feat: Add RAG service and modelfile

rag_service/rag_api.py

@@ -0,0 +1,37 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import OllamaEmbeddings
+
+# Configuration
+PERSIST_DIRECTORY = "/data/db"
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load the vector store
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
+db = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=embeddings)
+retriever = db.as_retriever()
+
+class RetrieveRequest(BaseModel):
+    query: str
+
+class RetrieveResponse(BaseModel):
+    context: str
+
+@app.post("/retrieve", response_model=RetrieveResponse)
+async def retrieve_context(request: RetrieveRequest):
+    """
+    Retrieves context from the vector store for a given query.
+    """
+    try:
+        docs = retriever.get_relevant_documents(request.query)
+        context = "\n\n".join([doc.page_content for doc in docs])
+        return RetrieveResponse(context=context)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)