BobAi
a189a4fe29
Add intelligent context window management for optimal RAG performance: ## Core Features - Dynamic context sizing based on model capabilities - User-friendly configuration menu with Development/Production/Advanced presets - Automatic validation against model limits (qwen3:0.6b/1.7b = 32K, qwen3:4b = 131K) - Educational content explaining context window importance for RAG ## Technical Implementation - Enhanced LLMConfig with context_window and auto_context parameters - Intelligent _get_optimal_context_size() method with model-specific limits - Consistent context application across synthesizer and explorer - YAML configuration output with helpful context explanations ## User Experience Improvements - Clear context window display in configuration status - Guided selection: Development (8K), Production (16K), Advanced (32K) - Memory usage estimates and performance guidance - Validation prevents invalid context/model combinations ## Educational Value - Explains why default 2048 tokens fails for RAG - Shows relationship between context size and conversation length - Guides users toward optimal settings for their use case - Highlights advanced capabilities (15+ results, 4000+ character chunks) This addresses the critical issue where Ollama's default context severely limits RAG performance, providing users with proper configuration tools and understanding of this crucial parameter.
709 lines
31 KiB
Python
709 lines
31 KiB
Python
#!/usr/bin/env python3
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"""
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LLM Synthesizer for RAG Results
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Provides intelligent synthesis of search results using Ollama LLMs.
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Takes raw search results and generates coherent, contextual summaries.
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"""
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import json
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import logging
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import time
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from typing import List, Dict, Any, Optional
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from dataclasses import dataclass
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import requests
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from pathlib import Path
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try:
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from .llm_safeguards import ModelRunawayDetector, SafeguardConfig, get_optimal_ollama_parameters
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except ImportError:
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# Graceful fallback if safeguards not available
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ModelRunawayDetector = None
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SafeguardConfig = None
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get_optimal_ollama_parameters = lambda x: {}
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logger = logging.getLogger(__name__)
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@dataclass
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class SynthesisResult:
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"""Result of LLM synthesis."""
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summary: str
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key_points: List[str]
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code_examples: List[str]
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suggested_actions: List[str]
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confidence: float
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class LLMSynthesizer:
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"""Synthesizes RAG search results using Ollama LLMs."""
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def __init__(self, ollama_url: str = "http://localhost:11434", model: str = None, enable_thinking: bool = False, config=None):
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self.ollama_url = ollama_url.rstrip('/')
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self.available_models = []
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self.model = model
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self.enable_thinking = enable_thinking # Default False for synthesis mode
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self._initialized = False
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self.config = config # For accessing model rankings
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# Initialize safeguards
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if ModelRunawayDetector:
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self.safeguard_detector = ModelRunawayDetector(SafeguardConfig())
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else:
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self.safeguard_detector = None
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def _get_available_models(self) -> List[str]:
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"""Get list of available Ollama models."""
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try:
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response = requests.get(f"{self.ollama_url}/api/tags", timeout=5)
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if response.status_code == 200:
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data = response.json()
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return [model['name'] for model in data.get('models', [])]
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except Exception as e:
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logger.warning(f"Could not fetch Ollama models: {e}")
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return []
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def _select_best_model(self) -> str:
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"""Select the best available model based on configuration rankings."""
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if not self.available_models:
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return "qwen2.5:1.5b" # Fallback preference
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# Get model rankings from config or use defaults
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if self.config and hasattr(self.config, 'llm') and hasattr(self.config.llm, 'model_rankings'):
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model_rankings = self.config.llm.model_rankings
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else:
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# Fallback rankings if no config
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model_rankings = [
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"qwen3:1.7b", "qwen3:0.6b", "qwen3:4b", "qwen2.5:3b",
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"qwen2.5:1.5b", "qwen2.5-coder:1.5b"
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]
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# Find first available model from our ranked list (exact matches first)
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for preferred_model in model_rankings:
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for available_model in self.available_models:
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# Exact match first (e.g., "qwen3:1.7b" matches "qwen3:1.7b")
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if preferred_model.lower() == available_model.lower():
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logger.info(f"Selected exact match model: {available_model}")
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return available_model
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# Partial match with version handling (e.g., "qwen3:1.7b" matches "qwen3:1.7b-q8_0")
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preferred_parts = preferred_model.lower().split(':')
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available_parts = available_model.lower().split(':')
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if len(preferred_parts) >= 2 and len(available_parts) >= 2:
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if (preferred_parts[0] == available_parts[0] and
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preferred_parts[1] in available_parts[1]):
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logger.info(f"Selected version match model: {available_model}")
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return available_model
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# If no preferred models found, use first available
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fallback = self.available_models[0]
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logger.warning(f"Using fallback model: {fallback}")
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return fallback
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def _ensure_initialized(self):
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"""Lazy initialization with LLM warmup."""
