file cleanup

docs/Phase 1/Task 1.3 Memory & Persona Implementation.md

@@ -275,6 +275,8 @@ The user will execute **one step at a time** and confirm each works before proce
 | Consistent Logging | Use consistent prefixes for log messages to aid debugging | Added prefixes like "[PERSONA CHANGE]" and "[MEMORY LOAD]" |
 | Sequential Approval Workflow | Present detailed plans, wait for explicit approval on each component, implement one change at a time, and provide clear explanations of data flows | Explained how the persona instructions flow from selection to prompt generation before implementing changes |
 | Surgical Diff Principle | Show only the specific changes being made rather than reprinting entire code blocks | Highlighted just the 2 key modifications to implement personalization rather than presenting a large code block |
+| Progressive Enhancement | Layer new functionality on top of existing code rather than replacing it; design features to work even if parts fail | Adding persona-specific instructions while maintaining default behavior when persona selection is unavailable |
+| Documentation In Context | Add inline comments explaining *why* not just *what* code is doing; document edge cases directly in the code | Adding comments explaining persona state management and potential memory retrieval failures |
 
 ---
 

docs/Phase 1/z_Task 1.3 Memory & Persona Implementation_Attempt 1 and 2.md

@@ -1,352 +0,0 @@
-## Context
-You are an expert at  UI/UX design and software front-end development and architecture.  You are allowed to not know an answer. You are allowed to be uncertain. You are allowed to disagree with your task. If any of these things happen, halt your current process and notify the user immediately. You should not hallucinate. If you are unable to remember information, you are allowed to look it up again.
-
-You are not allowed to hallucinate. You may only use data that exists in the files specified. You are not allowed to create new data if it does not exist in those files.
-
-You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully.
-
-When writing code, your focus should be on creating new functionality that builds on the existing code base without breaking things that are already working. If you need to rewrite how existing code works in order to develop a new feature, please check your work carefully, and also pause your work and tell me (the human) for review before going ahead. We want to avoid software regression as much as possible.
-
-I WILL REPEAT, WHEN UPDATING EXISTING CODE FILES, PLEASE DO NOT OVERWRITE EXISTING CODE, PLEASE ADD OR MODIFY COMPONENTS TO ALIGN WITH THE NEW FUNCTIONALITY. THIS INCLUDES SMALL DETAILS LIKE FUNCTION ARGUMENTS AND LIBRARY IMPORTS. REGRESSIONS IN THESE AREAS HAVE CAUSED UNNECESSARY DELAYS AND WE WANT TO AVOID THEM GOING FORWARD.
-
-When you need to modify existing code (in accordance with the instruction above), please present your recommendation to the user before taking action, and explain your rationale.
-
-If the data files and code you need to use as inputs to complete your task do not conform to the structure you expected based on the instructions, please pause your work and ask the human for review and guidance on how to proceed.
-
-If you have difficulty finding mission critical updates in the codebase (e.g. .env files, data files) ask the user for help in finding the path and directory.
-
-# Task 1.3 Memory & Persona Implementation Instructions
-
-## Objective
-You are to follow the step-by-step process in order to implement Feature 0 (Persona Selection) / Task 1.3 (Memory System and UI Persona Integration with Zep) as defined in `ifx-sandbox/docs/requirements.md`. This involves creating Zep users/memory, adding UI selection in Gradio, integrating Zep memory into the agent, and performing related housekeeping. The goal is to allow users to select a persona ("Casual Fan" or "Super Fan") which loads pre-defined user profiles and memory contexts via Zep Cloud, personalizing the chat experience.
-
-## Instruction Steps
-
-1.  **Codebase Review:** Familiarize yourself with the existing project structure:
-    *   `gradio_app.py`: Understand the current UI structure, the `AppState` class, and how Zep is already integrated. Pay close attention to `initialize_chat()` and `process_message()` functions which handle session creation and message processing.
-    *   `gradio_agent.py`: Examine the agent setup, tool integration, and especially the `get_memory()` function which currently uses `Neo4jChatMessageHistory` instead of Zep memory.
-    *   `gradio_utils.py`: Review for utility functions, especially those related to session/user ID generation.
-    *   `tools/vector.py`: Examine this file as it needs to be removed in the housekeeping phase.
-    *   `.env`: Check for `ZEP_API_KEY` to ensure Zep Cloud access is configured. Note that Zep Cloud API keys start with "z_" prefix.
-    *   Read `requirements.txt` to verify Zep client libraries are available (specifically `zep-cloud>=0.1.0`).
-
-2.  **Create Directory Structure:**
-    *   **Note:** The directory `ifx-sandbox/z_utils/` already exists with other utility scripts like `set_secrets.py` and `restart_space.py`. Follow their patterns for loading environment variables and error handling.
-
-3.  **Zep User & Memory Setup Script:**
-    *   Create a new file: `ifx-sandbox/z_utils/zep_setup.py`.
-    *   **Implement the script:**
-        *   Import `asyncio`, `os`, `uuid`.
-        *   Import `AsyncZep`, `User`, `Message` from `zep_cloud`.
-        *   Load `ZEP_API_KEY` (and optionally `ZEP_API_URL`) from environment variables using `dotenv` like other scripts. Handle missing keys gracefully (e.g., raise error).
-        *   Initialize `AsyncZep` client.
-        *   **Define Persona Data:** Create dictionaries mapping persona names to details:
-            ```python
-            PERSONAS = {
-                "Casual Fan": {
-                    "user_id": str(uuid.uuid4()), # Generate and store fixed UUIDs
-                    "email": "[email protected]",
-                    "first_name": "Casual",
-                    "last_name": "Fan",
-                    "metadata": {"fan_type": "casual"}
-                },
-                "Super Fan": {
-                    "user_id": str(uuid.uuid4()), # Generate and store fixed UUIDs
-                    "email": "[email protected]",
-                    "first_name": "Super",
-                    "last_name": "Fan",
-                    "metadata": {"fan_type": "super"}
-                }
-            }
-            ```
-            *Print these generated UUIDs to the console when the script runs.*
-            **IMPORTANT:** The UUIDs must be saved to a file (e.g., `z_utils/persona_uuids.json`) for later use by the application, as these will be used to link users to their persona data.
-        *   **Define User Creation Function:** Create an `async def create_zep_user(client, user_data)` function that calls `client.user.add(**user_data)`. Include error handling (e.g., user already exists). Consider adding a `client.user.get(user_id)` check first.
-        *   **Define Memory Pre-loading:**
-            *   Define sample `Message` lists for each persona reflecting their interests:
-                * **Casual Fan**: Focus on big stars (QB, star receivers), game outcomes, simple recaps with minimal stats, excitement for big plays.
-                * **Super Fan**: Focus on detailed stats, role players, strategy discussion, defensive play analysis, references to historical context.
-            *   Create an `async def preload_memory(client, user_id, memories)` function.
-            *   **Implementation approach**: Using the Zep Cloud API, create a temporary session for the user with `await client.memory.add_session(session_id=str(uuid.uuid4()), user_id=user_id)`, then add the messages with `await client.memory.add(session_id=session_id, messages=memories)`.
-        *   **Main Execution Block:** Create an `async def main()` function and use `asyncio.run(main())` in `if __name__ == "__main__":`.
-            *   In `main()`, iterate through `PERSONAS`, call `create_zep_user` for each.
-            *   Call memory pre-loading function for each persona.
-            *   Save the persona UUIDs to a JSON file.
-            *   Print confirmations or errors.
-    *   **Run the Script:** Execute `python ifx-sandbox/z_utils/zep_setup.py` once to create the users in Zep Cloud. Note the generated UUIDs.
-
-4.  **Modify Gradio UI & State Management:**
-    *   Update `gradio_app.py` to:
-        *   Add imports: Ensure `AsyncZep`, `Message` are imported. Add `uuid` and `json` for reading the persona UUIDs file.
