diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9389163 --- /dev/null +++ b/.gitignore @@ -0,0 +1,26 @@ +# Python +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +build/ +dist/ +*.egg-info/ +*.egg + +# Virtual environments +venv/ +.venv/ +ENV/ + +# IDE +.idea/ +.vscode/ +*.swp +*~ + +# Local +.env +.env.local +*.log diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..cdde590 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,29 @@ +FROM python:3.13-slim + +WORKDIR /app + +# Install uv for fast, reliable package management +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +# Install system dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl \ + && rm -rf /var/lib/apt/lists/* + +# Copy requirements first for better caching +COPY requirements.txt . +RUN uv pip install --system --no-cache -r requirements.txt + +# Copy application code +COPY chat_handler.py . + +# Set environment variables +ENV PYTHONUNBUFFERED=1 +ENV PYTHONDONTWRITEBYTECODE=1 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ + CMD python -c "print('healthy')" || exit 1 + +# Run the application +CMD ["python", "chat_handler.py"] diff --git a/Dockerfile.v2 b/Dockerfile.v2 new file mode 100644 index 0000000..9756a0b --- /dev/null +++ b/Dockerfile.v2 @@ -0,0 +1,11 @@ +# Chat Handler v2 - Using handler-base +ARG BASE_TAG=local +FROM ghcr.io/billy-davies-2/handler-base:${BASE_TAG} + +WORKDIR /app + +# Copy only the handler code (dependencies are in base image) +COPY chat_handler_v2.py ./chat_handler.py + +# Run the handler +CMD ["python", "chat_handler.py"] diff --git a/README.md b/README.md index d434084..55acd68 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,110 @@ -# chat-handler +# Chat Handler +Text-based chat pipeline for the DaviesTechLabs AI/ML platform. + +## Overview + +A NATS-based service that handles chat completion requests with RAG (Retrieval Augmented Generation). + +**Pipeline:** Query → Embeddings → Milvus → Rerank → LLM → (optional TTS) + +## Versions + +| File | Description | +|------|-------------| +| `chat_handler.py` | Standalone implementation (v1) | +| `chat_handler_v2.py` | Uses handler-base library (recommended) | +| `Dockerfile` | Standalone image | +| `Dockerfile.v2` | Handler-base image | + +## Architecture + +``` +NATS (ai.chat.request) + │ + ▼ +┌───────────────────┐ +│ Chat Handler │ +└───────────────────┘ + │ + ├──▶ BGE Embeddings (drizzt) + │ │ + │ ▼ + ├──▶ Milvus Vector Search + │ │ + │ ▼ + ├──▶ BGE Reranker (danilo) + │ │ + │ ▼ + ├──▶ vLLM (khelben) + │ │ + │ ▼ (optional) + └──▶ XTTS TTS (elminster) + │ + ▼ + NATS (ai.chat.response.{id}) +``` + +## NATS Message Format + +### Request (ai.chat.request) + +```json +{ + "request_id": "uuid", + "query": "What is the capital of France?", + "collection": "knowledge_base", + "enable_tts": false, + "system_prompt": "Optional custom system prompt" +} +``` + +### Response (ai.chat.response.{request_id}) + +```json +{ + "request_id": "uuid", + "response": "The capital of France is Paris.", + "sources": [ + {"text": "Paris is the capital...", "score": 0.95} + ], + "audio": "base64-encoded-audio (if TTS enabled)" +} +``` + +## Configuration + +| Environment Variable | Default | Description | +|---------------------|---------|-------------| +| `NATS_URL` | `nats://nats.ai-ml.svc.cluster.local:4222` | NATS server | +| `EMBEDDINGS_URL` | `http://embeddings-predictor.ai-ml.svc.cluster.local` | Embeddings | +| `RERANKER_URL` | `http://reranker-predictor.ai-ml.svc.cluster.local` | Reranker | +| `VLLM_URL` | `http://llm-draft.ai-ml.svc.cluster.local:8000` | LLM service | +| `TTS_URL` | `http://tts-predictor.ai-ml.svc.cluster.local` | TTS (optional) | +| `MILVUS_HOST` | `milvus.ai-ml.svc.cluster.local` | Vector DB | +| `COLLECTION_NAME` | `knowledge_base` | Default Milvus collection | +| `ENABLE_TTS` | `false` | Enable audio responses | + +## Building + +```bash +# Standalone image (v1) +docker build -f Dockerfile -t chat-handler:latest . + +# Handler-base image (v2 - recommended) +docker build -f Dockerfile.v2 -t chat-handler:v2 . +``` + +## Dependencies + +The v2 handler depends on [handler-base](https://git.daviestechlabs.io/daviestechlabs/handler-base): + +```bash +pip install git+https://git.daviestechlabs.io/daviestechlabs/handler-base.git +``` + +## Related + +- [handler-base](https://git.daviestechlabs.io/daviestechlabs/handler-base) - Base handler library +- [voice-assistant](https://git.daviestechlabs.io/daviestechlabs/voice-assistant) - Voice pipeline +- [homelab-design](https://git.daviestechlabs.io/daviestechlabs/homelab-design) - Architecture docs diff --git a/chat_handler.py b/chat_handler.py new file mode 100644 index 0000000..2ce7ef1 --- /dev/null +++ b/chat_handler.py @@ -0,0 +1,867 @@ +#!/usr/bin/env python3 +""" +Chat Handler Service + +Text-based chat pipeline: +1. Listen for text on NATS subject "ai.chat.request" +2. Generate embeddings for RAG (optional) +3. Retrieve context from Milvus +4. Rerank with BGE reranker +5. Generate response with vLLM +6. Optionally synthesize speech with XTTS +7. Publish result to NATS "ai.chat.response.