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Introduction

The Knowledge Base (KB) in Lyzr empowers AI agents to retrieve and utilize both structured and unstructured information for accurate, context-aware responses. It supports various file formats, advanced chunking strategies, and multiple retrieval methods to ensure high-quality information extraction.4

Knowledge Base Guide

  Learn how to manage data sources for your agents.

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Creating and Managing a Knowledge Base

Lyzr provides a streamlined interface via Lyzr Studio to manage Knowledge Bases:

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Create a Knowledge Base

  • Configure embedding, LLM, and vector store credentials.
  • Set retrieval and chunking strategies.
  • Define a unique name and description.

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Manage Content

  • Add content: Upload documents, enter text, or provide URLs.
  • Delete content: Remove outdated or irrelevant entries.
  • Update configuration: Change retrieval types or chunk settings anytime.

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Supported File Types

The following formats can be uploaded to a Lyzr KB:
  • .pdf
  • .doc, .docx
  • .txt
  • Website URLs (via scraping)

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Upload Limitations

To ensure optimal performance:
  • Max 5 files at a time
  • Each file must be less than 15MB
  • For better results, prefer batch-wise uploading

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Chunking Strategy

Chunking splits documents into smaller parts for better semantic indexing.

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Parameters:

  • Number of chunks: Number of sections generated.
  • Chunk size: Controls the length of each chunk.
  • Overlap: Adds context continuity across chunks.
This improves both retrieval quality and answer coherence.

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Available Retrieval Types

Lyzr offers multiple retrieval mechanisms to suit different information needs:

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a) Basic Retrieval

  • Default vector similarity-based retrieval.
  • Great for general knowledge lookups.

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b) MMR (Maximal Marginal Relevance)

  • Balances diversity and relevance.
  • Reduces duplicate content in retrieved results.

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c) HyDE (Hypothetical Document Embeddings)

  • Generates synthetic documents to simulate context.
  • Boosts open-ended query results.

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Retrieval-Augmented Generation (RAG)

Lyzr seamlessly integrates RAG to generate more accurate and grounded answers using knowledge base content.

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RAG Workflow

  1. Query Reception
    Agent receives a user question or instruction.
  2. Document Retrieval
    Top-N relevant documents are fetched using vector similarity.
  3. Reranking & Filtering
    Results are optionally refined for relevance.
  4. Prompt Assembly
    Retrieved context is combined with the original question.
  5. Generation
    LLM generates a grounded response using the assembled prompt.
  6. Citation & Delivery
    Output includes references to source documents for transparency.

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Core Components

  • Vector Store: Stores semantic vectors (e.g., Pinecone, FAISS, Qdrant)
  • Embedding Model: Transforms content into vectors (e.g., OpenAI, Cohere)
  • Reranker: Improves result ordering (optional)
  • Prompt Template: Defines how context + question are structured
  • Citation Module: Appends references to the output

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Simulator Testing

Once a Knowledge Base is created and populated:
  1. Navigate to the Agent Simulator in Lyzr Studio.
  2. Select the agent connected to your KB.
  3. Enter test prompts to evaluate:
    • Retrieval accuracy
    • Answer relevance
    • Citation correctness
  4. Adjust retrieval type, chunking, or KB content as needed.
Testing helps validate that the agent understands and uses the KB effectively before production deployment.

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Conclusion

Lyzr’s Knowledge Base system is a robust tool for enabling intelligent, grounded, and flexible AI responses. With support for diverse file types, retrieval strategies, and RAG integration, it provides a powerful foundation for domain-specific agents. Optimize your AI workflows by:
  • Configuring proper chunking
  • Choosing the right retrieval type
  • Uploading high-quality content in batches
  • Testing thoroughly with the simulator