load_summarize_chain -- attempt stuff chain_type first and then go to map_reduce or just go to map_reduce?

[RERobbins]

If a document can be summarized without splitting the text, is it more efficient to run the load_summarize_chain with the stuff chain_type first or is the map_reduce chain_type efficient enough to realize that when it is presented with a single document that it can go directly to the combine prompt and skip mapping? It would seem inefficient to run the map prompt on a single document and then pass the sole result to the combine prompt.

[dosubot[bot]]

🤖

Based on the information available in the LangChain repository, it appears that the MapReduceDocumentsChain class does not have a built-in mechanism to skip the mapping step and go directly to the combine prompt when presented with a single document. The map step is always performed first, regardless of the number of documents. This is indicated by the following code in the map_reduce.py file:

map_results = self.llm_chain.apply(
    [{self.document_variable_name: d.page_content, **kwargs} for d in docs],
    callbacks=callbacks,
)

This code applies the llm_chain (which represents the map step) to each document in the docs list. If there is only one document, the map step will still be performed on that single document.

Therefore, it would not be more efficient to run the load_summarize_chain with the stuff chain_type first for a single document. The map_reduce chain_type does not efficiently skip the mapping and go directly to the combine prompt when presented with a single document.

As for the load_summarize_chain method, it is responsible for loading a summarizing chain based on the provided chain type. It supports "stuff", "map_reduce", and "refine" chain types. If the provided chain_type is not in the loader_mapping, the method raises a ValueError. If the chain_type is valid, the corresponding loading function is called with the provided llm, verbose, and **kwargs as arguments.

I hope this answers your question. If you have any further queries, feel free to ask.

Sources

• Improvement: MapReduce summarization chains executes a map step on a single document
• libs/langchain/langchain/chains/combine_documents/map_reduce.py
• libs/langchain/langchain/chains/summarize/init.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[RERobbins]

@dosu-bot What I meant was that if I have a document that is small enough so that there is no need to split the text, I was wondering if I should just call the stuff chain and avoid the map_reduce chain outright. My original approach was to take a document, call the recursivetextsplitter and pass the resulting list of documents to map_reduce. However, if it turns out that I don't need to split at all, it appears I might as well just call the stuff_chain. And, since it appears that the Azure chat models check the prompt length first, I am inclined to just call the stuff chain and catch an error for the prompt being too large, in which case, I will go to the map_reduce chain. This seems to make sense as otherwise, I'd be checking length of the prompt and then calling a model which appears to repeat the same calculation.

…

On Sat, Sep 23, 2023 at 8:30 PM dosu-beta[bot] ***@***.***> wrote: *🤖* Based on the information available in the LangChain repository, it appears that the MapReduceDocumentsChain class does not have a built-in mechanism to skip the mapping step and go directly to the combine prompt when presented with a single document. The map step is always performed first, regardless of the number of documents. This is indicated by the following code in the map_reduce.py file: map_results = self.llm_chain.apply( [{self.document_variable_name: d.page_content, **kwargs} for d in docs], callbacks=callbacks, ) This code applies the llm_chain (which represents the map step) to each document in the docs list. If there is only one document, the map step will still be performed on that single document. Therefore, it would not be more efficient to run the load_summarize_chain with the stuff chain_type first for a single document. The map_reduce chain_type does not efficiently skip the mapping and go directly to the combine prompt when presented with a single document. As for the load_summarize_chain method, it is responsible for loading a summarizing chain based on the provided chain type. It supports "stuff", "map_reduce", and "refine" chain types. If the provided chain_type is not in the loader_mapping, the method raises a ValueError. If the chain_type is valid, the corresponding loading function is called with the provided llm, verbose, and **kwargs as arguments. I hope this answers your question. If you have any further queries, feel free to ask. Sources - Improvement: MapReduce summarization chains executes a map step on a single document <#1937> - libs/langchain/langchain/chains/combine_documents/map_reduce.py <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/combine_documents/map_reduce.py> - libs/langchain/langchain/chains/summarize/*init*.py <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/summarize/__init__.py> ------------------------------ This response is meant to be useful and save you time. It is *not meant to be a precise solution*, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot <https://github.com/dosu-bot>. — Reply to this email directly, view it on GitHub <#10980 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEYUR2IUAB5BBR4SVONFGB3X36EJXANCNFSM6AAAAAA5ESIUSY> . You are receiving this because you authored the thread.Message ID: ***@***.*** com>

