How to execute multiple Langchain queries simultaneously in Python

Other Questions langchain
1

I got a single query working with langchain using this setup:

prompt_template = """Answer the question using only the provided context below.
If the context doesn't contain the answer, respond with 'I don't have enough information'.
Be accurate and concise in your responses.
Return only numerical values when numbers are requested.

{context}

Query: {query}

Response:"""
my_prompt = PromptTemplate.from_template(prompt_template)

query_chain = (
    {"context": document_retriever | format_documents, "query": RunnablePassthrough()}
    | my_prompt
    | language_model
    | StrOutputParser()
)

query_chain.invoke(user_question)

Now I want to process multiple questions at once like this:

question_list = ["what is the main topic?", "how many items are there?"]

I need to run these questions simultaneously instead of one after another. What’s the best approach to handle parallel execution with langchain? I’m looking for a way to speed up the process when I have several queries to process at the same time.

2

u can use asyncio for this! make sure ur query_chain is async and then use await asyncio.gather(*[query_chain.ainvoke(q) for q in question_list]). itll speed things up a lot compared to running them 1 by 1!

3

Been doing this for years - all these coding approaches work, but they’re still manual. You’re writing Python, managing async patterns, debugging threads.

I skip that and build automated workflows instead. Take my langchain queries, drop them into visual automation that handles parallel processing without touching concurrency code.

Drop in my questions, connect to langchain, and it fans out queries automatically. No asyncio debugging, no thread management, no rate limits to code around. Just drag, drop, connect.

Real win is regular use. Instead of running Python scripts manually, I schedule workflows to process batches. Handles retries, logging, sends results via email or Slack.

Want preprocessing or different post-processing? Add nodes to the flow. Way more flexible than hardcoding in Python.

For langchain, it connects directly - keep your existing prompts and chains. Just move execution into visual workflow that handles the parallel stuff.

4

I’ve had good luck with Langchain’s batch processing instead of managing threads myself. Just swap invoke for query_chain.batch(question_list) - it handles all the parallelization behind the scenes. No need to mess with thread pools or async stuff. It’s way more reliable across different retrievers and models since Langchain optimizes based on what you’re actually using. You’ll see better performance even with 3-4 queries, and it won’t hammer your LLM provider’s API since it manages request pacing automatically.

5

Langchain’s abatch method is perfect for this. It gives you async handling with built-in concurrency control.

import asyncio

results = await query_chain.abatch(question_list)

I use this in production for hundreds of daily queries. Unlike ThreadPoolExecutor, it doesn’t spawn OS threads for each request - you can handle way more concurrent ops without hitting system limits.

Here’s what I learned the hard way: OpenAI and similar APIs already have rate limiting in their async clients. With abatch you get automatic backoff and retry handling. With thread pools, you’d code that yourself.

For 2 queries, anything works. But abatch scales way better as your lists grow.

6

The asyncio approach works great, but there’s something simpler if you don’t want to mess with async/await - just use ThreadPoolExecutor from concurrent.futures. I’ve gotten good results with this for batches of langchain queries. Wrap your query_chain.invoke calls in a thread pool and submit all questions at once: with ThreadPoolExecutor(max_workers=5) as executor: futures = [executor.submit(query_chain.invoke, q) for q in question_list]; results = [future.result() for future in futures]. You get parallel execution without changing your existing sync code. Just watch your worker count if you’re hitting external APIs - don’t exceed rate limits.

7

I’d use map for quick parallelization - from multiprocessing import Pool; with Pool(processes=4) as pool: results = pool.map(query_chain.invoke, question_list). No async knowledge needed and it’s perfect for CPU-bound tasks when your retrievers are doing heavy local processing.