向量存储集成 - Docs by LangChain

概述

向量存储嵌入数据并执行相似性搜索。

接口

LangChain 为向量存储提供了一个统一的接口，允许您：

add_documents - 向存储中添加文档。
delete - 按 ID 删除存储的文档。
similarity_search - 查询语义相似的文档。

这种抽象让您可以在不改变应用程序逻辑的情况下，在不同实现之间切换。

初始化

要初始化向量存储，请为其提供一个嵌入模型：

from langchain_core.vectorstores import InMemoryVectorStore
vector_store = InMemoryVectorStore(embedding=SomeEmbeddingModel())

添加文档

添加 Document 对象（包含 page_content 和可选元数据），如下所示：

vector_store.add_documents(documents=[doc1, doc2], ids=["id1", "id2"])

删除文档

通过指定 ID 删除：

vector_store.delete(ids=["id1"])

相似性搜索

使用 similarity_search 发出语义查询，该查询返回最接近的嵌入文档：

similar_docs = vector_store.similarity_search("your query here")

许多向量存储支持以下参数：

k — 要返回的结果数量
filter — 基于元数据的条件过滤

相似性度量与索引

嵌入相似性可以使用以下方法计算：

余弦相似度
欧几里得距离
点积

高效搜索通常采用索引方法，例如 HNSW（分层可导航小世界），但具体取决于向量存储。

元数据过滤

通过元数据（例如来源、日期）进行过滤可以优化搜索结果：

vector_store.similarity_search(
  "query",
  k=3,
  filter={"source": "tweets"}
)

主要集成

选择嵌入模型：

OpenAI

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

Azure

pip install -qU langchain-azure-ai

import getpass
import os

if not os.environ.get("AZURE_OPENAI_API_KEY"):
  os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass("Enter API key for Azure: ")

from langchain_openai import AzureOpenAIEmbeddings

embeddings = AzureOpenAIEmbeddings(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

Google Gemini

pip install -qU langchain-google-genai

import getpass
import os

if not os.environ.get("GOOGLE_API_KEY"):
  os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter API key for Google Gemini: ")

from langchain_google_genai import GoogleGenerativeAIEmbeddings

embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001")

Google Vertex

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="text-embedding-005")

AWS

pip install -qU langchain-aws

from langchain_aws import BedrockEmbeddings

embeddings = BedrockEmbeddings(model_id="amazon.titan-embed-text-v2:0")

HuggingFace

pip install -qU langchain-huggingface

from langchain_huggingface import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

Ollama

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="llama3")

Cohere

pip install -qU langchain-cohere

import getpass
import os

if not os.environ.get("COHERE_API_KEY"):
  os.environ["COHERE_API_KEY"] = getpass.getpass("Enter API key for Cohere: ")

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-v3.0")

Mistral AI

pip install -qU langchain-mistralai

import getpass
import os

if not os.environ.get("MISTRALAI_API_KEY"):
  os.environ["MISTRALAI_API_KEY"] = getpass.getpass("Enter API key for MistralAI: ")

from langchain_mistralai import MistralAIEmbeddings

embeddings = MistralAIEmbeddings(model="mistral-embed")

Nomic

pip install -qU langchain-nomic

import getpass
import os

if not os.environ.get("NOMIC_API_KEY"):
  os.environ["NOMIC_API_KEY"] = getpass.getpass("Enter API key for Nomic: ")

from langchain_nomic import NomicEmbeddings

embeddings = NomicEmbeddings(model="nomic-embed-text-v1.5")

NVIDIA

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

if not os.environ.get("NVIDIA_API_KEY"):
  os.environ["NVIDIA_API_KEY"] = getpass.getpass("Enter API key for NVIDIA: ")

from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings

embeddings = NVIDIAEmbeddings(model="NV-Embed-QA")

Voyage AI

pip install -qU langchain-voyageai

import getpass
import os

if not os.environ.get("VOYAGE_API_KEY"):
  os.environ["VOYAGE_API_KEY"] = getpass.getpass("Enter API key for Voyage AI: ")

from langchain-voyageai import VoyageAIEmbeddings

embeddings = VoyageAIEmbeddings(model="voyage-3")

