Documentation
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Index ¶
- func EmbeddingHashMatches(embedding *embeddinglib.Embedding, hash embeddinglib.Sha256Hash) bool
- func MarshalEmbedding(embedding *embeddinglib.Embedding) ([]byte, error)
- func UnmarshalEmbedding(bytes []byte) (*embeddinglib.Embedding, error)
- type BatchReducer
- type Chat
- func (chat *Chat) Clear()
- func (chat *Chat) Complete(ctx context.Context, userInput string, ...) (any, *tokens.TokenCount, error)
- func (chat *Chat) GetMessages() []openai.ChatCompletionMessage
- func (chat *Chat) Insert(role string, content string) int
- func (chat *Chat) Reply(ctx context.Context, userInput string, ...) (any, *tokens.TokenCount, error)
- type Client
- func (client *Client) ClassifyMessage(ctx context.Context, message string, classes map[string]string) (string, error)
- func (client *Client) CommandSummary(ctx context.Context, messages []openai.ChatCompletionMessage, ...) (string, *tokens.TokenCount, error)
- func (client *Client) ComputeEmbeddings(ctx context.Context, input []string) ([]embedding.Vector64, error)
- func (client *Client) NewAuditQuery(username string) *Chat
- func (client *Client) NewChat(toolContext *modeltools.ToolContext) *Chat
- func (client *Client) NewCommand(username string) *Chat
- func (client *Client) RunTool(ctx context.Context, toolContext *modeltools.ToolContext, ...) (any, *tokens.TokenCount, error)
- func (client *Client) Summary(ctx context.Context, message string) (string, error)
- type Document
- type EmbeddingProcessor
- type EmbeddingProcessorConfig
- type Embeddings
- type FilterFn
- type MockEmbedder
- type SimpleRetriever
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func EmbeddingHashMatches ¶
func EmbeddingHashMatches(embedding *embeddinglib.Embedding, hash embeddinglib.Sha256Hash) bool
EmbeddingHashMatches returns true if the hash of the embedding matches the given hash.
func MarshalEmbedding ¶
func MarshalEmbedding(embedding *embeddinglib.Embedding) ([]byte, error)
MarshalEmbedding marshals the ai.Embedding resource to binary ProtoBuf.
func UnmarshalEmbedding ¶
func UnmarshalEmbedding(bytes []byte) (*embeddinglib.Embedding, error)
UnmarshalEmbedding unmarshals binary ProtoBuf into an ai.Embedding resource.
Types ¶
type BatchReducer ¶
type BatchReducer[T, V any] struct { // contains filtered or unexported fields }
BatchReducer is a helper that processes data in batches.
func NewBatchReducer ¶
func NewBatchReducer[T, V any](processFn func(ctx context.Context, data []T) (V, error), batchSize int) *BatchReducer[T, V]
NewBatchReducer is a BatchReducer constructor.
func (*BatchReducer[T, V]) Add ¶
func (b *BatchReducer[T, V]) Add(ctx context.Context, data T) (V, error)
Add adds a new item to the batch. If the batch is full, it will be processed and the result will be returned. Otherwise, a zero value will be returned. Finalize must be called to process the remaining data in the batch.
type Chat ¶
type Chat struct {
// contains filtered or unexported fields
}
Chat represents a conversation between a user and an assistant with context memory.
func (*Chat) Complete ¶
func (chat *Chat) Complete(ctx context.Context, userInput string, progressUpdates func(*model.AgentAction)) (any, *tokens.TokenCount, error)
Complete completes the conversation with a message from the assistant based on the current context and user input. On success, it returns the message. Returned types: - message: one of the message types below - error: an error if one occurred Message types: - CompletionCommand: a command from the assistant - Message: a text message from the assistant - AccessRequest: an access request suggestion from the assistant
func (*Chat) GetMessages ¶
func (chat *Chat) GetMessages() []openai.ChatCompletionMessage
GetMessages returns the messages in the conversation.
type Client ¶
type Client struct {
// contains filtered or unexported fields
}
Client is a client for OpenAI API.
func NewClientFromConfig ¶
func NewClientFromConfig(config openai.ClientConfig) *Client
NewClientFromConfig creates a new client for OpenAI API from config.
func (*Client) ClassifyMessage ¶
func (client *Client) ClassifyMessage(ctx context.Context, message string, classes map[string]string) (string, error)
ClassifyMessage takes a user message, a list of categories, and uses the AI mode as a zero-shot classifier.
