feat: add FastAPI chat endpoints and streaming response support

app/core/config.py

@@ -0,0 +1,21 @@
+import os
+from pydantic_settings import BaseSettings
+
+class Settings(BaseSettings):
+    PROJECT_NAME: str = "Support Docs Copilot"
+    
+    # Ollama LLM Config
+    OLLAMA_BASE_URL: str = os.getenv("OLLAMA_BASE_URL", "http://ollama:11434")
+    OLLAMA_MODEL: str = os.getenv("OLLAMA_MODEL", "llama3")
+    
+    # Qdrant Vector DB Config
+    QDRANT_URL: str = os.getenv("QDRANT_URL", "")
+    QDRANT_LOCATION: str = os.getenv("QDRANT_LOCATION", "./qdrant_data")
+    COLLECTION_NAME: str = "support_docs"
+    
+    # Embeddings Config
+    DENSE_EMBEDDING_MODEL: str = "BAAI/bge-small-en-v1.5"
+    SPARSE_EMBEDDING_MODEL: str = "Qdrant/bm25"
+    RERANKER_MODEL: str = "BAAI/bge-reranker-base"
+
+settings = Settings()

app/main.py

@@ -0,0 +1,71 @@
+import asyncio
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from guardrails import Guard
+from langchain_core.prompts import PromptTemplate
+from langchain_ollama import ChatOllama
+
+from app.core.config import settings
+from app.graph.workflow import compile_workflow
+from app.guardrails.validators import DetectPromptInjection
+
+app = FastAPI(title=settings.PROJECT_NAME)
+rag_agent = compile_workflow()
+input_guard = Guard().use(DetectPromptInjection, on_fail="exception")
+
+class ChatRequest(BaseModel):
+    query: str
+
+class ChatResponse(BaseModel):
+    query: str
+    answer: str
+
+@app.post("/chat", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    try:
+        input_guard.validate(request.query)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(getattr(e, "message", e)))
+
+    initial_state = {"question": request.query, "run_count": 0}
+    try:
+        final_state = rag_agent.invoke(initial_state)
+        answer = final_state.get("generation", "Unable to compile answer.")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+    return ChatResponse(query=request.query, answer=answer)
+
+@app.post("/chat/stream")
+async def chat_stream_endpoint(request: ChatRequest):
+    try:
+        input_guard.validate(request.query)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(getattr(e, "message", e)))
+
+    async def token_generator():
+        initial_state = {"question": request.query, "run_count": 0}
+        final_state = rag_agent.invoke(initial_state)
+        documents = final_state.get("documents", [])
+        
+        if not documents:
+            yield "I am sorry, no reliable matching documentation was found."
+            return
+
+        context = "\n\n".join(doc.page_content for doc in documents)
+        prompt = PromptTemplate(
+            template="""You are a Support Docs Copilot. Use the retrieved context to answer the question concisely. If you don't know the answer, say "I don't know".
+            Question: {question} 
+            Context: {context} \n\nAnswer:""",
+            input_variables=["question", "context"],
+        )
+        async_llm = ChatOllama(model=settings.OLLAMA_MODEL, temperature=0, base_url=settings.OLLAMA_BASE_URL)
+        rag_chain = prompt | async_llm
+
+        async for chunk in rag_chain.astream({"context": context, "question": request.query}):
+            if chunk.content:
+                yield chunk.content
+                await asyncio.sleep(0.01)
+
+    return StreamingResponse(token_generator(), media_type="text/event-stream")