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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)

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Integrations

KnowledgeBase supports a rich ecosystem of integrations for vector stores, embedding providers, document loaders, and text splitters.

Vector Stores

Chroma, Qdrant, Pinecone, Milvus, PGVector, FAISS, Weaviate, SuperMemory

Embedding Providers

OpenAI, Azure, Google, AWS Bedrock, HuggingFace, FastEmbed, Ollama

Document Loaders

PDF, DOCX, CSV, JSON, Markdown, HTML, XML, YAML, Text & more

Text Splitters

Recursive, Semantic, Agentic, Character, Markdown, HTML, JSON, Python