VectorDbs
Clickhouse Agent Knowledge
Concepts
- Agents
- Models
- Tools
- Knowledge
- Chunking
- VectorDbs
- Storage
- Embeddings
- Workflows
VectorDbs
Clickhouse Agent Knowledge
Setup
Copy
docker run -d \
-e CLICKHOUSE_DB=ai \
-e CLICKHOUSE_USER=ai \
-e CLICKHOUSE_PASSWORD=ai \
-e CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT=1 \
-v clickhouse_data:/var/lib/clickhouse/ \
-v clickhouse_log:/var/log/clickhouse-server/ \
-p 8123:8123 \
-p 9000:9000 \
--ulimit nofile=262144:262144 \
--name clickhouse-server \
clickhouse/clickhouse-server
Example
agent_with_knowledge.py
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from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.storage.agent.sqlite import SqliteAgentStorage
from agno.vectordb.clickhouse import Clickhouse
agent = Agent(
storage=SqliteAgentStorage(table_name="recipe_agent"),
knowledge=PDFUrlKnowledgeBase(
urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
vector_db=Clickhouse(
table_name="recipe_documents",
host="localhost",
port=8123,
username="ai",
password="ai",
),
),
# Show tool calls in the response
show_tool_calls=True,
# Enable the agent to search the knowledge base
search_knowledge=True,
# Enable the agent to read the chat history
read_chat_history=True,
)
# Comment out after first run
agent.knowledge.load(recreate=False) # type: ignore
agent.print_response("How do I make pad thai?", markdown=True)
agent.print_response("What was my last question?", stream=True)
Developer Resources
- View Cookbook
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