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* Implement tests for migrating configuration from v1 to v2 for the litellm provider. * Validate the structure and values of the migrated configuration. * Ensure migration rejects newer versions of the configuration. fix(validate): enhance AI provider validation logic * Consolidate provider validation into a dedicated method. * Ensure at least one provider is specified and validate its type. * Check for required fields based on provider type. fix(mcpserver): update tool set to use new enrichment tool * Replace RetryMetadataTool with RetryEnrichmentTool in the ToolSet. fix(tools): refactor tools to use embedding and metadata runners * Update tools to utilize EmbeddingRunner and MetadataRunner instead of Provider. * Adjust method calls to align with the new runner interfaces.
45 lines
1.2 KiB
Go
45 lines
1.2 KiB
Go
package tools
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import (
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"context"
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"github.com/google/uuid"
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"git.warky.dev/wdevs/amcs/internal/ai"
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"git.warky.dev/wdevs/amcs/internal/config"
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"git.warky.dev/wdevs/amcs/internal/store"
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thoughttypes "git.warky.dev/wdevs/amcs/internal/types"
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)
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// semanticSearch runs vector similarity search if embeddings exist for the
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// primary embedding model in the given scope, otherwise falls back to Postgres
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// full-text search. Search always uses the primary model so query vectors
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// match rows stored under the primary model name.
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func semanticSearch(
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ctx context.Context,
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db *store.DB,
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embeddings *ai.EmbeddingRunner,
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search config.SearchConfig,
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query string,
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limit int,
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threshold float64,
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projectID *uuid.UUID,
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excludeID *uuid.UUID,
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) ([]thoughttypes.SearchResult, error) {
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model := embeddings.PrimaryModel()
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hasEmbeddings, err := db.HasEmbeddingsForModel(ctx, model, projectID)
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if err != nil {
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return nil, err
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}
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if hasEmbeddings {
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embedding, err := embeddings.EmbedPrimary(ctx, query)
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if err != nil {
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return nil, err
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}
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return db.SearchSimilarThoughts(ctx, embedding, model, threshold, limit, projectID, excludeID)
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}
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return db.SearchThoughtsText(ctx, query, limit, projectID, excludeID)
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}
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