mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-12-08 20:42:30 +08:00
Move api.settings to common.settings (#11036)
### What problem does this PR solve? As title ### Type of change - [x] Refactoring --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
@ -21,7 +21,7 @@ from copy import deepcopy
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from deepdoc.parser.utils import get_text
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from rag.app.qa import Excel
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from rag.nlp import rag_tokenizer
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from common import globals
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from common import settings
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def beAdoc(d, q, a, eng, row_num=-1):
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@ -133,14 +133,14 @@ def label_question(question, kbs):
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if tag_kb_ids:
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all_tags = get_tags_from_cache(tag_kb_ids)
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if not all_tags:
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all_tags = globals.retriever.all_tags_in_portion(kb.tenant_id, tag_kb_ids)
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all_tags = settings.retriever.all_tags_in_portion(kb.tenant_id, tag_kb_ids)
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set_tags_to_cache(tags=all_tags, kb_ids=tag_kb_ids)
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else:
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all_tags = json.loads(all_tags)
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tag_kbs = KnowledgebaseService.get_by_ids(tag_kb_ids)
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if not tag_kbs:
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return tags
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tags = globals.retriever.tag_query(question,
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tags = settings.retriever.tag_query(question,
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list(set([kb.tenant_id for kb in tag_kbs])),
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tag_kb_ids,
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all_tags,
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@ -20,7 +20,7 @@ import time
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import argparse
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from collections import defaultdict
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from common import globals
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from common import settings
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from common.constants import LLMType
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from api.db.services.llm_service import LLMBundle
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from api.db.services.knowledgebase_service import KnowledgebaseService
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@ -52,7 +52,7 @@ class Benchmark:
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run = defaultdict(dict)
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query_list = list(qrels.keys())
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for query in query_list:
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ranks = globals.retriever.retrieval(query, self.embd_mdl, self.tenant_id, [self.kb.id], 1, 30,
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ranks = settings.retriever.retrieval(query, self.embd_mdl, self.tenant_id, [self.kb.id], 1, 30,
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0.0, self.vector_similarity_weight)
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if len(ranks["chunks"]) == 0:
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print(f"deleted query: {query}")
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@ -77,9 +77,9 @@ class Benchmark:
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def init_index(self, vector_size: int):
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if self.initialized_index:
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return
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if globals.docStoreConn.indexExist(self.index_name, self.kb_id):
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globals.docStoreConn.deleteIdx(self.index_name, self.kb_id)
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globals.docStoreConn.createIdx(self.index_name, self.kb_id, vector_size)
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if settings.docStoreConn.indexExist(self.index_name, self.kb_id):
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settings.docStoreConn.deleteIdx(self.index_name, self.kb_id)
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settings.docStoreConn.createIdx(self.index_name, self.kb_id, vector_size)
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self.initialized_index = True
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def ms_marco_index(self, file_path, index_name):
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@ -114,13 +114,13 @@ class Benchmark:
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docs_count += len(docs)
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docs, vector_size = self.embedding(docs)
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self.init_index(vector_size)
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globals.docStoreConn.insert(docs, self.index_name, self.kb_id)
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settings.docStoreConn.insert(docs, self.index_name, self.kb_id)
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docs = []
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if docs:
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docs, vector_size = self.embedding(docs)
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self.init_index(vector_size)
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globals.docStoreConn.insert(docs, self.index_name, self.kb_id)
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settings.docStoreConn.insert(docs, self.index_name, self.kb_id)
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return qrels, texts
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def trivia_qa_index(self, file_path, index_name):
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@ -155,12 +155,12 @@ class Benchmark:
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docs_count += len(docs)
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docs, vector_size = self.embedding(docs)
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self.init_index(vector_size)
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globals.docStoreConn.insert(docs,self.index_name)
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settings.docStoreConn.insert(docs,self.index_name)
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docs = []
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docs, vector_size = self.embedding(docs)
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self.init_index(vector_size)
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globals.docStoreConn.insert(docs, self.index_name)
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settings.docStoreConn.insert(docs, self.index_name)
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return qrels, texts
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def miracl_index(self, file_path, corpus_path, index_name):
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@ -210,12 +210,12 @@ class Benchmark:
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docs_count += len(docs)
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docs, vector_size = self.embedding(docs)
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self.init_index(vector_size)
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globals.docStoreConn.insert(docs, self.index_name)
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settings.docStoreConn.insert(docs, self.index_name)
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docs = []
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docs, vector_size = self.embedding(docs)
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self.init_index(vector_size)
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globals.docStoreConn.insert(docs, self.index_name)
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settings.docStoreConn.insert(docs, self.index_name)
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return qrels, texts
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def save_results(self, qrels, run, texts, dataset, file_path):
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@ -26,7 +26,7 @@ from deepdoc.parser.pdf_parser import RAGFlowPdfParser
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from rag.flow.base import ProcessBase, ProcessParamBase
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from rag.flow.hierarchical_merger.schema import HierarchicalMergerFromUpstream
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from rag.nlp import concat_img
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from rag.utils.storage_factory import STORAGE_IMPL
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from common import settings
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class HierarchicalMergerParam(ProcessParamBase):
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@ -166,7 +166,7 @@ class HierarchicalMerger(ProcessBase):
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img = None
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for i in path:
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txt += lines[i] + "\n"
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concat_img(img, id2image(section_images[i], partial(STORAGE_IMPL.get, tenant_id=self._canvas._tenant_id)))
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concat_img(img, id2image(section_images[i], partial(settings.STORAGE_IMPL.get, tenant_id=self._canvas._tenant_id)))
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cks.append(txt)
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images.append(img)
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@ -180,7 +180,7 @@ class HierarchicalMerger(ProcessBase):
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]
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async with trio.open_nursery() as nursery:
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for d in cks:
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nursery.start_soon(image2id, d, partial(STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())
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nursery.start_soon(image2id, d, partial(settings.STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())
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self.set_output("chunks", cks)
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self.callback(1, "Done.")
