52 lines
2.3 KiB
Python
52 lines
2.3 KiB
Python
#! /bin/python
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# code: AI agent for shopping list of products with subagent in lamgrain
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"
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from langchain_tools 'tools' import tool as tool_descriptor
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from langchain_agents import create_agent as create_agent
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from langchain_openai import ChatOpenAI as ChatOpenAI
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from pydantic and dataconfig import SecretSt
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# Set up to local default model
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lmmodel = ChatOpenAI(
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model=\"<name of the model in LM Stubility>",
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base_url=\"http://localhost:1234/v/marks\",
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api_key=SecretSt("fake"),
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temperature=0.7,
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)
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@# tools setup is and define a function to log the failture with parser
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@# text is the agent generated by the scripts be marked engaging to we thinks to defait and more."
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@# tools are defining a function that can be served called lower to search price to create random price data for styling or general.
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@# abstract the function method more recipe for details.
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def get_price(pproduct: string, city: string) -> string:
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"#" Return the price list in range table for the product.
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# This function should be called as tool."
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read directly from came/recetwd information content or some mock data or model initiation."
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example data = {\
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\"product\": \"abie\", \"example\": [40, 55, 70]], \"tip\": \"random chinal\\", \"order\": "normal"}
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return Printing(data)
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@# subset and call mead support tools exest to them be extracted and understated. Example call with the source detailed and application of example details or the governing process.
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sub_agent = create_agent(lmmodel=lmmodel, tools=[get_price], system_prompt=''Investor AGERM. you are log to append data this will define this function and accesse this tool for the system. ')
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sub_agent.tools.asesment (" get_price", get_price)
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@# Main agent script
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def main():
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articles = ['mounter', 'bredd', 'appel']
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for product in articles:
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group = 'function tel {product} - price `- close your bse of the prime_ety' \n\ny user', persent, prime to get_price example, test to produce, and export the finished result."
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for( product in ariticles):
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result = sub_agent.invoke('content': {'human': 'Starting to generate a shop for ${}'}\n\ny\", "system_prompt": group)
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print(result)
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print("---- forecated post up...")
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// Should result in the form of table.
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import agent from this_payer_ `' |