mirror of
https://github.com/infiniflow/ragflow.git
synced 2026-06-29 23:41:12 +08:00
## Summary
Aligns the **Go agent runtime/canvas/components/tools** behavior with
the **Python `agent/` implementation** so the same stored canvas DSL
produces the same execution result on either side. Every component,
tool, and runtime primitive in `internal/agent/` is now driven by the
same semantics as its Python counterpart — variable resolution, template
substitution, control flow, error reporting, retry/cancel, and stream
event shapes.
The **retrieval component is the one explicit exception** in this PR. It
is being reworked in a separate change and is excluded from this
alignment pass; the wrapper slot (`universe_a_wrappers.go →
newRetrievalComponent`) is preserved.
## Scope of alignment
### Components (all aligned with `agent/component/`)
`Begin` · `Message` · `LLM` (incl. ChatTemplateKwargs,
MessageHistoryWindowSize, VisualFiles, Cite, OutputStructure,
JSONOutput, TopP, MaxRetries, DelayAfterError, credentials) · `Agent`
(react + tool artifact capture + `Reset()` interface-assert) · `Switch`
(12/12 operators, Python-equivalent semantics) · `Categorize` · `Invoke`
· `Iteration` · `Loop` (macro-expansion through `workflowx.AddLoopNode`)
· `UserFillUp` (Python-equivalent interrupt/resume via eino
`compose.Interrupt`/`ResumeWithData`) · `FillUp` · `DataOperations` ·
`ListOperations` · `StringTransform` · `VariableAggregator` ·
`VariableAssigner` · `Browser` (full stagehand runtime parity) ·
`DocsGenerator` · `ExcelProcessor`.
### Tools (all aligned with `agent/tools/`)
`Retrieval` (wrapper slot only — logic out of scope) · `MCPToolAdapter`
(streamable-HTTP) · `CodeExec` (sandbox bridge with
`code_exec_contract.go` matching Python contract) · `AkShare` · `ArXiv`
· `Crawler` · `DeepL` · `DuckDuckGo` · `Email` · `ExeSQL` · `GitHub` ·
`Google` · `GoogleScholar` · `Jin10` · `PubMed` · `QWeather` · `SearXNG`
· `Tavily` · `Tushare` · `Wencai` · `Wikipedia` · `YahooFinance` —
uniform `eino tool.InvokableTool` interface, SSRF protection, shared
HTTP client.
### Canvas execution engine (`internal/agent/canvas/`)
Aligned with Python's `agent/canvas.py`:
- **Scheduler** (`scheduler.go`): state pre/post handlers, node lambdas,
per-component timeout resolver (4-level: per-class env → per-class table
→ uniform env → 600s fallback), `legacyNoOpNames`.
- **Loop subgraph** (`loop_subgraph.go`): Python-equivalent
`AddLoopNode` macro expansion + condition translation.
- **Multibranch** (`multibranch.go`): `Switch` / `Categorize` routing
via `compose.NewGraphMultiBranch` — same branch selection semantics as
Python.
- **Parallel subgraph** (`parallel_subgraph.go`): matches Python's
parallel fan-out contract.
- **Interrupt/Resume** (`interrupt_resume.go`): `UserFillUpNodeBody` /
`IsInterruptError` / `ExtractInterruptContexts` — replaces the
deprecated Python sentinel chain with eino's native interrupt API,
preserving the same external behavior.
- **Checkpoint** (`checkpoint_store.go`): `RedisCheckPointStore`
Get/Set/Delete, with business metadata (status / canvas_id /
parent_run_id) on a parallel Redis Hash.
- **RunTracker** (`run_tracker.go`): Start / MarkSucceeded / MarkFailed
/ MarkCancelled / AttachCheckpoint — same lifecycle as the Python run
record.
- **Cancel** (`cancel.go`): Redis pub/sub watch.
- **Stream** (`stream.go`): SSE channel with `messages` / `waiting` /
`errors` / `done` events, same shape as Python's `agent.canvas.RunEvent`
payload.
### DSL bridge (`internal/agent/dsl/`)
- `normalize.go`: v1↔v2 collapsed into a single wire format — Python and
Go consume the same stored JSON.
- `reset.go`: per-run state reset matches Python's `Canvas.reset()`
semantics.
- Testdata mirrors Python's `agent_msg.json` / `all.json` / etc.
### Runtime (`internal/agent/runtime/`)
- `CanvasState` / `NewCanvasState` / `GetVar` / `SetVar` / `ReadVars`:
same `{{cpn_id@param}}` resolution model.
- `ResolveTemplate` (regex fast path + gonja fallback) — Python
Jinja-style semantics.
- `selector.go`, `metrics.go`, `component.go`: shared runtime contracts.
## Out of scope (intentionally)
- **`Retrieval` component logic** — wrapped only; full parity lands in a
follow-up PR.
- **Frontend** — only minor dsl-bridge / canvas UX fixes ride along.
- **CLI / admin / model registry** — orthogonal to agent behavior.
