Creating Knowledge Bases

Create knowledge bases using studio.create_knowledge_base() with customizable vector stores, embedding models, and configurations.

Quick Start

from lyzr import Studio

studio = Studio(api_key="your-api-key")

kb = studio.create_knowledge_base(
    name="product_docs",
    vector_store="qdrant",
    embedding_model="text-embedding-3-large",
    llm_model="gpt-4o",
    description="Product documentation knowledge base"
)

print(f"Created KB: {kb.id}")

Function Signature

studio.create_knowledge_base(
    name: str,
    vector_store: str = "qdrant",
    embedding_model: str = "text-embedding-3-large",
    llm_model: str = "gpt-4o",
    description: str = None,
    **kwargs
) -> KnowledgeBase

Parameters

Parameter	Type	Required	Default	Description
`name`	str	Yes	-	KB name (lowercase, numbers, underscores only)
`vector_store`	str	No	`"qdrant"`	Vector store provider
`embedding_model`	str	No	`"text-embedding-3-large"`	Embedding model for vectorization
`llm_model`	str	No	`"gpt-4o"`	LLM for query processing
`description`	str	No	None	KB description (max 1000 chars)

Name Requirements

The name parameter must:

Contain only lowercase letters, numbers, and underscores
Be 1-100 characters long

# Valid names
kb = studio.create_knowledge_base(name="customer_support")
kb = studio.create_knowledge_base(name="product_docs_2024")
kb = studio.create_knowledge_base(name="faq_kb")

# Invalid names (will raise ValidationError)
kb = studio.create_knowledge_base(name="Customer Support")  # Uppercase, spaces
kb = studio.create_knowledge_base(name="product-docs")      # Hyphens

Vector Store Options

Provider	Value	Description
Qdrant	`"qdrant"`	High-performance, default choice
Weaviate	`"weaviate"`	Open-source, GraphQL API
PG Vector	`"pg_vector"`	PostgreSQL extension
Milvus	`"milvus"`	Scalable, cloud-native
Neptune	`"neptune"`	Amazon Neptune

Examples

# Qdrant (default)
kb = studio.create_knowledge_base(
    name="docs_qdrant",
    vector_store="qdrant"
)

# Weaviate
kb = studio.create_knowledge_base(
    name="docs_weaviate",
    vector_store="weaviate"
)

# PG Vector (PostgreSQL)
kb = studio.create_knowledge_base(
    name="docs_postgres",
    vector_store="pg_vector"
)

# Milvus
kb = studio.create_knowledge_base(
    name="docs_milvus",
    vector_store="milvus"
)

# Amazon Neptune
kb = studio.create_knowledge_base(
    name="docs_neptune",
    vector_store="neptune"
)

Embedding Models

The embedding model converts text into vectors for similarity search.

# OpenAI text-embedding-3-large (default, recommended)
kb = studio.create_knowledge_base(
    name="my_kb",
    embedding_model="text-embedding-3-large"
)

# OpenAI text-embedding-3-small (faster, smaller)
kb = studio.create_knowledge_base(
    name="my_kb",
    embedding_model="text-embedding-3-small"
)

LLM Model

The LLM model processes queries and generates responses from retrieved content.

# GPT-4o (default)
kb = studio.create_knowledge_base(
    name="my_kb",
    llm_model="gpt-4o"
)

# GPT-4o Mini (faster, cheaper)
kb = studio.create_knowledge_base(
    name="my_kb",
    llm_model="gpt-4o-mini"
)

Examples

Basic Knowledge Base

kb = studio.create_knowledge_base(name="support_docs")

Full Configuration

kb = studio.create_knowledge_base(
    name="enterprise_kb",
    vector_store="qdrant",
    embedding_model="text-embedding-3-large",
    llm_model="gpt-4o",
    description="Enterprise documentation for customer support"
)

Multiple Knowledge Bases

# Product documentation
product_kb = studio.create_knowledge_base(
    name="product_docs",
    description="Product manuals and guides"
)

# FAQ knowledge base
faq_kb = studio.create_knowledge_base(
    name="faq_kb",
    description="Frequently asked questions"
)

# Policy documents
policy_kb = studio.create_knowledge_base(
    name="policy_docs",
    description="Company policies and procedures"
)

Return Value

Returns a KnowledgeBase object with methods:

Method	Description
`add_pdf()`	Add PDF document
`add_docx()`	Add Word document
`add_txt()`	Add text file
`add_website()`	Add website content
`add_text()`	Add raw text
`query()`	Search the knowledge base
`list_documents()`	List all documents
`delete_documents()`	Delete specific documents
`reset()`	Clear all documents
`update()`	Update configuration
`delete()`	Delete the knowledge base
`with_config()`	Create runtime configuration

KnowledgeBase Properties

Property	Type	Description
`id`	str	Unique identifier
`name`	str	KB name
`collection_name`	str	Vector DB collection name
`description`	str	KB description
`vector_store_provider`	str	Vector store display name
`embedding_model`	str	Embedding model name
`llm_model`	str	LLM model name
`created_at`	str	Creation timestamp
`updated_at`	str	Last update timestamp

Error Handling

from lyzr.exceptions import ValidationError, APIError

try:
    kb = studio.create_knowledge_base(
        name="Invalid Name!",  # Invalid characters
        vector_store="unknown_store"  # Unknown provider
    )
except ValidationError as e:
    print(f"Validation error: {e.message}")
except APIError as e:
    print(f"API error: {e.message}")

Next Steps

After creating a knowledge base, add documents:

kb = studio.create_knowledge_base(name="my_kb")

# Add documents
kb.add_pdf("manual.pdf")
kb.add_website("https://docs.example.com")

# Use with an agent
response = agent.run("Question?", knowledge_bases=[kb])

See Adding Documents for more details.

Getting Started

Agents

Knowledge Bases

Memory

Tools

Contexts

RAI Guardrails

Structured Outputs

File & Image Output

Streaming

Reference

Creating Knowledge Bases

Quick Start

Function Signature

Parameters

Name Requirements

Vector Store Options

Examples

Embedding Models

LLM Model

Examples

Basic Knowledge Base

Full Configuration

Multiple Knowledge Bases

Return Value

KnowledgeBase Properties

Error Handling

Next Steps

​Quick Start

​Function Signature

​Parameters

​Name Requirements

​Vector Store Options

​Examples

​Embedding Models

​LLM Model

​Examples

​Basic Knowledge Base

​Full Configuration

​Multiple Knowledge Bases

​Return Value

​KnowledgeBase Properties

​Error Handling

​Next Steps

Quick Start

Function Signature

Parameters

Name Requirements

Vector Store Options

Examples

Embedding Models

LLM Model

Examples

Basic Knowledge Base

Full Configuration

Multiple Knowledge Bases

Return Value

KnowledgeBase Properties

Error Handling

Next Steps