https://github.com/syed007hassan/document-querying-with-vectordb

Document Querying with LLMs - Google PaLM API: Semantic Search With LLM Embeddings
https://github.com/syed007hassan/document-querying-with-vectordb

chroma document-retrieval embeddings palm-api pdf-encoding vectordb

Last synced: 6 months ago
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Document Querying with LLMs - Google PaLM API: Semantic Search With LLM Embeddings

• Host: GitHub
• URL: https://github.com/syed007hassan/document-querying-with-vectordb
• Owner: Syed007Hassan
• Created: 2023-09-03T20:22:09.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-14T17:59:09.000Z (almost 2 years ago)
• Last Synced: 2025-03-25T00:41:37.100Z (6 months ago)
• Topics: chroma, document-retrieval, embeddings, palm-api, pdf-encoding, vectordb
• Language: Python
• Homepage: https://medium.com/@syed007hassan/9ba73a935e5
• Size: 395 KB
• Stars: 9
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Document-Querying-With-VectorDB

- Follow [this guide](https://medium.com/@syed007hassan/9ba73a935e5) to understand how document retrieval and question-answering work with LLMs and how to apply LLMs to your domain-specific data.

![image](https://github.com/Syed007Hassan/Document-Querying-With-VectorDB/assets/104893311/fa7d631e-4f0c-42eb-bf02-c92691786b8c)

- Example API

![image](https://github.com/Syed007Hassan/Document-Querying-With-VectorDB/assets/104893311/6a2544e8-d847-44ba-8c84-5dac7ffbe6ac)