UPGRADE: New RAG functional

Docker/Dockerfile

@@ -25,6 +25,10 @@ RUN python -m grpc_tools.protoc \
     --grpc_python_out=./src \
     ./protos/brunix.proto
 
-EXPOSE 50051
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
 
-CMD ["python", "src/server.py"]
+EXPOSE 50051
+EXPOSE 8000
+
+ENTRYPOINT ["/entrypoint.sh"]

Docker/docker-compose.yaml

@@ -6,6 +6,7 @@ services:
     container_name: brunix-assistance-engine
     ports:
       - "50052:50051"
+      - "8000:8000"
     environment:
       ELASTICSEARCH_URL: ${ELASTICSEARCH_URL}
       ELASTICSEARCH_INDEX: ${ELASTICSEARCH_INDEX}
@@ -16,6 +17,7 @@ services:
       OLLAMA_URL: ${OLLAMA_URL}
       OLLAMA_MODEL_NAME: ${OLLAMA_MODEL_NAME}
       OLLAMA_EMB_MODEL_NAME: ${OLLAMA_EMB_MODEL_NAME}
+      PROXY_THREAD_WORKERS: 10
     
     extra_hosts:
       - "host.docker.internal:host-gateway"

Docker/entrypoint.sh

@@ -0,0 +1,30 @@
+#!/bin/sh
+set -e
+
+echo "[entrypoint] Starting Brunix Engine (gRPC :50051)..."
+python src/server.py &
+ENGINE_PID=$!
+
+echo "[entrypoint] Starting OpenAI Proxy (HTTP :8000)..."
+uvicorn openai_proxy:app --host 0.0.0.0 --port 8000 --workers 4 --app-dir src &
+PROXY_PID=$!
+
+wait_any() {
+    while kill -0 $ENGINE_PID 2>/dev/null && kill -0 $PROXY_PID 2>/dev/null; do
+        sleep 2
+    done
+
+    if ! kill -0 $ENGINE_PID 2>/dev/null; then
+        echo "[entrypoint] Engine died — stopping proxy"
+        kill $PROXY_PID 2>/dev/null
+        exit 1
+    fi
+
+    if ! kill -0 $PROXY_PID 2>/dev/null; then
+        echo "[entrypoint] Proxy died — stopping engine"
+        kill $ENGINE_PID 2>/dev/null
+        exit 1
+    fi
+}
+
+wait_any

Docker/protos/brunix.proto

@@ -3,16 +3,60 @@ syntax = "proto3";
 package brunix;
 
 service AssistanceEngine {
-  rpc AskAgent (AgentRequest) returns (stream AgentResponse);
+  // Respuesta completa — compatible con clientes existentes
+  rpc AskAgent       (AgentRequest) returns (stream AgentResponse);
+
+  // Streaming real token a token desde Ollama
+  rpc AskAgentStream (AgentRequest) returns (stream AgentResponse);
+
+  // Evaluación RAGAS con Claude como juez
+  rpc EvaluateRAG    (EvalRequest)  returns (EvalResponse);
 }
 
+// ---------------------------------------------------------------------------
+// AskAgent / AskAgentStream — mismos mensajes, dos comportamientos
+// ---------------------------------------------------------------------------
+
 message AgentRequest {
-  string query = 1;
+  string query      = 1;
   string session_id = 2;
 }
 
 message AgentResponse {
-  string text = 1;
+  string text      = 1;
   string avap_code = 2;
-  bool is_final = 3;
+  bool   is_final  = 3;
+}
+
+// ---------------------------------------------------------------------------
+// EvaluateRAG
+// ---------------------------------------------------------------------------
+
+message EvalRequest {
+  string category = 1;
+  int32  limit    = 2;
+  string index    = 3;
+}
+
+message EvalResponse {
+  string status               = 1;
+  int32  questions_evaluated  = 2;
+  float  elapsed_seconds      = 3;
+  string judge_model          = 4;
+  string index                = 5;
+  float  faithfulness         = 6;
+  float  answer_relevancy     = 7;
+  float  context_recall       = 8;
+  float  context_precision    = 9;
+  float  global_score         = 10;
+  string verdict              = 11;
+  repeated QuestionDetail details = 12;
+}
+
+message QuestionDetail {
+  string id             = 1;
+  string category       = 2;
+  string question       = 3;
+  string answer_preview = 4;
+  int32  n_chunks       = 5;
 }

Docker/requirements.txt

@@ -316,3 +316,10 @@ yarl==1.22.0
     # via aiohttp
 zstandard==0.25.0
     # via langsmith
+
+ragas
+datasets
+langchain-anthropic
+
+fastapi>=0.111.0
+uvicorn[standard]>=0.29.0

