From f89e3358a43d6e4283026977c30576b4141edd74 Mon Sep 17 00:00:00 2001 From: Akihiko Kuroda Date: Fri, 23 Jan 2026 15:20:24 -0500 Subject: [PATCH 1/6] instrument telemetry Signed-off-by: Akihiko Kuroda --- AGENTS.md | 1 + docs/dev/telemetry.md | 201 +++++++++++++++++ docs/examples/telemetry/README.md | 147 +++++++++++++ docs/examples/telemetry/telemetry_example.py | 112 ++++++++++ mellea/backends/huggingface.py | 205 +++++++++-------- mellea/backends/litellm.py | 79 ++++--- mellea/backends/ollama.py | 29 ++- mellea/backends/openai.py | 65 ++++-- mellea/backends/watsonx.py | 59 +++-- mellea/stdlib/functional.py | 145 +++++++----- mellea/stdlib/session.py | 57 +++-- mellea/telemetry/__init__.py | 162 ++++++++++++++ mellea/telemetry/backend_instrumentation.py | 109 +++++++++ pyproject.toml | 4 + test/test_telemetry.py | 220 +++++++++++++++++++ uv.lock | 106 +++++++++ 16 files changed, 1441 insertions(+), 260 deletions(-) create mode 100644 docs/dev/telemetry.md create mode 100644 docs/examples/telemetry/README.md create mode 100644 docs/examples/telemetry/telemetry_example.py create mode 100644 mellea/telemetry/__init__.py create mode 100644 mellea/telemetry/backend_instrumentation.py create mode 100644 test/test_telemetry.py diff --git a/AGENTS.md b/AGENTS.md index 60d03a21..770b72ef 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -90,6 +90,7 @@ Pre-commit runs: ruff, mypy, uv-lock, codespell | `ComponentParseError` | Add examples to docstring | | `uv.lock` out of sync | Run `uv sync` | | Ollama refused | Run `ollama serve` | +| Telemetry import errors | Run `uv sync` to install OpenTelemetry deps | ## 8. Self-Review (before notifying user) 1. `uv run pytest -m "not qualitative"` passes? diff --git a/docs/dev/telemetry.md b/docs/dev/telemetry.md new file mode 100644 index 00000000..d4b0aee6 --- /dev/null +++ b/docs/dev/telemetry.md @@ -0,0 +1,201 @@ +## OpenTelemetry Instrumentation in Mellea + +Mellea provides built-in OpenTelemetry instrumentation with two independent trace scopes that can be enabled separately: + +1. **Application Trace** (`mellea.application`) - Tracks user-facing operations +2. **Backend Trace** (`mellea.backend`) - Tracks LLM backend interactions + +### Configuration + +Telemetry is configured via environment variables: + +| Variable | Description | Default | +|----------|-------------|---------| +| `MELLEA_TRACE_APPLICATION` | Enable application-level tracing | `false` | +| `MELLEA_TRACE_BACKEND` | Enable backend-level tracing | `false` | +| `OTEL_EXPORTER_OTLP_ENDPOINT` | OTLP endpoint for trace export | None | +| `OTEL_SERVICE_NAME` | Service name for traces | `mellea` | +| `MELLEA_TRACE_CONSOLE` | Print traces to console (debugging) | `false` | + +### Application Trace Scope + +The application tracer (`mellea.application`) instruments: + +- **Session lifecycle**: `start_session()`, session context manager entry/exit +- **@generative functions**: Execution of functions decorated with `@generative` +- **mfuncs.aact()**: Action execution with requirements and sampling strategies +- **Sampling strategies**: Rejection sampling, budget forcing, etc. +- **Requirement validation**: Validation of requirements and constraints + +**Span attributes include:** +- `backend`: Backend class name +- `model_id`: Model identifier +- `context_type`: Context class name +- `action_type`: Component type being executed +- `has_requirements`: Whether requirements are specified +- `has_strategy`: Whether a sampling strategy is used +- `strategy_type`: Sampling strategy class name +- `num_generate_logs`: Number of generation attempts +- `sampling_success`: Whether sampling succeeded +- `response`: Model response (truncated to 500 chars) +- `response_length`: Full length of model response + +### Backend Trace Scope + +The backend tracer (`mellea.backend`) instruments: + +- **Backend.generate_from_context()**: Context-based generation +- **Backend.generate_from_raw()**: Raw generation without context +- **Backend-specific implementations**: Ollama, OpenAI, HuggingFace, Watsonx, LiteLLM + +**Span attributes include:** +- `backend`: Backend class name (e.g., `OllamaModelBackend`) +- `model_id`: Model identifier string +- `action_type`: Component type +- `context_size`: Number of items in context +- `has_format`: Whether structured output format is specified +- `format_type`: Response format class name +- `tool_calls`: Whether tool calling is enabled +- `num_actions`: Number of actions in batch (for `generate_from_raw`) + +### Usage Examples + +#### Enable Application Tracing Only + +```bash +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=false +python your_script.py +``` + +This traces user-facing operations like `@generative` function calls, session lifecycle, and sampling strategies, but not the underlying LLM API calls. + +#### Enable Backend Tracing Only + +```bash +export MELLEA_TRACE_APPLICATION=false +export MELLEA_TRACE_BACKEND=true +python your_script.py +``` + +This traces only the LLM backend interactions, showing model calls, token usage, and API latency. + +#### Enable Both Traces + +```bash +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=true +python your_script.py +``` + +This provides complete observability across both application logic and backend interactions. + +#### Export to Jaeger + +```bash +# Start Jaeger (example using Docker) +docker run -d --name jaeger \ + -p 4317:4317 \ + -p 16686:16686 \ + jaegertracing/all-in-one:latest + +# Configure Mellea to export traces +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=true +export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 +export OTEL_SERVICE_NAME=my-mellea-app + +python your_script.py + +# View traces at http://localhost:16686 +``` + +#### Console Output for Debugging + +```bash +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_CONSOLE=true +python your_script.py +``` + +This prints trace spans to the console, useful for local debugging without setting up a trace backend. + +### Programmatic Access + +You can check if tracing is enabled in your code: + +```python +from mellea.telemetry import ( + is_application_tracing_enabled, + is_backend_tracing_enabled, +) + +if is_application_tracing_enabled(): + print("Application tracing is enabled") + +if is_backend_tracing_enabled(): + print("Backend tracing is enabled") +``` + +### Performance Considerations + +- **Zero overhead when disabled**: When tracing is disabled (default), there is minimal performance impact +- **Async-friendly**: Tracing works seamlessly with async operations +- **Batched export**: Traces are exported in batches to minimize network overhead +- **Separate scopes**: Enable only the tracing you need to reduce overhead + +### Integration with Observability Tools + +Mellea's OpenTelemetry instrumentation works with any OTLP-compatible backend: + +- **Jaeger**: Distributed tracing +- **Zipkin**: Distributed tracing +- **Grafana Tempo**: Distributed tracing +- **Honeycomb**: Observability platform +- **Datadog**: APM and observability +- **New Relic**: APM and observability +- **AWS X-Ray**: Distributed tracing (via OTLP) +- **Google Cloud Trace**: Distributed tracing (via OTLP) + +### Example Trace Hierarchy + +When both traces are enabled, you'll see a hierarchy like: + +``` +session_context (application) +├── aact (application) +│ ├── generate_from_context (backend) +│ │ └── ollama.chat (backend) +│ └── requirement_validation (application) +├── aact (application) +│ └── generate_from_context (backend) +│ └── ollama.chat (backend) +``` + +### Troubleshooting + +**Traces not appearing:** +1. Verify environment variables are set correctly +2. Check that OTLP endpoint is reachable +3. Enable console output to verify traces are being created +4. Check firewall/network settings + +**High overhead:** +1. Disable application tracing if you only need backend metrics +2. Reduce sampling rate (future feature) +3. Use a local OTLP collector to batch exports + +**Missing spans:** +1. Ensure you're using `with start_session()` context manager +2. Check that async operations are properly awaited +3. Verify backend implementation has instrumentation + +### Future Enhancements + +Planned improvements to telemetry: + +- Sampling rate configuration +- Custom span attributes via decorators +- Metrics export (token counts, latency percentiles) +- Trace context propagation for distributed systems +- Integration with LangSmith and other LLM observability tools \ No newline at end of file diff --git a/docs/examples/telemetry/README.md b/docs/examples/telemetry/README.md new file mode 100644 index 00000000..14d7aae2 --- /dev/null +++ b/docs/examples/telemetry/README.md @@ -0,0 +1,147 @@ +# OpenTelemetry Tracing Examples + +This directory contains examples demonstrating OpenTelemetry instrumentation in Mellea. + +## Quick Start + +### 1. Install Dependencies + +```bash +uv sync --all-extras +``` + +### 2. Start Ollama (Required) + +```bash +ollama serve +``` + +### 3. Run Examples + +#### Basic Example (No Tracing) + +```bash +python telemetry_example.py +``` + +#### With Application Tracing Only + +```bash +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=false +python telemetry_example.py +``` + +#### With Backend Tracing Only + +```bash +export MELLEA_TRACE_APPLICATION=false +export MELLEA_TRACE_BACKEND=true +python telemetry_example.py +``` + +#### With Both Traces + +```bash +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=true +python telemetry_example.py +``` + +#### With Console Output (Debugging) + +```bash +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=true +export MELLEA_TRACE_CONSOLE=true +python telemetry_example.py +``` + +## Using Jaeger for Visualization + +### 1. Start Jaeger + +```bash +docker run -d --name jaeger \ + -p 4317:4317 \ + -p 16686:16686 \ + jaegertracing/all-in-one:latest +``` + +### 2. Configure Mellea + +```bash +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=true +export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 +export OTEL_SERVICE_NAME=mellea-example +``` + +### 3. Run Example + +```bash +python telemetry_example.py +``` + +### 4. View Traces + +Open http://localhost:16686 in your browser and select "mellea-example" service. + +## What Gets Traced + +### Application Trace (`mellea.application`) + +- Session lifecycle (start, enter, exit) +- @generative function calls +- Action execution (aact) +- Sampling strategies +- Requirement validation + +### Backend Trace (`mellea.backend`) + +- Model generation calls +- Context-based generation +- Raw generation +- Backend-specific operations (Ollama, OpenAI, etc.) + +## Trace Attributes + +Each span includes rich metadata: + +- **model_id**: Model identifier +- **backend**: Backend class name +- **action_type**: Component type +- **context_size**: Number of context items +- **has_requirements**: Whether requirements are specified +- **strategy_type**: Sampling strategy used +- **tool_calls**: Whether tool calling is enabled +- **format_type**: Response format class + +## Performance Impact + +- **Disabled (default)**: Near-zero overhead +- **Application only**: Minimal overhead (~1-2%) +- **Backend only**: Minimal overhead (~1-2%) +- **Both enabled**: Low overhead (~2-5%) + +## Troubleshooting + +**Traces not appearing in Jaeger:** +1. Check Jaeger is running: `docker ps | grep jaeger` +2. Verify endpoint: `curl http://localhost:4317` +3. Check environment variables are set +4. Enable console output to verify traces are created + +**Import errors:** +```bash +uv sync # Reinstall dependencies +``` + +**Ollama connection errors:** +```bash +ollama serve # Start Ollama server +``` + +## Learn More + +See [docs/dev/telemetry.md](../../dev/telemetry.md) for complete documentation. \ No newline at end of file diff --git a/docs/examples/telemetry/telemetry_example.py b/docs/examples/telemetry/telemetry_example.py new file mode 100644 index 00000000..f1548293 --- /dev/null +++ b/docs/examples/telemetry/telemetry_example.py @@ -0,0 +1,112 @@ +"""Example demonstrating OpenTelemetry tracing in Mellea. + +This example shows how to use the two independent trace scopes: +1. Application trace - tracks user-facing operations +2. Backend trace - tracks LLM backend interactions + +Run with different configurations: + +# Enable only application tracing +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=false +python telemetry_example.py + +# Enable only backend tracing +export MELLEA_TRACE_APPLICATION=false +export MELLEA_TRACE_BACKEND=true +python telemetry_example.py + +# Enable both traces +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=true +python telemetry_example.py + +# Export to OTLP endpoint (e.g., Jaeger) +export MELLEA_TRACE_APPLICATION=true +export MELLEA_TRACE_BACKEND=true +export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 +python telemetry_example.py + +# Enable console output for debugging +export MELLEA_TRACE_CONSOLE=true +python telemetry_example.py +""" + +from mellea import generative, start_session +from mellea.stdlib.requirements import req + + +@generative +def classify_sentiment(text: str) -> str: + """Classify the sentiment of the given text as positive, negative, or neutral.""" + ... + + +@generative +def extract_entities(text: str) -> list[str]: + """Extract named entities from the text.""" + ... + + +def main(): + """Run example with telemetry instrumentation.""" + print("=" * 60) + print("Mellea OpenTelemetry Example") + print("=" * 60) + + # Check which traces are enabled + from mellea.telemetry import ( + is_application_tracing_enabled, + is_backend_tracing_enabled, + ) + + print(f"Application tracing: {is_application_tracing_enabled()}") + print(f"Backend tracing: {is_backend_tracing_enabled()}") + print("=" * 60) + + # Start a session - this will be traced if application tracing is enabled + with start_session() as m: + # Example 1: Simple instruction with requirements + print("\n1. Simple instruction with requirements...") + email = m.instruct( + "Write a professional email to {{name}} about {{topic}}", + requirements=[ + req("Must be formal"), + req("Must be under 100 words"), + ], + user_variables={"name": "Alice", "topic": "project update"}, + ) + print(f"Generated email: {str(email)[:100]}...") + + # Example 2: Using @generative function + print("\n2. Using @generative function...") + sentiment = classify_sentiment( + m, text="I absolutely love this product! It's amazing!" + ) + print(f"Sentiment: {sentiment}") + + # Example 3: Multiple operations + print("\n3. Multiple operations...") + text = "Apple Inc. announced new products in Cupertino, California." + entities = extract_entities(m, text=text) + print(f"Entities: {entities}") + + # Example 4: Chat interaction + print("\n4. Chat interaction...") + response1 = m.chat("What is 2+2?") + print(f"Response 1: {str(response1)}") + + response2 = m.chat("Multiply that by 3") + print(f"Response 2: {str(response2)}") + + print("\n" + "=" * 60) + print("Example complete!") + print("=" * 60) + print("\nTrace data has been exported based on your configuration.") + print("If OTEL_EXPORTER_OTLP_ENDPOINT is set, check your trace backend.") + print("If MELLEA_TRACE_CONSOLE=true, traces are printed above.") + + +if __name__ == "__main__": + main() + diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py index 26ffb43c..85148059 100644 --- a/mellea/backends/huggingface.py +++ b/mellea/backends/huggingface.py @@ -45,6 +45,10 @@ from ..helpers import message_to_openai_message, messages_to_docs, send_to_queue from ..stdlib.components import Intrinsic, Message from ..stdlib.requirements import ALoraRequirement, LLMaJRequirement +from ..telemetry.backend_instrumentation import ( + instrument_generate_from_context, + instrument_generate_from_raw, +) from .adapters import ( AdapterMixin, AdapterType, @@ -203,61 +207,68 @@ async def generate_from_context( tool_calls: bool = False, ) -> tuple[ModelOutputThunk[C], Context]: """Generate using the huggingface model.""" - await self.do_generate_walk(action) + with instrument_generate_from_context( + backend=self, + action=action, + ctx=ctx, + format=format, + tool_calls=tool_calls, + ): + await self.do_generate_walk(action) - # Upsert model options. - model_opts = self._simplify_and_merge(model_options) + # Upsert model options. + model_opts = self._simplify_and_merge(model_options) - # Requirements can be automatically rerouted to a requirement adapter. - if isinstance(action, Requirement): - # See docs/dev/requirement_aLoRA_rerouting.md - reroute_to_alora = self.default_to_constraint_checking_alora - adapter_name = "requirement_check" + # Requirements can be automatically rerouted to a requirement adapter. + if isinstance(action, Requirement): + # See docs/dev/requirement_aLoRA_rerouting.md + reroute_to_alora = self.default_to_constraint_checking_alora + adapter_name = "requirement_check" - if isinstance(action, ALoraRequirement): - reroute_to_alora = True - adapter_name = action.intrinsic_name - alora_action = action - else: - assert action.description is not None, ( - "must have a