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