{request_id}" +""" +import asyncio +import base64 +import json +import logging +import os +import signal +import subprocess +import sys +import time +from typing import List, Dict, Optional + +# Install dependencies on startup +subprocess.check_call([ + sys.executable, "-m", "pip", "install", "-q", + "--root-user-action=ignore", + "-r", "/app/requirements.txt" +]) + +import httpx +import msgpack +import nats +import redis.asyncio as redis +from pymilvus import connections, Collection, utility + +# OpenTelemetry imports +from opentelemetry import trace, metrics +from opentelemetry.sdk.trace import TracerProvider +from opentelemetry.sdk.trace.export import BatchSpanProcessor +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader +from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter +from opentelemetry.exporter.otlp.proto.grpc.metric_exporter import OTLPMetricExporter +from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter as OTLPSpanExporterHTTP +from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter as OTLPMetricExporterHTTP +from opentelemetry.sdk.resources import Resource, SERVICE_NAME, SERVICE_VERSION, SERVICE_NAMESPACE +from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor +from opentelemetry.instrumentation.logging import LoggingInstrumentor + +# MLflow inference tracking +try: + from mlflow_utils import InferenceMetricsTracker + from mlflow_utils.inference_tracker import InferenceMetrics + MLFLOW_AVAILABLE = True +except ImportError: + MLFLOW_AVAILABLE = False + InferenceMetricsTracker = None + InferenceMetrics = None + +# Configure logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger("chat-handler") + + +def setup_telemetry(): + """Initialize OpenTelemetry tracing and metrics.""" + otel_enabled = os.environ.get("OTEL_ENABLED", "true").lower() == "true" + if not otel_enabled: + logger.info("OpenTelemetry disabled") + return None, None + + otel_endpoint = os.environ.get("OTEL_EXPORTER_OTLP_ENDPOINT", "http://opentelemetry-collector.observability.svc.cluster.local:4317") + service_name = os.environ.get("OTEL_SERVICE_NAME", "chat-handler") + service_namespace = os.environ.get("OTEL_SERVICE_NAMESPACE", "ai-ml") + + # HyperDX configuration + hyperdx_api_key = os.environ.get("HYPERDX_API_KEY", "") + hyperdx_endpoint = os.environ.get("HYPERDX_ENDPOINT", "https://in-otel.hyperdx.io") + use_hyperdx = os.environ.get("HYPERDX_ENABLED", "false").lower() == "true" and hyperdx_api_key + + resource = Resource.create({ + SERVICE_NAME: service_name, + SERVICE_VERSION: os.environ.get("SERVICE_VERSION", "1.0.0"), + SERVICE_NAMESPACE: service_namespace, + "deployment.environment": os.environ.get("DEPLOYMENT_ENV", "production"), + "host.name": os.environ.get("HOSTNAME", "unknown"), + }) + + trace_provider = TracerProvider(resource=resource) + + if use_hyperdx: + logger.info(f"Configuring HyperDX exporter at {hyperdx_endpoint}") + headers = {"authorization": hyperdx_api_key} + otlp_span_exporter = OTLPSpanExporterHTTP( + endpoint=f"{hyperdx_endpoint}/v1/traces", + headers=headers + ) + otlp_metric_exporter = OTLPMetricExporterHTTP( + endpoint=f"{hyperdx_endpoint}/v1/metrics", + headers=headers + ) + else: + otlp_span_exporter = OTLPSpanExporter(endpoint=otel_endpoint, insecure=True) + otlp_metric_exporter = OTLPMetricExporter(endpoint=otel_endpoint, insecure=True) + + trace_provider.add_span_processor(BatchSpanProcessor(otlp_span_exporter)) + trace.set_tracer_provider(trace_provider) + + metric_reader = PeriodicExportingMetricReader(otlp_metric_exporter, export_interval_millis=60000) + meter_provider = MeterProvider(resource=resource, metric_readers=[metric_reader]) + metrics.set_meter_provider(meter_provider) + + HTTPXClientInstrumentor().instrument() + LoggingInstrumentor().instrument(set_logging_format=True) + + destination = "HyperDX" if use_hyperdx else "OTEL Collector" + logger.info(f"OpenTelemetry initialized - destination: {destination}, service: {service_name}") + + return trace.get_tracer(__name__), metrics.get_meter(__name__) + +# Configuration from environment +TTS_URL = os.environ.get("TTS_URL", "http://tts-predictor.ai-ml.svc.cluster.local") +EMBEDDINGS_URL = os.environ.get( + "EMBEDDINGS_URL", "http://embeddings-predictor.ai-ml.svc.cluster.local" +) +RERANKER_URL = os.environ.get( + "RERANKER_URL", "http://reranker-predictor.ai-ml.svc.cluster.local" +) +VLLM_URL = os.environ.get("VLLM_URL", "http://llm-draft.ai-ml.svc.cluster.local:8000") +LLM_MODEL = os.environ.get("LLM_MODEL", "mistralai/Mistral-7B-Instruct-v0.3") +MILVUS_HOST = os.environ.get("MILVUS_HOST", "milvus.ai-ml.svc.cluster.local") +MILVUS_PORT = int(os.environ.get("MILVUS_PORT", "19530")) +COLLECTION_NAME = os.environ.get("COLLECTION_NAME", "knowledge_base") +NATS_URL = os.environ.get("NATS_URL", "nats://nats.ai-ml.svc.cluster.local:4222") +VALKEY_URL = os.environ.get("VALKEY_URL", "redis://valkey.ai-ml.svc.cluster.local:6379") + +# MLflow configuration +MLFLOW_ENABLED = os.environ.get("MLFLOW_ENABLED", "true").lower() == "true" +MLFLOW_TRACKING_URI = os.environ.get( + "MLFLOW_TRACKING_URI", "http://mlflow.mlflow.svc.cluster.local:80" +) + +# Context window limits (characters) +MAX_CONTEXT_LENGTH = int(os.environ.get("MAX_CONTEXT_LENGTH", "8000")) # Prevent unbounded growth + +# NATS subjects (ai.