kenken999 commited on
Commit
49be7f2
1 Parent(s): ac6fdb9
controllers/【リッチメニュー】取/prompt ADDED
@@ -0,0 +1 @@
 
 
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+ 【リッチメニュー】取扱商材【リッチメニュー】取扱商材
controllers/買取強化キャンペーン/prompt ADDED
@@ -0,0 +1 @@
 
 
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+ 買取強化キャンペーン買取強化キャンペーン
mysite/interpreter/prompt.py CHANGED
@@ -9,14 +9,40 @@ from langchain_core.prompts import (
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  from langchain_core.messages import SystemMessage
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  from langchain.chains.conversation.memory import ConversationBufferWindowMemory
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  from langchain_groq import ChatGroq
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- def prompt_genalate(word):
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  # Get Groq API key
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  groq_api_key = os.getenv("api_key")
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  groq_chat = ChatGroq(groq_api_key=groq_api_key, model_name="llama3-70b-8192")
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- system_prompt = "あなたはプロンプト作成の優秀なアシスタントです。答えは日本語で答えます"
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  conversational_memory_length = 50
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  memory = ConversationBufferWindowMemory(
@@ -42,6 +68,7 @@ def prompt_genalate(word):
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  HumanMessagePromptTemplate.from_template("{human_input}"),
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  ]
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  )
 
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  conversation = LLMChain(
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  llm=groq_chat,
@@ -54,4 +81,4 @@ def prompt_genalate(word):
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  print("User: ", user_question)
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  print("Assistant:", response)
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- return user_question+"[役割]"+response
 
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  from langchain_core.messages import SystemMessage
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  from langchain.chains.conversation.memory import ConversationBufferWindowMemory
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  from langchain_groq import ChatGroq
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+ from groq import Groq
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+
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+ def test_prompt(prompt,question):
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+ client = Groq(api_key=os.getenv("api_key"))
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+ completion = client.chat.completions.create(
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+ model="llama3-8b-8192",
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+ messages=[
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+ {
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+ "role": "system",
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+ "content": prompt+" 毎回日本語で答える事"
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+ },
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+ {
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+ "role": "user",
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+ "content": question
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+ },
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+ ],
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+ temperature=1,
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+ max_tokens=1024,
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+ top_p=1,
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+ stream=False,
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+ stop=None,
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+ )
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+
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+ print(completion.choices[0].message)
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+ return completion.choices[0].message.content
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+
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+ def prompt_genalate(word,sys_prompt="あなたはプロンプト作成の優秀なアシスタントです。答えは日本語で答えます"):
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  # Get Groq API key
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  groq_api_key = os.getenv("api_key")
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  groq_chat = ChatGroq(groq_api_key=groq_api_key, model_name="llama3-70b-8192")
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+ system_prompt = sys_prompt
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  conversational_memory_length = 50
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  memory = ConversationBufferWindowMemory(
 
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  HumanMessagePromptTemplate.from_template("{human_input}"),
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  ]
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  )
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+
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  conversation = LLMChain(
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  llm=groq_chat,
 
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  print("User: ", user_question)
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  print("Assistant:", response)
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+ return user_question+"[役割]"+response,response