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

LangChain Memory Store

To add bigger memory space with LangChain, you can leverage the various memory modules that LangChain provides. Here's a brief guide on how to do it:

1. Use a Larger Memory Backend

LangChain allows you to use different types of memory backends. For larger memory capacity, you can use backends like databases or cloud storage. For instance, using a vector database like Pinecone or FAISS can help manage larger context effectively.

2. Implement a Custom Memory Class

You can implement your own memory class to handle larger context. Here’s an example of how to create a custom memory class:

```python

from langchain.memory import BaseMemory

class CustomMemory(BaseMemory):

    def __init__(self):

        self.memory = []

    def add_to_memory(self, message):

        self.memory.append(message)

    

    def get_memory(self):

        return self.memory

    def clear_memory(self):

        self.memory = []

```

3. Configure Memory in LangChain

When setting up the chain, you can specify the memory class you want to use:

```python

from langchain import LLMChain

from langchain.llms import OpenAI

# Create an instance of your custom memory class

custom_memory = CustomMemory()

# Initialize the language model

llm = OpenAI(api_key='your_openai_api_key')

# Create the chain with the custom memory

chain = LLMChain(llm=llm, memory=custom_memory)

# Add messages to memory

chain.memory.add_to_memory("Previous context or message")

# Retrieve memory

context = chain.memory.get_memory()

```

4. Use External Storage

For even larger memory, consider using external storage solutions like a database (e.g., PostgreSQL, MongoDB) or cloud storage (e.g., AWS S3, Google Cloud Storage). You can extend the memory class to interact with these external storage systems.

Example with SQLite:

```python

import sqlite3

from langchain.memory import BaseMemory

class SQLiteMemory(BaseMemory):

    def __init__(self, db_path):

        self.conn = sqlite3.connect(db_path)

        self.cursor = self.conn.cursor()

        self.cursor.execute('''CREATE TABLE IF NOT EXISTS memory (message TEXT)''')

    def add_to_memory(self, message):

        self.cursor.execute("INSERT INTO memory (message) VALUES (?)", (message,))

        self.conn.commit()

    

    def get_memory(self):

        self.cursor.execute("SELECT message FROM memory")

        return [row[0] for row in self.cursor.fetchall()]

    def clear_memory(self):

        self.cursor.execute("DELETE FROM memory")

        self.conn.commit()

        self.conn.close()

# Initialize SQLite memory

sqlite_memory = SQLiteMemory('memory.db')

# Create the chain with SQLite memory

chain = LLMChain(llm=llm, memory=sqlite_memory)

```

By using these methods, you can effectively increase the memory capacity for your LangChain application, ensuring it can handle and recall larger contexts across interactions.

Develop a Customize LLM Agent

 

Photo by MART PRODUCTION at pexel

If you’re interested in customizing an agent for a specific task, one way to do this is to fine-tune the models on your dataset. 

For preparing dataset you can see this article.

1. Curate the Dataset

- Using NeMo Curator:

  - Install NVIDIA NeMo: `pip install nemo_toolkit`

  - Use NeMo Curator to prepare your dataset according to your specific requirements.

2. Fine-Tune the Model

- Using NeMo Framework:

  1. Setup NeMo:

     ```python

     import nemo

     import nemo.collections.nlp as nemo_nlp

     ```

  2. Prepare the Data:

     ```python

     # Example to prepare dataset

     from nemo.collections.nlp.data.text_to_text import TextToTextDataset

     dataset = TextToTextDataset(file_path="path_to_your_dataset")

     ```

  3. Fine-Tune the Model:

     ```python

     model = nemo_nlp.models.NLPModel.from_pretrained("pretrained_model_name")

     model.train(dataset)

     model.save_to("path_to_save_fine_tuned_model")

     ```

- Using HuggingFace Transformers:

  1. Install Transformers:

     ```sh

     pip install transformers

     ```

  2. Load Pretrained Model:

     ```python

     from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, Trainer, TrainingArguments

     model_name = "pretrained_model_name"

     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

     tokenizer = AutoTokenizer.from_pretrained(model_name)