* schema) +# Per-user channels matching companions-frontend pattern +REQUEST_SUBJECT = "ai.chat.user.*.message" # Wildcard subscription for all users +PREMIUM_REQUEST_SUBJECT = "ai.chat.premium.user.*.message" # Premium users +RESPONSE_SUBJECT = "ai.chat.response" # Response published to specific request_id +STREAM_RESPONSE_SUBJECT = "ai.chat.response.stream" # Streaming responses (token chunks) + +# System prompt for the assistant +SYSTEM_PROMPT = """You are a helpful AI assistant. +Answer questions based on the provided context when available. +Be concise and informative. If you don't know the answer, say so clearly.""" + + +class ChatHandler: + def __init__(self): + self.nc = None + self.http_client = None + self.collection = None + self.valkey_client = None + self.running = True + self.tracer = None + self.meter = None + self.request_counter = None + self.request_duration = None + self.rag_search_duration = None + # MLflow inference tracker + self.mlflow_tracker = None + + async def setup(self): + """Initialize all connections.""" + # Initialize OpenTelemetry + self.tracer, self.meter = setup_telemetry() + + # Setup metrics + if self.meter: + self.request_counter = self.meter.create_counter( + "chat.requests", + description="Number of chat requests processed", + unit="1" + ) + self.request_duration = self.meter.create_histogram( + "chat.request_duration", + description="Duration of chat request processing", + unit="s" + ) + self.rag_search_duration = self.meter.create_histogram( + "chat.rag_search_duration", + description="Duration of RAG search operations", + unit="s" + ) + + # Initialize MLflow inference tracker + if MLFLOW_ENABLED and MLFLOW_AVAILABLE: + try: + self.mlflow_tracker = InferenceMetricsTracker( + service_name="chat-handler", + experiment_name="chat-inference", + tracking_uri=MLFLOW_TRACKING_URI, + batch_size=50, + flush_interval_seconds=60.0, + ) + await self.mlflow_tracker.start() + logger.info(f"MLflow inference tracking enabled at {MLFLOW_TRACKING_URI}") + except Exception as e: + logger.warning(f"MLflow initialization failed: {e}, tracking disabled") + self.mlflow_tracker = None + elif not MLFLOW_AVAILABLE: + logger.info("MLflow utils not available, inference tracking disabled") + else: + logger.info("MLflow tracking disabled via MLFLOW_ENABLED=false") + + # NATS connection with reconnection support + async def disconnected_cb(): + logger.warning("NATS disconnected, attempting reconnection...") + + async def reconnected_cb(): + logger.info(f"NATS reconnected to {self.nc.connected_url.netloc}") + + async def error_cb(e): + logger.error(f"NATS error: {e}") + + async def closed_cb(): + logger.warning("NATS connection closed") + + self.nc = await nats.connect( + NATS_URL, + reconnect_time_wait=2, + max_reconnect_attempts=-1, # Infinite reconnection attempts + disconnected_cb=disconnected_cb, + reconnected_cb=reconnected_cb, + error_cb=error_cb, + closed_cb=closed_cb, + ) + logger.info(f"Connected to NATS at {NATS_URL}") + + # HTTP client for services + self.http_client = httpx.AsyncClient(timeout=180.0) + + # Connect to Valkey for conversation history and context caching + try: + self.valkey_client = redis.from_url( + VALKEY_URL, + encoding="utf-8", + decode_responses=True, + socket_connect_timeout=5 + ) + await self.valkey_client.ping() + logger.info(f"Connected to Valkey at {VALKEY_URL}") + except Exception as e: + logger.warning(f"Valkey connection failed: {e}, conversation history disabled") + self.valkey_client = None + + # Connect to Milvus if collection exists + try: + connections.connect(host=MILVUS_HOST, port=MILVUS_PORT) + if utility.has_collection(COLLECTION_NAME): + self.collection = Collection(COLLECTION_NAME) + self.collection.load() + logger.info(f"Connected to Milvus collection: {COLLECTION_NAME}") + else: + logger.warning(f"Collection {COLLECTION_NAME} not found, RAG disabled") + except Exception as e: + logger.warning(f"Milvus connection failed: {e}, RAG disabled") + + async def get_embeddings(self, texts: List[str]) -> List[List[float]]: + """Get embeddings from the embedding service.""" + try: + response = await self.http_client.post( + f"{EMBEDDINGS_URL}/embeddings", json={"input": texts, "model": "bge"} + ) + result = response.json() + return [d["embedding"] for d in result.get("data", [])] + except Exception as e: + logger.error(f"Embedding failed: {e}") + return [] + + async def search_milvus( + self, query_embedding: List[float], top_k: int = 5 + ) -> List[Dict]: + """Search Milvus for relevant documents.""" + if not self.collection: + return [] + try: + results = self.collection.search( + data=[query_embedding], + anns_field="embedding", + param={"metric_type": "COSINE", "params": {"ef": 64}}, + limit=top_k, + output_fields=["text", "book_name", "page_num"], + ) + docs = [] + for hits in results: + for hit in hits: + docs.append( + { + "text": hit.entity.get("text", ""), + "source": f'{hit.entity.get("book_name", "")} p.