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Overview

KnowledgeBase can be used as a tool, allowing the agent to actively search and retrieve information from your knowledge base during task execution. When added as a tool, the agent gains access to a search method that it can call to query the knowledge base.

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Key Benefits

  • Active Retrieval: Agent decides when and how to search the knowledge base
  • Dynamic Queries: Agent can formulate multiple queries based on context
  • Tool Integration: Works seamlessly with other tools in the agent’s toolkit
  • Automatic Setup: KnowledgeBase is automatically set up when first used

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Basic Usage

Add KnowledgeBase to the tools parameter of a Task or Agent: 
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding, OpenAIEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Step 1: Setup embedding provider
embedding = OpenAIEmbedding(OpenAIEmbeddingConfig(
    model_name="text-embedding-3-small"
))

# Step 2: Setup vector database
vectordb = ChromaProvider(ChromaConfig(
    collection_name="my_rag_kb",
    vector_size=1536,
    connection=ConnectionConfig(
        mode=Mode.EMBEDDED,
        db_path="./rag_database"
    )
))

# Step 3: Create knowledge base with documents
kb = KnowledgeBase(
    sources=["document.pdf"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Step 4: Create agent
agent = Agent("openai/gpt-4o")

# Step 5: Create task with knowledge base as a tool
task = Task(
    description="What are the main topics discussed in the document?",
    tools=[kb]
)

# Step 6: Execute and get results
result = agent.do(task)
print(result)

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How It Works

  1. Tool Registration: When you add KnowledgeBase to tools, the agent automatically registers the search method as an available tool
  2. Automatic Setup: The KnowledgeBase is automatically set up.
  3. Agent Control: The agent decides when to call the search tool and what queries to make
  4. Results Integration: Search results are returned to the agent, which uses them to answer your questions

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Tool vs Context

Using as Tool (this feature):
  • Agent actively searches when needed
  • Agent can make multiple queries
  • Agent controls the search strategy
  • Best for: Complex tasks requiring multiple searches
Using as Context (traditional RAG):
  • Knowledge base is queried once before task execution
  • Results are added to the context automatically
  • Simpler, but less flexible
  • Best for: Simple Q&A tasks

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Multiple Knowledge Bases

You can add multiple KnowledgeBase instances as tools: 
kb1 = KnowledgeBase(
    sources=["technical_docs/"],
    embedding_provider=embedding,
    vectordb=ChromaProvider(ChromaConfig(...))
)

kb2 = KnowledgeBase(
    sources=["user_guides/"],
    embedding_provider=embedding,
    vectordb=ChromaProvider(ChromaConfig(...))
)

task = Task(
    description="Compare information from technical docs and user guides",
    tools=[kb1, kb2]
)

result = agent.do(task)

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Cleanup

Always close the KnowledgeBase when done to free resources: 
await kb.close()
Or use async context manager: 
async with kb:
    result = agent.do(task)