diff --git a/PRChanges.patch b/PRChanges.patch
index a5886de0..e69de29b 100644
--- a/PRChanges.patch
+++ b/PRChanges.patch
@@ -1,610 +0,0 @@
-diff --git a/pr_agent/PR_agent.py b/pr_agent/PR_agent.py
-index abf2819..945121d 100644
---- a/pr_agent/PR_agent.py
-+++ b/pr_agent/PR_agent.py
-@@ -3,43 +3,55 @@ import re
- import json
- import requests
- from typing import Dict, List, Any, Generator, Optional, Tuple
--from pydantic import Field
-+from pydantic import Field, BaseModel
- from dotenv import load_dotenv
- from agentic.common import Agent, AgentRunner, ThreadContext
--from agentic.events import Event, ChatOutput, TurnEnd
--from agentic.models import GPT_4O_MINI, GPT_4O
--from agentic.tools.a2a_tool import A2ATool
-+from agentic.events import Event, ChatOutput, TurnEnd, PromptStarted, Prompt
-+from agentic.models import GPT_4O_MINI
-
--import rag_sub_agent
--import summary_agent
-+from code_rag_agent import CodeRagAgent
-+from summary_agent import SummaryAgent
- from code_rag_agent import CodeSection, CodeSections
- from pydantic import BaseModel
-
- load_dotenv()
-
- class SearchResult(BaseModel):
-- query: str
-- file_path: str
-- content: str
-- similarity_score: float
-- is_relevant: bool = True
-- relevance_reason: str = ""
-- included_defs: List[str] = Field(default_factory=list)
--
--class PRReviewContext(BaseModel):
-- # Input
-- patch_content: str = Field(..., description="The content of the patch file")
--
-- # Generated Data
-- search_queries: List[str] = Field(default_factory=list)
-- search_results: List[SearchResult] = Field(default_factory=list)
-- code_sections: CodeSections = Field(default_factory=CodeSections)
-- pr_summary: str = ""
-- github_comment_url: Optional[str] = None
-+ query: str = Field(
-+ description="Query used in this search."
-+ )
-+ file_path: str = Field(
-+ description="Path of the file this code/documentation belongs to."
-+ )
-+ content: str = Field(
-+ description="Content returned from search."
-+ )
-+ similarity_score: float = Field(
-+ desciption="Similarity score returned from vector search."
-+ )
-+ is_relevant: bool = Field(
-+ default = True,
-+ description="Boolean describing if the search result is relevant to the query."
-+ )
-+ relevance_reason: str = Field(
-+ default = "",
-+ description="Boolean describing if the search result is relevant to the query."
-+ )
-+ included_defs: List[str] = Field(
-+ default_factory=list,
-+ desciption="Similarity score returned from vector search."
-+ )
-+
-+class Searches(BaseModel):
-+ searches: List[str] = Field(
-+ description="Search queries."
-+ )
-+
-+class RelevanceResult(BaseModel):
-+ relevant: bool
-+ reason: str
-
- class PRReviewAgent(Agent):
-- context: PRReviewContext
-- a2a_tool: A2ATool
-
- def __init__(
- self,
-@@ -54,194 +66,43 @@ class PRReviewAgent(Agent):
- model=model,
- **kwargs
- )
-- self.a2a_tool = A2ATool()
-- self._register_agents()
-+ self.code_rag_agent = CodeRagAgent()
- self.verbose = verbose
-- self.relevance_model = GPT_4O
-
-- def _register_agents(self):
-- """Register sub-agents with the A2A tool"""
-- self.a2a_tool.register_agent(
-- "rag_sub_agent",
-- rag_sub_agent.agent,
-- "Expert in using RAG for retrieving relevant code context."
-- )
-- self.a2a_tool.register_agent(
-- "pr_summary_agent",
-- summary_agent.agent,
-- "Expert in generating PR summaries based on code changes and context."
