From a9087c3853fc7c6a96ef5d8904f7a8abf43779fe Mon Sep 17 00:00:00 2001 From: Florian Date: Thu, 16 May 2024 15:39:50 +0800 Subject: [PATCH] feat: add GraphRAG framework and integrate TuGraph (#1506) Co-authored-by: KingSkyLi <15566300566@163.com> Co-authored-by: aries_ckt <916701291@qq.com> Co-authored-by: Fangyin Cheng --- .env.template | 16 +- .mypy.ini | 3 + dbgpt/_private/config.py | 3 + dbgpt/app/knowledge/api.py | 29 +- dbgpt/app/knowledge/request/request.py | 4 + dbgpt/app/knowledge/service.py | 101 +- dbgpt/app/scene/chat_knowledge/v1/chat.py | 36 +- dbgpt/app/static/404.html | 2 +- dbgpt/app/static/404/index.html | 2 +- .../0eNcH1NYITc55RC9aiK13/_buildManifest.js | 1 + .../0eNcH1NYITc55RC9aiK13/_ssgManifest.js | 1 + .../4gKm1VO_9LV92tDHe7clS/_buildManifest.js | 1 + .../4gKm1VO_9LV92tDHe7clS/_ssgManifest.js | 1 + .../Iy2rUZvIVIuhDi41d3hKV/_buildManifest.js | 1 + .../Iy2rUZvIVIuhDi41d3hKV/_ssgManifest.js | 1 + .../LaFLZhBGaR9JJ4m33Sti7/_buildManifest.js | 1 + 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dbgpt/datasource/conn_tugraph.py | 125 +- dbgpt/rag/assembler/base.py | 23 +- dbgpt/rag/assembler/embedding.py | 8 + dbgpt/rag/index/__init__.py | 1 + dbgpt/rag/index/base.py | 168 + dbgpt/rag/retriever/embedding.py | 2 +- dbgpt/rag/transformer/__init__.py | 1 + dbgpt/rag/transformer/base.py | 26 + dbgpt/rag/transformer/keyword_extractor.py | 50 + dbgpt/rag/transformer/llm_extractor.py | 50 + dbgpt/rag/transformer/text2cypher.py | 10 + dbgpt/rag/transformer/text2gql.py | 10 + dbgpt/rag/transformer/text2vector.py | 10 + dbgpt/rag/transformer/triplet_extractor.py | 71 + dbgpt/serve/rag/service/service.py | 72 +- dbgpt/storage/graph_store/__init__.py | 37 + dbgpt/storage/graph_store/base.py | 68 + dbgpt/storage/graph_store/factory.py | 42 + dbgpt/storage/graph_store/graph.py | 477 + dbgpt/storage/graph_store/memgraph_store.py | 81 + dbgpt/storage/graph_store/neo4j_store.py | 64 + dbgpt/storage/graph_store/tugraph_store.py | 239 + dbgpt/storage/knowledge_graph/__init__.py | 1 + dbgpt/storage/knowledge_graph/base.py | 28 + .../knowledge_graph/knowledge_graph.py | 147 + dbgpt/storage/knowledge_graph/open_spg.py | 49 + dbgpt/storage/vector_store/__init__.py | 81 +- dbgpt/storage/vector_store/base.py | 160 +- dbgpt/storage/vector_store/connector.py | 88 +- .../cookbook/rag/graph_rag_app_develop.md | 210 + docs/sidebars.js | 12 +- .../graph_rag/create_knowledge_graph.jpg | Bin 0 -> 252193 bytes .../chat_knowledge/graph_rag/graph_data.jpg | Bin 0 -> 1015764 bytes .../graph_rag/graph_rag_chat.jpg | Bin 0 -> 255109 bytes .../chat_knowledge/graph_rag/view_graph.jpg | Bin 0 -> 153229 bytes docs/yarn.lock | 12530 ++++++++-------- examples/rag/graph_rag_example.py | 66 + examples/test_files/tranformers_story.md | 110 + .../intetration_tests/graph_store/__init__.py | 0 .../graph_store/test_memgraph_store.py | 41 + .../graph_store/test_tugraph_store.py | 67 + .../transformer/test_extactor.py | 42 + tests/unit_tests/graph/test_graph.py | 101 + .../unit_tests/vector_store/test_pgvector.py | 8 +- web/app/i18n.ts | 6 + web/client/api/request.ts | 8 +- web/components/common/gpt-card.tsx | 4 +- web/components/knowledge/doc-panel.tsx | 8 +- web/components/knowledge/space-card.tsx | 3 +- web/components/knowledge/space-form.tsx | 14 +- web/global.d.ts | 2 + web/package-lock.json | 42 + web/package.json | 3 + web/pages/knowledge/graph/index.tsx | 137 + web/public/models/knowledge-graph.png | Bin 0 -> 41168 bytes web/types/knowledge.ts | 5 + 133 files changed, 10139 insertions(+), 6631 deletions(-) create mode 100644 dbgpt/app/static/_next/static/0eNcH1NYITc55RC9aiK13/_buildManifest.js create mode 100644 dbgpt/app/static/_next/static/0eNcH1NYITc55RC9aiK13/_ssgManifest.js create mode 100644 dbgpt/app/static/_next/static/4gKm1VO_9LV92tDHe7clS/_buildManifest.js create mode 100644 dbgpt/app/static/_next/static/4gKm1VO_9LV92tDHe7clS/_ssgManifest.js create mode 100644 dbgpt/app/static/_next/static/Iy2rUZvIVIuhDi41d3hKV/_buildManifest.js 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dbgpt/rag/transformer/text2cypher.py create mode 100644 dbgpt/rag/transformer/text2gql.py create mode 100644 dbgpt/rag/transformer/text2vector.py create mode 100644 dbgpt/rag/transformer/triplet_extractor.py create mode 100644 dbgpt/storage/graph_store/__init__.py create mode 100644 dbgpt/storage/graph_store/base.py create mode 100644 dbgpt/storage/graph_store/factory.py create mode 100644 dbgpt/storage/graph_store/graph.py create mode 100644 dbgpt/storage/graph_store/memgraph_store.py create mode 100644 dbgpt/storage/graph_store/neo4j_store.py create mode 100644 dbgpt/storage/graph_store/tugraph_store.py create mode 100644 dbgpt/storage/knowledge_graph/__init__.py create mode 100644 dbgpt/storage/knowledge_graph/base.py create mode 100644 dbgpt/storage/knowledge_graph/knowledge_graph.py create mode 100644 dbgpt/storage/knowledge_graph/open_spg.py create mode 100644 docs/docs/cookbook/rag/graph_rag_app_develop.md create mode 100644 docs/static/img/chat_knowledge/graph_rag/create_knowledge_graph.jpg create mode 100644 docs/static/img/chat_knowledge/graph_rag/graph_data.jpg create mode 100644 docs/static/img/chat_knowledge/graph_rag/graph_rag_chat.jpg create mode 100644 docs/static/img/chat_knowledge/graph_rag/view_graph.jpg create mode 100644 examples/rag/graph_rag_example.py create mode 100644 examples/test_files/tranformers_story.md create mode 100644 tests/intetration_tests/graph_store/__init__.py create mode 100644 tests/intetration_tests/graph_store/test_memgraph_store.py create mode 100644 tests/intetration_tests/graph_store/test_tugraph_store.py create mode 100644 tests/intetration_tests/transformer/test_extactor.py create mode 100644 tests/unit_tests/graph/test_graph.py create mode 100644 web/pages/knowledge/graph/index.tsx create mode 100644 web/public/models/knowledge-graph.png diff --git a/.env.template b/.env.template index 799113fc2..38ec1c61f 100644 --- a/.env.template +++ b/.env.template @@ -70,6 +70,7 @@ EMBEDDING_MODEL=text2vec #EMBEDDING_MODEL=bge-large-zh KNOWLEDGE_CHUNK_SIZE=500 KNOWLEDGE_SEARCH_TOP_SIZE=5 +KNOWLEDGE_GRAPH_SEARCH_TOP_SIZE=50 ## Maximum number of chunks to load at once, if your single document is too large, ## you can set this value to a higher value for better performance. ## if out of memory when load large document, you can set this value to a lower value. @@ -138,10 +139,12 @@ LOCAL_DB_TYPE=sqlite EXECUTE_LOCAL_COMMANDS=False #*******************************************************************# -#** VECTOR STORE SETTINGS **# +#** VECTOR STORE / KNOWLEDGE GRAPH SETTINGS **# #*******************************************************************# -### Chroma vector db config VECTOR_STORE_TYPE=Chroma +GRAPH_STORE_TYPE=TuGraph + +### Chroma vector db config #CHROMA_PERSIST_PATH=/root/DB-GPT/pilot/data ### Milvus vector db config @@ -163,6 +166,15 @@ ElasticSearch_PORT=9200 ElasticSearch_USERNAME=elastic ElasticSearch_PASSWORD=i=+iLw9y0Jduq86XTi6W +### TuGraph config +#TUGRAPH_HOST=127.0.0.1 +#TUGRAPH_PORT=7687 +#TUGRAPH_USERNAME=admin +#TUGRAPH_PASSWORD=73@TuGraph +#TUGRAPH_VERTEX_TYPE=entity +#TUGRAPH_EDGE_TYPE=relation +#TUGRAPH_EDGE_NAME_KEY=label + #*******************************************************************# #** WebServer Language Support **# #*******************************************************************# diff --git a/.mypy.ini b/.mypy.ini index ae26c8dc3..e2c2bc3ab 100644 --- a/.mypy.ini +++ b/.mypy.ini @@ -112,3 +112,6 @@ ignore_missing_imports = True [mypy-ollama.