tei-client 0.2.4

Convenience Client for Hugging Face Text Embeddings Inference (TEI) with synchronous and asynchronous HTTP/gRPC support

tei-client

Convenience Client for Hugging Face Text Embeddings Inference (TEI) with synchronous and asynchronous HTTP/gRPC support.

Implements the API defined in TEI Swagger.

Installation

You can easily install tei-client via pip:

pip install tei-client

Grpc Support

If you want to use grpc, you need to install tei-client with grpc support:

pip install tei-client[grpc]

Usage

Creating a Client

HTTP Example

To create an instance of the client, you can do the following:

from tei_client import HttpClient

url = 'http://localhost:8080'
client = HttpClient(url)

Example docker server

docker run -p 8080:80 -v ./tei_data:/data ghcr.io/huggingface/text-embeddings-inference:cpu-latest --model-id sentence-transformers/all-MiniLM-L6-v2

gRPC Example

Alternatively, you can use gRPC to connect to your server:

import grpc
from tei_client import GrpcClient

channel = grpc.insecure_channel('localhost:8080')
client = GrpcClient(channel)

Example docker server

docker run -p 8080:80 -v ./tei_data:/data ghcr.io/huggingface/text-embeddings-inference:cpu-latest-grpc --model-id sentence-transformers/all-MiniLM-L6-v2

Embedding

To generate embeddings, you can use the following methods:

Single Embedding Generation

You can generate a single embedding using the embed method:

result = client.embed("This is an example sentence")
print(result[0])

Batch Embedding Generation

To generate multiple embeddings in batch mode, use the embed method with a list of sentences:

results = client.embed(["This is an example sentence", "This is another example sentence"])
for result in results:
    print(result)

Asynchronous Embedding Generation

For asynchronous embedding generation, you can use the async_embed method:

result = await client.async_embed("This is an example sentence")

Classification

To generate classification results for a given text, you can use the following methods:

Basic Classification

You can classify a single text using the classify method:

result = client.classify("This is an example sentence")
print(result[0].scores)

NLI Style Classification

For Natural Language Inference (NLI) style classification, you can use the classify method with tuples as input. The first element of the tuple represents the premise and the second element represents the hypothesis.

premise = "This is an example sentence"
hypothesis = "An example was given"

result = client.classify((premise, hypothesis))
print(result[0].scores)

Asynchronous and Batched Classification

The classify method also supports batched requests by passing a list of tuples or strings. For asynchronous classification, you can use the async_classify method.

# Classify multiple texts in batch mode
results = client.classify(["This is an example sentence", "This is another example sentence"])
for result in results:
    print(result.scores)

# Asynchronous classification
result = await client.async_classify("This is an example sentence")

Reranking

Reranking allows you to refine the order of search results based on additional information. This feature is supported by the rerank method.

Basic Reranking

You can use the rerank method to rerank search results with the following syntax:

result = client.rerank(
    query="What is Deep Learning?",  # Search query
    texts=["Lore ipsum", "Deep Learning is ..."]  # List of text snippets
)
print(result)

Asynchronous Reranking

For asynchronous reranking, use the async_rerank method:

result = await client.async_rerank(
    query="What is Deep Learning?",  # Search query
    texts=["Lore ipsum", "Deep Learning is ..."]  # List of text snippets
)