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mikepastor11
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b31777c
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Parent(s):
d181195
Update app.py
Browse files
app.py
CHANGED
@@ -47,29 +47,32 @@ def get_text_chunks(text):
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return chunks
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# # embeddings = OpenAIEmbeddings()
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#
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# embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
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# # instruction = "Represent the Science title:"
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# # embeddings = model.encode([[instruction, sentence]])
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#
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# #
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# vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
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# print('FAISS succeeds: ')
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#
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# def get_conversation_chain(vectorstore):
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# # llm = ChatOpenAI()
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return chunks
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def get_vectorstore(text_chunks):
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st.write('Here in vector store....', unsafe_allow_html=True)
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# embeddings = OpenAIEmbeddings()
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# pip install InstructorEmbedding
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# pip install sentence-transformers==2.2.2
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embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
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st.write('Here in vector store - got embeddings ', unsafe_allow_html=True)
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# from InstructorEmbedding import INSTRUCTOR
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# model = INSTRUCTOR('hkunlp/instructor-xl')
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# sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
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# instruction = "Represent the Science title:"
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# embeddings = model.encode([[instruction, sentence]])
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# embeddings = model.encode(text_chunks)
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print('have Embeddings: ')
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# text_chunks="this is a test"
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# FAISS, Chroma and other vector databases
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#
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# vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
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print('FAISS succeeds: ')
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return vectorstore
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# def get_conversation_chain(vectorstore):
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# # llm = ChatOpenAI()
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