> ## Documentation Index
> Fetch the complete documentation index at: https://docs.servflow.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Vector Operations

> Store and query vector embeddings for similarity search and RAG applications

Vector operations allow you to store and retrieve vector embeddings from vector databases like Qdrant. These actions are essential for building semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG) applications.

***

## storevector

Stores vector embeddings in a vector database along with associated metadata.

### Integration ID

The vector database integration to store vectors in.

|              |                 |
| ------------ | --------------- |
| **YAML Key** | `integrationID` |
| **Type**     | string          |
| **Required** | Yes             |

### Vectors

The vector data to store, typically as a JSON array of floats.

|              |           |
| ------------ | --------- |
| **YAML Key** | `vectors` |
| **Type**     | string    |
| **Required** | Yes       |

<Tip>
  Use `{{ .generate_embedding }}` to reference vectors generated by a previous action, such as an AI embedding model.
</Tip>

### Fields

Metadata fields to store alongside the vector. These fields can be used for filtering during retrieval.

|              |          |
| ------------ | -------- |
| **YAML Key** | `fields` |
| **Type**     | map      |
| **Required** | Yes      |

### Options

Additional storage options such as the collection name.

|              |           |
| ------------ | --------- |
| **YAML Key** | `options` |
| **Type**     | map       |
| **Required** | No        |

Common options:

* `collection` — The collection name to store vectors in

### Example

```docs/actions/vector-operations.mdx theme={null}
actions:
  store_embedding:
    type: storevector
    config:
      integrationID: my_qdrant
      vectors: "{{ .generate_embedding }}"
      fields:
        content: "{{ param \"text\" }}"
        source: "user_input"
        timestamp: "{{ now }}"
      options:
        collection: documents
    next: response.success
    fail: response.error
```

***

## fetchvectors

Retrieves vectors from a vector database for similarity search. Returns the most similar vectors to the provided query vector.

### Integration ID

The vector database integration to query.

|              |                 |
| ------------ | --------------- |
| **YAML Key** | `integrationID` |
| **Type**     | string          |
| **Required** | Yes             |

### Vector

The query vector to search for similar vectors. This is typically an embedding of the search query.

|              |          |
| ------------ | -------- |
| **YAML Key** | `vector` |
| **Type**     | string   |
| **Required** | No       |

### Options

Query options such as collection name and result limit.

|              |           |
| ------------ | --------- |
| **YAML Key** | `options` |
| **Type**     | map       |
| **Required** | No        |

Common options:

* `collection` — The collection name to search in
* `limit` — Maximum number of results to return

### Example

```docs/actions/vector-operations.mdx theme={null}
actions:
  search_similar:
    type: fetchvectors
    config:
      integrationID: my_qdrant
      vector: "{{ .query_embedding }}"
      options:
        collection: documents
        limit: 10
    next: response.results
    fail: response.error
```

***

## Common Patterns

### RAG Pipeline

A typical RAG (Retrieval-Augmented Generation) workflow combines vector search with an AI agent:

```docs/actions/vector-operations.mdx theme={null}
actions:
  embed_query:
    type: agent
    config:
      integrationID: my_openai
      userPrompt: "Generate an embedding for: {{ param \"question\" }}"
    next: action.search_docs

  search_docs:
    type: fetchvectors
    config:
      integrationID: my_qdrant
      vector: "{{ .embed_query }}"
      options:
        collection: knowledge_base
        limit: 5
    next: action.generate_answer

  generate_answer:
    type: agent
    config:
      integrationID: my_openai
      systemPrompt: |
        Answer the user's question using only the provided context.
        Context: {{ .search_docs }}
      userPrompt: "{{ param \"question\" }}"
    next: response.answer
```

### Document Ingestion

Store documents with their embeddings for later retrieval:

```docs/actions/vector-operations.mdx theme={null}
actions:
  generate_embedding:
    type: agent
    config:
      integrationID: my_openai
      userPrompt: "Generate an embedding for: {{ param \"content\" }}"
    next: action.store_document

  store_document:
    type: storevector
    config:
      integrationID: my_qdrant
      vectors: "{{ .generate_embedding }}"
      fields:
        content: "{{ param \"content\" }}"
        title: "{{ param \"title\" }}"
        category: "{{ param \"category\" }}"
      options:
        collection: documents
    next: response.stored
```

***

## Next Steps

<CardGroup cols={2}>
  <Card title="AI Agents" icon="robot" href="/concepts/actions/ai-agents">
    Use AI agents to generate embeddings and process search results.
  </Card>

  <Card title="Data Operations" icon="database" href="/concepts/actions/data-operations">
    Combine vector search with traditional database queries.
  </Card>

  <Card title="Actions Overview" icon="play" href="/concepts/actions/overview">
    Learn the fundamentals of ServFlow actions.
  </Card>

  <Card title="Configuration Reference" icon="gear" href="/references/configuration">
    Explore all ServFlow configuration options.
  </Card>
</CardGroup>