cortexdb

CortexDB Skill

CortexDB is a pure-Go, single-file AI memory and knowledge graph library built on SQLite.

Current Architecture

Use the right layer:

pkg/cortexdb
  Main public DB facade: vectors, text search, knowledge, memory, KnowledgeMemory, KG, tools, MCP.

pkg/memoryflow
  Agent memory workflow: transcript ingest, recall, wake-up layers, diary, promotion.

pkg/graphflow
  Corpus-to-graph workflow: extraction schema, build, analyze, report, export, HTML.

pkg/importflow
  Structured-data import workflow: CSV / live Postgres / live MySQL -> RAG + knowledge graph.

pkg/connector
  Privacy gate in front of importflow: live-source introspection, PII classification,
  human-signed MaskingPlan, desensitization (masking + free-text redaction + reversible
  per-tenant AES-256-GCM token vault). Feeds RAG + KG; agent tools + MCP server.

pkg/graph
  Low-level graph engine: property graph, RDF triples/quads, SPARQL, RDFS, SHACL.

pkg/core
  SQLite storage, embeddings, FTS5, vector indexes, chat/session primitives.

Default recommendation:

• Use pkg/cortexdb for application code.
• Use pkg/memoryflow for chat/session/agent memory workflows.
• Use pkg/graphflow for document/corpus-to-graph extraction and report/export workflows.
• Use pkg/importflow to import structured data (CSV / live Postgres / MySQL) into RAG + KG.
• Use pkg/connector when that structured data contains PII and must be desensitized (under a human-signed plan) before it enters RAG/KG, with reversible un-mask via a vault.
• Use pkg/graph only for low-level RDF/SPARQL/RDFS/SHACL or property graph control.

Install

import "github.com/liliang-cn/cortexdb/v2/pkg/cortexdb"

Core DB Usage

db, err := cortexdb.Open(cortexdb.DefaultConfig("KnowledgeMemory.db"))
if err != nil {
    return err
}
defer db.Close()

quick := db.Quick()
_, _ = quick.Add(ctx, []float32{0.1, 0.2, 0.9}, "SQLite is a single-file database.")
hits, _ := quick.Search(ctx, []float32{0.1, 0.2, 0.8}, 3)
_ = hits

Knowledge and Memory

_, _ = db.SaveKnowledge(ctx, cortexdb.KnowledgeSaveRequest{
    KnowledgeID: "apollo-plan",
    Title:       "Apollo launch plan",
    Content:     "Alice owns Apollo. Apollo ships on Friday.",
    ChunkSize:   24,
    Entities: []cortexdb.ToolEntityInput{
        {Name: "Alice", Type: "person", ChunkIDs: []string{"chunk:apollo-plan:000"}},
        {Name: "Apollo", Type: "project", ChunkIDs: []string{"chunk:apollo-plan:000"}},
    },
    Relations: []cortexdb.ToolRelationInput{
        {From: "Alice", To: "Apollo", Type: "owns"},
    },
})

resp, _ := db.SearchKnowledge(ctx, cortexdb.KnowledgeSearchRequest{
    Query:         "Who owns Apollo?",
    Keywords:      []string{"Apollo", "Alice", "owns"},
    RetrievalMode: cortexdb.RetrievalModeLexical,
    TopK:          3,
})
_ = resp.Context

_, _ = db.SaveMemory(ctx, cortexdb.MemorySaveRequest{
    MemoryID:  "style",
    UserID:    "user-1",
    Scope:     cortexdb.MemoryScopeUser,
    Namespace: "assistant",
    Content:   "User prefers concise status updates.",
})

No-embedder mode is supported. Use lexical retrieval plus LLM-planned Keywords, AlternateQueries, EntityNames, and RetrievalMode.

