# -*- coding: utf-8 -*-
# Standard
from typing import Any
# Third-Party
from pydantic import BaseModel, Field
# OpenAI Chat API Models
class ChatMessage(BaseModel):
role: str = Field(..., description="Role of the message sender")
content: str = Field(..., description="Content of the message")
name: str | None = Field(None, description="Name of the sender")
class ChatCompletionRequest(BaseModel):
model: str = Field(..., description="Model to use for completion")
messages: list[ChatMessage] = Field(..., description="List of messages")
max_tokens: int | None = Field(None, description="Maximum tokens to generate")
temperature: float | None = Field(0.7, description="Sampling temperature")
top_p: float | None = Field(1.0, description="Nucleus sampling parameter")
n: int | None = Field(1, description="Number of completions to generate")
stream: bool | None = Field(False, description="Whether to stream responses")
stop: str | list[str] | None = Field(None, description="Stop sequences")
presence_penalty: float | None = Field(0.0, description="Presence penalty")
frequency_penalty: float | None = Field(0.0, description="Frequency penalty")
logit_bias: dict[str, float] | None = Field(None, description="Logit bias")
user: str | None = Field(None, description="User identifier")
class Usage(BaseModel):
prompt_tokens: int = Field(..., description="Tokens in the prompt")
completion_tokens: int = Field(..., description="Tokens in the completion")
total_tokens: int = Field(..., description="Total tokens used")
class ChatCompletionChoice(BaseModel):
index: int = Field(..., description="Choice index")
message: ChatMessage = Field(..., description="Generated message")
finish_reason: str = Field(..., description="Reason for finishing")
class ChatCompletionResponse(BaseModel):
id: str = Field(..., description="Unique identifier for the completion")
object: str = Field("chat.completion", description="Object type")
created: int = Field(..., description="Unix timestamp of creation")
model: str = Field(..., description="Model used for completion")
choices: list[ChatCompletionChoice] = Field(..., description="List of completion choices")
usage: Usage = Field(..., description="Token usage information")
# Health and Status Models
class HealthResponse(BaseModel):
status: str = Field(..., description="Health status")
timestamp: str = Field(..., description="Timestamp of health check")
details: dict[str, Any] | None = Field(None, description="Additional health details")
class ReadyResponse(BaseModel):
ready: bool = Field(..., description="Readiness status")
timestamp: str = Field(..., description="Timestamp of readiness check")
details: dict[str, Any] | None = Field(None, description="Additional readiness details")
# Tool Models
class ToolDefinition(BaseModel):
id: str = Field(..., description="Tool identifier")
name: str = Field(..., description="Tool name")
description: str = Field(..., description="Tool description")
input_schema: dict[str, Any] = Field(..., description="Tool input schema", alias="schema")
url: str | None = Field(None, description="Tool URL (for REST tools)")
method: str | None = Field(None, description="HTTP method")
integration_type: str | None = Field(None, description="Integration type")
class Config:
populate_by_name = True # Allow both 'schema' and 'input_schema'
class ToolListResponse(BaseModel):
tools: list[ToolDefinition] = Field(..., description="List of available tools")
count: int = Field(..., description="Number of tools")
# Agent Configuration Models
class AgentConfig(BaseModel):
# MCP Gateway Configuration
mcp_gateway_url: str = Field(..., description="MCP Gateway URL")
gateway_bearer_token: str | None = Field(None, description="Gateway authentication token")
tools_allowlist: list[str] | None = Field(None, description="List of allowed tool IDs")
# LLM Provider Configuration
llm_provider: str = Field("openai", description="LLM provider (openai, azure, bedrock, ollama, anthropic)")
default_model: str = Field("gpt-4o-mini", description="Default model to use")
# OpenAI Configuration
openai_api_key: str | None = Field(None, description="OpenAI API key")
openai_base_url: str | None = Field(None, description="Custom OpenAI base URL")
openai_organization: str | None = Field(None, description="OpenAI organization")
# Azure OpenAI Configuration
azure_openai_api_key: str | None = Field(None, description="Azure OpenAI API key")
azure_openai_endpoint: str | None = Field(None, description="Azure OpenAI endpoint")
azure_openai_api_version: str = Field("2024-02-15-preview", description="Azure OpenAI API version")
azure_deployment_name: str | None = Field(None, description="Azure deployment name")
# AWS Bedrock Configuration
aws_access_key_id: str | None = Field(None, description="AWS access key ID")
aws_secret_access_key: str | None = Field(None, description="AWS secret access key")
aws_region: str = Field("us-east-1", description="AWS region")
bedrock_model_id: str | None = Field(None, description="Bedrock model ID")
# OLLAMA Configuration
ollama_base_url: str = Field("http://localhost:11434", description="OLLAMA base URL")
ollama_model: str | None = Field(None, description="OLLAMA model name")
# Anthropic Configuration
anthropic_api_key: str | None = Field(None, description="Anthropic API key")
# Agent Configuration
max_iterations: int = Field(10, description="Maximum agent iterations")
temperature: float = Field(0.7, description="Default temperature")
streaming_enabled: bool = Field(True, description="Enable streaming responses")
debug_mode: bool = Field(False, description="Enable debug logging")
# Performance Configuration
request_timeout: int = Field(30, description="Request timeout in seconds")
max_tokens: int | None = Field(None, description="Maximum tokens per response")
top_p: float | None = Field(None, description="Top-p sampling parameter")
# Tool Invocation Models
class ToolInvocationRequest(BaseModel):
tool_id: str = Field(..., description="Tool to invoke")
args: dict[str, Any] = Field(default_factory=dict, description="Tool arguments")
class ToolInvocationResponse(BaseModel):
tool_id: str = Field(..., description="Tool that was invoked")
result: Any = Field(..., description="Tool execution result")
execution_time: float | None = Field(None, description="Execution time in seconds")
success: bool = Field(..., description="Whether execution was successful")
error: str | None = Field(None, description="Error message if any")
# Streaming Models
class StreamChunk(BaseModel):
id: str = Field(..., description="Stream identifier")
object: str = Field("chat.completion.chunk", description="Object type")
created: int = Field(..., description="Unix timestamp")
model: str = Field(..., description="Model used")
choices: list[dict[str, Any]] = Field(..., description="Stream choices")
# Error Models
class ErrorResponse(BaseModel):
error: str = Field(..., description="Error message")
code: str | None = Field(None, description="Error code")
details: dict[str, Any] | None = Field(None, description="Additional error details")
# JSON-RPC Models for A2A communication
class JSONRPCRequest(BaseModel):
jsonrpc: str = Field("2.0", description="JSON-RPC version")
method: str = Field(..., description="Method to call")
params: dict[str, Any] | None = Field(None, description="Method parameters")
id: str | int | None = Field(None, description="Request identifier")
class JSONRPCResponse(BaseModel):
jsonrpc: str = Field("2.0", description="JSON-RPC version")
result: Any | None = Field(None, description="Method result")
error: dict[str, Any] | None = Field(None, description="Error object")
id: str | int | None = Field(None, description="Request identifier")