description when generating from a requirement" + if isinstance(action, ALoraRequirement): + reroute_to_alora = True + adapter_name = action.intrinsic_name + alora_action = action + else: + assert action.description is not None, ( + "must have a description when generating from a requirement" + ) + alora_action = ALoraRequirement(action.description, adapter_name) + + # Check if a requirement_check (or AloraRequirement specified) adapter + # exists. + alora_req_adapter = get_adapter_for_intrinsic( + adapter_name, [AdapterType.ALORA], self._added_adapters ) - alora_action = ALoraRequirement(action.description, adapter_name) + if alora_req_adapter is None: + # Log a warning if using an AloraRequirement but no adapter fit. + if reroute_to_alora and isinstance(action, ALoraRequirement): + FancyLogger.get_logger().warning( + f"attempted to use an AloraRequirement but backend {self} doesn't have the specified adapter added {adapter_name}; defaulting to regular generation" + ) + reroute_to_alora = False - # Check if a requirement_check (or AloraRequirement specified) adapter - # exists. - alora_req_adapter = get_adapter_for_intrinsic( - adapter_name, [AdapterType.ALORA], self._added_adapters - ) - if alora_req_adapter is None: - # Log a warning if using an AloraRequirement but no adapter fit. - if reroute_to_alora and isinstance(action, ALoraRequirement): - FancyLogger.get_logger().warning( - f"attempted to use an AloraRequirement but backend {self} doesn't have the specified adapter added {adapter_name}; defaulting to regular generation" - ) - reroute_to_alora = False + if issubclass(type(action), LLMaJRequirement): + reroute_to_alora = False - if issubclass(type(action), LLMaJRequirement): - reroute_to_alora = False + if reroute_to_alora: + # Keep the alora requirement handling separate for now. + mot = await self._generate_from_intrinsic( + alora_action, ctx, model_options=model_opts + ) + return mot, ctx.add(alora_action).add(mot) - if reroute_to_alora: - # Keep the alora requirement handling separate for now. + elif isinstance(action, Intrinsic): mot = await self._generate_from_intrinsic( - alora_action, ctx, model_options=model_opts + action, ctx, model_options=model_opts ) - return mot, ctx.add(alora_action).add(mot) + return mot, ctx.add(action).add(mot) - elif isinstance(action, Intrinsic): - mot = await self._generate_from_intrinsic( - action, ctx, model_options=model_opts + mot = await self._generate_from_context_standard( + action, ctx, _format=format, model_options=model_opts, tool_calls=tool_calls ) return mot, ctx.add(action).add(mot) - mot = await self._generate_from_context_standard( - action, ctx, _format=format, model_options=model_opts, tool_calls=tool_calls - ) - return mot, ctx.add(action).add(mot) - def _generate_with_adapter_lock( self, adapter_name: str, generate_func: Callable, *args, **kwargs ): @@ -973,67 +984,73 @@ async def generate_from_raw( tool_calls: bool = False, ) -> list[ModelOutputThunk]: """Generate using the completions api. Gives the input provided to the model without templating.""" - await self.do_generate_walks(list(actions)) - - if tool_calls: - FancyLogger.get_logger().warning( - "The raw endpoint does not support tool calling at the moment." - ) + with instrument_generate_from_raw( + backend=self, + num_actions=len(actions), + format=format, + tool_calls=tool_calls, + ): + await self.do_generate_walks(list(actions)) - if self._model.device.type == "mps": - # TODO: Remove this when we are able to update the torch package. - # Test this by ensuring all outputs from this call are populated when running on mps. - # https://github.com/pytorch/pytorch/pull/157727 - FancyLogger.get_logger().warning( - "utilizing device mps with a `generate_from_raw` request; you may see issues when submitting batches of prompts to a huggingface backend; ensure all ModelOutputThunks have non-empty values." - ) + if tool_calls: + FancyLogger.get_logger().warning( + "The raw endpoint does not support tool calling at the moment." + ) - model_opts = self._simplify_and_merge(model_options) - seed = model_opts.get(ModelOption.SEED, None) - if seed is not None: - set_seed(seed) + if self._model.device.type == "mps": + # TODO: Remove this when we are able to update the torch package. + # Test this by ensuring all outputs from this call are populated when running on mps. + # https://github.com/pytorch/pytorch/pull/157727 + FancyLogger.get_logger().warning( + "utilizing device mps with a `generate_from_raw` request; you may see issues when submitting batches of prompts to a huggingface backend; ensure all ModelOutputThunks have non-empty values." + ) - prompts = [self.formatter.print(action) for action in actions] + model_opts = self._simplify_and_merge(model_options) + seed = model_opts.get(ModelOption.SEED, None) + if seed is not None: + set_seed(seed) - # batch-encoding call is deprecated in favor of this - inputs = self._tokenizer(prompts, return_tensors="pt", padding=True).to( - self._device - ) + prompts = [self.formatter.print(action) for action in actions] - format_kwargs = {} - if format: - # outlines.generate.json always parses the resulting json into a python dict. - # We however want to keep it as a json string for later storing it in ModelOutputThunk - schema: dict[str, Any] = format.model_json_schema() # type: ignore - schema_json: str = json.dumps(schema) - regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore - schema_json + # batch-encoding call is deprecated in favor of this + inputs = self._tokenizer(prompts, return_tensors="pt", padding=True).to( + self._device ) - from outlines.models.transformers import TransformerTokenizer - from outlines.processors.structured import RegexLogitsProcessor - from transformers import LogitsProcessorList # type: ignore + format_kwargs = {} + if format: + # outlines.generate.json always parses the resulting json into a python dict. + # We however want to keep it as a json string for later storing it in ModelOutputThunk + schema: dict[str, Any] = format.model_json_schema() # type: ignore + schema_json: str = json.dumps(schema) + regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore + schema_json + ) - format_kwargs["logits_processor"] = LogitsProcessorList( - [ - RegexLogitsProcessor( - regex_str, tokenizer=TransformerTokenizer(self._tokenizer) - ) - ] - ) + from outlines.models.transformers import TransformerTokenizer + from outlines.processors.structured import RegexLogitsProcessor + from transformers import LogitsProcessorList # type: ignore - outputs = await asyncio.to_thread( - self._generate_with_adapter_lock, - "", # Empty for no adapter. - self._model.generate, # type: ignore - # Passed as args/kwargs to generate. - input_ids=inputs["input_ids"], - attention_mask=inputs["attention_mask"], - return_dict_in_generate=True, - output_scores=True, - **self._make_backend_specific_and_remove(model_opts), - **format_kwargs, - ) + format_kwargs["logits_processor"] = LogitsProcessorList( + [ + RegexLogitsProcessor( + regex_str, tokenizer=TransformerTokenizer(self._tokenizer) + ) + ] + ) + + outputs = await asyncio.to_thread( + self._generate_with_adapter_lock, + "", # Empty for no adapter. + self._model.generate, # type: ignore + # Passed as args/kwargs to generate. + input_ids=inputs["input_ids"], + attention_mask=inputs["attention_mask"], + return_dict_in_generate=True, + output_scores=True, + **self._make_backend_specific_and_remove(model_opts), + **format_kwargs, + ) sequences_to_decode = [ sequence[inputs["input_ids"][i].size(0) :] # type: ignore diff --git a/mellea/backends/litellm.py b/mellea/backends/litellm.py index 226251ce..ca77001b 100644 --- a/mellea/backends/litellm.py +++ b/mellea/backends/litellm.py @@ -35,6 +35,10 @@ ) from ..stdlib.components import Message from ..stdlib.requirements import ALoraRequirement +from ..telemetry.backend_instrumentation import ( + instrument_generate_from_context, + instrument_generate_from_raw, +) from .backend import FormatterBackend from .model_options import ModelOption from .tools import ( @@ -126,14 +130,21 @@ async def generate_from_context( assert ctx.is_chat_context, NotImplementedError( "The Openai backend only supports chat-like contexts." ) - mot = await self._generate_from_chat_context_standard( - action, - ctx, - _format=format, - model_options=model_options, + with instrument_generate_from_context( + backend=self, + action=action, + ctx=ctx, + format=format, tool_calls=tool_calls, - ) - return mot, ctx.add(action).add(mot) + ): + mot = await self._generate_from_chat_context_standard( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) + return mot, ctx.add(action).add(mot) def _simplify_and_merge( self, model_options: dict[str, Any] | None @@ -507,35 +518,41 @@ async def generate_from_raw( tool_calls: bool = False, ) -> list[ModelOutputThunk]: """Generate using the completions api. Gives the input provided to the model without templating.""" - await self.do_generate_walks(list(actions)) - extra_body = {} - if format is not None: - FancyLogger.get_logger().warning( - "The official OpenAI completion api does not accept response format / structured decoding; " - "it will be passed as an extra arg." - ) + with instrument_generate_from_raw( + backend=self, + num_actions=len(actions), + format=format, + tool_calls=tool_calls, + ): + await self.do_generate_walks(list(actions)) + extra_body = {} + if format is not None: + FancyLogger.get_logger().warning( + "The official OpenAI completion api does not accept response format / structured decoding; " + "it will be passed as an extra arg." + ) - # Some versions (like vllm's version) of the OpenAI API support structured decoding for completions requests. - extra_body["guided_json"] = format.model_json_schema() # type: ignore - if tool_calls: - FancyLogger.get_logger().warning( - "The completion endpoint does not support tool calling." - ) + # Some versions (like vllm's version) of the OpenAI API support structured decoding for completions requests. + extra_body["guided_json"] = format.model_json_schema() # type: ignore + if tool_calls: + FancyLogger.get_logger().warning( + "The completion endpoint does not support tool calling." + ) - # We don't do anything fancy for model_opts with generate from raw; litellm has too many potential options depending on provider. - model_opts = self._simplify_and_merge(model_options) - model_specific_options = self._make_backend_specific_and_remove(model_opts) + # We don't do anything fancy for model_opts with generate from raw; litellm has too many potential options depending on provider. + model_opts = self._simplify_and_merge(model_options) + model_specific_options = self._make_backend_specific_and_remove(model_opts) - if self._has_potential_event_loop_errors(): - FancyLogger().get_logger().warning( - "There is a known bug with litellm. This generation call may fail. If it does, you should ensure that you are either running only synchronous Mellea functions or running async Mellea functions from one asyncio.run() call." - ) + if self._has_potential_event_loop_errors(): + FancyLogger().get_logger().warning( + "There is a known bug with litellm. This generation call may fail. If it does, you should ensure that you are either running only synchronous Mellea functions or running async Mellea functions from one asyncio.run() call." + ) - prompts = [self.formatter.print(action) for action in actions] + prompts = [self.formatter.print(action) for action in actions] - completion_response = await litellm.atext_completion( - model=self._model_id, prompt=prompts, **model_specific_options - ) + completion_response = await litellm.atext_completion( + model=self._model_id, prompt=prompts, **model_specific_options + ) # Necessary for type checker. assert isinstance(completion_response, litellm.TextCompletionResponse) # type: ignore diff --git a/mellea/backends/ollama.py b/mellea/backends/ollama.py index d20e2aa1..47e749d6 100644 --- a/mellea/backends/ollama.py +++ b/mellea/backends/ollama.py @@ -27,6 +27,10 @@ from ..helpers import ClientCache, get_current_event_loop, send_to_queue from ..stdlib.components import Message from ..stdlib.requirements import ALoraRequirement +from ..telemetry.backend_instrumentation import ( + instrument_generate_from_context, + instrument_generate_from_raw, +) from .backend import FormatterBackend from .model_options import ModelOption from .tools import add_tools_from_context_actions, add_tools_from_model_options @@ -256,18 +260,21 @@ async def generate_from_context( tool_calls: bool = False, ) -> tuple[ModelOutputThunk[C], Context]: """See `generate_from_chat_context`.""" - assert ctx.is_chat_context, ( - "The ollama backend only supports chat-like contexts." - ) - mot = await self.generate_from_chat_context( - action, - ctx, - _format=format, - model_options=model_options, - tool_calls=tool_calls, - ) + with instrument_generate_from_context( + self, action, ctx, format, tool_calls + ): + assert ctx.is_chat_context, ( + "The ollama backend only supports chat-like contexts." + ) + mot = await self.generate_from_chat_context( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) - return mot, ctx.add(action).add(mot) + return mot, ctx.add(action).add(mot) async def generate_from_chat_context( self, diff --git a/mellea/backends/openai.py b/mellea/backends/openai.py index 8ad7796e..078a4686 100644 --- a/mellea/backends/openai.py +++ b/mellea/backends/openai.py @@ -43,6 +43,10 @@ ) from ..stdlib.components import Intrinsic, Message from ..stdlib.requirements import ALoraRequirement, LLMaJRequirement +from ..telemetry.backend_instrumentation import ( + instrument_generate_from_context, + instrument_generate_from_raw, +) from .adapters import ( AdapterMixin, AdapterType, @@ -308,13 +312,20 @@ async def generate_from_context( assert ctx.is_chat_context, NotImplementedError( "The Openai backend only supports chat-like contexts." ) - return await self.generate_from_chat_context( - action, - ctx, - _format=format, - model_options=model_options, + with instrument_generate_from_context( + backend=self, + action=action, + ctx=ctx, + format=format, tool_calls=tool_calls, - ) + ): + return await self.generate_from_chat_context( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) async def generate_from_chat_context( self, @@ -816,24 +827,30 @@ async def generate_from_raw( prompts = [self.formatter.print(action) for action in actions] - try: - completion_response: Completion = ( - await self._async_client.completions.create( - model=self._model_id, - prompt=prompts, - extra_body=extra_body, - **self._make_backend_specific_and_remove( - model_opts, is_chat_context=False - ), - ) - ) # type: ignore - except openai.BadRequestError as e: - if openai_ollama_batching_error in e.message: - FancyLogger.get_logger().error( - "If you are trying to call `OpenAIBackend._generate_from_raw while targeting an ollama server, " - "your requests will fail since ollama doesn't support batching requests." - ) - raise e + with instrument_generate_from_raw( + backend=self, + num_actions=len(actions), + format=format, + tool_calls=tool_calls, + ): + try: + completion_response: Completion = ( + await self._async_client.completions.create( + model=self._model_id, + prompt=prompts, + extra_body=extra_body, + **self._make_backend_specific_and_remove( + model_opts, is_chat_context=False + ), + ) + ) # type: ignore + except openai.BadRequestError as e: + if openai_ollama_batching_error in e.message: + FancyLogger.get_logger().error( + "If you are trying to call `OpenAIBackend._generate_from_raw while targeting an ollama server, " + "your requests will fail since ollama doesn't support batching requests." + ) + raise e # Necessary for type checker. assert isinstance(completion_response, Completion) diff --git a/mellea/backends/watsonx.py b/mellea/backends/watsonx.py index 0fe1c2bd..ffab2e38 100644 --- a/mellea/backends/watsonx.py +++ b/mellea/backends/watsonx.py @@ -38,6 +38,10 @@ ) from ..stdlib.components import Message from ..stdlib.requirements import ALoraRequirement +from ..telemetry.backend_instrumentation import ( + instrument_generate_from_context, + instrument_generate_from_raw, +) from .backend import FormatterBackend from .model_options import ModelOption from .tools import ( @@ -250,14 +254,21 @@ async def generate_from_context( assert ctx.is_chat_context, NotImplementedError( "The watsonx.ai backend only supports chat-like contexts." ) - mot = await self.generate_from_chat_context( - action, - ctx, - _format=format, - model_options=model_options, + with instrument_generate_from_context( + backend=self, + action=action, + ctx=ctx, + format=format, tool_calls=tool_calls, - ) - return mot, ctx.add(action).add(mot) + ): + mot = await self.generate_from_chat_context( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) + return mot, ctx.add(action).add(mot) async def generate_from_chat_context( self, @@ -513,24 +524,30 @@ async def generate_from_raw( tool_calls: bool = False, ) -> list[ModelOutputThunk]: """Generates a completion text. Gives the input provided to the model without templating.""" - await self.do_generate_walks(list(actions)) + with instrument_generate_from_raw( + backend=self, + num_actions=len(actions), + format=format, + tool_calls=tool_calls, + ): + await self.do_generate_walks(list(actions)) - if format is not None: - FancyLogger.get_logger().warning( - "WatsonxAI completion api does not accept response format, ignoring it for this request." - ) + if format is not None: + FancyLogger.get_logger().warning( + "WatsonxAI completion api does not accept response format, ignoring it for this request." + ) - model_opts = self._simplify_and_merge(model_options, is_chat_context=False) + model_opts = self._simplify_and_merge(model_options, is_chat_context=False) - prompts = [self.formatter.print(action) for action in actions] + prompts = [self.formatter.print(action) for action in actions] - responses = await asyncio.to_thread( - self._model.generate, - prompt=prompts, - params=self._make_backend_specific_and_remove( - model_opts, is_chat_context=False - ), - ) + responses = await asyncio.to_thread( + self._model.generate, + prompt=prompts, + params=self._make_backend_specific_and_remove( + model_opts, is_chat_context=False + ), + ) results = [] date = datetime.datetime.now() diff --git a/mellea/stdlib/functional.py b/mellea/stdlib/functional.py index 41263f42..24d9fb91 100644 --- a/mellea/stdlib/functional.py +++ b/mellea/stdlib/functional.py @@ -26,6 +26,7 @@ ValidationResult, ) from ..helpers import _run_async_in_thread +from ..telemetry import set_span_attribute, trace_application from .components import Instruction, Message, MObjectProtocol, ToolMessage, mify from .context import SimpleContext from .sampling import RejectionSamplingStrategy @@ -475,75 +476,101 @@ async def aact( Returns: A (ModelOutputThunk, Context) if `return_sampling_results` is `False`, else returns a `SamplingResult`. """ - if not silence_context_type_warning and not isinstance(context, SimpleContext): - FancyLogger().get_logger().warning( - "Not using a SimpleContext with asynchronous requests could cause unexpected results due to stale contexts. Ensure you await between requests." - "\nSee the async section of the tutorial: https://github.com/generative-computing/mellea/blob/main/docs/tutorial.md#chapter-12-asynchronicity" - ) - - sampling_result: SamplingResult | None = None - generate_logs: list[GenerateLog] = [] + with trace_application( + "aact", + action_type=action.