mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-12-08 20:42:30 +08:00
360f5c1179
### What problem does this PR solve? As title ### Type of change - [x] Refactoring Signed-off-by: Jin Hai <haijin.chn@gmail.com>
874 lines
35 KiB
Python
874 lines
35 KiB
Python
#
|
||
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
|
||
#
|
||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
# you may not use this file except in compliance with the License.
|
||
# You may obtain a copy of the License at
|
||
#
|
||
# http://www.apache.org/licenses/LICENSE-2.0
|
||
#
|
||
# Unless required by applicable law or agreed to in writing, software
|
||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
# See the License for the specific language governing permissions and
|
||
# limitations under the License.
|
||
#
|
||
import binascii
|
||
import logging
|
||
import re
|
||
import time
|
||
from copy import deepcopy
|
||
from datetime import datetime
|
||
from functools import partial
|
||
from timeit import default_timer as timer
|
||
import trio
|
||
from langfuse import Langfuse
|
||
from peewee import fn
|
||
from agentic_reasoning import DeepResearcher
|
||
from api import settings
|
||
from api.db import LLMType, ParserType, StatusEnum
|
||
from api.db.db_models import DB, Dialog
|
||
from api.db.services.common_service import CommonService
|
||
from api.db.services.document_service import DocumentService
|
||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||
from api.db.services.langfuse_service import TenantLangfuseService
|
||
from api.db.services.llm_service import LLMBundle
|
||
from api.db.services.tenant_llm_service import TenantLLMService
|
||
from common.time_utils import current_timestamp, datetime_format
|
||
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.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 common.token_utils import num_tokens_from_string
|
||
from rag.utils.tavily_conn import Tavily
|
||
from common.string_utils import remove_redundant_spaces
|
||
|
||
|
||
class DialogService(CommonService):
|
||
model = Dialog
|
||
|
||
@classmethod
|
||
def save(cls, **kwargs):
|
||
"""Save a new record to database.
|
||
|
||
This method creates a new record in the database with the provided field values,
|
||
forcing an insert operation rather than an update.
|
||
|
||
Args:
|
||
**kwargs: Record field values as keyword arguments.
|
||
|
||
Returns:
|
||
Model instance: The created record object.
|
||
"""
|
||
sample_obj = cls.model(**kwargs).save(force_insert=True)
|
||
return sample_obj
|
||
|
||
@classmethod
|
||
def update_many_by_id(cls, data_list):
|
||
"""Update multiple records by their IDs.
|
||
|
||
This method updates multiple records in the database, identified by their IDs.
|
||
It automatically updates the update_time and update_date fields for each record.
|
||
|
||
Args:
|
||
data_list (list): List of dictionaries containing record data to update.
|
||
Each dictionary must include an 'id' field.
|
||
"""
|
||
with DB.atomic():
|
||
for data in data_list:
|
||
data["update_time"] = current_timestamp()
|
||
data["update_date"] = datetime_format(datetime.now())
|
||
cls.model.update(data).where(cls.model.id == data["id"]).execute()
|
||
|
||
@classmethod
|
||
@DB.connection_context()
|
||
def get_list(cls, tenant_id, page_number, items_per_page, orderby, desc, id, name):
|
||
chats = cls.model.select()
|
||
if id:
|
||
chats = chats.where(cls.model.id == id)
|
||
if name:
|
||
chats = chats.where(cls.model.name == name)
|
||
chats = chats.where((cls.model.tenant_id == tenant_id) & (cls.model.status == StatusEnum.VALID.value))
|
||
if desc:
|
||
chats = chats.order_by(cls.model.getter_by(orderby).desc())
|
||
else:
|
||
chats = chats.order_by(cls.model.getter_by(orderby).asc())
|
||
|
||
chats = chats.paginate(page_number, items_per_page)
|
||
|
||
return list(chats.dicts())
|
||
|
||
@classmethod
|
||
@DB.connection_context()
