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"] - Decompose — The agent breaks your question into smaller, focused sub-questions
- Browse — It maps out what documents are available in your notebook
- Search — It runs keyword and semantic searches for each sub-question
- Read — It reads the most relevant document chunks
- Reflect — It checks: "Do I have enough information to answer well?"
- Iterate — If gaps exist, it searches again with refined queries
- 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:
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