Update app.py

app.py

@@ -7,42 +7,6 @@ import pandas as pd
 from langchain_core.messages import HumanMessage
 from agent import build_graph
 
-import os
-from datasets import load_dataset
-from langchain.schema import Document
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import SupabaseVectorStore
-from supabase import create_client
-
-# 1. Load GAIA train split
-dataset = load_dataset("gaia-benchmark/GAIA", split="train")
-
-# 2. Build Documents: "Q: …\nA: …"
-docs = []
-for ex in dataset:
-    q, a = ex["question"], ex["answer"]
-    docs.append(Document(
-        page_content=f"Q: {q}\nA: {a}",
-        metadata={"task_id": ex.get("task_id"), "split": "train"}
-    ))
-
-# 3. Initialize embedding & Supabase client
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-supabase_url = os.environ["SUPABASE_URL"]
-supabase_key = os.environ["SUPABASE_SERVICE_KEY"]
-supabase = create_client(supabase_url, supabase_key)
-
-# 4. Upload to Supabase
-vectorstore = SupabaseVectorStore.from_documents(
-    docs,
-    embedding=embeddings,
-    client=supabase,
-    table_name="documents",
-    query_name="match_documents_langchain"
-)
-
-print(f"Seeded {len(docs)} GAIA examples into Supabase.")
-
 
 # (Keep Constants as is)
 # --- Constants ---