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Implement Phase 5: Distributed LLM Routing Architecture
Co-authored-by: mblanke <9078342+mblanke@users.noreply.github.com>
This commit is contained in:
copilot-swe-agent[bot]
259
backend/app/core/merger_agent.py
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259
backend/app/core/merger_agent.py
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"""
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Merger Agent
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Combines and synthesizes results from multiple LLM models.
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"""
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from typing import Dict, Any, List, Optional
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from enum import Enum
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class MergeStrategy(Enum):
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"""Strategies for merging LLM results"""
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CONSENSUS = "consensus" # Take majority vote
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WEIGHTED = "weighted" # Weight by model confidence
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CONCATENATE = "concatenate" # Combine all outputs
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BEST_QUALITY = "best_quality" # Select highest quality response
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ENSEMBLE = "ensemble" # Ensemble multiple results
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class MergerAgent:
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"""
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Merger Agent - Combines results from multiple LLM executions
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When multiple models process the same or related requests,
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this agent intelligently merges their outputs into a coherent response.
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"""
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def __init__(self, default_strategy: MergeStrategy = MergeStrategy.CONSENSUS):
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"""
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Initialize merger agent
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Args:
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default_strategy: Default merging strategy
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"""
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self.default_strategy = default_strategy
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def merge_results(
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self,
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results: List[Dict[str, Any]],
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strategy: Optional[MergeStrategy] = None
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) -> Dict[str, Any]:
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"""
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Merge multiple LLM results using specified strategy
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Args:
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results: List of results from different models
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strategy: Merging strategy (uses default if not specified)
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Returns:
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Merged result
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"""
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if not results:
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return {"error": "No results to merge"}
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if len(results) == 1:
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return results[0]
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merge_strategy = strategy or self.default_strategy
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if merge_strategy == MergeStrategy.CONSENSUS:
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return self._merge_consensus(results)
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elif merge_strategy == MergeStrategy.WEIGHTED:
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return self._merge_weighted(results)
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elif merge_strategy == MergeStrategy.CONCATENATE:
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return self._merge_concatenate(results)
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elif merge_strategy == MergeStrategy.BEST_QUALITY:
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return self._merge_best_quality(results)
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elif merge_strategy == MergeStrategy.ENSEMBLE:
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return self._merge_ensemble(results)
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else:
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return self._merge_consensus(results)
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def _merge_consensus(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Merge by consensus - take majority vote or most common response
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Args:
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results: List of results
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Returns:
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Consensus result
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"""
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# For classification tasks, take majority vote
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if all("classification" in r for r in results):
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classifications = [r["classification"] for r in results]
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most_common = max(set(classifications), key=classifications.count)
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return {
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"strategy": "consensus",
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"result": most_common,
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"confidence": classifications.count(most_common) / len(classifications),
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"votes": dict((k, classifications.count(k)) for k in set(classifications))
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}
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# For text generation, use first high-quality result
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valid_results = [r for r in results if "response" in r and r["response"]]
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if valid_results:
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return {
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"strategy": "consensus",
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"result": valid_results[0]["response"],
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"num_models": len(results)
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}
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return {"strategy": "consensus", "result": None}
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def _merge_weighted(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Merge by weighted average based on model confidence
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Args:
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results: List of results with confidence scores
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Returns:
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Weighted result
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"""
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# Extract results with confidence scores
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weighted_results = [
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(r.get("response", ""), r.get("confidence", 0.5))
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for r in results
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]
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# Sort by confidence
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weighted_results.sort(key=lambda x: x[1], reverse=True)
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return {
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"strategy": "weighted",
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"result": weighted_results[0][0],
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"confidence": weighted_results[0][1],
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"all_results": [
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{"response": resp, "confidence": conf}
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for resp, conf in weighted_results
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]
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}
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def _merge_concatenate(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Concatenate all results
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Args:
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results: List of results
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Returns:
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Concatenated result
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"""
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responses = [
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r.get("response", "") for r in results if r.get("response")
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]
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return {
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"strategy": "concatenate",
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"result": "\n\n---\n\n".join(responses),
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"num_responses": len(responses)
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}
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def _merge_best_quality(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Select the highest quality response
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Args:
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results: List of results
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Returns:
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Best quality result
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"""
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# Score responses by length and presence of key indicators
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scored_results = []
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for r in results:
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response = r.get("response", "")
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if not response:
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continue
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score = 0
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score += len(response) / 100 # Longer responses get higher score
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score += response.count(".") * 0.5 # More complete sentences
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score += response.count("\n") * 0.3 # Better formatting
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scored_results.append((response, score, r))
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if not scored_results:
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return {"strategy": "best_quality", "result": None}
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scored_results.sort(key=lambda x: x[1], reverse=True)
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best_response, best_score, best_result = scored_results[0]
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return {
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"strategy": "best_quality",
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"result": best_response,
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"quality_score": best_score,
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"model": best_result.get("model", "unknown")
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}
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def _merge_ensemble(self, results: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""
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Ensemble multiple results by combining their insights
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Args:
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results: List of results
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Returns:
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Ensemble result
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"""
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# Collect unique insights from all models
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all_responses = [r.get("response", "") for r in results if r.get("response")]
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# Create ensemble summary
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ensemble_summary = {
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"strategy": "ensemble",
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"num_models": len(results),
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"individual_results": [
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{
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"model": r.get("model", "unknown"),
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"response": r.get("response", ""),
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"confidence": r.get("confidence", 0.5)
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}
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for r in results
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],
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"synthesized_result": self._synthesize_insights(all_responses)
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}
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return ensemble_summary
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def _synthesize_insights(self, responses: List[str]) -> str:
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"""
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Synthesize insights from multiple responses
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Args:
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responses: List of response strings
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Returns:
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Synthesized summary
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"""
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if not responses:
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return ""
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# Simple synthesis - in production, use another LLM to synthesize
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unique_points = []
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for response in responses:
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sentences = response.split(". ")
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for sentence in sentences:
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if sentence and sentence not in unique_points:
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unique_points.append(sentence)
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return ". ".join(unique_points[:10]) # Top 10 insights
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def get_merger_agent(
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default_strategy: MergeStrategy = MergeStrategy.CONSENSUS
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) -> MergerAgent:
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"""
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Factory function to create merger agent
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Args:
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default_strategy: Default merging strategy
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Returns:
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Configured MergerAgent instance
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"""
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return MergerAgent(default_strategy)
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