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grit-app[bot]
89 lines
2.4 KiB
Python
89 lines
2.4 KiB
Python
from typing import List
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from enum import Enum
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from pydantic import BaseModel, Field
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import instructor
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from openai import OpenAI
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client = instructor.patch(OpenAI())
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class CRMSource(Enum):
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personal = "personal"
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business = "business"
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work_contacts = "work_contacts"
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all = "all"
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class CRMSearch(BaseModel):
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"""A CRM search query
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The search description is a natural language description of the search query
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the backend will use semantic search so use a range of phrases to describe the search
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"""
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source: CRMSource
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city_location: str = Field(
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..., description="City location used to match the desired customer profile"
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)
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search_description: str = Field(
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..., description="Search query used to match the desired customer profile"
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)
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class CRMSearchQuery(BaseModel):
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"""
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A set of CRM queries to be executed against a CRM system,
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for large locations decompose into multiple queries of smaller locations
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"""
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queries: List[CRMSearch]
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def query_crm(query: str) -> CRMSearchQuery:
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queries = client.chat.completions.create(
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model="gpt-3.5-turbo",
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response_model=CRMSearchQuery,
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messages=[
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{
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"role": "system",
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"content": """
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You are a world class CRM search career generator.
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You will take the user query and decompose it into a set of CRM queries queries.
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""",
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},
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{"role": "user", "content": query},
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],
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)
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return queries
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if __name__ == "__main__":
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query = "find me all the pottery businesses in San Francisco and my friends in the east coast big cities"
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print(query_crm(query).model_dump_json(indent=2))
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"""
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{
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"queries": [
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{
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"source": "business",
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"city_location": "San Francisco",
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"search_description": "pottery businesses"
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},
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{
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"source": "personal",
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"city_location": "New York",
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"search_description": "friends in New York"
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},
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{
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"source": "personal",
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"city_location": "Boston",
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"search_description": "friends in Boston"
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},
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{
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"source": "personal",
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"city_location": "Philadelphia",
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"search_description": "friends in Philadelphia"
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}
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]
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}
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"""
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