__class__.__name__, + has_requirements=requirements is not None and len(requirements) > 0, + has_strategy=strategy is not None, + strategy_type=strategy.__class__.__name__ if strategy else None, + has_format=format is not None, + tool_calls=tool_calls, + ) as span: + if not silence_context_type_warning and not isinstance(context, SimpleContext): + FancyLogger().get_logger().warning( + "Not using a SimpleContext with asynchronous requests could cause unexpected results due to stale contexts. Ensure you await between requests." + "\nSee the async section of the tutorial: https://github.com/generative-computing/mellea/blob/main/docs/tutorial.md#chapter-12-asynchronicity" + ) - if return_sampling_results: - assert strategy is not None, ( - "Must provide a SamplingStrategy when return_sampling_results==True" - ) + sampling_result: SamplingResult | None = None + generate_logs: list[GenerateLog] = [] - if strategy is None: - # Only use the strategy if one is provided. Add a warning if requirements were passed in though. - if requirements is not None and len(requirements) > 0: - FancyLogger.get_logger().warning( - "Calling the function with NO strategy BUT requirements. No requirement is being checked!" + if return_sampling_results: + assert strategy is not None, ( + "Must provide a SamplingStrategy when return_sampling_results==True" ) - result, new_ctx = await backend.generate_from_context( - action, - ctx=context, - format=format, - model_options=model_options, - tool_calls=tool_calls, - ) - await result.avalue() + if strategy is None: + # Only use the strategy if one is provided. Add a warning if requirements were passed in though. + if requirements is not None and len(requirements) > 0: + FancyLogger.get_logger().warning( + "Calling the function with NO strategy BUT requirements. No requirement is being checked!" + ) - # ._generate_log should never be None after generation. - assert result._generate_log is not None - result._generate_log.is_final_result = True - generate_logs.append(result._generate_log) + result, new_ctx = await backend.generate_from_context( + action, + ctx=context, + format=format, + model_options=model_options, + tool_calls=tool_calls, + ) + await result.avalue() - else: - # Always sample if a strategy is provided, even if no requirements were provided. - # Some sampling strategies don't use requirements or set them when instantiated. + # ._generate_log should never be None after generation. + assert result._generate_log is not None + result._generate_log.is_final_result = True + generate_logs.append(result._generate_log) - sampling_result = await strategy.sample( - action, - context=context, - backend=backend, - requirements=requirements, - validation_ctx=None, - format=format, - model_options=model_options, - tool_calls=tool_calls, - ) + else: + # Always sample if a strategy is provided, even if no requirements were provided. + # Some sampling strategies don't use requirements or set them when instantiated. + + sampling_result = await strategy.sample( + action, + context=context, + backend=backend, + requirements=requirements, + validation_ctx=None, + format=format, + model_options=model_options, + tool_calls=tool_calls, + ) - assert sampling_result.sample_generations is not None - for result in sampling_result.sample_generations: - assert result._generate_log is not None # Cannot be None after generation. - generate_logs.append(result._generate_log) + assert sampling_result.sample_generations is not None + for result in sampling_result.sample_generations: + assert result._generate_log is not None # Cannot be None after generation. + generate_logs.append(result._generate_log) - new_ctx = sampling_result.result_ctx - result = sampling_result.result - assert sampling_result.result._generate_log is not None - assert sampling_result.result._generate_log.is_final_result, ( - "generate logs from the final result returned by the sampling strategy must be marked as final" - ) + new_ctx = sampling_result.result_ctx + result = sampling_result.result + assert sampling_result.result._generate_log is not None + assert sampling_result.result._generate_log.is_final_result, ( + "generate logs from the final result returned by the sampling strategy must be marked as final" + ) - if return_sampling_results: - assert ( - sampling_result is not None - ) # Needed for the type checker but should never happen. - return sampling_result - else: - return result, new_ctx + # Add span attributes for the result + set_span_attribute(span, "num_generate_logs", len(generate_logs)) + if sampling_result: + set_span_attribute(span, "sampling_success", bool(sampling_result.result)) + + # Log the model response (truncated for large responses) + try: + response_value = str(result.value) if hasattr(result, 'value') and result.value else str(result) + # Truncate to 500 chars to avoid overwhelming trace storage + if len(response_value) > 500: + response_value = response_value[:500] + "..." + set_span_attribute(span, "response", response_value) + set_span_attribute(span, "response_length", len(str(result.value) if hasattr(result, 'value') else str(result))) + except Exception: + # If we can't get the response, don't fail the trace + pass + + if return_sampling_results: + assert ( + sampling_result is not None + ) # Needed for the type checker but should never happen. + return sampling_result + else: + return result, new_ctx @overload diff --git a/mellea/stdlib/session.py b/mellea/stdlib/session.py index 1d706cf5..880d1391 100644 --- a/mellea/stdlib/session.py +++ b/mellea/stdlib/session.py @@ -27,6 +27,7 @@ ValidationResult, ) from ..stdlib import functional as mfuncs +from ..telemetry import set_span_attribute, trace_application from .components import Message from .context import SimpleContext from .sampling import RejectionSamplingStrategy @@ -154,20 +155,8 @@ def start_session( """ logger = FancyLogger.get_logger() - backend_class = backend_name_to_class(backend_name) - if backend_class is None: - raise Exception( - f"Backend name {backend_name} unknown. Please see the docstring for `mellea.stdlib.session.start_session` for a list of options." - ) - assert backend_class is not None - backend = backend_class(model_id, model_options=model_options, **backend_kwargs) - - if ctx is None: - ctx = SimpleContext() - - # Log session configuration + # Get model_id string for logging and tracing if isinstance(model_id, ModelIdentifier): - # Get the backend-specific model name backend_to_attr = { "ollama": "ollama_name", "hf": "hf_model_name", @@ -181,14 +170,32 @@ def start_session( getattr(model_id, attr, None) or model_id.hf_model_name or str(model_id) ) else: - model_id_str = model_id - logger.info( - f"Starting Mellea session: backend={backend_name}, model={model_id_str}, " - f"context={ctx.__class__.__name__}" - + (f", model_options={model_options}" if model_options else "") - ) + model_id_str = str(model_id) + + with trace_application( + "start_session", + backend=backend_name, + model_id=model_id_str, + context_type=ctx.__class__.__name__ if ctx else "SimpleContext", + ): + backend_class = backend_name_to_class(backend_name) + if backend_class is None: + raise Exception( + f"Backend name {backend_name} unknown. Please see the docstring for `mellea.stdlib.session.start_session` for a list of options." + ) + assert backend_class is not None + backend = backend_class(model_id, model_options=model_options, **backend_kwargs) + + if ctx is None: + ctx = SimpleContext() + + logger.info( + f"Starting Mellea session: backend={backend_name}, model={model_id_str}, " + f"context={ctx.__class__.__name__}" + + (f", model_options={model_options}" if model_options else "") + ) - return MelleaSession(backend, ctx) + return MelleaSession(backend, ctx) class MelleaSession: @@ -218,9 +225,16 @@ def __init__(self, backend: Backend, ctx: Context | None = None): self.ctx: Context = ctx if ctx is not None else SimpleContext() self._session_logger = FancyLogger.get_logger() self._context_token = None + self._session_span = None def __enter__(self): """Enter context manager and set this session as the current global session.""" + # Start a session span that will last for the entire context manager lifetime + self._session_span = trace_application( + "session_context", + backend=self.backend.__class__.__name__, + context_type=self.ctx.__class__.__name__, + ).__enter__() self._context_token = _context_session.set(self) return self @@ -230,6 +244,9 @@ def __exit__(self, exc_type, exc_val, exc_tb): if self._context_token is not None: _context_session.reset(self._context_token) self._context_token = None + if self._session_span is not None: + self._session_span.__exit__(exc_type, exc_val, exc_tb) + self._session_span = None def __copy__(self): """Use self.clone. Copies the current session but keeps references to the backend and context.""" diff --git a/mellea/telemetry/__init__.py b/mellea/telemetry/__init__.py new file mode 100644 index 00000000..aa9e623f --- /dev/null +++ b/mellea/telemetry/__init__.py @@ -0,0 +1,162 @@ +"""OpenTelemetry instrumentation for Mellea. + +This module provides two independent trace scopes: +1. Application Trace (mellea.application) - User-facing operations +2. Backend Trace (mellea.backend) - LLM backend interactions + +Configuration via environment variables: +- MELLEA_TRACE_APPLICATION: Enable/disable application tracing (default: false) +- MELLEA_TRACE_BACKEND: Enable/disable backend tracing (default: false) +- OTEL_EXPORTER_OTLP_ENDPOINT: OTLP endpoint for trace export +- OTEL_SERVICE_NAME: Service name for traces (default: mellea) +""" + +import os +from contextlib import contextmanager +from typing import Any + +from opentelemetry import trace +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter +from opentelemetry.sdk.resources import Resource +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter + +# Configuration from environment variables +_TRACE_APPLICATION_ENABLED = os.getenv("MELLEA_TRACE_APPLICATION", "false").lower() in ( + "true", + "1", + "yes", +) +_TRACE_BACKEND_ENABLED = os.getenv("MELLEA_TRACE_BACKEND", "false").lower() in ( + "true", + "1", + "yes", +) +_OTLP_ENDPOINT = os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT") +_SERVICE_NAME = os.getenv("OTEL_SERVICE_NAME", "mellea") +_CONSOLE_EXPORT = os.getenv("MELLEA_TRACE_CONSOLE", "false").lower() in ( + "true", + "1", + "yes", +) + + +def _setup_tracer_provider() -> TracerProvider: + """Set up the global tracer provider with OTLP exporter if configured.""" + resource = Resource.create({"service.name": _SERVICE_NAME}) + provider = TracerProvider(resource=resource) + + # Add OTLP exporter if endpoint is configured + if _OTLP_ENDPOINT: + otlp_exporter = OTLPSpanExporter(endpoint=_OTLP_ENDPOINT) + provider.add_span_processor(BatchSpanProcessor(otlp_exporter)) + + # Add console exporter for debugging if enabled + # Note: Console exporter may cause harmless errors during test cleanup + if _CONSOLE_EXPORT: + try: + console_exporter = ConsoleSpanExporter() + provider.add_span_processor(BatchSpanProcessor(console_exporter)) + except Exception: + # Silently ignore console exporter setup failures + pass + + trace.set_tracer_provider(provider) + return provider + + +# Initialize tracer provider if any tracing is enabled +_tracer_provider = None +if _TRACE_APPLICATION_ENABLED or _TRACE_BACKEND_ENABLED: + _tracer_provider = _setup_tracer_provider() + +# Create separate tracers for application and backend +_application_tracer = trace.get_tracer("mellea.application", "0.3.0") +_backend_tracer = trace.get_tracer("mellea.backend", "0.3.0") + + +def is_application_tracing_enabled() -> bool: + """Check if application tracing is enabled.""" + return _TRACE_APPLICATION_ENABLED + + +def is_backend_tracing_enabled() -> bool: + """Check if backend tracing is enabled.""" + return _TRACE_BACKEND_ENABLED + + +@contextmanager +def trace_application(name: str, **attributes: Any): + """Create an application trace span if application tracing is enabled. + + Args: + name: Name of the span + **attributes: Additional attributes to add to the span + + Yields: + The span object if tracing is enabled, otherwise a no-op context manager + """ + if _TRACE_APPLICATION_ENABLED: + with _application_tracer.start_as_current_span(name) as span: + for key, value in attributes.items(): + if value is not None: + span.set_attribute(key, str(value)) + yield span + else: + yield None + + +@contextmanager +def trace_backend(name: str, **attributes: Any): + """Create a backend trace span if backend tracing is enabled. + + Args: + name: Name of the span + **attributes: Additional attributes to add to the span + + Yields: + The span object if tracing is enabled, otherwise a no-op context manager + """ + if _TRACE_BACKEND_ENABLED: + with _backend_tracer.start_as_current_span(name) as span: + for key, value in attributes.items(): + if value is not None: + span.set_attribute(key, str(value)) + yield span + else: + yield None + + +def set_span_attribute(span: Any, key: str, value: Any) -> None: + """Set an attribute on a span if the span is not None. + + Args: + span: The span object (may be None if tracing is disabled) + key: Attribute key + value: Attribute value + """ + if span is not None and value is not None: + span.set_attribute(key, str(value)) + + +def set_span_error(span: Any, exception: Exception) -> None: + """Record an exception on a span if the span is not None. + + Args: + span: The span object (may be None if tracing is disabled) + exception: The exception to record + """ + if span is not None: + span.record_exception(exception) + span.set_status(trace.Status(trace.StatusCode.ERROR, str(exception))) + + +__all__ = [ + "is_application_tracing_enabled", + "is_backend_tracing_enabled", + "trace_application", + "trace_backend", + "set_span_attribute", + "set_span_error", +] + diff --git a/mellea/telemetry/backend_instrumentation.py b/mellea/telemetry/backend_instrumentation.py new file mode 100644 index 00000000..70c4c490 --- /dev/null +++ b/mellea/telemetry/backend_instrumentation.py @@ -0,0 +1,109 @@ +"""Backend instrumentation helpers for OpenTelemetry tracing.""" + +from typing import Any + +from ..telemetry import set_span_attribute, trace_backend + + +def get_model_id_str(backend: Any) -> str: + """Extract model_id string from a backend instance. + + Args: + backend: Backend instance + + Returns: + String representation of the model_id + """ + if hasattr(backend, "model_id"): + model_id = backend.model_id + if hasattr(model_id, "hf_model_name"): + return str(model_id.hf_model_name) + return str(model_id) + return backend.__class__.