Fixing Litellm.APIConnectionError OllamaException Timeout Error In Crawl4AI

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Introduction

This article addresses a bug encountered while using the crawl4ai library, specifically the litellm.APIConnectionError: OllamaException - litellm.Timeout error. This issue arose during an attempt to extract content from a Wikipedia page using an Ollama model for language processing. This comprehensive guide will walk you through the details of the bug, the troubleshooting steps, and potential solutions, ensuring you can effectively use crawl4ai for your web crawling and content extraction needs.

Background

The user, using crawl4ai version 0.6.3, aimed to extract information from a Wikipedia page. The Ollama model was downloaded and expected to function correctly. The user replicated code examples to create a Proof of Concept (PoC) for future development. However, the litellm.APIConnectionError was encountered, indicating a timeout issue. This article provides a detailed analysis of the error, the environment in which it occurred, and the steps taken to reproduce it. Understanding the intricacies of this error is crucial for developers and researchers alike, as it sheds light on potential pitfalls when integrating large language models (LLMs) with web crawling tools.

Expected Behavior

The user expected the crawl4ai library to successfully extract content from the specified Wikipedia page using the Ollama model. The desired outcome was to retrieve and process the page's information without encountering any timeout errors. Specifically, the user aimed to leverage the LLMExtractionStrategy within crawl4ai to generate markdown content based on the provided query. The expected behavior included a successful crawl, extraction of relevant content, and generation of markdown output, all within a reasonable timeframe. Any deviation from this, such as a timeout error, would be considered a bug requiring investigation.

Current Behavior

Instead of successful content extraction, the user encountered the following error: litellm.APIConnectionError: OllamaException - litellm.Timeout: Connection timed out after 600.0 seconds. This error indicates that the connection to the Ollama model timed out after 600 seconds, preventing the extraction process from completing successfully. The error message suggests a failure in establishing or maintaining a connection with the Ollama service, leading to the interruption of the content extraction task. This behavior deviates from the expected outcome and necessitates a thorough examination of the configuration, network settings, and resource limitations to identify the root cause of the timeout.

Reproducibility

Yes, this bug is reproducible. The user confirmed that the error consistently occurs when attempting to run the provided code snippet with the specified URL and query. This reproducibility is crucial for debugging and resolving the issue, as it allows developers to reliably recreate the error and test potential fixes. The consistent nature of the bug suggests that it is likely tied to specific configurations, resource constraints, or network conditions, rather than being an intermittent or random occurrence. By confirming reproducibility, the user has provided valuable information that aids in the diagnostic process.

Steps to Reproduce

To reproduce the bug, follow these steps:

  1. Set up an environment with crawl4ai version 0.6.3 and Python 3.11.2.
  2. Ensure the Ollama model is downloaded and accessible.
  3. Run the provided Python code snippet.
  4. Input a query when prompted (e.g., "wer hat sie bestellt?").
  5. Observe the error message litellm.APIConnectionError: OllamaException - litellm.Timeout: Connection timed out after 600.0 seconds.

These steps provide a clear and concise way for others to replicate the issue, facilitating collaboration and efficient debugging. The detailed instructions ensure that anyone can recreate the environment and circumstances under which the error occurs, making it easier to identify the underlying cause and develop a solution.

Code Snippets

The following Python code snippet was used to reproduce the bug:

import asyncio
import os

from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai import LLMConfig
from crawl4ai.extraction_strategy import (
    LLMExtractionStrategy,
    JsonCssExtractionStrategy,
    JsonXPathExtractionStrategy,
)
from crawl4ai.content_filter_strategy import PruningContentFilter
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator

print("?")
query = input()


async def run_extraction(crawler: AsyncWebCrawler, url: str, strategy, name: str):
    """Helper function to run extraction with proper configuration"""
    try:
        # Configure the crawler run settings
        config = CrawlerRunConfig(
            cache_mode=CacheMode.BYPASS,
            extraction_strategy=strategy,
            markdown_generator=DefaultMarkdownGenerator(
                content_filter=PruningContentFilter()  # For fit_markdown support
            ),
        )

        # Run the crawler
        result = await crawler.arun(url=url, config=config)

        if result.success:
            print(f"\n=== {name} Results ===")
            print(f"Extracted Content: {result.extracted_content}")
            print(f"Raw Markdown Length: {len(result.markdown.raw_markdown)}")
            print(
                f"Citations Markdown Length: {len(result.markdown.markdown_with_citations)}"
            )
        else:
            print(f"Error in {name}: Crawl failed")

    except Exception as e:
        print(f"Error in {name}: {str(e)}")


async def main():
    # Example URL (replace with actual URL)
    url = "https://de.wikipedia.org/wiki/Kleinbahn_Philippsheim-Binsfeld_1%E2%80%932"

