Knowledge Base¶

This example shows knowledge base ingestion, embedding, and querying.

Source Code¶

from agently import Agently
from agently.integrations.chromadb import ChromaCollection


## Knowledge Base: Embedding + Retrieval + Answer
def knowledge_base_demo():
    # Embedding agent for vector search
    embedding = Agently.create_agent()
    embedding.set_settings(
        "OpenAICompatible",
        {
            "model": "qwen3-embedding:0.6b",
            "base_url": "http://127.0.0.1:11434/v1/",
            "auth": "nothing",
            "model_type": "embeddings",
        },
    )

    # Build a small knowledge base
    collection = ChromaCollection(
        collection_name="demo",
        embedding_agent=embedding,
    )
    collection.add(
        [
            {
                "document": "Book about Dogs",
                "metadata": {"book_name": "🐶"},
            },
            {
                "document": "Book about cars",
                "metadata": {"book_name": "🚗"},
            },
            {
                "document": "Book about vehicles",
                "metadata": {"book_name": "🚘"},
            },
            {
                "document": "Book about birds",
                "metadata": {"book_name": "🐦‍⬛"},
            },
        ]
    )

    # Query the knowledge base
    query = "Things that can move really fast"
    results = collection.query(query)
    print("[retrieval]", results)

    # Use retrieval results in a normal agent response
    agent = Agently.create_agent()
    agent.set_settings(
        "OpenAICompatible",
        {
            "base_url": "http://127.0.0.1:11434/v1",
            "model": "qwen2.5:7b",
        },
    )
    answer = (
        agent.input(query).info({"retrieval_results": results}).instruct("Answer based on {retrieval_results}.").start()
    )
    print("[answer]", answer)


# knowledge_base_demo()

Walkthrough¶

Embed documents once and reuse the collection.
Attach metadata for traceability.
Inject results into info or context slots.

What you'll learn¶

Understand knowledge base pipeline
Avoid rebuild per request

Exercises¶

Build a small knowledge base
Query and inject results