Sahayog AI agent

README.md

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-# iitm-submission-odyssey
-Submissions for IITM Odyssey Hackathon
+# 📘 README: AI Assistant Skills for Online Course Platform
+
+This assistant is designed to support students across various academic topics such as **Biology**, **Linguistics**, **Computer Science**, **Python Programming**, **Calculus**, and **Software Engineering**. It offers help in answering academic queries, providing logistics information for courses, and managing study schedules.
+
+# Drive Link for Demo and ppt
+https://drive.google.com/drive/folders/1w0c6QvOwNwAMBjCuIPPgDr5HPm1H3oLh?usp=drive_link
+---
+
+## 🔧 Skills Overview
+
+### 1. `Get_questions`
+
+**Purpose:**  
+Retrieve frequently asked questions from previous student logs for a specific course. This feature is locally hosted.
+
+**Usage:**  
+- Used only when the query is about **logistics** (e.g., deadlines, grading, syllabus).
+- Requires a `Course_name` field present in the user's request.
+- Only the **main topic** from each FAQ is returned.
+
+**Important Rules:**
+- Do **not** use any other skill when using this.
+- If `Course_name` is **missing**, reply:  
+  > "The course name is not present in the article."  
+  And provide the **list of available courses** directly.
+
+---
+
+### 2. `AddGoogleCalendarEvent`
+
+We have created Operations feature called Add_gevent
+**Purpose:**  
+Add academic events (like assignment deadlines, exam reminders, etc.) to a student’s calendar.
+
+**Usage:**  
+- Automatically assumes the **Asia/Kolkata** timezone.
+- Triggered when students ask to **add an event** (e.g., “remind me of the quiz tomorrow”).
+
+---
+
+### 3. `RAG`
+
+**Purpose:**  
+Answer academic questions by retrieving information from course materials using **Retrieval-Augmented Generation**. The data is vectorized and saved in local storage. This feature is locally hosted.
+
+**Usage:**  
+- First choice when the student asks a question on course content.
+- Summarized responses must include:
+  - **Lecture number**
+  - **Week number**
+
+---
+
+### 4. `WebScrape`
+
+**Purpose:**  
+Provide external context if `RAG` is **not sufficient** or **not relevant**.
+
+**Usage:**  
+- Used only **if RAG fails** to provide an adequate or relevant answer.
+- When summarizing, always include **relevant source links**.
+
+---
+
+## ✅ Assistant Behavior Summary
+
+- **Primary Focus:** Academic support in biology, linguistics, CS, Python, calculus, software engineering.
+- **Polite Redirection:** Gently guide students back to academic topics if the query goes off-track.
+- **Course Logistics:** Use `Get_questions` with `Course_name`, and do not mix it with other skills.
+- **Calendar Support:** Use `AddGoogleCalendarEvent` for event reminders (default to Asia/Kolkata).
+- **Contextual Help:** Use `RAG` for academic queries and fall back to `WebScrape` if needed.