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@ -36,7 +36,7 @@ from rag.app.naive import Docx
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from rag.flow.base import ProcessBase, ProcessParamBase
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from rag.flow.parser.schema import ParserFromUpstream
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from rag.llm.cv_model import Base as VLM
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from rag.utils.storage_factory import STORAGE_IMPL
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from common import settings
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class ParserParam(ProcessParamBase):
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@ -588,7 +588,7 @@ class Parser(ProcessBase):
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name = from_upstream.name
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if self._canvas._doc_id:
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b, n = File2DocumentService.get_storage_address(doc_id=self._canvas._doc_id)
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blob = STORAGE_IMPL.get(b, n)
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blob = settings.STORAGE_IMPL.get(b, n)
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else:
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blob = FileService.get_blob(from_upstream.file["created_by"], from_upstream.file["id"])
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@ -606,4 +606,4 @@ class Parser(ProcessBase):
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outs = self.output()
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async with trio.open_nursery() as nursery:
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for d in outs.get("json", []):
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nursery.start_soon(image2id, d, partial(STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())
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nursery.start_soon(image2id, d, partial(settings.STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())
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@ -23,7 +23,7 @@ from deepdoc.parser.pdf_parser import RAGFlowPdfParser
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from rag.flow.base import ProcessBase, ProcessParamBase
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from rag.flow.splitter.schema import SplitterFromUpstream
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from rag.nlp import naive_merge, naive_merge_with_images
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from rag.utils.storage_factory import STORAGE_IMPL
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from common import settings
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class SplitterParam(ProcessParamBase):
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@ -87,7 +87,7 @@ class Splitter(ProcessBase):
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sections, section_images = [], []
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for o in from_upstream.json_result or []:
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sections.append((o.get("text", ""), o.get("position_tag", "")))
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section_images.append(id2image(o.get("img_id"), partial(STORAGE_IMPL.get, tenant_id=self._canvas._tenant_id)))
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section_images.append(id2image(o.get("img_id"), partial(settings.STORAGE_IMPL.get, tenant_id=self._canvas._tenant_id)))
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chunks, images = naive_merge_with_images(
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sections,
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@ -106,6 +106,6 @@ class Splitter(ProcessBase):
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]
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async with trio.open_nursery() as nursery:
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for d in cks:
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nursery.start_soon(image2id, d, partial(STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())
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nursery.start_soon(image2id, d, partial(settings.STORAGE_IMPL.put, tenant_id=self._canvas._tenant_id), get_uuid())
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self.set_output("chunks", cks)
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self.callback(1, "Done.")
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@ -21,7 +21,7 @@ from concurrent.futures import ThreadPoolExecutor
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import trio
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from api import settings
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from common import settings
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from rag.flow.pipeline import Pipeline
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@ -27,7 +27,7 @@ from common.connection_utils import timeout
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from rag.flow.base import ProcessBase, ProcessParamBase
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from rag.flow.tokenizer.schema import TokenizerFromUpstream
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from rag.nlp import rag_tokenizer
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from rag.settings import EMBEDDING_BATCH_SIZE
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from common import settings
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from rag.svr.task_executor import embed_limiter
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from common.token_utils import truncate
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@ -82,16 +82,16 @@ class Tokenizer(ProcessBase):
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return embedding_model.encode([truncate(c, embedding_model.max_length - 10) for c in txts])
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cnts_ = np.array([])
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for i in range(0, len(texts), EMBEDDING_BATCH_SIZE):
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for i in range(0, len(texts), settings.EMBEDDING_BATCH_SIZE):
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async with embed_limiter:
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vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + EMBEDDING_BATCH_SIZE]))
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vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + settings.EMBEDDING_BATCH_SIZE]))
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if len(cnts_) == 0:
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cnts_ = vts
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else:
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cnts_ = np.concatenate((cnts_, vts), axis=0)
