v2.5.13

We’ve made ChromaDB operations more reliable by automatically splitting large upsert and query requests into smaller batches at runtime. This prevents failures that used to happen when requests exceeded ChromaDB’s per-request limits.

You can continue calling upsert and query operations the same way as before. The system now handles batching behind the scenes, so large payloads process smoothly without extra work.

import asyncio

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.chroma import ChromaDb

# Create Knowledge Instance with ChromaDB
knowledge = Knowledge(
    name="Basic SDK Knowledge Base",
    description="Agno 2.0 Knowledge Implementation with ChromaDB",
    vector_db=ChromaDb(
        collection="vectors", path="tmp/chromadb", persistent_client=True
    ),
)

asyncio.run(
    knowledge.ainsert(
        name="Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        metadata={"doc_type": "recipe_book"},
    )
)

# Create and use the agent
agent = Agent(knowledge=knowledge)
agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)

# Delete operations examples
vector_db = knowledge.vector_db
vector_db.delete_by_name("Recipes")
# or
vector_db.delete_by_metadata({"user_tag": "Recipes from website"})

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View the ChromaDB Vector Database docs for more.