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personas: elevate to server with per-persona fact scoping

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Move personas off the mobile client into ImageApi as first-class
records, and scope entity_facts by persona so each one builds its own
voice over a shared entity graph. The new include_all_memories flag
lets a persona opt back into the full hive-mind pool for human
browsing of /knowledge/*; agentic generation always stays in-voice.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Cameron Cordes
2026-05-09 17:59:20 -04:00
parent 55a986c249
commit 3e2f36a748
15 changed files with 1024 additions and 20 deletions
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19
src/ai/handlers.rs
View File

@@ -48,6 +48,11 @@ pub struct GeneratePhotoInsightRequest {
/// falls back to `DEFAULT_FEWSHOT_INSIGHT_IDS`.
#[serde(default)]
pub fewshot_insight_ids: Option<Vec<i32>>,
/// Active persona id for this generation. New facts are tagged with
/// it (`entity_facts.persona_id`); recall during the agentic loop is
/// scoped to it. Defaults to `"default"` when absent.
#[serde(default)]
pub persona_id: Option<String>,
}
#[derive(Debug, Deserialize)]
@@ -376,6 +381,13 @@ pub async fn generate_agentic_insight_handler(
.collect()
};
let persona_id = request
.persona_id
.clone()
.filter(|s| !s.trim().is_empty())
.unwrap_or_else(|| "default".to_string());
span.set_attribute(KeyValue::new("persona_id", persona_id.clone()));
let result = insight_generator
.generate_agentic_insight_for_photo(
&normalized_path,
@@ -390,6 +402,7 @@ pub async fn generate_agentic_insight_handler(
request.backend.clone(),
fewshot_examples,
fewshot_ids,
persona_id,
)
.await;
@@ -645,6 +658,10 @@ pub struct ChatTurnHttpRequest {
/// semantics. Also seeds the bootstrap path when no insight exists.
#[serde(default)]
pub system_prompt: Option<String>,
/// Active persona id for this turn. New facts/recalls scope to it.
/// Defaults to `"default"` when missing.
#[serde(default)]
pub persona_id: Option<String>,
#[serde(default)]
pub amend: bool,
/// When true, force the bootstrap path even if an insight already
@@ -707,6 +724,7 @@ pub async fn chat_turn_handler(
min_p: request.min_p,
max_iterations: request.max_iterations,
system_prompt: request.system_prompt.clone(),
persona_id: request.persona_id.clone(),
amend: request.amend,
regenerate: request.regenerate,
};
@@ -923,6 +941,7 @@ pub async fn chat_stream_handler(
min_p: request.min_p,
max_iterations: request.max_iterations,
system_prompt: request.system_prompt.clone(),
persona_id: request.persona_id.clone(),
amend: request.amend,
regenerate: request.regenerate,
};

26
src/ai/insight_chat.rs
View File

@@ -50,6 +50,10 @@ pub struct ChatTurnRequest {
/// In amend mode, persisted into the new insight row's system message.
/// None / empty = no change.
pub system_prompt: Option<String>,
/// Active persona id for this turn. Tools that write to
/// `entity_facts` tag the new rows with it; `recall_facts_for_photo`
/// scopes its read to it. None defaults to `"default"`.
pub persona_id: Option<String>,
/// When true, write a new insight row (regenerating title) instead of
/// updating training_messages on the existing row.
pub amend: bool,
@@ -231,6 +235,13 @@ impl InsightChatService {
bail!("user_message exceeds 8192 chars");
}
let active_persona = req
.persona_id
.clone()
.filter(|s| !s.trim().is_empty())
.unwrap_or_else(|| "default".to_string());
span.set_attribute(KeyValue::new("persona_id", active_persona.clone()));
let normalized = normalize_path(&req.file_path);
// 1. Acquire the per-(library, file) async mutex. Two concurrent
@@ -464,6 +475,7 @@ impl InsightChatService {
&ollama_client,
&image_base64,
&normalized,
&active_persona,
&loop_cx,
)
.await;
@@ -737,6 +749,11 @@ impl InsightChatService {
insight: crate::database::models::PhotoInsight,
tx: tokio::sync::mpsc::Sender<ChatStreamEvent>,
) -> Result<()> {
let active_persona = req
.persona_id
.clone()
.filter(|s| !s.trim().is_empty())
.unwrap_or_else(|| "default".to_string());
let raw_history = insight.training_messages.as_ref().ok_or_else(|| {
anyhow!("insight has no chat history; regenerate this insight in agentic mode")
})?;
@@ -826,6 +843,7 @@ impl InsightChatService {
tools,
&image_base64,
&normalized,
&active_persona,
max_iterations,
&tx,
)
@@ -915,6 +933,11 @@ impl InsightChatService {
normalized: String,
tx: tokio::sync::mpsc::Sender<ChatStreamEvent>,
) -> Result<()> {
let active_persona = req
.persona_id
.clone()
.filter(|s| !s.trim().is_empty())
.unwrap_or_else(|| "default".to_string());
let effective_backend = resolve_bootstrap_backend(req.backend.as_deref())?;
let is_hybrid = effective_backend == "hybrid";
@@ -1008,6 +1031,7 @@ impl InsightChatService {
tools,
&image_base64,
&normalized,
&active_persona,
max_iterations,
&tx,
)
@@ -1157,6 +1181,7 @@ impl InsightChatService {
tools: Vec<Tool>,
image_base64: &Option<String>,
normalized: &str,
active_persona: &str,
max_iterations: usize,
tx: &tokio::sync::mpsc::Sender<ChatStreamEvent>,
) -> Result<AgenticLoopOutcome> {
@@ -1235,6 +1260,7 @@ impl InsightChatService {
ollama_client,
image_base64,
normalized,
active_persona,
&cx,
)
.await;

