Spaces:
Sleeping
Sleeping
File size: 10,489 Bytes
53ae001 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | """
Integration Service for RAG Agent
Orchestrates the full RAG pipeline:
1. Embed query using Cohere
2. Retrieve relevant chunks from Qdrant
3. Generate answer using OpenAI
4. Deduplicate sources and format response
This service integrates retrieval.py (existing) with OpenAI generation.
"""
import os
import logging
from typing import List, Dict, Any, Optional
from dotenv import load_dotenv
from retrieval import SearchResult
from services.retrieval_service import RetrievalService
from services.prompt_builder import PromptBuilder
from services.generation_service import AnswerGenerationService
from models.response_models import QueryResponse, SourceReference
from exceptions import RetrievalError, GenerationError, RateLimitError
# Load environment variables
load_dotenv()
# Constants
DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "xiaomi/mimo-v2-flash:free")
MAX_TOKENS = int(os.getenv("MAX_TOKENS", "3000"))
# NOTE: Threshold set to 0.15 based on actual retrieval performance
# The current embedding model (Cohere embed-english-v3.0) returns lower scores than expected.
# Original requirement (FR-005) specified 0.7, but testing shows:
# - Technical queries get scores of 0.15-0.30
# - Irrelevant queries (like "hi") get scores of 0.10-0.23
# Setting to 0.15 filters out very low-quality matches while keeping relevant docs
RELEVANCE_THRESHOLD = 0.15 # Minimum relevance score for usable results (FR-005)
# Greeting patterns for detection (FR-003)
GREETING_PATTERNS = {
"hi", "hello", "hey", "helo", "hii", "heya", "greetings",
"good morning", "good afternoon", "good evening"
}
logger = logging.getLogger('integration_service')
class IntegrationService:
"""
Service for orchestrating RAG pipeline: retrieval + generation.
Methods:
process_query: Main entry point for RAG pipeline
_is_greeting: Detect if query is a casual greeting
_retrieve_chunks: Retrieve relevant documentation chunks
_deduplicate_sources: Deduplicate chunks by URL
_build_prompt: Build prompt with context and query
_generate_answer: Generate answer using OpenAI
_count_tokens: Count tokens in text
"""
def __init__(self, openai_api_key: Optional[str] = None):
"""
Initialize integration service.
Args:
openai_api_key: OpenRouter API key (default: from env)
"""
self.openai_api_key = openai_api_key or os.getenv("OPENROUTER_API_KEY")
if not self.openai_api_key:
raise ValueError("OPENROUTER_API_KEY not found in environment")
self.retrieval_service = RetrievalService()
self.prompt_builder = PromptBuilder(max_tokens=MAX_TOKENS)
self.generation_service = AnswerGenerationService(api_key=self.openai_api_key)
def _is_greeting(self, query: str) -> bool:
"""
Detect if query is a standalone greeting (FR-003).
Returns True for casual greetings like "hi", "hello", "hey".
Returns False for queries that contain greetings but have actual questions
(e.g., "Hi, how do I create a ROS 2 publisher?").
Args:
query: User query text
Returns:
True if query is a greeting, False otherwise
"""
# Normalize: strip, lowercase, remove punctuation
normalized = query.strip().lower().rstrip('!?.,:;')
# Check if entire query is just a greeting (standalone)
# Don't match if query contains other words after greeting
return normalized in GREETING_PATTERNS
async def process_query(
self,
query: str,
top_k: int = 5,
model: str = DEFAULT_MODEL
) -> QueryResponse:
"""
Process a query through the full RAG pipeline.
Args:
query: User's natural language question
top_k: Number of chunks to retrieve
model: OpenAI model to use
Returns:
QueryResponse with answer and sources
Raises:
RetrievalError: If retrieval fails
GenerationError: If generation fails
RateLimitError: If rate limit exceeded
"""
logger.info(f"Processing query", extra={"query": query, "top_k": top_k, "model": model})
# Step 0: Check for greetings FIRST (FR-003)
if self._is_greeting(query):
logger.info(f"Greeting detected: {query}")
return QueryResponse(
query=query,
answer="Hello! I'm here to help you learn about ROS 2 and humanoid robotics. What would you like to know?",
sources=[],
model_used="greeting-detector",
tokens_used=0,
retrieval_count=0
)
# Step 1: Retrieve chunks
chunks = await self._retrieve_chunks(query, top_k)
retrieval_count = len(chunks)
# Step 2: Deduplicate sources
deduplicated_sources = self._deduplicate_sources(chunks)
# Step 3: Build prompt
prompt = self._build_prompt(query, chunks, model)
# Step 4: Generate answer
answer, tokens_used = await self._generate_answer(prompt, model)
# Step 5: Format response
response = QueryResponse(
query=query,
answer=answer,
sources=deduplicated_sources,
model_used=model,
tokens_used=tokens_used,
retrieval_count=retrieval_count
)
logger.info(
f"Query processed successfully",
extra={
"query": query,
"tokens_used": tokens_used,
"retrieval_count": retrieval_count,
"sources_count": len(deduplicated_sources)
}
)
return response
async def _retrieve_chunks(self, query: str, top_k: int) -> List[SearchResult]:
"""
Retrieve relevant documentation chunks using RetrievalService.
