Advanced Settings

Fine-tune knowledge base behavior with advanced configuration options.

Search Methods

Semantic Search
Keyword Search
Hybrid Search
Reranking

Uses vector embeddings to understand meaning

Configuration	Options	Default
Embedding model	FastEmbed, OpenAI, Multilingual	FastEmbed
Similarity threshold	0.0 - 1.0	0.7
Top-K results	1 - 50	5

Best For: Natural language queries, conceptual similarity, finding related information

Example: “climate initiatives” matches “environmental programs”, “sustainability efforts”

Traditional keyword matching with BM25 ranking

Configuration	Options	Default
BM25 parameters	k1, b values	k1=1.2, b=0.75
Stop words	Enable/disable	Enabled
Stemming	Language-specific	English

Best For: Exact term searches, technical terminology, IDs and codes

Example: “SKU-12345” requires exact match

Combines semantic + keyword search

Configuration	Options	Default
Alpha parameter	0.0 - 1.0	0.5
Fusion method	RRF, Relative scoring	RRF
Reranking	Enable/disable	Disabled

Recommended: Start with alpha=0.5 for balanced results between keyword and semantic search

Best For: Most general-purpose queries with balance between precision and recall

Retrieval Settings

Top-K (Number of Chunks)

How many chunks to retrieve:Recommendations:

5-10 chunks: Most use cases
3-5 chunks: Quick answers, cost-sensitive
10-20 chunks: Complex questions, comprehensive answers

Trade-offs:

More chunks = more context but slower and costlier
Fewer chunks = faster but may miss information

Similarity Threshold

Filter out low-relevance chunks:Range: 0.0 (all results) to 1.0 (exact match only)Recommendations:

0.5-0.6: Lenient, more results
0.7: Balanced, good default
0.8+: Strict, high precision

Chunk Size

Size of document segments:Options:

256 tokens: Precise, more chunks needed
512 tokens: Balanced, recommended default
1024 tokens: More context per chunk

Considerations:

Smaller chunks = more precise but need more retrievals
Larger chunks = more context but less precise

Chunk Overlap

Overlap between consecutive chunks:Typical: 50-100 tokensPurpose:

Prevents information loss at boundaries
Ensures continuity of context
Improves retrieval quality

Embedding Models

Model	Quality	Speed	Cost	Best For
FastEmbed (Default)	⭐⭐⭐	⚡⚡⚡	💰	Most general use cases
OpenAI Embeddings	⭐⭐⭐⭐⭐	⚡⚡	💰💰💰	Critical applications, maximum accuracy
Multilingual Models	⭐⭐⭐⭐	⚡⚡	💰💰	International documents, 100+ languages

FastEmbed provides the best balance of quality, speed, and cost for most use cases. Upgrade to OpenAI for mission-critical applications or multilingual models for international content.

Advanced Features

Folder-Based Search

Limit search to specific folders:Benefits:

Faster searches
More relevant results
Domain-specific retrieval
Organized knowledge

Use Cases:

Search only “HR Policies” for HR questions
Search only “Product Docs” for technical questions
Separate public vs internal documents

Metadata Filtering

Filter by custom metadata fields:Examples:

Only documents from “Engineering” department
Only documents tagged “2024”
Only documents by specific author
Only documents of type “Policy”

Configuration:

{
  "metadata_filter": {
    "department": "Engineering",
    "year": "2024",
    "doc_type": "Technical Spec"
  }
}

Citation Configuration

Control how sources are cited:Options:

Include page numbers
Show file names
Display chunk IDs
Add custom metadata in citations

Example Output:

Source: Installation Guide.pdf, Page 3
Department: Engineering
Last Updated: 2024-01-15

Managing Through Flows

Automate knowledge base operations with flows

Overview

Concepts

Flows

Agents

Genie

Knowledge Bases

Data Tables

Variables & Secrets

Tests

Analytics

Search Methods

Retrieval Settings

Embedding Models

Advanced Features

Managing Through Flows

​Search Methods

​Retrieval Settings

​Embedding Models

​Advanced Features

Managing Through Flows

Search Methods

Retrieval Settings

Embedding Models

Advanced Features