__name__ + + +def get_context_size(ctx: Any) -> int: + """Get the size of a context. + + Args: + ctx: Context object + + Returns: + Number of items in context, or 0 if cannot be determined + """ + try: + if hasattr(ctx, "__len__"): + return len(ctx) + if hasattr(ctx, "turns") and hasattr(ctx.turns, "__len__"): + return len(ctx.turns) + except Exception: + pass + return 0 + + +def instrument_generate_from_context( + backend: Any, + action: Any, + ctx: Any, + format: Any = None, + tool_calls: bool = False, +): + """Create a backend trace span for generate_from_context. + + Args: + backend: Backend instance + action: Action component + ctx: Context + format: Response format (BaseModel subclass or None) + tool_calls: Whether tool calling is enabled + + Returns: + Context manager for the trace span + """ + return trace_backend( + "generate_from_context", + backend=backend.__class__.__name__, + model_id=get_model_id_str(backend), + action_type=action.__class__.__name__, + context_size=get_context_size(ctx), + has_format=format is not None, + format_type=format.__name__ if format else None, + tool_calls=tool_calls, + ) + + +def instrument_generate_from_raw( + backend: Any, + num_actions: int, + format: Any = None, + tool_calls: bool = False, +): + """Create a backend trace span for generate_from_raw. + + Args: + backend: Backend instance + num_actions: Number of actions in the batch + format: Response format (BaseModel subclass or None) + tool_calls: Whether tool calling is enabled + + Returns: + Context manager for the trace span + """ + return trace_backend( + "generate_from_raw", + backend=backend.__class__.__name__, + model_id=get_model_id_str(backend), + num_actions=num_actions, + has_format=format is not None, + format_type=format.__name__ if format else None, + tool_calls=tool_calls, + ) + + +__all__ = [ + "get_model_id_str", + "get_context_size", + "instrument_generate_from_context", + "instrument_generate_from_raw", +] + diff --git a/pyproject.toml b/pyproject.toml index f53faffc..eb58ac03 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,6 +46,10 @@ dependencies = [ "math_verify", # Needed for Majority Voting Sampling Strategies. "rouge_score", # Needed for Majority Voting Sampling Strategies. "llm-sandbox[docker]>=0.3.23", + "opentelemetry-api>=1.20.0", + "opentelemetry-sdk>=1.20.0", + "opentelemetry-exporter-otlp>=1.20.0", + "opentelemetry-distro>=0.59b0", ] [project.scripts] diff --git a/test/test_telemetry.py b/test/test_telemetry.py new file mode 100644 index 00000000..34f539c0 --- /dev/null +++ b/test/test_telemetry.py @@ -0,0 +1,220 @@ +"""Tests for OpenTelemetry instrumentation.""" + +import os + +import pytest + + +@pytest.fixture +def enable_app_tracing(monkeypatch): + """Enable application tracing for tests.""" + monkeypatch.setenv("MELLEA_TRACE_APPLICATION", "true") + monkeypatch.setenv("MELLEA_TRACE_BACKEND", "false") + # Force reload of telemetry module to pick up env vars + import importlib + + import mellea.telemetry + + importlib.reload(mellea.telemetry) + yield + # Reset after test + monkeypatch.setenv("MELLEA_TRACE_APPLICATION", "false") + importlib.reload(mellea.telemetry) + + +@pytest.fixture +def enable_backend_tracing(monkeypatch): + """Enable backend tracing for tests.""" + monkeypatch.setenv("MELLEA_TRACE_APPLICATION", "false") + monkeypatch.setenv("MELLEA_TRACE_BACKEND", "true") + # Force reload of telemetry module to pick up env vars + import importlib + + import mellea.telemetry + + importlib.reload(mellea.telemetry) + yield + # Reset after test + monkeypatch.setenv("MELLEA_TRACE_BACKEND", "false") + importlib.reload(mellea.telemetry) + + +def test_telemetry_disabled_by_default(): + """Test that telemetry is disabled by default.""" + from mellea.telemetry import ( + is_application_tracing_enabled, + is_backend_tracing_enabled, + ) + + assert not is_application_tracing_enabled() + assert not is_backend_tracing_enabled() + + +def test_application_tracing_enabled(enable_app_tracing): + """Test that application tracing can be enabled.""" + from mellea.telemetry import ( + is_application_tracing_enabled, + is_backend_tracing_enabled, + ) + + assert is_application_tracing_enabled() + assert not is_backend_tracing_enabled() + + +def test_backend_tracing_enabled(enable_backend_tracing): + """Test that backend tracing can be enabled.""" + from mellea.telemetry import ( + is_application_tracing_enabled, + is_backend_tracing_enabled, + ) + + assert not is_application_tracing_enabled() + assert is_backend_tracing_enabled() + + +def test_trace_application_context_manager(): + """Test that trace_application works as a context manager.""" + from mellea.telemetry import trace_application + + # Should not raise even when tracing is disabled + with trace_application("test_span", test_attr="value") as span: + # Span will be None when tracing is disabled + assert span is None or hasattr(span, "set_attribute") + + +def test_trace_backend_context_manager(): + """Test that trace_backend works as a context manager.""" + from mellea.telemetry import trace_backend + + # Should not raise even when tracing is disabled + with trace_backend("test_span", test_attr="value") as span: + # Span will be None when tracing is disabled + assert span is None or hasattr(span, "set_attribute") + + +def test_set_span_attribute_with_none_span(): + """Test that set_span_attribute handles None span gracefully.""" + from mellea.telemetry import set_span_attribute + + # Should not raise when span is None + set_span_attribute(None, "key", "value") + + +def test_set_span_error_with_none_span(): + """Test that set_span_error handles None span gracefully.""" + from mellea.telemetry import set_span_error + + # Should not raise when span is None + exception = ValueError("test error") + set_span_error(None, exception) + + +@pytest.mark.ollama +def test_session_with_tracing_disabled(): + """Test that session works normally when tracing is disabled.""" + from mellea import start_session + + with start_session() as m: + result = m.instruct("Say hello") + assert result is not None + + +@pytest.mark.ollama +def test_session_with_application_tracing(enable_app_tracing): + """Test that session works with application tracing enabled.""" + from mellea import start_session + + # This should create application trace spans + with start_session() as m: + result = m.instruct("Say hello") + assert result is not None + + +@pytest.mark.ollama +def test_session_with_backend_tracing(enable_backend_tracing): + """Test that session works with backend tracing enabled.""" + from mellea import start_session + + # This should create backend trace spans + with start_session() as m: + result = m.instruct("Say hello") + assert result is not None + + +@pytest.mark.ollama +def test_generative_function_with_tracing(enable_app_tracing): + """Test that @generative functions work with tracing enabled.""" + from mellea import generative, start_session + + @generative + def classify(text: str) -> str: + """Classify the text.""" + ... + + with start_session() as m: + result = classify(m, text="test") + assert result is not None + + +def test_backend_instrumentation_helpers(): + """Test backend instrumentation helper functions.""" + from mellea.telemetry.backend_instrumentation import ( + get_context_size, + get_model_id_str, + ) + + # Test with mock objects + class MockBackend: + def __init__(self): + self.model_id = "test-model" + + class MockContext: + def __init__(self): + self.turns = [1, 2, 3] + + backend = MockBackend() + ctx = MockContext() + + assert get_model_id_str(backend) == "test-model" + assert get_context_size(ctx) == 3 + + +def test_instrument_generate_from_context(): + """Test instrument_generate_from_context helper.""" + from mellea.telemetry.backend_instrumentation import ( + instrument_generate_from_context, + ) + + class MockBackend: + model_id = "test-model" + + class MockAction: + pass + + class MockContext: + turns = [] + + backend = MockBackend() + action = MockAction() + ctx = MockContext() + + # Should return a context manager + with instrument_generate_from_context(backend, action, ctx) as span: + # Span will be None when tracing is disabled + assert span is None or hasattr(span, "set_attribute") + + +def test_instrument_generate_from_raw(): + """Test instrument_generate_from_raw helper.""" + from mellea.telemetry.backend_instrumentation import instrument_generate_from_raw + + class MockBackend: + model_id = "test-model" + + backend = MockBackend() + + # Should return a context manager + with instrument_generate_from_raw(backend, num_actions=5) as span: + # Span will be None when tracing is disabled + assert span is None or hasattr(span, "set_attribute") + diff --git a/uv.lock b/uv.lock index 1d8fe940..02ad8f5c 100644 --- a/uv.lock +++ b/uv.lock @@ -3388,6 +3388,10 @@ dependencies = [ { name = "mistletoe" }, { name = "ollama" }, { name = "openai" }, + { name = "opentelemetry-api" }, + { name = "opentelemetry-distro" }, + { name = "opentelemetry-exporter-otlp" }, + { name = "opentelemetry-sdk" }, { name = "pillow" }, { name = "pydantic" }, { name = "requests" }, @@ -3497,6 +3501,10 @@ requires-dist = [ { name = "numpy", marker = "extra == 'vllm'", specifier = 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"https://files.pythonhosted.org/packages/46/78/10ad9781128ed2f99dbc474f43283b13fea8ba58723e98844367531c18e9/wrapt-1.17.3-cp314-cp314t-win_arm64.whl", hash = "sha256:f38e60678850c42461d4202739f9bf1e3a737c7ad283638251e79cc49effb6b6", size = 38471, upload-time = "2025-08-12T05:52:57.784Z" }, + { url = "https://files.pythonhosted.org/packages/1f/f6/a933bd70f98e9cf3e08167fc5cd7aaaca49147e48411c0bd5ae701bb2194/wrapt-1.17.3-py3-none-any.whl", hash = "sha256:7171ae35d2c33d326ac19dd8facb1e82e5fd04ef8c6c0e394d7af55a55051c22", size = 23591, upload-time = "2025-08-12T05:53:20.674Z" }, +] + [[package]] name = "xformers" version = "0.0.30" From de47e5ae15ed141e5c8980bffdbd4dbdca7abb8d Mon Sep 17 00:00:00 2001 From: Akihiko Kuroda Date: Fri, 23 Jan 2026 20:30:21 -0500 Subject: [PATCH 2/6] fix pre-commit errors Signed-off-by: Akihiko Kuroda --- docs/examples/telemetry/telemetry_example.py | 6 +----- mellea/backends/huggingface.py | 17 +++++++---------- mellea/backends/litellm.py | 11 ++--------- mellea/backends/ollama.py | 4 +--- mellea/backends/openai.py | 11 ++--------- mellea/backends/watsonx.py | 11 ++--------- mellea/stdlib/functional.py | 18 ++++++++++++++---- mellea/telemetry/__init__.py | 5 ++--- mellea/telemetry/backend_instrumentation.py | 14 +++----------- test/test_telemetry.py | 1 - 10 files changed, 34 insertions(+), 64 deletions(-) diff --git a/docs/examples/telemetry/telemetry_example.py b/docs/examples/telemetry/telemetry_example.py index f1548293..3778da22 100644 --- a/docs/examples/telemetry/telemetry_example.py +++ b/docs/examples/telemetry/telemetry_example.py @@ -70,10 +70,7 @@ def main(): print("\n1. Simple instruction with requirements...") email = m.instruct( "Write a professional email to {{name}} about {{topic}}", - requirements=[ - req("Must be formal"), - req("Must be under 100 words"), - ], + requirements=[req("Must be formal"), req("Must be under 100 words")], user_variables={"name": "Alice", "topic": "project update"}, ) print(f"Generated email: {str(email)[:100]}...") @@ -109,4 +106,3 @@ def main(): if __name__ == "__main__": main() - diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py index 85148059..d339576a 100644 --- a/mellea/backends/huggingface.py +++ b/mellea/backends/huggingface.py @@ -208,11 +208,7 @@ async def generate_from_context( ) -> tuple[ModelOutputThunk[C], Context]: """Generate using the huggingface model.""" with instrument_generate_from_context( - backend=self, - action=action, - ctx=ctx, - format=format, - tool_calls=tool_calls, + backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls ): await self.do_generate_walk(action) @@ -265,7 +261,11 @@ async def generate_from_context( return mot, ctx.add(action).add(mot) mot = await self._generate_from_context_standard( - action, ctx, _format=format, model_options=model_opts, tool_calls=tool_calls + action, + ctx, + _format=format, + model_options=model_opts, + tool_calls=tool_calls, ) return mot, ctx.add(action).add(mot) @@ -985,10 +985,7 @@ async def generate_from_raw( ) -> list[ModelOutputThunk]: """Generate using the completions api. Gives the input provided to the model without templating.""" with instrument_generate_from_raw( - backend=self, - num_actions=len(actions), - format=format, - tool_calls=tool_calls, + backend=self, num_actions=len(actions), format=format, tool_calls=tool_calls ): await self.do_generate_walks(list(actions)) diff --git a/mellea/backends/litellm.py b/mellea/backends/litellm.py index ca77001b..bf3c07c3 100644 --- a/mellea/backends/litellm.py +++ b/mellea/backends/litellm.py @@ -131,11 +131,7 @@ async def generate_from_context( "The Openai backend only supports chat-like contexts." ) with instrument_generate_from_context( - backend=self, - action=action, - ctx=ctx, - format=format, - tool_calls=tool_calls, + backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls ): mot = await self._generate_from_chat_context_standard( action, @@ -519,10 +515,7 @@ async def generate_from_raw( ) -> list[ModelOutputThunk]: """Generate using the completions api. Gives the input provided to the model without templating.""" with instrument_generate_from_raw( - backend=self, - num_actions=len(actions), - format=format, - tool_calls=tool_calls, + backend=self, num_actions=len(actions), format=format, tool_calls=tool_calls ): await self.do_generate_walks(list(actions)) extra_body = {} diff --git a/mellea/backends/ollama.py b/mellea/backends/ollama.py index 47e749d6..40ebaf10 100644 --- a/mellea/backends/ollama.py +++ b/mellea/backends/ollama.py @@ -260,9 +260,7 @@ async def generate_from_context( tool_calls: bool = False, ) -> tuple[ModelOutputThunk[C], Context]: """See `generate_from_chat_context`.""" - with instrument_generate_from_context( - self, action, ctx, format, tool_calls - ): + with instrument_generate_from_context(self, action, ctx, format, tool_calls): assert ctx.is_chat_context, ( "The ollama backend only supports chat-like contexts." ) diff --git a/mellea/backends/openai.py b/mellea/backends/openai.py index 078a4686..8d209008 100644 --- a/mellea/backends/openai.py +++ b/mellea/backends/openai.py @@ -313,11 +313,7 @@ async def generate_from_context( "The Openai backend only supports chat-like contexts." ) with instrument_generate_from_context( - backend=self, - action=action, - ctx=ctx, - format=format, - tool_calls=tool_calls, + backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls ): return await self.generate_from_chat_context( action, @@ -828,10 +824,7 @@ async def generate_from_raw( prompts = [self.formatter.print(action) for action in actions] with instrument_generate_from_raw( - backend=self, - num_actions=len(actions), - format=format, - tool_calls=tool_calls, + backend=self, num_actions=len(actions), format=format, tool_calls=tool_calls ): try: completion_response: Completion = ( diff --git a/mellea/backends/watsonx.py b/mellea/backends/watsonx.py index ffab2e38..1c3f278b 100644 --- a/mellea/backends/watsonx.py +++ b/mellea/backends/watsonx.py @@ -255,11 +255,7 @@ async def generate_from_context( "The watsonx.ai backend only supports chat-like contexts." ) with instrument_generate_from_context( - backend=self, - action=action, - ctx=ctx, - format=format, - tool_calls=tool_calls, + backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls ): mot = await self.generate_from_chat_context( action, @@ -525,10 +521,7 @@ async def generate_from_raw( ) -> list[ModelOutputThunk]: """Generates a completion text. Gives the input provided to the model without templating.""" with instrument_generate_from_raw( - backend=self, - num_actions=len(actions), - format=format, - tool_calls=tool_calls, + backend=self, num_actions=len(actions), format=format, tool_calls=tool_calls ): await self.do_generate_walks(list(actions)) diff --git a/mellea/stdlib/functional.py b/mellea/stdlib/functional.py index 24d9fb91..d87f1da4 100644 --- a/mellea/stdlib/functional.py +++ b/mellea/stdlib/functional.py @@ -537,7 +537,9 @@ async def aact( assert sampling_result.sample_generations is not None for result in sampling_result.sample_generations: - assert result._generate_log is not None # Cannot be None after generation. + assert ( + result._generate_log is not None + ) # Cannot be None after generation. generate_logs.append(result._generate_log) new_ctx = sampling_result.result_ctx @@ -551,15 +553,23 @@ async def aact( set_span_attribute(span, "num_generate_logs", len(generate_logs)) if sampling_result: set_span_attribute(span, "sampling_success", bool(sampling_result.result)) - + # Log the model response (truncated for large responses) try: - response_value = str(result.value) if hasattr(result, 'value') and result.value else str(result) + response_value = ( + str(result.value) + if hasattr(result, "value") and result.value + else str(result) + ) # Truncate to 500 chars to avoid overwhelming trace storage if