[dosubot[bot]]

🤖

Based on your description, it seems like your approach of calling the stuff_chain first and then falling back to the map_reduce chain if the prompt is too large could be a viable strategy. This approach could potentially save computational resources by avoiding unnecessary text splitting and combining operations when the document is small enough to be processed as a whole.

However, there are a few things to consider:

1. Error Handling: Make sure your application is capable of properly handling the error that is thrown when the prompt is too large. This includes not only catching the error but also ensuring that the transition to the map_reduce chain is smooth.

2. Efficiency: While this approach could be more efficient for smaller documents, it might be less efficient for larger documents. This is because, for larger documents, you would essentially be processing the document twice - once with the stuff_chain (which fails due to the large prompt size) and then with the map_reduce chain.

3. Code Complexity: This approach could potentially increase the complexity of your code. You would need to implement logic to switch between the stuff_chain and the map_reduce chain based on whether an error is thrown. This could make your code harder to maintain and debug.

As an alternative, you could consider implementing a check for the document size before deciding which chain to use. This could be done by checking the length of the document against the maximum prompt length of the language model you are using. If the document is smaller than the maximum prompt length, you could use the stuff_chain. Otherwise, you could use the map_reduce chain. This approach could potentially be more efficient and result in simpler code.

Here is a rough example of how you could implement this:

from langchain.chains import StuffDocumentsChain, MapReduceChain
from langchain.llms import OpenAI

# Initialize your chains
stuff_chain = StuffDocumentsChain(...)
map_reduce_chain = MapReduceChain(...)

# Get your document
document = ...

# Check the document size against the maximum prompt length
if len(document) <= OpenAI.MAX_PROMPT_LENGTH:
    # If the document is small enough, use the stuff_chain
    result = stuff_chain.run(document)
else:
    # If the document is too large, use the map_reduce_chain
    result = map_reduce_chain.run(document)

Please note that this is just a rough example and you would need to adapt it to fit your specific use case and codebase.

I hope this helps! If you have any further questions, feel free to ask.

Sources

• Request for a new approach to combineDocument Chain for efficient GPT processing
• libs/langchain/langchain/chains/combine_documents/stuff.py
• libs/langchain/langchain/chains/combine_documents/map_reduce.py
• libs/langchain/langchain/chains/mapreduce.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[RERobbins]

@dosu-bot While your recommended approach of just checking the document length has some appeal, it disregards other elements that go into assembling the prompt. So, I'd prefer a more precise way of calculating the prompt size, which would include system messages and the like. While I have seen elaborate code from the OpenAI cookbook, I find that a simple try/catch approach works very well, especially when the preponderance of my documents appear to be small enough to work with stuff.

