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Generative Q&A with LangChain, Gemini and Chroma

May 20, 2024 - alphasec.io
The article discusses the use of Gemini, a series of multimodal generative artificial intelligence (AI) models developed by Google, in conjunction with LangChain, an open-source framework for developing applications leveraging large language models, and Chroma, a lightweight embedding database. The author provides a detailed walkthrough of building a Streamlit app that uses these technologies to create a "Chat with PDF" function, which splits a PDF into individual pages, creates embeddings for each page using Google's embeddings API, and retrieves information from the vector database using a similarity search.

The article also explains how to deploy the Streamlit app on Railway, a modern app hosting platform. The author demonstrates the app's functionality by uploading Alphabet's latest quarterly earnings report and asking for specific information from the filing. The app successfully retrieves accurate information from the source document, showcasing the potential of the "Chat with PDF" capability.

Key takeaways:

  • The article discusses the use of Google's Gemini, a series of multimodal generative AI models, in conjunction with LangChain and Chroma for various applications such as chatbots, text summarisation, and data generation.
  • LangChain is an open-source framework that aids in the development of applications leveraging large language models, while Chroma is an open-source, lightweight embedding database used to store embeddings locally.
  • The article provides a detailed walkthrough of building a Streamlit app that allows users to "chat" with a PDF document, using Google's Gemini models, LangChain, and Chroma.
  • The Streamlit app can be deployed on Railway, a modern app hosting platform, and the article provides a step-by-step guide on how to do this.
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