IBM watsonx

pip install -qU langchain-ibm

import getpass
import os

if not os.environ.get("WATSONX_APIKEY"):
  os.environ["WATSONX_APIKEY"] = getpass.getpass("Enter API key for IBM watsonx: ")

from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="<WATSONX PROJECT_ID>",
)

Fake

pip install -qU langchain-core

from langchain_core.embeddings import DeterministicFakeEmbedding

embeddings = DeterministicFakeEmbedding(size=4096)

xAI

pip install -qU langchain-xai

import getpass
import os

if not os.environ.get("XAI_API_KEY"):
  os.environ["XAI_API_KEY"] = getpass.getpass("Enter API key for xAI: ")

from langchain.chat_models import init_chat_model

model = init_chat_model("grok-2", model_provider="xai")

Perplexity

pip install -qU langchain-perplexity

import getpass
import os

if not os.environ.get("PPLX_API_KEY"):
  os.environ["PPLX_API_KEY"] = getpass.getpass("Enter API key for Perplexity: ")

from langchain.chat_models import init_chat_model

model = init_chat_model("llama-3.1-sonar-small-128k-online", model_provider="perplexity")

DeepSeek

pip install -qU langchain-deepseek

import getpass
import os

if not os.environ.get("DEEPSEEK_API_KEY"):
  os.environ["DEEPSEEK_API_KEY"] = getpass.getpass("Enter API key for DeepSeek: ")

from langchain.chat_models import init_chat_model

model = init_chat_model("deepseek-chat", model_provider="deepseek")

选择向量存储：

In-memory

pip install -qU langchain-core

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(embeddings)

Amazon OpenSearch

pip

pip install -qU boto3

from opensearchpy import RequestsHttpConnection

service = "es"  # must set the service as 'es'
region = "us-east-2"
credentials = boto3.Session(
    aws_access_key_id="xxxxxx", aws_secret_access_key="xxxxx"
).get_credentials()
awsauth = AWS4Auth("xxxxx", "xxxxxx", region, service, session_token=credentials.token)

vector_store = OpenSearchVectorSearch.from_documents(
    docs,
    embeddings,
    opensearch_url="host url",
    http_auth=awsauth,
    timeout=300,
    use_ssl=True,
    verify_certs=True,
    connection_class=RequestsHttpConnection,
    index_name="test-index",
)

Astra DB

pip install -qU langchain-astradb

from langchain_astradb import AstraDBVectorStore

vector_store = AstraDBVectorStore(
    embedding=embeddings,
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    collection_name="astra_vector_langchain",
    token=ASTRA_DB_APPLICATION_TOKEN,
    namespace=ASTRA_DB_NAMESPACE,
)

Azure Cosmos DB NoSQL

pip install -qU langchain-azure-ai azure-cosmos

from langchain_azure_ai.vectorstores.azure_cosmos_db_no_sql import (
    AzureCosmosDBNoSqlVectorSearch,
)
vector_search = AzureCosmosDBNoSqlVectorSearch.from_documents(
    documents=docs,
    embedding=openai_embeddings,
    cosmos_client=cosmos_client,
    database_name=database_name,
    container_name=container_name,
    vector_embedding_policy=vector_embedding_policy,
    full_text_policy=full_text_policy,
    indexing_policy=indexing_policy,
    cosmos_container_properties=cosmos_container_properties,
    cosmos_database_properties={},
    full_text_search_enabled=True,
)

Azure Cosmos DB Mongo vCore

pip install -qU langchain-azure-ai pymongo

from langchain_azure_ai.vectorstores.azure_cosmos_db_mongo_vcore import (
    AzureCosmosDBMongoVCoreVectorSearch,
)

vectorstore = AzureCosmosDBMongoVCoreVectorSearch.from_documents(
    docs,
    openai_embeddings,
    collection=collection,
    index_name=INDEX_NAME,
)

Chroma

pip install -qU langchain-chroma

from langchain_chroma import Chroma

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
    persist_directory="./chroma_langchain_db",  # Where to save data locally, remove if not necessary
)

CockroachDB

pip install -qU langchain-cockroachdb

from langchain_cockroachdb import AsyncCockroachDBVectorStore, CockroachDBEngine

CONNECTION_STRING = "cockroachdb://user:pass@host:26257/db?sslmode=verify-full"

engine = CockroachDBEngine.from_connection_string(CONNECTION_STRING)
await engine.ainit_vectorstore_table(
    table_name="vectors",
    vector_dimension=1536,
)

vector_store = AsyncCockroachDBVectorStore(
    engine=engine,
    embeddings=embeddings,
    collection_name="vectors",
)