Knowledge Graph APIs

High-level APIs live in pkg/cortexdb:

• UpsertKnowledgeGraph
• FindKnowledgeGraph
• DeleteKnowledgeGraph
• ImportKnowledgeGraph
• ExportKnowledgeGraph
• QueryKnowledgeGraph
• ValidateKnowledgeGraphSHACL
• RefreshKnowledgeGraphInference
• SummarizeKnowledgeGraphInference
• ExplainKnowledgeGraphInference
• ExplainKnowledgeGraphInferenceMatch

_, _ = db.UpsertKnowledgeGraph(ctx, cortexdb.KnowledgeGraphUpsertRequest{
    Triples: []cortexdb.KnowledgeGraphTriple{
        {
            Subject:   graph.NewIRI("https://example.com/alice"),
            Predicate: graph.NewIRI(graph.RDFType),
            Object:    graph.NewIRI("https://example.com/Person"),
        },
    },
})

result, _ := db.QueryKnowledgeGraph(ctx, cortexdb.KnowledgeGraphQueryRequest{
    Query: `SELECT ?o WHERE { <https://example.com/alice> ?p ?o . }`,
})
_ = result

SPARQL is a practical embedded subset. It includes SELECT, ASK, CONSTRUCT, DESCRIBE, update forms, GRAPH, OPTIONAL, UNION, MINUS, VALUES, BIND, FILTER, EXISTS, NOT EXISTS, aggregates, subqueries, and constrained property paths: ^pred, p|q, p+, p*.

RDFS-lite:

refresh, _ := db.RefreshKnowledgeGraphInference(ctx, cortexdb.KnowledgeGraphInferenceRefreshRequest{
    Mode: cortexdb.KnowledgeGraphInferenceRefreshModeIncremental,
    Triples: []cortexdb.KnowledgeGraphTriple{
        {
            Subject:   graph.NewIRI("https://example.com/Employee"),
            Predicate: graph.NewIRI("http://www.w3.org/2000/01/rdf-schema#subClassOf"),
            Object:    graph.NewIRI("https://example.com/Person"),
        },
    },
})
_ = refresh

SHACL-lite:

report, _ := db.ValidateKnowledgeGraphSHACL(ctx, cortexdb.KnowledgeGraphSHACLValidateRequest{
    Shapes: []cortexdb.KnowledgeGraphTriple{
        {Subject: graph.NewIRI("https://example.com/PersonShape"), Predicate: graph.NewIRI(graph.RDFType), Object: graph.NewIRI(graph.SHACLNodeShape)},
        {Subject: graph.NewIRI("https://example.com/PersonShape"), Predicate: graph.NewIRI(graph.SHACLTargetClass), Object: graph.NewIRI("https://example.com/Person")},
    },
})
_ = report

MemoryFlow

Use pkg/memoryflow for agent memory workflows:

flow, _ := memoryflow.New(db, planner, extractor)

_, _ = flow.IngestTranscript(ctx, memoryflow.IngestTranscriptRequest{
    Transcript: memoryflow.Transcript{
        SessionID: "session-1",
        UserID:    "user-1",
        Source:    "chat",
        Turns: []memoryflow.TranscriptTurn{
            {Role: "user", Content: "Apollo ships on Friday."},
            {Role: "assistant", Content: "Captured."},
        },
    },
    Scope:     cortexdb.MemoryScopeSession,
    Namespace: "assistant",
})

layers, _ := flow.WakeUpLayers(ctx, memoryflow.WakeUpLayersRequest{
    Identity: "You are the Apollo project assistant.",
    Recall: memoryflow.RecallRequest{
        Query:     "startup context",
        SessionID: "session-1",
        Scope:     cortexdb.MemoryScopeSession,
        Namespace: "assistant",
    },
})
_ = layers

LLM-dependent interfaces:

• QueryPlanner
• SessionExtractor
• PromotionPolicy

Optional Hindsight recall strategy plugin:

flow, _ := memoryflow.New(
    db,
    planner,
    extractor,
    memoryflow.WithRecallStrategy(hindsight.NewStrategy(db, hindsight.StrategyOptions{
        BankID:      "apollo-agent",
        EntityNames: []string{"Apollo"},
        Keywords:    []string{"deadline"},
        UseKG:       true,
    })),
)

