Picking a vector database: a comparison and guide for 2023
Peter Hagen and added
There are numerous integrations for vector storage. These include Alibaba Cloud OpenSearch, AnalyticDB for PostgreSQL, Meta AI’s Annoy library for Approximate Nearest Neighbor (ANN) search, Cassandra, Chroma, Elasticsearch, Facebook AI Similarity Search (Faiss), MongoDB Atlas Vector Search, PGVector as a vector similarity search for Postgres, Pinec
... See moreBen Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
- Implement both full-text search and vector search
- Test the performance of each method on your specific use case
- Consider using a single database system to store both types of
Systematically Improving Your RAG - jxnl.co
Nicolay Gerold added
Vector databases can be used to store and serve machine learning models and their corresponding embeddings. The primary application is similarity search (also semantic search),
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Vector databases are widely used in NLP tasks such as sentiment analysis, text classification, and semantic search. By representing text as vector embeddings, it becomes easier to compare and analyze textual data.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
Your ML team (or maybe OpenAI) comes out with a new version of their... See more
6 Hard Problems Scaling Vector Search
Nicolay Gerold added