     ```

  3. Prepare the Data:

     ```python

     from datasets import load_dataset

     dataset = load_dataset("path_to_your_dataset")

     tokenized_dataset = dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding=True), batched=True)

     ```

  4. Fine-Tune the Model:

     ```python

     training_args = TrainingArguments(

         output_dir="./results",

         evaluation_strategy="epoch",

         learning_rate=2e-5,

         per_device_train_batch_size=16,

         per_device_eval_batch_size=16,

         num_train_epochs=3,

         weight_decay=0.01,

     )

     trainer = Trainer(

         model=model,

         args=training_args,

         train_dataset=tokenized_dataset['train'],

         eval_dataset=tokenized_dataset['validation']

     )

     trainer.train()

     model.save_pretrained("path_to_save_fine_tuned_model")

     tokenizer.save_pretrained("path_to_save_tokenizer")

     ```

3. Develop an Agent with LangChain

1. Install LangChain:

   ```sh

   pip install langchain

   ```

2. Load the Fine-Tuned Model:

   ```python

   from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

   from langchain.llms import HuggingFaceLLM

   model = AutoModelForSeq2SeqLM.from_pretrained("path_to_save_fine_tuned_model")

   tokenizer = AutoTokenizer.from_pretrained("path_to_save_tokenizer")

   llm = HuggingFaceLLM(model=model, tokenizer=tokenizer)

   ```

3. Define the Agent:

   ```python

   from langchain.agents import Agent

   agent = Agent(

       llm=llm,

       tools=["tool1", "tool2"],  # Specify the tools your agent will use

       memory="memory_option",    # Specify memory options if any

   )

   ```

4. Use the Agent:

   ```python

   response = agent("Your prompt here")

   print(response)

   ```

This process guides you through curating the dataset, fine-tuning the model, and integrating it into the LangChain framework to develop a custom agent.

You can get more details guide links following.

https://huggingface.co/docs/transformers/en/training

https://github.com/NVIDIA/NeMo-Curator/tree/main/examples

https://docs.smith.langchain.com/old/cookbook/fine-tuning-examples

Sentiment Analysis with LangChain and LLM

 

Here's a quick guide on how to perform sentiment analysis and other tasks using LangChain, LLM (Large Language Models), NLP (Natural Language Processing), and statistical analytics.

Sentiment Analysis with LangChain and LLM

1. Install Required Libraries:

   ```bash

   pip install langchain openai transformers

   ```

2. Set Up OpenAI API:

   ```python

   import openai

   openai.api_key = 'your_openai_api_key'

   ```

3. LangChain for Sentiment Analysis:

   ```python

   from langchain.llms import OpenAI

   from langchain import Chain

   # Initialize OpenAI LLM

   llm = OpenAI(model="text-davinci-003")

   # Define a function for sentiment analysis

   def analyze_sentiment(text):

       response = llm.completion(

           prompt=f"Analyze the sentiment of the following text: {text}",

           max_tokens=60

       )

       return response.choices[0].text.strip()

   # Example usage

   text = "I love the new design of the website!"

   sentiment = analyze_sentiment(text)

   print(f"Sentiment: {sentiment}")

   ```

Additional NLP Tasks with LangChain and LLM

Text Summarization

```python

def summarize_text(text):

    response = llm.completion(

        prompt=f"Summarize the following text: {text}",

        max_tokens=150

    )

    return response.choices[0].text.strip()

# Example usage

text = "Your detailed article or document here."

summary = summarize_text(text)

print(f"Summary: {summary}")

```

Named Entity Recognition (NER)

```python

def extract_entities(text):

    response = llm.completion(

        prompt=f"Extract the named entities from the following text: {text}",

        max_tokens=100

    )

    return response.choices[0].text.strip()

# Example usage

text = "OpenAI, founded in San Francisco, is a leading AI research institute."

entities = extract_entities(text)

print(f"Entities: {entities}")

