mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-12-08 20:42:30 +08:00
0d8791936e
### What problem does this PR solve? #10436 ### Type of change - [x] New Feature (non-breaking change which adds functionality)
188 lines
7.3 KiB
Python
188 lines
7.3 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 os
|
||
import re
|
||
from abc import ABC
|
||
from agent.tools.base import ToolParamBase, ToolBase, ToolMeta
|
||
from api.db import LLMType
|
||
from api.db.services.knowledgebase_service import KnowledgebaseService
|
||
from api.db.services.llm_service import LLMBundle
|
||
from api import settings
|
||
from api.utils.api_utils import timeout
|
||
from rag.app.tag import label_question
|
||
from rag.prompts.generator import cross_languages, kb_prompt
|
||
|
||
|
||
class RetrievalParam(ToolParamBase):
|
||
"""
|
||
Define the Retrieval component parameters.
|
||
"""
|
||
|
||
def __init__(self):
|
||
self.meta:ToolMeta = {
|
||
"name": "search_my_dateset",
|
||
"description": "This tool can be utilized for relevant content searching in the datasets.",
|
||
"parameters": {
|
||
"query": {
|
||
"type": "string",
|
||
"description": "The keywords to search the dataset. The keywords should be the most important words/terms(includes synonyms) from the original request.",
|
||
"default": "",
|
||
"required": True
|
||
}
|
||
}
|
||
}
|
||
super().__init__()
|
||
self.function_name = "search_my_dateset"
|
||
self.description = "This tool can be utilized for relevant content searching in the datasets."
|
||
self.similarity_threshold = 0.2
|
||
self.keywords_similarity_weight = 0.5
|
||
self.top_n = 8
|
||
self.top_k = 1024
|
||
self.kb_ids = []
|
||
self.kb_vars = []
|
||
self.rerank_id = ""
|
||
self.empty_response = ""
|
||
self.use_kg = False
|
||
self.cross_languages = []
|
||
self.toc_enhance = False
|
||
|
||
def check(self):
|
||
self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
|
||
self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keyword similarity weight")
|
||
self.check_positive_number(self.top_n, "[Retrieval] Top N")
|
||
|
||
def get_input_form(self) -> dict[str, dict]:
|
||
return {
|
||
"query": {
|
||
"name": "Query",
|
||
"type": "line"
|
||
}
|
||
}
|
||
|
||
class Retrieval(ToolBase, ABC):
|
||
component_name = "Retrieval"
|
||
|
||
@timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12)))
|
||
def _invoke(self, **kwargs):
|
||
if not kwargs.get("query"):
|
||
self.set_output("formalized_content", self._param.empty_response)
|
||
|
||
kb_ids: list[str] = []
|
||
for id in self._param.kb_ids:
|
||
if id.find("@") < 0:
|
||
kb_ids.append(id)
|
||
continue
|
||
kb_nm = self._canvas.get_variable_value(id)
|
||
# if kb_nm is a list
|
||
kb_nm_list = kb_nm if isinstance(kb_nm, list) else [kb_nm]
|
||
for nm_or_id in kb_nm_list:
|
||
e, kb = KnowledgebaseService.get_by_name(nm_or_id,
|
||
self._canvas._tenant_id)
|
||
if not e:
|
||
e, kb = KnowledgebaseService.get_by_id(nm_or_id)
|
||
if not e:
|
||
raise Exception(f"Dataset({nm_or_id}) does not exist.")
|
||
kb_ids.append(kb.id)
|
||
|
||
filtered_kb_ids: list[str] = list(set([kb_id for kb_id in kb_ids if kb_id]))
|
||
|
||
kbs = KnowledgebaseService.get_by_ids(filtered_kb_ids)
|
||
if not kbs:
|
||
raise Exception("No dataset is selected.")
|
||
|
||
embd_nms = list(set([kb.embd_id for kb in kbs]))
|
||
assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
|
||
|
||
embd_mdl = None
|
||
if embd_nms:
|
||
embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
|
||
|
||
rerank_mdl = None
|
||
if self._param.rerank_id:
|
||
rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
|
||
|
||
vars = self.get_input_elements_from_text(kwargs["query"])
|
||
vars = {k:o["value"] for k,o in vars.items()}
|
||
query = self.string_format(kwargs["query"], vars)
|
||
if self._param.cross_languages:
|
||
query = cross_languages(kbs[0].tenant_id, None, query, self._param.cross_languages)
|
||
|
||
if kbs:
|
||
query = re.sub(r"^user[:：\s]*", "", query, flags=re.IGNORECASE)
|
||
kbinfos = settings.retriever.retrieval(
|
||
query,
|
||
embd_mdl,
|
||
[kb.tenant_id for kb in kbs],
|
||
filtered_kb_ids,
|
||
1,
|
||
self._param.top_n,
|
||
self._param.similarity_threshold,
|
||
1 - self._param.keywords_similarity_weight,
|
||
aggs=False,
|
||
rerank_mdl=rerank_mdl,
|
||
rank_feature=label_question(query, kbs),
|
||
)
|
||
if self._param.toc_enhance:
|
||
chat_mdl = LLMBundle(self._canvas._tenant_id, LLMType.CHAT)
|
||
cks = settings.retriever.retrieval_by_toc(query, kbinfos["chunks"], [kb.tenant_id for kb in kbs], chat_mdl, self._param.top_n)
|
||
if cks:
|
||
kbinfos["chunks"] = cks
|
||
if self._param.use_kg:
|
||
ck = settings.kg_retriever.retrieval(query,
|
||
[kb.tenant_id for kb in kbs],
|
||
kb_ids,
|
||
embd_mdl,
|
||
LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT))
|
||
if ck["content_with_weight"]:
|
||
kbinfos["chunks"].insert(0, ck)
|
||
else:
|
||
kbinfos = {"chunks": [], "doc_aggs": []}
|
||
|
||
if self._param.use_kg and kbs:
|
||
ck = settings.kg_retriever.retrieval(query, [kb.tenant_id for kb in kbs], filtered_kb_ids, embd_mdl, LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
|
||
if ck["content_with_weight"]:
|
||
ck["content"] = ck["content_with_weight"]
|
||
del ck["content_with_weight"]
|
||
kbinfos["chunks"].insert(0, ck)
|
||
|
||
for ck in kbinfos["chunks"]:
|
||
if "vector" in ck:
|
||
del ck["vector"]
|
||
if "content_ltks" in ck:
|
||
del ck["content_ltks"]
|
||
|
||
if not kbinfos["chunks"]:
|
||
self.set_output("formalized_content", self._param.empty_response)
|
||
return
|
||
|
||
# Format the chunks for JSON output (similar to how other tools do it)
|
||
json_output = kbinfos["chunks"].copy()
|
||
|
||
self._canvas.add_reference(kbinfos["chunks"], kbinfos["doc_aggs"])
|
||
form_cnt = "\n".join(kb_prompt(kbinfos, 200000, True))
|
||
|
||
# Set both formalized content and JSON output
|
||
self.set_output("formalized_content", form_cnt)
|
||
self.set_output("json", json_output)
|
||
|
||
return form_cnt
|
||
|
||
def thoughts(self) -> str:
|
||
return """
|
||
Keywords: {}
|
||
Looking for the most relevant articles.
|
||
""".format(self.get_input().get("query", "-_-!"))
|