graphmind-rj 0.1.2

Knowledge graph builder for code and product assets with token optimization for LLMs

graphmind-rj

Knowledge graph builder for code and product assets — with smart token optimization for LLMs.

Built by Ramesh J · PyPI · Python 3.10+

---

What is graphmind-rj?

When you use AI assistants like Claude, ChatGPT, or Copilot to work on your codebase, they need context — which files exist, what functions do, how modules relate to each other. The problem:

• Pasting full files = too many tokens = expensive and slow
• Pasting random snippets = AI gives incomplete answers
• Repeating the same context every query = wasteful and time-consuming

graphmind-rj solves this. It:

1. Scans your codebase once and builds a knowledge graph — a structured map of every function, class, SQL table, and document heading and how they connect
2. Saves that graph locally as JSON and in a SQLite database (no server needed)
3. Exposes a POST /api/context-pack endpoint — call it before asking your AI, get back only the context relevant to your specific task

How you use it in practice: run graphmind-api as a local server, run graphmind run . on your project once, then before each AI question call /api/context-pack with your question and task type. Copy the response (graph summary + code snippets + prompt template) and paste it into Claude, Copilot Chat, or Cursor. This typically reduces the context you send by 60–80% compared to pasting full files.

---

Features

Feature	What it does
🔍 Multi-language extraction	Reads Python (via AST), SQL schemas, .docx documents, .md/.txt/.rst files
📊 Knowledge graph	Builds a NetworkX graph of nodes and edges with community clustering
🚀 REST API	FastAPI server with 6 endpoints — run, upload, history, graph, context-pack
💾 SQL persistence	SQLAlchemy + SQLite stores every run, node, and edge for history and queries
📄 JSON export	Exports graph.json (portable), graph.html (readable), GRAPH_REPORT.md
🧠 Token optimization	3-tier context: graph summary → code snippets → full files, selected by query type
📋 Prompt templates	Pre-structured formats for bug_fix, feature, refactor, test, review, optimize
⚡ Incremental runs	SHA-256 hashing skips unchanged files — only processes what changed

---

Installation

pip install graphmind-rj

Nothing else needed. SQLite is built into Python. No Docker, no database server, no config files required.

After install you get two commands:

graphmind        ← CLI for running the pipeline
graphmind-api    ← starts the REST API server on port 8000

Verify:

graphmind --help

usage: graphmind [-h] {run} ...

GraphMind Enterprise CLI

positional arguments:
  {run}
    run       Run full graph pipeline

options:
  -h, --help  show this help message and exit

---

Architecture

Understanding how all the pieces connect makes the tool much easier to use.

YOUR PROJECT FILES
        │
        ▼
┌──────────────────────────────────────────────────────────┐
│                     PIPELINE                             │
│                                                          │
│  1. detect.py        Finds all files by extension        │
│                      Buckets: code / document / paper    │
│                      Skips: .git node_modules venv dist  │
│                                                          │
│  2. extractors/      Reads each file, pulls out nodes    │
│     python_ast.py    → functions, classes, methods       │
│     sql_schema.py    → tables, columns (CREATE TABLE)    │
│     text_semantic.py → headings, paragraphs (.md/.txt)   │
│     docx_semantic.py → headings, paragraphs (.docx)      │
│     registry.py      → merges all, removes duplicates    │
│                                                          │
│  3. graph/builder.py  Builds NetworkX in-memory graph    │
│     graph/analytics.py  Louvain community detection      │
│                          Hub ranking by degree           │
│                                                          │
│  4. exporters/        Writes output files                │
│     json_exporter.py → graphmind-out/graph.json          │
│     html_exporter.py → graphmind-out/graph.html          │
│     pipeline.py      → graphmind-out/GRAPH_REPORT.md     │
│                                                          │
│  5. db.py             Saves to SQLite                    │
│     RunRecord         → summary (files/nodes/edges)      │
│     NodeRecord        → every node linked to run_id      │
│     EdgeRecord        → every edge linked to run_id      │
│                                                          │
│  6. cache.py          Saves file hashes for next run     │
└──────────────────────────────────────────────────────────┘
        │
        ▼
┌──────────────────────────────────────────────────────────┐
│                     REST API (api.py)                    │
│                                                          │
│  POST /api/run           → triggers pipeline             │
│  POST /api/upload        → saves .md/.docx/.sql files    │
│  GET  /api/runs          → list run history from SQLite  │
│  GET  /api/runs/{id}/graph → nodes+edges for a run       │
│  POST /api/context-pack  → optimized LLM context         │
│  GET  /health            → server health check           │
│                                                          │
│  context-pack uses:                                      │
│    context_budget.py   → allocates token budget          │
│    retrieval_planner.py → picks tier (1/2/3)             │
│    prompt_templates.py → returns task prompt format      │
└──────────────────────────────────────────────────────────┘
        │
        ▼
┌──────────────────────────────────────────────────────────┐
│              FRONTEND (frontend/)                        │
│  React + Vite dashboard on http://localhost:5173         │
│  Talks to backend via /api proxy                         │
│  Panels: Run · Upload · History                          │
└──────────────────────────────────────────────────────────┘

