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[
"Cheshire Cat - Qdrant",
"Next steps"
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[
"Cheshire Cat - Qdrant",
"Next steps"
] | /documentation/frameworks/cheshire-cat/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > ul:nth-of-type(4) > li:nth-of-type(3) | [
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"documentation",
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[
"Cheshire Cat - Qdrant",
"Next steps"
] | /documentation/frameworks/cheshire-cat/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > ul:nth-of-type(4) > li:nth-of-type(6) | [
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[
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] | /documentation/frameworks/fifty-one/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(1) | [
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"documentation",
"documentation/frameworks",
"documentation/frameworks/fifty-one"
] | 8,508 |
[
"FiftyOne - Qdrant",
"FiftyOne"
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"documentation",
"documentation/frameworks",
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[
"FiftyOne - Qdrant",
"FiftyOne"
] | /documentation/frameworks/fifty-one/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(3) | [
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"documentation",
"documentation/frameworks",
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[
"FiftyOne - Qdrant",
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"documentation",
"documentation/frameworks",
"documentation/frameworks/fifty-one"
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[
"Cohere - Qdrant"
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] | p | might be installed as any other package: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/cohere"
] | 8,516 |
[
"Cohere - Qdrant",
"Cohere"
] | /documentation/embeddings/cohere/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(2) | [
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[
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[
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] | p | It determines the type of task you want to use the embeddings for. | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/cohere"
] | 8,524 |
[
"Cohere - Qdrant",
"Embed v3"
] | /documentation/embeddings/cohere/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > ul > li:nth-of-type(1) | [
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"documentation",
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"documentation/embeddings/cohere"
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[
"Cohere - Qdrant",
"Embed v3"
] | /documentation/embeddings/cohere/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > ul > li:nth-of-type(4) | [
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"documentation",
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"documentation/embeddings/cohere"
] | 8,530 |
[
"Cohere - Qdrant",
"Embed v3"
] | /documentation/embeddings/cohere/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(5) | [
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] | p | The following example shows how to index | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/cohere"
] | 8,531 |
[
"Cohere - Qdrant",
"Embed v3"
] | /documentation/embeddings/cohere/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(5) | [
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[
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[
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] | p | Qdrant is compatible with Gemini Embedding Model API and its official Python SDK that can be installed as any other package: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,535 |
[
"Gemini - Qdrant",
"Gemini"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(2) | [
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"documentation",
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[
"Gemini - Qdrant",
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[
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] | p | The Embedding Model API supports various task types, outlined as follows: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,540 |
[
"Gemini - Qdrant",
"Gemini"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > ol > li:nth-of-type(1) | [
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[
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[
"Gemini - Qdrant",
"Gemini"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(5) | [
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] | p | If you’re building a semantic search application, such as RAG, you should use task_type="retrieval_document" for the indexed documents and task_type="retrieval_query" for the search queries. | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,547 |
[
"Gemini - Qdrant",
"Gemini"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(6) | [
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] | p | The following example shows how to do this with Qdrant: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,548 |
[
"Gemini - Qdrant"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h2:nth-of-type(1) | [
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] | p | The following example shows how to embed a document with the models/embedding-001 with the retrieval_document task type: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,551 |
[
"Gemini - Qdrant"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h2:nth-of-type(2) | [
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] | h2 | Embedding a document | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,552 |
[
"Gemini - Qdrant",
"Embedding a document"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(9) | [
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] | p | The returned result is a dictionary with a key: embedding. | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,553 |
[
"Gemini - Qdrant",
"Embedding a document"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(9) | [
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] | p | Once the documents are indexed, you can search for the most relevant documents using the same model with the retrieval_query task type: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,557 |
[
"Gemini - Qdrant"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h2:nth-of-type(5) | [
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] | h2 | Using Gemini Embedding Models with Binary Quantization | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/gemini"
] | 8,558 |
[
"Gemini - Qdrant",
"Using Gemini Embedding Models with Binary Quantization"
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[
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[
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"Using Gemini Embedding Models with Binary Quantization"
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[
"Gemini - Qdrant",
"Using Gemini Embedding Models with Binary Quantization"
] | /documentation/embeddings/gemini/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(14) | [
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[
"Mistral - Qdrant"
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"documentation/embeddings/mistral"
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[
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] | h3 | Install the client | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/mistral"
] | 8,567 |
[
"Mistral - Qdrant",
"Install the client"
] | /documentation/embeddings/mistral/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(2) | [
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] | p | And then we set this up: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/mistral"
] | 8,568 |
[
"Mistral - Qdrant",
"Install the client"
] | /documentation/embeddings/mistral/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(3) | [
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] | p | Let’s see how to use the Embedding Model API to embed a document for retrieval. | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/mistral"
] | 8,569 |
[
"Mistral - Qdrant",
"Install the client"
] | /documentation/embeddings/mistral/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(4) | [
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] | p | The following example shows how to embed a document with the models/embedding-001 with the retrieval_document task type: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/mistral"
] | 8,570 |
[
"Mistral - Qdrant"
] | /documentation/embeddings/mistral/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h2:nth-of-type(2) | [
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] | h2 | Embedding a document | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/mistral"
] | 8,571 |
[
"Mistral - Qdrant",
"Embedding a document"
] | /documentation/embeddings/mistral/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(5) | [
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[
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] | p | Once the documents are indexed, you can search for the most relevant documents using the same model with the retrieval_query task type: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/mistral"
] | 8,577 |
[
"Mistral - Qdrant"
] | /documentation/embeddings/mistral/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > h2:nth-of-type(5) | [
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[
"Mistral - Qdrant",
"Using Mistral Embedding Models with Binary Quantization"
] | /documentation/embeddings/mistral/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(10) | [
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[
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] | p | To call their endpoint, all you need is an API key obtainable here. | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/jina-embeddings"
] | 8,585 |
[
"Jina Embeddings - Qdrant",
"Jina Embeddings"
] | /documentation/embeddings/jina-embeddings/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(2) | [
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] | p | Qdrant can also easily work with OpenAI embeddings. | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/openai"
] | 8,588 |
[
"OpenAI - Qdrant",
"OpenAI"
] | /documentation/embeddings/openai/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(2) | [
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] | p | There is an official OpenAI Python package that simplifies obtaining them, and it might be installed with pip: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/openai"
] | 8,589 |
[
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[
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] | p | OpenAI requires an API key that has to be provided either as an environmental variable OPENAI_API_KEY or set in the source code directly, as presented below: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/openai"
] | 8,591 |
[
"Aleph Alpha - Qdrant"
] | /documentation/embeddings/aleph-alpha/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(1) | [
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] | p | Aleph Alpha is a multimodal and multilingual embeddings’ provider. | [
"documentation",
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[
"Aleph Alpha - Qdrant"
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"documentation",
"documentation/embeddings",
"documentation/embeddings/aleph-alpha"
] | 8,593 |
[
"Aleph Alpha - Qdrant"
] | /documentation/embeddings/aleph-alpha/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(1) | [
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[
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[
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[
"Aleph Alpha - Qdrant"
] | /documentation/embeddings/aleph-alpha/ | html > body > div:nth-of-type(1) > section > div > div > div:nth-of-type(2) > article > p:nth-of-type(2) | [
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] | p | be done in the following way: | [
"documentation",
"documentation/embeddings",
"documentation/embeddings/aleph-alpha"
] | 8,599 |
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