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app.py

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+
+import os
+import json
+from dotenv import load_dotenv
+import streamlit as st
+from huggingface_hub import login
+import google.generativeai as genai
+from sentence_transformers import SentenceTransformer
+from langchain_community.vectorstores import FAISS
+from langchain.embeddings.base import Embeddings
+from google.adk.agents import Agent
+from google.adk.sessions import InMemorySessionService
+from google.adk.runners import Runner
+from google.adk.tools import FunctionTool
+from google.genai import types
+from langchain_tavily import TavilySearch
+
+# === CONFIGURE ENV AND AUTH ===
+load_dotenv()
+hf_token = os.getenv("HUGGINGFACE_TOKEN")
+assert hf_token, "Please set HUGGINGFACE_TOKEN in your .env"
+login(token=hf_token)
+assert os.getenv("GOOGLE_API_KEY"), "Set GOOGLE_API_KEY in .env"
+assert os.getenv("TAVILY_API_KEY"), "Set TAVILY_API_KEY in .env"
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+def flatten_json(obj: dict) -> str:
+    pieces = []
+    def recurse(prefix, value):
+        if isinstance(value, dict):
+            for k, v in value.items(): recurse(f"{prefix}{k} > ", v)
+        elif value is not None:
+            pieces.append(f"{prefix}{value}")
+    recurse("", obj)
+    return "\n".join(pieces)
+
+# === LOAD AND INDEX LOCAL COLLEGE JSONS ===
+@st.cache_resource
+def load_vector_store(data_dir: str):
+    texts = []
+    for fname in os.listdir(data_dir):
+        if fname.lower().endswith('.json'):
+            path = os.path.join(data_dir, fname)
+            try:
+                with open(path, 'r', encoding='utf-8') as f: data = json.load(f)
+            except UnicodeDecodeError:
+                with open(path, 'r', encoding='latin-1') as f: data = json.load(f)
+            texts.append(flatten_json(data))
+    st.info(f"Loaded {len(texts)} documents.")
+
+    st_model = SentenceTransformer('all-MiniLM-L6-v2')
+    class LocalEmbeddings(Embeddings):
+        def embed_documents(self, docs): return st_model.encode(docs).tolist()
+        def embed_query(self, q): return st_model.encode([q])[0].tolist()
+
+    return FAISS.from_texts(texts, LocalEmbeddings())
+
+vector_store = load_vector_store('Jsons-Colleges/Jsons')
+
+# === TOOLS ===
+def db_search(query: str) -> dict:
+    docs = vector_store.similarity_search(query, k=6)
+    if not docs: return {"results": []}
+    return {"results": [d.page_content for d in docs]}
+
+
+def tavily_search(query: str) -> dict:
+    tool = TavilySearch(max_results=6, topic="general", include_raw_content=True)
+    result = tool.invoke({"query": query})
+    snippets = [item.get('content') for item in result.get('results', [])]
+    return {"results": snippets or []}
+
+# Wrap as FunctionTools
+from google.adk.tools import FunctionTool
+
+db_tool = FunctionTool(db_search)
+tavily_tool = FunctionTool(tavily_search)
+
+# === AGENT SETUP ===
+@st.cache_resource
+def create_agent():
+    agent = Agent(
+        name="college_info_agent",
+        model="gemini-2.0-flash",
+        instruction=(
+            "You are a college information specialist. For every user query about colleges or universities, "
+            "follow this exact workflow before replying:\n"
+            "1. Call `db_search` with the user’s query.\n"
+            "2. If `db_search` returns an empty `results` list, immediately call `tavily_search`.\n"
+            "3. Do not produce any output until one of those calls returns data.\n"
+            "4. As soon as you have non‑empty results, stop further searches and craft your answer using only that source.\n"
+            "5. Structure your response with key details: name, location, major/program offerings, rankings, tuition, "
+            "admissions criteria, campus highlights, and any notable facts.\n"
+            "6. Use a clear, conversational tone and include examples or comparable institutions when helpful."
+            "7. If something is not present in the database or you don't know about it automatically do web search and find the answer for it without asking the user."
+            "8. Always try to give complete answer in one go and let user ask follow up questions on the complete answer."
+        ),
+        tools=[db_tool, tavily_tool],
+        generate_content_config=types.GenerateContentConfig(
+            max_output_tokens=1500,
+            temperature=0
+        )
+    )
+    session_svc = InMemorySessionService()
+    session = session_svc.create_session(app_name="college_agent_app", user_id="user1", session_id="session1")
+    runner = Runner(agent=agent, app_name="college_agent_app", session_service=session_svc)
+    return runner, session
+
+runner, session = create_agent()
+
+# === STREAMLIT UI ===
+st.title("🎓 CollegeGPT")
+
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# Display chat history
+for role, msg in st.session_state.history:
+    if role == "user": st.chat_message("user").write(msg)
+    else: st.chat_message("assistant").write(msg)
+
+# Input
+query = st.chat_input("Ask me about any college…")
+if query:
+    st.session_state.history.append(("user", query))
+    # Run agent
+    user_msg = types.Content(role="user", parts=[types.Part(text=query)])
+    events = runner.run(user_id="user1", session_id=session.id, new_message=user_msg)
+    # Collect final response text
+    reply = ""
+    for ev in events:
+        if ev.is_final_response(): reply = ev.content.parts[0].text
+    st.session_state.history.append(("assistant", reply))
+    st.rerun()  

requirements.txt

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+google-adk
+google-generativeai
+sentence-transformers
+faiss-cpu
+tavily-python
+python-dotenv
+langchain-community
+langchain_tavily