add: sample

README.md

@@ -39,6 +39,8 @@ if __name__ == '__main__':
 
 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.
 
+For other samples, please refer to the following [sample directory](./sample/).
+
 ## Support and Bug Reports
 
 For support and bug reports, please use the GitHub Issues page. 

sample/llm_sample.py

@@ -10,9 +10,9 @@ def main():
         provider=Provider.Aichat,
     )
 
-    res = llm('hello')
+    res = llm("hello")
     print(res)  # Hello! How can I assist you today?
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

sample/prompt_template_sample.py

@@ -0,0 +1,27 @@
+from g4f import Provider, Model
+from langchain.llms.base import LLM
+from langchain import PromptTemplate
+
+from langchain_g4f import G4FLLM
+
+
+def main():
+    template = "What color is the {fruit}?"
+    prompt_template = PromptTemplate(template=template, input_variables=["fruit"])
+
+    llm: LLM = G4FLLM(
+        model=Model.gpt_35_turbo,
+        provider=Provider.Aichat,
+    )
+
+    res = llm(prompt_template.format(fruit="apple"))
+    print(res)
+    # The color of an apple can vary, but it is typically red, green, or yellow.
+
+    res = llm(prompt_template.format(fruit="lemon"))
+    print(res)
+    # The color of a lemon is typically yellow.
+
+
+if __name__ == "__main__":
+    main()

sample/sequential_chain_sample.py

@@ -0,0 +1,35 @@
+from g4f import Provider, Model
+from langchain.llms.base import LLM
+from langchain import PromptTemplate
+from langchain.chains import LLMChain, SimpleSequentialChain
+
+from langchain_g4f import G4FLLM
+
+
+def main():
+    llm: LLM = G4FLLM(
+        model=Model.gpt_35_turbo,
+        provider=Provider.DeepAi,
+    )
+
+    prompt_template_1 = PromptTemplate(
+        input_variables=["location"],
+        template="Please tell us one tourist attraction in {location}.",
+    )
+    chain_1 = LLMChain(llm=llm, prompt=prompt_template_1)
+
+    prompt_template_2 = PromptTemplate(
+        input_variables=["location"],
+        template="What is the train route from Tokyo Station to {location}?",
+    )
+    chain_2 = LLMChain(llm=llm, prompt=prompt_template_2)
+
+    simple_sequential_chain = SimpleSequentialChain(
+        chains=[chain_1, chain_2], verbose=True
+    )
+
+    print(simple_sequential_chain("tokyo"))
+
+
+if __name__ == "__main__":
+    main()

sample/simple_chain_sample.py

@@ -0,0 +1,23 @@
+from g4f import Provider, Model
+from langchain.llms.base import LLM
+from langchain import PromptTemplate
+from langchain.chains import LLMChain
+
+from langchain_g4f import G4FLLM
+
+
+def main():
+    llm: LLM = G4FLLM(
+        model=Model.gpt_35_turbo,
+        provider=Provider.Aichat,
+    )
+    prompt_template = PromptTemplate(
+        input_variables=["location"],
+        template="Where is the best tourist attraction in {location}?",
+    )
+    chain = LLMChain(llm=llm, prompt=prompt_template)
+    print(chain("tokyo"))
+
+
+if __name__ == "__main__":
+    main()