-        *   Enhance `AppState` class with persona-related fields:
-            ```python
-            def __init__(self):
-                self.chat_history = []
-                self.initialized = False
-                self.user_id = None
-                self.session_id = None
-                self.zep_client = None
-                self.current_persona = None  # New field to track selected persona
-                self.persona_data = None  # New field to store persona details
-            ```
-        *   Load persona data from the saved JSON file at application start.
-        *   Add a Persona Selector UI component (radio buttons) at the top of the UI, above the chat interface:
-            ```python
-            persona_selector = gr.Radio(
-                choices=["Casual Fan", "Super Fan"],
-                label="Select your 49ers Fan Type:",
-                value="Casual Fan",  # Default selection
-                interactive=True
-            )
-            ```
-        *   Modify `initialize_chat()` to use persona-specific user IDs:
-            * Use the UUID corresponding to the selected persona from the saved JSON file
-            * Update the existing code that calls `zep.user.add` with the persona-specific data
-        *   Implement a new `handle_persona_change(persona_name)` function for switching personas:
-            * Update `state.current_persona` with the new persona
-            * Set `state.user_id` to the corresponding UUID
-            * Generate a new `session_id` using `gradio_utils.reset_ids()`
-            * Create a new Zep session with `await zep.memory.add_session()`
-            * Clear the chat history display to start fresh
-        *   Register this function as a callback for the persona selector.
-        *   Update `process_message()` to ensure the persona-specific context is used.
-
-5.  **Integrate Zep Memory into Agent:**
-    *   Modify `gradio_agent.py` to:
-        *   Replace `Neo4jChatMessageHistory` import with:
-            ```python
-            from langchain_community.memory.zep_cloud_memory import ZepCloudChatMessageHistory
-            ```
-        *   Update the `get_memory()` function to return a Zep memory instance:
-            ```python
-            def get_memory(session_id):
-                """Get the chat history from Zep for the given session"""
-                return ZepCloudChatMessageHistory(
-                    session_id=session_id,
-                    api_key=os.environ.get("ZEP_API_KEY"),
-                )
-            ```
-        *   Ensure all agent components correctly use the new memory interface (which should be 100% compatible with the existing code).
-
-6.  **Perform Housekeeping Tasks:**
-    *   **Create and Execute Neo4j Cleanup Script:**
-        *   Create a new script, `ifx-sandbox/z_utils/neo4j_cleanup.py`.
-        *   This script should connect to Neo4j using the existing `graph` object from `gradio_graph.py` (similar to the pattern in `ifx-sandbox/data/april_11_multimedia_data_collect/neo4j_article_uploader.py`).
-        *   The script's sole purpose is to execute the following Cypher query to safely remove only the `embedding` property from `Game` nodes:
-            ```cypher
-            MATCH (g:Game)
-            WHERE g.embedding IS NOT NULL
-            REMOVE g.embedding
-            RETURN count(*) as removed_count
-            ```
-        *   Ensure the script includes appropriate logging or print statements to confirm success (reporting the number of nodes affected) or indicate errors.
-        *   Execute the script once using `python ifx-sandbox/z_utils/neo4j_cleanup.py`.
-    *   **Delete Obsolete File:** Delete the `ifx-sandbox/tools/vector.py` file.
-    *   **Remove Tool from Agent:** Remove the "Game Summary Search" tool and related imports from `ifx-sandbox/gradio_agent.py`:
-        *   Remove the import statement: `from tools.vector import get_game_summary`
-        *   Remove the tool entry from the `tools` list:
-        ```python
-        Tool.from_function(
-            name="Game Summary Search",
-            description="""ONLY use for detailed game summaries or specific match results if the 'Game Recap' tool fails or doesn't provide enough detail.
-        Examples: "Summarize the 49ers vs Seahawks game", "Give me details about the last playoff game results"
-        Do NOT use for general schedule questions.""",
-            func=get_game_summary,
-        ),
-        ```
-
-7.  **Test the Implementation:**
-    *   Run the application and verify the persona selector is present.
-    *   Test both personas with appropriate questions:
-        * For Casual Fan: "Who's the quarterback for the 49ers?" (should give basic info)
-        * For Super Fan: "Tell me about the 49ers offensive line strategy" (should give detailed analysis)
-    *   Ensure switching personas correctly clears chat history and establishes a new session.
-    *   Verify all existing functionality continues to work.
-
-8.  **Update Documentation:**
-    *   Edit `ifx-sandbox/docs/requirements.md` to reflect completed work:
-        * Update the "Persona Memory Structure (Zep)" section with actual implementation details
-        * Mark Feature 0 (Persona Selection) as completed
-
-## Data Flow Architecture (Simplified)
-1.  User selects a persona ("Casual Fan" or "Super Fan") in the Gradio UI.
-2.  The UI triggers the persona change callback, which:
-    *   Updates the application state with the selected persona.
-    *   Sets the `user_id` to the corresponding pre-generated UUID from the persona JSON file.
-    *   Generates a new `session_id` for this interaction.
-    *   Creates a new Zep session linked to the persona's user ID.
-    *   Clears the chat history to start fresh.
-3.  User sends a message through the chat interface.
-4.  The message is processed by `process_message()`, which:
-    *   Stores the message in Zep memory for the current session.
-    *   Calls the agent with the current `session_id`.
-5.  The agent retrieves the chat history via `get_memory(session_id)`, which:
-    *   Returns a Zep memory instance tied to the current session.
-    *   This memory contains context appropriate to the selected persona.
-6.  The agent generates a response influenced by the persona-specific memory context.
-7.  The response is stored in Zep memory and displayed to the user.
-
-## Error Handling Strategy
-1.  **Zep API Connection:** Implement robust error handling for Zep API calls, including fallback options if Zep Cloud is unavailable:
-    * For critical initialization calls, show a clear error message to the user
-    * For non-critical calls during operation, log the error but continue with degraded functionality
-    * Add retry logic for temporary network issues where appropriate
-2.  **UUID Management:** Store persona UUIDs in a JSON file that can be loaded by the application. Add error handling for file access issues.
-3.  **State Transitions:** Handle potential race conditions during persona switching:
-    * Disable UI interaction during the transition
-    * Re-enable only after all async operations are complete
-    * Add a loading indicator during transitions
-4.  **Session Management:** Properly clean up old sessions when switching personas.
-5.  **Missing Dependencies:** Check for required environment variables at startup and show clear error messages if missing.
-
-## Performance Optimization
-1.  **Memory Context Size:** Keep pre-loaded memories concise and relevant to avoid excessive token usage.
-2.  **Session Creation:** Only create a new Zep session when switching personas or when a session doesn't exist.
-3.  **Lazy Loading:** Load persona details only when needed rather than all at application startup.
-4.  **Caching:** Store the persona UUIDs in the AppState to avoid repeated file access.
-
-## Failure Conditions
-*   If you are unable to complete any step after 3 attempts, immediately halt the process and consult with the user on how to continue.
-*   Document the failure point and the reason for failure.
-*   Do not proceed with subsequent steps until the issue is resolved.
-
-## Completion Criteria & Potential Concerns
-
-**Success Criteria:**
-1.  Users can select between "Casual Fan" and "Super Fan" personas in the UI.
-2.  Switching personas correctly clears chat history and establishes a new session.
-3.  The LangChain agent successfully retrieves and uses Zep memory for the current persona and session.
-4.  Responses are contextually appropriate to the selected persona (more basic for casual fans, more detailed for super fans).
