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Update auditqa/doc_process.py
Browse files- auditqa/doc_process.py +8 -5
auditqa/doc_process.py
CHANGED
@@ -6,7 +6,7 @@ from torch import cuda
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from langchain_community.document_loaders import PyMuPDFLoader
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from langchain_community.embeddings import HuggingFaceEmbeddings, HuggingFaceInferenceAPIEmbeddings
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from langchain_community.vectorstores import Qdrant
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from auditqa.reports import files, report_list
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device = 'cuda' if cuda.is_available() else 'cpu'
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@@ -93,15 +93,18 @@ def process_pdf():
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print("done")
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return qdrant_collections
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def get_local_qdrant(
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print(client.get_collections())
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embeddings = HuggingFaceEmbeddings(
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model_kwargs = {'device': device},
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encode_kwargs = {'normalize_embeddings': True},
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model_name="BAAI/bge-small-en-v1.5")
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from langchain_community.document_loaders import PyMuPDFLoader
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from langchain_community.embeddings import HuggingFaceEmbeddings, HuggingFaceInferenceAPIEmbeddings
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from langchain_community.vectorstores import Qdrant
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from qdrant_client import QdrantClient
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from auditqa.reports import files, report_list
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device = 'cuda' if cuda.is_available() else 'cpu'
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print("done")
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return qdrant_collections
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def get_local_qdrant():
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print(client.get_collections())
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qdrant_collections = {}
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embeddings = HuggingFaceEmbeddings(
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model_kwargs = {'device': device},
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encode_kwargs = {'normalize_embeddings': True},
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model_name="BAAI/bge-small-en-v1.5")
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list_ = ['Consolidated','District','Ministry','allreports']
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for val in list_:
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client = QdrantClient(path="./data/{val}")
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qdrant_collections[val] = Qdrant(client=client, collection_name=val, embeddings=embeddings, )
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return qdrant_collections
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