---
title: "Knowledge Retrieval Settings — AVCodex Docs"
description: "Knowledge Retrieval Settings — AVCodex documentation for AV integrators, programmers, and ops teams."
lang: en
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---

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[Docs](/docs)/ Guides / Knowledge & Memory 

# Knowledge Retrieval Settings

Last updated · MAR 2026 · [Read as Markdown](/docs/guides/knowledge-retrieval-settings.md)

When you add knowledge sources (manufacturer datasheets, programming guides, internal SOPs, AS-built drawings) to an AVCodex agent, the AI uses **Retrieval-Augmented Generation (RAG)** to find and use relevant information. Advanced RAG Settings let you tune that process.

## [How your agent uses your documents# ](#how-your-agent-uses-your-documents)

Before the dials, here is how the agent actually finds and uses information from uploaded documents.

### [What is RAG# ](#what-is-rag)

**RAG (Retrieval-Augmented Generation)** is how the agent "reads" your documents to answer questions. Instead of memorizing everything, the AI:

1.  **Searches** your documents for relevant information when a user asks a question.
2.  **Retrieves** the most relevant sections.
3.  **Generates** a response using that information.

Think of it like a research-savvy junior engineer who pulls the exact spec page out of a Biamp manual before answering, rather than trying to recall the whole binder from memory.

### [What is a "chunk"# ](#what-is-a-chunk)

When you upload a document, AVCodex automatically breaks it into smaller pieces called **chunks**. A chunk is typically a paragraph or a few paragraphs of text.

**Why chunks?**

-   AI models cap how much text they can process at once.
-   Smaller pieces let the AI find the exact relevant section, not the whole document.
-   It is faster and more accurate than searching full documents.

**Example:** A 200-page Crestron programming guide might split into 600 chunks. When a programmer asks a question, the agent searches all 600 chunks and pulls only the 5 most relevant ones.

### [How search works# ](#how-search-works)

When a user asks a question, AVCodex turns both the question and your document chunks into mathematical representations (called embeddings). It then finds chunks whose meaning is closest to the question.

This is **semantic search**. It understands meaning, not only keywords. So "How do I reset a DM-NVX endpoint?" can match a chunk titled "Restoring a DigitalMedia receiver to factory defaults" even when the wording differs.

### [Standard search vs hybrid search# ](#standard-search-vs-hybrid-search)

**Standard search (default)**

Looks at each chunk independently and finds the ones most similar to the user's question.

-   Good for: FAQ-style content, focused topics, smaller knowledge bases.
-   Best when: each chunk contains a self-contained answer.

**Hybrid search**

Combines two strategies:

1.  **Chunk search**: Which individual chunks match the question best?
2.  **Document search**: Which whole documents are most relevant overall?

It then boosts chunks that come from highly relevant documents, even when those chunks are not the top individual matches.

-   Good for: multiple documents on similar topics, long documents, broad questions.
-   Best when: context from the same document matters, or you have 10+ files (think a full library of QSC, Crestron, and Extron programming guides).

**Example:** A user asks about "warranty replacement procedure." Standard search might pull one good chunk from your service SOP and another from a random product page. Hybrid search recognizes that your "RMA and Warranty Process" document is highly relevant overall, so it favors chunks from that document.

## [Accessing RAG settings# ](#accessing-rag-settings)

1.  Open your agent in the AVCodex dashboard.
2.  Go to the **Build** tab.
3.  Scroll to **Knowledge Sources**.
4.  Click **Advanced RAG Settings** to expand.

## [Settings reference# ](#settings-reference)

### [Relevance threshold# ](#relevance-threshold)

**Default: 0.15** | Range: 0.00 to 1.00

Controls how closely a document chunk must match the user's query to be included in the agent's context.

Value

Behavior

**Lower (0.00 to 0.15)**

More results included, may pull loosely related content.

**Medium (0.15 to 0.30)**

Balanced (recommended starting point).

**Higher (0.30 to 1.00)**

Stricter matching, only highly relevant content.