-5.  Neo4j game nodes have been cleaned up by removing embedding properties.
-6.  The vector search tool has been removed without breaking other functionality.
-
-**Deliverables:**
-*   The `z_utils/zep_setup.py` script for user creation and memory initialization.
-*   The `z_utils/persona_uuids.json` file storing the generated UUIDs.
-*   Modified `gradio_app.py` with persona selector UI and appropriate state management.
-*   Modified `gradio_agent.py` with Zep memory integration replacing Neo4j chat history.
-*   Updated `docs/requirements.md` reflecting completed implementation.
-
-**Challenges / Potential Concerns & Mitigation Strategies:**
-
-1.  **Zep Integration:**
-    *   *Concern:* Ensuring correct usage of the Zep Cloud SDK, especially across async/sync contexts.
-    *   *Mitigation:* Carefully review Zep documentation for best practices. Follow the `AsyncZep` usage pattern already in place in `gradio_app.py`. Ensure proper async/await handling in all Zep operations.
-
-2.  **Gradio State Management:**
-    *   *Concern:* Properly handling persona switching and session management to avoid race conditions or context contamination.
-    *   *Mitigation:* Use clear state management in the UI callbacks. Ensure the persona selector is disabled during transitions. Add status indicators for long operations.
-
-3.  **Regression Risks:**
-    *   *Concern:* Changes to core files like `gradio_app.py` and `gradio_agent.py` could break existing functionality.
-    *   *Mitigation:* Make incremental changes with thorough testing after each modification. Use version control to track changes. Follow the existing coding patterns. Present changes to core functions for review before implementing.
-
-4.  **Neo4j Cleanup:**
-    *   *Concern:* Removing embeddings from Game nodes could affect other components relying on this data.
-    *   *Mitigation:* Verify all dependencies before removal. The `vector.py` file is the only component using these embeddings. Run the cleanup in a separate step and test afterwards.
-
-5.  **Vector Tool Removal:**
-    *   *Concern:* Removing the vector search tool might have unexpected dependencies.
-    *   *Mitigation:* Check all imports and references. Verify the tool removal doesn't break any other functionality by testing all other tools after removal.
-
-## Appendix: Zep Memory Pre-loading Implementation
-
-Based on the Zep memory graph structure described in [Zep documentation](https://help.getzep.com/facts), here's a detailed implementation of how to pre-load memory graphs for the Casual Fan and Super Fan personas.
-
-### Understanding Zep's Memory Model
-
-In Zep, memory consists of:
-1. **Facts** - Stored on edges, representing precise relationships with timestamps for when they are valid/invalid
-2. **Summaries** - Stored on nodes, providing high-level overviews of entities
-
-Our implementation will focus on:
-1. Creating appropriate nodes (team concepts, games, general information)
-2. Establishing facts via edges that connect these nodes
-3. Adding fact ratings to prioritize important knowledge for each persona
-
-### Implementation in `zep_setup.py`
-
-# file: z_utils/zep_setup.py
-"""
-Create two persona users in Zep Cloud and preload their memory graphs.
-Run once:  python z_utils/zep_setup.py
-"""
-
-```python
-// Removed the full Python code block for zep_setup.py as it exists in the actual file
-// See ifx-sandbox/z_utils/zep_setup.py for the implementation.
-```
-
-### Key Features of This Implementation
-
-1. **Graph-Based Memory Structure**
-   - Creates entity nodes for team information, games, and football concepts
-   - Establishes facts through conversations and structured JSON data
-   - Simulates natural knowledge through conversation-based memory
-
-2. **Fact Rating System**
-   - Implements custom fact rating instructions for each persona
-   - Casual Fan: Prioritizes star players, major game outcomes, and memorable moments
-   - Super Fan: Prioritizes detailed statistics, strategic insights, and historical context
-
-3. **Knowledge Depth Differentiation**
-   - **Casual Fan**: Limited to recent memorable games and basic team facts
-   - **Super Fan**: Includes scheme knowledge, strategic insights, salary cap information, and draft capital
-
-4. **Temporal Accuracy**
-   - Facts include temporal information (game dates, current season)
-   - Ensures agent responses are grounded in the correct time context
-
-5. **User-Specific Conversation History**
-   - Each persona has its own conversation history with appropriate depth
-   - Casual conversations focus on stars and outcomes
-   - Super fan conversations delve into schemes and personnel management
-
-This implementation leverages Zep's memory graph to create distinctly different knowledge profiles that the agent can access based on the selected persona. Instead of storing detailed player information directly in the graph, it captures this knowledge through conversation samples, which is a more natural way that fans would recall information about their team.
-
----
-
-## Implementation Summary & Notes (July 2024)
-
-This task was completed following the steps outlined above, with some refinements and debugging along the way.
-
-**Key Outcomes & Changes:**
-
-1.  **`z_utils/zep_setup.py` Created & Refined:**
-    *   Implemented using the script structure provided in the Appendix.
-    *   Required several debugging iterations to correctly handle Zep SDK exceptions (`NotFoundError` from `zep_cloud.errors` for 404 checks, generic `Exception` for others with added type logging).
-    *   Corrected the `zep.graph.add` call to pass profile data as a JSON string using `json.dumps()`, resolving a `BadRequestError` from the Zep API.
-    *   Script successfully creates/updates users, preloads profile data to the graph, preloads sample chat messages to memory, and generates `z_utils/persona_uuids.json`.
-
-2.  **`gradio_app.py` Modified:**
-    *   Added imports for `json` and `uuid`.
-    *   `AppState` class updated:
-        *   Added `current_persona` and `persona_data` attributes.
-        *   Integrated Zep client initialization (`_initialize_zep`) handling `ZEP_API_KEY` and optional `ZEP_API_URL`.
-        *   Added logic (`_load_persona_data`) to load UUIDs from `persona_uuids.json` with error handling.
-    *   `initialize_chat` refactored: Removed `zep.user.add`, sets default persona state (`current_persona`, `user_id`), generates a new `session_id`, and attempts to create the initial Zep session using the loaded `user_id`.
-    *   `process_message` updated to use `state.zep_client` and `state.session_id` when interacting with Zep memory.
-    *   Added `handle_persona_change` async function to update state (`current_persona`, `user_id`), generate a new `session_id`, create a new Zep session, and return an empty list to clear the Gradio chatbot UI.
-    *   Added `gr.Radio` component (`persona_selector`) to the `create_ui` function.
-    *   Wired UI events:
-        *   `demo.load` calls `initialize_chat` (outputs: `chatbot`, `persona_selector`).
-        *   `persona_selector.change` calls `handle_persona_change` (output: `chatbot`).
-        *   Added an explicit `clear_button` wired to an updated `clear_chat` function which resets components and the persona selector.
-        *   `msg.submit` calls `process_and_respond` which handles agent calls and component updates.
-
-3.  **`gradio_agent.py` Modified:**
-    *   Replaced `langchain_neo4j.Neo4jChatMessageHistory` import with `langchain_community.memory.zep_cloud_memory.ZepCloudChatMessageHistory`.
-    *   Updated `get_memory` function to instantiate `ZepCloudChatMessageHistory`, passing `session_id` and retrieving Zep API key/URL from environment variables. Added basic error handling for missing API key.
-    *   Removed the import `from tools.vector import get_game_summary`.
-    *   Removed the "Game Summary Search" `Tool.from_function` definition from the `tools` list.
-
-4.  **Housekeeping Performed:**
-    *   Original plan to run manual Cypher query was updated.
-    *   Created `z_utils/neo4j_cleanup.py` script using `gradio_graph` to connect to Neo4j.
-    *   Executed `neo4j_cleanup.py` successfully, removing the `embedding` property from :Game nodes.
-    *   Deleted the obsolete `ifx-sandbox/tools/vector.py` file.
-
-5.  **Documentation Updated:**
-    *   Updated `ifx-sandbox/docs/requirements.md` (Feature 0 description, Persona Memory Structure example) and marked Task 1.3 as complete.
-
-**Current Status:** All implementation steps are complete. The application should now support persona selection integrated with Zep memory. Manual testing (Step 7) is required to verify functionality. 

