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توضیحات

This package contains toolkit for faiss-instant. It mainly helps to encode texts via Transformers and build Faiss indexes in an automatic way.

ویژگی	مقدار
سیستم عامل	-
نام فایل	faiss-instant-0.0.4
نام	faiss-instant
نسخه کتابخانه	0.0.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Kexin Wang
ایمیل نویسنده	kexin.wang.2049@gmail.com
آدرس صفحه اصلی	https://github.com/UKP-SQuARE/faiss-instant
آدرس اینترنتی	https://pypi.org/project/faiss-instant/
مجوز	-

# Faiss Instant Build a Faiss service instantly. Faiss-instant will simply load **existing** Faiss index (and the corresponding ID mapping) and provide the search service via POST request. **New features:** Now Faiss-instant also provides the toolkit for encoding texts into embeddings via [SBERT](https://sbert.net/) models and indexing the embeddings into a Faiss ANN index. One just needs to install the toolkit via ```bash pip install faiss-instant ``` and try this [example](https://github.com/UKP-SQuARE/faiss-instant/blob/main/examples/encode_and_index/beir_scifact/encode_and_index.sh). ## Usage First, one needs to put the resource files (the ID mapping and the Faiss index, please refer to [resources/README.md](resources/README.md)) under the folder [./resources](./resources): ```bash make download # This will download example resource files. The example index comes from building a SQ index (QT_8bit_uniform) on a 10K-document version of the NQ corpus (dpr-single-nq-base was used for encoding). For other indices, please find under https://public.ukp.informatik.tu-darmstadt.de/kwang/faiss-instant/. ``` Then, one needs to start the faiss-instant service via docker: ```bash docker pull kwang2049/faiss-instant # Or `make pull`; or `make build` to build the docker image docker run --detach --rm -it -p 5001:5000 -v ${PWD}/resources:/opt/faiss-instant/resources --name faiss-instant kwang2049/faiss-instant # Or `make run`; notice here a volume mapping will be made from ./resources to /opt/faiss-instant in the container ``` Finally, do the query: ```bash bash query_example.sh # curl 'localhost:5001/search' -X POST -d '{"k": 5, "vectors": [[0.31800827383995056, -0.19993115961551666, -0.029884858056902885, ...]]}' ``` This will return the mappings from document IDs to the corresponding scores: ```json [{"2426246":106.54305267333984,"4944584":107.05268096923828,"6195536":106.5833511352539,"6398884":107.19760131835938,"8077664":107.86164093017578}] ``` Whenever update the resources, one needs reload them: ```bash curl 'localhost:5001/reload' -X GET # Or `make reload` ``` ## Advanced ### Multiple Indices One can have multiple indices in the resource folder, to load a certain one (actually a pair of `index_name`.index and `index_name`.txt, here the index name is 'ivf-32-sq-QT_8bit_uniform'): ```bash curl -d '{"index_name":"ivf-32-sq-QT_8bit_uniform", "use_gpu":true}' -H "Content-Type: application/json" -X POST 'http://localhost:5001/reload' ``` To view the available indices under the resource folder and the index loaded, one can run: ```bash curl -X GET 'http://localhost:5001/index_list' ``` To load a specified index: ```bash curl -d '{"index_name":"ivf-32-sq-QT_8bit_uniform"}' -H "Content-Type: application/json" -X POST 'http://localhost:5001/reload' ``` ### Use GPU > Note Faiss only supports part of the index types: https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU#implemented-indexes. And for PQ, it cannot support large `m` such as 384. One can also use GPU to accelerate the search. To achieve that, one needs to use the GPU version: ```bash docker pull kwang2049/faiss-instant-gpu # The current image supports only CUDA 10.2 or higher version ``` And then start the GPU-version container: ```bash docker run --runtime=nvidia -e CUDA_VISIBLE_DEVICES=0 --detach --rm -it -p 5001:5000 -v ${PWD}/resources:/opt/faiss-instant/resources --name faiss-instant-gpu kwang2049/faiss-instant-gpu # Or `make run-gpu` ``` This will split and load the index onto all the GPUs available (in this example it uses only `gpu:0`). To load a specified index and make it on GPU, one can run: ```bash curl -d '{"index_name":"ivf-32-sq-QT_8bit_uniform", "use_gpu":true}' -H "Content-Type: application/json" -X POST 'http://localhost:5001/reload' ``` ### Reconstruct To get the original vector without indexing by its ID, run: ```bash curl -X 'GET' 'http://localhost:5001/reconstruct?id=1' # This example returns the vector by its ID='1' ``` ### Explain To compute the similarity score between a given query vector and a support vector by its ID: ```bash bash explain_example.sh ``` ## Philosophy Faiss-instant provides only the search service and relies on uploaded Faiss indices. By using the volume mapping, the huge pain of uploading index files to the docker service can be directly removed. Consequently, a minimal efficient Faiss system for search is born. For creating index files (and also benchmarking ANN methods), please refer to [kwang2049/benchmarking-ann](https://github.com/kwang2049/benchmarking-ann). ## Reference [plippe/faiss-web-service](https://github.com/plippe/faiss-web-service)

نیازمندی

مقدار	نام
-	beir
-	crash-ipdb
-	sentence-transformers
-	faiss-gpu

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl faiss-instant-0.0.4:

pip install faiss-instant-0.0.4.whl

نصب پکیج tar.gz faiss-instant-0.0.4:

pip install faiss-instant-0.0.4.tar.gz