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if self._initialized:
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return
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# Load available models
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self.available_models = self._get_available_models()
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if not self.model:
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self.model = self._select_best_model()
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# Skip warmup - models are fast enough and warmup causes delays
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# Warmup removed to eliminate startup delays and unwanted model calls
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self._initialized = True
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def _get_optimal_context_size(self, model_name: str) -> int:
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"""Get optimal context size based on model capabilities and configuration."""
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# Get configured context window
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if self.config and hasattr(self.config, 'llm'):
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configured_context = self.config.llm.context_window
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auto_context = getattr(self.config.llm, 'auto_context', True)
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else:
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configured_context = 16384 # Default to 16K
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auto_context = True
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# Model-specific maximum context windows (based on research)
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model_limits = {
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# Qwen3 models with native context support
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'qwen3:0.6b': 32768, # 32K native
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'qwen3:1.7b': 32768, # 32K native
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'qwen3:4b': 131072, # 131K with YaRN extension
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# Qwen2.5 models
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'qwen2.5:1.5b': 32768, # 32K native
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'qwen2.5:3b': 32768, # 32K native
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'qwen2.5-coder:1.5b': 32768, # 32K native
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# Fallback for unknown models
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'default': 8192
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}
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# Find model limit (check for partial matches)
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model_limit = model_limits.get('default', 8192)
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for model_pattern, limit in model_limits.items():
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if model_pattern != 'default' and model_pattern.lower() in model_name.lower():
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model_limit = limit
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break
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# If auto_context is enabled, respect model limits
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if auto_context:
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optimal_context = min(configured_context, model_limit)
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else:
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optimal_context = configured_context
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# Ensure minimum usable context for RAG
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optimal_context = max(optimal_context, 4096) # Minimum 4K for basic RAG
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logger.debug(f"Context for {model_name}: {optimal_context} tokens (configured: {configured_context}, limit: {model_limit})")
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return optimal_context
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def is_available(self) -> bool:
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"""Check if Ollama is available and has models."""
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self._ensure_initialized()
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return len(self.available_models) > 0
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def _call_ollama(self, prompt: str, temperature: float = 0.3, disable_thinking: bool = False, use_streaming: bool = True, collapse_thinking: bool = True) -> Optional[str]:
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"""Make a call to Ollama API with safeguards."""
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start_time = time.time()
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try:
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# Use the best available model
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model_to_use = self.model
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if self.model not in self.available_models:
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# Fallback to first available model
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if self.available_models:
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model_to_use = self.available_models[0]
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else:
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logger.error("No Ollama models available")
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return None
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# Handle thinking mode for Qwen3 models
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final_prompt = prompt
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use_thinking = self.enable_thinking and not disable_thinking
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# For non-thinking mode, add <no_think> tag for Qwen3
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if not use_thinking and "qwen3" in model_to_use.lower():
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if not final_prompt.endswith(" <no_think>"):
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final_prompt += " <no_think>"
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# Get optimal parameters for this model
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optimal_params = get_optimal_ollama_parameters(model_to_use)
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# Qwen3-specific optimal parameters based on research
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if "qwen3" in model_to_use.lower():
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if use_thinking:
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# Thinking mode: Temperature=0.6, TopP=0.95, TopK=20, PresencePenalty=1.5
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qwen3_temp = 0.6
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qwen3_top_p = 0.95
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qwen3_top_k = 20
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qwen3_presence = 1.5
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else:
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# Non-thinking mode: Temperature=0.7, TopP=0.8, TopK=20, PresencePenalty=1.5
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qwen3_temp = 0.7
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qwen3_top_p = 0.8
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qwen3_top_k = 20
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qwen3_presence = 1.5
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else:
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qwen3_temp = temperature
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qwen3_top_p = optimal_params.get("top_p", 0.9)
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qwen3_top_k = optimal_params.get("top_k", 40)
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qwen3_presence = optimal_params.get("presence_penalty", 1.0)
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payload = {
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"model": model_to_use,
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"prompt": final_prompt,
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"stream": use_streaming,
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"options": {
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"temperature": qwen3_temp,
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"top_p": qwen3_top_p,
