How do you use ChromaDB as a vector store with LangChain?

# pip install langchain langchain-chroma langchain-openai
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.documents import Document
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
import os

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

# --- Option 1: Create from documents ---
docs = [
    Document(page_content="ChromaDB is a vector database.", metadata={"source": "intro"}),
    Document(page_content="HNSW is used for ANN search.",  metadata={"source": "tech"}),
    Document(page_content="RAG improves LLM accuracy.",    metadata={"source": "ai"}),
]
vectorstore = Chroma.from_documents(
    documents=docs,
    embedding=embeddings,
    collection_name="lc_demo",
    persist_directory="./lc_chroma",  # persistent storage
)

# --- Option 2: Load existing ChromaDB ---
vectorstore = Chroma(
    collection_name="lc_demo",
    embedding_function=embeddings,
    persist_directory="./lc_chroma",
)

# Similarity search
results = vectorstore.similarity_search("vector databases", k=2)
for doc in results:
    print(doc.page_content)

# As retriever (for use in chains)
retriever = vectorstore.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 3, "filter": {"source": "tech"}},
)

# Build a simple RAG chain with LCEL
llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template(
    "Answer based on context:\n\n{context}\n\nQuestion: {question}"
)

def format_docs(docs):
    return "\n\n".join(d.page_content for d in docs)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt | llm | StrOutputParser()
)
print(rag_chain.invoke("What search algorithm does ChromaDB use?"))