Merge branch 'mrh-online-dev' of github.com:BRUNIX-AI/assistance-engine into mrh-online-dev

Docker/docker-compose.yaml

@@ -9,7 +9,7 @@ services:
     environment:
       ELASTICSEARCH_URL: ${ELASTICSEARCH_URL}
       DATABASE_URL: ${DATABASE_URL}
-      LLM_BASE_URL: ${LLM_BASE_URL}
+      OLLAMA_URL: ${OLLAMA_URL}
       LANGFUSE_HOST: ${LANGFUSE_HOST}
       LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
       LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}

Docker/requirements.txt

@@ -1,5 +1,7 @@
 # This file was autogenerated by uv via the following command:
-#    uv export --format requirements-txt --no-hashes --no-dev -o requirements.txt
+#    uv export --format requirements-txt --no-hashes --no-dev -o Docker/requirements.txt
+accelerate==1.12.0
+    # via assistance-engine
 aiohappyeyeballs==2.6.1
     # via aiohttp
 aiohttp==3.13.3
@@ -12,6 +14,12 @@ anyio==4.12.1
     # via httpx
 attrs==25.4.0
     # via aiohttp
+boto3==1.42.58
+    # via langchain-aws
+botocore==1.42.58
+    # via
+    #   boto3
+    #   s3transfer
 certifi==2026.1.4
     # via
     #   elastic-transport
@@ -20,115 +28,211 @@ certifi==2026.1.4
     #   requests
 charset-normalizer==3.4.4
     # via requests
+click==8.3.1
+    # via nltk
 colorama==0.4.6 ; sys_platform == 'win32'
     # via
+    #   click
     #   loguru
     #   tqdm
+cuda-bindings==12.9.4 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+cuda-pathfinder==1.3.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via cuda-bindings
 dataclasses-json==0.6.7
     # via langchain-community
 elastic-transport==8.17.1
     # via elasticsearch
 elasticsearch==8.19.3
     # via langchain-elasticsearch
+filelock==3.24.3
+    # via
+    #   huggingface-hub
+    #   torch
 frozenlist==1.8.0
     # via
     #   aiohttp
     #   aiosignal
-greenlet==3.3.1 ; platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'
+fsspec==2025.10.0
+    # via
+    #   huggingface-hub
+    #   torch
+greenlet==3.3.2 ; platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'
     # via sqlalchemy
-grpcio==1.78.0
+grpcio==1.78.1
     # via
     #   assistance-engine
     #   grpcio-reflection
     #   grpcio-tools
-grpcio-reflection==1.78.0
+grpcio-reflection==1.78.1
     # via assistance-engine
-grpcio-tools==1.78.0
+grpcio-tools==1.78.1
     # via assistance-engine
 h11==0.16.0
     # via httpcore
+hf-xet==1.3.0 ; platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'
+    # via huggingface-hub
 httpcore==1.0.9
     # via httpx
 httpx==0.28.1
     # via
     #   langgraph-sdk
     #   langsmith
+    #   ollama
 httpx-sse==0.4.3
     # via langchain-community
+huggingface-hub==0.36.2
+    # via
+    #   accelerate
+    #   langchain-huggingface
+    #   tokenizers
 idna==3.11
     # via
     #   anyio
     #   httpx
     #   requests
     #   yarl
+jinja2==3.1.6
+    # via torch
+jmespath==1.1.0
+    # via
+    #   boto3
+    #   botocore
+joblib==1.5.3
+    # via nltk
 jsonpatch==1.33
     # via langchain-core
 jsonpointer==3.0.0
     # via jsonpatch
 langchain==1.2.10
     # via assistance-engine
+langchain-aws==1.3.1
+    # via assistance-engine
 langchain-classic==1.0.1
     # via langchain-community
 langchain-community==0.4.1
     # via assistance-engine
-langchain-core==1.2.11
+langchain-core==1.2.15
     # via
     #   langchain
+    #   langchain-aws
     #   langchain-classic
     #   langchain-community
     #   langchain-elasticsearch
+    #   langchain-huggingface
+    #   langchain-ollama
     #   langchain-text-splitters
     #   langgraph
     #   langgraph-checkpoint
     #   langgraph-prebuilt
 langchain-elasticsearch==1.0.0
     # via assistance-engine
-langchain-text-splitters==1.1.0
+langchain-huggingface==1.2.0
+    # via assistance-engine
+langchain-ollama==1.0.1
+    # via assistance-engine
+langchain-text-splitters==1.1.1
     # via langchain-classic
-langgraph==1.0.8
+langgraph==1.0.9
     # via langchain
 langgraph-checkpoint==4.0.0
     # via
     #   langgraph
     #   langgraph-prebuilt
-langgraph-prebuilt==1.0.7
+langgraph-prebuilt==1.0.8
     # via langgraph
-langgraph-sdk==0.3.5
+langgraph-sdk==0.3.8
     # via langgraph
-langsmith==0.7.1
+langsmith==0.7.6
     # via
     #   langchain-classic
     #   langchain-community
     #   langchain-core
 loguru==0.7.3
     # via assistance-engine
+markupsafe==3.0.3
+    # via jinja2
 marshmallow==3.26.2
     # via dataclasses-json
