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LangChain

LangChain 是一個旨在為開發者提供一套工具和程式介接，以便更容易、更有效地利用大型語言模型（LLM）的開源開發框架，專注於情境感知和推理。它包含多個組件，如 Python 和 JavaScript 的函式庫、快速部署的模板、用於開發REST API的 LangServe，以及用於除錯和監控的 LangSmith。LangChain 簡化了開發、生產和部署過程，提供與語言模型互動、執行檢索策略和輔助建立複雜應用架構的工具。

LangSmith

LangChain 提供的雲端服務，可用來作程式除錯與監視後端程序，例如 RAG 的檢索資訊過程。

 

RAG

Using Retrievers in LCEL
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

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

{context}

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


def format_docs(docs):
    return "\n\n".join([d.page_content for d in docs])


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

chain.invoke("What did the president say about technology?")

ChatPromptTemplate

few_shot_examples = [
{"input":"Could you please clarify the terms outlined in section 3.2 of the contract?",
"output":"Certainly, I will provide clarification on the terms in section 3.2."},
{"input":"We are interested in extending the payment deadline to 30 days instead of the current 15 days. Additionally, we would like to add a clause regarding late payment penalties.",
"output":"Our request is to extend the payment deadline to 30 days and include a clause on late payment penalties."},
{"input":"""The current indemnification clause seems too broad. We would like to narrow it down to cover only direct damages and exclude consequential damages.
Additionally, we propose including a dispute resolution clause specifying arbitration as the preferred method of resolving disputes.""",
"output":"""We suggest revising the indemnification clause to limit it to covering direct damages and excluding consequential damages.
Furthermore, we recommend adding a dispute resolution clause that specifies arbitration as the preferred method of resolving disputes."""},
{"input":"I believe the proposed changes are acceptable.",
"output":"Thank you for your feedback. I will proceed with implementing the proposed changes."}
]

few_shot_template = ChatPromptTemplate.from_messages(
    [
        ("human", "{input}"),
        ("ai", "{output}")
    ]
)

few_shot_prompt = FewShotChatMessagePromptTemplate(
    example_prompt=few_shot_template,
    examples=few_shot_examples,
)

print(few_shot_prompt.format())
Loader

Web

from langchain_community.document_loaders import WebBaseLoader
loader = WebBaseLoader(
    web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
    bs_kwargs=dict(
        parse_only=bs4.SoupStrainer(
            class_=("post-content", "post-title", "post-header")
        )
    ),
)
docs = loader.load()

Text

from langchain_community.document_loaders import DirectoryLoader
loader = DirectoryLoader("../", glob="**/*.md")
docs = loader.load()
len(docs)
print(docs[0].page_content[:100])

 Markdown

'''
%pip install "unstructured[md]"
'''
from langchain_community.document_loaders import UnstructuredMarkdownLoader
markdown_path = "../../../README.md"
loader = UnstructuredMarkdownLoader(markdown_path)

data = loader.load()
assert len(data) == 1
readme_content = data[0].page_content
print(readme_content[:3])

常用函式

格式化輸出

# Helper function for printing docs
def pretty_print_docs(docs):
    print(
        f"\n{'-' * 100}\n".join(
            [f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
        )
    )

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