Chatbot and Local CoPilot with Local LLM, RAG, LangChain, and Guardrail

 

Chatbot Application with Local LLM, RAG, LangChain, and Guardrail

I've developed a chatbot application designed for informative and engaging conversationAs you already aware that Retrieval-augmented generation (RAG) is a technique that combines information retrieval with a set of carefully designed system prompts to provide more accurate, up-to-date, and contextually relevant responses from large language models (LLMs). By incorporating data from various sources such as relational databases, unstructured document repositories, internet data streams, and media news feeds, RAG can significantly improve the value of generative AI systems.

Developers must consider a variety of factors when building a RAG pipeline: from LLM response benchmarking to selecting the right chunk size.

In tapplication demopost, I demonstrate how to build a RAG pipeline uslocal LLM which can be converted to ing NVIDIA AI Endpoints for LangChain. FirI have you crdeate a vector storeconnecting with one of the Hugging Face dataset though we can by downding web p or can use any pdf etc easily.aThen and generating their embeddings using SentenceTransformer or you can use the NVIDIA NeMo Retriever embedding microservice and searching for similarity using FAISS. I then showcase two different chat chains for querying the vector store. For this example, I use local LangChain chain and a Python FastAPI based REST API services which is running in different thread within the Jupyter Notebook environment itself. At last I have preapred a small but beautiful front end with HTML, Bootstrap and Ajax as a Chat Bot front end to interact by users. However you can use the NVIDIA Triton Inference Server documentation, though the code can be easily modified to use any other soueok.

Introducing ChoiatBot Local CoPilot: Your Customizable Local Copilot Agent

ChoiatBot offers a revolutionary approach to personalized chatbot solutions, developed to operate entirely on CPU-based systems without the need for an internet connection. This ensures not only enhanced privacy but also unrestricted accessibility, making it ideal for environments where data security is paramount.

Key Features and Capabilities

ChoiatBot stands out with its ability to be seamlessly integrated with diverse datasets, allowing users to upload and train the bot with their own data and documents. This customization empowers businesses and individuals alike to tailor the bot's responses to specific needs, ensuring a truly personalized user experience.

Powered by the google/flan-t5-small model, ChoiatBot leverages state-of-the-art technology known for its robust performance across various benchmarks. This model's impressive few-shot learning capabilities, as evidenced by achievements like 75.2% on the five-shot MMLU benchmark, ensure that ChoiatBot delivers accurate and contextually relevant responses even with minimal training data.

The foundation of ChoiatBot's intelligence lies in its training on the "Wizard-of-Wikipedia" dataset, renowned for its groundbreaking approach to knowledge-grounded conversation generation. This dataset not only enriches the bot's understanding but also enhances its ability to provide nuanced and informative responses based on a broad spectrum of topics.

Performance and Security

One of ChoiatBot's standout features is its ability to function offline, offering unparalleled data security and privacy. This capability is particularly advantageous for sectors dealing with sensitive information or operating in environments with limited internet connectivity. By eliminating reliance on external servers, ChoiatBot ensures that sensitive data remains within the user's control, adhering to the strictest security protocols.

Moreover, ChoiatBot's implementation on CPU-based systems underscores its efficiency and accessibility. This approach not only reduces operational costs associated with cloud-based solutions but also enhances reliability by mitigating risks related to internet disruptions or server downtimes.

Applications and Use Cases

ChoiatBot caters to a wide array of applications, from customer support automation to educational tools and personalized assistants. Businesses can integrate ChoiatBot into their customer service frameworks to provide instant responses and streamline communication channels. Educational institutions can leverage ChoiatBot to create interactive learning environments where students can receive tailored explanations and guidance.

For developers and data scientists, ChoiatBot offers a versatile platform for experimenting with different datasets and fine-tuning models. The provided code, along with detailed documentation on usage, encourages innovation and facilitates the adaptation of advanced AI capabilities to specific project requirements.

Conclusion

In conclusion, ChoiatBot represents a leap forward in AI-driven conversational agents, combining cutting-edge technology with a commitment to user privacy and customization. Whether you are looking to enhance customer interactions, optimize educational experiences, or explore the frontiers of AI research, ChoiatBot stands ready as your reliable local copilot agent, empowering you to harness the full potential of AI in your endeavors. Discover ChoiatBot today and unlock a new era of intelligent, personalized interactions tailored to your unique needs and aspirations:

Development Environment:

Operating System: Windows 10 (widely used and compatible)

Hardware: CPU (no NVIDIA GPU required, making it accessible to a broader audience)

Language Model:

Local LLM (Large Language Model): This provides the core conversational caUsed Google Flan 5 small LLM.f using a CPU)

Hugging Face Dataset: You've leveraged a small dataset from Hugging Face, a valuable resource for pre-trained models and datasets. This enables you to fine-tune the LLM for your specific purposes.

