gen_ai.system
- The vendor of the LLM (e.g. OpenAI, Anthropic, etc.)
gen_ai.request.model
- The model requested (e.g. gpt-4
, claude
, etc.)
gen_ai.response.model
- The model actually used (e.g. gpt-4-0613
, etc.)
gen_ai.request.max_tokens
- The maximum number of response tokens requested
gen_ai.request.temperature
gen_ai.request.top_p
gen_ai.prompt
- An array of prompts as sent to the LLM model
gen_ai.completion
- An array of completions returned from the LLM model
gen_ai.usage.prompt_tokens
- The number of tokens used for the prompt in the request
gen_ai.usage.completion_tokens
- The number of tokens used for the completion response
gen_ai.usage.total_tokens
- The total number of tokens used
llm.request.type
- The type of request (e.g. completion
, chat
, etc.)
llm.usage.total_tokens
- The total number of tokens used
llm.request.functions
- An array of function definitions provided to the model in the request
llm.frequency_penalty
llm.presence_penalty
llm.chat.stop_sequences
llm.user
- The user ID sent with the request
llm.headers
- The headers used for the request
db.system
- The vendor of the Vector DB (e.g. Chroma, Pinecone, etc.)db.vector.query.top_k
- The top k used for the querydb.query.embeddings
is fired with this attribute:
db.query.embeddings.vector
- The vector used in the querydb.query.result
is fired for each vector in the response with the following attributes:
db.query.result.id
- The ID of the vectordb.query.result.score
- The score of the vector in relation to the querydb.query.result.distance
- The distance of the vector from the query vectordb.query.result.metadata
- Related metadata that was attached to the result vector in the DBdb.query.result.vector
- The vector returneddb.query.result.document
- The document that is represented by the vectorpinecone.query.id
pinecone.query.namespace
pinecone.query.top_k
pinecone.usage.read_units
- The number of read units used (as reported by Piinecone)pinecone.usage.write_units
- The number of write units used (as reported by Piinecone)traceloop.span.kind
- One of workflow
, task
, agent
, tool
.traceloop.workflow.name
- The name of the parent workflow/chain associated with this spantraceloop.entity.name
- Framework-related name for the entity (for example, in Langchain, this will be the name of the specific class that defined the chain / subchain).traceloop.association.properties
- Context on the request (relevant User ID, Chat ID, etc.)