> For the complete documentation index, see [llms.txt](https://karini-ai.gitbook.io/karini-ai-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://karini-ai.gitbook.io/karini-ai-documentation/observability.md).

# Observability

Every request in Karini AI includes a trace that outlines the steps orchestrated by the prompt, agent, recipe, or copilot. This trace allows you to follow the step-by-step process leading to the response at that point in the conversation.

<table><thead><tr><th width="170">Tracing Step</th><th width="212">Prompt</th><th>Attributes</th></tr></thead><tbody><tr><td>Detect Greeting Questions</td><td><ol><li><strong>Input</strong> : Greeting detection prompt with input question  </li><li><strong>Output</strong>: Classification output</li></ol></td><td><ol><li><strong>ServiceName-</strong>  Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li><li><strong>gen_ai.prompt.0.role -</strong></li><li><strong>gen_ai.completion.0.finish_reason</strong> -</li><li><strong>gen_ai.completion.0.role -</strong></li><li><strong>gen_ai.openai.api_base -</strong></li><li><strong>gen_ai.openai.system_fingerprint -</strong></li><li><strong>gen_ai.request.max_tokens -</strong>The maximum number of response tokens requested</li><li><strong>gen_ai.request.model -</strong> The model requested (e.g. <code>gpt-4</code>, <code>claude</code>, etc.)</li><li><strong>gen_ai.request.temperature</strong></li><li><strong>gen_ai.system -</strong> The vendor of the LLM (e.g. OpenAI, Anthropic, etc.)</li><li><strong>gen_ai.usage.completion_tokens -</strong> The number of tokens used for the completion response</li><li><strong>gen_ai.usage.prompt_tokens -</strong> The number of tokens used for the prompt in the request</li><li><strong>llm.headers -</strong> </li><li><strong>llm.is_streaming</strong> -</li><li><strong>llm.request.type -</strong> The type of request (e.g. <code>completion</code>, <code>chat</code>, etc.)</li><li><strong>llm.usage.total_tokens -</strong> The total number of tokens used</li></ol></td></tr><tr><td>Check Content Safety</td><td><ol><li><strong>Input  :</strong> User query</li><li><strong>Output :</strong> Content Safety Check Output</li></ol></td><td><ol><li><strong>ServiceName -</strong>  Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li></ol></td></tr><tr><td>Query Embeddings</td><td><p></p><ol><li><strong>Input  :</strong> User query</li><li><strong>Output :</strong> Vector embeddings of the user query</li></ol></td><td><ol><li><strong>ServiceName -</strong> Information about the application in the resource</li><li><strong>SpanName -</strong>  Internal function name</li><li><strong>gen_ai.openai.api_base</strong> -</li><li><strong>gen_ai.request.model</strong> - The model requested (e.g. <code>gpt-4</code>, <code>claude</code>, etc.)</li><li><strong>gen_ai.response.model -</strong> The model actually used (e.g. <code>gpt-4-0613</code>, etc.)</li><li><strong>gen_ai.system -</strong> The vendor of the LLM (e.g. OpenAI, Anthropic, etc.)</li><li><strong>gen_ai.usage.prompt_tokens -</strong>The number of tokens used for the prompt in the request</li><li><strong>llm.headers -</strong> The headers used for the request</li><li><strong>llm.is_streaming -</strong></li><li><strong>llm.request.type -</strong> The type of request (e.g. <code>completion</code>, <code>chat</code>, etc.)</li><li><strong>llm.usage.total_tokens -</strong> The total number of tokens used</li></ol></td></tr><tr><td>Get similar embeddings </td><td><ol><li><strong>Input :</strong> User query</li><li><strong>Output :</strong> Similar embeddings from the vector store</li></ol></td><td><p></p><ol><li><strong>ServiceName -</strong>  Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li></ol><p></p><p><br></p></td></tr><tr><td>Perform reranking  </td><td><p></p><ol><li><strong>Input :</strong> User query</li><li><strong>Output :</strong> Reranked similar embeddings using Cohere reranker</li></ol></td><td><ol><li><strong>ServiceName -</strong>  Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li></ol></td></tr><tr><td>Get Qna chain streaming  </td><td><p></p><ol><li><strong>Input :</strong> Prompt, user query and the reranked context</li><li><strong>Output :</strong> Response from the LLM</li></ol></td><td><p></p><ol><li><strong>ServiceName -</strong> Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li><li><strong>gen_ai.completion.0.finish_reason -</strong></li><li><strong>gen_ai.completion.0.role -</strong></li><li><strong>gen_ai.openai.api_base -</strong></li><li><strong>gen_ai.openai.api_version -</strong></li><li><strong>gen_ai.prompt.0.role</strong></li><li><strong>gen_ai.request.max_tokens -</strong>The maximum number of response tokens requested</li><li><strong>gen_ai.request.model -</strong> The model requested (e.g. <code>gpt-4</code>, <code>claude</code>, etc.)