[Question]: Chat engine takes long time to generate output for the first query

[Prem-Nitin]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

When using chat engine with stream_chat it takes long time to generate output just for the first query. The subsequent queries are processed quickly. Why does this problem occur and what to do to resolve this. I am using vertex ai for llm and embed model

[dosubot]

The delay you're experiencing with the first query in your chat engine, when using Vertex AI for LLM and an embed model, is likely due to a few key factors:

1. Model Initialization: The first query might be slow because the model takes time to load and initialize. Keeping the model loaded in memory between queries can mitigate this.

2. Cold Start in Serverless Deployments: If your application is serverless, the initial delay could be a cold start. Optimizing your serverless function's configuration can help reduce this latency.

3. Network Latency: Ensure your application and Vertex AI are in the same region to minimize network delays.

4. Retry Logic: The default retry logic, with max_retries set to 10, might cause delays if the initial requests to Vertex AI fail. Adjusting the retry logic based on error types can help.

5. Asynchronous Processing: Utilizing asynchronous methods for processing can improve responsiveness by allowing other tasks to proceed while waiting for the query to complete.

By addressing these areas, you should be able to reduce the initial delay and improve the responsiveness of your chat engine for the first query.

Details

Sources

• llama-index-integrations/llms/llama-index-llms-vertex/llama_index/llms/vertex/base.py
• llama-index-networks/llama_index/networks/network/query_engine.py
• llama-index-core/llama_index/core/tools/query_engine.py
• llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/utils.py
• llama-index-legacy/llama_index/legacy/llms/vertex_utils.py
• llama-index-core/llama_index/core/query_engine/retry_query_engine.py
• llama-index-legacy/llama_index/legacy/llms/litellm.py
• llama-index-core/llama_index/core/ingestion/cache.py
• llama-index-legacy/llama_index/legacy/ingestion/cache.py

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

[Prem-Nitin]

@dosu-bot the model is initialized and assigned to settings.llm. But it looks like it only connects to the api when the first query is given. How to overcome this

[dosubot]

To overcome the issue of the model only connecting to the API upon the first query, implementing a preload step for your model during your application's initialization process is an effective strategy. This involves making a dummy or lightweight request to the model right after your application starts, but before any real user queries are made. This step ensures that the model is "warmed up" and ready for immediate use, thus avoiding the initial delay you're experiencing.

Here's a step-by-step guide to preloading your model:

1. Initialize Vertex AI: Make sure that your Vertex AI environment is set up with the correct project, location, and credentials. This is crucial for establishing a successful connection to Vertex AI services.

2. Preload the Model: Implement a method that sends a dummy request to your model. This request is intended to activate and keep the model in a ready state.

Below is an example in Python that demonstrates how to preload your model:

from llama_index.legacy.llms.vertex_utils import init_vertexai, completion_with_retry
from llama_index.legacy.llms.vertex import Vertex

def preload_model(model_name, project, location, credentials):
    # Initialize Vertex AI with your project details and credentials
    init_vertexai(project=project, location=location, credentials=credentials)
    
    # Configure the model for preloading
    vertex_model = Vertex(
        model=model_name,
        project=project,
        location=location,
        credentials=credentials,
        temperature=0.1,
        max_tokens=512,
        max_retries=3,
    )
    
    # Execute a dummy request to preload the model
    try:
        completion_with_retry(
            client=vertex_model._client,
            prompt="Hello world!",  # This is a simple prompt for the dummy request
            max_retries=3,
        )
        print("Model preloaded successfully.")
    except Exception as e:
        print(f"Failed to preload model: {e}")

# Example usage
preload_model("your-model-name", "your-project-id", "your-location", "your-credentials")

This script initializes and preloads your model by making a dummy request, ensuring that it's primed for immediate use. Make sure to replace "your-model-name", "your-project-id", "your-location", and "your-credentials" with your actual model and project details.

By following this approach, you should be able to significantly reduce or eliminate the initial delay for the first query, enhancing the responsiveness of your chat engine.

Details

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.