Building a Real-time RAG Pipeline Using Doubao API and LangChain

Cursor's "Composer" mode combined with the Doubao API is a lethal combo for cranking out RAG pipelines, but the real challenge isn't the boilerplate—it's handling the synchronization between your vector store and the LLM's context window without hitting latency walls.

I spent the last few days migrating a prototype from OpenAI to Doubao to see how the latency holds up in a real-time streaming scenario. Using LangChain as the orchestrator, the setup is straightforward, but there are a few configuration tweaks you need to make to prevent the model from hallucinating when the retrieved documents are slightly off-topic.

The core of the pipeline relies on a fast embedding model and a vector store that supports asynchronous updates. I'm using FAISS for local testing, but the logic remains the same for Pinecone or Milvus. The trick to getting "real-time" feel isn't just the streaming response, but how you structure the prompt to force the LLM to cite its sources strictly.

Here is the implementation pattern I've found most reliable for the retrieval chain:

from langchain_community.chat_models import ChatDoubao
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings

# Critical: Set the temperature low for RAG to avoid creative drifting
llm = ChatDoubao(
    model="doubao-one-pro", 
    temperature=0.1, 
    streaming=True
)

prompt = ChatPromptTemplate.from_template("""
Answer the question based ONLY on the following context:
{context}

Question: {question}
Answer:""")

# Using a lightweight embedding model to keep retrieval under 100ms
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
vectorstore = FAISS.load_local("faiss_index", embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt 
    | llm
)

# Execute with streaming to reduce perceived latency
for chunk in chain.stream("How does the new API authentication work?"):
    print(chunk.content, end="", flush=True)

A few "gotchas" I hit during the build:

The Context Window Trap: Even if the model supports a massive context, shoving 10+ documents into the prompt slows down the Time To First Token (TTFT). I found that limiting k to 3-5 highly relevant chunks is the sweet spot for Doubao. If the answer isn't there, it's better to have the model say "I don't know" than to provide a sluggish, diluted answer.

Prompt Engineering for Citations: If you don't explicitly tell the model to use the context, it will lean on its pre-trained knowledge. I added a "Strict Mode" to my system prompt: If the answer is not contained within the context, state "Information not found in documentation" and do not attempt to guess.

Cursor Workflow Tip: When debugging the LangChain LCEL (LangChain Expression Language) chains, I use Cursor's @Codebase feature to index the LangChain documentation locally. Since LCEL syntax changes frequently, this prevents the AI from suggesting deprecated LLMChain syntax and keeps it focused on the newer Runnable pipes.

Productivity Gain: Switching to this asynchronous streaming setup reduced the perceived wait time from 3 seconds (blocking) to about 400ms (first token). For a real-time RAG app, that's the difference between a tool that feels like a bot and a tool that feels like an extension of the UI.