Filters results by relevance threshold (FR-005).
Args:
query: User query
top_k: Number of chunks to retrieve
Returns:
List of SearchResult objects with relevance >= 0.7
Raises:
RetrievalError: If retrieval fails or no relevant docs found (FR-006)
"""
# Delegate to RetrievalService (already has error handling and logging)
chunks = self.retrieval_service.retrieve_context(query, top_k=top_k)
# Filter by relevance threshold (FR-005)
filtered_chunks = [c for c in chunks if c.score >= RELEVANCE_THRESHOLD]
logger.info(
f"Relevance filtering: {len(filtered_chunks)}/{len(chunks)} chunks pass threshold {RELEVANCE_THRESHOLD}",
extra={
"total_chunks": len(chunks),
"filtered_chunks": len(filtered_chunks),
"threshold": RELEVANCE_THRESHOLD
}
)
# Handle no relevant docs case (FR-006)
if not filtered_chunks:
logger.warning(f"No documents meet relevance threshold {RELEVANCE_THRESHOLD}")
raise RetrievalError(
"I couldn't find relevant information in the documentation. "
"Try rephrasing your question or ask about: ROS 2, Unity, Digital Twins, or Humanoid Robotics."
)
return filtered_chunks
def _deduplicate_sources(self, chunks: List[SearchResult]) -> List[SourceReference]:
"""
Deduplicate chunks by URL, keeping highest-scoring chunk.
Args:
chunks: List of retrieved chunks
Returns:
List of deduplicated SourceReference objects
"""
# Group by URL, keep highest score
url_to_best_chunk: Dict[str, tuple[SearchResult, float]] = {}
for chunk in chunks:
url = chunk.metadata.get('url', '')
if not url:
continue
if url not in url_to_best_chunk or chunk.score > url_to_best_chunk[url][1]:
url_to_best_chunk[url] = (chunk, chunk.score)
# Convert to SourceReference
sources = []
for url, (chunk, score) in url_to_best_chunk.items():
source = SourceReference(
url=url,
page_title=chunk.metadata.get('page_title') or 'Unknown',
relevance_score=round(score, 4),
chunk_index=chunk.metadata.get('chunk_index')
)
sources.append(source)
# Sort by relevance score (descending)
sources.sort(key=lambda s: s.relevance_score, reverse=True)
logger.info(f"Deduplicated {len(chunks)} chunks to {len(sources)} unique sources")
return sources
def _build_prompt(self, query: str, chunks: List[SearchResult], model: str = "gpt-3.5-turbo") -> str:
"""
Build prompt with context and query using PromptBuilder.
Args:
query: User query
chunks: Retrieved chunks
model: OpenAI model for token counting
Returns:
Formatted prompt string
"""
# Delegate to PromptBuilder (already has XML formatting, token management, source URLs)
prompt = self.prompt_builder.build_prompt(query, chunks, model)
return prompt
async def _generate_answer(self, prompt: str, model: str) -> tuple[str, int]:
"""
Generate answer using AnswerGenerationService.
Args:
prompt: Full prompt with context and query
model: OpenAI model to use
Returns:
Tuple of (answer, tokens_used)
Raises:
GenerationError: If generation fails (propagated from AnswerGenerationService)
RateLimitError: If rate limit exceeded (propagated from AnswerGenerationService)
"""
# Delegate to AnswerGenerationService (already has retry logic, error handling, logging)
result = self.generation_service.generate_answer(
prompt=prompt,
model=model,
temperature=0.1, # Low temperature for deterministic, factual responses
max_tokens=500
)
return result['answer'], result['tokens_used']
|