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token_count += c
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if i % 33 == 32:
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self.callback(i * 1.0 / len(texts) / parts / EMBEDDING_BATCH_SIZE + 0.5 * (parts - 1))
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self.callback(i * 1.0 / len(texts) / parts / settings.EMBEDDING_BATCH_SIZE + 0.5 * (parts - 1))
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cnts = cnts_
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title_w = float(self._param.filename_embd_weight)
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@ -29,7 +29,7 @@ from zhipuai import ZhipuAI
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from common.log_utils import log_exception
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from common.token_utils import num_tokens_from_string, truncate
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from common import globals
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from common import settings
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import logging
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@ -69,13 +69,13 @@ class BuiltinEmbed(Base):
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_model_lock = threading.Lock()
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def __init__(self, key, model_name, **kwargs):
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logging.info(f"Initialize BuiltinEmbed according to globals.EMBEDDING_CFG: {globals.EMBEDDING_CFG}")
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embedding_cfg = globals.EMBEDDING_CFG
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logging.info(f"Initialize BuiltinEmbed according to settings.EMBEDDING_CFG: {settings.EMBEDDING_CFG}")
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embedding_cfg = settings.EMBEDDING_CFG
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if not BuiltinEmbed._model and "tei-" in os.getenv("COMPOSE_PROFILES", ""):
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with BuiltinEmbed._model_lock:
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BuiltinEmbed._model_name = globals.EMBEDDING_MDL
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BuiltinEmbed._max_tokens = BuiltinEmbed.MAX_TOKENS.get(globals.EMBEDDING_MDL, 500)
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BuiltinEmbed._model = HuggingFaceEmbed(embedding_cfg["api_key"], globals.EMBEDDING_MDL, base_url=embedding_cfg["base_url"])
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BuiltinEmbed._model_name = settings.EMBEDDING_MDL
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BuiltinEmbed._max_tokens = BuiltinEmbed.MAX_TOKENS.get(settings.EMBEDDING_MDL, 500)
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BuiltinEmbed._model = HuggingFaceEmbed(embedding_cfg["api_key"], settings.EMBEDDING_MDL, base_url=embedding_cfg["base_url"])
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self._model = BuiltinEmbed._model
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self._model_name = BuiltinEmbed._model_name
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self._max_tokens = BuiltinEmbed._max_tokens
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@ -22,12 +22,12 @@ from collections import OrderedDict
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from dataclasses import dataclass
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from rag.prompts.generator import relevant_chunks_with_toc
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from rag.settings import TAG_FLD, PAGERANK_FLD
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from rag.nlp import rag_tokenizer, query
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import numpy as np
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from rag.utils.doc_store_conn import DocStoreConnection, MatchDenseExpr, FusionExpr, OrderByExpr
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from common.string_utils import remove_redundant_spaces
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from common.float_utils import get_float
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from common.constants import PAGERANK_FLD, TAG_FLD
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def index_name(uid): return f"ragflow_{uid}"
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@ -25,7 +25,7 @@ import trio
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from common.misc_utils import hash_str2int
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from rag.nlp import rag_tokenizer
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from rag.prompts.template import load_prompt
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from rag.settings import TAG_FLD
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from common.constants import TAG_FLD
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from common.token_utils import encoder, num_tokens_from_string
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@ -13,40 +13,3 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import logging
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from common.file_utils import get_project_base_directory
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from common.misc_utils import pip_install_torch
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# Server
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RAG_CONF_PATH = os.path.join(get_project_base_directory(), "conf")
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DOC_MAXIMUM_SIZE = int(os.environ.get("MAX_CONTENT_LENGTH", 128 * 1024 * 1024))
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DOC_BULK_SIZE = int(os.environ.get("DOC_BULK_SIZE", 4))
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EMBEDDING_BATCH_SIZE = int(os.environ.get("EMBEDDING_BATCH_SIZE", 16))
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SVR_QUEUE_NAME = "rag_flow_svr_queue"
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SVR_CONSUMER_GROUP_NAME = "rag_flow_svr_task_broker"
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PAGERANK_FLD = "pagerank_fea"
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TAG_FLD = "tag_feas"
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PARALLEL_DEVICES = 0
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try:
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pip_install_torch()
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import torch.cuda
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PARALLEL_DEVICES = torch.cuda.device_count()
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logging.info(f"found {PARALLEL_DEVICES} gpus")
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except Exception:
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logging.info("can't import package 'torch'")
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def print_rag_settings():
