LangChain 入门5 Ollama 美化输出与Function-calling_ollama function calling

美化输出

代码实现

WikipediaRetriever

维基百科API检索器。
它将load（）包装为get_relevant_documents（）。它使用所有WikipediaAPIWrapper参数，没有任何更改。
通过分析和验证来自关键字参数的输入数据来创建一个新模型。
如果无法解析输入数据以形成有效的模型，则引发ValidationError。

#下载依赖
from langchain_community.retrievers import WikipediaRetriever
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.llms import Ollama
from langchain_experimental.llms.ollama_functions import OllamaFunctions
#模型实例化
llm = Ollama(model='llama2')
#下载维基百科信息
wiki = WikipediaRetriever(top_k_results=6, doc_content_chars_max=2000)
#提示词模版
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You're a helpful AI assistant. Given a user question and some Wikipedia article snippets, answer the user question. If none of the articles answer the question, just say you don't know.\n\nHere are the Wikipedia articles:{context}",
        ),
        ("human", "{question}"),
    ]
)
prompt.pretty_print()#美化输出
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结果返回

现在我们已经有了一个模型、检索器和提示，让将它们链接在一起。需要添加一些逻辑，将检索到的文档格式化为可以传递给提示的字符串。以便链的同时返回答案和检索到的文档。

依赖加载

from operator import itemgetter
from typing import List

from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import (
    RunnableLambda,
    RunnableParallel,
    RunnablePassthrough,
)

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格式转换

def format_docs(docs: List[Document]) -> str:
    """将文档转换为单个字符串.:"""
    formatted = [
        f"Article Title: {doc.metadata['title']}\nArticle Snippet: {doc.page_content}"
        for doc in docs
    ]
    return "\n\n" + "\n\n".join(formatted)
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输出

format = itemgetter("docs") | RunnableLambda(format_docs)
# 检索完成后生成答案的子通道
answer = prompt | llm | StrOutputParser()
# 调用wiki->将文档格式化为字符串的完整链->运行answer子链->返回答案和检索到的文档。
chain = (
    RunnableParallel(question=RunnablePassthrough(), docs=wiki) #格式化
    .assign(context=format)#输出样式
    .assign(answer=answer)
    .pick(["answer", "docs"])
)
print(chain.invoke("How fast are cheetahs?"))
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返回：

function-calling

在编程中，“function-calling”（函数调用）是一个基本的概念，指的是从程序的某一部分调用或激活一个函数（function）的行为。函数是一段封装了特定逻辑的代码块，加载在大模型中，使得大模型能有特定的输出。

代码实现

from langchain_experimental.llms.ollama_functions import OllamaFunctions
#实例化模型
model = OllamaFunctions(model="mistral")
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工具设计

llm_with_tool = model.bind(
    #函数
    functions=[ 
        {
            "name": "cited_answer", 
            "description": "Answer user questions based solely on the given source and cite the source used",#描述 仅根据给定的来源回答用户问题，并引用所使用的来源
            "parameters": { #参数
                "type": "object",#类型 object
                "properties": { #特性 这里的内容是自定义的，下面主要是希望能让大模型给出关于人身高的相关信息。
                         'human':{
                             "type": "string",
                             "description": "Remember the names of people that have appeared before",
                        },
                         'characteristic':{
                             "type": "string",
                             "description": "Remember everyone's characteristics",
                        },
                        'return':{
                             "type": "string",
                              "description": "Answer first questions based on the information obtained",
                        },
                    },
                },
        }
    ],
    function_call={"name": "cited_answer"},
)

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加载提示词

example_q = """What Brian's height?

Source: 1
Information: Suzy is 6'2"

Source: 2
Information: Jeremiah is blonde

Source: 3
Information: Brian is 3 inches shorted than Suzy"""
#上面可以是希望的任何信息。
return_ = llm_with_tool.invoke(example_q)
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return_
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返回

AIMessage(content=‘’, additional_kwargs={‘function_call’: {‘name’:
‘cited_answer’, ‘arguments’: ‘{“human”: “Brian, Suzy”, “return”:
“Brian is 5 inches shorter than Susie, so his height is approximately 5 feet 8 inches (66 inches).”}’}},
id=‘run-c76f129f-06a0-44e9-95a6-a3ffd1760ffb-0’)

#查看详细信息
import json
return_1= return_.to_json()['kwargs']['additional_kwargs']['function_call']['arguments']

# 使用 json.loads 方法将字符串转换为字典
dict_data = json.loads(return_1)

dict_data['return']
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Brian is 5 inches shorter than Susie, so his height is approximately 5 feet 8 inches (66 inches)
#布赖恩比苏西矮5英寸，所以他的身高大约是5英尺8英寸（66英寸）。这里看着回复可能比较合理

对比 没有使用function-calling的回答

提示词加载

#实例化模型
llm = Ollama(model='mistral')
#提示词模版
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            """"Source: 1
                Information: Suzy is 6'2"
                
                Source: 2
                Information: Jeremiah is blonde
                
                Source: 3
                Information: Brian is 3 inches shorted than Suzy""",
        ),
        ("human", "{question}"),
    ]
)
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输出

chain = prompt | llm 
return_=chain.invoke("What Brian's height?")
return_
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" Based on the information provided, Suzy is 6 feet 2 inches tall, and
Brian is 3 inches shorter than her. To find Brian’s height, we
subtract 3 inches from Suzy’s height:\n\n6 feet 2 inches (Suzy’s
height) - 3 inches (height difference) = 5 feet 11 inches (Brian’s
height)\n\nTherefore, Brian is 5 feet 11 inches tall."

从这里看回答的会有点繁琐。
以上是本文的全部内容，感谢阅读。
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