Examples
- Examples
- Getting Started
- Agents
- Teams
- Workflows
- Applications
- Streamlit Apps
- Evals
Agent Concepts
- Reasoning
- Multimodal
- RAG
- User Control Flows
- Knowledge
- Memory
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Context
- Embedders
- Agent State
- Observability
- Miscellaneous
Models
- Anthropic
- AWS Bedrock
- AWS Bedrock Claude
- Azure AI Foundry
- Azure OpenAI
- Cerebras
- Cerebras OpenAI
- Cohere
- DeepInfra
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- IBM
- LM Studio
- LiteLLM
- LiteLLM OpenAI
- Meta
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Perplexity
- Together
- XAI
- Vercel
Hybrid Search
MongoDB Hybrid Search
Code
cookbook/agent_concepts/knowledge/vector_dbs/mongo_db/mongo_db_hybrid_search.py
import typer
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.mongodb import MongoDb
from agno.vectordb.search import SearchType
from rich.prompt import Prompt
# MongoDB Atlas connection string
"""
Example connection strings:
"mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority"
"mongodb://localhost:27017/agno?authSource=admin"
"""
mdb_connection_string = "mongodb+srv://<username>:<password>@cluster0.mongodb.net/?retryWrites=true&w=majority"
vector_db = MongoDb(
collection_name="recipes",
db_url=mdb_connection_string,
search_index_name="recipes",
search_type=SearchType.hybrid
)
knowledge_base = PDFUrlKnowledgeBase(
urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
vector_db=vector_db,
)
def mongodb_agent(user: str = "user"):
agent = Agent(
user_id=user,
knowledge=knowledge_base,
search_knowledge=True,
)
while True:
message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
if message in ("exit", "bye"):
break
agent.print_response(message)
if __name__ == "__main__":
# Comment out after first run
knowledge_base.load(recreate=True)
typer.run(mongodb_agent)
Usage
1
Create a virtual environment
Open the Terminal
and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2
Set your API key
export OPENAI_API_KEY=xxx
3
Install libraries
pip install -U pymongo tantivy pypdf openai agno
4
Run Agent
python cookbook/agent_concepts/knowledge/vector_dbs/mongo_db/mongo_db_hybrid_search.py
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