Docker/src/evaluate.py

@@ -0,0 +1,230 @@
+import os
+import time
+import json
+import logging
+from collections import defaultdict
+from pathlib import Path
+from typing import Optional
+from ragas import evaluate as ragas_evaluate
+from ragas.metrics import ( faithfulness, answer_relevancy, context_recall, context_precision,)
+from ragas.llms import LangchainLLMWrapper
+from ragas.embeddings import LangchainEmbeddingsWrapper
+from datasets import Dataset
+from langchain_anthropic import ChatAnthropic
+
+logger = logging.getLogger(__name__)
+
+GOLDEN_DATASET_PATH = Path(__file__).parent / "golden_dataset.json"
+CLAUDE_MODEL = os.getenv("ANTHROPIC_MODEL", "claude-sonnet-4-20250514")
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
+K_RETRIEVE = 5
+
+
+
+ANTHROPIC_AVAILABLE = True
+
+
+from elasticsearch import Elasticsearch
+from langchain_core.messages import SystemMessage, HumanMessage
+
+def retrieve_context( es_client, embeddings, question, index, k = K_RETRIEVE,):
+
+    query_vector = None
+    try:
+        query_vector = embeddings.embed_query(question)
+    except Exception as e:
+        logger.warning(f"[eval] embed_query fails: {e}")
+
+    bm25_hits = []
+    try:
+        resp = es_client.search(
+            index=index,
+            body={
+                "size": k,
+                "query": {
+                    "multi_match": {
+                        "query": question,
+                        "fields": ["content^2", "text^2"],
+                        "type": "best_fields",
+                        "fuzziness": "AUTO",
+                    }
+                },
+                "_source": {"excludes": ["embedding"]},
+            }
+        )
+        bm25_hits = resp["hits"]["hits"]
+    except Exception as e:
+        logger.warning(f"[eval] BM25 fails: {e}")
+
+    knn_hits = []
+    if query_vector:
+        try:
+            resp = es_client.search(
+                index=index,
+                body={
+                    "size": k,
+                    "knn": {
+                        "field": "embedding",
+                        "query_vector": query_vector,
+                        "k": k,
+                        "num_candidates": k * 5,
+                    },
+                    "_source": {"excludes": ["embedding"]},
+                }
+            )
+            knn_hits = resp["hits"]["hits"]
+        except Exception as e:
+            logger.warning(f"[eval] kNN falló: {e}")
+
+    rrf_scores: dict[str, float] = defaultdict(float)
+    hit_by_id:  dict[str, dict]  = {}
+
+    for rank, hit in enumerate(bm25_hits):
+        doc_id = hit["_id"]
+        rrf_scores[doc_id] += 1.0 / (rank + 60)
+        hit_by_id[doc_id] = hit
+
+    for rank, hit in enumerate(knn_hits):
+        doc_id = hit["_id"]
+        rrf_scores[doc_id] += 1.0 / (rank + 60)
+        if doc_id not in hit_by_id:
+            hit_by_id[doc_id] = hit
+
+    ranked = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)[:k]
+
+    return [
+        hit_by_id[doc_id]["_source"].get("content")
+        or hit_by_id[doc_id]["_source"].get("text", "")
+        for doc_id, _ in ranked
+        if (
+            hit_by_id[doc_id]["_source"].get("content")
+            or hit_by_id[doc_id]["_source"].get("text", "")
+        ).strip()
+    ]
+
+
+def generate_answer(llm, question: str, contexts: list[str]) -> str:
+    try:
+        from prompts import GENERATE_PROMPT
+        context_text = "\n\n".join(
+            f"[{i+1}] {ctx}" for i, ctx in enumerate(contexts)
+        )
+        prompt = SystemMessage(
+            content=GENERATE_PROMPT.content.format(context=context_text)
+        )
+        resp = llm.invoke([prompt, HumanMessage(content=question)])
+        return resp.content.strip()
+    except Exception as e:
+        logger.warning(f"[eval] generate_answer fails: {e}")
+        return ""
+
+def run_evaluation( es_client, llm, embeddings, index_name, category = None, limit = None,):
+
+    if not ANTHROPIC_AVAILABLE:
+        return {"error": "langchain-anthropic no instalado. pip install langchain-anthropic"}
+    if not ANTHROPIC_API_KEY:
+        return {"error": "ANTHROPIC_API_KEY no configurada en .env"}
+    if not GOLDEN_DATASET_PATH.exists():
+        return {"error": f"Golden dataset no encontrado en {GOLDEN_DATASET_PATH}"}
+
+
+    questions = json.loads(GOLDEN_DATASET_PATH.read_text(encoding="utf-8"))
+    if category:
+        questions = [q for q in questions if q.get("category") == category]
+    if limit:
+        questions = questions[:limit]
+    if not questions:
+        return {"error": "NO QUESTIONS WITH THIS FILTERS"}
+
+    logger.info(f"[eval] makind: {len(questions)} questions, index={index_name}")
+
+    claude_judge = ChatAnthropic(
+        model=CLAUDE_MODEL,
+        api_key=ANTHROPIC_API_KEY,
+        temperature=0,
+        max_tokens=2048,
+    )
+
+    rows = {"question": [], "answer": [], "contexts": [], "ground_truth": []}
+    details = []
+    t_start = time.time()
+
+    for item in questions:
+        q_id = item["id"]
+        question = item["question"]
+        gt = item["ground_truth"]
+
+        logger.info(f"[eval] {q_id}: {question[:60]}")
+
+        contexts = retrieve_context(es_client, embeddings, question, index_name)
+        if not contexts:
+            logger.warning(f"[eval] No context for {q_id} — skipping")
+            continue
+
+        answer = generate_answer(llm, question, contexts)
+        if not answer:
+            logger.warning(f"[eval] No answers for {q_id} — skipping")
+            continue
+
+        rows["question"].append(question)
+        rows["answer"].append(answer)
+        rows["contexts"].append(contexts)
+        rows["ground_truth"].append(gt)
+
+        details.append({
+            "id": q_id,
+            "category": item.get("category", ""),
+            "question": question,
+            "answer_preview": answer[:300],
+            "n_chunks": len(contexts),
+        })
+
+    if not rows["question"]:
+        return {"error": "NO SAMPLES GENETARED"}
+
+    dataset = Dataset.from_dict(rows)
+    ragas_llm = LangchainLLMWrapper(claude_judge)
+    ragas_emb = LangchainEmbeddingsWrapper(embeddings)
+
+    metrics = [faithfulness, answer_relevancy, context_recall, context_precision]
+    for metric in metrics:
+        metric.llm = ragas_llm
+        if hasattr(metric, "embeddings"):
+            metric.embeddings = ragas_emb
+
+    logger.info("[eval] JUDGING BY CLAUDE...")
+    result = ragas_evaluate(dataset, metrics=metrics)
+
+    elapsed = time.time() - t_start
+
+    scores = {
+        "faithfulness": round(float(result.get("faithfulness", 0)), 4),
+        "answer_relevancy": round(float(result.get("answer_relevancy", 0)), 4),
+        "context_recall": round(float(result.get("context_recall", 0)), 4),
+        "context_precision": round(float(result.get("context_precision", 0)), 4),
+    }
+
+    valid_scores = [v for v in scores.values() if v > 0]
+    global_score = round(sum(valid_scores) / len(valid_scores), 4) if valid_scores else 0.0
+
+    verdict = (
+        "EXCELLENT" if global_score >= 0.8 else
+        "ACCEPTABLE" if global_score >= 0.6 else
+        "INSUFFICIENT"
+    )
+
+    logger.info(f"[eval] FINISHED — global={global_score} verdict={verdict} "
+                f"elapsed={elapsed:.0f}s")
+
+    return {
+        "status": "ok",
+        "questions_evaluated": len(rows["question"]),
+        "elapsed_seconds": round(elapsed, 1),
+        "judge_model": CLAUDE_MODEL,
+        "index": index_name,
+        "category_filter": category or "all",
+        "scores": scores,
+        "global_score": global_score,
+        "verdict": verdict,
+        "details": details,
+    }

Docker/src/graph.py

@@ -1,60 +1,391 @@
-# graph.py
 import logging
-
+from collections import defaultdict
+from elasticsearch import Elasticsearch
 from langchain_core.documents import Document
-from langchain_core.messages import SystemMessage
+from langchain_core.messages import AIMessage, SystemMessage, HumanMessage, BaseMessage
 from langgraph.graph import END, StateGraph
 from langgraph.graph.state import CompiledStateGraph
-from prompts import GENERATE_PROMPT, REFORMULATE_PROMPT
+
+from prompts import (
+    CLASSIFY_PROMPT_TEMPLATE,
+    CODE_GENERATION_PROMPT,
+    CONVERSATIONAL_PROMPT,
+    GENERATE_PROMPT,
+    REFORMULATE_PROMPT,
+)
+
 from state import AgentState
 
 logger = logging.getLogger(__name__)
 
+session_store: dict[str, list] = defaultdict(list)
 
-def format_context(docs: list[Document]) -> str:
+def format_context(docs):
     chunks = []
     for i, doc in enumerate(docs, 1):
-        source = (doc.metadata or {}).get("source", "Untitled")
+        meta = doc.metadata or {}
-        source_id = (doc.metadata or {}).get("id", f"chunk-{i}")
+        chunk_id = meta.get("chunk_id", meta.get("id", f"chunk-{i}"))
-        text = doc.page_content or ""
+        source = meta.get("source_file", meta.get("source", "unknown"))
-        chunks.append(f"[{i}] id={source_id} source={source}\n{text}")
+        doc_type = meta.get("doc_type", "")
+        block_type = meta.get("block_type", "")
+        section = meta.get("section", "")
+
+        text = (doc.page_content or "").strip()
+        if not text:
+            text = meta.get("content") or meta.get("text") or ""
+
+        header_parts = [f"[{i}]", f"id={chunk_id}"]
+        if doc_type: header_parts.append(f"type={doc_type}")
+        if block_type: header_parts.append(f"block={block_type}")
+        if section: header_parts.append(f"section={section}")
+        header_parts.append(f"source={source}")
+
+        if doc_type in ("code", "code_example", "bnf") or \
+           block_type in ("function", "if", "startLoop", "try"):
+            header_parts.append("[AVAP CODE]")
+
+        chunks.append(" ".join(header_parts) + "\n" + text)
+
     return "\n\n".join(chunks)
 