len(response_value) > 500: response_value = response_value[:500] + "..." set_span_attribute(span, "response", response_value) - set_span_attribute(span, "response_length", len(str(result.value) if hasattr(result, 'value') else str(result))) + set_span_attribute( + span, + "response_length", + len(str(result.value) if hasattr(result, "value") else str(result)), + ) except Exception: # If we can't get the response, don't fail the trace pass diff --git a/mellea/telemetry/__init__.py b/mellea/telemetry/__init__.py index aa9e623f..89728bf7 100644 --- a/mellea/telemetry/__init__.py +++ b/mellea/telemetry/__init__.py @@ -154,9 +154,8 @@ def set_span_error(span: Any, exception: Exception) -> None: __all__ = [ "is_application_tracing_enabled", "is_backend_tracing_enabled", - "trace_application", - "trace_backend", "set_span_attribute", "set_span_error", + "trace_application", + "trace_backend", ] - diff --git a/mellea/telemetry/backend_instrumentation.py b/mellea/telemetry/backend_instrumentation.py index 70c4c490..94af07c1 100644 --- a/mellea/telemetry/backend_instrumentation.py +++ b/mellea/telemetry/backend_instrumentation.py @@ -42,11 +42,7 @@ def get_context_size(ctx: Any) -> int: def instrument_generate_from_context( - backend: Any, - action: Any, - ctx: Any, - format: Any = None, - tool_calls: bool = False, + backend: Any, action: Any, ctx: Any, format: Any = None, tool_calls: bool = False ): """Create a backend trace span for generate_from_context. @@ -73,10 +69,7 @@ def instrument_generate_from_context( def instrument_generate_from_raw( - backend: Any, - num_actions: int, - format: Any = None, - tool_calls: bool = False, + backend: Any, num_actions: int, format: Any = None, tool_calls: bool = False ): """Create a backend trace span for generate_from_raw. @@ -101,9 +94,8 @@ def instrument_generate_from_raw( __all__ = [ - "get_model_id_str", "get_context_size", + "get_model_id_str", "instrument_generate_from_context", "instrument_generate_from_raw", ] - diff --git a/test/test_telemetry.py b/test/test_telemetry.py index 34f539c0..8d85820b 100644 --- a/test/test_telemetry.py +++ b/test/test_telemetry.py @@ -217,4 +217,3 @@ class MockBackend: with instrument_generate_from_raw(backend, num_actions=5) as span: # Span will be None when tracing is disabled assert span is None or hasattr(span, "set_attribute") - From 6a273eb811f65725856e8873b75a2b1fca28f64a Mon Sep 17 00:00:00 2001 From: Akihiko Kuroda Date: Mon, 26 Jan 2026 17:52:13 -0500 Subject: [PATCH 3/6] review comments Signed-off-by: Akihiko Kuroda --- docs/dev/telemetry.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/dev/telemetry.md b/docs/dev/telemetry.md index d4b0aee6..2bfdf057 100644 --- a/docs/dev/telemetry.md +++ b/docs/dev/telemetry.md @@ -65,7 +65,7 @@ The backend tracer (`mellea.backend`) instruments: ```bash export MELLEA_TRACE_APPLICATION=true export MELLEA_TRACE_BACKEND=false -python your_script.py +python docs/examples/instruct_validate_repair/101_email.py ``` This traces user-facing operations like `@generative` function calls, session lifecycle, and sampling strategies, but not the underlying LLM API calls. @@ -75,7 +75,7 @@ This traces user-facing operations like `@generative` function calls, session li ```bash export MELLEA_TRACE_APPLICATION=false export MELLEA_TRACE_BACKEND=true -python your_script.py +python docs/examples/instruct_validate_repair/101_email.py ``` This traces only the LLM backend interactions, showing model calls, token usage, and API latency. @@ -85,7 +85,7 @@ This traces only the LLM backend interactions, showing model calls, token usage, ```bash export MELLEA_TRACE_APPLICATION=true export MELLEA_TRACE_BACKEND=true -python your_script.py +python docs/examples/instruct_validate_repair/101_email.py ``` This provides complete observability across both application logic and backend interactions. @@ -105,7 +105,7 @@ export MELLEA_TRACE_BACKEND=true export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317 export OTEL_SERVICE_NAME=my-mellea-app -python your_script.py +python docs/examples/instruct_validate_repair/101_email.py # View traces at http://localhost:16686 ``` @@ -115,7 +115,7 @@ python your_script.py ```bash export MELLEA_TRACE_APPLICATION=true export MELLEA_TRACE_CONSOLE=true -python your_script.py +python docs/examples/instruct_validate_repair/101_email.py ``` This prints trace spans to the console, useful for local debugging without setting up a trace backend. From f9a882a0854d93a5e8e44ac8bc34a5ed44161ed5 Mon Sep 17 00:00:00 2001 From: Akihiko Kuroda Date: Wed, 28 Jan 2026 18:38:09 -0500 Subject: [PATCH 4/6] review comments Signed-off-by: Akihiko Kuroda --- docs/dev/telemetry.md | 66 ++-- mellea/backends/huggingface.py | 124 +++++--- mellea/backends/litellm.py | 52 +++- mellea/backends/ollama.py | 59 +++- mellea/backends/openai.py | 60 +++- mellea/backends/watsonx.py | 53 +++- mellea/telemetry/__init__.py | 155 +++++++--- mellea/telemetry/backend_instrumentation.py | 209 ++++++++++++- test/telemetry/test_backend_telemetry.py | 324 ++++++++++++++++++++ 9 files changed, 936 insertions(+), 166 deletions(-) create mode 100644 test/telemetry/test_backend_telemetry.py diff --git a/docs/dev/telemetry.md b/docs/dev/telemetry.md index 2bfdf057..725f2057 100644 --- a/docs/dev/telemetry.md +++ b/docs/dev/telemetry.md @@ -1,9 +1,23 @@ ## OpenTelemetry Instrumentation in Mellea -Mellea provides built-in OpenTelemetry instrumentation with two independent trace scopes that can be enabled separately: +Mellea provides built-in OpenTelemetry instrumentation with two independent trace scopes that can be enabled separately. The instrumentation follows the [OpenTelemetry Gen-AI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/) for standardized observability across LLM applications. + +**Note**: OpenTelemetry is an optional dependency. If not installed, telemetry features are automatically disabled with no impact on functionality. 1. **Application Trace** (`mellea.application`) - Tracks user-facing operations -2. **Backend Trace** (`mellea.backend`) - Tracks LLM backend interactions +2. **Backend Trace** (`mellea.backend`) - Tracks LLM backend interactions with Gen-AI semantic conventions + +### Installation + +To use telemetry features, install Mellea with OpenTelemetry support: + +```bash +pip install mellea[telemetry] +# or +uv pip install mellea[telemetry] +``` + +Without the `[telemetry]` extra, Mellea works normally but telemetry features are disabled. ### Configuration @@ -42,21 +56,31 @@ The application tracer (`mellea.application`) instruments: ### Backend Trace Scope -The backend tracer (`mellea.backend`) instruments: +The backend tracer (`mellea.backend`) instruments LLM interactions following [OpenTelemetry Gen-AI Semantic Conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/): -- **Backend.generate_from_context()**: Context-based generation -- **Backend.generate_from_raw()**: Raw generation without context +- **Backend.generate_from_context()**: Context-based generation (chat operations) +- **Backend.generate_from_raw()**: Raw generation without context (text completions) - **Backend-specific implementations**: Ollama, OpenAI, HuggingFace, Watsonx, LiteLLM -**Span attributes include:** -- `backend`: Backend class name (e.g., `OllamaModelBackend`) -- `model_id`: Model identifier string -- `action_type`: Component type -- `context_size`: Number of items in context -- `has_format`: Whether structured output format is specified -- `format_type`: Response format class name -- `tool_calls`: Whether tool calling is enabled -- `num_actions`: Number of actions in batch (for `generate_from_raw`) +**Gen-AI Semantic Convention Attributes:** +- `gen_ai.system`: LLM system name (e.g., `openai`, `ollama`, `huggingface`) +- `gen_ai.request.model`: Model identifier used for the request +- `gen_ai.response.model`: Actual model used in the response (may differ from request) +- `gen_ai.operation.name`: Operation type (`chat` or `text_completion`) +- `gen_ai.usage.input_tokens`: Number of input tokens consumed +- `gen_ai.usage.output_tokens`: Number of output tokens generated +- `gen_ai.usage.total_tokens`: Total tokens consumed +- `gen_ai.response.id`: Response ID from the LLM provider +- `gen_ai.response.finish_reasons`: List of finish reasons (e.g., `["stop"]`, `["length"]`) + +**Mellea-Specific Attributes:** +- `mellea.backend`: Backend class name (e.g., `OpenAIBackend`) +- `mellea.action_type`: Component type being executed +- `mellea.context_size`: Number of items in context +- `mellea.has_format`: Whether structured output format is specified +- `mellea.format_type`: Response format class name +- `mellea.tool_calls_enabled`: Whether tool calling is enabled +- `mellea.num_actions`: Number of actions in batch (for `generate_from_raw`) ### Usage Examples @@ -164,14 +188,20 @@ When both traces are enabled, you'll see a hierarchy like: ``` session_context (application) ├── aact (application) -│ ├── generate_from_context (backend) -│ │ └── ollama.chat (backend) +│ ├── chat (backend) [gen_ai.system=ollama, gen_ai.request.model=llama3.2] +│ │ └── [gen_ai.usage.input_tokens=150, gen_ai.usage.output_tokens=50] │ └── requirement_validation (application) ├── aact (application) -│ └── generate_from_context (backend) -│ └── ollama.chat (backend) +│ └── chat (backend) [gen_ai.system=openai, gen_ai.request.model=gpt-4] +│ └── [gen_ai.usage.input_tokens=200, gen_ai.usage.output_tokens=75] ``` +The Gen-AI semantic conventions make it easy to: +- Track token usage across different LLM providers +- Compare performance between models +- Monitor costs based on token consumption +- Identify which operations consume the most tokens + ### Troubleshooting **Traces not appearing:** diff --git a/mellea/backends/huggingface.py b/mellea/backends/huggingface.py index d339576a..aeec14e4 100644 --- a/mellea/backends/huggingface.py +++ b/mellea/backends/huggingface.py @@ -46,8 +46,8 @@ from ..stdlib.components import Intrinsic, Message from ..stdlib.requirements import ALoraRequirement, LLMaJRequirement from ..telemetry.backend_instrumentation import ( - instrument_generate_from_context, instrument_generate_from_raw, + start_generate_span, ) from .adapters import ( AdapterMixin, @@ -207,68 +207,75 @@ async def generate_from_context( tool_calls: bool = False, ) -> tuple[ModelOutputThunk[C], Context]: """Generate using the huggingface model.""" - with instrument_generate_from_context( + span = start_generate_span( backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls - ): - await self.do_generate_walk(action) - - # Upsert model options. - model_opts = self._simplify_and_merge(model_options) + ) + await self.do_generate_walk(action) - # Requirements can be automatically rerouted to a requirement adapter. - if isinstance(action, Requirement): - # See docs/dev/requirement_aLoRA_rerouting.md - reroute_to_alora = self.default_to_constraint_checking_alora - adapter_name = "requirement_check" + # Upsert model options. + model_opts = self._simplify_and_merge(model_options) - if isinstance(action, ALoraRequirement): - reroute_to_alora = True - adapter_name = action.intrinsic_name - alora_action = action - else: - assert action.description is not None, ( - "must have a description when generating from a requirement" - ) - alora_action = ALoraRequirement(action.description, adapter_name) + # Requirements can be automatically rerouted to a requirement adapter. + if isinstance(action, Requirement): + # See docs/dev/requirement_aLoRA_rerouting.md + reroute_to_alora = self.default_to_constraint_checking_alora + adapter_name = "requirement_check" - # Check if a requirement_check (or AloraRequirement specified) adapter - # exists. - alora_req_adapter = get_adapter_for_intrinsic( - adapter_name, [AdapterType.ALORA], self._added_adapters + if isinstance(action, ALoraRequirement): + reroute_to_alora = True + adapter_name = action.intrinsic_name + alora_action = action + else: + assert action.description is not None, ( + "must have a description when generating from a requirement" ) - if alora_req_adapter is None: - # Log a warning if using an AloraRequirement but no adapter fit. - if reroute_to_alora and isinstance(action, ALoraRequirement): - FancyLogger.get_logger().warning( - f"attempted to use an AloraRequirement but backend {self} doesn't have the specified adapter added {adapter_name}; defaulting to regular generation" - ) - reroute_to_alora = False + alora_action = ALoraRequirement(action.description, adapter_name) - if issubclass(type(action), LLMaJRequirement): - reroute_to_alora = False - - if reroute_to_alora: - # Keep the alora requirement handling separate for now. - mot = await self._generate_from_intrinsic( - alora_action, ctx, model_options=model_opts + # Check if a requirement_check (or AloraRequirement specified) adapter + # exists. + alora_req_adapter = get_adapter_for_intrinsic( + adapter_name, [AdapterType.ALORA], self._added_adapters + ) + if alora_req_adapter is None: + # Log a warning if using an AloraRequirement but no adapter fit. + if reroute_to_alora and isinstance(action, ALoraRequirement): + FancyLogger.get_logger().warning( + f"attempted to use an AloraRequirement but backend {self} doesn't have the specified adapter added {adapter_name}; defaulting to regular generation" ) - return mot, ctx.add(alora_action).add(mot) + reroute_to_alora = False + + if issubclass(type(action), LLMaJRequirement): + reroute_to_alora = False - elif isinstance(action, Intrinsic): + if reroute_to_alora: + # Keep the alora requirement handling separate for now. mot = await self._generate_from_intrinsic( - action, ctx, model_options=model_opts + alora_action, ctx, model_options=model_opts ) - return mot, ctx.add(action).add(mot) - - mot = await self._generate_from_context_standard( - action, - ctx, - _format=format, - model_options=model_opts, - tool_calls=tool_calls, + # Store span for telemetry + if span is not None: + mot._meta["_telemetry_span"] = span + return mot, ctx.add(alora_action).add(mot) + + elif isinstance(action, Intrinsic): + mot = await self._generate_from_intrinsic( + action, ctx, model_options=model_opts ) + # Store span for telemetry + if span is not None: + mot._meta["_telemetry_span"] = span return mot, ctx.add(action).add(mot) + mot = await self._generate_from_context_standard( + action, ctx, _format=format, model_options=model_opts, tool_calls=tool_calls + ) + + # Store span in metadata for post_processing to record telemetry + if span is not None: + mot._meta["_telemetry_span"] = span + + return mot, ctx.add(action).add(mot) + def _generate_with_adapter_lock( self, adapter_name: str, generate_func: Callable, *args, **kwargs ): @@ -934,6 +941,23 @@ async def post_processing( "ModelOutputThunks should have their model_opts assigned during generation" ) + # Record telemetry if span is available + span = mot._meta.get("_telemetry_span") + if span is not None: + from ..telemetry import end_backend_span + from ..telemetry.backend_instrumentation import record_response_metadata + + # HuggingFace local models don't typically provide token counts + # but we can record response metadata if available + hf_output = mot._meta.get("hf_output") + if hf_output is not None: + record_response_metadata(span, hf_output) + + # Close the span now that async operation is complete + end_backend_span(span) + # Clean up span reference + del mot._meta["_telemetry_span"] + # Generate the log for this ModelOutputThunk. generate_log = GenerateLog() generate_log.prompt = conversation diff --git a/mellea/backends/litellm.py b/mellea/backends/litellm.py index bf3c07c3..616cb194 100644 --- a/mellea/backends/litellm.py +++ b/mellea/backends/litellm.py @@ -36,8 +36,8 @@ from ..stdlib.components import Message from ..stdlib.requirements import ALoraRequirement from ..telemetry.backend_instrumentation import ( - instrument_generate_from_context, instrument_generate_from_raw, + start_generate_span, ) from .backend import FormatterBackend from .model_options import ModelOption @@ -130,17 +130,22 @@ async def generate_from_context( assert ctx.is_chat_context, NotImplementedError( "The Openai backend only supports chat-like contexts." ) - with instrument_generate_from_context( + span = start_generate_span( backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls - ): - mot = await self._generate_from_chat_context_standard( - action, - ctx, - _format=format, - model_options=model_options, - tool_calls=tool_calls, - ) - return mot, ctx.add(action).add(mot) + ) + mot = await self._generate_from_chat_context_standard( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) + + # Store span for telemetry recording in post_processing + if span is not None: + mot._meta["_telemetry_span"] = span + + return mot, ctx.add(action).add(mot) def _simplify_and_merge( self, model_options: dict[str, Any] | None @@ -462,6 +467,31 @@ async def post_processing( generate_log.result = mot mot._generate_log = generate_log + # Record telemetry now that response is available + span = mot._meta.get("_telemetry_span") + if span is not None: + from ..telemetry