Unstract Adapters
Project description
Unstract Adapters
This is Unstract's python package which helps to configure to a number of different LLMs, Embeddings and VectorDBs.
LLMs
The following LLMs are supported:
LLM | Version |
---|---|
OpenAI | 1.3.9 |
Azure OpenAI | 1.3.9 |
Anthropic | 0.7.8 |
PaLM | 0.3.1 |
Replicate | 0.22.0 |
AnyScale | 0.5.165 |
Mistral | 0.0.8 |
Embeddings
The following Embeddings are supported:
Embedding | Version |
---|---|
OpenAI | 1.3.9 |
Azure OpenAI | 1.3.9 |
Qdrant FastEmbed | 0.1.3 |
HuggingFace | 0.0.1 |
PaLM | 0.3.1 |
VectorDBs
The following VectorDBs are supported:
Vector DB | Version |
---|---|
Milvus | 2.3.4 |
Pinecone | 2.2.4 |
Postgres | 0.2.4 |
Qdrant | 1.7.0 |
Supabase | 2.2.1 |
Weaviate | 3.25.3 |
Installation
Local Development
To get started with local development,
- Create and source a virtual environment if you haven't already following these steps.
- If you're using Mac, install the below library needed for PyMSSQL
brew install pkg-config freetds
- Install the required dependencies with
pdm install
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
unstract_adapters-0.0.2.tar.gz
(16.1 kB
view details)
Built Distribution
File details
Details for the file unstract_adapters-0.0.2.tar.gz
.
File metadata
- Download URL: unstract_adapters-0.0.2.tar.gz
- Upload date:
- Size: 16.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: pdm/2.10.4 CPython/3.9.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 52e64952a54d9d296cc750048a7df45534e4c6f2f97686e1dd0f25b95f9e6de0 |
|
MD5 | ae30b8629cee55d90d19ffc345a843ad |
|
BLAKE2b-256 | 0520149dc62b7f87f32ec68725f115ff5dbb67db615ffe9e65932a8ba6a1ab5b |
Provenance
File details
Details for the file unstract_adapters-0.0.2-py3-none-any.whl
.
File metadata
- Download URL: unstract_adapters-0.0.2-py3-none-any.whl
- Upload date:
- Size: 55.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: pdm/2.10.4 CPython/3.9.6
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8cf1a596746b0966f4448b8804939ca7e45e08cd2875c106d746a5497aedc065 |
|
MD5 | e323460d00e99e60ad194d905f52e132 |
|
BLAKE2b-256 | ad002775ee2cb53770809dbd7e39191d1e929270f94a076229efd5f791464015 |