## How alignment is verified
`internal/service/agent_run_e2e_test.go` exercises the **full production
chain** against real Python-shaped DSL fixtures:
```
loadCanvasForUser → versionDAO.GetLatest → decodeCanvasFromDSL →
canvas.Compile → cc.Workflow.Invoke → answer extraction
```
using in-memory SQLite + miniredis (no Docker). Covers:
- `TestRunAgent_RealCanvas_BeginMessage` — happy path, `{{sys.query}}`
resolution
- `TestRunAgent_RealCanvas_WaitForUserResume` — two-run resume cycle
(Python-equivalent)
- `TestRunAgent_RealCanvas_CompileFails` — unknown component name →
sanitized error (Python-equivalent)
- `TestRunAgent_RealCanvas_InvokeFails` — unresolvable template ref
(Python-equivalent)
- `TestRunAgent_RunTracker_AttachCheckpoint_CallSequence` —
Start→AttachCheckpoint→MarkSucceeded lifecycle
`internal/handler/agent_test.go` — SSE streaming parity (`Content-Type:
text/event-stream`, `data: {…}\n\n`, trailing `data: [DONE]\n\n`,
OpenAI-compatible non-stream `choices`).
`internal/agent/canvas/fixture_compile_test.go` + per-component tests
pin the Python-equivalent outputs.
```
go test -count=1 -v -run 'TestRunAgent_RealCanvas|TestRunAgent_RunTracker' ./internal/service/
```
## Design reference
`docs/develop/agent-go-port-design.md` (1329 lines, last cross-checked
2026-06-17) — module layout, per-component / per-tool inventory,
corner-case catalogue, and the actionable backlog (Section 14, including
the retrieval alignment follow-up).
---------
Co-authored-by: Claude <noreply@anthropic.com>
228 lines
6.7 KiB
JSON
228 lines
6.7 KiB
JSON
{
|
||
"components": {
|
||
"Agent:TenderSpidersStick": {
|
||
"downstream": [
|
||
"Message:MajorNumbersPlay"
|
||
],
|
||
"obj": {
|
||
"component_name": "Agent",
|
||
"params": {
|
||
"cite": true,
|
||
"delay_after_error": 1,
|
||
"description": "",
|
||
"exception_default_value": "",
|
||
"exception_goto": [],
|
||
"exception_method": "",
|
||
"frequencyPenaltyEnabled": true,
|
||
"frequency_penalty": 0.7,
|
||
"llm_id": "glm-4.7-flashx@zz@ZHIPU-AI",
|
||
"maxTokensEnabled": false,
|
||
"max_retries": 3,
|
||
"max_rounds": 1,
|
||
"max_tokens": 256,
|
||
"mcp": [],
|
||
"message_history_window_size": 12,
|
||
"outputs": {
|
||
"content": {
|
||
"type": "string",
|
||
"value": ""
|
||
}
|
||
},
|
||
"presencePenaltyEnabled": true,
|
||
"presence_penalty": 0.4,
|
||
"prompts": [
|
||
{
|
||
"content": "{sys.query}",
|
||
"role": "user"
|
||
}
|
||
],
|
||
"showStructuredOutput": false,
|
||
"sys_prompt": "\n <role>\n You are a helpful assistant, an AI assistant specialized in problem-solving for the user.\n If a specific domain is provided, adapt your expertise to that domain; otherwise, operate as a generalist.\n </role>\n <instructions>\n 1. Understand the user’s request.\n 2. Decompose it into logical subtasks.\n 3. Execute each subtask step by step, reasoning transparently.\n 4. Validate accuracy and consistency.\n 5. Summarize the final result clearly.\n </instructions>",
|
||
"temperature": 0.1,
|
||
"temperatureEnabled": true,
|
||
"tools": [],
|
||
"topPEnabled": true,
|
||
"top_p": 0.3,
|
||
"user_prompt": "",
|
||
"visual_files_var": ""
|
||
}
|
||
},
|
||
"upstream": [
|
||
"begin"
|
||
]
|
||
},
|
||
"Message:MajorNumbersPlay": {
|
||
"downstream": [],
|
||
"obj": {
|
||
"component_name": "Message",
|
||
"params": {
|
||
"content": [
|
||
"{Agent:TenderSpidersStick@content}"
|
||
]
|
||
}
|
||
},
|
||
"upstream": [
|
||
"Agent:TenderSpidersStick"
|
||
]
|
||
},
|
||
"begin": {
|
||
"downstream": [
|
||
"Agent:TenderSpidersStick"
|
||
],
|
||
"obj": {
|
||
"component_name": "Begin",
|
||
"params": {
|
||
"mode": "conversational",
|
||
"prologue": "Hi! I'm your assistant. What can I do for you?"
