I have implemented a complex chain. I perform stream operations on the chain. I hope that one of the intermediate steps will be invoke output instead of stream output. How should I operate it?

[shell-nlp]

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Example Code

prompt = (
    RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
    | chat_prompt_template
)
qa_bot = prompt | llm
event_extraction_chain = event_extraction_prompt | llm  # | JsonOutputParser()

qa_bot_with_docs = RunnableParallel(
    {
        "context": itemgetter("question") | contextual_rerank_retriever,
        "question": itemgetter("question"),
    }
).assign(answer=qa_bot, prompt=prompt, event=event_extraction_chain)

async for output in qa_bot_.astream({"question": query}, config={"callbacks": [token_callback]}):
  if "prompt" in output:
     ...
  if "context" in output:
     ...
  if "event" in output:
     ...
    #  I don't want streaming output
    # I hope this step uses invoke

Description

我实现了一个复杂的chain，我对chain进行stream操作,我希望中间的某一步是invoke输出，而不是stream输出，我应该怎么操作。
I have implemented a complex chain. I perform stream operations on the chain. I hope that one of the intermediate steps will be invoke output instead of stream output. How should I operate it?

System Info

System Information

OS: Linux
OS Version: #101~20.04.1-Ubuntu SMP Thu Nov 16 14:22:28 UTC 2023
Python Version: 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0]

Package Information

langchain_core: 0.2.28
langchain: 0.2.9
langchain_community: 0.2.7
langsmith: 0.1.85
langchain_experimental: 0.0.62
langchain_openai: 0.1.7
langchain_text_splitters: 0.2.2
langchainhub: 0.1.14
langgraph: 0.1.8

Packages not installed (Not Necessarily a Problem)

[dosubot[bot]]

Hello @shell-nlp! I'm here to help you with any bugs, questions, or contributions while you wait for a human maintainer.

To modify your complex chain to use invoke output instead of stream output for one of the intermediate steps, you can replace the astream method with the invoke method for that specific step. Here is an example of how you can do this:

Assume you have a sequence of RunnableLambda steps, and you want to change the output of the second step from streaming to invoking:

from langchain_core.runnables import RunnableLambda

# Define your functions
def add_one(x: int) -> int:
    return x + 1

def mul_two(x: int) -> int:
    return x * 2

def mul_three(x: int) -> int:
    return x * 3

# Create the sequence
sequence = (
    RunnableLambda(add_one) |
    RunnableLambda(mul_two) |
    RunnableLambda(mul_three)
)

# Modify the second step to use invoke instead of stream
class ModifiedRunnableLambda(RunnableLambda):
    async def astream(self, input, config=None, **kwargs):
        return self.invoke(input, config, **kwargs)

# Replace the second step with the modified version
sequence = (
    RunnableLambda(add_one) |
    ModifiedRunnableLambda(mul_two) |
    RunnableLambda(mul_three)
)

# Now, when you invoke the sequence, the second step will use invoke instead of stream
result = sequence.invoke(1)
print(result)  # Output will be 8 (1 + 1 = 2, 2 * 2 = 4, 4 * 2 = 8)

In your specific case, you can modify the event_extraction_chain to use invoke instead of astream:

class ModifiedEventExtractionChain(RunnableLambda):
    async def astream(self, input, config=None, **kwargs):
        return self.invoke(input, config, **kwargs)

event_extraction_chain = ModifiedEventExtractionChain(event_extraction_prompt | llm)

qa_bot_with_docs = RunnableParallel(
    {
        "context": itemgetter("question") | contextual_rerank_retriever,
        "question": itemgetter("question"),
    }
).assign(answer=qa_bot, prompt=prompt, event=event_extraction_chain)

async for output in qa_bot_with_docs.astream({"question": query}, config={"callbacks": [token_callback]}):
    if "prompt" in output:
        ...
    if "context" in output:
        ...
    if "event" in output:
        ...
        # This step now uses invoke instead of stream

In this example, the ModifiedEventExtractionChain class overrides the astream method to call invoke instead. This ensures that the event_extraction_chain step in your sequence uses invoke output instead of stream output ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[shell-nlp]

我实现了ModifiedEventExtractionChain，但运行代码的时候 并没有执行ModifiedEventExtractionChain

[dosubot[bot]]

确保 ModifiedEventExtractionChain 正确实现并覆盖了 astream 方法。以下是一个完整的示例，展示如何实现 ModifiedEventExtractionChain 并确保其在运行时被正确调用：

from langchain_core.runnables import RunnableLambda

# 假设 event_extraction_prompt 和 llm 已经定义
# event_extraction_prompt = ...
# llm = ...

class ModifiedEventExtractionChain(RunnableLambda):
    async def astream(self, input, config=None, **kwargs):
        # 调用 invoke 方法而不是 astream
        result = self.invoke(input, config, **kwargs)
        yield result

# 创建 ModifiedEventExtractionChain 实例
event_extraction_chain = ModifiedEventExtractionChain(event_extraction_prompt | llm)

# 定义其他组件
prompt = (
    RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
    | chat_prompt_template
)
qa_bot = prompt | llm

# 创建 qa_bot_with_docs
qa_bot_with_docs = RunnableParallel(
    {
        "context": itemgetter("question") | contextual_rerank_retriever,
        "question": itemgetter("question"),
    }
).assign(answer=qa_bot, prompt=prompt, event=event_extraction_chain)

# 运行代码并检查输出
async for output in qa_bot_with_docs.astream({"question": query}, config={"callbacks": [token_callback]}):
    if "prompt" in output:
        ...
    if "context" in output:
        ...
    if "event" in output:
        ...
        # 这里的 event_extraction_chain 使用 invoke 而不是 stream

确保 ModifiedEventExtractionChain 的 astream 方法正确实现，并且在 qa_bot_with_docs 中正确使用。这样可以确保 event_extraction_chain 在运行时使用 invoke 而不是 astream ^[1][2].

To continue talking to Dosu, mention @dosu.

[shell-nlp]

qa_bot_with_docs = RunnableParallel(
{
"context": itemgetter("question") | contextual_rerank_retriever,
"question": itemgetter("question"),
}
).assign(answer=qa_bot, prompt=prompt, event=event_extraction_chain)
我怎么设置 assign 后面的chain 的并发数为 2，即 先并发执行answer、prompt、然后再执行event、

[dosubot[bot]]

要在 qa_bot_with_docs 中设置 assign 后的链的并发数为 2，即先并发执行 answer 和 prompt，然后再执行 event，可以使用 RunnableParallel 类。以下是如何修改链的示例：

from langchain_core.runnables import RunnableParallel, RunnablePassthrough

# 假设 `answer` 和 `prompt` 已经定义
# 定义 `event` 步骤
event = event_extraction_chain  # 这里使用你定义的 event_extraction_chain

# 创建并发数为 2 的链
qa_bot_with_docs = (
    RunnableParallel(question=RunnablePassthrough(), docs=contextual_rerank_retriever)
    .assign(context=format_docs)
    .assign(
        RunnableParallel(
            answer=qa_bot,
            prompt=prompt,
            concurrency=2
        )
    )
    .assign(event=event)
    .pick(["answer", "docs"])
)

在这个设置中，answer 和 prompt 将首先并发执行，然后再执行 event ^[1].

To continue talking to Dosu, mention @dosu.