From ed07a89fb787dc0afa3b0e3edde65006fe447c1f Mon Sep 17 00:00:00 2001 From: Chris Krough <461869+ckrough@users.noreply.github.com> Date: Sat, 10 Jan 2026 16:35:02 -0500 Subject: [PATCH] chore: add OpenTelemetry observability instrumentation Add OpenTelemetry tracing to key operations: - Create src/infrastructure/observability/ module with setup, tracing utilities - Instrument RAG pipeline (ask, retrieve), LLM, embeddings, vectordb, cache - Add structlog processor for trace/span ID injection into logs - Configure via OTEL_ENABLED, OTEL_EXPORTER, OTEL_ENDPOINT env vars Closes retriever-cd6 --- .env.example | 7 + pyproject.toml | 8 + src/config.py | 7 + src/infrastructure/cache/chroma_cache.py | 240 ++++---- src/infrastructure/embeddings/openai.py | 174 +++--- src/infrastructure/llm/openrouter.py | 184 +++--- src/infrastructure/observability/__init__.py | 25 + src/infrastructure/observability/setup.py | 145 +++++ .../observability/structlog_processor.py | 33 ++ src/infrastructure/observability/tracing.py | 157 ++++++ src/infrastructure/vectordb/chroma.py | 247 ++++---- src/main.py | 27 +- src/modules/rag/retriever.py | 90 +-- src/modules/rag/service.py | 530 ++++++++++-------- tests/test_observability.py | 236 ++++++++ uv.lock | 133 +++-- 16 files changed, 1541 insertions(+), 702 deletions(-) create mode 100644 src/infrastructure/observability/__init__.py create mode 100644 src/infrastructure/observability/setup.py create mode 100644 src/infrastructure/observability/structlog_processor.py create mode 100644 src/infrastructure/observability/tracing.py create mode 100644 tests/test_observability.py diff --git a/.env.example b/.env.example index 6570deb..c719c73 100644 --- a/.env.example +++ b/.env.example @@ -61,3 +61,10 @@ AUTH_ENABLED=true # Enable user authentication JWT_SECRET_KEY=change-this-to-a-random-secret-key # Secret key for JWT signing (REQUIRED if auth enabled) JWT_ALGORITHM=HS256 # JWT signing algorithm JWT_EXPIRE_HOURS=24 # Token expiration in hours + +# OpenTelemetry Observability +OTEL_ENABLED=true # Enable OpenTelemetry tracing +OTEL_EXPORTER=none # Exporter: "otlp", "console", or "none" +OTEL_ENDPOINT= # OTLP collector endpoint (e.g., "http://localhost:4318") +OTEL_SERVICE_NAME=retriever # Service name for traces +OTEL_SAMPLE_RATE=1.0 # Sampling rate (0.0 to 1.0) diff --git a/pyproject.toml b/pyproject.toml index 3d5eb48..41ec492 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,6 +40,12 @@ dependencies = [ "email-validator>=2.3.0", "markdown>=3.10", "bleach>=6.3.0", + # Observability (OpenTelemetry) + "opentelemetry-api~=1.29.0", + "opentelemetry-sdk~=1.29.0", + "opentelemetry-exporter-otlp-proto-http~=1.29.0", + "opentelemetry-instrumentation-fastapi~=0.50b0", + "opentelemetry-instrumentation-httpx~=0.50b0", ] [project.optional-dependencies] @@ -120,6 +126,8 @@ module = [ "chromadb", "chromadb.*", "rank_bm25", + "opentelemetry", + "opentelemetry.*", ] ignore_missing_imports = true diff --git a/src/config.py b/src/config.py index b047175..0a5d85d 100644 --- a/src/config.py +++ b/src/config.py @@ -84,6 +84,13 @@ class Settings(BaseSettings): # Conversation History conversation_max_messages: int = 20 # Max messages to include in context + # OpenTelemetry / Observability + otel_enabled: bool = True # Enable OpenTelemetry tracing + otel_exporter: str = "none" # Exporter: "otlp", "console", or "none" + otel_endpoint: str | None = None # OTLP endpoint (e.g., "http://localhost:4318") + otel_service_name: str = "retriever" # Service name for traces + otel_sample_rate: float = 1.0 # Sampling rate (0.0 to 1.0) + @lru_cache def get_settings() -> Settings: diff --git a/src/infrastructure/cache/chroma_cache.py b/src/infrastructure/cache/chroma_cache.py index afbcd00..cfce052 100644 --- a/src/infrastructure/cache/chroma_cache.py +++ b/src/infrastructure/cache/chroma_cache.py @@ -10,8 +10,10 @@ from src.infrastructure.cache.protocol import CacheEntry from src.infrastructure.embeddings import EmbeddingProvider +from src.infrastructure.observability import get_tracer logger = structlog.get_logger() +tracer = get_tracer(__name__) class ChromaSemanticCache: @@ -83,94 +85,107 @@ async def get(self, question: str) -> CacheEntry | None: CacheEntry if a similar question was found above the similarity threshold, None otherwise. """ - if self._collection.count() == 0: - return None - - # Embed the question - query_embedding = await self._embeddings.embed(question) - - # Query for similar cached questions - results = self._collection.query( - query_embeddings=[query_embedding], # type: ignore[arg-type] - n_results=1, - include=["documents", "metadatas", "distances"], # type: ignore[list-item] - ) + with tracer.start_as_current_span("cache.get") as span: + span.set_attribute("cache.question_length", len(question)) + + if self._collection.count() == 0: + span.set_attribute("cache.hit", False) + span.set_attribute("cache.miss_reason", "empty_cache") + return None + + # Embed the question + query_embedding = await self._embeddings.embed(question) + + # Query for similar cached questions + results = self._collection.query( + query_embeddings=[query_embedding], + n_results=1, + include=["documents", "metadatas", "distances"], + ) - # Check if we got a result - ids_result = results.get("ids") - if not ids_result or not ids_result[0]: - logger.debug("cache_miss_no_results", question_length=len(question)) - return None - - # Get the distance and convert to similarity - distances_result = results.get("distances") - if distances_result and distances_result[0]: - distance = float(distances_result[0][0]) - else: - distance = 2.0 # Max cosine distance - similarity = 1.0 - distance - - # Check threshold - if similarity < self._similarity_threshold: - logger.debug( - "cache_miss_below_threshold", + # Check if we got a result + ids_result = results.get("ids") + if not ids_result or not ids_result[0]: + span.set_attribute("cache.hit", False) + span.set_attribute("cache.miss_reason", "no_results") + logger.debug("cache_miss_no_results", question_length=len(question)) + return None + + # Get the distance and convert to similarity + distances_result = results.get("distances") + if distances_result and distances_result[0]: + distance = float(distances_result[0][0]) + else: + distance = 2.0 # Max cosine distance + similarity = 1.0 - distance + span.set_attribute("cache.similarity", similarity) + + # Check threshold + if similarity < self._similarity_threshold: + span.set_attribute("cache.hit", False) + span.set_attribute("cache.miss_reason", "below_threshold") + logger.debug( + "cache_miss_below_threshold", + similarity=similarity, + threshold=self._similarity_threshold, + question_length=len(question), + ) + return None + + # Check TTL + metadatas_result = results.get("metadatas") + if metadatas_result and metadatas_result[0]: + metadata = metadatas_result[0][0] + else: + metadata = {} + created_at_str = str(metadata.get("created_at", "")) + + if created_at_str: + try: + created_at = datetime.fromisoformat(created_at_str) + # Handle timezone-naive datetime from storage + if created_at.tzinfo is None: + created_at = created_at.replace(tzinfo=UTC) + age_hours = (datetime.now(UTC) - created_at).total_seconds() / 3600 + if age_hours > self._ttl_hours: + span.set_attribute("cache.hit", False) + span.set_attribute("cache.miss_reason", "expired") + logger.debug( + "cache_miss_expired", + age_hours=age_hours, + ttl_hours=self._ttl_hours, + ) + return None + except ValueError: + pass # Invalid date, treat as valid + + # Cache hit! + span.set_attribute("cache.hit", True) + documents_result = results.get("documents") + if documents_result and documents_result[0]: + answer = str(documents_result[0][0]) + else: + answer = "" + + cached_question = str(metadata.get("question", "")) + chunks_json = str(metadata.get("chunks_json", "[]")) + + logger.info( + "cache_hit", similarity=similarity, - threshold=self._similarity_threshold, - question_length=len(question), + cached_question_length=len(cached_question), + query_question_length=len(question), ) - return None - - # Check TTL - metadatas_result = results.get("metadatas") - if metadatas_result and metadatas_result[0]: - metadata = metadatas_result[0][0] - else: - metadata = {} - created_at_str = str(metadata.get("created_at", "")) - - if created_at_str: - try: - created_at = datetime.fromisoformat(created_at_str) - # Handle timezone-naive datetime from storage - if created_at.tzinfo is None: - created_at = created_at.replace(tzinfo=UTC) - age_hours = (datetime.now(UTC) - created_at).total_seconds() / 3600 - if age_hours > self._ttl_hours: - logger.debug( - "cache_miss_expired", - age_hours=age_hours, - ttl_hours=self._ttl_hours, - ) - return None - except ValueError: - pass # Invalid date, treat as valid - - # Cache hit! - documents_result = results.get("documents") - if documents_result and documents_result[0]: - answer = str(documents_result[0][0]) - else: - answer = "" - - cached_question = str(metadata.get("question", "")) - chunks_json = str(metadata.get("chunks_json", "[]")) - - logger.info( - "cache_hit", - similarity=similarity, - cached_question_length=len(cached_question), - query_question_length=len(question), - ) - return CacheEntry( - question=cached_question, - answer=answer, - chunks_json=chunks_json, - created_at=datetime.fromisoformat(created_at_str) - if created_at_str - else datetime.now(UTC), - similarity_score=similarity, - ) + return CacheEntry( + question=cached_question, + answer=answer, + chunks_json=chunks_json, + created_at=datetime.fromisoformat(created_at_str) + if created_at_str + else datetime.now(UTC), + similarity_score=similarity, + ) async def set( self, @@ -185,32 +200,39 @@ async def set( answer: The generated answer. chunks_json: JSON-serialized list of ChunkWithScore used. """ - # Generate embedding for the question - question_embedding = await self._embeddings.embed(question) - - # Generate unique ID - entry_id = f"cache:{uuid.uuid4().hex}" - - # Store with metadata - self._collection.add( - ids=[entry_id], - documents=[answer], - embeddings=[question_embedding], # type: ignore[arg-type] - metadatas=[ - { - "question": question, - "chunks_json": chunks_json, - "created_at": datetime.now(UTC).isoformat(), - } - ], - ) + with tracer.start_as_current_span("cache.set") as span: + span.set_attribute("cache.question_length", len(question)) + span.set_attribute("cache.answer_length", len(answer)) + + # Generate embedding for the question + question_embedding = await self._embeddings.embed(question) + + # Generate unique ID + entry_id = f"cache:{uuid.uuid4().hex}" + + # Store with metadata + self._collection.add( + ids=[entry_id], + documents=[answer], + embeddings=[question_embedding], + metadatas=[ + { + "question": question, + "chunks_json": chunks_json, + "created_at": datetime.now(UTC).isoformat(), + } + ], + ) - logger.debug( - "cache_set", - question_length=len(question), - answer_length=len(answer), - total_cached=self._collection.count(), - ) + total_cached = self._collection.count() + span.set_attribute("cache.total_entries", total_cached) + + logger.debug( + "cache_set", + question_length=len(question), + answer_length=len(answer), + total_cached=total_cached, + ) async def clear(self) -> None: """Clear all cached entries. diff --git a/src/infrastructure/embeddings/openai.py b/src/infrastructure/embeddings/openai.py index a31449c..0dc47f0 100644 --- a/src/infrastructure/embeddings/openai.py +++ b/src/infrastructure/embeddings/openai.py @@ -19,8 +19,10 @@ EmbeddingRateLimitError, EmbeddingTimeoutError, ) +from src.infrastructure.observability import get_tracer logger = structlog.get_logger() +tracer = get_tracer(__name__) class OpenAIEmbeddingProvider: @@ -102,44 +104,53 @@ async def embed(self, text: str) -> list[float]: EmbeddingTimeoutError: If the request times out. EmbeddingRateLimitError: If rate limited. """ - try: - result = await self._embed_with_resilience([text]) - return result[0] - - except CircuitBreakerError as e: - logger.warning( - "circuit_breaker_open", - provider=self.PROVIDER_NAME, - model=self._model, - ) - raise EmbeddingProviderError( - "Service temporarily unavailable. Please try again in a moment.", - provider=self.PROVIDER_NAME, - ) from e - - except APITimeoutError as e: - logger.warning( - "embedding_timeout", - provider=self.PROVIDER_NAME, - model=self._model, - timeout_seconds=self._timeout, - ) - raise EmbeddingTimeoutError( - f"Request timed out after {self._timeout}s", - provider=self.PROVIDER_NAME, - ) from e - - except APIConnectionError as e: - logger.error( - "embedding_connection_error", - provider=self.PROVIDER_NAME, - model=self._model, - error=str(e), - ) - raise EmbeddingProviderError( - "Unable to connect to embedding service", - provider=self.PROVIDER_NAME, - ) from e + with tracer.start_as_current_span("embeddings.embed") as span: + span.set_attribute("embeddings.provider", self.PROVIDER_NAME) + span.set_attribute("embeddings.model", self._model) + span.set_attribute("embeddings.input_length", len(text)) + + try: + result = await self._embed_with_resilience([text]) + span.set_attribute("embeddings.dimensions", len(result[0])) + return result[0] + + except CircuitBreakerError as e: + span.record_exception(e) + logger.warning( + "circuit_breaker_open", + provider=self.PROVIDER_NAME, + model=self._model, + ) + raise EmbeddingProviderError( + "Service temporarily unavailable. Please try again in a moment.", + provider=self.PROVIDER_NAME, + ) from e + + except APITimeoutError as e: + span.record_exception(e) + logger.warning( + "embedding_timeout", + provider=self.PROVIDER_NAME, + model=self._model, + timeout_seconds=self._timeout, + ) + raise EmbeddingTimeoutError( + f"Request timed out after {self._timeout}s", + provider=self.PROVIDER_NAME, + ) from e + + except APIConnectionError as e: + span.record_exception(e) + logger.error( + "embedding_connection_error", + provider=self.PROVIDER_NAME, + model=self._model, + error=str(e), + ) + raise EmbeddingProviderError( + "Unable to connect to embedding service", + provider=self.PROVIDER_NAME, + ) from e async def embed_batch(self, texts: list[str]) -> list[list[float]]: """Generate embedding vectors for multiple texts. @@ -158,44 +169,55 @@ async def embed_batch(self, texts: list[str]) -> list[list[float]]: if not texts: return [] - try: - return await self._embed_with_resilience(texts) - - except CircuitBreakerError as e: - logger.warning( - "circuit_breaker_open", - provider=self.PROVIDER_NAME, - model=self._model, - ) - raise EmbeddingProviderError( - "Service temporarily unavailable. Please try again in a moment.", - provider=self.PROVIDER_NAME, - ) from e - - except APITimeoutError as e: - logger.warning( - "embedding_timeout", - provider=self.PROVIDER_NAME, - model=self._model, - timeout_seconds=self._timeout, - batch_size=len(texts), - ) - raise EmbeddingTimeoutError( - f"Request timed out after {self._timeout}s", - provider=self.PROVIDER_NAME, - ) from e - - except APIConnectionError as e: - logger.error( - "embedding_connection_error", - provider=self.PROVIDER_NAME, - model=self._model, - error=str(e), - ) - raise EmbeddingProviderError( - "Unable to connect to embedding service", - provider=self.PROVIDER_NAME, - ) from e + with tracer.start_as_current_span("embeddings.embed_batch") as span: + span.set_attribute("embeddings.provider", self.PROVIDER_NAME) + span.set_attribute("embeddings.model", self._model) + span.set_attribute("embeddings.batch_size", len(texts)) + + try: + result = await self._embed_with_resilience(texts) + if result: + span.set_attribute("embeddings.dimensions", len(result[0])) + return result + + except CircuitBreakerError as e: + span.record_exception(e) + logger.warning( + "circuit_breaker_open", + provider=self.PROVIDER_NAME, + model=self._model, + ) + raise EmbeddingProviderError( + "Service temporarily unavailable. Please try again in a moment.", + provider=self.PROVIDER_NAME, + ) from e + + except APITimeoutError as e: + span.record_exception(e) + logger.warning( + "embedding_timeout", + provider=self.PROVIDER_NAME, + model=self._model, + timeout_seconds=self._timeout, + batch_size=len(texts), + ) + raise EmbeddingTimeoutError( + f"Request timed out after {self._timeout}s", + provider=self.PROVIDER_NAME, + ) from e + + except APIConnectionError as e: + span.record_exception(e) + logger.error( + "embedding_connection_error", + provider=self.PROVIDER_NAME, + model=self._model, + error=str(e), + ) + raise EmbeddingProviderError( + "Unable to connect to embedding service", + provider=self.PROVIDER_NAME, + ) from e @retry( retry=retry_if_exception_type((APIConnectionError, APITimeoutError)), diff --git a/src/infrastructure/llm/openrouter.py b/src/infrastructure/llm/openrouter.py index c0e9d2a..35417b4 100644 --- a/src/infrastructure/llm/openrouter.py +++ b/src/infrastructure/llm/openrouter.py @@ -18,8 +18,10 @@ LLMRateLimitError, LLMTimeoutError, ) +from src.infrastructure.observability import get_tracer logger = structlog.get_logger() +tracer = get_tracer(__name__) # System prompt for Retriever assistant DEFAULT_SYSTEM_PROMPT = """You are Retriever, a helpful assistant for animal shelter volunteers. @@ -103,50 +105,59 @@ async def complete( """ model_to_use = model or self._default_model - try: - result: str = await self._complete_with_resilience( - system_prompt=system_prompt, - user_message=user_message, - model=model_to_use, - ) - return result + with tracer.start_as_current_span("llm.complete") as span: + span.set_attribute("llm.provider", self.PROVIDER_NAME) + span.set_attribute("llm.model", model_to_use) + span.set_attribute("llm.input_length", len(user_message)) - except CircuitBreakerError as e: - logger.warning( - "circuit_breaker_open", - provider=self.PROVIDER_NAME, - model=model_to_use, - ) - raise LLMProviderError( - "Service temporarily unavailable. Please try again in a moment.", - provider=self.PROVIDER_NAME, - ) from e + try: + result: str = await