Introducing the Local Copilot Chatbot Application: Your Ultimate Document-Based Query Assistant

                                        

actual screenshot taken of the knowledge bot

Introducing the Local Copilot Chatbot Application: Your Ultimate Document-Based Query Assistant

In today's fast-paced world, finding precise information quickly can make a significant difference. Our Local Copilot Chatbot Application offers a cutting-edge solution for accessing and querying document-based knowledge with remarkable efficiency. This Flask-based application utilizes the powerful Ollama and Phi3 models to deliver an interactive, intuitive chatbot experience. Here's a deep dive into what our application offers and how it leverages modern technologies to enhance your productivity.

What is the Local Copilot Chatbot Application?

The Local Copilot Chatbot Application is designed to serve as your personal assistant for document-based queries. Imagine having a copilot that understands your documents, provides precise answers, and adapts to your needs. That's exactly what our application does. It transforms your document uploads into a dynamic knowledge base that you can query using natural language.

Key Features

- Interactive Chatbot Interface: Engage with a responsive chatbot that provides accurate answers based on your document content.

- Document Upload and Processing: Upload your documents, and our system processes them into a searchable knowledge base.

- Vector Knowledge Base with RAG System: Utilize a sophisticated Retrieval-Augmented Generation (RAG) system that combines vector embeddings and document retrieval to deliver precise responses.

- Microservices Architecture: Our application uses a microservices approach, keeping the front-end and back-end isolated for greater flexibility and scalability.

- Session Management: Each user's interaction is managed through unique sessions, allowing for individualized queries and responses.

- Redis Cache with KNN: Used KNN algorithm with Redis cache to find similar questions already asked in session to get a faster response back.

Technologies Used

1. Flask: The back-end of our application is powered by Flask, a lightweight web framework that facilitates smooth interaction between the front-end and the chatbot service.

2. Ollama and Phi3 Models: These models form the core of our chatbot’s capabilities, enabling sophisticated language understanding and generation.

3. Chroma and Sentence Transformers: Chroma handles the vector database for document retrieval, while Sentence Transformers provide embeddings to compare and find relevant documents.

4. Redis: Used for caching responses to improve performance and reduce query times.

5. Docker: The entire application, including all its components, runs within Docker containers. This approach ensures consistent development and deployment environments, making it easy to manage dependencies and run the application locally.

6. Asynchronous Processing: Handles multiple user requests simultaneously, ensuring a smooth and efficient user experience.

How It Works

1. Document Upload: Start by uploading your documents through the front-end application. These documents are processed and stored in a vector knowledge base.

2. Knowledge Base Creation: Our system converts the document content into vector embeddings, making it searchable through the Chroma database.

3. Query Handling: When you pose a question, the chatbot uses the RAG system to retrieve relevant documents and generate a precise response.

4. Caching and Performance Optimization: Responses are cached in Redis to speed up future queries and enhance the overall performance of the system.

5. Session Management: Each session is tracked independently, ensuring personalized interactions and allowing multiple users to operate concurrently without interference.

What Can You Expect?

- Accurate Responses: The combination of advanced models and efficient retrieval systems ensures that you receive relevant and accurate answers.

- Flexible Integration: The microservices architecture allows for easy integration with various front-end frameworks and other back-end services.

- Enhanced Productivity: Quickly find and retrieve information from large volumes of documents, saving time and improving decision-making.

- Local Development: With all components running in Docker containers, you can easily set up and run the application on your local system.

Get Started

To explore the Local Copilot Chatbot Application, follow the setup instructions provided in our GitHub repository. Experience the power of a well-integrated chatbot system that understands your documents and delivers insightful answers at your fingertips.

System Used:

Medium power low RAM. However, if you can use 32GB RAM with Nvidia GPU and i7 CPU would be great and run after the first compilation.

GitHub Repo

https://github.com/dhirajpatra/ollama-langchain-streamlit

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

Local Copilot with SLM

 

Photo by ZHENYU LUO on Unsplash

What is a Copilot?

A copilot in the context of software development and artificial intelligence refers to an AI-powered assistant that helps users by providing suggestions, automating repetitive tasks, and enhancing productivity. These copilots can be integrated into various applications, such as code editors, customer service platforms, or personal productivity tools, to provide real-time assistance and insights.

Benefits of a Copilot

1. Increased Productivity:

   - Copilots can automate repetitive tasks, allowing users to focus on more complex and creative aspects of their work.

2. Real-time Assistance:

   - Provides instant suggestions and corrections, reducing the time spent on debugging and error correction.

