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42 lines
2.8 KiB
Markdown
42 lines
2.8 KiB
Markdown
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# CombineDocuments Chains
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CombineDocuments chains are useful for when you need to run a language over multiple documents.
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Common use cases for this include question answering, question answering with sources, summarization, and more.
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For more information on specific use cases as well as different methods for **fetching** these documents, please see
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[this overview](/use_cases/combine_docs.md).
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This documentation now picks up from after you've fetched your documents - now what?
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How do you pass them to the language model in a format it can understand?
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There are a few different methods, or chains, for doing so. LangChain supports three of the more common ones - and
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we are actively looking to include more, so if you have any ideas please reach out! Note that there is not
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one best method - the decision of which one to use is often very context specific. In order from simplest to
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most complex:
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## Stuffing
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Stuffing is the simplest method, whereby you simply stuff all the related data into the prompt as context
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to pass to the language model. This is implemented in LangChain as the `StuffDocumentsChain`.
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**Pros:** Only makes a single call to the LLM. When generating text, the LLM has access to all the data at once.
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**Cons:** Most LLMs have a context length, and for large documents (or many documents) this will not work as it will result in a prompt larger than the context length.
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The main downside of this method is that it only works one smaller pieces of data. Once you are working
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with many pieces of data, this approach is no longer feasible. The next two approaches are designed to help deal with that.
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## Map Reduce
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This method involves an initial prompt on each chunk of data (for summarization tasks, this
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could be a summary of that chunk; for question-answering tasks, it could be an answer based solely on that chunk).
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Then a different prompt is run to combine all the initial outputs. This is implemented in the LangChain as the `MapReduceDocumentsChain`.
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**Pros:** Can scale to larger documents (and more documents) than `StuffDocumentsChain`. The calls to the LLM on individual documents are independent and can therefore be parallelized.
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**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. Loses some information during the final combining call.
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## Refine
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This method involves an initial prompt on the first chunk of data, generating some output.
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For the remaining documents, that output is passed in, along with the next document,
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asking the LLM to refine the output based on the new document.
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**Pros:** Can pull in more relevant context, and may be less lossy than `MapReduceDocumentsChain`.
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**Cons:** Requires many more calls to the LLM than `StuffDocumentsChain`. The calls are also NOT independent, meaning they cannot be paralleled like `MapReduceDocumentsChain`. There is also some potential dependencies on the ordering of the documents.
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