FI Semantic Conventions

Standardizing span attributes across various models, frameworks, and vendors When sending traces, you might want to define custom attributes for each span. Semantic conventions are specific attribute keys or values that hold special significance. In Future AGI, certain attribute keys are highlighted more prominently, in addition to showing up in the attributes tab like other keys.

Types of Attributes

Span
Message
Document
Reranker
Embedding
Tool Call

Python
TypeScript

class SpanAttributes:
    # Output related attributes
    OUTPUT_VALUE = "output.value"
    OUTPUT_MIME_TYPE = "output.mime_type"
    # The type of output.value. If unspecified, the type is plain text by default.
    # If type is JSON, the value is a string representing a JSON object.

    INPUT_VALUE = "input.value"
    INPUT_MIME_TYPE = "input.mime_type"
    # The type of input.value. If unspecified, the type is plain text by default.
    # If type is JSON, the value is a string representing a JSON object.

    # Embedding related attributes
    EMBEDDING_EMBEDDINGS = "embedding.embeddings"
    # A list of objects containing embedding data, including the vector and represented piece of text.

    EMBEDDING_MODEL_NAME = "embedding.model_name"
    # The name of the embedding model.

    # LLM related attributes
    LLM_FUNCTION_CALL = "llm.function_call"
    # For models and APIs that support function calling. Records attributes such as the function
    # name and arguments to the called function.

    LLM_INVOCATION_PARAMETERS = "llm.invocation_parameters"
    # Invocation parameters passed to the LLM or API, such as the model name, temperature, etc.

    LLM_INPUT_MESSAGES = "llm.input_messages"
    # Messages provided to a chat API.

    LLM_OUTPUT_MESSAGES = "llm.output_messages"
    # Messages received from a chat API.

    LLM_MODEL_NAME = "llm.model_name"
    # The name of the model being used.

    LLM_PROVIDER = "llm.provider"
    # The provider of the model, such as OpenAI, Azure, Google, etc.

    LLM_SYSTEM = "llm.system"
    # The AI product as identified by the client or server

    LLM_PROMPTS = "llm.prompts"
    # Prompts provided to a completions API.

    LLM_PROMPT_TEMPLATE = "llm.prompt_template.template"
    # The prompt template as a Python f-string.

    LLM_PROMPT_TEMPLATE_VARIABLES = "llm.prompt_template.variables"
    # A list of input variables to the prompt template.

    LLM_PROMPT_TEMPLATE_VERSION = "llm.prompt_template.version"
    # The version of the prompt template being used.

    LLM_TOKEN_COUNT_PROMPT = "llm.token_count.prompt"
    # Number of tokens in the prompt.

    LLM_TOKEN_COUNT_COMPLETION = "llm.token_count.completion"
    # Number of tokens in the completion.

    LLM_TOKEN_COUNT_TOTAL = "llm.token_count.total"
    # Total number of tokens, including both prompt and completion.

    LLM_TOOLS = "llm.tools"
    # List of tools that are advertised to the LLM to be able to call

    # Tool related attributes
    TOOL_NAME = "tool.name"
    # Name of the tool being used.

    TOOL_DESCRIPTION = "tool.description"
    # Description of the tool's purpose, typically used to select the tool.

    TOOL_PARAMETERS = "tool.parameters"
    # Parameters of the tool represented a dictionary JSON string

    RETRIEVAL_DOCUMENTS = "retrieval.documents"

    METADATA = "metadata"
    # Metadata attributes are used to store user-defined key-value pairs.

    TAG_TAGS = "tag.tags"
    # Custom categorical tags for the span.

    FI_SPAN_KIND = "fi.span.kind"

    SESSION_ID = "session.id"
    # The id of the session

    USER_ID = "user.id"
    # The id of the user

    INPUT_IMAGES = "llm.input.images"
    # A list of input images provided to the model.

    EVAL_INPUT = "eval.input"
    # Input being sent to the eval

    RAW_INPUT = "raw.input"
    # Raw input being sent to otel

    RAW_OUTPUT = "raw.output"
    # Raw output being sent from otel

    QUERY = "query"
    # The query being sent to the model

    RESPONSE = "response"
    # The response being sent from the model

class MessageAttributes:
    # Attributes for a message sent to or from an LLM

    MESSAGE_ROLE = "message.role"
    # The role of the message, such as "user", "agent", "function".

    MESSAGE_CONTENT = "message.content"
    # The content of the message to or from the llm, must be a string.

