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* chore(README.md): update links and fix formatting in README.md

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NirantK
2023-09-27 18:00:19 +05:30
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README.md
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@@ -1,6 +1,6 @@
# ⚡️ What is FastEmbed?
FastEmbed is a lightweight, fast, Python library built for embedding generation. We [support popular text models](https://qdrant.github.io/fastembed/examples/Supported_Models/) and please [open an issue](https://github.com/qdrant/fastembed/issues/new) if you want us to add a new model.
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 embedding supports "query" and "passage" prefixes for the input text. The default model is Flag Embedding, which is top of the [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard. Here is an example for [Retrieval Embedding Generation](https://qdrant.github.io/fastembed/examples/Retrieval%20with%20FastEmbed/) and how to use [FastEmbed with Qdrant](https://qdrant.github.io/fastembed/examples/Usage_With_Qdrant/).
@@ -30,8 +30,7 @@ documents: List[str] = [
"passage: Hello, World!",
"query: Hello, World!", # these are two different embedding
"passage: This is an example passage.",
# You can leave out the prefix but it's recommended
"fastembed is supported by and maintained by Qdrant."
"fastembed is supported by and maintained by Qdrant." # You can leave out the prefix but it's recommended
]
embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512)
embeddings: List[np.ndarray] = embedding_model.embed(documents) # If you use