update

Dockerfile

@@ -8,20 +8,18 @@ WORKDIR /app
 RUN apt-get update && apt-get install -y --no-install-recommends \
     build-essential \
     curl \
-    libpq-dev \
     protobuf-compiler \ 
     && rm -rf /var/lib/apt/lists/*
 
 RUN pip install --no-cache-dir --upgrade pip
+
 RUN pip install --no-cache-dir \
     langchain==0.1.0 \
-    langfuse>=2.0.0 \
+    langchain-community==0.0.10 \
-    langgraph \
-    langchain-openai \
     langchain-elasticsearch \
     grpcio \
     grpcio-tools \
-    psycopg2-binary \
+    grpcio-reflection \
     pydantic 
 
 COPY ./protos ./protos
@@ -34,5 +32,5 @@ RUN python -m grpc_tools.protoc \
     ./protos/brunix.proto
 
 EXPOSE 50051
-#CMD ["tail", "-f", "/dev/null"]
+
 CMD ["python", "src/server.py"]

README.md

@@ -14,35 +14,21 @@ The following diagram illustrates the interaction between the AVAP technology, t
 
 ```mermaid
 graph TD
-    subgraph Client_Layer [External Interface]
+    subgraph Local_Dev [Laptop Ivar/Rafael]
-        Client[External Services / UI]
-    end
-    
-    subgraph Engine_Layer
         BE[Brunix Assistance Engine]
-        LG[LangGraph Logic]
+        KT[Kubectl Tunnel]
-        LC[LangChain Framework]
     end
     
-    subgraph Intelligence_Layer 
+    subgraph Vultr_K8s_Cluster [Production - Vultr Cloud]
-        LLM[Fine-tuned Model / OpenAI or other]
+        EDB[(Elasticsearch Vector DB - HDD)]
-        Prompt[Prompt Engineering]
-    end
-
-    subgraph Data_Observability_Layer [System Support]
-        EDB[(Elasticsearch Vector DB)]
-        LF[Langfuse Observability]
         PG[(Postgres - System Data)]
+        LF[Langfuse UI]
     end
 
-    Client -- gRPC:50052 --> BE
+    BE -- localhost:9200/5432 --> KT
-    BE --> LG
+    KT -- Secure Tunnel --> EDB
-    LG --> LC
+    KT -- Secure Tunnel --> PG
-    LC --> LLM
+    BE -- Public IP --> LF
-    LLM --> Prompt
-    LC -- Semantic Search --> EDB
-    LC -- Tracing/Metrics --> LF
-    LF -- Persistence --> PG
 ```
 
 ---

docker-compose.yaml

@@ -7,57 +7,15 @@ services:
     ports:
       - "50052:50051"
     environment:
-      - ELASTICSEARCH_URL=http://elasticsearch:9200
+      - ELASTICSEARCH_URL=http://host.docker.internal:9200
+      - DATABASE_URL=postgresql://postgres:brunix_pass@host.docker.internal:5432/postgres
+
+      - LANGFUSE_HOST=http://45.77.119.180
       - LANGFUSE_PUBLIC_KEY=${LANGFUSE_PUBLIC_KEY}
       - LANGFUSE_SECRET_KEY=${LANGFUSE_SECRET_KEY}
-      - LANGFUSE_HOST=http://langfuse:3000
+      - LLM_BASE_URL=http://host.docker.internal:11434
-      - OPENAI_API_KEY=${OPENAI_API_KEY} # O el proveedor que elija Ivar
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
-    depends_on:
-      - elasticsearch
-      - langfuse
-    networks:
-      - avap-network
     
-  elasticsearch:
+    extra_hosts:
-    image: docker.elastic.co/elasticsearch/elasticsearch:8.12.0
+      - "host.docker.internal:host-gateway"
-    container_name: brunix-vector-db
-    environment:
-      - discovery.type=single-node
-      - xpack.security.enabled=false
-      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
-    ports:
-      - "9200:9200"
-    networks:
-      - avap-network
 
