Add AGENTS.md documentation for AI agent guidance

app/utils/transformers.py

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+"""Utilities for transforming between OpenAI and watsonx formats."""
+
+import time
+import uuid
+from typing import Any, Dict, List, Optional
+from app.models.openai_models import (
+    ChatMessage,
+    ChatCompletionChoice,
+    ChatCompletionUsage,
+    ChatCompletionResponse,
+    ChatCompletionChunk,
+    ChatCompletionChunkChoice,
+    ChatCompletionChunkDelta,
+    CompletionChoice,
+    CompletionResponse,
+    EmbeddingData,
+    EmbeddingUsage,
+    EmbeddingResponse,
+)
+
+
+def transform_messages_to_watsonx(messages: List[ChatMessage]) -> List[Dict[str, Any]]:
+    """Transform OpenAI messages to watsonx format.
+    
+    Args:
+        messages: List of OpenAI ChatMessage objects
+        
+    Returns:
+        List of watsonx-compatible message dicts
+    """
+    watsonx_messages = []
+    
+    for msg in messages:
+        watsonx_msg = {
+            "role": msg.role,
+        }
+        
+        if msg.content:
+            watsonx_msg["content"] = msg.content
+        
+        if msg.name:
+            watsonx_msg["name"] = msg.name
+        
+        if msg.tool_calls:
+            watsonx_msg["tool_calls"] = msg.tool_calls
+        
+        if msg.function_call:
+            watsonx_msg["function_call"] = msg.function_call
+        
+        watsonx_messages.append(watsonx_msg)
+    
+    return watsonx_messages
+
+
+def transform_tools_to_watsonx(tools: Optional[List[Dict]]) -> Optional[List[Dict]]:
+    """Transform OpenAI tools to watsonx format.
+    
+    Args:
+        tools: List of OpenAI tool definitions
+        
+    Returns:
+        List of watsonx-compatible tool definitions
+    """
+    if not tools:
+        return None
+    
+    # watsonx uses similar format to OpenAI for tools
+    return tools
+
+
+def transform_watsonx_to_openai_chat(
+    watsonx_response: Dict[str, Any],
+    model: str,
+    request_id: Optional[str] = None,
+) -> ChatCompletionResponse:
+    """Transform watsonx chat response to OpenAI format.
+    
+    Args:
+        watsonx_response: Response from watsonx chat API
+        model: Model name to include in response
+        request_id: Optional request ID, generates one if not provided
+        
+    Returns:
+        OpenAI-compatible ChatCompletionResponse
+    """
+    response_id = request_id or f"chatcmpl-{uuid.uuid4().hex[:24]}"
+    created = int(time.time())
+    
+    # Extract choices
+    choices = []
+    watsonx_choices = watsonx_response.get("choices", [])
+    
+    for idx, choice in enumerate(watsonx_choices):
+        message_data = choice.get("message", {})
+        
+        message = ChatMessage(
+            role=message_data.get("role", "assistant"),
+            content=message_data.get("content"),
+            tool_calls=message_data.get("tool_calls"),
+            function_call=message_data.get("function_call"),
+        )
+        
+        choices.append(
+            ChatCompletionChoice(
+                index=idx,
+                message=message,
+                finish_reason=choice.get("finish_reason"),
+            )
+        )
+    
+    # Extract usage
+    usage_data = watsonx_response.get("usage", {})
+    usage = ChatCompletionUsage(
+        prompt_tokens=usage_data.get("prompt_tokens", 0),
+        completion_tokens=usage_data.get("completion_tokens", 0),
+        total_tokens=usage_data.get("total_tokens", 0),
+    )
+    
+    return ChatCompletionResponse(
+        id=response_id,
+        created=created,
+        model=model,
+        choices=choices,
+        usage=usage,
+    )
+
+
+def transform_watsonx_to_openai_chat_chunk(
+    watsonx_chunk: Dict[str, Any],
+    model: str,
+    request_id: str,
+) -> ChatCompletionChunk:
+    """Transform watsonx streaming chunk to OpenAI format.
