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app.py
CHANGED
@@ -196,6 +196,11 @@ def set_custom_prompt():
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'chat_history'])
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return prompt
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@st.cache_resource
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def load_embeddings():
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embeddings = HuggingFaceEmbeddings(model_name = "thenlper/gte-base",
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@@ -241,7 +246,7 @@ def main():
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text = ""
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for page in pdf_reader.pages:
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text += page.extract_text()
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-
db =
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qa_chain = ConversationalRetrievalChain(
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retriever =db.as_retriever(search_type="similarity_score_threshold", search_kwargs={'k':2, "score_threshold": 0.7}),
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question_generator=question_generator,
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@@ -268,7 +273,7 @@ def main():
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response = qa_chain({'question': query})
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url_list = set([i.metadata['source'] for i in response['source_documents']])
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#print(f"condensed quesion : {question_generator.run({'chat_history': response['chat_history'], 'question' : query})}")
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with st.chat_message("assistant"):
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@@ -280,10 +285,10 @@ def main():
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": response['answer']})
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with st.expander("See the related documents"):
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-
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-
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clear_button = st.button("Start new convo")
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if clear_button :
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'chat_history'])
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return prompt
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@st.cache_resource
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def create_db(text,embeddings):
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return FAISS.from_texts(text, embeddings)
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@st.cache_resource
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def load_embeddings():
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embeddings = HuggingFaceEmbeddings(model_name = "thenlper/gte-base",
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text = ""
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for page in pdf_reader.pages:
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text += page.extract_text()
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db = create_db(text,embeddings)
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qa_chain = ConversationalRetrievalChain(
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retriever =db.as_retriever(search_type="similarity_score_threshold", search_kwargs={'k':2, "score_threshold": 0.7}),
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question_generator=question_generator,
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response = qa_chain({'question': query})
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# url_list = set([i.metadata['source'] for i in response['source_documents']])
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#print(f"condensed quesion : {question_generator.run({'chat_history': response['chat_history'], 'question' : query})}")
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with st.chat_message("assistant"):
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": response['answer']})
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# with st.expander("See the related documents"):
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# for count, url in enumerate(url_list):
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# #url_reg = regex_source(url)
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# st.write(str(count+1)+":", url)
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clear_button = st.button("Start new convo")
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if clear_button :
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