    MESSAGE_CONTENTS = "message.contents"
    # The message contents to the llm, it is an array of message_content prefixed attributes.

    MESSAGE_NAME = "message.name"
    # The name of the message, often used to identify the function that was used to generate the message.

    MESSAGE_TOOL_CALLS = "message.tool_calls"
    # The tool calls generated by the model, such as function calls.

    MESSAGE_FUNCTION_CALL_NAME = "message.function_call_name"
    # The function name that is a part of the message list.
    # This is populated for role 'function' or 'agent' as a mechanism to identify
    # the function that was called during the execution of a tool.

    MESSAGE_FUNCTION_CALL_ARGUMENTS_JSON = "message.function_call_arguments_json"
    # The JSON string representing the arguments passed to the function during a function call.

    MESSAGE_TOOL_CALL_ID = "message.tool_call_id"
    # The id of the tool call.

class DocumentAttributes:
    # Attributes for a document.

    DOCUMENT_ID = "document.id"
    # The id of the document.

    DOCUMENT_SCORE = "document.score"
    # The score of the document

    DOCUMENT_CONTENT = "document.content"
    # The content of the document.

    DOCUMENT_METADATA = "document.metadata"
    # The metadata of the document represented as a dictionary JSON string

class RerankerAttributes:
    # Attributes for a reranker

    RERANKER_INPUT_DOCUMENTS = "reranker.input_documents"
    # List of documents as input to the reranker

    RERANKER_OUTPUT_DOCUMENTS = "reranker.output_documents"
    # List of documents as output from the reranker

    RERANKER_QUERY = "reranker.query"
    # Query string for the reranker

    RERANKER_MODEL_NAME = "reranker.model_name"
    # Model name of the reranker

    RERANKER_TOP_K = "reranker.top_k"
    # Top K parameter of the reranker

class EmbeddingAttributes:
    # Attributes for an embedding

    EMBEDDING_TEXT = "embedding.text"
    # The text represented by the embedding.

    EMBEDDING_VECTOR = "embedding.vector"
    # The embedding vector.

class ToolCallAttributes:
    # Attributes for a tool call

    TOOL_CALL_ID = "tool_call.id"
    # The id of the tool call.

    TOOL_CALL_FUNCTION_NAME = "tool_call.function.name"
    # The name of function that is being called during a tool call.

    TOOL_CALL_FUNCTION_ARGUMENTS_JSON = "tool_call.function.arguments"
    # The JSON string representing the arguments passed to the function during a tool call.

class ImageAttributes:
    IMAGE_URL = "image.url"
    # An http or base64 image url


class AudioAttributes:
    AUDIO_URL = "audio.url"
    # The url to an audio file
    AUDIO_MIME_TYPE = "audio.mime_type"
    # The mime type of the audio file
    AUDIO_TRANSCRIPT = "audio.transcript"
    # The transcript of the audio file

// Semantic Conventions for Span Attributes
export const SemanticConventions = {
  // Input/Output related attributes
  INPUT_VALUE: "input.value",
  INPUT_MIME_TYPE: "input.mime_type",
  OUTPUT_VALUE: "output.value", 
  OUTPUT_MIME_TYPE: "output.mime_type",

  // LLM related attributes
  LLM_INPUT_MESSAGES: "llm.input_messages",
  LLM_OUTPUT_MESSAGES: "llm.output_messages",
  LLM_MODEL_NAME: "llm.model_name",
  LLM_PROVIDER: "llm.provider",
  LLM_SYSTEM: "llm.system",
  LLM_PROMPTS: "llm.prompts",
  LLM_INVOCATION_PARAMETERS: "llm.invocation_parameters",
  LLM_FUNCTION_CALL: "llm.function_call",
  LLM_TOOLS: "llm.tools",

  // Token count attributes
  LLM_TOKEN_COUNT_PROMPT: "llm.token_count.prompt",
  LLM_TOKEN_COUNT_COMPLETION: "llm.token_count.completion",
  LLM_TOKEN_COUNT_TOTAL: "llm.token_count.total",
  LLM_TOKEN_COUNT_COMPLETION_DETAILS_REASONING: "llm.token_count.completion_details.reasoning",
  LLM_TOKEN_COUNT_COMPLETION_DETAILS_AUDIO: "llm.token_count.completion_details.audio",
  LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_WRITE: "llm.token_count.prompt_details.cache_write",
  LLM_TOKEN_COUNT_PROMPT_DETAILS_CACHE_READ: "llm.token_count.prompt_details.cache_read",
  LLM_TOKEN_COUNT_PROMPT_DETAILS_AUDIO: "llm.token_count.prompt_details.audio",

