vectorless 0.1.29

// Copyright (c) 2026 vectorless developers
// SPDX-License-Identifier: Apache-2.0

//! Core types for the retrieval system.

use serde::{Deserialize, Serialize};

use super::context::{PruningStrategy, TokenEstimation};
use crate::document::NodeId;

/// Query complexity level for adaptive strategy selection.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum QueryComplexity {
    /// Simple queries that can be solved with keyword matching.
    Simple,

    /// Medium complexity queries requiring semantic understanding.
    Medium,

    /// Complex queries requiring deep LLM reasoning.
    Complex,
}

impl Default for QueryComplexity {
    fn default() -> Self {
        Self::Medium
    }
}

/// Strategy preference for retrieval.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum StrategyPreference {
    /// Automatically select strategy based on query complexity.
    Auto,

    /// Force keyword-based strategy (fast, no LLM).
    ForceKeyword,

    /// Force LLM strategy (deep reasoning).
    ForceLlm,

    /// Force hybrid strategy (BM25 + LLM refinement).
    ForceHybrid,

    /// Force cross-document strategy (multi-document retrieval).
    ForceCrossDocument,

    /// Force page-range strategy (filter by page range).
    ForcePageRange,
}

impl Default for StrategyPreference {
    fn default() -> Self {
        Self::Auto
    }
}

/// Sufficiency level for incremental retrieval.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SufficiencyLevel {
    /// Information is sufficient, stop retrieving.
    Sufficient,

    /// Partial information, can continue if needed.
    PartialSufficient,

    /// Information is insufficient, continue retrieving.
    Insufficient,
}

impl Default for SufficiencyLevel {
    fn default() -> Self {
        Self::Insufficient
    }
}

/// Options for retrieval operations.
#[derive(Debug, Clone)]
pub struct RetrieveOptions {
    /// Maximum number of results to return.
    pub top_k: usize,

    /// Beam width for multi-path search.
    pub beam_width: usize,

    /// Maximum search iterations.
    pub max_iterations: usize,

    /// Whether to include node content in results.
    pub include_content: bool,

    /// Whether to include node summaries in results.
    pub include_summaries: bool,

    /// Minimum relevance score (0.0 - 1.0).
    pub min_score: f32,

    /// Strategy preference.
    pub strategy: StrategyPreference,

    /// Enable sufficiency checking for incremental retrieval.
    pub sufficiency_check: bool,

    /// Maximum tokens for sufficiency threshold.
    pub max_tokens: usize,

    /// Enable result caching.
    pub enable_cache: bool,

    /// Pruning strategy for context building.
    pub pruning_strategy: super::PruningStrategy,

    /// Token estimation mode.
    pub token_estimation: super::TokenEstimation,

    /// Whether to use async context building for large documents.
    pub use_async_context: bool,

    /// Enable streaming retrieval results.
    ///
    /// When enabled, use `query_stream()` to receive incremental
    /// `RetrieveEvent`s as each pipeline stage completes. When disabled
    /// (default), the standard `query()` returns a single final result.
    pub streaming: bool,

    /// Cross-document graph for graph-aware retrieval boosting.
    pub document_graph: Option<std::sync::Arc<crate::graph::DocumentGraph>>,

    /// Search fallback chain: algorithm names tried in order until min_score is met.
    /// Options: "beam", "mcts", "pure_pilot".
    /// Default: ["beam", "mcts", "pure_pilot"]
    pub fallback_chain: Vec<String>,
}

impl Default for RetrieveOptions {
    fn default() -> Self {
        Self {
            top_k: 5,
            beam_width: 3,
            max_iterations: 10,
            include_content: true,
            include_summaries: true,
            min_score: 0.1,
            strategy: StrategyPreference::Auto,
            sufficiency_check: true,
            max_tokens: 4000,
            enable_cache: true,
            pruning_strategy: super::PruningStrategy::default(),
            token_estimation: super::TokenEstimation::default(),
            use_async_context: false,
            streaming: false,
            document_graph: None,
            fallback_chain: vec!["beam".into(), "mcts".into(), "pure_pilot".into()],
        }
    }
}

impl RetrieveOptions {
    /// Create new retrieve options with defaults.
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }

    /// Set the maximum number of results to return.
    #[must_use]
    pub fn with_top_k(mut self, top_k: usize) -> Self {
        self.top_k = top_k;
        self
    }

