Documentation
¶
Index ¶
- func CosineDistance(v1, v2 []float64) float64
- func CreateEmbedding(ollamaUrl string, query llm.Query4Embedding, id string) (llm.VectorRecord, error)
- func GenerateContextFromSimilarities(similarities []llm.VectorRecord) string
- 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
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func CosineDistance ¶
func CreateEmbedding ¶
func CreateEmbedding(ollamaUrl string, query llm.Query4Embedding, id string) (llm.VectorRecord, error)
func GenerateContextFromSimilarities ¶ added in v0.0.4
func GenerateContextFromSimilarities(similarities []llm.VectorRecord) string
GenerateContextFromSimilarities generates the context content from a slice of vector records.
Parameters: - similarities: a slice of llm.VectorRecord representing the similarities.
Returns: - string: the generated context content in XML format.
Types ¶
type BboltVectorStore ¶ added in v0.0.2
type BboltVectorStore struct {
// contains filtered or unexported fields
}
func (*BboltVectorStore) Get ¶ added in v0.0.2
func (bvs *BboltVectorStore) Get(id string) (llm.VectorRecord, error)
func (*BboltVectorStore) GetAll ¶ added in v0.0.2
func (bvs *BboltVectorStore) GetAll() ([]llm.VectorRecord, error)
func (*BboltVectorStore) Initialize ¶ added in v0.0.2
func (bvs *BboltVectorStore) Initialize(dbPath string) error
func (*BboltVectorStore) Save ¶ added in v0.0.2
func (bvs *BboltVectorStore) Save(vectorRecord llm.VectorRecord) (llm.VectorRecord, error)
TODO: if vectorRecord.Id == "" create a uuid
func (*BboltVectorStore) SearchMaxSimilarity ¶ added in v0.0.2
func (bvs *BboltVectorStore) SearchMaxSimilarity(embeddingFromQuestion llm.VectorRecord) (llm.VectorRecord, error)
SearchMaxSimilarity searches for the vector record in the BboltVectorStore that has the maximum cosine distance similarity to the given embeddingFromQuestion.
Parameters: - embeddingFromQuestion: the vector record to compare similarities with.
Returns: - llm.VectorRecord: the vector record with the maximum cosine distance similarity. - error: an error if any occurred during the search.
func (*BboltVectorStore) SearchSimilarities ¶ added in v0.0.2
func (bvs *BboltVectorStore) SearchSimilarities(embeddingFromQuestion llm.VectorRecord, limit float64) ([]llm.VectorRecord, error)
SearchSimilarities searches for vector records in the BboltVectorStore 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 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)
}
Source Files