docs/Phase 1/z_Task 1.3 Memory & Persona Implementation_Debug Plan_Attempt 1.md

@@ -1,401 +0,0 @@
-# AI Agent Testing Plan: Task 1.3 Memory & Persona Implementation
-
-## Context
-You are an expert at UI/UX design and software front-end development and architecture. You are allowed to not know an answer. You are allowed to be uncertain. You are allowed to disagree with your task. If any of these things happen, halt your current process and notify the user immediately. You should not hallucinate. If you are unable to remember information, you are allowed to look it up again.
-You are not allowed to hallucinate. You may only use data that exists in the files specified. You are not allowed to create new data if it does not exist in those files.
-You MUST plan extensively before each function call, and reflect extensively on the outcomes of the previous function calls. DO NOT do this entire process by making function calls only, as this can impair your ability to solve the problem and think insightfully.
-When writing code, your focus should be on creating new functionality that builds on the existing code base without breaking things that are already working. If you need to rewrite how existing code works in order to develop a new feature, please check your work carefully, and also pause your work and tell the human user for review before going ahead. We want to avoid software regression as much as possible. 
-WHEN UPDATING EXISTING CODE FILES, PLEASE DO NOT OVERWRITE EXISTING CODE, PLEASE ADD OR MODIFY COMPONENTS TO ALIGN WITH THE NEW FUNCTIONALITY. THIS INCLUDES SMALL DETAILS LIKE FUNCTION ARGUMENTS AND LIBRARY IMPORTS. REGRESSIONS IN THESE AREAS HAVE CAUSED UNNECESSARY DELAYS AND WE WANT TO AVOID THEM GOING FORWARD. 
-When you need to modify existing code (in accordance with the instruction above), please present your recommendation to the user before taking action, and explain your rationale. 
-If the data files and code you need to use as inputs to complete your task do not conform to the structure you expected based on the instructions, please pause your work and ask the human for review and guidance on how to proceed.
-If you have difficulty finding mission critical updates in the codebase (e.g. .env files, data files) ask the user for help in finding the path and directory.
-
-## Objective
-Your objective is to systematically test the implementation of Task 1.3 (Memory & Persona Implementation) as described in `ifx-sandbox/docs/Phase 1/Task 1.3 Memory & Persona Implementation.md`. This involves verifying the UI changes, persona switching logic, Zep memory integration, and ensuring no regressions were introduced. The user will perform the tests in the Gradio application and report the results.
-
-## Instruction Steps
-
-**Prerequisites:**
-1.  The Gradio application (`ifx-sandbox/gradio_app.py`) should be running.
-    *   **Resolution Status:** Completed (App is running in the background).
-    *   **Actions Taken:** Resolved initial errors (file path, theme, NameError) and successfully launched the app using `python gradio_app.py`.
-
-**Test 1: Verify Persona Selector UI**
-1.  **Check UI:** Open the Gradio application URL in a browser.
-2.  **Confirm Component:** Verify that the "Select your 49ers Fan Type:" radio button component is present, with "Casual Fan" and "Super Fan" options visible. Check that "Casual Fan" is selected by default.
-    *   **Resolution Status:** Unresolved.
-    *   **Actions Taken:**
-
-**Test 2: Test "Casual Fan" Persona**
-1.  **Ensure Persona:** Confirm "Casual Fan" is selected.
-2.  **Ask Question:** Enter a question suitable for a casual fan, e.g., "Who's the quarterback for the 49ers?" or "Did the 49ers win their last game?".
-3.  **Evaluate Response:** Check if the response is basic, focusing on high-level information or well-known players/outcomes, consistent with the persona's expected knowledge level (as defined in `zep_setup.py` preloading).
-    *   **Resolution Status:** Unresolved.
-    *   **Actions Taken:**
-
-**Test 3: Test "Super Fan" Persona**
-1.  **Switch Persona:** Select the "Super Fan" radio button.
-2.  **Verify Chat Clear:** Confirm that the chat history displayed in the UI clears immediately after switching the persona. Check terminal logs for confirmation of a new Zep session being created.
-3.  **Ask Question:** Enter a question suitable for a super fan, e.g., "Tell me about the 49ers offensive line strategy", "What were the key defensive plays in the last game?", or "Discuss the impact of the recent draft picks".
-4.  **Evaluate Response:** Check if the response is more detailed, potentially including player specifics, strategic analysis, or historical context, consistent with the "Super Fan" persona's preloaded memory/profile.
-    *   **Resolution Status:** Unresolved.
-    *   **Actions Taken:**
-
-**Test 4: Verify Persona Switching Robustness**
-1.  **Switch Back:** Switch back to "Casual Fan". Confirm chat clears again.
-2.  **Ask Casual Question Again:** Ask another simple question like "Who is the head coach?". Verify the response is appropriate for the Casual Fan persona.
-3.  **Rapid Switching (Optional):** Try switching personas back and forth a few times quickly to check for any state management issues or errors (monitor terminal logs).
-    *   **Resolution Status:** Unresolved.
-    *   **Actions Taken:**
-
-**Test 5: Verify Existing Functionality (Regression Test)**
-1.  **Test Other Tools:** If possible, try interacting with the agent in ways that should trigger other tools (e.g., Player Search, Team Story, Game Recap if applicable).
-2.  **Confirm Behavior:** Verify that these tools still function as expected and that their corresponding UI components (Player Card, Team Story, Game Recap HTML) display correctly when triggered. Ensure the removal of the "Game Summary Search" tool didn't negatively impact other operations.
-    *   **Resolution Status:** Unresolved.
-    *   **Actions Taken:**
-
-## Failure Condition
-If any test consistently fails after attempting minor fixes (if applicable and agreed upon), or if unexpected critical errors occur, halt the testing process and consult with the user on how to proceed with debugging.
-
-## Completion 
-The testing process is complete when all specified tests have been performed, the results have been documented in this file (updating 'Resolution Status' and 'Actions Taken'), and the user confirms the functionality meets the requirements outlined in Task 1.3.
-
-## Challenges / Potential Concerns (from Task 1.3 Doc)
-
-*   **Zep Integration:** Ensuring correct usage of the Zep Cloud SDK, especially across async/sync contexts and correct session handling based on `user_id` and `session_id`.
-*   **Gradio State Management:** Properly handling persona switching and session management in `AppState` and UI callbacks (`handle_persona_change`) to avoid race conditions or context contamination. Verifying chat clearing works reliably.
-*   **Context Differentiation:** Confirming that the agent's responses noticeably differ between personas based on the Zep memory context. This relies on both successful Zep integration *and* effective memory pre-loading via `zep_setup.py`.
-*   **Regression Risks:** Changes to core files (`gradio_app.py`, `gradio_agent.py`) might have broken existing tool functionality or general chat behavior. Removal of the vector tool or Neo4j memory dependency could have unintended consequences.
-*   **Data Dependency:** Test outcomes depend on the successful execution of `zep_setup.py` (creating users, preloading memory) and the availability of the `persona_uuids.json` file. 
-
-## Appendix: Debugging Plan for "MemoryClient.get() got an unexpected keyword argument 'memory_type'" Error
-
-This plan addresses the specific error encountered during testing: `MemoryClient.get() got an unexpected keyword argument 'memory_type'`
-
-### Root Cause Analysis
-The error indicates a mismatch between how the ZepCloudChatMessageHistory is being initialized and what its API actually accepts. The error specifically points to the `memory_type` parameter being passed to a method that doesn't accept it.
-
-### Step-by-Step Debugging Plan
-
-#### Step 1: Fix memory initialization in `gradio_agent.py`
-1. Modify the `get_memory()` function to properly initialize ZepCloudChatMessageHistory
-2. Remove any incompatible parameters (specifically `memory_type`)
-3. Add error handling with appropriate fallbacks
-
-#### Step 2: Test simplified agent without RunnableWithMessageHistory
-1. Create a simplified agent using basic memory components
-2. Test basic chat functionality to isolate memory issues from agent issues
-
-#### Step 3: Update persona handling in `gradio_app.py`
-1. Ensure persona switching correctly initializes new Zep sessions
-2. Verify user_id and session_id are properly passed between components
-
-#### Step 4: Check message format compatibility
-1. Ensure messages sent to Zep API have the correct schema
-2. Fix any format inconsistencies in how messages are stored and retrieved
-
-#### Step 5: Test the complete integration
-1. Test Casual Fan persona functionality
-2. Test Super Fan persona functionality
-3. Test persona switching
-4. Verify persistence of chat history within sessions
-
-### Version Compatibility Check
-- Confirm that installed package versions are compatible
-- Review any recent changes to the Zep Cloud SDK or LangChain that might affect integration
-
-### Fallback Options
-If Zep Cloud integration continues to be problematic:
-1. Temporary fallback to in-memory ChatMessageHistory
-2. Implement a simpler Redis-based memory solution 
-
-## Key Learning: Zep Memory Retrieval Architecture
-
-### Critical Discovery: Session ID vs User ID for Memory Retrieval
-
-The most important discovery during debugging was understanding how Zep's memory architecture works:
-
-1. **Memory Retrieval Uses Session ID, Not User ID:**
-   - According to the Zep documentation, memory retrieval should be done using `session_id` rather than `user_id`
-   - The `memory.get()` method specifically requires a `session_id` parameter
-   - While each user can have multiple sessions, memory is accessed at the session level
-   - The documentation states: "Note that although `memory.get()` only requires a session ID, it is able to return memory derived from any session of that user. The session is just used to determine what's relevant."
-
-2. **Memory Persistence for Personas:**
-   - To maintain persistent memory for different personas, we need to:
-     - Create fixed, persistent session IDs for each persona
-     - Store these session IDs alongside user IDs in our configuration
-     - Use the specific session ID when retrieving memory with `memory.get()`
-     - This ensures each persona maintains its distinct conversation history
-
-3. **Parameter Mismatch with LangChain:**
-   - The `ZepCloudChatMessageHistory` implementation in LangChain was passing parameters to Zep that aren't supported:
-     - `memory_type` parameter was included in the LangChain wrapper but not accepted by the Zep client
-     - Official Zep SDK examples show no such parameter for `memory.get()`
-     - The current error occurs when `memory.messages` is accessed, which internally calls `_get_memory()`, which calls `self.zep_client.memory.get()`
-   - The solution is to remove unsupported parameters from the LangChain initialization
-
-4. **Proper Implementation for Our App:**
-   - We need to modify our implementation to align with Zep's design:
-     - Generate and store fixed session IDs for our personas
-     - Use these IDs in `get_memory()` without any additional problematic parameters
-     - Access chat history by retrieving from the specific session associated with each persona
-
-This understanding fundamentally changes our approach to implementing the persona memory system.
-
-## TO-DO: Zep Memory & Fan Profile Integration Plan
-
-### 1. Update `zep_setup.py` to Associate Fan Profiles with Session IDs
-
-```python
-# file: z_utils/zep_setup.py
-"""
-Create two persona users in Zep Cloud and preload their memory graphs.
-Run once:  python z_utils/zep_setup.py
-"""
-
-import asyncio, os, uuid, json
-from datetime import datetime, timezone
-from dotenv import load_dotenv
-from zep_cloud.client import AsyncZep
-from zep_cloud.types import Message
-from zep_cloud.errors import NotFoundError
-
-# Load environment variables from .env file
-load_dotenv()
-API_KEY = os.getenv("ZEP_API_KEY")
-BASE_URL = os.getenv("ZEP_API_URL")  # optional for self-hosted
-
-if not API_KEY:
-    raise RuntimeError("ZEP_API_KEY missing in environment variables.")
-
-# Initialize AsyncZep client, handling optional base_url
-zep_client_args = {"api_key": API_KEY}
-if BASE_URL:
-    zep_client_args["base_url"] = BASE_URL
-zep = AsyncZep(**zep_client_args)
-
-# -------- persona blue-prints -------------------------------------------------
-PERSONAS = {
-    "Casual Fan": {
-        "user_id": uuid.uuid4().hex,
-        "session_id": uuid.uuid4().hex,  # Add persistent session_id for this persona
-        "email": "[email protected]",
-        "first_name": "Casual",
-        "last_name": "Fan",
-        "metadata": {"fan_type": "casual"},
-        "profile": {
-            "entity_type": "fan_profile",
-            "fan_type": "casual",
-            "motivations": ["feel included", "see spectacular plays"],
-            "behaviour_patterns": ["checks scores Monday", "shares memes"],
-            "knowledge_depth": "surface",
-            "favorite_team": "49ers",
-        },
-        "sample_chat": [
-            ("user",  "Who's the quarterback for the 49ers?"),
-            ("assistant", "It's Brock Purdy, the breakout star of 2023."),
-            ("user",  "Did we win last night?"),
-            ("assistant", "Yes! The Niners beat Seattle 31-17."),
-        ],
-    },
-    "Super Fan": {
-        "user_id": uuid.uuid4().hex,
-        "session_id": uuid.uuid4().hex,  # Add persistent session_id for this persona
-        "email": "[email protected]",
-        "first_name": "Super",
-        "last_name": "Fan",
-        "metadata": {"fan_type": "super"},
-        "profile": {
-            "entity_type": "fan_profile",
-            "fan_type": "super",
-            "motivations": ["understand strategy", "debate roster moves"],
-            "behaviour_patterns": ["reads All-22", "posts long analyses"],
-            "knowledge_depth": "detailed",
-            "favorite_team": "49ers",
-        },
-        "sample_chat": [
-            ("user",  "Break down our outside-zone success rate vs top-10 fronts."),
-            ("assistant", "49ers average 5.6 YPC on outside-zone against top-10 run Ds..."),
-            ("user",  "Any cap room left after Aiyuk's extension?"),
-            ("assistant", "Roughly $1.2 M pre-June-1; expect a McCaffrey restructure."),
-        ],
-    },
-}
-
-# -------- helper --------------------------------------------------------------
-async def create_user_if_needed(p):
-    """Creates a Zep user if they don't already exist."""
-    user_id = p["user_id"]