Elasticsearch

安装包并使用 start-local 脚本在本地启动 Elasticsearch：

pip install -qU langchain-elasticsearch
curl -fsSL https://elastic.co/start-local | sh

这将创建一个 elastic-start-local 文件夹。要启动 Elasticsearch：

cd elastic-start-local
./start.sh

Elasticsearch 将在 http://localhost:9200 处可用。elastic 用户的密码和 API 密钥存储在 elastic-start-local 文件夹的 .env 文件中。

from langchain_elasticsearch import ElasticsearchStore

vector_store = ElasticsearchStore(
    index_name="langchain-demo",
    embedding=embeddings,
    es_url="http://localhost:9200",
)

FAISS

pip install -qU langchain-community

import faiss
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.vectorstores import FAISS

embedding_dim = len(embeddings.embed_query("hello world"))
index = faiss.IndexFlatL2(embedding_dim)

vector_store = FAISS(
    embedding_function=embeddings,
    index=index,
    docstore=InMemoryDocstore(),
    index_to_docstore_id={},
)

Milvus

pip install -qU langchain-milvus

from langchain_milvus import Milvus

URI = "./milvus_example.db"

vector_store = Milvus(
    embedding_function=embeddings,
    connection_args={"uri": URI},
    index_params={"index_type": "FLAT", "metric_type": "L2"},
)

MongoDB

pip install -qU langchain-mongodb

from langchain_mongodb import MongoDBAtlasVectorSearch

vector_store = MongoDBAtlasVectorSearch(
    embedding=embeddings,
    collection=MONGODB_COLLECTION,
    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
    relevance_score_fn="cosine",
)

PGVector

pip install -qU langchain-postgres

from langchain_postgres import PGVector

vector_store = PGVector(
    embeddings=embeddings,
    collection_name="my_docs",
    connection="postgresql+psycopg://..."
)

PGVectorStore

pip install -qU langchain-postgres

from langchain_postgres import PGEngine, PGVectorStore

$engine = PGEngine.from_connection_string(
    url="postgresql+psycopg://..."
)

vector_store = PGVectorStore.create_sync(
    engine=pg_engine,
    table_name='test_table',
    embedding_service=embedding
)

Pinecone

pip install -qU langchain-pinecone

from langchain_pinecone import PineconeVectorStore
from pinecone import Pinecone

pc = Pinecone(api_key=...)
index = pc.Index(index_name)

vector_store = PineconeVectorStore(embedding=embeddings, index=index)

Qdrant

pip install -qU langchain-qdrant

from qdrant_client.models import Distance, VectorParams
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient

client = QdrantClient(":memory:")

vector_size = len(embeddings.embed_query("sample text"))

if not client.collection_exists("test"):
    client.create_collection(
        collection_name="test",
        vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE)
    )
vector_store = QdrantVectorStore(
    client=client,
    collection_name="test",
    embedding=embeddings,
)

Oracle AI Database

pip install -qU langchain-oracledb

import oracledb
from langchain_oracledb.vectorstores import OracleVS
from langchain_oracledb.vectorstores.oraclevs import create_index
from langchain_community.vectorstores.utils import DistanceStrategy

username = "<username>"
password = "<password>"
dsn = "<hostname>:<port>/<service_name>"

connection = oracledb.connect(user=username, password=password, dsn=dsn)

vector_store = OracleVS(
    client=connection,
    embedding_function=embedding_model,
    table_name="VECTOR_SEARCH_DEMO",
    distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE
)

turbopuffer

pip install -qU langchain-turbopuffer

from langchain_turbopuffer import TurbopufferVectorStore
from turbopuffer import Turbopuffer

tpuf = Turbopuffer(region="gcp-us-central1")
ns = tpuf.namespace("langchain-test")

vector_store = TurbopufferVectorStore(embedding=embeddings, namespace=ns)

Valkey

pip install -qU "langchain-aws[valkey]"

from langchain_aws.vectorstores import ValkeyVectorStore

vector_store = ValkeyVectorStore(
    embedding=embeddings,
    valkey_url="valkey://localhost:6379",
    index_name="my_index"
)