|
||
def get_by_tenant_ids(cls, joined_tenant_ids, user_id, page_number, items_per_page, orderby, desc, keywords, parser_id=None):
|
||
from api.db.db_models import User
|
||
|
||
fields = [
|
||
cls.model.id,
|
||
cls.model.tenant_id,
|
||
cls.model.name,
|
||
cls.model.description,
|
||
cls.model.language,
|
||
cls.model.llm_id,
|
||
cls.model.llm_setting,
|
||
cls.model.prompt_type,
|
||
cls.model.prompt_config,
|
||
cls.model.similarity_threshold,
|
||
cls.model.vector_similarity_weight,
|
||
cls.model.top_n,
|
||
cls.model.top_k,
|
||
cls.model.do_refer,
|
||
cls.model.rerank_id,
|
||
cls.model.kb_ids,
|
||
cls.model.icon,
|
||
cls.model.status,
|
||
User.nickname,
|
||
User.avatar.alias("tenant_avatar"),
|
||
cls.model.update_time,
|
||
cls.model.create_time,
|
||
]
|
||
if keywords:
|
||
dialogs = (
|
||
cls.model.select(*fields)
|
||
.join(User, on=(cls.model.tenant_id == User.id))
|
||
.where(
|
||
(cls.model.tenant_id.in_(joined_tenant_ids) | (cls.model.tenant_id == user_id)) & (cls.model.status == StatusEnum.VALID.value),
|
||
(fn.LOWER(cls.model.name).contains(keywords.lower())),
|
||
)
|
||
)
|
||
else:
|
||
dialogs = (
|
||
cls.model.select(*fields)
|
||
.join(User, on=(cls.model.tenant_id == User.id))
|
||
.where(
|
||
(cls.model.tenant_id.in_(joined_tenant_ids) | (cls.model.tenant_id == user_id)) & (cls.model.status == StatusEnum.VALID.value),
|
||
)
|
||
)
|
||
if parser_id:
|
||
dialogs = dialogs.where(cls.model.parser_id == parser_id)
|
||
if desc:
|
||
dialogs = dialogs.order_by(cls.model.getter_by(orderby).desc())
|
||
else:
|
||
dialogs = dialogs.order_by(cls.model.getter_by(orderby).asc())
|
||
|
||
count = dialogs.count()
|
||
|
||
if page_number and items_per_page:
|
||
dialogs = dialogs.paginate(page_number, items_per_page)
|
||
|
||
return list(dialogs.dicts()), count
|
||
|
||
@classmethod
|
||
@DB.connection_context()
|
||
def get_all_dialogs_by_tenant_id(cls, tenant_id):
|
||
fields = [cls.model.id]
|
||
dialogs = cls.model.select(*fields).where(cls.model.tenant_id == tenant_id)
|
||
dialogs.order_by(cls.model.create_time.asc())
|
||
offset, limit = 0, 100
|
||
res = []
|
||
while True:
|
||
d_batch = dialogs.offset(offset).limit(limit)
|
||
_temp = list(d_batch.dicts())
|
||
if not _temp:
|
||
break
|
||
res.extend(_temp)
|
||
offset += limit
|
||
return res
|
||
|
||
def chat_solo(dialog, messages, stream=True):
|
||
if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text":
|
||
chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
|
||
else:
|
||
chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
|
||
|
||
prompt_config = dialog.prompt_config
|
||
tts_mdl = None
|
||
if prompt_config.get("tts"):
|
||
tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
|
||
msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"]
|
||
if stream:
|
||
last_ans = ""
|
||
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):]
|
||
if num_tokens_from_string(delta_ans) < 16:
|
||
continue
|
||
last_ans = answer
|
||
yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt": "", "created_at": time.time()}
|
||
delta_ans = ""
|
||
if delta_ans:
|
||
yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt": "", "created_at": time.time()}
|
||
else:
|
||
answer = chat_mdl.chat(prompt_config.get("system", ""), msg, dialog.llm_setting)
|
||
user_content = msg[-1].get("content", "[content not available]")
|
||
logging.debug("User: {}|Assistant: {}".format(user_content, answer))
|
||
yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, answer), "prompt": "", "created_at": time.time()}
|
||
|
||
|
||
def get_models(dialog):
|
||
embd_mdl, chat_mdl, rerank_mdl, tts_mdl = None, None, None, None
|
||
kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
|
||
embedding_list = list(set([kb.embd_id for kb in kbs]))
|
||
if len(embedding_list) > 1:
|
||
raise Exception("**ERROR**: Knowledge bases use different embedding models.")
