# Knowledge retrieval Node

### Overview

The Knowledge Retrieval Node searches and retrieves relevant documents from your knowledge collections based on a query. It supports semantic search through vector embeddings and allows filtering of results using vector store-specific filtering syntax. Each knowledge collection can be configured with different vector stores (Milvus, Qdrant, Chroma, Pinecone, ...).

Usage cost: 0.1 credit / document

### Configuration

#### Settings

1. **Collection Selection**
   * Collection\*: Select the knowledge collection to search
   * Number of Documents: Number of results to return (default: 5)
   * Query\*: Search query used to find relevant documents
   * Filtering: Optional filtering condition based on metadata

#### Output Ports

* `knowledges` (Document\[]): Retrieved documents with full metadata
* `documents_content` (string\[]): Array of document contents only

### Filtering example depending on vector store

**Milvus/Zilliz**

```sql
metadata["field"] == "value"
```

**Qdrant**

```json
{
  "must": [
    {"key": "metadata.field", "match": {"value": "exact_match"}}
  ]
}
```

**Chroma**

```json
{
  "field": {"$eq": "value"}
}
```

**Pinecone**

```json
{
  "field": {"$eq": "value"}
}
```

### Best Practices

1. **Query Construction**
   * Keep queries clear and focused
   * Include key terms relevant to desired content
   * Consider semantic meaning rather than exact keywords
   * Test queries with different phrasings
2. **Result Optimization**
   * Adjust number of documents based on use case
   * Use filtering to narrow down results, or to built multi-tenant data retrieval
   * Consider document length when setting limits
3. **Filtering Usage**
   * Follow vector store-specific syntax
   * Test filters with sample data
   * Use appropriate operators for data types

For vector store specific filter syntax, refer to:

* Milvus: [Filtering Documentation](https://milvus.io/docs/boolean.md)
* Zilliz: [Filtering Documentation](https://docs.zilliz.com/docs/filtering)
* Qdrant: [Filtering Documentation](https://qdrant.tech/documentation/concepts/filtering/)
* Chroma: [Where Filters Documentation](https://docs.trychroma.com/docs/querying-collections/metadata-filtering)
* Pinecone: [Metadata Filtering Guide](https://docs.pinecone.io/guides/data/filter-with-metadata#metadata-query-language)