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f4ef3b8 | 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 | """Observability setup using Arize Phoenix for LLM tracing.
Provides OpenTelemetry-compatible distributed tracing for LLM calls,
retrieval operations, and LangGraph execution. Gracefully degrades
when Phoenix is not installed or configured.
Usage:
Call setup_tracing() once at application startup (e.g., in app/main.py).
All trace_* functions will automatically emit spans when tracing is enabled.
"""
from __future__ import annotations
from config.settings import settings
from utils.logging import get_logger
_log = get_logger(__name__)
# Module-level state
_tracer = None
_phoenix_configured = False
_phoenix_project_name: str = settings.app_name
def setup_tracing() -> bool:
"""Initialize Phoenix tracing if ``settings.phoenix_endpoint`` is set.
This function is safe to call unconditionally at startup — it will
log a message and return immediately if Phoenix is not configured.
Tracing failures never crash the application.
Returns:
True if tracing was successfully enabled, False otherwise.
"""
global _tracer, _phoenix_configured, _phoenix_project_name
# BYOK mode mandates: no third-party telemetry sees a request. Phoenix
# spans capture LLM prompts and completions, which would include the
# visitor's keys-in-context and any private text they uploaded. Hard
# disable in BYOK regardless of phoenix_endpoint configuration.
if settings.byok_mode:
_log.info("phoenix_tracing_disabled", reason="BYOK mode forbids external telemetry")
return False
if not settings.phoenix_endpoint:
_log.info("phoenix_tracing_disabled", reason="No phoenix_endpoint configured")
return False
try:
from phoenix.otel import register
tracer_provider = register(
project_name=settings.app_name,
endpoint=settings.phoenix_endpoint,
)
# Attempt to instrument LLM and retrieval calls
_instrument_providers()
_phoenix_configured = True
_phoenix_project_name = settings.app_name
_log.info(
"phoenix_tracing_enabled",
endpoint=settings.phoenix_endpoint,
project=settings.app_name,
tracer_provider=str(tracer_provider),
)
return True
except ImportError:
_log.warning(
"phoenix_import_failed",
msg=(
"arize-phoenix not installed; tracing unavailable. "
"Install with: pip install 'arize-phoenix-otel'"
),
)
return False
except Exception as exc:
_log.error(
"phoenix_tracing_init_error",
error=str(exc),
endpoint=settings.phoenix_endpoint,
)
return False
def _instrument_providers() -> None:
"""Instrument LLM and retrieval providers with OpenTelemetry.
Attempts to auto-instrument supported providers. Failures are
logged but never raised — partial instrumentation is acceptable.
"""
# Instrument LangChain/LangGraph if available
try:
from openinference.instrumentation.langchain import LangChainInstrumentor
LangChainInstrumentor().instrument()
_log.info("instrumented_langchain")
except ImportError:
_log.debug(
"langchain_instrumentation_skipped",
reason="openinference-instrumentation-langchain not installed",
)
except Exception as exc:
_log.debug("langchain_instrumentation_error", reason=str(exc))
# Instrument OpenAI-compatible calls if available
try:
from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument()
_log.info("instrumented_openai")
except ImportError:
_log.debug(
"openai_instrumentation_skipped",
reason="openinference-instrumentation-openai not installed",
)
except Exception as exc:
_log.debug("openai_instrumentation_error", reason=str(exc))
def trace_llm_call(
provider: str,
model: str,
prompt: str,
response: str,
latency_ms: float,
tokens: dict[str, int] | None = None,
) -> None:
"""Record a manual trace span for an LLM call.
Can be used as an explicit trace point when auto-instrumentation
is unavailable or for custom tracking.
Args:
provider: LLM provider name (e.g., "ollama", "groq").
model: Model identifier used for generation.
prompt: The input prompt text.
response: The generated response text.
latency_ms: Response latency in milliseconds.
tokens: Optional token usage dict with keys like
"prompt_tokens", "completion_tokens", "total_tokens".
"""
if not _phoenix_configured:
return
try:
from opentelemetry import trace
tracer = trace.get_tracer("secureagentrag.llm")
with tracer.start_as_current_span("llm_call") as span:
span.set_attribute("llm.provider", provider)
span.set_attribute("llm.model", model)
span.set_attribute("llm.prompt_length", len(prompt))
span.set_attribute("llm.response_length", len(response))
span.set_attribute("llm.latency_ms", latency_ms)
if tokens:
for key, value in tokens.items():
span.set_attribute(f"llm.tokens.{key}", value)
except Exception as exc:
_log.debug("trace_llm_call_failed", error=str(exc))
def trace_retrieval(
query: str,
num_results: int,
latency_ms: float,
method: str = "hybrid",
) -> None:
"""Record a manual trace span for a retrieval operation.
Args:
query: The search query string.
num_results: Number of results returned.
latency_ms: Retrieval latency in milliseconds.
method: Retrieval method used ("hybrid", "dense", "bm25").
"""
if not _phoenix_configured:
return
try:
from opentelemetry import trace
tracer = trace.get_tracer("secureagentrag.retrieval")
with tracer.start_as_current_span("retrieval") as span:
span.set_attribute("retrieval.query_length", len(query))
span.set_attribute("retrieval.num_results", num_results)
span.set_attribute("retrieval.latency_ms", latency_ms)
span.set_attribute("retrieval.method", method)
except Exception as exc:
_log.debug("trace_retrieval_failed", error=str(exc))
def trace_graph_execution(
query: str,
nodes_executed: list[str],
total_latency_ms: float,
final_confidence: float,
retries: int = 0,
) -> None:
"""Record a manual trace span for LangGraph pipeline execution.
Args:
query: The original user query.
nodes_executed: List of graph node names that were executed.
total_latency_ms: Total pipeline execution time in milliseconds.
final_confidence: Final confidence score of the generated answer.
retries: Number of corrective retrieval retries performed.
"""
if not _phoenix_configured:
return
try:
from opentelemetry import trace
tracer = trace.get_tracer("secureagentrag.graph")
with tracer.start_as_current_span("graph_execution") as span:
span.set_attribute("graph.query_length", len(query))
span.set_attribute("graph.nodes_executed", ",".join(nodes_executed))
span.set_attribute("graph.total_latency_ms", total_latency_ms)
span.set_attribute("graph.confidence", final_confidence)
span.set_attribute("graph.retries", retries)
except Exception as exc:
_log.debug("trace_graph_execution_failed", error=str(exc))
def get_trace_url() -> str | None:
"""Return the Phoenix dashboard URL if tracing is configured.
Returns:
Phoenix UI URL string, or None if Phoenix is not configured.
"""
if not _phoenix_configured or not settings.phoenix_endpoint:
return None
# Phoenix UI typically runs on the same host
endpoint = settings.phoenix_endpoint.rstrip("/")
# Replace gRPC/collector port with UI port if needed
if ":4317" in endpoint:
return endpoint.replace(":4317", ":6006")
if ":6006" in endpoint:
return endpoint
return endpoint
def is_tracing_enabled() -> bool:
"""Check if Phoenix tracing is currently active.
Returns:
True if tracing was successfully configured, False otherwise.
"""
return _phoenix_configured
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