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.
The following attributes are reserved and MUST be supported by all OpenInference Tracing SDKs:
| Attribute | Type | Example | Description |
|---|---|---|---|
exception.type |
String | "NullPointerException" |
The type of exception that was thrown |
exception.message |
String | "Null value encountered" |
Detailed message describing the exception |
exception.escaped |
Boolean | true |
Indicator if the exception has escaped the span's scope |
exception.stacktrace |
String | "at app.main(app.java:16)" |
The stack trace of the exception |
output.value |
String | "Hello, World!" |
The output value of an operation |
output.mime_type |
String | "text/plain" or "application/json" |
MIME type representing the format of output.value |
input.value |
String | "{'query': 'What is the weather today?'}" |
The input value to an operation |
input.mime_type |
String | "text/plain" or "application/json" |
MIME type representing the format of input.value |
embedding.embeddings |
List of objects | [{"embeeding.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 |
llm.function_call |
JSON String | "{function_name: 'add', args: [1, 2]}" |
Object recording details of a function call in models or APIs |
llm.invocation_parameters |
JSON string | "{model_name: 'gpt-3', temperature: 0.7}" |
Parameters used during the invocation of an LLM or API |
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.output_messages |
List of objects | [{"message.role": "user", "message.content": "hello"}] |
List of messages received from the LLM in a chat API request |
message.role |
String | "user" or "system" |
Role of the entity in a message (e.g., user, system) |
message.function_call_name |
String | "multiply" or "subtract" |
Function call function name |
message.function_call_arguments_json |
JSON String | "{ 'x': 2 }" |
The arguments to the function call in JSON |
message.content |
String | "What's the weather today?" |
The content of a message in a chat |
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 |
tool_call.function.name |
String | get_current_weather |
The name of the function being invoked by a tool call |
tool_call.function.arguments |
JSON string | "{'city': 'London'}" |
The arguments for the function being invoked by a tool call |
llm.model_name |
String | "gpt-3.5-turbo" |
The name of the language model being utilized |
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.prompt |
Integer | 5 |
The number of tokens in the prompt |
llm.token_count.completion |
Integer | 15 |
The number of tokens in the completion |
llm.token_count.total |
Integer | 20 |
Total number of tokens, including prompt and completion |
tool.name |
String | "WeatherAPI" |
The name of the tool being utilized |
tool.description |
String | "An API to get weather data." |
Description of the tool's purpose and functionality |
tool.parameters |
JSON string | "{ 'a': 'int' }" |
The parameters definition for invoking the tool |
retrieval.documents |
List of objects | [{"document.id": "1", "document.score": 0.9, "document.content": "..."}] |
List of retrieved documents |
document.id |
String/Integer | "1234" or 1 |
Unique identifier for a document |
document.score |
Float | 0.98 |
Score representing the relevance of a document |
document.content |
String | "This is a sample document content." |
The content of a retrieved document |
document.metadata |
Object | {"author": "John Doe", "date": "2023-09-09"} |
Metadata associated with a document |
reranker.input_documents |
List of objects | [{"document.id": "1", "document.score": 0.9, "document.content": "..."}] |
List of documents as input to 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.model_name |
String | "cross-encoder/ms-marco-MiniLM-L-12-v2" |
Model name of the reranker |
reranker.top_k |
Integer | 3 | Top K parameter of the reranker |
Note: the object type refers to a set of key-value pairs also known as a struct, mapping, dictionary, etc.