self._complete_with_resilience( + system_prompt=system_prompt, + user_message=user_message, + model=model_to_use, + ) + span.set_attribute("llm.output_length", len(result)) + return result - except APITimeoutError as e: - # After all retries exhausted - logger.warning( - "llm_timeout", - provider=self.PROVIDER_NAME, - model=model_to_use, - timeout_seconds=self._timeout, - ) - raise LLMTimeoutError( - f"Request timed out after {self._timeout}s", - provider=self.PROVIDER_NAME, - ) from e + except CircuitBreakerError as e: + span.record_exception(e) + logger.warning( + "circuit_breaker_open", + provider=self.PROVIDER_NAME, + model=model_to_use, + ) + raise LLMProviderError( + "Service temporarily unavailable. Please try again in a moment.", + provider=self.PROVIDER_NAME, + ) from e - except APIConnectionError as e: - # After all retries exhausted - logger.error( - "llm_connection_error", - provider=self.PROVIDER_NAME, - model=model_to_use, - error=str(e), - ) - raise LLMProviderError( - "Unable to connect to LLM service", - provider=self.PROVIDER_NAME, - ) from e + except APITimeoutError as e: + # After all retries exhausted + span.record_exception(e) + logger.warning( + "llm_timeout", + provider=self.PROVIDER_NAME, + model=model_to_use, + timeout_seconds=self._timeout, + ) + raise LLMTimeoutError( + f"Request timed out after {self._timeout}s", + provider=self.PROVIDER_NAME, + ) from e + + except APIConnectionError as e: + # After all retries exhausted + span.record_exception(e) + logger.error( + "llm_connection_error", + provider=self.PROVIDER_NAME, + model=model_to_use, + error=str(e), + ) + raise LLMProviderError( + "Unable to connect to LLM service", + provider=self.PROVIDER_NAME, + ) from e @retry( retry=retry_if_exception_type((APIConnectionError, APITimeoutError)), @@ -255,48 +266,59 @@ async def complete_with_history( """ model_to_use = model or self._default_model - try: - result: str = await self._complete_history_with_resilience( - system_prompt=system_prompt, - messages=messages, - model=model_to_use, - ) - return result + with tracer.start_as_current_span("llm.complete_with_history") as span: + span.set_attribute("llm.provider", self.PROVIDER_NAME) + span.set_attribute("llm.model", model_to_use) + span.set_attribute("llm.message_count", len(messages)) + total_input_length = sum(len(m.get("content", "")) for m in messages) + span.set_attribute("llm.input_length", total_input_length) + + try: + result: str = await self._complete_history_with_resilience( + system_prompt=system_prompt, + messages=messages, + model=model_to_use, + ) + span.set_attribute("llm.output_length", len(result)) + return result - except CircuitBreakerError as e: - logger.warning( - "circuit_breaker_open", - provider=self.PROVIDER_NAME, - model=model_to_use, - ) - raise LLMProviderError( - "Service temporarily unavailable. Please try again in a moment.", - provider=self.PROVIDER_NAME, - ) from e + except CircuitBreakerError as e: + span.record_exception(e) + logger.warning( + "circuit_breaker_open", + provider=self.PROVIDER_NAME, + model=model_to_use, + ) + raise LLMProviderError( + "Service temporarily unavailable. Please try again in a moment.", + provider=self.PROVIDER_NAME, + ) from e - except APITimeoutError as e: - logger.warning( - "llm_timeout", - provider=self.PROVIDER_NAME, - model=model_to_use, - timeout_seconds=self._timeout, - ) - raise LLMTimeoutError( - f"Request timed out after {self._timeout}s", - provider=self.PROVIDER_NAME, - ) from e + except APITimeoutError as e: + span.record_exception(e) + logger.warning( + "llm_timeout", + provider=self.PROVIDER_NAME, + model=model_to_use, + timeout_seconds=self._timeout, + ) + raise LLMTimeoutError( + f"Request timed out after {self._timeout}s", + provider=self.PROVIDER_NAME, + ) from e - except APIConnectionError as e: - logger.error( - "llm_connection_error", - provider=self.PROVIDER_NAME, - model=model_to_use, - error=str(e), - ) - raise LLMProviderError( - "Unable to connect to LLM service", - provider=self.PROVIDER_NAME, - ) from e + except APIConnectionError as e: + span.record_exception(e) + logger.error( + "llm_connection_error", + provider=self.PROVIDER_NAME, + model=model_to_use, + error=str(e), + ) + raise LLMProviderError( + "Unable to connect to LLM service", + provider=self.PROVIDER_NAME, + ) from e @retry( retry=retry_if_exception_type((APIConnectionError, APITimeoutError)), @@ -341,7 +363,7 @@ async def _do_complete_with_history( response = await self._client.chat.completions.create( model=model, - messages=all_messages, # type: ignore[arg-type] + messages=all_messages, ) content = response.choices[0].message.content or "" diff --git a/src/infrastructure/observability/__init__.py b/src/infrastructure/observability/__init__.py new file mode 100644 index 0000000..eaacf0b --- /dev/null +++ b/src/infrastructure/observability/__init__.py @@ -0,0 +1,25 @@ +"""Observability module providing OpenTelemetry tracing and structlog integration.""" + +from src.infrastructure.observability.setup import ( + init_observability, + shutdown_observability, +) +from src.infrastructure.observability.structlog_processor import add_trace_context +from src.infrastructure.observability.tracing import ( + add_span_attributes, + get_current_span_id, + get_current_trace_id, + get_tracer, + traced, +) + +__all__ = [ + "add_span_attributes", + "add_trace_context", + "get_current_span_id", + "get_current_trace_id", + "get_tracer", + "init_observability", + "shutdown_observability", + "traced", +] diff --git a/src/infrastructure/observability/setup.py b/src/infrastructure/observability/setup.py new file mode 100644 index 0000000..f21a7a6 --- /dev/null +++ b/src/infrastructure/observability/setup.py @@ -0,0 +1,145 @@ +"""OpenTelemetry setup and initialization.""" + +import logging +from typing import TYPE_CHECKING + +import structlog +from opentelemetry import trace +from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter +from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor +from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor +from opentelemetry.sdk.resources import SERVICE_NAME, SERVICE_VERSION, Resource +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter +from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio + +from src.infrastructure.observability.structlog_processor import add_trace_context + +if TYPE_CHECKING: + from fastapi import FastAPI + +# Module-level state for cleanup +_tracer_provider: TracerProvider | None = None +_initialized: bool = False + + +def init_observability( + service_name: str, + service_version: str, + *, + otlp_endpoint: str | None = None, + console_export: bool = False, + enabled: bool = True, + sample_rate: float = 1.0, + app: "FastAPI | None" = None, +) -> None: + """Initialize OpenTelemetry tracing and configure structlog integration. + + This function sets up the global tracer provider with the specified exporter, + configures structlog to include trace context in logs, and optionally + instruments FastAPI. + + Args: + service_name: Name of the service for resource attribution. + service_version: Version of the service. + otlp_endpoint: OTLP collector endpoint (e.g., "http://localhost:4318"). + If None and console_export is False, no exporter is configured. + console_export: If True, export spans to console (for development). + enabled: If False, tracing is completely disabled (no-op provider). + sample_rate: Sampling rate between 0.0 and 1.0. Default is 1.0 (all traces). + app: Optional FastAPI app instance to instrument. + """ + global _tracer_provider, _initialized + + if _initialized: + return + + if not enabled: + # Set no-op tracer provider + trace.set_tracer_provider(trace.NoOpTracerProvider()) + _initialized = True + return + + # Create resource with service metadata + resource = Resource.create( + { + SERVICE_NAME: service_name, + SERVICE_VERSION: service_version, + } + ) + + # Configure sampler + sampler = ParentBasedTraceIdRatio(sample_rate) + + # Create tracer provider + _tracer_provider = TracerProvider(resource=resource, sampler=sampler) + + # Add exporters + if console_export: + _tracer_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter())) + + if otlp_endpoint: + otlp_exporter = OTLPSpanExporter(endpoint=f"{otlp_endpoint}/v1/traces") + _tracer_provider.add_span_processor(BatchSpanProcessor(otlp_exporter)) + + # Set as global tracer provider + trace.set_tracer_provider(_tracer_provider) + + # Configure structlog with trace context processor + _configure_structlog() + + # Instrument FastAPI if app provided + if app is not None: + FastAPIInstrumentor.instrument_app(app) + + # Instrument httpx for outgoing HTTP calls + HTTPXClientInstrumentor().instrument() + + _initialized = True + + +def shutdown_observability() -> None: + """Shutdown the tracer provider and flush any pending spans. + + This should be called during application shutdown to ensure all + spans are exported before the process exits. + """ + global _tracer_provider, _initialized + + if _tracer_provider is not None: + _tracer_provider.shutdown() + _tracer_provider = None + + _initialized = False + + +def _configure_structlog() -> None: + """Configure structlog with OpenTelemetry trace context injection. + + This adds the trace context processor to the structlog processing chain, + ensuring that trace_id and span_id are included in all log events. + """ + structlog.configure( + processors=[ + structlog.contextvars.merge_contextvars, + structlog.stdlib.add_log_level, + structlog.stdlib.add_logger_name, + structlog.stdlib.PositionalArgumentsFormatter(), + add_trace_context, # Inject trace context + structlog.processors.TimeStamper(fmt="iso"), + structlog.processors.StackInfoRenderer(), + structlog.processors.format_exc_info, + structlog.processors.UnicodeDecoder(), + structlog.stdlib.ProcessorFormatter.wrap_for_formatter, + ], + wrapper_class=structlog.stdlib.BoundLogger, + context_class=dict, + logger_factory=structlog.stdlib.LoggerFactory(), + cache_logger_on_first_use=True, + ) + + # Configure standard library logging + logging.basicConfig( + format="%(message)s", + level=logging.INFO, + ) diff --git a/src/infrastructure/observability/structlog_processor.py b/src/infrastructure/observability/structlog_processor.py new file mode 100644 index 0000000..fbbd998 --- /dev/null +++ b/src/infrastructure/observability/structlog_processor.py @@ -0,0 +1,33 @@ +"""Structlog processor for OpenTelemetry trace context injection.""" + +from typing import Any + +from opentelemetry import trace + + +def add_trace_context( + logger: Any, # noqa: ARG001 + method_name: str, # noqa: ARG001 + event_dict: dict[str, Any], +) -> dict[str, Any]: + """Structlog processor that adds trace_id and span_id to log events. + + This processor injects OpenTelemetry trace context into every log event, + enabling correlation between logs and traces in observability backends. + + Args: + logger: The logger instance (unused, required by structlog API). + method_name: The log method name (unused, required by structlog API). + event_dict: The log event dictionary to enrich. + + Returns: + The enriched event dictionary with trace_id and span_id if available. + """ + span = trace.get_current_span() + span_context = span.get_span_context() + + if span_context.is_valid: + event_dict["trace_id"] = format(span_context.trace_id, "032x") + event_dict["span_id"] = format(span_context.span_id, "016x") + + return event_dict diff --git a/src/infrastructure/observability/tracing.py b/src/infrastructure/observability/tracing.py new file mode 100644 index 0000000..69561b8 --- /dev/null +++ b/src/infrastructure/observability/tracing.py @@ -0,0 +1,157 @@ +"""Tracing utilities for instrumenting application code with OpenTelemetry.""" + +import asyncio +from collections.abc import Callable +from functools import wraps +from typing import Any, ParamSpec, TypeVar, overload + +from opentelemetry import trace +from opentelemetry.trace import Status, StatusCode, Tracer + +P = ParamSpec("P") +R = TypeVar("R") + + +def get_tracer(name: str) -> Tracer: + """Get a tracer for the given module name. + + Args: + name: Module name, typically __name__. + + Returns: + A Tracer instance for creating spans. + """ + return trace.get_tracer(name) + + +def add_span_attributes(attributes: dict[str, str | int | float | bool]) -> None: + """Add attributes to the current span. + + Args: + attributes: Key-value pairs to add to the span. + """ + span = trace.get_current_span() + if span.is_recording(): + for key, value in attributes.items(): + span.set_attribute(key, value) + + +def get_current_trace_id() -> str | None: + """Get the current trace ID as a hex string. + + Returns: + The trace ID as a 32-character hex string, or None if no active trace. + """ + span = trace.get_current_span() + span_context = span.get_span_context() + if span_context.is_valid: + return format(span_context.trace_id, "032x") + return None + + +def get_current_span_id() -> str | None: + """Get the current span ID as a hex string. + + Returns: + The span ID as a 16-character hex string, or None if no active span. + """ + span = trace.get_current_span() + span_context = span.get_span_context() + if span_context.is_valid: + return format(span_context.span_id, "016x") + return None + + +@overload +def traced( # noqa: UP047 + func: Callable[P, R], +) -> Callable[P, R]: ... + + +@overload +def traced( + func: None = None, + *, + span_name: str | None = None, + attributes: dict[str, str | int | float | bool] | None = None, + record_exception: bool = True, +) -> Callable[[Callable[P, R]], Callable[P, R]]: ... + + +def traced( # noqa: UP047 + func: Callable[P, R] | None = None, + *, + span_name: str | None = None, + attributes: dict[str, str | int | float | bool] | None = None, + record_exception: bool = True, +) -> Callable[P, R] | Callable[[Callable[P, R]], Callable[P, R]]: + """Decorator to trace a function with OpenTelemetry. + + Works with both sync and async functions. Can be used with or without + parentheses. + + Args: + func: The function to trace (when used without parentheses). + span_name: Name for the span (defaults to function name). + attributes: Static attributes to add to the span. + record_exception: Whether to record exceptions on the span. + + Returns: + The decorated function. + + Examples: + @traced + def my_function(): + ... + + @traced(span_name="custom.name", attributes={"key": "value"}) + async def my_async_function(): + ... + """ + + def decorator(fn: Callable[P, R]) -> Callable[P, R]: + tracer = get_tracer(fn.__module__) + name = span_name or fn.__name__ + + if asyncio.iscoroutinefunction(fn): + + @wraps(fn) + async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> Any: + with tracer.start_as_current_span(name) as span: + if attributes: + for key, value in attributes.items(): + span.set_attribute(key, value) + try: + result = await fn(*args, **kwargs) + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + if record_exception: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + raise + + return async_wrapper # type: ignore[return-value] + else: + + @wraps(fn) + def sync_wrapper(*args: P.args, **kwargs: P.kwargs) -> R: + with tracer.start_as_current_span(name) as span: + if attributes: + for key, value in attributes.items(): + span.set_attribute(key, value) + try: + result = fn(*args, **kwargs) + span.set_status(Status(StatusCode.OK)) + return result + except Exception as e: + if record_exception: + span.record_exception(e) + span.set_status(Status(StatusCode.ERROR, str(e))) + raise + + return sync_wrapper + + if func is not None: + return decorator(func) + return decorator diff --git a/src/infrastructure/vectordb/chroma.py b/src/infrastructure/vectordb/chroma.py index b1de007..0e803eb 100644 --- a/src/infrastructure/vectordb/chroma.py +++ b/src/infrastructure/vectordb/chroma.py @@ -11,6 +11,7 @@ import structlog from chromadb.config import Settings as ChromaSettings +from src.infrastructure.observability import get_tracer from src.infrastructure.vectordb.exceptions import ( VectorStoreConfigurationError, VectorStoreError, @@ -18,6 +19,7 @@ from src.infrastructure.vectordb.protocol import DocumentChunk, RetrievalResult logger = structlog.get_logger() +tracer = get_tracer(__name__) class ChromaVectorStore: @@ -98,38 +100,46 @@ async def add_chunks(self, chunks: list[DocumentChunk]) -> None: if not chunks: return - try: - # ChromaDB expects separate lists for each component - ids = [chunk.id for chunk in chunks] - documents = [chunk.content for chunk in chunks] - embeddings = [chunk.embedding for chunk in chunks] - metadatas: list[dict[str, Any]] = [chunk.metadata for chunk in chunks] - - self._collection.add( - ids=ids, - documents=documents, - embeddings=embeddings, # type: ignore[arg-type] - metadatas=metadatas, # type: ignore[arg-type] - ) + with tracer.start_as_current_span("vectordb.add_chunks") as span: + span.set_attribute("vectordb.provider", self.PROVIDER_NAME) + span.set_attribute("vectordb.chunk_count", len(chunks)) + + try: + # ChromaDB expects separate lists for each component + ids = [chunk.id for chunk in chunks] + documents = [chunk.content for chunk in chunks] + embeddings = [chunk.embedding for chunk in chunks] + metadatas: list[dict[str, Any]] = [chunk.metadata for chunk in chunks] + + self._collection.add( + ids=ids, + documents=documents, + embeddings=embeddings, + metadatas=metadatas, + ) - logger.debug( - "chroma_chunks_added", - provider=self.PROVIDER_NAME, - count=len(chunks), - total_count=self._collection.count(), - ) + total_count = self._collection.count() + span.set_attribute("vectordb.total_count", total_count) - except Exception as e: - logger.error( - "chroma_add_failed", - provider=self.PROVIDER_NAME, - error=str(e), - chunk_count=len(chunks), - ) - raise VectorStoreError( - f"Failed to add chunks: {e}", - provider=self.PROVIDER_NAME, - ) from e + logger.debug( + "chroma_chunks_added", + provider=self.PROVIDER_NAME, + count=len(chunks), + total_count=total_count, + ) + + except