refactor: consolidate to handler-base, migrate to pyproject.toml, add tests

Dockerfile

@@ -1,29 +1,9 @@
-FROM python:3.13-slim
+# Chat Handler - Using handler-base
+ARG BASE_TAG=latest
+FROM ghcr.io/billy-davies-2/handler-base:${BASE_TAG}
 
 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"]

Dockerfile.v2

@@ -1,11 +0,0 @@
-# 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"]

README.md

@@ -4,19 +4,10 @@ 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).
+A NATS-based service that handles chat completion requests with RAG (Retrieval Augmented Generation). It uses the [handler-base](https://git.daviestechlabs.io/daviestechlabs/handler-base) library for standardized NATS handling, telemetry, and health checks.
 
 **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
 
 ```
@@ -88,19 +79,10 @@ NATS (ai.chat.request)
 ## Building
 
 ```bash
-# Standalone image (v1)
-docker build -f Dockerfile -t chat-handler:latest .
+docker build -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
+# With specific handler-base tag
+docker build --build-arg BASE_TAG=latest -t chat-handler:latest .
 ```
 
 ## Related

chat_handler_v2.py

@@ -1,233 +0,0 @@
-#!/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()

pyproject.toml

@@ -0,0 +1,40 @@
+[project]
+name = "chat-handler"
+version = "1.0.0"
+description = "Text chat pipeline with RAG - Query → Embeddings → Milvus → Rerank → LLM"
+readme = "README.md"
+requires-python = ">=3.11"
+license = { text = "MIT" }
+authors = [{ name = "Davies Tech Labs" }]
+
+dependencies = [
+    "handler-base @ git+https://git.daviestechlabs.io/daviestechlabs/handler-base.git",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0.0",
+    "pytest-asyncio>=0.23.0",
+    "ruff>=0.1.0",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["."]
+only-include = ["chat_handler.py"]
+
+[tool.ruff]
+line-length = 100
+target-version = "py311"
+
+[tool.pytest.ini_options]
+asyncio_mode = "auto"
+testpaths = ["tests"]
+python_files = ["test_*.py"]
+python_classes = ["Test*"]
+python_functions = ["test_*"]
+addopts = "-v --tb=short"
+filterwarnings = ["ignore::DeprecationWarning"]

requirements.txt

@@ -1,15 +0,0 @@
-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

tests/__init__.py

@@ -0,0 +1 @@
+# Chat Handler Tests

tests/conftest.py

@@ -0,0 +1,81 @@
+"""
+Pytest configuration and fixtures for chat-handler tests.
+"""
+import asyncio
+import os
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+# Set test environment variables before importing
+os.environ.setdefault("NATS_URL", "nats://localhost:4222")
+os.environ.setdefault("REDIS_URL", "redis://localhost:6379")
+os.environ.setdefault("MILVUS_HOST", "localhost")
+os.environ.setdefault("OTEL_ENABLED", "false")
+os.environ.setdefault("MLFLOW_ENABLED", "false")
+
+
+@pytest.fixture(scope="session")
+def event_loop():
+    """Create event loop for async tests."""
+    loop = asyncio.new_event_loop()
+    yield loop
+    loop.close()
+
+
+@pytest.fixture
+def sample_embedding():
+    """Sample embedding vector."""
+    return [0.1] * 1024
+
+
+@pytest.fixture
+def sample_documents():
+    """Sample search results."""
+    return [
+        {"text": "Machine learning is a subset of AI.", "score": 0.95},
+        {"text": "Deep learning uses neural networks.", "score": 0.90},
+        {"text": "AI enables intelligent automation.", "score": 0.85},
+    ]
+
+
+@pytest.fixture
+def sample_reranked():
+    """Sample reranked results."""
+    return [
+        {"document": "Machine learning is a subset of AI.", "score": 0.98},
+        {"document": "Deep learning uses neural networks.", "score": 0.85},
+    ]
+
+
+@pytest.fixture
+def mock_nats_message():
+    """Create a mock NATS message."""
+    msg = MagicMock()
+    msg.subject = "ai.chat.request"
+    msg.reply = "ai.chat.response.test-123"
+    return msg
+
+
+@pytest.fixture
+def mock_chat_request():
+    """Sample chat request payload."""
+    return {
+        "request_id": "test-request-123",
+        "query": "What is machine learning?",
+        "collection": "test_collection",
+        "enable_tts": False,
+        "system_prompt": None,
+    }
+
+
+@pytest.fixture
+def mock_chat_request_with_tts():
+    """Sample chat request with TTS enabled."""
+    return {
+        "request_id": "test-request-456",
+        "query": "Tell me about AI",
+        "collection": "documents",
+        "enable_tts": True,
+        "system_prompt": "You are a helpful assistant.",
+    }

tests/test_chat_handler.py

@@ -0,0 +1,262 @@
+"""
+Unit tests for ChatHandler.
+"""
+import base64
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+from chat_handler import ChatHandler, ChatSettings
+
+
+class TestChatSettings:
