You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1e892a4ae0 | 4 months ago | |
---|---|---|
.. | ||
PolarDB | 8 months ago | |
SingleStoreDB | 8 months ago | |
analyticdb | 8 months ago | |
azuresearch | 8 months ago | |
cassandra_astradb | 4 months ago | |
chroma | 8 months ago | |
deeplake | 8 months ago | |
elasticsearch | 8 months ago | |
hologres | 8 months ago | |
kusto | 8 months ago | |
milvus | 8 months ago | |
mongodb_atlas | 8 months ago | |
myscale | 8 months ago | |
neon | 8 months ago | |
pinecone | 8 months ago | |
qdrant | 4 months ago | |
redis | 8 months ago | |
supabase | 8 months ago | |
tair | 8 months ago | |
typesense | 8 months ago | |
weaviate | 8 months ago | |
zilliz | 8 months ago | |
README.md | 5 months ago |
README.md
Vector Databases
This section of the OpenAI Cookbook showcases many of the vector databases available to support your semantic search use cases.
Vector databases can be a great accompaniment for knowledge retrieval applications, which reduce hallucinations by providing the LLM with the relevant context to answer questions.
Each provider has their own named directory, with a standard notebook to introduce you to using our API with their product, and any supplementary notebooks they choose to add to showcase their functionality.