3. Knowledge Enhancement:

   - Offers context-aware suggestions that help users learn and apply best practices, improving their skills over time.

4. Consistency:

   - Ensures consistent application of coding standards, style guides, and other best practices across projects.

What is a Local Copilot?

A local copilot is a variant of AI copilots that runs entirely on local compute resources rather than relying on cloud-based services. This setup involves deploying smaller, yet powerful, language models on local machines. 

Benefits of a Local Copilot

1. Privacy and Security:

   - Running models locally ensures that sensitive data does not leave the user's environment, mitigating risks associated with data breaches and unauthorized access.

2. Reduced Latency:

   - Local execution eliminates the need for data transmission to and from remote servers, resulting in faster response times.

3. Offline Functionality:

   - Local copilots can operate without an internet connection, making them reliable even in environments with limited or no internet access.

4. Cost Efficiency:

   - Avoids the costs associated with cloud-based services and data storage.

How to Implement a Local Copilot

Implementing a local copilot involves selecting a smaller language model, optimizing it to fit on local hardware, and integrating it with a framework like LangChain to build and run AI agents. Here are the high-level steps:

1. Model Selection:

   - Choose a language model that has 8 billion parameters or less.

2. Optimization with TensorRT:

   - Quantize and optimize the model using NVIDIA TensorRT-LLM to reduce its size and ensure it fits on your GPU.

3. Integration with LangChain:

   - Use the LangChain framework to build and manage the AI agents that will run locally.

4. Deployment:

   - Deploy the optimized model on local compute resources, ensuring it can handle the tasks required by the copilot.

By leveraging local compute resources and optimized language models, you can create a robust, privacy-conscious, and efficient local copilot to assist with various tasks and enhance productivity.

To develop a local copilot using smaller language models with LangChain and NVIDIA TensorRT-LLM, follow these steps:

Step-by-Step Guide

1. Set Up Your Environment

1. Install Required Libraries:

   Ensure you have Python installed and then install the necessary libraries:

   ```bash

   pip install langchain nvidia-pyindex nvidia-tensorrt

   ```

2. Prepare Your GPU:

   Make sure your system has an NVIDIA GPU and CUDA drivers installed. You'll also need TensorRT libraries which can be installed via the NVIDIA package index:

   ```bash

   sudo apt-get install nvidia-cuda-toolkit

   sudo apt-get install tensorrt

   ```

2. Model Preparation

1. Select a Smaller Language Model:

   Choose a language model that has 8 billion parameters or less. You can find many such models on platforms like Hugging Face.

2. Quantize the Model Using NVIDIA TensorRT-LLM:

   Use TensorRT to optimize and quantize the model to make it fit on your GPU.

   ```python

   import tensorrt as trt

   # Load your model here

   model = load_your_model_function()

   # Create a TensorRT engine

   builder = trt.Builder(trt.Logger(trt.Logger.WARNING))

   network = builder.create_network()

   parser = trt.OnnxParser(network, trt.Logger(trt.Logger.WARNING))

   with open("your_model.onnx", "rb") as f:

       parser.parse(f.read())

   engine = builder.build_cuda_engine(network)

   ```

3. Integrate with LangChain

1. Set Up LangChain:

   Create a LangChain project and configure it to use your local model.

   ```python

   from langchain import LangChain, LanguageModel

   # Assuming you have a function to load your TensorRT engine

   def load_trt_engine(engine_path):

       with open(engine_path, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.WARNING)) as runtime:

           return runtime.deserialize_cuda_engine(f.read())

   trt_engine = load_trt_engine("your_model.trt")

   class LocalLanguageModel(LanguageModel):

       def __init__(self, engine):

           self.engine = engine

       def predict(self, input_text):

           # Implement prediction logic using TensorRT engine

           pass

   local_model = LocalLanguageModel(trt_engine)

   ```

2. Develop the Agent:

   Use LangChain to develop your agent utilizing the local language model.

   ```python

   from langchain.agents import Agent

   class LocalCopilotAgent(Agent):

       def __init__(self, model):

           self.model = model

       def respond(self, input_text):

           return self.model.predict(input_text)

   agent = LocalCopilotAgent(local_model)

   ```

4. Run the Agent Locally

1. Execute the Agent:

   Run the agent locally to handle tasks as required.

   ```python

   if __name__ == "__main__":

       user_input = "Enter your input here"

       response = agent.respond(user_input)

       print(response)

   ```

By following these steps, you can develop a local copilot using LangChain and NVIDIA TensorRT-LLM. This approach ensures privacy and security by running the model on local compute resources.