llama-index packs resume_screener integration
Project description
Resumer Screener Pack
This LlamaPack loads a resume file, and review it against a user specified job description and screening criteria.
CLI Usage
You can download llamapacks directly using llamaindex-cli
, which comes installed with the llama-index
python package:
llamaindex-cli download-llamapack ResumeScreenerPack --download-dir ./resume_screener_pack
You can then inspect the files at ./resume_screener_pack
and use them as a template for your own project!
Code Usage
You can download the pack to a ./resume_screener_pack
directory:
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
ResumeScreenerPack = download_llama_pack(
"ResumeScreenerPack", "./resume_screener_pack"
)
From here, you can use the pack, or inspect and modify the pack in ./resume_screener_pack
.
Then, you can set up the pack like so:
# create the pack
resume_screener = ResumeScreenerPack(
job_description="<general job description>",
criteria=["<job criterion>", "<another job criterion>"],
)
response = resume_screener.run(resume_path="resume.pdf")
print(response.overall_decision)
The response
will be a pydantic model with the following schema
class CriteriaDecision(BaseModel):
"""The decision made based on a single criteria"""
decision: Field(
type=bool, description="The decision made based on the criteria"
)
reasoning: Field(type=str, description="The reasoning behind the decision")
class ResumeScreenerDecision(BaseModel):
"""The decision made by the resume screener"""
criteria_decisions: Field(
type=List[CriteriaDecision],
description="The decisions made based on the criteria",
)
overall_reasoning: Field(
type=str, description="The reasoning behind the overall decision"
)
overall_decision: Field(
type=bool,
description="The overall decision made based on the criteria",
)
Project details
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