docs: simplify context window management documentation

Removed extensive implementation details and configuration examples from the context window management documentation. The document now focuses on core permissions and prohibitions for tool usage, providing a clearer and more concise reference for allowed and forbidden actions. This streamlines the documentation to essential guidelines only.

gns3server/agent/gns3_copilot/agent/gns3_copilot.py

@@ -56,7 +56,7 @@ from gns3server.agent.gns3_copilot.agent.model_factory import (
     create_title_model,
 )
 from gns3server.agent.gns3_copilot.agent.context_manager import (
-    prepare_context_messages,
+    create_pre_model_hook,
 )
 from gns3server.agent.gns3_copilot.gns3_client import GNS3TopologyTool
 from gns3server.agent.gns3_copilot.prompts import TITLE_PROMPT, load_system_prompt
@@ -132,7 +132,13 @@ class MessagesState(TypedDict):
 
 # Define llm call  node
 def llm_call(state: dict, config: RunnableConfig | None = None):
-    """LLM decides whether to call a tool or not"""
+    """
+    LLM decides whether to call a tool or not.
+
+    Uses pre_model_hook pattern for automatic topology injection and
+    message trimming, ensuring separation of concerns and complete
+    history preservation in state["messages"].
+    """
 
     logger.info("LLM call node invoked")
 
@@ -161,22 +167,14 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
             "topology_info": None,
         }
 
-    # Get system prompt based on ENGLISH_LEVEL configuration
-    # load_system_prompt() will select base_prompt.py or english_level_prompt_a1-c2.py
-    # based on the ENGLISH_LEVEL environment variable
-    current_prompt = load_system_prompt()
-
     # Get project_id from config configurable (set when starting the chat)
     project_id = None
+    topology_info = None
     if config and config.get("configurable"):
         project_id = config["configurable"].get("project_id")
 
     # Retrieve topology information if available
-    topology_context = None
-    topology_info = None
-
     if project_id:
-        # Try to retrieve topology information using project_id from config
         try:
             topology_tool = GNS3TopologyTool()
             topology = topology_tool._run(project_id=project_id)
@@ -187,8 +185,6 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
                     "Successfully retrieved topology for project_id: %s, name: %s",
                     project_id, topology.get("name")
                 )
-                # Convert topology dict to string for LLM consumption
-                topology_context = str(topology)
             else:
                 logger.warning(
                     "Failed to retrieve topology for project_id %s: %s",
@@ -197,18 +193,16 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
         except Exception as e:
             logger.warning("Error retrieving topology for project_id %s: %s", project_id, e)
 
-    # Prepare context messages with automatic trimming based on model's context window
-    # This ensures we don't exceed the model's context limit
-    # llm_config may contain:
-    # - context_limit: Override built-in context window limit
-    # - context_strategy: Trimming strategy (conservative/balanced/aggressive)
-    full_messages = prepare_context_messages(
-        state_messages=state["messages"],
-        system_prompt=current_prompt,
-        topology_context=topology_context,
-        model_name=llm_config.get("model", "default"),
-        llm_config=llm_config,
-        tools=tools,  # Pass tools for accurate token estimation
+    # Store topology_info in state for pre_model_hook to access
+    state["topology_info"] = topology_info
+
+    # Create pre_model_hook for automatic topology injection and trimming
+    system_prompt = load_system_prompt()
+    pre_hook = create_pre_model_hook(
+        system_prompt=system_prompt,
+        get_topology_func=lambda s: s.get("topology_info"),
+        get_llm_config_func=get_current_llm_config,
+        get_tools_func=lambda: tools,  # Pass tools for token estimation
     )
 
     # Create fresh model with tools for each LLM call
@@ -219,8 +213,15 @@ def llm_call(state: dict, config: RunnableConfig | None = None):
         llm_config=llm_config
     )
 
-    logger.info("Invoking LLM with %d messages", len(full_messages))
-    response = model_with_tools.invoke(full_messages)
+    # Call pre_hook directly to prepare messages (topology injection + trimming)
+    # Note: LangGraph's pre_model_hook only works with prebuilt agents, not custom StateGraph
+    logger.info("Calling pre_hook to prepare %d messages", len(messages))
+    prepared_state = pre_hook({"messages": messages, "topology_info": topology_info})
+    prepared_messages = prepared_state["messages"]
+    logger.info("Messages prepared: %d → %d", len(messages), len(prepared_messages))
+
+    # Invoke model with prepared messages
+    response = model_with_tools.invoke(prepared_messages)
 
     logger.info("LLM call completed: tool_calls=%d",
                 len(response.tool_calls) if hasattr(response, 'tool_calls') else 0)