    /// Set the beam width for multi-path search.
    #[must_use]
    pub fn with_beam_width(mut self, beam_width: usize) -> Self {
        self.beam_width = beam_width;
        self
    }

    /// Set the maximum search iterations.
    #[must_use]
    pub fn with_max_iterations(mut self, max_iterations: usize) -> Self {
        self.max_iterations = max_iterations;
        self
    }

    /// Set whether to include node content in results.
    #[must_use]
    pub fn with_include_content(mut self, include: bool) -> Self {
        self.include_content = include;
        self
    }

    /// Set whether to include node summaries in results.
    #[must_use]
    pub fn with_include_summaries(mut self, include: bool) -> Self {
        self.include_summaries = include;
        self
    }

    /// Set the minimum relevance score.
    #[must_use]
    pub fn with_min_score(mut self, min_score: f32) -> Self {
        self.min_score = min_score;
        self
    }

    /// Set the strategy preference.
    #[must_use]
    pub fn with_strategy(mut self, strategy: StrategyPreference) -> Self {
        self.strategy = strategy;
        self
    }

    /// Set whether to enable sufficiency checking.
    #[must_use]
    pub fn with_sufficiency_check(mut self, enable: bool) -> Self {
        self.sufficiency_check = enable;
        self
    }

    /// Set the maximum tokens for sufficiency threshold.
    #[must_use]
    pub fn with_max_tokens(mut self, max_tokens: usize) -> Self {
        self.max_tokens = max_tokens;
        self
    }

    /// Set whether to enable result caching.
    #[must_use]
    pub fn with_enable_cache(mut self, enable: bool) -> Self {
        self.enable_cache = enable;
        self
    }

    /// Set pruning strategy for context building.
    #[must_use]
    pub fn with_pruning_strategy(mut self, strategy: PruningStrategy) -> Self {
        self.pruning_strategy = strategy;
        self
    }

    /// Set token estimation mode.
    #[must_use]
    pub fn with_token_estimation(mut self, mode: TokenEstimation) -> Self {
        self.token_estimation = mode;
        self
    }

    /// Enable async context building for large documents.
    #[must_use]
    pub fn with_async_context(mut self, enable: bool) -> Self {
        self.use_async_context = enable;
        self
    }

    /// Enable streaming retrieval results.
    #[must_use]
    pub fn with_streaming(mut self, enable: bool) -> Self {
        self.streaming = enable;
        self
    }

    /// Set the cross-document graph for graph-aware retrieval boosting.
    #[must_use]
    pub fn with_document_graph(
        mut self,
        graph: std::sync::Arc<crate::graph::DocumentGraph>,
    ) -> Self {
        self.document_graph = Some(graph);
        self
    }

    /// Set the search fallback chain.
    ///
    /// Algorithm names: "beam", "mcts", "pure_pilot".
    /// Primary algorithm is prepended automatically by the Plan stage.
    #[must_use]
    pub fn with_fallback_chain(mut self, chain: Vec<String>) -> Self {
        self.fallback_chain = chain;
        self
    }
}

/// A single retrieval result.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RetrievalResult {
    /// Node ID in the tree.
    pub node_id: Option<String>,

    /// Node title.
    pub title: String,

    /// Node content (if included).
    pub content: Option<String>,

    /// Node summary (if included).
    pub summary: Option<String>,

    /// Relevance score (0.0 - 1.0).
    pub score: f32,

    /// Depth in the tree.
    pub depth: usize,

    /// Page range (for PDFs).
    pub page_range: Option<(usize, usize)>,
}

impl RetrievalResult {
    /// Create a new retrieval result.
    #[must_use]
    pub fn new(title: impl Into<String>) -> Self {
        Self {
            node_id: None,
            title: title.into(),
            content: None,
            summary: None,
            score: 1.0,
            depth: 0,
            page_range: None,
        }
    }

    /// Set the node ID.
    #[must_use]
    pub fn with_node_id(mut self, id: impl Into<String>) -> Self {
        self.node_id = Some(id.into());
        self
    }

    /// Set the content.
    #[must_use]
    pub fn with_content(mut self, content: impl Into<String>) -> Self {
        self.content = Some(content.into());
        self
    }

    /// Set the summary.
    #[must_use]
    pub fn with_summary(mut self, summary: impl Into<String>) -> Self {
        self.summary = Some(summary.into());
        self
    }

    /// Set the score.
    #[must_use]
    pub fn with_score(mut self, score: f32) -> Self {
        self.score = score;
        self
    }

