File Search

File Search Configuration

Overview

The File Search node queries your knowledge folders to retrieve relevant information and context. Connect your workflow to your organization’s knowledge base - search through documents, files, and data stored in knowledge folders to enrich AI responses, validate information, or provide context for decisions.

Best for: Knowledge retrieval, document search, context enrichment, RAG (Retrieval Augmented Generation), and accessing organizational knowledge.

When to Use File Search

Perfect for:

• Searching company documentation and knowledge bases

• Retrieving relevant context for AI agent responses

• Finding specific information across multiple documents

• Implementing RAG (Retrieval Augmented Generation) patterns

• Validating information against internal knowledge

• Enriching workflows with organizational data

Not ideal for:

• Real-time web search (use Web Search node)

• Fetching data from external APIs (use HTTP Request node)

• Processing individual files (use direct file attachments)

Configuration

File Search Configuration

Knowledge Folder

Select the knowledge folder to search from your workspace’s available folders.

Options:

• Choose from connected knowledge folders

• Each folder contains your uploaded documents and files

• Folders can include PDFs, Word docs, spreadsheets, and more

Search Query

The search query to find relevant information. Supports Manual, Auto, and Prompt AI modes.

Manual mode examples:

{{trigger.output.customer_question}}

Find information about {{trigger.output.product_name}} pricing and features

Prompt mode example:

Generate a search query to find relevant information about the customer's question: {{trigger.output.question}}

Max Results

The maximum number of relevant results to return (default: 5)

Recommendations:

• 1-3 results: Focused, specific queries

• 5-10 results: Broader context needed

• 10+ results: Comprehensive searches (may impact performance)

How It Works

1

Query Processing

Query is processed against the selected knowledge folder.

2

Semantic Search

Semantic search finds the most relevant document chunks.

3

Ranking

Results are ranked by relevance score.

4

Return Top Results

Top N results are returned based on max results setting.

5

Use Results

Retrieved information is available to subsequent nodes.

Example Use Cases

Customer Support with Knowledge Base

Form Trigger (Customer question)
→ File Search: Query knowledge folder with {{trigger.question}}
  Knowledge Folder: "Support Documentation"
  Max Results: 5
→ Agent: Answer question using search results
  Context: {{file_search.output.results}}
  Question: {{trigger.question}}
→ Notification: Send answer to customer

Product Information Lookup

Integration Trigger (Slack question about product)
→ File Search: Search product knowledge
  Knowledge Folder: "Product Information"
  Query: {{trigger.message}}
  Max Results: 3
→ Agent: Summarize relevant product details
→ Action: Reply in Slack thread

Document Validation

Form Trigger (User claim submission)
→ File Search: Find relevant policies
  Knowledge Folder: "Company Policies"
  Query: "{{trigger.claim_type}} policy requirements"
  Max Results: 5
→ Agent: Validate claim against policies
  Policies: {{file_search.output.results}}
  Claim: {{trigger.claim_details}}
→ Condition: Approved or requires review?

Accessing Search Results

Access the retrieved information in subsequent nodes:

{{file_search.output.results}}
{{file_search.output.results[0].content}}
{{file_search.output.results[0].score}}
{{file_search.output.results[0].source}}

Result Structure

Each result contains:

• content: The relevant text chunk from the document

• score: Relevance score (0-1, higher is more relevant)

• source: Source file name and location

• metadata: Additional file metadata

Using in Agent prompts:

Context from knowledge base:
{{file_search.output.results}}

Based on the above context, answer this question:
{{trigger.output.question}}

Limitations

• Knowledge Folder Scope: Only searches within the selected knowledge folder

• Result Quality: Depends on quality and completeness of uploaded documents

• Chunk Size: Large documents are split into chunks; relevant information might span multiple results

• Real-time Updates: Document changes require reprocessing before they appear in search results

Important: Ensure your knowledge folders are regularly updated with current information for accurate search results.

Best Practices

• Write Specific Search Queries More specific queries return more relevant results. Include key terms, product names, or topics rather than generic searches.

• Adjust Max Results Based on Use Case Start with 5 results and adjust based on response quality. Too few might miss important context, too many can dilute relevance.

• Keep Knowledge Folders Organized Organize knowledge folders by topic or domain for more targeted searches. Separate technical docs from marketing content.

• Combine with Agent Nodes File Search is most powerful when combined with Agent nodes. The agent can synthesize and interpret the retrieved information.

• Test with Real Queries Test your file search with actual questions users might ask to ensure knowledge folder content is sufficient and queries return relevant results.

• Handle No Results Add a condition after file search to handle cases where no relevant results are found. Provide fallback responses or escalation paths.

Next Steps

Topic

Agent Node — Process and synthesize search results with AI

Target

Agent

Topic

Knowledge Folders — Learn how to set up and manage knowledge folders

Target

https://docs.langdock.com/resources/integrations/knowledge-folders

Topic

Web Search — Search the internet for current information

Target

Web Search

Topic

Condition Node — Route based on search result quality

Target

Condition