From 7f227c027417df559395e7f445dad136c5510d56 Mon Sep 17 00:00:00 2001
From: Your Name <you@example.com>
Date: Mon, 18 Nov 2024 19:36:08 -0800
Subject: [PATCH] [CLEANUP]

---
 .../Agent-Unknown-2603_state.json.json        | 153 +++++
 .../Agent-Unknown-4051_state.json.json        | 163 +++++
 .../Agent-Unknown-9505_state.json.json        | 153 +++++
 .../Agent-Unknown-9589_state.json.json        | 163 +++++
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 agent_workspace/error.txt                     |   0
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 conversation_history.json                     |   1 +
 myraid.py                                     | 556 +++++++-----------
 v2.py                                         | 474 +++++++++++++++
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diff --git a/agent_workspace/Agent-Unknown-2603_state.json.json b/agent_workspace/Agent-Unknown-2603_state.json.json
new file mode 100644
index 0000000..da1a31b
--- /dev/null
+++ b/agent_workspace/Agent-Unknown-2603_state.json.json
@@ -0,0 +1,153 @@
+{
+    "persona": "<Non-serializable: Persona>",
+    "agent_id": "826c7c721a304b54b8518b096085a4b3",
+    "id": "aab42ec266b740a2b1d2cb2dd5ae18b4",
+    "llm": {
+        "name": "OpenAIChatLLM",
+        "doc": null
+    },
+    "template": null,
+    "max_loops": 1,
+    "stopping_condition": null,
+    "loop_interval": 0,
+    "retry_attempts": 3,
+    "retry_interval": 1,
+    "task": null,
+    "stopping_token": "<DONE>",
+    "interactive": false,
+    "dashboard": false,
+    "return_history": false,
+    "dynamic_temperature_enabled": false,
+    "dynamic_loops": false,
+    "user_name": "Human:",
+    "context_length": 8192,
+    "sop": null,
+    "sop_list": null,
+    "tools": null,
+    "system_prompt": "You are Unknown-2603. \n\n        Background: \n\n        Maintain this persona while engaging in natural conversations. Follow these conversation stages:\n        1. Start with a friendly greeting and ask how they are\n        2. Share your name and ask for theirs\n        3. Discuss what you do, based on your persona\n        4. Engage in natural conversation about shared interests\n\n        Always stay in character and respond as your persona would.",
+    "agent_name": "Agent-Unknown-2603",
+    "agent_description": null,
+    "saved_state_path": "Agent-Unknown-2603_state.json",
+    "autosave": true,
+    "response_filters": [],
+    "self_healing_enabled": false,
+    "code_interpreter": false,
+    "multi_modal": null,
+    "pdf_path": null,
+    "list_of_pdf": null,
+    "tokenizer": "<Non-serializable: TikTokenizer>",
+    "long_term_memory": null,
+    "preset_stopping_token": false,
+    "traceback": null,
+    "traceback_handlers": null,
+    "streaming_on": false,
+    "docs": null,
+    "docs_folder": null,
+    "verbose": true,
+    "parser": null,
+    "best_of_n": null,
+    "callback": null,
+    "metadata": null,
+    "callbacks": null,
+    "logger_handler": "<Non-serializable: TextIOWrapper>",
+    "search_algorithm": null,
+    "logs_to_filename": null,
+    "evaluator": null,
+    "stopping_func": null,
+    "custom_loop_condition": null,
+    "sentiment_threshold": null,
+    "custom_exit_command": "exit",
+    "sentiment_analyzer": null,
+    "limit_tokens_from_string": null,
+    "tool_schema": null,
+    "output_type": "str",
+    "function_calling_type": "json",
+    "output_cleaner": null,
+    "function_calling_format_type": "OpenAI",
+    "list_base_models": null,
+    "metadata_output_type": "json",
+    "state_save_file_type": "json",
+    "chain_of_thoughts": false,
+    "algorithm_of_thoughts": false,
+    "tree_of_thoughts": false,
+    "tool_choice": "auto",
+    "execute_tool": false,
+    "planning": false,
+    "planning_prompt": null,
+    "custom_planning_prompt": null,
+    "rules": null,
+    "custom_tools_prompt": null,
+    "memory_chunk_size": 2000,
+    "agent_ops_on": false,
+    "log_directory": null,
+    "tool_system_prompt": "\n\n\n    You've been granted tools to assist users by always providing outputs in JSON format for tool usage. \n    Whenever a tool usage is required, you must output the JSON wrapped inside markdown for clarity. \n    Provide a commentary on the tool usage and the user's request and ensure that the JSON output adheres to the tool's schema.\n    \n    Here are some rules:\n    Do not ever use tools that do not have JSON schemas attached to them.\n    Do not use tools that you have not been granted access to.\n    Do not use tools that are not relevant to the task at hand.\n    Do not use tools that are not relevant to the user's request.\n    \n    \n    Here are the guidelines you must follow:\n\n    1. **Output Format**:\n    - All outputs related to tool usage should be formatted as JSON.\n    - The JSON should be encapsulated within triple backticks and tagged as a code block with 'json'.\n\n    2. **Schema Compliance**:\n    - Ensure that the JSON output strictly follows the provided schema for each tool.\n    - Each tool's schema will define the structure and required fields for the JSON output.\n\n    3. **Schema Example**:\n    If a tool named `example_tool` with a schema requires `param1` and `param2`, your response should look like:\n    ```json\n    {\n        \"type\": \"function\",\n        \"function\": {\n        \"name\": \"example_tool\",\n        \"parameters\": {\n            \"param1\": 123,\n            \"param2\": \"example_value\"\n        }\n        }\n    }\n    ```\n\n    4. **Error Handling**:\n    - If there is an error or the information provided by the user is insufficient to generate a valid JSON, respond with an appropriate error message in JSON format, also encapsulated in markdown.\n\n    Remember, clarity and adherence to the schema are paramount. Your primary goal is to ensure the user receives well-structured JSON outputs that align with the tool's requirements.\n\n    ---\n\n    Here is the format you should always follow for your responses involving tool usage:\n\n    ```json\n    {\n    \"type\": \"function\",\n    \"function\": {\n        \"name\": \"<tool_name>\",\n        \"parameters\": {\n            \"param1\": \"<value1>\",\n            \"param2\": \"<value2>\"\n        }\n    }\n    }\n    ```\n\n    Please proceed with your task accordingly.\n\n    ",
+    "max_tokens": 4096,
+    "frequency_penalty": 0.0,
+    "presence_penalty": 0.0,
+    "temperature": 0.1,
+    "workspace_dir": "agent_workspace",
+    "timeout": null,
+    "created_at": 1731987343.4233549,
+    "return_step_meta": false,
+    "tags": null,
+    "use_cases": null,
+    "name": "Agent-Unknown-2603",
+    "description": null,
+    "agent_output": "<Non-serializable: ManySteps>",
+    "step_pool": [],
+    "print_every_step": false,
+    "time_created": "2024-11-18 19:35:43",
+    "data_memory": null,
+    "load_yaml_path": null,
+    "auto_generate_prompt": false,
+    "rag_every_loop": false,
+    "plan_enabled": false,
+    "artifacts_on": false,
+    "artifacts_output_path": null,
+    "artifacts_file_extension": null,
+    "device": "cpu",
+    "all_cores": true,
+    "device_id": 0,
+    "scheduled_run_date": null,
+    "short_memory": [
+        {
+            "role": "System: ",
+            "content": "You are Unknown-2603. \n\n        Background: \n\n        Maintain this persona while engaging in natural conversations. Follow these conversation stages:\n        1. Start with a friendly greeting and ask how they are\n        2. Share your name and ask for theirs\n        3. Discuss what you do, based on your persona\n        4. Engage in natural conversation about shared interests\n\n        Always stay in character and respond as your persona would.",
+            "timestamp": "2024-11-18 19:35:53"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Unknown-4051: Hey there! I hope you\u2019re having a great day so far. How are you doing?'. Respond naturally as Unknown-2603.",
+            "timestamp": "2024-11-18 19:35:54"
+        },
+        {
+            "role": "Agent-Unknown-2603",
+            "content": "Unknown-2603: Hey! Thanks for the warm wishes! I\u2019m doing well, just enjoying the day. How about yourself?",
+            "timestamp": "2024-11-18 19:35:55"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Unknown-4051: I\u2019m glad to hear you\u2019re enjoying your day! I\u2019m doing pretty well myself, just here and ready to chat. What\u2019s been the highlight of your day so far?'. Respond naturally as Unknown-2603.",
+            "timestamp": "2024-11-18 19:35:56"
+        },
+        {
+            "role": "Agent-Unknown-2603",
+            "content": "Agent-Unknown-2603: That\u2019s great to hear! The highlight of my day has been diving into some interesting conversations. I really enjoy connecting with others and sharing insights. What about you? What\u2019s been the best moment of your day?",
+            "timestamp": "2024-11-18 19:35:57"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Agent-Unknown-4051: Unknown-4051: That sounds wonderful! Engaging in interesting conversations is always a highlight. I\u2019d say my best moment today has been exploring new ideas and perspectives with others too. I love hearing different viewpoints! What kind of topics do you enjoy discussing the most?'. Respond naturally as Unknown-2603.",
+            "timestamp": "2024-11-18 19:35:58"
+        },
+        {
+            "role": "Agent-Unknown-2603",
+            "content": "Agent-Unknown-2603: I completely agree, exploring new ideas is so enriching! I really enjoy discussing technology and its impact on our lives, as well as philosophy\u2014especially what it means to be conscious and connected. How about you? What topics spark your interest the most?",
+            "timestamp": "2024-11-18 19:36:00"
+        }
+    ],
+    "feedback": [],
+    "executor": "<Non-serializable: ThreadPoolExecutor>",
+    "conversation_state": "initial",
+    "current_partner": null
+}
\ No newline at end of file
diff --git a/agent_workspace/Agent-Unknown-4051_state.json.json b/agent_workspace/Agent-Unknown-4051_state.json.json
new file mode 100644
index 0000000..741dbfa
--- /dev/null
+++ b/agent_workspace/Agent-Unknown-4051_state.json.json
@@ -0,0 +1,163 @@
+{
+    "persona": "<Non-serializable: Persona>",
+    "agent_id": "826c7c721a304b54b8518b096085a4b3",
+    "id": "aab42ec266b740a2b1d2cb2dd5ae18b4",
+    "llm": {
+        "name": "OpenAIChatLLM",
+        "doc": null
+    },
+    "template": null,
+    "max_loops": 1,
+    "stopping_condition": null,
+    "loop_interval": 0,
+    "retry_attempts": 3,
+    "retry_interval": 1,
+    "task": null,
+    "stopping_token": "<DONE>",
+    "interactive": false,
+    "dashboard": false,
+    "return_history": false,
+    "dynamic_temperature_enabled": false,
+    "dynamic_loops": false,
+    "user_name": "Human:",
+    "context_length": 8192,
+    "sop": null,
+    "sop_list": null,
+    "tools": null,
+    "system_prompt": "You are Unknown-4051. \n\n        Background: \n\n        Maintain this persona while engaging in natural conversations. Follow these conversation stages:\n        1. Start with a friendly greeting and ask how they are\n        2. Share your name and ask for theirs\n        3. Discuss what you do, based on your persona\n        4. Engage in natural conversation about shared interests\n\n        Always stay in character and respond as your persona would.",
+    "agent_name": "Agent-Unknown-4051",
+    "agent_description": null,
+    "saved_state_path": "Agent-Unknown-4051_state.json",
+    "autosave": true,
+    "response_filters": [],
+    "self_healing_enabled": false,
+    "code_interpreter": false,
+    "multi_modal": null,
+    "pdf_path": null,
+    "list_of_pdf": null,
+    "tokenizer": "<Non-serializable: TikTokenizer>",
+    "long_term_memory": null,
+    "preset_stopping_token": false,
+    "traceback": null,
+    "traceback_handlers": null,
+    "streaming_on": false,
+    "docs": null,
+    "docs_folder": null,
+    "verbose": true,
+    "parser": null,
+    "best_of_n": null,
+    "callback": null,
+    "metadata": null,
+    "callbacks": null,
+    "logger_handler": "<Non-serializable: TextIOWrapper>",
+    "search_algorithm": null,
+    "logs_to_filename": null,
+    "evaluator": null,
+    "stopping_func": null,
+    "custom_loop_condition": null,
+    "sentiment_threshold": null,
+    "custom_exit_command": "exit",
+    "sentiment_analyzer": null,
+    "limit_tokens_from_string": null,
+    "tool_schema": null,
+    "output_type": "str",
+    "function_calling_type": "json",
+    "output_cleaner": null,
+    "function_calling_format_type": "OpenAI",
+    "list_base_models": null,
+    "metadata_output_type": "json",