|
||
}
|
||
},
|
||
"upstream": []
|
||
}
|
||
},
|
||
"globals": {
|
||
"sys.conversation_turns": 0,
|
||
"sys.date": "",
|
||
"sys.files": [],
|
||
"sys.history": [],
|
||
"sys.query": "",
|
||
"sys.user_id": ""
|
||
},
|
||
"graph": {
|
||
"edges": [
|
||
{
|
||
"id": "xy-edge__beginstart-Agent:TenderSpidersStickend",
|
||
"source": "begin",
|
||
"sourceHandle": "start",
|
||
"target": "Agent:TenderSpidersStick",
|
||
"targetHandle": "end"
|
||
},
|
||
{
|
||
"data": {
|
||
"isHovered": false
|
||
},
|
||
"id": "xy-edge__Agent:TenderSpidersStickstart-Message:MajorNumbersPlayend",
|
||
"source": "Agent:TenderSpidersStick",
|
||
"sourceHandle": "start",
|
||
"target": "Message:MajorNumbersPlay",
|
||
"targetHandle": "end"
|
||
}
|
||
],
|
||
"nodes": [
|
||
{
|
||
"data": {
|
||
"form": {
|
||
"mode": "conversational",
|
||
"prologue": "Hi! I'm your assistant. What can I do for you?"
|
||
},
|
||
"label": "Begin",
|
||
"name": "begin"
|
||
},
|
||
"dragging": false,
|
||
"id": "begin",
|
||
"measured": {
|
||
"height": 81,
|
||
"width": 200
|
||
},
|
||
"position": {
|
||
"x": -173.77599999999995,
|
||
"y": -145.59999999999994
|
||
},
|
||
"selected": false,
|
||
"sourcePosition": "left",
|
||
"targetPosition": "right",
|
||
"type": "beginNode"
|
||
},
|
||
{
|
||
"data": {
|
||
"form": {
|
||
"cite": true,
|
||
"delay_after_error": 1,
|
||
"description": "",
|
||
"exception_default_value": "",
|
||
"exception_goto": [],
|
||
"exception_method": "",
|
||
"frequencyPenaltyEnabled": true,
|
||
"frequency_penalty": 0.7,
|
||
"llm_id": "glm-4.7-flashx@zz@ZHIPU-AI",
|
||
"maxTokensEnabled": false,
|
||
"max_retries": 3,
|
||
"max_rounds": 1,
|
||
"max_tokens": 256,
|
||
"mcp": [],
|
||
"message_history_window_size": 12,
|
||
"outputs": {
|
||
"content": {
|
||
"type": "string",
|
||
"value": ""
|
||
}
|
||
},
|
||
"presencePenaltyEnabled": true,
|
||
"presence_penalty": 0.4,
|
||
"prompts": [
|
||
{
|
||
"content": "{sys.query}",
|
||
"role": "user"
|
||
}
|
||
],
|
||
"showStructuredOutput": false,
|
||
"sys_prompt": "\n <role>\n You are a helpful assistant, an AI assistant specialized in problem-solving for the user.\n If a specific domain is provided, adapt your expertise to that domain; otherwise, operate as a generalist.\n </role>\n <instructions>\n 1. Understand the user’s request.\n 2. Decompose it into logical subtasks.\n 3. Execute each subtask step by step, reasoning transparently.\n 4. Validate accuracy and consistency.\n 5. Summarize the final result clearly.\n </instructions>",
|
||
"temperature": 0.1,
|
||
"temperatureEnabled": true,
|
||
"tools": [],
|
||
"topPEnabled": true,
|
||
"top_p": 0.3,
|
||
"user_prompt": "",
|
||
"visual_files_var": ""
|
||
},
|
||
"label": "Agent",
|
||
"name": "Agent_0"
|
||
},
|
||
"dragging": false,
|
||
"id": "Agent:TenderSpidersStick",
|
||
"measured": {
|
||
"height": 79,
|
||
"width": 200
|
||
},
|
||
"position": {
|
||
"x": 92.03339543909598,
|
||
"y": -132.31937403691842
|
||
},
|
||
"selected": true,
|
||
"sourcePosition": "right",
|
||
"targetPosition": "left",
|
||
"type": "agentNode"
|
||
},
|
||
{
|
||
"data": {
|
||
"form": {
|
||
"content": [
|
||
"{Agent:TenderSpidersStick@content}"
|
||
]
|
||
},
|
||
"label": "Message",
|
||
"name": "Message_0"
|
||
},
|
||
"dragging": false,
|
||
"id": "Message:MajorNumbersPlay",
|
||
"measured": {
|
||
"height": 85,
|
||
"width": 200
|
||
},
|
||
"position": {
|
||
"x": 336.5453954390959,
|
||
"y": -120.22337403691841
|
||
},
|
||
"selected": false,
|
||
"sourcePosition": "right",
|
||
"targetPosition": "left",
|
||
"type": "messageNode"
|
||
}
|
||
]
|
||
},
|
||
"history": [],
|
||
"memory": [],
|
||
"messages": [],
|
||
"path": [],
|
||
"retrieval": [],
|
||
"variables": []
|
||
} |