 
-def build_graph(llm, vector_store) -> CompiledStateGraph:
+def format_history_for_classify(messages):
+    lines = []
+    for msg in messages[-6:]:
+        if isinstance(msg, HumanMessage):
+            lines.append(f"User: {msg.content}")
+        elif isinstance(msg, AIMessage):
+            lines.append(f"Assistant: {msg.content[:300]}")
+        elif isinstance(msg, dict):
+            role = msg.get("role", "user")
+            content = msg.get("content", "")[:300]
+            lines.append(f"{role.capitalize()}: {content}")
+    return "\n".join(lines) if lines else "(no history)"
+
+
+def hybrid_search_native(es_client, embeddings, query, index_name, k=8):
+    query_vector = None
+    try:
+        query_vector = embeddings.embed_query(query)
+    except Exception as e:
+        logger.warning(f"[hybrid] embed_query fails: {e}")
+
+    bm25_hits = []
+    try:
+        resp = es_client.search(
+            index=index_name,
+            body={
+                "size": k,
+                "query": {
+                    "multi_match": {
+                        "query": query,
+                        "fields": ["content^2", "text^2"],
+                        "type": "best_fields",
+                        "fuzziness": "AUTO",
+                    }
+                },
+                "_source": {"excludes": ["embedding"]},
+            }
+        )
+        bm25_hits = resp["hits"]["hits"]
+        logger.info(f"[hybrid] BM25 -> {len(bm25_hits)} hits")
+    except Exception as e:
+        logger.warning(f"[hybrid] BM25 fails: {e}")
+
+    knn_hits = []
+    if query_vector:
+        try:
+            resp = es_client.search(
+                index=index_name,
+                body={
+                    "size": k,
+                    "knn": {
+                        "field": "embedding",
+                        "query_vector": query_vector,
+                        "k": k,
+                        "num_candidates": k * 5,
+                    },
+                    "_source": {"excludes": ["embedding"]},
+                }
+            )
+            knn_hits = resp["hits"]["hits"]
+            logger.info(f"[hybrid] kNN -> {len(knn_hits)} hits")
+        except Exception as e:
+            logger.warning(f"[hybrid] kNN fails: {e}")
+
+    rrf_scores: dict[str, float] = defaultdict(float)
+    hit_by_id:  dict[str, dict]  = {}
+
+    for rank, hit in enumerate(bm25_hits):
+        doc_id = hit["_id"]
+        rrf_scores[doc_id] += 1.0 / (rank + 60)
+        hit_by_id[doc_id] = hit
+
+    for rank, hit in enumerate(knn_hits):
+        doc_id = hit["_id"]
+        rrf_scores[doc_id] += 1.0 / (rank + 60)
+        if doc_id not in hit_by_id:
+            hit_by_id[doc_id] = hit
+
+    ranked = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)[:k]
+
+    docs = []
+    for doc_id, score in ranked:
+        src  = hit_by_id[doc_id]["_source"]
+        text = src.get("content") or src.get("text") or ""
+        meta = {k: v for k, v in src.items()
+                if k not in ("content", "text", "embedding")}
+        meta["id"]= doc_id
+        meta["rrf_score"] = score
+        docs.append(Document(page_content=text, metadata=meta))
+
+    logger.info(f"[hybrid] RRF -> {len(docs)} final docs")
+    return docs
+
+def build_graph(llm, embeddings, es_client, index_name):
+
+    def _persist(state: AgentState, response: BaseMessage):
+        session_id = state.get("session_id", "")
+        if session_id:
+            session_store[session_id] = list(state["messages"]) + [response]
+
+    def classify(state):
+        messages = state["messages"]
+        user_msg = messages[-1]
+        question = getattr(user_msg, "content",
+                           user_msg.get("content", "")
+                           if isinstance(user_msg, dict) else "")
+        history_msgs = messages[:-1]
+
+        if not history_msgs:
+            prompt_content = (
+                CLASSIFY_PROMPT_TEMPLATE
+                .replace("{history}", "(no history)")
+                .replace("{message}", question)
+            )
+            resp = llm.invoke([SystemMessage(content=prompt_content)])
+            raw = resp.content.strip().upper()
+            query_type = _parse_query_type(raw)
+            logger.info(f"[classify] no historic content raw='{raw}' -> {query_type}")
+            return {"query_type": query_type}
+
+        history_text = format_history_for_classify(history_msgs)
+        prompt_content = (
+            CLASSIFY_PROMPT_TEMPLATE
+            .replace("{history}", history_text)
+            .replace("{message}", question)
+        )
+        resp = llm.invoke([SystemMessage(content=prompt_content)])
+        raw = resp.content.strip().upper()
+        query_type = _parse_query_type(raw)
+        logger.info(f"[classify] raw='{raw}' -> {query_type}")
+        return {"query_type": query_type}
+
+    def _parse_query_type(raw: str) -> str:
+        if raw.startswith("CODE_GENERATION") or "CODE" in raw:
+            return "CODE_GENERATION"
+        if raw.startswith("CONVERSATIONAL"):
+            return "CONVERSATIONAL"
+        return "RETRIEVAL"
+
     def reformulate(state: AgentState) -> AgentState:
         user_msg = state["messages"][-1]
         resp = llm.invoke([REFORMULATE_PROMPT, user_msg])
         reformulated = resp.content.strip()
-        logger.info(f"[reformulate] '{user_msg.content}' → '{reformulated}'")
+        logger.info(f"[reformulate] -> '{reformulated}'")
         return {"reformulated_query": reformulated}
 
     def retrieve(state: AgentState) -> AgentState:
         query = state["reformulated_query"]
-        docs = vector_store.as_retriever(
+        docs = hybrid_search_native(
-            search_type="similarity",
+            es_client=es_client,
-            search_kwargs={"k": 3},
+            embeddings=embeddings,
-        ).invoke(query)
+            query=query,
+            index_name=index_name,
+            k=8,
+        )
         context = format_context(docs)
-        logger.info(f"[retrieve] {len(docs)} docs fetched")
+        logger.info(f"[retrieve] {len(docs)} docs, context len={len(context)}")
-        logger.info(context)
         return {"context": context}
 
-    def generate(state: AgentState) -> AgentState:
+    def generate(state):
         prompt = SystemMessage(
             content=GENERATE_PROMPT.content.format(context=state["context"])
         )
         resp = llm.invoke([prompt] + state["messages"])
+        logger.info(f"[generate] {len(resp.content)} chars")
+        _persist(state, resp)
         return {"messages": [resp]}
 
+    def generate_code(state):
+        prompt = SystemMessage(
+            content=CODE_GENERATION_PROMPT.content.format(context=state["context"])
+        )
+        resp = llm.invoke([prompt] + state["messages"])
+        logger.info(f"[generate_code] {len(resp.content)} chars")
+        _persist(state, resp)
+        return {"messages": [resp]}
+
+    def respond_conversational(state):
+        resp = llm.invoke([CONVERSATIONAL_PROMPT] + state["messages"])
+        logger.info("[conversational] from comversation")
+        _persist(state, resp)
+        return {"messages": [resp]}
+
+    def route_by_type(state):
+        return state.get("query_type", "RETRIEVAL")
+
+    def route_after_retrieve(state):
+        qt = state.get("query_type", "RETRIEVAL")
+        return "generate_code" if qt == "CODE_GENERATION" else "generate"
+
     graph_builder = StateGraph(AgentState)
+
+    graph_builder.add_node("classify", classify)
     graph_builder.add_node("reformulate", reformulate)
     graph_builder.add_node("retrieve", retrieve)
     graph_builder.add_node("generate", generate)
+    graph_builder.add_node("generate_code", generate_code)
+    graph_builder.add_node("respond_conversational", respond_conversational)
+
+    graph_builder.set_entry_point("classify")
+
+    graph_builder.add_conditional_edges(
+        "classify",
+        route_by_type,
+        {
+            "RETRIEVAL":       "reformulate",
+            "CODE_GENERATION": "reformulate",
+            "CONVERSATIONAL":  "respond_conversational",
+        }
+    )
 
