Reducing inference time for Agents and Chains (GPT-3.5-turbo-16k)

[JasonIsaac]

Hello Everyone,

I have created a LangChain application in Flask with Agent+ custom tools+3 LLM Chains. For every request the flow goes through all the components. Because every step depends on the previous step( i.e, sequential), so response time is slow.

Flow:

1. Chain 1
2. Chain 2
3. Agent
4. Chain 3

Any ideas how to optimize this and reduce response time?.

[dosubot[bot]]

🤖

Hello,

To optimize your LangChain application and reduce response time, you can consider the following strategies:

1. Concurrency: Python's asyncio library can be used to run multiple tasks concurrently. This can significantly reduce the total execution time if the tasks can be run in parallel.

2. Early Stopping: The AgentExecutor class has a max_iterations attribute that limits the number of steps in the execution loop. This can prevent the application from spending too much time on tasks that are unlikely to complete. Similarly, the max_execution_time attribute can be used to limit the total execution time.

3. Trimming Intermediate Steps: The _prepare_intermediate_steps method can be used to trim the list of intermediate steps. This can reduce the amount of data that needs to be processed in each iteration of the execution loop.

4. Error Handling: The handle_parsing_errors attribute can be used to control how errors are handled. By setting this attribute to True, errors will be sent back to the LLM as an observation, which can prevent the application from crashing and wasting time on error handling.

5. Direct Return from Tools: The _get_tool_return method checks if a tool should return directly. If a tool has the return_direct attribute set to True, it will return immediately without going through the rest of the execution loop. This can save time if the tool's output is already the final result. However, note that in the context shared, tools that have return_direct=True are not allowed in multi-action agents.

Here is an example of how you can implement these strategies in your Flask application:

from flask import Flask, request
from libs.langchain.langchain.agents.agent import AgentExecutor, LLMSingleActionAgent
from libs.langchain.langchain.chains.llm import LLMChain
from libs.langchain.langchain.tools.base import BaseTool

app = Flask(__name__)

@app.route('/process', methods=['POST'])
async def process():
    data = request.get_json()
    # Initialize your LLM chains, tools, and agent here
    llm_chains = [LLMChain(), LLMChain(), LLMChain()]
    tools = [BaseTool(), BaseTool(), BaseTool()]
    agent = LLMSingleActionAgent(llm_chain=llm_chains, tools=tools)
    executor = AgentExecutor(agent=agent, tools=tools, max_iterations=10, max_execution_time=60.0)
    result = await executor.run(data)
    return result

In this example, the /process endpoint accepts POST requests with JSON data. The AgentExecutor is initialized with the LLM chains, tools, and agent, and it is configured to stop after 10 iterations or 60 seconds. The run method is then called with the input data, and the result is returned as the response.

To get a better understanding of the execution time for each Chain and the Agent in your current setup, you might need to add some logging or timing code to your application. This will help you identify which parts of your application are taking the most time and where you should focus your optimization efforts.

I hope this helps! Let me know if you have any other questions.

Sources

• libs/langchain/langchain/agents/agent.py

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