knowledge-graph
Build and query knowledge graphs with PostgreSQL + pgvector. Ingest episodes, extract entities/relations via LLM, and search facts by semantic similarity or keyword. Use when working with knowledge graphs, entity extraction, or graph storage.
- License 许可
- Apache-2.0
- 所属仓库
- urmzd/saige
- 最近更新
- 2026-11-12
- Stars
- ★ 6
- Forks
- 0
01 前言 Front Matter
- name
- knowledge-graph
- metadata
- { "argument-hint": [ "query" ] }
- description
- Build and query knowledge graphs with PostgreSQL + pgvector. Ingest episodes, extract entities/relations via LLM, and search facts by semantic similarity or keyword. Use when working with knowledge graphs, entity extraction, or graph storage.
03 SKILL.md 正文
knowledge-graph
Build and query knowledge graphs using saige/knowledge.
Quick Start
import (
"github.com/urmzd/saige/rag/knowledge"
"github.com/urmzd/saige/postgres"
"github.com/urmzd/saige/agent/provider/ollama"
)
// Connect to PostgreSQL (requires pgvector extension).
pool, _ := postgres.NewPool(ctx, postgres.Config{URL: "postgres://localhost:5432/mydb"})
postgres.RunMigrations(ctx, pool, postgres.MigrationOptions{})
client, _ := ollama.NewClient(ollama.Config{Host: "http://localhost:11434", Model: "qwen3.5:4b", EmbeddingModel: "nomic-embed-text"})
graph, _ := knowledge.New(knowledge.Config{},
knowledge.WithPostgres(pool),
knowledge.WithExtractor(knowledge.NewOllamaExtractor(client)),
knowledge.WithEmbedder(knowledge.NewOllamaEmbedder(client)),
)
defer graph.Close(ctx)
// Ingest
graph.IngestEpisode(ctx, &knowledge.EpisodeInput{
Name: "notes", Body: "Alice presented the roadmap.", Source: "meeting",
})
// Search
facts, _ := graph.SearchFacts(ctx, "roadmap")
Key Operations
| Method | Purpose |
|---|---|
IngestEpisode |
Extract entities/relations from text and store them |
SearchFacts |
Full-text search on relation facts |
GetEntity |
Retrieve a single entity by ID |
GetNode |
Get a node with its neighborhood (depth N) |
GetGraph |
Full graph snapshot for visualization |
ApplyOntology |
Constrain entity/relation types |
Ontology
graph.ApplyOntology(ctx, &knowledge.Ontology{
EntityTypes: []knowledge.EntityTypeDef{{Name: "Person", Description: "A human"}},
RelationTypes: []knowledge.RelationTypeDef{{Name: "works_on", SourceType: "Person", TargetType: "Project"}},
})
The ontology is passed to the extractor, which is asked to use its types. Extracted types that match an ontology type ignoring case and punctuation are rewritten to the ontology's spelling; other types are kept as extracted. Build the graph with knowledge.WithStrictOntology() to drop entities and relations whose type is not in the ontology.
Agent Tool Bindings
import "github.com/urmzd/saige/rag/knowledge/tool"
tools := tool.NewTools(graph, tool.WithGroupID(tenantID))
// kg_search, kg_ingest, both bound to one graph group the model cannot change
In a RAG pipeline, rag.WithGraphNamespace(tenantID) sets the group documents are ingested into and the graph retriever searches.
CLI
saige knowledge search --backend kg --db "$SAIGE_KG_DB" --query "Who presented?"
saige knowledge ingest --backend kg --db "$SAIGE_KG_DB" --name "meeting" --text "Alice presented the roadmap."
saige knowledge graph export --db "$SAIGE_KG_DB"
saige knowledge graph neighbors --db "$SAIGE_KG_DB" --id <entity-uuid> --depth 2
04 同仓库更多 Skills
rag
Build multi-modal RAG pipelines with graph-enhanced retrieval in Go using saige/rag. Covers chunking, multi-retriever fusion, reranking, HyDE, evaluation metrics, and PostgreSQL + pgvector storage.
agent
Build streaming LLM agent loops in Go with typed parts and part deltas, tool execution, media input, context compaction, sub-agent delegation, and RLHF feedback. Use when building AI agents, integrating LLM providers, or implementing tool-use patterns.
review
Codex copy, shadowed by the Claude one.
lint
Run the linters.
review
Review a diff for correctness.
hand
Claude copy of a skill already in .agents/skills.