</li><li><strong>gen_ai.request.temperature -</strong></li><li><strong>gen_ai.response.model -</strong> The model actually used (e.g. <code>gpt-4-0613</code>, etc.)</li><li><strong>gen_ai.system -</strong> The vendor of the LLM (e.g. OpenAI, Anthropic, etc.)</li><li><strong>gen_ai.usage.completion_tokens -</strong> The number of tokens used for the completion response</li><li><strong>gen_ai.usage.prompt_tokens -</strong> The number of tokens used for the prompt in the request</li><li><strong>llm.headers -</strong></li><li><strong>llm.is_streaming -</strong></li><li><strong>llm.request.type -</strong> The type of request (e.g. <code>completion</code>, <code>chat</code>, etc.)</li><li><strong>llm.usage.total_tokens -</strong> The total number of tokens used</li></ol></td></tr><tr><td>Get Followup Questions</td><td><ol><li><strong>Input :</strong> Follow up question generation prompt, user query and LLM generated answer to the user query</li><li><strong>Output :</strong> Followup questions</li></ol></td><td><ol><li><strong>ServiceName -</strong>  Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li><li><strong>gen_ai.completion.0.finish_reason -</strong></li><li><strong>gen_ai.completion.0.role -</strong></li><li><strong>gen_ai.openai.api_base -</strong></li><li>gen_ai.openai.system_fingerprint</li><li><strong>gen_ai.openai.api_version -</strong></li><li><strong>gen_ai.prompt.0.role -</strong></li><li><strong>gen_ai.request.max_tokens -</strong>The maximum number of response tokens requested</li><li><strong>gen_ai.request.model -</strong>The model actually used (e.g. <code>gpt-4-0613</code>, etc.)</li><li><strong>gen_ai.request.temperature -</strong></li><li><strong>gen_ai.response.model</strong> -</li><li><strong>gen_ai.system -</strong>The vendor of the LLM (e.g. OpenAI, Anthropic, etc.)</li><li><strong>gen_ai.usage.completion_tokens -</strong>The number of tokens used for the completion response</li><li><strong>gen_ai.usage.prompt_tokens -</strong>The number of tokens used for the prompt in the request</li><li><strong>llm.headers -</strong></li><li><strong>llm.is_streaming</strong> -</li><li><strong>llm.request.type</strong> - The type of request (e.g. <code>completion</code>, <code>chat</code>, etc.)</li><li><strong>llm.usage.total_tokens</strong><br></li></ol></td></tr><tr><td>Agent Executor</td><td><ol><li><strong>Input</strong> : Prompt, user question,  agent thoughts and actions</li><li><strong>Output:</strong> Response to the agent action</li></ol></td><td><p></p><ol><li><strong>ServiceName -</strong> Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li><li><strong>gen_ai.prompt.0.role</strong></li><li><strong>gen_ai.request.max_tokens -</strong> The maximum number of response tokens requested</li><li><strong>gen_ai.request.model -</strong>The model requested (e.g. <code>gpt-4</code>, <code>claude</code>, etc.)</li><li><strong>gen_ai.request.temperature</strong></li><li><strong>gen_ai.system-</strong>he vendor of the LLM (e.g. OpenAI, Anthropic, etc.)</li><li><strong>gen_ai.usage.completion_tokens -</strong>The number of tokens used for the completion response</li><li><strong>gen_ai.usage.prompt_tokens -</strong>The number of tokens used for the prompt in the request</li><li><strong>llm.request.type -</strong> The type of request (e.g. <code>completion</code>, <code>chat</code>, etc.)</li><li><strong>llm.usage.total_tokens -</strong> The total number of tokens used</li></ol></td></tr><tr><td><p>Get LLM Chain Streaming</p><p><br></p></td><td><p></p><ol><li><strong>Input</strong> : Prompt with user query</li><li><strong>Output:</strong> Response from LLM</li></ol></td><td><p></p><p></p><ol><li><strong>ServiceName -</strong> Information about the application in the resource</li><li><strong>SpanName -</strong> Internal function name</li><li><strong>gen_ai.prompt.0.role</strong></li><li><strong>gen_ai.request.max_tokens -</strong> The maximum number of response tokens requested</li><li><strong>gen_ai.request.model -</strong>The model requested (e.g. <code>gpt-4</code>, <code>claude</code>, etc.)</li><li><strong>gen_ai.request.temperature</strong></li><li><strong>gen_ai.system-</strong>he vendor of the LLM (e.g. OpenAI, Anthropic, etc.)</li><li><strong>gen_ai.usage.completion_tokens -</strong>The number of tokens used for the completion response</li><li><strong>gen_ai.usage.prompt_tokens -</strong>The number of tokens used for the prompt in the request</li><li><strong>llm.request.type -</strong> The type of request (e.g. <code>completion</code>, <code>chat</code>, etc.)</li><li><strong>llm.usage.total_tokens -</strong> The total number of tokens used</li></ol></td></tr></tbody></table>