token cache improvements#9652
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| // Insert the context prompt just before the trailing user prompt. It is regenerated on every | ||
| // call and not stored in the session, so keeping it at the end preserves the cached prefix of | ||
| // the earlier messages. | ||
| messages = slices.Insert(messages, len(messages)-1, NewTextCompletionMessage(RoleUser, contextPrompt)) |
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When a project defines ai_instructions, this inserts the new contextPrompt as a user message immediately before the actual user prompt, and that prompt now contains the administrator project-wide instructions. That downgrades those instructions from the previous system prompt to user-level content that the following user message can contradict or override, so configured analyst behavior/guardrails can be ignored. Keep admin instructions in the system prompt or another higher-priority message while moving only truly dynamic dashboard context here.
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| // Insert the context prompt just before the trailing user prompt. It is regenerated on every | ||
| // call and not stored in the session, so keeping it at the end preserves the cached prefix of | ||
| // the earlier messages. | ||
| messages = slices.Insert(messages, len(messages)-1, NewTextCompletionMessage(RoleUser, contextPrompt)) |
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When an analyst completion runs long enough for token truncation after tool calls have been appended, this inserted context message is no longer near the tail; maybeTruncateMessages only preserves the first four messages and a suffix, so it can drop the current dashboard filters/time range/date while keeping later tool-call history. In large dashboard sessions, later iterations can therefore query or answer without the per-turn context that previously lived in the always-kept system prompt.
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max_input_tokensconfigurable per connector.prompt_cache_keyfor per session cache for openai connector.Checklist: