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add: sample
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@ -39,6 +39,8 @@ if __name__ == '__main__':
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The above sample code demonstrates the basic usage of langchain_g4f. Choose the appropriate model and provider, initialize the LLM, and then pass input text to the LLM object to obtain the result.
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The above sample code demonstrates the basic usage of langchain_g4f. Choose the appropriate model and provider, initialize the LLM, and then pass input text to the LLM object to obtain the result.
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For other samples, please refer to the following [sample directory](./sample/).
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## Support and Bug Reports
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## Support and Bug Reports
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For support and bug reports, please use the GitHub Issues page.
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For support and bug reports, please use the GitHub Issues page.
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@ -10,9 +10,9 @@ def main():
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provider=Provider.Aichat,
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provider=Provider.Aichat,
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)
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)
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res = llm('hello')
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res = llm("hello")
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print(res) # Hello! How can I assist you today?
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print(res) # Hello! How can I assist you today?
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if __name__ == '__main__':
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if __name__ == "__main__":
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main()
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main()
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27
sample/prompt_template_sample.py
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sample/prompt_template_sample.py
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from g4f import Provider, Model
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from langchain.llms.base import LLM
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from langchain import PromptTemplate
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from langchain_g4f import G4FLLM
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def main():
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template = "What color is the {fruit}?"
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prompt_template = PromptTemplate(template=template, input_variables=["fruit"])
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llm: LLM = G4FLLM(
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model=Model.gpt_35_turbo,
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provider=Provider.Aichat,
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)
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res = llm(prompt_template.format(fruit="apple"))
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print(res)
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# The color of an apple can vary, but it is typically red, green, or yellow.
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res = llm(prompt_template.format(fruit="lemon"))
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print(res)
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# The color of a lemon is typically yellow.
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if __name__ == "__main__":
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main()
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35
sample/sequential_chain_sample.py
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35
sample/sequential_chain_sample.py
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from g4f import Provider, Model
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from langchain.llms.base import LLM
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from langchain import PromptTemplate
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from langchain.chains import LLMChain, SimpleSequentialChain
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from langchain_g4f import G4FLLM
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def main():
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llm: LLM = G4FLLM(
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model=Model.gpt_35_turbo,
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provider=Provider.DeepAi,
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)
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prompt_template_1 = PromptTemplate(
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input_variables=["location"],
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template="Please tell us one tourist attraction in {location}.",
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)
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chain_1 = LLMChain(llm=llm, prompt=prompt_template_1)
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prompt_template_2 = PromptTemplate(
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input_variables=["location"],
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template="What is the train route from Tokyo Station to {location}?",
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)
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chain_2 = LLMChain(llm=llm, prompt=prompt_template_2)
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simple_sequential_chain = SimpleSequentialChain(
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chains=[chain_1, chain_2], verbose=True
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)
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print(simple_sequential_chain("tokyo"))
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if __name__ == "__main__":
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main()
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23
sample/simple_chain_sample.py
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sample/simple_chain_sample.py
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from g4f import Provider, Model
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from langchain.llms.base import LLM
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from langchain import PromptTemplate
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from langchain.chains import LLMChain
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from langchain_g4f import G4FLLM
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def main():
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llm: LLM = G4FLLM(
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model=Model.gpt_35_turbo,
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provider=Provider.Aichat,
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)
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prompt_template = PromptTemplate(
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input_variables=["location"],
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template="Where is the best tourist attraction in {location}?",
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)
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chain = LLMChain(llm=llm, prompt=prompt_template)
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print(chain("tokyo"))
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if __name__ == "__main__":
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main()
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