+mpmath==1.3.0
+    # via sympy
 multidict==6.7.1
     # via
     #   aiohttp
     #   yarl
 mypy-extensions==1.1.0
     # via typing-inspect
+networkx==3.6.1
+    # via torch
+nltk==3.9.3
+    # via assistance-engine
 numpy==2.4.2
     # via
+    #   accelerate
     #   assistance-engine
     #   elasticsearch
+    #   langchain-aws
     #   langchain-community
     #   pandas
+    #   torchvision
+nvidia-cublas-cu12==12.8.4.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-cuda-runtime-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-cudnn-cu12==9.10.2.21 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-cufft-cu12==11.3.3.83 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-cufile-cu12==1.13.1.3 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-curand-cu12==10.3.9.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-cusolver-cu12==11.7.3.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-cusparse-cu12==12.5.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cusparselt-cu12==0.7.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-nccl-cu12==2.27.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via
+    #   nvidia-cufft-cu12
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvshmem-cu12==3.4.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+nvidia-nvtx-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
+ollama==0.6.1
+    # via langchain-ollama
 orjson==3.11.7
     # via
     #   langgraph-sdk
     #   langsmith
 ormsgpack==1.12.2
     # via langgraph-checkpoint
-packaging==26.0
+packaging==24.2
     # via
+    #   accelerate
+    #   huggingface-hub
     #   langchain-core
     #   langsmith
     #   marshmallow
-pandas==3.0.0
+pandas==3.0.1
     # via assistance-engine
+pillow==12.1.1
+    # via torchvision
 propcache==0.4.1
     # via
     #   aiohttp
@@ -137,20 +241,25 @@ protobuf==6.33.5
     # via
     #   grpcio-reflection
     #   grpcio-tools
+psutil==7.2.2
+    # via accelerate
 pydantic==2.12.5
     # via
     #   langchain
+    #   langchain-aws
     #   langchain-classic
     #   langchain-core
     #   langgraph
     #   langsmith
+    #   ollama
     #   pydantic-settings
 pydantic-core==2.41.5
     # via pydantic
-pydantic-settings==2.12.0
+pydantic-settings==2.13.1
     # via langchain-community
 python-dateutil==2.9.0.post0
     # via
+    #   botocore
     #   elasticsearch
     #   pandas
 python-dotenv==1.2.1
@@ -159,20 +268,33 @@ python-dotenv==1.2.1
     #   pydantic-settings
 pyyaml==6.0.3
     # via
+    #   accelerate
+    #   huggingface-hub
     #   langchain-classic
     #   langchain-community
     #   langchain-core
+rapidfuzz==3.14.3
+    # via assistance-engine
+regex==2026.2.19
+    # via nltk
 requests==2.32.5
     # via
+    #   huggingface-hub
     #   langchain-classic
     #   langchain-community
     #   langsmith
     #   requests-toolbelt
 requests-toolbelt==1.0.0
     # via langsmith
+s3transfer==0.16.0
+    # via boto3
+safetensors==0.7.0
+    # via accelerate
 setuptools==82.0.0
-    # via grpcio-tools
+    # via
-simsimd==6.5.12
+    #   grpcio-tools
+    #   torch
+simsimd==6.5.13
     # via elasticsearch
 six==1.17.0
     # via python-dateutil
@@ -180,22 +302,40 @@ sqlalchemy==2.0.46
     # via
     #   langchain-classic
     #   langchain-community
+sympy==1.14.0
+    # via torch
 tenacity==9.1.4
     # via
     #   langchain-community
     #   langchain-core
-tqdm==4.67.3
+tokenizers==0.22.2
+    # via langchain-huggingface
+torch==2.10.0
+    # via
+    #   accelerate
+    #   assistance-engine
+    #   torchvision
+torchvision==0.25.0
     # via assistance-engine
+tqdm==4.67.3
+    # via
+    #   assistance-engine
+    #   huggingface-hub
+    #   nltk
+triton==3.6.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
+    # via torch
 typing-extensions==4.15.0
     # via
     #   aiosignal
     #   anyio
     #   elasticsearch
     #   grpcio
+    #   huggingface-hub
     #   langchain-core
     #   pydantic
     #   pydantic-core
     #   sqlalchemy
+    #   torch
     #   typing-inspect
     #   typing-inspection
 typing-inspect==0.9.0
@@ -208,9 +348,10 @@ tzdata==2025.3 ; sys_platform == 'emscripten' or sys_platform == 'win32'
     # via pandas
 urllib3==2.6.3
     # via
+    #   botocore
     #   elastic-transport
     #   requests
-uuid-utils==0.14.0
+uuid-utils==0.14.1
     # via
     #   langchain-core
     #   langsmith

