memory

type ChromaClient interface {
	// CreateCollection creates a new collection
	CreateCollection(ctx context.Context, name string, metadata map[string]interface{}) error

	// AddDocuments adds documents to a collection
	AddDocuments(ctx context.Context, collection string, ids []string, embeddings [][]float32, documents []map[string]interface{}) error

	// Query queries a collection
	Query(ctx context.Context, collection string, queryEmbeddings [][]float32, k int) (*ChromaQueryResult, error)

	// Delete deletes documents from a collection
	Delete(ctx context.Context, collection string, ids []string) error

	// DeleteCollection deletes a collection
	DeleteCollection(ctx context.Context, name string) error
}

type ChromaQueryResult struct {
	IDs       [][]string                 `json:"ids"`
	Distances [][]float64                `json:"distances"`
	Documents [][]map[string]interface{} `json:"documents"`
}

type ChromaVectorStore struct {
	// contains filtered or unexported fields
}

type Config struct {
	// 对话记忆配置
	EnableConversation    bool `json:"enable_conversation"`     // 是否启用对话记忆
	MaxConversationLength int  `json:"max_conversation_length"` // 最大对话长度

	// 向量存储配置
	EnableVectorStore  bool   `json:"enable_vector_store"` // 是否启用向量存储
	VectorStoreType    string `json:"vector_store_type"`   // 向量存储类型
	VectorStorePath    string `json:"vector_store_path"`   // 向量存储路径
	EmbeddingDimension int    `json:"embedding_dimension"` // 嵌入维度

	// 嵌入器配置
	EmbeddingModel    string `json:"embedding_model"`    // 嵌入模型
	EmbeddingProvider string `json:"embedding_provider"` // 嵌入提供商

	// 搜索配置
	DefaultSearchLimit  int     `json:"default_search_limit"` // 默认搜索结果数量
	SimilarityThreshold float64 `json:"similarity_threshold"` // 相似度阈值
}

type ConversationStore interface {
	// Add 添加对话
	Add(ctx context.Context, conv *interfaces.Conversation) error

	// Get 获取对话历史
	Get(ctx context.Context, sessionID string, limit int) ([]*interfaces.Conversation, error)

	// Clear 清空会话
	Clear(ctx context.Context, sessionID string) error

	// Count 获取会话对话数量
	Count(ctx context.Context, sessionID string) (int, error)
}

type Embedder interface {
	// Embed 生成文本嵌入
	Embed(ctx context.Context, text string) ([]float64, error)

	// EmbedBatch 批量生成嵌入
	EmbedBatch(ctx context.Context, texts []string) ([][]float64, error)
}

type EmbeddingModel interface {
	// Embed generates embeddings for text
	Embed(ctx context.Context, text string) ([]float32, error)

	// EmbedBatch generates embeddings for multiple texts
	EmbedBatch(ctx context.Context, texts []string) ([][]float32, error)

	// Dimension returns the embedding dimension
	Dimension() int
}

type EmbeddingVectorStore struct {
	*InMemoryVectorStore
	// contains filtered or unexported fields
}

type EnhancedMemory interface {
	// Store memory with specific type
	StoreTyped(ctx context.Context, key string, value interface{}, memType MemoryType, opts StoreOptions) error

	// Retrieve memories by type
	GetByType(ctx context.Context, memType MemoryType, limit int) ([]*MemoryEntry, error)

	// Search memories using vector similarity
	VectorSearch(ctx context.Context, embedding []float32, limit int, threshold float64) ([]*MemoryEntry, error)

	// Consolidate short-term to long-term memory
	Consolidate(ctx context.Context) error

	// Forget old or unimportant memories
	Forget(ctx context.Context, threshold float64) error

	// Get memory statistics
	GetStats() *MemoryStats

	// Create associations between memories
	Associate(ctx context.Context, id1, id2 string, strength float64) error

	// Get associated memories
	GetAssociated(ctx context.Context, id string, limit int) ([]*MemoryEntry, error)
}

type HierarchicalMemory struct {
	// contains filtered or unexported fields
}

type InMemoryManager struct {
	// contains filtered or unexported fields
}

type InMemoryVectorStore struct {
	// contains filtered or unexported fields
}

type LongTermMemory struct {
	// contains filtered or unexported fields
}

type MemoryConsolidator struct {
	// contains filtered or unexported fields
}

type MemoryEntry struct {
	ID          string                 `json:"id"`
	Type        MemoryType             `json:"type"`
	Content     interface{}            `json:"content"`
	Embedding   []float32              `json:"embedding,omitempty"`
	Timestamp   time.Time              `json:"timestamp"`
	LastAccess  time.Time              `json:"last_access"`
	AccessCount int                    `json:"access_count"`
	Importance  float64                `json:"importance"`
	Decay       float64                `json:"decay"`
	Tags        []string               `json:"tags,omitempty"`
	Metadata    map[string]interface{} `json:"metadata,omitempty"`
	Related     []string               `json:"related,omitempty"` // IDs of related memories
}

type MemoryOption func(*HierarchicalMemory)

type MemoryStats struct {
	TotalEntries      int            `json:"total_entries"`
	ShortTermCount    int            `json:"short_term_count"`
	LongTermCount     int            `json:"long_term_count"`
	EpisodicCount     int            `json:"episodic_count"`
	SemanticCount     int            `json:"semantic_count"`
	ProceduralCount   int            `json:"procedural_count"`
	TotalSize         int64          `json:"total_size"`
	LastConsolidation time.Time      `json:"last_consolidation"`
	AccessPatterns    map[string]int `json:"access_patterns"`
}

type MemoryType string

type SearchResult struct {
	ID        string                 `json:"id"`        // ID
	Score     float64                `json:"score"`     // 相似度分数
	Embedding []float64              `json:"embedding"` // 向量
	Metadata  map[string]interface{} `json:"metadata"`  // 元数据
}

type ShortTermMemory struct {
	// contains filtered or unexported fields
}

type SimpleEmbeddingModel struct {
	// contains filtered or unexported fields
}

type SimpleVectorStore interface {
	// Store stores a vector with an ID
	Store(ctx context.Context, id string, vector []float32) error

	// Search searches for similar vectors
	Search(ctx context.Context, query []float32, k int, threshold float64) (ids []string, scores []float64, err error)

	// Delete removes a vector
	Delete(ctx context.Context, id string) error

	// GenerateEmbedding generates an embedding for content
	GenerateEmbedding(ctx context.Context, content interface{}) ([]float32, error)

	// Clear clears all vectors
	Clear(ctx context.Context) error

	// Size returns the number of stored vectors
	Size() int
}

type StoreOptions struct {
	TTL      time.Duration          // Time to live
	Priority float64                // Priority for retrieval
	Tags     []string               // Tags for categorization
	Metadata map[string]interface{} // Additional metadata
}

type VectorStore interface {
	// Add 添加向量
	Add(ctx context.Context, id string, embedding []float64, metadata map[string]interface{}) error

	// Search 搜索相似向量
	Search(ctx context.Context, embedding []float64, limit int) ([]*SearchResult, error)

	// Delete 删除向量
	Delete(ctx context.Context, id string) error

	// Clear 清空存储
	Clear(ctx context.Context) error
}