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import os
from langchain.chains.llm import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.document_loaders import PDFPlumberLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import ReduceDocumentsChain, MapReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
os.environ['OPENAI_API_KEY'] = os.getenv("OPENAI_API_KEY")
llm = ChatOpenAI(temperature=0, model_name="gpt-4-0125-preview")
def get_summ(path):
loader = PDFPlumberLoader(path)
docs = loader.load()
# Map
map_template = """The following is a set of documents
{docs}
Based on this list of docs, please identify the main themes and determine the genes relevant or irrelevant to the discussed disease followed by any associated p-values if available.
Helpful Answer:"""
map_prompt = PromptTemplate.from_template(map_template)
map_chain = LLMChain(llm=llm, prompt=map_prompt)
# Reduce
reduce_template = """The following is set of summaries:
{doc_summaries}
Take these and distill it into a final, consolidated summary of the main themes.
Helpful Answer:"""
reduce_prompt = PromptTemplate.from_template(reduce_template)
# Run chain
reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)
# Takes a list of documents, combines them into a single string, and passes this to an LLMChain
combine_documents_chain = StuffDocumentsChain(
llm_chain=reduce_chain, document_variable_name="doc_summaries"
)
# Combines and iteravely reduces the mapped documents
reduce_documents_chain = ReduceDocumentsChain(
# This is final chain that is called.
combine_documents_chain=combine_documents_chain,
# If documents exceed context for `StuffDocumentsChain`
collapse_documents_chain=combine_documents_chain,
# The maximum number of tokens to group documents into.
token_max=100000,
)
# Combining documents by mapping a chain over them, then combining results
map_reduce_chain = MapReduceDocumentsChain(
# Map chain
llm_chain=map_chain,
# Reduce chain
reduce_documents_chain=reduce_documents_chain,
# The variable name in the llm_chain to put the documents in
document_variable_name="docs",
# Return the results of the map steps in the output
return_intermediate_steps=False,
)
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=100000, chunk_overlap=0
)
split_docs = text_splitter.split_documents(docs)
return map_reduce_chain.run(split_docs) |