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"top_k": qwen3_top_k,
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"num_ctx": self._get_optimal_context_size(model_to_use), # Dynamic context based on model and config
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"num_predict": optimal_params.get("num_predict", 2000),
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"repeat_penalty": optimal_params.get("repeat_penalty", 1.1),
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"presence_penalty": qwen3_presence
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}
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}
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# Handle streaming with thinking display
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if use_streaming:
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return self._handle_streaming_with_thinking_display(payload, model_to_use, use_thinking, start_time, collapse_thinking)
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response = requests.post(
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f"{self.ollama_url}/api/generate",
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json=payload,
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timeout=65 # Slightly longer than safeguard timeout
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)
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if response.status_code == 200:
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result = response.json()
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# All models use standard response format
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# Qwen3 thinking tokens are embedded in the response content itself as <think>...</think>
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raw_response = result.get('response', '').strip()
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# Log thinking content for Qwen3 debugging
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if "qwen3" in model_to_use.lower() and use_thinking and "<think>" in raw_response:
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thinking_start = raw_response.find("<think>")
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thinking_end = raw_response.find("</think>")
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if thinking_start != -1 and thinking_end != -1:
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thinking_content = raw_response[thinking_start+7:thinking_end]
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logger.info(f"Qwen3 thinking: {thinking_content[:100]}...")
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# Apply safeguards to check response quality
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if self.safeguard_detector and raw_response:
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is_valid, issue_type, explanation = self.safeguard_detector.check_response_quality(
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raw_response, prompt[:100], start_time # First 100 chars of prompt for context
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)
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if not is_valid:
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logger.warning(f"Safeguard triggered: {issue_type}")
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# Preserve original response but add safeguard warning
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return self._create_safeguard_response_with_content(issue_type, explanation, raw_response)
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return raw_response
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else:
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logger.error(f"Ollama API error: {response.status_code}")
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return None
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except Exception as e:
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logger.error(f"Ollama call failed: {e}")
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return None
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def _create_safeguard_response(self, issue_type: str, explanation: str, original_prompt: str) -> str:
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"""Create a helpful response when safeguards are triggered."""
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return f"""⚠️ Model Response Issue Detected
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{explanation}
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**Original query context:** {original_prompt[:200]}{'...' if len(original_prompt) > 200 else ''}
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**What happened:** The AI model encountered a common issue with small language models and was prevented from giving a problematic response.
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**Your options:**
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1. **Try again**: Ask the same question (often resolves itself)
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2. **Rephrase**: Make your question more specific or break it into parts
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3. **Use exploration mode**: `rag-mini explore` for complex questions
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4. **Different approach**: Try synthesis mode: `--synthesize` for simpler responses
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This is normal with smaller AI models and helps ensure you get quality responses."""
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def _create_safeguard_response_with_content(self, issue_type: str, explanation: str, original_response: str) -> str:
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"""Create a response that preserves the original content but adds a safeguard warning."""
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# For Qwen3, extract the actual response (after thinking)
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actual_response = original_response
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if "<think>" in original_response and "</think>" in original_response:
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thinking_end = original_response.find("</think>")
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if thinking_end != -1:
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actual_response = original_response[thinking_end + 8:].strip()
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# If we have useful content, preserve it with a warning
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if len(actual_response.strip()) > 20:
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return f"""⚠️ **Response Quality Warning** ({issue_type})
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{explanation}
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---
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**AI Response (use with caution):**
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{actual_response}
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---
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💡 **Note**: This response may have quality issues. Consider rephrasing your question or trying exploration mode for better results."""
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else:
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# If content is too short or problematic, use the original safeguard response
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return f"""⚠️ Model Response Issue Detected
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{explanation}
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**What happened:** The AI model encountered a common issue with small language models.
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**Your options:**
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1. **Try again**: Ask the same question (often resolves itself)
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2. **Rephrase**: Make your question more specific or break it into parts
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3. **Use exploration mode**: `rag-mini explore` for complex questions
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This is normal with smaller AI models and helps ensure you get quality responses."""
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def _handle_streaming_with_thinking_display(self, payload: dict, model_name: str, use_thinking: bool, start_time: float, collapse_thinking: bool = True) -> Optional[str]:
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"""Handle streaming response with real-time thinking token display."""
