🎉\n",
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"\n",
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"### Credentials\n",
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"\n",
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"To interact with your Meilisearch instance, the Meilisearch SDK needs a host (URL of your instance) and an API key.\n",
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"\n",
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"**Host**\n",
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"\n",
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"- In **local**, the default host is `localhost:7700`\n",
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"- On **Meilisearch Cloud**, find the host in your project _Settings_ page\n",
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"\n",
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"**API keys**\n",
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"\n",
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"Meilisearch instance provides you with three API keys out of the box: \n",
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"- A `MASTER KEY` — it should only be used to create your Meilisearch instance\n",
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"- A `ADMIN KEY` — use it only server-side to update your database and its settings\n",
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"- A `SEARCH KEY` — a key that you can safely share in front-end applications\n",
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"\n",
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"You can create [additional API keys](https://www.meilisearch.com/docs/learn/security/master_api_keys) as needed."
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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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"### Installing dependencies\n",
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"\n",
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"This guide uses the [Meilisearch Python SDK](https://github.com/meilisearch/meilisearch-python). You can install it by running:"
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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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"!pip install meilisearch"
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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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"For more information, refer to the [Meilisearch Python SDK documentation](https://meilisearch.github.io/meilisearch-python/)."
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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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"## Examples\n",
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"\n",
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"There are multiple ways to initialize the Meilisearch vector store: providing a Meilisearch client or the _URL_ and _API key_ as needed. In our examples, the credentials will be loaded from the environment.\n",
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"\n",
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"You can make environment variables available in your Notebook environment by using `os` and `getpass`. You can use this technique for all the following examples."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ[\"MEILI_HTTP_ADDR\"] = getpass.getpass(\"Meilisearch HTTP address and port:\")\n",
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"os.environ[\"MEILI_MASTER_KEY\"] = getpass.getpass(\"Meilisearch API Key:\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We want to use OpenAIEmbeddings so we have to get the OpenAI API Key."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"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": "markdown",
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"metadata": {},
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"source": [
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"### Adding text and embeddings\n",
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"\n",
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"This example adds text to the Meilisearch vector database without having to initialize a Meilisearch vector store."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.vectorstores import Meilisearch\n",
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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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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"../../../state_of_the_union.txt\") as f:\n",
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" state_of_the_union = f.read()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"texts = text_splitter.split_text(state_of_the_union)"
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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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"# Use Meilisearch vector store to store texts & associated embeddings as vector\n",
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"vector_store = Meilisearch.from_texts(texts=texts, embedding=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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"Behind the scenes, Meilisearch will convert the text to multiple vectors. This will bring us to the same result as the following example."
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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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"### Adding documents and embeddings\n",
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"\n",
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"In this example, we'll use Langchain TextSplitter to split the text in multiple documents. Then, we'll store these documents along with their embeddings."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"# Load text\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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"\n",
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"# Create documents\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"# Import documents & embeddings in the vector store\n",
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"vector_store = Meilisearch.from_documents(documents=documents, embedding=embeddings)\n",
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"\n",
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"# Search in our vector store\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = vector_store.similarity_search(query)\n",
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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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"metadata": {},
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"source": [
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"## Add documents by creating a Meilisearch Vectorstore"
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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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"In this approach, we create a vector store object and add documents to it."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.vectorstores import Meilisearch\n",
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"import meilisearch\n",
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"\n",
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"client = meilisearch.Client(url=\"http://127.0.0.1:7700\", api_key=\"***\")\n",
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"vector_store = Meilisearch(\n",
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" embedding=embeddings, client=client, index_name=\"langchain_demo\", text_key=\"text\"\n",
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")\n",
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"vector_store.add_documents(documents)"
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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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"## Similarity Search with score\n",
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"\n",
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"This specific method allows you to return the documents and the distance score of the query to them."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"docs_and_scores = vector_store.similarity_search_with_score(query)\n",
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"docs_and_scores[0]"
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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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"## Similarity Search by vector"
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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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"embedding_vector = embeddings.embed_query(query)\n",
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"docs_and_scores = vector_store.similarity_search_by_vector(embedding_vector)\n",
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"docs_and_scores[0]"
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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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"## Additional resources\n",
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"\n",
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"Documentation\n",
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"- [Meilisearch](https://www.meilisearch.com/docs/)\n",
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"- [Meilisearch Python SDK](https://python-sdk.meilisearch.com)\n",
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"\n",
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"Open-source repositories\n",
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"- [Meilisearch repository](https://github.com/meilisearch/meilisearch)\n",
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"- [Meilisearch Python SDK](https://github.com/meilisearch/meilisearch-python)"
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]
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}
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],
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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