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* docs: Mastra integration Signed-off-by: Anush008 <anushshetty90@gmail.com> * chore: _index.md Signed-off-by: Anush008 <anushshetty90@gmail.com> * fix: distance Signed-off-by: Anush008 <anushshetty90@gmail.com> --------- Signed-off-by: Anush008 <anushshetty90@gmail.com>
105 lines
3.9 KiB
Markdown
105 lines
3.9 KiB
Markdown
---
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title: Mastra
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---
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# Mastra
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[Mastra](https://mastra.ai/) is a Typescript framework to build AI applications and features quickly. It gives you the set of primitives you need: workflows, agents, RAG, integrations, syncs and evals. You can run Mastra on your local machine, or deploy to a serverless cloud.
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Qdrant is available as a vector store in Mastra node to augment application with retrieval capabilities.
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## Setup
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```bash
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npm install @mastra/core
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```
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## Usage
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```typescript
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import { QdrantVector } from "@mastra/rag";
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const qdrant = new QdrantVector({
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url: "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333"
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apiKey: "<YOUR_API_KEY>",
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https: true
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});
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```
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## Constructor Options
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| Name | Type | Description |
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|--------|-----------|-------------------------------------------------------------------------------------------------------|
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| `url` | `string` | REST URL of the Qdrant instance. Eg. <https://xyz-example.eu-central.aws.cloud.qdrant.io:6333> |
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| `apiKey` | `string` | Optional Qdrant API key |
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| `https` | `boolean` | Whether to use TLS when setting up the connection. Recommended. |
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## Methods
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### `createIndex()`
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| Name | Type | Description | Default Value |
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|------------|------------------------------------------|-------------------------------------------------|--------------|
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| `indexName` | `string` | Name of the index to create | |
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| `dimension` | `number` | Vector dimension size | |
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| `metric` | `string` | Distance metric for similarity search | `cosine` |
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### `upsert()`
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| Name | Type | Description | Default Value |
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|-------------|---------------------------|-----------------------------------------|--------------|
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| `vectors` | `number[][]` | Array of embedding vectors | |
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| `metadata` | `Record<string, any>[]` | Metadata for each vector (optional) | |
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| `namespace` | `string` | Optional namespace for organization | |
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### `query()`
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| Name | Type | Description | Default Value |
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|------------|-------------------------|---------------------------------------------|--------------|
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| `vector` | `number[]` | Query vector to find similar vectors | |
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| `topK` | `number` | Number of results to return (optional) | `10` |
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| `filter` | `Record<string, any>` | Metadata filters for the query (optional) | |
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### `listIndexes()`
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Returns an array of index names as strings.
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### `describeIndex()`
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| Name | Type | Description |
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|-------------|----------|----------------------------------|
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| `indexName` | `string` | Name of the index to describe |
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#### Returns
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```typescript
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interface IndexStats {
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dimension: number;
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count: number;
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metric: "cosine" | "euclidean" | "dotproduct";
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}
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```
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### `deleteIndex()`
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| Name | Type | Description |
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|-------------|----------|----------------------------------|
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| `indexName` | `string` | Name of the index to delete |
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## Response Types
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Query results are returned in this format:
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```typescript
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interface QueryResult {
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id: string;
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score: number;
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metadata: Record<string, any>;
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}
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```
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## Further Reading
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- [Mastra Examples](https://github.com/mastra-ai/mastra/tree/main/examples)
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- [Mastra Documentation](http://mastra.ai/docs/)
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