mirror of
https://github.com/hwchase17/langchain.git
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537 lines
14 KiB
Plaintext
537 lines
14 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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"# Faiss\n",
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"\n",
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">[Facebook AI Similarity Search (FAISS)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also includes supporting code for evaluation and parameter tuning.\n",
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">\n",
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">See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper.\n",
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"\n",
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"You can find the FAISS documentation at [this page](https://faiss.ai/).\n",
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"\n",
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"This notebook shows how to use functionality related to the `FAISS` vector database. It will show functionality specific to this integration. After going through, it may be useful to explore [relevant use-case pages](/docs/how_to#qa-with-rag) to learn how to use this vectorstore as part of a larger chain."
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]
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},
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{
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"cell_type": "markdown",
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"id": "601ac1d5-48a2-4e41-bf51-f1d5fdd5639d",
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"metadata": {
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"tags": []
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},
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"source": [
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"## Setup\n",
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"\n",
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"The integration lives in the `langchain-community` package. We also need to install the `faiss` package itself. We can install these with:\n",
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"\n",
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"Note that you can also install `faiss-gpu` if you want to use the GPU enabled version"
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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": "08165d56",
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"metadata": {},
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"outputs": [],
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"source": [
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"pip install -qU langchain-community faiss-cpu"
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]
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},
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{
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"cell_type": "markdown",
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"id": "408be78f-7b0e-44d4-8d48-56a6cb9b3fb9",
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"metadata": {},
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"source": [
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"If you want to get best in-class automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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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": "951c82cb-40bf-46ac-9f3f-d2fca7d204b8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "78dde98a-584f-4f2a-98d5-e776fd9558fa",
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"metadata": {},
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"source": [
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"## Initialization\n",
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"\n",
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"import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
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"\n",
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"<EmbeddingTabs/>\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": 1,
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"id": "5b394da3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# | output: false\n",
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"# | echo: false\n",
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"from langchain_openai import OpenAIEmbeddings\n",
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"\n",
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"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
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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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"import faiss\n",
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"from langchain_community.docstore.in_memory import InMemoryDocstore\n",
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"from langchain_community.vectorstores import FAISS\n",
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"\n",
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"index = faiss.IndexFlatL2(len(embeddings.embed_query(\"hello world\")))\n",
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"\n",
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"vector_store = FAISS(\n",
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" embedding_function=embeddings,\n",
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" index=index,\n",
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" docstore=InMemoryDocstore(),\n",
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" index_to_docstore_id={},\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": "d8761614",
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"metadata": {},
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"source": [
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"## Manage vector store\n",
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"\n",
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"### Add items to 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": 3,
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"id": "3867e154",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['22f5ce99-cd6f-4e0c-8dab-664128307c72',\n",
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" 'dc3f061b-5f88-4fa1-a966-413550c51891',\n",
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" 'd33d890b-baad-47f7-b7c1-175f5f7b4e59',\n",
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" '6e6c01d2-6020-4a7b-95da-ef43d43f01b5',\n",
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" 'e677223d-ad75-4c1a-bef6-b5912bd1de03',\n",
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" '47e2a168-6462-4ed2-b1d9-d9edfd7391d6',\n",
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" '1e4d66d6-e155-4891-9212-f7be97f36c6a',\n",
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" 'c0663096-e1a5-4665-b245-1c2e6c4fb653',\n",
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" '8297474a-7f7c-4006-9865-398c1781b1bc',\n",
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" '44e4be03-0a8d-4316-b3c4-f35f4bb2b532']"
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]
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},
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"execution_count": 3,
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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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"from uuid import uuid4\n",
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"\n",
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"from langchain_core.documents import Document\n",
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"\n",
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"document_1 = Document(\n",
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" page_content=\"I had chocalate chip pancakes and scrambled eggs for breakfast this morning.\",\n",
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" metadata={\"source\": \"tweet\"},\n",
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")\n",
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"\n",
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"document_2 = Document(\n",
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" page_content=\"The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.\",\n",
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" metadata={\"source\": \"news\"},\n",
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")\n",
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"\n",
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"document_3 = Document(\n",
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" page_content=\"Building an exciting new project with LangChain - come check it out!\",\n",
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" metadata={\"source\": \"tweet\"},\n",
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")\n",
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"\n",
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"document_4 = Document(\n",
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" page_content=\"Robbers broke into the city bank and stole $1 million in cash.\",\n",
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" metadata={\"source\": \"news\"},\n",
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")\n",
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"\n",
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"document_5 = Document(\n",
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" page_content=\"Wow! That was an amazing movie. I can't wait to see it again.\",\n",
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" metadata={\"source\": \"tweet\"},\n",
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")\n",
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"\n",
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"document_6 = Document(\n",
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" page_content=\"Is the new iPhone worth the price? Read this review to find out.\",\n",
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" metadata={\"source\": \"website\"},\n",
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")\n",
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"\n",
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"document_7 = Document(\n",
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" page_content=\"The top 10 soccer players in the world right now.\",\n",
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" metadata={\"source\": \"website\"},\n",
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")\n",
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"\n",
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"document_8 = Document(\n",
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" page_content=\"LangGraph is the best framework for building stateful, agentic applications!\",\n",
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" metadata={\"source\": \"tweet\"},\n",
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")\n",
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"\n",
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"document_9 = Document(\n",
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" page_content=\"The stock market is down 500 points today due to fears of a recession.\",\n",
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" metadata={\"source\": \"news\"},\n",
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")\n",
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"\n",
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"document_10 = Document(\n",
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" page_content=\"I have a bad feeling I am going to get deleted :(\",\n",
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" metadata={\"source\": \"tweet\"},\n",
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")\n",
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"\n",
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"documents = [\n",
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" document_1,\n",
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" document_2,\n",
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" document_3,\n",
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" document_4,\n",
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" document_5,\n",
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" document_6,\n",
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" document_7,\n",
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" document_8,\n",
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" document_9,\n",
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" document_10,\n",
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"]\n",
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"uuids = [str(uuid4()) for _ in range(len(documents))]\n",
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"\n",
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"vector_store.add_documents(documents=documents, ids=uuids)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a410a2dc",
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"metadata": {},
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"source": [
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"### Delete items from 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": 4,
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"id": "c3db04bd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 4,
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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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"vector_store.delete(ids=[uuids[-1]])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "77de24ff",
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"metadata": {},
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"source": [
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"## Query vector store\n",
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"\n",
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"Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent. \n",
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"\n",
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"### Query directly\n",
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"\n",
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"#### Similarity search\n",
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"\n",
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"Performing a simple similarity search with filtering on metadata can be done as follows:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "53d95d3f",
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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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"* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]\n",
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"* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]\n"
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]
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}
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],
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"source": [
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"results = vector_store.similarity_search(\n",
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" \"LangChain provides abstractions to make working with LLMs easy\",\n",
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" k=2,\n",
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" filter={\"source\": \"tweet\"},\n",
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")\n",
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"for res in results:\n",
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" print(f\"* {res.page_content} [{res.metadata}]\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5ae35069",
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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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"You can also search with score:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "a9078ce9",
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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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"* [SIM=0.893688] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]\n"
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]
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}
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],
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"source": [
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"results = vector_store.similarity_search_with_score(\n",
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" \"Will it be hot tomorrow?\", k=1, filter={\"source\": \"news\"}\n",
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")\n",
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"for res, score in results:\n",
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" print(f\"* [SIM={score:3f}] {res.page_content} [{res.metadata}]\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e9091b1f",
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"metadata": {},
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"source": [
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"#### Other search methods\n",
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"\n",
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"\n",
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"There are a variety of other ways to search a FAISS vector store. For a complete list of those methods, please refer to the [API Reference](https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html)\n",
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"\n",
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"### Query by turning into retriever\n",
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"\n",
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"You can also transform the vector store into a retriever for easier usage in your chains. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "10da64fa",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"retriever = vector_store.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 1})\n",
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"retriever.invoke(\"Stealing from the bank is a crime\", filter={\"source\": \"news\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5edd1909",
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"metadata": {},
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"source": [
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"## Usage for retrieval-augmented generation\n",
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"\n",
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"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
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"\n",
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"- [Tutorials](/docs/tutorials/)\n",
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"- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
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"- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "31bda7fd",
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"metadata": {},
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"source": [
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"## Saving and loading\n",
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"You can also save and load a FAISS index. This is useful so you don't have to recreate it everytime you use 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": 11,
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"id": "1b31fe27-e0b3-42c6-b17c-8270b517ee1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store.save_local(\"faiss_index\")\n",
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"\n",
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"new_vector_store = FAISS.load_local(\n",
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" \"faiss_index\", embeddings, allow_dangerous_deserialization=True\n",
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")\n",
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"\n",
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"docs = new_vector_store.similarity_search(\"qux\")"
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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": "98378c4e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!')"
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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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"docs[0]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "57da60d4",
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"metadata": {},
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"source": [
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"## Merging\n",
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"You can also merge two FAISS vectorstores"
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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": "9b8f5e31-3f40-4e94-8d97-5883125efba7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'b752e805-350e-4cf5-ba54-0883d46a3a44': Document(page_content='foo')}"
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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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"db1 = FAISS.from_texts([\"foo\"], embeddings)\n",
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"db2 = FAISS.from_texts([\"bar\"], embeddings)\n",
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"\n",
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"db1.docstore._dict"
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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": "83392605",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'08192d92-746d-4cd1-b681-bdfba411f459': Document(page_content='bar')}"
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]
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},
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"execution_count": 14,
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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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"db2.docstore._dict"
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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": "a3fcc1c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"db1.merge_from(db2)"
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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": "41c51f89",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'b752e805-350e-4cf5-ba54-0883d46a3a44': Document(page_content='foo'),\n",
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" '08192d92-746d-4cd1-b681-bdfba411f459': Document(page_content='bar')}"
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]
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},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"db1.docstore._dict"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "65654d80",
|
|
"metadata": {},
|
|
"source": [
|
|
"## API reference\n",
|
|
"\n",
|
|
"For detailed documentation of all `FAISS` vector store features and configurations head to the API reference: https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|