{hit.entity.get("page_num", "")}', + "score": hit.score, + } + ) + return docs + except Exception as e: + logger.error(f"Milvus search failed: {e}") + return [] + + async def rerank(self, query: str, documents: List[str]) -> List[Dict]: + """Rerank documents using the reranker service.""" + if not documents: + return [] + try: + response = await self.http_client.post( + f"{RERANKER_URL}/v1/rerank", + json={"query": query, "documents": documents}, + ) + return response.json().get("results", []) + except Exception as e: + logger.error(f"Reranking failed: {e}") + return [{"index": i, "relevance_score": 0.5} for i in range(len(documents))] + + async def get_conversation_history(self, session_id: str, max_messages: int = 10) -> List[Dict]: + """Retrieve conversation history from Valkey.""" + if not self.valkey_client or not session_id: + return [] + try: + key = f"chat:history:{session_id}" + # Get the most recent messages (stored as a list) + history_json = await self.valkey_client.lrange(key, -max_messages, -1) + history = [json.loads(msg) for msg in history_json] + logger.info(f"Retrieved {len(history)} messages from history for session {session_id}") + return history + except Exception as e: + logger.error(f"Failed to get conversation history: {e}") + return [] + + async def save_message_to_history(self, session_id: str, role: str, content: str, ttl: int = 3600): + """Save a message to conversation history in Valkey.""" + if not self.valkey_client or not session_id: + return + try: + key = f"chat:history:{session_id}" + message = json.dumps({"role": role, "content": content, "timestamp": time.time()}) + # Use RPUSH to append to the list + await self.valkey_client.rpush(key, message) + # Set TTL on the key (1 hour by default) + await self.valkey_client.expire(key, ttl) + logger.debug(f"Saved {role} message to history for session {session_id}") + except Exception as e: + logger.error(f"Failed to save message to history: {e}") + + async def get_context_window(self, session_id: str) -> Optional[str]: + """Retrieve cached context window from Valkey for attention offloading.""" + if not self.valkey_client or not session_id: + return None + try: + key = f"chat:context:{session_id}" + context = await self.valkey_client.get(key) + if context: + logger.info(f"Retrieved cached context window for session {session_id}") + return context + except Exception as e: + logger.error(f"Failed to get context window: {e}") + return None + + async def save_context_window(self, session_id: str, context: str, ttl: int = 3600): + """Save context window to Valkey for attention offloading.""" + if not self.valkey_client or not session_id: + return + try: + key = f"chat:context:{session_id}" + await self.valkey_client.set(key, context, ex=ttl) + logger.debug(f"Saved context window for session {session_id}") + except Exception as e: + logger.error(f"Failed to save context window: {e}") + + async def generate_response(self, query: str, context: str = "", session_id: str = None) -> str: + """Generate response using vLLM with conversation history from Valkey.""" + try: + messages = [{"role": "system", "content": SYSTEM_PROMPT}] + + # Add conversation history from Valkey if session exists + if session_id: + history = await self.get_conversation_history(session_id) + messages.extend(history) + + if context: + messages.append( + { + "role": "user", + "content": f"Context:\n{context}\n\nQuestion: {query}", + } + ) + else: + messages.append({"role": "user", "content": query}) + + response = await self.http_client.post( + f"{VLLM_URL}/v1/chat/completions", + json={ + "model": LLM_MODEL, + "messages": messages, + "max_tokens": 1000, + "temperature": 0.7, + }, + ) + result = response.json() + answer = result["choices"][0]["message"]["content"] + logger.info(f"Generated response: {answer[:100]}...") + + # Save messages to conversation history + if session_id: + await self.save_message_to_history(session_id, "user", query) + await self.save_message_to_history(session_id, "assistant", answer) + + return answer + except Exception as e: + logger.error(f"LLM generation failed: {e}") + return "I'm sorry, I couldn't generate a response." + + async def generate_response_streaming(self, query: str, context: str = "", request_id: str = "", session_id: str = None): + """Generate streaming response using vLLM and publish chunks to NATS. + + Yields tokens as they are generated and publishes them to NATS streaming subject. + Returns the complete response text. + """ + try: + messages = [{"role": "system", "content": SYSTEM_PROMPT}] + + # Add conversation history from Valkey if session exists + if session_id: + history = await self.get_conversation_history(session_id) + messages.extend(history) + + if context: + messages.append( + { + "role": "user", + "content": f"Context:\n{context}\n\nQuestion: {query}", + } + ) + else: + messages.append({"role": "user", "content": query}) + + full_response = "" + + # Stream response from vLLM + async with self.http_client.stream( + "POST", + f"{VLLM_URL}/v1/chat/completions", + json={ + "model": LLM_MODEL, + "messages": messages, + "max_tokens": 1000, + "temperature": 0.7, + "stream": True, + }, + timeout=60.0, + ) as response: + # Parse SSE (Server-Sent Events) stream + async for line in response.aiter_lines(): + if not line or not line.startswith("data: "): + continue + + data_str = line[6:] # Remove "data: " prefix + if data_str.strip() == "[DONE]": + break + + try: + chunk_data = json.loads(data_str) + + # Extract token from delta + if chunk_data.get("choices") and len(chunk_data["choices"]) > 0: + delta = chunk_data["choices"][0].get("delta", {}) + content = delta.get("content", "") + + if content: + full_response += content + + # Publish token chunk to NATS streaming subject + chunk_msg = { + "request_id": request_id, + "type": "chunk", + "content": content, + "done": False, + } + await self.nc.publish( + f"{STREAM_RESPONSE_SUBJECT}.{request_id}", + msgpack.packb(chunk_msg) + ) + except json.JSONDecodeError: + continue + + # Send completion message + completion_msg = { + "request_id": request_id, + "type": "done", + "content": "", + "done": True, + } + await self.nc.publish( + f"{STREAM_RESPONSE_SUBJECT}.{request_id}", + msgpack.packb(completion_msg) + ) + + logger.info(f"Streamed complete response ({len(full_response)} chars) for request {request_id}") + + # Save messages to conversation history + if session_id: + await self.save_message_to_history(session_id, "user", query) + await self.save_message_to_history(session_id, "assistant", full_response) + + return full_response + + except Exception as e: + logger.error(f"Streaming LLM generation failed: {e}") + # Send error message + error_msg = { + "request_id": request_id, + "type": "error", + "content": "I'm sorry, I couldn't generate a response.", + "done": True, + "error": str(e), + } + await self.nc.publish( + f"{STREAM_RESPONSE_SUBJECT}.{request_id}", + msgpack.packb(error_msg) + ) + return "I'm sorry, I couldn't generate a response." + + async def synthesize_speech(self, text: str, language: str = "en") -> str: + """Convert text to speech using XTTS (Coqui TTS).""" + try: + response = await self.http_client.get( + f"{TTS_URL}/api/tts", params={"text": text, "language_id": language} + ) + if response.status_code == 200: + audio_b64 = base64.b64encode(response.content).decode("utf-8") + logger.info(f"Synthesized {len(response.content)} bytes of audio") + return audio_b64 + else: + logger.error( + f"TTS returned status {response.status_code}: {response.text}" + ) + return "" + except Exception as e: + logger.error(f"TTS failed: {e}") + return "" + + async def process_request(self, msg, is_premium=False): + """Process a chat request.""" + start_time = time.time() + span = None + + # MLflow metrics tracking + mlflow_metrics = None + embedding_start = None + rag_start = None + rerank_start = None + llm_start = None + + try: + data = msgpack.unpackb(msg.data, raw=False) + + # Support companions-frontend format (user_id, username, message, premium) + # as well as the original format (request_id, text, enable_rag, etc.) + user_id = data.get("user_id") + username = data.get("username", "") + + # Get text from either 'message' (companions-frontend) or 'text' (original) + text = data.get("message") or data.get("text", "") + + # Generate request_id from user_id if not provided + import uuid + request_id = data.get("request_id") or f"{user_id or 'anon'}-{uuid.uuid4().hex[:8]}" + + # Initialize MLflow metrics if available + if self.mlflow_tracker and MLFLOW_AVAILABLE: + mlflow_metrics = InferenceMetrics( + request_id=request_id, + user_id=user_id, + session_id=data.get("session_id"), + model_name=LLM_MODEL, + model_endpoint=VLLM_URL, + ) + + # Start tracing span + if self.tracer: + span = self.tracer.start_span("chat.process_request") + span.set_attribute("request_id", request_id) + span.set_attribute("user_id", user_id or "anonymous") + span.set_attribute("premium", is_premium) + + # Premium status from message or channel + is_premium = is_premium or data.get("premium", False) + + # Support both new parameters and backward compatibility with use_rag + use_rag = data.get("use_rag") # Legacy parameter + enable_rag = data.get( + "enable_rag", use_rag if use_rag is not None else True + ) + enable_reranker = data.get( + "enable_reranker", use_rag if use_rag is not None else True + ) + + # Premium users get more documents for deeper RAG + default_top_k = 15 if is_premium else 5 + top_k = data.get("top_k", default_top_k) + + # Get request parameters + enable_tts = data.get("enable_tts", False) + enable_streaming = data.get("enable_streaming", False) # New parameter for streaming + language = data.get("language", "en") + session_id = data.get("session_id") + + # Update MLflow metrics with request params + if mlflow_metrics: + mlflow_metrics.rag_enabled = enable_rag + mlflow_metrics.reranker_enabled = enable_reranker + mlflow_metrics.is_streaming = enable_streaming + mlflow_metrics.is_premium = is_premium + mlflow_metrics.prompt_length = len(text) + + # Add attributes to span + if span: + span.set_attribute("enable_rag", enable_rag) + span.set_attribute("enable_reranker", enable_reranker) + span.set_attribute("top_k", top_k) + span.set_attribute("enable_tts", enable_tts) + span.set_attribute("enable_streaming", enable_streaming) + + logger.info( + f"Processing {'premium ' if is_premium else ''}chat request {request_id} from {username or user_id or 'anonymous'}: {text[:50]}... (RAG: {enable_rag}, Reranker: {enable_reranker}, top_k: {top_k})" + ) + + # Warn if reranker is enabled without RAG + if enable_reranker and not enable_rag: + logger.warning( + f"Request {request_id}: Reranker enabled without RAG - no documents to rerank" + ) + + if not text: + await self.publish_error(request_id, "No text provided") + return + + context = "" + rag_sources = [] + docs = [] + + # Step 1: RAG retrieval (if enabled) + if enable_rag and self.collection: + # Get embeddings for RAG + embedding_start = time.time() + embeddings = await self.get_embeddings([text]) + if mlflow_metrics: + mlflow_metrics.embedding_latency = time.time() - embedding_start + + if embeddings: + # Search Milvus with configurable top_k + rag_start = time.time() + docs = await self.search_milvus(embeddings[0], top_k=top_k) + if mlflow_metrics: + mlflow_metrics.rag_search_latency = time.time() - rag_start + mlflow_metrics.rag_documents_retrieved = len(docs) + if docs: + rag_sources = [d.get("source", "") for d in docs] + + # Step 2: Reranking (if enabled and we have documents) + if enable_reranker and docs: + # Rerank documents + rerank_start = time.time() + doc_texts = [d["text"] for d in docs] + reranked = await self.rerank(text, doc_texts) + if mlflow_metrics: + mlflow_metrics.rerank_latency = time.time() - rerank_start + # Take top 3 reranked documents with bounds checking + sorted_docs = sorted( + reranked, key=lambda x: x.get("relevance_score", 0), reverse=True + )[:3] + # Build context with bounds checking + # Note: doc_texts and docs have the same length (doc_texts derived from docs) + context_parts = [] + sources = [] + for item in sorted_docs: + idx = item.get("index", -1) + if 0 <= idx < len(docs): + context_parts.append(doc_texts[idx]) + sources.append(docs[idx].get("source", "")) + else: + logger.warning( + f"Reranker returned invalid index {idx} for {len(docs)} docs" + ) + context = "\n\n".join(context_parts) + rag_sources = sources + elif docs: + # Use documents without reranking (take top 3) + doc_texts = [d["text"] for d in docs[:3]] + context = "\n\n".join(doc_texts) + rag_sources = [d.get("source", "") for d in docs[:3]] + + # Step 3: Generate response (streaming or non-streaming) + # Check for cached context window from Valkey (for attention offloading) + cached_context = None + if session_id: + cached_context = await self.get_context_window(session_id) + + # Combine RAG context with cached context if available + if cached_context and context: + # Prepend cached context to current RAG context + combined_context = f"{cached_context}\n\n{context}" + # Truncate to prevent unbounded growth + if len(combined_context) > MAX_CONTEXT_LENGTH: + logger.warning(f"Context length {len(combined_context)} exceeds max {MAX_CONTEXT_LENGTH}, truncating") + # Keep the most recent context (from the end) + combined_context = combined_context[-MAX_CONTEXT_LENGTH:] + context = combined_context + elif cached_context: + # Only cached context, still need to check length + if len(cached_context) > MAX_CONTEXT_LENGTH: + logger.warning(f"Cached context length {len(cached_context)} exceeds max {MAX_CONTEXT_LENGTH}, truncating") + cached_context = cached_context[-MAX_CONTEXT_LENGTH:] + context = cached_context + + # Save the combined context for future use (already truncated if needed) + if session_id and context: + await self.save_context_window(session_id, context) + + # Track number of RAG docs used after reranking + if mlflow_metrics and enable_rag: + mlflow_metrics.rag_documents_used = min(3, len(docs)) if docs else 0 + + llm_start = time.time() + if enable_streaming: + # Use streaming response + answer = await self.generate_response_streaming(text, context, request_id, session_id) + else: + # Use non-streaming response + answer = await self.generate_response(text, context, session_id) + + if mlflow_metrics: + mlflow_metrics.llm_latency = time.time() - llm_start + mlflow_metrics.response_length = len(answer) + # Estimate token counts (rough approximation: 4 chars per token) + mlflow_metrics.input_tokens = len(text) // 4 + mlflow_metrics.output_tokens = len(answer) // 4 + mlflow_metrics.total_tokens = mlflow_metrics.input_tokens + mlflow_metrics.output_tokens + + # Step 4: Optionally synthesize speech + audio_b64 = "" + if enable_tts: + audio_b64 = await self.synthesize_speech(answer, language) + + # Publish result + # Include both 'response' (companions-frontend) and 'response_text' (original) for compatibility + result = { + "request_id": request_id, + "user_id": user_id, + "text": text, + "response": answer, # companions-frontend expects 'response' + "response_text": answer, # original format + "audio_b64": audio_b64 if enable_tts else None, + "used_rag": bool(context), + "rag_enabled": enable_rag, + "reranker_enabled": enable_reranker, + "rag_sources": rag_sources, + "session_id": session_id, + "success": True, + } + await self.nc.publish( + f"{RESPONSE_SUBJECT}.