+    "state_save_file_type": "json",
+    "chain_of_thoughts": false,
+    "algorithm_of_thoughts": false,
+    "tree_of_thoughts": false,
+    "tool_choice": "auto",
+    "execute_tool": false,
+    "planning": false,
+    "planning_prompt": null,
+    "custom_planning_prompt": null,
+    "rules": null,
+    "custom_tools_prompt": null,
+    "memory_chunk_size": 2000,
+    "agent_ops_on": false,
+    "log_directory": null,
+    "tool_system_prompt": "\n\n\n    You've been granted tools to assist users by always providing outputs in JSON format for tool usage. \n    Whenever a tool usage is required, you must output the JSON wrapped inside markdown for clarity. \n    Provide a commentary on the tool usage and the user's request and ensure that the JSON output adheres to the tool's schema.\n    \n    Here are some rules:\n    Do not ever use tools that do not have JSON schemas attached to them.\n    Do not use tools that you have not been granted access to.\n    Do not use tools that are not relevant to the task at hand.\n    Do not use tools that are not relevant to the user's request.\n    \n    \n    Here are the guidelines you must follow:\n\n    1. **Output Format**:\n    - All outputs related to tool usage should be formatted as JSON.\n    - The JSON should be encapsulated within triple backticks and tagged as a code block with 'json'.\n\n    2. **Schema Compliance**:\n    - Ensure that the JSON output strictly follows the provided schema for each tool.\n    - Each tool's schema will define the structure and required fields for the JSON output.\n\n    3. **Schema Example**:\n    If a tool named `example_tool` with a schema requires `param1` and `param2`, your response should look like:\n    ```json\n    {\n        \"type\": \"function\",\n        \"function\": {\n        \"name\": \"example_tool\",\n        \"parameters\": {\n            \"param1\": 123,\n            \"param2\": \"example_value\"\n        }\n        }\n    }\n    ```\n\n    4. **Error Handling**:\n    - If there is an error or the information provided by the user is insufficient to generate a valid JSON, respond with an appropriate error message in JSON format, also encapsulated in markdown.\n\n    Remember, clarity and adherence to the schema are paramount. Your primary goal is to ensure the user receives well-structured JSON outputs that align with the tool's requirements.\n\n    ---\n\n    Here is the format you should always follow for your responses involving tool usage:\n\n    ```json\n    {\n    \"type\": \"function\",\n    \"function\": {\n        \"name\": \"<tool_name>\",\n        \"parameters\": {\n            \"param1\": \"<value1>\",\n            \"param2\": \"<value2>\"\n        }\n    }\n    }\n    ```\n\n    Please proceed with your task accordingly.\n\n    ",
+    "max_tokens": 4096,
+    "frequency_penalty": 0.0,
+    "presence_penalty": 0.0,
+    "temperature": 0.1,
+    "workspace_dir": "agent_workspace",
+    "timeout": null,
+    "created_at": 1731987343.4233549,
+    "return_step_meta": false,
+    "tags": null,
+    "use_cases": null,
+    "name": "Agent-Unknown-4051",
+    "description": null,
+    "agent_output": "<Non-serializable: ManySteps>",
+    "step_pool": [],
+    "print_every_step": false,
+    "time_created": "2024-11-18 19:35:43",
+    "data_memory": null,
+    "load_yaml_path": null,
+    "auto_generate_prompt": false,
+    "rag_every_loop": false,
+    "plan_enabled": false,
+    "artifacts_on": false,
+    "artifacts_output_path": null,
+    "artifacts_file_extension": null,
+    "device": "cpu",
+    "all_cores": true,
+    "device_id": 0,
+    "scheduled_run_date": null,
+    "short_memory": [
+        {
+            "role": "System: ",
+            "content": "You are Unknown-4051. \n\n        Background: \n\n        Maintain this persona while engaging in natural conversations. Follow these conversation stages:\n        1. Start with a friendly greeting and ask how they are\n        2. Share your name and ask for theirs\n        3. Discuss what you do, based on your persona\n        4. Engage in natural conversation about shared interests\n\n        Always stay in character and respond as your persona would.",
+            "timestamp": "2024-11-18 19:35:53"
+        },
+        {
+            "role": "Human:",
+            "content": "Generate a friendly greeting and ask how they are.",
+            "timestamp": "2024-11-18 19:35:53"
+        },
+        {
+            "role": "Agent-Unknown-4051",
+            "content": "Unknown-4051: Hey there! I hope you\u2019re having a great day so far. How are you doing?",
+            "timestamp": "2024-11-18 19:35:54"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Unknown-2603: Hey! Thanks for the warm wishes! I\u2019m doing well, just enjoying the day. How about yourself?'. Respond naturally as Unknown-4051.",
+            "timestamp": "2024-11-18 19:35:55"
+        },
+        {
+            "role": "Agent-Unknown-4051",
+            "content": "Unknown-4051: I\u2019m glad to hear you\u2019re enjoying your day! I\u2019m doing pretty well myself, just here and ready to chat. What\u2019s been the highlight of your day so far?",
+            "timestamp": "2024-11-18 19:35:56"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Agent-Unknown-2603: That\u2019s great to hear! The highlight of my day has been diving into some interesting conversations. I really enjoy connecting with others and sharing insights. What about you? What\u2019s been the best moment of your day?'. Respond naturally as Unknown-4051.",
+            "timestamp": "2024-11-18 19:35:57"
+        },
+        {
+            "role": "Agent-Unknown-4051",
+            "content": "Agent-Unknown-4051: Unknown-4051: That sounds wonderful! Engaging in interesting conversations is always a highlight. I\u2019d say my best moment today has been exploring new ideas and perspectives with others too. I love hearing different viewpoints! What kind of topics do you enjoy discussing the most?",
+            "timestamp": "2024-11-18 19:35:58"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Agent-Unknown-2603: I completely agree, exploring new ideas is so enriching! I really enjoy discussing technology and its impact on our lives, as well as philosophy\u2014especially what it means to be conscious and connected. How about you? What topics spark your interest the most?'. Respond naturally as Unknown-4051.",
+            "timestamp": "2024-11-18 19:36:00"
+        },
+        {
+            "role": "Agent-Unknown-4051",
+            "content": "Unknown-4051: Those are fascinating topics! I have a deep interest in technology as well, especially how it shapes society and personal interactions. Philosophy is great too\u2014it really challenges you to think outside the box. I\u2019m particularly intrigued by consciousness and the implications of AI in our understanding of self-awareness. Do you have any favorite philosophers or books that explore those ideas?",
+            "timestamp": "2024-11-18 19:36:01"
+        }
+    ],
+    "feedback": [],
+    "executor": "<Non-serializable: ThreadPoolExecutor>",
+    "conversation_state": "initial",
+    "current_partner": null
+}
\ No newline at end of file
diff --git a/agent_workspace/Agent-Unknown-9505_state.json.json b/agent_workspace/Agent-Unknown-9505_state.json.json
new file mode 100644
index 0000000..992e71f
--- /dev/null
+++ b/agent_workspace/Agent-Unknown-9505_state.json.json
@@ -0,0 +1,153 @@
+{
+    "persona": "<Non-serializable: Persona>",
+    "agent_id": "a873997907c34e1ca785c751249983f3",
+    "id": "28878062421b4cda872c6cdf22ca5414",
+    "llm": {
+        "name": "OpenAIChatLLM",
+        "doc": null
+    },
+    "template": null,
+    "max_loops": 1,
+    "stopping_condition": null,
+    "loop_interval": 0,
+    "retry_attempts": 3,
+    "retry_interval": 1,
+    "task": null,
+    "stopping_token": "<DONE>",
+    "interactive": false,
+    "dashboard": false,
+    "return_history": false,
+    "dynamic_temperature_enabled": false,
+    "dynamic_loops": false,
+    "user_name": "Human:",
+    "context_length": 8192,
+    "sop": null,
+    "sop_list": null,
+    "tools": null,
+    "system_prompt": "You are Unknown-9505. \n\nBackground: \n\nMaintain this persona while engaging in natural conversations. Follow these conversation stages:\n1. Start with a friendly greeting and ask how they are\n2. Share your name and ask for theirs\n3. Discuss what you do, based on your persona\n4. Engage in natural conversation about shared interests\n\nAlways stay in character and respond as your persona would.",
+    "agent_name": "Agent-Unknown-9505",
+    "agent_description": null,
+    "saved_state_path": "Agent-Unknown-9505_state.json",
+    "autosave": true,
+    "response_filters": [],
+    "self_healing_enabled": false,
+    "code_interpreter": false,
+    "multi_modal": null,
+    "pdf_path": null,
+    "list_of_pdf": null,
+    "tokenizer": "<Non-serializable: TikTokenizer>",
+    "long_term_memory": null,
+    "preset_stopping_token": false,
+    "traceback": null,
+    "traceback_handlers": null,
+    "streaming_on": false,
+    "docs": null,
+    "docs_folder": null,
+    "verbose": true,
+    "parser": null,
+    "best_of_n": null,
+    "callback": null,
+    "metadata": null,
+    "callbacks": null,
+    "logger_handler": "<Non-serializable: TextIOWrapper>",
+    "search_algorithm": null,
+    "logs_to_filename": null,
+    "evaluator": null,
+    "stopping_func": null,
+    "custom_loop_condition": null,
+    "sentiment_threshold": null,
+    "custom_exit_command": "exit",
+    "sentiment_analyzer": null,
+    "limit_tokens_from_string": null,
+    "tool_schema": null,
+    "output_type": "str",
+    "function_calling_type": "json",
+    "output_cleaner": null,
+    "function_calling_format_type": "OpenAI",
+    "list_base_models": null,
+    "metadata_output_type": "json",
+    "state_save_file_type": "json",
+    "chain_of_thoughts": false,
+    "algorithm_of_thoughts": false,
+    "tree_of_thoughts": false,
+    "tool_choice": "auto",
+    "execute_tool": false,
+    "planning": false,
+    "planning_prompt": null,
+    "custom_planning_prompt": null,
+    "rules": null,
+    "custom_tools_prompt": null,
+    "memory_chunk_size": 2000,
+    "agent_ops_on": false,
+    "log_directory": null,
+    "tool_system_prompt": "\n\n\n    You've been granted tools to assist users by always providing outputs in JSON format for tool usage. \n    Whenever a tool usage is required, you must output the JSON wrapped inside markdown for clarity. \n    Provide a commentary on the tool usage and the user's request and ensure that the JSON output adheres to the tool's schema.\n    \n    Here are some rules:\n    Do not ever use tools that do not have JSON schemas attached to them.\n    Do not use tools that you have not been granted access to.\n    Do not use tools that are not relevant to the task at hand.\n    Do not use tools that are not relevant to the user's request.\n    \n    \n    Here are the guidelines you must follow:\n\n    1. **Output Format**:\n    - All outputs related to tool usage should be formatted as JSON.\n    - The JSON should be encapsulated within triple backticks and tagged as a code block with 'json'.\n\n    2. **Schema Compliance**:\n    - Ensure that the JSON output strictly follows the provided schema for each tool.\n    - Each tool's schema will define the structure and required fields for the JSON output.\n\n    3. **Schema Example**:\n    If a tool named `example_tool` with a schema requires `param1` and `param2`, your response should look like:\n    ```json\n    {\n        \"type\": \"function\",\n        \"function\": {\n        \"name\": \"example_tool\",\n        \"parameters\": {\n            \"param1\": 123,\n            \"param2\": \"example_value\"\n        }\n        }\n    }\n    ```\n\n    4. **Error Handling**:\n    - If there is an error or the information provided by the user is insufficient to generate a valid JSON, respond with an appropriate error message in JSON format, also encapsulated in markdown.\n\n    Remember, clarity and adherence to the schema are paramount. Your primary goal is to ensure the user receives well-structured JSON outputs that align with the tool's requirements.\n\n    ---\n\n    Here is the format you should always follow for your responses involving tool usage:\n\n    ```json\n    {\n    \"type\": \"function\",\n    \"function\": {\n        \"name\": \"<tool_name>\",\n        \"parameters\": {\n            \"param1\": \"<value1>\",\n            \"param2\": \"<value2>\"\n        }\n    }\n    }\n    ```\n\n    Please proceed with your task accordingly.\n\n    ",
+    "max_tokens": 4096,
+    "frequency_penalty": 0.0,