    /// Set the depth.
    #[must_use]
    pub fn with_depth(mut self, depth: usize) -> Self {
        self.depth = depth;
        self
    }

    /// Set the page range.
    #[must_use]
    pub fn with_page_range(mut self, start: usize, end: usize) -> Self {
        self.page_range = Some((start, end));
        self
    }
}

/// Complete retrieval response.
#[derive(Debug, Clone)]
pub struct RetrieveResponse {
    /// Retrieved results.
    pub results: Vec<RetrievalResult>,

    /// Aggregated content.
    pub content: String,

    /// Overall confidence score.
    pub confidence: f32,

    /// Whether information is sufficient.
    pub is_sufficient: bool,

    /// Strategy that was used.
    pub strategy_used: String,

    /// Detected query complexity.
    pub complexity: QueryComplexity,

    /// Reasoning chain explaining how results were found.
    pub reasoning_chain: ReasoningChain,

    /// Total tokens used.
    pub tokens_used: usize,
}

impl Default for RetrieveResponse {
    fn default() -> Self {
        Self {
            results: Vec::new(),
            content: String::new(),
            confidence: 0.0,
            is_sufficient: false,
            strategy_used: String::new(),
            complexity: QueryComplexity::Medium,
            reasoning_chain: ReasoningChain::default(),
            tokens_used: 0,
        }
    }
}

impl RetrieveResponse {
    /// Create a new empty response.
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }

    /// Check if there are any results.
    #[must_use]
    pub fn is_empty(&self) -> bool {
        self.results.is_empty()
    }

    /// Get the number of results.
    #[must_use]
    pub fn len(&self) -> usize {
        self.results.len()
    }
}

/// A single navigation step in the search trace.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NavigationStep {
    /// Node ID visited.
    pub node_id: String,

    /// Node title.
    pub title: String,

    /// Relevance score at this step.
    pub score: f32,

    /// Decision made at this step.
    pub decision: NavigationDecision,

    /// Depth in tree.
    pub depth: usize,
}

/// Navigation decision at each step.
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum NavigationDecision {
    /// Go to the specified child.
    GoToChild(usize),

    /// This node contains the answer.
    ThisIsTheAnswer,

    /// Explore multiple children.
    ExploreMore,

    /// Skip this branch.
    Skip,

    /// Backtrack from a dead-end node to a previously shelved alternative.
    /// Contains the title of the dead-end node being abandoned.
    BacktrackFrom(String),
}

/// Pipeline stage name for reasoning chain provenance.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum StageName {
    /// Query analysis stage.
    Analyze,
    /// Strategy planning stage.
    Plan,
    /// Tree search stage.
    Search,
    /// Sufficiency evaluation stage.
    Evaluate,
}

impl std::fmt::Display for StageName {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            Self::Analyze => write!(f, "analyze"),
            Self::Plan => write!(f, "plan"),
            Self::Search => write!(f, "search"),
            Self::Evaluate => write!(f, "evaluate"),
        }
    }
}

/// Summary of an LLM call made during a reasoning step.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LlmCallSummary {
    /// Truncated prompt summary for display.
    pub prompt_summary: String,
    /// Tokens consumed by this call.
    pub tokens_used: usize,
    /// Model identifier.
    pub model: String,
}

/// A candidate node considered but not selected during reasoning.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningCandidate {
    /// Node ID.
    pub node_id: String,
    /// Node title.
    pub title: String,
    /// Relevance score of this candidate.
    pub score: f32,
}

/// A single step in the reasoning chain.
///
/// Unlike `NavigationStep` which only records "where" the search went,
/// `ReasoningStep` also records "why" — the decision rationale,
/// candidates considered, strategy used, and any LLM calls made.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReasoningStep {
    /// Which pipeline stage produced this step.
    pub stage: StageName,
    /// Node ID visited (if applicable).
    pub node_id: Option<String>,
    /// Node title (if applicable).
    pub title: Option<String>,
    /// Relevance score at this step.
    pub score: f32,
    /// Decision made at this step.
    pub decision: NavigationDecision,
    /// Depth in tree.
    pub depth: usize,
    /// Human-readable explanation of why this decision was made.
    pub reasoning: String,
    /// Candidates considered but not selected at this step.
    pub candidates: Vec<ReasoningCandidate>,
    /// Strategy used at this step (e.g. "keyword", "hybrid").
    pub strategy_used: Option<String>,
    /// LLM call summary, if an LLM was consulted.
    pub llm_call: Option<LlmCallSummary>,
    /// Reference identifiers followed from this step (cross-reference tracking).
    pub references_followed: Vec<String>,
}