```

Statistical Analytics with NLP

Word Frequency Analysis

```python

from collections import Counter

import re

def word_frequency_analysis(text):

    words = re.findall(r'\w+', text.lower())

    frequency = Counter(words)

    return frequency

# Example usage

text = "This is a sample text with several words. This text is for testing."

frequency = word_frequency_analysis(text)

print(f"Word Frequency: {frequency}")

```

Sentiment Score Aggregation

```python

def sentiment_score(text):

    sentiment = analyze_sentiment(text)

    if "positive" in sentiment.lower():

        return 1

    elif "negative" in sentiment.lower():

        return -1

    else:

        return 0

# Example usage

texts = ["I love this!", "This is bad.", "It's okay."]

scores = [sentiment_score(t) for t in texts]

average_score = sum(scores) / len(scores)

print(f"Average Sentiment Score: {average_score}")

```

For more advanced uses and customization, refer to the [LangChain documentation](https://langchain.com/docs) and the [OpenAI API documentation](https://beta.openai.com/docs/).

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.

Steps to Create Bot

 

Photo by Kindel Media at pexel

If you want to develop a ChatBot with Azure and OpenAi in a few simple steps. You can follow the steps below.

1. Design and Requirements Gathering:

   - Define the purpose and functionalities of the chatbot.

   - Gather requirements for integration with Azure, OpenAI, Langchain, Promo Engineering, Document Intelligence System, KNN-based question similarities with Redis, vector database, and Langchain memory.

2. Azure Setup:

   - Create an Azure account if you don't have one.

   - Set up Azure Functions for serverless architecture.

   - Request access to Azure OpenAI Service.

3. OpenAI Integration:

   - Obtain API access to OpenAI.

   - Integrate OpenAI's GPT models for natural language understanding and generation into your chatbot.

4. Langchain Integration:

   - Explore Langchain's capabilities for language processing and understanding.

   - Integrate Langchain into your chatbot for multilingual support or specialized language tasks.

   - Implement Langchain memory for retaining context across conversations.

5. Promo Engineering Integration:

   - Understand Promo Engineering's features for promotional content generation and analysis.

   - Integrate Promo Engineering into your chatbot for creating and optimizing promotional messages.

6. Document Intelligence System Integration:

   - Investigate the Document Intelligence System's functionalities for document processing and analysis.

   - Integrate Document Intelligence System for tasks such as extracting information from documents or providing insights.

7. Development of Chatbot Logic:

   - Develop the core logic of your chatbot using Python.

   - Utilize Azure Functions for serverless execution of the chatbot logic.

   - Implement KNN-based question similarities using Redis for efficient retrieval and comparison of similar questions.

8. Integration Testing:

   - Test the integrated components of the chatbot together to ensure seamless functionality.

9. Azure AI Studio Deployment:

   - Deploy LLM model in Azure AI Studio.

   - Create an Azure AI Search service.

   - Connect Azure AI Search service to Azure AI Studio.

   - Add data to the chatbot in the Playground.

   - Add data using various methods like uploading files or programmatically creating an index.

   - Use Azure AI Search service to index documents by creating an index and defining fields for document properties.

10. Deployment and Monitoring:

   - Deploy the chatbot as an App.

   - Navigate to the App in Azure.

   - Set up monitoring and logging to track performance and user interactions.

11. Continuous Improvement:

   - Collect user feedback and analyze chatbot interactions.

   - Iterate on the chatbot's design and functionality to enhance user experience and performance.

https://github.com/Azure-Samples/azureai-samples

Integrate and Optimize Large Language Model (LLM) Framework by Python

Integrating and optimizing Large Language Model (LLM) frameworks with various prompting strategies in Python requires careful consideration of the specific libraries and your desired use case. 