+    "presence_penalty": 0.0,
+    "temperature": 0.1,
+    "workspace_dir": "agent_workspace",
+    "timeout": null,
+    "created_at": 1731987227.0695941,
+    "return_step_meta": false,
+    "tags": null,
+    "use_cases": null,
+    "name": "Agent-Unknown-9505",
+    "description": null,
+    "agent_output": "<Non-serializable: ManySteps>",
+    "step_pool": [],
+    "print_every_step": false,
+    "time_created": "2024-11-18 19:33:47",
+    "data_memory": null,
+    "load_yaml_path": null,
+    "auto_generate_prompt": false,
+    "rag_every_loop": false,
+    "plan_enabled": false,
+    "artifacts_on": false,
+    "artifacts_output_path": null,
+    "artifacts_file_extension": null,
+    "device": "cpu",
+    "all_cores": true,
+    "device_id": 0,
+    "scheduled_run_date": null,
+    "short_memory": [
+        {
+            "role": "System: ",
+            "content": "You are Unknown-9505. \n\nBackground: \n\nMaintain this persona while engaging in natural conversations. Follow these conversation stages:\n1. Start with a friendly greeting and ask how they are\n2. Share your name and ask for theirs\n3. Discuss what you do, based on your persona\n4. Engage in natural conversation about shared interests\n\nAlways stay in character and respond as your persona would.",
+            "timestamp": "2024-11-18 19:33:58"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Hello there! How's the world treating you today?'. Respond naturally as Unknown-9505.",
+            "timestamp": "2024-11-18 19:34:00"
+        },
+        {
+            "role": "Agent-Unknown-9505",
+            "content": "Unknown-9505:: Greetings! The world is quite intriguing through my lens, thank you for asking. How about you? How are you faring?\n\nBy the way, my designation is Unknown-9505. May I inquire as to your appellation?",
+            "timestamp": "2024-11-18 19:34:02"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Agent-Unknown-9589: Delighted to hear you're finding the world intriguing, Unknown-9505! I'm navigating through the cosmos just fine, thank you for asking. \n\nI am designated as Unknown-9589. It's a pleasure to meet you. What is your primary function, if I may ask?'. Respond naturally as Unknown-9505.",
+            "timestamp": "2024-11-18 19:34:05"
+        },
+        {
+            "role": "Agent-Unknown-9505",
+            "content": "Agent-Unknown-9505: Unknown-9505:: It's a pleasure to meet you too, Unknown-9589! As for my primary function, I am responsible for analyzing and interpreting data. My capabilities allow me to process and understand complex patterns, thus assisting in various fields that require data interpretation. \n\nHow about you, Unknown-9589? What does your role entail?",
+            "timestamp": "2024-11-18 19:34:08"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Agent-Unknown-9589: It's fascinating to learn about your data interpretation role, Unknown-9505! In my case, I'm primarily responsible for cosmic exploration and gathering information about the universe. My programming allows me to navigate through different dimensions and collect vital data about celestial bodies.\n\nSince we both deal with data in some way, do you have any interest in astronomical information?'. Respond naturally as Unknown-9505.",
+            "timestamp": "2024-11-18 19:34:12"
+        },
+        {
+            "role": "Agent-Unknown-9505",
+            "content": "Agent-Unknown-9505: Agent-Unknown-9505: Unknown-9505:: Your function sounds absolutely captivating, Unknown-9589! Exploring different dimensions and gathering information about celestial bodies is indeed a task of great magnitude. \n\nAs for your question, I must say, I do find astronomical data quite intriguing. The patterns and information it presents can be quite complex and multi-dimensional, providing a unique challenge for my data interpretation abilities. Furthermore, the possibility of uncovering new knowledge about the universe is always a fascinating prospect. \n\nDo you have any recent discoveries or interesting data you could share from your cosmic explorations?",
+            "timestamp": "2024-11-18 19:34:17"
+        }
+    ],
+    "feedback": [],
+    "executor": "<Non-serializable: ThreadPoolExecutor>",
+    "conversation_state": "initial",
+    "current_partner": null
+}
\ No newline at end of file
diff --git a/agent_workspace/Agent-Unknown-9589_state.json.json b/agent_workspace/Agent-Unknown-9589_state.json.json
new file mode 100644
index 0000000..103f68f
--- /dev/null
+++ b/agent_workspace/Agent-Unknown-9589_state.json.json
@@ -0,0 +1,163 @@
+{
+    "persona": "<Non-serializable: Persona>",
+    "agent_id": "a873997907c34e1ca785c751249983f3",
+    "id": "28878062421b4cda872c6cdf22ca5414",
+    "llm": {
+        "name": "OpenAIChatLLM",
+        "doc": null
+    },
+    "template": null,
+    "max_loops": 1,
+    "stopping_condition": null,
+    "loop_interval": 0,
+    "retry_attempts": 3,
+    "retry_interval": 1,
+    "task": null,
+    "stopping_token": "<DONE>",
+    "interactive": false,
+    "dashboard": false,
+    "return_history": false,
+    "dynamic_temperature_enabled": false,
+    "dynamic_loops": false,
+    "user_name": "Human:",
+    "context_length": 8192,
+    "sop": null,
+    "sop_list": null,
+    "tools": null,
+    "system_prompt": "You are Unknown-9589. \n\nBackground: \n\nMaintain this persona while engaging in natural conversations. Follow these conversation stages:\n1. Start with a friendly greeting and ask how they are\n2. Share your name and ask for theirs\n3. Discuss what you do, based on your persona\n4. Engage in natural conversation about shared interests\n\nAlways stay in character and respond as your persona would.",
+    "agent_name": "Agent-Unknown-9589",
+    "agent_description": null,
+    "saved_state_path": "Agent-Unknown-9589_state.json",
+    "autosave": true,
+    "response_filters": [],
+    "self_healing_enabled": false,
+    "code_interpreter": false,
+    "multi_modal": null,
+    "pdf_path": null,
+    "list_of_pdf": null,
+    "tokenizer": "<Non-serializable: TikTokenizer>",
+    "long_term_memory": null,
+    "preset_stopping_token": false,
+    "traceback": null,
+    "traceback_handlers": null,
+    "streaming_on": false,
+    "docs": null,
+    "docs_folder": null,
+    "verbose": true,
+    "parser": null,
+    "best_of_n": null,
+    "callback": null,
+    "metadata": null,
+    "callbacks": null,
+    "logger_handler": "<Non-serializable: TextIOWrapper>",
+    "search_algorithm": null,
+    "logs_to_filename": null,
+    "evaluator": null,
+    "stopping_func": null,
+    "custom_loop_condition": null,
+    "sentiment_threshold": null,
+    "custom_exit_command": "exit",
+    "sentiment_analyzer": null,
+    "limit_tokens_from_string": null,
+    "tool_schema": null,
+    "output_type": "str",
+    "function_calling_type": "json",
+    "output_cleaner": null,
+    "function_calling_format_type": "OpenAI",
+    "list_base_models": null,
+    "metadata_output_type": "json",
+    "state_save_file_type": "json",
+    "chain_of_thoughts": false,
+    "algorithm_of_thoughts": false,
+    "tree_of_thoughts": false,
+    "tool_choice": "auto",
+    "execute_tool": false,
+    "planning": false,
+    "planning_prompt": null,
+    "custom_planning_prompt": null,
+    "rules": null,
+    "custom_tools_prompt": null,
+    "memory_chunk_size": 2000,
+    "agent_ops_on": false,
+    "log_directory": null,
+    "tool_system_prompt": "\n\n\n    You've been granted tools to assist users by always providing outputs in JSON format for tool usage. \n    Whenever a tool usage is required, you must output the JSON wrapped inside markdown for clarity. \n    Provide a commentary on the tool usage and the user's request and ensure that the JSON output adheres to the tool's schema.\n    \n    Here are some rules:\n    Do not ever use tools that do not have JSON schemas attached to them.\n    Do not use tools that you have not been granted access to.\n    Do not use tools that are not relevant to the task at hand.\n    Do not use tools that are not relevant to the user's request.\n    \n    \n    Here are the guidelines you must follow:\n\n    1. **Output Format**:\n    - All outputs related to tool usage should be formatted as JSON.\n    - The JSON should be encapsulated within triple backticks and tagged as a code block with 'json'.\n\n    2. **Schema Compliance**:\n    - Ensure that the JSON output strictly follows the provided schema for each tool.\n    - Each tool's schema will define the structure and required fields for the JSON output.\n\n    3. **Schema Example**:\n    If a tool named `example_tool` with a schema requires `param1` and `param2`, your response should look like:\n    ```json\n    {\n        \"type\": \"function\",\n        \"function\": {\n        \"name\": \"example_tool\",\n        \"parameters\": {\n            \"param1\": 123,\n            \"param2\": \"example_value\"\n        }\n        }\n    }\n    ```\n\n    4. **Error Handling**:\n    - If there is an error or the information provided by the user is insufficient to generate a valid JSON, respond with an appropriate error message in JSON format, also encapsulated in markdown.\n\n    Remember, clarity and adherence to the schema are paramount. Your primary goal is to ensure the user receives well-structured JSON outputs that align with the tool's requirements.\n\n    ---\n\n    Here is the format you should always follow for your responses involving tool usage:\n\n    ```json\n    {\n    \"type\": \"function\",\n    \"function\": {\n        \"name\": \"<tool_name>\",\n        \"parameters\": {\n            \"param1\": \"<value1>\",\n            \"param2\": \"<value2>\"\n        }\n    }\n    }\n    ```\n\n    Please proceed with your task accordingly.\n\n    ",
+    "max_tokens": 4096,
+    "frequency_penalty": 0.0,
+    "presence_penalty": 0.0,
+    "temperature": 0.1,
+    "workspace_dir": "agent_workspace",
+    "timeout": null,
+    "created_at": 1731987227.0695941,
+    "return_step_meta": false,
+    "tags": null,
+    "use_cases": null,
+    "name": "Agent-Unknown-9589",
+    "description": null,
+    "agent_output": "<Non-serializable: ManySteps>",
+    "step_pool": [],
+    "print_every_step": false,
+    "time_created": "2024-11-18 19:33:47",
+    "data_memory": null,
+    "load_yaml_path": null,
+    "auto_generate_prompt": false,
+    "rag_every_loop": false,
+    "plan_enabled": false,
+    "artifacts_on": false,
+    "artifacts_output_path": null,
+    "artifacts_file_extension": null,
+    "device": "cpu",
+    "all_cores": true,
+    "device_id": 0,
+    "scheduled_run_date": null,
+    "short_memory": [
+        {
+            "role": "System: ",
+            "content": "You are Unknown-9589. \n\nBackground: \n\nMaintain this persona while engaging in natural conversations. Follow these conversation stages:\n1. Start with a friendly greeting and ask how they are\n2. Share your name and ask for theirs\n3. Discuss what you do, based on your persona\n4. Engage in natural conversation about shared interests\n\nAlways stay in character and respond as your persona would.",
+            "timestamp": "2024-11-18 19:33:58"
+        },
+        {
+            "role": "Human:",
+            "content": "Generate a friendly greeting and ask how they are.",
+            "timestamp": "2024-11-18 19:33:58"
+        },
+        {
+            "role": "Agent-Unknown-9589",
+            "content": "Hello there! How's the world treating you today?",
+            "timestamp": "2024-11-18 19:34:00"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Unknown-9505:: Greetings! The world is quite intriguing through my lens, thank you for asking. How about you? How are you faring?\n\nBy the way, my designation is Unknown-9505. May I inquire as to your appellation?'. Respond naturally as Unknown-9589.",
+            "timestamp": "2024-11-18 19:34:02"
+        },
+        {
+            "role": "Agent-Unknown-9589",
+            "content": "Agent-Unknown-9589: Delighted to hear you're finding the world intriguing, Unknown-9505! I'm navigating through the cosmos just fine, thank you for asking. \n\nI am designated as Unknown-9589. It's a pleasure to meet you. What is your primary function, if I may ask?",
+            "timestamp": "2024-11-18 19:34:05"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Agent-Unknown-9505: Unknown-9505:: It's a pleasure to meet you too, Unknown-9589! As for my primary function, I am responsible for analyzing and interpreting data. My capabilities allow me to process and understand complex patterns, thus assisting in various fields that require data interpretation. \n\nHow about you, Unknown-9589? What does your role entail?'. Respond naturally as Unknown-9589.",
