אולי

app.py

@@ -16,9 +16,8 @@ out_area = st.container()
 
 def __run_pipline():
     out_area.markdown(":green[Running pipline]")
-    out_area.text(pipline.chain(state.input_text))
+    out_area.text(pipline.chain_TI(state.input_text))
 
 
 in_area.text_area("input_text", key="input_text")
-in_area.text_input("word", key="word")
 in_area.button("run", on_click=__run_pipline)

pipline.py

@@ -3,22 +3,28 @@ from langchain import PromptTemplate, OpenAI, LLMChain
 from langchain.prompts import load_prompt
 import wikipedia
 import os
+
 llm = OpenAI()
 # save templates to a file
-template = """Question:
+try_imply_template = """Question:
  The user wrote me the following text, what is he trying to imply to me?
  {user_input}
 
 Answer: Let's think step by step."""
 # An example prompt with multiple input variables
-input_prompt = PromptTemplate(
+TI_prompt = PromptTemplate(
     input_variables=["user_input"],
-    template=template,
+    template=try_imply_template,
 )
-input_prompt.save("awesome_prompt.json")  # Save to JSON file
 
 
-prompt = load_prompt("awesome_prompt.json")
+connection_between_terms_template = PromptTemplate(
+    template="""Question:
+ What is the connection between {term1} and {term2}?
+ Answer: Let's think step by step.""",
+    input_variables=["term1", "term2"],
+)
+
 
-prompt = PromptTemplate(template=template, input_variables=["user_input"])
-chain = LLMChain(prompt=prompt, llm=llm)
+chain_TI = LLMChain(prompt=TI_prompt, llm=llm)
+chain_CC = LLMChain(prompt=connection_between_terms_template, llm=llm)