    # Configure browser settings
    browser_config = BrowserConfig(headless=True, verbose=True)

    # Initialize extraction strategies

    # 1. LLM Extraction with different input formats
    markdown_strategy = LLMExtractionStrategy(
        llm_config = LLMConfig(provider="ollama/glm4", api_token=None),
        instruction= query,
    )
        # Use context manager for proper resource handling
        
    async with AsyncWebCrawler(config=browser_config) as crawler:
        # Run
        await run_extraction(crawler, url, markdown_strategy, "Markdown LLM")


if __name__ == "__main__":
    asyncio.run(main())

This code snippet showcases the implementation using crawl4ai to extract content from a Wikipedia page. It includes configurations for the browser, extraction strategy, and LLM settings. The key part is the LLMExtractionStrategy with llm_config set to use ollama/glm4 without an API token. The run_extraction function handles the crawling and extraction process, while the main function sets up the configurations and runs the crawler. The error occurs during the crawler.arun call, specifically within the LLM processing step, leading to the timeout. Analyzing this code helps identify potential areas for optimization and debugging, such as the LLM configuration, network settings, or resource limitations.

Operating System and Environment

The bug was reproduced on the following operating system:

  • Debian
  • Python version: Python 3.11.2

Understanding the operating system and Python version is crucial for identifying environment-specific issues. Debian, a widely used Linux distribution, is known for its stability and security. However, specific configurations or limitations within the Debian environment could contribute to the timeout issue. Similarly, the Python version can impact the behavior of libraries and dependencies. By specifying the OS and Python version, developers can narrow down potential compatibility issues or environment-related factors that might be causing the error. This information is essential for a comprehensive troubleshooting process.

Browser Information

Browser information was not provided in the initial report. However, the BrowserConfig in the code snippet indicates that the crawler was configured to run in headless mode with verbose logging. This means that the browser operates in the background without a graphical user interface. While the specific browser and version are not specified, the headless mode itself can influence resource usage and performance. Providing browser details, such as the type (e.g., Chrome, Firefox) and version, can further assist in diagnosing browser-related issues that might contribute to the timeout error. Future bug reports should include browser information for completeness.

Error Logs and Analysis

The following error logs were provided:

(crawl4ai) llama@debian-ki:~/crawlTest$ /home/llama/.venvs/crawl4ai/bin/python /home/llama/crawlTest/wikipediaTest.py
?
wer hat sie bestellt?
[INIT].... → Crawl4AI 0.6.3 
[FETCH]... ↓ https://de.wikipedia.org/wiki/Kleinbahn_Philippsheim-Binsfeld_1–2                                    | ✓ | ⏱: 0.48s 
[SCRAPE].. ◆ https://de.wikipedia.org/wiki/Kleinbahn_Philippsheim-Binsfeld_1–2                                    | ✓ | ⏱: 0.15s 

Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new
LiteLLM.Info: If you need to debug this error, use `litellm._turn_on_debug()'.


Provider List: https://docs.litellm.ai/docs/providers

[EXTRACT]. ■ Completed for https://de.wikipedia.org/wiki/Kleinbahn_Philippshe... | Time: 601.5632811149999s 
[COMPLETE] ● https://de.wikipedia.org/wiki/Kleinbahn_Philippsheim-Binsfeld_1–2                                    | ✓ | ⏱: 602.19s 

=== Markdown LLM Results ===
Extracted Content: [
    {
        "index": 0,
        "error": true,
        "tags": [
            "error"
        ],
        "content": "litellm.APIConnectionError: OllamaException - litellm.Timeout: Connection timed out after 600.0 seconds."
    }
]
Raw Markdown Length: 23581
Citations Markdown Length: 9943

The error logs provide valuable insights into the sequence of events leading to the timeout. The logs show that the initial steps of fetching and scraping the Wikipedia page were successful, with timings of 0.48s and 0.15s respectively. However, the extraction process timed out after 601.56 seconds, exceeding the 600-second timeout limit. The extracted content array includes an error object with the message litellm.APIConnectionError: OllamaException - litellm.Timeout: Connection timed out after 600.0 seconds. This clearly indicates that the issue occurred during the interaction with the Ollama model. Additionally, the logs suggest debugging with litellm._turn_on_debug(), which can provide more detailed information about the Litellm library's internal operations. The raw and citations markdown lengths indicate the amount of data processed before the error occurred, suggesting that the timeout is not necessarily due to the volume of data but rather the processing time or connectivity issues with the Ollama model. Analyzing these logs is crucial for pinpointing the exact stage where the error occurs and identifying potential solutions.