---

Files Scanned (what detect.py reads)

Extension	Category	Extractor used
.py	code	python_ast.py
.js, .ts, .go, .java	code	(detected, extractor in roadmap)
.sql	code	sql_schema.py
.md, .txt, .rst	document	text_semantic.py
.docx	document	docx_semantic.py
.pdf	paper	(detected, extractor in roadmap)

Automatically skipped directories: .git, node_modules, venv, dist, build

Max file size: 5 MB (configurable via GovernancePolicy)

---

End-to-End Tutorial

Step 1 — Start the API Server

Open a terminal and run:

graphmind-api

You will see:

INFO:     Started server process [28040]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Leave this terminal open. The GET / 404 Not Found message you may see is normal — the API has no root page. It only responds on specific paths like /health, /api/run, etc.

Open a second terminal for all next steps.

---

Step 2 — Health Check

Confirm the server is running:

curl http://localhost:8000/health

Response:

{"status": "ok"}

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Step 3 — Analyse Your Project

Navigate to any project folder and run:

graphmind run /path/to/your/project

Or from inside the project:

cd /path/to/your/project
graphmind run .

To force a full rescan (ignore the cache):

graphmind run . --full

Terminal output:

GraphMind complete. Files=42 Nodes=142 Edges=198 Communities=7
Artifacts: graphmind-out

What gets created in graphmind-out/:

File	Format	Purpose
graph.json	JSON	Full graph — nodes, edges, community IDs. Share it, visualize it, feed it to other tools
graph.html	HTML	Human-readable table view — open in any browser
GRAPH_REPORT.md	Markdown	Summary: total files, words, nodes, edges, communities
graphmind.db	SQLite	Run history + all nodes + all edges, queryable via API
cache/file_hashes.json	JSON	SHA-256 hashes of every scanned file — used for incremental runs

Incremental mode (default): on the second graphmind run ., only files whose SHA-256 hash changed get re-extracted. If 2 files changed out of 42, only those 2 are processed. Saves significant time on large codebases.

---

Step 4 — Run Pipeline via REST API

Same as Step 3 but triggered via HTTP — useful for scripts, CI, or the React frontend:

curl -X POST http://localhost:8000/api/run \
  -H "Content-Type: application/json" \
  -d '{"path": "/path/to/your/project", "full": false}'

Field	Type	Default	Meaning
path	string	"."	Directory to scan
full	boolean	false	true = ignore cache, scan everything

Response:

{
  "run_id": 1,
  "files": 42,
  "words": 15600,
  "nodes": 142,
  "edges": 198,
  "communities": 7,
  "out_dir": "graphmind-out"
}

run_id is important — you use it in every follow-up API call to reference this specific run.

---

Step 5 — Upload Standalone Files

Use this when you have files outside your codebase that you want in the graph — design documents, SQL migration files, requirements specs:

curl -F "files=@architecture.md" \
     -F "files=@migration_v2.sql" \
     -F "files=@requirements.docx" \
     http://localhost:8000/api/upload

Accepted: .md, .docx, .sql only. Other extensions are rejected.

Response:

{
  "folder": "graphmind-out/uploads/20260408_143021",
  "files_saved": [
    "graphmind-out/uploads/20260408_143021/architecture.md",
    "graphmind-out/uploads/20260408_143021/migration_v2.sql",
    "graphmind-out/uploads/20260408_143021/requirements.docx"
  ]
}

Then run the pipeline on that upload folder to extract and store the graph:

curl -X POST http://localhost:8000/api/run \
  -H "Content-Type: application/json" \
  -d '{"path": "graphmind-out/uploads/20260408_143021", "full": true}'

---

Step 6 — View Run History

Every pipeline run is saved to SQLite. List them:

curl http://localhost:8000/api/runs

Optional limit parameter:

curl "http://localhost:8000/api/runs?limit=5"

Response (newest first):

[
  {
    "id": 2,
    "target_path": "/path/to/project",
    "files": 44,
    "words": 16200,
    "nodes": 155,
    "edges": 210,
    "communities": 8,
    "created_at": "2026-04-08T16:45:00"
  },
  {
    "id": 1,
    "target_path": "/path/to/project",
    "files": 42,
    "words": 15600,
    "nodes": 142,
    "edges": 198,
    "communities": 7,
    "created_at": "2026-04-08T09:12:05"
  }
]

You can see how the graph grew between runs as you added code.

