jetxu-llm / llama-github Goto Github PK

Task 1.1: Initial Repository Content Analysis

• Use GitHub API to retrieve repository content including README, code, issues, and discussions.
• Implement basic content extraction and preliminary analysis features.
• Estimated Time: 2 weeks

Task 1.2: Integrate Advanced Algorithms for In-depth Analysis

• Integrate advanced algorithms such as knowledge graphs, multi-hop reasoning, PageRank, hybrid search, and BERT.
• Optimize the content analysis process to ensure accurate context retrieval for user queries.
• Estimated Time: 4 weeks

The advanced_integration.md file in the llama-github library's documentation provides guidance on advanced integration scenarios and techniques. However, the current examples in the document are limited and may not cover all the common use cases. To make the library more accessible and easier to understand, we aim to enhance the advanced_integration.md file by adding more comprehensive and diverse usage examples.

Tasks:

1. Review the existing content in the advanced_integration.md file and identify areas where additional examples would be beneficial.
2. Brainstorm and collect real-world use cases and scenarios that showcase the advanced features and capabilities of the llama-github library.
3. Create clear and concise code examples demonstrating how to integrate and utilize the library's functionality in each identified scenario.
4. Provide step-by-step explanations and comments within the code examples to guide users through the integration process.
5. Ensure that the examples cover a range of difficulty levels, from beginner-friendly to more advanced techniques.
6. Organize the examples in a logical manner, grouping related scenarios together or categorizing them based on their purpose.
7. Test the code examples to verify their accuracy and functionality.
8. Integrate the new examples seamlessly into the existing advanced_integration.md file, maintaining a consistent formatting and writing style.
9. Proofread the updated document for clarity, grammar, and spelling errors.

Skill level: Beginner
Language: Markdown, Python

Your contributions to enhancing the documentation will greatly benefit the llama-github library's users and make it more accessible to a wider audience. If you have any questions or suggestions during the process, feel free to discuss them in the comments.

Allow users to customize retrieval strategies and criteria.

Objective: Enable Llama-github to perform a detailed analysis of a single repository to retrieve context for user queries.

1. Task 1.1: Initial Repository Content Analysis

   • Use GitHub API to retrieve repository content including README, code, issues, and discussions.
   • Implement basic content extraction and preliminary analysis features.
   • Estimated Time: 2 weeks

2. Task 1.2: Integrate Advanced Algorithms for In-depth Analysis

   • Integrate advanced algorithms such as knowledge graphs, multi-hop reasoning, PageRank, hybrid search, and BERT.
   • Optimize the content analysis process to ensure accurate context retrieval for user queries.
   • Estimated Time: 4 weeks

3. Task 1.3: Optimize Retrieval Results

   • Refine retrieval algorithms based on user feedback and testing.
   • Implement relevance scoring and ranking mechanisms for retrieval results.
   • Estimated Time: 2 weeks

• Add support for more LLM providers (e.g., Google API, Claude3).
• Optimize model selection mechanism to automatically choose the most suitable model for specific tasks.

When calling the retrieve_context function in the llama-github library, a warning message is occasionally displayed in the console. This warning message may be related to deprecated usage or potential issues within the library's dependencies. The goal of this issue is to investigate the cause of the warning and eliminate it to provide a cleaner and more user-friendly experience.

Steps to Reproduce:

1. Install the llama-github library following the installation guidelines.
2. Use the retrieve_context function with a valid query and other required parameters.
3. Observe the warning message displayed in the console.

Tasks:

1. Investigate the source of the warning message by examining the library's codebase and its dependencies.
2. Identify the specific line or section of code that triggers the warning.
3. Analyze the warning message and determine the appropriate course of action to resolve it.
4. Implement the necessary changes to eliminate the warning, ensuring that the functionality of the library remains unaffected.
5. Test the modified code thoroughly to verify that the warning no longer appears and that the retrieve_context function behaves as expected.
6. Update the relevant documentation, including any release notes or changelog, to mention the resolved warning.

Skill level: Beginner
Language: Python

If you encounter any difficulties or have questions during the process, don't hesitate to reach out for assistance. We value your effort in improving the llama-github library's user experience.

• Support code retrieval and analysis for multiple programming languages.
• Optimize language models to ensure high accuracy in a multilingual environment.

