# Weaviate

The **Weaviate Node** in Stack AI is used to query a Weaviate vector database. Weaviate is a popular open-source vector database that allows you to store and search data using vector embeddings, which is especially useful for semantic search, recommendation systems, and AI-powered retrieval tasks.

Here’s a detailed explanation of Vector Search in Weaviate and how to establish a connection in Stack AI:

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### Available Actions

**Vector Search** in Weaviate allows you to search for objects (such as documents, images, or data entries) that are most similar to a given query, using vector embeddings. This is especially useful for semantic search, recommendations, and AI-powered retrieval.

#### How the "Vector Search" Action Works

* **Action Name:** Vector Search
* **Provider:** Weaviate
* **Purpose:** Search for similar vectors in a Weaviate database.

**Required Input Parameters**

1. **Class Name** (`class_name`):\
   The name of the Weaviate class (collection/table) you want to search in.\
   \&#xNAN;*Example: "Article", "Document", "Product"*
2. **Properties** (`properties`):\
   Comma-separated list of properties to return in the results.\
   \&#xNAN;*Example: "title,description,url"*
3. **Query** (`query`):\
   The search query (text) to find similar vectors.\
   \&#xNAN;*Example: "AI workflow automation"*
4. **Number of Results** (`top_k`):\
   How many results to return.\
   \&#xNAN;*Example: 5 or 10*

**Output**

* **Results** (`results`):\
  The results of the vector search query, typically a list of objects with the requested properties.

***

### How to Establish a Connection to Weaviate

To connect Stack AI to your Weaviate instance, you need to provide connection details. Here’s how:

1. **Connection ID:**\
   If you have a private Weaviate instance, you’ll need a connection ID. This is usually set up in your Stack AI integrations or connections panel.
   * In the action node’s `action_configurations`, add:

     ```json
     { "connection_id": "<your-weaviate-connection-id>" }
     ```
   * If you’re using the default Stack AI Weaviate instance, you may not need to set this.
2. **Configure the Action Node:**
   * Set the required input parameters (`class_name`, `properties`, `query`, `top_k`).
   * If you want to use dynamic input (e.g., from an Input node or LLM), reference the node like `{in-0}` or `{llm-0}` in the `query` field.

***

### Summary Table

| Parameter      | Description                                   | Example Value            |
| -------------- | --------------------------------------------- | ------------------------ |
| class\_name    | Weaviate class to search                      | "Article"                |
| properties     | Properties to return (comma-separated)        | "title,description,url"  |
| query          | Search query (text or node reference)         | "AI workflow automation" |
| top\_k         | Number of results to return                   | 5                        |
| connection\_id | (Optional) Connection ID for private Weaviate | "abc123-connection-id"   |

How to Use the Weaviate Node

1. **Connect to a Weaviate Database**: You typically need to configure the node with a connection to your Weaviate instance (this may require a connection ID if you have a private instance).
2. **Set Query Parameters**: Define what you want to search for—this usually involves providing a query vector or text, and specifying parameters like the number of results (`top_k`), the class or collection to search, and any filters.
3. **Input Data**: The node can take input from other nodes (like an LLM or Input node) to dynamically generate queries.
4. **Use Output**: The results from the Weaviate node can be passed to downstream nodes, such as an LLM for summarization or an Output node for display.

#### Example Workflow

* User provides a search query (Input node).
* The query is embedded (optionally by an LLM or embedding node).
* The Weaviate node performs a vector search using the embedding.
* The results are passed to an LLM for summarization or directly to the Output node.

#### When to Use the Weaviate Node

* When you need to perform advanced, AI-powered search over large datasets.
* When you want to build applications that require semantic understanding, such as chatbots, document search, or recommendation engines.