LlamaExtract Setup in n8n
Prerequisites
In order to make LlamaExtract work, you need to first create an Extraction Agent on LlamaCloud Platform with your designed schema.
With python code (current SDK), that looks like:
from pydantic import BaseModel, Field
from llama_cloud import LlamaCloud, AsyncLlamaCloud
# Define schema using Pydantic
class Resume(BaseModel):
name: str = Field(description="Full name of candidate")
email: str = Field(description="Email address")
skills: list[str] = Field(description="Technical skills and technologies")
client = LlamaCloud(api_key="your_api_key")
# Create extraction agent
agent = client.extraction.extraction_agents.create(
name="resume-parser",
data_schema=Resume,
config={}
)
# retrieve agent ID for future usage
print(agent.id)
With the legacy SDK, you can do:
from llama_cloud_services import LlamaExtract
from pydantic import BaseModel, Field
# Initialize client
extractor = LlamaExtract(api_key="your_llama_cloud_api_key")
# Define schema using Pydantic
class Resume(BaseModel):
name: str = Field(description="Full name of candidate")
email: str = Field(description="Email address")
skills: list[str] = Field(description="Technical skills and technologies")
# Create extraction agent
agent = extractor.create_agent(name="resume-parser", data_schema=Resume)
# retrieve agent ID for future usage
print(agent.id)
You can also configure your agents from the LlamaCloud Platform UI.
Setup
Select the 'LlamaExtract' node with the 'Extract data from a file' action.
When setting up the node, pass your Agent ID (copy it from the LlamaCloud Platform UI or from the outputs of the code snippets), and the path to a file:
As for LlamaParse, you can also set the node to receive inputs from other nodes, such as a n8n form.