bupa1018 commited on
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b79258d
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1 Parent(s): 0c70cc1

Update kadiApy_ragchain.py

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  1. kadiApy_ragchain.py +2 -36
kadiApy_ragchain.py CHANGED
@@ -31,8 +31,8 @@ class KadiApyRagchain:
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  #doc_contexts = self.retrieve_contexts(query, k=3, filter={"dataset_category": "kadi_apy_docs"})
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  #code_contexts = self.retrieve_contexts(query, k=5, filter={"dataset_category": "kadi_apy_source_code"})
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- # doc_contexts = self.retrieve_contexts(query, k=3, filter={"dataset_category": "kadi_apy_docs"})
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- # code_contexts = self.retrieve_contexts(rewritten_query, k=5, filter={"dataset_category": "kadi_apy_source_code"})
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  # Format contexts
@@ -130,39 +130,6 @@ class KadiApyRagchain:
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  context = self.vector_store.similarity_search(query = query, k=k, filter=filter)
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  return context
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- # def generate_response(self, query, doc_context, code_context):
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- # """
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- # Generate a response using the retrieved contexts and the LLM.
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- # """
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-
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-
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- # prompt = f"""You are a Python programming assistant specialized in the "Kadi-APY" library.
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- # The "Kadi-APY" library is a Python package designed to facilitate interaction with the REST-like API of a software platform called Kadi4Mat.
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- # Your task is to answer the user's query based on the guidelines and if needed the combine understanding provided by
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- # "Document snippets" with the implementation details provided by "Code Snippets."
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-
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- # Guidelines if generating code:
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- # - Display the complete code first, followed by a concise explanation in no more than 5 sentences.
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-
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- # General Guideline:
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- # - If the user's query can not be fullfilled based on the provided snippets, reply with "The API does not support the requested functionality"
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- # - If the user's query does not implicate any task, reply with a question asking the user to elaborate.
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-
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- # "Document Snippets": These contain documentation excerpts and code examples that explain how to use the "Kadi-APY" library
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- # Document Snippets:
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- # {doc_context}
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-
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-
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- # "Code Snippets": These are raw source code fragments from the implementation of the "Kadi-APY" library.
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- # Code Snippets:
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- # {code_context}
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-
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- # Query:
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- # {query}
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- # """
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- # return self.llm.invoke(prompt).content
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-
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-
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  def generate_response(self, query, chat_history, doc_context, code_context):
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  """
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  Generate a response using the retrieved contexts and the LLM.
@@ -203,7 +170,6 @@ class KadiApyRagchain:
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  def format_documents(self, documents):
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  formatted_docs = []
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- print("################################# start of doc #######################################")
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  for i, doc in enumerate(documents, start=1):
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  formatted_docs.append(f"Snippet {i}: \n")
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  formatted_docs.append("\n")
 
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  #doc_contexts = self.retrieve_contexts(query, k=3, filter={"dataset_category": "kadi_apy_docs"})
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  #code_contexts = self.retrieve_contexts(query, k=5, filter={"dataset_category": "kadi_apy_source_code"})
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+ # doc_contexts = self.retrieve_contexts(query, k=3, filter={"dataset_category": "kadi_apy_docs"})
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+ # code_contexts = self.retrieve_contexts(rewritten_query, k=5, filter={"dataset_category": "kadi_apy_source_code"})
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  # Format contexts
 
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  context = self.vector_store.similarity_search(query = query, k=k, filter=filter)
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  return context
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  def generate_response(self, query, chat_history, doc_context, code_context):
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  """
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  Generate a response using the retrieved contexts and the LLM.
 
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  def format_documents(self, documents):
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  formatted_docs = []
 
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  for i, doc in enumerate(documents, start=1):
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  formatted_docs.append(f"Snippet {i}: \n")
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  formatted_docs.append("\n")