30
src/ai/insight_generator.rs
View File

@@ -1534,7 +1534,15 @@ Return ONLY the summary, nothing else."#,
// ── Tool executors for agentic loop ────────────────────────────────
/// Dispatch a tool call to the appropriate executor
/// Dispatch a tool call to the appropriate executor.
///
/// `persona_id` identifies the persona this loop is generating for —
/// `store_fact` tags new facts with it, `recall_facts_for_photo`
/// filters reads to it (always Single in the agentic loop, even when
/// the persona has `include_all_memories=true`; the hive-mind toggle
/// is for human browsing of `/knowledge/*`, where mixing voices is
/// the explicit goal — during generation the persona's own voice
/// must stay clean).
pub(crate) async fn execute_tool(
&self,
tool_name: &str,
@@ -1542,6 +1550,7 @@ Return ONLY the summary, nothing else."#,
ollama: &OllamaClient,
image_base64: &Option<String>,
file_path: &str,
persona_id: &str,
cx: &opentelemetry::Context,
) -> String {
let result = match tool_name {
@@ -1556,9 +1565,15 @@ Return ONLY the summary, nothing else."#,
"reverse_geocode" => self.tool_reverse_geocode(arguments).await,
"get_personal_place_at" => self.tool_get_personal_place_at(arguments).await,
"recall_entities" => self.tool_recall_entities(arguments, cx).await,
"recall_facts_for_photo" => self.tool_recall_facts_for_photo(arguments, cx).await,
"recall_facts_for_photo" => {
self.tool_recall_facts_for_photo(arguments, persona_id, cx)
.await
}
"store_entity" => self.tool_store_entity(arguments, ollama, cx).await,
"store_fact" => self.tool_store_fact(arguments, file_path, cx).await,
"store_fact" => {
self.tool_store_fact(arguments, file_path, persona_id, cx)
.await
}
"get_current_datetime" => Self::tool_get_current_datetime(),
unknown => format!("Unknown tool: {}", unknown),
};
@@ -2391,8 +2406,11 @@ Return ONLY the summary, nothing else."#,
async fn tool_recall_facts_for_photo(
&self,
args: &serde_json::Value,
persona_id: &str,
cx: &opentelemetry::Context,
) -> String {
use crate::database::PersonaFilter;
let persona_filter = PersonaFilter::Single(persona_id.to_string());
let file_path = match args.get("file_path").and_then(|v| v.as_str()) {
Some(p) => p.to_string(),
None => return "Error: missing required parameter 'file_path'".to_string(),
@@ -2432,7 +2450,7 @@ Return ONLY the summary, nothing else."#,
"Entity: {} ({}, role: {})",
e.name, e.entity_type, role
));
if let Ok(facts) = kdao.get_facts_for_entity(cx, entity_id) {
if let Ok(facts) = kdao.get_facts_for_entity(cx, entity_id, &persona_filter) {
for f in facts.iter().filter(|f| f.status == "active") {
let obj = if let Some(ref v) = f.object_value {
v.clone()
@@ -2577,6 +2595,7 @@ Return ONLY the summary, nothing else."#,
&self,
args: &serde_json::Value,
file_path: &str,
persona_id: &str,
cx: &opentelemetry::Context,
) -> String {
use crate::database::models::{InsertEntityFact, InsertEntityPhotoLink};
@@ -2627,6 +2646,7 @@ Return ONLY the summary, nothing else."#,
confidence: 0.6,
status: "active".to_string(),
created_at: chrono::Utc::now().timestamp(),
persona_id: persona_id.to_string(),
};
let mut kdao = self
@@ -3176,6 +3196,7 @@ Return ONLY the summary, nothing else."#,
backend: Option<String>,
fewshot_examples: Vec<Vec<ChatMessage>>,
fewshot_source_ids: Vec<i32>,
persona_id: String,
) -> Result<(Option<i32>, Option<i32>)> {
let tracer = global_tracer();
let current_cx = opentelemetry::Context::current();
@@ -3652,6 +3673,7 @@ Return ONLY the summary, nothing else."#,
&ollama_client,
&image_base64,
&file_path,
&persona_id,
&loop_cx,
)
.await;