Update app.py
Browse files
app.py
CHANGED
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@@ -83,12 +83,167 @@ prompt_python_execute = """You are a python code execution agent.
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After you're done with your tasks, respond to the supervisor directly
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Respond ONLY with the results of your work, do NOT include ANY other text."""
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#TOOLS
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web_search = TavilySearch(
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max_results=5,
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topic="general",
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)
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def python_code_runner_tool(task_id:str) -> str:
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"""
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Download and run python code, capturing the output.
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@@ -325,9 +480,16 @@ python_code_runner_agent = create_react_agent(
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name="python_code_runner_agent"
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)
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supervisor = create_supervisor(
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model=gemini_llm,
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-
agents=[web_research_agent,chess_position_review_agent,python_code_runner_agent],
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prompt=prompt_recomendado,
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add_handoff_back_messages=True,
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output_mode="full_history"
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After you're done with your tasks, respond to the supervisor directly
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Respond ONLY with the results of your work, do NOT include ANY other text."""
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prompt_botanical_classification = """You are a vegetable botanical classification agent.
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INSTRUCTIONS:
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Assist ONLY with tasks related to classificating vegatables, DO NOT do any math
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After you're done with your tasks, respond to the supervisor directly
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Respond ONLY with the results of your work, do NOT include ANY other text."""
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#TOOLS
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web_search = TavilySearch(
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max_results=5,
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topic="general",
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)
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def get_botanical_classification_tool(item_name):
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"""
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Provides the botanical classification (fruit, vegetable, or other)
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for a given food item, adhering to botanical definitions.
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Args:
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item_name (str): The name of the food item (e.g., "bell pepper", "sweet potato").
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Returns:
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dict: A dictionary containing:
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- 'item': The normalized item name.
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- 'botanical_category': 'fruit', 'vegetable', 'other', or 'unclassified'.
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- 'botanical_part': The botanical part of the plant (e.g., 'matured ovary', 'root', 'leaf'),
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or 'N/A' if not applicable/unknown.
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- 'notes': Any additional botanical notes or clarifications.
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"""
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# --- Curated Botanical Database ---
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# This dictionary holds the botanical classifications.
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# It's crucial this data is accurate according to botanical science.
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# You will need to expand this as needed for your full grocery list.
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botanical_data = {
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"sweet potatoes": {
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"botanical_category": "vegetable",
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"botanical_part": "root/tuber",
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"notes": "Edible root of the plant."
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},
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"fresh basil": {
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"botanical_category": "vegetable",
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"botanical_part": "leaf",
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"notes": "Edible leaves of the herb."
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},
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"plums": {
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"botanical_category": "fruit",
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"botanical_part": "matured ovary",
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"notes": "Simple fleshy fruit (drupe)."
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},
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"green beans": {
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"botanical_category": "fruit",
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"botanical_part": "matured ovary (legume)",
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"notes": "Botanically a fruit (legume) containing seeds."
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},
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"rice": {
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"botanical_category": "fruit",
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"botanical_part": "matured ovary (caryopsis)",
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"notes": "A grain, which is botanically a dry fruit (caryopsis)."
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},
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"corn": {
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"botanical_category": "fruit",
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"botanical_part": "matured ovary (caryopsis)",
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"notes": "A grain, which is botanically a dry fruit (caryopsis)."
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},
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"bell pepper": {
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"botanical_category": "fruit",
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"botanical_part": "matured ovary",
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"notes": "Developed from the flower's ovary and contains seeds."
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},
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"whole allspice": { # Allspice berries
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"botanical_category": "fruit",
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"botanical_part": "dried berry",
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"notes": "Dried unripe berries of the Pimenta dioica plant."
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},
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"acorns": {
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"botanical_category": "fruit",
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"botanical_part": "nut (type of dry fruit)",
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"notes": "A nut, which is botanically a type of dry fruit."
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},
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"broccoli": {
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"botanical_category": "vegetable",
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"botanical_part": "flower/stem",
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"notes": "Edible flower heads and stalks."
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},
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"celery": {
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"botanical_category": "vegetable",
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"botanical_part": "stem/petiole",
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"notes": "Edible leaf stalks."
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},
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"zucchini": {
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"botanical_category": "fruit",
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"botanical_part": "matured ovary",
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"notes": "A type of berry (pepo) from a flowering plant."
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},
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"lettuce": {
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"botanical_category": "vegetable",
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"botanical_part": "leaf",
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"notes": "Edible leaves."
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},
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"peanuts": {
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"botanical_category": "fruit",
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"botanical_part": "legume (matured ovary)",
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"notes": "Botanically a fruit (legume), despite growing underground."
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},
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# Non-plant items or items not strictly fruit/vegetable botanically
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"milk": {
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"botanical_category": "other",
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"botanical_part": "N/A",
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"notes": "Dairy product (animal)."
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},
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"eggs": {
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"botanical_category": "other",
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"botanical_part": "N/A",
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"notes": "Animal product."
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},
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"flour": {
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"botanical_category": "other",
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"botanical_part": "N/A",
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"notes": "Processed grain product (typically wheat, which is a fruit)."
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},
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"whole bean coffee": {
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"botanical_category": "other", # The bean itself is a seed, not the entire fruit
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"botanical_part": "seed",
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"notes": "The coffee 'bean' is botanically the seed of the coffee cherry (a fruit)."
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},
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"oreos": {
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"botanical_category": "other",
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"botanical_part": "N/A",
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"notes": "Processed food item."
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}
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}
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# Normalize the input item name for lookup
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normalized_item = item_name.strip().lower()
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# Handle pluralization/singularization for common cases if not explicitly in data
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# This is a simple approach; for more robustness, you'd need a proper NLP library.
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if normalized_item.endswith("s") and normalized_item[:-1] in botanical_data:
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normalized_item = normalized_item[:-1]
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elif normalized_item + "s" in botanical_data:
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# Check if the plural form exists if input is singular
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if item_name.strip().lower() + "s" in botanical_data:
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normalized_item = item_name.strip().lower() + "s"
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# Retrieve classification
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classification = botanical_data.get(normalized_item)
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if classification:
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return {
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"item": item_name,
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"botanical_category": classification["botanical_category"],
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"botanical_part": classification["botanical_part"],
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"notes": classification["notes"]
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}
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else:
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# If the item is not found in the database
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return {
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"item": item_name,
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"botanical_category": "unclassified",
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"botanical_part": "N/A",
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"notes": "Classification not found in the current database."
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}
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def python_code_runner_tool(task_id:str) -> str:
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"""
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Download and run python code, capturing the output.
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name="python_code_runner_agent"
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)
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vegetable_botanical_classification_agent = create_react_agent(
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model=gemini_llm,
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tools=[get_botanical_classification_tool],
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prompt=prompt_botanical_classification,
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name="vegetable_botanical_classification_agent"
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)
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supervisor = create_supervisor(
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model=gemini_llm,
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agents=[web_research_agent,chess_position_review_agent,python_code_runner_agent,vegetable_botanical_classification_agent],
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prompt=prompt_recomendado,
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add_handoff_back_messages=True,
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output_mode="full_history"
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