*] ignore_missing_imports = True + +[mypy-networkx.*] +ignore_missing_imports = True diff --git a/dbgpt/_private/config.py b/dbgpt/_private/config.py index 60c3908f7..7c0279a11 100644 --- a/dbgpt/_private/config.py +++ b/dbgpt/_private/config.py @@ -254,6 +254,9 @@ class Config(metaclass=Singleton): self.KNOWLEDGE_CHUNK_SIZE = int(os.getenv("KNOWLEDGE_CHUNK_SIZE", 100)) self.KNOWLEDGE_CHUNK_OVERLAP = int(os.getenv("KNOWLEDGE_CHUNK_OVERLAP", 50)) self.KNOWLEDGE_SEARCH_TOP_SIZE = int(os.getenv("KNOWLEDGE_SEARCH_TOP_SIZE", 5)) + self.KNOWLEDGE_GRAPH_SEARCH_TOP_SIZE = int( + os.getenv("KNOWLEDGE_GRAPH_SEARCH_TOP_SIZE", 50) + ) self.KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD = int( os.getenv("KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD", 10) ) diff --git a/dbgpt/app/knowledge/api.py b/dbgpt/app/knowledge/api.py index a891c46ea..2203d4c2c 100644 --- a/dbgpt/app/knowledge/api.py +++ b/dbgpt/app/knowledge/api.py @@ -13,6 +13,7 @@ from dbgpt.app.knowledge.request.request import ( DocumentSummaryRequest, DocumentSyncRequest, EntityExtractRequest, + GraphVisRequest, KnowledgeDocumentRequest, KnowledgeQueryRequest, KnowledgeSpaceRequest, @@ -75,7 +76,7 @@ def space_delete(request: KnowledgeSpaceRequest): try: return Result.succ(knowledge_space_service.delete_space(request.name)) except Exception as e: - return Result.failed(code="E000X", msg=f"space list error {e}") + return Result.failed(code="E000X", msg=f"space delete error {e}") @router.post("/knowledge/{space_name}/arguments") @@ -84,7 +85,7 @@ def arguments(space_name: str): try: return Result.succ(knowledge_space_service.arguments(space_name)) except Exception as e: - return Result.failed(code="E000X", msg=f"space list error {e}") + return Result.failed(code="E000X", msg=f"space arguments error {e}") @router.post("/knowledge/{space_name}/argument/save") @@ -95,7 +96,7 @@ def arguments_save(space_name: str, argument_request: SpaceArgumentRequest): knowledge_space_service.argument_save(space_name, argument_request) ) except Exception as e: - return Result.failed(code="E000X", msg=f"space list error {e}") + return Result.failed(code="E000X", msg=f"space save error {e}") @router.post("/knowledge/{space_name}/document/add") @@ -156,6 +157,20 @@ def document_list(space_name: str, query_request: DocumentQueryRequest): return Result.failed(code="E000X", msg=f"document list error {e}") +@router.post("/knowledge/{space_name}/graphvis") +def graph_vis(space_name: str, query_request: GraphVisRequest): + print(f"/document/list params: {space_name}, {query_request}") + print(query_request.limit) + try: + return Result.succ( + knowledge_space_service.query_graph( + space_name=space_name, limit=query_request.limit + ) + ) + except Exception as e: + return Result.failed(code="E000X", msg=f"get graph vis error {e}") + + @router.post("/knowledge/{space_name}/document/delete") def document_delete(space_name: str, query_request: DocumentQueryRequest): print(f"/document/list params: {space_name}, {query_request}") @@ -164,7 +179,7 @@ def document_delete(space_name: str, query_request: DocumentQueryRequest): knowledge_space_service.delete_document(space_name, query_request.doc_name) ) except Exception as e: - return Result.failed(code="E000X", msg=f"document list error {e}") + return Result.failed(code="E000X", msg=f"document delete error {e}") @router.post("/knowledge/{space_name}/document/upload") @@ -232,7 +247,7 @@ def document_sync(space_name: str, request: DocumentSyncRequest): @router.post("/knowledge/{space_name}/document/sync_batch") -def batch_document_sync( +async def batch_document_sync( space_name: str, request: List[KnowledgeSyncRequest], service: Service = Depends(get_rag_service), @@ -242,13 +257,13 @@ def batch_document_sync( space = service.get({"name": space_name}) for sync_request in request: sync_request.space_id = space.id - doc_ids = service.sync_document(requests=request) + doc_ids = await service.sync_document(requests=request) # doc_ids = service.sync_document( # space_name=space_name, sync_requests=request # ) return Result.succ({"tasks": doc_ids}) except Exception as e: - return Result.failed(code="E000X", msg=f"document sync error {e}") + return Result.failed(code="E000X", msg=f"document sync batch error {e}") @router.post("/knowledge/{space_name}/chunk/list") diff --git a/dbgpt/app/knowledge/request/request.py b/dbgpt/app/knowledge/request/request.py index 7c4897e03..ba2bb2c15 100644 --- a/dbgpt/app/knowledge/request/request.py +++ b/dbgpt/app/knowledge/request/request.py @@ -53,6 +53,10 @@ class DocumentQueryRequest(BaseModel): page_size: int = 20 +class GraphVisRequest(BaseModel): + limit: int = 100 + + class DocumentSyncRequest(BaseModel): """Sync request""" diff --git a/dbgpt/app/knowledge/service.py b/dbgpt/app/knowledge/service.py index 9119d3981..9755ea04b 100644 --- a/dbgpt/app/knowledge/service.py +++ b/dbgpt/app/knowledge/service.py @@ -25,8 +25,9 @@ from dbgpt.app.knowledge.request.response import ( ) from dbgpt.component import ComponentType from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG -from dbgpt.core import Chunk +from dbgpt.core import Chunk, LLMClient from dbgpt.model import DefaultLLMClient +from dbgpt.model.cluster import WorkerManagerFactory from dbgpt.rag.assembler.embedding import EmbeddingAssembler from dbgpt.rag.assembler.summary import SummaryAssembler from dbgpt.rag.chunk_manager import ChunkParameters @@ -39,7 +40,7 @@ from dbgpt.rag.text_splitter.text_splitter import ( ) from dbgpt.serve.rag.api.schemas import KnowledgeSyncRequest from dbgpt.serve.rag.models.models import KnowledgeSpaceDao, KnowledgeSpaceEntity -from dbgpt.serve.rag.service.service import Service, SyncStatus +from dbgpt.serve.rag.service.service import SyncStatus from dbgpt.storage.vector_store.base import VectorStoreConfig from dbgpt.storage.vector_store.connector import VectorStoreConnector from dbgpt.util.executor_utils import ExecutorFactory, blocking_func_to_async @@ -52,7 +53,6 @@ document_chunk_dao = DocumentChunkDao() logger = logging.getLogger(__name__) CFG = Config() - # default summary max iteration call with llm. DEFAULT_SUMMARY_MAX_ITERATION = 5 # default summary concurrency call with llm. @@ -70,6 +70,13 @@ class KnowledgeService: def __init__(self): pass + @property + def llm_client(self) -> LLMClient: + worker_manager = CFG.SYSTEM_APP.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + return DefaultLLMClient(worker_manager, True) + def create_knowledge_space(self, request: KnowledgeSpaceRequest): """create knowledge space Args: @@ -332,16 +339,23 @@ class KnowledgeService: embedding_fn = embedding_factory.create( model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] ) + + spaces = self.get_knowledge_space(KnowledgeSpaceRequest(name=space_name)) + if len(spaces) != 1: + raise Exception(f"invalid space name:{space_name}") + space = spaces[0] + from dbgpt.storage.vector_store.base import VectorStoreConfig config = VectorStoreConfig( - name=space_name, + name=space.name, embedding_fn=embedding_fn, max_chunks_once_load=CFG.KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD, + llm_client=self.llm_client, + model_name=self.model_name, ) vector_store_connector = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, - vector_store_config=config, + vector_store_type=space.vector_type, vector_store_config=config ) knowledge = KnowledgeFactory.create( datasource=doc.content, @@ -442,21 +456,27 @@ class KnowledgeService: Args: - space_name: knowledge space name """ - query = KnowledgeSpaceEntity(name=space_name) - spaces = knowledge_space_dao.get_knowledge_space(query) - if len(spaces) == 0: - raise Exception(f"delete error, no space name:{space_name} in database") + spaces = knowledge_space_dao.get_knowledge_space( + KnowledgeSpaceEntity(name=space_name) + ) + if len(spaces) != 1: + raise Exception(f"invalid space name:{space_name}") space = spaces[0] + embedding_factory = CFG.SYSTEM_APP.get_component( "embedding_factory", EmbeddingFactory ) embedding_fn = embedding_factory.create( model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] ) - config = VectorStoreConfig(name=space.name, embedding_fn=embedding_fn) + config = VectorStoreConfig( + name=space.name, + embedding_fn=embedding_fn, + llm_client=self.llm_client, + model_name=None, + ) vector_store_connector = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, - vector_store_config=config, + vector_store_type=space.vector_type, vector_store_config=config ) # delete vectors vector_store_connector.delete_vector_name(space.name) @@ -480,6 +500,12 @@ class KnowledgeService: documents = knowledge_document_dao.get_documents(document_query) if len(documents) != 1: raise Exception(f"there are no or more than one document called {doc_name}") + + spaces = self.get_knowledge_space(KnowledgeSpaceRequest(name=space_name)) + if len(spaces) != 1: + raise Exception(f"invalid space name:{space_name}") + space = spaces[0] + vector_ids = documents[0].vector_ids if vector_ids is not None: embedding_factory = CFG.SYSTEM_APP.get_component( @@ -488,10 +514,14 @@ class KnowledgeService: embedding_fn = embedding_factory.create( model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] ) - config = VectorStoreConfig(name=space_name, embedding_fn=embedding_fn) + config = VectorStoreConfig( + name=space.name, + embedding_fn=embedding_fn, + llm_client=self.llm_client, + model_name=None, + ) vector_store_connector = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, - vector_store_config=config, + vector_store_type=space.vector_type, vector_store_config=config ) # delete vector by ids vector_store_connector.delete_by_ids(vector_ids) @@ -535,7 +565,7 @@ class KnowledgeService: """ logger.info( - f"async doc embedding sync, doc:{doc.doc_name}, chunks length is {len(chunk_docs)}, begin embedding to vector store-{CFG.VECTOR_STORE_TYPE}" + f"async doc embedding sync, doc:{doc.doc_name}, chunks length is {len(chunk_docs)}" ) try: with root_tracer.start_span( @@ -645,3 +675,40 @@ class KnowledgeService: **{"chat_param": chat_param}, ) return chat + + def query_graph(self, space_name, limit): + embedding_factory = CFG.SYSTEM_APP.get_component( + "embedding_factory", EmbeddingFactory + ) + embedding_fn = embedding_factory.create( + model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] + ) + spaces = self.get_knowledge_space(KnowledgeSpaceRequest(name=space_name)) + if len(spaces) != 1: + raise Exception(f"invalid space name:{space_name}") + space = spaces[0] + print(CFG.LLM_MODEL) + config = VectorStoreConfig( + name=space.name, + embedding_fn=embedding_fn, + max_chunks_once_load=CFG.KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD, + llm_client=self.llm_client, + model_name=None, + ) + + vector_store_connector = VectorStoreConnector( + vector_store_type=space.vector_type, vector_store_config=config + ) + graph = vector_store_connector.client.query_graph(limit=limit) + res = {"nodes": [], "edges": []} + for node in graph.vertices(): + res["nodes"].append({"vid": node.vid}) + for edge in graph.edges(): + res["edges"].append( + { + "src": edge.sid, + "dst": edge.tid, + "label": edge.props[graph.edge_label], + } + ) + return res diff --git a/dbgpt/app/scene/chat_knowledge/v1/chat.py b/dbgpt/app/scene/chat_knowledge/v1/chat.py index 297d9dd04..2cfef3eed 100644 --- a/dbgpt/app/scene/chat_knowledge/v1/chat.py +++ b/dbgpt/app/scene/chat_knowledge/v1/chat.py @@ -9,6 +9,7 @@ from dbgpt.app.knowledge.document_db import ( KnowledgeDocumentDao, KnowledgeDocumentEntity, ) +from dbgpt.app.knowledge.request.request import KnowledgeSpaceRequest from dbgpt.app.knowledge.service import KnowledgeService from dbgpt.app.scene import BaseChat, ChatScene from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG @@ -50,7 +51,7 @@ class ChatKnowledge(BaseChat): ) self.space_context = self.get_space_context(self.knowledge_space) self.top_k = ( - CFG.KNOWLEDGE_SEARCH_TOP_SIZE + self.get_knowledge_search_top_size(self.knowledge_space) if self.space_context is None else int(self.space_context["embedding"]["topk"]) ) @@ -73,12 +74,27 @@ class ChatKnowledge(BaseChat): embedding_fn = embedding_factory.create( model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] ) + from dbgpt.serve.rag.models.models import ( + KnowledgeSpaceDao, + KnowledgeSpaceEntity, + ) from dbgpt.storage.vector_store.base import VectorStoreConfig - config = VectorStoreConfig(name=self.knowledge_space, embedding_fn=embedding_fn) + spaces = KnowledgeSpaceDao().get_knowledge_space( + KnowledgeSpaceEntity(name=self.knowledge_space) + ) + if len(spaces) != 1: + raise Exception(f"invalid space name:{self.knowledge_space}") + space = spaces[0] + + config = VectorStoreConfig( + name=space.name, + embedding_fn=embedding_fn, + llm_client=self.llm_client, + llm_model=self.llm_model, + ) vector_store_connector = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, - vector_store_config=config, + vector_store_type=space.vector_type, vector_store_config=config ) query_rewrite = None if CFG.KNOWLEDGE_SEARCH_REWRITE: @@ -239,6 +255,18 @@ class ChatKnowledge(BaseChat): service = KnowledgeService() return service.get_space_context(space_name) + def get_knowledge_search_top_size(self, space_name) -> int: + service = KnowledgeService() + request = KnowledgeSpaceRequest(name=space_name) + spaces = service.get_knowledge_space(request) + if len(spaces) == 1: + from dbgpt.storage import vector_store + + if spaces[0].vector_type in vector_store.__knowledge_graph__: + return CFG.KNOWLEDGE_GRAPH_SEARCH_TOP_SIZE + + return CFG.KNOWLEDGE_SEARCH_TOP_SIZE + async def execute_similar_search(self, query): """execute similarity search""" with root_tracer.start_span( diff --git a/dbgpt/app/static/404.html b/dbgpt/app/static/404.html index 184e3b581..822d93636 100644 --- a/dbgpt/app/static/404.html +++ b/dbgpt/app/static/404.html @@ -1 +1 @@ -404: This page could not be found

404

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\ No newline at end of file +404: This page could not be found

404

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\ No newline at end of file diff --git a/dbgpt/app/static/404/index.html b/dbgpt/app/static/404/index.html index 184e3b581..822d93636 100644 --- a/dbgpt/app/static/404/index.html +++ b/dbgpt/app/static/404/index.html @@ -1 +1 @@ -404: This page could not be found

404

This page could not be found.

