Files
ragflow/internal/cli
Zhichang Yu 12787996d1 feat(agent): Go ingestion pipeline progress mirroring and DeepDOC parser hardening (#16795)
feat(ingestion): mirror Go pipeline progress into the document table;
harden resume guards
- pipeline: bind the owning document via WithDocumentID; after each
TrackProgress event aggregate ingestion_task_log progress and mirror
progress/run/progress_msg back into the document table, so GET
/api/v1/datasets/{dataset_id}/documents reflects live Go pipeline
progress without a bespoke endpoint.
- canvas: extend the S3 resume guard to reject legacy no-op nodes (e.g.
ExitLoop) so component_total equals the count of progress-reporting
components and the aggregate percent can reach 100%.
- runtime/canvas: route progress through TrackProgress; add interrupt
test coverage (r3_interrupt_test.go).
- dao/entity: add IngestionTask.DocumentID column and AggregateProgress
support used by the mirror; IngestionTaskLog keeps a Checkpoint column
alongside the progress fields.

feat(deepdoc): cache DocAnalyzer inference results in Redis (1h TTL)
- Redis-backed DocAnalyzerCache decorator over inference.Client; cache
key = "ddoc:cache:<method>:" + sha256 of the JPEG-encoded image bytes
(deterministic).
- TTL = 1h; hits skip the inner HTTP call and return cached JSON; inner
errors are not cached.

refactor(deepdoc): align figure cropping with Python cropout + bounded
page caches
- CropSectionByDLA mirrors Python cropout: best-overlap DLA
figure/equation region, fallback to section bbox per page, vertical
concat on gray background.
- sliding-window page-image cache bounds peak memory to the recent
window instead of the whole PDF.
- rename DLADebug -> DLARegions across parser/chunker/tests.

refactor(parser): drop lib_type selector; align NewXxxParser with
NewPDFParser
- remove config["lib_type"] lookup and the libType param/field/switch
from all nine constructors; surface the CGO-required error at
ParseWithResult time instead of construction time; drop resolveLibType,
its test, and the four lib_type constants.

feat(utility): add a reusable workerpool for bounded concurrent
execution
- internal/utility/workerpool.go (+ tests).

refactor: translate Chinese prose comments to English in non-harness Go
files.

chore: upgrade github.com/cloudwego/eino from v0.9.9 to v0.9.12.
2026-07-10 10:36:10 +08:00
..
2026-06-24 16:50:40 +08:00
2026-06-23 17:43:26 +08:00
2026-06-29 19:09:32 +08:00
2026-06-23 22:04:34 +08:00

RAGFlow CLI (Go Version)

This is the Go implementation of the RAGFlow command-line interface, compatible with the Python version's syntax.

Features

  • Interactive mode and single command execution
  • Full compatibility with Python CLI syntax
  • Recursive descent parser for SQL-like commands
  • Virtual Filesystem for intuitive resource management
  • Support for all major commands:
    • User management: LOGIN, REGISTER, CREATE USER, DROP USER, LIST USERS, etc.
    • Service management: LIST SERVICES, SHOW SERVICE, STARTUP/SHUTDOWN/RESTART SERVICE
    • Role management: CREATE ROLE, DROP ROLE, LIST ROLES, GRANT/REVOKE PERMISSION
    • Dataset management via Virtual Filesystem: ls, search, mkdir, cat, rm
    • Model management: SET/RESET DEFAULT LLM/VLM/EMBEDDING/etc.
    • And more...

Usage

Build and run

go build -o ragflow-cli ./cmd/ragflow-cli.go
./ragflow-cli

Architecture

internal/cli/
├── cli.go              # Main CLI loop and interaction
├── client.go           # RAGFlowClient with Filesystem integration
├── http_client.go      # HTTP client for API communication
├── parser/             # Command parser package
│   ├── types.go        # Token and Command types
│   ├── lexer.go        # Lexical analyzer
│   └── parser.go       # Recursive descent parser
└── filesystem/         # Virtual Filesystem
    ├── engine.go       # Core engine: path resolution, command routing
    ├── types.go        # Node, Command, Result types
    ├── base.go         # Provider interface definition    
    ├── dataset.go      # Dataset provider implementation
    ├── file.go         # File manager provider implementation
    └── utils.go        # Helper functions

Virtual Filesystem

The Virtual Filesystem provides a unified filesystem interface over RAGFlow's RESTful APIs.

Design Principles

  1. No Server-Side Changes: All logic implemented client-side using existing APIs
  2. Provider Pattern: Modular providers for different resource types (datasets, files, etc.)
  3. Unified Interface: Common ls, search, mkdir commands across all providers
  4. Path-Based Navigation: Virtual paths like /datasets, /datasets/{name}/files

Supported Paths

Path Description
/datasets List all datasets
/datasets/{name} List documents in dataset (default behavior)
/datasets/{name}/{doc} Get document info

Commands

ls [path] [options] - List nodes at path

List contents of a path in the context filesystem.

Arguments:

  • [path] - Path to list (default: "datasets")

Options:

  • -n, --limit <number> - Maximum number of items to display (default: 10)
  • -h, --help - Show ls help message

Examples:

ls                              # List all datasets (default 10)
ls -n 20                        # List 20 datasets
ls datasets/kb1                 # List files in kb1 dataset
ls datasets/kb1 -n 50           # List 50 files in kb1 dataset

search [options] - Search for content

Semantic search in datasets.

Options:

  • -n, --number - Number of top results to return (default: 10)

Output Formats:

  • Default: JSON format
  • --output plain - Plain text format
  • --output table - Table format with borders

Examples:

search "machine learning"                    # Search all datasets (JSON output)
search "neural networks" datasets/kb1        # Search in kb1
search "AI" datasets/kb1  --output plain     # Plain text output
search "RAG" -n 20                           # Return 20 results
SEARCH 'machine learning' ON DATASETS 'kb1' 'kb2'
SEARCH 'AI' ON DATASETS 'kb1' WITH top_k 1024 similarity_threshold 0.0 vector_similarity_weight 0.3 keyword true
SEARCH 'AI' ON DATASETS 'kb1' WITH cross_languages ['Chinese']

cat <path> - Display content

Display document content (if available).

Examples:

cat myskills/doc.md   # Show content of doc.md file
cat datasets/kb1/document.pdf   # Error: cannot display binary file content

Command Examples

-- Authentication
LOGIN USER 'admin@example.com';

-- User management
REGISTER USER 'john' AS 'John Doe' PASSWORD 'secret';
CREATE USER 'jane' 'password123';
DROP USER 'jane';
LIST USERS;
SHOW USER 'john';

-- Service management
LIST SERVICES;
SHOW SERVICE 1;
STARTUP SERVICE 1;
SHUTDOWN SERVICE 1;
RESTART SERVICE 1;
PING;

-- Role management
CREATE ROLE admin DESCRIPTION 'Administrator role';
LIST ROLES;
GRANT read,write ON datasets TO ROLE admin;

-- Dataset management
CREATE DATASET 'my_dataset' WITH EMBEDDING 'text-embedding-ada-002' PARSER 'naive';
LIST DATASETS;
DROP DATASET 'my_dataset';

-- Model configuration
SET DEFAULT LLM 'gpt-4';
SET DEFAULT EMBEDDING 'text-embedding-ada-002';
RESET DEFAULT LLM;


## Parser Implementation

The parser uses a hand-written recursive descent approach instead of go-yacc for:
- Better control over error messages
- Easier to extend and maintain
- No code generation step required

The parser structure follows the grammar defined in the Python version, ensuring full syntax compatibility.