feat: upgraded rag model from ollama to chatgpt

examples/rag/README.md

@@ -1,89 +1,66 @@
-# RAG Example
+# Intramind 
 
-This example demonstrates how to use PyTiDB to build a minimal RAG application.
-
-* Use Ollama to deploy local embedding model and LLM model
-* Use Streamlit to build a Web UI for the RAG application
-* Use PyTiDB to build a minimal RAG application
-
-<p align="center">
-  <img src="https://github.com/user-attachments/assets/dfd85672-65ce-4a46-8dd2-9f77d826363e" alt="RAG application built with PyTiDB" width="600" />
-  <p align="center"><i>RAG application built with PyTiDB</i></p>
-</p>
+* An RAG-Driven AI Chatbot that allows users to manage a private knowledge base by feeding private files, therefore obtaining more accurate, reliable, and secure answers about any private fields that simple LLMs cannot answer
+* Use `pytidb` to connect to TiDB
+* Use `openai` to deploy embedding model and response generation
+* Use Streamlit as web ui
 
 ## Prerequisites
-
-- **Python 3.10+**
-- **A TiDB Cloud Starter cluster**: Create a free cluster here: [tidbcloud.com ↗️](https://tidbcloud.com/?utm_source=github&utm_medium=referral&utm_campaign=pytidb_readme)
-- **Ollama**: You can install it from [Ollama ↗️](https://ollama.com/download)
+* Python 3.10+
+* A TiDB Cloud Serverless cluster: Create a free cluster here: tidbcloud.com
+* OpenAI API key: Go to Open AI to get your own API key
+* Google Auth: Create a web application in Google Cloud Console (https://docs.streamlit.io/develop/tutorials/authentication/google)
 
 ## How to run
 
-**Step 1**: Prepare the inference API
-
-Pull the embedding and LLM model via ollama CLI:
-
-```bash
-ollama pull mxbai-embed-large
-ollama pull gemma3:4b
-ollama run gemma3:4b
-```
-
-Test the `/embed` and `/generate` endpoints to make sure they are running:
-
-```bash
-curl http://localhost:11434/api/embed -d '{
-  "model": "mxbai-embed-large",
-  "input": "Llamas are members of the camelid family"
-}'
-```
+**Step1**: Clone the repo
 
 ```bash
-curl http://localhost:11434/api/generate -d '{
-  "model": "gemma3:4b",
-  "prompt": "Hello, Who are you?"
-}'
+git clone https://github.com/cindywxw1/Intramind.git
 ```
 
-**Step 2**: Clone the repository to local
-
-```bash
-git clone https://github.com/pingcap/pytidb.git
-cd pytidb/examples/rag/;
-```
-
-**Step 3**: Install the required packages and setup environment
+**Step2**: Install the required packages and setup environment
 
 ```bash
 python -m venv .venv
 source .venv/bin/activate
-pip install -r reqs.txt
+pip install -r requirements.txt
 ```
 
-**Step 4**: Set up environment to connect to database
+**Step3**: Set up environment to connect to storage
 
-Go to [TiDB Cloud console](https://tidbcloud.com/clusters) and get the connection parameters, then set up the environment variable like this:
+As you are using a local TiDB server, you can set up the environment variable like this:
+(You can also referense)
 
 ```bash
 cat > .env <<EOF
-TIDB_HOST={gateway-region}.prod.aws.tidbcloud.com
+OPENAI_API_KEY=
+TIDB_HOST=localhost
 TIDB_PORT=4000
-TIDB_USERNAME={prefix}.root
-TIDB_PASSWORD={password}
-TIDB_DATABASE=rag_example
+TIDB_USERNAME=root
+TIDB_PASSWORD=
+TIDB_DATABASE=test
 EOF
 ```
 
-**Step 5**: Run the Streamlit app
+**Step4**: Set up Google Auth Platform info
 
 ```bash
-streamlit run main.py
+cat > .streamlit/secrets.toml <<EOF
+[auth]
+redirect_uri = "http://localhost:8501/oauth2callback"
+cookie_secret = 
+client_id =
+client_secret =
+server_metadata_url = "https://accounts.google.com/.well-known/openid-configuration"
+EOF
 ```
 
-**Step 6**: Open the browser and visit `http://localhost:8501`
+**Step5**: Run the Streamlit app
 
-## Troubleshooting
+```bash
+streamlit run src/app.py
+```
 
-### `502 Bad Gateway` Error
+**Step6**: open the browser and visit `http://localhost:8501`
 
-Try to disable the global proxy settings.

examples/rag/env.example

@@ -0,0 +1,7 @@
+OPENAI_API_KEY = "sk-xxxxxx"
+
+TIDB_HOST=xxxxxx
+TIDB_PORT=xxxxxx
+TIDB_USERNAME=xxxxxx
+TIDB_PASSWORD=xxxxxx
+TIDB_DATABASE=test

examples/rag/requirements.txt

@@ -0,0 +1,11 @@
+streamlit
+openai
+pytidb
+
+python-dotenv
+streamlit-authenticator
+
+sqlalchemy
+litellm
+PyPDF2
+langchain_text_splitters

examples/rag/src/app.py

@@ -0,0 +1,17 @@
+import streamlit as st
+
+doc_page = st.Page("page_files/doc_page.py", title = "Manage Uploaded Files")
+main_page = st.Page("page_files/main_page.py", title = "Chats")
+login_page = st.Page("page_files/login_page.py")
+
+
+def main():
+    if not st.user.is_logged_in:
+        pg = st.navigation([login_page])
+    else:
+        pg = st.navigation([main_page, doc_page])
+    pg.run()
+
+if __name__ == "__main__":
+    main()
+