/// Complete reasoning chain for a retrieval operation.
///
/// Provides an ordered, auditable trace of every decision the engine made
/// from query analysis through final evaluation. This is the core
/// differentiator — not just results, but *why* these results.
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct ReasoningChain {
    /// Ordered reasoning steps.
    pub steps: Vec<ReasoningStep>,
}

impl ReasoningChain {
    /// Create an empty reasoning chain.
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }

    /// Append a reasoning step.
    pub fn push(&mut self, step: ReasoningStep) {
        self.steps.push(step);
    }

    /// Number of reasoning steps.
    #[must_use]
    pub fn len(&self) -> usize {
        self.steps.len()
    }

    /// Whether the chain is empty.
    #[must_use]
    pub fn is_empty(&self) -> bool {
        self.steps.is_empty()
    }

    /// Build a human-readable summary of the full chain.
    #[must_use]
    pub fn summary(&self) -> String {
        self.steps
            .iter()
            .map(|s| {
                let node_info = s.title.as_deref().unwrap_or("(no node)");
                format!(
                    "[{}] {} (score={:.2}): {}",
                    s.stage, node_info, s.score, s.reasoning
                )
            })
            .collect::<Vec<_>>()
            .join("\n")
    }
}

/// Search path for multi-path algorithms.
///
/// Tracks the sequence of nodes visited, along with the reasoning
/// for each navigation step. This reasoning is fed back into the
/// LLM context so the Pilot can understand how it arrived at the
/// current position and avoid repeating mistakes.
#[derive(Debug, Clone)]
pub struct SearchPath {
    /// Nodes in the path.
    pub nodes: Vec<NodeId>,

    /// Cumulative score.
    pub score: f32,

    /// Leaf node (if path ends at leaf).
    pub leaf: Option<NodeId>,

    /// Per-step reasoning for why each node was chosen.
    ///
    /// Same length as `nodes`. Each entry is the reason the
    /// corresponding node was selected. `None` means no reason
    /// was captured (e.g., algorithm-only fallback).
    pub step_reasons: Vec<Option<String>>,
}

impl SearchPath {
    /// Create a new empty path.
    #[must_use]
    pub fn new() -> Self {
        Self {
            nodes: Vec::new(),
            score: 0.0,
            leaf: None,
            step_reasons: Vec::new(),
        }
    }

    /// Create a path from a single node.
    #[must_use]
    pub fn from_node(node_id: NodeId, score: f32) -> Self {
        Self {
            nodes: vec![node_id],
            score,
            leaf: Some(node_id),
            step_reasons: vec![None],
        }
    }

    /// Extend the path with a new node and optional reason.
    #[must_use]
    pub fn extend(&self, node_id: NodeId, score: f32) -> Self {
        let mut nodes = self.nodes.clone();
        let mut step_reasons = self.step_reasons.clone();
        nodes.push(node_id);
        step_reasons.push(None);
        Self {
            nodes,
            score: self.score + score,
            leaf: Some(node_id),
            step_reasons,
        }
    }

    /// Extend the path with a new node and a reason for choosing it.
    #[must_use]
    pub fn extend_with_reason(
        &self,
        node_id: NodeId,
        score: f32,
        reason: impl Into<String>,
    ) -> Self {
        let mut nodes = self.nodes.clone();
        let mut step_reasons = self.step_reasons.clone();
        nodes.push(node_id);
        step_reasons.push(Some(reason.into()));
        Self {
            nodes,
            score: self.score + score,
            leaf: Some(node_id),
            step_reasons,
        }
    }
}

impl Default for SearchPath {
    fn default() -> Self {
        Self::new()
    }
}

/// Statistics for a retrieval operation.
#[derive(Debug, Clone, Default)]
pub struct RetrievalStats {
    /// Number of nodes visited.
    pub nodes_visited: usize,

    /// Number of LLM calls made.
    pub llm_calls: usize,

    /// Time spent in milliseconds.
    pub time_ms: u64,

    /// Tokens consumed.
    pub tokens_used: usize,

    /// Cache hits.
    pub cache_hits: usize,

    /// Cache misses.
    pub cache_misses: usize,
}