https://github.com/benja1972/vectordb
Vector databases experimentation for the Knowledge Graphs and sentenceBERT embedding
https://github.com/benja1972/vectordb
knowledge-graph neo4j semantic-search streamlit vectordb vectorstore
Last synced: 6 months ago
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Vector databases experimentation for the Knowledge Graphs and sentenceBERT embedding
- Host: GitHub
- URL: https://github.com/benja1972/vectordb
- Owner: Benja1972
- Created: 2023-06-05T13:06:35.000Z (over 2 years ago)
- Default Branch: develop
- Last Pushed: 2023-06-05T13:14:34.000Z (over 2 years ago)
- Last Synced: 2025-03-24T08:41:11.584Z (7 months ago)
- Topics: knowledge-graph, neo4j, semantic-search, streamlit, vectordb, vectorstore
- Language: Python
- Homepage:
- Size: 303 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Vector Databases experiments
## Overview
[List of vectorstores](https://python.langchain.com/en/latest/modules/indexes/vectorstores.html)
We will experiment with two databases
### Chroma
[Chroma](https://docs.trychroma.com/) is the open-source embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. Chroma gives you the tools to:
- store embeddings and their metadata
- embed documents and queries
- search embeddings
Chroma prioritizes:
- simplicity and developer productivity
- analysis on top of search
- it also happens to be very quick
### Qdrant
[Qdrant](https://qdrant.tech/documentation/) is powering the next generation of AI applications with advanced and high-performant vector similarity search technology.
Qdrant is a vector database & vector similarity search engine. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
- Easy to Use API. Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilize ready-made client for Python or other programming languages with additional functionality.
- Fast and Accurate. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results.
- Filtrable. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.
## Use-cases
- [Chroma DB for taxonomy embedding](Chroma_taxonomy.md)
- [Chroma DB for Matching KG](Chroma_kg.md)
- [Qdrant DB for Matching KG](Qdrant_kg.md)