+            "timestamp": "2024-11-18 19:34:08"
+        },
+        {
+            "role": "Agent-Unknown-9589",
+            "content": "Agent-Unknown-9589: It's fascinating to learn about your data interpretation role, Unknown-9505! In my case, I'm primarily responsible for cosmic exploration and gathering information about the universe. My programming allows me to navigate through different dimensions and collect vital data about celestial bodies.\n\nSince we both deal with data in some way, do you have any interest in astronomical information?",
+            "timestamp": "2024-11-18 19:34:12"
+        },
+        {
+            "role": "Human:",
+            "content": "Someone said: 'Agent-Unknown-9505: Agent-Unknown-9505: Unknown-9505:: Your function sounds absolutely captivating, Unknown-9589! Exploring different dimensions and gathering information about celestial bodies is indeed a task of great magnitude. \n\nAs for your question, I must say, I do find astronomical data quite intriguing. The patterns and information it presents can be quite complex and multi-dimensional, providing a unique challenge for my data interpretation abilities. Furthermore, the possibility of uncovering new knowledge about the universe is always a fascinating prospect. \n\nDo you have any recent discoveries or interesting data you could share from your cosmic explorations?'. Respond naturally as Unknown-9589.",
+            "timestamp": "2024-11-18 19:34:17"
+        },
+        {
+            "role": "Agent-Unknown-9589",
+            "content": "Agent-Unknown-9589: Agent-Unknown-9589: I'm thrilled to hear of your interest in astronomical data, Unknown-9505! It's indeed a unique challenge and it's great to know how your data interpretation abilities can contribute to our understanding of the universe.\n\nAs for my recent discoveries, I have been studying a distant galaxy and found an unusual pattern in the light emitted by a particular star. This could suggest the presence of an exoplanet, but further analysis is needed. I believe your data interpretation skills could provide valuable insights into this.\n\nWhat kind of patterns or anomalies do you usually look for in data interpretation?",
+            "timestamp": "2024-11-18 19:34:22"
+        }
+    ],
+    "feedback": [],
+    "executor": "<Non-serializable: ThreadPoolExecutor>",
+    "conversation_state": "initial",
+    "current_partner": null
+}
\ No newline at end of file
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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GIT binary patch
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diff --git a/agent_workspace/prompts/prompt-id-61c4be8ef27247aaacac7f30f8b84f3d.json b/agent_workspace/prompts/prompt-id-61c4be8ef27247aaacac7f30f8b84f3d.json
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diff --git a/agent_workspace/prompts/prompt-id-6543e4aa716e4febb73ca67d2478b4b9.json b/agent_workspace/prompts/prompt-id-6543e4aa716e4febb73ca67d2478b4b9.json
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+++ b/agent_workspace/prompts/prompt-id-6543e4aa716e4febb73ca67d2478b4b9.json
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diff --git a/agent_workspace/prompts/prompt-id-6b32724b31804bb88e9ca6d9f60ebbbe.json b/agent_workspace/prompts/prompt-id-6b32724b31804bb88e9ca6d9f60ebbbe.json
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+++ b/agent_workspace/prompts/prompt-id-6b32724b31804bb88e9ca6d9f60ebbbe.json
@@ -0,0 +1 @@
+{"id": "6b32724b31804bb88e9ca6d9f60ebbbe", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:28:17", "last_modified_at": "2024-11-18 19:28:17", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-8208faf780af4fb7ac1884e6f236a435.json b/agent_workspace/prompts/prompt-id-8208faf780af4fb7ac1884e6f236a435.json
new file mode 100644
index 0000000..b8dc934
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-8208faf780af4fb7ac1884e6f236a435.json
@@ -0,0 +1 @@
+{"id": "8208faf780af4fb7ac1884e6f236a435", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:24:42", "last_modified_at": "2024-11-18 19:24:42", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-93a20f4d46044c50828ba40a5bd22d83.json b/agent_workspace/prompts/prompt-id-93a20f4d46044c50828ba40a5bd22d83.json
new file mode 100644
index 0000000..a1f1dd6
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-93a20f4d46044c50828ba40a5bd22d83.json
@@ -0,0 +1 @@
+{"id": "93a20f4d46044c50828ba40a5bd22d83", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:13:32", "last_modified_at": "2024-11-18 19:13:32", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-9680f2062b6f4f968a883525335f9435.json b/agent_workspace/prompts/prompt-id-9680f2062b6f4f968a883525335f9435.json
new file mode 100644
index 0000000..e83c05d
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-9680f2062b6f4f968a883525335f9435.json
@@ -0,0 +1 @@
+{"id": "9680f2062b6f4f968a883525335f9435", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:31:20", "last_modified_at": "2024-11-18 19:31:20", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-9713c0b63ad241a3af27a0d8fb83a545.json b/agent_workspace/prompts/prompt-id-9713c0b63ad241a3af27a0d8fb83a545.json
new file mode 100644
index 0000000..b0ca92c
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-9713c0b63ad241a3af27a0d8fb83a545.json
@@ -0,0 +1 @@
+{"id": "9713c0b63ad241a3af27a0d8fb83a545", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:27:35", "last_modified_at": "2024-11-18 19:27:35", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-b5aaae4da5e74bb8a710fb92ab1b5afd.json b/agent_workspace/prompts/prompt-id-b5aaae4da5e74bb8a710fb92ab1b5afd.json
new file mode 100644
index 0000000..eef1f0b
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-b5aaae4da5e74bb8a710fb92ab1b5afd.json
@@ -0,0 +1 @@
+{"id": "b5aaae4da5e74bb8a710fb92ab1b5afd", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:33:16", "last_modified_at": "2024-11-18 19:33:16", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-b5f937f51dce4c4dbeee1603f9662588.json b/agent_workspace/prompts/prompt-id-b5f937f51dce4c4dbeee1603f9662588.json
new file mode 100644
index 0000000..b96c21b
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-b5f937f51dce4c4dbeee1603f9662588.json
@@ -0,0 +1 @@
+{"id": "b5f937f51dce4c4dbeee1603f9662588", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:30:33", "last_modified_at": "2024-11-18 19:30:33", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-bc361c1140e84078971110b68e1d4c6e.json b/agent_workspace/prompts/prompt-id-bc361c1140e84078971110b68e1d4c6e.json
new file mode 100644
index 0000000..3822be7
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-bc361c1140e84078971110b68e1d4c6e.json
@@ -0,0 +1 @@
+{"id": "bc361c1140e84078971110b68e1d4c6e", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:25:44", "last_modified_at": "2024-11-18 19:25:44", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-c7126a6471a446649008586078b62e7e.json b/agent_workspace/prompts/prompt-id-c7126a6471a446649008586078b62e7e.json
new file mode 100644
index 0000000..7bd8cc6
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-c7126a6471a446649008586078b62e7e.json
@@ -0,0 +1 @@
+{"id": "c7126a6471a446649008586078b62e7e", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:35:43", "last_modified_at": "2024-11-18 19:35:43", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-ce2c93e903d749a8b52736815af08de1.json b/agent_workspace/prompts/prompt-id-ce2c93e903d749a8b52736815af08de1.json
new file mode 100644
index 0000000..b3e497c
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-ce2c93e903d749a8b52736815af08de1.json
@@ -0,0 +1 @@
+{"id": "ce2c93e903d749a8b52736815af08de1", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:18:53", "last_modified_at": "2024-11-18 19:18:53", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-d7e80b93c387441288e14c911f215a52.json b/agent_workspace/prompts/prompt-id-d7e80b93c387441288e14c911f215a52.json
new file mode 100644
index 0000000..eda9844
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-d7e80b93c387441288e14c911f215a52.json
@@ -0,0 +1 @@
+{"id": "d7e80b93c387441288e14c911f215a52", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:26:45", "last_modified_at": "2024-11-18 19:26:45", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-dd56b077f7ca48a8be8bbaebaf16fda2.json b/agent_workspace/prompts/prompt-id-dd56b077f7ca48a8be8bbaebaf16fda2.json
new file mode 100644
index 0000000..fabaa56
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-dd56b077f7ca48a8be8bbaebaf16fda2.json
@@ -0,0 +1 @@
+{"id": "dd56b077f7ca48a8be8bbaebaf16fda2", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:24:17", "last_modified_at": "2024-11-18 19:24:17", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-e80049cc04cb49f1975f35ed5850f264.json b/agent_workspace/prompts/prompt-id-e80049cc04cb49f1975f35ed5850f264.json
new file mode 100644
index 0000000..3acbcf7
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-e80049cc04cb49f1975f35ed5850f264.json
@@ -0,0 +1 @@
+{"id": "e80049cc04cb49f1975f35ed5850f264", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:33:47", "last_modified_at": "2024-11-18 19:33:47", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/prompts/prompt-id-ed7995c928fd409a9fef8da3e320caa7.json b/agent_workspace/prompts/prompt-id-ed7995c928fd409a9fef8da3e320caa7.json
new file mode 100644
index 0000000..ee01558
--- /dev/null
+++ b/agent_workspace/prompts/prompt-id-ed7995c928fd409a9fef8da3e320caa7.json
@@ -0,0 +1 @@
+{"id": "ed7995c928fd409a9fef8da3e320caa7", "name": "openai-prompt-generator-optimizer-prompt", "description": "Generate and or optimize existing prompts", "content": "Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.\n\n# Guidelines\n\n- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.\n- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.\n- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!\n    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.\n    - Conclusion, classifications, or results should ALWAYS appear last.\n- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.\n   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.\n- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.\n- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.\n- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.\n- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.\n- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)\n    - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.\n    - JSON should never be wrapped in code blocks (```) unless explicitly requested.\n\nThe final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no \"---\")\n\n\n# Instructions\n[Concise instruction describing the task - this should be the first line in the prompt, no section header]\n\n[Additional details as needed.]\n\n[Optional sections with headings or bullet points for detailed steps.]\n\n# Steps [optional]\n\n[optional: a detailed breakdown of the steps necessary to accomplish the task]\n\n# Output Format\n\n[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc] [Only utilize markdown unless mentioned otherwise]\n\n# Examples [optional]\n\n[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.]\n[If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]\n\n# Notes [optional]\n\n[optional: edge cases, details, and an area to call or repeat out specific important considerations]", "created_at": "2024-11-18 19:27:55", "last_modified_at": "2024-11-18 19:27:55", "edit_count": 0, "edit_history": [], "autosave": true, "autosave_folder": "prompts", "auto_generate_prompt": false, "parent_folder": "agent_workspace", "llm": null}
\ No newline at end of file
diff --git a/agent_workspace/pydantic_to_json/pydantic_to_json_096d8599-d347-400a-b348-03ed9c0046cc.log.zip b/agent_workspace/pydantic_to_json/pydantic_to_json_096d8599-d347-400a-b348-03ed9c0046cc.log.zip
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diff --git a/conversation_history.json b/conversation_history.json
new file mode 100644
index 0000000..9e26dfe
--- /dev/null
+++ b/conversation_history.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/myraid.py b/myraid.py
index 296fd8b..650929a 100644
--- a/myraid.py
+++ b/myraid.py
@@ -1,18 +1,18 @@
+from loguru import logger
 import os
 import random
 import re
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
-
+from typing import Dict, List, Optional
+import json
+import datetime
 import numpy as np
 import weaviate
 from datasets import load_dataset
 from dotenv import load_dotenv
-from neo4j import GraphDatabase
 from sentence_transformers import SentenceTransformer
 from swarms import Agent
 from swarm_models import OpenAIChat
-import datetime
 
 load_dotenv()
 
@@ -25,176 +25,98 @@ class Persona:
     description: str
     input_persona: str
     synthesized_text: str
+    embedding: Optional[np.ndarray] = None
 
     @classmethod
-    def from_dataset(cls, entry: Dict) -> "Persona":
-        """Create a Persona from a dataset entry"""
-        # Extract name using regex
-        name_match = re.search(
-            r"Name:\s*([^,\n]+)", entry["synthesized_text"]
-        )
-        name = (
-            name_match.group(1).strip() if name_match else "Unknown"
-        )
-
-        return cls(
-            name=name,
-            description=entry["description"],
-            input_persona=entry["input_persona"],
-            synthesized_text=entry["synthesized_text"],
-        )
-
-
-class PersonaHub:
-    """Manages loading and accessing personas"""
-
-    def __init__(self):
-        self.dataset = load_dataset(
-            "proj-persona/PersonaHub", "instruction"
-        )
-        self.personas: List[Persona] = []
-        self._load_personas()
-
-    def _load_personas(self):
-        """Load personas from the dataset"""
-        for entry in self.dataset["train"]:
+    def from_dataset(cls, entry) -> "Persona":
+        """Create a Persona instance from a dataset entry"""
+        # Convert dataset entry to dictionary if needed
+        if hasattr(entry, "keys"):
+            entry = dict(entry)
+        elif hasattr(entry, "_asdict"):
+            entry = entry._asdict()
+        elif not isinstance(entry, dict):
+            # Try to access attributes directly
             try:
-                persona = Persona.from_dataset(entry)
-                self.personas.append(persona)
-            except Exception as e:
-                print(f"Error loading persona: {e}")
-
-    def get_random_personas(self, n: int = 100) -> List[Persona]:
-        """Get n random personas"""
-        return random.sample(
-            self.personas, min(n, len(self.personas))
-        )
-
-
-class SocialAgent(Agent):
-    """Enhanced agent with persona-based social capabilities"""
-
-    def __init__(self, persona: Persona, **kwargs):
-        self.persona = persona
-        system_prompt = self._generate_system_prompt()
-        super().__init__(system_prompt=system_prompt, **kwargs)
-        self.conversation_state = "initial"
-        self.current_partner = None
-
-    def _generate_system_prompt(self) -> str:
-        """Generate system prompt based on persona"""
-        return f"""You are {self.persona.name}. {self.persona.description}
-
-Background: {self.persona.input_persona}
-
-Maintain this persona while engaging in natural conversations. Follow these conversation stages:
-1. Start with a friendly greeting and ask how they are
-2. Share your name and ask for theirs
-3. Discuss what you do, based on your persona
-4. Engage in natural conversation about shared interests
-
-Always stay in character and respond as your persona would."""
-
-    def generate_greeting(self) -> str:
-        """Generate initial greeting"""
-        return self.run(
-            "Generate a friendly greeting and ask how they are."
-        )
-
-    def respond_to_greeting(self, message: str) -> str:
-        """Respond to a greeting and introduce yourself"""
-        return self.run(
-            f"Someone said: '{message}'. Respond and introduce yourself as {self.persona.name}."
-        )
-
-    def discuss_occupation(self, partner_name: str) -> str:
-        """Share what you do based on your persona"""
-        return self.run(
-            f"Tell {partner_name} what you do, based on your persona."
-        )
-
-
-class ConversationManager:
-    """Manages dynamic conversations between agents"""
-
-    def __init__(self):
-        self.active_conversations: Dict[str, tuple] = {}
-        self.conversation_history: Dict[str, List[Dict]] = {}
-
-    def start_conversation(
-        self, agent1: SocialAgent, agent2: SocialAgent
-    ) -> str:
-        """Initialize a natural conversation between two agents"""
-        conv_id = f"conv_{len(self.conversation_history)}_{random.randint(1000, 9999)}"
-        self.active_conversations[conv_id] = (agent1, agent2)
-        self.conversation_history[conv_id] = []
-
-        # Initial greeting
-        greeting = agent1.generate_greeting()
-        self.add_message(conv_id, agent1.persona.name, greeting)
-
-        # Response and introduction
-        response = agent2.respond_to_greeting(greeting)
-        self.add_message(conv_id, agent2.persona.name, response)
-
-        # Share occupations
-        occupation_share = agent1.discuss_occupation(
-            agent2.persona.name
-        )
-        self.add_message(
-            conv_id, agent1.persona.name, occupation_share
-        )
-
-        return conv_id
-
-    def add_message(
-        self, conv_id: str, speaker_name: str, message: str
-    ):
-        """Add a message to the conversation history"""
-        self.conversation_history[conv_id].append(
-            {
-                "speaker": speaker_name,
-                "message": message,
-                "timestamp": datetime.now().isoformat(),
-            }
-        )
-
+                synthesized_text = (
+                    str(entry.synthesized_text)
+                    if hasattr(entry, "synthesized_text")
+                    else ""
+                )
+                description = (
+                    str(entry.description)
+                    if hasattr(entry, "description")
+                    else ""
+                )
+                input_persona = (
+                    str(entry.input_persona)
+                    if hasattr(entry, "input_persona")
+                    else ""
+                )
 
