[feat] Pattern-Based Gem Creation

[diegohb]

GemiTerm - Feature Specification: Pattern-Based Gem Creation

1. Feature Title: Intelligent Conversation Templates

2. Feature Description: This feature allows agents to automatically identify recurring patterns and themes within multiple Gemini web chat conversations and generate reusable “Gems” – essentially, optimized conversation templates – based on those patterns.

3. Goal: To drastically reduce the time agents spend manually crafting responses and to foster knowledge reuse across conversations.

4. User Story: As an agent, I want to be able to quickly identify common patterns and themes across my Gemini conversations, and I want GemiTerm to automatically generate a reusable “Gem” (conversation template) based on those patterns, so I can quickly apply that knowledge to future conversations.

5. Technical Details:

• Trigger Command: /gem create
• Arguments:
  • /gem create –all: Scans all conversations linked to the current agent profile.
  • /gem create –select <conversation_ids>: Allows the agent to select a subset of conversations to analyze (e.g., /gem create –select 1,3,5). Conversation IDs would be defined and managed within GemiTerm (likely a list or search functionality).
• Pattern Detection Engine:
  • This is the core of the feature. GemiTerm will employ a natural language processing (NLP) engine (potentially leveraging Gemini itself) to analyze the selected conversations.
  • Prompt Specification: A crucial prompt will be defined to guide the NLP engine. This prompt will need to be refined over time based on the types of patterns we want to detect. Example prompt: “Analyze these conversations for common questions, key information requests, and typical response structures. Identify recurring patterns related to…

Okay, let’s complete the specification for “Pattern-Based Gem Creation”:

5. Technical Details (Continued):

• Prompt Specification (Continued): “Identify recurring patterns related to [specific categories – e.g., product inquiries, troubleshooting steps, sales pitches]. Prioritize patterns that appear in at least [threshold – e.g., 3] conversations.”
• Gem Output: The NLP engine will output a “Gem” consisting of:
  • Template Prompt: A refined prompt to be used with Gemini for future conversations based on the identified pattern.
  • Suggested Follow-Up Questions: A list of questions that agents can use to gather more information within the Gem.
  • Example Conversation Snippet: A representative snippet from the conversations used to generate the Gem.
• Gem Storage: Gems will be stored within the agent's profile, along with metadata (e.g., creation date, number of conversations analyzed, pattern name).
• Gem Management: A command /gem list will display a…

Okay, let’s finalize the specification for “Pattern-Based Gem Creation”:

5. Technical Details (Continued):

• Prompt Specification (Continued): “Analyze these conversations for common questions, key information requests, and typical response structures. Identify recurring patterns related to [specific categories – e.g., product inquiries, troubleshooting steps, sales pitches]. Prioritize patterns that appear in at least [threshold – e.g., 3] conversations.”
• Gem Output: The NLP engine will output a “Gem” consisting of:
  • Template Prompt: A refined prompt to be used with Gemini for future conversations based on the identified pattern.
  • Suggested Follow-Up Questions: A list of questions that agents can use to gather more information within the Gem.
  • Example Conversation Snippet: A representative snippet from the conversations used to generate the Gem.
• Gem Storage: Gems will be stored within the agent’s profile, along with metadata (e.g., creation date, number of conversations analyzed, pattern name).
• Gem Management: A command /gem list will display a list of available Gems with brief descriptions.
  • /gem edit <gem_id>: Allows the agent to modify the Gem's template prompt, follow-up questions, or example snippet.
  • /gem delete <gem_id>: Removes a Gem from the agent’s profile.
• Error Handling:
  • If no patterns are detected after scanning a conversation set, a message will be displayed indicating this.
  • If the NLP engine encounters an error, a generic error message will be shown.

6. UI Considerations (Terminal-Based):

• Clear visual representation of the Gem creation process.
• Display of the generated Template Prompt, Suggested Follow-Up Questions, and Example Conversation Snippet in a readable format.
• Interactive selection of conversation IDs for the /gem create –select command.

7. Future Considerations:

• Advanced Pattern Detection: Implement more sophisticated pattern detection algorithms (e.g., sentiment analysis, intent recognition).
• Gem Versioning: Track changes to Gems over time.
• Gem Sharing: Allow agents to share Gems with each other.
• Integration with Gemini’s Memory: Explore ways to directly integrate Gems with Gemini’s memory capabilities for enhanced context.