Classic Knowledge Base

Lyzr Studio’s Classic Knowledge Base feature allows you to create a no-code RAG pipeline for fast, searchable document understanding. It’s ideal for building lightweight Q&A systems from files, URLs, and plain text sources — optimized for quick setup and cost-efficient usage.

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.

---

Creating and Managing a Knowledge Base

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

Create a Knowledge Base

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

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• Delete content: Remove outdated or irrelevant entries.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.

---

Supported File Types

The following formats can be uploaded to a Lyzr KB:

• .pdf
• .doc, .docx
• .txt
• Website URLs (via scraping)

---

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

---

Chunking Strategy

Chunking splits documents into smaller parts for better semantic indexing.

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.

---

Available Retrieval Types

Lyzr offers multiple retrieval mechanisms to suit different information needs:

a) Basic Retrieval

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

b) MMR (Maximal Marginal Relevance)

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

c) HyDE (Hypothetical Document Embeddings)

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

---

Retrieval-Augmented Generation (RAG)

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

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.

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

---

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.

---

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

1. Choose Knowledge Base Type

Before creation, you’ll be prompted to choose the type of Knowledge Base:

• Classic (for general documents and text)
• Knowledge Graph (for relationship-heavy data)
• Semantic Model (for structured databases)

Select Classic Knowledge Base to proceed.

---

2. Create a Classic Knowledge Base

Define your new KB by entering essential details:

• Name: A meaningful title (e.g., “Marketing FAQs”).
• Description: Briefly explain the purpose of this KB.
• Vector Database: Choose where embeddings will be stored — options include Qdrant, Weaviate, or others integrated with Lyzr.

Click Create to initialize your Classic Knowledge Base.

---

3. Add Content from Multiple Sources

Lyzr supports uploading or linking multiple data types. Content types supported:

• File Upload: PDF, DOCX, TXT, CSV, JSON.
• Web Links: Direct URLs of documentation pages or websites.
• Copy-paste text: Manually input chunks of content.

Lyzr auto-detects file formats and applies the right parser. Behind the scenes:

• Content is split into semantically coherent chunks.
• Embeddings are generated using LLMs.
• The vector store indexes these chunks for fast retrieval.

---

4. Query & Test

Once your Classic KB is populated, test it directly inside Studio:

• Search Input: Ask natural language questions.
• Chunk Count: Control number of results returned (default: 10).
• Retrieval Type: Basic (similarity-based).
• Score Threshold: Filter out low-score responses for higher precision.

You can continue uploading more files or editing your KB over time. Lyzr will automatically update the index.

---

5. Integrate with Agents

The Classic KB can now be connected to Lyzr agents:

• Choose the KB as a data source during agent creation.
• The agent will use this KB to perform Retrieval-Augmented Generation (RAG).
• No coding required — fully visual interface for mapping knowledge and deploying agents.

---

Summary

Feature	Description
Fast Setup	Upload or link content in minutes.
Cost-Effective Retrieval	Optimized for quick queries and basic document Q&A.
No-Code Interface	Simple visual UI for all KB operations.
Agent-Ready	Seamlessly connect to agents for real-time semantic Q&A.

---

The Classic Knowledge Base is best suited when you want to get started fast with document-based Q&A and lightweight RAG — no database or complex configuration needed.