add generic RAG chatbot example

community/generic-rag-bot/.streamlit/config.toml

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+[client]
+showErrorDetails = false
+
+[theme]
+primaryColor = "#76b900"
+backgroundColor = "white"
+
+[browser]
+gatherUsageStats = false

community/generic-rag-bot/README.md

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+# Tutorial for a Generic RAG-Based Chatbot
+
+This is a tutorial for how to build your own generic RAG chatbot. It is intended as a foundation for building more complex, domain-specific RAG-based assistants or tools. Note that no local GPU is needed to run this, as it is using NIMs from the NVIDIA NGC catalog.
+
+## Acknowledgements
+
+ - This implementation is based on [Rag in 5 Minutes](https://github.com/NVIDIA/GenerativeAIExamples/tree/main/community/5_mins_rag_no_gpu), with changes primarily made to the UI as well as updates to some outdated libraries, models, and dependencies. 
+ - Alyssa Sawyer also contributed to updating and further developing this repo in her project, [Resume RAG Bot](https://github.com/alysawyer/resume-rag-nv).
+
+## Steps
+
+1. Create a python virtual environment and activate it:
+
+   ```console
+   python3 -m venv genai
+   source genai/bin/activate
+   ```
+
+1. From the root of this folder, `generic-rag-chatbot`, install the requirements:
+
+   ```console
+   pip install -r requirements.txt
+   ```
+
+1. Add your NVIDIA API key as an environment variable:
+
+   ```console
+   export NVIDIA_API_KEY="nvapi-*"
+   ```
+
+   If you don't already have an API key, visit the NVIDIA API Catalog, select on any model, then click on **Get API Key**.
+
+1. Run the example using Streamlit:
+
+   ```console
+   streamlit run main.py
+   ```
+
+1. Test the deployed example by going to `http://<host_ip>:8501` in a web browser.
+
+   Click **Browse Files** and select the documents for your knowledge base.
+   After selecting, click **Upload!** to complete the ingestion process.

