DeterministicIntegrationChatModel

Deterministic chat model for CLI integration tests.

This subclasses LangChain's GenericFakeChatModel so the implementation stays aligned with the core fake-chat-model test surface, while overriding generation to remain prompt-driven and restart-safe for real CLI server integration tests.

Why the existing langchain_core fakes cannot be reused here:

1. Every core fake (GenericFakeChatModel, FakeListChatModel, FakeMessagesListChatModel) pops from an iterator or cycles an index — the actual prompt is ignored. CLI integration tests start and stop the server process, which resets in-memory state. An iterator-based model either raises StopIteration or replays from the beginning after a restart, producing wrong or missing responses. This model derives output solely from the prompt text, so identical input always produces identical output regardless of process lifecycle.

2. The agent runtime calls model.bind_tools(schemas) during initialization. None of the core fakes implement bind_tools, so they raise AttributeError in any agent-loop context. This model provides a no-op passthrough.

3. The CLI server reads model.profile for capability negotiation (e.g. tool_calling, max_input_tokens). Core fakes have no such attribute, causing AttributeError or silent misconfiguration at runtime.

Additionally, the compact middleware issues summarization prompts mid- conversation. A list-based model cannot distinguish these from normal user turns without pre-knowledge of exact call ordering, whereas this model detects summary requests by inspecting the prompt content.