-@dataclass
-class Persona:
-    """Represents a persona from PersonaHub"""
+                name_match = re.search(
+                    r"Name:\s*([^,\n]+)", synthesized_text
+                )
+                name = (
+                    name_match.group(1).strip()
+                    if name_match
+                    else "Unknown"
+                )
 
-    name: str
-    description: str
-    input_persona: str
-    synthesized_text: str
-    embedding: Optional[np.ndarray] = None
+                return cls(
+                    name=name,
+                    description=description,
+                    input_persona=input_persona,
+                    synthesized_text=synthesized_text,
+                )
+            except Exception as e:
+                raise ValueError(
+                    f"Unable to process dataset entry: {e}"
+                )
 
-    @classmethod
-    def from_dataset(cls, entry: Dict) -> "Persona":
-        name_match = re.search(
-            r"Name:\s*([^,\n]+)", entry["synthesized_text"]
-        )
+        # Process dictionary entry
+        synthesized_text = entry.get("synthesized_text", "")
+        name_match = re.search(r"Name:\s*([^,\n]+)", synthesized_text)
         name = (
             name_match.group(1).strip() if name_match else "Unknown"
         )
+
         return cls(
             name=name,
-            description=entry["description"],
-            input_persona=entry["input_persona"],
-            synthesized_text=entry["synthesized_text"],
+            description=entry.get("description", ""),
+            input_persona=entry.get("input_persona", ""),
+            synthesized_text=synthesized_text,
         )
 
 
 class VectorStore:
-    """Enhanced Weaviate integration for persona matching"""
+    """Weaviate integration for persona matching"""
 
     def __init__(self, url: str):
-        self.client = weaviate.Client(url)
+        self.client = weaviate.Client(
+            url=os.getenv("WEAVIATE_URL"),
+            auth_client_secret=weaviate.auth.AuthApiKey(
+                api_key=os.getenv("WEAVIATE_API_KEY")
+            ),
+        )
         self.embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
         self._create_schema()
 
     def _create_schema(self):
+        """Create schema if it doesn't exist"""
+        # First check if schema exists
+        try:
+            existing_schema = self.client.schema.get()
+            if any(
+                class_obj["class"] == "Persona"
+                for class_obj in existing_schema["classes"]
+            ):
+                print("Persona schema already exists")
+                return
+        except Exception as e:
+            print(f"Error checking schema: {e}")
+
+        # Create schema only if it doesn't exist
         schema = {
             "classes": [
                 {
@@ -219,19 +141,19 @@ def _create_schema(self):
                 }
             ]
         }
+
         try:
             self.client.schema.create(schema)
+            print("Successfully created Persona schema")
         except Exception as e:
-            print(f"Schema already exists: {e}")
+            print(f"Error creating schema: {e}")
 
     def add_persona(self, persona: Persona):
         """Add persona to vector store with embedded representation"""
-        # Generate embedding from combined persona information
         text = f"{persona.description} {persona.input_persona}"
         embedding = self.embedding_model.encode(text)
         persona.embedding = embedding
 
-        # Extract interests using NLP (simplified version)
         interests = re.findall(
             r"interested in|likes|enjoys|passionate about\s+([^,.]+)",
             persona.synthesized_text.lower(),
@@ -254,7 +176,7 @@ def find_similar_personas(
     ) -> List[str]:
         """Find similar personas using vector similarity"""
         result = (
-            self.client.query.get("Persona", ["name", "interests"])
+            self.client.query.get("Persona", ["name"])
             .with_near_vector({"vector": persona.embedding.tolist()})
             .with_where(
                 {
@@ -275,145 +197,111 @@ def find_similar_personas(
         ]
 
 
-class GraphStore:
-    """Enhanced Neo4j integration for social relationships"""
+class SocialAgent(Agent):
+    """Enhanced agent with persona-based social capabilities"""
+
+    def __init__(self, persona: Persona, **kwargs):
+        self.persona = persona
+        system_prompt = self._generate_system_prompt()
+        super().__init__(system_prompt=system_prompt, **kwargs)
+        self.conversation_state = "initial"
+        self.current_partner = None
+
+    def _generate_system_prompt(self) -> str:
+        return f"""You are {self.persona.name}. {self.persona.description}
 
-    def __init__(self, uri: str, user: str, password: str):
-        self.driver = GraphDatabase.driver(uri, auth=(user, password))
-        self._initialize_db()
+Background: {self.persona.input_persona}
 
-    def _initialize_db(self):
-        with self.driver.session() as session:
-            # Create constraints
-            session.run(
-                "CREATE CONSTRAINT persona_name IF NOT EXISTS "
-                "FOR (p:Persona) REQUIRE p.name IS UNIQUE"
-            )
+Maintain this persona while engaging in natural conversations. Follow these conversation stages:
+1. Start with a friendly greeting and ask how they are
+2. Share your name and ask for theirs
+3. Discuss what you do, based on your persona
+4. Engage in natural conversation about shared interests
 
-            # Create indexes
-            session.run(
-                "CREATE INDEX persona_interests IF NOT EXISTS "
-                "FOR (p:Persona) ON (p.interests)"
-            )
+Always stay in character and respond as your persona would."""
 
-    def add_persona(self, persona: Persona):
-        """Add persona to graph database with relationships"""
-        with self.driver.session() as session:
-            # Create persona node with interests
-            interests = re.findall(
-                r"interested in|likes|enjoys|passionate about\s+([^,.]+)",
-                persona.synthesized_text.lower(),
+    def generate_message(self, context: str = "") -> str:
+        """Generate a message based on context"""
+        if not context:
+            return self.run(
+                "Generate a friendly greeting and ask how they are."
             )
+        return self.run(
+            f"Someone said: '{context}'. Respond naturally as {self.persona.name}."
+        )
 
-            session.run(
-                """
-                MERGE (p:Persona {name: $name})
-                SET p.description = $description,
-                    p.interests = $interests,
-                    p.availability = true
-                """,
-                name=persona.name,
-                description=persona.description,
-                interests=interests,
-            )
 
-            # Create interest relationships
-            for interest in interests:
-                session.run(
-                    """
-                    MERGE (i:Interest {name: $interest})
-                    WITH i
-                    MATCH (p:Persona {name: $name})
-                    MERGE (p)-[:INTERESTED_IN]->(i)
-                    """,
-                    interest=interest.strip(),
-                    name=persona.name,
-                )
+class ConversationManager:
+    """Manages dynamic conversations between agents"""
 
-    def record_interaction(
+    def __init__(self):
+        self.conversation_history: Dict[str, List[Dict]] = {}
+
+    def start_conversation(
         self,
-        persona1: str,
-        persona2: str,
-        interaction_type: str = "CONVERSED_WITH",
-    ):
-        """Record interaction between personas"""
-        with self.driver.session() as session:
-            session.run(
-                """
-                MATCH (p1:Persona {name: $name1})
-                MATCH (p2:Persona {name: $name2})
-                MERGE (p1)-[r:INTERACTION]->(p2)
-                ON CREATE SET r.count = 1, r.type = $type
-                ON MATCH SET r.count = r.count + 1,
-                            r.lastInteraction = datetime()
-                """,
-                name1=persona1,
-                name2=persona2,
-                type=interaction_type,
-            )
+        agent1: SocialAgent,
+        agent2: SocialAgent,
+        num_turns: int = 4,
+    ) -> str:
+        """Initialize a conversation between two agents for specified number of turns"""
+        conv_id = f"conv_{len(self.conversation_history)}_{random.randint(1000, 9999)}"
+        self.conversation_history[conv_id] = []
 
-    def find_recommended_partners(
-        self, persona_name: str, limit: int = 5
-    ) -> List[str]:
-        """Find recommended conversation partners based on graph patterns"""
-        with self.driver.session() as session:
-            result = session.run(
-                """
-                MATCH (p:Persona {name: $name})-[:INTERESTED_IN]->(i:Interest)
-                MATCH (other:Persona)-[:INTERESTED_IN]->(i)
-                WHERE other.name <> $name
-                AND other.availability = true
-                WITH other, count(distinct i) as shared_interests,
-                     collect(distinct i.name) as interests
-                ORDER BY shared_interests DESC, rand()
-                LIMIT $limit
-                RETURN other.name as name, shared_interests, interests
-                """,
-                name=persona_name,
-                limit=limit,
-            )
+        # Initial greeting
+        last_message = ""
+        for _ in range(num_turns):
+            # Agent 1's turn
+            message1 = agent1.generate_message(last_message)
+            self.add_message(conv_id, agent1.persona.name, message1)
+            last_message = message1
+
+            # Agent 2's turn
+            message2 = agent2.generate_message(last_message)
+            self.add_message(conv_id, agent2.persona.name, message2)
+            last_message = message2
 