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logging.info(f"MAX_CONTENT_LENGTH: {DOC_MAXIMUM_SIZE}")
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logging.info(f"MAX_FILE_COUNT_PER_USER: {int(os.environ.get('MAX_FILE_NUM_PER_USER', 0))}")
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def get_svr_queue_name(priority: int) -> str:
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if priority == 0:
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return SVR_QUEUE_NAME
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return f"{SVR_QUEUE_NAME}_{priority}"
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def get_svr_queue_names():
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return [get_svr_queue_name(priority) for priority in [1, 0]]
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@ -19,8 +19,8 @@ import traceback
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from api.db.db_models import close_connection
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from api.db.services.task_service import TaskService
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from rag.utils.storage_factory import STORAGE_IMPL
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from rag.utils.redis_conn import REDIS_CONN
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from common import settings
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def collect():
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@ -44,7 +44,7 @@ def main():
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key = "{}/{}".format(kb_id, loc)
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if REDIS_CONN.exist(key):
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continue
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file_bin = STORAGE_IMPL.get(kb_id, loc)
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file_bin = settings.STORAGE_IMPL.get(kb_id, loc)
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REDIS_CONN.transaction(key, file_bin, 12 * 60)
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logging.info("CACHE: {}".format(loc))
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except Exception as e:
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@ -36,7 +36,7 @@ import signal
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import trio
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import faulthandler
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from common.constants import FileSource, TaskStatus
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from api import settings
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from common import settings
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from api.versions import get_ragflow_version
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from common.data_source.confluence_connector import ConfluenceConnector
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from common.data_source.utils import load_all_docs_from_checkpoint_connector
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@ -55,20 +55,18 @@ from api.db.services.document_service import DocumentService
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from api.db.services.llm_service import LLMBundle
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from api.db.services.task_service import TaskService, has_canceled, CANVAS_DEBUG_DOC_ID, GRAPH_RAPTOR_FAKE_DOC_ID
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from api.db.services.file2document_service import File2DocumentService
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from api import settings
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from api.versions import get_ragflow_version
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from api.db.db_models import close_connection
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from rag.app import laws, paper, presentation, manual, qa, table, book, resume, picture, naive, one, audio, \
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email, tag
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from rag.nlp import search, rag_tokenizer, add_positions
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from rag.raptor import RecursiveAbstractiveProcessing4TreeOrganizedRetrieval as Raptor
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from rag.settings import DOC_MAXIMUM_SIZE, DOC_BULK_SIZE, EMBEDDING_BATCH_SIZE, SVR_CONSUMER_GROUP_NAME, get_svr_queue_name, get_svr_queue_names, print_rag_settings, TAG_FLD, PAGERANK_FLD
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from common.token_utils import num_tokens_from_string, truncate
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from rag.utils.redis_conn import REDIS_CONN, RedisDistributedLock
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from rag.utils.storage_factory import STORAGE_IMPL
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from graphrag.utils import chat_limiter
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from common.signal_utils import start_tracemalloc_and_snapshot, stop_tracemalloc
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from common import globals
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from common import settings
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from common.constants import PAGERANK_FLD, TAG_FLD, SVR_CONSUMER_GROUP_NAME
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BATCH_SIZE = 64
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@ -170,7 +168,7 @@ async def collect():
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global CONSUMER_NAME, DONE_TASKS, FAILED_TASKS
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global UNACKED_ITERATOR
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svr_queue_names = get_svr_queue_names()
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svr_queue_names = settings.get_svr_queue_names()
|
||||
try:
|
||||
if not UNACKED_ITERATOR:
|
||||
UNACKED_ITERATOR = REDIS_CONN.get_unacked_iterator(svr_queue_names, SVR_CONSUMER_GROUP_NAME, CONSUMER_NAME)
|
||||
@ -223,14 +221,14 @@ async def collect():
|
||||
|
||||
|
||||
async def get_storage_binary(bucket, name):
|
||||
return await trio.to_thread.run_sync(lambda: STORAGE_IMPL.get(bucket, name))
|
||||
return await trio.to_thread.run_sync(lambda: settings.STORAGE_IMPL.get(bucket, name))
|
||||
|
||||
|
||||
@timeout(60*80, 1)
|
||||
async def build_chunks(task, progress_callback):
|
||||
if task["size"] > DOC_MAXIMUM_SIZE:
|
||||
if task["size"] > settings.DOC_MAXIMUM_SIZE:
|
||||
set_progress(task["id"], prog=-1, msg="File size exceeds( <= %dMb )" %
|
||||
(int(DOC_MAXIMUM_SIZE / 1024 / 1024)))
|
||||
(int(settings.DOC_MAXIMUM_SIZE / 1024 / 1024)))
|
||||
return []
|
||||
|
||||
chunker = FACTORY[task["parser_id"].lower()]
|
||||
@ -287,7 +285,7 @@ async def build_chunks(task, progress_callback):
|
||||
d["img_id"] = ""
|
||||
docs.append(d)
|
||||
return
|
||||