Root Cause Analysis

Based on the error logs and the code provided, the root cause of the issue appears to be a timeout while connecting to or processing data with the Ollama model. The litellm.APIConnectionError: OllamaException - litellm.Timeout error specifically indicates that the connection timed out after 600 seconds. Several factors could contribute to this:

  1. Resource Constraints: The Ollama model might be resource-intensive, and the Debian environment might not have sufficient CPU, memory, or GPU resources to process the request within the timeout period. This is particularly relevant if the model is large or the input query requires extensive computation.
  2. Network Issues: There might be network connectivity problems between the crawl4ai application and the Ollama model. This could include slow network speeds, intermittent connectivity, or firewall restrictions that prevent the application from reaching the model.
  3. Ollama Model Performance: The Ollama model itself might be experiencing performance issues, such as slow response times or internal errors. This could be due to the model being overloaded, bugs in the model implementation, or issues with the underlying infrastructure hosting the model.
  4. Configuration Issues: There might be misconfigurations in the crawl4ai or Litellm settings that are causing the timeout. This could include incorrect API keys, improperly configured timeouts, or other settings that affect the connection to the Ollama model.
  5. Input Complexity: The complexity of the input query or the size of the content being processed might be exceeding the model's capabilities within the timeout period. Complex queries require more processing time, and large documents take longer to analyze.

To further investigate the root cause, it's essential to:

  • Monitor resource usage (CPU, memory, GPU) during the extraction process.
  • Check network connectivity and latency between the application and the Ollama model.
  • Review the Ollama model's logs for any internal errors or performance issues.
  • Verify the configuration settings for crawl4ai and Litellm.
  • Simplify the input query or reduce the size of the content being processed.

By systematically examining these factors, it is possible to identify the specific cause of the timeout and implement appropriate solutions.

Proposed Solutions

To address the litellm.APIConnectionError: OllamaException - litellm.Timeout error, consider the following solutions:

  1. Increase Timeout Settings: Adjust the timeout settings in the crawl4ai configuration to allow more time for the Ollama model to process the request. This can be done by modifying the LLMConfig within the LLMExtractionStrategy. For example:

    markdown_strategy = LLMExtractionStrategy(
    llm_config=LLMConfig(provider="ollama/glm4", api_token=None, timeout=1200),
    instruction=query,
)

    Here, the timeout parameter is increased to 1200 seconds (20 minutes). This provides the Ollama model with more time to process complex queries or large documents.

  2. Optimize Resource Allocation: Ensure that the Debian environment has sufficient resources (CPU, memory, GPU) allocated to the Ollama model. This might involve increasing the system's resources or optimizing the model's resource usage. If running Ollama in a containerized environment (e.g., Docker), ensure the container has adequate resource limits. Monitoring resource usage during the extraction process can help identify bottlenecks.

  3. Improve Network Connectivity: Check the network connection between the crawl4ai application and the Ollama model. Ensure there are no network bottlenecks, firewalls, or other issues that might be causing connectivity problems. Using a more stable or faster network connection can help reduce the likelihood of timeouts.

  4. Use a More Robust LLM Provider: Consider using a different LLM provider or model that is known for its reliability and performance. While Ollama is a powerful tool for local LLM execution, it may be subject to resource limitations or performance issues. Cloud-based LLM providers (e.g., OpenAI, Cohere) offer scalable and reliable services, albeit with associated costs.

  5. Implement Error Handling and Retries: Add error handling and retry mechanisms to the code to gracefully handle timeout errors. This can involve catching the litellm.APIConnectionError and retrying the request after a delay. Exponential backoff strategies can be used to avoid overwhelming the Ollama model with retries.

  6. Simplify Input Queries: Break down complex queries into smaller, more manageable parts. This reduces the processing time required by the Ollama model and decreases the likelihood of timeouts. Instead of a single complex query, use multiple simpler queries and combine the results.

  7. Implement Caching: Cache the results of previous queries to avoid redundant processing. This can significantly reduce the load on the Ollama model and improve overall performance. Implement a caching mechanism that stores the responses for frequently used queries and reuses them when appropriate.

By implementing these solutions, you can mitigate the litellm.APIConnectionError and improve the reliability of your crawl4ai application.

Conclusion

In conclusion, the litellm.APIConnectionError: OllamaException - litellm.Timeout error encountered while using crawl4ai with the Ollama model highlights the importance of proper configuration, resource management, and error handling when integrating LLMs into web crawling applications. This article has provided a comprehensive analysis of the bug, including the expected behavior, current behavior, steps to reproduce, code snippets, environmental details, error logs, root cause analysis, and proposed solutions. By addressing the timeout issue through increased timeout settings, optimized resource allocation, improved network connectivity, and robust error handling, developers can ensure the successful extraction of content using crawl4ai and LLMs. This detailed exploration serves as a valuable resource for anyone facing similar challenges, promoting a deeper understanding of the complexities involved in web crawling and LLM integration. Remember, a systematic approach to debugging and implementing proactive solutions is key to building reliable and efficient applications.