Skip to content

KB Explorer

The KB Explorer is Beyond Retrieval's most advanced retrieval strategy. Instead of running a single search query, it deploys an AI agent that autonomously researches your question — browsing files, searching by keywords and meaning, reading documents, and reasoning about what it finds.

Think of it as an AI research assistant that explores your knowledge base the way a human would: look around, search, read, think, and repeat until it has enough information to give a thorough answer.


When to Use KB Explorer

Scenario Best Strategy
Simple factual lookup Fusion or Semantic
Keyword-specific search Full-Text or Hybrid
Complex or multi-part questions KB Explorer
Questions requiring reasoning across multiple documents KB Explorer
Exploratory research ("What do we know about X?") KB Explorer

KB Explorer shines when:

  • Your question has multiple parts (e.g., "Compare Q3 and Q4 results and identify trends")
  • The answer requires connecting information from different documents
  • You're not sure which documents contain the answer
  • The question is broad or exploratory

How It Works

When you send a message with KB Explorer enabled, the AI agent follows a research loop:

graph TD
    A["Your Question"] --> B["Break Down Question"]
    B --> C["Browse & Search"]
    C --> D["Read Documents"]
    D --> E{"Enough Info?"}
    E -->|"No — gaps found"| C
    E -->|"Yes — sufficient"| F["Generate Answer"]
  1. Decompose — The agent breaks your question into smaller, focused sub-questions
  2. Browse — It maps out what documents are available in your notebook
  3. Search — It runs keyword and semantic searches for each sub-question
  4. Read — It reads the most relevant document chunks
  5. Reflect — It checks: "Do I have enough information to answer well?"
  6. Iterate — If gaps exist, it searches again with refined queries
  7. Answer — Once satisfied, it synthesizes everything into a comprehensive response

The 15 Tools

The KB Explorer agent has 15 specialized tools at its disposal. During a research session, you'll see these appear in the "Researching your question" panel.

Browse Tools

These tools help the agent understand what's in your knowledge base.

Tool What You'll See What It Does
Browsing documents "Listing files in your knowledge base" Lists all files in the notebook — like opening a folder to see what's inside
Browsing documents "Mapping your knowledge base" Shows the full file structure as a tree — gives the big picture of all documents and their sizes
Browsing documents "Finding files by pattern" Finds files by name pattern — e.g., all PDFs, or files with "report" in the name

Search Tools

These tools find relevant content across your documents.

Tool What You'll See What It Does
Searching for keywords "Scanning text content" Keyword search — scans all documents for exact words or phrases, like ++ctrl+f++ across everything
Finding relevant passages "Semantic search across documents" Smart search — finds passages by meaning, not just exact words. Understands that "revenue growth" and "sales increase" mean similar things
Finding relevant passages "Precision search with tuned parameters" Same as above but with fine-tuned parameters for more precise results
Finding related content "Discovering similar passages" Given a passage it already found, looks for other passages that discuss similar topics
Expanding search terms "Broadening search for better results" Rewrites the search query to be broader or more specific — helps find content the original search missed

Read Tool

Tool What You'll See What It Does
Reading document "Extracting content from source" Reads a specific document chunk in full — this is how the agent actually reads your files. Every chunk it reads becomes a potential source for the answer.

Reasoning Tools

These tools allow the agent to think, analyze, and evaluate what it has found.

Tool What You'll See What It Does
Breaking down your question "Identifying sub-questions" Splits your question into smaller, focused sub-questions. For example, "What are the Q3 financial trends?" might become: (1) What are current revenue trends? (2) How do they compare to Q2? (3) What's the projected growth?
Analyzing findings "Evaluating collected information" Examines all collected evidence to find patterns, contradictions, and key findings. Flags if two documents say conflicting things.
Checking answer quality "Evaluating sufficiency of evidence" Asks: "Do I have enough to answer well?" Returns a confidence score (0-100%). If below threshold, the agent keeps searching.
Summarizing findings "Condensing collected information" Creates a structured summary of everything found so far — key points, entities, dates, and topics.
Extracting key facts "Pulling structured data from sources" Pulls out specific facts, numbers, dates, and data points from the sources it has read.

Quality Check Tool

Tool What You'll See What It Does
Checking completeness "Verifying all questions are covered" Maps each sub-question to a status: Covered (fully answered), Partial (some info found), or Missing (no info yet). Shows an overall coverage percentage.

What You See in the Chat

When KB Explorer is active, you'll see a live progress panel showing each research step:

During Research (Live)

The panel shows each step as it happens:

  • Step number and icon — Color-coded by category (blue for browse, amber for search, green for read, purple for reasoning)
  • Friendly tool name — e.g., "Finding relevant passages" instead of technical names
  • Description — What the tool is doing right now
  • Result count — How many results were found (shown as a badge)
  • Duration — How long each step took
  • Sub-questions — When the agent breaks down your question, the sub-questions appear indented
  • Confidence score — When the agent checks answer quality, a progress bar shows 0-100%

After Research (Collapsed)

Once the agent finishes researching, the panel collapses to a single line:

Researched 6 steps in 1.8s · Complete

Click it to expand and see the full research trail.


Typical Research Flows

Simple Question (3-5 steps)

1. Browsing documents     — Map the knowledge base
2. Searching for keywords — Find relevant content
3. Reading document       — Read the best match
→ Answer generated

Complex Question (8-12 steps)

1. Breaking down your question  — Split into 3 sub-questions
2. Browsing documents           — Map available files
3. Searching for keywords       — Search for sub-question 1
4. Finding relevant passages    — Semantic search for sub-question 2
5. Reading document             — Read Q3_Report.pdf, page 4
6. Reading document             — Read Annual_Review.pdf, page 12
7. Checking answer quality      — Score: 65% — gaps found
8. Expanding search terms       — Broaden search for missing info
9. Finding relevant passages    — New search with expanded query
10. Reading document            — Read Market_Analysis.docx
11. Checking answer quality     — Score: 92% — sufficient
→ Answer generated with citations from 3 sources

Configuration

Enable KB Explorer

In Notebook Settings > Intelligence > RAG Strategy, select KB Explorer.

Alternatively, via the API:

{
  "strategies_config": {
    "strategy_id": "kb-explorer"
  }
}

Parameters

Parameter Default Description
max_steps 15 Maximum tool calls the agent can make (0 = unlimited, capped at 50)
use_keyword_fallback false If true, also runs a keyword search before the agent loop

Choosing max_steps

For most questions, 8-12 steps is sufficient. Increase to 20+ for very broad research questions. The agent will stop earlier if it reaches high confidence before the limit.


How It Compares

Feature Standard Search KB Explorer
Query understanding Literal match Decomposes into sub-questions
Search strategy Single pass Multiple targeted searches
Iteration None Searches again if gaps found
Reasoning None Analyzes, reflects, checks coverage
Best for Simple lookups Complex research
Speed ~200ms 2-15 seconds
LLM calls 1 (generation only) 3-8 (research + generation)

Troubleshooting

Agent takes too long

Reduce max_steps to 8-10, or switch to a faster LLM model (e.g., openai/gpt-4o-mini).

Agent doesn't find relevant content

  • Make sure your documents are ingested and embedded
  • Try a different embedding model with better multilingual support
  • Check if the document content was parsed correctly in the Documents page

"Checking answer quality" shows low score

This means the agent couldn't find enough relevant content. This usually indicates:

  • The answer isn't in your documents
  • The documents need to be re-ingested with a different parser
  • Try rephrasing your question to be more specific