github/DB-GPT
1
0
Fork 0
mirror of https://github.com/csunny/DB-GPT.git synced 2026-07-17 01:58:47 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix: react agent chat with selected LLM

Browse Source
This commit is contained in:
alan.cl
2026-03-08 16:46:51 +08:00
parent 4258ad5335
commit 80f7b44587
2 changed files with 116 additions and 34 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
.gitignore vendored
View File

@@ -191,4 +191,18 @@ thirdparty
/i18n/locales/**/**/*~
configs/my
.devcontainer/dev.toml
test_docs
test_docs
# AI coding assistant configs
.opencode/
.cursor/
.aone_copilot/
.windsurf/
.continue/
.codeium/
.github/copilot-instructions.md
.claude/
.codex/
.trade/
.qoder/
.qwencode/

134
packages/dbgpt-app/src/dbgpt_app/openapi/api_v1/agentic_data_api.py
View File

@@ -38,6 +38,33 @@ if TYPE_CHECKING:
REACT_AGENT_MEMORY_CACHE: Dict[str, "GptsMemory"] = {}
DEFAULT_SKILLS_DIR = resolve_root_path("skills") or "skills"
AUTO_DATA_MARKER_PATTERN = re.compile(
r"###([A-Z0-9_]+)_START###\s*(.*?)\s*###\1_END###", re.DOTALL
)
def _extract_auto_data_markers(text: str) -> tuple[str, Dict[str, str]]:
"""Extract generic marker blocks from script output text.
Marker format:
###KEY_START###...###KEY_END###
"""
if not text or "###" not in text:
return text, {}
extracted: Dict[str, str] = {}
def _replace(match: re.Match) -> str:
key = match.group(1)
value = match.group(2).strip()
if value:
extracted[key] = value
return ""
cleaned = AUTO_DATA_MARKER_PATTERN.sub(_replace, text)
cleaned = re.sub(r"\n{3,}", "\n\n", cleaned).strip()
return cleaned, extracted
async def _execute_skill_script_impl(
@@ -440,7 +467,7 @@ async def _react_agent_stream(
from dbgpt.agent.resource import ResourcePack, ToolPack, tool
from dbgpt.agent.resource.base import AgentResource, ResourceType
from dbgpt.agent.resource.manage import get_resource_manager
from dbgpt.agent.util.llm.llm import LLMConfig
from dbgpt.agent.util.llm.llm import LLMConfig, LLMStrategyType
from dbgpt.agent.util.react_parser import ReActOutputParser
from dbgpt.core import StorageConversation
from dbgpt.model.cluster.client import DefaultLLMClient
@@ -1532,7 +1559,9 @@ print(json.dumps(summary, ensure_ascii=False))
# Might be {"charts": {...}} or flat dict
chart_map = chart_output.get("charts", chart_output)
for name, abs_path in chart_map.items():
if isinstance(abs_path, str) and os.path.isfile(abs_path):
if isinstance(abs_path, str) and os.path.isfile(
abs_path
):
ext = os.path.splitext(abs_path)[1].lower()
if ext in IMAGE_EXTS:
unique_name = (
@@ -1550,9 +1579,9 @@ print(json.dumps(summary, ensure_ascii=False))
orig_stem = os.path.splitext(
os.path.basename(abs_path)
)[0].lower()
react_state.setdefault(
"image_url_map", {}
)[orig_stem] = img_url
react_state.setdefault("image_url_map", {})[
orig_stem
] = img_url
except (json.JSONDecodeError, TypeError):
pass
# Also scan the output dir for any new .png files
@@ -1569,9 +1598,7 @@ print(json.dumps(summary, ensure_ascii=False))
if orig_stem not in react_state.get(
"image_url_map", {}
):
unique_name = (
f"{uuid.uuid4().hex[:8]}_{fname}"
)
unique_name = f"{uuid.uuid4().hex[:8]}_{fname}"
dest = os.path.join(
STATIC_MESSAGE_IMG_PATH, unique_name
)
@@ -1580,9 +1607,9 @@ print(json.dumps(summary, ensure_ascii=False))
react_state.setdefault(
"generated_images", []
).append(img_url)
react_state.setdefault(
"image_url_map", {}
)[orig_stem] = img_url
react_state.setdefault("image_url_map", {})[
orig_stem
] = img_url
# Append image URL summary for LLM reference
all_images = react_state.get("generated_images", [])
if all_images:
@@ -1590,9 +1617,7 @@ print(json.dumps(summary, ensure_ascii=False))
"\u5df2\u751f\u6210\u7684\u56fe\u7247URL\uff08\u5728\u751f\u6210HTML\u62a5\u544a\u65f6\u8bf7\u4f7f\u7528\u8fd9\u4e9bURL\uff09:\n"
+ "\n".join(f" - {url}" for url in all_images)
)
chunks.append(
{"output_type": "text", "content": img_summary}
)
chunks.append({"output_type": "text", "content": img_summary})
logger.info(
"shell_interpreter: captured %d images for skill script",
len(react_state.get("image_url_map", {})),
@@ -1720,10 +1745,31 @@ print(json.dumps(summary, ensure_ascii=False))
+ "\n".join(f" - {url}" for url in all_images)
)
chunks.append({"output_type": "text", "content": img_summary})
# Special handling for calculate_ratios.py output:
# Store its output in react_state so html_interpreter can use
# it automatically. This prevents the LLM from having to echo
# back 30 keys of data in JSON
auto_data = react_state.get("auto_data")
if not isinstance(auto_data, dict):
auto_data = {}
react_state["auto_data"] = auto_data
filtered_chunks = []
for chunk in chunks:
if chunk.get("output_type") != "text":
filtered_chunks.append(chunk)
continue
content = chunk.get("content") or ""
cleaned, extracted = _extract_auto_data_markers(content)
if extracted:
auto_data.update(extracted)
logger.info(
"execute_skill_script_file: captured auto_data keys=%s",
sorted(extracted.keys()),
)
if cleaned:
chunk["content"] = cleaned
filtered_chunks.append(chunk)
elif not extracted:
filtered_chunks.append(chunk)
chunks = filtered_chunks
# Compatibility path for existing financial-report skill.
if script_file_name == "calculate_ratios.py":
for chunk in chunks:
if chunk.get("output_type") == "text":
@@ -1849,10 +1895,14 @@ print(json.dumps(summary, ensure_ascii=False))
replacements = {}
if not isinstance(replacements, dict):
replacements = {}
auto_data = react_state.get("auto_data", {})
if isinstance(auto_data, dict):
replacements = {**auto_data, **replacements}
# Merge LLM replacements with ratio_data from calculate_ratios.py
ratio_data = react_state.get("ratio_data", {})
if isinstance(ratio_data, dict):
# LLM's data overwrites ratio_data if keys overlap
# auto_data / LLM data overwrites ratio_data if keys overlap
merged = {**ratio_data, **replacements}
replacements = merged
@@ -1872,7 +1922,7 @@ print(json.dumps(summary, ensure_ascii=False))
def _replace_placeholder(m):
key = m.group(1)
return str(replacements.get(key, "NA"))
return str(replacements.get(key, ""))
html = re.sub(r"\{\{([A-Z_0-9]+)\}\}", _replace_placeholder, raw_template)
if not title or title == "Report":
@@ -1931,8 +1981,13 @@ print(json.dumps(summary, ensure_ascii=False))
)
# ── Mode 3: inline html ──────────────────────────────────────
# Unescape literal \n sequences that LLM may produce
if html and isinstance(html, str):
# Unescape literal \n sequences that LLM may produce.
# IMPORTANT: Only apply this unescape when html was provided directly
# (inline mode). Template mode (Mode 1) and file mode (Mode 2) produce
# real HTML that already contains actual newlines and may contain JS
# regex literals like /\\n/ which must NOT be collapsed into real
# newlines — doing so corrupts the JS and breaks chart rendering.
if html and isinstance(html, str) and not template_path and not file_path:
if "\\n" in html:
html = html.replace("\\n", "\n")
if "\\t" in html:
@@ -2023,6 +2078,7 @@ print(json.dumps(summary, ensure_ascii=False))
r'<img[^>]+src=["\']([^"\']+)["\']', fixed_html, re.IGNORECASE
)
)
# An image is "missing" only when neither its exact URL nor its
# stem (filename with UUID prefix stripped) is already covered.
def _img_stem(url):
@@ -2072,7 +2128,16 @@ print(json.dumps(summary, ensure_ascii=False))
).create(),
auto_convert_message=True,
)
llm_config = LLMConfig(llm_client=llm_client)
# If user specified a model_name, use Priority strategy to ensure the
# agent uses the requested model instead of picking the first available one.
if dialogue.model_name:
llm_config = LLMConfig(
llm_client=llm_client,
llm_strategy=LLMStrategyType.Priority,
strategy_context=json.dumps([dialogue.model_name]),
)
else:
llm_config = LLMConfig(llm_client=llm_client)
conv_id = dialogue.conv_uid or str(uuid.uuid4())
react_state["conv_id"] = conv_id
@@ -2188,11 +2253,11 @@ Please always response in the same language as the user's input language.
## Available Tools Description
1. **execute_skill_script_file** (recommended for executing skill scripts): Execute script files in the skills scripts directory, automatically handling post-processing such as copying images to the static directory and recording calculation results.
Parameters: {{"skill_name": "skill name", "script_file_name": "script file name", "args": {{parameters}}}}
- Example: {{"skill_name": "{pre_matched_skill.metadata.name if pre_matched_skill else 'skill'}", "script_file_name": "calculate_ratios.py", "args": {{"input_data": "..."}}}}
- Example: {{"skill_name": "{pre_matched_skill.metadata.name if pre_matched_skill else "skill"}", "script_file_name": "calculate_ratios.py", "args": {{"input_data": "..."}}}}
- **Must use this tool when executing skill scripts**, do not use shell_interpreter.
2. **get_skill_resource**: Read reference documents, configurations, templates, and other non-script resource files in the skill.
Parameters: {{"skill_name": "skill name", "resource_path": "resource path"}}
- Read reference document: {{"skill_name": "{pre_matched_skill.metadata.name if pre_matched_skill else 'skill'}", "resource_path": "references/analysis_framework.md"}}
- Read reference document: {{"skill_name": "{pre_matched_skill.metadata.name if pre_matched_skill else "skill"}", "resource_path": "references/analysis_framework.md"}}
- Note: The report template does not need to be read using this tool; directly use the template_path parameter of html_interpreter.
3. **execute_skill_script**: Execute the inline script defined in the skill (backup). Parameters: {{"skill_name": "skill name", "script_name": "script name", "args": {{"parameter name": "parameter value"}}}}
4. **shell_interpreter**: Execute shell/bash commands (only for non-skill script system commands, such as ls, cat, etc.).
@@ -2534,14 +2599,16 @@ Action Input: The JSON format of tool parameters
if round_num in round_step_map:
# Step already exists (from thinking) - update title/phase with same id
react_step_id = round_step_map[round_num]
updated_event = _sse_event({
"type": "step.start",
"step": step,
"id": react_step_id,
"title": action_title,
"detail": "Thought/Action/Observation",
"phase": inferred_phase,
})
updated_event = _sse_event(
{
"type": "step.start",
"step": step,
"id": react_step_id,
"title": action_title,
"detail": "Thought/Action/Observation",
"phase": inferred_phase,
}
)
yield updated_event
else:
react_step_id, react_step_event = build_step(
@@ -2939,6 +3006,7 @@ async def download_skill_package(
},
)
@router.post("/v1/chat/react-agent")
async def chat_react_agent(
dialogue: ConversationVo = Body(),