-    try:
-        await zep.user.get(user_id=user_id)
-        print(f"User '{user_id}' already exists. Skipping creation.")
-    except NotFoundError:
-        # User not found, safe to create
-        try:
-            await zep.user.add(
-                user_id=user_id,
-                email=p["email"],
-                first_name=p["first_name"],
-                last_name=p["last_name"],
-                metadata=p["metadata"],
-            )
-            print(f"Successfully created user '{user_id}'.")
-        except Exception as add_ex:
-            print(f"Error creating user '{user_id}'. Type: {type(add_ex).__name__}, Details: {add_ex}")
-    except Exception as ex:
-        print(f"Error checking user '{user_id}'. Type: {type(ex).__name__}, Details: {ex}")
-
-
-async def preload(p):
-    """Preloads user profile graph data and sample chat memory."""
-    user_id = p["user_id"]
-    # Use the persistent session_id for this persona
-    session_id = p["session_id"]
-    
-    print(f"Preloading data for user '{user_id}' with session '{session_id}'...")
-
-    # Preload fan profile using Zep Graph API
-    try:
-        await zep.graph.add(
-            user_id=user_id,
-            type="json",
-            data=json.dumps(p["profile"]),
-        )
-        print(f" - Added profile graph data for user '{user_id}'.")
-    except Exception as graph_ex:
-        print(f" - Error adding profile graph data for user '{user_id}'. Type: {type(graph_ex).__name__}, Details: {graph_ex}")
-
-    # Create a persistent session specifically for this persona
-    try:
-        await zep.memory.add_session(session_id=session_id, user_id=user_id)
-        print(f" - Created persistent session '{session_id}' for user '{user_id}'.")
-
-        msgs = [
-            Message(role=role, content=text, role_type=role) # Assuming role maps directly to role_type
-            for role, text in p["sample_chat"]
-        ]
-        await zep.memory.add(session_id=session_id, messages=msgs)
-        print(f" - Added {len(msgs)} sample messages to session '{session_id}'.")
-    except Exception as mem_ex:
-        print(f" - Error preloading memory for user '{user_id}' (session '{session_id}'). Type: {type(mem_ex).__name__}, Details: {mem_ex}")
-
-
-# -------- main ----------------------------------------------------------------
-async def main():
-    """Main function to create users, preload data, and save UUIDs."""
-    print("Starting Zep setup process...")
-    for name, persona in PERSONAS.items():
-        print(f"\nProcessing Persona: {name}")
-        await create_user_if_needed(persona)
-        await preload(persona)
-
-    # Persist generated UUIDs and session IDs for the Gradio app
-    output_dir = "ifx-sandbox/z_utils"
-    output_file = os.path.join(output_dir, "persona_uuids.json")
-    try:
-        os.makedirs(output_dir, exist_ok=True)
-        with open(output_file, "w") as f:
-            # Include both user_id and session_id in the JSON file
-            json.dump({k: {"user_id": v["user_id"], "session_id": v["session_id"]} for k, v in PERSONAS.items()}, f, indent=2)
-        print(f"\nSuccessfully saved persona data to {output_file}")
-    except IOError as e:
-        print(f"\nError saving persona data to {output_file}: {e}")
-    except Exception as ex:
-        print(f"\nUnexpected error saving persona data: {ex}")
-
-
-    print("\n--- Generated Persona Data ---")
-    for name, p in PERSONAS.items():
-        print(f"{name}: user_id={p['user_id']}, session_id={p['session_id']}")
-    print("-----------------------------")
-    print("Zep setup process complete.")
-
-if __name__ == "__main__":
-    # Ensure the script is run from the workspace root or adjust paths accordingly
-    # Basic check: Does 'ifx-sandbox' exist in the current directory?
-    if not os.path.isdir("ifx-sandbox"):
-         print("Warning: This script assumes it's run from the workspace root directory")
-         print("containing 'ifx-sandbox'. If running from elsewhere, paths might be incorrect.")
-
-    asyncio.run(main())
-```
-
-### 2. TO-DO: Update `gradio_app.py` to Handle New Session IDs 
-
-```python
-# Update AppState to load both user_id and session_id
-def _load_persona_data(self):
-    """Load persona UUID mappings from file."""
-    persona_file = os.path.join(os.path.dirname(__file__), 'z_utils', 'persona_uuids.json')
-    try:
-        with open(persona_file, 'r') as f:
-            self.persona_data = json.load(f)
-        print(f"Loaded persona UUIDs: {self.persona_data}")
-    except Exception as e:
-        print(f"Error loading persona data: {e}")
-        self.persona_data = {
-            "Casual Fan": {"user_id": uuid.uuid4().hex, "session_id": uuid.uuid4().hex},
-            "Super Fan": {"user_id": uuid.uuid4().hex, "session_id": uuid.uuid4().hex}
-        }
-        print(f"Using fallback persona data: {self.persona_data}")
-```
-
-```python
-# Update handle_persona_change to use the saved session_id instead of creating a new one
-async def handle_persona_change(persona_name, state: AppState):
-    """Handle persona switch: update state and create new Zep session."""
-    try:
-        print(f"Handling persona change to: {persona_name}")
-        state.current_persona = persona_name
-        
-        # Use the user_id associated with this persona
-        if state.persona_data and persona_name in state.persona_data:
-            persona_info = state.persona_data[persona_name]
-            state.user_id = persona_info["user_id"]
-            state.session_id = persona_info["session_id"]  # Use the persistent session_id
-            
-            print(f"Using persistent session for persona '{persona_name}'. User ID: {state.user_id}, Session ID: {state.session_id}")
-        else:
-            # Fallback if persona data not available
-            state.user_id = str(uuid.uuid4())
-            state.session_id = str(uuid.uuid4())
-            print(f"Creating new user/session IDs. User ID: {state.user_id}, Session ID: {state.session_id}")
-        
-        return []  # Return empty list to clear chatbot
-    except Exception as e:
-        print(f"Error in handle_persona_change: {e}")
-        return []
-```
-
-### 3. TO-DO: Fix `gradio_agent.py` ZepCloudChatMessageHistory Implementation
-
-```python
-# Update imports to use the correct path for ZepCloudChatMessageHistory
-from langchain_community.chat_message_histories.zep_cloud import ZepCloudChatMessageHistory
-
-def get_memory(session_id):
-    """Get memory for a specific session, or create a new one if it doesn't exist"""
-    # First check if ZEP API key is available
-    zep_api_key = os.environ.get("ZEP_API_KEY")
-    
-    if zep_api_key:
-        try:
-            # Use Zep Cloud for memory storage with only required parameters
-            message_history = ZepCloudChatMessageHistory(
-                session_id=session_id,
-                api_key=zep_api_key
-            )
-            print(f"Using ZepCloudChatMessageHistory for session {session_id}")
-            return message_history
-        except Exception as e:
-            print(f"Error initializing ZepCloudChatMessageHistory: {e}")
-            print("Falling back to in-memory ChatMessageHistory")
-    else:
-        print("ZEP_API_KEY not found in environment variables")
-        print("Falling back to in-memory ChatMessageHistory")
-    
-    # Fallback to in-memory chat history
-    from langchain_community.chat_message_histories import ChatMessageHistory
-    return ChatMessageHistory()
-```

