Introduction to AI Embedding Techniques
AI-native open-source embedding databases are revolutionizing the way developers build applications, particularly in the realm of large language models (LLMs). One such leading example is Chroma, which simplifies the process of integrating knowledge and skills into LLM-based applications. For those looking to enhance their projects, Chroma offers a plethora of tutorials and resources.
Cohere is another powerful tool that empowers developers to create AI-driven applications using a minimal amount of coding. Through its intuitive API, users can build diverse applications ranging from chatbots to sophisticated summarization tools. Explore the creative applications crafted by the community at lablab.ai!
Lastly, Stable Diffusion is an innovative generative model capable of creating high-resolution images with remarkable efficiency, making it an outstanding choice for various applications.
What Will We Achieve?
In this tutorial, we will harness the capabilities of Chroma DB and Cohere embeddings to bring book personas to life through the Stable Diffusion image generation model. So grab your favorite drink and prepare for a fascinating journey into AI art creation!
Learning Outcomes
- Understanding how to utilize Google Colab effectively.
- Gaining familiarity with the operations of Chroma, Cohere, and Stable Diffusion.
- Employing Cohere LLM for embedding large files.
- Storing embeddings using Chroma.
- Querying the database with Chroma.
- Generating images with the Stable Diffusion SDK.
Prerequisites
To proceed with Cohere embeddings, you will need to obtain an API key:
- Visit the Cohere website, and click TRY NOW in the top right corner.
- Log in or create an account.
- After logging in, navigate to the API Keys section on the left sidebar to copy your API key and store it securely.
Next, for Stable Diffusion:
- Go to Dream Studio and sign up for an account.
- Upon successful registration, access your API key from the dashboard.
No previous knowledge of Google Colab is necessary, as step-by-step guidance will be provided.
Getting Started
Create a New Project
Kick off by opening Google Colab and creating a new notebook. Navigate to File > New notebook, then rename it (e.g., Coach Chroma Stable Diffusion Tutorial).
Install Dependencies
Add a new code cell to install the necessary libraries:
!pip install chromadb cohere stable-diffusion
Run the code to install all libraries, which may take a few moments.
Import Dependencies
Make another code cell to import the required libraries:
import chromadb
import cohere
from stable_diffusion import StableDiffusion
Execute the code; any warning messages can typically be ignored.
Exporting Environment Variables
Create a new code cell and input your API keys as environment variables, then execute them to ensure they are correctly set up.
Creating the Prompt for Stable Diffusion
Upload the chosen book (e.g., Harry Potter and the Sorcerer's Stone) to Google Colab.
- Download the PDF version and upload it through the Files tab.
- Copy the path to the uploaded file for processing.
Generate chunks of the document to ensure the language model can parse it:
def split_document(document):
return [document[i:i + 1000] for i in range(0, len(document), 1000)]
Image Generation Using Stable Diffusion
With the prompt ready, initiate the Stable Diffusion SDK:
stable_diffusion_client = StableDiffusion(api_key="YOUR_API_KEY")
Now, by utilizing the prompt acquired from our previous queries, let's generate an image:
image = stable_diffusion_client.generate_image(prompt)
Finally, save the generated image using:
image.save("output_image.png")
Once saved, download the image to view the final artwork!
Conclusion
This tutorial encompassed various tools and libraries such as Chroma, Cohere embeddings, PyMuPDFLoader, Stability SDK, and demonstrated stepwise instructions on leveraging Chroma DB and Cohere embeddings for image generation. For further exploration, I highly recommend reviewing the documentation for Chroma, Cohere, and Stable Diffusion.
Thank you for participating in this tutorial, and if you have any questions, feel free to connect with me on LinkedIn or Twitter. Happy generating!
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