Upload helper.py
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helper.py
CHANGED
@@ -42,7 +42,7 @@ llm = HuggingFacePipeline(pipeline=pipe)
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# # Initialize instructor embeddings using the HF中国镜像站 model
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# instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="C:/Users/arasu/Workspace/Projects/GenAI/embeddings/hkunlp_instructor-large")
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instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-large")
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def create_vector_db():
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# Load data from pdf
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@@ -64,13 +64,13 @@ def create_vector_db():
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texts = text_splitter.split_text(raw_text)
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# Create a vector database from 'text'
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vector_db = Chroma.from_texts(texts,instructor_embeddings
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vector_db.persist()
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vector_db = None
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def get_qa_chain():
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# Load the vector database from the local folder
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vector_db = Chroma(persist_directory=db_path, embedding_function = instructor_embeddings)
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# Create a retriever for querying the vector database
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retriever = vector_db.as_retriever(search_kwargs={"k":3})
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# # Initialize instructor embeddings using the HF中国镜像站 model
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# instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="C:/Users/arasu/Workspace/Projects/GenAI/embeddings/hkunlp_instructor-large")
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instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-large")
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vector_db = ""
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def create_vector_db():
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# Load data from pdf
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texts = text_splitter.split_text(raw_text)
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# Create a vector database from 'text'
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vector_db = Chroma.from_texts(texts,instructor_embeddings)
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# vector_db.persist()
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# vector_db = None
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def get_qa_chain():
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# Load the vector database from the local folder
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# vector_db = Chroma(persist_directory=db_path, embedding_function = instructor_embeddings)
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# Create a retriever for querying the vector database
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retriever = vector_db.as_retriever(search_kwargs={"k":3})
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