|
||
|
||
if embedding_list:
|
||
embd_mdl = LLMBundle(dialog.tenant_id, LLMType.EMBEDDING, embedding_list[0])
|
||
if not embd_mdl:
|
||
raise LookupError("Embedding model(%s) not found" % embedding_list[0])
|
||
|
||
if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text":
|
||
chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
|
||
else:
|
||
chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
|
||
|
||
if dialog.rerank_id:
|
||
rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id)
|
||
|
||
if dialog.prompt_config.get("tts"):
|
||
tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
|
||
return kbs, embd_mdl, rerank_mdl, chat_mdl, tts_mdl
|
||
|
||
|
||
BAD_CITATION_PATTERNS = [
|
||
re.compile(r"\(\s*ID\s*[: ]*\s*(\d+)\s*\)"), # (ID: 12)
|
||
re.compile(r"\[\s*ID\s*[: ]*\s*(\d+)\s*\]"), # [ID: 12]
|
||
re.compile(r"【\s*ID\s*[: ]*\s*(\d+)\s*】"), # 【ID: 12】
|
||
re.compile(r"ref\s*(\d+)", flags=re.IGNORECASE), # ref12、REF 12
|
||
]
|
||
|
||
|
||
def repair_bad_citation_formats(answer: str, kbinfos: dict, idx: set):
|
||
max_index = len(kbinfos["chunks"])
|
||
|
||
def safe_add(i):
|
||
if 0 <= i < max_index:
|
||
idx.add(i)
|
||
return True
|
||
return False
|
||
|
||
def find_and_replace(pattern, group_index=1, repl=lambda i: f"ID:{i}", flags=0):
|
||
nonlocal answer
|
||
|
||
def replacement(match):
|
||
try:
|
||
i = int(match.group(group_index))
|
||
if safe_add(i):
|
||
return f"[{repl(i)}]"
|
||
except Exception:
|
||
pass
|
||
return match.group(0)
|
||
|
||
answer = re.sub(pattern, replacement, answer, flags=flags)
|
||
|
||
for pattern in BAD_CITATION_PATTERNS:
|
||
find_and_replace(pattern)
|
||
|
||
return answer, idx
|
||
|
||
|
||
def convert_conditions(metadata_condition):
|
||
if metadata_condition is None:
|
||
metadata_condition = {}
|
||
op_mapping = {
|
||
"is": "=",
|
||
"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", [])
|
||
]
|
||
|
||
|
||
def meta_filter(metas: dict, filters: list[dict]):
|
||
doc_ids = set([])
|
||
|
||
def filter_out(v2docs, operator, value):
|
||
ids = []
|
||
for input, docids in v2docs.items():
|
||
try:
|
||
input = float(input)
|
||
value = float(value)
|
||
except Exception:
|
||
input = str(input)
|
||
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),
|
||
]:
|
||
try:
|
||
if all(conds):
|
||
ids.extend(docids)
|
||
break
|
||
except Exception:
|
||
pass
|
||
return ids
|
||
|
||
for k, v2docs in metas.items():
|
||
for f in filters:
|
||
if k != f["key"]:
|
||
continue
|
||
ids = filter_out(v2docs, f["op"], f["value"])
|
||
if not doc_ids:
|
||
doc_ids = set(ids)
|
||
else:
|
||
doc_ids = doc_ids & set(ids)
|
||
if not doc_ids:
|
||
return []
|
||
return list(doc_ids)
|
||
|
||
|
||
def chat(dialog, messages, stream=True, **kwargs):
|
||
assert messages[-1]["role"] == "user", "The last content of this conversation is not from user."
|
||
if not dialog.kb_ids and not dialog.prompt_config.get("tavily_api_key"):
|
||
for ans in chat_solo(dialog, messages, stream):
|
||
yield ans
|
||
return
|
||
|
||
chat_start_ts = timer()
|
||
|
||
if TenantLLMService.llm_id2llm_type(dialog.llm_id) == "image2text":
|
||
llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
|
||
else:
|
||
llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
|
||
|
||
max_tokens = llm_model_config.get("max_tokens", 8192)
|
||
|
||
check_llm_ts = timer()
|
||
|
||
langfuse_tracer = None
|
||
trace_context = {}
|
||
langfuse_keys = TenantLangfuseService.filter_by_tenant(tenant_id=dialog.tenant_id)
|
||
if langfuse_keys:
|
||
langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key, host=langfuse_keys.host)
|
||
if langfuse.auth_check():
|
||
langfuse_tracer = langfuse
|
||
trace_id = langfuse_tracer.create_trace_id()
|
||
trace_context = {"trace_id": trace_id}
|
||
|
||
check_langfuse_tracer_ts = timer()
|
||
kbs, embd_mdl, rerank_mdl, chat_mdl, tts_mdl = get_models(dialog)
|
||
toolcall_session, tools = kwargs.get("toolcall_session"), kwargs.get("tools")
|
||
if toolcall_session and tools:
|
||
chat_mdl.bind_tools(toolcall_session, tools)
|
||
bind_models_ts = timer()
|
||
|
||
retriever = settings.retriever
|
||
questions = [m["content"] for m in messages if m["role"] == "user"][-3:]
|
||
attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else []
|
||
if "doc_ids" in messages[-1]:
|
||
attachments = messages[-1]["doc_ids"]
|
||
|
||
prompt_config = dialog.prompt_config
|
||
field_map = KnowledgebaseService.get_field_map(dialog.kb_ids)
|
||
# try to use sql if field mapping is good to go
|
||
if field_map:
|
||
logging.debug("Use SQL to retrieval:{}".format(questions[-1]))
|
||
ans = use_sql(questions[-1], field_map, dialog.tenant_id, chat_mdl, prompt_config.get("quote", True), dialog.kb_ids)
|
||
if ans:
|
||
yield ans
|
||
return
|
||
|
||
for p in prompt_config["parameters"]:
|
||
if p["key"] == "knowledge":
|
||
continue
|
||
if p["key"] not in kwargs and not p["optional"]:
|
||
raise KeyError("Miss parameter: " + p["key"])
|
||
if p["key"] not in kwargs:
|
||
prompt_config["system"] = prompt_config["system"].replace("{%s}" % p["key"], " ")
|
||
|
||
if len(questions) > 1 and prompt_config.get("refine_multiturn"):
|
||
questions = [full_question(dialog.tenant_id, dialog.llm_id, messages)]
|
||
else:
|
||
questions = questions[-1:]
|
||
|
||
if prompt_config.get("cross_languages"):
|
||
questions = [cross_languages(dialog.tenant_id, dialog.llm_id, questions[0], prompt_config["cross_languages"])]
|
||
|
||
if dialog.meta_data_filter:
|
||
metas = DocumentService.get_meta_by_kbs(dialog.kb_ids)
|
||
if dialog.meta_data_filter.get("method") == "auto":
|
||
filters = gen_meta_filter(chat_mdl, metas, questions[-1])
|
||
attachments.extend(meta_filter(metas, filters))
|
||
if not attachments:
|
||
attachments = None
|
||
elif dialog.meta_data_filter.get("method") == "manual":
|
||
attachments.extend(meta_filter(metas, dialog.meta_data_filter["manual"]))
|
||
if not attachments:
|
||
attachments = None
|
||
|
||
if prompt_config.get("keyword", False):
|
||
questions[-1] += keyword_extraction(chat_mdl, questions[-1])
|
||
|
||
refine_question_ts = timer()
|
||
|
||
thought = ""
|
||
kbinfos = {"total": 0, "chunks": [], "doc_aggs": []}
|
||
knowledges = []
|
||
|
||
if attachments is not None and "knowledge" in [p["key"] for p in prompt_config["parameters"]]:
|
||
tenant_ids = list(set([kb.tenant_id for kb in kbs]))
|
||
knowledges = []
|
||
if prompt_config.get("reasoning", False):
|
||
reasoner = DeepResearcher(
|
||
chat_mdl,
|
||
prompt_config,
|
||
partial(
|
||
retriever.retrieval,
|
||
embd_mdl=embd_mdl,
|
||
tenant_ids=tenant_ids,
|
||
kb_ids=dialog.kb_ids,
|
||
page=1,
|
||
page_size=dialog.top_n,
|
||
similarity_threshold=0.2,
|
||
vector_similarity_weight=0.3,
|
||
doc_ids=attachments,
|
||
),
|
||
)
|
||
|
||
for think in reasoner.thinking(kbinfos, " ".join(questions)):
|
||
if isinstance(think, str):
|
||
thought = think
|
||
knowledges = [t for t in think.split("\n") if t]
|
||
elif stream:
|
||
yield think