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import json
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import sys
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try:
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response = requests.post(
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f"{self.ollama_url}/api/generate",
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json=payload,
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stream=True,
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timeout=65
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)
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if response.status_code != 200:
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logger.error(f"Ollama API error: {response.status_code}")
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return None
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full_response = ""
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thinking_content = ""
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is_in_thinking = False
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is_thinking_complete = False
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thinking_lines_printed = 0
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# ANSI escape codes for colors and cursor control
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GRAY = '\033[90m' # Dark gray for thinking
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LIGHT_GRAY = '\033[37m' # Light gray alternative
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RESET = '\033[0m' # Reset color
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CLEAR_LINE = '\033[2K' # Clear entire line
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CURSOR_UP = '\033[A' # Move cursor up one line
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print(f"\n💭 {GRAY}Thinking...{RESET}", flush=True)
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for line in response.iter_lines():
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if line:
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try:
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chunk_data = json.loads(line.decode('utf-8'))
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chunk_text = chunk_data.get('response', '')
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if chunk_text:
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full_response += chunk_text
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# Handle thinking tokens
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if use_thinking and '<think>' in chunk_text:
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is_in_thinking = True
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chunk_text = chunk_text.replace('<think>', '')
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if is_in_thinking and '</think>' in chunk_text:
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is_in_thinking = False
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is_thinking_complete = True
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chunk_text = chunk_text.replace('</think>', '')
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if collapse_thinking:
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# Clear thinking content and show completion
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# Move cursor up to clear thinking lines
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for _ in range(thinking_lines_printed + 1):
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print(f"{CURSOR_UP}{CLEAR_LINE}", end='', flush=True)
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print(f"💭 {GRAY}Thinking complete ✓{RESET}", flush=True)
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thinking_lines_printed = 0
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else:
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# Keep thinking visible, just show completion
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print(f"\n💭 {GRAY}Thinking complete ✓{RESET}", flush=True)
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print("🤖 AI Response:", flush=True)
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continue
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# Display thinking content in gray with better formatting
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if is_in_thinking and chunk_text.strip():
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thinking_content += chunk_text
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# Handle line breaks and word wrapping properly
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if ' ' in chunk_text or '\n' in chunk_text or len(thinking_content) > 100:
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# Split by sentences for better readability
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sentences = thinking_content.replace('\n', ' ').split('. ')
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for sentence in sentences[:-1]: # Process complete sentences
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sentence = sentence.strip()
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if sentence:
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# Word wrap long sentences
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words = sentence.split()
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line = ""
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for word in words:
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if len(line + " " + word) > 70:
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if line:
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print(f"{GRAY} {line.strip()}{RESET}", flush=True)
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thinking_lines_printed += 1
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line = word
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else:
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line += " " + word if line else word
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if line.strip():
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print(f"{GRAY} {line.strip()}.{RESET}", flush=True)
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thinking_lines_printed += 1
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# Keep the last incomplete sentence for next iteration
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thinking_content = sentences[-1] if sentences else ""
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# Display regular response content (skip any leftover thinking)
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elif not is_in_thinking and is_thinking_complete and chunk_text.strip():
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# Filter out any remaining thinking tags that might leak through
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clean_text = chunk_text
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if '<think>' in clean_text or '</think>' in clean_text:
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clean_text = clean_text.replace('<think>', '').replace('</think>', '')
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if clean_text.strip():
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print(clean_text, end='', flush=True)
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# Check if response is done
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if chunk_data.get('done', False):
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print() # Final newline
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break
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except json.JSONDecodeError:
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continue
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except Exception as e:
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logger.error(f"Error processing stream chunk: {e}")
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continue
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return full_response
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except Exception as e:
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logger.error(f"Streaming failed: {e}")
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return None
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def _handle_streaming_with_early_stop(self, payload: dict, model_name: str, use_thinking: bool, start_time: float) -> Optional[str]:
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"""Handle streaming response with intelligent early stopping."""
