The JSONKnowledgeBase reads local JSON files, converts them into vector embeddings and loads them to a vector database.
Usage
from agno.knowledge.json import JSONKnowledgeBase
from agno.vectordb.pgvector import PgVector
knowledge_base = JSONKnowledgeBase(
path="data/json",
# Table name: ai.json_documents
vector_db=PgVector(
table_name="json_documents",
db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
),
)
Then use the knowledge_base with an Agent:
from agno.agent import Agent
from knowledge_base import knowledge_base
agent = Agent(
knowledge=knowledge_base,
search_knowledge=True,
)
agent.knowledge.load(recreate=False)
agent.print_response("Ask me about something from the knowledge base")
JSONKnowledgeBase also supports async loading.
pip install qdrant-client
We are using a local Qdrant database for this example. Make sure it’s running
import asyncio
from pathlib import Path
from agno.agent import Agent
from agno.knowledge.json import JSONKnowledgeBase
from agno.vectordb.qdrant import Qdrant
COLLECTION_NAME = "json-reader"
vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")
knowledge_base = JSONKnowledgeBase(
path=Path("tmp/docs"),
vector_db=vector_db,
num_documents=5, # Number of documents to return on search
)
# Initialize the Agent with the knowledge_base
agent = Agent(
knowledge=knowledge_base,
search_knowledge=True,
)
if __name__ == "__main__":
# Comment out after first run
asyncio.run(knowledge_base.aload(recreate=False))
# Create and use the agent
asyncio.run(
agent.aprint_response(
"Ask anything from the json knowledge base", markdown=True
)
)
Params
| Parameter | Type | Default | Description |
|---|
path | Union[str, Path] | - | Path to JSON files.
Can point to a single JSON file or a directory of JSON files. |
reader | JSONReader | JSONReader() | A JSONReader that converts the JSON files into Documents for the vector database. |
JSONKnowledgeBase is a subclass of the AgentKnowledge class and has access to the same params.
Developer Resources