Docker/src/emb_factory.py

@@ -0,0 +1,67 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict
+
+
+class BaseEmbeddingFactory(ABC):
+    @abstractmethod
+    def create(self, model: str, **kwargs: Any):
+        raise NotImplementedError
+
+
+class OpenAIEmbeddingFactory(BaseEmbeddingFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_openai import OpenAIEmbeddings
+
+        return OpenAIEmbeddings(model=model, **kwargs)
+
+
+class OllamaEmbeddingFactory(BaseEmbeddingFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_ollama import OllamaEmbeddings
+
+        return OllamaEmbeddings(model=model, **kwargs)
+
+
+class BedrockEmbeddingFactory(BaseEmbeddingFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_aws import BedrockEmbeddings
+
+        return BedrockEmbeddings(model_id=model, **kwargs)
+
+
+class HuggingFaceEmbeddingFactory(BaseEmbeddingFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_huggingface import HuggingFaceEmbeddings
+
+        return HuggingFaceEmbeddings(model_name=model, **kwargs)
+
+
+EMBEDDING_FACTORIES: Dict[str, BaseEmbeddingFactory] = {
+    "openai": OpenAIEmbeddingFactory(),
+    "ollama": OllamaEmbeddingFactory(),
+    "bedrock": BedrockEmbeddingFactory(),
+    "huggingface": HuggingFaceEmbeddingFactory(),
+}
+
+
+def create_embedding_model(provider: str, model: str, **kwargs: Any):
+    """
+    Create an embedding model instance for the given provider.
+
+    Args:
+        provider: The provider name (openai, ollama, bedrock, huggingface).
+        model: The model identifier.
+        **kwargs: Additional keyword arguments passed to the model constructor.
+
+    Returns:
+        An embedding model instance.
+    """
+    key = provider.strip().lower()
+
+    if key not in EMBEDDING_FACTORIES:
+        raise ValueError(
+            f"Unsupported embedding provider: {provider}. "
+            f"Available providers: {list(EMBEDDING_FACTORIES.keys())}"
+        )
+
+    return EMBEDDING_FACTORIES[key].create(model=model, **kwargs)