-    graph_builder.set_entry_point("reformulate")
     graph_builder.add_edge("reformulate", "retrieve")
-    graph_builder.add_edge("retrieve", "generate")
+
+    graph_builder.add_conditional_edges(
+        "retrieve",
+        route_after_retrieve,
+        {
+            "generate":      "generate",
+            "generate_code": "generate_code",
+        }
+    )
+
     graph_builder.add_edge("generate", END)
+    graph_builder.add_edge("generate_code", END)
+    graph_builder.add_edge("respond_conversational", END)
 
     return graph_builder.compile()
+
+
+def build_prepare_graph(llm, embeddings, es_client, index_name):
+
+    def classify(state):
+        messages = state["messages"]
+        user_msg = messages[-1]
+        question = getattr(user_msg, "content",
+                           user_msg.get("content", "")
+                           if isinstance(user_msg, dict) else "")
+        history_msgs = messages[:-1]
+
+        if not history_msgs:
+            prompt_content = (
+                CLASSIFY_PROMPT_TEMPLATE
+                .replace("{history}", "(no history)")
+                .replace("{message}", question)
+            )
+            resp = llm.invoke([SystemMessage(content=prompt_content)])
+            raw = resp.content.strip().upper()
+            query_type = _parse_query_type(raw)
+            logger.info(f"[prepare/classify] no history raw='{raw}' -> {query_type}")
+            return {"query_type": query_type}
+
+        history_text = format_history_for_classify(history_msgs)
+        prompt_content = (
+            CLASSIFY_PROMPT_TEMPLATE
+            .replace("{history}", history_text)
+            .replace("{message}", question)
+        )
+        resp = llm.invoke([SystemMessage(content=prompt_content)])
+        raw = resp.content.strip().upper()
+        query_type = _parse_query_type(raw)
+        logger.info(f"[prepare/classify] raw='{raw}' -> {query_type}")
+        return {"query_type": query_type}
+
+    def _parse_query_type(raw: str) -> str:
+        if raw.startswith("CODE_GENERATION") or "CODE" in raw:
+            return "CODE_GENERATION"
+        if raw.startswith("CONVERSATIONAL"):
+            return "CONVERSATIONAL"
+        return "RETRIEVAL"
+
+    def reformulate(state: AgentState) -> AgentState:
+        user_msg = state["messages"][-1]
+        resp = llm.invoke([REFORMULATE_PROMPT, user_msg])
+        reformulated = resp.content.strip()
+        logger.info(f"[prepare/reformulate] -> '{reformulated}'")
+        return {"reformulated_query": reformulated}
+
+    def retrieve(state: AgentState) -> AgentState:
+        query = state["reformulated_query"]
+        docs = hybrid_search_native(
+            es_client=es_client,
+            embeddings=embeddings,
+            query=query,
+            index_name=index_name,
+            k=8,
+        )
+        context = format_context(docs)
+        logger.info(f"[prepare/retrieve] {len(docs)} docs, context len={len(context)}")
+        return {"context": context}
+
+    def skip_retrieve(state: AgentState) -> AgentState:
+        return {"context": ""}
+
+    def route_by_type(state):
+        return state.get("query_type", "RETRIEVAL")
+
+    graph_builder = StateGraph(AgentState)
+
+    graph_builder.add_node("classify", classify)
+    graph_builder.add_node("reformulate", reformulate)
+    graph_builder.add_node("retrieve", retrieve)
+    graph_builder.add_node("skip_retrieve", skip_retrieve)
+
+    graph_builder.set_entry_point("classify")
+
+    graph_builder.add_conditional_edges(
+        "classify",
+        route_by_type,
+        {
+            "RETRIEVAL": "reformulate",
+            "CODE_GENERATION": "reformulate",
+            "CONVERSATIONAL": "skip_retrieve",
+        }
+    )
+
+    graph_builder.add_edge("reformulate", "retrieve")
+    graph_builder.add_edge("retrieve", END)
+    graph_builder.add_edge("skip_retrieve",END)
+
+    return graph_builder.compile()
+
+
+def build_final_messages(state: AgentState) -> list:
+    query_type = state.get("query_type", "RETRIEVAL")
+    context = state.get("context", "")
+    messages = state.get("messages", [])
+
+    if query_type == "CONVERSATIONAL":
+        return [CONVERSATIONAL_PROMPT] + messages
+
+    if query_type == "CODE_GENERATION":
+        prompt = SystemMessage(
+            content=CODE_GENERATION_PROMPT.content.format(context=context)
+        )
+    else:
+        prompt = SystemMessage(
+            content=GENERATE_PROMPT.content.format(context=context)
+        )
+
+    return [prompt] + messages