**When to adjust:**

-   **Lower the threshold** if users say the agent is missing information that is in your documents.
-   **Raise the threshold** if responses bring in too much off-topic material or the agent looks confused by conflicting sources.

### [Max chunks# ](#max-chunks)

**Default: 5** | Range: 1 to 20

The maximum number of document chunks retrieved per user query. Each chunk is a section of your uploaded content.

Value

Behavior

**Fewer chunks (1 to 3)**

Focused responses, lower token use, faster.

**Moderate (4 to 7)**

Balanced context (recommended).

**More chunks (8 to 20)**

Wide coverage, higher token use.

**When to adjust:**

-   **Use fewer chunks** for simple Q&A where the answer is usually in one place (a touch panel error message lookup, for example).
-   **Use more chunks** when questions need synthesis across sources (a design engineering question that pulls from a Biamp DSP manual, an Extron switcher datasheet, and a network spec).

**Note:** More chunks means more context, but also more tokens and slower responses.

### [Hybrid search# ](#hybrid-search)

**Default: Off**

When on, combines two search strategies:

1.  **Chunk-level search**: Finds the chunks most similar to the query.
2.  **Document-level search**: Considers which documents are most relevant overall.

This helps when the best answer comes from a document that is highly relevant overall, even if no single chunk perfectly matches the query.

**When to enable:**

-   You have many documents on similar topics (a full set of Q-SYS Lua reference docs, for example).
-   Users ask broad questions that span sections.
-   You notice the agent missing context from documents you know are relevant.

**When to keep off:**

-   Simple, focused knowledge bases.
-   When chunk-level matching is already working well.

### [Document weight# ](#document-weight)

**Default: 0.30** | Range: 0.00 to 1.00 | Only visible when Hybrid Search is on.

Controls the balance between chunk-level and document-level relevance when Hybrid Search is enabled.

Value

Behavior

**Lower (0.00 to 0.25)**

Prioritize individual chunk matches (chunk focus).

**Balanced (0.25 to 0.50)**

Equal weight to both signals.

**Higher (0.50 to 1.00)**

Prioritize chunks from highly relevant documents (document focus).

**When to adjust:**

-   **Lower values** when your documents cover very different topics and chunk relevance is most important.
-   **Higher values** when you have long documents (a 300-page programming guide) where context from the same document is worth keeping together.

## [Recommended configurations# ](#recommended-configurations)

### [Field tech support agent# ](#field-tech-support-agent)

code 

```
Relevance Threshold: 0.20
Max Chunks: 5
Hybrid Search: Off
```

Good for FAQ-style knowledge bases where answers are self-contained, like a touch panel error code lookup.

### [Design engineering assistant# ](#design-engineering-assistant)

code 

```
Relevance Threshold: 0.10
Max Chunks: 10
Hybrid Search: On
Document Weight: 0.40
```

Pulls more context for synthesizing answers across multiple manufacturer manuals and internal standards.

### [Programming reference agent# ](#programming-reference-agent)

code 

```
Relevance Threshold: 0.25
Max Chunks: 7
Hybrid Search: On
Document Weight: 0.30
```

Balances precision with coverage for Crestron SIMPL, SIMPL#, and Q-SYS Lua questions.

### [RFP and compliance agent# ](#rfp-and-compliance-agent)

code 

```
Relevance Threshold: 0.15
Max Chunks: 8
Hybrid Search: On
Document Weight: 0.50
```

Keeps relevant policy and standards documents (corporate AV standards, accessibility requirements, network security policies) well-represented.

## [Troubleshooting# ](#troubleshooting)

### [Agent does not find information that exists in my documents# ](#agent-does-not-find-information-that-exists-in-my-documents)

1.  Lower **Relevance Threshold** to 0.10.
2.  Raise **Max Chunks** to 8 to 10.
3.  Turn on **Hybrid Search** if you have multiple related documents.

### [Responses include irrelevant information# ](#responses-include-irrelevant-information)

1.  Raise **Relevance Threshold** to 0.25 to 0.35.
2.  Drop **Max Chunks** to 3 to 4.
3.  Review your document organization.