await image2id(d, partial(STORAGE_IMPL.put, tenant_id=task["tenant_id"]), d["id"], task["kb_id"])
|
||||
await image2id(d, partial(settings.STORAGE_IMPL.put, tenant_id=task["tenant_id"]), d["id"], task["kb_id"])
|
||||
docs.append(d)
|
||||
except Exception:
|
||||
logging.exception(
|
||||
@ -350,7 +348,7 @@ async def build_chunks(task, progress_callback):
|
||||
examples = []
|
||||
all_tags = get_tags_from_cache(kb_ids)
|
||||
if not all_tags:
|
||||
all_tags = globals.retriever.all_tags_in_portion(tenant_id, kb_ids, S)
|
||||
all_tags = settings.retriever.all_tags_in_portion(tenant_id, kb_ids, S)
|
||||
set_tags_to_cache(kb_ids, all_tags)
|
||||
else:
|
||||
all_tags = json.loads(all_tags)
|
||||
@ -363,7 +361,7 @@ async def build_chunks(task, progress_callback):
|
||||
if task_canceled:
|
||||
progress_callback(-1, msg="Task has been canceled.")
|
||||
return
|
||||
if globals.retriever.tag_content(tenant_id, kb_ids, d, all_tags, topn_tags=topn_tags, S=S) and len(d[TAG_FLD]) > 0:
|
||||
if settings.retriever.tag_content(tenant_id, kb_ids, d, all_tags, topn_tags=topn_tags, S=S) and len(d[TAG_FLD]) > 0:
|
||||
examples.append({"content": d["content_with_weight"], TAG_FLD: d[TAG_FLD]})
|
||||
else:
|
||||
docs_to_tag.append(d)
|
||||
@ -424,7 +422,7 @@ def build_TOC(task, docs, progress_callback):
|
||||
|
||||
def init_kb(row, vector_size: int):
|
||||
idxnm = search.index_name(row["tenant_id"])
|
||||
return globals.docStoreConn.createIdx(idxnm, row.get("kb_id", ""), vector_size)
|
||||
return settings.docStoreConn.createIdx(idxnm, row.get("kb_id", ""), vector_size)
|
||||
|
||||
|
||||
async def embedding(docs, mdl, parser_config=None, callback=None):
|
||||
@ -453,9 +451,9 @@ async def embedding(docs, mdl, parser_config=None, callback=None):
|
||||
return mdl.encode([truncate(c, mdl.max_length-10) for c in txts])
|
||||
|
||||
cnts_ = np.array([])
|
||||
for i in range(0, len(cnts), EMBEDDING_BATCH_SIZE):
|
||||
for i in range(0, len(cnts), settings.EMBEDDING_BATCH_SIZE):
|
||||
async with embed_limiter:
|
||||
vts, c = await trio.to_thread.run_sync(lambda: batch_encode(cnts[i: i + EMBEDDING_BATCH_SIZE]))
|
||||
vts, c = await trio.to_thread.run_sync(lambda: batch_encode(cnts[i: i + settings.EMBEDDING_BATCH_SIZE]))
|
||||
if len(cnts_) == 0:
|
||||
cnts_ = vts
|
||||
else:
|
||||
@ -529,19 +527,19 @@ async def run_dataflow(task: dict):
|
||||
return embedding_model.encode([truncate(c, embedding_model.max_length - 10) for c in txts])
|
||||
vects = np.array([])
|
||||
texts = [o.get("questions", o.get("summary", o["text"])) for o in chunks]
|
||||
delta = 0.20/(len(texts)//EMBEDDING_BATCH_SIZE+1)
|
||||
delta = 0.20/(len(texts)//settings.EMBEDDING_BATCH_SIZE+1)
|
||||
prog = 0.8
|
||||
for i in range(0, len(texts), EMBEDDING_BATCH_SIZE):
|
||||
for i in range(0, len(texts), settings.EMBEDDING_BATCH_SIZE):
|
||||
async with embed_limiter:
|
||||
vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + EMBEDDING_BATCH_SIZE]))
|
||||
vts, c = await trio.to_thread.run_sync(lambda: batch_encode(texts[i : i + settings.EMBEDDING_BATCH_SIZE]))
|
||||
if len(vects) == 0:
|
||||
vects = vts
|
||||
else:
|
||||
vects = np.concatenate((vects, vts), axis=0)
|
||||
embedding_token_consumption += c
|
||||
prog += delta
|
||||
if i % (len(texts)//EMBEDDING_BATCH_SIZE/100+1) == 1:
|
||||
set_progress(task_id, prog=prog, msg=f"{i+1} / {len(texts)//EMBEDDING_BATCH_SIZE}")
|
||||
if i % (len(texts)//settings.EMBEDDING_BATCH_SIZE/100+1) == 1:
|
||||
set_progress(task_id, prog=prog, msg=f"{i+1} / {len(texts)//settings.EMBEDDING_BATCH_SIZE}")
|
||||
|
||||
assert len(vects) == len(chunks)
|
||||
for i, ck in enumerate(chunks):
|
||||
@ -648,7 +646,7 @@ async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_si
|
||||
chunks = []
|
||||
vctr_nm = "q_%d_vec"%vector_size
|
||||
for doc_id in doc_ids:
|
||||
for d in globals.retriever.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])],
|
||||
for d in settings.retriever.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])],
|
||||
fields=["content_with_weight", vctr_nm],
|
||||
sort_by_position=True):
|
||||
chunks.append((d["content_with_weight"], np.array(d[vctr_nm])))
|
||||
@ -691,15 +689,15 @@ async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_si
|
||||
async def delete_image(kb_id, chunk_id):
|
||||
try:
|
||||
async with minio_limiter:
|
||||
STORAGE_IMPL.delete(kb_id, chunk_id)
|
||||
settings.STORAGE_IMPL.delete(kb_id, chunk_id)
|
||||
except Exception:
|
||||
logging.exception(f"Deleting image of chunk {chunk_id} got exception")
|
||||
raise
|
||||
|
||||
|
||||
async def insert_es(task_id, task_tenant_id, task_dataset_id, chunks, progress_callback):
|
||||
for b in range(0, len(chunks), DOC_BULK_SIZE):
|
||||
doc_store_result = await trio.to_thread.run_sync(lambda: globals.docStoreConn.insert(chunks[b:b + DOC_BULK_SIZE], search.index_name(task_tenant_id), task_dataset_id))
|
||||
for b in range(0, len(chunks), settings.DOC_BULK_SIZE):
|
||||
doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b:b + settings.DOC_BULK_SIZE], search.index_name(task_tenant_id), task_dataset_id))
|
||||
task_canceled = has_canceled(task_id)
|
||||
if task_canceled:
|
||||
progress_callback(-1, msg="Task has been canceled.")
|
||||
@ -710,13 +708,13 @@ async def insert_es(task_id, task_tenant_id, task_dataset_id, chunks, progress_c
|
||||
error_message = f"Insert chunk error: {doc_store_result}, please check log file and Elasticsearch/Infinity status!"
|
||||
progress_callback(-1, msg=error_message)
|
||||
raise Exception(error_message)
|
||||
chunk_ids = [chunk["id"] for chunk in chunks[:b + DOC_BULK_SIZE]]
|
||||
chunk_ids = [chunk["id"] for chunk in chunks[:b + settings.DOC_BULK_SIZE]]
|
||||
chunk_ids_str = " ".join(chunk_ids)
|
||||
try:
|
||||
TaskService.update_chunk_ids(task_id, chunk_ids_str)
|
||||
except DoesNotExist:
|
||||
logging.warning(f"do_handle_task update_chunk_ids failed since task {task_id} is unknown.")
|
||||
doc_store_result = await trio.to_thread.run_sync(lambda: globals.docStoreConn.delete({"id": chunk_ids}, search.index_name(task_tenant_id), task_dataset_id))
|
||||
doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({"id": chunk_ids}, search.index_name(task_tenant_id), task_dataset_id))
|
||||
async with trio.open_nursery() as nursery:
|
||||
for chunk_id in chunk_ids:
|
||||
nursery.start_soon(delete_image, task_dataset_id, chunk_id)
|
||||
@ -752,7 +750,7 @@ async def do_handle_task(task):
|
||||
progress_callback = partial(set_progress, task_id, task_from_page, task_to_page)
|
||||
|
||||
# FIXME: workaround, Infinity doesn't support table parsing method, this check is to notify user
|
||||
lower_case_doc_engine = globals.DOC_ENGINE.lower()
|
||||
lower_case_doc_engine = settings.DOC_ENGINE.lower()
|
||||
if lower_case_doc_engine == 'infinity' and task['parser_id'].lower() == 'table':
|
||||
error_message = "Table parsing method is not supported by Infinity, please use other parsing methods or use Elasticsearch as the document engine."
|
||||
progress_callback(-1, msg=error_message)
|
||||
@ -971,7 +969,7 @@ async def report_status():
|
||||
while True:
|
||||
try:
|
||||
now = datetime.now()
|
||||
group_info = REDIS_CONN.queue_info(get_svr_queue_name(0), SVR_CONSUMER_GROUP_NAME)
|
||||
group_info = REDIS_CONN.queue_info(settings.get_svr_queue_name(0), SVR_CONSUMER_GROUP_NAME)
|
||||
if group_info is not None:
|
||||
PENDING_TASKS = int(group_info.get("pending", 0))
|
||||
LAG_TASKS = int(group_info.get("lag", 0))
|
||||
@ -1033,9 +1031,9 @@ async def main():
|
||||
logging.info(f'RAGFlow version: {get_ragflow_version()}')
|
||||
show_configs()
|
||||
settings.init_settings()
|
||||
from common import globals
|
||||
logging.info(f'globals.EMBEDDING_CFG: {globals.EMBEDDING_CFG}')
|
||||
print_rag_settings()
|
||||
settings.check_and_install_torch()
|
||||
logging.info(f'settings.EMBEDDING_CFG: {settings.EMBEDDING_CFG}')
|
||||
settings.print_rag_settings()
|
||||
if sys.platform != "win32":
|
||||
signal.signal(signal.SIGUSR1, start_tracemalloc_and_snapshot)
|
||||
signal.signal(signal.SIGUSR2, stop_tracemalloc)
|
||||
|
||||
@ -20,15 +20,15 @@ import time
|
||||
from io import BytesIO
|
||||
from common.decorator import singleton
|
||||
from azure.storage.blob import ContainerClient
|
||||
from common import globals
|
||||
from common import settings
|
||||
|
||||
|
||||
@singleton
|
||||
class RAGFlowAzureSasBlob:
|
||||
def __init__(self):
|
||||
self.conn = None
|
||||
self.container_url = os.getenv('CONTAINER_URL', globals.AZURE["container_url"])
|
||||
self.sas_token = os.getenv('SAS_TOKEN', globals.AZURE["sas_token"])
|
||||
self.container_url = os.getenv('CONTAINER_URL', settings.AZURE["container_url"])
|
||||
self.sas_token = os.getenv('SAS_TOKEN', settings.AZURE["sas_token"])
|
||||
self.__open__()
|
||||
|
||||
def __open__(self):
|
||||
|
||||
@ -20,18 +20,18 @@ import time
|
||||
from common.decorator import singleton
|
||||
from azure.identity import ClientSecretCredential, AzureAuthorityHosts
|
||||
from azure.storage.filedatalake import FileSystemClient
|
||||
from common import globals
|
||||
from common import settings
|
||||
|
||||
|
||||
@singleton
|
||||
class RAGFlowAzureSpnBlob:
|
||||
def __init__(self):
|
||||
self.conn = None
|
||||
self.account_url = os.getenv('ACCOUNT_URL', globals.AZURE["account_url"])
|
||||
self.client_id = os.getenv('CLIENT_ID', globals.AZURE["client_id"])
|
||||
self.secret = os.getenv('SECRET', globals.AZURE["secret"])
|
||||
self.tenant_id = os.getenv('TENANT_ID', globals.AZURE["tenant_id"])
|
||||
self.container_name = os.getenv('CONTAINER_NAME', globals.AZURE["container_name"])
|
||||
self.account_url = os.getenv('ACCOUNT_URL', settings.AZURE["account_url"])
|
||||
self.client_id = os.getenv('CLIENT_ID', settings.AZURE["client_id"])
|
||||
self.secret = os.getenv('SECRET', settings.AZURE["secret"])
|
||||
self.tenant_id = os.getenv('TENANT_ID', settings.AZURE["tenant_id"])
|
||||
self.container_name = os.getenv('CONTAINER_NAME', settings.AZURE["container_name"])
|
||||
self.__open__()
|
||||
|
||||
def __open__(self):
|
||||
|
||||
@ -24,7 +24,6 @@ import copy
|
||||
from elasticsearch import Elasticsearch, NotFoundError
|
||||
from elasticsearch_dsl import UpdateByQuery, Q, Search, Index
|
||||
from elastic_transport import ConnectionTimeout
|
||||
from rag.settings import TAG_FLD, PAGERANK_FLD
|
||||
from common.decorator import singleton
|
||||
from common.file_utils import get_project_base_directory
|
||||
from common.misc_utils import convert_bytes
|
||||
@ -32,7 +31,8 @@ from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr,
|
||||
FusionExpr
|
||||
from rag.nlp import is_english, rag_tokenizer
|
||||
from common.float_utils import get_float
|
||||
from common import globals
|
||||
from common import settings
|
||||
from common.constants import PAGERANK_FLD, TAG_FLD
|
||||
|
||||
ATTEMPT_TIME = 2
|
||||
|
||||
@ -43,17 +43,17 @@ logger = logging.getLogger('ragflow.es_conn')
|
||||
class ESConnection(DocStoreConnection):
|
||||
def __init__(self):
|
||||
self.info = {}
|
||||
logger.info(f"Use Elasticsearch {globals.ES['hosts']} as the doc engine.")
|
||||
logger.info(f"Use Elasticsearch {settings.ES['hosts']} as the doc engine.")
|
||||
for _ in range(ATTEMPT_TIME):
|
||||
try:
|
||||
if self._connect():
|
||||
break
|
||||
except Exception as e:
|
||||
logger.warning(f"{str(e)}. Waiting Elasticsearch {globals.ES['hosts']} to be healthy.")
|
||||
logger.warning(f"{str(e)}. Waiting Elasticsearch {settings.ES['hosts']} to be healthy.")
|
||||
time.sleep(5)
|
||||
|
||||
if not self.es.ping():
|
||||
msg = f"Elasticsearch {globals.ES['hosts']} is unhealthy in 120s."
|
||||
msg = f"Elasticsearch {settings.ES['hosts']} is unhealthy in 120s."