1. RAG

• RAG (Retrieval-Augmented Generation) is a technique that uses a retrieval model to retrieve relevant documents from a knowledge base, and then uses a generative model to generate text based on the retrieved documents.
• To integrate RAG with an LLM framework, you can use the rag module in LangChain. This module provides a simple interface for using RAG with different LLMs.
• To optimize RAG, you can use a variety of techniques, such as:
  • Using a larger knowledge base
  • Using a more powerful retrieval model
  • Using a more powerful generative model
  • Tuning the hyperparameters of the RAG model

2. ReAct Prompting

• ReAct prompting is a technique that uses prompts to guide the LLM towards generating the desired output.
• To integrate ReAct prompting with an LLM framework, you can use the react module in LangChain. This module provides a simple interface for using ReAct prompting with different LLMs.
• To optimize ReAct prompting, you can use a variety of techniques, such as:
  • Using more informative prompts
  • Using longer prompts
  • Using prompts that are more specific to the desired output

3. Function Calling

• Function Calling is a technique that allows you to call functions from within the LLM.
• To integrate Function Calling with an LLM framework, you can use the function_calling module in LangChain. This module provides a simple interface for using Function Calling with different LLMs.
• To optimize Function Calling, you can use a variety of techniques, such as:
  • Using more efficient functions
  • Using functions that are more specific to the desired output
  • Caching the results of functions

Here's a breakdown of how you might approach it:

1. Choosing Frameworks:

• LangChain: This framework focuses on building applications powered by LLMs. It excels in managing prompts, responses, and data awareness.
• AutoGPT: This library simplifies interacting with OpenAI's GPT-3 models through an easy-to-use API.
• LlamaIndex: This is a research project by Google AI, not a readily available library. However, it explores efficient retrieval and summarization of factual information from large datasets.

Integration Strategies:

a) LangChain with AutoGPT:

1. Install Libraries:
   Bash

   pip install langchain autogpt
   

2. Import Libraries:
   Python

   import langchain
   from langchain.llms import AutoGPT
   

3. Configure AutoGPT Model:
   Python

   model = AutoGPT(temperature=0.7, max_length=150)  # Adjust parameters as needed
   

4. Create a LangChain Pipeline:
   Python

   @langchain.llms.llm_call
   def query_llm(prompt):
       response = model.run(prompt)
       return response['choices'][0]['text']  # Extract response text
   
   pipeline = langchain.Pipeline(query_llm)
   

5. Use the Pipeline:
   Python

   prompt = "What is the capital of France?"
   answer = pipeline.run(prompt)
   print(answer)  # Output: Paris
   

b) Utilizing RAG (Retrieval-Augmented Generation):

• RAG involves retrieving relevant information from external sources before generating text. You'll need an additional library like Haystack for information retrieval.

c) ReAct Prompting (Reasoning with Activated Conditioning Tokens):

• This strategy involves adding special tokens to the prompt to guide the LLM towards specific reasoning processes. Specific libraries might be under development for this approach.

d) Function Calling:

• While LLMs are not designed for direct function calls, you can achieve a similar effect by crafting prompts that guide the LLM towards completing specific actions. For example, prompting to "Summarize the following article" or "Write a poem in the style of Shakespeare."

2. Optimization Tips:

• Fine-tune Prompts: Experiment with different prompts to achieve the desired outcome and reduce the number of LLM calls needed.
• Batch Processing: If you have multiple prompts, consider batching them together for efficiency when using frameworks like LangChain.
• Cloud Resources: Consider using cloud-based LLM services for access to powerful hardware and potentially lower costs compared to running models locally.

3. Additional Notes:

• Be aware of potential limitations of each framework and choose the one that aligns with your specific needs.
• Explore the documentation and tutorials provided by each library for detailed guidance and advanced functionalities.
• Remember that responsible LLM usage involves cost considerations, potential biases in models, and proper interpretation of generated text.

Python code

import langchain

# Create a RAG model

rag_model = langchain.llms.RAG(

    retrieval_model=langchain.retrieval.DensePassageRetrieval(

        knowledge_base="wikipedia"

    ),

    generative_model=langchain.llms.GPTNeo()

)

# Create a ReAct prompt

react_prompt = "Write a poem about a cat."

# Create a function that calls the RAG model

def generate_poem(prompt):

    return rag_model.generate(prompt)

# Call the function to generate a poem

poem = generate_poem(react_prompt)

# Print the poem

print(poem)

This provides a starting point for integrating and optimizing LLMs with prompting strategies in Python. Remember to adapt and enhance this approach based on your specific use case and chosen libraries.