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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
RAG Installation
pip install upsonic[vectordb]
pip install upsonic[loaders]
pip install upsonic[embeddings]
These install Upsonic with RAG dependencies:
  • [vectordb] - Vector database clients (Chroma, Milvus, Qdrant, Pinecone, Weaviate, FAISS, PGVector)
  • [loaders] - Document loaders for PDFs, Markdown, DOCX, CSV, JSON, HTML, and more
  • [embeddings] - Embedding providers for creating vector representations
You’ll have access to all knowledge base components through a unified interface without needing to configure each one separately.

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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

# 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)

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

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

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