|
||
else:
|
||
if embd_mdl:
|
||
kbinfos = retriever.retrieval(
|
||
" ".join(questions),
|
||
embd_mdl,
|
||
tenant_ids,
|
||
dialog.kb_ids,
|
||
1,
|
||
dialog.top_n,
|
||
dialog.similarity_threshold,
|
||
dialog.vector_similarity_weight,
|
||
doc_ids=attachments,
|
||
top=dialog.top_k,
|
||
aggs=False,
|
||
rerank_mdl=rerank_mdl,
|
||
rank_feature=label_question(" ".join(questions), kbs),
|
||
)
|
||
if prompt_config.get("toc_enhance"):
|
||
cks = retriever.retrieval_by_toc(" ".join(questions), kbinfos["chunks"], tenant_ids, chat_mdl, dialog.top_n)
|
||
if cks:
|
||
kbinfos["chunks"] = cks
|
||
if prompt_config.get("tavily_api_key"):
|
||
tav = Tavily(prompt_config["tavily_api_key"])
|
||
tav_res = tav.retrieve_chunks(" ".join(questions))
|
||
kbinfos["chunks"].extend(tav_res["chunks"])
|
||
kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
|
||
if prompt_config.get("use_kg"):
|
||
ck = settings.kg_retriever.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)
|
||
|
||
knowledges = kb_prompt(kbinfos, max_tokens)
|
||
|
||
logging.debug("{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
|
||
|
||
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)}
|
||
return {"answer": prompt_config["empty_response"], "reference": kbinfos}
|
||
|
||
kwargs["knowledge"] = "\n------\n" + "\n\n------\n\n".join(knowledges)
|
||
gen_conf = dialog.llm_setting
|
||
|
||
msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}]
|
||
prompt4citation = ""
|
||
if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
|
||
prompt4citation = citation_prompt()
|
||
msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"])
|
||
used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.95))
|
||
assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
|
||
prompt = msg[0]["content"]
|
||
|
||
if "max_tokens" in gen_conf:
|
||
gen_conf["max_tokens"] = min(gen_conf["max_tokens"], max_tokens - used_token_count)
|
||
|
||
def decorate_answer(answer):
|
||
nonlocal embd_mdl, prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_ts, questions, langfuse_tracer
|
||
|
||
refs = []
|
||
ans = answer.split("</think>")
|
||
think = ""
|
||
if len(ans) == 2:
|
||
think = ans[0] + "</think>"
|
||
answer = ans[1]
|
||
|
||
if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
|
||
idx = set([])
|
||
if embd_mdl and not re.search(r"\[ID:([0-9]+)\]", answer):
|
||
answer, idx = retriever.insert_citations(
|
||
answer,
|
||
[ck["content_ltks"] for ck in kbinfos["chunks"]],
|
||
[ck["vector"] for ck in kbinfos["chunks"]],
|
||
embd_mdl,
|
||
tkweight=1 - dialog.vector_similarity_weight,
|
||
vtweight=dialog.vector_similarity_weight,
|
||
)
|
||
else:
|
||
for match in re.finditer(r"\[ID:([0-9]+)\]", answer):
|
||
i = int(match.group(1))
|
||
if i < len(kbinfos["chunks"]):
|
||
idx.add(i)
|
||
|
||
answer, idx = repair_bad_citation_formats(answer, kbinfos, idx)
|
||
|
||
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:
|
||
recall_docs = kbinfos["doc_aggs"]
|
||
kbinfos["doc_aggs"] = recall_docs
|
||
|
||
refs = deepcopy(kbinfos)
|
||
for c in refs["chunks"]:
|
||
if c.get("vector"):
|
||
del c["vector"]
|
||
|
||
if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
|
||
answer += " Please set LLM API-Key in 'User Setting -> Model providers -> API-Key'"
|
||
finish_chat_ts = timer()
|
||
|
||
total_time_cost = (finish_chat_ts - chat_start_ts) * 1000
|
||
check_llm_time_cost = (check_llm_ts - chat_start_ts) * 1000
|
||
check_langfuse_tracer_cost = (check_langfuse_tracer_ts - check_llm_ts) * 1000
|
||
bind_embedding_time_cost = (bind_models_ts - check_langfuse_tracer_ts) * 1000
|
||
refine_question_time_cost = (refine_question_ts - bind_models_ts) * 1000
|
||
retrieval_time_cost = (retrieval_ts - refine_question_ts) * 1000
|
||
generate_result_time_cost = (finish_chat_ts - retrieval_ts) * 1000
|
||
|
||
tk_num = num_tokens_from_string(think + answer)
|
||
prompt += "\n\n### Query:\n%s" % " ".join(questions)
|
||
prompt = (
|
||
f"{prompt}\n\n"
|
||
"## Time elapsed:\n"
|
||
f" - Total: {total_time_cost:.1f}ms\n"
|
||
f" - Check LLM: {check_llm_time_cost:.1f}ms\n"
|
||
