vectorboard 0.0.1

Hyperparameter optimisation for your LLM Emebddings.

💛 Vectorboard - alpha 0.0.1

Embeddings Optimization and Eval Framework for RAG/LLM Applications

Find the best hyperparameters for Embedding your data in RAG Pipeline

TL;DR

1. Instal vectorboard

pip install vectorboard

2. Create a grid search of parameters you want to experiment. For example:

param_grid = {
    "chunk_size": [50, 300, 500],
    "vector_store": [FAISS],
    "embeddings": [OpenAIEmbeddings(), HuggingFaceEmbeddings()],
}

3. Run the search using a GridSearch() (more search types upcoming).

from vectorboard.search import GridSearch

# Create a GridSearch with the chain you'd like to try.
grid_search = GridSearch(chain=RetrievalQA)

# Use a document loader
grid_search.create_experiments(loader, param_grid=param_grid)
grid_search.run(eval_queries=eval_queries)
grid_search.results()

Step by step overview of the example

Import GridSearch() from vectorboard.search

from vectorboard.search import GridSearch

Create a dict with parameters and steps you want to search over.

param_grid = {
    "chunk_size": [50, 300, 500],
    "vector_store": [FAISS],
    "embeddings": [OpenAIEmbeddings(), HuggingFaceEmbeddings()],
}

If the parameter is not one of the simple types (int, str, ...), you need to import the Class. E.g. to try different Embedding algorithms, first import them (we use langchain for simplicity):

from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings

Initilize a Vectorboard object with the chain you want to run your experiment. Currently supporting RetrivalQA. More chains and custom chains are on the roadmap.

search = GridSearch(chain=RetrievalQA)

Import a loader relevant to your data and include it as a parameter to .create_experiments().

from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("recycling.pdf") # For example
search.create_experiments(param_grid=param_grid, loader=loader)

If you already loaded your data or have it available, use:

search.create_experiments(param_grid=param_grid, documents=YOUR_DOCS)

Define eval queries and run the experiments:

eval_queries = [
    "what percentage of waste is recyvled into materials in 2022?",
    # ...
]
search.run(eval_queries=eval_queries)

Finally, view the results in a Gradio app using .results() method. To get a publicly available link to share with your team, set the share=True paramater.

search.results(share=True)

Overview and Core concepts

RAG (Retreival Augmentation Generation) is great but it has a huge emphasis on perfect embeddings.

But it has challenges. It's hard to find the right

Current status

• Built on top of 🦜⛓️Langchain
• Using Gradio for the final result page (with shareable links)

Currently supported steps and parameters

1. Embeddings
2. Text and Document transformers
3. Vector Databases

Roadmap

• Support more types of Search.
• Support more chains. LLMChain and custom chains in progress.
• Add async support to run Experiments() in parallel.
• TS/JS support.
• Add Eval tools and metrics.

Have a special feature request? Send your feedback/suggestion on our Discord community: