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96 lines
2.9 KiB
Markdown
96 lines
2.9 KiB
Markdown
---
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title: Mistral
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weight: 700
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---
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| Time: 10 min | Level: Beginner | [](https://githubtocolab.com/qdrant/examples/blob/mistral-getting-started/mistral-embed-getting-started/mistral_qdrant_getting_started.ipynb) |
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| --- | ----------- | ----------- |
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# Mistral
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Qdrant is compatible with the new released Mistral Embed and its official Python SDK that can be installed as any other package:
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## Setup
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### Install the client
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```bash
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pip install mistralai
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```
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```python
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from mistralai.client import MistralClient
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from qdrant_client import QdrantClient
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from qdrant_client.http.models import PointStruct, VectorParams, Distance
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collection_name = "example_collection"
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MISTRAL_API_KEY = "your_mistral_api_key"
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search_client = QdrantClient(":memory:")
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mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
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texts = [
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"Qdrant is the best vector search engine!",
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"Loved by Enterprises and everyone building for low latency, high performance, and scale.",
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]
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```
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Let's see how to use the Embedding Model API to embed a document for retrieval.
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The following example shows how to embed a document with the `models/embedding-001` with the `retrieval_document` task type:
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## Embedding a document
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```python
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result = mistral_client.embeddings(
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model="mistral-embed",
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input=texts,
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)
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```
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The returned result has a data field with a key: `embedding`. The value of this key is a list of floats representing the embedding of the document.
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### Converting this into Qdrant Points
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```python
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points = [
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PointStruct(
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id=idx,
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vector=response.embedding,
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payload={"text": text},
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)
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for idx, (response, text) in enumerate(zip(result.data, texts))
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]
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```
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## Create a collection and Insert the documents
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```python
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search_client.create_collection(collection_name, vectors_config=
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VectorParams(
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size=1024,
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distance=Distance.COSINE,
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)
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)
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search_client.upsert(collection_name, points)
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```
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## Searching for documents with Qdrant
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Once the documents are indexed, you can search for the most relevant documents using the same model with the `retrieval_query` task type:
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```python
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search_client.search(
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collection_name=collection_name,
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query_vector=mistral_client.embeddings(
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model="mistral-embed", input=["What is the best to use for vector search scaling?"]
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).data[0].embedding,
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)
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```
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## Using Mistral Embedding Models with Binary Quantization
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You can use Mistral Embedding Models with [Binary Quantization](/articles/binary-quantization/) - a technique that allows you to reduce the size of the embeddings by 32 times without losing the quality of the search results too much.
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At an oversampling of 3 and a limit of 100, we've a 95% recall against the exact nearest neighbors with rescore enabled.
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That's it! You can now use Mistral Embedding Models with Qdrant! |