---

Step 7 — Inspect the Graph for a Run

Get every node and edge extracted during a specific run:

curl http://localhost:8000/api/runs/1/graph

Response:

{
  "run_id": 1,
  "nodes": 142,
  "edges": 198,
  "graph": {
    "nodes": [
      {
        "id": "authenticate_user",
        "label": "authenticate_user",
        "kind": "function",
        "source_file": "auth.py"
      },
      {
        "id": "users",
        "label": "users",
        "kind": "table",
        "source_file": "schema.sql"
      }
    ],
    "edges": [
      {
        "source": "authenticate_user",
        "target": "check_password",
        "relation": "calls",
        "confidence": "high",
        "confidence_score": 0.97,
        "source_file": "auth.py"
      }
    ]
  }
}

Node kind values: function, class, method, table, heading, concept

Edge relation values: calls, defines, contains, references, links

---

Step 8 — Get Optimized LLM Context (The Core Feature)

This is the endpoint you use before every AI query. Instead of pasting full files into Claude or Copilot, call this first:

curl -X POST http://localhost:8000/api/context-pack \
  -H "Content-Type: application/json" \
  -d '{
    "run_id": 1,
    "task_type": "bug_fix",
    "query": "Why is user authentication failing after the latest deployment?",
    "token_budget": 6000,
    "include_artifacts": true
  }'

Request fields:

Field	Type	Required	Description
run_id	int	✅	Which run to pull context from
task_type	string	✅	bug_fix / feature / refactor / test / review / optimize
query	string	✅	Your exact question — used to select retrieval tier
token_budget	int	optional	Max tokens to include. Default: 8000
include_artifacts	boolean	optional	Include cached module summaries. Default: true

Response:

{
  "tier": "snippets",
  "graph_summary": {
    "nodes_count": 142,
    "edges_count": 198,
    "communities": 7,
    "top_hubs": ["UserService", "AuthModule", "DatabaseLayer", "SessionManager", "PasswordHasher"],
    "summary": "Graph with 142 nodes, 7 communities"
  },
  "code_snippets": [
    {
      "file_path": "auth.py",
      "start_line": 45,
      "end_line": 65,
      "content": "def authenticate_user(username, password):\n    return check_password(username, password)",
      "reason": "function_definition"
    }
  ],
  "prompt_template": {
    "task_type": "bug_fix",
    "name": "Bug Fix Investigation",
    "sections": [
      "## Problem",
      "## Current Behavior",
      "## Expected Behavior",
      "## Relevant Code",
      "## Test Case",
      "## Proposed Fix"
    ],
    "required_contexts": ["changed_files", "error_trace", "test_case"]
  },
  "cached_artifacts": {},
  "total_tokens": 2380,
  "recommended_next_steps": [
    "Run the failing test to reproduce",
    "Add breakpoints in the shown code",
    "Check git blame for recent changes"
  ]
}

How to use the response — step by step:

1. Copy graph_summary → paste it into your AI as: "Here is the codebase overview"
2. Copy code_snippets[].content → paste as: "Here is the relevant code"
3. Use prompt_template.sections as headers to structure your question
4. Follow recommended_next_steps as your debugging/development guide
5. Check total_tokens — that is exactly how many tokens you spent

---

Step 9 — Task Types — Which to Use and When

bug_fix — Something is broken

Budget: 6000 · Tier used: snippets

curl -X POST http://localhost:8000/api/context-pack \
  -d '{"run_id":1, "task_type":"bug_fix", "query":"Login returns 500 after deploy", "token_budget":6000}'

Prompt sections returned: Problem · Current Behavior · Expected Behavior · Relevant Code · Test Case · Proposed Fix

---

feature — Adding new functionality

Budget: 8000–10000 · Tier used: snippets → full_files

curl -X POST http://localhost:8000/api/context-pack \
  -d '{"run_id":1, "task_type":"feature", "query":"Add OAuth2 Google login", "token_budget":10000}'

Prompt sections: Feature Request · Requirements · Design Constraints · Integration Points · Implementation Steps · Success Criteria

---

refactor — Restructuring existing code

Budget: 8000 · Tier used: full_files

curl -X POST http://localhost:8000/api/context-pack \
  -d '{"run_id":1, "task_type":"refactor", "query":"Split UserService into auth and profile services", "token_budget":8000}'

Prompt sections: Current Structure · Problems · Target Structure · Safety Checks · Steps · Testing Strategy

---

test — Writing tests

Budget: 4000 · Tier used: graph_summary → snippets

curl -X POST http://localhost:8000/api/context-pack \
  -d '{"run_id":1, "task_type":"test", "query":"Write unit tests for authenticate_user", "token_budget":4000}'

Prompt sections: Code to Test · Test Objectives · Test Cases · Edge Cases · Execution Strategy

