File size: 17,294 Bytes
055a3a7 7d28de6 7baf8ba 055a3a7 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 b50fffd 3aa91e9 ff16d8e 3aa91e9 20c7bad 3aa91e9 7d28de6 3aa91e9 20c7bad 3aa91e9 055a3a7 3aa91e9 20c7bad 3aa91e9 2f0030b 055a3a7 3aa91e9 055a3a7 20c7bad 3aa91e9 055a3a7 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 055a3a7 3aa91e9 7baf8ba 3aa91e9 055a3a7 3aa91e9 7baf8ba 055a3a7 3aa91e9 055a3a7 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 055a3a7 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 055a3a7 3aa91e9 3f90da8 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 055a3a7 3aa91e9 7baf8ba 3aa91e9 7baf8ba 3aa91e9 ff16d8e 3aa91e9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 |
"""LlamaIndex RAG service for evidence retrieval and indexing.
Requires optional dependencies: uv sync --extra modal
Migration Note (v1.0 rebrand):
Default collection_name changed from "deepcritical_evidence" to "deepboner_evidence".
To preserve existing data, explicitly pass collection_name="deepcritical_evidence".
Protocol Compliance:
This service implements EmbeddingServiceProtocol via async wrapper methods:
- add_evidence() - async wrapper for ingest_evidence()
- search_similar() - async wrapper for retrieve()
- deduplicate() - async wrapper using search_similar() + add_evidence()
These wrappers use asyncio.run_in_executor() to avoid blocking the event loop.
"""
import asyncio
from typing import Any
import structlog
from src.utils.config import settings
from src.utils.exceptions import ConfigurationError, EmbeddingError
from src.utils.models import Citation, Evidence
logger = structlog.get_logger()
class LlamaIndexRAGService:
"""RAG service using LlamaIndex with ChromaDB vector store.
Note:
This service is currently OpenAI-only. It uses OpenAI embeddings and LLM
regardless of the global `settings.llm_provider` configuration.
Requires OPENAI_API_KEY to be set.
"""
def __init__(
self,
collection_name: str = "deepboner_evidence",
persist_dir: str | None = None,
embedding_model: str | None = None,
similarity_top_k: int = 5,
) -> None:
"""
Initialize LlamaIndex RAG service.
Args:
collection_name: Name of the ChromaDB collection (default changed from
"deepcritical_evidence" to "deepboner_evidence" in v1.0 rebrand)
persist_dir: Directory to persist ChromaDB data
embedding_model: OpenAI embedding model (defaults to settings.openai_embedding_model)
similarity_top_k: Number of top results to retrieve
"""
# Lazy import - only when instantiated
try:
import chromadb
from llama_index.core import Document, Settings, StorageContext, VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.vector_stores.chroma import ChromaVectorStore
except ImportError as e:
raise ImportError(
"LlamaIndex dependencies not installed. Run: uv sync --extra modal"
) from e
# Store references for use in other methods
self._chromadb = chromadb
self._Document = Document
self._Settings = Settings
self._StorageContext = StorageContext
self._VectorStoreIndex = VectorStoreIndex
self._VectorIndexRetriever = VectorIndexRetriever
self._ChromaVectorStore = ChromaVectorStore
self.collection_name = collection_name
self.persist_dir = persist_dir or settings.chroma_db_path
self.similarity_top_k = similarity_top_k
self.embedding_model = embedding_model or settings.openai_embedding_model
# Validate API key before use
if not settings.openai_api_key:
raise ConfigurationError("OPENAI_API_KEY required for LlamaIndex RAG service")
# Configure LlamaIndex settings (use centralized config)
self._Settings.llm = OpenAI(
model=settings.openai_model,
api_key=settings.openai_api_key,
)
self._Settings.embed_model = OpenAIEmbedding(
model=self.embedding_model,
api_key=settings.openai_api_key,
)
# Initialize ChromaDB client
self.chroma_client = self._chromadb.PersistentClient(path=self.persist_dir)
# Get or create collection
# ChromaDB raises different exceptions depending on version:
# - ValueError (older versions)
# - InvalidCollectionException / NotFoundError (newer versions)
try:
self.collection = self.chroma_client.get_collection(self.collection_name)
logger.info("loaded_existing_collection", name=self.collection_name)
except Exception as e:
# Catch any collection-not-found error and create it
if (
"not exist" in str(e).lower()
or "not found" in str(e).lower()
or isinstance(e, ValueError)
):
self.collection = self.chroma_client.create_collection(self.collection_name)
logger.info("created_new_collection", name=self.collection_name)
else:
raise
# Initialize vector store and index
self.vector_store = self._ChromaVectorStore(chroma_collection=self.collection)
self.storage_context = self._StorageContext.from_defaults(vector_store=self.vector_store)
# Try to load existing index, or create empty one
# LlamaIndex raises ValueError for empty/invalid stores
try:
self.index = self._VectorStoreIndex.from_vector_store(
vector_store=self.vector_store,
storage_context=self.storage_context,
)
logger.info("loaded_existing_index")
except (ValueError, KeyError):
# Empty or newly created store - create fresh index
self.index = self._VectorStoreIndex([], storage_context=self.storage_context)
logger.info("created_new_index")
def ingest_evidence(self, evidence_list: list[Evidence]) -> None:
"""
Ingest evidence into the vector store.