  // Prompt template attributes
  PROMPT_TEMPLATE_TEMPLATE: "llm.prompt_template.template",
  PROMPT_TEMPLATE_VARIABLES: "llm.prompt_template.variables", 
  PROMPT_TEMPLATE_VERSION: "llm.prompt_template.version",

  // Tool related attributes
  TOOL_NAME: "tool.name",
  TOOL_DESCRIPTION: "tool.description",
  TOOL_PARAMETERS: "tool.parameters",
  TOOL_JSON_SCHEMA: "tool.json_schema",

  // Embedding attributes
  EMBEDDING_EMBEDDINGS: "embedding.embeddings",
  EMBEDDING_MODEL_NAME: "embedding.model_name",
  EMBEDDING_TEXT: "embedding.text",
  EMBEDDING_VECTOR: "embedding.vector",

  // Retrieval attributes
  RETRIEVAL_DOCUMENTS: "retrieval.documents",

  // Session and user tracking
  SESSION_ID: "session.id", 
  USER_ID: "user.id",

  // Metadata and tagging
  METADATA: "metadata",
  TAG_TAGS: "tag.tags",
  FI_SPAN_KIND: "fi.span.kind",

  // Raw input/output
  RAW_INPUT: "raw.input",
  RAW_OUTPUT: "raw.output",
} as const;

// Span kind enumeration
export enum FISpanKind {
  LLM = "LLM",
  CHAIN = "CHAIN", 
  TOOL = "TOOL",
  RETRIEVER = "RETRIEVER",
  RERANKER = "RERANKER",
  EMBEDDING = "EMBEDDING",
  AGENT = "AGENT",
  GUARDRAIL = "GUARDRAIL",
  EVALUATOR = "EVALUATOR",
  UNKNOWN = "UNKNOWN",
}

// Message related semantic conventions
export const MessageConventions = {
  MESSAGE_ROLE: "message.role",
  MESSAGE_CONTENT: "message.content", 
  MESSAGE_CONTENTS: "message.contents",
  MESSAGE_NAME: "message.name",
  MESSAGE_TOOL_CALLS: "message.tool_calls",
  MESSAGE_TOOL_CALL_ID: "message.tool_call_id",
  MESSAGE_FUNCTION_CALL_NAME: "message.function_call_name",
  MESSAGE_FUNCTION_CALL_ARGUMENTS_JSON: "message.function_call_arguments_json",

  // Message content attributes
  MESSAGE_CONTENT_TYPE: "message_content.type",
  MESSAGE_CONTENT_TEXT: "message_content.text", 
  MESSAGE_CONTENT_IMAGE: "message_content.image",
} as const;

// Message content types
export const MessageContentTypes = {
  TEXT: "text",
  IMAGE: "image",
} as const;

// Document related semantic conventions
export const DocumentConventions = {
  DOCUMENT_ID: "document.id",
  DOCUMENT_CONTENT: "document.content",
  DOCUMENT_SCORE: "document.score", 
  DOCUMENT_METADATA: "document.metadata",
} as const;

// Reranker related semantic conventions
export const RerankerConventions = {
  RERANKER_INPUT_DOCUMENTS: "reranker.input_documents",
  RERANKER_OUTPUT_DOCUMENTS: "reranker.output_documents",
  RERANKER_QUERY: "reranker.query",
  RERANKER_MODEL_NAME: "reranker.model_name", 
  RERANKER_TOP_K: "reranker.top_k",
} as const;

// Embedding related semantic conventions
export const EmbeddingConventions = {
  EMBEDDING_TEXT: "embedding.text",
  EMBEDDING_VECTOR: "embedding.vector",
  EMBEDDING_MODEL_NAME: "embedding.model_name",
  EMBEDDING_EMBEDDINGS: "embedding.embeddings",
} as const;

// Tool call related semantic conventions
export const ToolCallConventions = {
  TOOL_CALL_ID: "tool_call.id",
  TOOL_CALL_FUNCTION_NAME: "tool_call.function.name", 
  TOOL_CALL_FUNCTION_ARGUMENTS_JSON: "tool_call.function.arguments",
} as const;

// Image related semantic conventions
export const ImageConventions = {
  IMAGE_URL: "image.url",
} as const;