…

On Sat, Sep 23, 2023 at 8:44 PM dosu-beta[bot] ***@***.***> wrote: *🤖* Based on your description, it seems like your approach of calling the stuff_chain first and then falling back to the map_reduce chain if the prompt is too large could be a viable strategy. This approach could potentially save computational resources by avoiding unnecessary text splitting and combining operations when the document is small enough to be processed as a whole. However, there are a few things to consider: 1. Error Handling: Make sure your application is capable of properly handling the error that is thrown when the prompt is too large. This includes not only catching the error but also ensuring that the transition to the map_reduce chain is smooth. 2. Efficiency: While this approach could be more efficient for smaller documents, it might be less efficient for larger documents. This is because, for larger documents, you would essentially be processing the document twice - once with the stuff_chain (which fails due to the large prompt size) and then with the map_reduce chain. 3. Code Complexity: This approach could potentially increase the complexity of your code. You would need to implement logic to switch between the stuff_chain and the map_reduce chain based on whether an error is thrown. This could make your code harder to maintain and debug. As an alternative, you could consider implementing a check for the document size before deciding which chain to use. This could be done by checking the length of the document against the maximum prompt length of the language model you are using. If the document is smaller than the maximum prompt length, you could use the stuff_chain. Otherwise, you could use the map_reduce chain. This approach could potentially be more efficient and result in simpler code. Here is a rough example of how you could implement this: from langchain.chains import StuffDocumentsChain, MapReduceChainfrom langchain.llms import OpenAI # Initialize your chainsstuff_chain = StuffDocumentsChain(...)map_reduce_chain = MapReduceChain(...) # Get your documentdocument = ... # Check the document size against the maximum prompt lengthif len(document) <= OpenAI.MAX_PROMPT_LENGTH: # If the document is small enough, use the stuff_chain result = stuff_chain.run(document)else: # If the document is too large, use the map_reduce_chain result = map_reduce_chain.run(document) Please note that this is just a rough example and you would need to adapt it to fit your specific use case and codebase. I hope this helps! If you have any further questions, feel free to ask. Sources - Request for a new approach to combineDocument Chain for efficient GPT processing <#1247> - libs/langchain/langchain/chains/combine_documents/stuff.py <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/combine_documents/stuff.py> - libs/langchain/langchain/chains/combine_documents/map_reduce.py <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/combine_documents/map_reduce.py> - libs/langchain/langchain/chains/mapreduce.py <https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/mapreduce.py> ------------------------------ This response is meant to be useful and save you time. It is *not meant to be a precise solution*, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot <https://github.com/dosu-bot>. — Reply to this email directly, view it on GitHub <#10980 (reply in thread)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEYUR2O7OEB74H4XDA5LTYLX36F6BANCNFSM6AAAAAA5ESIUSY> . You are receiving this because you authored the thread.Message ID: ***@***.*** com>

[dosubot[bot]]

🤖

Hello,

Thank you for your detailed question. It seems like you're looking for a way to more accurately calculate the prompt size in the LangChain framework, especially when using the stuff_chain method.

The StuffDocumentsChain class in LangChain combines documents by stuffing them into context. It formats each document into a string with the document_prompt and then joins them together with document_separator. This new string is added to the inputs with the variable name set by document_variable_name. These inputs are then passed to the llm_chain.

To calculate the prompt size more precisely, you can modify the prompt_length method in the StuffDocumentsChain class. This method currently returns the length of the prompt in tokens, but you can adjust it to include the size of system messages and other elements.

Here's a simple approach to modify the prompt_length method:

def prompt_length(self, docs: List[Document], **kwargs: Any) -> Optional[int]:
    """Return the prompt length given the documents passed in.

    This can be used by a caller to determine whether passing in a list
    of documents would exceed a certain prompt length. This useful when
    trying to ensure that the size of a prompt remains below a certain
    context limit.

    Args:
        docs: List[Document], a list of documents to use to calculate the
            total prompt length.

    Returns:
        Returns None if the method does not depend on the prompt length,
        otherwise the length of the prompt in tokens.
    """
    inputs = self._get_inputs(docs, **kwargs)
    prompt = self.llm_chain.prompt.format(**inputs)
    total_size = len(prompt) + sum(len(str(v)) for v in inputs.values())
    return total_size

In this modified method, the prompt_length not only returns the length of the prompt in tokens but also the total size of the prompt including the size of the variables.

However, if most of your documents are small enough to not require splitting, you might not need to worry about the precise calculation of the prompt size. The language model should be able to handle small documents without any issues. If you're facing specific problems with small documents, it would be helpful to know more about those issues to provide a more targeted solution.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Best,
Dosu

Sources

• libs/langchain/langchain/chains/qa_with_sources/stuff_prompt.py
• libs/langchain/langchain/prompts/prompt.py
• libs/langchain/langchain/chains/combine_documents/stuff.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.