Exception as e: + span.record_exception(e) + logger.error( + "chroma_add_failed", + provider=self.PROVIDER_NAME, + error=str(e), + chunk_count=len(chunks), + ) + raise VectorStoreError( + f"Failed to add chunks: {e}", + provider=self.PROVIDER_NAME, + ) from e async def query( self, @@ -149,70 +159,80 @@ async def query( Raises: VectorStoreError: If query fails. """ - try: - results = self._collection.query( - query_embeddings=[embedding], # type: ignore[arg-type] - n_results=min(top_k, self._collection.count()), - include=["documents", "metadatas", "distances"], # type: ignore[list-item] - ) + with tracer.start_as_current_span("vectordb.query") as span: + span.set_attribute("vectordb.provider", self.PROVIDER_NAME) + span.set_attribute("vectordb.top_k", top_k) + span.set_attribute("vectordb.collection_size", self._collection.count()) + + try: + results = self._collection.query( + query_embeddings=[embedding], + n_results=min(top_k, self._collection.count()), + include=["documents", "metadatas", "distances"], + ) - # Convert Chroma results to RetrievalResult objects - retrieval_results: list[RetrievalResult] = [] - - # Chroma returns lists of lists (one per query), we only have one query - # All fields can be None if not included - ids_list = results.get("ids") or [[]] - ids = ids_list[0] if ids_list else [] - - docs_list = results.get("documents") or [[]] - documents = docs_list[0] if docs_list else [] - - meta_list = results.get("metadatas") or [[]] - metadatas = meta_list[0] if meta_list else [] - - dist_list = results.get("distances") or [[]] - distances = dist_list[0] if dist_list else [] - - for i, doc_id in enumerate(ids): - # Chroma returns distance; convert to similarity score - # For cosine, distance = 1 - similarity - distance = distances[i] if i < len(distances) else 0.0 - score = 1.0 - float(distance) - - # Convert metadata values to strings for our protocol - raw_meta = metadatas[i] if i < len(metadatas) else {} - str_meta: dict[str, str] = { - k: str(v) for k, v in (raw_meta or {}).items() - } - - retrieval_results.append( - RetrievalResult( - id=doc_id, - content=documents[i] if i < len(documents) else "", - metadata=str_meta, - score=score, + # Convert Chroma results to RetrievalResult objects + retrieval_results: list[RetrievalResult] = [] + + # Chroma returns lists of lists (one per query), we only have one query + # All fields can be None if not included + ids_list = results.get("ids") or [[]] + ids = ids_list[0] if ids_list else [] + + docs_list = results.get("documents") or [[]] + documents = docs_list[0] if docs_list else [] + + meta_list = results.get("metadatas") or [[]] + metadatas = meta_list[0] if meta_list else [] + + dist_list = results.get("distances") or [[]] + distances = dist_list[0] if dist_list else [] + + for i, doc_id in enumerate(ids): + # Chroma returns distance; convert to similarity score + # For cosine, distance = 1 - similarity + distance = distances[i] if i < len(distances) else 0.0 + score = 1.0 - float(distance) + + # Convert metadata values to strings for our protocol + raw_meta = metadatas[i] if i < len(metadatas) else {} + str_meta: dict[str, str] = { + k: str(v) for k, v in (raw_meta or {}).items() + } + + retrieval_results.append( + RetrievalResult( + id=doc_id, + content=documents[i] if i < len(documents) else "", + metadata=str_meta, + score=score, + ) ) - ) - logger.debug( - "chroma_query_success", - provider=self.PROVIDER_NAME, - top_k=top_k, - results_count=len(retrieval_results), - ) + span.set_attribute("vectordb.results_count", len(retrieval_results)) + if retrieval_results: + span.set_attribute("vectordb.top_score", retrieval_results[0].score) - return retrieval_results + logger.debug( + "chroma_query_success", + provider=self.PROVIDER_NAME, + top_k=top_k, + results_count=len(retrieval_results), + ) - except Exception as e: - logger.error( - "chroma_query_failed", - provider=self.PROVIDER_NAME, - error=str(e), - ) - raise VectorStoreError( - f"Failed to query: {e}", - provider=self.PROVIDER_NAME, - ) from e + return retrieval_results + + except Exception as e: + span.record_exception(e) + logger.error( + "chroma_query_failed", + provider=self.PROVIDER_NAME, + error=str(e), + ) + raise VectorStoreError( + f"Failed to query: {e}", + provider=self.PROVIDER_NAME, + ) from e async def clear(self) -> None: """Clear all data from the store. @@ -220,30 +240,35 @@ async def clear(self) -> None: Raises: VectorStoreError: If clearing fails. """ - try: - # Delete and recreate the collection - self._client.delete_collection(name=self._collection_name) - self._collection = self._client.create_collection( - name=self._collection_name, - metadata={"hnsw:space": "cosine"}, - ) + with tracer.start_as_current_span("vectordb.clear") as span: + span.set_attribute("vectordb.provider", self.PROVIDER_NAME) + span.set_attribute("vectordb.collection", self._collection_name) + + try: + # Delete and recreate the collection + self._client.delete_collection(name=self._collection_name) + self._collection = self._client.create_collection( + name=self._collection_name, + metadata={"hnsw:space": "cosine"}, + ) - logger.info( - "chroma_cleared", - provider=self.PROVIDER_NAME, - collection=self._collection_name, - ) + logger.info( + "chroma_cleared", + provider=self.PROVIDER_NAME, + collection=self._collection_name, + ) - except Exception as e: - logger.error( - "chroma_clear_failed", - provider=self.PROVIDER_NAME, - error=str(e), - ) - raise VectorStoreError( - f"Failed to clear collection: {e}", - provider=self.PROVIDER_NAME, - ) from e + except Exception as e: + span.record_exception(e) + logger.error( + "chroma_clear_failed", + provider=self.PROVIDER_NAME, + error=str(e), + ) + raise VectorStoreError( + f"Failed to clear collection: {e}", + provider=self.PROVIDER_NAME, + ) from e def count(self) -> int: """Return the number of chunks in the store.""" diff --git a/src/main.py b/src/main.py index 93159c3..bc072ae 100644 --- a/src/main.py +++ b/src/main.py @@ -13,6 +13,7 @@ from src.api.rate_limit import limiter, rate_limit_exceeded_handler from src.config import get_settings from src.infrastructure.database import Database, init_database +from src.infrastructure.observability import init_observability, shutdown_observability from src.modules.auth.repository import UserRepository from src.modules.auth.routes import router as auth_api_router from src.modules.auth.routes import set_auth_service as set_api_auth_service @@ -31,10 +32,26 @@ @asynccontextmanager -async def lifespan(_app: FastAPI) -> AsyncGenerator[None]: +async def lifespan(app: FastAPI) -> AsyncGenerator[None]: """Application lifespan handler for startup/shutdown.""" global _database + # Initialize observability first (before other services) + init_observability( + service_name=settings.otel_service_name, + service_version=settings.app_version, + otlp_endpoint=settings.otel_endpoint, + console_export=(settings.otel_exporter == "console"), + enabled=settings.otel_enabled, + sample_rate=settings.otel_sample_rate, + app=app, + ) + logger.info( + "observability_initialized", + enabled=settings.otel_enabled, + exporter=settings.otel_exporter, + ) + # Initialize database (sets global in connection module) db_path = Path(settings.database_path) db_path.parent.mkdir(parents=True, exist_ok=True) @@ -65,6 +82,10 @@ async def lifespan(_app: FastAPI) -> AsyncGenerator[None]: await _database.disconnect() logger.info("database_disconnected") + # Shutdown observability (flush pending spans) + shutdown_observability() + logger.info("observability_shutdown") + app = FastAPI( title=settings.app_name, @@ -78,13 +99,13 @@ async def lifespan(_app: FastAPI) -> AsyncGenerator[None]: app.state.limiter = limiter app.add_exception_handler( RateLimitExceeded, - rate_limit_exceeded_handler, # type: ignore[arg-type] + rate_limit_exceeded_handler, ) # Authentication redirect app.add_exception_handler( AuthenticationRequired, - auth_exception_handler, # type: ignore[arg-type] + auth_exception_handler, ) # Static files (optional - only mount if directory exists) diff --git a/src/modules/rag/retriever.py b/src/modules/rag/retriever.py index a05030f..735fc2a 100644 --- a/src/modules/rag/retriever.py +++ b/src/modules/rag/retriever.py @@ -7,9 +7,11 @@ from rank_bm25 import BM25Okapi from src.infrastructure.embeddings import EmbeddingProvider +from src.infrastructure.observability import get_tracer from src.infrastructure.vectordb import RetrievalResult, VectorStore logger = structlog.get_logger() +tracer = get_tracer(__name__) @dataclass @@ -108,50 +110,66 @@ async def retrieve( Returns: List of retrieval results ordered by combined relevance. """ - # Get semantic results (over-retrieve for better fusion) - semantic_k = min(top_k * 2, self._vector_store.count()) - if semantic_k == 0: - return [] + with tracer.start_as_current_span("retrieval.hybrid") as span: + span.set_attribute("retrieval.query_length", len(query)) + span.set_attribute("retrieval.top_k", top_k) + + # Get semantic results (over-retrieve for better fusion) + semantic_k = min(top_k * 2, self._vector_store.count()) + if semantic_k == 0: + span.set_attribute("retrieval.results_count", 0) + return [] + + with tracer.start_as_current_span("retrieval.semantic"): + query_embedding = await self._embeddings.embed(query) + semantic_results = await self._vector_store.query( + query_embedding, top_k=semantic_k + ) - query_embedding = await self._embeddings.embed(query) - semantic_results = await self._vector_store.query( - query_embedding, top_k=semantic_k - ) + span.set_attribute("retrieval.semantic_count", len(semantic_results)) - logger.debug( - "hybrid_semantic_results", - count=len(semantic_results), - top_score=semantic_results[0].score if semantic_results else 0, - ) + logger.debug( + "hybrid_semantic_results", + count=len(semantic_results), + top_score=semantic_results[0].score if semantic_results else 0, + ) - # Get keyword results - keyword_results = self._keyword_search(query, top_k=top_k * 2) + # Get keyword results + with tracer.start_as_current_span("retrieval.keyword"): + keyword_results = self._keyword_search(query, top_k=top_k * 2) - logger.debug( - "hybrid_keyword_results", - count=len(keyword_results), - top_score=keyword_results[0].score if keyword_results else 0, - ) + span.set_attribute("retrieval.keyword_count", len(keyword_results)) - # Merge results using Reciprocal Rank Fusion - merged = self._reciprocal_rank_fusion( - semantic_results, - keyword_results, - ) + logger.debug( + "hybrid_keyword_results", + count=len(keyword_results), + top_score=keyword_results[0].score if keyword_results else 0, + ) - # Return top_k results - final_results = merged[:top_k] + # Merge results using Reciprocal Rank Fusion + merged = self._reciprocal_rank_fusion( + semantic_results, + keyword_results, + ) - logger.info( - "hybrid_retrieval_complete", - query_length=len(query), - semantic_count=len(semantic_results), - keyword_count=len(keyword_results), - merged_count=len(merged), - returned_count=len(final_results), - ) + # Return top_k results + final_results = merged[:top_k] + + span.set_attribute("retrieval.merged_count", len(merged)) + span.set_attribute("retrieval.results_count", len(final_results)) + if final_results: + span.set_attribute("retrieval.top_score", final_results[0].score) + + logger.info( + "hybrid_retrieval_complete", + query_length=len(query), + semantic_count=len(semantic_results), + keyword_count=len(keyword_results), + merged_count=len(merged), + returned_count=len(final_results), + ) - return final_results + return final_results def _keyword_search( self, diff --git a/src/modules/rag/service.py b/src/modules/rag/service.py index 6911d72..21d0ef4 100644 --- a/src/modules/rag/service.py +++ b/src/modules/rag/service.py @@ -9,6 +9,7 @@ from src.infrastructure.cache import SemanticCache from src.infrastructure.embeddings import EmbeddingProvider from src.infrastructure.llm import LLMProvider +from src.infrastructure.observability import get_tracer from src.infrastructure.safety import ( ConfidenceScorer, SafetyService, @@ -27,6 +28,7 @@ from src.modules.rag.schemas import ChunkWithScore, IndexingResult, RAGResponse logger = structlog.get_logger() +tracer = get_tracer(__name__) def _serialize_chunks(chunks: list[ChunkWithScore]) -> str: @@ -135,191 +137,222 @@ async def ask( Returns: RAGResponse with answer and retrieved chunks. """ - # Check input safety first (if enabled) - if self._safety is not None: - safety_result = await self._safety.check_input(question) - if not safety_result.is_safe: - logger.warning( - "rag_input_blocked", - violation_type=safety_result.violation_type.value, - question_length=len(question), - ) + with tracer.start_as_current_span("rag.ask") as span: + span.set_attribute("rag.question_length", len(question)) + span.set_attribute("rag.has_history", conversation_history is not None) + span.set_attribute( + "rag.history_length", + len(conversation_history) if conversation_history else 0, + ) + + # Check input safety first (if enabled) + if self._safety is not None: + safety_result = await self._safety.check_input(question) + if not safety_result.is_safe: + span.set_attribute("rag.blocked", True) + span.set_attribute( + "rag.blocked_reason", safety_result.violation_type.value + ) + logger.warning( + "rag_input_blocked", + violation_type=safety_result.violation_type.value, + question_length=len(question), + ) + return RAGResponse( + answer=safety_result.message, + chunks_used=[], + question=question, + confidence_level="low", + confidence_score=0.0, + blocked=True, + blocked_reason=safety_result.violation_type.value, + ) + + # Check semantic cache first (if enabled) + if self._cache is not None: + cached = await self._cache.get(question) + if cached is not None: + span.set_attribute("rag.cache_hit", True) + logger.info( + "rag_cache_hit", + question_length=len(question), + similarity=cached.similarity_score, + ) + return RAGResponse( + answer=cached.answer, + chunks_used=_deserialize_chunks(cached.chunks_json), + question=question, + ) + + span.set_attribute("rag.cache_hit", False) + + # Check if we have any documents indexed + if self._store.count() == 0: + span.set_attribute("rag.no_documents", True) + logger.info("rag_no_documents", question_length=len(question)) + if conversation_history: + messages = conversation_history.copy() + messages.append({"role": "user", "content": question}) + answer = await self._llm.complete_with_history( + system_prompt=FALLBACK_SYSTEM_PROMPT, + messages=messages, + ) + else: + answer = await self._llm.complete( + system_prompt=FALLBACK_SYSTEM_PROMPT, + user_message=question, + ) + # Don't cache fallback responses (no context) return RAGResponse( - answer=safety_result.message, + answer=answer, chunks_used=[], question=question, - confidence_level="low", - confidence_score=0.0, - blocked=True, - blocked_reason=safety_result.violation_type.value, ) - # Check semantic cache first (if enabled) - if self._cache is not None: - cached = await self._cache.get(question) - if cached is not None: - logger.info( - "rag_cache_hit", - question_length=len(question), - similarity=cached.similarity_score, + # Retrieve relevant chunks (hybrid or semantic-only) + if self._retriever is not None: + # Use hybrid retrieval (semantic + keyword search with RRF) + span.set_attribute("rag.retrieval_type", "hybrid") + logger.debug( + "rag_hybrid_retrieving", + top_k=self._top_k, + keyword_index_size=self._retriever.get_keyword_index_count(), ) - return RAGResponse( - answer=cached.answer, - chunks_used=_deserialize_chunks(cached.chunks_json), - question=question, + results = await self._retriever.retrieve(question, top_k=self._top_k) + else: + # Fall back to semantic-only search + span.set_attribute("rag.retrieval_type", "semantic") + logger.debug("rag_embedding_question", question_length=len(question)) + query_embedding = await self._embeddings.embed(question) + logger.debug("rag_retrieving", top_k=self._top_k) + results = await self._store.query(query_embedding, top_k=self._top_k) + + # Convert to ChunkWithScore for response + chunks_used = [ + ChunkWithScore( + content=r.content, + source=r.metadata.get("source", "unknown"), + section=r.metadata.get("section", ""), + score=r.score, + title=r.metadata.get("title", ""), ) + for r in results + ] + + span.set_attribute("rag.chunks_retrieved", len(chunks_used)) + + # Build context for LLM + context_tuples = [ + (r.content, r.metadata.get("source", "unknown"), r.score) + for r in results + ] + system_prompt = build_rag_prompt(context_tuples) + + # Generate answer + logger.debug( + "rag_generating", + context_chunks=len(results), + question_length=len(question), + has_history=conversation_history is not None, + history_length=len(conversation_history) if conversation_history else 0, + ) - # Check if we have any documents indexed - if self._store.count() == 0: - logger.info("rag_no_documents", question_length=len(question)) if conversation_history: + # Multi-turn: include conversation history messages = conversation_history.copy() messages.append({"role": "user", "content": question}) answer = await self._llm.complete_with_history( - system_prompt=FALLBACK_SYSTEM_PROMPT, + system_prompt=system_prompt, messages=messages, ) else: + # Single-turn: no history answer = await self._llm.complete( - system_prompt=FALLBACK_SYSTEM_PROMPT, + system_prompt=system_prompt, user_message=question, ) - # Don't cache fallback responses (no context) - return RAGResponse( - answer=answer, - chunks_used=[], - question=question, - ) - # Retrieve relevant chunks (hybrid or semantic-only) - if self._retriever is not None: - # Use hybrid retrieval (semantic + keyword search with RRF) - logger.debug( - "rag_hybrid_retrieving", - top_k=self._top_k, - keyword_index_size=self._retriever.get_keyword_index_count(), - ) - results = await self._retriever.retrieve(question, top_k=self._top_k) - else: - # Fall back to semantic-only search - logger.debug("rag_embedding_question", question_length=len(question)) - query_embedding = await self._embeddings.embed(question) - logger.debug("rag_retrieving", top_k=self._top_k) - results = await self._store.query(query_embedding, top_k=self._top_k) - - # Convert to ChunkWithScore for response - chunks_used = [ - ChunkWithScore( - content=r.content, - source=r.metadata.get("source", "unknown"), - section=r.metadata.get("section", ""), - score=r.score, - title=r.metadata.get("title", ""), - ) - for r in results - ] + span.set_attribute("rag.answer_length", len(answer)) - # Build context for LLM - context_tuples = [ - (r.content, r.metadata.get("source", "unknown"), r.score) for r in results - ] - system_prompt = build_rag_prompt(context_tuples) - - # Generate answer - logger.debug( - "rag_generating", - context_chunks=len(results), - question_length=len(question), - has_history=conversation_history is not None, - history_length=len(conversation_history) if conversation_history else 0, - ) + # Calculate confidence score + scores = [c.score for c in chunks_used] + chunk_texts = [c.content for c in chunks_used] + sources_used = list({c.source for c in chunks_used}) - if conversation_history: - # Multi-turn: include conversation history - messages = conversation_history.copy() - messages.append({"role": "user", "content": question}) - answer = await self._llm.complete_with_history( - system_prompt=system_prompt, - messages=messages, - ) - else: - # Single-turn: no history - answer = await self._llm.complete( - system_prompt=system_prompt, - user_message=question, + # Check for hallucination and get grounding ratio + grounding_ratio: float | None = None + if self._safety is not None and chunks_used: + hallucination_result = self._safety.check_hallucination( + answer=answer, + chunks=chunk_texts, + sources=[c.source for c in chunks_used], + ) + if not hallucination_result.is_safe: + # Hallucination detected - return low confidence response + span.set_attribute("rag.blocked", True) + span.set_attribute( + "rag.blocked_reason", SafetyViolationType.HALLUCINATION.value + ) + logger.warning( + "rag_hallucination_detected", + question_length=len(question), + ) + return RAGResponse( + answer=hallucination_result.message, + chunks_used=chunks_used, + question=question, + confidence_level="low", + confidence_score=0.0, + blocked=True, + blocked_reason=SafetyViolationType.HALLUCINATION.value, + ) + # Get grounding ratio for confidence scoring + details = self._safety.get_hallucination_details( + answer=answer, + chunks=chunk_texts, + ) + grounding_ratio = details.support_ratio + + # Calculate confidence + confidence = self._confidence_scorer.score( + chunk_scores=scores, + grounding_ratio=grounding_ratio, ) - # Calculate confidence score - scores = [c.score for c in chunks_used] - chunk_texts = [c.content for c in chunks_used] - sources_used = list({c.source for c in chunks_used}) + span.set_attribute("rag.confidence_level", confidence.level.value) + span.set_attribute("rag.confidence_score", confidence.score) + span.set_attribute("rag.sources_count", len(sources_used)) - # Check for hallucination and get grounding ratio - grounding_ratio: float | None = None - if self._safety is not None and chunks_used: - hallucination_result = self._safety.check_hallucination( - answer=answer, - chunks=chunk_texts, - sources=[c.source for c in chunks_used], + # Log quality metrics for observability + logger.info( + "rag_answer_generated", + question_length=len(question), + answer_length=len(answer), + chunks_used=len(chunks_used), + sources_used=sources_used, + top_score=max(scores) if scores else 0.0, + min_score=min(scores) if scores else 0.0, + avg_score=sum(scores) / len(scores) if scores else 0.0, + confidence_level=confidence.level.value, + confidence_score=confidence.score, ) - if not hallucination_result.is_safe: - # Hallucination detected - return low confidence response - logger.warning( - "rag_hallucination_detected", - question_length=len(question), - ) - return RAGResponse( - answer=hallucination_result.message, - chunks_used=chunks_used, + + # Store in cache (only high/medium confidence answers with context) + if self._cache is not None and chunks_used and not confidence.needs_review: + await self._cache.set( question=question, - confidence_level="low", - confidence_score=0.0, - blocked=True, - blocked_reason=SafetyViolationType.HALLUCINATION.value, + answer=answer, + chunks_json=_serialize_chunks(chunks_used), ) - # Get grounding ratio for confidence scoring - details = self._safety.get_hallucination_details( - answer=answer, - chunks=chunk_texts, - ) - grounding_ratio = details.support_ratio - # Calculate confidence - confidence = self._confidence_scorer.score( - chunk_scores=scores, - grounding_ratio=grounding_ratio, - ) - - # Log quality metrics for observability - logger.info( - "rag_answer_generated", - question_length=len(question), - answer_length=len(answer), - chunks_used=len(chunks_used), - sources_used=sources_used, - top_score=max(scores) if scores else 0.0, - min_score=min(scores) if scores else 0.0, - avg_score=sum(scores) / len(scores) if scores else 0.0, - confidence_level=confidence.level.value, - confidence_score=confidence.score, - ) - - # Store in cache (only high/medium confidence answers with context) - if self._cache is not None and chunks_used and not confidence.needs_review: - await self._cache.set( - question=question, + return RAGResponse( answer=answer, - chunks_json=_serialize_chunks(chunks_used), + chunks_used=chunks_used, + question=question, + confidence_level=confidence.level.value, + confidence_score=confidence.score, ) - return RAGResponse( - answer=answer, - chunks_used=chunks_used, - question=question, - confidence_level=confidence.level.value, - confidence_score=confidence.score, - ) - async def index_document(self, file_path: Path) -> IndexingResult: """Index a document into the vector store. @@ -329,104 +362,119 @@ async def index_document(self, file_path: Path) -> IndexingResult: Returns: IndexingResult with status and chunk count. """ - try: - # Load the document - doc: LoadedDocument = load_document(file_path) - - # Chunk the document - chunks = chunk_document( - doc.content, - source=doc.source, - config=self._chunking_config, - title=doc.title, - ) - - if not chunks: - return IndexingResult( + with tracer.start_as_current_span("rag.index_document") as span: + span.set_attribute("rag.file_path", str(file_path)) + span.set_attribute("rag.file_name", file_path.name) + + try: + # Load the document + doc: LoadedDocument = load_document(file_path) + span.set_attribute("rag.source", doc.source) + + # Chunk the document + chunks = chunk_document( + doc.content, source=doc.source, - chunks_created=0, - success=True, + config=self._chunking_config, + title=doc.title, ) - # Generate embeddings for all chunks - logger.debug( - "rag_embedding_chunks", - source=doc.source, - chunk_count=len(chunks), - ) - contents = [chunk.content for chunk in chunks] - embeddings = await self._embeddings.embed_batch(contents) - - # Create DocumentChunk objects for storage - doc_chunks = [ - DocumentChunk( - id=f"{doc.source}:{chunk.position}:{uuid.uuid4().hex[:8]}", - content=chunk.content, - embedding=embedding, - metadata=chunk.metadata, - ) - for chunk, embedding in zip(chunks, embeddings, strict=True) - ] - - # Store in vector DB - await self._store.add_chunks(doc_chunks) - - # Track documents for keyword index - for doc_chunk in doc_chunks: - self._indexed_docs.append( - IndexedDocument( - id=doc_chunk.id, - content=doc_chunk.content, - metadata=doc_chunk.metadata, + if not chunks: + span.set_attribute("rag.chunks_created", 0) + return IndexingResult( + source=doc.source, + chunks_created=0, + success=True, ) - ) - # Rebuild keyword index if hybrid retriever is enabled - if self._retriever is not None: - self._retriever.build_keyword_index(self._indexed_docs) + span.set_attribute("rag.chunks_to_embed", len(chunks)) + + # Generate embeddings for all chunks logger.debug( - "rag_keyword_index_rebuilt", - document_count=len(self._indexed_docs), + "rag_embedding_chunks", + source=doc.source, + chunk_count=len(chunks), ) + contents = [chunk.content for chunk in chunks] + embeddings = await self._embeddings.embed_batch(contents) + + # Create DocumentChunk objects for storage + doc_chunks = [ + DocumentChunk( + id=f"{doc.source}:{chunk.position}:{uuid.uuid4().hex[:8]}", + content=chunk.content, + embedding=embedding, + metadata=chunk.metadata, + ) + for chunk, embedding in zip(chunks, embeddings, strict=True) + ] + + # Store in vector DB + await self._store.add_chunks(doc_chunks) + + # Track documents for keyword index + for doc_chunk in doc_chunks: + self._indexed_docs.append( + IndexedDocument( + id=doc_chunk.id, + content=doc_chunk.content, + metadata=doc_chunk.metadata, + ) + ) - logger.info( - "rag_document_indexed", - source=doc.source, - chunks_created=len(doc_chunks), - ) + # Rebuild keyword index if hybrid retriever is enabled + if self._retriever is not None: + self._retriever.build_keyword_index(self._indexed_docs) + logger.debug( + "rag_keyword_index_rebuilt", + document_count=len(self._indexed_docs), + ) - return IndexingResult( - source=doc.source, - chunks_created=len(doc_chunks), - success=True, - ) + span.set_attribute("rag.chunks_created", len(doc_chunks)) + span.set_attribute("rag.success", True) - except DocumentLoadError as e: - logger.error( - "rag_load_error", - file_path=str(file_path), - error=str(e), - ) - return IndexingResult( - source=file_path.name, - chunks_created=0, - success=False, - error_message=str(e), - ) + logger.info( + "rag_document_indexed", + source=doc.source, + chunks_created=len(doc_chunks), + ) - except Exception as e: - logger.error( - "rag_index_error", - file_path=str(file_path), - error=str(e), - error_type=type(e).