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import json
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try:
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response = requests.post(
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f"{self.ollama_url}/api/generate",
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json=payload,
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stream=True,
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timeout=65
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)
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if response.status_code != 200:
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logger.error(f"Ollama API error: {response.status_code}")
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return None
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full_response = ""
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word_buffer = []
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repetition_window = 30 # Check last 30 words for repetition (more context)
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stop_threshold = 0.8 # Stop only if 80% of recent words are repetitive (very permissive)
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min_response_length = 100 # Don't early stop until we have at least 100 chars
|
|
|
|
for line in response.iter_lines():
|
|
if line:
|
|
try:
|
|
chunk_data = json.loads(line.decode('utf-8'))
|
|
chunk_text = chunk_data.get('response', '')
|
|
|
|
if chunk_text:
|
|
full_response += chunk_text
|
|
|
|
# Add words to buffer for repetition detection
|
|
new_words = chunk_text.split()
|
|
word_buffer.extend(new_words)
|
|
|
|
# Keep only recent words in buffer
|
|
if len(word_buffer) > repetition_window:
|
|
word_buffer = word_buffer[-repetition_window:]
|
|
|
|
# Check for repetition patterns after we have enough words AND content
|
|
if len(word_buffer) >= repetition_window and len(full_response) >= min_response_length:
|
|
unique_words = set(word_buffer)
|
|
repetition_ratio = 1 - (len(unique_words) / len(word_buffer))
|
|
|
|
# Early stop only if repetition is EXTREMELY high (80%+)
|
|
if repetition_ratio > stop_threshold:
|
|
logger.info(f"Early stopping due to repetition: {repetition_ratio:.2f}")
|
|
|
|
# Add a gentle completion to the response
|
|
if not full_response.strip().endswith(('.', '!', '?')):
|
|
full_response += "..."
|
|
|
|
# Send stop signal to model (attempt to gracefully stop)
|
|
try:
|
|
stop_payload = {"model": model_name, "stop": True}
|
|
requests.post(f"{self.ollama_url}/api/generate", json=stop_payload, timeout=2)
|
|
except:
|
|
pass # If stop fails, we already have partial response
|
|
|
|
break
|
|
|
|
if chunk_data.get('done', False):
|
|
break
|
|
|
|
except json.JSONDecodeError:
|
|
continue
|
|
|
|
return full_response.strip()
|
|
|
|
except Exception as e:
|
|
logger.error(f"Streaming with early stop failed: {e}")
|
|
return None
|
|
|
|
def synthesize_search_results(self, query: str, results: List[Any], project_path: Path) -> SynthesisResult:
|
|
"""Synthesize search results into a coherent summary."""
|
|
|
|
self._ensure_initialized()
|
|
if not self.is_available():
|
|
return SynthesisResult(
|
|
summary="LLM synthesis unavailable (Ollama not running or no models)",
|
|
key_points=[],
|
|
code_examples=[],
|
|
suggested_actions=["Install and run Ollama with a model"],
|
|
confidence=0.0
|
|
)
|
|
|
|
# Prepare context from search results
|
|
context_parts = []
|
|
for i, result in enumerate(results[:8], 1): # Limit to top 8 results
|
|
file_path = result.file_path if hasattr(result, 'file_path') else 'unknown'
|
|
content = result.content if hasattr(result, 'content') else str(result)
|
|
score = result.score if hasattr(result, 'score') else 0.0
|
|
|
|
context_parts.append(f"""
|
|
Result {i} (Score: {score:.3f}):
|
|
File: {file_path}
|
|
Content: {content[:500]}{'...' if len(content) > 500 else ''}
|
|
""")
|
|
|
|
context = "\n".join(context_parts)
|
|
|
|
# Create synthesis prompt
|
|
prompt = f"""You are a senior software engineer analyzing code search results. Your task is to synthesize the search results into a helpful, actionable summary.
|
|
|
|
SEARCH QUERY: "{query}"
|
|
PROJECT: {project_path.name}
|
|
|
|
SEARCH RESULTS:
|
|
{context}
|
|
|
|
Please provide a synthesis in the following JSON format:
|
|
{{
|
|
"summary": "A 2-3 sentence overview of what the search results show",
|
|
"key_points": [
|
|
"Important finding 1",
|
|
"Important finding 2",
|
|
"Important finding 3"
|
|
],
|
|
"code_examples": [
|
|
"Relevant code snippet or pattern from the results",
|
|
"Another important code example"
|
|
],
|
|
"suggested_actions": [
|
|
"What the developer should do next",
|
|
"Additional recommendations"
|
|
],
|
|
"confidence": 0.85
|
|
}}
|
|
|
|
Focus on:
|
|
- What the code does and how it works
|
|
- Patterns and relationships between the results
|
|
- Practical next steps for the developer
|
|
- Code quality observations
|
|
|
|
Respond with ONLY the JSON, no other text."""