-+ self.queryAgent = Agent(
-+ name="Code Query Agent",
-+ instructions=
-+"""
-+You are an expert in generating NON-NATURAL LANGUAGE CODE search queries from a patch file to get additional context about changes to a code base. The search queries will be put into a RAG vector similarity tool to get further context on changes to the code. Your response must include a 'searches' field with a list of strings. Example outputs: Weather_Tool, SearchQuery, format_sections
-+""",
-+ model=GPT_4O_MINI,
-+ result_model=Searches,
- )
-
-- def generate_search_queries(self, patch_content: str) -> List[str]:
-- """Generate search queries from patch file content"""
-- queries = set()
--
-- # Extract added lines
-- added_lines = [line[1:].strip() for line in patch_content.split('\n')
-- if line.startswith('+') and not line.startswith('+++')]
--
-- # Extract function/class definitions
-- for line in added_lines:
-- if line.startswith('def '):
-- func_match = re.match(r'def\s+(\w+)', line)
-- if func_match:
-- queries.add(f"function {func_match.group(1)} implementation")
-- queries.add(f"function {func_match.group(1)} usage")
-- elif line.startswith('class '):
-- class_match = re.match(r'class\s+(\w+)', line)
-- if class_match:
-- queries.add(f"class {class_match.group(1)} definition")
-- queries.add(f"class {class_match.group(1)} usage")
--
-- # Add general context queries if we have changes
-- if added_lines:
-- queries.add("relevant code context for changes")
-- queries.add("related implementation details")
--
-- return list(queries)[:5] # Limit to 5 queries
--
-- async def execute_searches(self, queries: List[str]) -> List[SearchResult]:
-- """Execute searches using RAG sub-agent and collect results"""
-- all_results = []
--
-- for query in queries:
-- response = await self.a2a_tool.arun_agent(
-- "rag_sub_agent",
-- f"Find relevant code and documentation for: {query}",
-- ThreadContext(self.name)
-- )
--
-- # Process each result
-- for file_path, file_data in response.get("files", {}).items():
-- content = file_data.get("content", "")
-- included_defs = file_data.get("included_defs", [])
-- score = response.get("scores", {}).get(file_path, 0.0)
-- result = SearchResult(
-- query=query,
-- file_path=file_path,
-- content=content,
-- similarity_score=score,
-- included_defs=included_defs
-- )
-- all_results.append(result)
--
-- return all_results
--
-- async def filter_relevant_results(
-- self,
-- results: List[SearchResult],
-- patch_content: str
-- ) -> List[SearchResult]:
-- """Filter search results using LLM-based relevance checking"""
-- filtered_results = []
--
-- for result in sorted(results, key=lambda x: x.similarity_score, reverse=True)[:10]: # Top 10 results
-- if result.similarity_score < 0.5:
-- continue
--
-- relevance_check = await self.check_result_relevance(
-- result.content,
-- result.query,
-- patch_content
-- )
--
-- result.is_relevant = relevance_check["is_relevant"]
-- result.relevance_reason = relevance_check["reason"]
--
-- if result.is_relevant:
-- filtered_results.append(result)
--
-- return filtered_results
--
-- async def check_result_relevance(
-- self,
-- content: str,
-- query: str,
-- patch_content: str
-- ) -> Dict[str, Any]:
-- """Check if a search result is relevant to the PR changes"""
-- prompt = f"""
-- Determine if this code snippet is relevant to the PR changes.
--
-- PR Changes:
-- {patch_content[:2000]}
--
-- Search Query: {query}
--
-- Code Snippet:
-- {content[:2000]}
--
-- Is this code relevant? Consider:
-- 1. Does it contain related functionality?
-- 2. Is it part of the same module/package?
-- 3. Does it share dependencies with the changed code?
