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NirantandAtita Arora
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* * feat(gemini.md): add documentation for integrating Gemini embeddings with Qdrant * * refactor(integrations): move cohere.md to embeddings folder * refactor(integrations): move openai.md to embeddings folder * refactor(integrations): move autogen.md to frameworks folder * refactor(integrations): move langchain.md to frameworks folder * blacken * * feat(embedding, frameworks): reorganise integrations into embedding and frameworks, add _index.md to both * * chore(gemini.md): remove old Gemini integration documentation * * chore(embedding/_index.md): update weight from 24 to 23 and set is_empty to false * chore(frameworks/_index.md): update weight from 24 to 23 and set is_empty to false * Split integrations into embedding and frameworks * Update heading level for embedding a document * Update Gemini embedding documentation * Update titles for embedding and frameworks sections * Try again with nesting * Add documentation for integrated frameworks and embedding options * Delete integrations documentation file * Add Delimiter; unknown weights * Change all weights to 3x * Delimiter reorg * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * Update qdrant-landing/content/documentation/embedding/gemini.md Co-authored-by: Atita Arora <atarora@users.noreply.github.com> * * docs(embedding/gemini.md): update Gemini Embedding Model API documentation * * - Add information about the new Gemini Embedding Model and its compatibility with Qdrant * - Clarify the usage of the `task_type` parameter in the API call * - Provide a list of supported task types and * * docs(embedding): update list of embedding integrations * * refactor(fifty-one.md): Rename file from embedding/fifty-one.md to frameworks/fifty-one.md * refactor(txtai.md): Rename file from embedding/txtai.md to frameworks/txtai.md * * chore(embedding): update is_empty value to true in _index.md * chore(embedding): remove Fifty One from embedding/_index.md * embedding -> embeddings --------- Co-authored-by: Atita Arora <atarora@users.noreply.github.com>
43 lines
1.7 KiB
Markdown
43 lines
1.7 KiB
Markdown
---
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title: Haystack
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weight: 400
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---
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# Haystack
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[Haystack](https://haystack.deepset.ai/) serves as a comprehensive NLP framework, offering a modular methodology for constructing
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cutting-edge generative AI, QA, and semantic knowledge base search systems. A critical element in contemporary NLP systems is an
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efficient database for storing and retrieving extensive text data. Vector databases excel in this role, as they house vector
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representations of text and implement effective methods for swift retrieval. Thus, we are happy to announce the integration
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with Haystack - `QdrantDocumentStore`. This document store is unique, as it is maintained externally by the Qdrant team.
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The new document store comes as a separate package and can be updated independently of Haystack:
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```bash
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pip install qdrant-haystack
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```
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`QdrantDocumentStore` supports [all the configuration properties](/documentation/collections/#create-collection) available in
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the Qdrant Python client. If you want to customize the default configuration of the collection used under the hood, you can
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provide that settings when you create an instance of the `QdrantDocumentStore`. For example, if you'd like to enable the
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Scalar Quantization, you'd make that in the following way:
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```python
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from qdrant_haystack.document_stores import QdrantDocumentStore
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from qdrant_client.http import models
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document_store = QdrantDocumentStore(
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":memory:",
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index="Document",
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embedding_dim=512,
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recreate_index=True,
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quantization_config=models.ScalarQuantization(
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scalar=models.ScalarQuantizationConfig(
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type=models.ScalarType.INT8,
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quantile=0.99,
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always_ram=True,
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),
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),
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)
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```
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