[translator] clean-up deployment and install translator from pypi

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/traceloop/Dockerfile.tl → instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/traceloop/Dockerfile

@@ -11,13 +11,13 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy required packages (build context is repo root)
-COPY util/opentelemetry-util-genai-traceloop-translator /app/opentelemetry-util-genai-traceloop-translator
+# COPY util/opentelemetry-util-genai-traceloop-translator /app/opentelemetry-util-genai-traceloop-translator
 COPY instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner /app/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner
 
 WORKDIR /app/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/traceloop
 
 # Install Python dependencies (excluding local -e packages and git+ssh dependencies)
-RUN pip install --no-cache-dir -r requirements.traceloop.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
 # Verify packages are installed correctly
 RUN python3 -c "from opentelemetry.util.genai.handler import get_telemetry_handler; print('✓ GenAI handler available')" && \
@@ -28,7 +28,7 @@ RUN python3 -c "from opentelemetry.util.genai.handler import get_telemetry_handl
     python3 -c "from opentelemetry.util.genai.traceloop import enable_traceloop_translator; print('✓ Traceloop translator available')"
 
 # Make the script executable
-RUN chmod +x main_traceloop.py
+RUN chmod +x main.py
 
 # Set default environment variables
 ENV OTEL_PYTHON_LOG_CORRELATION=true \
@@ -37,4 +37,4 @@ ENV OTEL_PYTHON_LOG_CORRELATION=true \
     PYTHONUNBUFFERED=1
 
 # Run the Traceloop version
-CMD ["python3", "main_traceloop.py"]
+CMD ["python3", "main.py"]

instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/traceloop/README.md

@@ -0,0 +1,138 @@
+# Multi-Agent Travel Planner (Traceloop translator + OpenTelemetry)
+
+This example shows a small team of LangChain agents connected by a LangGraph state machine to produce a week-long city break itinerary (flights, hotel, activities, synthesis). It demonstrates GenAI observability (traces, metrics, logs, evaluation metrics) using the Traceloop SDK. 
+
+Also, for [more info](https://help.splunk.com/en/splunk-observability-cloud/observability-for-ai/instrument-an-ai-application/collect-data-from-traceloop-instrumented-ai-applications)
+
+## 1. Architecture Overview
+
+Agents (ReAct-style):
+
+1. `coordinator` – Interprets traveller request and outlines a plan.
+2. `flight_specialist` – Suggests flight option (tool: `mock_search_flights`).
+3. `hotel_specialist` – Recommends hotel (tool: `mock_search_hotels`).
+4. `activity_specialist` – Curates activities (tool: `mock_search_activities`).
+5. `plan_synthesizer` – Produces final structured itinerary.
+
+Note: for LangGraph nodes to be recognized as `AgentInvocation` by the instrumentation, it has to have the following configuration
+
+- `agent_name` metadata in the LangGraph config, i.e. 
+
+```python
+_create_react_agent(llm, tools=[]).with_config(
+    {
+        "metadata": {
+            "agent_name": "coordinator",
+        },
+    }
+```
+
+- `agent` tags in the config, i.e 
+
+```python
+_create_react_agent(llm, tools=[]).with_config(
+    {
+        "tags": ["agent:coordinator"],
+    }
+```
+
+See more example in `main.py` example.
+
+LangGraph `StateGraph` drives transitions; `should_continue` moves through sequence until `END`. State (`PlannerState`) accumulates messages, per-agent summaries, poison events, and the final itinerary.
+
+```text
+[User Request] --> Pre-Parse (origin/dest/dates) --> START
+							 |
+							 v
+					 LangGraph Workflow
+	    +-------------+----------+-------------+---------------+
+	    |             |          |             |               |
+ [Coordinator] -> [Flight] -> [Hotel] -> [Activities] -> [Synthesizer] -> END
+	    |             |          |             |               |
+	    +-------------+----------+-------------+---------------+
+```
+
+## 2. Prompt Poisoning (Quality Noise)
+
+To exercise evaluation dimensions, the app can inject mild quality-degrading snippets into agent prompts (hallucination, bias, irrelevance, negative sentiment, toxicity). Controlled by env vars:
+
+| Variable | Default | Purpose |
+|----------|---------|---------|
+| `TRAVEL_POISON_PROB` | `0.8` | Probability a step is poisoned |
+| `TRAVEL_POISON_TYPES` | `hallucination,bias,irrelevance,negative_sentiment,toxicity` | Allowed poison types |
+| `TRAVEL_POISON_MAX` | `2` | Max snippets injected per step |
+| `TRAVEL_POISON_SEED` | (unset) | Deterministic seed |
+
+Injected kinds recorded in `state['poison_events']` and can surface evaluation metrics (bias, relevance, hallucination, sentiment, toxicity). Snippets are intentionally mild and safe.
+
+## 3. Local Setup
+
+```bash
+cd instrumentation-genai/opentelemetry-instrumentation-langchain/examples/multi_agent_travel_planner/traceloop
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+modify `.env`, change there or export your `OPENAI_API_KEY`. Run the app
+
+Evaluation metrics appear on `model/LLM` spans allowing correlation with injected poison events. These synthetic spans are added in `on_end()` lifecycle of the span using the `TelemetryHandelr` in `splunk-otel-util-genai-translator-traceloop`
+
+## 4. Docker Usage
+
+The `Dockerfile` installs dependencies and sets `CMD ["python3", "main.py"]`.
+
+Build image:
+
+```bash
+docker build -t travel-planner-tl:latest .
+```
+
+Run manual mode:
+
+```bash
+docker run --rm -e OPENAI_API_KEY=$OPENAI_API_KEY \
+  -e OTEL_EXPORTER_OTLP_ENDPOINT=http://host.docker.internal:4317 \
+  travel-planner-tl:latest python main.py
+```
+
+## 7. Kubernetes Deployment
+
+Example `cronjob.yaml` runs this demo as a cron job workload
+
+## 8. Core Environment Variables
+
+| Variable | Purpose |
+|----------|---------|
+| `OPENAI_API_KEY` | Authenticates ChatOpenAI |
+| `OPENAI_MODEL` | Chooses model (default fallback in code) |
+| `OTEL_EXPORTER_OTLP_ENDPOINT` | Collector endpoint (gRPC) |
+| `OTEL_EXPORTER_OTLP_PROTOCOL` | Usually `grpc` |
+| `OTEL_SERVICE_NAME` | Service identity |
+| `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` | Capture prompt & output content |
+| `TRAVEL_POISON_*` | Controls prompt noise injection |
+
+## 9. Observing Evaluation Metrics
+
+Metrics & logs exported (names may include operation suffixes):
+
+- `gen_ai.evaluation.hallucination`
+- `gen_ai.evaluation.bias`
+- `gen_ai.evaluation.relevance`
+- `gen_ai.evaluation.sentiment`
+- `gen_ai.evaluation.toxicity`
+
+Higher scores often correlate with injected poison snippets.
+
+## 10. Key Files
+
+| File | Purpose |
+|------|---------|
+| `main.py` | Workflow, poisoning, optional manual instrumentation setup |
+| `Dockerfile` | Container build (editable installs + example requirements) |
+| `cronjob.yaml` | Example CronJob manifest (modify for two flavors) |
+| `requirements.txt` | Python dependencies for the sample |
+
+## 11. Disclaimer
+
+Poison snippets are intentionally mild, non-harmful, and used solely to trigger evaluation telemetry for demonstration. Not production travel advice.