From 2797d903805cddc135bb8392a2de563ac8855d1b Mon Sep 17 00:00:00 2001
From: MIDORIBIN <aquarion123@gmail.com>
Date: Sun, 23 Jul 2023 10:20:03 +0900
Subject: [PATCH] add: sample

---
 README.md                         |  2 ++
 sample.py => sample/llm_sample.py |  4 ++--
 sample/prompt_template_sample.py  | 27 ++++++++++++++++++++++++
 sample/sequential_chain_sample.py | 35 +++++++++++++++++++++++++++++++
 sample/simple_chain_sample.py     | 23 ++++++++++++++++++++
 5 files changed, 89 insertions(+), 2 deletions(-)
 rename sample.py => sample/llm_sample.py (84%)
 create mode 100644 sample/prompt_template_sample.py
 create mode 100644 sample/sequential_chain_sample.py
 create mode 100644 sample/simple_chain_sample.py

diff --git a/README.md b/README.md
index 453b42a..c930b18 100644
--- a/README.md
+++ b/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. 
diff --git a/sample.py b/sample/llm_sample.py
similarity index 84%
rename from sample.py
rename to sample/llm_sample.py
index a1a5314..804d7bb 100644
--- a/sample.py
+++ b/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()
diff --git a/sample/prompt_template_sample.py b/sample/prompt_template_sample.py
new file mode 100644
index 0000000..b45b087
--- /dev/null
+++ b/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()
diff --git a/sample/sequential_chain_sample.py b/sample/sequential_chain_sample.py
new file mode 100644
index 0000000..356ae0d
--- /dev/null
+++ b/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()
diff --git a/sample/simple_chain_sample.py b/sample/simple_chain_sample.py
new file mode 100644
index 0000000..d5b289b
--- /dev/null
+++ b/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()