Create api.py

api.py

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+import os
+import uvicorn
+import requests
+import json
+import numpy as np
+import faiss
+from dotenv import load_dotenv
+from collections import defaultdict
+from fastapi import FastAPI, HTTPException, Request
+from pydantic import BaseModel
+from langchain_nomic import NomicEmbeddings # ✅ Updated Embedding Model
+
+# Initialize FastAPI
+app = FastAPI()
+
+# --- Load Environment Variables ---
+load_dotenv()
+api_key = os.getenv("AIPIPE_API_KEY")
+
+if not api_key:
+    raise RuntimeError("Missing API key in environment variables.")
+
+# --- Load Discourse Data ---
+try:
+    with open("data/discourse_posts.json", "r", encoding="utf-8") as f:
+        posts_data = json.load(f)
+except FileNotFoundError:
+    raise RuntimeError("Could not find 'data/discourse_posts.json'. Ensure the file is in the correct location.")
+
+# Group posts by topic
+topics = defaultdict(lambda: {"topic_title": "", "posts": []})
+for post in posts_data:
+    tid = post["topic_id"]
+    topics[tid]["posts"].append(post)
+    if "topic_title" in post:
+        topics[tid]["topic_title"] = post["topic_title"]
+
+# Sort posts within topics by post_number
+for topic in topics.values():
+    topic["posts"].sort(key=lambda x: x.get("post_number", 0))
+
+# --- Embedding Setup ---
+def normalize(v):
+    norm = np.linalg.norm(v)
+    return v / norm if norm != 0 else v
+
+embedder = NomicEmbeddings(model="nomic-embed-text")  # ✅ Updated Embedding Model
+embedding_data = []
+embeddings = []
+
+# Process topics for FAISS
+for tid, data in topics.items():
+    posts = data["posts"]
+    title = data["topic_title"]
+    reply_map = defaultdict(list)
+    by_number = {}
+
+    for p in posts:
+        pn = p.get("post_number")
+        if pn is not None:
+            by_number[pn] = p
+        parent = p.get("reply_to_post_number")
+        reply_map[parent].append(p)
+
+    def extract(pn):
+        collected = []
+        def dfs(n):
+            if n not in by_number:
+                return
+            p = by_number[n]
+            collected.append(p)
+            for child in reply_map.get(n, []):
+                dfs(child.get("post_number"))
+        dfs(pn)
+        return collected
+
+    roots = [p for p in posts if not p.get("reply_to_post_number")]
+    for root in roots:
+        root_num = root.get("post_number", 1)
+        thread = extract(root_num)
+        text = f"Topic: {title}\n\n" + "\n\n---\n\n".join(
+            p.get("content", "").strip() for p in thread if p.get("content")
+        )
+        emb = normalize(embedder.embed_query(text))  # ✅ Updated Embedding Call
+        embedding_data.append({
+            "topic_id": tid,
+            "topic_title": title,
+            "root_post_number": root_num,
+            "post_numbers": [p.get("post_number") for p in thread],
+            "combined_text": text
+        })
+        embeddings.append(emb)
+
+# Create FAISS index
+index = faiss.IndexFlatIP(len(embeddings[0]))
+index.add(np.vstack(embeddings).astype("float32"))
+
+# --- API Input Model ---
+class QuestionInput(BaseModel):
+    question: str
+    image: str = None  # Optional image input, unused here
+
+# --- AIPIPE API Configuration ---
+AIPIPE_URL = "https://your-aipipe-endpoint.com/chat/completions"
+AIPIPE_KEY = api_key
+
+def query_aipipe(prompt):
+    headers = {"Authorization": f"Bearer {AIPIPE_KEY}", "Content-Type": "application/json"}
+    data = {"model": "gpt-4o-mini", "messages": [{"role": "user", "content": prompt}], "temperature": 0.7}
+
+    response = requests.post(AIPIPE_URL, json=data, headers=headers)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        raise HTTPException(status_code=500, detail=f"AIPIPE API error: {response.text}")
+
+# --- API Endpoint for Answer Generation ---
+@app.post("/api/")
+async def answer_question(payload: QuestionInput):
+    q = payload.question
+
+    # Ensure query is valid
+    if not q:
+        raise HTTPException(status_code=400, detail="Question field cannot be empty.")
+
+    # Search FAISS Index
+    q_emb = normalize(embedder.embed_query(q)).astype("float32")  # ✅ Updated Query Embedding Call
+    D, I = index.search(np.array([q_emb]), 3)
+
+    top_results = []
+    for score, idx in zip(D[0], I[0]):
+        data = embedding_data[idx]
+        top_results.append({
+            "score": float(score),
+            "text": data["combined_text"],
+            "topic_id": data["topic_id"],
+            "url": f"https://discourse.onlinedegree.iitm.ac.in/t/{data['topic_id']}"
+        })
+
+    # Generate answer using AIPIPE
+    try:
+        answer_response = query_aipipe(q)
+        answer = answer_response.get("choices", [{}])[0].get("message", {}).get("content", "No response.")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error fetching response from AIPIPE: {str(e)}")
+
+    links = [{"url": r["url"], "text": r["text"][:120]} for r in top_results]
+    return {"answer": answer, "links": links}
+
+# --- Run the Server ---
+if __name__ == "__main__":
+    uvicorn.run("api:app", reload=True)