Docker/src/llm_factory.py

@@ -0,0 +1,72 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict
+
+
+class BaseProviderFactory(ABC):
+    @abstractmethod
+    def create(self, model: str, **kwargs: Any):
+        raise NotImplementedError
+
+
+class OpenAIChatFactory(BaseProviderFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_openai import ChatOpenAI
+
+        return ChatOpenAI(model=model, **kwargs)
+
+
+class OllamaChatFactory(BaseProviderFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_ollama import ChatOllama
+
+        return ChatOllama(model=model, **kwargs)
+
+
+class BedrockChatFactory(BaseProviderFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_aws import ChatBedrockConverse
+
+        return ChatBedrockConverse(model=model, **kwargs)
+
+
+class HuggingFaceChatFactory(BaseProviderFactory):
+    def create(self, model: str, **kwargs: Any):
+        from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline
+
+        llm = HuggingFacePipeline.from_model_id(
+            model_id=model,
+            task="text-generation",
+            pipeline_kwargs=kwargs,
+        )
+        return ChatHuggingFace(llm=llm)
+
+
+CHAT_FACTORIES: Dict[str, BaseProviderFactory] = {
+    "openai": OpenAIChatFactory(),
+    "ollama": OllamaChatFactory(),
+    "bedrock": BedrockChatFactory(),
+    "huggingface": HuggingFaceChatFactory(),
+}
+
+
+def create_chat_model(provider: str, model: str, **kwargs: Any):
+    """
+    Create a chat model instance for the given provider.
+
+    Args:
+        provider: The provider name (openai, ollama, bedrock, huggingface).
+        model: The model identifier.
+        **kwargs: Additional keyword arguments passed to the model constructor.
+
+    Returns:
+        A chat model instance.
+    """
+    key = provider.strip().lower()
+
+    if key not in CHAT_FACTORIES:
+        raise ValueError(
+            f"Unsupported chat provider: {provider}. "
+            f"Available providers: {list(CHAT_FACTORIES.keys())}"
+        )
+
+    return CHAT_FACTORIES[key].create(model=model, **kwargs)

