Send multiple requests at the same time (concurrency in chain.invoke)

[givkashi]

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Example Code

import asyncio
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate

# Define your LangChain components
llm = ChatOpenAI(model_name='openai/gpt-4o-mini')

template = "Translate the following English text to French: {text}"
prompt = PromptTemplate(template=template, input_variables=["text"])

chain = llm | prompt

# Define an async function to run the chain
async def run_chain(text):
    return await chain.run_async({"text": text})

# Define a function to run multiple chains concurrently
async def run_multiple_chains(texts):
    tasks = [run_chain(text) for text in texts]
    results = await asyncio.gather(*tasks)
    return results

# List of texts to process
texts = [
    "Hello, how are you?",
    "What is your name?",
    "Where are you from?",
    "What do you do?"
]

# Run the chains concurrently
results = asyncio.run(run_multiple_chains(texts))

# Print the results
for result in results:
    print(result)

Description

I am trying to use the Langchain library to send multiple requests at the same time to increase the speed of my program.
After doing some search I found the mentioned code but it didn't work. I got below error.
Is there any other approach to send requests concurrently?

return await chain.run_async({"text": text})

AttributeError: 'RunnableSequence' object has no attribute 'run_async'the

System Info

System Information

OS: Windows
OS Version: 10.0.19045
Python Version: 3.8.19 (default, Mar 20 2024, 19:55:45) [MSC v.1916 64 bit (AMD64)]

Package Information

langchain_core: 0.2.12
langchain: 0.2.7
langchain_community: 0.2.7
langsmith: 0.1.84
langchain_openai: 0.1.8
langchain_text_splitters: 0.2.0
langserve: 0.2.1

Packages not installed (Not Necessarily a Problem)

The following packages were not found:

langgraph

[dosubot[bot]]

To send multiple requests concurrently using the LangChain library, you should use the ainvoke method instead of run_async. Here's how you can modify your code to achieve this:

import asyncio
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnableSequence

# Define your LangChain components
llm = ChatOpenAI(model_name='openai/gpt-4o-mini')

template = "Translate the following English text to French: {text}"
prompt = PromptTemplate(template=template, input_variables=["text"])

# Combine them into a RunnableSequence
chain = RunnableSequence(first=prompt, last=llm)

# Define an async function to run the chain
async def run_chain(text):
    return await chain.ainvoke({"text": text})

# Define a function to run multiple chains concurrently
async def run_multiple_chains(texts):
    tasks = [run_chain(text) for text in texts]
    results = await asyncio.gather(*tasks)
    return results

# List of texts to process
texts = [
    "Hello, how are you?",
    "What is your name?",
    "Where are you from?",
    "What do you do?"
]

# Run the chains concurrently
results = asyncio.run(run_multiple_chains(texts))

# Print the results
for result in results:
    print(result)

In this updated code, ainvoke is used to run the chain asynchronously, which should resolve the AttributeError you encountered. This approach ensures that multiple requests are sent concurrently ^[1].

To continue talking to Dosu, mention @dosu.

[arpita739]

How to pass a json file with questions and options in it to get correct predicted answer sequentially in a data frame?

I followed this rule but I am getting error: coroutine object process_question_with_langch...

from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnableSequence
import asyncio

# Define a template for the question-prompt
template = """
You are an expert in the USMLE exam. Analyze the following clinical question and choose the correct answer.

Question: {question}

Options:
{options}

Provide the correct letter (A, B, C, D, or E) based on the question and options above. (NO PREAMBLE)
"""

# Set up a Langchain prompt template
prompt = PromptTemplate(input_variables=["question", "options"], template=template)

# Initialize Groq's LLM (ensure you replace `groq_api_key` with your actual API key)
llm = ChatGroq(groq_api_key=groq_api_key, temperature=0, model_name="llama-3.2-90b-text-preview")

# Create a RunnableSequence to process the prompt
llm_chain = RunnableSequence(first=prompt, last=llm)

# Define an async function to run the chain for a single question
async def run_chain(question, options):
    try:
        # Join options into a formatted string
        options_str = "\n".join([f"{key}) {value}" for key, value in options.items()])

        # Invoke the chain and await the result
        response = await llm_chain.ainvoke({"question": question, "options": options_str})

        # Extract the predicted answer
        predicted_answer = response.strip()
        return predicted_answer
    except Exception as e:
        print(f"Error processing question: {e}")
        return None

# Define a function to run multiple chains concurrently
async def run_multiple_chains(questions, options_list):
    # Create tasks for all questions
    tasks = [run_chain(question, options) for question, options in zip(questions, options_list)]

    # Run all tasks concurrently and gather results
    results = await asyncio.gather(*tasks)
    return results

# Example function to process a single question
async def process_question_with_langchain(question, options):
    """Use Langchain to get the LLM's predicted answer."""
    return await run_chain(question, options)
 

[givkashi]

@dosu I run the below code but I got this error how can I fix it?

