Introduction
To store and search over unstructured data, a widely adopted approach is embedding data into vectors, stored and indexed in vector databases. The promptflow-vectordb SDK is designed for PromptFlow, provides essential tools for vector similarity search within popular vector databases, including FAISS, Qdrant, Azure Congnitive Search, and more.
1 maintainer
Metadata
1 maintainer
Release files for gega-promptflow-vectordb 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gega_promptflow_vectordb-0.0.2.tar.gz | 57.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gega_promptflow_vectordb-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 161.1 kB
Release files / gega_promptflow_vectordb-0.0.2.tar.gz
| Download URL | gega_promptflow_vectordb-0.0.2.tar.gz |
|---|---|
| Size | 57.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
0583bee77503410efb559d0309903508ce715a2343a9530fe580546364fa28f4
|
|
BLAKE2b-256 checksum How to use checksums |
81b1c80e07552cbb77e3c4856e6eb3d303912df80d09659a729eef8dcead9059
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
|
Release files / gega_promptflow_vectordb-0.0.2-py3-none-any.whl
| Download URL | gega_promptflow_vectordb-0.0.2-py3-none-any.whl |
|---|---|
| Size | 103.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c3c6d139b08fb1d4462e33c294c15cdb59e6b061da8c615a41235a4e9fc048c1
|
|
BLAKE2b-256 checksum How to use checksums |
c914384fccb56638f3316f30e3ec2bedb589955c67f6569551edb561b284ad25
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.9.18
|
