feat(chat): include article context in AI Discussion and merge with RAG context

Author: ashi2004

backend/app/modules/chat/llm_processing.py

@@ -39,11 +39,23 @@ def build_context(docs):
     return "\n".join(
         f"{m['metadata'].get('explanation') or m['metadata'].get('reasoning', '')}"
         for m in docs
-    )
+    ).strip()
+
+
+def ask_llm(question, docs, article_context=""):
+    rag_context = build_context(docs)
+    article_context = (article_context or "").strip()
+
+    if rag_context and article_context:
+        context = f"Article:\n{article_context}\n\nRetrieved Insights:\n{rag_context}"
+    else:
+        context = rag_context or article_context
 
+    if not context:
+        return (
+            "I don't have article context yet. Please analyze an article first and then ask me again."
+        )
 
-def ask_llm(question, docs):
-    context = build_context(docs)
     logger.debug(f"Generated context for LLM:\n{context}")
     prompt = f"""You are an assistant that answers based on context.
 

backend/app/routes/routes.py

@@ -30,7 +30,7 @@
 """
 
 
-from fastapi import APIRouter
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
 from app.modules.pipeline import run_scraper_pipeline
 from app.modules.pipeline import run_langgraph_workflow
@@ -52,6 +52,7 @@ class URlRequest(BaseModel):
 
 class ChatQuery(BaseModel):
     message: str
+    article_context: str | None = None
 
 
 @router.get("/")
@@ -77,9 +78,20 @@ async def run_pipelines(request: URlRequest):
 
 @router.post("/chat")
 async def answer_query(request: ChatQuery):
-    query = request.message
-    results = search_pinecone(query)
-    answer = ask_llm(query, results)
-    logger.info(f"Chat answer generated: {answer}")
-
-    return {"answer": answer}
+    try:
+        query = request.message.strip()
+        if not query:
+            raise HTTPException(status_code=400, detail="Message cannot be empty.")
+
+        article_context = (request.article_context or "").strip()
+
+        results = search_pinecone(query)
+        answer = ask_llm(query, results, article_context)
+        logger.info("Chat answer generated successfully.")
+
+        return {"answer": answer}
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.exception(f"Chat request failed: {e}")
+        raise HTTPException(status_code=500, detail="Failed to generate chat response.")

backend/package-lock.json

@@ -19,7 +19,7 @@ import { Badge } from "@/components/ui/badge";
 import BiasMeter from "@/components/bias-meter";
 import axios from "axios";
 
-const backend_url = process.env.NEXT_PUBLIC_API_URL;
+const backendUrl = (process.env.NEXT_PUBLIC_API_URL ?? "http://localhost:8000").trim();
 
 /**
  * Renders the article analysis page with summary, perspectives, fact checks, bias meter, AI chat, and sources.
@@ -32,6 +32,7 @@ export default function AnalyzePage() {
   const [activeTab, setActiveTab] = useState("summary");
   const [message, setMessage] = useState("");
   const [isLoading, setIsLoading] = useState(true);
+  const [isChatLoading, setIsChatLoading] = useState(false);
   const [messages, setMessages] = useState<{ role: string; content: string }[]>(
     [
       {
@@ -80,20 +81,42 @@ export default function AnalyzePage() {
 
   async function handleSendMessage(e: React.FormEvent) {
     e.preventDefault();
-    if (!message.trim()) return;
-    const newMessages = [...messages, { role: "user", content: message }];
+    const userMessage = message.trim();
+    if (!userMessage || isChatLoading) return;
+
+    const newMessages = [...messages, { role: "user", content: userMessage }];
     setMessages(newMessages);
     setMessage("");
+    setIsChatLoading(true);
 
-    const res = await axios.post(`${backend_url}/api/chat`, {
-      message: message,
-    });
-    const data = res.data;
-
-    console.log(data);
+    try {
+      const articleContext = analysisData?.cleaned_text?.trim() ?? "";
+      const res = await axios.post(
+        `${backendUrl}/api/chat`,
+        {
+          message: userMessage,
+          article_context: articleContext,
+        },
+        { timeout: 45000 }
+      );
+      const answer = res.data?.answer ?? "I could not generate an answer right now.";
 
-    // 🔹 Step 2: Append LLM’s response
-    setMessages([...newMessages, { role: "assistant", content: data.answer }]);
+      setMessages([...newMessages, { role: "assistant", content: answer }]);
+    } catch (error: any) {
+      const fallback =
+        error?.response?.data?.detail ||
+        error?.message ||
+        "Chat request failed. Please try again.";
+      setMessages([
+        ...newMessages,
+        {
+          role: "assistant",
+          content: `Sorry, I couldn't fetch a reply: ${fallback}`,
+        },
+      ]);
+    } finally {
+      setIsChatLoading(false);
+    }
   }
    if (isLoading) {
     return (
@@ -187,10 +210,10 @@ export default function AnalyzePage() {
                             <Badge
                               variant={
                                 fact.verdict === "True"
-                                  ? "success"
+                                  ? "secondary"
                                   : fact.verdict === "False"
                                   ? "destructive"
-                                  : "warning"
+                                  : "outline"
                               }
                             >
                               {fact.verdict}
@@ -255,7 +278,7 @@ export default function AnalyzePage() {
                     value={message}
                     onChange={(e) => setMessage(e.target.value)}
                   />
-                  <Button type="submit" disabled={!message.trim()}>
+                  <Button type="submit" disabled={!message.trim() || isChatLoading}>
                     <Send />
                   </Button>
                 </form>