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* added a table of content * wide layout for docs, styles for the table of content * fixes for docs layout * wide footer at the docs section * added support for nested docs, added toggling groups of links, delimiters, external links * external link icon * added active state for nested links, styles for the external link icon * styles fix * remove doc sync * update directory structure and doc titles * fix outstanding links * fix more links for merge * Revert "fix more links for merge" This reverts commit 46c9ccaf1b7765f2cda8dc85d625fa6b4e3f5436. * Revert "fix outstanding links" This reverts commit 28e6380b74f1ab74690c8184551f186656d6d4e9. * fix remaining broken links * move how-to tutorials in the different page * split tutorials * fix link * upd github edit link * skip empty index pages --------- Co-authored-by: Andrey Vasnetsov <andrey@vasnetsov.com> Co-authored-by: David Sertic <62056091+davidmyriel@users.noreply.github.com>
58 lines
1.6 KiB
Markdown
58 lines
1.6 KiB
Markdown
---
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title: Aleph Alpha
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weight: 900
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---
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Aleph Alpha is a multimodal and multilingual embeddings' provider. Their API allows creating the embeddings for text and images, both
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in the same latent space. They maintain an [official Python client](https://github.com/Aleph-Alpha/aleph-alpha-client) that might be
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installed with pip:
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```bash
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pip install aleph-alpha-client
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```
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There is both synchronous and asynchronous client available. Obtaining the embeddings for an image and storing it into Qdrant might
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be done in the following way:
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```python
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import qdrant_client
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from aleph_alpha_client import (
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Prompt,
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AsyncClient,
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SemanticEmbeddingRequest,
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SemanticRepresentation,
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ImagePrompt
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)
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from qdrant_client.http.models import Batch
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aa_token = "<< your_token >>"
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model = "luminous-base"
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qdrant_client = qdrant_client.QdrantClient()
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async with AsyncClient(token=aa_token) as client:
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prompt = ImagePrompt.from_file("./path/to/the/image.jpg")
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prompt = Prompt.from_image(prompt)
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query_params = {
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"prompt": prompt,
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"representation": SemanticRepresentation.Symmetric,
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"compress_to_size": 128,
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}
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query_request = SemanticEmbeddingRequest(**query_params)
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query_response = await client.semantic_embed(
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request=query_request, model=model
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)
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qdrant_client.upsert(
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collection_name="MyCollection",
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points=Batch(
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ids=[1],
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vectors=[query_response.embedding],
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)
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)
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```
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If we wanted to create text embeddings with the same model, we wouldn't use `ImagePrompt.from_file`, but simply provide the input
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text into the `Prompt.from_text` method.
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