An integration of Qdrant ANN vector database backend with txtai
Project description
qdrant-txtai
txtai simplifies building AI-powered semantic search applications using Transformers. It leverages the neural embeddings and their properties to encode high-dimensional data in a lower-dimensional space and allows to find similar objects based on their embeddings' proximity.
Implementing such applications in real-world use cases requires storing the embeddings efficiently, namely in a vector database like Qdrant. It offers not only a powerful engine for neural search, but also allows setting up a whole cluster if your data does not fit a single machine anymore. It is production-grade and can be launched easily with Docker.
Combining the easiness of txtai with Qdrant's performance enables you to build production-ready semantic search applications way faster than before.
Installation
The library might be installed with pip as following:
pip install qdrant-txtai
Usage
Running the txtai application with Qdrant as vector storage requires launching a Qdrant instance. That might be done easily with Docker:
docker run -p 6333:6333 -p:6334:6334 qdrant/qdrant:latest
Running the txtai application might be done either programmatically or by providing configuration in a YAML file.
Programmatically
from txtai.embeddings import Embeddings
embeddings = Embeddings({
"path": "sentence-transformers/all-MiniLM-L6-v2",
"backend": "qdrant_txtai.ann.qdrant.Qdrant",
})
embeddings.index([(0, "Correct", None), (1, "Not what we hoped", None)])
result = embeddings.search("positive", 1)
print(result)
Via YAML configuration
# app.yml
embeddings:
path: sentence-transformers/all-MiniLM-L6-v2
backend: qdrant_txtai.ann.qdrant.Qdrant
CONFIG=app.yml uvicorn "txtai.api:app"
curl -X GET "http://localhost:8000/search?query=positive"
Configuration properties
qdrant-txtai allows you to configure both the connection details and some internal properties of the vector collection, which may impact both speed and accuracy. Please refer to Qdrant docs if you are interested in the meaning of each property.
The example below presents all the available options, if we connect to Qdrant server:
embeddings:
path: sentence-transformers/all-MiniLM-L6-v2
backend: qdrant_txtai.ann.qdrant.Qdrant
metric: l2 # allowed values: l1 / l2 / cosine / ip
qdrant:
url: qdrant.host
port: 6333
grpc_port: 6334
prefer_grpc: true
collection: CustomCollectionName
https: true # for Qdrant Cloud
api_key: XYZ # for Qdrant Cloud
search_params:
hnsw_ef: 100
Local in-memory/disk-persisted mode
Qdrant Python client, from version 1.1.1, supports local in-memory/disk-persisted mode. That's a good choice for any test scenarios and quick experiments in which you do not plan to store lots of vectors. In such a case, spinning a Docker container might be even not required.
In-memory storage
In case you want to have transient storage, for example, in the case of automated tests launched during your CI/CD pipeline, using Qdrant Local mode with in-memory storage might be a preferred option.
embeddings:
path: sentence-transformers/all-MiniLM-L6-v2
backend: qdrant_txtai.ann.qdrant.Qdrant
metric: l2 # allowed values: l1 / l2 / cosine / ip
qdrant:
location: ':memory:'
prefer_grpc: true
On disk storage
However, if you prefer to keep the vectors between different runs of your application, it might be better to use disk storage and pass the path that should be used to persist the data.
embeddings:
path: sentence-transformers/all-MiniLM-L6-v2
backend: qdrant_txtai.ann.qdrant.Qdrant
metric: l2 # allowed values: l1 / l2 / cosine / ip
qdrant:
path: '/home/qdrant/storage_local'
prefer_grpc: true
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
Built Distribution
File details
Details for the file qdrant_txtai-2.0.0.tar.gz
.
File metadata
- Download URL: qdrant_txtai-2.0.0.tar.gz
- Upload date:
- Size: 7.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.9.19
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 951ea0a70a56f1f475769a7b711f3aa3774a3788ba0d917662d7cfe9a508b60b |
|
MD5 | c9f41b02c7312641666de9f682ae18c1 |
|
BLAKE2b-256 | ed8961b43a0748aa30a1c66e6d945beae5b584d00ed9919e413c9018ed6d2c2e |
File details
Details for the file qdrant_txtai-2.0.0-py3-none-any.whl
.
File metadata
- Download URL: qdrant_txtai-2.0.0-py3-none-any.whl
- Upload date:
- Size: 8.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.9.19
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9e90e4cb1fec2187a3a16de3aff8442c8fd288adc1384e25917f05d6231742af |
|
MD5 | 58a255a58da73a797d2bd5ad10fbb00e |
|
BLAKE2b-256 | 9375bffd430d1616d40206b449f75e461c6170a633dac21d06d1873067962124 |