Update main.py

main.py

@@ -0,0 +1,72 @@
+import pandas as pd
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import re
+
+app = FastAPI()
+
+# Allow CORS for local frontend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load data
+faq_df = pd.read_csv("BankFAQs.csv", usecols=["Question", "Answer"])
+questions = faq_df["Question"].astype(str).tolist()
+answers = faq_df["Answer"].astype(str).tolist()
+
+# Chunking function: split text into sentences
+sentence_splitter = re.compile(r'(?<=[.!?]) +')
+def chunk_text(text):
+    return [chunk.strip() for chunk in sentence_splitter.split(text) if chunk.strip()]
+
+# Prepare chunked data
+chunked_questions = []  # Parent question for each chunk
+chunks = []             # The actual chunk text
+chunked_answers = []    # Full answer for reference
+for q, a in zip(questions, answers):
+    answer_chunks = chunk_text(a)
+    for chunk in answer_chunks:
+        chunked_questions.append(q)
+        chunks.append(chunk)
+        chunked_answers.append(a)
+
+# Load model and build index
+model = SentenceTransformer("all-MiniLM-L6-v2")
+chunk_embeddings = model.encode(chunks)
+chunk_embeddings = np.array(chunk_embeddings).astype("float32")
+chunk_index = faiss.IndexFlatL2(chunk_embeddings.shape[1])
+chunk_index.add(chunk_embeddings)
+
+class QueryRequest(BaseModel):
+    query: str
+    k: int = 1
+
+@app.post("/search")
+async def search_faq(req: QueryRequest):
+    query_embedding = model.encode([req.query]).astype("float32")
+    D, I = chunk_index.search(query_embedding, req.k)
+    # Calculate cosine similarity from L2 distance
+    # cosine_sim = 1 - (L2_distance^2 / 2)
+    similarities = 1 - (D[0] / 2)
+    threshold = 0.6
+    results = []
+    for idx, sim in zip(I[0], similarities):
+        if sim >= threshold:
+            results.append({
+                "question": chunked_questions[idx],
+                "full_answer": chunked_answers[idx]
+            })
+    return {"results": results}
+
+@app.get("/")
+def root():
+    return {"message": "FAQ Assistant is running with chunking."}