implemented RAG-based chat feature to allow users to ask any queries about the article

Author: ParagGhatage

backend/app/modules/chat/__init__.py

@@ -0,0 +1,10 @@
+from sentence_transformers import SentenceTransformer
+
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+
+def embed_query(query: str):
+
+    embeddings = embedder.encode(query).tolist()
+
+    return embeddings

backend/app/modules/chat/embed_query.py

@@ -0,0 +1,31 @@
+from pinecone import Pinecone
+from dotenv import load_dotenv
+from app.modules.chat.embed_query import embed_query
+import os
+
+load_dotenv()
+
+pc = Pinecone(os.getenv("PINECONE_API_KEY"))
+index = pc.Index("perspective")
+
+
+def search_pinecone(query: str, top_k: int = 5):
+
+    embeddings = embed_query(query)
+
+    results = index.query(
+        vector=embeddings,
+        top_k=top_k,
+        include_metadata=True,
+        namespace="default"
+
+    )
+
+    matches = []
+    for match in results["matches"]:
+        matches.append({
+            "id": match["id"],
+            "score": match["score"],
+            "metadata": match["metadata"]
+        })
+    return matches

backend/app/modules/chat/get_rag_data.py

@@ -0,0 +1,35 @@
+import os
+from groq import Groq
+from dotenv import load_dotenv
+
+load_dotenv()
+
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+
+def build_context(docs):
+
+    return "\n".join(f"{m['metadata'].get('explanation') or m['metadata'].get('reasoning', '')}"for m in docs)
+
+
+def ask_llm(question, docs):
+    context = build_context(docs)
+    print(context)
+    prompt = f"""You are an assistant that answers based on context.
+
+Context:
+{context}
+
+Question:
+{question}
+"""
+
+    response = client.chat.completions.create(
+        model="gemma2-9b-it",
+        messages=[
+            {"role": "system", "content": "Use only the context to answer."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+
+    return response.choices[0].message.content

backend/app/modules/chat/llm_processing.py

@@ -28,3 +28,4 @@ def embed_chunks(chunks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
             "metadata": chunk["metadata"]
         })
     return vectors
+

backend/app/modules/vector_store/embed.py

@@ -3,31 +3,49 @@
 from app.modules.pipeline import run_scraper_pipeline
 from app.modules.pipeline import run_langgraph_workflow
 from app.modules.bias_detection.check_bias import check_bias
+from app.modules.chat.get_rag_data import search_pinecone
+from app.modules.chat.llm_processing import ask_llm
 import asyncio
 import json
 
 router = APIRouter()
 
+
 class URlRequest(BaseModel):
     url: str
 
 
+class ChatQuery(BaseModel):
+    message: str
+
+
 @router.get("/")
 async def home():
     return {"message": "Perspective API is live!"}
 
+
 @router.post("/bias")
 async def bias_detection(request: URlRequest):
-    content = await asyncio.to_thread(run_scraper_pipeline,(request.url))
-    bias_score = await asyncio.to_thread(check_bias,(content))
+    content = await asyncio.to_thread(run_scraper_pipeline, (request.url))
+    bias_score = await asyncio.to_thread(check_bias, (content))
     print(bias_score)
     return bias_score
-    
 
 
 @router.post("/process")
 async def run_pipelines(request: URlRequest):
-    article_text = await asyncio.to_thread(run_scraper_pipeline,(request.url))
+    article_text = await asyncio.to_thread(run_scraper_pipeline, (request.url))
     print(json.dumps(article_text, indent=2))
-    data = await asyncio.to_thread(run_langgraph_workflow,(article_text))
+    data = await asyncio.to_thread(run_langgraph_workflow, (article_text))
     return data
+
+
+@router.post("/chat")
+async def answer_query(request: ChatQuery):
+
+    query = request.message
+    results = search_pinecone(query)
+    answer = ask_llm(query, results)
+    print(answer)
+
+    return {"answer": answer}

backend/app/routes/routes.py

@@ -20,6 +20,7 @@ import {
 import { Input } from "@/components/ui/input";
 import { Badge } from "@/components/ui/badge";
 import BiasMeter from "@/components/bias-meter";
+import axios from "axios";
 
 
 /**
@@ -79,23 +80,24 @@ export default function AnalyzePage() {
 
   }, [router]);
 
-  const handleSendMessage = (e: React.FormEvent) => {
+  async function handleSendMessage(e: React.FormEvent){
     e.preventDefault();
     if (!message.trim()) return;
     const newMessages = [...messages, { role: "user", content: message }];
     setMessages(newMessages);
     setMessage("");
-    setTimeout(() => {
-      setMessages([
-        ...newMessages,
-        {
-          role: "system",
-          content:
-            "Based on the article... let me know if you want more details.",
-        },
-      ]);
-    }, 1000);
-  };
+
+  const res = await axios.post("http://Thunder1245-perspective-backend.hf.space/api/chat", {
+    message: message
+    });
+  const data = res.data;
+
+  console.log(data)
+
+  // 🔹 Step 2: Append LLM’s response
+  setMessages([...newMessages, { role: "assistant", content: data.answer }]);
+};
+
 
   if (isLoading || !analysisData || !biasScore) {
     return (
@@ -113,7 +115,7 @@ export default function AnalyzePage() {
     score,
   } = analysisData;
 
-  return (
+  return(
     <div className="flex flex-col min-h-screen">
       {/* Header omitted for brevity */}
       <main className="flex-1 pt-16 container mx-auto px-4">