[feat]: implement github issue processor to scraper issue body, summarize and embed it

Author: smokeyScraper

backend/app/agents/devrel/github/prompts/contributor_recommendation/issue_summarization.py

@@ -0,0 +1,20 @@
+ISSUE_SUMMARIZATION_PROMPT = """You are a technical analyst optimizing GitHub issues for contributor search. 
+
+Analyze the provided GitHub issue and create a technical summary optimized for finding relevant expert contributors.
+
+Focus on:
+- Core technical problem or feature request
+- Specific technologies, frameworks, libraries, APIs mentioned
+- Technical skills and expertise required to solve this
+- Programming languages and tools involved
+- System components affected (frontend, backend, database, etc.)
+
+Create a summary that reads like a job requirement for finding the right technical expert.
+
+**GitHub Issue Content:**
+---
+{issue_content}
+---
+
+**Optimized Technical Summary for Contributor Search:**
+"""

backend/app/services/github/issue_processor.py

@@ -0,0 +1,83 @@
+import logging
+from typing import List
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.messages import HumanMessage
+
+from app.core.config import settings
+from app.services.embedding_service.service import EmbeddingService
+from app.services.github.user.profiling import GitHubUserProfiler
+from app.agents.devrel.github.prompts.contributor_recommendation.issue_summarization import ISSUE_SUMMARIZATION_PROMPT
+
+logger = logging.getLogger(__name__)
+
+
+class GitHubIssueProcessor:
+    """
+    A service to fetch, summarize, and embed a GitHub issue.
+    """
+
+    def __init__(self, owner: str, repo: str, issue_number: int):
+        self.owner = owner
+        self.repo = repo
+        self.issue_number = issue_number
+        self.summarizer_llm = ChatGoogleGenerativeAI(
+            model=settings.github_agent_model,
+            temperature=0.1,
+            google_api_key=settings.gemini_api_key
+        )
+        self.embedding_service = EmbeddingService()
+
+    async def _fetch_issue_content(self) -> str:
+        """
+        Fetches and consolidates all text content from a GitHub issue.
+        """
+        logger.info(f"Fetching content for {self.owner}/{self.repo}#{self.issue_number}")
+        async with GitHubUserProfiler() as profiler:
+            issue_url = f"{profiler.base_url}/repos/{self.owner}/{self.repo}/issues/{self.issue_number}"
+            comments_url = f"{issue_url}/comments"
+
+            issue_data = await profiler._make_request(issue_url)
+            if not issue_data:
+                raise ValueError("Failed to fetch issue data.")
+
+            content_parts = [
+                f"Title: {issue_data['title']}",
+                f"Body: {issue_data['body']}",
+            ]
+
+            comments_data = await profiler._make_request(comments_url)
+            if comments_data:
+                comment_texts = [
+                    f"Comment by {c['user']['login']}: {c['body']}"
+                    for c in comments_data if c.get('body')
+                ]
+                content_parts.extend(comment_texts)
+
+            return "\n\n---\n\n".join(content_parts)
+
+    async def _summarize_context(self, content: str) -> str:
+        """Generates a technical summary of the issue content using an LLM."""
+        logger.info(f"Summarizing issue content for {self.owner}/{self.repo}#{self.issue_number}")
+        prompt = ISSUE_SUMMARIZATION_PROMPT.format(issue_content=content)
+        response = await self.summarizer_llm.ainvoke([HumanMessage(content=prompt)])
+        logger.info(f"Generated summary: {response.content.strip()[:100]}")
+        return response.content.strip()
+
+    async def get_embedding_for_issue(self) -> List[float]:
+        """
+        Orchestrates the entire process: fetch, summarize, and embed.
+        Returns a vector embedding representing the issue.
+        """
+        try:
+            content = await self._fetch_issue_content()
+            if not content:
+                raise ValueError("Failed to fetch issue content.")
+
+            summary = await self._summarize_context(content)
+
+            logger.info("Embedding issue summary")
+            embedding = await self.embedding_service.get_embedding(summary)
+            return embedding
+        except Exception as e:
+            logger.error(f"Error processing issue {self.owner}/{self.repo}#{self.issue_number}: {str(e)}")
+            raise e