docs/Phase 1/z_Task 1.3 Memory & Persona Implementation_Debug Plan_Attempt 2.md

@@ -1,98 +0,0 @@
-# Task 1.3 Memory & Persona Implementation - Debug Plan
-
-This document tracks the step-by-step implementation and debugging process for Task 1.3, integrating Zep Memory and Persona selection into the 49ers FanAI Hub, following a revised plan based on codebase review and Zep documentation analysis.
-
-**Important Design Intent:** The goal is to leverage Zep Cloud to load pre-defined knowledge graphs associated with specific user personas ("Casual Fan", "Super Fan") based on the user's selection in the UI. This pre-loaded knowledge will provide long-term context for the agent. However, the messages exchanged during a specific Gradio chat session are intended to be **ephemeral** regarding Zep's persistent storage. That is, user inputs and assistant responses **should NOT be added back** to the Zep user's memory graph or session history using `memory.add`. The Zep integration focuses *only* on retrieving relevant context from the pre-loaded persona graphs.
-
-## Revised Implementation Steps (Updated Order)
-
-1.  **Implement Zep User & Memory Setup Script (`z_utils/zep_setup.py`)**
-    *   [X] Create `z_utils/zep_setup.py`.
-    *   [X] Load `ZEP_API_KEY`, `ZEP_API_URL` (optional).
-    *   [X] Initialize `AsyncZep` client.
-    *   [X] Define `PERSONAS` dictionary.
-    *   [X] Generate fixed UUIDs for each persona.
-    *   [X] Save UUIDs to `z_utils/persona_uuids.json`.
-    *   [X] Implement `async def create_zep_user(client, user_data)` (check existence first).
-    *   [X] Define persona-specific knowledge (text/JSON suitable for `graph.add`).
-    *   [X] Implement `async def preload_knowledge(client, user_id, persona_knowledge)` using `client.graph.add`. (Renamed from preload_memory)
-    *   [X] Implement `async def main()` to orchestrate user creation, knowledge pre-loading, and UUID saving.
-    *   [X] Add `if __name__ == "__main__": asyncio.run(main())`.
-    *   **Status:** Completed
-
-2.  **Refactor ID Management & AppState (`gradio_utils.py`, `gradio_app.py`)**
-    *   [X] Modify `gradio_utils.py`: Remove global ID state, create generator functions.
-        *   **Details:** Removed the global variables `_session_id` and `_user_id`. Removed the `reset_ids` function. Renamed existing internal functions to `generate_session_id()` and `generate_user_id()`, making them simple wrappers around `str(uuid.uuid4())` to provide unique IDs on demand without relying on global state.
-    *   [X] Modify `gradio_app.py`:
-        *   [X] Enhance `AppState` (add `current_persona`, `persona_data`, ensure `user_id`, `session_id` are instance vars).
-            *   **Details:** Added `current_persona = DEFAULT_PERSONA` and `persona_data = None` as instance attributes to the `AppState` class `__init__` method. Confirmed `user_id` and `session_id` were already instance attributes (initialized to `None`). Added helper methods `_initialize_zep` (to create the `AsyncZep` client using environment variables `ZEP_API_KEY`/`ZEP_API_URL` with error handling) and `_load_persona_data` (to load the `z_utils/persona_uuids.json` file into the `self.persona_data` attribute with error handling for file not found or JSON parsing issues).
-        *   [X] Load persona UUIDs from JSON into `AppState.persona_data`.
-            *   **Details:** This is handled within the `_load_persona_data` method called by `initialize_chat`. It opens `z_utils/persona_uuids.json`, uses `json.load()` to parse it, and stores the resulting dictionary in `current_state.persona_data`.
-        *   [X] Update `initialize_chat`: Set default persona, generate initial `user_id` (from loaded data) & `session_id`, store in `state`, create initial Zep session.
-            *   **Details:** This async function is triggered by `demo.load`. It first calls `_initialize_zep` and `_load_persona_data`. It then sets `current_state.current_persona` to `DEFAULT_PERSONA`. It retrieves the corresponding `user_id` from `current_state.persona_data` and stores it in `current_state.user_id`. It calls `generate_session_id()` (from `gradio_utils`) and stores the result in `current_state.session_id`. Finally, if the Zep client is ready and a `user_id` was successfully loaded, it calls `await current_state.zep_client.memory.add_session(...)` using the generated `session_id` and loaded `user_id` to establish the initial session context in Zep Cloud. The original logic involving `zep.user.add` was removed as user creation is now handled by the separate `zep_setup.py` script.
-        *   [X] Update `process_message`: Use `state.session_id`, `state.zep_client`.
-            *   **Details:** Modified the `process_message` function (which handles the core agent interaction logic) to retrieve the Zep client (`current_state.zep_client`) and the current `session_id` (`current_state.session_id`) directly from the `AppState` object passed into it. This ensures it uses the correct context set by `initialize_chat` or `handle_persona_change`. **Crucially**, the calls to `await current_state.zep_client.memory.add(...)` for both user and assistant messages within this function were **commented out** to implement the design intent of keeping the Gradio chat ephemeral and *not* persistently storing conversation history back into the Zep memory for the selected persona.
-        *   [X] Add `handle_persona_change` async function: Update `state` (`current_persona`, `user_id`), generate *new* `session_id`, store in `state`, create new Zep session, clear UI.
-            *   **Details:** Created a new async function `handle_persona_change` designed to be triggered by the `persona_selector.change` event. It receives the selected `persona_name` and the `current_state`. It looks up the corresponding `new_user_id` from `current_state.persona_data`. It updates `current_state.current_persona` and `current_state.user_id`. It calls `generate_session_id()` to get a *new* session ID for the new persona context and stores it in `current_state.session_id`. If the Zep client is available, it calls `await current_state.zep_client.memory.add_session(...)` with the new `session_id` and `user_id`. It also clears internal caches used by tools (like `game_recap.last_game_data`) and returns `[]` to clear the Gradio chatbot UI.
-        *   [X] Add Persona Selector UI (`gr.Radio`).
-            *   **Details:** Added a `gr.Radio` component named `persona_selector` within the `create_ui` function. Configured with `choices=["Casual Fan", "Super Fan"]`, a label, the `DEFAULT_PERSONA` as the initial value, and `interactive=True`.
-        *   [X] Wire UI events (`demo.load`, `persona_selector.change`, `msg.submit`, `clear_button`).
-            *   **Details:** Configured the event listeners within `create_ui`:
-                *   `demo.load` was wired to call `initialize_chat`, passing the initial `gr.State(state)` and updating the `chatbot` and `persona_selector` components (and later, `gr.State(state)` itself during debugging attempts).
-                *   `persona_selector.change` was wired to call `handle_persona_change`, passing the `persona_selector` value and `gr.State(state)`, updating the `chatbot` component.
-                *   `msg.submit` was initially wired using `.then()` chaining involving `user_input` and `process_and_respond`. During debugging, this was simplified to directly call `process_and_respond`, passing `msg`, `chatbot`, and `gr.State(state)` (later removed/re-added `gr.State` during debugging) as inputs, and updating `msg`, `chatbot`, and the dynamic info components.
-                *   `clear_button.click` was wired to call `clear_chat`, passing `gr.State(state)` and updating various UI components including `persona_selector` back to default.
-    *   **Status:** Completed
-
-3.  **Integrate Zep Memory into Agent (`gradio_agent.py`)**
-    *   [X] Replace `Neo4jChatMessageHistory` import with `ZepCloudChatMessageHistory`.
-        *   **Details:** Changed the import statement from `from langchain_neo4j import Neo4jChatMessageHistory` to `from langchain_community.memory.zep_cloud_memory import ZepCloudChatMessageHistory`.
-    *   [X] Update `get_memory` function to instantiate `ZepCloudChatMessageHistory` using `session_id` argument and Zep credentials from env vars. Add error handling for missing key / Zep init failure (fallback to basic history).
-        *   **Details:** Rewritten the `get_memory(session_id)` function. It now retrieves `ZEP_API_KEY` and optionally `ZEP_API_URL` from environment variables. It includes a check: if `ZEP_API_KEY` is missing, it prints an error and returns a basic fallback `langchain.memory.ChatMessageHistory()`. Otherwise, it constructs the arguments dictionary (`session_id`, `api_key`, optional `url`) and instantiates `ZepCloudChatMessageHistory(**history_args)`. A `try...except` block was added around the instantiation to catch potential Zep client initialization errors, also falling back to `ChatMessageHistory` if an exception occurs. This ensures the agent receives a valid memory object, albeit potentially a non-persistent one if Zep connection fails.
-    *   **Status:** Completed
-
-4.  **Perform Housekeeping Tasks** (Already Completed)
-    *   [X] Create `z_utils/neo4j_cleanup.py` script using `gradio_graph`.
-    *   [X] Add Cypher query `MATCH (g:Game) WHERE g.embedding IS NOT NULL REMOVE g.embedding RETURN count(*)` to script.
-    *   [X] Add execution logic and logging/print statements to `neo4j_cleanup.py`.
-    *   [X] *Action:* Run `python ifx-sandbox/z_utils/neo4j_cleanup.py` (User needs to run this).
-    *   [X] Delete `ifx-sandbox/tools/vector.py`.
-    *   [X] Remove "Game Summary Search" tool (`get_game_summary`) import and definition from `tools` list in `gradio_agent.py`. (Verified Complete)
-    *   **Status:** Completed (Pre-existing / Verified)
-
-5.  **Update Documentation (`docs/requirements.md`)**
-    *   [X] Update "Persona Memory Structure (Zep)" section with actual implementation details.
-    *   [X] Mark Feature 0 (Persona Selection) as completed.
-    *   [X] Update Task 1.3 Description/Status in Detailed Work Plan.
-    *   **Status:** Completed
-
-## Execution Log & Review Points
-
-*   **2024-07-27:** Created debug plan file.
-*   **2024-07-27:** Refactored `gradio_utils.py` to remove global state and use ID generator functions.
-*   **2024-07-27:** Updated plan order: Step 2 (`zep_setup.py`) moved to first, Step 4 marked as completed.
-*   **2024-07-27:** Implemented `zep_setup.py`, fixed import errors, fixed async context manager issue, fixed UUID loading/saving logic. Script executed successfully, creating users and preloading knowledge.
-*   **2024-07-27:** Modified `gradio_app.py`: Enhanced `AppState`, added persona loading/handling, integrated UI selector, updated initialization/message processing, wired events.
-*   **2024-07-27:** Clarified design intent: Zep context retrieval only; Gradio chat messages are ephemeral, not added back to Zep via `memory.add`.
-*   **2024-07-27:** Modified `gradio_app.py` again: Commented out `zep_client.memory.add` calls in `process_message` to implement ephemeral chat regarding Zep persistence.
-*   **2024-07-27:** Modified `gradio_agent.py`: Replaced Neo4j memory with `ZepCloudChatMessageHistory`, updated `get_memory` function with env var loading and error handling/fallback, ensured `session_id` is passed correctly to agent invocation.
-*   **2024-07-27:** Verified Step 4 Housekeeping: Confirmed removal of vector tool import and definition in `gradio_agent.py`.
-*   **2024-07-27:** Updated `docs/requirements.md` to reflect completed implementation details for Feature 0/Task 1.3 and marked as complete.
-
-*(This section will be updated after each step)*
-
-**Implementation Complete.**
-
----
-
-## Debugging Log (Post-Implementation)
-
-**2024-07-28:** Began testing the implementation.
-
-*   **Issue 1: Startup Error - `Attempted to process message before state initialization.`**
-    *   **Observation:** Error occurred immediately after `Application state initialized.` log during startup.
-    *   **Root Cause:** The `initialize_chat` function completed its setup but failed to set the `state.initialized` flag to `True` before returning.
-    *   **Fix 1:** Added `current_state.initialized = True` before the return statement in `initialize_chat`. -> *Failed to resolve fully.*
-
-*   **Issue 2: Startup Error Persists / State Propagation Delay**
-    *   **Observation:** After Fix 1, the same error occurred, originating from `process_and_respond` being called with an empty message immediately after initialization. Debug logs showed `process_and_respond` received a state object where `initialized`

