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chains.py
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chains.py
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from functools import partial
from langchain.chains import LLMChain
from langchain.chains.combine_documents import collapse_docs, split_list_of_docs
from langchain.chat_models import AzureChatOpenAI
from langchain.docstore.document import Document
from langchain.prompts import PromptTemplate
from langchain.schema import StrOutputParser
from langchain_core.prompts import format_document
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
def create_map_reduce_chain(llm: AzureChatOpenAI, document_prompt: PromptTemplate, summary_prompt: PromptTemplate) -> LLMChain:
partial_format_document = partial(format_document, prompt=document_prompt)
# The chain we'll apply to each individual document.
# Returns a summary of the document.
map_chain = (
{"context": partial_format_document}
| summary_prompt
| llm
| StrOutputParser()
)
# A wrapper chain to keep the original Document metadata
map_as_doc_chain = (
RunnableParallel({"doc": RunnablePassthrough(), "content": map_chain})
| (lambda x: Document(page_content=x["content"], metadata=x["doc"].metadata))
).with_config(run_name="Summarize (return doc)")
# The chain we'll repeatedly apply to collapse subsets of the documents
# into a consolidate document until the total token size of our
# documents is below some max size.
def format_docs(docs):
return "\n\n".join(partial_format_document(doc) for doc in docs)
collapse_chain = (
{"context": format_docs}
| PromptTemplate.from_template("Collapse this content:\n\n{context}")
| llm
| StrOutputParser()
)
def get_num_tokens(docs):
return llm.get_num_tokens(format_docs(docs))
def collapse(
docs,
config,
token_max=4000,
):
collapse_ct = 1
while get_num_tokens(docs) > token_max:
config["run_name"] = f"Collapse {collapse_ct}"
invoke = partial(collapse_chain.invoke, config=config)
split_docs = split_list_of_docs(docs, get_num_tokens, token_max)
docs = [collapse_docs(_docs, invoke) for _docs in split_docs]
collapse_ct += 1
return docs
# The chain we'll use to combine our individual document summaries
# (or summaries over subset of documents if we had to collapse the map results)
# into a final summary.
reduce_chain = (
{"context": format_docs}
| PromptTemplate.from_template("Combine these summaries:\n\n{context}")
| llm
| StrOutputParser()
).with_config(run_name="Reduce")
# The final full chain
return (map_as_doc_chain.map() | collapse | reduce_chain).with_config(run_name="Map reduce")