Slfagrouche
commited on
Commit
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e777fd3
1
Parent(s):
96764ee
Update app.py
Browse files
app.py
CHANGED
@@ -1,5 +1,5 @@
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# -*- coding: utf-8 -*-
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"""Said
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"""
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import getpass
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@@ -37,7 +37,7 @@ OPENAI_API_KEYY = os.getenv('OPENAI_API_KEY')
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MONGO_URII = os.getenv('MONGO_URI')
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# Append our directory path to the Python system path.
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directory_path = "
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sys.path.append(directory_path)
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@@ -149,7 +149,7 @@ vector_search = Weaviate.from_documents(
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# Semantic Search.
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# query = "When is the spring recess at The City College of New York for Spring 2024?"
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query = "
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results = vector_search.similarity_search(query=query, k=10) # 10 most similar documents.
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print("\n")
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@@ -158,7 +158,7 @@ pprint.pprint(results)
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# Semantic Search with Score.
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# query = "When is the spring recess at The City College of New York for Spring 2024?"
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query = "
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results = vector_search.similarity_search_with_score(
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query = query, k = 10 # 10 most similar documents.
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)
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@@ -216,7 +216,7 @@ rag_chain = (
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)
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# Prompt the chain.
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query = "
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answer = rag_chain.invoke(query)
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print("\nQuestion: " + query)
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@@ -234,10 +234,10 @@ def get_response(query):
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return rag_chain.invoke(query)
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# Gradio application.
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with gr.Blocks(theme=Base(), title="RAG QA App
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gr.Markdown(
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"""
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# RAG
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""")
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textbox = gr.Textbox(label="Question:")
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with gr.Row():
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@@ -252,3 +252,4 @@ with gr.Blocks(theme=Base(), title="RAG QA App Using Spring 2024 Syllabuses PDFs
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demo.launch(share=True)
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# -*- coding: utf-8 -*-
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"""Said Lfagrouche: RAG_QA_App_base_on_BROOKLYN_COLLEGE STUDENT_HANDBOOK_2023-2024_app
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"""
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import getpass
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MONGO_URII = os.getenv('MONGO_URI')
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# Append our directory path to the Python system path.
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directory_path = "Data"
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sys.path.append(directory_path)
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# Semantic Search.
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# query = "When is the spring recess at The City College of New York for Spring 2024?"
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query = "Who is Prof. Langsam"
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results = vector_search.similarity_search(query=query, k=10) # 10 most similar documents.
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print("\n")
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# Semantic Search with Score.
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# query = "When is the spring recess at The City College of New York for Spring 2024?"
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query = "Is there a pool in campus?"
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results = vector_search.similarity_search_with_score(
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query = query, k = 10 # 10 most similar documents.
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)
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)
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# Prompt the chain.
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query = "Where is Student Center located at?"
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answer = rag_chain.invoke(query)
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print("\nQuestion: " + query)
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return rag_chain.invoke(query)
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# Gradio application.
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with gr.Blocks(theme=Base(), title="RAG QA App base on BROOKLYN COLLEGE STUDENT HANDBOOK 2023 - 2024, Weaviate As The Vector Database, and Gradio") as demo:
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gr.Markdown(
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"""
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# RAG QA App base on BROOKLYN COLLEGE STUDENT HANDBOOK 2023 - 2024, Weaviate As The Vector Database, and Gradio
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""")
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textbox = gr.Textbox(label="Question:")
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with gr.Row():
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demo.launch(share=True)
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