Update file permissions and add documentation for LLM integration

cline_docs/productContext.md

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+# Product Context
+Last Updated: 2024-12-27
+
+## Why we're building this
+- To create an engaging Discord bot that learns from and interacts with server conversations
+- To provide natural, contextually relevant responses using both Markov chains and LLM capabilities
+- To maintain conversation history and generate responses that feel authentic to each server's culture
+
+## Core user problems/solutions
+Problems:
+- Current Markov responses can be incoherent or lack context
+- No semantic understanding of conversation context
+- Limited ability to generate coherent long-form responses
+
+Solutions:
+- Integrate LLM to enhance response quality while maintaining server-specific voice
+- Use existing message database for both Markov and LLM training
+- Combine Markov's randomness with LLM's coherence
+
+## Key workflows
+1. Message Collection
+   - Listen to channels
+   - Store messages in SQLite
+   - Track message context and metadata
+
+2. Response Generation
+   - Current: Markov chain generation
+   - Proposed: Hybrid Markov-LLM generation
+   - Context-aware responses
+
+3. Training
+   - Batch processing of channel history
+   - JSON import support
+   - Continuous learning from new messages
+
+## Product direction and priorities
+1. Short term
+   - Implement LLM integration for response generation
+   - Maintain existing Markov functionality as fallback
+   - Add context window for more relevant responses
+
+2. Medium term
+   - Fine-tune LLM on server-specific data
+   - Implement response quality metrics
+   - Add conversation memory
+
+3. Long term
+   - Advanced context understanding
+   - Personality adaptation per server
+   - Multi-modal response capabilities