first commit

docker_svc/agent/app/libs/prompt_helper.py

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+from llama_index.core.base.llms.types import ChatMessage as LlamaChatMessage
+import logging
+from libs.models import ChatMessage
+from typing import List, Dict, Any, Optional, AsyncGenerator
+import httpx
+import json
+import os
+import asyncio
+
+logger = logging.getLogger(__name__)
+
+def format_system_prompt(system_prompt_template: str, language_prompts: dict, language: str,
+                         retrieved_docs: str, is_medical: bool, personality: str = "supportive",
+                         personality_prompts: dict = {}) -> str:
+    """Formatta il prompt di sistema con il contenuto specifico della lingua, personalità e i documenti recuperati."""
+    language_prompt = language_prompts[language]["prompt"]
+    language_disclaimer = language_prompts[language]["disclaimer"]
+    language_constraint = "" if language == "auto" else language_prompts[language]["constraint"]
+    
+    # Miglioro il log e la gestione della personalità
+    if personality not in personality_prompts:
+        logger.warning(f"Personality '{personality}' not found in prompts, using default empty prompt")
+        personality_prompt = ""
+    else:
+        personality_prompt = personality_prompts[personality]["prompt"]
+        logger.info(f"Using '{personality}' personality: {personality_prompts[personality]['description'][:50]}...")
+    
+    logger.info(f"Formatting system prompt with language {language}, personality {personality}")
+    system_message_content = system_prompt_template.format(
+        language_prompt=language_prompt,
+        context=retrieved_docs,
+        language_disclaimer=language_disclaimer if is_medical else "",
+        personality_prompt=personality_prompt,
+        language_constraint=language_constraint
+    )
+    logger.debug(f"System message content: {system_message_content[:200]}...")
+    return system_message_content
+
+async def perform_inference_streaming(
+    llm,
+    system_message: str,
+    history: List[Dict],
+    current_message: str
+) -> AsyncGenerator[str, None]:
+    """Stream inference results from Ollama API"""
+    base_url = os.getenv("OLLAMA_BASE_URL", "http://ollama:11434")
+    
+    # Prepare messages for Ollama API
+    messages = []
+    
+    # Add system message
+    messages.append({
+        "role": "system",
+        "content": system_message
+    })
+    
+    # Add history
+    for msg in history:
+        messages.append({
+            "role": "user" if msg.role == "user" else "assistant",
+            "content": msg.content
+        })
+    
+    # Add current user message
+    messages.append({
+        "role": "user",
+        "content": current_message
+    })
+    
+    # Prepare request payload
+    payload = {
+        "model": llm.model,
+        "messages": messages,
+        "stream": True,
+        "options": {
+            "temperature": llm.temperature
+        }
+    }
+    
+    logger.debug(f"Sending streaming request to Ollama API: {base_url}/api/chat")
+    
+    try:
+        async with httpx.AsyncClient() as client:
+            async with client.stream("POST", f"{base_url}/api/chat", json=payload, timeout=60.0) as response:
+                if response.status_code != 200:
+                    error_detail = await response.aread()
+                    logger.error(f"Error from Ollama API: {response.status_code}, {error_detail}")
+                    yield f"Error: Failed to get response from language model (Status {response.status_code})"
+                    return
+
+                # Variable to accumulate the full response
+                full_response = ""
+                
+                # Process the streaming response
+                async for chunk in response.aiter_text():
+                    if not chunk.strip():
+                        continue
+                    
+                    # Each chunk might contain one JSON object
+                    try:
+                        data = json.loads(chunk)
+                        # Process message content if available
+                        if 'message' in data and 'content' in data['message']:
+                            content = data['message']['content']
+                            full_response += content
+                            yield content
+                        
+                        # Check if this is the final message with done flag
+                        if data.get('done', False):
+                            logger.debug("Streaming response completed")
+                    except json.JSONDecodeError as e:
+                        logger.error(f"Failed to parse streaming response: {e}, chunk: {chunk}")
+                
+    except Exception as e:
+        logger.error(f"Error during streaming inference: {str(e)}")
+        yield f"Error: {str(e)}"
+        
+    # Return empty string at the end to signal completion
+    yield ""
+
+def perform_inference(
+    llm,
+    system_message: str,
+    history: List[Dict],
+    current_message: str,
+    stream: bool = False
+) -> str:
+    """Perform inference with the given LLM."""
+    if stream:
+        # This will be handled by the streaming endpoint
+        raise ValueError("Streaming not supported in synchronous inference")
+    
+    # Prepare messages for the API
+    messages = []
+    
+    # Add system message
+    messages.append({
+        "role": "system",
+        "content": system_message
+    })
+    
+    # Add history
+    for msg in history:
+        messages.append({
+            "role": "user" if msg.role == "user" else "assistant",
+            "content": msg.content
+        })
+    
+    # Add current user message
+    messages.append({
+        "role": "user",
+        "content": current_message
+    })
+    
+    # For non-streaming, we'll use the httpx client directly to call Ollama API
+    base_url = os.getenv("OLLAMA_BASE_URL", "http://ollama:11434")
+    
+    # Prepare request payload
+    payload = {
+        "model": llm.model,
+        "messages": messages,
+        "stream": False,
+        "options": {
+            "temperature": llm.temperature
+        }
+    }
+    
+    logger.debug(f"Sending non-streaming request to Ollama API: {base_url}/api/chat")
+    
+    try:
+        with httpx.Client(timeout=60.0) as client:
+            response = client.post(f"{base_url}/api/chat", json=payload)
+            
+            if response.status_code != 200:
+                logger.error(f"Error from Ollama API: {response.status_code}, {response.text}")
+                return f"Error: Failed to get response from language model (Status {response.status_code})"
+            
+            data = response.json()
+            if 'message' in data and 'content' in data['message']:
+                return data['message']['content']
+            else:
+                logger.error(f"Unexpected response format: {data}")
+                return "Error: Unexpected response format from language model"
+    
+    except Exception as e:
+        logger.error(f"Error during non-streaming inference: {str(e)}")
+        return f"Error: {str(e)}"
+
+def select_llm(llm, llm_reasoning, reasoning: bool):
+    """Select the LLM model based on the reasoning flag."""
+    selected_llm = llm_reasoning if reasoning else llm
+    return selected_llm