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docs: update intro page (#28639)

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ccurme
2024-12-13 15:24:14 -05:00
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parent ce90b25313
commit 4802c31a53
2 changed files with 42 additions and 25 deletions
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22
docs/src/theme/VectorStoreTabs.js
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@@ -4,71 +4,73 @@ import TabItem from "@theme/TabItem";
import CodeBlock from "@theme-original/CodeBlock";
export default function VectorStoreTabs(props) {
const { customVarName } = props;
const { customVarName, useFakeEmbeddings = false } = props;
const vectorStoreVarName = customVarName ?? "vector_store";
const fakeEmbeddingsString = `from langchain_core.embeddings import DeterministicFakeEmbedding\n\nembeddings = DeterministicFakeEmbedding(size=100)`;
const tabItems = [
{
value: "In-memory",
label: "In-memory",
text: `from langchain_core.vectorstores import InMemoryVectorStore\n\n${vectorStoreVarName} = InMemoryVectorStore(embeddings)`,
text: `from langchain_core.vectorstores import InMemoryVectorStore\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n${vectorStoreVarName} = InMemoryVectorStore(embeddings)`,
packageName: "langchain-core",
default: true,
},
{
value: "AstraDB",
label: "AstraDB",
text: `from langchain_astradb import AstraDBVectorStore\n\n${vectorStoreVarName} = AstraDBVectorStore(\n embedding=embeddings,\n api_endpoint=ASTRA_DB_API_ENDPOINT,\n collection_name="astra_vector_langchain",\n token=ASTRA_DB_APPLICATION_TOKEN,\n namespace=ASTRA_DB_NAMESPACE,\n)`,
text: `from langchain_astradb import AstraDBVectorStore\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n${vectorStoreVarName} = AstraDBVectorStore(\n embedding=embeddings,\n api_endpoint=ASTRA_DB_API_ENDPOINT,\n collection_name="astra_vector_langchain",\n token=ASTRA_DB_APPLICATION_TOKEN,\n namespace=ASTRA_DB_NAMESPACE,\n)`,
packageName: "langchain-astradb",
default: false,
},
{
value: "Chroma",
label: "Chroma",
text: `from langchain_chroma import Chroma\n\n${vectorStoreVarName} = Chroma(embedding_function=embeddings)`,
text: `from langchain_chroma import Chroma\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n${vectorStoreVarName} = Chroma(embedding_function=embeddings)`,
packageName: "langchain-chroma",
default: false,
},
{
value: "FAISS",
label: "FAISS",
text: `from langchain_community.vectorstores import FAISS\n\n${vectorStoreVarName} = FAISS(embedding_function=embeddings)`,
text: `from langchain_community.vectorstores import FAISS\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n${vectorStoreVarName} = FAISS(embedding_function=embeddings)`,
packageName: "langchain-community",
default: false,
},
{
value: "Milvus",
label: "Milvus",
text: `from langchain_milvus import Milvus\n\n${vectorStoreVarName} = Milvus(embedding_function=embeddings)`,
text: `from langchain_milvus import Milvus\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n${vectorStoreVarName} = Milvus(embedding_function=embeddings)`,
packageName: "langchain-milvus",
default: false,
},
{
value: "MongoDB",
label: "MongoDB",
text: `from langchain_mongodb import MongoDBAtlasVectorSearch\n\n${vectorStoreVarName} = MongoDBAtlasVectorSearch(\n embedding=embeddings,\n collection=MONGODB_COLLECTION,\n index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n relevance_score_fn="cosine",\n)`,
text: `from langchain_mongodb import MongoDBAtlasVectorSearch\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n${vectorStoreVarName} = MongoDBAtlasVectorSearch(\n embedding=embeddings,\n collection=MONGODB_COLLECTION,\n index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,\n relevance_score_fn="cosine",\n)`,
packageName: "langchain-mongodb",
default: false,
},
{
value: "PGVector",
label: "PGVector",
text: `from langchain_postgres import PGVector\n\n${vectorStoreVarName} = PGVector(\n embedding=embeddings,\n collection_name="my_docs",\n connection="postgresql+psycopg://...",\n)`,
text: `from langchain_postgres import PGVector\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n${vectorStoreVarName} = PGVector(\n embedding=embeddings,\n collection_name="my_docs",\n connection="postgresql+psycopg://...",\n)`,
packageName: "langchain-postgres",
default: false,
},
{
value: "Pinecone",
label: "Pinecone",
text: `from langchain_pinecone import PineconeVectorStore\nfrom pinecone import Pinecone\n\npc = Pinecone(api_key=...)\nindex = pc.Index(index_name)\n\n${vectorStoreVarName} = PineconeVectorStore(embedding=embeddings, index=index)`,
text: `from langchain_pinecone import PineconeVectorStore\nfrom pinecone import Pinecone\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\npc = Pinecone(api_key=...)\nindex = pc.Index(index_name)\n\n${vectorStoreVarName} = PineconeVectorStore(embedding=embeddings, index=index)`,
packageName: "langchain-pinecone",
default: false,
},
{
value: "Qdrant",
label: "Qdrant",
text: `from langchain_qdrant import QdrantVectorStore\nfrom qdrant_client import QdrantClient\n\nclient = QdrantClient(":memory:")\n${vectorStoreVarName} = QdrantVectorStore(\n client=client,\n collection_name="test",\n embedding=embeddings,\n)`,
text: `from langchain_qdrant import QdrantVectorStore\nfrom qdrant_client import QdrantClient\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\nclient = QdrantClient(":memory:")\n${vectorStoreVarName} = QdrantVectorStore(\n client=client,\n collection_name="test",\n embedding=embeddings,\n)`,
packageName: "langchain-qdrant",
default: false,
},