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Author SHA1 Message Date
George Panchuk 5afa07b921 new: update dev workflow 2024-05-02 20:34:47 +02:00
Arun 432da42c11 fix links (#215) 2024-04-27 22:24:29 +05:30
2 changed files with 3 additions and 5 deletions
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name: Tests
run-name: Tests (dev)
on:
push:
branches: [ master, main ]
schedule:
- cron: 0 0 * * *
branches: [ dev ]
pull_request:
env:
@@ -12,7 +11,6 @@ env:
jobs:
test:
strategy:
matrix:
python-version:
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FastEmbed is a lightweight, fast, Python library built for embedding generation. We [support popular text models](https://qdrant.github.io/fastembed/examples/Supported_Models/). Please [open a GitHub issue](https://github.com/qdrant/fastembed/issues/new) if you want us to add a new model.
The default text embedding (`TextEmbedding`) model is Flag Embedding, presented in the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/examples/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/examples/Usage_With_Qdrant/).
The default text embedding (`TextEmbedding`) model is Flag Embedding, presented in the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. It supports "query" and "passage" prefixes for the input text. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/qdrant/Retrieval_with_FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/qdrant/Usage_With_Qdrant/).
## 📈 Why FastEmbed?