{request_id}", msgpack.packb(result) + ) + logger.info(f"Published response for request {request_id}") + + # Record metrics + duration = time.time() - start_time + if self.request_counter: + self.request_counter.add(1, {"premium": str(is_premium), "rag_enabled": str(enable_rag), "success": "true"}) + if self.request_duration: + self.request_duration.record(duration, {"premium": str(is_premium), "rag_enabled": str(enable_rag)}) + if span: + span.set_attribute("success", True) + span.set_attribute("response_length", len(answer)) + + # Log to MLflow + if self.mlflow_tracker and mlflow_metrics: + mlflow_metrics.total_latency = duration + await self.mlflow_tracker.log_inference(mlflow_metrics) + + except Exception as e: + logger.error(f"Request processing failed: {e}") + if self.request_counter: + self.request_counter.add(1, {"premium": str(is_premium), "success": "false"}) + if span: + span.set_attribute("success", False) + span.set_attribute("error", str(e)) + + # Log error to MLflow + if self.mlflow_tracker and mlflow_metrics: + mlflow_metrics.has_error = True + mlflow_metrics.error_message = str(e) + mlflow_metrics.total_latency = time.time() - start_time + await self.mlflow_tracker.log_inference(mlflow_metrics) + + await self.publish_error(data.get("request_id", "unknown"), str(e)) + finally: + if span: + span.end() + + async def publish_error(self, request_id: str, error: str): + """Publish an error response.""" + result = {"request_id": request_id, "error": error, "success": False} + await self.nc.publish( + f"{RESPONSE_SUBJECT}.{request_id}", msgpack.packb(result) + ) + + async def process_premium_request(self, msg): + """Process a premium chat request (wrapper for deeper RAG).""" + await self.process_request(msg, is_premium=True) + + async def run(self): + """Main run loop.""" + await self.setup() + + # Subscribe to standard chat requests + sub = await self.nc.subscribe(REQUEST_SUBJECT, cb=self.process_request) + logger.info(f"Subscribed to {REQUEST_SUBJECT}") + + # Subscribe to premium chat requests (deeper RAG retrieval) + premium_sub = await self.nc.subscribe( + PREMIUM_REQUEST_SUBJECT, cb=self.process_premium_request + ) + logger.info(f"Subscribed to {PREMIUM_REQUEST_SUBJECT}") + + # Handle shutdown + def signal_handler(): + self.running = False + + loop = asyncio.get_event_loop() + for sig in (signal.SIGTERM, signal.SIGINT): + loop.add_signal_handler(sig, signal_handler) + + # Keep running + while self.running: + await asyncio.sleep(1) + + # Cleanup + await sub.unsubscribe() + await premium_sub.unsubscribe() + await self.nc.close() + if self.valkey_client: + await self.valkey_client.close() + if self.collection: + connections.disconnect("default") + if self.mlflow_tracker: + await self.mlflow_tracker.stop() + logger.info("Shutdown complete") + + +if __name__ == "__main__": + handler = ChatHandler() + asyncio.run(handler.run()) diff --git a/chat_handler_v2.py b/chat_handler_v2.py new file mode 100644 index 0000000..f720e4e --- /dev/null +++ b/chat_handler_v2.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +""" +Chat Handler Service (Refactored) + +Text-based chat pipeline using handler-base: +1. Listen for text on NATS subject "ai.chat.request" +2. Generate embeddings for RAG +3. Retrieve context from Milvus +4. Rerank with BGE reranker +5. Generate response with vLLM +6. Optionally synthesize speech with XTTS +7. Publish result to NATS "ai.chat.response.{request_id}" +""" +import base64 +import logging +from typing import Any, Optional + +from nats.aio.msg import Msg + +from handler_base import Handler, Settings +from handler_base.clients import ( + EmbeddingsClient, + RerankerClient, + LLMClient, + TTSClient, + MilvusClient, +) +from handler_base.telemetry import create_span + +logger = logging.getLogger("chat-handler") + + +class ChatSettings(Settings): + """Chat handler specific settings.""" + + service_name: str = "chat-handler" + + # RAG settings + rag_top_k: int = 10 + rag_rerank_top_k: int = 5 + rag_collection: str = "documents" + + # Response settings + include_sources: bool = True + enable_tts: bool = False + tts_language: str = "en" + + +class ChatHandler(Handler): + """ + Chat request handler with RAG pipeline. + + Request format: + { + "request_id": "uuid", + "query": "user question", + "collection": "optional collection name", + "enable_tts": false, + "system_prompt": "optional custom system prompt" + } + + Response format: + { + "request_id": "uuid", + "response": "generated response", + "sources": [{"text": "...", "score": 0.95}], + "audio": "base64 encoded audio (if tts enabled)" + } + """ + + def __init__(self): + self.chat_settings = ChatSettings() + super().