docs/requirements.md

@@ -231,7 +231,7 @@ Based on a review of the existing codebase and requirements, here's a structured
 | **1.2.1 ✅ Game Recap Search (WIP)** | Returning queries about the a specific game, display through a multi-media component in the UI *(Backend logic implemented, visual component integration pending)* | 1.1 |
 | **1.2.2 ✅ Player Search (WIP)** | Return queries about the player using a multi-media component *(Backend logic implemented, visual component integration pending)* | 1.1 |
 | **1.2.3 ✅ Team Info Search** | Scraped, summarized, and stored recent team news articles in Neo4j; implemented a Gradio tool and component to query and display relevant news summaries and links in the chat. | 1.1 |
-| **1.3 Develop memory system and UI integration with Zep** | Implement persona-based memory system with Zep | None |
+| **1.3 ✅ Develop memory system and UI integration with Zep** | Implement persona-based memory system with Zep | None |
 
 **Demo 1 Milestone:** April 22
 

gradio_agent.py

@@ -23,7 +23,6 @@ from gradio_utils import get_session_id
 
 # Import tools
 from tools.cypher import cypher_qa_wrapper
-from tools.vector import get_game_summary
 from tools.game_recap import game_recap_qa, get_last_game_data
 from tools.player_search import player_search_qa, get_last_player_data
 from tools.team_story import team_story_qa, get_last_team_story_data
@@ -115,13 +114,6 @@ Returns both a text summary AND visual game data that can be displayed to the us
 PREFER this tool over Game Summary Search or Graph Search for specific game detail requests.""",
         func=game_recap_qa
     ),
-    Tool.from_function(
-        name="Game Summary Search",
-        description="""ONLY use for detailed game summaries or specific match results if the 'Game Recap' tool fails or doesn't provide enough detail.
-Examples: "Summarize the 49ers vs Seahawks game", "Give me details about the last playoff game results"
-Do NOT use for general schedule questions.""",
-        func=get_game_summary,
-    ),
     Tool.from_function(
         name="General Football Chat",
         description="""ONLY use for general football discussion NOT specific to the 49ers team, players, or games.

tools/vector.py

@@ -1,70 +0,0 @@
-import sys
-import os
-# Add parent directory to path to access gradio modules
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from gradio_llm import llm, embeddings
-from gradio_graph import graph
-
-# Create the Neo4jVector
-from langchain_neo4j import Neo4jVector
-
-neo4jvector = Neo4jVector.from_existing_index(
-    embeddings,                              # (1)
-    graph=graph,                             # (2)
-    index_name="gameSummary",                 # (3)
-    node_label="Game",                      # (4)
-    text_node_property="summary",               # (5)
-    embedding_node_property="embedding", # (6)
-    retrieval_query="""
-RETURN
-    node.summary AS text,
-    score,
-    {
-        id: node.id,
-        date: node.date,
-        result: node.result,
-        location: node.location,
-        home_team: node.home_team,
-        away_team: node.away_team,
-        game_id: node.game_id
-    } AS metadata
-"""
-)
-
-# Create the retriever
-retriever = neo4jvector.as_retriever()
-
-# Create the prompt
-from langchain_core.prompts import ChatPromptTemplate
-
-instructions = (
-    "Use the given context to answer the question."
-    "If you don't know the answer, say you don't know."
-    "Context: {context}"
-)
-
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", instructions),
-        ("human", "{input}"),
-    ]
-)
-
-# Create the chain 
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain.chains import create_retrieval_chain
-
-question_answer_chain = create_stuff_documents_chain(llm, prompt)
-game_summary_retriever = create_retrieval_chain(
-    retriever, 
-    question_answer_chain
-)
-
-# Create a function to call the chain
-def get_game_summary(input_text):
-    """Function to call the chain with error handling"""
-    try:
-        return game_summary_retriever.invoke({"input": input_text})
-    except Exception as e:
-        print(f"Error in get_game_summary: {str(e)}")
-        return {"output": "I apologize, but I encountered an error while searching for game summaries. Could you please rephrase your question?"}