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feat(agent): inject topology into system prompt via template variable

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Refactor context preparation to embed topology information directly into the system prompt using a {{topology_info}} placeholder, replacing the previous approach of appending a separate SystemMessage. This consolidates context into a single system message and simplifies token accounting. The logging is updated to reflect the new token breakdown, showing combined system prompt tokens (base + topology) instead of separate components.
This commit is contained in:
YueGuobin committed 2026-03-05 11:05:51 +08:00
1 parent 33df674cbf
commit ed5fd7d17c
2 files changed
+26 -17
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gns3server/agent/gns3_copilot/agent/context_manager.py
+20 -15
View File
@@ -515,19 +515,22 @@ def prepare_context_messages(
# Estimate tool tokens (tools are sent with each LLM call)
tool_tokens = estimate_tool_tokens(tools) if tools else 0
# Build base context (system + topology)
context_messages = [SystemMessage(content=system_prompt)]
# Inject topology info into system prompt using template variable
# The system_prompt contains {{topology_info}} placeholder
if topology_context:
topology_formatted = f"Current Topology:\n{topology_context}"
formatted_prompt = system_prompt.replace("{{topology_info}}", topology_formatted)
else:
# If no topology, remove the placeholder
formatted_prompt = system_prompt.replace("{{topology_info}}", "(No topology information available)")
# Calculate token breakdown
system_prompt_tokens = count_tokens_accurately(system_prompt)
topology_tokens = 0
topology_tokens = count_tokens_accurately(topology_formatted) if topology_context else 0
formatted_tokens = count_tokens_accurately(formatted_prompt)
if topology_context:
topology_context_full = f"Current Topology:\n{topology_context}"
topology_tokens = count_tokens_accurately(topology_context_full)
context_messages.append(
SystemMessage(content=topology_context_full)
)
# Build context messages (single system message with topology injected)
context_messages = [SystemMessage(content=formatted_prompt)]
# Calculate conversation history tokens
history_tokens = count_messages_tokens(state_messages)
@@ -556,28 +559,30 @@ def prepare_context_messages(
if trimmed_history_tokens < history_tokens:
# Trimming happened
logger.info(
"Context prepared (trimmed): system=%d + topology=%d + history=%d→%d + tools=%d = %d total / %dK limit (%.1f%%), strategy=%s",
"Context prepared (trimmed): system=%d (base=%d + topology=%d) + history=%d→%d + tools=%d = %d total / %dK limit (%.1f%%), strategy=%s",
formatted_tokens,
system_prompt_tokens,
topology_tokens,
history_tokens,
trimmed_history_tokens,
tool_tokens,
trimmed_total_tokens,
formatted_tokens + trimmed_history_tokens + tool_tokens,
model_limit_k,
(trimmed_total_tokens / (model_limit_k * 1000)) * 100,
((formatted_tokens + trimmed_history_tokens) / (model_limit_k * 1000)) * 100,
trim_strategy
)
else:
# No trimming
logger.info(
"Context prepared: system=%d + topology=%d + history=%d + tools=%d = %d total / %dK limit (%.1f%%), strategy=%s",
"Context prepared: system=%d (base=%d + topology=%d) + history=%d + tools=%d = %d total / %dK limit (%.1f%%), strategy=%s",
formatted_tokens,
system_prompt_tokens,
topology_tokens,
history_tokens,
tool_tokens,
total_context_tokens,
formatted_tokens + history_tokens + tool_tokens,
model_limit_k,
(total_context_tokens / (model_limit_k * 1000)) * 100,
(formatted_tokens / (model_limit_k * 1000)) * 100,
trim_strategy
)