+    """Tests for ChatSettings configuration."""
+    
+    def test_default_settings(self):
+        """Test default settings values."""
+        settings = ChatSettings()
+        
+        assert settings.service_name == "chat-handler"
+        assert settings.rag_top_k == 10
+        assert settings.rag_rerank_top_k == 5
+        assert settings.rag_collection == "documents"
+        assert settings.include_sources is True
+        assert settings.enable_tts is False
+        assert settings.tts_language == "en"
+    
+    def test_custom_settings(self):
+        """Test custom settings."""
+        settings = ChatSettings(
+            rag_top_k=20,
+            rag_collection="custom_docs",
+            enable_tts=True,
+        )
+        
+        assert settings.rag_top_k == 20
+        assert settings.rag_collection == "custom_docs"
+        assert settings.enable_tts is True
+
+
+class TestChatHandler:
+    """Tests for ChatHandler."""
+    
+    @pytest.fixture
+    def handler(self):
+        """Create handler with mocked clients."""
+        with patch("chat_handler.EmbeddingsClient"), \
+             patch("chat_handler.RerankerClient"), \
+             patch("chat_handler.LLMClient"), \
+             patch("chat_handler.TTSClient"), \
+             patch("chat_handler.MilvusClient"):
+            
+            handler = ChatHandler()
+            
+            # Setup mock clients
+            handler.embeddings = AsyncMock()
+            handler.reranker = AsyncMock()
+            handler.llm = AsyncMock()
+            handler.milvus = AsyncMock()
+            handler.tts = None  # TTS disabled by default
+            handler.nats = AsyncMock()
+            
+            yield handler
+    
+    @pytest.fixture
+    def handler_with_tts(self):
+        """Create handler with TTS enabled."""
+        with patch("chat_handler.EmbeddingsClient"), \
+             patch("chat_handler.RerankerClient"), \
+             patch("chat_handler.LLMClient"), \
+             patch("chat_handler.TTSClient"), \
+             patch("chat_handler.MilvusClient"):
+            
+            handler = ChatHandler()
+            handler.chat_settings.enable_tts = True
+            
+            # Setup mock clients
+            handler.embeddings = AsyncMock()
+            handler.reranker = AsyncMock()
+            handler.llm = AsyncMock()
+            handler.milvus = AsyncMock()
+            handler.tts = AsyncMock()
+            handler.nats = AsyncMock()
+            
+            yield handler
+    
+    def test_init(self, handler):
+        """Test handler initialization."""
+        assert handler.subject == "ai.chat.request"
+        assert handler.queue_group == "chat-handlers"
+        assert handler.chat_settings.service_name == "chat-handler"
+    
+    @pytest.mark.asyncio
+    async def test_handle_message_success(
+        self,
+        handler,
+        mock_nats_message,
+        mock_chat_request,
+        sample_embedding,
+        sample_documents,
+        sample_reranked,
+    ):
+        """Test successful chat request handling."""
+        # Setup mocks
+        handler.embeddings.embed_single.return_value = sample_embedding
+        handler.milvus.search_with_texts.return_value = sample_documents
+        handler.reranker.rerank.return_value = sample_reranked
+        handler.llm.generate.return_value = "Machine learning is a subset of AI that..."
+        
+        # Execute
+        result = await handler.handle_message(mock_nats_message, mock_chat_request)
+        
+        # Verify
+        assert result["request_id"] == "test-request-123"
+        assert "response" in result
+        assert result["response"] == "Machine learning is a subset of AI that..."
+        assert "sources" in result  # include_sources is True by default
+        
+        # Verify pipeline was called
+        handler.embeddings.embed_single.assert_called_once()
+        handler.milvus.search_with_texts.assert_called_once()
+        handler.reranker.rerank.assert_called_once()
+        handler.llm.generate.assert_called_once()
+    
+    @pytest.mark.asyncio
+    async def test_handle_message_without_sources(
+        self,
+        handler,
+        mock_nats_message,
+        mock_chat_request,
+        sample_embedding,
+        sample_documents,
+        sample_reranked,
+    ):
+        """Test response without sources when disabled."""
+        handler.chat_settings.include_sources = False
+        
+        handler.embeddings.embed_single.return_value = sample_embedding
+        handler.milvus.search_with_texts.return_value = sample_documents
+        handler.reranker.rerank.return_value = sample_reranked
+        handler.llm.generate.return_value = "Response text"
+        