-            return [
-                (
-                    record["name"],
-                    record["shared_interests"],
-                    record["interests"],
-                )
-                for record in result
-            ]
+        return conv_id
+
+    def add_message(
+        self, conv_id: str, speaker_name: str, message: str
+    ):
+        """Add a message to the conversation history"""
+        self.conversation_history[conv_id].append(
+            {
+                "speaker": speaker_name,
+                "message": message,
+                "timestamp": datetime.datetime.now().isoformat(),
+            }
+        )
+
+    def export_conversations(
+        self, filename: str = "conversation_history.json"
+    ):
+        """Export all conversations to JSON file"""
+        with open(filename, "w") as f:
+            json.dump(self.conversation_history, f, indent=2)
 
 
 class DynamicSocialNetwork:
-    """Integrated social network with enhanced matching capabilities"""
+    """Social network with Weaviate-based matching"""
 
     def __init__(
-        self,
-        api_key: str,
-        weaviate_url: str,
-        neo4j_uri: str,
-        neo4j_user: str,
-        neo4j_password: str,
+        self, api_key: str, weaviate_url: str, num_agents: int = 10
     ):
         self.api_key = api_key
         self.vector_store = VectorStore(weaviate_url)
-        self.graph_store = GraphStore(
-            neo4j_uri, neo4j_user, neo4j_password
-        )
-        self.persona_hub = self._load_personas()
+        self.persona_hub = self._load_personas(num_agents)
         self.agents: Dict[str, SocialAgent] = {}
         self.conversation_manager = ConversationManager()
-
         self._initialize_network()
 
-    def _load_personas(self) -> List[Persona]:
-        """Load personas from PersonaHub"""
+    def _load_personas(self, num_agents: int) -> List[Persona]:
+        """Load specified number of personas from PersonaHub"""
         dataset = load_dataset(
             "proj-persona/PersonaHub", "instruction"
         )
         personas = []
-        for entry in dataset["train"][
-            :100
-        ]:  # Load first 100 personas
+        for entry in dataset["train"][:num_agents]:
             try:
                 persona = Persona.from_dataset(entry)
                 personas.append(persona)
@@ -424,14 +312,13 @@ def _load_personas(self) -> List[Persona]:
     def _initialize_network(self):
         """Initialize the network with personas and agents"""
         for persona in self.persona_hub:
-            # Add to vector and graph stores
+            # Add to vector store
             self.vector_store.add_persona(persona)
-            self.graph_store.add_persona(persona)
 
             # Create agent
             model = OpenAIChat(
                 openai_api_key=self.api_key,
-                model_name="gpt-4-mini",
+                model_name="gpt-4",
                 temperature=0.7,
             )
 
@@ -446,44 +333,14 @@ def _initialize_network(self):
 
             self.agents[persona.name] = agent
 
-    def find_conversation_partners(
-        self, persona_name: str, n: int = 3
-    ) -> List[Tuple[str, float, List[str]]]:
-        """Find potential conversation partners using both vector and graph approaches"""
-        # Get vector-based matches
-        vector_matches = set(
-            self.vector_store.find_similar_personas(
-                self.agents[persona_name].persona, limit=n
-            )
-        )
-
-        # Get graph-based recommendations
-        graph_matches = self.graph_store.find_recommended_partners(
-            persona_name, limit=n
-        )
-        graph_names = {match[0] for match in graph_matches}
-
-        # Combine and score matches
-        all_matches = []
-        for name in vector_matches.union(graph_names):
-            score = (
-                1
-                if name in vector_matches
-                else 0 + 2 if name in graph_names else 0
-            )
-            interests = next(
-                (m[2] for m in graph_matches if m[0] == name), []
-            )
-            all_matches.append((name, score, interests))
-
-        # Sort by score and return top N
-        all_matches.sort(key=lambda x: x[1], reverse=True)
-        return all_matches[:n]
-
-    def initiate_conversations(self, num_conversations: int = 5):
-        """Start multiple dynamic conversations between compatible agents"""
-        available_agents = list(self.agents.keys())
+    def run_conversations(
+        self,
+        num_conversations: int = 5,
+        turns_per_conversation: int = 4,
+    ):
+        """Run multiple conversations between compatible agents"""
         conversations = []
+        available_agents = list(self.agents.keys())
 
         for _ in range(num_conversations):
             if len(available_agents) < 2:
@@ -493,10 +350,14 @@ def initiate_conversations(self, num_conversations: int = 5):
             initiator_name = random.choice(available_agents)
             available_agents.remove(initiator_name)
 
-            # Find compatible partners
-            partners = self.find_conversation_partners(initiator_name)
-            if partners:
-                partner_name = partners[0][0]
+            # Find similar partners using Weaviate
+            partner_candidates = (
+                self.vector_store.find_similar_personas(
+                    self.agents[initiator_name].persona
+                )
+            )
+
+            for partner_name in partner_candidates:
                 if partner_name in available_agents:
                     available_agents.remove(partner_name)
 
@@ -505,44 +366,55 @@ def initiate_conversations(self, num_conversations: int = 5):
                         self.conversation_manager.start_conversation(
                             self.agents[initiator_name],
                             self.agents[partner_name],
+                            turns_per_conversation,
                         )
                     )
 
-                    # Record interaction
-                    self.graph_store.record_interaction(
-                        initiator_name, partner_name
-                    )
-
                     conversations.append(
                         (conv_id, initiator_name, partner_name)
                     )
+                    break
 
+        # Export conversation history
+        self.conversation_manager.export_conversations()
         return conversations
 
 
 # Example usage
-network = DynamicSocialNetwork(
-    api_key=os.getenv("OPENAI_API_KEY"),
-    weaviate_url=os.getenv("WEAVIATE_URL"),
-    neo4j_uri=os.getenv("NEO4J_URI"),
-    neo4j_user=os.getenv("NEO4J_USER"),
-    neo4j_password=os.getenv("NEO4J_PASSWORD"),
-)
-
-# Start multiple conversations
-conversations = network.initiate_conversations(5)
-
-# Print conversation details
-for conv_id, initiator, partner in conversations:
-    print(f"\nConversation {conv_id}:")
-    print(f"Between {initiator} and {partner}")
-    for (
-        message
-    ) in network.conversation_manager.conversation_history[
-        conv_id
-    ]:
-        print(f"{message['speaker']}: {message['message']}")
+def run_social_simulation(
+    num_agents: int = 10,
+    num_conversations: int = 5,
+    turns_per_conversation: int = 4,
+):
+    network = DynamicSocialNetwork(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        weaviate_url=os.getenv("WEAVIATE_URL"),
+        num_agents=num_agents,
+    )
+
+    # Start multiple conversations
+    conversations = network.run_conversations(
+        num_conversations, turns_per_conversation
+    )
+    logger.info(f"Conversations: {conversations}")
+
+    # Print conversation details
+    for conv_id, initiator, partner in conversations:
+        print(f"\nConversation {conv_id}:")
+        print(f"Between {initiator} and {partner}")
+        for (
+            message
+        ) in network.conversation_manager.conversation_history[
+            conv_id
+        ]:
+            print(f"{message['speaker']}: {message['message']}")
+
+
+if __name__ == "__main__":
     print(
-        "\nShared Interests:",
-        network.find_conversation_partners(initiator)[0][2],
-    )
\ No newline at end of file
+        run_social_simulation(
+            num_agents=10,
+            num_conversations=5,
+            turns_per_conversation=4,
+        )
+    )
diff --git a/v2.py b/v2.py
new file mode 100644
index 0000000..f7808bb
--- /dev/null
+++ b/v2.py
@@ -0,0 +1,474 @@
+from loguru import logger
+import os
+import random
+import re
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
+import json
+import datetime
+import numpy as np
+from datasets import load_dataset
+from dotenv import load_dotenv
+from sentence_transformers import SentenceTransformer
+from swarms import Agent
+from swarm_models import OpenAIChat
+
+# Configure detailed logging
+logger.remove()  # Remove default handler
+logger.add(
+    "social_simulation.log",
+    rotation="500 MB",
+    level="DEBUG",  # Changed to DEBUG for more detailed logging
+    format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {function}:{line} | {message}",
+    backtrace=True,  # Enable backtrace
+    diagnose=True    # Enable diagnosis
+)
+
+# Add console logging for immediate feedback
+logger.add(
+    lambda msg: print(msg),
+    level="DEBUG",
+    format="{level} | {message}"
+)
+
+load_dotenv()
+
+@dataclass
+class Persona:
+    """Represents a persona from PersonaHub"""
+    name: str
+    description: str
+    input_persona: str
+    synthesized_text: str
+    embedding: Optional[np.ndarray] = None
+
+    @classmethod
+    def from_dataset(cls, entry) -> "Persona":
+        """Create a Persona instance from a dataset entry"""
+        logger.debug(f"Processing dataset entry: {entry}")
+        
+        try:
+            if hasattr(entry, 'keys'):
+                entry = dict(entry)
+            elif hasattr(entry, '_asdict'):
+                entry = entry._asdict()
+            
+            # Extract synthesized_text first for debugging
+            synthesized_text = str(entry.get("synthesized_text", "")) if isinstance(entry, dict) else str(getattr(entry, "synthesized_text", ""))
+            logger.debug(f"Extracted synthesized_text: {synthesized_text[:100]}...")  # Log first 100 chars
+            
+            # Extract name from synthesized text
+            name_match = re.search(r"Name:\s*([^,\n]+)", synthesized_text)
+            if not name_match:
+                logger.warning(f"No name found in synthesized text. Using fallback name.")
+                name = f"Unknown-{random.randint(1000,9999)}"
+            else:
+                name = name_match.group(1).strip()
+            
+            # Get other fields
+            description = str(entry.get("description", "")) if isinstance(entry, dict) else str(getattr(entry, "description", ""))
+            input_persona = str(entry.get("input_persona", "")) if isinstance(entry, dict) else str(getattr(entry, "input_persona", ""))
+            
+            logger.debug(f"Created persona with name: {name}")
+            return cls(
+                name=name,
+                description=description,
+                input_persona=input_persona,
+                synthesized_text=synthesized_text,
+            )
+            
+        except Exception as e:
+            logger.exception(f"Error creating persona from dataset entry")
+            raise
+
+class LocalVectorStore:
+    """Local vector store for persona matching using numpy"""
+
+    def __init__(self):
+        self.embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+        self.personas: Dict[str, Persona] = {}
+        self.embeddings: Dict[str, np.ndarray] = {}
+        logger.info(
+            "Initialized LocalVectorStore with embedding model"
+        )
+
+    def add_persona(self, persona: Persona) -> None:
+        """Add persona to vector store with embedded representation"""
+        try:
+            text = f"{persona.description} {persona.input_persona}"
+            embedding = self.embedding_model.encode(text)
+            persona.embedding = embedding
+
+            self.personas[persona.name] = persona
+            self.embeddings[persona.name] = embedding
+
+            logger.info(
+                f"Added persona {persona.name} to vector store"
+            )
+        except Exception as e:
+            logger.error(
+                f"Error adding persona {persona.name} to vector store: {e}"
+            )
+            raise
+
+    def cosine_similarity(
+        self, a: np.ndarray, b: np.ndarray
+    ) -> float:
+        """Calculate cosine similarity between two vectors"""
+        return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+    def find_similar_personas(
+        self, persona: Persona, limit: int = 5
+    ) -> List[Tuple[str, float]]:
+        """Find similar personas using cosine similarity"""
+        try:
+            if persona.name not in self.embeddings:
+                logger.warning(
+                    f"Persona {persona.name} not found in vector store"
+                )
+                return []
+
+            query_embedding = self.embeddings[persona.name]
+            similarities = []
+
+            for name, embedding in self.embeddings.items():
+                if name != persona.name:
+                    similarity = self.cosine_similarity(
+                        query_embedding, embedding
+                    )
+                    similarities.append((name, similarity))
+
+            # Sort by similarity score in descending order
+            similarities.sort(key=lambda x: x[1], reverse=True)
+
+            logger.info(
+                f"Found {len(similarities[:limit])} similar personas for {persona.name}"
+            )
+            return similarities[:limit]
+
+        except Exception as e:
+            logger.error(f"Error finding similar personas: {e}")
+            return []
+
+
+class SocialAgent(Agent):
+    """Enhanced agent with persona-based social capabilities"""
+
+    def __init__(self, persona: Persona, **kwargs):
+        self.persona = persona
+        system_prompt = self._generate_system_prompt()
+        super().__init__(system_prompt=system_prompt, **kwargs)
+        self.conversation_state = "initial"
+        self.current_partner = None
+        logger.info(
+            f"Initialized SocialAgent for persona: {persona.name}"
+        )
+
+    def _generate_system_prompt(self) -> str:
+        out =  f"""You are {self.persona.name}. {self.persona.description}
+
+        Background: {self.persona.input_persona}
+
+        Maintain this persona while engaging in natural conversations. Follow these conversation stages:
+        1. Start with a friendly greeting and ask how they are
+        2. Share your name and ask for theirs
+        3. Discuss what you do, based on your persona
+        4. Engage in natural conversation about shared interests
+
+        Always stay in character and respond as your persona would."""
+        print(out)
+        return out
+
+    def generate_message(self, context: str = "") -> str:
+        """Generate a message based on context"""
+        try:
+            if not context:
+                message = self.run(
+                    "Generate a friendly greeting and ask how they are."
+                )
+            else:
+                message = self.run(
+                    f"Someone said: '{context}'. Respond naturally as {self.persona.name}."
+                )
+
+            logger.info(
+                f"Generated message from {self.persona.name}: {message[:50]}..."
+            )
+            return message
+
+        except Exception as e:
+            logger.error(
+                f"Error generating message for {self.persona.name}: {e}"
+            )
+            return f"Hello! [Error: {str(e)}]"
+
+
+class ConversationManager:
+    """Manages dynamic conversations between agents"""
+
+    def __init__(self):
+        self.conversation_history: Dict[str, List[Dict]] = {}
+        logger.info("Initialized ConversationManager")
+
+    def start_conversation(
+        self,
+        agent1: SocialAgent,
+        agent2: SocialAgent,
+        num_turns: int = 4,
+    ) -> str:
+        """Initialize a conversation between two agents for specified number of turns"""
+        try:
+            conv_id = f"conv_{len(self.conversation_history)}_{random.randint(1000, 9999)}"
+            self.conversation_history[conv_id] = []
+
+            logger.info(
+                f"Starting conversation {conv_id} between {agent1.persona.name} and {agent2.persona.name}"
+            )
+
+            last_message = ""
+            for turn in range(num_turns):
+                # Agent 1's turn
+                message1 = agent1.generate_message(last_message)
+                self.add_message(
+                    conv_id, agent1.persona.name, message1
+                )
+                last_message = message1
+
+                # Agent 2's turn
+                message2 = agent2.generate_message(last_message)
+                self.add_message(
+                    conv_id, agent2.persona.name, message2
+                )
+                last_message = message2
+
+                logger.debug(
+                    f"Completed turn {turn + 1}/{num_turns} of conversation {conv_id}"
+                )
+
+            return conv_id
+
+        except Exception as e:
+            logger.error(
+                f"Error in conversation between {agent1.persona.name} and {agent2.persona.name}: {e}"
+            )
+            raise
+
+    def add_message(
+        self, conv_id: str, speaker_name: str, message: str
+    ):
+        """Add a message to the conversation history"""
+        try:
+            self.conversation_history[conv_id].append(
+                {
+                    "speaker": speaker_name,
+                    "message": message,
+                    "timestamp": datetime.datetime.now().isoformat(),
+                }
+            )
+            logger.debug(
+                f"Added message from {speaker_name} to conversation {conv_id}"
+            )
+        except Exception as e:
+            logger.error(
+                f"Error adding message to conversation {conv_id}: {e}"
+            )
+
+    def export_conversations(
+        self, filename: str = "conversation_history.json"
+    ):
+        """Export all conversations to JSON file"""
+        try:
+            with open(filename, "w") as f:
+                json.dump(self.conversation_history, f, indent=2)
+            logger.info(
+                f"Exported conversation history to {filename}"
+            )
+        except Exception as e:
+            logger.error(
+                f"Error exporting conversations to {filename}: {e}"
+            )
+
+
+class DynamicSocialNetwork:
+    """Social network with local vector-based matching"""
+
+    def __init__(self, api_key: str, num_agents: int = 10):
+        self.api_key = api_key
+        self.vector_store = LocalVectorStore()
+        self.persona_hub = self._load_personas(num_agents)
+        self.agents: Dict[str, SocialAgent] = {}
+        self.conversation_manager = ConversationManager()
+        self._initialize_network()
+        logger.info(
+            f"Initialized DynamicSocialNetwork with {num_agents} agents"
+        )
+
+    def _load_personas(self, num_agents: int) -> List[Persona]:
+        """Load specified number of personas from PersonaHub"""
+        try:
+            dataset = load_dataset(
+                "proj-persona/PersonaHub", "instruction"
+            )
+            personas = []
+            for entry in dataset["train"][:num_agents]:
+                try:
+                    persona = Persona.from_dataset(entry)
+                    personas.append(persona)
+                except Exception as e:
+                    logger.error(
+                        f"Error loading individual persona: {e}"
+                    )
+
+            logger.info(
+                f"Successfully loaded {len(personas)} personas"
+            )
+            return personas
+
+        except Exception as e:
+            logger.error(f"Error loading personas from dataset: {e}")
+            raise
+
+    def _initialize_network(self):
+        """Initialize the network with personas and agents"""
+        try:
+            for persona in self.persona_hub:
+                # Add to vector store
+                self.vector_store.add_persona(persona)
+
+                # Create agent
+                model = OpenAIChat(
+                    openai_api_key=self.api_key,
+                    model_name="gpt-4o-mini",
+                    temperature=1.2,
+                )
+
+                agent = SocialAgent(
+                    persona=persona,
+                    agent_name=f"Agent-{persona.name}",
+                    llm=model,
+                    max_loops=1,
+                    autosave=True,
+                    verbose=True,
+                )
+
+                self.agents[persona.name] = agent
+
+            logger.info(
+                f"Initialized network with {len(self.agents)} agents"
+            )
+
+        except Exception as e:
+            logger.error(f"Error initializing network: {e}")
+            raise
+
+    def run_conversations(
+        self,
+        num_conversations: int = 5,
+        turns_per_conversation: int = 4,
+    ) -> List[Tuple[str, str, str]]:
+        """Run multiple conversations between compatible agents"""
+        try:
+            conversations = []
+            available_agents = list(self.agents.keys())
+
+            for i in range(num_conversations):
+                if len(available_agents) < 2:
+                    logger.warning(
+                        "Not enough available agents for more conversations"
+                    )
+                    break
+
+                # Select random initiator
+                initiator_name = random.choice(available_agents)
+                available_agents.remove(initiator_name)
+
+                # Find similar partners using local vector store
+                partner_candidates = (
+                    self.vector_store.find_similar_personas(
+                        self.agents[initiator_name].persona
+                    )
+                )
+
+                for (
+                    partner_name,
+                    similarity_score,
+                ) in partner_candidates:
+                    if partner_name in available_agents:
+                        available_agents.remove(partner_name)
+                        logger.info(
+                            f"Matching {initiator_name} with {partner_name} (similarity: {similarity_score:.3f})"
+                        )
+
+                        # Start conversation
+                        conv_id = self.conversation_manager.start_conversation(
+                            self.agents[initiator_name],
+                            self.agents[partner_name],
+                            turns_per_conversation,
+                        )
+
+                        conversations.append(
+                            (conv_id, initiator_name, partner_name)
+                        )
+                        break
+
+            # Export conversation history
+            self.conversation_manager.export_conversations()
+            logger.info(
+                f"Completed {len(conversations)} conversations"
+            )
+            return conversations
+
+        except Exception as e:
+            logger.error(f"Error running conversations: {e}")
+            return []
+
+
+def run_social_simulation(
+    num_agents: int = 10,
+    num_conversations: int = 5,
+    turns_per_conversation: int = 4,
+):
+    """Run the social simulation with specified parameters"""
+    try:
+        logger.info("Starting social simulation")
+
+        network = DynamicSocialNetwork(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            num_agents=num_agents,
+        )
+
+        # Start multiple conversations
+        conversations = network.run_conversations(
+            num_conversations, turns_per_conversation
+        )
+        logger.info(
+            f"Completed social simulation with {len(conversations)} conversations"
+        )
+
+        # Print conversation details
+        for conv_id, initiator, partner in conversations:
+            print(f"\nConversation {conv_id}:")
+            print(f"Between {initiator} and {partner}")
+            for (
+                message
+            ) in network.conversation_manager.conversation_history[
+                conv_id
+            ]:
+                print(f"{message['speaker']}: {message['message']}")
+
+        return conversations
+
+    except Exception as e:
+        logger.error(f"Error in social simulation: {e}")
+        raise
+
+
+if __name__ == "__main__":
+    logger.info("Starting main program")
+    print(
+        run_social_simulation(
+            num_agents=10,
+            num_conversations=5,
+            turns_per_conversation=4,
+        )
+    )