FI Semantic Conventions

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",
}