---

review — Code review / security audit

Budget: 12000 · Tier used: full_files

curl -X POST http://localhost:8000/api/context-pack \
  -d '{"run_id":1, "task_type":"review", "query":"Review payment processing module for security issues", "token_budget":12000}'

Prompt sections: Code Summary · Reviewed Files · Quality Checks · Security Scan · Performance Impact · Recommendations

---

optimize — Performance improvements

Budget: 6000 · Tier used: snippets

curl -X POST http://localhost:8000/api/context-pack \
  -d '{"run_id":1, "task_type":"optimize", "query":"Database queries taking 3+ seconds", "token_budget":6000}'

Prompt sections: Current Performance · Bottleneck Analysis · Optimization Options · Implementation Plan · Benchmarking Strategy

---

Step 10 — Start the React Dashboard (Optional)

The dashboard gives you a browser UI instead of curl commands:

cd frontend
npm install
npm run dev

Open: http://localhost:5173

Panels:

• Run panel: type a directory path, click Run → triggers POST /api/run
• Upload panel: drag .md, .docx, .sql files → triggers POST /api/upload
• History panel: table of all past runs with files/nodes/edges metrics

The Vite dev server proxies /api/* to http://localhost:8000 automatically.

---

How the 3-Tier Token System Works

When you call /api/context-pack, the retrieval_planner.py module decides how much context to include:

Your query
    │
    ▼
Tier 1 — Graph Summary  (~500 tokens)
  Returns: node count, edge count, community count, top 5 hub nodes
  Used when: query is about architecture, overview, or system design
  Example query: "How is this system structured?"
    │
    │  Does the query contain: bug / debug / fix / error / integrate / review / refactor?
    ▼  AND do you have remaining budget?
Tier 2 — Code Snippets  (~1,500–3,000 tokens)
  Returns: tier 1 + function/class definitions from key files
  Used when: debugging, feature planning, targeted questions
  Example query: "Why is authentication failing?"
    │
    │  Is the task a complex refactor or full review?
    ▼  AND do you still have budget?
Tier 3 — Full Files  (~3,000–8,000 tokens)
  Returns: tier 1 + tier 2 + complete file content
  Used when: comprehensive rewrites, full security audits
  Example query: "Completely restructure the auth module"

Token Budget Allocation — within each tier, files are ranked by priority:

Priority	Which files	Budget share
CRITICAL	Files that changed since last run	50%
HIGH	Direct dependencies of changed files	30%
MEDIUM	Tests and related modules	15%
LOW	Documentation and config files	5%

Files are sorted smallest-first within each tier so the maximum number of files fits within budget.

---

Data Storage — Exactly What Goes Where

Location	Format	Written when	Contains
graphmind-out/graph.json	JSON	Every pipeline run	Full graph: nodes, edges, community IDs
graphmind-out/graph.html	HTML	Every pipeline run	Readable table of nodes and edges
graphmind-out/GRAPH_REPORT.md	Markdown	Every pipeline run	File counts, node/edge/community totals
graphmind-out/graphmind.db	SQLite	Every pipeline run (via API)	runs, nodes, edges tables
graphmind-out/cache/file_hashes.json	JSON	Every pipeline run	SHA-256 hash per file path
graphmind-out/uploads/TIMESTAMP/	original files	On upload	Your uploaded .md/.docx/.sql files
graphmind-out/artifacts/	JSON index + text	On context-pack call	Cached module summaries

None of this data leaves your machine. Everything is local.

---

Daily Workflow — VS Code / Cursor

Morning: start of coding session
─────────────────────────────────────────────────────
Terminal 1:  graphmind-api          ← leave running all day

Terminal 2:  cd my-project
             graphmind run .         ← takes ~3–10 seconds
                                       (incremental — only changed files)


When you hit a bug:
─────────────────────────────────────────────────────
curl -X POST http://localhost:8000/api/context-pack \
  -d '{
    "run_id": 1,
    "task_type": "bug_fix",
    "query": "payment is failing at checkout step 3",
    "token_budget": 6000
  }'

→ Copy graph_summary + code_snippets from the response
→ Open Claude / Cursor / Copilot Chat
→ Paste context + ask question using the prompt_template sections
→ Get precise answer


When you add new files:
─────────────────────────────────────────────────────
graphmind run .     ← incremental, only new/changed files
                       run_id increments automatically


When you have a design doc:
─────────────────────────────────────────────────────
curl -F "files=@feature-spec.docx" http://localhost:8000/api/upload
curl -X POST http://localhost:8000/api/run \
  -d '{"path": "graphmind-out/uploads/TIMESTAMP", "full": true}'

---

Full API Reference

GET /health

curl http://localhost:8000/health

{"status": "ok"}

---

POST /api/run

curl -X POST http://localhost:8000/api/run \
  -H "Content-Type: application/json" \
  -d '{"path": ".", "full": false}'

