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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>
97 lines
2.8 KiB
Markdown
97 lines
2.8 KiB
Markdown
---
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title: MindsDB
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weight: 1100
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---
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# MindsDB
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[MindsDB](https://mindsdb.com) is an AI automation platform for building AI/ML powered features and applications. It works by connecting any source of data with any AI/ML model or framework and automating how real-time data flows between them.
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With the MindsDB-Qdrant integration, you can now select Qdrant as a database to load into and retrieve from with semantic search and filtering.
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**MindsDB allows you to easily**:
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- Connect to any store of data or end-user application.
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- Pass data to an AI model from any store of data or end-user application.
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- Plug the output of an AI model into any store of data or end-user application.
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- Fully automate these workflows to build AI-powered features and applications
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## Usage
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To get started with Qdrant and MindsDB, the following syntax can be used.
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```sql
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CREATE DATABASE qdrant_test
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WITH ENGINE = "qdrant",
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PARAMETERS = {
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"location": ":memory:",
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"collection_config": {
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"size": 386,
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"distance": "Cosine"
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}
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}
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```
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The available arguments for instantiating Qdrant can be found [here](https://github.com/mindsdb/mindsdb/blob/23a509cb26bacae9cc22475497b8644e3f3e23c3/mindsdb/integrations/handlers/qdrant_handler/qdrant_handler.py#L408-L468).
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## Creating a new table
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- Qdrant options for creating a collection can be specified as `collection_config` in the `CREATE DATABASE` parameters.
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- By default, UUIDs are set as collection IDs. You can provide your own IDs under the `id` column.
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```sql
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CREATE TABLE qdrant_test.test_table (
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SELECT embeddings,'{"source": "bbc"}' as metadata FROM mysql_demo_db.test_embeddings
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);
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```
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## Querying the database
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#### Perform a full retrieval using the following syntax.
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```sql
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SELECT * FROM qdrant_test.test_table
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```
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By default, the `LIMIT` is set to 10 and the `OFFSET` is set to 0.
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#### Perform a similarity search using your embeddings
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<aside role="status">Qdrant supports <a href="https://qdrant.tech/documentation/concepts/indexing/#payload-index">payload indexing</a> that vastly improves retrieval efficiency with filters and is highly recommended. Please note that this feature currently cannot be configured via MindsDB and must be set up separately if needed.</aside>
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```sql
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SELECT * FROM qdrant_test.test_table
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WHERE search_vector = (select embeddings from mysql_demo_db.test_embeddings limit 1)
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```
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#### Perform a search using filters
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```sql
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SELECT * FROM qdrant_test.test_table
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WHERE `metadata.source` = 'bbc';
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```
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#### Delete entries using IDs
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```sql
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DELETE FROM qtest.test_table_6
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WHERE id = 2
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```
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#### Delete entries using filters
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```sql
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DELETE * FROM qdrant_test.test_table
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WHERE `metadata.source` = 'bbc';
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```
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#### Drop a table
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```sql
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DROP TABLE qdrant_test.test_table;
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```
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## Next steps
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You can find more information pertaining to MindsDB and its datasources [here](https://docs.mindsdb.com/).
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