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Documentation Index

Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

By default, KnowledgeBase stores document chunks only in the vector database. When you pass a storage backend, KnowledgeBase also writes a document registry — a relational record of every document it has processed, including metadata, content hashes, chunk counts, and processing status. This is useful when you need to:
  • Track which documents are indexed across restarts without querying the vector database
  • Share a storage backend between Memory and KnowledgeBase for a unified persistence layer
  • Audit document lifecycle — see when documents were added, their status, and source paths
  • Enable source removal by document ID — storage lets remove_document() look up the original file path and clean up sources

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Quick Start

Pass any Upsonic storage backend as the storage parameter: 
from upsonic import KnowledgeBase
from upsonic.storage.sqlite import SqliteStorage
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Reuse the same storage you use for Memory, or create a dedicated one
storage = SqliteStorage(db_file="app.db")

kb = KnowledgeBase(
    sources=["docs/"],
    vectordb=ChromaProvider(ChromaConfig(
        collection_name="my_kb",
        vector_size=1536,
        connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
    )),
    embedding_provider=OpenAIEmbedding(OpenAIEmbeddingConfig()),
    storage=storage,  # enables document registry persistence
)

kb.setup()
After setup(), every processed document is recorded in the storage’s knowledge table (upsonic_knowledge by default). When you call add_source(), add_text(), or remove_document(), the registry is updated automatically.

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What Gets Persisted

Each processed document creates a row in the knowledge table:
FieldDescription
idDocument ID (content-based hash)
nameHuman-readable document name
typeFile extension (e.g., pdf, md)
sizeFile size in bytes
knowledge_base_idID of the parent KnowledgeBase
content_hashMD5 hash of document content for deduplication
chunk_countNumber of chunks created from this document
sourceOriginal file path
statusProcessing status (indexed, failed)
metadataFull document metadata as JSON
created_atTimestamp of first indexing
updated_atTimestamp of last update
See Storage Tables for the full schema.

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Supported Backends

Any Upsonic storage backend works — the same ones used for Memory:
BackendExample
SqliteStorageSqliteStorage(db_file="app.db")
PostgresStoragePostgresStorage(db_url="postgresql://...")
RedisStorageRedisStorage(db_url="redis://...")
MongoStorageMongoStorage(db_url="mongodb://...")
JSONStorageJSONStorage(db_path="./data")
InMemoryStorageInMemoryStorage()
Mem0StorageMem0Storage(api_key="...")
Async storage backends (AsyncSqliteStorage, AsyncPostgresStorage, AsyncMongoStorage, AsyncMem0Storage) are also supported.

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Sharing Storage with Memory

You can use the same storage instance for both Memory and KnowledgeBase. Each system writes to its own tables: 
from upsonic import Agent, Task, KnowledgeBase
from upsonic.storage.sqlite import SqliteStorage
from upsonic.storage.memory import Memory
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Single storage for everything
storage = SqliteStorage(db_file="app.db")

# KnowledgeBase uses the knowledge table
kb = KnowledgeBase(
    sources=["docs/"],
    vectordb=ChromaProvider(ChromaConfig(
        collection_name="my_kb",
        vector_size=1536,
        connection=ConnectionConfig(mode=Mode.EMBEDDED, db_path="./chroma_db")
    )),
    embedding_provider=OpenAIEmbedding(OpenAIEmbeddingConfig()),
    storage=storage,
)

# Memory uses the sessions and user_memory tables
memory = Memory(
    storage=storage,
    session_id="session_001",
    user_id="user_123",
    full_session_memory=True,
    model="anthropic/claude-sonnet-4-5"
)

agent = Agent("anthropic/claude-sonnet-4-5", memory=memory)
task = Task("Summarize the documentation", context=[kb])
result = agent.do(task)

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Custom Table Name

Override the default knowledge table name via the storage constructor: 
storage = SqliteStorage(
    db_file="app.db",
    knowledge_table="my_custom_knowledge_table"
)