+    
+    Args:
+        watsonx_chunk: Streaming chunk from watsonx
+        model: Model name to include in response
+        request_id: Request ID for this stream
+        
+    Returns:
+        OpenAI-compatible ChatCompletionChunk
+    """
+    created = int(time.time())
+    
+    # Extract choices
+    choices = []
+    watsonx_choices = watsonx_chunk.get("choices", [])
+    
+    for idx, choice in enumerate(watsonx_choices):
+        delta_data = choice.get("delta", {})
+        
+        delta = ChatCompletionChunkDelta(
+            role=delta_data.get("role"),
+            content=delta_data.get("content"),
+            tool_calls=delta_data.get("tool_calls"),
+            function_call=delta_data.get("function_call"),
+        )
+        
+        choices.append(
+            ChatCompletionChunkChoice(
+                index=idx,
+                delta=delta,
+                finish_reason=choice.get("finish_reason"),
+            )
+        )
+    
+    return ChatCompletionChunk(
+        id=request_id,
+        created=created,
+        model=model,
+        choices=choices,
+    )
+
+
+def transform_watsonx_to_openai_completion(
+    watsonx_response: Dict[str, Any],
+    model: str,
+    request_id: Optional[str] = None,
+) -> CompletionResponse:
+    """Transform watsonx text generation response to OpenAI completion format.
+    
+    Args:
+        watsonx_response: Response from watsonx text generation API
+        model: Model name to include in response
+        request_id: Optional request ID, generates one if not provided
+        
+    Returns:
+        OpenAI-compatible CompletionResponse
+    """
+    response_id = request_id or f"cmpl-{uuid.uuid4().hex[:24]}"
+    created = int(time.time())
+    
+    # Extract results
+    results = watsonx_response.get("results", [])
+    choices = []
+    
+    for idx, result in enumerate(results):
+        choices.append(
+            CompletionChoice(
+                text=result.get("generated_text", ""),
+                index=idx,
+                finish_reason=result.get("stop_reason"),
+            )
+        )
+    
+    # Extract usage
+    usage_data = watsonx_response.get("usage", {})
+    usage = ChatCompletionUsage(
+        prompt_tokens=usage_data.get("prompt_tokens", 0),
+        completion_tokens=usage_data.get("generated_tokens", 0),
+        total_tokens=usage_data.get("prompt_tokens", 0) + usage_data.get("generated_tokens", 0),
+    )
+    
+    return CompletionResponse(
+        id=response_id,
+        created=created,
+        model=model,
+        choices=choices,
+        usage=usage,
+    )
+
+
+def transform_watsonx_to_openai_embeddings(
+    watsonx_response: Dict[str, Any],
+    model: str,
+) -> EmbeddingResponse:
+    """Transform watsonx embeddings response to OpenAI format.
+    
+    Args:
+        watsonx_response: Response from watsonx embeddings API
+        model: Model name to include in response
+        
+    Returns:
+        OpenAI-compatible EmbeddingResponse
+    """
+    # Extract results
+    results = watsonx_response.get("results", [])
+    data = []
+    
+    for idx, result in enumerate(results):
+        embedding = result.get("embedding", [])
+        data.append(
+            EmbeddingData(
+                embedding=embedding,
+                index=idx,
+            )
+        )
+    
+    # Calculate usage
+    input_token_count = watsonx_response.get("input_token_count", 0)
+    usage = EmbeddingUsage(
+        prompt_tokens=input_token_count,
+        total_tokens=input_token_count,
+    )
+    
+    return EmbeddingResponse(
+        data=data,
+        model=model,
+        usage=usage,
+    )
+
+
+def format_sse_event(data: str) -> str:
+    """Format data as Server-Sent Event.
+    
+    Args:
+        data: JSON string to send
+        
+    Returns:
+        Formatted SSE string
+    """
+    return f"data: {data}\n\n"