ryanrwatkins commited on
Commit
78b8854
1 Parent(s): 8ad7e64

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +9 -3
app.py CHANGED
@@ -320,6 +320,7 @@ vectorstore,search_type="similarity",k=4,score_threshold=None
320
  k: number of documents to return (Default: 4)
321
  score_threshold: Minimum relevance threshold for similarity_score_threshold (default=None)
322
  """
 
323
  search_kwargs={}
324
  if k is not None:
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  search_kwargs['k'] = k
@@ -330,6 +331,7 @@ vectorstore,search_type="similarity",k=4,score_threshold=None
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  search_type=search_type,
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  search_kwargs=search_kwargs
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  )
 
333
  return retriever
334
 
335
  # similarity search
@@ -353,7 +355,7 @@ def create_compression_retriever(embeddings, base_retriever, chunk_size=500, k=1
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  k (int): top k relevant chunks to the query are filtered using the EmbeddingsFilter. default =16.
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  similarity_threshold : minimum relevance threshold used by the EmbeddingsFilter. default =None.
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  """
356
-
357
  # 1. splitting documents into smaller chunks
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  splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0, separator=". ")
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@@ -378,10 +380,11 @@ def create_compression_retriever(embeddings, base_retriever, chunk_size=500, k=1
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  base_compressor=pipeline_compressor,
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  base_retriever=base_retriever
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  )
381
-
382
  return compression_retriever
383
 
384
  def CohereRerank_retriever(
 
385
  base_retriever,
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  cohere_api_key,cohere_model="rerank-multilingual-v2.0", top_n=8
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  ):
@@ -403,6 +406,7 @@ def CohereRerank_retriever(
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  base_compressor=compressor,
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  base_retriever=base_retriever
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  )
 
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  return retriever_Cohere
407
 
408
 
@@ -418,6 +422,7 @@ def retrieval_blocks(
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  compression_retriever_k=16,
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  cohere_api_key="***", cohere_model="rerank-multilingual-v2.0", cohere_top_n=8,
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  ):
 
421
  """
422
  Rertieval includes: document loaders, text splitter, vectorstore and retriever.
423
 
@@ -506,7 +511,7 @@ def retrieval_blocks(
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  print(f"\n{retriever_type} is created successfully!")
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  print(f"Relevant documents will be retrieved from vectorstore ({vectorstore_name}) which uses {LLM_service} embeddings \
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  and has {vector_store._collection.count()} chunks.")
509
-
510
  return retriever
511
  except Exception as e:
512
  print(e)
@@ -652,6 +657,7 @@ def answer_template(language="english"):
652
  </context>
653
 
654
  Question: {{question}}
 
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  Language: {language}.
656
 
657
  """
 
320
  k: number of documents to return (Default: 4)
321
  score_threshold: Minimum relevance threshold for similarity_score_threshold (default=None)
322
  """
323
+ print("vector_backed retriever started")
324
  search_kwargs={}
325
  if k is not None:
326
  search_kwargs['k'] = k
 
331
  search_type=search_type,
332
  search_kwargs=search_kwargs
333
  )
334
+ print("vector_backed retriever done")
335
  return retriever
336
 
337
  # similarity search
 
355
  k (int): top k relevant chunks to the query are filtered using the EmbeddingsFilter. default =16.
356
  similarity_threshold : minimum relevance threshold used by the EmbeddingsFilter. default =None.
357
  """
358
+ print("compression retriever started")
359
  # 1. splitting documents into smaller chunks
360
  splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0, separator=". ")
361
 
 
380
  base_compressor=pipeline_compressor,
381
  base_retriever=base_retriever
382
  )
383
+ print("compression retriever done")
384
  return compression_retriever
385
 
386
  def CohereRerank_retriever(
387
+ print("cohere rerank started")
388
  base_retriever,
389
  cohere_api_key,cohere_model="rerank-multilingual-v2.0", top_n=8
390
  ):
 
406
  base_compressor=compressor,
407
  base_retriever=base_retriever
408
  )
409
+ print("cohere rerank done")
410
  return retriever_Cohere
411
 
412
 
 
422
  compression_retriever_k=16,
423
  cohere_api_key="***", cohere_model="rerank-multilingual-v2.0", cohere_top_n=8,
424
  ):
425
+ print("retrieval blocks started")
426
  """
427
  Rertieval includes: document loaders, text splitter, vectorstore and retriever.
428
 
 
511
  print(f"\n{retriever_type} is created successfully!")
512
  print(f"Relevant documents will be retrieved from vectorstore ({vectorstore_name}) which uses {LLM_service} embeddings \
513
  and has {vector_store._collection.count()} chunks.")
514
+ print("retrieval blocks done")
515
  return retriever
516
  except Exception as e:
517
  print(e)
 
657
  </context>
658
 
659
  Question: {{question}}
660
+ Question: {question}
661
  Language: {language}.
662
 
663
  """