Docker/src/server copy.py

@@ -0,0 +1,140 @@
+import logging
+import os
+import sys
+from concurrent import futures
+from pathlib import Path
+from typing import Any
+
+import brunix_pb2
+import brunix_pb2_grpc
+import grpc
+from grpc_reflection.v1alpha import reflection
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_elasticsearch import ElasticsearchStore
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("brunix-engine")
+
+
+def _provider_kwargs(provider: str, base_url: str) -> dict[str, Any]:
+    if provider == "ollama":
+        return {"base_url": base_url}
+    return {}
+
+
+class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
+    def __init__(self):
+        from src.emb_factory import create_embedding_model
+        from src.llm_factory import create_chat_model
+
+        self.base_url = os.getenv("OLLAMA_LOCAL_URL", "http://ollama-light-service:11434")
+        self.chat_provider = os.getenv("CHAT_PROVIDER", "ollama")
+        self.embedding_provider = os.getenv("EMBEDDING_PROVIDER", self.chat_provider)
+        self.chat_model_name = os.getenv("OLLAMA_MODEL_NAME")
+        self.embedding_model_name = os.getenv(
+            "OLLAMA_EMB_MODEL_NAME", self.chat_model_name
+        )
+
+        if not self.chat_model_name:
+            raise ValueError("OLLAMA_MODEL_NAME is required")
+
+        logger.info("Starting server")
+
+        self.llm = create_chat_model(
+            provider=self.chat_provider,
+            model=self.chat_model_name,
+            **_provider_kwargs(self.chat_provider, self.base_url),
+        )
+
+        self.embeddings = create_embedding_model(
+            provider=self.embedding_provider,
+            model=self.embedding_model_name,
+            **_provider_kwargs(self.embedding_provider, self.base_url),
+        )
+
+        es_url = os.getenv("ELASTICSEARCH_URL", "http://elasticsearch:9200")
+        logger.info("ElasticSearch on: %s", es_url)
+
+        self.vector_store = ElasticsearchStore(
+            es_url=es_url,
+            index_name=os.getenv("ELASTICSEARCH_INDEX"),
+            embedding=self.embeddings,
+            query_field="text",
+            vector_query_field="embedding",
+        )
+
+    def format_context(self, docs) -> str:
+        parts = []
+        for i, d in enumerate(docs, start=1):
+            meta = d.metadata or {}
+            source = meta.get("source", "unknown")
+            doc_id = meta.get("doc_id", "unknown")
+            chunk_id = meta.get("chunk_id", "unknown")
+
+            parts.append(
+                f"[{i}] source={source} doc_id={doc_id} chunk_id={chunk_id}\n{d.page_content}"
+            )
+        return "\n\n---\n\n".join(parts)
+
+    def AskAgent(self, request, context):
+        logger.info(f"request {request.session_id}): {request.query[:50]}.")
+
+        docs_and_scores = self.vector_store.similarity_search_with_score(
+            request.query, k=4
+        )
+
+        try:
+            context_text = self.format_context([doc for doc, _ in docs_and_scores])
+            # 4. Prompt Engineering
+            prompt = ChatPromptTemplate.from_template("""
+            You are a helpful assistant. Use the following retrieved documents to answer the question. 
+                                                      If you don't know the answer, say you don't know.
+            
+            CONTEXT:
+            {context}
+
+            QUESTION:
+            {question}
+            """)
+
+            chain = prompt | self.llm
+
+            result = chain.invoke({"context": context_text, "question": request.query})
+            result_text = getattr(result, "content", str(result))
+            yield brunix_pb2.AgentResponse(
+                text=str(result_text), avap_code="AVAP-2026", is_final=True
+            )
+
+            yield brunix_pb2.AgentResponse(text="", avap_code="", is_final=True)
+
+        except Exception as e:
+            logger.error(f"Error in AskAgent: {str(e)}")
+            yield brunix_pb2.AgentResponse(
+                text=f"[Error Motor]: {str(e)}", is_final=True
+            )
+
+
+def serve():
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+
+    brunix_pb2_grpc.add_AssistanceEngineServicer_to_server(BrunixEngine(), server)
+
+    SERVICE_NAMES = (
+        brunix_pb2.DESCRIPTOR.services_by_name["AssistanceEngine"].full_name,
+        reflection.SERVICE_NAME,
+    )
+    reflection.enable_server_reflection(SERVICE_NAMES, server)
+
+    server.add_insecure_port("[::]:50051")
+    logger.info("Brunix Engine on port 50051")
+    server.start()
+    server.wait_for_termination()
+
+
+if __name__ == "__main__":
+    serve()

Docker/src/server.py

@@ -1,36 +1,63 @@
-import os
-import grpc
 import logging
+import os
+import sys
 from concurrent import futures
-from grpc_reflection.v1alpha import reflection
+from pathlib import Path
+from typing import Any
+
 import brunix_pb2
 import brunix_pb2_grpc
-
+import grpc
-from langchain_community.llms import Ollama
+from grpc_reflection.v1alpha import reflection
-from langchain_community.embeddings import OllamaEmbeddings
-from langchain_elasticsearch import ElasticsearchStore
 from langchain_core.prompts import ChatPromptTemplate
+from langchain_elasticsearch import ElasticsearchStore
+
+# PROJECT_ROOT = Path(__file__).resolve().parents[2]
+# if str(PROJECT_ROOT) not in sys.path:
+#     sys.path.insert(0, str(PROJECT_ROOT))
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("brunix-engine")
 