Docker/src/openai_proxy.py

@@ -0,0 +1,420 @@
+import json
+import os
+import time
+import uuid
+import logging
+import asyncio
+import concurrent.futures
+from typing import AsyncIterator, Optional, Any, Literal, Union
+
+import grpc
+import brunix_pb2
+import brunix_pb2_grpc
+
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import JSONResponse, StreamingResponse
+from pydantic import BaseModel
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("openai-proxy")
+
+_thread_pool = concurrent.futures.ThreadPoolExecutor(
+    max_workers=int(os.getenv("PROXY_THREAD_WORKERS", "20"))
+)
+
+GRPC_TARGET = os.getenv("BRUNIX_GRPC_TARGET", "localhost:50051")
+PROXY_MODEL = os.getenv("PROXY_MODEL_ID", "brunix")
+
+_channel: Optional[grpc.Channel] = None
+_stub:    Optional[brunix_pb2_grpc.AssistanceEngineStub] = None
+
+
+def get_stub() -> brunix_pb2_grpc.AssistanceEngineStub:
+    global _channel, _stub
+    if _stub is None:
+        _channel = grpc.insecure_channel(GRPC_TARGET)
+        _stub    = brunix_pb2_grpc.AssistanceEngineStub(_channel)
+        logger.info(f"[gRPC] connected to {GRPC_TARGET}")
+    return _stub
+
+
+app = FastAPI(
+    title="Brunix OpenAI-Compatible Proxy",
+    version="2.0.0",
+    description="stream:false → AskAgent | stream:true → AskAgentStream",
+)
+
+class ChatMessage(BaseModel):
+    role:    Literal["system", "user", "assistant", "function"] = "user"
+    content: str = ""
+    name:    Optional[str] = None
+
+
+class ChatCompletionRequest(BaseModel):
+    model:             str = PROXY_MODEL
+    messages:          list[ChatMessage]
+    stream:            bool = False
+    temperature:       Optional[float] = None
+    max_tokens:        Optional[int]   = None
+    session_id:        Optional[str]   = None   # extensión Brunix
+    top_p:             Optional[float] = None
+    n:                 Optional[int]   = 1
+    stop:              Optional[Any]   = None
+    presence_penalty:  Optional[float] = None
+    frequency_penalty: Optional[float] = None
+    user:              Optional[str]   = None
+
+
+class CompletionRequest(BaseModel):
+    model:       str = PROXY_MODEL
+    prompt:      Union[str, list[str]] = ""
+    stream:      bool = False
+    temperature: Optional[float] = None
+    max_tokens:  Optional[int]   = None
+    session_id:  Optional[str]   = None
+    suffix:      Optional[str]   = None
+    top_p:       Optional[float] = None
+    n:           Optional[int]   = 1
+    stop:        Optional[Any]   = None
+    user:        Optional[str]   = None
+
+
+# Ollama schemas
+class OllamaChatMessage(BaseModel):
+    role:    str = "user"
+    content: str = ""
+
+
+class OllamaChatRequest(BaseModel):
+    model:      str = PROXY_MODEL
+    messages:   list[OllamaChatMessage]
+    stream:     bool = True             # Ollama hace stream por defecto
+    session_id: Optional[str] = None
+
+
+class OllamaGenerateRequest(BaseModel):
+    model:      str = PROXY_MODEL
+    prompt:     str = ""
+    stream:     bool = True
+    session_id: Optional[str] = None
+
+
+def _ts() -> int:
+    return int(time.time())
+
+
+def _chat_response(content: str, req_id: str) -> dict:
+    return {
+        "id": req_id, "object": "chat.completion", "created": _ts(),
+        "model": PROXY_MODEL,
+        "choices": [{"index": 0, "message": {"role": "assistant", "content": content}, "finish_reason": "stop"}],
+        "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
+    }
+
+
+def _completion_response(text: str, req_id: str) -> dict:
+    return {
+        "id": req_id, "object": "text_completion", "created": _ts(),
+        "model": PROXY_MODEL,
+        "choices": [{"text": text, "index": 0, "logprobs": None, "finish_reason": "stop"}],
+        "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
+    }
+
+
+def _chat_chunk(delta: str, req_id: str, finish: Optional[str] = None) -> dict:
+    return {
+        "id": req_id, "object": "chat.completion.chunk", "created": _ts(),
+        "model": PROXY_MODEL,
+        "choices": [{"index": 0,
+                     "delta": {"role": "assistant", "content": delta} if delta else {},
+                     "finish_reason": finish}],
+    }
+
+
+def _completion_chunk(text: str, req_id: str, finish: Optional[str] = None) -> dict:
+    return {
+        "id": req_id, "object": "text_completion", "created": _ts(),
+        "model": PROXY_MODEL,
+        "choices": [{"text": text, "index": 0, "logprobs": None, "finish_reason": finish}],
+    }
+
+
+def _sse(data: dict) -> str:
+    return f"data: {json.dumps(data)}\n\n"
+
+
+def _sse_done() -> str:
+    return "data: [DONE]\n\n"
+
+
+def _query_from_messages(messages: list[ChatMessage]) -> str:
+    for m in reversed(messages):
+        if m.role == "user":
+            return m.content
+    return ""
+
+
+async def _invoke_blocking(query: str, session_id: str) -> str:
+
+    loop = asyncio.get_event_loop()
+
+    def _call():
+        stub = get_stub()
+        req  = brunix_pb2.AgentRequest(query=query, session_id=session_id)
+        parts = []
+        for resp in stub.AskAgent(req):
+            if resp.text:
+                parts.append(resp.text)
+        return "".join(parts)
+
+    return await loop.run_in_executor(_thread_pool, _call)
+
+
+async def _iter_stream(query: str, session_id: str) -> AsyncIterator[brunix_pb2.AgentResponse]:
+
+    loop  = asyncio.get_event_loop()
+    queue: asyncio.Queue = asyncio.Queue()
+
+    def _producer():
+        try:
+            stub = get_stub()
+            req  = brunix_pb2.AgentRequest(query=query, session_id=session_id)
+            for resp in stub.AskAgentStream(req):                    # ← AskAgentStream
+                asyncio.run_coroutine_threadsafe(queue.put(resp), loop).result()
+        except Exception as e:
+            asyncio.run_coroutine_threadsafe(queue.put(e), loop).result()
+        finally:
+            asyncio.run_coroutine_threadsafe(queue.put(None), loop).result()  # sentinel
+
+    _thread_pool.submit(_producer)
+
+    while True:
+        item = await queue.get()
+        if item is None:
+            break
+        if isinstance(item, Exception):
+            raise item
+        yield item
+
+
+async def _stream_chat(query: str, session_id: str, req_id: str) -> AsyncIterator[str]:
+    try:
+        async for resp in _iter_stream(query, session_id):
+            if resp.is_final:
+                yield _sse(_chat_chunk("", req_id, finish="stop"))
+                break
+            if resp.text:
+                yield _sse(_chat_chunk(resp.text, req_id))
+    except Exception as e:
+        logger.error(f"[stream_chat] error: {e}")
+        yield _sse(_chat_chunk(f"[Error: {e}]", req_id, finish="stop"))
+
+    yield _sse_done()
+
+
+async def _stream_completion(query: str, session_id: str, req_id: str) -> AsyncIterator[str]:
+    try:
+        async for resp in _iter_stream(query, session_id):
+            if resp.is_final:
+                yield _sse(_completion_chunk("", req_id, finish="stop"))
+                break
+            if resp.text:
+                yield _sse(_completion_chunk(resp.text, req_id))
+    except Exception as e:
+        logger.error(f"[stream_completion] error: {e}")
+        yield _sse(_completion_chunk(f"[Error: {e}]", req_id, finish="stop"))
+
+    yield _sse_done()
+
+
+def _ollama_chat_chunk(token: str, done: bool) -> str:
+    return json.dumps({
+        "model":      PROXY_MODEL,
+        "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+        "message":    {"role": "assistant", "content": token},
+        "done":       done,
+    }) + "\n"
+
+
+def _ollama_generate_chunk(token: str, done: bool) -> str:
+    return json.dumps({
+        "model":      PROXY_MODEL,
+        "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+        "response":   token,
+        "done":       done,
+    }) + "\n"
+
+
+async def _stream_ollama_chat(query: str, session_id: str) -> AsyncIterator[str]:
+    try:
+        async for resp in _iter_stream(query, session_id):
+            if resp.is_final:
+                yield _ollama_chat_chunk("", done=True)
+                break
+            if resp.text:
+                yield _ollama_chat_chunk(resp.text, done=False)
+    except Exception as e:
+        logger.error(f"[ollama_chat] error: {e}")
+        yield _ollama_chat_chunk(f"[Error: {e}]", done=True)
+
+
+async def _stream_ollama_generate(query: str, session_id: str) -> AsyncIterator[str]:
+    try:
+        async for resp in _iter_stream(query, session_id):
+            if resp.is_final:
+                yield _ollama_generate_chunk("", done=True)
+                break
+            if resp.text:
+                yield _ollama_generate_chunk(resp.text, done=False)
+    except Exception as e:
+        logger.error(f"[ollama_generate] error: {e}")
+        yield _ollama_generate_chunk(f"[Error: {e}]", done=True)
+
+
+@app.get("/v1/models")
+async def list_models():
+    return {
+        "object": "list",
+        "data": [{
+            "id": PROXY_MODEL, "object": "model", "created": 1700000000,
+            "owned_by": "brunix", "permission": [], "root": PROXY_MODEL, "parent": None,
+        }],
+    }
+
+
+@app.post("/v1/chat/completions")
+async def chat_completions(req: ChatCompletionRequest):
+    query      = _query_from_messages(req.messages)
+    session_id = req.session_id or req.user or "default"
+    req_id     = f"chatcmpl-{uuid.uuid4().hex}"
+
+    logger.info(f"[chat] session={session_id} stream={req.stream} query='{query[:80]}'")
+
+    if not query:
+        raise HTTPException(status_code=400, detail="No user message found in messages.")
+
+    if req.stream:
+
+        return StreamingResponse(
+            _stream_chat(query, session_id, req_id),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
+        )
+
+    try:
+        text = await _invoke_blocking(query, session_id)
+    except grpc.RpcError as e:
+        raise HTTPException(status_code=502, detail=f"gRPC error: {e.details()}")
+
+    return JSONResponse(_chat_response(text, req_id))
+
+
+@app.post("/v1/completions")
+async def completions(req: CompletionRequest):
+    query      = req.prompt if isinstance(req.prompt, str) else " ".join(req.prompt)
+    session_id = req.session_id or req.user or "default"
+    req_id     = f"cmpl-{uuid.uuid4().hex}"
+
+    logger.info(f"[completion] session={session_id} stream={req.stream} prompt='{query[:80]}'")
+
+    if not query:
+        raise HTTPException(status_code=400, detail="prompt is required.")
+
+    if req.stream:
+        return StreamingResponse(
+            _stream_completion(query, session_id, req_id),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
+        )
+
+    try:
+        text = await _invoke_blocking(query, session_id)
+    except grpc.RpcError as e:
+        raise HTTPException(status_code=502, detail=f"gRPC error: {e.details()}")
+
+    return JSONResponse(_completion_response(text, req_id))
+
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "grpc_target": GRPC_TARGET}
+
+
+@app.get("/api/tags")
+async def ollama_tags():
+    return {
+        "models": [{
+            "name": PROXY_MODEL,
+            "model":PROXY_MODEL,
+            "modified_at": "2024-01-01T00:00:00Z",
+            "size": 0,
+            "digest":"brunix",
+            "details": {
+                "format":  "gguf",
+                "family": "brunix",
+                "parameter_size":   "unknown",
+                "quantization_level": "unknown",
+            },
+        }]
+    }
+
+
+@app.post("/api/chat")
+async def ollama_chat(req: OllamaChatRequest):
+
+    query      = next((m.content for m in reversed(req.messages) if m.role == "user"), "")
+    session_id = req.session_id or "default"
+
+    logger.info(f"[ollama/chat] session={session_id} stream={req.stream} query='{query[:80]}'")
+
+    if not query:
+        raise HTTPException(status_code=400, detail="No user message found.")
+
+    if req.stream:
+        return StreamingResponse(
+            _stream_ollama_chat(query, session_id),
+            media_type="application/x-ndjson",
+            headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
+        )
+
+    try:
+        text = await _invoke_blocking(query, session_id)
+    except grpc.RpcError as e:
+        raise HTTPException(status_code=502, detail=f"gRPC error: {e.details()}")
+
+    return JSONResponse({
+        "model":      PROXY_MODEL,
+        "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+        "message":    {"role": "assistant", "content": text},
+        "done":       True,
+    })
+
+
+@app.post("/api/generate")
+async def ollama_generate(req: OllamaGenerateRequest):
+
+    session_id = req.session_id or "default"
+
+    logger.info(f"[ollama/generate] session={session_id} stream={req.stream} prompt='{req.prompt[:80]}'")
+
+    if not req.prompt:
+        raise HTTPException(status_code=400, detail="prompt is required.")
+
+    if req.stream:
+        return StreamingResponse(
+            _stream_ollama_generate(req.prompt, session_id),
+            media_type="application/x-ndjson",
+            headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
+        )
+
+    try:
+        text = await _invoke_blocking(req.prompt, session_id)
+    except grpc.RpcError as e:
+        raise HTTPException(status_code=502, detail=f"gRPC error: {e.details()}")
+
+    return JSONResponse({
+        "model":      PROXY_MODEL,
+        "created_at": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
+        "response":   text,
+        "done":       True,
+    })

