mirror of
https://github.com/langchain-ai/lcel-teacher.git
synced 2026-07-19 13:33:54 -04:00
Lance Martin
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
from bs4 import BeautifulSoup as Soup
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from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader
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from langchain_openai import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnablePassthrough
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from langchain_core.pydantic_v1 import BaseModel
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# Load LCEL docs
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url = "https://python.langchain.com/docs/expression_language/"
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loader = RecursiveUrlLoader(
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url=url, max_depth=20, extractor=lambda x: Soup(x, "html.parser").text
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)
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docs = loader.load()
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# LCEL w/ PydanticOutputParser (outside the primary LCEL docs)
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url = "https://python.langchain.com/docs/modules/model_io/output_parsers/quick_start"
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loader = RecursiveUrlLoader(
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url=url, max_depth=1, extractor=lambda x: Soup(x, "html.parser").text
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)
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docs_pydantic = loader.load()
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# LCEL w/ Self Query (outside the primary LCEL docs)
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url = "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/"
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loader = RecursiveUrlLoader(
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url=url, max_depth=1, extractor=lambda x: Soup(x, "html.parser").text
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)
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docs_sq = loader.load()
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# Add
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docs.extend([*docs_pydantic, *docs_sq])
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# Sort the list based on the URLs in 'metadata' -> 'source'
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d_sorted = sorted(docs, key=lambda x: x.metadata["source"])
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d_reversed = list(reversed(d_sorted))
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# Concatenate the 'page_content' of each sorted dictionary
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concatenated_content = "\n\n\n --- \n\n\n".join(
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[doc.page_content for doc in d_reversed]
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)
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# Prompt template
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template = """You are a coding assistant with expertise in LCEL, LangChain expression language. Here is a full set of documentation:
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{context}
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Now, answer the user question based on the above provided documentation: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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model = ChatOpenAI(temperature=0, model="gpt-4-1106-preview")
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chain = (
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{
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"context": lambda x: concatenated_content,
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"question": RunnablePassthrough(),
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}
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| prompt
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| model
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| StrOutputParser()
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)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question) |