 
+def _provider_kwargs(provider: str, base_url: str) -> dict[str, Any]:
+    if provider == "ollama":
+        return {"base_url": base_url}
+    return {}
+
+
 class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
     def __init__(self):
+        from emb_factory import create_embedding_model
+        from llm_factory import create_chat_model
 
-        self.base_url = os.getenv("LLM_BASE_URL", "http://ollama-light-service:11434")
+        self.base_url = os.getenv("OLLAMA_LOCAL_URL", "http://ollama-light-service:11434")
-        self.model_name = os.getenv("OLLAMA_MODEL_NAME")
+        self.chat_provider = os.getenv("CHAT_PROVIDER", "ollama")
+        self.embedding_provider = os.getenv("EMBEDDING_PROVIDER", self.chat_provider)
+        self.chat_model_name = os.getenv("OLLAMA_MODEL_NAME")
+        self.embedding_model_name = os.getenv(
+            "OLLAMA_EMB_MODEL_NAME", self.chat_model_name
+        )
 
-        logger.info(f"Starting server")
+        if not self.chat_model_name:
+            raise ValueError("OLLAMA_MODEL_NAME is required")
 
-        self.llm = Ollama(base_url=self.base_url, model=self.model_name)
+        logger.info("Starting server")
 
-        self.embeddings = OllamaEmbeddings(
+        self.llm = create_chat_model(
-            base_url=self.base_url, model=self.model_name
+            provider=self.chat_provider,
+            model=self.chat_model_name,
+            **_provider_kwargs(self.chat_provider, self.base_url),
+        )
+
+        self.embeddings = create_embedding_model(
+            provider=self.embedding_provider,
+            model=self.embedding_model_name,
+            **_provider_kwargs(self.embedding_provider, self.base_url),
         )
 
         es_url = os.getenv("ELASTICSEARCH_URL", "http://elasticsearch:9200")
-        logger.info(f"ElasticSearch on: {es_url}")
+        logger.info("ElasticSearch on: %s", es_url)
 
         self.vector_store = ElasticsearchStore(
             es_url=es_url,
@@ -77,8 +104,9 @@ class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
             chain = prompt | self.llm
 
             result = chain.invoke({"context": context_text, "question": request.query})
+            result_text = getattr(result, "content", str(result))
             yield brunix_pb2.AgentResponse(
-                text=str(result), avap_code="AVAP-2026", is_final=True
+                text=str(result_text), avap_code="AVAP-2026", is_final=True
             )
 
             yield brunix_pb2.AgentResponse(text="", avap_code="", is_final=True)

scripts/start-tunnels.sh

@@ -22,9 +22,9 @@ if [ ! -f "$KUBECONFIG_PATH" ]; then
 fi
 
 # 1. AI Model Tunnel (Ollama)
-echo -e "${YELLOW}[1/3]${NC} Starting Ollama Light Service tunnel (localhost:11434)..."
+# echo -e "${YELLOW}[1/3]${NC} Starting Ollama Light Service tunnel (localhost:11434)..."
-kubectl port-forward --address 0.0.0.0 svc/ollama-light-service 11434:11434 -n brunix --kubeconfig "$KUBECONFIG_PATH" &
+# kubectl port-forward --address 0.0.0.0 svc/ollama-light-service 11434:11434 -n brunix --kubeconfig "$KUBECONFIG_PATH" &
-OLLAMA_PID=$!
+# OLLAMA_PID=$!
 
 # 2. Knowledge Base Tunnel (Elasticsearch)
 echo -e "${YELLOW}[2/3]${NC} Starting Elasticsearch Vector DB tunnel (localhost:9200)..."