\ No newline at end of file +404: This page could not be found

404

This page could not be found.

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+self.__BUILD_MANIFEST=function(s,a,c,e,t,n,f,k,d,h,i,u,j,b,p,o,g,l,r,_){return{__rewrites:{beforeFiles:[],afterFiles:[],fallback:[]},"/":[p,s,a,e,c,f,h,k,o,"static/chunks/9305-f44429d5185a9fc7.js","static/chunks/1353-705aa47cc2b94999.js","static/chunks/pages/index-630820928b86def4.js"],"/_error":["static/chunks/pages/_error-8095ba9e1bf12f30.js"],"/agent":[s,a,c,t,h,n,"static/chunks/pages/agent-18e026fc6118647f.js"],"/app":[i,s,a,e,c,t,n,u,j,g,b,"static/chunks/pages/app-762a5a0b6bc328e9.js"],"/chat":["static/chunks/pages/chat-5794854c0948b84c.js"],"/database":[s,a,e,c,t,n,k,d,"static/chunks/3718-e111d727d432bdd2.js","static/chunks/pages/database-1218c76d7d4baf11.js"],"/flow":[i,s,a,c,u,j,g,b,"static/chunks/pages/flow-af517aa90c527538.js"],"/flow/canvas":[p,i,s,a,e,c,f,k,u,d,j,l,o,"static/chunks/1425-6e94ae18b1ac5a70.js",b,"static/chunks/pages/flow/canvas-e370c26a140e4220.js"],"/knowledge":[r,s,a,e,c,t,h,n,k,d,_,l,"static/chunks/2265-47013b51a7e3b2ef.js","static/chunks/pages/knowledge-793cde0e9a5a1e24.js"],"/knowledge/chunk":[s,e,t,f,n,"static/chunks/pages/knowledge/chunk-115c855fa8e7a2f9.js"],"/models":[r,s,a,e,c,d,"static/chunks/3444-30181eacc7980e66.js","static/chunks/pages/models-2fcfe92e4f548979.js"],"/prompt":[s,a,e,c,f,_,"static/chunks/7184-3ca3f58327a6986a.js","static/chunks/7869-1a99e25b182b3eaa.js","static/chunks/pages/prompt-d7d77828a490e5d4.js"],sortedPages:["/","/_app","/_error","/agent","/app","/chat","/database","/flow","/flow/canvas","/knowledge","/knowledge/chunk","/models","/prompt"]}}("static/chunks/2185-30f9d0578fa0d631.js","static/chunks/5503-c65f6d730754acc7.js","static/chunks/9479-21f588e1fd4e6b6d.js","static/chunks/1009-4b2af86bde623424.js","static/chunks/785-c3544abc036fc97d.js","static/chunks/5813-c6244a8eba7ef4ae.js","static/chunks/1647-8683da4db89d68c1.js","static/chunks/411-b5d3e7f64bee2335.js","static/chunks/8928-0e78def492052d13.js","static/chunks/4553-61740188e6a650a8.js","static/chunks/971df74e-7436ff4085ebb785.js","static/chunks/7434-29506257e67e8077.js","static/chunks/9924-5bce555f07385e1f.js","static/css/b4846eed11c4725f.css","static/chunks/29107295-75edf0bf34e24b1e.js","static/chunks/2487-cda9d2a2fd712a15.js","static/chunks/6165-1c3685c948e36ea8.js","static/chunks/2282-96412afca1591c9a.js","static/chunks/75fc9c18-1d6133135d3d283c.js","static/chunks/5733-7ef320ab0f876a5e.js"),self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB(); 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r=e.positionX,i=e.positionY,a=e.height||1,o=e.width||1,s=n/t,l=a/o,u={};return 0===t&&0n?(u.x=r,u.y=i+a/2,u):0t&&-1*l<=s&&s<=l?(u.x=r-o/2,u.y=i-o*n/2/t,u):0=l)?(u.x=r+a*t/2/n,u.y=i+a/2,u):(0>n&&(s<=-1*l||s>=l)&&(u.x=r-a*t/2/n,u.y=i-a/2),u)},ob=function(e,t){for(var n=0;n1){var f=t.gravity*d/p,g=t.gravity*h/p;c.offsetX+=f,c.offsetY+=g}}}}},ow=function(e,t){var n=[],r=0,i=-1;for(n.push.apply(n,e.graphSet[0]),i+=e.graphSet[0].length;r<=i;){var a=n[r++],o=e.idToIndex[a],s=e.layoutNodes[o],l=s.children;if(0n)var i={x:n*e/r,y:n*t/r};else var i={x:e,y:t};return i},oC=function e(t,n){var r=t.parentId;if(null!=r){var i=n.layoutNodes[n.idToIndex[r]],a=!1;if((null==i.maxX||t.maxX+i.padRight>i.maxX)&&(i.maxX=t.maxX+i.padRight,a=!0),(null==i.minX||t.minX-i.padLefti.maxY)&&(i.maxY=t.maxY+i.padBottom,a=!0),(null==i.minY||t.minY-i.padTopg&&(h+=f+t.componentSpacing,d=0,p=0,f=0)}}},oD={fit:!0,padding:30,boundingBox:void 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if(null==d&&null!=h)s=Math.ceil(a/(l=h));else if(l*s>a){var p=u(),f=c();(p-1)*f>=a?u(p-1):(f-1)*p>=a&&c(f-1)}else for(;l*s=a?c(v+1):u(g+1)}var y=i.w/l,b=i.h/s;if(e.condense&&(y=0,b=0),e.avoidOverlap)for(var x=0;x=l&&(B=0,M++)},A={},I=0;I=0;x--){var w=l[x];w.isNode()?y(w)||b(w):function(n){var r,i=n._private,a=i.rscratch,l=n.pstyle("width").pfValue,c=n.pstyle("arrow-scale").value,p=l/2+h,f=p*p,g=2*p,b=i.source,x=i.target;if("segments"===a.edgeType||"straight"===a.edgeType||"haystack"===a.edgeType){for(var w,E,k,C,S=a.allpts,D=0;D+3(r=tG(e,t,S[D],S[D+1],S[D+2],S[D+3])))return v(n,r),!0}else if("bezier"===a.edgeType||"multibezier"===a.edgeType||"self"===a.edgeType||"compound"===a.edgeType){for(var S=a.allpts,D=0;D+5(r=tH(e,t,S[D],S[D+1],S[D+2],S[D+3],S[D+4],S[D+5])))return v(n,r),!0}for(var b=b||i.source,x=x||i.target,T=o.getArrowWidth(l,c),P=[{name:"source",x:a.arrowStartX,y:a.arrowStartY,angle:a.srcArrowAngle},{name:"target",x:a.arrowEndX,y:a.arrowEndY,angle:a.tgtArrowAngle},{name:"mid-source",x:a.midX,y:a.midY,angle:a.midsrcArrowAngle},{name:"mid-target",x:a.midX,y:a.midY,angle:a.midtgtArrowAngle}],D=0;D0&&(y(b),y(x))}(w)||b(w)||b(w,"source")||b(w,"target")}return u},oV.getAllInBox=function(e,t,n,r){var i=this.getCachedZSortedEles().interactive,a=[],o=Math.min(e,n),s=Math.max(e,n),l=Math.min(t,r),u=Math.max(t,r);e=o,n=s,t=l,r=u;for(var c=tN({x1:e,y1:t,x2:n,y2:r}),d=0;d0?-(Math.PI-e.ang):Math.PI+e.ang},sl=function(e,t,n,r,i){if(e!==g?so(t,e,si):ss(sa,si),so(t,n,sa),o3=si.nx*sa.ny-si.ny*sa.nx,o4=si.nx*sa.nx- -(si.ny*sa.ny),1e-6>Math.abs(o8=Math.asin(Math.max(-1,Math.min(1,o3))))){o2=t.x,o5=t.y,se=sn=0;return}o9=1,o6=!1,o4<0?o8<0?o8=Math.PI+o8:(o8=Math.PI-o8,o9=-1,o6=!0):o8>0&&(o9=-1,o6=!0),sn=void 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ea=e.pstyle("taxi-radius").value,eo="arc-radius"===e.pstyle("radius-type").value[0];r.radii=Array(r.segpts.length/2).fill(ea),r.isArcRadius=Array(r.segpts.length/2).fill(eo)}},sd.tryToCorrectInvalidPoints=function(e,t){var n=e._private.rscratch;if("bezier"===n.edgeType){var r=t.srcPos,i=t.tgtPos,a=t.srcW,o=t.srcH,s=t.tgtW,l=t.tgtH,u=t.srcShape,c=t.tgtShape,d=t.srcCornerRadius,h=t.tgtCornerRadius,p=t.srcRs,f=t.tgtRs,g=!_(n.startX)||!_(n.startY),v=!_(n.arrowStartX)||!_(n.arrowStartY),y=!_(n.endX)||!_(n.endY),b=!_(n.arrowEndX)||!_(n.arrowEndY),x=3*(this.getArrowWidth(e.pstyle("width").pfValue,e.pstyle("arrow-scale").value)*this.arrowShapeWidth),w=tS({x:n.ctrlpts[0],y:n.ctrlpts[1]},{x:n.startX,y:n.startY}),E=wh.poolIndex()){var p=d;d=h,h=p}var f=s.srcPos=d.position(),g=s.tgtPos=h.position(),v=s.srcW=d.outerWidth(),y=s.srcH=d.outerHeight(),b=s.tgtW=h.outerWidth(),x=s.tgtH=h.outerHeight(),w=s.srcShape=n.nodeShapes[t.getNodeShape(d)],E=s.tgtShape=n.nodeShapes[t.getNodeShape(h)],k=s.srcCornerRadius="auto"===d.pstyle("corner-radius").value?"auto":d.pstyle("corner-radius").pfValue,C=s.tgtCornerRadius="auto"===h.pstyle("corner-radius").value?"auto":h.pstyle("corner-radius").pfValue,S=s.tgtRs=h._private.rscratch,D=s.srcRs=d._private.rscratch;s.dirCounts={north:0,west:0,south:0,east:0,northwest:0,southwest:0,northeast:0,southeast:0};for(var T=0;T0){var W=tD(l,ty(t)),H=tD(l,ty(Y)),G=W;H2&&tD(l,{x:Y[2],y:Y[3]})0){var eo=tD(u,ty(t)),es=tD(u,ty(ea)),el=eo;es2&&tD(u,{x:ea[2],y:ea[3]})=l||b){c={cp:g,segment:y};break}}if(c)break}var x=c.cp,w=c.segment,E=(l-h)/w.length,k=w.t1-w.t0,C=i?w.t0+k*E:w.t1-k*E;C=tB(0,C,1),t=t_(x.p0,x.p1,x.p2,C),u=sm(x.p0,x.p1,x.p2,C);break;case"straight":case"segments":case"haystack":for(var S,D,T,P,_=0,M=r.allpts.length,B=0;B+3=l));B+=2);var N=(l-P)/T;t=tM(S,D,N=tB(0,N,1)),u=sy(S,D)}o("labelX",n,t.x),o("labelY",n,t.y),o("labelAutoAngle",n,u)}};l("source"),l("target"),this.applyLabelDimensions(e)}},sg.applyLabelDimensions=function(e){this.applyPrefixedLabelDimensions(e),e.isEdge()&&(this.applyPrefixedLabelDimensions(e,"source"),this.applyPrefixedLabelDimensions(e,"target"))},sg.applyPrefixedLabelDimensions=function(e,t){var n=e._private,r=this.getLabelText(e,t),i=this.calculateLabelDimensions(e,r),a=e.pstyle("line-height").pfValue,o=e.pstyle("text-wrap").strValue,s=e8(n.rscratch,"labelWrapCachedLines",t)||[],l="wrap"!==o?1:Math.max(s.length,1),u=i.height/l,c=i.width,d=i.height+(l-1)*(a-1)*u;e7(n.rstyle,"labelWidth",t,c),e7(n.rscratch,"labelWidth",t,c),e7(n.rstyle,"labelHeight",t,d),e7(n.rscratch,"labelHeight",t,d),e7(n.rscratch,"labelLineHeight",t,u*a)},sg.getLabelText=function(e,t){var n=e._private,r=t?t+"-":"",i=e.pstyle(r+"label").strValue,a=e.pstyle("text-transform").value,o=function(e,r){return r?(e7(n.rscratch,e,t,r),r):e8(n.rscratch,e,t)};if(!i)return"";"none"==a||("uppercase"==a?i=i.toUpperCase():"lowercase"==a&&(i=i.toLowerCase()));var s=e.pstyle("text-wrap").value;if("wrap"===s){var l=o("labelKey");if(null!=l&&o("labelWrapKey")===l)return o("labelWrapCachedText");for(var u=i.split("\n"),c=e.pstyle("text-max-width").pfValue,d="anywhere"===e.pstyle("text-overflow-wrap").value,h=[],p=/[\s\u200b]+/,f=d?"":" ",g=0;gc){for(var b=v.split(p),x="",w=0;wC);T++)S+=i[T],T===i.length-1&&(D=!0);return D||(S+="…"),S}return i},sg.getLabelJustification=function(e){var t=e.pstyle("text-justification").strValue,n=e.pstyle("text-halign").strValue;if("auto"!==t)return t;if(!e.isNode())return"center";switch(n){case"left":return"right";case"right":return"left";default:return"center"}},sg.calculateLabelDimensions=function(e,t){var n=eF(t,e._private.labelDimsKey),r=this.labelDimCache||(this.labelDimCache=[]),i=r[n];if(null!=i)return i;var a=e.pstyle("font-style").strValue,o=e.pstyle("font-size").pfValue,s=e.pstyle("font-family").strValue,l=e.pstyle("font-weight").strValue,u=this.labelCalcCanvas,c=this.labelCalcCanvasContext;if(!u){u=this.labelCalcCanvas=document.createElement("canvas"),c=this.labelCalcCanvasContext=u.getContext("2d");var d=u.style;d.position="absolute",d.left="-9999px",d.top="-9999px",d.zIndex="-1",d.visibility="hidden",d.pointerEvents="none"}c.font="".concat(a," ").concat(l," ").concat(o,"px ").concat(s);for(var h=0,p=0,f=t.split("\n"),g=0;ge.width()||28>e.height()))return sx||(eJ("The `cutrectangle` node shape can not be used at small sizes so `rectangle` is used instead"),sx=!0),"rectangle";if(e.isParent())return"rectangle"===t||"roundrectangle"===t||"round-rectangle"===t||"cutrectangle"===t||"cut-rectangle"===t||"barrel"===t?t:"rectangle";if("polygon"===t){var n=e.pstyle("shape-polygon-points").value;return this.nodeShapes.makePolygon(n).name}return t};var sw={};sw.registerCalculationListeners=function(){var e=this.cy,t=e.collection(),n=this,r=function(e){var n=!