### [Token usage is too high# ](#token-usage-is-too-high)

1.  Drop **Max Chunks** to 3 to 4.
2.  Raise **Relevance Threshold** to cut low-quality matches.
3.  Consider splitting large documents into focused topics (per manufacturer or per system, for example).

### [Agent gives conflicting information# ](#agent-gives-conflicting-information)

1.  Raise **Relevance Threshold** to be more selective.
2.  With Hybrid Search on, raise **Document Weight** to favor cohesive document sources.
3.  Audit documents for outdated or contradictory content (old AS-builts vs. current ones).

## [Resetting to defaults# ](#resetting-to-defaults)

Click **Reset to defaults** in the Advanced RAG Settings panel to restore all values. Useful if you have been experimenting and want to start fresh.

## [Using with MCP# ](#using-with-mcp)

You can manage knowledge sources and RAG settings through code using the AVCodex MCP Server.

### [Available tools# ](#available-tools)

Tool

Description

`list_knowledge_sources`

List all sources with processing status.

`add_url_source`

Add a URL with crawl depth options.

`add_document_source`

Upload documents (base64 encoded).

`delete_knowledge_source`

Remove a knowledge source.

`refresh_knowledge_source`

Re-crawl or re-process a source.

`update_settings`

Modify RAG settings (topK, temperature, etc.).

### [Example prompts# ](#example-prompts)

code 

```
"List all knowledge sources for my field tech agent"
"Add https://docs.crestron.com with crawl depth 3"
"Refresh the outdated Biamp programming guide"
"Update my agent to retrieve 10 chunks instead of 5"
```

### [Workflow: knowledge base setup# ](#workflow-knowledge-base-setup)

code 

```
You: Set up knowledge sources for my service desk agent.

AI: [Calls add_url_source for docs site]
[Calls add_url_source for FAQ page]
Added 2 knowledge sources:
- docs.crestron.com (crawling, depth 2)
- support.qsc.com/faq (crawling, depth 1)

You: Increase max chunks to 8 for better coverage.

AI: [Calls update_settings with topK=8]
Updated RAG settings. Your agent will now retrieve up to 8
chunks per query.
```

> **Note:** MCP server access requires a Builder plan or higher. See the [MCP Setup Guide](/docs/guides/mcp/setup) to get started.

\*AVCodex · Your AV expertise. Amplified by AI.\*

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On this page

-   [How your agent uses your documents](#how-your-agent-uses-your-documents)
-   [What is RAG](#what-is-rag)
-   [What is a "chunk"](#what-is-a-chunk)
-   [How search works](#how-search-works)
-   [Standard search vs hybrid search](#standard-search-vs-hybrid-search)
-   [Accessing RAG settings](#accessing-rag-settings)
-   [Settings reference](#settings-reference)
-   [Relevance threshold](#relevance-threshold)
-   [Max chunks](#max-chunks)
-   [Hybrid search](#hybrid-search)
-   [Document weight](#document-weight)
-   [Recommended configurations](#recommended-configurations)
-   [Field tech support agent](#field-tech-support-agent)
-   [Design engineering assistant](#design-engineering-assistant)
-   [Programming reference agent](#programming-reference-agent)
-   [RFP and compliance agent](#rfp-and-compliance-agent)
-   [Troubleshooting](#troubleshooting)
-   [Agent does not find information that exists in my documents](#agent-does-not-find-information-that-exists-in-my-documents)
-   [Responses include irrelevant information](#responses-include-irrelevant-information)
-   [Token usage is too high](#token-usage-is-too-high)
-   [Agent gives conflicting information](#agent-gives-conflicting-information)
-   [Resetting to defaults](#resetting-to-defaults)
-   [Using with MCP](#using-with-mcp)
-   [Available tools](#available-tools)
-   [Example prompts](#example-prompts)
-   [Workflow: knowledge base setup](#workflow-knowledge-base-setup)

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