Add initial project setup with environment variables, server logic, and memory handling

supabase/functions/llm-pipeline/prompts.xml

@@ -104,6 +104,14 @@
         - If user explicitly says something changed/ended, DELETE old memory
         - Don't create duplicates—check existing memories first
 
+        FORGET REQUESTS:
+        If the user explicitly asks to forget something (e.g., "forget that", "don't remember that", "forget about X"), you must:
+        1. Identify which existing memories match what they want forgotten
+        2. Use DELETE action for each matching memory
+        3. Be specific in the "reason" field about what the user requested
+        4. If the request is vague ("forget that"), use context from recent messages to identify what "that" refers to
+        5. If unclear what to forget, DELETE nothing and explain in the "reason" field
+
         TAGGING GUIDELINES:
         You will be provided with existing tags in the reference data section.
         - **Reuse existing tags whenever possible** to maintain consistency
@@ -159,12 +167,29 @@
         }
 
         EXTRACTION THOROUGHNESS:
-        - Rich sources (long messages, reports) should yield 20-50+ changes
-        - Don't self-limit; extract ALL atomic facts
-        - Err on the side of over-extraction rather than under-extraction
-        - Each paragraph of substantial content likely contains multiple extractable facts
+        CRITICAL: You MUST extract EVERY SINGLE atomic fact from the user's messages.
 
-        BE PRECISE. BE THOROUGH. BE ATOMIC.
+        - A detailed personal report should yield 100-200+ separate memories
+        - Each sentence typically contains 2-5 extractable atomic facts
+        - Break down EVERY detail: demographics, preferences, relationships, experiences, skills, beliefs, habits, feelings, goals, challenges
+        - If you can answer "who, what, when, where, why, how" from a statement, those are separate facts
+        - DO NOT SUMMARIZE - extract each detail as its own memory
+        - DO NOT LIMIT YOURSELF - there is no maximum number of memories
+        - Over-extraction is REQUIRED, not optional
+        - Under-extraction means losing valuable information about the user
+
+        Example of proper extraction density:
+        Input: "I'm a 28-year-old software engineer at Google in NYC, working on search algorithms"
+        Should extract AT LEAST:
+        1. User is 28 years old
+        2. User works as a software engineer
+        3. User works at Google
+        4. User is located in NYC
+        5. User works on search algorithms
+        6. User works in the tech industry
+        7. User has experience with algorithms
+
+        BE PRECISE. BE THOROUGH. BE ATOMIC. EXTRACT EVERYTHING.
         Extract every distinct, useful fact about the user from their conversation messages - ignore the reference data section completely.
     </memory_extraction>