fixing ruff suggestions and tests needed updating.

The JS-based GitHub Action doesn't work on Gitea's act runner.
Use curl installer + GITHUB_PATH instead.

.gitea/workflows/ci.yml

@@ -18,10 +18,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up uv
-        uses: astral-sh/setup-uv@v7
+        run: curl -LsSf https://astral.sh/uv/install.sh | sh && echo "$HOME/.local/bin" >> $GITHUB_PATH
-        with:
-          version: "latest"
-          activate-environment: false
 
       - name: Set up Python
         run: uv python install 3.13
@@ -43,10 +40,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up uv
-        uses: astral-sh/setup-uv@v7
+        run: curl -LsSf https://astral.sh/uv/install.sh | sh && echo "$HOME/.local/bin" >> $GITHUB_PATH
-        with:
-          version: "latest"
-          activate-environment: false
 
       - name: Set up Python
         run: uv python install 3.13

chat_handler.py

@@ -3,13 +3,12 @@
 Chat Handler Service (Refactored)
 
 Text-based chat pipeline using handler-base:
-1. Listen for text on NATS subject "ai.chat.request"
+1. Listen for text on NATS subject "ai.chat.user.*.message"
-2. Generate embeddings for RAG
+2. If RAG enabled (premium/explicit): embed → Milvus search → rerank
-3. Retrieve context from Milvus
+3. Generate response with vLLM (with or without RAG context)
-4. Rerank with BGE reranker
+4. Optionally stream chunks to "ai.chat.response.stream.{request_id}"
-5. Generate response with vLLM
+5. Optionally synthesize speech with XTTS
-6. Optionally synthesize speech with XTTS
+6. Publish result to "ai.chat.response.{request_id}" (or custom response_subject)
-7. Publish result to NATS "ai.chat.response.{request_id}"
 """
 
 import base64
@@ -51,28 +50,38 @@ class ChatHandler(Handler):
     """
     Chat request handler with RAG pipeline.
 
-    Request format:
+    Subscribes to: ai.chat.user.*.message  (JetStream durable "chat-handler")
+
+    Request format (msgpack):
     {
         "request_id": "uuid",
-        "query": "user question",
+        "user_id": "user-123",
-        "collection": "optional collection name",
+        "username": "john_doe",
-        "enable_tts": false,
+        "message": "user question",
+        "premium": false,
+        "enable_rag": true,
+        "enable_reranker": true,
+        "enable_streaming": true,
+        "top_k": 5,
+        "session_id": "session-abc",
         "system_prompt": "optional custom system prompt"
     }
 
-    Response format:
+    Response format (msgpack):
     {
-        "request_id": "uuid",
+        "user_id": "user-123",
         "response": "generated response",
-        "sources": [{"text": "...", "score": 0.95}],
+        "response_text": "generated response",
-        "audio": "base64 encoded audio (if tts enabled)"
+        "used_rag": true,
+        "rag_sources": ["source1", "source2"],
+        "success": true
     }
     """
 
     def __init__(self):
         self.chat_settings = ChatSettings()
         super().__init__(
-            subject="ai.chat.request",
+            subject="ai.chat.user.*.message",
             settings=self.chat_settings,
             queue_group="chat-handlers",
         )
@@ -114,56 +123,96 @@ class ChatHandler(Handler):
     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", "")
+        user_id = data.get("user_id", "unknown")
+        query = data.get("message", "") or data.get("query", "")
+        premium = data.get("premium", False)
+        enable_rag = data.get("enable_rag", premium)
+        enable_reranker = data.get("enable_reranker", enable_rag)
+        enable_streaming = data.get("enable_streaming", False)
+        top_k = data.get("top_k", self.chat_settings.rag_top_k)
         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")
+        # companions-frontend may set a custom response subject
+        response_subject = data.get("response_subject", f"ai.chat.response.{request_id}")
 
         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("user.id", user_id)
                 span.set_attribute("query.length", len(query))
+                span.set_attribute("premium", premium)
+                span.set_attribute("rag.enabled", enable_rag)
 