__init__( + subject="ai.chat.request", + settings=self.chat_settings, + queue_group="chat-handlers", + ) + + async def setup(self) -> None: + """Initialize service clients.""" + logger.info("Initializing service clients...") + + self.embeddings = EmbeddingsClient(self.chat_settings) + self.reranker = RerankerClient(self.chat_settings) + self.llm = LLMClient(self.chat_settings) + self.milvus = MilvusClient(self.chat_settings) + + # TTS is optional + if self.chat_settings.enable_tts: + self.tts = TTSClient(self.chat_settings) + else: + self.tts = None + + # Connect to Milvus + await self.milvus.connect(self.chat_settings.rag_collection) + + logger.info("Service clients initialized") + + async def teardown(self) -> None: + """Clean up service clients.""" + logger.info("Closing service clients...") + + await self.embeddings.close() + await self.reranker.close() + await self.llm.close() + await self.milvus.close() + + if self.tts: + await self.tts.close() + + logger.info("Service clients closed") + + async def handle_message(self, msg: Msg, data: Any) -> Optional[dict]: + """Handle incoming chat request.""" + request_id = data.get("request_id", "unknown") + query = data.get("query", "") + collection = data.get("collection", self.chat_settings.rag_collection) + enable_tts = data.get("enable_tts", self.chat_settings.enable_tts) + system_prompt = data.get("system_prompt") + + logger.info(f"Processing request {request_id}: {query[:50]}...") + + with create_span("chat.process") as span: + if span: + span.set_attribute("request.id", request_id) + span.set_attribute("query.length", len(query)) + + # 1. Generate query embedding + embedding = await self._get_embedding(query) + + # 2. Search Milvus for context + documents = await self._search_context(embedding, collection) + + # 3. Rerank documents + reranked = await self._rerank_documents(query, documents) + + # 4. Build context from top documents + context = self._build_context(reranked) + + # 5. Generate LLM response + response_text = await self._generate_response( + query, context, system_prompt + ) + + # 6. Optionally synthesize speech + audio_b64 = None + if enable_tts and self.tts: + audio_b64 = await self._synthesize_speech(response_text) + + # Build response + result = { + "request_id": request_id, + "response": response_text, + } + + if self.chat_settings.include_sources: + result["sources"] = [ + {"text": d["document"][:200], "score": d["score"]} + for d in reranked[:3] + ] + + if audio_b64: + result["audio"] = audio_b64 + + logger.info(f"Completed request {request_id}") + + # Publish to response subject + response_subject = f"ai.chat.response.{request_id}" + await self.nats.publish(response_subject, result) + + return result + + async def _get_embedding(self, text: str) -> list[float]: + """Generate embedding for query text.""" + with create_span("chat.embedding"): + return await self.embeddings.embed_single(text) + + async def _search_context( + self, embedding: list[float], collection: str + ) -> list[dict]: + """Search Milvus for relevant documents.""" + with create_span("chat.search"): + return await self.milvus.search_with_texts( + embedding, + limit=self.chat_settings.rag_top_k, + text_field="text", + metadata_fields=["source", "title"], + ) + + async def _rerank_documents( + self, query: str, documents: list[dict] + ) -> list[dict]: + """Rerank documents by relevance to query.""" + with create_span("chat.rerank"): + texts = [d.get("text", "") for d in documents] + return await self.reranker.rerank( + query, texts, top_k=self.chat_settings.rag_rerank_top_k + ) + + def _build_context(self, documents: list[dict]) -> str: + """Build context string from ranked documents.""" + context_parts = [] + for i, doc in enumerate(documents, 1): + text = doc.get("document", "") + context_parts.append(f"[{i}] {text}") + return "\n\n".join(context_parts) + + async def _generate_response( + self, + query: str, + context: str, + system_prompt: Optional[str] = None, + ) -> str: + """Generate LLM response with context.""" + with create_span("chat.generate"): + return await self.llm.generate( + query, + context=context, + system_prompt=system_prompt, + ) + + async def _synthesize_speech(self, text: str) -> str: + """Synthesize speech and return base64 encoded audio.""" + with create_span("chat.tts"): + audio_bytes = await self.tts.synthesize( + text, + language=self.chat_settings.tts_language, + ) + return base64.b64encode(audio_bytes).decode() + + +if __name__ == "__main__": + ChatHandler().run() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..ef68ffa --- /dev/null +++ b/requirements.txt @@ -0,0 +1,15 @@ +nats-py +httpx +pymilvus +numpy +msgpack +redis>=5.0.0 +opentelemetry-api +opentelemetry-sdk +opentelemetry-exporter-otlp-proto-grpc +opentelemetry-exporter-otlp-proto-http +opentelemetry-instrumentation-httpx +opentelemetry-instrumentation-logging +# MLflow for inference metrics tracking +mlflow>=2.10.0 +psycopg2-binary>=2.9.0