f" - Check Langfuse tracer: {check_langfuse_tracer_cost:.1f}ms\n"
|
||
f" - Bind models: {bind_embedding_time_cost:.1f}ms\n"
|
||
f" - Query refinement(LLM): {refine_question_time_cost:.1f}ms\n"
|
||
f" - Retrieval: {retrieval_time_cost:.1f}ms\n"
|
||
f" - Generate answer: {generate_result_time_cost:.1f}ms\n\n"
|
||
"## Token usage:\n"
|
||
f" - Generated tokens(approximately): {tk_num}\n"
|
||
f" - Token speed: {int(tk_num / (generate_result_time_cost / 1000.0))}/s"
|
||
)
|
||
|
||
# Add a condition check to call the end method only if langfuse_tracer exists
|
||
if langfuse_tracer and "langfuse_generation" in locals():
|
||
langfuse_output = "\n" + re.sub(r"^.*?(### Query:.*)", r"\1", prompt, flags=re.DOTALL)
|
||
langfuse_output = {"time_elapsed:": re.sub(r"\n", " \n", langfuse_output), "created_at": time.time()}
|
||
langfuse_generation.update(output=langfuse_output)
|
||
langfuse_generation.end()
|
||
|
||
return {"answer": think + answer, "reference": refs, "prompt": re.sub(r"\n", " \n", prompt), "created_at": time.time()}
|
||
|
||
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}
|
||
)
|
||
|
||
if stream:
|
||
last_ans = ""
|
||
answer = ""
|
||
for ans in chat_mdl.chat_streamly(prompt + prompt4citation, msg[1:], gen_conf):
|
||
if thought:
|
||
ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
|
||
answer = 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):]
|
||
if delta_ans:
|
||
yield {"answer": thought + answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
|
||
yield decorate_answer(thought + answer)
|
||
else:
|
||
answer = chat_mdl.chat(prompt + prompt4citation, msg[1:], gen_conf)
|
||
user_content = msg[-1].get("content", "[content not available]")
|
||
logging.debug("User: {}|Assistant: {}".format(user_content, answer))
|
||
res = decorate_answer(answer)
|
||
res["audio_binary"] = tts(tts_mdl, answer)
|
||
yield res
|
||
|
||
|
||
def use_sql(question, field_map, tenant_id, chat_mdl, quota=True, kb_ids=None):
|
||
sys_prompt = "You are a Database Administrator. You need to check the fields of the following tables based on the user's list of questions and write the SQL corresponding to the last question."
|
||
user_prompt = """
|
||
Table name: {};
|
||
Table of database fields are as follows:
|
||
{}
|
||
|
||
Question are as follows:
|
||
{}
|
||
Please write the SQL, only SQL, without any other explanations or text.
|
||
""".format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question)
|
||
tried_times = 0
|
||
|
||
def get_table():
|
||
nonlocal sys_prompt, user_prompt, question, tried_times
|
||
sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {"temperature": 0.06})
|
||
sql = re.sub(r"^.*</think>", "", sql, flags=re.DOTALL)
|
||
logging.debug(f"{question} ==> {user_prompt} get SQL: {sql}")
|
||
sql = re.sub(r"[\r\n]+", " ", sql.lower())
|
||
sql = re.sub(r".*select ", "select ", sql.lower())
|
||
sql = re.sub(r" +", " ", sql)
|
||
sql = re.sub(r"([;；]|```).*", "", sql)
|
||
if sql[: len("select ")] != "select ":
|
||
return None, None
|
||
if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()):
|
||
if sql[: len("select *")] != "select *":
|
||
sql = "select doc_id,docnm_kwd," + sql[6:]
|
||
else:
|
||
flds = []
|
||
for k in field_map.keys():
|
||
if k in forbidden_select_fields4resume:
|
||
continue
|
||
if len(flds) > 11:
|
||
break
|
||
flds.append(k)
|
||
sql = "select doc_id,docnm_kwd," + ",".join(flds) + sql[8:]
|
||
|
||
if kb_ids:
|
||
kb_filter = "(" + " OR ".join([f"kb_id = '{kb_id}'" for kb_id in kb_ids]) + ")"
|
||
if "where" not in sql.lower():
|
||
sql += f" WHERE {kb_filter}"
|
||
else:
|
||
sql += f" AND {kb_filter}"
|
||
|
||
logging.debug(f"{question} get SQL(refined): {sql}")
|
||
tried_times += 1
|
||
return settings.retriever.sql_retrieval(sql, format="json"), sql
|
||
|
||
tbl, sql = get_table()
|
||
if tbl is None:
|
||
return None
|
||
if tbl.get("error") and tried_times <= 2:
|
||
user_prompt = """
|
||
Table name: {};
|
||
Table of database fields are as follows:
|
||
{}
|
||
|
||
Question are as follows:
|
||
{}
|
||
Please write the SQL, only SQL, without any other explanations or text.