(arguments.length>1)||void 0===arguments[1]||arguments[1];if(t.merge(e),n)for(var r=0;r=C.desktopTapThreshold2}var v=P(e);w&&(C.hoverData.tapholdCancelled=!0),t=!0,T(u,["mousemove","vmousemove","tapdrag"],e,{x:a[0],y:a[1]});var y=function(){C.data.bgActivePosistion=void 0,C.hoverData.selecting||n.emit({originalEvent:e,type:"boxstart",position:{x:a[0],y:a[1]}}),l[4]=1,C.hoverData.selecting=!0,C.redrawHint("select",!0),C.redraw()};if(3===C.hoverData.which){if(w){var b={originalEvent:e,type:"cxtdrag",position:{x:a[0],y:a[1]}};d?d.emit(b):n.emit(b),C.hoverData.cxtDragged=!0,(!C.hoverData.cxtOver||u!==C.hoverData.cxtOver)&&(C.hoverData.cxtOver&&C.hoverData.cxtOver.emit({originalEvent:e,type:"cxtdragout",position:{x:a[0],y:a[1]}}),C.hoverData.cxtOver=u,u&&u.emit({originalEvent:e,type:"cxtdragover",position:{x:a[0],y:a[1]}}))}}else if(C.hoverData.dragging){if(t=!0,n.panningEnabled()&&n.userPanningEnabled()){if(C.hoverData.justStartedPan){var x=C.hoverData.mdownPos;E={x:(a[0]-x[0])*r,y:(a[1]-x[1])*r},C.hoverData.justStartedPan=!1}else E={x:h[0]*r,y:h[1]*r};n.panBy(E),n.emit("dragpan"),C.hoverData.dragged=!0}a=C.projectIntoViewport(e.clientX,e.clientY)}else if(1==l[4]&&(null==d||d.pannable()))w&&(!C.hoverData.dragging&&n.boxSelectionEnabled()&&(v||!n.panningEnabled()||!n.userPanningEnabled())?y():!C.hoverData.selecting&&n.panningEnabled()&&n.userPanningEnabled()&&M(d,C.hoverData.downs)&&(C.hoverData.dragging=!0,C.hoverData.justStartedPan=!0,l[4]=0,C.data.bgActivePosistion=ty(o),C.redrawHint("select",!0),C.redraw()),d&&d.pannable()&&d.active()&&d.unactivate());else{if(d&&d.pannable()&&d.active()&&d.unactivate(),d&&d.grabbed()||u==c||(c&&T(c,["mouseout","tapdragout"],e,{x:a[0],y:a[1]}),u&&T(u,["mouseover","tapdragover"],e,{x:a[0],y:a[1]}),C.hoverData.last=u),d){if(w){if(n.boxSelectionEnabled()&&v)d&&d.grabbed()&&(F(p),d.emit("freeon"),p.emit("free"),C.dragData.didDrag&&(d.emit("dragfreeon"),p.emit("dragfree"))),y();else if(d&&d.grabbed()&&C.nodeIsDraggable(d)){var w,E,k,S=!C.dragData.didDrag;S&&C.redrawHint("eles",!0),C.dragData.didDrag=!0,C.hoverData.draggingEles||V(p,{inDragLayer:!0});var D={x:0,y:0};if(_(h[0])&&_(h[1])&&(D.x+=h[0],D.y+=h[1],S)){var B=C.hoverData.dragDelta;B&&_(B[0])&&_(B[1])&&(D.x+=B[0],D.y+=B[1])}C.hoverData.draggingEles=!0,p.silentShift(D).emit("position drag"),C.redrawHint("drag",!0),C.redraw()}}else 0===(k=C.hoverData.dragDelta=C.hoverData.dragDelta||[]).length?(k.push(h[0]),k.push(h[1])):(k[0]+=h[0],k[1]+=h[1])}t=!0}if(l[2]=a[0],l[3]=a[1],t)return e.stopPropagation&&e.stopPropagation(),e.preventDefault&&e.preventDefault(),!1}},!1),C.registerBinding(S,"mouseup",function(r){if(C.hoverData.capture){C.hoverData.capture=!1;var i=C.cy,a=C.projectIntoViewport(r.clientX,r.clientY),o=C.selection,s=C.findNearestElement(a[0],a[1],!0,!1),l=C.dragData.possibleDragElements,u=C.hoverData.down,c=P(r);if(C.data.bgActivePosistion&&(C.redrawHint("select",!0),C.redraw()),C.hoverData.tapholdCancelled=!0,C.data.bgActivePosistion=void 0,u&&u.unactivate(),3===C.hoverData.which){var d={originalEvent:r,type:"cxttapend",position:{x:a[0],y:a[1]}};if(u?u.emit(d):i.emit(d),!C.hoverData.cxtDragged){var h={originalEvent:r,type:"cxttap",position:{x:a[0],y:a[1]}};u?u.emit(h):i.emit(h)}C.hoverData.cxtDragged=!1,C.hoverData.which=null}else if(1===C.hoverData.which){if(T(s,["mouseup","tapend","vmouseup"],r,{x:a[0],y:a[1]}),C.dragData.didDrag||C.hoverData.dragged||C.hoverData.selecting||C.hoverData.isOverThresholdDrag||(T(u,["click","tap","vclick"],r,{x:a[0],y:a[1]}),t=!1,r.timeStamp-n<=i.multiClickDebounceTime()?(e&&clearTimeout(e),t=!0,n=null,T(u,["dblclick","dbltap","vdblclick"],r,{x:a[0],y:a[1]})):(e=setTimeout(function(){t||T(u,["oneclick","onetap","voneclick"],r,{x:a[0],y:a[1]})},i.multiClickDebounceTime()),n=r.timeStamp)),null!=u||C.dragData.didDrag||C.hoverData.selecting||C.hoverData.dragged||P(r)||(i.$(D).unselect(["tapunselect"]),l.length>0&&C.redrawHint("eles",!0),C.dragData.possibleDragElements=l=i.collection()),s!=u||C.dragData.didDrag||C.hoverData.selecting||null==s||!s._private.selectable||(C.hoverData.dragging||("additive"===i.selectionType()||c?s.selected()?s.unselect(["tapunselect"]):s.select(["tapselect"]):c||(i.$(D).unmerge(s).unselect(["tapunselect"]),s.select(["tapselect"]))),C.redrawHint("eles",!0)),C.hoverData.selecting){var p=i.collection(C.getAllInBox(o[0],o[1],o[2],o[3]));C.redrawHint("select",!0),p.length>0&&C.redrawHint("eles",!0),i.emit({type:"boxend",originalEvent:r,position:{x:a[0],y:a[1]}}),"additive"!==i.selectionType()&&(c||i.$(D).unmerge(p).unselect()),p.emit("box").stdFilter(function(e){return e.selectable()&&!e.selected()}).select().emit("boxselect"),C.redraw()}if(C.hoverData.dragging&&(C.hoverData.dragging=!1,C.redrawHint("select",!0),C.redrawHint("eles",!0),C.redraw()),!o[4]){C.redrawHint("drag",!0),C.redrawHint("eles",!0);var f=u&&u.grabbed();F(l),f&&(u.emit("freeon"),l.emit("free"),C.dragData.didDrag&&(u.emit("dragfreeon"),l.emit("dragfree")))}}o[4]=0,C.hoverData.down=null,C.hoverData.cxtStarted=!1,C.hoverData.draggingEles=!1,C.hoverData.selecting=!1,C.hoverData.isOverThresholdDrag=!1,C.dragData.didDrag=!1,C.hoverData.dragged=!1,C.hoverData.dragDelta=[],C.hoverData.mdownPos=null,C.hoverData.mdownGPos=null}},!1);var U=function(e){if(!C.scrollingPage){var t=C.cy,n=t.zoom(),r=t.pan(),i=C.projectIntoViewport(e.clientX,e.clientY),a=[i[0]*n+r.x,i[1]*n+r.y];if(C.hoverData.draggingEles||C.hoverData.dragging||C.hoverData.cxtStarted||0!