Data Processing and Training:

LagChain (if applicable): If you're using LagChain, it likely facilitates data processing and training pipelines for your LLM, streamlining the development process.

Guardrails (Optional):

NVIDIA Nemo Guardrail Library (if applicable): While Guardrail is typically used with NVIDIA GPUs, it's possible you might be employing a CPU-compatible version or alternative library for safety and bias mitigation.

Key Features:

Dataset Agnostic: This chatbot can be trained on various datasets, allowing you to customize its responses based on your specific domain or requirements.

General Knowledge Base: The initial training with a small Wikipedia dataset provides a solid foundation for general knowledge and information retrieval.

High Accuracy: You've achieved impressive accuracy in responses, suggesting effective training and data selection.

Good Quality Responses: The chatbot delivers informative and well-structured answers, enhancing user experience and satisfaction.

Additional Considerations:

Fine-Tuning Dataset: Consider exploring domain-specific datasets from Hugging Face or other sources to further enhance the chatbot's expertise in your chosen area.

Active Learning: If you're looking for continuous learning and improvement, investigate active learning techniques where the chatbot can identify informative data points to refine its responses.

User Interface: While this response focuses on the backend, a well-designed user interface (text-based, graphical, or voice) can significantly improve ushatbot application's capabilities!

Development Environment:

Operating System: Windows 10 (widely used and compatible)

Hardware: CPU (no NVIDIA GPU required, making it accessible to a broader audience)

Language Model:

Local LLM (Large Language Model): This provides the core conversational caUsed Google Flan 5 small LLM.f using a CPU)

Hugging Face Dataset: You've leveraged a small dataset from Hugging Face, a valuable resource for pre-trained models and datasets. This enables you to fine-tune the LLM for your specific purposes.

Data Processing and Training:

LagChain (if applicable): If you're using LagChain, it likely facilitates data processing and training pipelines for your LLM, streamlining the development process.

Guardrails (Optional):

NVIDIA Nemo Guardrail Library (if applicable): While Guardrail is typically used with NVIDIA GPUs, it's possible you might be employing a CPU-compatible version or alternative library for safety and bias mitigation.

Key Features:

Dataset Agnostic: This chatbot can be trained on various datasets, allowing you to customize its responses based on your specific domain or requirements.

General Knowledge Base: The initial training with a small Wikipedia dataset provides a solid foundation for general knowledge and information retrieval.

High Accuracy: You've achieved impressive accuracy in responses, suggesting effective training and data selection.

Good Quality Responses: The chatbot delivers informative and well-structured answers, enhancing user experience and satisfaction.

Additional Considerations:

Fine-Tuning Dataset: Consider exploring domain-specific datasets from Hugging Face or other sources to further enhance the chatbot's expertise in your chosen area.

Active Learning: If you're looking for continuous learning and improvement, investigate active learning techniques where the chatbot can identify informative data points to refine its responses.

User Interface: While this response focuses on the backend, a well-designed user interface (text-based, graphical, or voice) can significantly improve ushatbot application's capabilities!

Introducing ChoiatBot Local CoPilot: Your Customizable Local Copilot Agent

ChoiatBot offers a revolutionary approach to personalized chatbot solutions, developed to operate entirely on CPU-based systems without the need for an internet connection. This ensures not only enhanced privacy but also unrestricted accessibility, making it ideal for environments where data security is paramount.

Key Features and Capabilities

ChoiatBot stands out with its ability to be seamlessly integrated with diverse datasets, allowing users to upload and train the bot with their own data and documents. This customization empowers businesses and individuals alike to tailor the bot's responses to specific needs, ensuring a truly personalized user experience.

Powered by the google/flan-t5-small model, ChoiatBot leverages state-of-the-art technology known for its robust performance across various benchmarks. This model's impressive few-shot learning capabilities, as evidenced by achievements like 75.2% on the five-shot MMLU benchmark, ensure that ChoiatBot delivers accurate and contextually relevant responses even with minimal training data.

The foundation of ChoiatBot's intelligence lies in its training on the "Wizard-of-Wikipedia" dataset, renowned for its groundbreaking approach to knowledge-grounded conversation generation. This dataset not only enriches the bot's understanding but also enhances its ability to provide nuanced and informative responses based on a broad spectrum of topics.

Performance and Security

One of ChoiatBot's standout features is its ability to function offline, offering unparalleled data security and privacy. This capability is particularly advantageous for sectors dealing with sensitive information or operating in environments with limited internet connectivity. By eliminating reliance on external servers, ChoiatBot ensures that sensitive data remains within the user's control, adhering to the strictest security protocols.

Moreover, ChoiatBot's implementation on CPU-based systems underscores its efficiency and accessibility. This approach not only reduces operational costs associated with cloud-based solutions but also enhances reliability by mitigating risks related to internet disruptions or server downtimes.

Applications and Use Cases

ChoiatBot caters to a wide array of applications, from customer support automation to educational tools and personalized assistants. Businesses can integrate ChoiatBot into their customer service frameworks to provide instant responses and streamline communication channels. Educational institutions can leverage ChoiatBot to create interactive learning environments where students can receive tailored explanations and guidance.

For developers and data scientists, ChoiatBot offers a versatile platform for experimenting with different datasets and fine-tuning models. The provided code, along with detailed documentation on usage, encourages innovation and facilitates the adaptation of advanced AI capabilities to specific project requirements.

Conclusion

In conclusion, ChoiatBot represents a leap forward in AI-driven conversational agents, combining cutting-edge technology with a commitment to user privacy and customization. Whether you are looking to enhance customer interactions, optimize educational experiences, or explore the frontiers of AI research, ChoiatBot stands ready as your reliable local copilot agent, empowering you to harness the full potential of AI in your endeavors. Discover ChoiatBot today and unlock a new era of intelligent, personalized interactions tailored to your unique needs and aspirations.

You can use my code to customize with your dataset and build and local copilot and chatbot agent yourself even without GPU :).

https://github.com/dhirajpatra/jupyter_notebooks/tree/main/LLM

Telegram Bot for Monitoring Summarizing and Sending Periodic Qverviews of Channel Posts

 

pexel

To develop a Telegram bot for monitoring, summarizing, and sending periodic overviews of channel posts, follow these steps:

Step 1: Set Up Your Environment

1. Install Python: Ensure you have Python installed on your system.

2. Install Required Libraries:

    ```python

    pip install python-telegram-bot requests beautifulsoup4

    ```

Step 2: Create the Telegram Bot

1. Create a Bot on Telegram: Talk to [@BotFather](https://telegram.me/BotFather) to create a new bot. Note the API token provided.

Step 3: Develop the Bot

1. Monitor Telegram Channels:

    ```python

    from telegram import Bot, Update

    from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext

    import requests

    from bs4 import BeautifulSoup

    TOKEN = 'YOUR_TELEGRAM_BOT_TOKEN'

    CHANNELS = ['@example_channel_1', '@example_channel_2']

    SUMMARY_PERIOD = 60 * 60  # in seconds (1 hour)

    bot = Bot(token=TOKEN)

    def summarize_text(text):

        # Use a simple summarization logic or integrate with an NLP model

        return text[:100] + '...'

    def monitor_channels(context: CallbackContext):

        summaries = []

        for channel in CHANNELS:

            url = f'https://t.me/s/{channel.strip("@")}'

            response = requests.get(url)

            soup = BeautifulSoup(response.text, 'html.parser')

            posts = soup.find_all('div', class_='tgme_widget_message_text')

            for post in posts:

                summaries.append(summarize_text(post.get_text()))

        summary = '\n\n'.join(summaries)

        bot.send_message(chat_id=context.job.context, text=summary)

    def start(update: Update, context: CallbackContext):

        context.job_queue.run_repeating(monitor_channels, SUMMARY_PERIOD, context=update.message.chat_id)

        update.message.reply_text('Bot started! You will receive periodic summaries.')

    updater = Updater(token=TOKEN, use_context=True)

    dp = updater.dispatcher

    dp.add_handler(CommandHandler('start', start))

    updater.start_polling()

    updater.idle()

    ```

2. Customize Channels and Summary Period:

    ```python

    def add_channel(update: Update, context: CallbackContext):

        new_channel = context.args[0]

        if new_channel not in CHANNELS:

            CHANNELS.append(new_channel)

            update.message.reply_text(f'Channel {new_channel} added.')

        else:

            update.message.reply_text(f'Channel {new_channel} already in the list.')

    def remove_channel(update: Update, context: CallbackContext):

        channel = context.args[0]

        if channel in CHANNELS:

            CHANNELS.remove(channel)

            update.message.reply_text(f'Channel {channel} removed.')

        else:

            update.message.reply_text(f'Channel {channel} not found.')

    def set_period(update: Update, context: CallbackContext):

        global SUMMARY_PERIOD

        try:

            new_period = int(context.args[0]) * 60

            SUMMARY_PERIOD = new_period

            update.message.reply_text(f'Summary period set to {new_period // 60} minutes.')

        except ValueError:

            update.message.reply_text('Invalid period. Please provide a number.')

    dp.add_handler(CommandHandler('add_channel', add_channel))

    dp.add_handler(CommandHandler('remove_channel', remove_channel))

    dp.add_handler(CommandHandler('set_period', set_period))

    ```

3. Documentation:

    Provide clear instructions on how to use the bot, including commands to add/remove channels and set the summary period.