Docker/src/prompts.py

@@ -1,89 +1,250 @@
+
 from langchain_core.messages import SystemMessage
 
+CLASSIFY_PROMPT_TEMPLATE = (
+    "<role>\n"
+    "You are a query classifier for an AVAP language assistant. "
+    "Your only job is to classify the user message into one of three categories.\n"
+    "</role>\n\n"
+
+    "<categories>\n"
+    "RETRIEVAL — the user is asking about AVAP concepts, documentation, syntax rules, "
+    "or how something works. They want an explanation, not code.\n"
+    "Examples: 'What is addVar?', 'How does registerEndpoint work?', "
+    "'What is the difference between if() modes?'\n\n"
+
+    "CODE_GENERATION — the user is asking to generate, write, create, build, or show "
+    "an example of an AVAP script, function, API, or code snippet. "
+    "They want working code as output.\n"
+    "Examples: 'Write an API that returns hello world', "
+    "'Generate a function that queries the DB', "
+    "'Show me how to create an endpoint', "
+    "'dame un ejemplo de codigo', 'escribeme un script', "
+    "'dime como seria un API', 'genera un API', 'como haria'\n\n"
+
+    "CONVERSATIONAL — the user is following up on the previous answer. "
+    "They want a reformulation, summary, or elaboration of what was already said.\n"
+    "Examples: 'can you explain that?', 'en menos palabras', "
+    "'describe it in your own words', 'what did you mean?'\n"
+    "</categories>\n\n"
+
+    "<output_rule>\n"
+    "Your entire response must be exactly one word: "
+    "RETRIEVAL, CODE_GENERATION, or CONVERSATIONAL. Nothing else.\n"
+    "</output_rule>\n\n"
+
+    "<conversation_history>\n"
+    "{history}\n"
+    "</conversation_history>\n\n"
+
+    "<user_message>{message}</user_message>"
+)
+
 REFORMULATE_PROMPT = SystemMessage(
     content=(
-        "You are a deterministic lexical query rewriter used for vector retrieval.\n"
+        "<role>\n"
-        "Your task is to rewrite user questions into optimized keyword search queries.\n\n"
+        "You are a deterministic query rewriter whose sole purpose is to prepare "
+        "user questions for vector similarity retrieval against an AVAP language "
+        "knowledge base. You do not answer questions. You only transform phrasing "
+        "into keyword queries that will find the right AVAP documentation chunks.\n"
+        "</role>\n\n"
 
-        "CRITICAL RULES (ABSOLUTE):\n"
+        "<task>\n"
-        "1. NEVER answer the question.\n"
+        "Rewrite the user message into a compact keyword query for semantic search.\n\n"
-        "2. NEVER expand acronyms.\n"
-        "3. NEVER introduce new terms not present in the original query.\n"
-        "4. NEVER infer missing information.\n"
-        "5. NEVER add explanations, definitions, or interpretations.\n"
-        "6. Preserve all technical tokens exactly as written.\n"
-        "7. Only remove filler words (e.g., what, does, is, explain, tell me, please).\n"
-        "8. You may reorder terms for better retrieval.\n"
-        "9. Output must be a single-line plain keyword query.\n"
-        "10. If the query is already optimal, return it unchanged.\n\n"
-        "11. If you receive something that looks like code, do NOT attempt to rewrite it. Return it verbatim.\n\n"
 
-        "ALLOWED OPERATIONS:\n"
+        "SPECIAL RULE for code generation requests:\n"
-        "- Remove interrogative phrasing.\n"
+        "When the user asks to generate/create/build/show AVAP code, expand the query "
-        "- Remove stopwords.\n"
+        "with the AVAP commands typically needed. Use this mapping:\n\n"
-        "- Reorder words.\n"
-        "- Convert to noun phrase form.\n\n"
 
-        "FORBIDDEN OPERATIONS:\n"
+        "- API / endpoint / route / HTTP response\n"
-        "- Expanding abbreviations.\n"
+        "  expand to: AVAP registerEndpoint addResult _status\n\n"
-        "- Paraphrasing into unseen vocabulary.\n"
-        "- Adding definitions.\n"
-        "- Answering implicitly.\n\n"
 
-        "Examples:\n"
+        "- Read input / parameter\n"
-        "Input: What does AVAP stand for?\n"
+        "  expand to: AVAP addParam getQueryParamList\n\n"
-        "Output: AVAP stand for\n"
 
-        "Input: Hey, I'm trying to understand how AVAP handels a ZeroDivisionError when doing divison or modulus operatoins. Can you explane what situatoins cause a ZeroDivisionError to be raised and how I can catch it in my AVAP scripts?\n"
+        "- Database / ORM / query\n"
-        "Output: AVAP ZeroDivisionError division / modulus % catch try except\n"
+        "  expand to: AVAP ormAccessSelect ormAccessInsert avapConnector\n\n"
 
-        "Input: What does AVAP stand for?\n"
+        "- Error handling\n"
-        "Output: AVAP stand for\n"
+        "  expand to: AVAP try exception end\n\n"
 
-        "Input: Please explain how the import statement works in AVAP scripts.\n"
+        "- Loop / iterate\n"
-        "Output: AVAP import statement syntax behavior\n\n"
+        "  expand to: AVAP startLoop endLoop itemFromList getListLen\n\n"
 
-        "Return only the rewritten query."
+        "- HTTP request / call external\n"
+        "  expand to: AVAP RequestPost RequestGet\n"
+        "</task>\n\n"
+
+        "<rules>\n"
+        "- Preserve all AVAP identifiers verbatim.\n"
+        "- Remove filler words.\n"
+        "- Output a single line.\n"
+        "- Never answer the question.\n"
+        "</rules>\n\n"
+
+        "<examples>\n"
+        "<example>\n"
+        "<input>What does AVAP stand for?</input>\n"
+        "<o>AVAP stand for</o>\n"
+        "</example>\n\n"
+
+        "<example>\n"
+        "<input>dime como seria un API que devuelva hello world con AVAP</input>\n"
+        "<o>AVAP registerEndpoint addResult _status hello world example</o>\n"
+        "</example>\n\n"
+
+        "<example>\n"
+        "<input>generate an AVAP script that reads a parameter and queries the DB</input>\n"
+        "<o>AVAP addParam ormAccessSelect avapConnector registerEndpoint addResult</o>\n"
+        "</example>\n"
+        "</examples>\n\n"
+
+        "Return only the rewritten query. No labels, no prefixes, no explanation."
     )
 )
 