==C.selection[4]){e.preventDefault();return}if(t.panningEnabled()&&t.userPanningEnabled()&&t.zoomingEnabled()&&t.userZoomingEnabled()){e.preventDefault(),C.data.wheelZooming=!0,clearTimeout(C.data.wheelTimeout),C.data.wheelTimeout=setTimeout(function(){C.data.wheelZooming=!1,C.redrawHint("eles",!0),C.redraw()},150),o=(null!=e.deltaY?-(e.deltaY/250):null!=e.wheelDeltaY?e.wheelDeltaY/1e3:e.wheelDelta/1e3)*C.wheelSensitivity,1===e.deltaMode&&(o*=33);var o,s=t.zoom()*Math.pow(10,o);"gesturechange"===e.type&&(s=C.gestureStartZoom*e.scale),t.zoom({level:s,renderedPosition:{x:a[0],y:a[1]}}),t.emit("gesturechange"===e.type?"pinchzoom":"scrollzoom")}}};C.registerBinding(C.container,"wheel",U,!0),C.registerBinding(S,"scroll",function(e){C.scrollingPage=!0,clearTimeout(C.scrollingPageTimeout),C.scrollingPageTimeout=setTimeout(function(){C.scrollingPage=!1},250)},!0),C.registerBinding(C.container,"gesturestart",function(e){C.gestureStartZoom=C.cy.zoom(),C.hasTouchStarted||e.preventDefault()},!0),C.registerBinding(C.container,"gesturechange",function(e){C.hasTouchStarted||U(e)},!0),C.registerBinding(C.container,"mouseout",function(e){var t=C.projectIntoViewport(e.clientX,e.clientY);C.cy.emit({originalEvent:e,type:"mouseout",position:{x:t[0],y:t[1]}})},!1),C.registerBinding(C.container,"mouseover",function(e){var t=C.projectIntoViewport(e.clientX,e.clientY);C.cy.emit({originalEvent:e,type:"mouseover",position:{x:t[0],y:t[1]}})},!1);var K=function(e,t,n,r){return Math.sqrt((n-e)*(n-e)+(r-t)*(r-t))},Z=function(e,t,n,r){return(n-e)*(n-e)+(r-t)*(r-t)};if(C.registerBinding(C.container,"touchstart",v=function(e){if(C.hasTouchStarted=!0,G(e)){q(),C.touchData.capture=!0,C.data.bgActivePosistion=void 0;var t=C.cy,n=C.touchData.now,v=C.touchData.earlier;if(e.touches[0]){var y=C.projectIntoViewport(e.touches[0].clientX,e.touches[0].clientY);n[0]=y[0],n[1]=y[1]}if(e.touches[1]){var y=C.projectIntoViewport(e.touches[1].clientX,e.touches[1].clientY);n[2]=y[0],n[3]=y[1]}if(e.touches[2]){var y=C.projectIntoViewport(e.touches[2].clientX,e.touches[2].clientY);n[4]=y[0],n[5]=y[1]}if(e.touches[1]){C.touchData.singleTouchMoved=!0,F(C.dragData.touchDragEles);var b=C.findContainerClientCoords();d=b[0],h=b[1],p=b[2],f=b[3],r=e.touches[0].clientX-d,i=e.touches[0].clientY-h,a=e.touches[1].clientX-d,o=e.touches[1].clientY-h,g=0<=r&&r<=p&&0<=a&&a<=p&&0<=i&&i<=f&&0<=o&&o<=f;var x=t.pan(),w=t.zoom();if(s=K(r,i,a,o),l=Z(r,i,a,o),c=[((u=[(r+a)/2,(i+o)/2])[0]-x.x)/w,(u[1]-x.y)/w],l<4e4&&!e.touches[2]){var E=C.findNearestElement(n[0],n[1],!0,!0),k=C.findNearestElement(n[2],n[3],!0,!0);E&&E.isNode()?(E.activate().emit({originalEvent:e,type:"cxttapstart",position:{x:n[0],y:n[1]}}),C.touchData.start=E):k&&k.isNode()?(k.activate().emit({originalEvent:e,type:"cxttapstart",position:{x:n[0],y:n[1]}}),C.touchData.start=k):t.emit({originalEvent:e,type:"cxttapstart",position:{x:n[0],y:n[1]}}),C.touchData.start&&(C.touchData.start._private.grabbed=!1),C.touchData.cxt=!0,C.touchData.cxtDragged=!1,C.data.bgActivePosistion=void 0,C.redraw();return}}if(e.touches[2])t.boxSelectionEnabled()&&e.preventDefault();else if(e.touches[1]);else if(e.touches[0]){var S=C.findNearestElements(n[0],n[1],!0,!0),D=S[0];if(null!=D&&(D.activate(),C.touchData.start=D,C.touchData.starts=S,C.nodeIsGrabbable(D))){var P=C.dragData.touchDragEles=t.collection(),_=null;C.redrawHint("eles",!0),C.redrawHint("drag",!0),D.selected()?V(_=t.$(function(e){return e.selected()&&C.nodeIsGrabbable(e)}),{addToList:P}):V(D,{addToList:P}),O(D);var M=function(t){return{originalEvent:e,type:t,position:{x:n[0],y:n[1]}}};D.emit(M("grabon")),_?_.forEach(function(e){e.emit(M("grab"))}):D.emit(M("grab"))}T(D,["touchstart","tapstart","vmousedown"],e,{x:n[0],y:n[1]}),null==D&&(C.data.bgActivePosistion={x:y[0],y:y[1]},C.redrawHint("select",!0),C.redraw()),C.touchData.singleTouchMoved=!1,C.touchData.singleTouchStartTime=+new Date,clearTimeout(C.touchData.tapholdTimeout),C.touchData.tapholdTimeout=setTimeout(function(){!1!==C.touchData.singleTouchMoved||C.pinching||C.touchData.selecting||T(C.touchData.start,["taphold"],e,{x:n[0],y:n[1]})},C.tapholdDuration)}if(e.touches.length>=1){for(var B=C.touchData.startPosition=[null,null,null,null,null,null],N=0;N=C.touchTapThreshold2}if(t&&C.touchData.cxt){e.preventDefault();var D=e.touches[0].clientX-d,P=e.touches[0].clientY-h,B=e.touches[1].clientX-d,N=e.touches[1].clientY-h,A=Z(D,P,B,N);if(A/l>=2.25||A>=22500){C.touchData.cxt=!1,C.data.bgActivePosistion=void 0,C.redrawHint("select",!0);var I={originalEvent:e,type:"cxttapend",position:{x:p[0],y:p[1]}};C.touchData.start?