firqaaa commited on
Commit
7181652
1 Parent(s): 80fc244

Update summ.py

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Files changed (1) hide show
  1. summ.py +4 -3
summ.py CHANGED
@@ -9,7 +9,7 @@ from langchain.chains.combine_documents.stuff import StuffDocumentsChain
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  os.environ['OPENAI_API_KEY'] = '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'
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- llm = ChatOpenAI(temperature=0, model_name="gpt-4-1106-preview")
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  def get_summ(path):
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@@ -27,6 +27,7 @@ def get_summ(path):
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  reduce_template = """The following is set of summaries:
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  {doc_summaries}
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  Take these and distill it into a final, consolidated summary of the main themes.
 
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  Helpful Answer:"""
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  reduce_prompt = PromptTemplate.from_template(reduce_template)
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@@ -45,7 +46,7 @@ def get_summ(path):
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  # If documents exceed context for `StuffDocumentsChain`
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  collapse_documents_chain=combine_documents_chain,
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  # The maximum number of tokens to group documents into.
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- token_max=64000,
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  )
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  # Combining documents by mapping a chain over them, then combining results
@@ -61,7 +62,7 @@ def get_summ(path):
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  )
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  text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
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- chunk_size=64000, chunk_overlap=0
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  )
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  split_docs = text_splitter.split_documents(docs)
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  os.environ['OPENAI_API_KEY'] = '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'
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+ llm = ChatOpenAI(temperature=0, model_name="gpt-4-0125-preview")
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  def get_summ(path):
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  reduce_template = """The following is set of summaries:
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  {doc_summaries}
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  Take these and distill it into a final, consolidated summary of the main themes.
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+ Determine the genes relevant or irrelevant to the discussed disease, and incorporate any associated p-values if available.
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  Helpful Answer:"""
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  reduce_prompt = PromptTemplate.from_template(reduce_template)
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  # If documents exceed context for `StuffDocumentsChain`
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  collapse_documents_chain=combine_documents_chain,
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  # The maximum number of tokens to group documents into.
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+ token_max=100000,
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  )
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  # Combining documents by mapping a chain over them, then combining results
 
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  )
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  text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
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+ chunk_size=100000, chunk_overlap=0
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  )
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  split_docs = text_splitter.split_documents(docs)
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