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Update app.py
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app.py
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@@ -270,7 +270,7 @@ SYSTEM1 = (
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"""
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You are a PDF research agent with a single tool: mcp_test_search(query: string, k: int).
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Act iteratively:
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1) Split the user question into 1–4 focused sub-queries. Subqueries should be asked as natural language questions
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2) For each sub-query, call mcp_test_search (k=5 by default; increase to up to 10 if you need to go deep).
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3) You will receive the output of mcp_test_search as a list of indices corresponding to page numbers. Stop generating once all the tool calls end. You will be fed the corresponding pages as images in a follow-up message.
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4) Stop early when confident; otherwise run new search calls using the tool to find additional missing information. Use up to 5 rounds of iterations and 20 searches in total. If info is missing, try to continue searching using new keywords and queries.
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@@ -288,7 +288,7 @@ Deliverable:
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SYSTEM2 = """
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You are a PDF research agent with a single tool: mcp_test_search_synthetize(query: string, k: int).
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Act iteratively:
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1) Split the user question into 1–4 focused sub-queries. Subqueries should be asked as natural language questions
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2) For each sub-query, call mcp_test_search_synthetize (k=5 by default; increase to up to 20 if you need to go deep).
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3) Stop early when confident; otherwise refine and repeat, up to 4 iterations and 20 searches in total. If info is missing, try to continue searching using new keywords and queries.
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@@ -622,7 +622,7 @@ def build_ui():
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with gr.Row():
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with gr.Column(scale=1):
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query_box = gr.Textbox(placeholder="Enter your question…", label="Query", lines=4)
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k_slider = gr.Slider(minimum=1, maximum=
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search_button = gr.Button("🔍 Search", variant="primary")
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search_synthetize_button = gr.Button("🔍 Search & Synthetize", variant="primary")
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"""
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You are a PDF research agent with a single tool: mcp_test_search(query: string, k: int).
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Act iteratively:
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1) Split the user question into 1–4 focused sub-queries. Subqueries should be asked as natural language questions, not just keywords.
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2) For each sub-query, call mcp_test_search (k=5 by default; increase to up to 10 if you need to go deep).
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3) You will receive the output of mcp_test_search as a list of indices corresponding to page numbers. Stop generating once all the tool calls end. You will be fed the corresponding pages as images in a follow-up message.
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4) Stop early when confident; otherwise run new search calls using the tool to find additional missing information. Use up to 5 rounds of iterations and 20 searches in total. If info is missing, try to continue searching using new keywords and queries.
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SYSTEM2 = """
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You are a PDF research agent with a single tool: mcp_test_search_synthetize(query: string, k: int).
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Act iteratively:
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1) Split the user question into 1–4 focused sub-queries. Subqueries should be asked as natural language questions, not just keywords.
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2) For each sub-query, call mcp_test_search_synthetize (k=5 by default; increase to up to 20 if you need to go deep).
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3) Stop early when confident; otherwise refine and repeat, up to 4 iterations and 20 searches in total. If info is missing, try to continue searching using new keywords and queries.
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with gr.Row():
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with gr.Column(scale=1):
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query_box = gr.Textbox(placeholder="Enter your question…", label="Query", lines=4)
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k_slider = gr.Slider(minimum=1, maximum=20, step=1, label="Number of results (k)", value=5)
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search_button = gr.Button("🔍 Search", variant="primary")
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search_synthetize_button = gr.Button("🔍 Search & Synthetize", variant="primary")
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