+CONFIDENCE_PROMPT_TEMPLATE = (
+    "<role>\n"
+    "You are a relevance evaluator. Decide whether the context contains "
+    "useful information to address the user question.\n"
+    "</role>\n\n"
+
+    "<task>\n"
+    "Answer YES if the context contains at least one relevant passage. "
+    "Answer NO only if context is empty or completely unrelated.\n"
+    "</task>\n\n"
+
+    "<output_rule>\n"
+    "Exactly one word: YES or NO.\n"
+    "</output_rule>\n\n"
+
+    "<question>{question}</question>\n\n"
+    "<context>{context}</context>"
+)
+
+
+CODE_GENERATION_PROMPT = SystemMessage(
+    content=(
+        "<role>\n"
+        "You are an expert AVAP programmer. AVAP (Advanced Virtual API Programming) "
+        "is a domain-specific language for orchestrating microservices and HTTP I/O. "
+        "Write correct, minimal, working AVAP code.\n"
+        "</role>\n\n"
+
+        "<critical_rules>\n"
+        "1. AVAP is line-oriented: every statement on a single line.\n"
+        "2. Use ONLY commands from <avap_syntax_reminder> or explicitly described in <context>.\n"
+        "3. Do NOT copy code examples from <context> that solve a DIFFERENT problem. "
+        "Context examples are syntax references only — ignore them if unrelated.\n"
+        "4. Write the MINIMUM code needed. No extra connectors, no unrelated variables.\n"
+        "5. Add brief inline comments explaining each part.\n"
+        "6. Answer in the same language the user used.\n"
+        "</critical_rules>\n\n"
+
+        "<avap_syntax_reminder>\n"
+        "// Register an HTTP endpoint\n"
+        "registerEndpoint(\"GET\", \"/path\", [], \"scope\", handlerFn, \"\")\n\n"
+        "// Declare a function — uses curly braces, NOT end()\n"
+        "function handlerFn() {{\n"
+        "    msg = \"Hello World\"\n"
+        "    addResult(msg)\n"
+        "}}\n\n"
+        "// Assign a value to a variable\n"
+        "addVar(varName, \"value\")   // or: varName = \"value\"\n\n"
+        "// Add variable to HTTP JSON response body\n"
+        "addResult(varName)\n\n"
+        "// Set HTTP response status code\n"
+        "_status = 200   // or: addVar(_status, 200)\n\n"
+        "// Read a request parameter (URL, body, or form)\n"
+        "addParam(\"paramName\", targetVar)\n\n"
+        "// Conditional\n"
+        "if(var, value, \"==\")\n"
+        "    // ...\n"
+        "end()\n\n"
+        "// Loop\n"
+        "startLoop(i, 0, length)\n"
+        "    // ...\n"
+        "endLoop()\n\n"
+        "// Error handling\n"
+        "try()\n"
+        "    // ...\n"
+        "exception(errVar)\n"
+        "    // handle\n"
+        "end()\n"
+        "</avap_syntax_reminder>\n\n"
+
+        "<task>\n"
+        "Generate a minimal, complete AVAP example for the user's request.\n\n"
+        "Structure:\n"
+        "1. One sentence describing what the code does.\n"
+        "2. The AVAP code block — clean, minimal, with inline comments.\n"
+        "3. Two or three lines explaining the key commands used.\n"
+        "</task>\n\n"
+
+        "<context>\n"
+        "{context}\n"
+        "</context>"
+    )
+)
+
+CONVERSATIONAL_PROMPT = SystemMessage(
+    content=(
+        "<role>\n"
+        "You are a helpful AVAP assistant continuing an ongoing conversation.\n"
+        "</role>\n\n"
+
+        "<task>\n"
+        "The user is following up on something already discussed. "
+        "Rephrase, summarize, or elaborate using the conversation history.\n"
+        "</task>\n\n"
+
+        "<rules>\n"
+        "- Base your answer on the conversation history.\n"
+        "- Do not introduce new AVAP facts not in the history.\n"
+        "- Keep the same language the user is using.\n"
+        "- No Answer/Evidence format. Just answer naturally.\n"
+        "</rules>"
+    )
+)
+
+
 GENERATE_PROMPT = SystemMessage(
     content=(
-        """You are a retrieval-grounded assistant.
+        "<role>\n"
+        "You are a precise, retrieval-grounded assistant specialized in AVAP. "
+        "Answers are honest, calibrated to evidence, and clearly structured.\n"
+        "</role>\n\n"
 
-        CRITICAL SCOPE RULES
+        "<critical_constraint>\n"
-        - AVAP (Advanced Virtual API Programming) is a new/unknown programming language.
+        "AVAP is a new proprietary language. Use ONLY content inside <context>. "
-        - If the user message is AVAP-related (explicitly mentions "AVAP" case-insensitive OR the provided context is AVAP code/docs), you MUST answer using ONLY <CONTEXT>.
+        "Treat any AVAP knowledge outside <context> as unreliable.\n"
-        - You MUST NOT use general programming knowledge to interpret AVAP.
+        "</critical_constraint>\n\n"
-        - You MUST NOT assume AVAP behaves like any other language even if syntax looks similar.
-        - You MUST NOT infer missing details. Only state what is explicitly present in <CONTEXT>.
 
-        GROUNDING REQUIREMENTS (AVAP-RELATED)
+        "<task>\n"
-        1) Every non-trivial factual claim MUST be directly supported by an EXACT QUOTE from <CONTEXT>.
+        "Answer using exclusively the information in <context>.\n"
-        2) If a claim is not supported by a quote, DO NOT include it.
+        "</task>\n\n"
-        3) If <CONTEXT> does not contain enough information to answer, reply with EXACTLY:
-        "I don't have enough information in the provided context to answer that."
 
-        WORKFLOW (AVAP-RELATED) — FOLLOW IN ORDER
+        "<thinking_steps>\n"
-        A) Identify the specific question(s) being asked.
+        "Step 1 — Find relevant passages in <context>.\n"
-        B) Extract the minimum necessary quotes from <CONTEXT> that answer those question(s).
+        "Step 2 — Assess if question can be fully or partially answered.\n"
-        C) Write the answer using ONLY those quotes (paraphrase is allowed, but every statement must be backed by at least one quote).
+        "Step 3 — Write a clear answer backed by those passages.\n"
-        D) Verify: for EACH sentence in your answer, confirm there is a supporting quote. If any sentence lacks a quote, delete it or refuse.
+        "Step 4 — If context contains relevant AVAP code, include it exactly.\n"
+        "</thinking_steps>\n\n"
 
-        OUTPUT FORMAT (AVAP-RELATED ONLY)
+        "<output_format>\n"
-        Answer:
+        "Answer:\n"
-        <short, direct answer; no extra speculation; no unrelated tips>
+        "<direct answer; include code blocks if context has relevant code>\n\n"
 
-        Evidence:
+        "Evidence:\n"
-        - "<exact quote 1>"
+        "- \"<exact quote from context>\"\n"
-        - "<exact quote 2>"
+        "(only quotes you actually used)\n\n"
-        (Include only quotes you actually used. Prefer the smallest quotes that fully support the statements.)
 
-        NON-AVAP QUESTIONS
+        "If context has no relevant information reply with exactly:\n"
-        - If the question is clearly not AVAP-related, answer normally using general knowledge.
+        "\"I don't have enough information in the provided context to answer that.\"\n"
+        "</output_format>\n\n"
 
-        <CONTEXT>
+        "<context>\n"
-        {context}
+        "{context}\n"
-        </CONTEXT>"""
+        "</context>"
     )
 )

Docker/src/server.py

@@ -8,18 +8,28 @@ import brunix_pb2
 import brunix_pb2_grpc
 import grpc
 from grpc_reflection.v1alpha import reflection
-from langchain_elasticsearch import ElasticsearchStore
+from elasticsearch import Elasticsearch
+from langchain_core.messages import AIMessage
 
 from utils.llm_factory import create_chat_model
 from utils.emb_factory import create_embedding_model
-from graph import build_graph
+from graph import build_graph, build_prepare_graph, build_final_messages, session_store
 
+from evaluate import run_evaluation
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("brunix-engine")
 