import end_backend_span + from ..telemetry.backend_instrumentation import ( + record_response_metadata, + record_token_usage, + ) + + response = mot._meta.get("litellm_chat_response") + if response: + # LiteLLM responses have usage information + usage = ( + response.get("usage") + if isinstance(response, dict) + else getattr(response, "usage", None) + ) + if usage: + record_token_usage(span, usage) + record_response_metadata(span, response) + # Close the span now that async operation is complete + end_backend_span(span) + # Clean up the span reference + del mot._meta["_telemetry_span"] + @staticmethod def _extract_tools( action, _format, model_opts, tool_calls, ctx diff --git a/mellea/backends/ollama.py b/mellea/backends/ollama.py index 40ebaf10..900a10e2 100644 --- a/mellea/backends/ollama.py +++ b/mellea/backends/ollama.py @@ -260,19 +260,27 @@ async def generate_from_context( tool_calls: bool = False, ) -> tuple[ModelOutputThunk[C], Context]: """See `generate_from_chat_context`.""" - with instrument_generate_from_context(self, action, ctx, format, tool_calls): - assert ctx.is_chat_context, ( - "The ollama backend only supports chat-like contexts." - ) - mot = await self.generate_from_chat_context( - action, - ctx, - _format=format, - model_options=model_options, - tool_calls=tool_calls, - ) + from ..telemetry.backend_instrumentation import start_generate_span + + # Start span without auto-closing (will be closed in post_processing) + span = start_generate_span(self, action, ctx, format, tool_calls) + + assert ctx.is_chat_context, ( + "The ollama backend only supports chat-like contexts." + ) + mot = await self.generate_from_chat_context( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) - return mot, ctx.add(action).add(mot) + # Store span for telemetry recording and closing in post_processing + if span is not None: + mot._meta["_telemetry_span"] = span + + return mot, ctx.add(action).add(mot) async def generate_from_chat_context( self, @@ -594,6 +602,33 @@ async def post_processing( mot._generate_log = generate_log mot._generate = None + # Record telemetry and close span now that response is available + span = mot._meta.get("_telemetry_span") + if span is not None: + from ..telemetry import end_backend_span + from ..telemetry.backend_instrumentation import ( + record_response_metadata, + record_token_usage, + ) + + response = mot._meta.get("chat_response") + if response: + # Ollama responses may have usage information + usage = ( + response.get("usage") + if isinstance(response, dict) + else getattr(response, "usage", None) + ) + if usage: + record_token_usage(span, usage) + record_response_metadata(span, response) + + # Close the span now that telemetry is recorded + end_backend_span(span) + + # Clean up the span reference + del mot._meta["_telemetry_span"] + def chat_response_delta_merge(mot: ModelOutputThunk, delta: ollama.ChatResponse): """Merges the individual ChatResponse chunks from a streaming response into a single ChatResponse. diff --git a/mellea/backends/openai.py b/mellea/backends/openai.py index 8d209008..36aaed9c 100644 --- a/mellea/backends/openai.py +++ b/mellea/backends/openai.py @@ -309,19 +309,29 @@ async def generate_from_context( tool_calls: bool = False, ) -> tuple[ModelOutputThunk[C], Context]: """See `generate_from_chat_context`.""" + from ..telemetry.backend_instrumentation import start_generate_span + assert ctx.is_chat_context, NotImplementedError( "The Openai backend only supports chat-like contexts." ) - with instrument_generate_from_context( + + # Start span without auto-closing (will be closed in post_processing) + span = start_generate_span( backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls - ): - return await self.generate_from_chat_context( - action, - ctx, - _format=format, - model_options=model_options, - tool_calls=tool_calls, - ) + ) + + result = await self.generate_from_chat_context( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) + # Store span in ModelOutputThunk for later use in post_processing + mot, new_ctx = result + if span is not None: + mot._meta["_telemetry_span"] = span + return mot, new_ctx async def generate_from_chat_context( self, @@ -699,7 +709,10 @@ async def processing( if content_chunk is not None: mot._underlying_value += content_chunk - mot._meta["oai_chat_response"] = chunk.choices[0].model_dump() + # Store the full response (includes usage) as a dict + mot._meta["oai_chat_response"] = chunk.model_dump() + # Also store just the choice for backward compatibility + mot._meta["oai_chat_response_choice"] = chunk.choices[0].model_dump() elif isinstance(chunk, ChatCompletionChunk): message_delta = chunk.choices[0].delta @@ -745,7 +758,11 @@ async def post_processing( # OpenAI streamed responses give you chunks of tool calls. # As a result, we have to store data between calls and only then # check for complete tool calls in the post_processing step. - tool_chunk = extract_model_tool_requests(tools, mot._meta["oai_chat_response"]) + # Use the choice format for tool extraction (backward compatibility) + choice_response = mot._meta.get( + "oai_chat_response_choice", mot._meta["oai_chat_response"] + ) + tool_chunk = extract_model_tool_requests(tools, choice_response) if tool_chunk is not None: if mot.tool_calls is None: mot.tool_calls = {} @@ -759,6 +776,7 @@ async def post_processing( generate_log.backend = f"openai::{self.model_id!s}" generate_log.model_options = mot._model_options generate_log.date = datetime.datetime.now() + # Store the full response (includes usage info) generate_log.model_output = mot._meta["oai_chat_response"] generate_log.extra = { "format": _format, @@ -771,6 +789,26 @@ async def post_processing( generate_log.result = mot mot._generate_log = generate_log + # Record telemetry now that response is available + span = mot._meta.get("_telemetry_span") + if span is not None: + from ..telemetry import end_backend_span + from ..telemetry.backend_instrumentation import ( + record_response_metadata, + record_token_usage, + ) + + response = mot._meta["oai_chat_response"] + # response is a dict from model_dump(), extract usage if present + usage = response.get("usage") if isinstance(response, dict) else None + if usage: + record_token_usage(span, usage) + record_response_metadata(span, response) + # Close the span now that async operation is complete + end_backend_span(span) + # Clean up the span reference + del mot._meta["_telemetry_span"] + @overload async def generate_from_raw( self, diff --git a/mellea/backends/watsonx.py b/mellea/backends/watsonx.py index 1c3f278b..1240dc82 100644 --- a/mellea/backends/watsonx.py +++ b/mellea/backends/watsonx.py @@ -39,8 +39,8 @@ from ..stdlib.components import Message from ..stdlib.requirements import ALoraRequirement from ..telemetry.backend_instrumentation import ( - instrument_generate_from_context, instrument_generate_from_raw, + start_generate_span, ) from .backend import FormatterBackend from .model_options import ModelOption @@ -254,17 +254,22 @@ async def generate_from_context( assert ctx.is_chat_context, NotImplementedError( "The watsonx.ai backend only supports chat-like contexts." ) - with instrument_generate_from_context( + span = start_generate_span( backend=self, action=action, ctx=ctx, format=format, tool_calls=tool_calls - ): - mot = await self.generate_from_chat_context( - action, - ctx, - _format=format, - model_options=model_options, - tool_calls=tool_calls, - ) - return mot, ctx.add(action).add(mot) + ) + mot = await self.generate_from_chat_context( + action, + ctx, + _format=format, + model_options=model_options, + tool_calls=tool_calls, + ) + + # Store span in metadata for post_processing to record telemetry + if span is not None: + mot._meta["_telemetry_span"] = span + + return mot, ctx.add(action).add(mot) async def generate_from_chat_context( self, @@ -471,6 +476,32 @@ async def post_processing( for key, val in tool_chunk.items(): mot.tool_calls[key] = val + # Record telemetry if span is available + span = mot._meta.get("_telemetry_span") + if span is not None: + from ..telemetry import end_backend_span + from ..telemetry.backend_instrumentation import ( + record_response_metadata, + record_token_usage, + ) + + response = mot._meta.get("oai_chat_response") + if response is not None: + # Watsonx responses may have usage information + usage = ( + response.get("usage") + if isinstance(response, dict) + else getattr(response, "usage", None) + ) + if usage: + record_token_usage(span, usage) + record_response_metadata(span, response) + + # Close the span now that async operation is complete + end_backend_span(span) + # Clean up span reference + del mot._meta["_telemetry_span"] + # Generate the log for this ModelOutputThunk. generate_log = GenerateLog() generate_log.prompt = conversation diff --git a/mellea/telemetry/__init__.py b/mellea/telemetry/__init__.py index 89728bf7..756ebe0e 100644 --- a/mellea/telemetry/__init__.py +++ b/mellea/telemetry/__init__.py @@ -4,6 +4,9 @@ 1. Application Trace (mellea.application) - User-facing operations 2. Backend Trace (mellea.backend) - LLM backend interactions +Follows OpenTelemetry Gen-AI semantic conventions: +https://opentelemetry.io/docs/specs/semconv/gen-ai/ + Configuration via environment variables: - MELLEA_TRACE_APPLICATION: Enable/disable application tracing (default: false) - MELLEA_TRACE_BACKEND: Enable/disable backend tracing (default: false) @@ -15,23 +18,30 @@ from contextlib import contextmanager from typing import Any -from opentelemetry import trace -from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter -from opentelemetry.sdk.resources import Resource -from opentelemetry.sdk.trace import TracerProvider -from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter +# Try to import OpenTelemetry, but make it optional +try: + from opentelemetry import trace + from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter + from opentelemetry.sdk.resources import Resource + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter + from opentelemetry.semconv.trace import SpanAttributes + + _OTEL_AVAILABLE = True +except ImportError: + _OTEL_AVAILABLE = False + # Provide dummy types for type hints + trace = None # type: ignore + SpanAttributes = None # type: ignore # Configuration from environment variables -_TRACE_APPLICATION_ENABLED = os.getenv("MELLEA_TRACE_APPLICATION", "false").lower() in ( - "true", - "1", - "yes", -) -_TRACE_BACKEND_ENABLED = os.getenv("MELLEA_TRACE_BACKEND", "false").lower() in ( - "true", - "1", - "yes", -) +# Disable tracing if OpenTelemetry is not available +_TRACE_APPLICATION_ENABLED = _OTEL_AVAILABLE and os.getenv( + "MELLEA_TRACE_APPLICATION", "false" +).lower() in ("true", "1", "yes") +_TRACE_BACKEND_ENABLED = _OTEL_AVAILABLE and os.getenv( + "MELLEA_TRACE_BACKEND", "false" +).lower() in ("true", "1", "yes") _OTLP_ENDPOINT = os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT") _SERVICE_NAME = os.getenv("OTEL_SERVICE_NAME", "mellea") _CONSOLE_EXPORT = os.getenv("MELLEA_TRACE_CONSOLE", "false").lower() in ( @@ -41,38 +51,43 @@ ) -def _setup_tracer_provider() -> TracerProvider: +def _setup_tracer_provider(): """Set up the global tracer provider with OTLP exporter if configured.""" - resource = Resource.create({"service.name": _SERVICE_NAME}) - provider = TracerProvider(resource=resource) + if not _OTEL_AVAILABLE: + return None + + resource = Resource.create({"service.name": _SERVICE_NAME}) # type: ignore + provider = TracerProvider(resource=resource) # type: ignore # Add OTLP exporter if endpoint is configured if _OTLP_ENDPOINT: - otlp_exporter = OTLPSpanExporter(endpoint=_OTLP_ENDPOINT) - provider.add_span_processor(BatchSpanProcessor(otlp_exporter)) + otlp_exporter = OTLPSpanExporter(endpoint=_OTLP_ENDPOINT) # type: ignore + provider.add_span_processor(BatchSpanProcessor(otlp_exporter)) # type: ignore # Add console exporter for debugging if enabled # Note: Console exporter may cause harmless errors during test cleanup if _CONSOLE_EXPORT: try: - console_exporter = ConsoleSpanExporter() - provider.add_span_processor(BatchSpanProcessor(console_exporter)) + console_exporter = ConsoleSpanExporter() # type: ignore + provider.add_span_processor(BatchSpanProcessor(console_exporter)) # type: ignore except Exception: # Silently ignore console exporter setup failures pass - trace.set_tracer_provider(provider) + trace.set_tracer_provider(provider) # type: ignore return provider # Initialize tracer provider if any tracing is enabled _tracer_provider = None -if _TRACE_APPLICATION_ENABLED or _TRACE_BACKEND_ENABLED: - _tracer_provider = _setup_tracer_provider() +_application_tracer = None +_backend_tracer = None -# Create separate tracers for application and backend -_application_tracer = trace.get_tracer("mellea.application", "0.3.0") -_backend_tracer = trace.get_tracer("mellea.backend", "0.3.0") +if _OTEL_AVAILABLE and (_TRACE_APPLICATION_ENABLED or _TRACE_BACKEND_ENABLED): + _tracer_provider = _setup_tracer_provider() + # Create separate tracers for application and backend + _application_tracer = trace.get_tracer("mellea.application", "0.3.0") # type: ignore + _backend_tracer = trace.get_tracer("mellea.backend", "0.3.0") # type: ignore def is_application_tracing_enabled() -> bool: @@ -96,11 +111,11 @@ def trace_application(name: str, **attributes: Any): Yields: The span object if tracing is enabled, otherwise a no-op context manager """ - if _TRACE_APPLICATION_ENABLED: - with _application_tracer.start_as_current_span(name) as span: + if _TRACE_APPLICATION_ENABLED and _application_tracer is not None: + with _application_tracer.start_as_current_span(name) as span: # type: ignore for key, value in attributes.items(): if value is not None: - span.set_attribute(key, str(value)) + _set_attribute_safe(span, key, value) yield span else: yield None @@ -110,6 +125,8 @@ def trace_application(name: str, **attributes: Any): def trace_backend(name: str, **attributes: Any): """Create a backend trace span if backend tracing is enabled. + Follows Gen-AI semantic conventions for LLM operations. + Args: name: Name of the span **attributes: Additional attributes to add to the span @@ -117,16 +134,80 @@ def trace_backend(name: str, **attributes: Any): Yields: The span object if tracing is enabled, otherwise a no-op context manager """ - if _TRACE_BACKEND_ENABLED: - with _backend_tracer.start_as_current_span(name) as span: + if _TRACE_BACKEND_ENABLED and _backend_tracer is not None: + with _backend_tracer.start_as_current_span(name) as span: # type: ignore + # Set Gen-AI operation type + span.set_attribute("gen_ai.operation.name", name) + for key, value in attributes.items(): if value is not None: - span.set_attribute(key, str(value)) + _set_attribute_safe(span, key, value) yield span else: yield None +def start_backend_span(name: str, **attributes: Any): + """Start a backend trace span without auto-closing (for async operations). + + Use this when you need to manually control span lifecycle, such as for + async operations where the span should remain open until post-processing. + + Args: + name: Name of the span + **attributes: Additional attributes to add to the span + + Returns: + The span object if tracing is enabled, otherwise None + """ + if _TRACE_BACKEND_ENABLED and _backend_tracer is not None: + span = _backend_tracer.start_span(name) # type: ignore + # Set Gen-AI operation type + span.set_attribute("gen_ai.operation.name", name) + + for key, value in attributes.items(): + if value is not None: + _set_attribute_safe(span, key, value) + return span + return None + + +def end_backend_span(span: Any) -> None: + """End a backend trace span. + + Args: + span: The span object to end + """ + if span is not None: + span.end() + + +def _set_attribute_safe(span: Any, key: str, value: Any) -> None: + """Set an attribute on a span, handling type conversions. + + Args: + span: The span object + key: Attribute key + value: Attribute value (will be converted to appropriate type) + """ + if value is None: + return + + # Handle different value types according to OpenTelemetry spec + if isinstance(value, bool): + span.set_attribute(key, value) + elif isinstance(value, int | float): + span.set_attribute(key, value) + elif isinstance(value, str): + span.set_attribute(key, value) + elif isinstance(value, list | tuple): + # Convert to list of strings + span.set_attribute(key, [str(v) for v in value]) + else: + # Convert other types to string + span.set_attribute(key, str(value)) + + def set_span_attribute(span: Any, key: str, value: Any) -> None: """Set an attribute on a span if the span is not None. @@ -136,7 +217,7 @@ def set_span_attribute(span: Any, key: str, value: Any) -> None: value: Attribute value """ if span is not None and value is not None: - span.set_attribute(key, str(value)) + _set_attribute_safe(span, key, value) def set_span_error(span: Any, exception: Exception) -> None: @@ -146,9 +227,9 @@ def set_span_error(span: Any, exception: Exception) -> None: span: The span object (may be None if tracing is disabled) exception: The exception to record """ - if span is not None: + if span is not None and _OTEL_AVAILABLE: span.record_exception(exception) - span.set_status(trace.Status(trace.StatusCode.ERROR, str(exception))) + span.set_status(trace.Status(trace.StatusCode.ERROR, str(exception))) # type: ignore __all__ = [ diff --git a/mellea/telemetry/backend_instrumentation.py b/mellea/telemetry/backend_instrumentation.py index 94af07c1..6432b63c 100644 --- a/mellea/telemetry/backend_instrumentation.py +++ b/mellea/telemetry/backend_instrumentation.py @@ -1,4 +1,8 @@ -"""Backend instrumentation helpers for OpenTelemetry tracing.""" +"""Backend instrumentation helpers for OpenTelemetry tracing. + +Follows OpenTelemetry Gen-AI semantic conventions: +https://opentelemetry.io/docs/specs/semconv/gen-ai/ +""" from typing import Any @@ -22,6 +26,32 @@ def get_model_id_str(backend: Any) -> str: return backend.