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

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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}")

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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

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Parameters

ParameterTypeRequiredDefaultDescription
namestrYes-KB name (lowercase, numbers, underscores only)
vector_storestrNo"qdrant"Vector store provider
embedding_modelstrNo"text-embedding-3-large"Embedding model for vectorization
llm_modelstrNo"gpt-4o"LLM for query processing
descriptionstrNoNoneKB description (max 1000 chars)

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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

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Vector Store Options

ProviderValueDescription
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

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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"
)

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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"
)

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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"
)

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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"
)

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Return Value

Returns a KnowledgeBase object with methods:
MethodDescription
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

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KnowledgeBase Properties

PropertyTypeDescription
idstrUnique identifier
namestrKB name
collection_namestrVector DB collection name
descriptionstrKB description
vector_store_providerstrVector store display name
embedding_modelstrEmbedding model name
llm_modelstrLLM model name
created_atstrCreation timestamp
updated_atstrLast update timestamp

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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.