LangChain integration

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

# Load and split documents
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.create_documents(["Your text here"])

# Create Chroma vector store
vectorstore = Chroma.from_documents(
    documents=documents,
    embedding=OpenAIEmbeddings(),
    collection_name="langchain_store",
    persist_directory="./chroma_langchain_db"
)

# Query the vector store
results = vectorstore.similarity_search("your query", k=3)

from langchain.chains import RetrievalQA
from langchain_openai import OpenAI

# Create a retriever from the vector store
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

# Create a QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=retriever
)

# Ask questions
response = qa_chain.invoke("What is this document about?")

# Maximum Marginal Relevance (MMR) search
results = vectorstore.max_marginal_relevance_search(
    "query text",
    k=5,
    fetch_k=20,
    lambda_mult=0.5
)

# Search with metadata filtering
results = vectorstore.similarity_search(
    "query text",
    filter={"source": "documentation"},
    k=3
)

import { Chroma } from "@langchain/community/vectorstores/chroma";
import { OpenAIEmbeddings } from "@langchain/openai";

// Create vector store
const vectorStore = await Chroma.fromTexts(
  ["Text 1", "Text 2", "Text 3"],
  [{ source: "doc1" }, { source: "doc2" }, { source: "doc3" }],
  new OpenAIEmbeddings(),
  {
    collectionName: "langchain_collection",
    url: "http://localhost:8000",
  }
);

// Query
const results = await vectorStore.similaritySearch("query text", 3);