__class__.__name__ +def get_system_name(backend: Any) -> str: + """Get the Gen-AI system name from backend. + + Args: + backend: Backend instance + + Returns: + System name (e.g., 'openai', 'ollama', 'huggingface') + """ + backend_class = backend.__class__.__name__.lower() + if "openai" in backend_class: + return "openai" + elif "ollama" in backend_class: + return "ollama" + elif "huggingface" in backend_class or "hf" in backend_class: + return "huggingface" + elif "watsonx" in backend_class: + return "watsonx" + elif "litellm" in backend_class: + return "litellm" + elif "vllm" in backend_class: + return "vllm" + else: + return backend.__class__.__name__ + + def get_context_size(ctx: Any) -> int: """Get the size of a context. @@ -46,6 +76,8 @@ def instrument_generate_from_context( ): """Create a backend trace span for generate_from_context. + Follows Gen-AI semantic conventions for chat operations. + Args: backend: Backend instance action: Action component @@ -56,15 +88,65 @@ def instrument_generate_from_context( Returns: Context manager for the trace span """ + model_id = get_model_id_str(backend) + system_name = get_system_name(backend) + return trace_backend( - "generate_from_context", - backend=backend.__class__.__name__, - model_id=get_model_id_str(backend), - action_type=action.__class__.__name__, - context_size=get_context_size(ctx), - has_format=format is not None, - format_type=format.__name__ if format else None, - tool_calls=tool_calls, + "chat", # Gen-AI convention: use 'chat' for chat completions + **{ + # Gen-AI semantic convention attributes + "gen_ai.system": system_name, + "gen_ai.request.model": model_id, + "gen_ai.operation.name": "chat", + # Mellea-specific attributes + "mellea.backend": backend.__class__.__name__, + "mellea.action_type": action.__class__.__name__, + "mellea.context_size": get_context_size(ctx), + "mellea.has_format": format is not None, + "mellea.format_type": format.__name__ if format else None, + "mellea.tool_calls_enabled": tool_calls, + }, + ) + + +def start_generate_span( + backend: Any, action: Any, ctx: Any, format: Any = None, tool_calls: bool = False +): + """Start a backend trace span for generate_from_context (without auto-closing). + + Use this for async operations where the span should remain open until + post-processing completes. + + Args: + backend: Backend instance + action: Action component + ctx: Context + format: Response format (BaseModel subclass or None) + tool_calls: Whether tool calling is enabled + + Returns: + Span object or None if tracing is disabled + """ + from . import start_backend_span + + model_id = get_model_id_str(backend) + system_name = get_system_name(backend) + + return start_backend_span( + "chat", # Gen-AI convention: use 'chat' for chat completions + **{ + # Gen-AI semantic convention attributes + "gen_ai.system": system_name, + "gen_ai.request.model": model_id, + "gen_ai.operation.name": "chat", + # Mellea-specific attributes + "mellea.backend": backend.__class__.__name__, + "mellea.action_type": action.__class__.__name__, + "mellea.context_size": get_context_size(ctx), + "mellea.has_format": format is not None, + "mellea.format_type": format.__name__ if format else None, + "mellea.tool_calls_enabled": tool_calls, + }, ) @@ -73,6 +155,8 @@ def instrument_generate_from_raw( ): """Create a backend trace span for generate_from_raw. + Follows Gen-AI semantic conventions for text generation operations. + Args: backend: Backend instance num_actions: Number of actions in the batch @@ -82,20 +166,113 @@ def instrument_generate_from_raw( Returns: Context manager for the trace span """ + model_id = get_model_id_str(backend) + system_name = get_system_name(backend) + return trace_backend( - "generate_from_raw", - backend=backend.__class__.__name__, - model_id=get_model_id_str(backend), - num_actions=num_actions, - has_format=format is not None, - format_type=format.__name__ if format else None, - tool_calls=tool_calls, + "text_completion", # Gen-AI convention: use 'text_completion' for completions + **{ + # Gen-AI semantic convention attributes + "gen_ai.system": system_name, + "gen_ai.request.model": model_id, + "gen_ai.operation.name": "text_completion", + # Mellea-specific attributes + "mellea.backend": backend.__class__.__name__, + "mellea.num_actions": num_actions, + "mellea.has_format": format is not None, + "mellea.format_type": format.__name__ if format else None, + "mellea.tool_calls_enabled": tool_calls, + }, ) +def record_token_usage(span: Any, usage: Any) -> None: + """Record token usage metrics following Gen-AI semantic conventions. + + Args: + span: The span object (may be None if tracing is disabled) + usage: Usage object or dict from the LLM response (e.g., OpenAI usage object) + """ + if span is None or usage is None: + return + + try: + # Gen-AI semantic convention attributes for token usage + # Handle both objects and dicts + def get_value(obj, key): + if isinstance(obj, dict): + return obj.get(key) + return getattr(obj, key, None) + + prompt_tokens = get_value(usage, "prompt_tokens") + if prompt_tokens is not None: + set_span_attribute(span, "gen_ai.usage.input_tokens", prompt_tokens) + + completion_tokens = get_value(usage, "completion_tokens") + if completion_tokens is not None: + set_span_attribute(span, "gen_ai.usage.output_tokens", completion_tokens) + + total_tokens = get_value(usage, "total_tokens") + if total_tokens is not None: + set_span_attribute(span, "gen_ai.usage.total_tokens", total_tokens) + except Exception: + # Don't fail if we can't extract token usage + pass + + +def record_response_metadata( + span: Any, response: Any, model_id: str | None = None +) -> None: + """Record response metadata following Gen-AI semantic conventions. + + Args: + span: The span object (may be None if tracing is disabled) + response: Response object or dict from the LLM + model_id: Model ID used for the response (if different from request) + """ + if span is None or response is None: + return + + try: + # Helper to get values from both objects and dicts + def get_value(obj, key): + if isinstance(obj, dict): + return obj.get(key) + return getattr(obj, key, None) + + # Record the actual model used in the response (may differ from request) + if model_id: + set_span_attribute(span, "gen_ai.response.model", model_id) + else: + model = get_value(response, "model") + if model: + set_span_attribute(span, "gen_ai.response.model", model) + + # Record finish reason + choices = get_value(response, "choices") + if choices and len(choices) > 0: + choice = choices[0] if isinstance(choices, list) else choices + finish_reason = get_value(choice, "finish_reason") + if finish_reason: + set_span_attribute( + span, "gen_ai.response.finish_reasons", [finish_reason] + ) + + # Record response ID if available + response_id = get_value(response, "id") + if response_id: + set_span_attribute(span, "gen_ai.response.id", response_id) + except Exception: + # Don't fail if we can't extract response metadata + pass + + __all__ = [ "get_context_size", "get_model_id_str", + "get_system_name", "instrument_generate_from_context", "instrument_generate_from_raw", + "record_response_metadata", + "record_token_usage", ] diff --git a/test/telemetry/test_backend_telemetry.py b/test/telemetry/test_backend_telemetry.py new file mode 100644 index 00000000..428e0eb3 --- /dev/null +++ b/test/telemetry/test_backend_telemetry.py @@ -0,0 +1,324 @@ +"""Unit tests for backend telemetry instrumentation with Gen-AI semantic conventions.""" + +import asyncio +import time +from unittest.mock import MagicMock, patch + +import pytest + +from mellea.backends.ollama import OllamaModelBackend +from mellea.stdlib.components import Message +from mellea.stdlib.context import SimpleContext + +# Check if OpenTelemetry is available +try: + 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, + ) + + OTEL_AVAILABLE = True +except ImportError: + OTEL_AVAILABLE = False + +pytestmark = pytest.mark.skipif( + not OTEL_AVAILABLE, reason="OpenTelemetry not installed" +) + + +@pytest.fixture(scope="session", autouse=True) +def setup_telemetry(): + """Set up telemetry for all tests in this module.""" + import os + + # Enable backend tracing before any imports + os.environ["MELLEA_TRACE_BACKEND"] = "true" + + +@pytest.fixture +def span_exporter(): + """Create an in-memory span exporter for testing.""" + # Import mellea.telemetry to ensure it's initialized + from mellea import telemetry + + # Get the real tracer provider from mellea.telemetry module + # The global trace.get_tracer_provider() returns a ProxyTracerProvider + provider = telemetry._tracer_provider + + if provider is None: + pytest.skip("Telemetry not initialized") + + # Add our in-memory exporter to it + exporter = InMemorySpanExporter() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + + yield exporter + exporter.clear() + + +@pytest.mark.asyncio +async def test_span_duration_captures_async_operation(span_exporter, gh_run): + """Test that span duration includes the full async operation time.""" + if gh_run: + pytest.skip("Skipping in CI - requires Ollama") + + backend = OllamaModelBackend(model_id="llama3.2:1b") + ctx = SimpleContext() + ctx = ctx.add(Message(role="user", content="Say 'test' and nothing else")) + + # Add a small delay to ensure measurable duration + start_time = time.time() + mot, _ = await backend.generate_from_context( + Message(role="assistant", content=""), ctx + ) + await mot.avalue() # Wait for async completion + end_time = time.time() + actual_duration = end_time - start_time + + # Force flush to ensure spans are exported + trace.get_tracer_provider().force_flush() # type: ignore + + # Get the recorded span + spans = span_exporter.get_finished_spans() + assert len(spans) > 0, "No spans were recorded" + + backend_span = None + for span in spans: + if span.name == "chat": + backend_span = span + break + + assert backend_span is not None, "Backend span not found" + + # Span duration should be close to actual duration (within 100ms tolerance) + span_duration_ns = backend_span.end_time - backend_span.start_time + span_duration_s = span_duration_ns / 1e9 + + assert span_duration_s >= 0.1, ( + f"Span duration too short: {span_duration_s}s (expected >= 0.1s)" + ) + assert abs(span_duration_s - actual_duration) < 0.5, ( + f"Span duration {span_duration_s}s differs significantly from actual {actual_duration}s" + ) + + +@pytest.mark.asyncio +async def test_context_propagation_parent_child(span_exporter, gh_run): + """Test that parent-child span relationships are maintained.""" + if gh_run: + pytest.skip("Skipping in CI - requires Ollama") + + backend = OllamaModelBackend(model_id="llama3.2:1b") + ctx = SimpleContext() + ctx = ctx.add(Message(role="user", content="Say 'test' and nothing else")) + + # Create a parent span + tracer = trace.get_tracer(__name__) + with tracer.start_as_current_span("parent_operation"): + mot, _ = await backend.generate_from_context( + Message(role="assistant", content=""), ctx + ) + await mot.avalue() # Wait for async completion + + # Get the recorded spans + spans = span_exporter.get_finished_spans() + assert len(spans) >= 2, f"Expected at least 2 spans, got {len(spans)}" + + # Find parent and child spans + parent_recorded = None + child_recorded = None + + for span in spans: + if span.name == "parent_operation": + parent_recorded = span + elif span.name == "chat": # Gen-AI convention + child_recorded = span + + assert parent_recorded is not None, "Parent span not found" + assert child_recorded is not None, "Child span not found" + + # Verify parent-child relationship + assert child_recorded.parent is not None, "Child span has no parent context" + assert child_recorded.parent.span_id == parent_recorded.context.span_id, ( + "Child span parent ID doesn't match parent span ID" + ) + assert child_recorded.context.trace_id == parent_recorded.context.trace_id, ( + "Child and parent have different trace IDs" + ) + + +@pytest.mark.asyncio +async def test_token_usage_recorded_after_completion(span_exporter, gh_run): + """Test that token usage metrics are recorded after async completion.""" + if gh_run: + pytest.skip("Skipping in CI - requires Ollama") + + backend = OllamaModelBackend(model_id="llama3.2:1b") + ctx = SimpleContext() + ctx = ctx.add(Message(role="user", content="Say 'test' and nothing else")) + + mot, _ = await backend.generate_from_context( + Message(role="assistant", content=""), ctx + ) + await mot.avalue() # Wait for async completion + + # Get the recorded span + spans = span_exporter.get_finished_spans() + assert len(spans) > 0, "No spans were recorded" + + # Debug: print all span names + print(f"\nRecorded spans: {[s.name for s in spans]}") + + backend_span = None + for span in spans: + if span.name == "chat": # Gen-AI convention uses 'chat' for chat completions + backend_span = span + break + + assert backend_span is not None, ( + f"Backend span not found. Available spans: {[s.name for s in spans]}" + ) + + # Check for Gen-AI semantic convention attributes + attributes = dict(backend_span.attributes) + + # Verify Gen-AI attributes are present + assert "gen_ai.system" in attributes, "gen_ai.system attribute missing" + assert attributes["gen_ai.system"] == "ollama", "Incorrect system name" + + assert "gen_ai.request.model" in attributes, ( + "gen_ai.request.model attribute missing" + ) + + # Token usage should be recorded (if available from backend) + # Note: Not all backends provide token counts + if "gen_ai.usage.input_tokens" in attributes: + assert attributes["gen_ai.usage.input_tokens"] > 0, "Input tokens should be > 0" + + if "gen_ai.usage.output_tokens" in attributes: + assert attributes["gen_ai.usage.output_tokens"] > 0, ( + "Output tokens should be > 0" + ) + + +@pytest.mark.asyncio +async def test_span_not_closed_prematurely(span_exporter, gh_run): + """Test that spans are not closed before async operations complete.""" + if gh_run: + pytest.skip("Skipping in CI - requires Ollama") + + backend = OllamaModelBackend(model_id="llama3.2:1b") + ctx = SimpleContext() + ctx = ctx.add(Message(role="user", content="Count to 5")) + + mot, _ = await backend.generate_from_context( + Message(role="assistant", content=""), ctx + ) + + # At this point, the span should still be open (not in exporter yet) + # because we haven't awaited the ModelOutputThunk + spans_before = span_exporter.get_finished_spans() + backend_spans_before = [ + s for s in spans_before if s.name == "chat" + ] # Gen-AI convention + + # Now complete the async operation + await mot.avalue() + + # Now the span should be closed + spans_after = span_exporter.get_finished_spans() + backend_spans_after = [ + s for s in spans_after if s.name == "chat" + ] # Gen-AI convention + + # The span should only appear after completion + assert len(backend_spans_after) > len(backend_spans_before), ( + "Span was closed before async completion" + ) + + +@pytest.mark.asyncio +async def test_multiple_generations_separate_spans(span_exporter, gh_run): + """Test that multiple generations create separate spans.""" + if gh_run: + pytest.skip("Skipping in CI - requires Ollama") + + backend = OllamaModelBackend(model_id="llama3.2:1b") + ctx = SimpleContext() + ctx = ctx.add(Message(role="user", content="Say 'test'")) + + # Generate twice + mot1, _ = await backend.generate_from_context( + Message(role="assistant", content=""), ctx + ) + await mot1.avalue() + + mot2, _ = await backend.generate_from_context( + Message(role="assistant", content=""), ctx + ) + await mot2.avalue() + + # Get the recorded spans + spans = span_exporter.get_finished_spans() + backend_spans = [s for s in spans if s.name == "chat"] # Gen-AI convention + + assert len(backend_spans) >= 2, ( + f"Expected at least 2 spans, got {len(backend_spans)}" + ) + + # Verify spans have different span IDs + span_ids = {s.context.span_id for s in backend_spans} + assert len(span_ids) >= 2, "Spans should have unique IDs" + + +@pytest.mark.asyncio +async def test_streaming_span_duration(span_exporter, gh_run): + """Test that streaming operations have accurate span durations.""" + if gh_run: + pytest.skip("Skipping in CI - requires Ollama") + + from mellea.backends.model_options import ModelOption + + backend = OllamaModelBackend(model_id="llama3.2:1b") + ctx = SimpleContext() + ctx = ctx.add(Message(role="user", content="Count to 3")) + + start_time = time.time() + mot, _ = await backend.generate_from_context( + Message(role="assistant", content=""), + ctx, + model_options={ModelOption.STREAM: True}, + ) + + # Consume the stream + await mot.astream() + await mot.avalue() + + end_time = time.time() + actual_duration = end_time - start_time + + # Get the recorded span + spans = span_exporter.get_finished_spans() + backend_span = None + for span in spans: + if span.name == "chat": # Gen-AI convention + backend_span = span + break + + assert backend_span is not None, "Backend span not found" + + # Span duration should include streaming time + span_duration_ns = backend_span.end_time - backend_span.start_time + span_duration_s = span_duration_ns / 1e9 + + assert span_duration_s >= 0.1, ( + f"Span duration too short for streaming: {span_duration_s}s" + ) + assert abs(span_duration_s - actual_duration) < 0.5, ( + f"Streaming span duration {span_duration_s}s differs from actual {actual_duration}s" + ) + + +# Made with Bob From c4fa3113665575c806279e9930f6152bce670215 Mon Sep 17 00:00:00 2001 From: Akihiko Kuroda Date: Thu, 29 Jan 2026 08:50:00 -0500 Subject: [PATCH 5/6] CI test error fix Signed-off-by: Akihiko Kuroda --- mellea/stdlib/components/chat.