Building a chatbot for your data and pipelines is challenging because they are often too large (e.g., 1,000+ tables) to fit within the LLM context window. Cocoon addresses this by creating a RAG layer for your data and pipelines. With Cocoon's RAG, we offer a cursor-style chatbot for your data tasks.

• Live Demo for Data Pipeline RAG (Enter your question and we will generate response live)

• Live Demo for Data Warehouse RAG (Enter your question and we will generate response live)

• Learn more about all the features

• 👉 Try this Google Collab Notebook for Data Warehouse RAG
• 👉 Try this Google Collab Notebook for Data Pipeline RAG

Cocoon is available on PyPI. Create a virtual env and then:

pip install cocoon_data -U

To get started, you need to connect to

• LLMs (e.g., GPT-4, Claude-3, Gemini-Ultra, or your local LLMs)
• Data Warehouses (e.g., Snowflake, Big Query, Duckdb...)

from cocoon_data import *

# if you use Open AI GPT-4
openai.api_key  = 'xycabc'

# if you use Snowflake
con = snowflake.connector.connect(...)

query_widget, cocoon_workflow = create_cocoon_workflow(con)

# a helper widget to query your data warehouse
query_widget.display()

# the main panel to interact with Cocoon
cocoon_workflow.start()

🎉 You shall see the following on a notebook:

We also offer a browser UI, only for the chat over RAG feature. Simply:

pip install cocoon_data -U
cocoon_data

You shall see