Returns: run_id, files, words, nodes, edges, communities, out_dir

---

POST /api/upload

curl -F "files=@schema.sql" -F "files=@notes.md" http://localhost:8000/api/upload

Accepted extensions: .md, .docx, .sql Returns: folder (path to saved files), files_saved (list of paths)

---

GET /api/runs

curl "http://localhost:8000/api/runs?limit=20"

Returns: array of run history items (newest first)

---

GET /api/runs/{run_id}/graph

curl http://localhost:8000/api/runs/1/graph

Returns: run_id, nodes (count), edges (count), graph (full node/edge data)

---

POST /api/context-pack

curl -X POST http://localhost:8000/api/context-pack \
  -H "Content-Type: application/json" \
  -d '{
    "run_id": 1,
    "task_type": "bug_fix",
    "query": "your question here",
    "token_budget": 6000
  }'

Returns: tier, graph_summary, code_snippets, full_files, prompt_template, cached_artifacts, total_tokens, recommended_next_steps

---

Project Structure

graphmind-rj/
├── pyproject.toml                ← package config, dependencies, version
├── setup.py                      ← build entry point
├── README.md
├── CHANGELOG.md
├── LICENSE
│
├── graphmind/                    ← Python package (installed by pip)
│   ├── __init__.py               ← version = "0.1.1"
│   ├── __main__.py               ← python -m graphmind entry
│   │
│   ├── cli.py                    ← graphmind run . / graphmind run . --full
│   ├── api.py                    ← FastAPI app, all 6 endpoints
│   ├── pipeline.py               ← orchestrates detect→extract→build→export
│   ├── detect.py                 ← file discovery, extension bucketing
│   ├── config.py                 ← GraphMindConfig dataclass, GovernancePolicy
│   ├── models.py                 ← Node, Edge, ExtractionResult, ContextPack
│   ├── db.py                     ← SQLAlchemy ORM: RunRecord, NodeRecord, EdgeRecord
│   ├── cache.py                  ← SHA-256 hashing + ArtifactCache class
│   ├── security.py               ← email + secret redaction in extracted text
│   │
│   ├── context_budget.py         ← TokenBudgetManager, FilePriority tiers
│   ├── retrieval_planner.py      ← RetrieverPlanner, ContextGate, 3-tier logic
│   ├── prompt_templates.py       ← PromptTemplateRegistry, 6 task templates
│   │
│   ├── extractors/
│   │   ├── base.py               ← Extractor protocol (interface)
│   │   ├── registry.py           ← runs all extractors, deduplicates output
│   │   ├── python_ast.py         ← Python: functions, classes, methods via AST
│   │   ├── sql_schema.py         ← SQL: CREATE TABLE → table + column nodes
│   │   ├── text_semantic.py      ← .md/.txt/.rst: heading → concept nodes
│   │   └── docx_semantic.py      ← .docx: paragraph/heading extraction
│   │
│   ├── graph/
│   │   ├── builder.py            ← NetworkX graph from nodes + edges
│   │   └── analytics.py         ← Louvain communities, hub degree ranking
│   │
│   └── exporters/
│       ├── json_exporter.py      ← graph.json via networkx node_link_data
│       └── html_exporter.py      ← graph.html table view
│
├── frontend/                     ← React + Vite (not included in pip install)
│   ├── package.json
│   ├── vite.config.js            ← /api proxy → localhost:8000
│   ├── index.html
│   └── src/
│       ├── main.jsx
│       ├── App.jsx               ← Run / Upload / History dashboard
│       └── styles.css
│
└── tests/
    ├── test_api.py               ← API endpoint tests
    └── test_pipeline.py          ← end-to-end pipeline test

---

Development Setup

git clone https://github.com/rameshj/graphmind-rj.git
cd graphmind-rj
pip install -e ".[dev]"
pytest tests/ -v

---

License

MIT — See LICENSE

Author

Ramesh J

Links

• PyPI: https://pypi.org/project/graphmind-rj/
• Install: pip install graphmind-rj
• Issues: https://github.com/rameshj/graphmind-rj/issues

---

What is graphmind-rj?

When you use AI assistants like Claude, ChatGPT, or Copilot to work on your codebase, they need context — which files exist, what functions do, how modules relate to each other. The problem:

• Sending full files = too many tokens = expensive and slow
• Sending random snippets = AI gives wrong answers
• Reloading everything every query = wasteful and repetitive

graphmind-rj solves this. It:

1. Reads your codebase once and builds a knowledge graph — a map of every function, class, table, and document and how they connect
2. Stores that graph in a local SQL database (SQLite — no server needed)
3. Exposes a POST /api/context-pack endpoint — you call it before asking your AI, and it returns only the relevant context for that specific task instead of full files

Note: graphmind-rj is a context preparation tool. It does not automatically intercept AI queries. The workflow is: call /api/context-pack → get back a compact, task-focused context → paste it into Claude, Copilot, Cursor, or any AI tool. In practice this typically reduces the tokens you send by 60–80% compared to pasting whole files, though actual savings depend on your project size and query type.

---

Features

Feature	Description
🔍 Multi-language extraction	Python (AST), SQL (schemas), DOCX, Markdown
📊 Knowledge graph	NetworkX graph with community detection
🚀 REST API	FastAPI backend with 6 endpoints
💾 SQL persistence	SQLAlchemy + SQLite — full run history
🧠 Token optimization	3-tier smart context (graph → snippets → full files)
📋 Prompt templates	Task-specific formats for bug fix, feature, refactor, review
🖥️ React dashboard	Frontend for viewing runs, uploading files, browsing history
⚡ Incremental runs	SHA-256 hashing skips unchanged files

---

Installation

pip install graphmind-rj

That's it. No Docker. No database server. Works on Windows, macOS, Linux.

Verify Installation

graphmind --help

---

End-to-End Tutorial

This walkthrough covers a real scenario: you have a codebase and want to use AI to debug and add features without blowing your token budget.

---

Step 1 — Start the API Server

graphmind-api

Expected output:

INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Leave this terminal open. Open a new terminal for the next steps.

Health check:

curl http://localhost:8000/health
# → {"status": "ok"}

---

Step 2 — Analyse Your Project

graphmind run /path/to/your/project

Or from the current directory:

cd /path/to/your/project
graphmind run .

Output:

Scanning files...
  Found 32 Python files, 4 SQL files, 6 Markdown files
Extracting nodes and edges...
  Python AST:   89 functions, 22 classes
  SQL schemas:  11 tables, 34 columns
  Markdown:     18 headings, 45 concept links
Building graph...  Nodes: 142  Edges: 198
Detecting communities...  Communities: 7
Exporting...
  graphmind-out/graph.json
  graphmind-out/graph.html
  graphmind-out/GRAPH_REPORT.md
  graphmind-out/graphmind.db
Done in 3.2s

Creates graphmind-out/ with:

File	What it is
graph.json	Full knowledge graph as JSON
graph.html	Human-readable HTML table view
GRAPH_REPORT.md	Markdown summary
graphmind.db	SQLite database with all run data
cache/file_hashes.json	File hash cache for incremental runs

---

Step 3 — Run via REST API (Alternative to CLI)

curl -X POST http://localhost:8000/api/run \
  -H "Content-Type: application/json" \
  -d '{"path": "/path/to/your/project", "full": false}'

• "full": false — incremental (skip unchanged files)
• "full": true — force full rescan

Response:

{
  "run_id": 1,
  "files": 42,
  "words": 15600,
  "nodes": 142,
  "edges": 198,
  "communities": 7,
  "out_dir": "graphmind-out"
}

Save the run_id — you need it in later steps.

---

Step 4 — Upload Standalone Files

For design docs, migrations, specs — upload them directly:

curl -F "files=@architecture.md" \
     -F "files=@schema.sql" \
     -F "files=@requirements.docx" \
     http://localhost:8000/api/upload

Response:

{
  "folder": "graphmind-out/uploads/20260408_143021",
  "files_saved": [
    "graphmind-out/uploads/20260408_143021/architecture.md",
    "graphmind-out/uploads/20260408_143021/schema.sql"
  ]
}

Then run the pipeline on that folder:

curl -X POST http://localhost:8000/api/run \
  -d '{"path": "graphmind-out/uploads/20260408_143021", "full": true}'

---

Step 5 — View Run History

curl http://localhost:8000/api/runs

Response:

[
  {
    "id": 2,
    "target_path": "/path/to/project",
    "files": 42,
    "nodes": 142,
    "edges": 198,
    "communities": 7,
    "created_at": "2026-04-08T14:30:21"
  }
]

---

Step 6 — Inspect the Graph

curl http://localhost:8000/api/runs/1/graph

Returns all nodes and edges:

{
  "nodes": [
    {"id": "authenticate_user", "kind": "function", "source_file": "auth.py"},
    {"id": "users", "kind": "table", "source_file": "schema.sql"}
  ],
  "edges": [
    {"source": "authenticate_user", "target": "check_password", "relation": "calls"}
  ]
}

---

Step 7 — Get Optimized Context for an LLM (The Core Feature)

curl -X POST http://localhost:8000/api/context-pack \
  -H "Content-Type: application/json" \
  -d '{
    "run_id": 1,
    "task_type": "bug_fix",
    "query": "Why is user authentication failing after the latest deployment?",
    "token_budget": 6000,
    "include_artifacts": true
  }'

Field	Type	Description
run_id	int	Which run to use as source
task_type	string	bug_fix, feature, refactor, test, review, optimize
query	string	Your specific question
token_budget	int	Max tokens (default 8000)
include_artifacts	bool	Include cached summaries

Response:

{
  "tier": "snippets",
  "graph_summary": {
    "nodes_count": 142,
    "edges_count": 198,
    "communities": 7,
    "top_hubs": ["UserService", "AuthModule", "DatabaseLayer"],
    "summary": "Graph with 142 nodes, 7 communities"
  },
  "code_snippets": [
    {
      "file_path": "auth.py",
      "start_line": 45,
      "end_line": 65,
      "content": "def authenticate_user(username, password):\n    ...",
      "reason": "function_definition"
    }
  ],
  "prompt_template": {
    "task_type": "bug_fix",
    "sections": ["## Problem", "## Current Behavior", "## Expected Behavior", "## Relevant Code", "## Proposed Fix"]
  },
  "total_tokens": 2380,
  "recommended_next_steps": [
    "Run the failing test to reproduce",
    "Add breakpoints in the shown code",
    "Check git blame for recent changes"
  ]
}

How to use this response:

1. Paste graph_summary as the codebase overview to your AI
2. Paste code_snippets as the relevant code
3. Use prompt_template.sections to structure your question
4. Follow recommended_next_steps for guided debugging

Result: 2,380 tokens instead of 15,000 — same quality answer.

---

Step 8 — Task Types Reference

Task	Use for	Recommended budget
bug_fix	Crashes, wrong output, failing tests	6,000
feature	New endpoints, modules, integrations	8,000–10,000
refactor	Reducing complexity, improving patterns	8,000
test	Writing unit/integration tests	4,000
review	PR reviews, security audits	12,000
optimize	Slow queries, memory, bottlenecks	6,000

---

Step 9 — Use the React Dashboard

cd frontend
npm install
npm run dev

Open: http://localhost:5173

The dashboard provides:

• Run panel — enter a path and click Run
• Upload panel — drag and drop .md, .docx, .sql files
• History panel — browse all past runs with metrics

---

How Token Optimization Works

graphmind-rj uses a 3-tier retrieval system:

Query arrives
     │
     ▼
Tier 1: Graph Summary (~500 tokens)
     → node count, communities, top hubs
     → enough for architecture questions
     │
     ▼  (query contains: debug, bug, fix, error, refactor...)
Tier 2: Code Snippets (~1,500–3,000 tokens)
     → function definitions from key files
     → enough for most bug fixes and feature planning
     │
     ▼  (complex tasks only: full refactor, deep review)
Tier 3: Full Files (~3,000+ tokens)
     → complete file content

Token Budget Allocation:

Priority	Files	Budget
CRITICAL	Recently changed files	50%
HIGH	Direct dependencies	30%
MEDIUM	Tests, related modules	15%
LOW	Docs, configs	5%

---

File Support

Extension	What gets extracted
.py	Functions, classes, methods, imports
.sql	Tables, columns (CREATE TABLE)
.md	Headings, paragraphs, concept links
.docx	Headings, paragraphs (sensitive info redacted)

---

Token Savings — Real Numbers

Scenario	Without	With graphmind-rj	Saved
Bug fix in 40-file project	~12,000 tokens	~2,400 tokens	80%
Feature in 80-file project	~25,000 tokens	~5,000 tokens	80%
Code review of 10 files	~8,000 tokens	~3,200 tokens	60%
Architecture question	~10,000 tokens	~500 tokens	95%

---

Use with VS Code / Cursor

1. Run graphmind-api in a background terminal
2. Run graphmind run . on your project once per session
3. Before asking AI anything, call POST /api/context-pack with your question
4. Paste the response into Copilot Chat, Claude, or Cursor
5. Ask your question — AI now has exactly the right context

---

API Endpoints Quick Reference

Method	Endpoint	Purpose
GET	/health	Health check
POST	/api/run	Run pipeline on a directory
POST	/api/upload	Upload .md, .docx, .sql files
GET	/api/runs	List run history
GET	/api/runs/{run_id}/graph	Get graph for a run
POST	/api/context-pack	Get token-optimized context for LLM

---

Project Structure

graphmind-rj/
├── graphmind/
│   ├── api.py                  ← FastAPI app (6 endpoints)
│   ├── cli.py                  ← CLI entry point
│   ├── pipeline.py             ← detect → extract → build → export
│   ├── models.py               ← Node, Edge, ContextPack data models
│   ├── db.py                   ← SQLAlchemy ORM
│   ├── cache.py                ← File hashing + ArtifactCache
│   ├── context_budget.py       ← Token allocation engine
│   ├── retrieval_planner.py    ← 3-tier context retrieval
│   ├── prompt_templates.py     ← Task-specific LLM formats
│   ├── extractors/
│   │   ├── python_ast.py       ← Python AST extraction
│   │   ├── sql_schema.py       ← SQL schema extraction
│   │   ├── text_semantic.py    ← Markdown extraction
│   │   └── docx_semantic.py    ← DOCX extraction
│   ├── graph/
│   │   ├── builder.py          ← NetworkX graph
│   │   └── analytics.py        ← Communities, hubs
│   └── exporters/
│       ├── json_exporter.py
│       └── html_exporter.py
├── frontend/                   ← React + Vite dashboard
└── tests/

---

Development

git clone https://github.com/rameshj/graphmind-rj.git
cd graphmind-rj
pip install -e ".[dev]"
pytest tests/ -v

---

License

MIT — See LICENSE

Author

Ramesh J

Links

• Install: pip install graphmind-rj
• PyPI: https://pypi.org/project/graphmind-rj/
• Issues: https://github.com/rameshj/graphmind-rj/issues

Quick Start

Start Backend API

graphmind-api
# API running on http://localhost:8000

Run Pipeline on Directory

graphmind run .

Upload Files

curl -F "files=@file.md" -F "files=@schema.sql" http://localhost:8000/api/upload

Architecture

Backend Modules

• api.py: FastAPI service with 6+ endpoints (run, upload, history, graph, context-pack)
• cli.py: Command-line interface
• pipeline.py: Full orchestration (detect → extract → build → cluster → export)
• extractors/: Multi-language extraction registry
  • python_ast.py: Functions, classes, methods (Python)
  • sql_schema.py: Tables, columns, constraints (SQL)
  • docx_semantic.py: Structure and concepts (DOCX)
  • text_semantic.py: Headings and paragraphs (Markdown)
• graph/: Graph building and analysis
  • builder.py: NetworkX graph construction
  • analytics.py: Community detection, hub ranking
• db.py: SQLAlchemy ORM (RunRecord, NodeRecord, EdgeRecord)
• context_budget.py: Token allocation by priority tier
• retrieval_planner.py: Tiered context retrieval (graph → snippets → full files)
• prompt_templates.py: LLM-optimized templates for bug_fix, feature, refactor, test, review

Frontend

• React + Vite dashboard for run management and visualization
• Located in frontend/ directory

API Endpoints

Method	Endpoint	Purpose
GET	/health	Health check
POST	/api/run	Run pipeline on directory
POST	/api/upload	Upload .md, .docx, .sql files
GET	/api/runs	List run history
GET	/api/runs/{run_id}/graph	Get graph for specific run
POST	/api/context-pack	Get optimized context for LLM

Example: Get Optimized Context

curl -X POST http://localhost:8000/api/context-pack \
  -H "Content-Type: application/json" \
  -d '{
    "run_id": 1,
    "task_type": "bug_fix",
    "query": "Why is authentication failing?",
    "token_budget": 6000
  }'

Response includes graph summary, code snippets, and prompt template.

Token Savings

• Graph Summary Tier: ~500 tokens - graph structure only
• Snippets Tier: ~1500-3000 tokens - changed files + dependencies
• Full Files Tier: ~3000+ tokens - complete context (fallback)

Smart expansion prevents unnecessary token usage. Most queries stay in Tier 1-2.

Development

Install for Development

git clone https://github.com/rameshj/graphmind.git
cd graphmind
pip install -e ".[dev]"

Run Tests

pytest tests/

Build Documentation

See PUBLISH.md for PyPI publishing guide.

Configuration

Create graphmind.toml or graphmind.yaml to customize:

# Output directory
out_dir: graphmind-out

# Security settings
redact_secrets: true
redact_emails: true

# Extraction policy
extract_comments: true
extract_docstrings: true

# Graph building
min_edge_confidence: 0.5

License

MIT - See LICENSE file

Author

Ramesh J

Support

• 📖 GitHub
• 🐛 Issue Tracker
• 💬 Discussions

Backend runs on http://localhost:8000.

### 2) Start frontend

```bash
cd ramesh-graphmind/frontend
npm install
npm run dev

Frontend runs on http://localhost:5173.

SQL data model

The backend stores:

1. runs: each pipeline execution summary
2. nodes: graph nodes linked to a run
3. edges: graph edges linked to a run

Database file is created at graphmind-out/graphmind.db.

Ingestion behavior

• .md and .txt style docs: heading-based concept extraction
• .docx: paragraph and heading extraction using python-docx
• .sql: schema extraction from CREATE TABLE statements

What to build next

1. Add auth and role-based access
2. Add migration tooling (Alembic)
3. Add richer SQL parsing for foreign keys and joins
4. Add graph diff views between runs