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46 lines
1.3 KiB
Markdown
46 lines
1.3 KiB
Markdown
---
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title: MixedBread
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---
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# Using MixedBread with Qdrant
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MixedBread is a unique provider offering embeddings across multiple domains. Their models are versatile for various search tasks when integrated with Qdrant. MixedBread is creating state-of-the-art models and tools that make search smarter, faster, and more relevant. Whether you're building a next-gen search engine or RAG (Retrieval Augmented Generation) systems, or whether you're enhancing your existing search solution, they've got the ingredients to make it happen.
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## Installation
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You can install the required package using the following pip command:
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```bash
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pip install mixedbread
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```
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## Integration Example
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Below is an example of how to obtain embeddings using MixedBread's API and store them in a Qdrant collection:
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```python
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import qdrant_client
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from qdrant_client.models import Batch
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from mixedbread import MixedBreadModel
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# Initialize MixedBread model
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model = MixedBreadModel("mixedbread-variant")
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# Generate embeddings
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text = "MixedBread provides versatile embeddings for various domains."
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embeddings = model.embed(text)
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# Initialize Qdrant client
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qdrant_client = qdrant_client.QdrantClient(host="localhost", port=6333)
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# Upsert the embedding into Qdrant
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qdrant_client.upsert(
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collection_name="VersatileEmbeddings",
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points=Batch(
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ids=[1],
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vectors=[embeddings],
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)
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)
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```
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