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Overview

KnowledgeBase enables you to build Retrieval-Augmented Generation (RAG) systems by automatically processing documents, creating embeddings, and storing them in vector databases. It integrates seamlessly with Agent and Task to provide relevant context for AI-powered queries.

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Key Features

  • Automatic Processing: Loads documents, chunks text, creates embeddings, and stores in vector databases
  • Multiple Formats: Supports PDFs, Markdown, DOCX, CSV, JSON, HTML, and more
  • Intelligent Chunking: Auto-detects optimal text splitting strategies
  • Flexible Storage: Works with Chroma, Milvus, Qdrant, Pinecone, Weaviate, FAISS, and PGVector
  • Hybrid Search: Combines dense vector search with full-text search for better results
  • Tool Integration: Can be used as a tool, allowing agents to actively search and retrieve information

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Installation

To use KnowledgeBase, you’ll need to install the required dependencies for your chosen vector database, document loaders, and embedding providers.
Example: Setting up KnowledgeBase with ChromaFor a complete RAG setup using Chroma as the vector database, PDF loader, and OpenAI embeddings:
uv pip install "upsonic[chroma]"
uv pip install "upsonic[pdf-loader]"
uv pip install "upsonic[embeddings]"
What each optional group provides:
  • [chroma] - ChromaDB vector database client
  • [pdf-loader] - PDF document loader (PyPDF)
  • [embeddings] - Embedding providers (OpenAI, Anthropic, etc.)
For other vector databases, replace chroma with qdrant, milvus, weaviate, pinecone, faiss, or pgvector. For other loaders, see the Loaders documentation.

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Example

Create a KnowledgeBase from documents and use it with an Agent: 
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode
from upsonic.loaders.pdf import PdfLoader
from upsonic.loaders.config import PdfLoaderConfig

# Setup embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())

# Setup vector database
config = ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
)
vectordb = ChromaProvider(config)

# Setup PDF loader
loader = PdfLoader(PdfLoaderConfig())

# Create knowledge base
kb = KnowledgeBase(
    sources=["document.pdf", "data/"],
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader]
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the main topics in the documents?",
    context=[kb]
)

result = agent.do(task)
print(result)