Creating Knowledge Bases

Create and configure knowledge bases for RAG

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.