community/generic-rag-bot/main.py

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+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This is a simple standalone implementation showing rag pipeline using Nvidia AI Foundational Models.
+# It uses a simple Streamlit UI and one file implementation of a minimalistic RAG pipeline.
+
+
+############################################
+# Component #0.5 - UI / Header
+############################################
+
+import streamlit as st
+import os
+
+# Page settings 
+st.set_page_config(
+    layout="wide",
+    page_title="RAG Chatbot", 
+    page_icon = "🤖",
+    initial_sidebar_state="expanded")
+
+# Page title 
+st.header('Generic RAG Chatbot 🤖📝', divider='rainbow')
+
+# Custom CSS
+def local_css(file_name):
+    with open(file_name, "r") as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+local_css("style.css")
+
+# Page description 
+st.markdown('''Manually looking through vast amounts of data can be tedious and time-consuming. This chatbot can expedite that process by providing a platform to query your documents.''')
+st.warning("This system leverages generative AI. Any output from the AI agent should be used in conjunction with the original data.", icon="⚠️")
+
+############################################
+# Component #1 - Document Loader
+############################################
+
+with st.sidebar:
+    st.subheader("Upload your Documents")
+
+    DOCS_DIR = os.path.abspath("./uploaded_docs")
+
+    # Make dir to store uploaded documents
+    if not os.path.exists(DOCS_DIR):
+        os.makedirs(DOCS_DIR)
+
+    # Define form on Streamlit page for uploading files to KB
+    with st.form("my-form", clear_on_submit=True):
+        uploaded_files = st.file_uploader("Upload a file to the Knowledge Base:", accept_multiple_files=True)
+        submitted = st.form_submit_button("Upload!")
+
+    # Acknowledge successful file uploads
+    if uploaded_files and submitted:
+        for uploaded_file in uploaded_files:
+            st.success(f"File {uploaded_file.name} uploaded successfully!")
+            with open(os.path.join(DOCS_DIR, uploaded_file.name), "wb") as f:
+                f.write(uploaded_file.read())
+
+############################################
+# Component #2 - Initalizing Embedding Model and LLM
+############################################
+
+from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings
+
+# Make sure to export your NGC NV-Developer API key as NVIDIA_API_KEY! 
+API_KEY = os.environ['NVIDIA_API_KEY']
+
+# Select embedding model and LLM
+document_embedder = NVIDIAEmbeddings(model="nvidia/nv-embedqa-e5-v5", api_key=API_KEY, model_type="passage", truncate="END")
+llm = ChatNVIDIA(model="meta/llama-3.1-70b-instruct", api_key=API_KEY, temperature=0)
+
+############################################
+# Component #3 - Vector Database Store
+############################################
+
+import pickle
+import nltk
+import ssl
+
+# Disable SSL check NLTK uses on nltk.download calls
+try:
+    _create_unverified_https_context = ssl._create_unverified_context
+except AttributeError:
+    pass
+else:
+    ssl._create_default_https_context = _create_unverified_https_context
+
+# Download tokenizer models from NLTK
+nltk.download('punkt')
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import DirectoryLoader
+from langchain_community.vectorstores import FAISS
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+
+# Option for using an existing vector store
+with st.sidebar:
+    use_existing_vector_store = st.radio("Use existing vector store if available", ["Yes", "No"], horizontal=True)
+
+# Load raw documents from the directory
+DOCS_DIR = os.path.abspath("./uploaded_docs")
+raw_documents = DirectoryLoader(DOCS_DIR).load()
+
+# Check for existing vector store file
+vector_store_path = "vectorstore.pkl"
+vector_store_exists = os.path.exists(vector_store_path)
+vectorstore = None
+
+if use_existing_vector_store == "Yes" and vector_store_exists:
+    # Load existing vector store
+    with open(vector_store_path, "rb") as f:
+        vectorstore = pickle.load(f)
+    with st.sidebar:
+        st.info("Existing vector store loaded successfully.")
+else:
+    with st.sidebar:
+        if raw_documents and use_existing_vector_store == "Yes":
+            # Chunk documents
+            with st.spinner("Splitting documents into chunks..."):
+                text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=100)
+                documents = text_splitter.split_documents(raw_documents)
+
+            # Convert document chunks to embeddings, and save in a vector store
+            with st.spinner("Adding document chunks to vector database..."):
+                vectorstore = FAISS.from_documents(documents, document_embedder)
+
+            # Save vector store
+            with st.spinner("Saving vector store"):
+                with open(vector_store_path, "wb") as f:
+                    pickle.dump(vectorstore, f)
+            st.success("Vector store created and saved.")
+        else:
+            st.warning("No documents available to process!", icon="⚠️")
+
+############################################
+# Component #4 - LLM Response Generation and Chat
+############################################
+
+st.subheader("Chat with your AI Assistant")
+
+# Save chat history for this user session
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Define prompt for LLM
+prompt_template = ChatPromptTemplate.from_messages([
+    ("system", "You are a helpful AI assistant. Use the provided context to inform your responses. If no context is available, please state that."),
+    ("human", "{input}")
+])
+
+# Define simple prompt chain 
+chain = prompt_template | llm | StrOutputParser()
+
+# Display an example query for user 
+user_query = st.chat_input("Please summarize these documents.")
+
+# Complete this section of code every time the user inputs a new query
+if user_query:
+    # Add user query to chat history
+    st.session_state.messages.append({"role": "user", "content": user_query})
+    with st.chat_message("user"):
+        st.markdown(user_query)
+
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+
+        if vectorstore is not None and use_existing_vector_store == "Yes":
+            # Retrieve relevant chunks for the given user query from the vector store
+            retriever = vectorstore.as_retriever()
+            retrieved_docs = retriever.invoke(user_query)
+
+            # Concatenate retrieved chunks together as context for LLM
+            context = "\n\n".join([doc.page_content for doc in retrieved_docs])
+            augmented_user_input = f"Context: {context}\n\nQuestion: {user_query}\n"
+        else:
+            augmented_user_input = f"Question: {user_query}\n"
+
+        # Get output from LLM
+        for response in chain.stream({"input": augmented_user_input}):
+            full_response += response
+            message_placeholder.markdown(full_response + "▌")
+        message_placeholder.markdown(full_response)
+    # Add AI assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})

community/generic-rag-bot/requirements.txt

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+streamlit
+faiss-cpu==1.7.4
+unstructured[all-docs]==0.11.2
+langchain
+langchain-community
+langchain-core
+langchain-nvidia-ai-endpoints
+langchain-text-splitters
+nltk==3.8.1
+numpy==1.23.5
+onnx==1.16.1 
+onnxruntime==1.15.1
+python-magic

community/generic-rag-bot/style.css

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+/* style.css */
+
+/* custom footer */
+.footer {
+    text-align: center;
+    color: #666;
+    font-size: 14px;
+}
+
+/* NVIDIA green for headers */
+h1, h2, h3, h4, h5 {
+    color: #76b900;
+}
+
+
+/* add line when hovering over link */
+.hover-link {
+    text-decoration: none;
+    color: inherit;
+    position: relative;
+  }
+
+.hover-link::after {
+    content: '';
+    position: absolute;
+    width: 100%;
+    height: 1px;
+    bottom: 0;
+    left: 0;
+    background-color: #000;
+    transform: scaleX(0);
+    transition: transform 0.3s ease-in-out;
+}
+
+.hover-link:hover::after {
+    transform: scaleX(1);   
+}
+
+/* Remove default formatting for links */
+a {
+    color: #666; 
+    text-decoration: none;
+}
+
+/* Remove streamlit bar */
+header {
+    visibility: hidden;
+}
+
+/* custom container */
+
+.custom-image-container img {
+    border-radius: 10px;
+}
+
+.custom-column-container {
+    background-color: #f0f0f0;
+    border-radius: 10px;
+    padding: 20px;
+}
+
+.custom-column-container .stMarkdown {
+    padding-right: 20px;
+}
+
+.streamlit-expanderHeader {
+    background-color: white;
+    color: #76b900; 
+}
+.streamlit-expanderContent {
+    background-color: white;
+    color: black; 
+}