> ## Documentation Index
> Fetch the complete documentation index at: https://pathlit.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Knowledge Base Search Node
> Search for similar data chunks from a knowledge base
## Overview
The Knowledge Base Search Node (embed-search) retrieves the most relevant chunks from a [Knowledge Base](/docs/guides/knowledge-base) for a given search query. It uses vector search (with optional reranking and continuation) so you can feed retrieved context into LLM nodes—a common [Retrieval Augmented Generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) pattern.
**Recommendation:** For most RAG and Q\&A use cases, we recommend using the **[Agent Node](/docs/nodes/agent) with the Knowledge Base Search tool** configured instead of this node. The agent can decide when and how to query the knowledge base and combine search results with other tools (e.g. web search). Use this **Knowledge Base Search Node** when you need a single, deterministic search step in the graph (e.g. a fixed query that always runs and passes results to an LLM).
You select a knowledge base, provide a search string (which can use format strings from workflow input), and optionally include XML-formatted metadata for citations. Retrieval can be **dense** (embeddings only), **sparse**, or **hybrid**; results can be reranked and expanded with neighboring chunks.
## Configuration Parameters
* **Knowledge Base**
Select the knowledge base to search. If you haven't created one yet, do so from the Knowledge Base page. The node will retrieve chunks from this index.
* **Search String**
The query used for semantic search. Supports format strings from workflow input (e.g. `{user_question}`).
Example: *What are the benefits of AI?*
* **Include Document Metadata for Citations**
When enabled, the output includes XML-formatted metadata for citations.
### Advanced Settings
* **\[VectorDB] Search Type**
**Similarity** — Retrieve by vector similarity only.
**MMR** — Maximal Marginal Relevance; balances similarity with diversity to reduce redundant chunks.
* **\[VectorDB] Retrieval Mode**
**Dense** — Use dense embeddings only (default).
**Sparse** — Use sparse embeddings only.
**Hybrid** — Combine dense and sparse.
Sparse and Hybrid require the knowledge base to have a sparse embedding model configured; otherwise the node returns an error and only Dense is supported.
* **\[VectorDB] Retrieve Top-N Chunks**
Number of most similar chunks to fetch from the vector store before reranking (0–100). Default: 40.
* **\[VectorDB] Similarity Score Threshold**
Minimum similarity score for results (0.0–1.0). Chunks below this threshold are excluded.
* **\[Reranker] Rerank Model**
Cohere model used to rerank retrieved chunks:
* **Cohere Rerank-v3.5**
* **Cohere Rerank-English-v3.0**
* **Cohere Rerank-Multilingual-v3.0**
* **\[Reranker] Return Top-N Chunks**
Number of chunks to keep after reranking (0–100). Default: 20.
* **\[Advanced RAG] Max Continuation Radius**
Maximum number of neighboring continuation chunks to add to each returned chunk for better context (0–20, step 5). Helps avoid cutting off mid-sentence or mid-paragraph.
## Expected Inputs and Outputs
* **Inputs**:
* **input**: Optional. Values can be referenced in the Search String using format strings (e.g. `{user_question}`). If omitted, the search string is used as-is. A search string must be provided (non-empty after formatting).
* **Outputs**:
* **output**: A string containing the retrieved data chunks. If "Include document metadata for citations" is enabled, the output includes XML-formatted citations.
## Use Case Examples
1. **Research Analysis**
Use this node to quickly find specific information or references within a large collection of research documents, streamlining your research workflow.
2. **Content Recommendation**
Content creators can use this node to identify similar articles or documents, aiding in the development of well-researched and informed content.
3. **Customer Support**
Enhance customer support by enabling chatbots to search and retrieve relevant knowledge base articles, providing quick and accurate responses to customer inquiries.
4. **Data Insights Extraction**
Extract specific insights from large datasets by setting a relevant search string, allowing for efficient data analysis without manual data sifting.
## Error Handling and Troubleshooting
* **Missing Knowledge Base**
If you see "A knowledge base must be selected," select a knowledge base from the dropdown.
* **Search String Required**
If you see "A search string must be provided," ensure the Search String is non-empty (after format-string substitution from input).
* **Sparse / Hybrid Retrieval**
If you see "Sparse embedding model is not set for this knowledge base. Only 'dense' retrieval mode is supported," the knowledge base does not have a sparse embedding model configured. Use **Dense** retrieval mode, or configure a sparse embedding model for the knowledge base to use Sparse or Hybrid.
If you experience any other issues not covered here, please contact our [support team](mailto:dev@pathlit.ai).
## Related Nodes
Recommended: use the Agent with Knowledge Base Search tool for RAG and Q\&A
Add content to knowledge bases for search
Create, populate, and search knowledge bases
Use retrieved chunks as context for RAG (when using this node)