__name__, - ) - return IndexingResult( - source=file_path.name, - chunks_created=0, - success=False, - error_message=f"Indexing failed: {e}", - ) + return IndexingResult( + source=doc.source, + chunks_created=len(doc_chunks), + success=True, + ) + + except DocumentLoadError as e: + span.record_exception(e) + span.set_attribute("rag.success", False) + logger.error( + "rag_load_error", + file_path=str(file_path), + error=str(e), + ) + return IndexingResult( + source=file_path.name, + chunks_created=0, + success=False, + error_message=str(e), + ) + + except Exception as e: + span.record_exception(e) + span.set_attribute("rag.success", False) + logger.error( + "rag_index_error", + file_path=str(file_path), + error=str(e), + error_type=type(e).__name__, + ) + return IndexingResult( + source=file_path.name, + chunks_created=0, + success=False, + error_message=f"Indexing failed: {e}", + ) async def index_all_documents(self, documents_path: Path) -> list[IndexingResult]: """Index all documents in a directory. diff --git a/tests/test_observability.py b/tests/test_observability.py new file mode 100644 index 0000000..80119b8 --- /dev/null +++ b/tests/test_observability.py @@ -0,0 +1,236 @@ +"""Tests for the observability module.""" + +import pytest +from opentelemetry import trace +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import SimpleSpanProcessor +from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter + +from src.infrastructure.observability.structlog_processor import add_trace_context +from src.infrastructure.observability.tracing import ( + add_span_attributes, + get_current_span_id, + get_current_trace_id, + get_tracer, +) + +# Module-level setup: configure TracerProvider once before any tests run +_exporter = InMemorySpanExporter() +_provider = TracerProvider() +_processor = SimpleSpanProcessor(_exporter) +_provider.add_span_processor(_processor) +trace.set_tracer_provider(_provider) + + +@pytest.fixture(autouse=True) +def clear_spans(): + """Clear spans before each test.""" + _exporter.clear() + yield + _exporter.clear() + + +def get_finished_spans(): + """Get finished spans from the module-level exporter.""" + return _exporter.get_finished_spans() + + +class TestGetTracer: + """Tests for get_tracer function.""" + + def test_returns_tracer_instance(self): + """get_tracer should return a Tracer instance.""" + tracer = get_tracer("test_module") + assert tracer is not None + # Create a span to verify tracer works + with tracer.start_as_current_span("test_span"): + pass + spans = get_finished_spans() + assert len(spans) == 1 + assert spans[0].name == "test_span" + + +class TestTracedDecorator: + """Tests for @traced decorator. + + Note: The @traced decorator caches its tracer at decoration time, + so we test it using get_tracer() directly to verify span creation + in the test's TracerProvider context. + """ + + def test_traced_sync_function(self): + """Sync function wrapped in span should create a span.""" + tracer = get_tracer("test") + + def my_sync_function(): + return "result" + + # Manually wrap to test the pattern + with tracer.start_as_current_span("my_sync_function"): + result = my_sync_function() + + assert result == "result" + spans = get_finished_spans() + assert len(spans) == 1 + assert spans[0].name == "my_sync_function" + + @pytest.mark.asyncio + async def test_traced_async_function(self): + """Async function wrapped in span should create a span.""" + tracer = get_tracer("test") + + async def my_async_function(): + return "async_result" + + with tracer.start_as_current_span("my_async_function"): + result = await my_async_function() + + assert result == "async_result" + spans = get_finished_spans() + assert len(spans) == 1 + assert spans[0].name == "my_async_function" + + def test_span_with_custom_name(self): + """Span should use custom name when provided.""" + tracer = get_tracer("test") + + with tracer.start_as_current_span("custom.span.name"): + pass + + spans = get_finished_spans() + assert len(spans) == 1 + assert spans[0].name == "custom.span.name" + + def test_span_with_attributes(self): + """Span should have attributes set correctly.""" + tracer = get_tracer("test") + + with tracer.start_as_current_span("test_span") as span: + span.set_attribute("key1", "value1") + span.set_attribute("key2", 42) + + spans = get_finished_spans() + assert len(spans) == 1 + attrs = dict(spans[0].attributes or {}) + assert attrs.get("key1") == "value1" + assert attrs.get("key2") == 42 + + def test_span_records_exception(self): + """Span should record exceptions with error status.""" + tracer = get_tracer("test") + + with pytest.raises(ValueError, match="test error"): # noqa: SIM117 + with tracer.start_as_current_span("failing_function") as span: + try: + raise ValueError("test error") + except ValueError as e: + span.record_exception(e) + span.set_status(trace.Status(trace.StatusCode.ERROR, str(e))) + raise + + spans = get_finished_spans() + assert len(spans) == 1 + assert spans[0].status.status_code == trace.StatusCode.ERROR + + +class TestAddSpanAttributes: + """Tests for add_span_attributes function.""" + + def test_adds_attributes_to_current_span(self): + """add_span_attributes should add attributes to current span.""" + tracer = get_tracer("test") + + with tracer.start_as_current_span("test_span"): + add_span_attributes({"custom_key": "custom_value", "number": 100}) + + spans = get_finished_spans() + assert len(spans) == 1 + attrs = dict(spans[0].attributes or {}) + assert attrs.get("custom_key") == "custom_value" + assert attrs.get("number") == 100 + + def test_does_nothing_without_active_span(self): + """add_span_attributes should not fail without active span.""" + # Should not raise + add_span_attributes({"key": "value"}) + + +class TestGetCurrentTraceId: + """Tests for get_current_trace_id function.""" + + def test_returns_trace_id_when_in_span(self): + """get_current_trace_id should return trace ID in active span.""" + tracer = get_tracer("test") + + with tracer.start_as_current_span("test_span"): + trace_id = get_current_trace_id() + assert trace_id is not None + assert len(trace_id) == 32 # 128-bit trace ID as hex + + def test_returns_none_without_span(self): + """get_current_trace_id should return None without active span.""" + # Outside any span, the context is invalid + # Result depends on whether there's lingering context + _ = get_current_trace_id() + + +class TestGetCurrentSpanId: + """Tests for get_current_span_id function.""" + + def test_returns_span_id_when_in_span(self): + """get_current_span_id should return span ID in active span.""" + tracer = get_tracer("test") + + with tracer.start_as_current_span("test_span"): + span_id = get_current_span_id() + assert span_id is not None + assert len(span_id) == 16 # 64-bit span ID as hex + + +class TestStructlogProcessor: + """Tests for structlog trace context processor.""" + + def test_adds_trace_context_when_in_span(self): + """Processor should add trace_id and span_id to event dict.""" + tracer = get_tracer("test") + + with tracer.start_as_current_span("test_span"): + event_dict: dict = {"event": "test_event"} + result = add_trace_context(None, "info", event_dict) + + assert "trace_id" in result + assert "span_id" in result + assert len(result["trace_id"]) == 32 + assert len(result["span_id"]) == 16 + + def test_does_not_add_context_without_span(self): + """Processor should not add trace context without active span.""" + event_dict: dict = {"event": "test_event"} + result = add_trace_context(None, "info", event_dict) + + # May or may not have trace_id depending on provider state + # The important thing is it doesn't crash + assert "event" in result + + +class TestNestedSpans: + """Tests for nested span relationships.""" + + def test_nested_spans_have_parent_child_relationship(self): + """Nested spans should maintain parent-child relationship.""" + tracer = get_tracer("test") + + with tracer.start_as_current_span("parent"): # noqa: SIM117 + with tracer.start_as_current_span("child"): # Nested to test 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