semantic_conventions.md

Semantic Conventions

The Semantic Conventions define the keys and values which describe commonly observed concepts, protocols, and operations used by applications. These conventions are used to populate the attributes of spans and span events.

Reserved Attributes

The following attributes are reserved and MUST be supported by all OpenInference Tracing SDKs:

Attribute	Type	Example	Description
document.content	String	"This is a sample document content."	The content of a retrieved document
document.id	String/Integer	"1234" or 1	Unique identifier for a document
document.metadata	JSON String	"{'author': 'John Doe', 'date': '2023-09-09'}"	Metadata associated with a document
document.score	Float	0.98	Score representing the relevance of a document
embedding.embeddings	List of objects†	[{"embedding.vector": [...], "embedding.text": "hello"}]	List of embedding objects including text and vector data
embedding.model_name	String	"BERT-base"	Name of the embedding model used
embedding.text	String	"hello world"	The text represented in the embedding
embedding.vector	List of floats	[0.123, 0.456, ...]	The embedding vector consisting of a list of floats
exception.escaped	Boolean	true	Indicator if the exception has escaped the span's scope
exception.message	String	"Null value encountered"	Detailed message describing the exception
exception.stacktrace	String	"at app.main(app.java:16)"	The stack trace of the exception
exception.type	String	"NullPointerException"	The type of exception that was thrown
input.mime_type	String	"text/plain" or "application/json"	MIME type representing the format of input.value
input.value	String	"{'query': 'What is the weather today?'}"	The input value to an operation
llm.function_call	JSON String	"{function_name: 'add', args: [1, 2]}"	Object recording details of a function call in models or APIs
llm.input_messages	List of objects†	[{"message.role": "user", "message.content": "hello"}]	List of messages sent to the LLM in a chat API request
llm.invocation_parameters	JSON string	"{model_name: 'gpt-3', temperature: 0.7}"	Parameters used during the invocation of an LLM or API
llm.model_name	String	"gpt-3.5-turbo"	The name of the language model being utilized
llm.output_messages	List of objects†	[{"message.role": "user", "message.content": "hello"}]	List of messages received from the LLM in a chat API request
llm.prompt_template.template	String	"Weather forecast for {city} on {date}"	Template used to generate prompts as Python f-strings
llm.prompt_template.variables	JSON String	{ context: "<context from retrieval>", subject: "math" }	JSON of key value pairs applied to the prompt template
llm.prompt_template.version	String	"v1.0"	The version of the prompt template
llm.token_count.completion	Integer	15	The number of tokens in the completion
llm.token_count.prompt	Integer	5	The number of tokens in the prompt
llm.token_count.total	Integer	20	Total number of tokens, including prompt and completion
message.content	String	"What's the weather today?"	The content of a message in a chat
message.function_call_arguments_json	JSON String	"{ 'x': 2 }"	The arguments to the function call in JSON
message.function_call_name	String	"multiply" or "subtract"	Function call function name
message.role	String	"user" or "system"	Role of the entity in a message (e.g., user, system)
message.tool_calls	List of objects†	[{"tool_call.function.name": "get_current_weather"}]	List of tool calls (e.g. function calls) generated by the LLM
metadata	JSON String	"{'author': 'John Doe', 'date': '2023-09-09'}"	Metadata associated with a span
openinference.span.kind	String	"CHAIN"	The kind of span (e.g., CHAIN, LLM, RETRIEVER, RERANKER)
output.mime_type	String	"text/plain" or "application/json"	MIME type representing the format of output.value
output.value	String	"Hello, World!"	The output value of an operation
reranker.input_documents	List of objects†	[{"document.id": "1", "document.score": 0.9, "document.content": "..."}]	List of documents as input to the reranker
reranker.model_name	String	"cross-encoder/ms-marco-MiniLM-L-12-v2"	Model name of the reranker
reranker.output_documents	List of objects†	[{"document.id": "1", "document.score": 0.9, "document.content": "..."}]	List of documents outputted by the reranker
reranker.query	String	"How to format timestamp?"	Query parameter of the reranker
reranker.top_k	Integer	3	Top K parameter of the reranker
retrieval.documents	List of objects†	[{"document.id": "1", "document.score": 0.9, "document.content": "..."}]	List of retrieved documents
session.id	String	"26bcd3d2-cad2-443d-a23c-625e47f3324a"	Unique identifier for a session
tag.tags	List of strings	["shopping", "travel"]	List of tags to give the span a category
tool.description	String	"An API to get weather data."	Description of the tool's purpose and functionality
tool.name	String	"WeatherAPI"	The name of the tool being utilized
tool.parameters	JSON string	"{ 'a': 'int' }"	The parameters definition for invoking the tool
tool_call.function.arguments	JSON string	"{'city': 'London'}"	The arguments for the function being invoked by a tool call
tool_call.function.name	String	get_current_weather	The name of the function being invoked by a tool call
user.id	String	"9328ae73-7141-4f45-a044-8e06192aa465"	Unique identifier for a user

^† To get a list of objects exported as OpenTelemetry span attributes, flattening of the list is necessary as shown in the examples below.

Python

messages = [{"message.role": "user", "message.content": "hello"},
            {"message.role": "assistant", "message.content": "hi"}]

for i, obj in enumerate(messages):
    for key, value in obj.items():
        span.set_attribute(f"input.messages.{i}.{key}", value)

JavaScript/TypeScript (ES6)

const messages = [{ "message.role": "user", "message.content": "hello", }, {
  "message.role": "assistant",
  "message.content": "hi",
}];

for (const [i, obj] of messages.entries()) {
  for (const [key, value] of Object.entries(obj)) {
    span.setAttribute(`input.messages.${i}.${key}`, value);
  }
}

If the objects are further nested, flattening should continue until the attribute values are either simple values, i.e. bool, str, bytes, int, float or simple lists, i.e. List[bool], List[str], List[bytes], List[int], List[float].