locally_hosted_apis/devrev_api.py

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+from flask import Flask, render_template, request, jsonify
+import json
+import sys
+import os
+from datetime import datetime
+import requests
+
+
+from rag_agent import token
+from rag_agent import serverHandler
+
+TAVILY_API_KEY = "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"
+access_token = "ya29.a0AZYkNZhRCtH1m2Rhuo32QzLvKquNvgBtBj68a8T7L9rf-4CXOdNAAm8_ljcuaPvrHdW6lUg5pvz5pOsKwcdihuvScIsYmGDhbTjcAn5-lTac58tDTet73PVwDL2hZOcTWYTM-BAoLaze5nY5F3fKupdGd4Fb0VXtsumwLM-lPwaCgYKAUgSARMSFQHGX2MiSv-sR4oCvgtz0ZfmdKsIKg0177"
+
+app = Flask(__name__)
+
+server = serverHandler(vdb_path="updated_vectordb",TAVILY_API_KEY=TAVILY_API_KEY)
+
+@app.route('/rag_message', methods=['POST'])
+def rag_message():
+
+    try:
+        data = request.get_json()
+        token1 = token(data["username"],data["message"],data["course"])
+        final_token = server.handle_token_rag(token1)
+
+
+        generated_response = final_token.response
+        data["response"] = generated_response
+        return jsonify({"status": "success", "message": "Message saved", "response": generated_response}), 200
+
+    except Exception as e:
+        return jsonify({"error": "Error Generating Response","message":str(e)}), 500
+
+@app.route('/web_message', methods=['POST'])
+def web_message():
+
+    try:
+        data = request.get_json()
+        token1 = token(data["username"],data["message"],data["course"])
+        final_token = server.handle_token_web(token1)
+
+
+        generated_response = final_token.response
+        data["response"] = generated_response
+        return jsonify({"status": "success", "message": "Message saved", "response": generated_response}), 200
+
+    except Exception as e:
+        return jsonify({"error": "Error Generating Response","message":str(e)}), 500
+
+
+@app.route('/add_event', methods=['POST'])
+def add_event():
+    print("add_event")
+    url = 'https://www.googleapis.com/calendar/v3/calendars/primary/events'
+
+    headers = {
+        'Authorization': f'Bearer {access_token}',
+        'Content-Type': 'application/json'
+    }
+    try:
+        data = request.get_json()
+        event_data = {
+            "summary": data["summary"],
+            "start": {
+                "dateTime": data["start_time"],
+                "timeZone": data["timezone"]
+            },
+            "end": {
+                "dateTime": data["end_time"],
+                "timeZone": data["timezone"]
+            }
+        }
+        print("event_data",event_data)
+        response = requests.post(url, headers=headers, json=event_data)
+
+        return jsonify({"status": "success", "message": "Event added successfully"}), 200
+    except Exception:
+        return jsonify({"status": "error", "message": "Failed to add event"}), 500
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)

locally_hosted_apis/rag_agent.py

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+import os
+from tavily import TavilyClient
+import chromadb
+from chromadb.utils import embedding_functions
+
+
+# Token class
+class token:
+    def __init__(self,username,query,courseID):
+        self.courseID = courseID
+        self.query = query
+        self.response = None
+        self.username = username
+
+class vectordbclass:
+    def __init__(self, path):
+        self.path = path
+        self.chroma_client = chromadb.PersistentClient(path=path)
+        self.embedding_fn = embedding_functions
+        self.sentence_transformer_ef = self.embedding_fn.SentenceTransformerEmbeddingFunction(model_name="all-mpnet-base-v2")
+        self.collection = self.chroma_client.get_or_create_collection(name=path, embedding_function=self.sentence_transformer_ef)
+
+    def query(self, query_text, n_results=3):
+        results = self.collection.query(query_texts=[query_text], n_results=n_results, include=['documents', 'metadatas'])
+        actual_results = min(n_results, len(results['documents'][0]) if results['documents'] else 0)
+        context = f'''The following are {actual_results} results from the vector database:\n'''
+
+        for i in range(actual_results):
+            context += '''\nResult {} Lecture No:{} Week:{} Course Name:{}\n Context: {}\n'''.format(
+                i+1,
+                results['metadatas'][0][i].get('lecture_no', 'N/A'),
+                results['metadatas'][0][i].get('week', 'N/A'),
+                results['metadatas'][0][i].get('courseID', 'N/A'),
+                results['documents'][0][i]
+            )
+        return context if actual_results > 0 else "No results found."
+
+# RAG Agent class
+class RAG_Agent:
+    def __init__(self,vectordb):
+        self.vectordb = vectordb
+
+    # Generating response based on the question
+    def generate_response(self,tok1):
+        context = self.vectordb.query(tok1.query,3)
+        tok1.response = context
+        return tok1
+
+class Web_Agent:
+    def __init__(self,TAVILY_API_KEY):
+        self.web = TavilyClient(api_key=TAVILY_API_KEY)
+    # Generating response based on the question
+    def generate_response(self,tok1):
+        context= self.web.search(tok1.query, search_depth="basic")["results"][:3]
+        tok1.response = context
+        return tok1
+
+# Server handler class
+class serverHandler:
+    def __init__(self,vdb_path,TAVILY_API_KEY):
+        self.vectordb = vectordbclass(vdb_path)
+        self.TAVILY_API_KEY = TAVILY_API_KEY
+
+    def handle_token_rag(self,tok1):
+        new_model = RAG_Agent(self.vectordb)
+        final_token = new_model.generate_response(tok1)
+        return final_token
+
+    def handle_token_web(self,tok1):
+        new_model = Web_Agent(self.TAVILY_API_KEY)
+        final_token = new_model.generate_response(tok1)
+        return final_token