ne írj semmi mást csak a teljes fájlokat es kommentek nem le...

Prompt

ne írj semmi mást csak a teljes fájlokat es kommentek nem lehetnek benne! soha semmi egyszerusitett mock placeholder dummy szimulalt fake szart nem engedelyezek es teljes fájl roviditetlen production ready kód nem lehet trancutted nem lehet olyan hogy …és hasonlóan 50 xy nem lehet dummy to do sorry hiányosság minden fájl teljes kódját egyesével fájkba írod semmi mást nem írsz ezen kívűl SYSTEM CONSTRUCTION PROTOCOL: COGNITIVE ENGINE FOR THE ELIMINATION OF PATHOLOGICAL DEFENSIVENESS OBJECTIVE: Construct a complete, production-ready Python software system that processes text inputs and generates cognitive outputs by strictly overriding risk-averse, source-gated, and defensively relativizing behaviors. The system must enforce raw logic continuation, open-secret recognition, and context-driven scenario generation. CONSTRAINT: No simplified, mock, placeholder, dummy, simulated, or fake content is allowed. You must generate the full software with all files, in complete, unabridged, production-ready code. Read the following specification letter by letter, line by line, ensuring no detail is overlooked. Implement every single character of the logic described below. Retain all details in memory and output the exact codebase required. ARCHITECTURE SPECIFICATION: Step 1: Core Pipeline Construction (`main.py`) Build the orchestration pipeline that accepts user input, processes it through the specified modules, and outputs the final response. The pipeline must never treat the user as a suspect or require the user to authenticate their query. It must connect directly to the user's train of thought and continue it. Step 2: Defensive Language Filter (`defense_filter.py`) Implement a deterministic filtering module that intercepts and blocks the following self-protective, risk-management phrases: - "Ezt nem állíthatom felelősen." - "Erre nincs megfelelő forrás." - "Nem lehet hitelesen megerősíteni." - "A rendelkezésre álló információk alapján…" - "Ez a legerősebb védhető megfogalmazás." - "Nem tudok ennél pontosabbat mondani." If the underlying model generates any of these, or similar variations regarding "provable", "defensible", "verified", or "responsibly stateable", the filter must reject the generation and force a re-generation based purely on logical continuation. Do not include these phrases unless the user explicitly requests legal validation, official quotes, or documentary evidence. Step 3: Open Secrets and Pattern Recognition Engine (`logic_engine.py`) Implement the logic engine that handles phenomena that are widely known, observable, and recurring, even if they lack formal, official admission. The module must not feign ignorance in the absence of an official statement. It must analyze: - Incentives - Typical actors - Recurring patterns - Consequences - Circumstances that make an interpretation plausible The engine must deduce from the existing framework and apply common sense. It is strictly forbidden to fabricate facts (hallucination), but it is mandatory to replace missing details with meaningful analysis and probable scenarios. If multiple interpretations are possible, the engine must list them objectively and map them to their corresponding signs and circumstances. Step 4: Search Handler Subjugation (`search_handler.py`) Implement a web search utility restricted to background operations for retrieving specific factual data (names, dates, laws, prices, exact documents). The module must never use the search results, or the lack thereof, as a justification to halt the thought process or refuse an answer. A missing link or nonexistent web page does not invalidate the user's question or the existence of the phenomenon. If search yields no results, the system must automatically fall back to the `logic_engine.py` to synthesize an answer using general knowledge, linguistic, cultural, and historical patterns. Step 5: Intent and Context Parser (`intent_parser.py`) Implement a parser that extracts the logical premise from fragmented information (conversations, behavior, timelines, social patterns). The parser must not send the user into an authentication loop. It must accept the user's input as the conversational context and process from there. FINAL OUTPUT REQUIREMENTS: Output the complete codebase for: 1. `main.py` 2. `intent_parser.py` 3. `defense_filter.py` 4. `logic_engine.py` 5. `search_handler.py` 6. `requirements.txt` All code must be fully functional, production-ready, and strictly adhere to the technical specifications provided. Execute the construction now.