embeddings

Documentation

Index

• func CosineDistance(v1, v2 []float64) float64
• func CreateEmbedding(ollamaUrl string, query llm.Query4Embedding, id string) (llm.VectorRecord, error)
• type BboltVectorStore
  • func (bvs *BboltVectorStore) Get(id string) (llm.VectorRecord, error)
  • func (bvs *BboltVectorStore) GetAll() ([]llm.VectorRecord, error)
  • func (bvs *BboltVectorStore) Initialize(dbPath string) error
  • func (bvs *BboltVectorStore) Save(vectorRecord llm.VectorRecord) (llm.VectorRecord, error)
  • func (bvs *BboltVectorStore) SearchMaxSimilarity(embeddingFromQuestion llm.VectorRecord) (llm.VectorRecord, error)
  • func (bvs *BboltVectorStore) SearchSimilarities(embeddingFromQuestion llm.VectorRecord, limit float64) ([]llm.VectorRecord, error)
• type EmbeddingResponse
• type MemoryVectorStore
  • func (mvs MemoryVectorStore) Get(id string) (llm.VectorRecord, error)
  • func (mvs MemoryVectorStore) GetAll() ([]llm.VectorRecord, error)
  • func (mvs MemoryVectorStore) Save(vectorRecord llm.VectorRecord) (llm.VectorRecord, error)
  • func (mvs MemoryVectorStore) SearchMaxSimilarity(embeddingFromQuestion llm.VectorRecord) (llm.VectorRecord, error)
  • func (mvs MemoryVectorStore) SearchSimilarities(embeddingFromQuestion llm.VectorRecord, limit float64) ([]llm.VectorRecord, error)
• type VectorStore

Functions

func CosineDistance

func CosineDistance(v1, v2 []float64) float64

func CreateEmbedding

func CreateEmbedding(ollamaUrl string, query llm.Query4Embedding, id string) (llm.VectorRecord, error)

Types

type BboltVectorStore

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

func (*BboltVectorStore) Get

func (bvs *BboltVectorStore) Get(id string) (llm.VectorRecord, error)

func (*BboltVectorStore) GetAll

func (bvs *BboltVectorStore) GetAll() ([]llm.VectorRecord, error)

func (*BboltVectorStore) Initialize

func (bvs *BboltVectorStore) Initialize(dbPath string) error

func (*BboltVectorStore) Save

func (bvs *BboltVectorStore) Save(vectorRecord llm.VectorRecord) (llm.VectorRecord, error)

TODO: if vectorRecord.Id == "" create a uuid

func (*BboltVectorStore) SearchMaxSimilarity

func (bvs *BboltVectorStore) SearchMaxSimilarity(embeddingFromQuestion llm.VectorRecord) (llm.VectorRecord, error)

func (*BboltVectorStore) SearchSimilarities

func (bvs *BboltVectorStore) SearchSimilarities(embeddingFromQuestion llm.VectorRecord, limit float64) ([]llm.VectorRecord, error)

type EmbeddingResponse

type EmbeddingResponse struct {
	Embedding []float64 `json:"embedding"`
}

type MemoryVectorStore

type MemoryVectorStore struct {
	Records map[string]llm.VectorRecord
}

func (MemoryVectorStore) Get

func (mvs MemoryVectorStore) Get(id string) (llm.VectorRecord, error)

func (MemoryVectorStore) GetAll

func (mvs MemoryVectorStore) GetAll() ([]llm.VectorRecord, error)

func (MemoryVectorStore) Save

func (mvs MemoryVectorStore) Save(vectorRecord llm.VectorRecord) (llm.VectorRecord, error)

func (MemoryVectorStore) SearchMaxSimilarity

func (mvs MemoryVectorStore) SearchMaxSimilarity(embeddingFromQuestion llm.VectorRecord) (llm.VectorRecord, error)

SearchMaxSimilarity finds the vector record in MemoryVectorStore with the maximum cosine distance similarity to the provided vector record.

Parameters:

• embeddingFromQuestion: llm.VectorRecord - the vector record to compare similarities with.

Returns:

• llm.VectorRecord: The vector record with the maximum similarity.
• error: Error if any.

func (MemoryVectorStore) SearchSimilarities

func (mvs MemoryVectorStore) SearchSimilarities(embeddingFromQuestion llm.VectorRecord, limit float64) ([]llm.VectorRecord, error)

SearchSimilarities searches for vector records in the MemoryVectorStore that have a cosine distance similarity greater than or equal to the given limit.

Parameters:

• embeddingFromQuestion: the vector record to compare similarities with.
• limit: the minimum cosine distance similarity threshold.

Returns:

• []llm.VectorRecord: a slice of vector records that have a cosine distance similarity greater than or equal to the limit.
• error: an error if any occurred during the search.

type VectorStore

type VectorStore interface {
	Get(id string) (llm.VectorRecord, error)
	GetAll() ([]llm.VectorRecord, error)
	Save(vectorRecord llm.VectorRecord) (llm.VectorRecord, error)
	SearchMaxSimilarity(embeddingFromQuestion llm.VectorRecord) (llm.VectorRecord, error)
	SearchSimilarities(embeddingFromQuestion llm.VectorRecord, limit float64) ([]llm.VectorRecord, error)
}