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检查你的可运行对象

一旦你使用LCEL创建了一个可运行对象，你可能经常想要检查它以更好地了解正在发生的情况。本笔记本介绍了一些方法来实现这一点。

首先，让我们创建一个示例的LCEL。我们将创建一个用于检索的LCEL

%pip install --upgrade --quiet  langchain langchain-openai faiss-cpu tiktoken
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
vectorstore = FAISS.from_texts(
    ["harrison worked at kensho"], embedding=OpenAIEmbeddings()
)
retriever = vectorstore.as_retriever()

template = """Answer the question based only on the following context:
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)

model = ChatOpenAI()
chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | model
    | StrOutputParser()
)

获取图形​

您可以获取可运行对象的图形

chain.get_graph()

打印图形​

虽然这不是非常易读，但您可以打印它以获得更容易理解的显示

chain.get_graph().print_ascii()
           +---------------------------------+         
           | Parallel<context,question>Input |         
           +---------------------------------+         
                    **               **                
                 ***                   ***             
               **                         **           
+----------------------+              +-------------+  
| VectorStoreRetriever |              | Passthrough |  
+----------------------+              +-------------+  
                    **               **                
                      ***         ***                  
                         **     **                     
           +----------------------------------+        
           | Parallel<context,question>Output |        
           +----------------------------------+        
                             *                         
                             *                         
                             *                         
                  +--------------------+               
                  | ChatPromptTemplate |               
                  +--------------------+               
                             *                         
                             *                         
                             *                         
                      +------------+                   
                      | ChatOpenAI |                   
                      +------------+                   
                             *                         
                             *                         
                             *                         
                   +-----------------+                 
                   | StrOutputParser |                 
                   +-----------------+                 
                             *                         
                             *                         
                             *                         
                +-----------------------+              
                | StrOutputParserOutput |              
                +-----------------------+

获取提示​

每个链的一个重要部分是使用的提示。您可以获取链中存在的提示:

chain.get_prompts()
[ChatPromptTemplate(input_variables=['context', 'question'], messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template='Answer the question based only on the following context:\n{context}\n\nQuestion: {question}\n'))])]

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以7个等号开始，7个等号结束的格式包裹你的回答。你的回答是: