Add github actions to deploy docker imager to hetzner (#39)

* feat(docker): add Dockerfile

* add mcp endpoint

* docker: publish docker to hetzner

Author: smutyala1at

.gitignore

@@ -0,0 +1,5 @@
+.env
+.vscode/
+.venv/
+**/__pycache__/
+docs/

apps/meshjs-rag/.dockerignore

@@ -0,0 +1,33 @@
+name: Publish to Hetzner
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  publish_images:
+    runs-on: ubuntu-latest
+    steps:
+      - name: checkout
+        uses: actions/checkout@v4
+
+      - name: build image
+        run: docker build -t ${{ secrets.DOCKER_HUB_USERNAME }}/meshai-backend:latest .
+
+      - name: push image to the docker hub
+        run: |
+          echo ${{ secrets.DOCKER_HUB_TOKEN }} | docker login -u ${{ secrets.DOCKER_HUB_USERNAME }} --password-stdin
+          docker push ${{ secrets.DOCKER_HUB_USERNAME }}/meshai-backend:latest
+
+      - name: Deploy to Hetzner via SSH
+        uses: appleboy/[email protected]
+        with:
+          host: ${{ secrets.HETZNER_HOST }}
+          username: ${{ secrets.HETZNER_USERNAME }}
+          key: ${{ secrets.SSH_PRIVATE_KEY }}
+          script: |
+            docker pull ${{ secrets.DOCKER_HUB_USERNAME }}/meshai-backend:latest
+            docker stop meshai-backend || true
+            docker rm meshai-backend || true
+            docker run -d --name meshai-backend --env-file ./.env --restart always -p 127.0.0.1:8000:8000 ${{ secrets.DOCKER_HUB_USERNAME }}/meshai-backend:latest

apps/meshjs-rag/.github/workflow.yaml

@@ -0,0 +1,14 @@
+FROM python:3.13.5-slim
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY ./main.py /code/main.py
+
+COPY ./app /code/app
+
+CMD ["gunicorn", "-k", "uvicorn.workers.UvicornWorker", "main:app", "-b", "0.0.0.0:8000", "--workers", "4"]
+

apps/meshjs-rag/Dockerfile

@@ -1,18 +1,20 @@
 from typing import Literal, List, Optional
-from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi import APIRouter, Depends, HTTPException, status, Header
 from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 from supabase import AsyncClient
 import openai
+from dotenv import load_dotenv
+load_dotenv()
 import os
 
 from app.services.openai import OpenAIService
 from app.utils.get_context import get_context
 from app.db.client import get_db_client
 
+
 router = APIRouter()
-openai_service = OpenAIService()
 security = HTTPBearer()
 
 ###########################################################################################################
@@ -27,6 +29,10 @@ class ChatCompletionRequest(BaseModel):
   messages: List[ChatMessage]
   stream: Optional[bool] = False
 
+class MCPRequestBody(BaseModel):
+  query: str
+  model: str
+
 ###########################################################################################################
 # ENDPOINTS
 ###########################################################################################################
@@ -40,6 +46,12 @@ async def ask_mesh_ai(body: ChatCompletionRequest, credentials: HTTPAuthorizatio
       detail="You are not authorized"
     )
 
+  openai_api_key = os.getenv("OPENAI_KEY") or None
+  if openai_api_key is None:
+    raise ValueError("OpenAI api key is missing")
+
+  openai_service = OpenAIService(openai_api_key)
+
   try:
     question = body.messages[-1].content
 
@@ -54,6 +66,43 @@ async def ask_mesh_ai(body: ChatCompletionRequest, credentials: HTTPAuthorizatio
       detail=f"An OpenAI API error occurred: {e}"
     )
   except Exception as e:
+    raise HTTPException(
+      status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+      detail=f"An unexpected error occurred: {e}"
+    )
+
+
+###########################################################################################################
+@router.post("/mcp")
+async def ask_mesh_ai(body: MCPRequestBody, authorization: str = Header(None), supabase: AsyncClient = Depends(get_db_client)):
+
+  if not authorization or not authorization.startswith("Bearer"):
+    print("error")
+    raise HTTPException(
+      status_code=status.HTTP_401_UNAUTHORIZED,
+      detail="You are not authorized"
+    )
+
+  try:
+    OPENAI_KEY = authorization.split(" ")[-1]
+    openai_service = OpenAIService(OPENAI_KEY)
+
+    question = body.query
+    model = body.model
+
+    embedded_query = await openai_service.embed_query(question)
+    context = await get_context(embedded_query, supabase)
+    response = await openai_service.get_mcp_answer(question=question, context=context, model=model)
+    return response
+
+  except (openai.APIError, openai.AuthenticationError, openai.RateLimitError) as e:
+    print(e)
+    raise HTTPException(
+      status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+      detail=f"An OpenAI API error occurred: {e}"
+    )
+  except Exception as e:
+    print(e)
     raise HTTPException(
       status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
       detail=f"An unexpected error occurred: {e}"