func (*Client) CommandSummary ¶
func (client *Client) CommandSummary(ctx context.Context, messages []openai.ChatCompletionMessage, output map[string][]byte) (string, *tokens.TokenCount, error)
CommandSummary creates a command summary based on the command output. The message history is also passed to the model in order to keep context and extract relevant information from the output.
func (*Client) ComputeEmbeddings ¶
func (client *Client) ComputeEmbeddings(ctx context.Context, input []string) ([]embedding.Vector64, error)
ComputeEmbeddings takes a map of nodes and calls openAI to generate embeddings for those nodes. ComputeEmbeddings is responsible for implementing a retry mechanism if the embedding computation is flaky.
func (*Client) NewAuditQuery ¶
func (*Client) NewChat ¶
func (client *Client) NewChat(toolContext *modeltools.ToolContext) *Chat
NewChat creates a new chat. The username is set in the conversation context, so that the AI can use it to personalize the conversation. embeddingServiceClient is used to get the embeddings from the Auth Server.
func (*Client) NewCommand ¶
func (*Client) RunTool ¶
func (client *Client) RunTool(ctx context.Context, toolContext *modeltools.ToolContext, toolName, toolInput string) (any, *tokens.TokenCount, error)
type EmbeddingProcessor ¶
type EmbeddingProcessor struct {
// contains filtered or unexported fields
}
EmbeddingProcessor is responsible for processing nodes, generating embeddings and storing their embeddings in the backend.
func NewEmbeddingProcessor ¶
func NewEmbeddingProcessor(cfg *EmbeddingProcessorConfig) *EmbeddingProcessor
NewEmbeddingProcessor returns a new EmbeddingProcessor.
type EmbeddingProcessorConfig ¶
type EmbeddingProcessorConfig struct {
AIClient embeddinglib.Embedder
EmbeddingSrv Embeddings
EmbeddingsRetriever *SimpleRetriever
NodeSrv *services.UnifiedResourceCache
Log logrus.FieldLogger
Jitter retryutils.Jitter
}
EmbeddingProcessorConfig is the configuration for EmbeddingProcessor.
type Embeddings ¶
type Embeddings interface {
// GetAllEmbeddings returns all embeddings.
GetAllEmbeddings(ctx context.Context) stream.Stream[*embeddinglib.Embedding]
// UpsertEmbedding creates or update a single ai.Embedding in the backend.
UpsertEmbedding(ctx context.Context, embedding *embeddinglib.Embedding) (*embeddinglib.Embedding, error)
}
Embeddings implements the minimal interface used by the Embedding processor.
type FilterFn ¶
FilterFn is a function that filters out embeddings. If the function returns false, the embedding is filtered out.
type MockEmbedder ¶
MockEmbedder returns embeddings based on the sha256 hash function. Those embeddings have no semantic meaning but ensure different embedded content provides different embeddings.
func (*MockEmbedder) ComputeEmbeddings ¶
type SimpleRetriever ¶
type SimpleRetriever struct {
// contains filtered or unexported fields
}
SimpleRetriever is a simple implementation of embeddings retriever. It stores all the embeddings in memory and retrieves the k nearest neighbors by iterating over all the embeddings. Do not use for large datasets.
func NewSimpleRetriever ¶
func NewSimpleRetriever() *SimpleRetriever
func (*SimpleRetriever) GetRelevant ¶
func (r *SimpleRetriever) GetRelevant(query *embedding.Embedding, k int, filter FilterFn) []*Document
GetRelevant returns the k nearest neighbors to the query embedding. If a filter is provided, only the embeddings that pass the filter are considered.
func (*SimpleRetriever) Insert ¶
func (r *SimpleRetriever) Insert(id string, embedding *embedding.Embedding) bool
Insert adds the embedding to the retriever. If the retriever is full, the embedding is not added and false is returned.
func (*SimpleRetriever) Remove ¶
func (r *SimpleRetriever) Remove(id string)
Remove removes the embedding from the retriever by ID.
func (*SimpleRetriever) Swap ¶
func (r *SimpleRetriever) Swap(s *SimpleRetriever)
Swap replaces the embeddings in the retriever with the embeddings from the provided retriever. The mutex is acquired for the receiver, but not for the provided retriever.
Source Files
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