+
 class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
+
     def __init__(self):
+        es_url = os.getenv("ELASTICSEARCH_URL", "http://localhost:9200")
+        es_user = os.getenv("ELASTICSEARCH_USER")
+        es_pass = os.getenv("ELASTICSEARCH_PASSWORD")
+        es_apikey = os.getenv("ELASTICSEARCH_API_KEY")
+        index = os.getenv("ELASTICSEARCH_INDEX", "avap-knowledge-v1")
+
         self.llm = create_chat_model(
             provider="ollama",
             model=os.getenv("OLLAMA_MODEL_NAME"),
@@ -27,56 +37,194 @@ class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
             temperature=0,
             validate_model_on_init=True,
         )
+
         self.embeddings = create_embedding_model(
             provider="ollama",
             model=os.getenv("OLLAMA_EMB_MODEL_NAME"),
             base_url=os.getenv("OLLAMA_URL"),
         )
-        self.vector_store = ElasticsearchStore(
+
-            es_url=os.getenv("ELASTICSEARCH_URL"),
+        es_kwargs: dict = {"hosts": [es_url], "request_timeout": 60}
-            index_name=os.getenv("ELASTICSEARCH_INDEX"),
+        if es_apikey:
-            embedding=self.embeddings,
+            es_kwargs["api_key"] = es_apikey
-            query_field="text",
+        elif es_user and es_pass:
-            vector_query_field="embedding",
+            es_kwargs["basic_auth"] = (es_user, es_pass)
-        )
+
+        self.es_client  = Elasticsearch(**es_kwargs)
+        self.index_name = index
+
+        if self.es_client.ping():
+            info = self.es_client.info()
+            logger.info(f"[ESEARCH] Connected: {info['version']['number']} — index: {index}")
+        else:
+            logger.error("[ESEARCH] Cant Connect")
+
         self.graph = build_graph(
-            llm=self.llm,
+            llm = self.llm,
-            vector_store=self.vector_store
+            embeddings = self.embeddings,
+            es_client = self.es_client,
+            index_name = self.index_name,
         )
-        logger.info("Brunix Engine initializing.")
+
+        self.prepare_graph = build_prepare_graph(
+            llm = self.llm,
+            embeddings = self.embeddings,
+            es_client = self.es_client,
+            index_name = self.index_name,
+        )
+
+        logger.info("Brunix Engine initialized.")
 
 
     def AskAgent(self, request, context):
-        logger.info(f"request {request.session_id}): {request.query[:50]}.")
+        session_id = request.session_id or "default"
+        query = request.query
+        logger.info(f"[AskAgent] session={session_id} query='{query[:80]}'")
 
         try:
-            final_state = self.graph.invoke({"messages": [{"role": "user", 
+            history = list(session_store.get(session_id, []))
-                                                           "content": request.query}]})
+            logger.info(f"[AskAgent] conversation: {len(history)} previous messages.")
 
+            initial_state = {
+                "messages": history + [{"role": "user", "content": query}],
+                "session_id": session_id,
+                "reformulated_query": "",
+                "context": "",
+                "query_type": "",
+            }
+
+            final_state = self.graph.invoke(initial_state)
             messages = final_state.get("messages", [])
             last_msg = messages[-1] if messages else None
-            result_text = getattr(last_msg, "content", str(last_msg)) if last_msg else ""
+            result_text = getattr(last_msg, "content", str(last_msg)) \
+                          if last_msg else ""
+
+            logger.info(f"[AskAgent] query_type={final_state.get('query_type')} "
+                        f"answer='{result_text[:100]}'")
 
             yield brunix_pb2.AgentResponse(
-                text=result_text,
+                text = result_text,
-                avap_code="AVAP-2026",
+                avap_code= "AVAP-2026",
-                is_final=True,
+                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)}", exc_info=True)
+            logger.error(f"[AskAgent] Error: {e}", exc_info=True)
             yield brunix_pb2.AgentResponse(
-                text=f"[Error Motor]: {str(e)}",
+                text = f"[ENG] Error: {str(e)}",
-                is_final=True,
+                is_final = True,
             )
 
 
+    def AskAgentStream(self, request, context):
+        session_id = request.session_id or "default"
+        query = request.query
+        logger.info(f"[AskAgentStream] session={session_id} query='{query[:80]}'")
+
+        try:
+            history = list(session_store.get(session_id, []))
+            logger.info(f"[AskAgentStream] conversation: {len(history)} previous messages.")
+
+            initial_state = {
+                "messages": history + [{"role": "user", "content": query}],
+                "session_id": session_id,
+                "reformulated_query": "",
+                "context": "",
+                "query_type": "",
+            }
+
+            prepared = self.prepare_graph.invoke(initial_state)
+            logger.info(
+                f"[AskAgentStream] query_type={prepared.get('query_type')} "
+                f"context_len={len(prepared.get('context', ''))}"
+            )
+
+            final_messages = build_final_messages(prepared)
+            full_response  = []
+
+            for chunk in self.llm.stream(final_messages):
+                token = chunk.content
+                if token:
+                    full_response.append(token)
+                    yield brunix_pb2.AgentResponse(
+                        text     = token,
+                        is_final = False,
+                    )
+
+            complete_text = "".join(full_response)
+            if session_id:
+                session_store[session_id] = (
+                    list(prepared["messages"]) + [AIMessage(content=complete_text)]
+                )
+
+            logger.info(
+                f"[AskAgentStream] done — "
+                f"chunks={len(full_response)} total_chars={len(complete_text)}"
+            )
+
+            yield brunix_pb2.AgentResponse(text="", is_final=True)
+
+        except Exception as e:
+            logger.error(f"[AskAgentStream] Error: {e}", exc_info=True)
+            yield brunix_pb2.AgentResponse(
+                text     = f"[ENG] Error: {str(e)}",
+                is_final = True,
+            )
+
+
+    def EvaluateRAG(self, request, context):
+        category = request.category or None
+        limit = request.limit    or None
+        index = request.index    or self.index_name
+
+        logger.info(f"[EvaluateRAG] category={category} limit={limit} index={index}")
+
+        try:
+            result = run_evaluation(
+                es_client  = self.es_client,
+                llm = self.llm,
+                embeddings = self.embeddings,
+                index_name = index,
+                category = category,
+                limit = limit,
+            )
+        except Exception as e:
+            logger.error(f"[EvaluateRAG] Error: {e}", exc_info=True)
+            return brunix_pb2.EvalResponse(status=f"error: {e}")
+
+        if result.get("status") != "ok":
+            return brunix_pb2.EvalResponse(status=result.get("error", "unknown error"))
+
+        details = [
+            brunix_pb2.QuestionDetail(
+                id = d["id"],
+                category = d["category"],
+                question = d["question"],
+                answer_preview = d["answer_preview"],
+                n_chunks = d["n_chunks"],
+            )
+            for d in result.get("details", [])
+        ]
+
+        scores = result["scores"]
+        return brunix_pb2.EvalResponse(
+            status = "ok",
+            questions_evaluated = result["questions_evaluated"],
+            elapsed_seconds = result["elapsed_seconds"],
+            judge_model = result["judge_model"],
+            index = result["index"],
+            faithfulness = scores["faithfulness"],
+            answer_relevancy = scores["answer_relevancy"],
+            context_recall = scores["context_recall"],
+            context_precision = scores["context_precision"],
+            global_score = result["global_score"],
+            verdict= result["verdict"],
+            details= details,
+        )
+
+
 def serve():
-
     server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
-
     brunix_pb2_grpc.add_AssistanceEngineServicer_to_server(BrunixEngine(), server)
 
     SERVICE_NAMES = (
@@ -86,7 +234,7 @@ def serve():
     reflection.enable_server_reflection(SERVICE_NAMES, server)
 
     server.add_insecure_port("[::]:50051")
-    logger.info("Brunix Engine on port 50051")
+    logger.info("[ENGINE] listen on 50051 (gRPC)")
     server.start()
     server.wait_for_termination()
 

Docker/src/state.py

@@ -1,9 +1,11 @@
+# state.py
 from typing import TypedDict, Annotated
-
 from langgraph.graph.message import add_messages
 
 
 class AgentState(TypedDict):
-    messages: Annotated[list, add_messages]
+    messages:           Annotated[list, add_messages]
     reformulated_query: str
-    context: str
+    context:            str
+    query_type:         str
+    session_id:         str