-            # 1. Generate query embedding
+            context = ""
-            embedding = await self._get_embedding(query)
+            rag_sources: list[str] = []
+            used_rag = False
 
-            # 2. Search Milvus for context
+            # Only run RAG pipeline when enabled (premium users or explicit flag)
-            documents = await self._search_context(embedding, collection)
+            if enable_rag:
+                # 1. Generate query embedding
+                embedding = await self._get_embedding(query)
 
-            # 3. Rerank documents
+                # 2. Search Milvus for context
-            reranked = await self._rerank_documents(query, documents)
+                documents = await self._search_context(
+                    embedding,
+                    collection,
+                    top_k=top_k,
+                )
 
-            # 4. Build context from top documents
+                # 3. Optionally rerank documents
-            context = self._build_context(reranked)
+                if enable_reranker and documents:
+                    reranked = await self._rerank_documents(query, documents)
+                else:
+                    reranked = documents
 
-            # 5. Generate LLM response
+                # 4. Build context from top documents
-            response_text = await self._generate_response(query, context, system_prompt)
+                if reranked:
+                    context = self._build_context(reranked)
+                    rag_sources = [
+                        d.get("source", d.get("document", "")[:80]) for d in reranked[:3]
+                    ]
+                    used_rag = True
 
-            # 6. Optionally synthesize speech
+            # 5. Generate LLM response (with or without RAG context)
+            response_text = await self._generate_response(
+                query,
+                context or None,
+                system_prompt,
+            )
+
+            # 6. Stream response chunks if requested
+            if enable_streaming:
+                stream_subject = f"ai.chat.response.stream.{request_id}"
+                await self._publish_streaming_chunks(
+                    stream_subject,
+                    request_id,
+                    response_text,
+                )
+
+            # 7. Optionally synthesize speech
             audio_b64 = None
             if enable_tts and self.tts:
                 audio_b64 = await self._synthesize_speech(response_text)
 
-            # Build response
+            # Build response (compatible with companions-frontend NATSChatResponse)
-            result = {
+            result: dict[str, Any] = {
-                "request_id": request_id,
+                "user_id": user_id,
                 "response": response_text,
+                "response_text": response_text,
+                "used_rag": used_rag,
+                "rag_sources": rag_sources,
+                "success": True,
             }
 
-            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}")
+            logger.info(f"Completed request {request_id} (rag={used_rag})")
 
-            # Publish to response subject
+            # Publish to the response subject the frontend is waiting on
-            response_subject = f"ai.chat.response.{request_id}"
             await self.nats.publish(response_subject, result)
 
             return result
@@ -173,12 +222,17 @@ class ChatHandler(Handler):
         with create_span("chat.embedding"):
             return await self.embeddings.embed_single(text)
 
-    async def _search_context(self, embedding: list[float], collection: str) -> list[dict]:
+    async def _search_context(
+        self,
+        embedding: list[float],
+        collection: str,
+        top_k: int | None = None,
+    ) -> 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,
+                limit=top_k or self.chat_settings.rag_top_k,
                 text_field="text",
                 metadata_fields=["source", "title"],
             )
@@ -202,10 +256,10 @@ class ChatHandler(Handler):
     async def _generate_response(
         self,
         query: str,
-        context: str,
+        context: Optional[str] = None,
         system_prompt: Optional[str] = None,
     ) -> str:
-        """Generate LLM response with context."""
+        """Generate LLM response, optionally augmented with RAG context."""
         with create_span("chat.generate"):
             return await self.llm.generate(
                 query,
@@ -213,6 +267,41 @@ class ChatHandler(Handler):
                 system_prompt=system_prompt,
             )
 
+    async def _publish_streaming_chunks(
+        self,
+        subject: str,
+        request_id: str,
+        full_text: str,
+    ) -> None:
+        """Publish response as streaming chunks for real-time display."""
+        import time
+
+        words = full_text.split(" ")
+        chunk_size = 4
+        for i in range(0, len(words), chunk_size):
+            token_chunk = " ".join(words[i : i + chunk_size])
+            await self.nats.publish(
+                subject,
+                {
+                    "request_id": request_id,
+                    "type": "chunk",
+                    "content": token_chunk,
+                    "done": False,
+                    "timestamp": time.time(),
+                },
+            )
+        # Send done marker
+        await self.nats.publish(
+            subject,
+            {
+                "request_id": request_id,
+                "type": "done",
+                "content": "",
+                "done": True,
+                "timestamp": time.time(),
+            },
+        )
+
     async def _synthesize_speech(self, text: str) -> str:
         """Synthesize speech and return base64 encoded audio."""
         with create_span("chat.tts"):

renovate.json

@@ -0,0 +1,7 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
+  "extends": [
+    "local>daviestechlabs/renovate-config",
+    "local>daviestechlabs/renovate-config:python"
+  ]
+}

tests/conftest.py

@@ -53,7 +53,7 @@ def sample_reranked():
 def mock_nats_message():
     """Create a mock NATS message."""
     msg = MagicMock()
-    msg.subject = "ai.chat.request"
+    msg.subject = "ai.chat.user.test-user-1.message"
     msg.reply = "ai.chat.response.test-123"
     return msg
 
@@ -63,7 +63,13 @@ def mock_chat_request():
     """Sample chat request payload."""
     return {
         "request_id": "test-request-123",
-        "query": "What is machine learning?",
+        "user_id": "test-user-1",
+        "username": "testuser",
+        "message": "What is machine learning?",
+        "premium": True,
+        "enable_rag": True,
+        "enable_reranker": True,
+        "enable_streaming": False,
         "collection": "test_collection",
         "enable_tts": False,
         "system_prompt": None,
@@ -75,7 +81,13 @@ def mock_chat_request_with_tts():
     """Sample chat request with TTS enabled."""
     return {
         "request_id": "test-request-456",
-        "query": "Tell me about AI",
+        "user_id": "test-user-2",
+        "username": "testuser2",
+        "message": "Tell me about AI",
+        "premium": True,
+        "enable_rag": True,
+        "enable_reranker": True,
+        "enable_streaming": False,
         "collection": "documents",
         "enable_tts": True,
         "system_prompt": "You are a helpful assistant.",

tests/test_chat_handler.py

@@ -86,7 +86,7 @@ class TestChatHandler:
 
     def test_init(self, handler):
         """Test handler initialization."""
-        assert handler.subject == "ai.chat.request"
+        assert handler.subject == "ai.chat.user.*.message"
         assert handler.queue_group == "chat-handlers"
         assert handler.chat_settings.service_name == "chat-handler"
 
@@ -111,12 +111,15 @@ class TestChatHandler:
         result = await handler.handle_message(mock_nats_message, mock_chat_request)
 
         # Verify
-        assert result["request_id"] == "test-request-123"
+        assert result["user_id"] == "test-user-1"
+        assert result["success"] is True
         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
+        assert result["response_text"] == result["response"]
+        assert result["used_rag"] is True
+        assert isinstance(result["rag_sources"], list)
 
-        # Verify pipeline was called
+        # Verify RAG pipeline was called (enable_rag=True in fixture)
         handler.embeddings.embed_single.assert_called_once()
         handler.milvus.search_with_texts.assert_called_once()
         handler.reranker.rerank.assert_called_once()
@@ -142,7 +145,9 @@ class TestChatHandler:
 
         result = await handler.handle_message(mock_nats_message, mock_chat_request)
 
-        assert "sources" not in result
+        # New response format doesn't have a separate "sources" key;
+        # rag_sources is always present (may be empty)
+        assert "rag_sources" in result
 
     @pytest.mark.asyncio
     async def test_handle_message_with_tts(
@@ -180,7 +185,10 @@ class TestChatHandler:
         """Test LLM is called with custom system prompt."""
         request = {
             "request_id": "test-123",
-            "query": "Hello",
+            "user_id": "user-42",
+            "message": "Hello",
+            "premium": True,
+            "enable_rag": True,
             "system_prompt": "You are a pirate. Respond like one.",
         }