RuntimeError Traceback (most recent call last)
in <cell line: 26>()
24
25 # Run the chains concurrently
---> 26 results = asyncio.run(run_multiple_chains(input_texts, images_path))
27 # Print the results
28 for result in results:

/usr/lib/python3.10/asyncio/runners.py in run(main, debug)
31 """
32 if events._get_running_loop() is not None:
---> 33 raise RuntimeError(
34 "asyncio.run() cannot be called from a running event loop")
35

RuntimeError: asyncio.run() cannot be called from a running event loop

import asyncio
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnableSequence

llm = ChatOpenAI(model_name='openai/gpt-4o-mini')

Combine them into a RunnableSequence

chain = RunnableSequence(first=chat_prompt_template, middle=llm, last=parser)

Define an async function to run the chain

async def run_chain(text, image_path):
with open(image_path, "rb") as image_file:
image = base64.b64encode(image_file.read()).decode('utf-8')
return await chain.ainvoke({"image": image, "input": text, "instructions" : parser.get_format_instructions()})

Define a function to run multiple chains concurrently

async def run_multiple_chains(images, texts):
tasks = [run_chain(image, text) for image, text in zip(images, texts)]
results = await asyncio.gather(*tasks)
return results

Run the chains concurrently

results = asyncio.run(run_multiple_chains(input_texts, images_path))

Print the results

for result in results:
print(result)

[dosubot[bot]]

To fix the RuntimeError: asyncio.run() cannot be called from a running event loop error, you can use the nest_asyncio library to allow nested use of asyncio.run(). This is particularly useful if you are running the code in an environment like Jupyter notebooks, which already have an event loop running. Here is how you can modify your code:

1. Install the nest_asyncio library if you haven't already:

   pip install nest_asyncio

2. Apply nest_asyncio in your script:

   import nest_asyncio
   nest_asyncio.apply()

3. Modify your code to use the existing event loop:

import asyncio
import nest_asyncio
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnableSequence

nest_asyncio.apply()

llm = ChatOpenAI(model_name='openai/gpt-4o-mini')

# Combine them into a RunnableSequence
chain = RunnableSequence(first=chat_prompt_template, middle=llm, last=parser)

# Define an async function to run the chain
async def run_chain(text, image_path):
    with open(image_path, "rb") as image_file:
        image = base64.b64encode(image_file.read()).decode('utf-8')
        return await chain.ainvoke({"image": image, "input": text, "instructions": parser.get_format_instructions()})

# Define a function to run multiple chains concurrently
async def run_multiple_chains(images, texts):
    tasks = [run_chain(image, text) for image, text in zip(images, texts)]
    results = await asyncio.gather(*tasks)
    return results

# List of texts and images to process
input_texts = [
    "Hello, how are you?",
    "What is your name?",
    "Where are you from?",
    "What do you do?"
]

images_path = [
    "path_to_image1.jpg",
    "path_to_image2.jpg",
    "path_to_image3.jpg",
    "path_to_image4.jpg"
]

# Run the chains concurrently
results = asyncio.run(run_multiple_chains(input_texts, images_path))

# Print the results
for result in results:
    print(result)

This should resolve the RuntimeError by allowing asyncio.run() to be called within an existing event loop ^[1][2].

To continue talking to Dosu, mention @dosu.

[givkashi]

@dosu I got this error now. fix it and tell me how can I create a chain with 3 or more parts with RunnableSequence

2624 if not steps:
2625 if first is not None and last is not None:
-> 2626 steps_flat = [first] + (middle or []) + [last]
2627 for step in steps:
2628 if isinstance(step, RunnableSequence):

TypeError: can only concatenate list (not "ChatOpenAI") to list

[dosubot[bot]]

The error TypeError: can only concatenate list (not "ChatOpenAI") to list occurs because you are trying to concatenate a ChatOpenAI object directly to a list. To fix this, you need to ensure that all parts of the chain are correctly combined using the | operator.

Here's how you can create a chain with 3 or more parts using RunnableSequence:

1. Define each part of the chain.
2. Use the | operator to chain them together.

Here is an example:

import asyncio
import nest_asyncio
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_core.output_parsers.json import SimpleJsonOutputParser

nest_asyncio.apply()

# Define your LangChain components
llm = ChatOpenAI(model_name='openai/gpt-4o-mini')

template = "Translate the following English text to French: {text}"
prompt = PromptTemplate(template=template, input_variables=["text"])

# Define a simple output parser
output_parser = SimpleJsonOutputParser()

# Combine them into a RunnableSequence using the | operator
chain = prompt | llm | output_parser

# Define an async function to run the chain
async def run_chain(text):
    return await chain.ainvoke({"text": text})

# Define a function to run multiple chains concurrently
async def run_multiple_chains(texts):
    tasks = [run_chain(text) for text in texts]
    results = await asyncio.gather(*tasks)
    return results

# List of texts to process
input_texts = [
    "Hello, how are you?",
    "What is your name?",
    "Where are you from?",
    "What do you do?"
]

# Run the chains concurrently
results = asyncio.run(run_multiple_chains(input_texts))

# Print the results
for result in results:
    print(result)

In this example:

1. prompt is the first part of the chain.
2. llm (an instance of ChatOpenAI) is the second part.
3. output_parser (an instance of SimpleJsonOutputParser) is the third part.

By using the | operator, you correctly chain these parts together into a RunnableSequence ^[1].

To continue talking to Dosu, mention @dosu.