base

Chain to run queries against LLMs.

This class is deprecated. See below for an example implementation using LangChain runnables:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI

prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
model = OpenAI()
chain = prompt | model | StrOutputParser()

chain.invoke("your adjective here")

Base class for question-answer generation chains.

This class is deprecated. See below for an alternative implementation.

Advantages of this implementation include:

• Supports async and streaming;
• Surfaces prompt and text splitter for easier customization;
• Use of JsonOutputParser supports JSONPatch operations in streaming mode, as well as robustness to markdown.

from langchain_classic.chains.qa_generation.prompt import (
    CHAT_PROMPT as prompt,
)

# Note: import PROMPT if using a legacy non-chat model.
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.runnables import (
    RunnableLambda,
    RunnableParallel,
    RunnablePassthrough,
)
from langchain_core.runnables.base import RunnableEach
from langchain_openai import ChatOpenAI
from langchain_text_splitters import RecursiveCharacterTextSplitter

model = ChatOpenAI()
text_splitter = RecursiveCharacterTextSplitter(chunk_overlap=500)
split_text = RunnableLambda(lambda x: text_splitter.create_documents([x]))

chain = RunnableParallel(
    text=RunnablePassthrough(),
    questions=(
        split_text | RunnableEach(bound=prompt | model | JsonOutputParser())
    ),
)