Add Mistral Embedding documentation and image

qdrant-landing/content/documentation/embeddings/mistral.md

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+---
+title: Mistral 
+weight: 700
+---
+
+# Mistral
+Qdrant is compatible with the new released Mistral Embed and its official Python SDK that can be installed as any other package:
+
+## Setup
+
+### Install the client
+
+```bash
+pip install mistralai
+```
+
+```python
+from mistralai.client import MistralClient
+
+api_key = os.environ["MISTRAL_API_KEY"]
+client = MistralClient(api_key=api_key)
+```
+
+Let's see how to use the Embedding Model API to embed a document for retrieval. 
+
+The following example shows how to embed a document with the `models/embedding-001` with the `retrieval_document` task type:
+
+## Embedding a document
+
+```python
+import pathlib
+from mistralai.client import MistralClient
+import qdrant_client
+
+MISTRAL_API_KEY = "YOUR MISTRAL API KEY"  # add your key here
+
+mistral_client = MistralClient(api_key=MISTRAL_API_KEY)
+
+result = mistral_client.embeddings(
+        model="mistral-embed", input=["Qdrant is the best vector search engine to use with Mistral"]
+    )
+```
+
+The returned result is a dictionary with a key: `embedding`. The value of this key is a list of floats representing the embedding of the document.
+
+## Searching for documents with Qdrant
+
+Once the documents are indexed, you can search for the most relevant documents using the same model with the `retrieval_query` task type:
+
+```python
+qdrant_client.search(
+    collection_name="MistralCollection",
+    query=client.embeddings(
+        model="mistral-embed", input=["What is the best to use with Mistral?"]
+    )["embedding"],
+)
+```
+
+## Using Mistral Embedding Models with Binary Quantization
+
+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. 
+
+At an oversampling of 3 and a limit of 100, we've a 95% recall against the exact nearest neighbors with rescore enabled.
+
+![](../../../static/documentation/embeddings/mistral-binary-quantization.png)
+
+That's it! You can now use Mistral Embedding Models with Qdrant!