// Audio related semantic conventions  
export const AudioConventions = {
  AUDIO_URL: "audio.url",
  AUDIO_MIME_TYPE: "audio.mime_type",
  AUDIO_TRANSCRIPT: "audio.transcript", 
} as const;

// Prompt related semantic conventions
export const PromptConventions = {
  PROMPT_VENDOR: "prompt.vendor",
  PROMPT_ID: "prompt.id",
  PROMPT_URL: "prompt.url", 
} as const;

// Common enums
export enum MimeType {
  TEXT = "text/plain",
  JSON = "application/json", 
  AUDIO_WAV = "audio/wav",
}

export enum LLMSystem {
  OPENAI = "openai",
  ANTHROPIC = "anthropic",
  MISTRALAI = "mistralai", 
  COHERE = "cohere",
  VERTEXAI = "vertexai",
}

export enum LLMProvider {
  OPENAI = "openai",
  ANTHROPIC = "anthropic", 
  MISTRALAI = "mistralai",
  COHERE = "cohere",
  // Cloud Providers of LLM systems
  GOOGLE = "google",
  AWS = "aws", 
  AZURE = "azure",
}

For comprehensive guides to semantic conventions, refer to the following resources:

Python: See the Python examples above for implementation details
TypeScript: See the TypeScript examples above for implementation details

Attribute Overview

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
fi.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

Using Semantic Conventions

Here are examples of how to implement semantic conventions in both Python and TypeScript:

Python Usage
TypeScript Usage

# pip install fi-instrumentation-otel

from fi_instrumentation.fi_types import SpanAttributes, FiSpanKindValues

def chat(message: str):
    with tracer.start_as_current_span("an_llm_span") as span:
        span.set_attribute(
            SpanAttributes.FI_SPAN_KIND,
            FiSpanKindValues.LLM.value
        )
        
        # Equivalent to:
        # span.set_attribute(
        #     "fi.span.kind",
        #     "LLM",
        # )
        
        span.set_attribute(
            SpanAttributes.INPUT_VALUE,
            message,
        )

import { SemanticConventions, FISpanKind } from '@traceai/fi-semantic-conventions';

function chat(message: string) {
    const span = tracer.startSpan("an_llm_span");
    
    span.setAttributes({
        [SemanticConventions.FI_SPAN_KIND]: FISpanKind.LLM,
        [SemanticConventions.INPUT_VALUE]: message,
        [SemanticConventions.LLM_MODEL_NAME]: "gpt-4",
    });

    // Your LLM logic here...
    
    span.setAttributes({
        [SemanticConventions.OUTPUT_VALUE]: response,
        [SemanticConventions.LLM_TOKEN_COUNT_TOTAL]: tokenCount,
    });
    
    span.end();
}

Converting Messages to OpenTelemetry Span Attributes

To export a list of objects as OpenTelemetry span attributes, flatten the list until the attribute values are simple types, such as bool, str, bytes, int, float, or simple lists like List[bool], List[str], List[bytes], List[int], List[float].

Python Example
TypeScript Example

# List of messages from OpenAI or another LLM provider
messages = [{"message.role": "user", "message.content": "hello"},
            {"message.role": "assistant", "message.content": "hi"}]

# Assuming you have a span object already created
for i, obj in enumerate(messages):
    for key, value in obj.items():
        span.set_attribute(f"input.messages.{i}.{key}", value)

import { MessageConventions } from '@traceai/fi-semantic-conventions';

// List of messages from OpenAI or another LLM provider
const messages = [
    { "message.role": "user", "message.content": "hello" },
    { "message.role": "assistant", "message.content": "hi" }
];

// Assuming you have a span object already created
messages.forEach((obj, i) => {
    Object.entries(obj).forEach(([key, value]) => {
        span.setAttribute(`input.messages.${i}.${key}`, value);
    });
});

// Or using semantic conventions constants:
messages.forEach((message, i) => {
    span.setAttributes({
        [`input.messages.${i}.${MessageConventions.MESSAGE_ROLE}`]: message["message.role"],
        [`input.messages.${i}.${MessageConventions.MESSAGE_CONTENT}`]: message["message.content"],
    });
});

Get Started

Guides

Types of Attributes

Attribute Overview

Using Semantic Conventions

Converting Messages to OpenTelemetry Span Attributes

​Types of Attributes

​Attribute Overview

​Using Semantic Conventions

​Converting Messages to OpenTelemetry Span Attributes

Types of Attributes

Attribute Overview

Using Semantic Conventions

Converting Messages to OpenTelemetry Span Attributes