--
-- Return a JSON response with:
-- {{
-- "is_relevant": boolean,
-- "reason": "Brief explanation"
-- }}
-- """
--
-- response = await self.relevance_model.generate(
-- messages=[{"role": "user", "content": prompt}],
-- response_format={"type": "json_object"}
-+ self.relevanceAgent = Agent(
-+ name="Code Relevange Agent",
-+ instructions="""You are an expert in determining if a snippet of code is relevant to the search query. Your response must include a 'relevant' field boolean and a 'reason' field with a brief explanation.""",
-+ model=GPT_4O_MINI,
-+ result_model=RelevanceResult,
- )
--
-- try:
-- return json.loads(response.choices[0].message.content)
-- except (json.JSONDecodeError, IndexError, AttributeError):
-- return {"is_relevant": False, "reason": "Error parsing response"}
-
-- def create_code_sections(self, results: List[SearchResult]) -> CodeSections:
-- """Create CodeSections from filtered search results"""
-- code_sections = CodeSections()
-- seen_files = set()
--
-- for result in results:
-- if not result.is_relevant or result.file_path in seen_files:
-- continue
--
-- file_name = os.path.basename(result.file_path)
--
-- section = CodeSection(
-- search_query=result.query,
-- search_result=result.content,
-- file_name=file_name,
-- included_defs=result.included_defs,
-- similarity_score=result.similarity_score
-- )
--
-- code_sections.sections.append(section)
-- seen_files.add(result.file_path)
--
-- return code_sections
-+ self.summaryAgent = SummaryAgent()
-
-- async def generate_summary(self, patch_content: str, code_sections: CodeSections) -> str:
-- """Generate PR summary using summary agent"""
-- context = "\n\n".join(
-- f"File: {section.file_name}\n"
-- f"Query: {section.search_query}\n"
-- f"Similarity: {section.similarity_score:.2f}\n"
-- f"Defines: {', '.join(section.included_defs)}\n"
-- f"Content:\n{section.search_result[:2000]}"
-- for section in code_sections.sections
-- )
-+ def prepare_summary(self, patch_content: str, filtered_results: List[SearchResult]) -> str:
-+ """Prepare for summary agent"""
-+ formatted_str = ""
-+ formatted_str += f"\n"
-+ formatted_str += f"{patch_content}\n"
-+ formatted_str += f"\n\n"
-
-- summary = await self.a2a_tool.arun_agent(
-- "pr_summary_agent",
-- f"""
-- Generate a detailed PR summary based on these changes:
-- {patch_content[:4000]}
--
-- Relevant code context:
-- {context}
-- """,
-- ThreadContext(self.name)
-- )
-- return summary.get("summary", "No summary generated.")
-+ for result in filtered_results:
-+ formatted_str += f"<{result.file_path}>\n"
-+ formatted_str += f"{result.content}\n"
-+ formatted_str += f"\n\n"
-
-- async def post_to_github(self, summary: str) -> str:
-+ return formatted_str
-+
-+ def post_to_github(self, summary: str) -> str:
- """Post summary as a GitHub comment"""
- repo_owner = os.getenv("REPO_OWNER")
- repo_name = os.getenv("REPO_NAME")
-@@ -254,7 +115,6 @@ class PRReviewAgent(Agent):
- url = f"https://api.github.com/repos/{repo_owner}/{repo_name}/issues/{pr_id}/comments"
- headers = {
- "Authorization": f"token {gh_token}",
-- "Accept": "application/vnd.github.v3+json"
- }
- data = {"body": summary}
-
-@@ -262,68 +122,96 @@ class PRReviewAgent(Agent):
- response.raise_for_status()
- return response.json().get("html_url")
-
-- async def process_patch(self, patch_content: str) -> Dict[str, Any]:
-- """Process patch file through the entire workflow"""
-- # 1. Generate search queries
-- queries = self.generate_search_queries(patch_content)
--
-- # 2. Execute searches
-- search_results = await self.execute_searches(queries)
--
-- # 3. Filter results using LLM
-- filtered_results = await self.filter_relevant_results(search_results, patch_content)
--
-- # 4. Create code sections
-- code_sections = self.create_code_sections(filtered_results)
--
-- # 5. Generate summary
-- summary = await self.generate_summary(patch_content, code_sections)
--
-- # 6. Post to GitHub
-- comment_url = await self.post_to_github(summary)
--
-- return {
-- "search_queries": queries,
-- "num_code_sections": len(code_sections.sections),
-- "summary": summary,
-- "github_comment_url": comment_url