向量存储	按 ID 删除	过滤	按向量搜索	带分数搜索	异步	通过标准测试	多租户	在添加文档中包含 ID
`AstraDBVectorStore`	✅	✅	✅	✅	✅	✅	✅	✅
`AzureCosmosDBNoSqlVectorStore`	✅	✅	✅	✅	❌	✅	✅	✅
`AzureCosmosDBMongoVCoreVectorStore`	✅	✅	✅	✅	❌	✅	✅	✅
`Chroma`	✅	✅	✅	✅	✅	✅	✅	✅
`Clickhouse`	✅	✅	❌	✅	❌	❌	❌	✅
`AsyncCockroachDBVectorStore`	✅	✅	✅	✅	✅	✅	✅	✅
`CouchbaseSearchVectorStore`	✅	✅	✅	✅	✅	❌	✅	✅
`DatabricksVectorSearch`	✅	✅	✅	✅	✅	❌	❌	✅
`ElasticsearchStore`	✅	✅	✅	✅	✅	❌	❌	✅
`FAISS`	✅	✅	✅	✅	✅	❌	❌	✅
`InMemoryVectorStore`	✅	✅	❌	✅	✅	❌	❌	✅
`LambdaDB`	✅	✅	✅	✅	✅	✅	❌	✅
`Milvus`	✅	✅	✅	✅	✅	✅	✅	✅
`Moorcheh`	✅	✅	✅	✅	✅	✅	✅	✅
`MongoDBAtlasVectorSearch`	✅	✅	✅	✅	✅	✅	✅	✅
`openGauss`	✅	✅	✅	✅	❌	✅	❌	✅
`PGVector`	✅	✅	✅	✅	✅	❌	❌	✅
`PGVectorStore`	✅	✅	✅	✅	✅	✅	❌	✅
`PineconeVectorStore`	✅	✅	✅	❌	✅	❌	❌	✅
`QdrantVectorStore`	✅	✅	✅	✅	✅	❌	✅	✅
`Weaviate`	✅	✅	✅	✅	✅	❌	✅	✅
`SQLServer`	✅	✅	✅	✅	❌	❌	❌	✅
`TurbopufferVectorStore`	✅	✅	✅	✅	❌	✅	✅	✅
`ValkeyVectorStore`	✅	✅	✅	✅	❌	❌	❌	✅
`ZeusDB`	✅	✅	✅	✅	✅	✅	❌	✅
`Oracle AI Database`	✅	✅	✅	✅	✅	✅	❌	✅

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Integrations by component

​概述

​接口

​初始化

​添加文档

​删除文档

​相似性搜索

​相似性度量与索引

​元数据过滤

​主要集成

​所有向量存储

Activeloop Deep Lake

Alibaba Cloud MySQL

Alibaba Cloud OpenSearch

AnalyticDB

Annoy

Apache Doris

ApertureDB

Astra DB Vector Store

Atlas

AwaDB

Azure Cosmos DB Mongo vCore

Azure Cosmos DB No SQL

Azure Database for PostgreSQL - Flexible Server

Azure AI Search

Bagel

BagelDB

Baidu Cloud ElasticSearch VectorSearch

Baidu VectorDB

Apache Cassandra

Chroma

Clarifai

ClickHouse

CockroachDB

Couchbase

DashVector

Databricks

IBM Db2

DingoDB

DocArray HnswSearch

DocArray InMemorySearch

Amazon Document DB

DuckDB

China Mobile ECloud ElasticSearch

Elasticsearch

Epsilla

Faiss

Faiss (Async)

FalkorDB

Gel

Google AlloyDB

Google BigQuery Vector Search

Google Cloud SQL for MySQL

Google Cloud SQL for PostgreSQL

Firestore

Google Memorystore for Redis

Google Spanner

Google Bigtable

Google Vertex AI Feature Store

Google Vertex AI Vector Search

Hippo

Hologres

Jaguar Vector Database

Kinetica

LambdaDB

LanceDB

Lantern

Lindorm

LLMRails

ManticoreSearch

MariaDB

Marqo

Meilisearch

Amazon MemoryDB

Milvus

Momento Vector Index

Moorcheh

MongoDB Atlas

MyScale

概述

接口

初始化