openinference

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
image.url	String	"https://sample-link-to-image.jpg"	The link to the image or its base64 encoding
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.provider	String	openai, azure	The hosting provider of the llm, e.x. azure
llm.system	String	anthropic, openai	The AI product as identified by the client or server instrumentation.
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
llm.tools	List of objects†	[{"tool": {"json_schema": "{}"}, ...]	List of tools that are advertised to the LLM to be able to call
message.content	String	"What's the weather today?"	The content of a message in a chat
message.contents	List of objects†	[{"message_content.type": "text", "message_content.text": "Hello"}, ...]	The message contents to the llm, it is an array of message_content objects.
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.tool_call_id	String	"call_62136355"	Tool call result identifier corresponding to tool_call.id
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
messagecontent.type	String	"text" or "image"	The type of the content, such as “text” or “image”.
messagecontent.text	String	"This is a sample text"	The text content of the message, if the type is “text”.
messagecontent.image	Image Object	{"image.url": "https://sample-link-to-image.jpg"}	The image content of the message, if the type is “image”.
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.json_schema	JSON String	"{'type': 'function', 'function': {'name': 'get_weather'}}"	The json schema of a tool input
tool.name	String	"WeatherAPI"	The name of the tool being utilized
tool.id	String	"WeatherAPI"	The identifier for the result of the tool call (corresponding to tool_call.id)
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
tool_call.id	string	"call_62136355"	The id of the a tool call (useful when there are more than one call at the same time)
user.id	String	"9328ae73-7141-4f45-a044-8e06192aa465"	Unique identifier for a user
audio.url	String	https://storage.com/buckets/1/file.wav	The url to an audio file (e.x. cloud storage)
audio.mime_type	String	audio/mpeg	The mime type of the audio file (e.x. audio/mpeg, audio/wav )
audio.transcript	String	"Hello, how are you?"	The transcript of the audio file (e.x. whisper transcription)

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

llm.system has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description
anthropic	Anthropic
openai	OpenAI
vertexai	Vertex AI
cohere	Cohere
mistralai	Mistral AI

llm.provider has the following list of well-known values. If one of them applies, then the respective value MUST be used; otherwise, a custom value MAY be used.

Value	Description
anthropic	Anthropic
openai	OpenAI
cohere	Cohere
mistralai	Mistral AI
azure	Azure
google	Google (Vertex)
aws	AWS Bedrock

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].

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