+        result = await handler.handle_message(mock_nats_message, mock_chat_request)
+        
+        assert "sources" not in result
+    
+    @pytest.mark.asyncio
+    async def test_handle_message_with_tts(
+        self,
+        handler_with_tts,
+        mock_nats_message,
+        mock_chat_request_with_tts,
+        sample_embedding,
+        sample_documents,
+        sample_reranked,
+    ):
+        """Test response with TTS audio."""
+        handler = handler_with_tts
+        
+        handler.embeddings.embed_single.return_value = sample_embedding
+        handler.milvus.search_with_texts.return_value = sample_documents
+        handler.reranker.rerank.return_value = sample_reranked
+        handler.llm.generate.return_value = "AI response"
+        handler.tts.synthesize.return_value = b"audio_bytes"
+        
+        result = await handler.handle_message(mock_nats_message, mock_chat_request_with_tts)
+        
+        assert "audio" in result
+        handler.tts.synthesize.assert_called_once()
+    
+    @pytest.mark.asyncio
+    async def test_handle_message_with_custom_system_prompt(
+        self,
+        handler,
+        mock_nats_message,
+        sample_embedding,
+        sample_documents,
+        sample_reranked,
+    ):
+        """Test LLM is called with custom system prompt."""
+        request = {
+            "request_id": "test-123",
+            "query": "Hello",
+            "system_prompt": "You are a pirate. Respond like one.",
+        }
+        
+        handler.embeddings.embed_single.return_value = sample_embedding
+        handler.milvus.search_with_texts.return_value = sample_documents
+        handler.reranker.rerank.return_value = sample_reranked
+        handler.llm.generate.return_value = "Ahoy!"
+        
+        await handler.handle_message(mock_nats_message, request)
+        
+        # Verify system_prompt was passed to LLM
+        handler.llm.generate.assert_called_once()
+        call_kwargs = handler.llm.generate.call_args.kwargs
+        assert call_kwargs.get("system_prompt") == "You are a pirate. Respond like one."
+    
+    def test_build_context(self, handler):
+        """Test context building with numbered sources."""
+        documents = [
+            {"document": "First doc content"},
+            {"document": "Second doc content"},
+        ]
+        
+        context = handler._build_context(documents)
+        
+        assert "[1]" in context
+        assert "[2]" in context
+        assert "First doc content" in context
+        assert "Second doc content" in context
+    
+    @pytest.mark.asyncio
+    async def test_setup_initializes_clients(self):
+        """Test that setup initializes all required clients."""
+        with patch("chat_handler.EmbeddingsClient") as emb_cls, \
+             patch("chat_handler.RerankerClient") as rer_cls, \
+             patch("chat_handler.LLMClient") as llm_cls, \
+             patch("chat_handler.TTSClient") as tts_cls, \
+             patch("chat_handler.MilvusClient") as mil_cls:
+            
+            mil_cls.return_value.connect = AsyncMock()
+            
+            handler = ChatHandler()
+            await handler.setup()
+            
+            emb_cls.assert_called_once()
+            rer_cls.assert_called_once()
+            llm_cls.assert_called_once()
+            mil_cls.assert_called_once()
+            # TTS should not be initialized when disabled
+            tts_cls.assert_not_called()
+    
+    @pytest.mark.asyncio
+    async def test_teardown_closes_clients(self, handler):
+        """Test that teardown closes all clients."""
+        await handler.teardown()
+        
+        handler.embeddings.close.assert_called_once()
+        handler.reranker.close.assert_called_once()
+        handler.llm.close.assert_called_once()
+        handler.milvus.close.assert_called_once()
+    
+    @pytest.mark.asyncio
+    async def test_publishes_to_response_subject(
+        self,
+        handler,
+        mock_nats_message,
+        mock_chat_request,
+        sample_embedding,
+        sample_documents,
+        sample_reranked,
+    ):
+        """Test that result is published to response subject."""
+        handler.embeddings.embed_single.return_value = sample_embedding
+        handler.milvus.search_with_texts.return_value = sample_documents
+        handler.reranker.rerank.return_value = sample_reranked
+        handler.llm.generate.return_value = "Response"
+        
+        await handler.handle_message(mock_nats_message, mock_chat_request)
+        
+        handler.nats.publish.assert_called_once()
+        call_args = handler.nats.publish.call_args
+        assert "ai.chat.response.test-request-123" in str(call_args)