Docs/Guides/Knowledge & Memory

    Knowledge Retrieval Settings

    Last updated · MAR 2026·Read as Markdown

    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.

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

    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"#

    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#

    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 (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.

    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.

    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#

    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.

    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#

    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.

    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#

    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#

    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#

    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.

    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#

    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#

    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#

    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).

    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.

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

    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#

    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#

    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 to get started.

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