|
|
|
|
# Get LLM response
|
|
response = self._call_ollama(prompt, temperature=0.2)
|
|
|
|
if not response:
|
|
return SynthesisResult(
|
|
summary="LLM synthesis failed (API error)",
|
|
key_points=[],
|
|
code_examples=[],
|
|
suggested_actions=["Check Ollama status and try again"],
|
|
confidence=0.0
|
|
)
|
|
|
|
# Parse JSON response
|
|
try:
|
|
# Extract JSON from response (in case there's extra text)
|
|
start_idx = response.find('{')
|
|
end_idx = response.rfind('}') + 1
|
|
if start_idx >= 0 and end_idx > start_idx:
|
|
json_str = response[start_idx:end_idx]
|
|
data = json.loads(json_str)
|
|
|
|
return SynthesisResult(
|
|
summary=data.get('summary', 'No summary generated'),
|
|
key_points=data.get('key_points', []),
|
|
code_examples=data.get('code_examples', []),
|
|
suggested_actions=data.get('suggested_actions', []),
|
|
confidence=float(data.get('confidence', 0.5))
|
|
)
|
|
else:
|
|
# Fallback: use the raw response as summary
|
|
return SynthesisResult(
|
|
summary=response[:300] + '...' if len(response) > 300 else response,
|
|
key_points=[],
|
|
code_examples=[],
|
|
suggested_actions=[],
|
|
confidence=0.3
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to parse LLM response: {e}")
|
|
return SynthesisResult(
|
|
summary="LLM synthesis failed (JSON parsing error)",
|
|
key_points=[],
|
|
code_examples=[],
|
|
suggested_actions=["Try the search again or check LLM output"],
|
|
confidence=0.0
|
|
)
|
|
|
|
def format_synthesis_output(self, synthesis: SynthesisResult, query: str) -> str:
|
|
"""Format synthesis result for display."""
|
|
|
|
output = []
|
|
output.append("🧠 LLM SYNTHESIS")
|
|
output.append("=" * 50)
|
|
output.append("")
|
|
|
|
output.append(f"📝 Summary:")
|
|
output.append(f" {synthesis.summary}")
|
|
output.append("")
|
|
|
|
if synthesis.key_points:
|
|
output.append("🔍 Key Findings:")
|
|
for point in synthesis.key_points:
|
|
output.append(f" • {point}")
|
|
output.append("")
|
|
|
|
if synthesis.code_examples:
|
|
output.append("💡 Code Patterns:")
|
|
for example in synthesis.code_examples:
|
|
output.append(f" {example}")
|
|
output.append("")
|
|
|
|
if synthesis.suggested_actions:
|
|
output.append("🎯 Suggested Actions:")
|
|
for action in synthesis.suggested_actions:
|
|
output.append(f" • {action}")
|
|
output.append("")
|
|
|
|
confidence_emoji = "🟢" if synthesis.confidence > 0.7 else "🟡" if synthesis.confidence > 0.4 else "🔴"
|
|
output.append(f"{confidence_emoji} Confidence: {synthesis.confidence:.1%}")
|
|
output.append("")
|
|
|
|
return "\n".join(output)
|
|
|
|
# Quick test function
|
|
def test_synthesizer():
|
|
"""Test the synthesizer with sample data."""
|
|
from dataclasses import dataclass
|
|
|
|
@dataclass
|
|
class MockResult:
|
|
file_path: str
|
|
content: str
|
|
score: float
|
|
|
|
synthesizer = LLMSynthesizer()
|
|
|
|
if not synthesizer.is_available():
|
|
print("❌ Ollama not available for testing")
|
|
return
|
|
|
|
# Mock search results
|
|
results = [
|
|
MockResult("auth.py", "def authenticate_user(username, password):\n return verify_credentials(username, password)", 0.95),
|
|
MockResult("models.py", "class User:\n def login(self):\n return authenticate_user(self.username, self.password)", 0.87)
|
|
]
|
|
|
|
synthesis = synthesizer.synthesize_search_results(
|
|
"user authentication",
|
|
results,
|
|
Path("/test/project")
|
|
)
|
|
|
|
print(synthesizer.format_synthesis_output(synthesis, "user authentication"))
|
|
|
|
if __name__ == "__main__":
|
|
test_synthesizer() |