Args:
evidence_list: List of Evidence objects to ingest
"""
if not evidence_list:
logger.warning("no_evidence_to_ingest")
return
# Convert Evidence objects to LlamaIndex Documents
documents = []
for evidence in evidence_list:
metadata = {
"source": evidence.citation.source,
"title": evidence.citation.title,
"url": evidence.citation.url,
"date": evidence.citation.date,
"authors": ", ".join(evidence.citation.authors),
}
doc = self._Document(
text=evidence.content,
metadata=metadata,
doc_id=evidence.citation.url, # Use URL as unique ID
)
documents.append(doc)
# Insert documents into index
try:
for doc in documents:
self.index.insert(doc)
logger.info("ingested_evidence", count=len(documents))
except (ValueError, RuntimeError) as e:
logger.error("failed_to_ingest_evidence", error=str(e))
raise EmbeddingError(f"Failed to ingest evidence: {e}") from e
def ingest_documents(self, documents: list[Any]) -> None:
"""
Ingest raw LlamaIndex Documents.
Args:
documents: List of LlamaIndex Document objects
"""
if not documents:
logger.warning("no_documents_to_ingest")
return
try:
for doc in documents:
self.index.insert(doc)
logger.info("ingested_documents", count=len(documents))
except (ValueError, RuntimeError) as e:
logger.error("failed_to_ingest_documents", error=str(e))
raise EmbeddingError(f"Failed to ingest documents: {e}") from e
def retrieve(self, query: str, top_k: int | None = None) -> list[dict[str, Any]]:
"""
Retrieve relevant documents for a query.
Args:
query: Query string
top_k: Number of results to return (defaults to similarity_top_k)
Returns:
List of retrieved documents with metadata and scores
"""
k = top_k or self.similarity_top_k
# Create retriever
retriever = self._VectorIndexRetriever(
index=self.index,
similarity_top_k=k,
)
try:
# Retrieve nodes
nodes = retriever.retrieve(query)
# Convert to dict format
results = []
for node in nodes:
results.append(
{
"text": node.node.get_content(),
"score": node.score,
"metadata": node.node.metadata,
}
)
logger.info("retrieved_documents", query=query[:50], count=len(results))
return results
except (ValueError, RuntimeError) as e:
logger.error("failed_to_retrieve", error=str(e), query=query[:50])
raise EmbeddingError(f"Failed to retrieve documents: {e}") from e
def query(self, query_str: str, top_k: int | None = None) -> str:
"""
Query the RAG system and get a synthesized response.
Args:
query_str: Query string
top_k: Number of results to use (defaults to similarity_top_k)
Returns:
Synthesized response string
"""
k = top_k or self.similarity_top_k
# Create query engine
query_engine = self.index.as_query_engine(
similarity_top_k=k,
)
try:
response = query_engine.query(query_str)
logger.info("generated_response", query=query_str[:50])
return str(response)
except (ValueError, RuntimeError) as e:
logger.error("failed_to_query", error=str(e), query=query_str[:50])
raise EmbeddingError(f"Failed to query RAG system: {e}") from e
def clear_collection(self) -> None:
"""Clear all documents from the collection."""
try:
self.chroma_client.delete_collection(self.collection_name)
self.collection = self.chroma_client.create_collection(self.collection_name)
self.vector_store = self._ChromaVectorStore(chroma_collection=self.collection)
self.storage_context = self._StorageContext.from_defaults(
vector_store=self.vector_store
)
self.index = self._VectorStoreIndex([], storage_context=self.storage_context)
logger.info("cleared_collection", name=self.collection_name)
except (ValueError, RuntimeError) as e:
logger.error("failed_to_clear_collection", error=str(e))
raise EmbeddingError(f"Failed to clear collection: {e}") from e
# ─────────────────────────────────────────────────────────────────
# Async Protocol Methods (EmbeddingServiceProtocol compliance)
# ─────────────────────────────────────────────────────────────────
async def embed(self, text: str) -> list[float]:
"""Embed a single text using OpenAI embeddings (Protocol-compatible).
Uses the LlamaIndex Settings.embed_model which was configured in __init__.