Step 4: Ensure Security and Compliance

- Secure Your Bot: Implement security measures to ensure the bot only responds to authorized users.

- Adhere to Telegram's API Usage Policies: Follow Telegram's guidelines and avoid actions that may lead to the bot being banned.

Step 5: Deployment and Support

- Deploy: Host your bot on a server to keep it running continuously.

- Ongoing Support: Be prepared to troubleshoot issues and update the bot as needed.

By following these steps, you can create a robust Telegram bot for monitoring, summarizing, and sending periodic overviews of channel posts.

Bot State with Azure

We can use Azure Bot Application using FastAPI that integrates with Azure Cache for Redis for session management and uses Azure Cosmos DB for state management. Here are the steps to achieve this:

1. State Management with Azure Cosmos DB:

   • Why do you need state?
     • Maintaining state allows your bot to have more meaningful conversations by remembering certain things about a user or conversation.
     • For example, if you’ve talked to a user previously, you can save previous information about them, so that you don’t have to ask for it again.
     • State also keeps data for longer than the current turn, so your bot retains information over the course of a multi-turn conversation.

2. Storage Layer:

   • The backend storage layer is where the state information is actually stored.
   • You can choose from different storage options:
     • Memory Storage: For local testing only; volatile and temporary.
     • Azure Blob Storage: Connects to an Azure Blob Storage object database.
     • Azure Cosmos DB Partitioned Storage: Connects to a partitioned Cosmos DB NoSQL database.
     • Note: The legacy Cosmos DB storage class has been deprecated.

3. State Management:

   • State management automates reading and writing bot state to the underlying storage layer.
   • State is stored as state properties (key-value pairs).
   • The Bot Framework SDK abstracts the underlying implementation.
   • You can use state property accessors to read and write state without worrying about storage specifics.

4. Setting Up Azure Cosmos DB for Bot State:

   • Create an Azure Cosmos DB Account (globally distributed, multi-model database service).
   • Within the Cosmos DB account, create a SQL Database to store the state of your bot effectively.

5. Implementing in Your Bot:

   • In your bot code, use the appropriate storage provider (e.g., Cosmos DB) to manage state.
   • Initialize state management and property accessors.
   • Example (using FastAPI):

     import azure.functions as func
     from WrapperFunction import app as fastapi_app
     from bot_state import BotState, CosmosDbPartitionedStorage
     
     # Initialize Cosmos DB storage
     cosmos_db_storage = CosmosDbPartitionedStorage(
         cosmos_db_endpoint="your_cosmos_db_endpoint",
         cosmos_db_key="your_cosmos_db_key",
         database_id="your_database_id",
         container_id="your_container_id"
     )
     
     # Initialize bot state
     bot_state = BotState(cosmos_db_storage)
     mple: Writing a user-specific property
     async def save_user_preference(turn_context, preference_value):
         user_id = turn_context.activity.from_property.id
         await bot_state.user_state.set_property(turn_context, f"user_preference_{user_id}", preference_value)
     
     # Example: Reading a user-specific property
     async def get_user_preference(turn_context):
         user_id = turn_context.activity.from_property.id
         preference_value = await bot_state.user_state.get_property(turn_context, f"user_preference_{user_id}")
         return preference_value
     
     # Usage in your bot logic
     async def on_message_activity(turn_context):
         # Get user preference
         preference = await get_user_preference(turn_context)
         await turn_context.send_activity(f"Your preference: {preference}")
     
         # Set user preference
         await save_user_preference(turn_context, "New Preference Value")
         await turn_context.send_activity("Preference updated!")
     # Example: Writing a user-specific property
     async def save_user_preference(turn_context, preference_value):
         user_id = turn_context.activity.from_property.id
         await bot_state.user_state.set_property(turn_context, f"user_preference_{user_id}", preference_value)
     
     # Example: Reading a user-specific property
     async def get_user_preference(turn_context):
         user_id = turn_context.activity.from_property.id
         preference_value = await bot_state.user_state.get_property(turn_context, f"user_preference_{user_id}")
         return preference_value
     
     # Usage in your bot logic
     async def on_message_activity(turn_context):
         # Get user preference
         preference = await get_user_preference(turn_context)
         await turn_context.send_activity(f"Your preference: {preference}")
     
         # Set user preference
         await save_user_preference(turn_context, "New Preference Value")
         await turn_context.send_activity("Preference updated!")
     
     app = func.AsgiFunctionApp(app=fastapi_app, http_auth_level=func.AuthLevel.ANONYMOUS)
     

6. Testing Locally and Deployment:

• Test your bot locally using VS Code or Azure CLI.
• Deploy your bot to Azure using the VS Code Azure Functions extension or Azure CLI.