Currently, the GithubRAG class in the llama-github library provides a retrieve_context method that retrieves relevant context based on a user's query. However, users often need a more straightforward way to obtain direct answers to their questions without explicitly handling the context retrieval process. To enhance the usability and convenience of the library, we propose adding an answer_with_context method to the GithubRAG class.

The answer_with_context method should accept a user's query and an optional contexts parameter. If the contexts parameter is not provided, the method should internally call the retrieve_context method to obtain the relevant context based on the user's query. Once the context is available, the method should invoke the self.rag_processor.llm_handler.ainvoke() method to generate the final answer based on the retrieved context.

Tasks:

1. Design the interface for the answer_with_context method, including the method signature and parameter definitions.
2. Implement the logic to check if the contexts parameter is provided. If not, call the retrieve_context method to obtain the relevant context based on the user's query.
3. Integrate the self.rag_processor.llm_handler.ainvoke() method to generate the final answer based on the retrieved or provided context.
4. Handle any necessary error handling and edge cases, such as empty context or invalid queries.
5. Write unit tests to verify the functionality of the answer_with_context method, covering different scenarios such as providing context explicitly and relying on the internal context retrieval.
6. Update the library's documentation, including the API reference and usage examples, to showcase the new answer_with_context method and its usage.
7. Provide clear and concise inline comments within the implemented code to explain the purpose and functionality of each section.
8. Ensure that the implementation follows the coding style and best practices maintained in the llama-github library.
9. Test the answer_with_context method thoroughly with various queries and context scenarios to ensure its robustness and reliability.

Skill level: Intermediate
Language: Python

By implementing the answer_with_context method, we aim to provide a more user-friendly and efficient way for users to obtain direct answers to their queries without the need to manually handle context retrieval. This enhancement will improve the overall user experience and make the llama-github library more versatile and convenient to use.

If you have any suggestions, ideas, or concerns regarding the implementation or design of the answer_with_context method, please share them in the comments. We value your feedback and collaboration in making this feature addition successful.

Let's work together to enhance the capabilities of the llama-github library and provide a smoother experience for our users!

Task 1.3: Optimize Retrieval Results

• Refine retrieval algorithms based on user feedback and testing.
• Implement relevance scoring and ranking mechanisms for retrieval results.
• Estimated Time: 2 weeks

Hello, developers!

We're thrilled to have you interested in contributing to llama-github. This library aims to empower LLM Chatbots, AI Agents, and Auto-dev Agents by retrieving relevant context from GitHub through search and public repositories.

To get started, please take a moment to review our contribution guidelines: CONTRIBUTING.md. It outlines the process for submitting issues, proposing changes, and submitting pull requests.

If you're new to the library, we recommend checking out our documentation: Documentation. It provides installation instructions, usage examples, and API references to help you understand how to use and contribute to llama-github effectively.

We have identified a few beginner-friendly issues that you can work on to familiarize yourself with the library:

• Issue 1: Add meta data to output contexts, e.g. URL
• Issue 2: Eliminate the warning during retrieve context call
• Issue 3: Enhance advanded_integration.md documentation with more usage examples

Feel free to explore these issues and pick one that interests you. If you have any questions or need assistance, don't hesitate to reach out to us through GitHub Issues or join our Discussion.

We appreciate your contributions and look forward to collaborating with you to make llama-github even better!

Thank you,
The llama-github Team

Currently, the retrieve_context function in the llama-github library returns a list of relevant context strings based on the provided query. However, it would be beneficial to include additional meta data, such as the URL of the source code or documentation, along with each context string. This enhancement will provide more context and make it easier for users to trace back the origin of the retrieved information.

Tasks:

1. Modify the retrieve_context function to include meta data, such as the URL, alongside each context string.
2. Update the return type of retrieve_context to accommodate the meta data, e.g., using a dictionary or a custom object.
3. Ensure that the meta data is correctly populated during the retrieval process.
4. Update the relevant documentation and docstrings to reflect the changes in the function's input and output.
5. Add unit tests to verify the correctness of the meta data retrieval and association with the context strings.

Skill level: Beginner
Language: Python

If you have any questions or need further clarification, feel free to ask! We appreciate your contribution to enhancing the llama-github library.