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92 lines
2.6 KiB
Markdown
92 lines
2.6 KiB
Markdown
---
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title: "Nomic"
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weight: 2300
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---
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# Nomic
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The `nomic-embed-text-v1` model is an open source [8192 context length](https://github.com/nomic-ai/contrastors) text encoder.
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While you can find it on the [Hugging Face Hub](https://huggingface.co/nomic-ai/nomic-embed-text-v1),
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you may find it easier to obtain them through the [Nomic Text Embeddings](https://docs.nomic.ai/reference/endpoints/nomic-embed-text).
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Once installed, you can configure it with the official Python client, FastEmbed or through direct HTTP requests.
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<aside role="status">Using Nomic Embeddings via the Nomic API/SDK requires configuring the <a href="https://atlas.nomic.ai/cli-login">Nomic API token</a>.</aside>
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You can use Nomic embeddings directly in Qdrant client calls. There is a difference in the way the embeddings
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are obtained for documents and queries.
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#### Upsert using [Nomic SDK](https://github.com/nomic-ai/nomic)
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The `task_type` parameter defines the embeddings that you get.
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For documents, set the `task_type` to `search_document`:
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```python
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from qdrant_client import QdrantClient, models
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from nomic import embed
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output = embed.text(
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texts=["Qdrant is the best vector database!"],
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model="nomic-embed-text-v1",
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task_type="search_document",
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)
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client = QdrantClient()
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client.upsert(
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collection_name="my-collection",
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points=models.Batch(
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ids=[1],
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vectors=output["embeddings"],
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),
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)
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```
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#### Upsert using [FastEmbed](https://github.com/qdrant/fastembed)
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```python
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from fastembed import TextEmbedding
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from client import QdrantClient, models
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model = TextEmbedding("nomic-ai/nomic-embed-text-v1")
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output = model.embed(["Qdrant is the best vector database!"])
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client = QdrantClient()
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client.upsert(
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collection_name="my-collection",
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points=models.Batch(
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ids=[1],
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vectors=[embeddings.tolist() for embeddings in output],
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),
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)
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```
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#### Search using [Nomic SDK](https://github.com/nomic-ai/nomic)
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To query the collection, set the `task_type` to `search_query`:
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```python
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output = embed.text(
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texts=["What is the best vector database?"],
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model="nomic-embed-text-v1",
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task_type="search_query",
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)
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client.search(
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collection_name="my-collection",
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query_vector=output["embeddings"][0],
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)
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```
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#### Search using [FastEmbed](https://github.com/qdrant/fastembed)
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```python
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output = next(model.embed("What is the best vector database?"))
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client.search(
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collection_name="my-collection",
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query_vector=output.tolist(),
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)
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```
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For more information, see the Nomic documentation on [Text embeddings](https://docs.nomic.ai/reference/endpoints/nomic-embed-text).
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