GraphFlow

Use pkg/graphflow for corpus-to-graph workflows:

extraction := graphflow.ExtractionResult{ /* nodes + edges */ }
_, _ = graphflow.Build(ctx, db, []graphflow.ExtractionResult{extraction}, graphflow.BuildOptions{})
analysis, _ := graphflow.Analyze(ctx, db, graphflow.AnalyzeRequest{TopN: 10})
report, _ := graphflow.RenderReport(ctx, analysis)
_, _ = graphflow.Export(ctx, db, graphflow.ExportRequest{OutputDir: "graphflow-out", Analysis: analysis, Report: report})
_, _ = graphflow.ExportHTML(ctx, db, graphflow.ExportRequest{OutputDir: "graphflow-out", Analysis: analysis})

LLM extraction uses only this interface:

type JSONGenerator interface {
    GenerateJSON(ctx context.Context, systemPrompt string, userPrompt string) ([]byte, error)
}

The example examples/05_graphflow uses github.com/openai/openai-go/v3 with JSON Schema structured output:

OPENAI_API_KEY=...
OPENAI_BASE_URL=http://43.167.167.6:8080/v1
OPENAI_MODEL=gpt-5.4

Tools and MCP

In-process tool calls:

tools := db.GraphRAGTools()
defs := tools.Definitions()
resp, err := tools.Call(ctx, "knowledge_graph_query", payload)
_, _, _ = defs, resp, err

MCP server:

server := db.NewMCPServer(cortexdb.MCPServerOptions{})
_ = server

Important tools:

• GraphRAG: ingest_document, search_text, expand_graph, build_context
• Knowledge/memory: knowledge_save, knowledge_search, memory_save, memory_search
• Knowledge graph: knowledge_graph_upsert, knowledge_graph_query, knowledge_graph_shacl_validate, knowledge_graph_infer_refresh
• KnowledgeMemory: knowledge_memory_recall, knowledge_memory_build_context_pack, knowledge_memory_reflect, knowledge_memory_consolidate
• Ontology/inference: ontology_save, apply_inference

Separate workflow toolboxes:

• memoryflow: memoryflow_ingest_transcript, memoryflow_recall, memoryflow_wake_up_layers, memoryflow_prepare_reply
• graphflow: graphflow_build, graphflow_analyze, graphflow_report, graphflow_export, graphflow_run
• importflow: importflow_plan, importflow_run (via importflow.NewToolbox(im) / importflow.NewMCPServer(im, opts) / importflow.RunMCPStdio(ctx, im, opts))
• connector: connector_introspect, connector_plan, connector_run, connector_unmask — privacy gate over a live DB/source feeding RAG + KG; pseudonymized join keys become deterministic tokens so KG edges survive while raw PII never enters the graph or the vault-free retrieval path. Wire via connector.NewToolbox(db, opts) + connector.NewMCPServer(tb, opts) / connector.RunMCPStdio(ctx, tb, opts), connector.RegisterMCPTools(server, tb) to ride another server, or run cmd/cortexdb-connector-mcp.

Optional Semantic Router

pkg/semantic-router is optional. Use it before CortexDB tools when you need intent routing.

No-embedder lexical router:

router, _ := semanticrouter.NewLexicalRouter(semanticrouter.WithSparseThreshold(0.1))
_ = router.Add(&semanticrouter.SparseRoute{Name: "memory_save", Utterances: []string{"remember this", "save to memory"}})
route, _ := router.Route(ctx, "please remember this")
_ = route.RouteName

Examples

The examples are architecture-oriented:

go run ./examples/01_core
go run ./examples/02_rag
go run ./examples/03_memoryflow
go run ./examples/04_knowledge_graph
go run ./examples/05_graphflow
go run ./examples/06_tools_mcp

Use examples/05_graphflow to verify OpenAI-compatible LLM graph extraction with structured output.

Checks

When changing CortexDB, run:

go build ./...
go test ./...