fehmikaya commited on
Commit
92293bb
·
verified ·
1 Parent(s): 467f506

Update ragagent.py

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Files changed (1) hide show
  1. ragagent.py +8 -22
ragagent.py CHANGED
@@ -99,29 +99,15 @@ class RAGAgent():
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  embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
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  collection_name = re.sub(r'[^a-zA-Z0-9]', '', doc_splits[0].metadata.get('source'))
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- print(collection_name)
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-
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- '''
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- client = chromadb.EphemeralClient()
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-
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- try:
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- # If it exists, delete the existing collection
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- collection = client.get_collection(collection_name)
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- client.delete_collection(collection_name)
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- except:
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- pass
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-
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- collection = client.create_collection(collection_name)
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- '''
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  persistent_client = chromadb.PersistentClient(settings=Settings(allow_reset=True))
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- persistent_client.reset()
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- # if collection_name in [c.name for c in persistent_client.list_collections()]:
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- # print("deleted: ",collection_name)
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- # persistent_client.delete_collection(collection_name)
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  collection = persistent_client.create_collection(collection_name)
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- print("created: ",collection_name)
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  # Add to vectorDB
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  vectorstore = Chroma(
@@ -176,12 +162,12 @@ class RAGAgent():
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  web_search = "Yes"
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  for d in documents:
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- print("question: ",question)
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- print("document: ",d.page_content)
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  score = RAGAgent.retrieval_grader.invoke(
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  {"question": question, "document": d.page_content}
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  )
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- print("score: ",score)
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  grade = score["score"]
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  # Document relevant
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  if grade.lower() == "yes":
 
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  embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
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  collection_name = re.sub(r'[^a-zA-Z0-9]', '', doc_splits[0].metadata.get('source'))
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  persistent_client = chromadb.PersistentClient(settings=Settings(allow_reset=True))
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+ # persistent_client.reset()
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+ if collection_name in [c.name for c in persistent_client.list_collections()]:
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+ print("\ndeleted: ",collection_name)
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+ persistent_client.delete_collection(collection_name)
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  collection = persistent_client.create_collection(collection_name)
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+ print("\ncreated: ",collection_name)
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  # Add to vectorDB
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  vectorstore = Chroma(
 
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  web_search = "Yes"
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  for d in documents:
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+ print("\n---- question: ",question)
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+ print("\n---- document: ",d.page_content)
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  score = RAGAgent.retrieval_grader.invoke(
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  {"question": question, "document": d.page_content}
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  )
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+ print("\n---- score: ",score)
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  grade = score["score"]
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  # Document relevant
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  if grade.lower() == "yes":