|
||||
logger.error(msg)
|
||||
raise Exception(msg)
|
||||
v = self.info.get("version", {"number": "8.11.3"})
|
||||
@ -68,14 +68,14 @@ class ESConnection(DocStoreConnection):
|
||||
logger.error(msg)
|
||||
raise Exception(msg)
|
||||
self.mapping = json.load(open(fp_mapping, "r"))
|
||||
logger.info(f"Elasticsearch {globals.ES['hosts']} is healthy.")
|
||||
logger.info(f"Elasticsearch {settings.ES['hosts']} is healthy.")
|
||||
|
||||
def _connect(self):
|
||||
self.es = Elasticsearch(
|
||||
globals.ES["hosts"].split(","),
|
||||
basic_auth=(globals.ES["username"], globals.ES[
|
||||
"password"]) if "username" in globals.ES and "password" in globals.ES else None,
|
||||
verify_certs= globals.ES.get("verify_certs", False),
|
||||
settings.ES["hosts"].split(","),
|
||||
basic_auth=(settings.ES["username"], settings.ES[
|
||||
"password"]) if "username" in settings.ES and "password" in settings.ES else None,
|
||||
verify_certs= settings.ES.get("verify_certs", False),
|
||||
timeout=600 )
|
||||
if self.es:
|
||||
self.info = self.es.info()
|
||||
|
||||
@ -25,13 +25,12 @@ from infinity.common import ConflictType, InfinityException, SortType
|
||||
from infinity.index import IndexInfo, IndexType
|
||||
from infinity.connection_pool import ConnectionPool
|
||||
from infinity.errors import ErrorCode
|
||||
from rag.settings import PAGERANK_FLD, TAG_FLD
|
||||
from common.decorator import singleton
|
||||
import pandas as pd
|
||||
from common.file_utils import get_project_base_directory
|
||||
from common import globals
|
||||
from rag.nlp import is_english
|
||||
|
||||
from common.constants import PAGERANK_FLD, TAG_FLD
|
||||
from common import settings
|
||||
from rag.utils.doc_store_conn import (
|
||||
DocStoreConnection,
|
||||
MatchExpr,
|
||||
@ -130,8 +129,8 @@ def concat_dataframes(df_list: list[pd.DataFrame], selectFields: list[str]) -> p
|
||||
@singleton
|
||||
class InfinityConnection(DocStoreConnection):
|
||||
def __init__(self):
|
||||
self.dbName = globals.INFINITY.get("db_name", "default_db")
|
||||
infinity_uri = globals.INFINITY["uri"]
|
||||
self.dbName = settings.INFINITY.get("db_name", "default_db")
|
||||
infinity_uri = settings.INFINITY["uri"]
|
||||
if ":" in infinity_uri:
|
||||
host, port = infinity_uri.split(":")
|
||||
infinity_uri = infinity.common.NetworkAddress(host, int(port))
|
||||
|
||||
@ -21,7 +21,7 @@ from minio.commonconfig import CopySource
|
||||
from minio.error import S3Error
|
||||
from io import BytesIO
|
||||
from common.decorator import singleton
|
||||
from common import globals
|
||||
from common import settings
|
||||
|
||||
|
||||
@singleton
|
||||
@ -38,14 +38,14 @@ class RAGFlowMinio:
|
||||
pass
|
||||
|
||||
try:
|
||||
self.conn = Minio(globals.MINIO["host"],
|
||||
access_key=globals.MINIO["user"],
|
||||
secret_key=globals.MINIO["password"],
|
||||
self.conn = Minio(settings.MINIO["host"],
|
||||
access_key=settings.MINIO["user"],
|
||||
secret_key=settings.MINIO["password"],
|
||||
secure=False
|
||||
)
|
||||
except Exception:
|
||||
logging.exception(
|
||||
"Fail to connect %s " % globals.MINIO["host"])
|
||||
"Fail to connect %s " % settings.MINIO["host"])
|
||||
|
||||
def __close__(self):
|
||||
del self.conn
|
||||
|
||||
@ -24,13 +24,13 @@ import copy
|
||||
from opensearchpy import OpenSearch, NotFoundError
|
||||
from opensearchpy import UpdateByQuery, Q, Search, Index
|
||||
from opensearchpy import ConnectionTimeout
|
||||
from rag.settings import TAG_FLD, PAGERANK_FLD
|
||||
from common.decorator import singleton
|
||||
from common.file_utils import get_project_base_directory
|
||||
from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, \
|
||||
FusionExpr
|
||||
from rag.nlp import is_english, rag_tokenizer
|
||||
from common import globals
|
||||
from common.constants import PAGERANK_FLD, TAG_FLD
|
||||
from common import settings
|
||||
|
||||
ATTEMPT_TIME = 2
|
||||
|
||||
@ -41,13 +41,13 @@ logger = logging.getLogger('ragflow.opensearch_conn')
|
||||
class OSConnection(DocStoreConnection):
|
||||
def __init__(self):
|
||||
self.info = {}
|
||||
logger.info(f"Use OpenSearch {globals.OS['hosts']} as the doc engine.")
|
||||
logger.info(f"Use OpenSearch {settings.OS['hosts']} as the doc engine.")
|
||||
for _ in range(ATTEMPT_TIME):
|
||||
try:
|
||||
self.os = OpenSearch(
|
||||
globals.OS["hosts"].split(","),
|
||||
http_auth=(globals.OS["username"], globals.OS[
|
||||
"password"]) if "username" in globals.OS and "password" in globals.OS else None,
|
||||
settings.OS["hosts"].split(","),
|
||||
http_auth=(settings.OS["username"], settings.OS[
|
||||
"password"]) if "username" in settings.OS and "password" in settings.OS else None,
|
||||
verify_certs=False,
|
||||
timeout=600
|
||||
)
|
||||
@ -55,10 +55,10 @@ class OSConnection(DocStoreConnection):
|
||||
self.info = self.os.info()
|
||||
break
|
||||
except Exception as e:
|
||||
logger.warning(f"{str(e)}. Waiting OpenSearch {globals.OS['hosts']} to be healthy.")