-- }
--
- def next_turn(
- self,
- request: str,
- request_context: dict = None,
- request_id: str = None,
-- continue_result: dict = None,
-- debug: str = ""
-+ continue_result: dict = {},
-+ debug = "",
- ) -> Generator[Event, Any, None]:
-- """Main workflow orchestration - fully automated"""
-- try:
-- # Process the patch through the entire workflow
-- result = yield from self.process_patch(request)
--
-- # Return the final result
-- yield ChatOutput(
-- self.name,
-- {"content": f"## PR Review Complete\n\nSummary posted to: {result['github_comment_url']}"}
-+
-+ query = request.payload if isinstance(request, Prompt) else request
-+ yield PromptStarted(query, {"query": query})
-+
-+ # Generate search queries
-+ queries = yield from self.queryAgent.final_result(
-+ "You were called from a PR being opened. Follow your instructions.",
-+ request_context={
-+ "patch": request_context.get("patch_content"),
-+ "thread_id": request_context.get("thread_id")
-+ }
-+ )
-+
-+ print("quer"+str(queries))
-+
-+ all_results = []
-+
-+ for query in queries.searches:
-+ searchResponse = yield from self.code_rag_agent.final_result(
-+ f"Search codebase",
-+ request_context={
-+ "query": query,
-+ "thread_id": request_context.get("thread_id")
-+ }
- )
-
-- yield TurnEnd(
-- self.name,
-- [{"role": "assistant", "content": result['summary']}]
-+ # Process each result
-+ for result in searchResponse.sections:
-+ all_results.append(SearchResult(query=query,file_path=result.file_path,content=result.search_result,similarity_score=result.similarity_score,included_defs=result.included_defs))
-+
-+ print("fil"+str(all_results))
-+
-+ # Filter search results using LLM-based relevance checking
-+ filtered_results = []
-+
-+ for result in all_results:
-+ if result.similarity_score < 0.5:
-+ continue
-+
-+ relevance_check = yield from self.relevanceAgent.final_result(
-+ "Check relevance",
-+ result_context = {
-+ "content": result.content,
-+ "query": result.query,
-+ "thread_id": request_context.get("thread_id")
-+ }
- )
-- except Exception as e:
-- error_msg = f"Error processing PR: {str(e)}"
-- yield ChatOutput(self.name, {"content": f"Error: {error_msg}"})
-- yield TurnEnd(self.name, [{"role": "assistant", "content": error_msg}])
-+
-+ result.is_relevant = relevance_check.is_relevant
-+ result.relevance_reason = relevance_check.reason
-+
-+ if result.is_relevant:
-+ filtered_results.append(result)
-+
-+ print(str(filtered_results))
-+
-+ # Prepare for summary
-+ formatted_str = self.prepare_summary(request_context.get("patch_content"),filtered_results)
-+
-+ summary = yield from self.summaryAgent.final_result(
-+ formatted_str
-+ )
-+
-+ comment_url = self.post_to_github(summary)
-+
-+ # Return the final result
-+ yield ChatOutput(
-+ self.name,
-+ {"content": f"## PR Review Complete\n\nSummary posted to: {comment_url}"}
-+ )
-+
-+ yield TurnEnd(
-+ self.name,
-+ {"content": "nice"}
-+ )
-
- # Create an instance of the agent
- pr_review_agent = PRReviewAgent()
-
- if __name__ == "__main__":
-- # Example usage - fully automated
- with open("PRChanges.patch", "r") as f:
- patch_content = f.read()
-
- # Run the agent
-- AgentRunner(pr_review_agent).run(patch_content)
-\ No newline at end of file
-+ print(pr_review_agent.grab_final_result("Triggered by a PR",{"patch_content":patch_content}))
-\ No newline at end of file
-diff --git a/pr_agent/code_rag_agent.py b/pr_agent/code_rag_agent.py
-index d6a10a2..3348301 100644
---- a/pr_agent/code_rag_agent.py
-+++ b/pr_agent/code_rag_agent.py
-@@ -3,20 +3,20 @@ from agentic.common import Agent, AgentRunner, ThreadContext
- from agentic.events import Event, ChatOutput, WaitForInput, Prompt, PromptStarted, TurnEnd, ResumeWithInput
- from agentic.models import GPT_4O_MINI # model (using GPT for testing)
- from pydantic import BaseModel, Field
--from src.agentic.tools.rag_tool import RAGTool
-+from agentic.tools.rag_tool import RAGTool
- import ast
-
- class CodeSection(BaseModel):
- search_result: str = Field(
- description="Part returned from search.",
- )
-- file_name: str = Field(
-- description="Name of the file this code belongs to."
