py_mnn_kb — MNN Knowledge Base Skill#

Local GraphRAG knowledge base backed by SQLite + MNN embeddings. Fully compatible with Android OfflineAI RAG database format.

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Setup#

1. Install dependencies#

bash

pip install -r requirements.txt

2. Configure#

bash

cp config.example.json config.json
# Edit config.json: set llm_api.api_key, optionally change default_name

Key fields in config.json:

FieldDefaultDescriptionknowledge_base.default_name``defaultKB used when --kb is omittedknowledge_base.storage_dir``assets/knowledge_basesWhere DB files are storedllm_api.api_key_(required for query+LLM)_OpenAI-compatible API keygraph_ner.custom_dict_path``assets/example_terms.jsonDomain terminology for NER

3. First run (auto-downloads embedding model)#

bash

python scripts/py_mnn_kb.py status

On first use, Qwen3-Embedding-0.6B-MNN-int4 (~400 MB) is auto-downloaded into assets/.

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Tools#

kb_build — Build / append knowledge base from files#

Indexes a directory of documents. Runs in background; returns immediately. Check progress with kb_status.

Parameters:

NameTypeRequiredDescriptiondir_pathstringyesDirectory path to index (recursive)kb_namestringnoKB name (default: value of default_name in config.json)

Returns: { status, command, kb_name, pid, files, message }

Supported formats: .txt .md .pdf .docx .pptx .xlsx .csv .html .json .jsonl

CLI:

bash

python scripts/py_mnn_kb.py build ./my_docs/ --kb my_kb
python scripts/py_mnn_kb.py build ./my_docs/          # uses default KB name

Trigger phrases: "加入知识库", "索引这个目录", "build KB", "index these files"

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kb_note — Insert a text note directly into the knowledge base#

Embeds and stores a free-form text snippet. Synchronous. Refused while build is running.

Parameters:

NameTypeRequiredDescriptiontextstringyesText content to storekb_namestringnoKB name (default: default_name)titlestringnoOptional title, stored as source label

Returns: { status, kb_name, chunks_added, elapsed_sec }

CLI:

bash

python scripts/py_mnn_kb.py note "Q1 roadmap: focus on modules A and B" --kb my_kb
python scripts/py_mnn_kb.py note "$(cat meeting.txt)" --kb my_kb --title "Weekly meeting"

Trigger phrases: "记住这个", "记录一下", "加个笔记", "save this", "remember this"

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kb_query — Retrieve relevant chunks (RAG retrieval)#

Runs vector + BM25 + GraphRAG fusion and returns the top-N context string. The agent appends this context to its prompt — no LLM call is made inside this tool. Synchronous. Refused while build is running.

Parameters:

NameTypeRequiredDescriptionpromptstringyesQuery question or keywordskb_namestringnoKB name (default: default_name)

Returns: Multi-document context string, e.g.:

text

Document1 [ID:42 source:manual.pdf]:
Deployment has three steps...

Document2 [ID:55 source:notes.md]:
...

CLI:

bash

python scripts/py_mnn_kb.py query "NAND筛选核心流程" --kb my_kb --no-llm
python scripts/py_mnn_kb.py --output json query "产品路线图" --kb my_kb

Agent usage pattern:

text

context = kb_query("用户的问题", kb_name="my_kb")
# Then: f"Based on the following context:\n{context}\n\nQuestion: {user_question}"

Trigger phrases: "查知识库", "查一下", "知识库里有没有", "search KB"

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kb_status — Check build progress or last build result#

No KB initialization needed. Always returns instantly.

Parameters:

NameTypeRequiredDescriptionkb_namestringno(informational only, does not affect result)

Returns:

• While building: { status: "building", progress: 0-100, message }
• After success: { status: "ok", message, stats: { chunks_added, elapsed_sec, ... } }
• After failure: { status: "error", error }
• Not yet run: { status: "idle", message }

CLI:

bash

python scripts/py_mnn_kb.py status

Trigger phrases: "构建进度", "build status", "知识库建好了吗"

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Workflow Examples#

A · User uploads files → auto-index

text

User: "把这些文档加入知识库"
Agent → save files to temp dir
      → kb_build(dir_path=tmp_dir, kb_name="my_kb")   # returns immediately
      → "已开始后台构建，用 kb_status 检查进度"

B · User dictates a note → insert

text

User: "记住：STAR2000 低温写性能提升 8%"
Agent → kb_note(text="STAR2000 低温写性能提升 8%", kb_name="my_kb", title="技术发现")
      → "已保存到知识库 my_kb"

C · User asks a question → KB-assisted answer

text

User: "NAND 筛选核心流程是什么？"
Agent → context = kb_query("NAND 筛选核心流程", kb_name="my_kb")
      → append context to LLM prompt → generate answer

D · Check if build finished before querying

text

Agent → st = kb_status()
      → if st["status"] == "building": tell user to wait
      → else: proceed with kb_query(...)

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Notes#

• kb_build is incremental append — re-running on the same directory adds only new content
• kb_note and kb_query are blocked (return status: building) while a build is running
• --output json on any CLI command returns machine-parseable JSON on stdout
• KB name default is used when --kb is omitted; configure default_name in config.json