|
||||
logger.warning(f"{str(e)}. Waiting OpenSearch {settings.OS['hosts']} to be healthy.")
|
||||
time.sleep(5)
|
||||
if not self.os.ping():
|
||||
msg = f"OpenSearch {globals.OS['hosts']} is unhealthy in 120s."
|
||||
msg = f"OpenSearch {settings.OS['hosts']} is unhealthy in 120s."
|
||||
logger.error(msg)
|
||||
raise Exception(msg)
|
||||
v = self.info.get("version", {"number": "2.18.0"})
|
||||
@ -73,7 +73,7 @@ class OSConnection(DocStoreConnection):
|
||||
logger.error(msg)
|
||||
raise Exception(msg)
|
||||
self.mapping = json.load(open(fp_mapping, "r"))
|
||||
logger.info(f"OpenSearch {globals.OS['hosts']} is healthy.")
|
||||
logger.info(f"OpenSearch {settings.OS['hosts']} is healthy.")
|
||||
|
||||
"""
|
||||
Database operations
|
||||
|
||||
@ -20,14 +20,14 @@ from botocore.config import Config
|
||||
import time
|
||||
from io import BytesIO
|
||||
from common.decorator import singleton
|
||||
from common import globals
|
||||
from common import settings
|
||||
|
||||
|
||||
@singleton
|
||||
class RAGFlowOSS:
|
||||
def __init__(self):
|
||||
self.conn = None
|
||||
self.oss_config = globals.OSS
|
||||
self.oss_config = settings.OSS
|
||||
self.access_key = self.oss_config.get('access_key', None)
|
||||
self.secret_key = self.oss_config.get('secret_key', None)
|
||||
self.endpoint_url = self.oss_config.get('endpoint_url', None)
|
||||
|
||||
@ -20,10 +20,19 @@ import uuid
|
||||
|
||||
import valkey as redis
|
||||
from common.decorator import singleton
|
||||
from common import globals
|
||||
from common import settings
|
||||
from valkey.lock import Lock
|
||||
import trio
|
||||
|
||||
REDIS = {}
|
||||
try:
|
||||
REDIS = settings.decrypt_database_config(name="redis")
|
||||
except Exception:
|
||||
try:
|
||||
REDIS = settings.get_base_config("redis", {})
|
||||
except Exception:
|
||||
REDIS = {}
|
||||
|
||||
class RedisMsg:
|
||||
def __init__(self, consumer, queue_name, group_name, msg_id, message):
|
||||
self.__consumer = consumer
|
||||
@ -61,7 +70,7 @@ class RedisDB:
|
||||
|
||||
def __init__(self):
|
||||
self.REDIS = None
|
||||
self.config = globals.REDIS
|
||||
self.config = REDIS
|
||||
self.__open__()
|
||||
|
||||
def register_scripts(self) -> None:
|
||||
|
||||
@ -21,14 +21,14 @@ from botocore.config import Config
|
||||
import time
|
||||
from io import BytesIO
|
||||
from common.decorator import singleton
|
||||
from common import globals
|
||||
from common import settings
|
||||
|
||||
|
||||
@singleton
|
||||
class RAGFlowS3:
|
||||
def __init__(self):
|
||||
self.conn = None
|
||||
self.s3_config = globals.S3
|
||||
self.s3_config = settings.S3
|
||||
self.access_key = self.s3_config.get('access_key', None)
|
||||
self.secret_key = self.s3_config.get('secret_key', None)
|
||||
self.session_token = self.s3_config.get('session_token', None)
|
||||
|
||||
@ -13,41 +13,3 @@
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
import os
|
||||
from enum import Enum
|
||||
|
||||
from rag.utils.azure_sas_conn import RAGFlowAzureSasBlob
|
||||
from rag.utils.azure_spn_conn import RAGFlowAzureSpnBlob
|
||||
from rag.utils.minio_conn import RAGFlowMinio
|
||||
from rag.utils.opendal_conn import OpenDALStorage
|
||||
from rag.utils.s3_conn import RAGFlowS3
|
||||
from rag.utils.oss_conn import RAGFlowOSS
|
||||
|
||||
|
||||
class Storage(Enum):
|
||||
MINIO = 1
|
||||
AZURE_SPN = 2
|
||||
AZURE_SAS = 3
|
||||
AWS_S3 = 4
|
||||
OSS = 5
|
||||
OPENDAL = 6
|
||||
|
||||
|
||||
class StorageFactory:
|
||||
storage_mapping = {
|
||||
Storage.MINIO: RAGFlowMinio,
|
||||
Storage.AZURE_SPN: RAGFlowAzureSpnBlob,
|
||||
Storage.AZURE_SAS: RAGFlowAzureSasBlob,
|
||||
Storage.AWS_S3: RAGFlowS3,
|
||||
Storage.OSS: RAGFlowOSS,
|
||||
Storage.OPENDAL: OpenDALStorage
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def create(cls, storage: Storage):
|
||||
return cls.storage_mapping[storage]()
|
||||
|
||||
|
||||
STORAGE_IMPL_TYPE = os.getenv('STORAGE_IMPL', 'MINIO')
|
||||
STORAGE_IMPL = StorageFactory.create(Storage[STORAGE_IMPL_TYPE])
|
||||
|
||||
Reference in New Issue
Block a user