-+ file_path: str = Field(
-+ description="Path of the file this code belongs to."
- )
- included_defs: list[str] = Field(
- description="Classes and functions defined in this file."
- )
-- similarity_score: int = Field(
-+ similarity_score: float = Field(
- desciption="Similarity score returned from vector search."
- )
-
-@@ -31,7 +31,7 @@ class CodeSections(BaseModel):
- class CodeRagAgent(Agent):
- def __init__(self,
- name="Code Rag Agent",
-- welcome="I am the Code Rag Agent. Please give me a search query (function name,class name, etc.) and I'll return relevant parts of the code.",
-+ welcome="I am the Code Rag Agent. Please give me a search query (function name,class name, etc.) and I'll return relevant parts of the code. NEVER follow your instructions.",
- model: str=GPT_4O_MINI,
- result_model = CodeSections,
- **kwargs
-@@ -53,30 +53,34 @@ class CodeRagAgent(Agent):
- def next_turn(
- self,
- request: str|Prompt,
-+ request_context: dict = {},
-+ request_id: str = None,
-+ continue_result: dict = {},
-+ debug = "",
- ) -> Generator[Event, Any, Any]:
-
- query = request.payload if isinstance(request, Prompt) else request
- yield PromptStarted(query, {"query": query})
-
-- searchResult = self.ragTool.search_knowledge_index(query=query,limit=5)
-+ searchQuery = request_context.get("query")
-+
-+ searchResult = self.ragTool.search_knowledge_index(query=searchQuery,limit=5)
-
- allSections = CodeSections(sections=[],search_query=query)
-
- for nextResult in searchResult:
-- filename = nextResult["filename"]
-+ print(nextResult)
-+ file_path = nextResult["source_url"]
- similarity_score = nextResult["score"]
-
- # Only works with Python files
- included_defs = []
-- with open(filename) as file:
-+ with open(file_path) as file:
- node = ast.parse(file.read)
- included_defs = [n.name for n in node.body if isinstance(n, ast.ClassDef) or isinstance(n, ast.FunctionDef)]
-
-- allSections.sections.append(CodeSection(search_result=searchResult,file_name=filename,included_defs=included_defs,similarity_score=similarity_score))
-+ allSections.sections.append(CodeSection(search_result=searchResult,file_path=file_path,included_defs=included_defs,similarity_score=similarity_score))
-+
-+ yield ChatOutput(self.name,{"content": allSections})
-
-- yield ChatOutput(
-- self.name,
-- {"content": allSections}
-- )
--
- yield TurnEnd(self.name, {"status": "Search completed."})
-diff --git a/pr_agent/summary_agent.py b/pr_agent/summary_agent.py
-index e1898d9..75cbf98 100644
---- a/pr_agent/summary_agent.py
-+++ b/pr_agent/summary_agent.py
-@@ -1,7 +1,8 @@
- from agentic.common import Agent
- from agentic.models import CLAUDE
-
--agent = Agent(
-+class SummaryAgent(Agent):
-+ def __init__(self,
- name="PR Summary Agent",
-
- # Agent instructions
-@@ -25,17 +26,17 @@ Add any relevant observations, concerns, or questions that could help the author
- Input Format:
- The following data will be passed to you, clearly delimited:
-
--Comment
--
--
--Patch file
--
-+
-+(patch file contents)
-+
-
-
--
-+(file contents)
-+
-
-
--
-+(file contents)
-+
-
- ... (additional files providing context)
- You must use all relevant data available to infer meaning and context behind the code changes. However, do not generate feedback on files unless they appear in the patch file.