-  langfuse:
-    image: langfuse/langfuse:2.33.0
-    container_name: brunix-observability
-    ports:
-      - "3000:3000"
-    environment:
-      - DATABASE_URL=postgresql://postgres:brunix_pass@langfuse-db:5432/postgres
-      - NEXTAUTH_URL=http://localhost:3000
-      - NEXTAUTH_SECRET=my_ultra_secret
-      - SALT=my_salt
-    depends_on:
-      - langfuse-db
-    networks:
-      - avap-network
-
-  langfuse-db:
-    image: postgres:15
-    container_name: brunix-postgres
-    environment:
-      - POSTGRES_PASSWORD=brunix_pass
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-    networks:
-      - avap-network
-
-networks:
-  avap-network:
-    driver: bridge
-
-volumes:
-  postgres_data:

src/server.py

@@ -1,68 +1,85 @@
 import os
 import grpc
-from concurrent import futures
 import logging
-
+from concurrent import futures
+from grpc_reflection.v1alpha import reflection
 import brunix_pb2
 import brunix_pb2_grpc
-#from langfuse.callback import CallbackHandler       # Descomentar cuando este configurado
+
-#from langchain_openai import ChatOpenAI             # Cambiar por el que corresponda
+from langchain_community.llms import Ollama
-from langchain_core.messages import HumanMessage
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_elasticsearch import ElasticsearchStore
+from langchain_core.prompts import ChatPromptTemplate
 
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("BrunixServer")
+logger = logging.getLogger("brunix-engine")
 
-class BrunixService(brunix_pb2_grpc.AssistanceEngineServicer):
+class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
     def __init__(self):
-        #self.langfuse_handler = CallbackHandler(          # Descomentar cuando este configurado
-        #    public_key=os.getenv("LANGFUSE_PUBLIC_KEY"),
-        #    secret_key=os.getenv("LANGFUSE_SECRET_KEY"),
-        #    host=os.getenv("LANGFUSE_HOST")
-        #)
-        # AQUI IMPLEMENTAR EL MODELO QUE CORRESPONDA - IVAR
-        #self.llm = ChatOpenAI(
-        #    model="gpt-4-turbo-preview", 
-        #    temperature=0.2,
-        #    streaming=True
-        #)
-        logger.info("Brunix Engine initializing.")
 
-    def AskAgent(self, request, context): # PLACEHOLDER DE FUNCIONAMIENTO
+        self.base_url = os.getenv("LLM_BASE_URL", "http://ollama-light-service:11434")
-        logger.info(f"Request received: {request.query}")
+        self.model_name = os.getenv("LLM_MODEL", "qwen2.5:1.5b")
         
+        logger.info(f"Starting server")
+
+        self.llm = Ollama(base_url=self.base_url, model=self.model_name)
+
+        self.embeddings = OllamaEmbeddings(base_url=self.base_url, model="nomic-embed-text")
+
+        es_url = os.getenv("ELASTICSEARCH_URL", "http://elasticsearch:9200")
+        logger.info(f"ElasticSearch on: {es_url}")
+        
+        self.vector_store = ElasticsearchStore(
+            es_url=es_url,
+            index_name="avap_manuals",
+            embedding=self.embeddings
+        )
+
+    def AskAgent(self, request, context):
+        logger.info(f"request {request.session_id}): {request.query[:50]}.")
 
         try:
+            context_text = "AVAP is a virtual programming language for API development."
+            # 4. Prompt Engineering
+            prompt = ChatPromptTemplate.from_template("""
+            You are Brunix, the 101OBEX artificial intelligence for the AVAP Sphere platform. Respond in a professional manner.
             
-            config = {"callbacks": [self.langfuse_handler], "run_name": "Brunix_Query"}
+            CONTEXT:
+            {context}
 
-            full_response_text = ""
+            QUESTION:
+            {question}
+            """)
 
-            chunks = [
+            chain = prompt | self.llm
-                {"text": ""},
-            ]
             
-            for chunk in chunks:
+            for chunk in chain.stream({"context": context_text, "question": request.query}):
                 yield brunix_pb2.AgentResponse(
-                    text=chunk["text"],
+                    text=str(chunk),
-                    avap_code="",
+                    avap_code="AVAP-2026",
-                    node_id=chunk["node"],
                     is_final=False
                 )
             
-            yield brunix_pb2.AgentResponse(text="", is_final=True)
+            yield brunix_pb2.AgentResponse(text="", avap_code="", is_final=True)
 
         except Exception as e:
-            logger.error(f"AGENT ERROR: {str(e)}")
+            logger.error(f"Error in AskAgent: {str(e)}")
-            context.set_details(str(e))
+            yield brunix_pb2.AgentResponse(text=f"[Error Motor]: {str(e)}", is_final=True)
-            context.set_code(grpc.StatusCode.INTERNAL)
 
 def serve():
+    
     server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
-    brunix_pb2_grpc.add_AssistanceEngineServicer_to_server(BrunixService(), server)
+    
+    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 gRPC Server listen on port 50051")
+    logger.info("Brunix Engine on port 50051")
-    
     server.start()
     server.wait_for_termination()