Use return_full_result to extract raw answer_with_sources output

- Upgraded smolagents from 1.4.1 to 1.22.0 for return_full_result support
- Extract answer_with_sources observations from result.steps to bypass final_answer reformatting
- Separate CONTEXT (for answer) from metadata (for sources) in retriever output
- Updated summarizer prompt to use CONTEXT only for answer generation
- Metadata (summary and key findings) used only for Sources section
- Reduces token usage and improves answer quality by focusing on relevant context

src/second_brain_online/application/agents/agents.py

@@ -71,26 +71,24 @@ class AgentWrapper:
 
     @opik.track(name="Agent.run")
     def run(self, task: str, **kwargs) -> Any:
-        result = self.__agent.run(task, **kwargs)
-
-        model = self.__agent.model
-        metadata = {
-            "system_prompt": self.__agent.system_prompt,
-            "system_prompt_template": self.__agent.system_prompt_template,
-            "tool_description_template": self.__agent.tool_description_template,
-            "tools": self.__agent.tools,
-            "model_id": self.__agent.model.model_id,
-            "api_base": self.__agent.model.api_base,
-            "input_token_count": model.last_input_token_count,
-            "output_token_count": model.last_output_token_count,
-        }
-        if hasattr(self.__agent, "step_number"):
-            metadata["step_number"] = self.__agent.step_number
-        opik_context.update_current_trace(
-            tags=["agent"],
-            metadata=metadata,
-        )
-
+        result = self.__agent.run(task, return_full_result=True, **kwargs)
+        
+        # Extract the raw output from answer_with_sources (Step 2) instead of using final_answer
+        if hasattr(result, 'steps') and len(result.steps) >= 2:
+            # Find the step where answer_with_sources was called
+            for step in result.steps:
+                if 'tool_calls' in step and step['tool_calls']:
+                    for tool_call in step['tool_calls']:
+                        if tool_call.get('function', {}).get('name') == 'answer_with_sources':
+                            # Found the answer_with_sources step - return its observations
+                            if 'observations' in step and step['observations']:
+                                logger.info("Returning raw answer_with_sources output (bypassing final reformatting)")
+                                return step['observations']
+        
+        # Fallback to regular result.output
+        if hasattr(result, 'output'):
+            return result.output
+        
         return result
 
 

src/second_brain_online/application/agents/tools/mongodb_retriever.py

@@ -186,16 +186,17 @@ class MongoDBRetrieverTool(Tool):
                 for chunk_idx, chunk_context in enumerate(doc_info['chunks'], 1):
                     doc_text += f"{chunk_idx}. {chunk_context}\n"
                 
-                # Add conversation insights (only once per conversation)
+                # Add conversation insights (for Sources section only - not for answer generation)
                 insights = doc_info['insights']
+                doc_text += f"\n[METADATA FOR SOURCES SECTION]\n"
+                
                 summary = insights.get("summary", "")
                 if summary:
-                    doc_text += f"\nINSIGHTS SUMMARY: {summary}\n"
+                    doc_text += f"Summary: {summary}\n"
                 
-                # Add key findings
                 key_findings = insights.get("key_findings", [])
                 if key_findings:
-                    doc_text += "\nKEY FINDINGS:\n"
+                    doc_text += "Key Findings:\n"
                     for finding in key_findings:
                         insight_type = finding.get("insight_type", "Unknown")
                         finding_text = finding.get("finding", "")

src/second_brain_online/application/agents/tools/summarizer.py

@@ -80,10 +80,16 @@ CRITICAL: This tool generates the final answer that will be returned to the user
 
 {content}
 
+IMPORTANT INSTRUCTIONS:
+- Use the CONTEXT sections from each document to generate your answer
+- Use the [METADATA FOR SOURCES SECTION] to populate the Sources section (Summary and Key Findings)
+- DO NOT use the metadata to answer the question - only use CONTEXT for the answer
+
 Create a two-part response:
 
 1. **ANSWER** (with inline citations):
-   - Focus on the core issues, concerns, or highlights identified
+   - Base your answer ONLY on the CONTEXT sections from the documents
+   - Focus on the core issues, concerns, or highlights identified in the CONTEXT
    - DO NOT mention specific customer names or personal identifiers
    - Group related insights by topic with bullet points
    - Be concise and general, highlighting the problem/concern rather than individuals
@@ -104,6 +110,7 @@ Create a two-part response:
 2. **📚 Sources** (at the end):
    - List ONLY UNIQUE documents (de-duplicate by Document ID)
    - Number each unique source to match the inline citations (Doc 1, Doc 2, etc.)
+   - Use the information from [METADATA FOR SOURCES SECTION] to populate Summary and Key Findings
    - Format URLs as markdown links: [View Chat](url) or [View Recording](url)
    
    For EACH unique document, use this EXACT structure with proper spacing and NO bold/italic formatting:
@@ -111,11 +118,11 @@ Create a two-part response:
    Doc X: [Title (Date)]
    Source: [Type] | Document ID: [ID] | [Hyperlinked URL if available] | [User ID if available]
    
-   Summary: [One-line summary of the conversation]
+   Summary: [Copy from the metadata section]
    
    Key Findings:
-   - [Type/Impact] Finding text here
-   - [Type/Impact] Finding text here
+   - [Type/Impact] [Copy from the metadata section]
+   - [Type/Impact] [Copy from the metadata section]
    
    Example:
    

uv.lock

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