Args:
text: Text to embed
Returns:
Embedding vector as list of floats
"""
loop = asyncio.get_running_loop()
# LlamaIndex embed_model has get_text_embedding method
embedding = await loop.run_in_executor(
None, self._Settings.embed_model.get_text_embedding, text
)
return list(embedding)
async def embed_batch(self, texts: list[str]) -> list[list[float]]:
"""Embed multiple texts efficiently (Protocol-compatible).
Uses LlamaIndex's batch embedding for efficiency.
Args:
texts: List of texts to embed
Returns:
List of embedding vectors
"""
if not texts:
return []
loop = asyncio.get_running_loop()
# LlamaIndex embed_model has get_text_embedding_batch method
embeddings = await loop.run_in_executor(
None, self._Settings.embed_model.get_text_embedding_batch, texts
)
return [list(emb) for emb in embeddings]
async def add_evidence(self, evidence_id: str, content: str, metadata: dict[str, Any]) -> None:
"""Async wrapper for adding evidence (Protocol-compatible).
Converts the sync ingest_evidence pattern to the async protocol interface.
Uses run_in_executor to avoid blocking the event loop.
Args:
evidence_id: Unique identifier (typically URL)
content: Text content to embed and store
metadata: Additional metadata (source, title, date, authors)
"""
# Reconstruct Evidence from parts
authors_str = metadata.get("authors", "")
authors = [a.strip() for a in authors_str.split(",")] if authors_str else []
citation = Citation(
source=metadata.get("source", "web"),
title=metadata.get("title", "Unknown"),
url=evidence_id,
date=metadata.get("date", "Unknown"),
authors=authors,
)
evidence = Evidence(content=content, citation=citation)
loop = asyncio.get_running_loop()
await loop.run_in_executor(None, self.ingest_evidence, [evidence])
async def search_similar(self, query: str, n_results: int = 5) -> list[dict[str, Any]]:
"""Async wrapper for retrieve (Protocol-compatible).
Returns results in the same format as EmbeddingService.search_similar()
for seamless interchangeability.
Args:
query: Search query text
n_results: Maximum number of results to return
Returns:
List of dicts with keys: id, content, metadata, distance
"""
loop = asyncio.get_running_loop()
results = await loop.run_in_executor(None, self.retrieve, query, n_results)
# Convert LlamaIndex format to EmbeddingService format for compatibility
# LlamaIndex: {"text": ..., "score": ..., "metadata": ...}
# EmbeddingService: {"id": ..., "content": ..., "metadata": ..., "distance": ...}
return [
{
"id": r.get("metadata", {}).get("url", ""),
"content": r.get("text", ""),
"metadata": r.get("metadata", {}),
# Convert similarity score to distance
# LlamaIndex score: 0-1 (higher = more similar)
# Output distance: 0-1 (lower = more similar, matches ChromaDB behavior)
"distance": 1.0 - r.get("score", 0.5),
}
for r in results
]
async def deduplicate(self, evidence: list[Evidence], threshold: float = 0.9) -> list[Evidence]:
"""Async wrapper for deduplication (Protocol-compatible).
Uses search_similar() to check for existing similar content.
Stores unique evidence and returns the deduplicated list.
Args:
evidence: List of evidence items to deduplicate
threshold: Similarity threshold (0.9 = 90% similar is duplicate)
Distance range: 0-1 (0 = identical, 1 = orthogonal)
Duplicate if: distance < (1 - threshold), e.g., < 0.1 for 90%
Returns:
List of unique evidence items (duplicates removed)
"""
unique = []
for ev in evidence:
try:
# Check for similar existing content
similar = await self.search_similar(ev.content, n_results=1)
# Check similarity threshold
# distance 0 = identical, higher = more different
is_duplicate = similar and similar[0]["distance"] < (1 - threshold)
if not is_duplicate:
unique.append(ev)
# Store the new evidence
await self.add_evidence(
evidence_id=ev.citation.url,
content=ev.content,
metadata={
"source": ev.citation.source,
"title": ev.citation.title,
"date": ev.citation.date,
"authors": ",".join(ev.citation.authors or []),
},
)
except Exception as e:
# Log but don't fail - better to have duplicates than lose data
logger.warning(
"Failed to process evidence in deduplicate",
url=ev.citation.url,
error=str(e),
)
unique.append(ev)
return unique
def get_rag_service(
collection_name: str = "deepboner_evidence",
**kwargs: Any,
) -> LlamaIndexRAGService:
"""
Get or create a RAG service instance.
Args:
collection_name: Name of the ChromaDB collection
**kwargs: Additional arguments for LlamaIndexRAGService
Returns:
Configured LlamaIndexRAGService instance
"""
return LlamaIndexRAGService(collection_name=collection_name, **kwargs)
|