phoenix-tracing

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## Data Masking (PII Protection) **Environment variables:** ```bash export OPENINFERENCE_HIDE_INPUTS=true # Hide input.value export OPENINFERENCE_HIDE_OUTPUTS=true # Hide output.value export OPENINFERENCE_HIDE_INPUT_MESSAGES=true # Hide LLM input messages export OPENINFERENCE_HIDE_OUTPUT_MESSAGES=true # Hide LLM output messages export OPENINFERENCE_HIDE_INPUT_IMAGES=true # Hide image content export OPENINFERENCE_HIDE_INPUT_TEXT=true # Hide embedding text export OPENINFE

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Tool: rules/fundamentals-universal-attributes.md Description: # Universal Attributes This document covers attributes that can be used on any span kind in OpenInference.

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## Supported Frameworks **Python:** - LLM SDKs: OpenAI, Anthropic, Bedrock, Mistral, Vertex AI, Groq, Ollama - Frameworks: LangChain, LlamaIndex, DSPy, CrewAI, Instructor, Haystack - Install: `pip install openinference-instrumentation-{name}` ## Setup **Install and enable:** ```bash pip install arize-phoenix-otel pip install openinference-instrumentation-openai # Add others as needed ``` ```python from phoenix.otel import register register(project_name="my-app", auto_instrument=True) # Discove

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Tool: rules/instrumentation-auto-typescript.md Description: # Auto-Instrumentation (TypeScript) Automatically create spans for LLM calls without code changes.

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Tool: rules/instrumentation-manual-python.md Description: # Manual Instrumentation (Python) Add custom spans using decorators or context managers for fine-grained tracing control.

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``` ## Combined (using_attributes) ```python from openinference.instrumentation import using_attributes with using_attributes( session_id="my-session-id", user_id="my-user-id", metadata={"environment": "production"}, tags=["prod", "v2"], prompt_template="Answer: {question}", prompt_template_version="v1.0", prompt_template_variables={"question": "What is Phoenix?"}, ): # All attributes applied to spans in this context ...

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Tool: rules/metadata-typescript.md Description: # Phoenix Tracing: Custom Metadata (TypeScript) Add custom attributes to spans for richer observability.

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Tool: rules/production-typescript.md Description: # Phoenix Tracing: Production Guide (TypeScript) **CRITICAL: Configure batching, data masking, and span filtering for production deployment.** ## Metadata | Attribute | Value | |-----------|-------| | Priority | Critical - production readiness | | Impact | Security, Performance | | Setup Time | 5-15 min | ## Batch Processing **Enable batch processing for production efficiency.** Batching reduces network overhead by sending spans in groups rather

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Tool: rules/projects-python.md Description: # Phoenix Tracing: Projects (Python) **Organize traces by application using projects (Phoenix's top-level grouping).** ## Overview Projects group traces for a single application or experiment.

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Query traces by `session.id` in Phoenix to see all interactions ## Implementation (Best Practice) ### 1. Setup (instrumentation.ts) ```typescript import { register } from "@arizeai/phoenix-otel"; import { randomUUID } from "node:crypto"; // Initialize Phoenix register({ projectName: "your-app", url: process.env.PHOENIX_COLLECTOR_ENDPOINT || "http://localhost:6006", apiKey: process.env.PHOENIX_API_KEY, batch: true, }); // Generate and export session ID export const SESSION_ID = randomUUID(); ```

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Configure via environment variables: ```bash export OTEL_BSP_SCHEDULE_DELAY=5000 # Batch every 5s export OTEL_BSP_MAX_QUEUE_SIZE=2048 # Queue 2048 spans export OTEL_BSP_MAX_EXPORT_BATCH_SIZE=512 # Send 512 spans/batch ``` **Link:** https://opentelemetry.io/docs/specs/otel/configuration/sdk-environment-variables/ ## Verification 1.

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## Configuration ```typescript import { register } from "@arizeai/phoenix-otel"; register({ projectName: "my-app", url: "http://localhost:6006", apiKey: process.env.PHOENIX_API_KEY, batch: true }); ``` **Environment variables:** ```bash export PHOENIX_API_KEY="your-api-key" export PHOENIX_COLLECTOR_ENDPOINT="http://localhost:6006" export PHOENIX_PROJECT_NAME="my-app" ``` ## ESM vs CommonJS **CommonJS (automatic):** ```javascript const { register } = require("@arizeai/phoenix-otel"); register({ p

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Tool: rules/span-chain.md Description: # CHAIN Spans ## Purpose CHAIN spans represent orchestration layers in your application (LangChain chains, custom workflows, application entry points).

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"embedding.embeddings.1.embedding.vector": "[0.6, 0.7, 0.8, ..., 0.9]" } ``` ### Vector Format Vectors stored as JSON array strings: - Dimensions: Typically 384, 768, 1536, or 3072 - Format: `"[0.123, -0.456, 0.789, ...]"` - Precision: Usually 3-6 decimal places **Storage Considerations:** - Large vectors can significantly increase trace size - Consider omitting vectors in production (keep `embedding.text` for debugging) - Use separate vector database for actual similarity search ## Examples ###

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Tool: rules/span-evaluator.md Description: # EVALUATOR Spans ## Purpose EVALUATOR spans represent quality assessment operations (answer relevance, faithfulness, hallucination detection).

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Tool: rules/span-guardrail.md Description: # GUARDRAIL Spans ## Purpose GUARDRAIL spans represent safety and policy checks (content moderation, PII detection, toxicity scoring).

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Tool: rules/span-llm.md Description: # LLM Spans Represent calls to language models (OpenAI, Anthropic, local models, etc.).

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"reranker.input_documents.0.document.score": 0.7, "reranker.input_documents.1.document.id": "doc_B", "reranker.input_documents.1.document.score": 0.9, "reranker.output_documents.0.document.id": "doc_B", "reranker.output_documents.0.document.score": 0.95, "reranker.output_documents.1.document.id": "doc_A", "reranker.output_documents.1.document.score": 0.85 } ``` In this example: - Input: doc_B (0.9) ranked higher than doc_A (0.7) - Output: doc_B still highest but both scores increased - Reranker

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Preserve retrieval order in your flattened attributes. ### Large Document Handling For very long documents: - Consider truncating `document.content` to first N characters - Store full content in separate document store - Use `document.id` to reference full content ## Examples ### Basic Vector Search ```json { "openinference.span.kind": "RETRIEVER", "input.value": "What is machine learning?", "retrieval.documents.0.document.id": "doc_123", "retrieval.documents.0.document.content": "Machine learni

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Tool: rules/span-tool.md Description: # TOOL Spans ## Purpose TOOL spans represent external tool or function invocations (API calls, database queries, calculators, custom functions).

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