awinml commited on
Commit
1227998
1 Parent(s): c6634c4

Upload 17 files (#17)

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- Upload 17 files (313a678dec11f2f1512c293afe1e86559cf20a38)

Files changed (2) hide show
  1. utils/entity_extraction.py +36 -1
  2. utils/retriever.py +13 -6
utils/entity_extraction.py CHANGED
@@ -21,6 +21,41 @@ def expand_list_of_lists(list_of_lists):
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  return expanded_list
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23
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  def all_keywords_combs(texts):
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  texts = [text.split(" ") for text in texts]
@@ -47,7 +82,7 @@ def extract_keywords(query_text, model):
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  prompt = f"###Instruction:Extract the important keywords which describe the context accurately.\n\nInput:{query_text}\n\n###Response:"
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  response = model.predict(prompt)
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  keywords = response.split(", ")
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- keywords = all_keywords_combs(keywords)
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  return keywords
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53
 
 
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  return expanded_list
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+ def keywords_no_companies(texts):
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+ # Company list (to remove companies from extracted entities)
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+
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+ company_list = [
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+ "apple",
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+ "amd",
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+ "amazon",
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+ "cisco",
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+ "google",
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+ "microsoft",
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+ "nvidia",
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+ "asml",
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+ "intel",
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+ "micron",
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+ "aapl",
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+ "csco",
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+ "msft",
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+ "asml",
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+ "nvda",
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+ "googl",
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+ "mu",
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+ "intc",
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+ "amzn",
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+ "amd",
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+ ]
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+
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+ texts = [text.split(" ") for text in texts]
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+ texts = expand_list_of_lists(texts)
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+
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+ # Convert all strings to lowercase.
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+ lower_texts = [text.lower() for text in texts]
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+ keywords = [text for text in lower_texts if text not in company_list]
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+ return keywords
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+
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+
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  def all_keywords_combs(texts):
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  texts = [text.split(" ") for text in texts]
 
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  prompt = f"###Instruction:Extract the important keywords which describe the context accurately.\n\nInput:{query_text}\n\n###Response:"
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  response = model.predict(prompt)
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  keywords = response.split(", ")
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+ keywords = keywords_no_companies(keywords)
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  return keywords
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utils/retriever.py CHANGED
@@ -15,6 +15,9 @@ def query_pinecone_sparse(
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  else:
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  participant = "Question"
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  if year == "All":
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  if quarter == "All":
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  xc = index.query(
@@ -34,7 +37,7 @@ def query_pinecone_sparse(
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  "Quarter": {"$in": ["Q1", "Q2", "Q3", "Q4"]},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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- "Keywords": {"$in": keywords}
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  },
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  include_metadata=True,
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  )
@@ -56,7 +59,7 @@ def query_pinecone_sparse(
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  "Quarter": {"$eq": quarter},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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- "Keywords": {"$in": keywords}
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  },
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  include_metadata=True,
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  )
@@ -71,7 +74,7 @@ def query_pinecone_sparse(
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  "Quarter": {"$eq": quarter},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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- "Keywords": {"$in": keywords}
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  },
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  include_metadata=True,
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  )
@@ -100,6 +103,10 @@ def query_pinecone(
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  else:
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  participant = "Question"
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  if year == "All":
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  if quarter == "All":
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  xc = index.query(
@@ -118,7 +125,7 @@ def query_pinecone(
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  "Quarter": {"$in": ["Q1", "Q2", "Q3", "Q4"]},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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- "Keywords": {"$in": keywords}
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  },
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  include_metadata=True,
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  )
@@ -139,7 +146,7 @@ def query_pinecone(
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  "Quarter": {"$eq": quarter},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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- "Keywords": {"$in": keywords}
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  },
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  include_metadata=True,
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  )
@@ -153,7 +160,7 @@ def query_pinecone(
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  "Quarter": {"$eq": quarter},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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- "Keywords": {"$in": keywords}
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  },
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  include_metadata=True,
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  )
 
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  else:
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  participant = "Question"
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+ # Create filter dictionary based on keywords
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+ filter_dict = [{'Keywords': word} for word in keywords]
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+
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  if year == "All":
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  if quarter == "All":
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  xc = index.query(
 
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  "Quarter": {"$in": ["Q1", "Q2", "Q3", "Q4"]},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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+ '$and': filter_dict
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  },
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  include_metadata=True,
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  )
 
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  "Quarter": {"$eq": quarter},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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+ '$and': filter_dict
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  },
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  include_metadata=True,
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  )
 
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  "Quarter": {"$eq": quarter},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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+ '$and': filter_dict
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  },
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  include_metadata=True,
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  )
 
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  else:
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  participant = "Question"
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+ # Create filter dictionary based on keywords
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+ filter_dict = [{'Keywords': word} for word in keywords]
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+
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+
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  if year == "All":
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  if quarter == "All":
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  xc = index.query(
 
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  "Quarter": {"$in": ["Q1", "Q2", "Q3", "Q4"]},
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  "Ticker": {"$eq": ticker},
127
  "QA_Flag": {"$eq": participant},
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+ '$and': filter_dict
129
  },
130
  include_metadata=True,
131
  )
 
146
  "Quarter": {"$eq": quarter},
147
  "Ticker": {"$eq": ticker},
148
  "QA_Flag": {"$eq": participant},
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+ '$and': filter_dict
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  },
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  include_metadata=True,
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  )
 
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  "Quarter": {"$eq": quarter},
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  "Ticker": {"$eq": ticker},
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  "QA_Flag": {"$eq": participant},
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+ '$and': filter_dict
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  },
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  include_metadata=True,
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  )