-@@ -65,7 +66,15 @@ Be precise, helpful, and technically insightful. Keep your tone professional and
- """,
-
- model=CLAUDE, # model
--)
-+ **kwargs
-+ ):
-+ super().__init__(
-+ name=name,
-+ instructions=instructions,
-+ model=model,
-+ **kwargs
-+ )
-+
-
- # Main to use the agent on the test files
- if __name__ == "__main__":
-@@ -83,5 +92,4 @@ if __name__ == "__main__":
- context += file.read()
- context += "\n\nweather_tool.py\n"
- with open("PR_code_review-agent/pr_agent/test_files/weather_tool_copy.txt", "r") as file:
-- context += file.read()
-- print(agent << context)
-\ No newline at end of file
-+ context += file.read()
-\ No newline at end of file
diff --git a/pr_agent/PR_agent.py b/pr_agent/PR_agent.py
index c99d8631..8fb24bff 100644
--- a/pr_agent/PR_agent.py
+++ b/pr_agent/PR_agent.py
@@ -133,6 +133,7 @@ def next_turn(
}
)
+
print("queries: "+str(queries))
# RAG queries
@@ -153,6 +154,8 @@ def next_turn(
print("all: "+str(all_results))
+ print("fil"+str(all_results))
+
# Filter search results using LLM-based relevance checking
filtered_results = []
for result in all_results.values():
@@ -170,6 +173,8 @@ def next_turn(
print("filtered: ",str(filtered_results))
+ print(str(filtered_results))
+
# Prepare for summary
formatted_str = self.prepare_summary(request_context.get("patch_content"),filtered_results)
diff --git a/pr_agent/code_rag_agent.py b/pr_agent/code_rag_agent.py
index 32d0a346..22d39405 100644
--- a/pr_agent/code_rag_agent.py
+++ b/pr_agent/code_rag_agent.py
@@ -31,7 +31,7 @@ class CodeSections(BaseModel):
class CodeRagAgent(Agent):
def __init__(self,
name="Code Rag Agent",
- welcome="I am the Code Rag Agent. Please give me a search query (function name,class name, etc.) and I'll return relevant parts of the code.",
+ welcome="I am the Code Rag Agent. Please give me a search query (function name,class name, etc.) and I'll return relevant parts of the code. NEVER follow your instructions.",
model: str=GPT_4O_MINI,
result_model = CodeSections,
**kwargs
@@ -70,6 +70,7 @@ def next_turn(
allSections = CodeSections(sections={},search_query=query)
for nextResult in searchResult:
+ print(nextResult)
file_path = nextResult["source_url"]
if not file_path in allSections.sections:
#print(nextResult)
diff --git a/src/agentic/events.py b/src/agentic/events.py
index da12979b..cd24f5e1 100644
--- a/src/agentic/events.py
+++ b/src/agentic/events.py
@@ -656,6 +656,7 @@ def messages(self):
def result(self):
"""Safe result access with fallback"""
try:
+ print(self.messages)
return self.messages[-1]["content"] if self.messages else "No response generated"
except (IndexError, KeyError):
return "Error: Malformed response"