|
||
|
||
|
||
The SQL error you provided last time is as follows:
|
||
{}
|
||
|
||
Error issued by database as follows:
|
||
{}
|
||
|
||
Please correct the error and write SQL again, only SQL, without any other explanations or text.
|
||
""".format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question, sql, tbl["error"])
|
||
tbl, sql = get_table()
|
||
logging.debug("TRY it again: {}".format(sql))
|
||
|
||
logging.debug("GET table: {}".format(tbl))
|
||
if tbl.get("error") or len(tbl["rows"]) == 0:
|
||
return None
|
||
|
||
docid_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "doc_id"])
|
||
doc_name_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "docnm_kwd"])
|
||
column_idx = [ii for ii in range(len(tbl["columns"])) if ii not in (docid_idx | doc_name_idx)]
|
||
|
||
# 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 "|")
|
||
)
|
||
|
||
line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + ("|------|" if docid_idx and docid_idx else "")
|
||
|
||
rows = ["|" + "|".join([remove_redundant_spaces(str(r[i])) for i in column_idx]).replace("None", " ") + "|" for r in tbl["rows"]]
|
||
rows = [r for r in rows if re.sub(r"[ |]+", "", r)]
|
||
if quota:
|
||
rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
|
||
else:
|
||
rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)])
|
||
rows = re.sub(r"T[0-9]{2}:[0-9]{2}:[0-9]{2}(\.[0-9]+Z)?\|", "|", rows)
|
||
|
||
if not docid_idx or not doc_name_idx:
|
||
logging.warning("SQL missing field: " + sql)
|
||
return {"answer": "\n".join([columns, line, rows]), "reference": {"chunks": [], "doc_aggs": []}, "prompt": sys_prompt}
|
||
|
||
docid_idx = list(docid_idx)[0]
|
||
doc_name_idx = list(doc_name_idx)[0]
|
||
doc_aggs = {}
|
||
for r in tbl["rows"]:
|
||
if r[docid_idx] not in doc_aggs:
|
||
doc_aggs[r[docid_idx]] = {"doc_name": r[doc_name_idx], "count": 0}
|
||
doc_aggs[r[docid_idx]]["count"] += 1
|
||
return {
|
||
"answer": "\n".join([columns, line, rows]),
|
||
"reference": {
|
||
"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[doc_name_idx]} for r in tbl["rows"]],
|
||
"doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in doc_aggs.items()],
|
||
},
|
||
"prompt": sys_prompt,
|
||
}
|
||
|
||
|
||
def tts(tts_mdl, text):
|
||
if not tts_mdl or not text:
|
||
return
|
||
bin = b""
|
||
for chunk in tts_mdl.tts(text):
|
||
bin += chunk
|
||
return binascii.hexlify(bin).decode("utf-8")
|
||
|
||
|
||
def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
|
||
doc_ids = search_config.get("doc_ids", [])
|
||
rerank_mdl = None
|
||
kb_ids = search_config.get("kb_ids", kb_ids)
|
||
chat_llm_name = search_config.get("chat_id", chat_llm_name)
|
||
rerank_id = search_config.get("rerank_id", "")
|
||
meta_data_filter = search_config.get("meta_data_filter")
|
||
|
||
kbs = KnowledgebaseService.get_by_ids(kb_ids)
|
||
embedding_list = list(set([kb.embd_id for kb in kbs]))
|
||
|
||
is_knowledge_graph = all([kb.parser_id == ParserType.KG for kb in kbs])
|
||
retriever = settings.retriever if not is_knowledge_graph else settings.kg_retriever
|
||
|
||
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0])
|
||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_llm_name)
|
||
if rerank_id:
|
||
rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id)
|
||
max_tokens = chat_mdl.max_length
|
||
tenant_ids = list(set([kb.tenant_id for kb in kbs]))
|
||
|
||
if meta_data_filter:
|
||
metas = DocumentService.get_meta_by_kbs(kb_ids)
|
||
if meta_data_filter.get("method") == "auto":
|
||
filters = gen_meta_filter(chat_mdl, metas, question)
|
||
doc_ids.extend(meta_filter(metas, filters))
|
||
if not doc_ids:
|
||
doc_ids = None
|
||
elif meta_data_filter.get("method") == "manual":
|
||
doc_ids.extend(meta_filter(metas, meta_data_filter["manual"]))
|
||
if not doc_ids:
|
||
doc_ids = None
|
||
|
||
kbinfos = retriever.retrieval(
|
||
question=question,
|
||
embd_mdl=embd_mdl,
|
||
tenant_ids=tenant_ids,
|
||
kb_ids=kb_ids,
|
||
page=1,
|
||
page_size=12,
|
||
similarity_threshold=search_config.get("similarity_threshold", 0.1),
|
||
vector_similarity_weight=search_config.get("vector_similarity_weight", 0.3),
|
||
top=search_config.get("top_k", 1024),
|
||
doc_ids=doc_ids,
|
||
aggs=False,
|
||
rerank_mdl=rerank_mdl,
|
||
rank_feature=label_question(question, kbs)
|
||
)
|
||
|
||
knowledges = kb_prompt(kbinfos, max_tokens)
|
||
sys_prompt = PROMPT_JINJA_ENV.from_string(ASK_SUMMARY).render(knowledge="\n".join(knowledges))
|
||
|
||
msg = [{"role": "user", "content": question}]
|
||
|
||
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)
|
||
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:
|
||
recall_docs = kbinfos["doc_aggs"]
|
||
kbinfos["doc_aggs"] = recall_docs
|
||
refs = deepcopy(kbinfos)
|
||
for c in refs["chunks"]:
|
||
if c.get("vector"):
|
||
del c["vector"]
|
||
|
||
if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
|
||
answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'"
|
||
refs["chunks"] = chunks_format(refs)
|
||
return {"answer": answer, "reference": refs}
|
||
|
||
answer = ""
|
||
for ans in chat_mdl.chat_streamly(sys_prompt, msg, {"temperature": 0.1}):
|
||
answer = ans
|
||
yield {"answer": answer, "reference": {}}
|
||
yield decorate_answer(answer)
|
||
|
||
|
||
def gen_mindmap(question, kb_ids, tenant_id, search_config={}):
|
||
meta_data_filter = search_config.get("meta_data_filter", {})
|
||
doc_ids = search_config.get("doc_ids", [])
|
||
rerank_id = search_config.get("rerank_id", "")
|
||
rerank_mdl = None
|
||
kbs = KnowledgebaseService.get_by_ids(kb_ids)
|
||
if not kbs:
|
||
return {"error": "No KB selected"}
|
||
embedding_list = list(set([kb.embd_id for kb in kbs]))
|
||
tenant_ids = list(set([kb.tenant_id for kb in kbs]))
|
||
|
||
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, llm_name=embedding_list[0])
|
||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=search_config.get("chat_id", ""))
|
||
if rerank_id:
|
||
rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id)
|
||
|
||
if meta_data_filter:
|
||
metas = DocumentService.get_meta_by_kbs(kb_ids)
|
||
if meta_data_filter.get("method") == "auto":
|
||
filters = gen_meta_filter(chat_mdl, metas, question)
|
||
doc_ids.extend(meta_filter(metas, filters))
|
||
if not doc_ids:
|
||
doc_ids = None
|
||
elif meta_data_filter.get("method") == "manual":
|
||
doc_ids.extend(meta_filter(metas, meta_data_filter["manual"]))
|
||
if not doc_ids:
|
||
doc_ids = None
|
||
|
||
ranks = settings.retriever.retrieval(
|
||
question=question,
|
||
embd_mdl=embd_mdl,
|
||
tenant_ids=tenant_ids,
|
||
kb_ids=kb_ids,
|
||
page=1,
|
||
page_size=12,
|
||
similarity_threshold=search_config.get("similarity_threshold", 0.2),
|
||
vector_similarity_weight=search_config.get("vector_similarity_weight", 0.3),
|
||
top=search_config.get("top_k", 1024),
|
||
doc_ids=doc_ids,
|
||
aggs=False,
|
||
rerank_mdl=rerank_mdl,
|
||
rank_feature=label_question(question, kbs),
|
||
)
|
||
mindmap = MindMapExtractor(chat_mdl)
|
||
mind_map = trio.run(mindmap, [c["content_with_weight"] for c in ranks["chunks"]])
|
||
return mind_map.output
|