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Usage

llama-github has two primary workflows:

  • retrieve GitHub-derived context blocks with retrieve_context()
  • answer a question from already available context with answer_with_context()

Initialization

from llama_github import GithubRAG

github_rag = GithubRAG(
    github_access_token="your_github_access_token",
    mistral_api_key="your_mistral_api_key",
)

Supported chat model strategies:

  • openai_api_key=...
  • mistral_api_key=...
  • llm=your_langchain_compatible_chat_model

For request-scoped or serverless usage, pass repo_cleanup_enabled=False. For a long-lived process, call github_rag.close() during shutdown or use GithubRAG as a context manager.

Context Retrieval

contexts = github_rag.retrieve_context("How do I create a NumPy array in Python?")

Return type:

List[Dict[str, str]]

Each item contains at least:

  • context
  • url

Simple Mode

contexts = github_rag.retrieve_context(
    "How do I create a NumPy array in Python?",
    simple_mode=True,
)

simple_mode=True:

  • skips embedding and reranker loading
  • uses deterministic fallback ranking
  • is the recommended mode for examples and smoke tests

Answering With Existing Context

answer = github_rag.answer_with_context(
    "How do I create a NumPy array in Python?",
    contexts=[
        {
            "context": "Use numpy.array([...]) to create a NumPy array.",
            "url": "https://numpy.org/doc/stable/reference/generated/numpy.array.html",
        }
    ],
)

answer_with_context() also accepts context items using a content key for backward compatibility.

Asynchronous Usage

import asyncio

async def main():
    contexts = await github_rag.async_retrieve_context(
        "How do I create a NumPy array in Python?"
    )
    print(contexts)

asyncio.run(main())

Pull Request Content Retrieval

repo = github_rag.RepositoryPool.get_repository("JetXu-LLM/llama-github")
pr_content = repo.get_pr_content(number=15)
print(pr_content["pr_metadata"]["title"])
print(pr_content["pr_metadata"]["head_sha"])
print(pr_content["_retrieval_meta"]["pr_files"])

This method is useful when you want structured PR metadata, changed files, interactions, and related issue context in one object. Related issues come only from the PR title/body and top-level PR comments. Review summaries and inline review comments remain separate interaction records, so callers do not lose multiple inline comments attached to one review.

_retrieval_meta records bounded-fetch outcomes. A partial or error result is an unknown, not evidence that a file, comment, or match does not exist.

To refresh CI evidence later without refetching the whole pull request:

ci_snapshot = repo.get_ci_status_with_status(pr_content["pr_metadata"]["head_sha"])
print(ci_snapshot.outcome.value)
print(ci_snapshot.to_dict())

This helper is pinned to the supplied head SHA and keeps commit statuses and check runs independently typed. Status history is reduced to the newest result per GitHub context, while retrieval metadata retains both fetched and current item counts. Its aggregate outcome is retrieval metadata, not a merge verdict.

Logging

llama-github does not auto-configure logging on import. If you want library logs:

import logging
from llama_github import configure_logging

configure_logging(level=logging.INFO)

Library logs contain operation names, counts, lengths, status codes, and error types. They intentionally omit raw queries, retrieved contexts, response bodies, and private source content.