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Overview

KnowledgeBase accepts multiple types of sources: individual files, directories, or direct string content. It automatically detects file types and uses appropriate loaders.

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Installation

To add files to your KnowledgeBase, you’ll need a vector database provider and document loaders for the file types you want to process.
Example: Setting up for PDF files with ChromaTo process PDF files and store them in ChromaDB:
uv pip install "upsonic[chroma]"
uv pip install "upsonic[pdf-loader]"
Or install both at once:
uv pip install "upsonic[chroma,pdf-loader]"
What you need:
  • A vector database provider (e.g., chroma, qdrant, milvus, weaviate, pinecone, faiss, or pgvector)
  • Document loaders for your file types (e.g., pdf-loader, docx-loader, csv-loader, markdown-loader, html-loader, json-loader, xml-loader, yaml-loader, text-loader)
The examples below use Chroma and PDF loader, but you can use any combination of supported providers and loaders. See Storage Providers and Loaders for all options.

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Examples

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Single File

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",
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader]
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What was the total revenue in Q3 2024 according to the report?",
    context=[kb]
)

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

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Multiple Files

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Setup dependencies
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
))

# Create knowledge base with multiple sources
kb = KnowledgeBase(
    sources=["doc1.pdf", "doc2.md", "doc3.docx"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the three main conclusions drawn from the A/B testing results?",
    context=[kb]
)

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

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Directory

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 dependencies
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
))

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

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

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="What are the dimensions and power requirements for the Model X unit?",
    context=[kb]
)

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

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Mixed Sources

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 dependencies
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig())
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_kb",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
))

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

# Create knowledge base with mixed sources
kb = KnowledgeBase(
    sources=["doc1.pdf", "data/", "This is direct content text."],
    embedding_provider=embedding,
    vectordb=vectordb,
    loaders=[loader]
)

# Use with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task(
    description="Find the warranty terms for the battery component in the text",
    context=[kb]
)

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

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Supported File Types

  • PDF: .pdf (PyPDF, PDFPlumber, PyMuPDF)
  • Markdown: .md, .markdown
  • Documents: .docx
  • Spreadsheets: .csv
  • Data: .json, .jsonl, .xml, .yaml, .yml
  • Code: .py, .js, .ts, .java, .c, .cpp, .h, .cs, .go, .rs, .php, .rb
  • Web: .html, .htm, .xhtml, .css
  • Text: .txt, .log, .rst