apps/meshjs-rag/app/api/v1/ask_mesh_ai.py

@@ -17,8 +17,12 @@
 from app.utils.checksum import calculate_checksum
 from app.utils.extract_title import extract_chunk_title
 
+openai_api_key = os.getenv("OPENAI_KEY") or None
+if openai_api_key is None:
+  raise ValueError("OpenAI api key is missing")
+
 router = APIRouter()
-openai_service = OpenAIService()
+openai_service = OpenAIService(openai_api_key=openai_api_key)
 security = HTTPBearer()
 
 ###########################################################################################################

apps/meshjs-rag/app/api/v1/ingest.py

@@ -1,15 +1,8 @@
 from openai import AsyncOpenAI
-from dotenv import load_dotenv
-load_dotenv()
-import os
 from typing import List
 from tenacity import retry, wait_random_exponential, stop_after_attempt
 import json
 
-openai_api_key = os.getenv("OPENAI_KEY") or None
-if openai_api_key is None:
-  raise ValueError("OpenAI api key is missing")
-
 DOCUMENT_CONTEXT_PROMPT = """
 <document>
 {doc_content}
@@ -27,7 +20,7 @@
 """
 
 class OpenAIService:
-  def __init__(self):
+  def __init__(self, openai_api_key):
     self.client = AsyncOpenAI(api_key=openai_api_key)
 
   @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
@@ -82,7 +75,7 @@ async def embed_query(self, text: str) -> List[float]:
     return response.data[0].embedding
 
   @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6), reraise=True)
-  async def get_answer(self, question: str, context: str):
+  async def get_answer(self, question: str, context: str, model="gpt-4o-mini"):
     messages = [
       {
         "role": "system",
@@ -94,9 +87,25 @@ async def get_answer(self, question: str, context: str):
       }
     ]
 
-    stream = await self._chat(messages=messages, stream=True)
+    stream = await self._chat(messages=messages, stream=True, model=model)
 
     async for chunk in stream:
       yield f"data: {json.dumps(chunk.model_dump())}\n\n"
 
-    yield "data: [DONE]\n\n"
+    yield "data: [DONE]\n\n"
+
+  @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6), reraise=True)
+  async def get_mcp_answer(self, question: str, context: str, model="gpt-4o-mini"):
+    messages = [
+      {
+        "role": "system",
+        "content": "You are a MeshJS expert assistant. Help developers with MeshJS questions using the provided context.\n\nUse the documentation context to answer questions about MeshJS and Cardano development. Provide accurate code examples and explanations based on the context provided.\n\nWhen answering:\n- Give direct, helpful answers based on the context\n- Include relevant code examples when available\n- Explain concepts clearly for developers\n- Include any links present in the context for additional resources\n- If the context doesn't cover something, say so\n- Don't make up APIs or methods not in the documentation\n\nBe concise but thorough. Focus on practical, actionable guidance for MeshJS development."
+      },
+      {
+        "role": "user",
+        "content": f"""Context: {context}\n\nQuestion: {question}""",
+      }
+    ]
+
+    response = await self._chat(messages=messages, model=model)
+    return response.choices[0].message.content

apps/meshjs-rag/app/services/openai.py

@@ -2,7 +2,7 @@
 from app.api.v1.api import api_router
 import uvicorn
 
-app = FastAPI()
+app = FastAPI(title="MeshAI Backend")
 app.include_router(api_router, prefix="/api/v1", tags=["api"])
 
 @app.get("/")

apps/meshjs-rag/main.py

@@ -8,6 +8,7 @@ deprecation==2.1.0
 distro==1.9.0
 fastapi==0.116.1
 gotrue==2.12.3
+gunicorn==23.0.0
 h11==0.16.0
 h2==4.2.0
 hpack==4.1.0