Liss, Alex (NYC-HUG) commited on
Commit
3ab0381
·
1 Parent(s): 79a1c17

brought fan memory into applicaiton! now just need to use it :smile:

Browse files
docs/Phase 1/Task 1.3 Memory & Persona Implementation.md CHANGED
@@ -83,6 +83,29 @@ The user will execute **one step at a time** and confirm each works before proce
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  3. **Hard‑code** one session‑ID first to verify import works inside the agent.
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  4. Run the app. Ensure chat history loads without breaking existing features.
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  ---
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  ### 4 │ Add Radio Button (Skeleton Only)
@@ -138,6 +161,7 @@ The user will execute **one step at a time** and confirm each works before proce
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  | 5 | Missing env variables | At startup, assert `ZEP_API_KEY` is set; show clear error if not. |
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  | 6 | Session ID mismatch | Verify that session IDs in code match those actually created in Zep Cloud. |
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  | 7 | Message history creation | Ensure messages follow proper format for Zep; implement fallbacks if message history retrieval fails. |
 
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  ---
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  3. **Hard‑code** one session‑ID first to verify import works inside the agent.
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  4. Run the app. Ensure chat history loads without breaking existing features.
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+ **Status Update:**
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+ ✅ Successfully imported Zep libraries and dependencies in `gradio_agent.py`.
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+ ✅ Updated the `get_memory` function to use `ZepCloudChatMessageHistory` with a hardcoded session ID.
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+ ❌ Encountered an error with `MemoryClient.get()` related to an unexpected `memory_type` parameter.
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+ ✅ Created a workaround approach that:
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+ - Removes the problematic `memory_type` parameter
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+ - Uses a new `initialize_memory_from_zep` function to directly load chat history from Zep
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+ - Uses the Zep client directly to retrieve message history using the hardcoded session ID
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+ - Converts Zep messages to LangChain format
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+ - Initializes a `ConversationBufferMemory` with this history
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+ - Provides this memory to the agent executor for each session
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+ ✅ Fixed import compatibility issues between different versions of LangChain
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+ ✅ Successfully retrieving conversation history from Zep! Terminal output confirms:
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+ - "Loading 6 messages from Zep for Casual Fan persona"
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+ - "Successfully loaded message history from Zep"
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+ ✅ Agent now has access to the pre-existing context in Zep, and the application works without errors
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+
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+ **TODO:**
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+ - Implement persona-specific behavior based on user context
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+ - Currently we're loading conversation history successfully, but the agent's responses aren't explicitly personalized based on the casual/super fan persona context
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+ - We should update agent system prompts to explicitly use facts from Zep memory when responding to questions
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+ - This will be addressed after the initial implementation is complete
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+
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  ---
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  ### 4 │ Add Radio Button (Skeleton Only)
 
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  | 5 | Missing env variables | At startup, assert `ZEP_API_KEY` is set; show clear error if not. |
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  | 6 | Session ID mismatch | Verify that session IDs in code match those actually created in Zep Cloud. |
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  | 7 | Message history creation | Ensure messages follow proper format for Zep; implement fallbacks if message history retrieval fails. |
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+ | 8 | Library compatibility issues | Use direct API calls to workaround parameter inconsistencies; maintain fallbacks for memory initialization to avoid breaking the application when parameters change. |
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  ---
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gradio_agent.py CHANGED
@@ -131,13 +131,13 @@ Do NOT use for any 49ers-specific questions.""",
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  )
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  ]
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- session_id = "241b3478c7634492abee9f178b5341cb"
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  # Create the memory manager
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  def get_memory(session_id):
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  """Get the chat history from Zep for the given session"""
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  return ZepCloudChatMessageHistory(
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- session_id=session_id,
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  api_key=os.environ.get("ZEP_API_KEY")
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  # No memory_type parameter
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  )
@@ -169,7 +169,8 @@ def initialize_memory_from_zep(session_id):
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  try:
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  # Get history from Zep
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  zep = Zep(api_key=os.environ.get("ZEP_API_KEY"))
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- memory = zep.memory.get(session_id=session_id)
 
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  # Create a conversation memory with the history
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  conversation_memory = ConversationBufferMemory(
@@ -178,7 +179,7 @@ def initialize_memory_from_zep(session_id):
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  )
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  if memory and memory.messages:
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- print(f"Loading {len(memory.messages)} messages from Zep for session {session_id}")
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  # Add messages to the conversation memory
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  for msg in memory.messages:
 
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  )
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  ]
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+ memory_session_id = "241b3478c7634492abee9f178b5341cb"
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  # Create the memory manager
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  def get_memory(session_id):
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  """Get the chat history from Zep for the given session"""
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  return ZepCloudChatMessageHistory(
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+ session_id=memory_session_id,
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  api_key=os.environ.get("ZEP_API_KEY")
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  # No memory_type parameter
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  )
 
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  try:
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  # Get history from Zep
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  zep = Zep(api_key=os.environ.get("ZEP_API_KEY"))
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+ print(f"Attempting to get memory for hardcoded session ID: {memory_session_id}")
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+ memory = zep.memory.get(session_id=memory_session_id)
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  # Create a conversation memory with the history
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  conversation_memory = ConversationBufferMemory(
 
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  )
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  if memory and memory.messages:
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+ print(f"Loading {len(memory.messages)} messages from Zep for Casual Fan persona")
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  # Add messages to the conversation memory
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  for msg in memory.messages: