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Fix bug: broken import from rag.prompts.prompts (#10217)

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### What problem does this PR solve?

Fix broken imports

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

---------

Signed-off-by: jinhai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
2025-09-23 10:19:25 +08:00
committed by GitHub
parent 46a61e5aff
commit 4eb7659499
23 changed files with 64 additions and 63 deletions
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2
api/apps/api_app.py
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@ -39,7 +39,7 @@ from api.utils.api_utils import server_error_response, get_data_error_result, ge
from api.utils.file_utils import filename_type, thumbnail
from rag.app.tag import label_question
from rag.prompts import keyword_extraction
from rag.prompts.generator import keyword_extraction
from rag.utils.storage_factory import STORAGE_IMPL
from api.db.services.canvas_service import UserCanvasService

4
api/apps/canvas_app.py
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@ -23,7 +23,7 @@ import trio
from flask import request, Response
from flask_login import login_required, current_user
from agent.component import LLM
from agent.component.llm import LLM
from api.db import CanvasCategory, FileType
from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService, API4ConversationService
from api.db.services.document_service import DocumentService
@ -474,7 +474,7 @@ def sessions(canvas_id):
@manager.route('/prompts', methods=['GET']) # noqa: F821
@login_required
def prompts():
from rag.prompts.prompts import ANALYZE_TASK_SYSTEM, ANALYZE_TASK_USER, NEXT_STEP, REFLECT, CITATION_PROMPT_TEMPLATE
from rag.prompts.generator import ANALYZE_TASK_SYSTEM, ANALYZE_TASK_USER, NEXT_STEP, REFLECT, CITATION_PROMPT_TEMPLATE
return get_json_result(data={
"task_analysis": ANALYZE_TASK_SYSTEM +"\n\n"+ ANALYZE_TASK_USER,
"plan_generation": NEXT_STEP,

3
api/apps/chunk_app.py
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@ -33,8 +33,7 @@ from api.utils.api_utils import get_data_error_result, get_json_result, server_e
from rag.app.qa import beAdoc, rmPrefix
from rag.app.tag import label_question
from rag.nlp import rag_tokenizer, search
from rag.prompts import cross_languages, keyword_extraction
from rag.prompts.prompts import gen_meta_filter
from rag.prompts.generator import gen_meta_filter, cross_languages, keyword_extraction
from rag.settings import PAGERANK_FLD
from rag.utils import rmSpace

4
api/apps/conversation_app.py
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@ -29,8 +29,8 @@ from api.db.services.search_service import SearchService
from api.db.services.tenant_llm_service import TenantLLMService
from api.db.services.user_service import TenantService, UserTenantService
from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request
from rag.prompts.prompt_template import load_prompt
from rag.prompts.prompts import chunks_format
from rag.prompts.template import load_prompt
from rag.prompts.generator import chunks_format
@manager.route("/set", methods=["POST"]) # noqa: F821

2
api/apps/dataflow_app.py
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@ -24,7 +24,7 @@ from flask import request
from flask_login import current_user, login_required
from agent.canvas import Canvas
from agent.component import LLM
from agent.component.llm import LLM
from api.db import CanvasCategory, FileType
from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService
from api.db.services.document_service import DocumentService

2
api/apps/sdk/doc.py
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@ -40,7 +40,7 @@ from api.utils.api_utils import check_duplicate_ids, construct_json_result, get_
from rag.app.qa import beAdoc, rmPrefix
from rag.app.tag import label_question
from rag.nlp import rag_tokenizer, search
from rag.prompts import cross_languages, keyword_extraction
from rag.prompts.generator import cross_languages, keyword_extraction
from rag.utils import rmSpace
from rag.utils.storage_factory import STORAGE_IMPL

5
api/apps/sdk/session.py
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@ -38,9 +38,8 @@ from api.db.services.user_service import UserTenantService
from api.utils import get_uuid
from api.utils.api_utils import check_duplicate_ids, get_data_openai, get_error_data_result, get_json_result, get_result, server_error_response, token_required, validate_request
from rag.app.tag import label_question
from rag.prompts import chunks_format
from rag.prompts.prompt_template import load_prompt
from rag.prompts.prompts import cross_languages, gen_meta_filter, keyword_extraction
from rag.prompts.template import load_prompt
from rag.prompts.generator import cross_languages, gen_meta_filter, keyword_extraction, chunks_format
@manager.route("/chats/<chat_id>/sessions", methods=["POST"]) # noqa: F821

2
api/db/services/conversation_service.py
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@ -23,7 +23,7 @@ from api.db.services.dialog_service import DialogService, chat
from api.utils import get_uuid
import json
from rag.prompts import chunks_format
from rag.prompts.generator import chunks_format
class ConversationService(CommonService):

68
api/db/services/dialog_service.py
View File

@ -39,8 +39,8 @@ from graphrag.general.mind_map_extractor import MindMapExtractor
from rag.app.resume import forbidden_select_fields4resume
from rag.app.tag import label_question
from rag.nlp.search import index_name
from rag.prompts import chunks_format, citation_prompt, cross_languages, full_question, kb_prompt, keyword_extraction, message_fit_in
from rag.prompts.prompts import gen_meta_filter, PROMPT_JINJA_ENV, ASK_SUMMARY
from rag.prompts.generator import chunks_format, citation_prompt, cross_languages, full_question, kb_prompt, keyword_extraction, message_fit_in, \
gen_meta_filter, PROMPT_JINJA_ENV, ASK_SUMMARY
from rag.utils import num_tokens_from_string, rmSpace
from rag.utils.tavily_conn import Tavily
@ -176,7 +176,7 @@ def chat_solo(dialog, messages, stream=True):
delta_ans = ""
for ans in chat_mdl.chat_streamly(prompt_config.get("system", ""), msg, dialog.llm_setting):
answer = ans
delta_ans = ans[len(last_ans) :]
delta_ans = ans[len(last_ans):]
if num_tokens_from_string(delta_ans) < 16:
continue
last_ans = answer
@ -261,13 +261,13 @@ def convert_conditions(metadata_condition):
"not is": "≠"
}
return [
{
"op": op_mapping.get(cond["comparison_operator"], cond["comparison_operator"]),
"key": cond["name"],
"value": cond["value"]
}
for cond in metadata_condition.get("conditions", [])
]
{
"op": op_mapping.get(cond["comparison_operator"], cond["comparison_operator"]),
"key": cond["name"],
"value": cond["value"]
}
for cond in metadata_condition.get("conditions", [])
]
def meta_filter(metas: dict, filters: list[dict]):
@ -284,19 +284,19 @@ def meta_filter(metas: dict, filters: list[dict]):
value = str(value)
for conds in [
(operator == "contains", str(value).lower() in str(input).lower()),
(operator == "not contains", str(value).lower() not in str(input).lower()),
(operator == "start with", str(input).lower().startswith(str(value).lower())),
(operator == "end with", str(input).lower().endswith(str(value).lower())),
(operator == "empty", not input),
(operator == "not empty", input),
(operator == "=", input == value),
(operator == "≠", input != value),
(operator == ">", input > value),
(operator == "<", input < value),
(operator == "≥", input >= value),
(operator == "≤", input <= value),
]:
(operator == "contains", str(value).lower() in str(input).lower()),
(operator == "not contains", str(value).lower() not in str(input).lower()),
(operator == "start with", str(input).lower().startswith(str(value).lower())),
(operator == "end with", str(input).lower().endswith(str(value).lower())),
(operator == "empty", not input),
(operator == "not empty", input),
(operator == "=", input == value),
(operator == "≠", input != value),
(operator == ">", input > value),
(operator == "<", input < value),
(operator == "≥", input >= value),
(operator == "≤", input <= value),
]:
try:
if all(conds):
ids.extend(docids)
@ -456,7 +456,8 @@ def chat(dialog, messages, stream=True, **kwargs):
kbinfos["chunks"].extend(tav_res["chunks"])
kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
if prompt_config.get("use_kg"):
ck = settings.kg_retrievaler.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl, LLMBundle(dialog.tenant_id, LLMType.CHAT))
ck = settings.kg_retrievaler.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl,
LLMBundle(dialog.tenant_id, LLMType.CHAT))
if ck["content_with_weight"]:
kbinfos["chunks"].insert(0, ck)
@ -467,7 +468,8 @@ def chat(dialog, messages, stream=True, **kwargs):
retrieval_ts = timer()
if not knowledges and prompt_config.get("empty_response"):
empty_res = prompt_config["empty_response"]
yield {"answer": empty_res, "reference": kbinfos, "prompt": "\n\n### Query:\n%s" % " ".join(questions), "audio_binary": tts(tts_mdl, empty_res)}
yield {"answer": empty_res, "reference": kbinfos, "prompt": "\n\n### Query:\n%s" % " ".join(questions),
"audio_binary": tts(tts_mdl, empty_res)}
return {"answer": prompt_config["empty_response"], "reference": kbinfos}
kwargs["knowledge"] = "\n------\n" + "\n\n------\n\n".join(knowledges)
@ -565,7 +567,8 @@ def chat(dialog, messages, stream=True, **kwargs):
if langfuse_tracer:
langfuse_generation = langfuse_tracer.start_generation(
trace_context=trace_context, name="chat", model=llm_model_config["llm_name"], input={"prompt": prompt, "prompt4citation": prompt4citation, "messages": msg}
trace_context=trace_context, name="chat", model=llm_model_config["llm_name"],
input={"prompt": prompt, "prompt4citation": prompt4citation, "messages": msg}
)
if stream:
@ -575,12 +578,12 @@ def chat(dialog, messages, stream=True, **kwargs):
if thought:
ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
answer = ans
delta_ans = ans[len(last_ans) :]
delta_ans = ans[len(last_ans):]
if num_tokens_from_string(delta_ans) < 16:
continue
last_ans = answer
yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
delta_ans = answer[len(last_ans) :]
delta_ans = answer[len(last_ans):]
if delta_ans:
yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
yield decorate_answer(thought + answer)
@ -676,7 +679,9 @@ Please write the SQL, only SQL, without any other explanations or text.
# compose Markdown table
columns = (
"|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], tbl["columns"][i]["name"])) for i in column_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
"|" + "|".join(
[re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], tbl["columns"][i]["name"])) for i in column_idx]) + (
"|Source|" if docid_idx and docid_idx else "|")
)
line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + ("|------|" if docid_idx and docid_idx else "")
@ -753,7 +758,7 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
doc_ids = None
kbinfos = retriever.retrieval(
question = question,
question=question,
embd_mdl=embd_mdl,
tenant_ids=tenant_ids,
kb_ids=kb_ids,
@ -775,7 +780,8 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
def decorate_answer(answer):
nonlocal knowledges, kbinfos, sys_prompt
answer, idx = retriever.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], embd_mdl, tkweight=0.7, vtweight=0.3)
answer, idx = retriever.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]],
embd_mdl, tkweight=0.7, vtweight=0.3)
idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
if not recall_docs: