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clementsan
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eb94a8f
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Parent(s):
8aeeae1
Improve PDF chatbot description
Browse files
app.py
CHANGED
@@ -98,7 +98,7 @@ def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, pr
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"trust_remote_code": True, "torch_dtype": "auto"}
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)
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progress(0.
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memory = ConversationBufferMemory(
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memory_key="chat_history",
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return_messages=True
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@@ -185,9 +185,11 @@ def demo():
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# qa_chain = gr.Variable()
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gr.Markdown(
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"""<center><h2>
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<h3>Ask any questions
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<
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""")
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with gr.Tab("Step 1 - Document pre-processing"):
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with gr.Row():
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@@ -206,9 +208,6 @@ def demo():
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db_btn = gr.Button("Generating vector database...")
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with gr.Tab("Step 2 - QA chain initialization"):
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gr.Markdown(
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"""<b>Note:</b> This space uses the free CPU Basic hardware from Hugging Face. The LLM models used below (free inference endpoints) can take some time to generate a reply.
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""")
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with gr.Row():
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llm_btn = gr.Radio(list_llm_simple, \
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label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
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"trust_remote_code": True, "torch_dtype": "auto"}
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)
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progress(0.75, desc="Defining buffer memory...")
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memory = ConversationBufferMemory(
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memory_key="chat_history",
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return_messages=True
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# qa_chain = gr.Variable()
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gr.Markdown(
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"""<center><h2>PDF-based chatbot, powered by LangChain and open-source LLMs</center></h2>
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<h3>Ask any questions about your PDF documents, along with follow-ups</h3>
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<b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents. \
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When generating answers, it takes past questions into account (via conversational memory), and points to specific document sources for clarity purposes</i>
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<b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
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""")
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with gr.Tab("Step 1 - Document pre-processing"):
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with gr.Row():
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db_btn = gr.Button("Generating vector database...")
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with gr.Tab("Step 2 - QA chain initialization"):
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with gr.Row():
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llm_btn = gr.Radio(list_llm_simple, \
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label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model")
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