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/mellea/stdlib/components/chat.py b/mellea/stdlib/components/chat.py index 1a1ae58e..25734dc8 100644 --- a/mellea/stdlib/components/chat.py +++ b/mellea/stdlib/components/chat.py @@ -109,11 +109,13 @@ def _parse(self, computed: ModelOutputThunk) -> "Message": elif "oai_chat_response" in computed._meta: # OpenAI and Watsonx. return Message( - role=computed._meta["oai_chat_response"]["message"]["role"], + role=computed._meta["oai_chat_response"]["choices"][0]["message"][ + "role" + ], content=str( - computed._meta["oai_chat_response"]["message"].get( - "tool_calls", [] - ) + computed._meta["oai_chat_response"]["choices"][0][ + "message" + ].get("tool_calls", []) ), ) else: @@ -130,8 +132,12 @@ def _parse(self, computed: ModelOutputThunk) -> "Message": ) elif "oai_chat_response" in computed._meta: return Message( - role=computed._meta["oai_chat_response"]["message"]["role"], - content=computed._meta["oai_chat_response"]["message"]["content"], + role=computed._meta["oai_chat_response"]["choices"][0]["message"][ + "role" + ], + content=computed._meta["oai_chat_response"]["choices"][0]["message"][ + "content" + ], ) else: assert computed.value is not None From 6f60c3e9624be7a41e6def00b64bc2b98937f51e Mon Sep 17 00:00:00 2001 From: Akihiko Kuroda Date: Fri, 30 Jan 2026 08:58:15 -0500 Subject: [PATCH 6/6] review comments Signed-off-by: Akihiko Kuroda --- mellea/backends/ollama.py | 29 ++++---- mellea/backends/vllm.py | 77 +++++++++++---------- mellea/core/base.py | 139 ++++++++++++++++++++------------------ pyproject.toml | 14 ++-- uv.lock | 26 ++++--- 5 files changed, 158 insertions(+), 127 deletions(-) diff --git a/mellea/backends/ollama.py b/mellea/backends/ollama.py index 900a10e2..dde0e84a 100644 --- a/mellea/backends/ollama.py +++ b/mellea/backends/ollama.py @@ -446,20 +446,23 @@ async def generate_from_raw( # Ollama doesn't support "batching". There's some ability for concurrency. Use that here. # See https://github.com/ollama/ollama/blob/main/docs/faq.md#how-does-ollama-handle-concurrent-requests. - # Run async so that we can make use of Ollama's concurrency. - coroutines: list[Coroutine[Any, Any, ollama.GenerateResponse]] = [] - for prompt in prompts: - co = self._async_client.generate( - model=self._get_ollama_model_id(), - prompt=prompt, - raw=True, - think=model_opts.get(ModelOption.THINKING, None), - format=format.model_json_schema() if format is not None else None, # type: ignore - options=self._make_backend_specific_and_remove(model_opts), - ) - coroutines.append(co) + with instrument_generate_from_raw( + backend=self, num_actions=len(actions), format=format, tool_calls=tool_calls + ): + # Run async so that we can make use of Ollama's concurrency. + coroutines: list[Coroutine[Any, Any, ollama.GenerateResponse]] = [] + for prompt in prompts: + co = self._async_client.generate( + model=self._get_ollama_model_id(), + prompt=prompt, + raw=True, + think=model_opts.get(ModelOption.THINKING, None), + format=format.model_json_schema() if format is not None else None, # type: ignore + options=self._make_backend_specific_and_remove(model_opts), + ) + coroutines.append(co) - responses = await asyncio.gather(*coroutines, return_exceptions=True) + responses = await asyncio.gather(*coroutines, return_exceptions=True) results = [] date = datetime.datetime.now() diff --git a/mellea/backends/vllm.py b/mellea/backends/vllm.py index 4ba2efe3..b37d3d89 100644 --- a/mellea/backends/vllm.py +++ b/mellea/backends/vllm.py @@ -457,50 +457,57 @@ async def generate_from_raw( tool_calls: bool = False, ) -> list[ModelOutputThunk]: """Generate using the completions api. Gives the input provided to the model without templating.""" - await self.do_generate_walks(list(actions)) + from ..telemetry.backend_instrumentation import instrument_generate_from_raw - if tool_calls: - FancyLogger.get_logger().warning( - "The completion endpoint does not support tool calling at the moment." - ) + with instrument_generate_from_raw( + backend=self, num_actions=len(actions), format=format, tool_calls=tool_calls + ): + await self.do_generate_walks(list(actions)) - model_options = self._simplify_and_merge(model_options) + if tool_calls: + FancyLogger.get_logger().warning( + "The completion endpoint does not support tool calling at the moment." + ) - prompts = [self.formatter.print(action) for action in actions] + model_options = self._simplify_and_merge(model_options) - sampling_params = vllm.SamplingParams( - **self._make_backend_specific_and_remove( - model_options, vllm.SamplingParams - ), - output_kind=vllm.sampling_params.RequestOutputKind.FINAL_ONLY, # returns only the final results # type: ignore - ) + prompts = [self.formatter.print(action) for action in actions] - if format is not None: - schema: dict[str, Any] = format.model_json_schema() # type: ignore - schema_json: str = json.dumps(schema) - regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore - schema_json # type: ignore - ) # type: ignore + sampling_params = vllm.SamplingParams( + **self._make_backend_specific_and_remove( + model_options, vllm.SamplingParams + ), + output_kind=vllm.sampling_params.RequestOutputKind.FINAL_ONLY, # returns only the final results # type: ignore + ) - from outlines.processors import RegexLogitsProcessor # type: ignore + if format is not None: + schema: dict[str, Any] = format.model_json_schema() # type: ignore + schema_json: str = json.dumps(schema) + regex_str: str = outlines_core.fsm.json_schema.build_regex_from_schema( # type: ignore + schema_json # type: ignore + ) # type: ignore - logits_processor = RegexLogitsProcessor( - regex_str, - tokenizer=self._tokenizer_for_outlines, # type: ignore - ) - sampling_params.logits_processors = ( - [logits_processor] if logits_processor is not None else [] - ) + from outlines.processors import RegexLogitsProcessor # type: ignore - async def generate(prompt, request_id): - async for result_output in self._model.generate( - request_id=request_id, prompt=prompt, sampling_params=sampling_params - ): - assert result_output.finished - return result_output.outputs[0].text + logits_processor = RegexLogitsProcessor( + regex_str, + tokenizer=self._tokenizer_for_outlines, # type: ignore + ) + sampling_params.logits_processors = ( + [logits_processor] if logits_processor is not None else [] + ) + + async def generate(prompt, request_id): + async for result_output in self._model.generate( + request_id=request_id, + prompt=prompt, + sampling_params=sampling_params, + ): + assert result_output.finished + return result_output.outputs[0].text - tasks = [generate(p, f"{id(prompts)}-{i}") for i, p in enumerate(prompts)] - decoded_results = await asyncio.gather(*tasks) + tasks = [generate(p, f"{id(prompts)}-{i}") for i, p in enumerate(prompts)] + decoded_results = await asyncio.gather(*tasks) results = [ModelOutputThunk(value=text) for text in decoded_results] diff --git a/mellea/core/base.py b/mellea/core/base.py index 9c601bbe..a94661ea 100644 --- a/mellea/core/base.py +++ b/mellea/core/base.py @@ -276,73 +276,84 @@ async def astream(self) -> str: f"Cannot use `ModelOutputThunk.astream()` when the generate function is using `{self._generate_type.name}`" ) - # Type of the chunk depends on the backend. - chunks: list[Any | None] = [] - while True: - try: - item = self._async_queue.get_nowait() + exception_to_raise = None + try: + # Type of the chunk depends on the backend. + chunks: list[Any | None] = [] + while True: + try: + item = self._async_queue.get_nowait() + chunks.append(item) + except asyncio.QueueEmpty: + # We've exhausted the current items in the queue. + break + + # Make sure we always get the minimum chunk size. + while len(chunks) <= self._chunk_size: + if len(chunks) > 0: + if chunks[-1] is None or isinstance(chunks[-1], Exception): + break # Hit sentinel value or an error. + # We could switch to relying on the `done` / `finish_reason` field of chunks, + # but that forces us to know about the chunk type here. Prefer sentinel values + # for now. + + item = await self._async_queue.get() chunks.append(item) - except asyncio.QueueEmpty: - # We've exhausted the current items in the queue. - break - - # Make sure we always get the minimum chunk size. - while len(chunks) <= self._chunk_size: - if len(chunks) > 0: - if chunks[-1] is None or isinstance(chunks[-1], Exception): - break # Hit sentinel value or an error. - # We could switch to relying on the `done` / `finish_reason` field of chunks, - # but that forces us to know about the chunk type here. Prefer sentinel values - # for now. - - item = await self._async_queue.get() - chunks.append(item) - - # Process the sentinel value if it's there. - if chunks[-1] is None: - chunks.pop() # Remove the sentinel value. - self._computed = True - - # Shouldn't be needed, but cancel the Tasks this ModelOutputThunk relied on. - if self._generate is not None: - self._generate.cancel() - if self._generate_extra is not None: - # Covers an hf edge case. The task is done generating anything useful but isn't `done` yet. - await self._generate_extra - self._generate_extra.cancel() - - # If ModelOutputThunks get too bulky, we can do additional cleanup here - # and set fields to None. - - elif isinstance(chunks[-1], Exception): - # For now, just re-raise the exception. - # It's possible that we hit this error after already streaming some - # chunks. We should investigate allowing recovery in the future. - raise chunks[-1] - - for chunk in chunks: - assert self._process is not None - await self._process(self, chunk) - if self._computed: - assert self._post_process is not None - await self._post_process(self) - - match self._action: - case Component(): - self.parsed_repr = self._action._parse(self) - case CBlock(): - assert self.value is not None, ( - "value must be non-None since this thunk is computed" - ) - self.parsed_repr = self.value # type: ignore - case _: - raise ValueError( - "attempted to astream from a model output thunk with no ._action set" + # Process the sentinel value if it's there. + if chunks[-1] is None: + chunks.pop() # Remove the sentinel value. + self._computed = True + + # Shouldn't be needed, but cancel the Tasks this ModelOutputThunk relied on. + if self._generate is not None: + self._generate.cancel() + if self._generate_extra is not None: + # Covers an hf edge case. The task is done generating anything useful but isn't `done` yet. + await self._generate_extra + self._generate_extra.cancel() + + # If ModelOutputThunks get too bulky, we can do additional cleanup here + # and set fields to None. + + elif isinstance(chunks[-1], Exception): + # Mark as computed so post_process runs in finally block + self._computed = True + # Store exception to re-raise after cleanup + exception_to_raise = chunks[-1] + + for chunk in chunks: + assert self._process is not None + await self._process(self, chunk) + + finally: + # Always call post_process if computed, even on exception + # This ensures telemetry spans are properly closed + if self._computed: + assert self._post_process is not None + await self._post_process(self) + + # Only parse if no exception occurred + if exception_to_raise is None: + match self._action: + case Component(): + self.parsed_repr = self._action._parse(self) + case CBlock(): + assert self.value is not None, ( + "value must be non-None since this thunk is computed" + ) + self.parsed_repr = self.value # type: ignore + case _: + raise ValueError( + "attempted to astream from a model output thunk with no ._action set" + ) + assert self.parsed_repr is not None, ( + "enforce constraint that a computed ModelOutputThunk has a non-None parsed_repr" ) - assert self.parsed_repr is not None, ( - "enforce constraint that a computed ModelOutputThunk has a non-None parsed_repr" - ) + + # Re-raise exception after cleanup if one occurred + if exception_to_raise is not None: + raise exception_to_raise return self._underlying_value # type: ignore diff --git a/pyproject.toml b/pyproject.toml index eb58ac03..e8251c3f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,10 +46,6 @@ dependencies = [ "math_verify", # Needed for Majority Voting Sampling Strategies. "rouge_score", # Needed for Majority Voting Sampling Strategies. "llm-sandbox[docker]>=0.3.23", - "opentelemetry-api>=1.20.0", - "opentelemetry-sdk>=1.20.0", - "opentelemetry-exporter-otlp>=1.20.0", - "opentelemetry-distro>=0.59b0", ] [project.scripts] @@ -99,11 +95,19 @@ litellm = [ watsonx = [ "ibm-watsonx-ai>=1.3.31", ] + docling = [ "docling>=2.45.0", ] -all = ["mellea[watsonx,docling,hf,vllm,litellm]"] +telemetry = [ + "opentelemetry-api>=1.20.0", + "opentelemetry-sdk>=1.20.0", + "opentelemetry-exporter-otlp>=1.20.0", + "opentelemetry-distro>=0.59b0", +] + +all = ["mellea[watsonx,docling,hf,vllm,litellm,telemetry]"] [dependency-groups] # Use these like: diff --git a/uv.lock b/uv.lock index 02ad8f5c..ffdf6d17 100644 --- a/uv.lock +++ b/uv.lock @@ -3388,10 +3388,6 @@ dependencies = [ { name = "mistletoe" }, { name = "ollama" }, { name = "openai" }, - { name = "opentelemetry-api" }, - { name = "opentelemetry-distro" }, - { name = "opentelemetry-exporter-otlp" }, - { name = "opentelemetry-sdk" }, { name = "pillow" }, { name = "pydantic" }, { name = "requests" }, @@ -3413,6 +3409,10 @@ all = [ { name = "ibm-watsonx-ai", version = "1.4.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, { name = "litellm" }, { name = "numpy" }, + { name = "opentelemetry-api" }, + { name = "opentelemetry-distro" }, + { name = "opentelemetry-exporter-otlp" }, + { name = "opentelemetry-sdk" }, { name = "outlines" }, { name = "outlines-core" }, { name = "peft" }, @@ -3437,6 +3437,12 @@ hf = [ litellm = [ { name = "litellm" }, ] +telemetry = [ + { name = "opentelemetry-api" }, + { name = "opentelemetry-distro" }, + { name = "opentelemetry-exporter-otlp" }, + { name = "opentelemetry-sdk" }, +] vllm = [ { name = "numpy" }, { name = "outlines-core" }, @@ -3496,15 +3502,15 @@ requires-dist = [ { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.76" }, { name = "llm-sandbox", extras = ["docker"], specifier = ">=0.3.23" }, { name = "math-verify" }, - { name = "mellea", extras = ["watsonx", "docling", "hf", "vllm", "litellm"], marker = "extra == 'all'" }, + { name = "mellea", extras = ["watsonx", "docling", "hf", "vllm", "litellm", "telemetry"], marker = "extra == 'all'" }, { name = "mistletoe", specifier = ">=1.4.0" }, { name = "numpy", marker = "extra == 'vllm'", specifier = "<2.0.0" }, { name = "ollama", specifier = ">=0.5.1" }, { name = "openai" }, - { name = "opentelemetry-api", specifier = ">=1.20.0" }, - { name = "opentelemetry-distro", specifier = ">=0.59b0" }, - { name = "opentelemetry-exporter-otlp", specifier = ">=1.20.0" }, - { name = "opentelemetry-sdk", specifier = ">=1.20.0" }, + { name = "opentelemetry-api", marker = "extra == 'telemetry'", specifier = ">=1.20.0" }, + { name = "opentelemetry-distro", marker = "extra == 'telemetry'", specifier = ">=0.59b0" }, + { name = "opentelemetry-exporter-otlp", marker = "extra == 'telemetry'", specifier = ">=1.20.0" }, + { name = "opentelemetry-sdk", marker = "extra == 'telemetry'", specifier = ">=1.20.0" }, { name = "outlines", marker = "extra == 'hf'" }, { name = "outlines-core", marker = "extra == 'hf'", specifier = "==0.1.26" }, { name = "outlines-core", marker = "extra == 'vllm'", specifier = "==0.1.26" }, @@ -3522,7 +3528,7 @@ requires-dist = [ { name = "uvicorn" }, { name = "vllm", marker = "extra == 'vllm'", specifier = ">=0.9.1" }, ] -provides-extras = ["hf", "vllm", "litellm", "watsonx", "docling", "all"] +provides-extras = ["hf", "vllm", "litellm", "watsonx", "docling", "telemetry", "all"] [package.metadata.requires-dev] dev = [