Enhance Your Chatbot Knowledge Base with Anthropic's Claude Model and Chroma

Introduction

Anthropic, a leading global AI company, has recently introduced the revolutionary Claude Model. This innovative AI tool is being adopted by major tech players like Notion and Quora, showcasing its remarkable versatility in various applications, including conversations, text processing, and coding. In this tutorial, we will explore the unique features of the Claude model and how to leverage its capabilities for enhanced chatbot functionality.

The Power of ChromaDB and Embeddings

Embeddings play a pivotal role in natural language processing and machine learning by capturing semantic meaning through vector representations. ChromaDB, an open-source embedding database, allows users to store and query embeddings effectively. This section delves into the dynamic world of embeddings and how ChromaDB can improve text search and retrieval.

Prerequisites

• Basic knowledge of Python
• Access to Anthropic's Claude API
• Access to OpenAI's API (for embedding function)
• A Chroma database set up

Outline

1. Initializing the Project
2. Setting Up the Required Libraries
3. Writing the Project Files
4. Testing the Basic Chatbot without Context Awareness
5. Utilizing Claude's Large Context Window
6. Testing the Chatbot with Context Awareness
7. Providing Contextual Information to Claude
8. Evaluating the Enhanced Chatbot
9. Building a Knowledge Base for Claude
10. Testing Claude's Final Form

1. Initializing the Project

To start, we will create a new directory for our project called chroma-claude. This helps to maintain an organized workspace. Next, we will set up a virtual environment, which allows us to keep our project dependencies separate from the global Python environment, thereby avoiding conflicts across different projects.

To create the virtual environment, use the following command in your terminal:

python -m venv env

Activate the environment using:

• For Windows: . \env\Scripts\activate
• For Unix-based systems: source env/bin/activate

2. Setting Up the Required Libraries

We will install the necessary libraries using pip. This includes:

• chromadb: For storing and querying embeddings.
• anthropic: To interact with Anthropic's Claude model.
• halo: For loading indicators during requests.

Run the following command to install the libraries:

pip install chromadb anthropic halo

3. Writing the Project Files

Next, open your code editor and create a new file named main.py. In this file, we will import necessary libraries and define our chatbot functionality.

Step 1: Import Necessary Libraries

import os
from dotenv import load_dotenv
from anthropic import ChatCompletion
from halo import Halo

load_dotenv()

Step 2: Load Environment Variables

Use the following code to load sensitive data such as your API key:

API_KEY = os.getenv('CLAUDE_KEY')

Step 3: Define Response Generation Function

def generate_response(messages):
    spinner = Halo(text='Generating response...', spinner='dots')
    spinner.start()
    # Initialize Claude client and send request
    client = ChatCompletion(api_key=API_KEY)
    response = client.create(messages=messages)
    spinner.stop()
    return response['choices'][0]['message']['content']

Step 4: Define Main Function and Handle User Input

def main():
    messages = [] # Initialize conversation history
    while True:
        user_input = input('You: ')
        if user_input.lower() == 'quit':
            break
        messages.append({'role': 'user', 'content': user_input})
        response = generate_response(messages)
        messages.append({'role': 'assistant', 'content': response})
        print(f'Claude: {response}')

4. Testing the Basic Chatbot

Run the script using the command python main.py. Type messages to interact with the bot. At this stage, the bot does not remember prior messages, illustrating a lack of context awareness.

5. Making Use of Claude's Large Context Window

To enhance functionality, modify the code to maintain conversation history by appending user inputs and chatbot responses to the messages list. Increase max_tokens_to_sample to allow for longer conversations, acknowledging that responses may vary based on previous interactions.

6. Testing the Chatbot with Context Awareness

After modifying the code, test the chatbot by discussing specific topics, such as the video game Dota2. This will illustrate Claude's ability to reference earlier parts of the conversation.

7. Providing Contextual Information to Claude

To enhance Claude's responses, feed it contextually relevant information, such as recent updates or wiki data. Read in this data and use it to form the context for Claude's responses, ensuring richer interactions.

8. Evaluating the Enhanced Chatbot

Test the bot’s knowledge of provided supplements by querying specific details about Dota2 patches. Monitor the token usage to ensure it stays within limits set by Claude's architecture.

9. Building a Knowledge Base for Claude

Establish a structured knowledge base using ChromaDB to enable Claude to access extensive, relevant data as needed. This will allow for more accurate, context-aware responses to complex inquiries.

10. Testing Claude's Final Form

Conduct further tests by asking specific and complex questions. Claude's responses will illustrate the integration of context-awareness and the knowledge base, showcasing its capabilities.

Conclusion

Through this detailed guide, we have demonstrated how to effectively use Anthropic's Claude model and integrate it with ChromaDB to create a powerful chatbot. By providing context and leveraging a knowledge base, Claude can deliver accurate and engaging responses, paving the way for innovative AI applications.