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OpenInference Core\n\n[![npm version](https://badge.fury.io/js/@arizeai%2Fopeninference-core.svg)](https://badge.fury.io/js/@arizeai%2Fopeninference-core)\n\nThis package provides OpenInference Core utilities for LLM Traces.\n\n## Installation\n\n```bash\nnpm install @arizeai/openinference-core # npm\npnpm add @arizeai/openinference-core # pnpm\nyarn add @arizeai/openinference-core # yarn\n```\n\n## Customizing Spans\n\nThe `@arizeai/openinference-core` package offers utilities to track important application metadata such as sessions and users using context attribute propagation:\n\n- `setSession`: to specify a session ID to track and group multi-turn conversations\n- `setUser`: to specify a user ID to track different conversations with a given user\n- `setMetadata`: to add custom metadata that can provide extra information to support a wide range of operational needs\n- `setTag`: to add tags, to filter spans on specific keywords\n- `setPromptTemplate`: to reflect the prompt template used, with its version and variables. This is useful for prompt template tracking\n- `setAttributes`: to add multiple custom attributes at the same time\n\n> [!NOTE] All @arizeai/openinference auto instrumentation packages will pull attributes off of context and add them to spans\n\n### Examples\n\n`setSession`\n\n```typescript\nimport { context } from \"@opentelemetry/api\";\nimport { setSession } from \"@arizeai/openinference-core\";\n\ncontext.with(setSession(context.active(), { sessionId: \"session-id\" }), () => {\n  // Calls within this block will generate spans with the attributes:\n  // \"session.id\" = \"session-id\"\n});\n```\n\nEach setter function returns a new active context, so they can be chained together.\n\n```typescript\nimport { context } from \"@opentelemetry/api\";\nimport { setAttributes, setSession } from \"@arizeai/openinference-core\";\n\ncontext.with(\n  setAttributes(setSession(context.active(), { sessionId: \"session-id\" }), {\n    myAttribute: \"test\",\n  }),\n  () => {\n    // Calls within this block will generate spans with the attributes:\n    // \"myAttribute\" = \"test\"\n    // \"session.id\" = \"session-id\"\n  },\n);\n```\n\nAdditionally, they can be used in conjunction with the [OpenInference Semantic Conventions](../openinference-semantic-conventions/).\n\n```typescript\nimport { context } from \"@opentelemetry/api\"\nimport { setAttributes } from \"@openinference-core\"\nimport { SemanticConventions } from \"@arizeai/openinference-semantic-conventions\";\n\n\ncontext.with(\n  setAttributes(\n    { [SemanticConventions.SESSION_ID: \"session-id\" }\n  ),\n  () => {\n      // Calls within this block will generate spans with the attributes:\n      // \"session.id\" = \"session-id\"\n  }\n)\n```\n\nIf you are creating spans manually and want to propagate context attributes you've set to those spans as well you can use the `getAttributesFromContext` utility to do that. you can read more about customizing spans in our [docs](https://docs.arize.com/phoenix/tracing/how-to-tracing/customize-spans).\n\n```typescript\nimport { getAttributesFromContext } from \"@arizeai/openinference-core\";\nimport { context, trace } from \"@opentelemetry/api\";\n\nconst contextAttributes = getAttributesFromContext(context.active());\nconst tracer = trace.getTracer(\"example\");\nconst span = tracer.startSpan(\"example span\");\nspan.setAttributes(contextAttributes);\nspan.end();\n```\n\n## Tracing Helpers\n\nThis package provides convenient helpers to instrument your functions, agents, and LLM operations with OpenInference spans.\n\n### Function Tracing\n\n**`withSpan`** - Wraps any function (sync or async) with OpenTelemetry tracing:\n\n```typescript\nimport { withSpan } from \"@arizeai/openinference-core\";\nimport { OpenInferenceSpanKind } from \"@arizeai/openinference-semantic-conventions\";\n\nconst processUserQuery = async (query: string) => {\n  // Your business logic here\n  const response = await fetch(`/api/process?q=${query}`);\n  return response.json();\n};\n\nconst tracedProcess = withSpan(processUserQuery, {\n  name: \"user-query-processor\",\n  kind: OpenInferenceSpanKind.CHAIN,\n});\n```\n\n**`traceChain`** - Convenience wrapper for tracing workflow sequences:\n\n```typescript\nimport { traceChain } from \"@arizeai/openinference-core\";\n\nconst ragPipeline = async (question: string) => {\n  const documents = await retrieveDocuments(question);\n  const context = documents.map((d) => d.content).join(\"\\n\");\n  const answer = await generateAnswer(question, context);\n  return answer;\n};\n\nconst tracedRag = traceChain(ragPipeline, { name: \"rag-pipeline\" });\n```\n\n**`traceAgent`** - Convenience wrapper for tracing autonomous agents:\n\n```typescript\nimport { traceAgent } from \"@arizeai/openinference-core\";\n\nconst simpleAgent = async (question: string) => {\n  // Agent logic that may call tools, make decisions, etc.\n  const documents = await retrieveDocuments(question);\n  const analysis = await analyzeContext(question, documents);\n  return await executePlan(analysis);\n};\n\nconst tracedAgent = traceAgent(simpleAgent, { name: \"qa-agent\" });\n```\n\n**`traceTool`** - Convenience wrapper for tracing external tools:\n\n```typescript\nimport { traceTool } from \"@arizeai/openinference-core\";\n\nconst weatherTool = async (city: string) => {\n  const response = await fetch(`https://api.weather.com/v1/${city}`);\n  return response.json();\n};\n\nconst tracedWeatherTool = traceTool(weatherTool, { name: \"weather-api\" });\n```\n\n### Decorators\n\n**`@observe`** - Decorator for automatically tracing class methods:\n\n```typescript\nimport { observe } from \"@arizeai/openinference-core\";\n\nclass ChatService {\n  @observe({ kind: \"chain\" })\n  async processMessage(message: string) {\n    // Your method implementation\n    return `Processed: ${message}`;\n  }\n\n  @observe({ name: \"llm-call\", kind: \"llm\" })\n  async callLLM(prompt: string) {\n    // LLM invocation\n    return await llmClient.generate(prompt);\n  }\n}\n```\n\n### Attribute Helpers\n\n**`getLLMAttributes`** - Generate attributes for LLM operations:\n\n```typescript\nimport { getLLMAttributes } from \"@arizeai/openinference-core\";\nimport { trace } from \"@opentelemetry/api\";\n\nconst tracer = trace.getTracer(\"llm-service\");\n\ntracer.startActiveSpan(\"llm-inference\", (span) => {\n  const attributes = getLLMAttributes({\n    provider: \"openai\",\n    modelName: \"gpt-4\",\n    inputMessages: [{ role: \"user\", content: \"What is AI?\" }],\n    outputMessages: [{ role: \"assistant\", content: \"AI is...\" }],\n    tokenCount: { prompt: 10, completion: 50, total: 60 },\n  });\n  span.setAttributes(attributes);\n  span.end();\n});\n```\n\n**`getEmbeddingAttributes`** - Generate attributes for embedding operations:\n\n```typescript\nimport { getEmbeddingAttributes } from \"@arizeai/openinference-core\";\nimport { trace } from \"@opentelemetry/api\";\n\nconst tracer = trace.getTracer(\"embedding-service\");\n\ntracer.startActiveSpan(\"generate-embeddings\", (span) => {\n  const attributes = getEmbeddingAttributes({\n    modelName: \"text-embedding-ada-002\",\n    embeddings: [\n      { text: \"The quick brown fox\", vector: [0.1, 0.2, 0.3, ...] },\n      { text: \"jumps over the lazy dog\", vector: [0.4, 0.5, 0.6, ...] },\n    ],\n  });\n  span.setAttributes(attributes);\n  span.end();\n});\n```\n\n**`getRetrieverAttributes`** - Generate attributes for document retrieval:\n\n```typescript\nimport { getRetrieverAttributes } from \"@arizeai/openinference-core\";\nimport { trace } from \"@opentelemetry/api\";\n\nconst tracer = trace.getTracer(\"retriever-service\");\n\nasync function retrieveDocuments(query: string) {\n  return tracer.startActiveSpan(\"retrieve-documents\", async (span) => {\n    const documents = await vectorStore.similaritySearch(query, 5);\n    const attributes = getRetrieverAttributes({\n      documents: documents.map((doc) => ({\n        content: doc.pageContent,\n        id: doc.metadata.id,\n        score: doc.score,\n        metadata: doc.metadata,\n      })),\n    });\n    span.setAttributes(attributes);\n    span.end();\n    return documents;\n  });\n}\n```\n\n**`getToolAttributes`** - Generate attributes for tool definitions:\n\n```typescript\nimport { getToolAttributes } from \"@arizeai/openinference-core\";\nimport { trace } from \"@opentelemetry/api\";\n\nconst tracer = trace.getTracer(\"tool-service\");\n\ntracer.startActiveSpan(\"define-tool\", (span) => {\n  const attributes = getToolAttributes({\n    name: \"search_web\",\n    description: \"Search the web for information\",\n    parameters: {\n      query: { type: \"string\", description: \"The search query\" },\n      maxResults: { type: \"number\", description: \"Maximum results to return\" },\n    },\n  });\n  span.setAttributes(attributes);\n  span.end();\n});\n```\n\n## Trace Config\n\nThis package also provides support for controlling settings like data privacy and payload sizes. For instance, you may want to keep sensitive information from being logged for security reasons, or you may want to limit the size of the base64 encoded images logged to reduced payload size.\n\n> [!NOTE] These values can also be controlled via environment variables, see more information [here](https://github.com/Arize-ai/openinference/blob/main/spec/configuration.md).\n\nHere is an example of how to configure these settings using the OpenAI auto instrumentation. Note that all of our auto instrumentations will accept a traceConfig object.\n\n```typescript\nimport { OpenAIInstrumentation } from \"@arizeai/openinference-instrumentation-openai\";\n\n/**\n * Everything left out of here will fallback to\n * environment variables then defaults\n */\nconst traceConfig = { hideInputs: true };\n\nconst instrumentation = new OpenAIInstrumentation({ traceConfig });\n```","users":{}}