Refactor agent API structure and add core functionality with FastAPI integration

agent-api/Dockerfile

@@ -1,11 +0,0 @@
-FROM python:3.11-slim
-
-WORKDIR /app
-
-COPY requirements.txt .
-
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

agent-api/app.py

@@ -1,37 +0,0 @@
-from fastapi import FastAPI
-from openai import OpenAI
-import os
-
-app = FastAPI()
-
-# DeepSeek OpenAI-compatible endpoint
-client = OpenAI(
-    api_key=os.getenv("DEEPSEEK_API_KEY"),
-    base_url="https://api.deepseek.com"
-)
-
-@app.post("/v1/chat/completions")
-async def chat(data: dict):
-
-    messages = data["messages"]
-
-    repo_context = """
-    This assistant has access to the repository.
-    Help the user understand the codebase.
-    This repo is building an chatbot, currently still in early stages. The main files are:
-        - app.py: The main FastAPI application that defines the API endpoints and integrates with the Deep seek API.
-        - agent.py: Contains the logic for the chatbot agent, including how it processes messages and generates responses.
-    """
-
-    messages.insert(0, {
-        "role": "system",
-        "content": repo_context
-    })
-
-    response = client.chat.completions.create(
-        model="deepseek-v4-flash",
-        messages=messages,
-        temperature=0.3
-    )
-
-    return response.model_dump()

agent-api/requirements.txt

@@ -1,3 +0,0 @@
-fastapi
-openai
-uvicorn

agent_api/core/config.py

@@ -0,0 +1,7 @@
+from dotenv import load_dotenv
+from pathlib import Path
+import os
+
+load_dotenv(Path(__file__).resolve().parent.parent.parent / ".env")
+
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")

agent_api/core/llm.py

@@ -0,0 +1,7 @@
+from openai import OpenAI
+from .config import DEEPSEEK_API_KEY
+
+client = OpenAI(
+    api_key=DEEPSEEK_API_KEY,
+    base_url="https://api.deepseek.com"
+)

agent_api/main.py

@@ -0,0 +1,32 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+from .core.llm import client
+
+app = FastAPI()
+
+
+# ---- request model ----
+class ChatRequest(BaseModel):
+    message: str
+
+
+# ---- basic health check ----
+@app.get("/")
+def root():
+    return {"status": "ok"}
+
+
+# ---- test LLM endpoint ----
+@app.post("/chat")
+def chat(req: ChatRequest):
+    response = client.chat.completions.create(
+        model="deepseek-chat",
+        messages=[
+            {"role": "user", "content": req.message}
+        ]
+    )
+
+    return {
+        "response": response.choices[0].message.content
+    }

docker/Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . /app
+
+ENV PYTHONPATH=/app
+
+CMD ["uvicorn", "agent-api.main:app", "--host", "0.0.0.0", "--port", "8000"]

requirements.txt

@@ -0,0 +1,4 @@
+fastapi
+openai
+uvicorn
+python-dotenv