修改相关的prompt
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CounterFire2023
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c21de6bdda
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87
app.py
87
app.py
@ -4,23 +4,18 @@
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from langchain.prompts import PromptTemplate
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from langchain_core.output_parsers import JsonOutputParser, StrOutputParser
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from langchain_community.chat_models import ChatOllama
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from langchain_community.tools import DuckDuckGoSearchRun
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from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
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from langgraph.graph import END, StateGraph
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# For State Graph
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from typing_extensions import TypedDict
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import os
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import json
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# Defining LLM
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local_llm = 'llama3.2'
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llama3 = ChatOllama(model=local_llm, temperature=0)
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llama3_json = ChatOllama(model=local_llm, format='json', temperature=0)
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# Web Search Tool
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wrapper = DuckDuckGoSearchAPIWrapper(max_results=25)
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web_search_tool = DuckDuckGoSearchRun(api_wrapper=wrapper)
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# Generation Prompt
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generate_prompt = PromptTemplate(
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@ -40,7 +35,7 @@ generate_prompt = PromptTemplate(
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<|start_header_id|>user<|end_header_id|>
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Question: {question}
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Web Search Context: {context}
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Send Order Context: {context}
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Answer:
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<|eot_id|>
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@ -67,11 +62,11 @@ router_prompt = PromptTemplate(
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<|start_header_id|>system<|end_header_id|>
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You are an expert at routing a user question to either the generation stage or web search.
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Use the web search for questions that require more context for a better answer, or recent events.
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You are an expert at routing a user question to either the generation stage or send order.
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Use the send order for questions that user want buy CEC from you.
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Otherwise, you can skip and go straight to the generation phase to respond.
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You do not need to be stringent with the keywords in the question related to these topics.
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Give a binary choice 'web_search' or 'generate' based on the question.
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Give a binary choice 'send_order' or 'generate' based on the question.
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Return the JSON with a single key 'choice' with no premable or explanation.
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Question to route: {question}
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@ -100,10 +95,8 @@ query_prompt = PromptTemplate(
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<|start_header_id|>system<|end_header_id|>
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You are an expert at crafting web search queries for research questions.
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More often than not, a user will ask a basic question that they wish to learn more about, however it might not be in the best format.
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Reword their query to be the most effective web search string possible.
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Return the JSON with a single key 'query' with no premable or explanation.
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You are an expert at sell CEC,
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Return the JSON with a single key 'count' with amount which user want to buy.
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Question to transform: {question}
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@ -131,12 +124,12 @@ class GraphState(TypedDict):
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Attributes:
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question: question
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generation: LLM generation
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search_query: revised question for web search
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context: web_search result
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send_order: revised question for send order
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context: send_order result
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"""
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question : str
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generation : str
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search_query : str
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send_order : str
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context : str
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# Node - Generate
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@ -154,18 +147,20 @@ def generate(state):
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print("Step: Generating Final Response")
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question = state["question"]
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context = state["context"]
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context = state.get("context", None)
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print(context)
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# TODO:: 根据context特定的内容生产答案
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# Answer Generation
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generation = generate_chain.invoke({"context": context, "question": question})
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return {"generation": generation}
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if context.index("orderinfo") != -1:
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return {"generation": context.replace("orderinfo:", "")}
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else:
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generation = generate_chain.invoke({"context": context, "question": question})
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return {"generation": generation}
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# Node - Query Transformation
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def transform_query(state):
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"""
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Transform user question to web search
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Transform user question to order info
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Args:
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state (dict): The current graph state
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@ -174,18 +169,19 @@ def transform_query(state):
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state (dict): Appended search query
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"""
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print("Step: Optimizing Query for Web Search")
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print("Step: Optimizing Query for Send Order")
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question = state['question']
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gen_query = query_chain.invoke({"question": question})
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search_query = gen_query["query"]
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return {"search_query": search_query}
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search_query = gen_query["count"]
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print("send_order", search_query)
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return {"send_order": search_query}
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# Node - Web Search
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# Node - Send Order
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def web_search(state):
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def send_order(state):
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"""
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Web search based on the question
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Send order based on the question
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Args:
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state (dict): The current graph state
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@ -193,14 +189,12 @@ def web_search(state):
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Returns:
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state (dict): Appended web results to context
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"""
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# search_query = state['search_query']
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# print(f'Step: Searching the Web for: "{search_query}"')
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# # Web search tool call
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# search_result = web_search_tool.invoke(search_query)
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print("Step: Web Search")
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search_result = "Web Search Results"
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print("Step: before Send Order")
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amount = state['send_order']
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print(amount)
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print(f'Step: build order info for : "{amount}" CEC')
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order_info = {"amount": amount, "price": 0.1, "name": "CEC", "url": "https://www.example.com"}
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search_result = f"orderinfo:{json.dumps(order_info)}"
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return {"context": search_result}
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@ -208,7 +202,7 @@ def web_search(state):
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def route_question(state):
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"""
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route question to web search or generation.
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route question to send order or generation.
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Args:
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state (dict): The current graph state
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@ -220,16 +214,16 @@ def route_question(state):
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print("Step: Routing Query")
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question = state['question']
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output = question_router.invoke({"question": question})
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if output['choice'] == "web_search":
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print("Step: Routing Query to Web Search")
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return "websearch"
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if output['choice'] == "send_order":
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print("Step: Routing Query to Send Order")
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return "sendorder"
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elif output['choice'] == 'generate':
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print("Step: Routing Query to Generation")
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return "generate"
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# Build the nodes
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workflow = StateGraph(GraphState)
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workflow.add_node("websearch", web_search)
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workflow.add_node("sendorder", send_order)
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workflow.add_node("transform_query", transform_query)
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workflow.add_node("generate", generate)
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@ -237,12 +231,12 @@ workflow.add_node("generate", generate)
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workflow.set_conditional_entry_point(
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route_question,
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{
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"websearch": "transform_query",
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"sendorder": "transform_query",
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"generate": "generate",
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},
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)
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workflow.add_edge("transform_query", "websearch")
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workflow.add_edge("websearch", "generate")
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workflow.add_edge("transform_query", "sendorder")
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workflow.add_edge("sendorder", "generate")
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workflow.add_edge("generate", END)
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# Compile the workflow
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@ -254,4 +248,5 @@ def run_agent(query):
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print(output["generation"])
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# display(Markdown(output["generation"]))
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run_agent("What is Latest news About Open AI?")
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run_agent("I want to buy 100 CEC")
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# run_agent("What the weather of New York today?")
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