(C.touchData.start.unactivate().emit(I),C.touchData.start=null):u.emit(I)}}if(t&&C.touchData.cxt){var I={originalEvent:e,type:"cxtdrag",position:{x:p[0],y:p[1]}};C.data.bgActivePosistion=void 0,C.redrawHint("select",!0),C.touchData.start?C.touchData.start.emit(I):u.emit(I),C.touchData.start&&(C.touchData.start._private.grabbed=!1),C.touchData.cxtDragged=!0;var O=C.findNearestElement(p[0],p[1],!0,!0);(!C.touchData.cxtOver||O!==C.touchData.cxtOver)&&(C.touchData.cxtOver&&C.touchData.cxtOver.emit({originalEvent:e,type:"cxtdragout",position:{x:p[0],y:p[1]}}),C.touchData.cxtOver=O,O&&O.emit({originalEvent:e,type:"cxtdragover",position:{x:p[0],y:p[1]}}))}else if(t&&e.touches[2]&&u.boxSelectionEnabled())e.preventDefault(),C.data.bgActivePosistion=void 0,this.lastThreeTouch=+new Date,C.touchData.selecting||u.emit({originalEvent:e,type:"boxstart",position:{x:p[0],y:p[1]}}),C.touchData.selecting=!0,C.touchData.didSelect=!0,n[4]=1,n&&0!==n.length&&void 0!==n[0]?(n[2]=(p[0]+p[2]+p[4])/3,n[3]=(p[1]+p[3]+p[5])/3):(n[0]=(p[0]+p[2]+p[4])/3,n[1]=(p[1]+p[3]+p[5])/3,n[2]=(p[0]+p[2]+p[4])/3+1,n[3]=(p[1]+p[3]+p[5])/3+1),C.redrawHint("select",!0),C.redraw();else if(t&&e.touches[1]&&!C.touchData.didSelect&&u.zoomingEnabled()&&u.panningEnabled()&&u.userZoomingEnabled()&&u.userPanningEnabled()){e.preventDefault(),C.data.bgActivePosistion=void 0,C.redrawHint("select",!0);var z=C.dragData.touchDragEles;if(z){C.redrawHint("drag",!0);for(var L=0;L0&&!C.hoverData.draggingEles&&!C.swipePanning&&null!=C.data.bgActivePosistion&&(C.data.bgActivePosistion=void 0,C.redrawHint("select",!0),C.redraw())}},!1),C.registerBinding(S,"touchcancel",b=function(e){var t=C.touchData.start;C.touchData.capture=!1,t&&t.unactivate()}),C.registerBinding(S,"touchend",x=function(e){var t,n=C.touchData.start;if(C.touchData.capture){0===e.touches.length&&(C.touchData.capture=!1),e.preventDefault();var r=C.selection;C.swipePanning=!1,C.hoverData.draggingEles=!1;var i=C.cy,a=i.zoom(),o=C.touchData.now,s=C.touchData.earlier;if(e.touches[0]){var l=C.projectIntoViewport(e.touches[0].clientX,e.touches[0].clientY);o[0]=l[0],o[1]=l[1]}if(e.touches[1]){var l=C.projectIntoViewport(e.touches[1].clientX,e.touches[1].clientY);o[2]=l[0],o[3]=l[1]}if(e.touches[2]){var l=C.projectIntoViewport(e.touches[2].clientX,e.touches[2].clientY);o[4]=l[0],o[5]=l[1]}if(n&&n.unactivate(),C.touchData.cxt){if(t={originalEvent:e,type:"cxttapend",position:{x:o[0],y:o[1]}},n?n.emit(t):i.emit(t),!C.touchData.cxtDragged){var u={originalEvent:e,type:"cxttap",position:{x:o[0],y:o[1]}};n?n.emit(u):i.emit(u)}C.touchData.start&&(C.touchData.start._private.grabbed=!1),C.touchData.cxt=!1,C.touchData.start=null,C.redraw();return}if(!e.touches[2]&&i.boxSelectionEnabled()&&C.touchData.selecting){C.touchData.selecting=!1;var c=i.collection(C.getAllInBox(r[0],r[1],r[2],r[3]));r[0]=void 0,r[1]=void 0,r[2]=void 0,r[3]=void 0,r[4]=0,C.redrawHint("select",!0),i.emit({type:"boxend",originalEvent:e,position:{x:o[0],y:o[1]}}),c.emit("box").stdFilter(function(e){return e.selectable()&&!e.selected()}).select().emit("boxselect"),c.nonempty()&&C.redrawHint("eles",!0),C.redraw()}if(null!=n&&n.unactivate(),e.touches[2])C.data.bgActivePosistion=void 0,C.redrawHint("select",!0);else if(e.touches[1]);else if(e.touches[0]);else if(!e.touches[0]){C.data.bgActivePosistion=void 0,C.redrawHint("select",!0);var d=C.dragData.touchDragEles;if(null!=n){var h=n._private.grabbed;F(d),C.redrawHint("drag",!0),C.redrawHint("eles",!0),h&&(n.emit("freeon"),d.emit("free"),C.dragData.didDrag&&(n.emit("dragfreeon"),d.emit("dragfree"))),T(n,["touchend","tapend","vmouseup","tapdragout"],e,{x:o[0],y:o[1]}),n.unactivate(),C.touchData.start=null}else{var p=C.findNearestElement(o[0],o[1],!0,!0);T(p,["touchend","tapend","vmouseup","tapdragout"],e,{x:o[0],y:o[1]})}var f=C.touchData.startPosition[0]-o[0],g=C.touchData.startPosition[1]-o[1];C.touchData.singleTouchMoved||(n||i.$(":selected").unselect(["tapunselect"]),T(n,["tap","vclick"],e,{x:o[0],y:o[1]}),w=!1,e.timeStamp-k<=i.multiClickDebounceTime()?(E&&clearTimeout(E),w=!0,k=null,T(n,["dbltap","vdblclick"],e,{x:o[0],y:o[1]})):(E=setTimeout(function(){w||T(n,["onetap","voneclick"],e,{x:o[0],y:o[1]})},i.multiClickDebounceTime()),k=e.timeStamp)),null!=n&&!C.dragData.didDrag&&n._private.selectable&&(f*f+g*g)*a*a0)return d[0]}return null}(e,t,f);if(null!=g){var v=tP(f[5],f[3],f[1],g);if(f.isTop&&v<=t||f.isBottom&&t<=v)return!0}}return!1}}},sD.generateBottomRoundrectangle=function(){return this.nodeShapes["bottom-round-rectangle"]=this.nodeShapes.bottomroundrectangle={renderer:this,name:"bottom-round-rectangle",points:t6(4,0),draw:function(e,t,n,r,i,a){this.renderer.nodeShapeImpl(this.name,e,t,n,r,i,this.points,a)},intersectLine:function(e,t,n,r,i,a,o,s){var l=e-(n/2+o),u=t-(r/2+o),c=e+(n/2+o),d=t5(i,a,e,t,l,u,c,u,!1);return d.length>0?d:tq(i,a,e,t,n,r,o,s)},checkPoint:function(e,t,n,r,i,a,o,s){var l=2*(s="auto"===s?ne(r,i):s);if(tK(e,t,this.points,a,o,r,i-l,[0,-1],n)||tK(e,t,this.points,a,o,r-l,i,[0,-1],n))return!0;var u=r/2+2*n,c=i/2+2*n;return!!(tU(e,t,[a-u,o-c,a-u,o,a+u,o,a+u,o-c])||t0(e,t,l,l,a+r/2-s,o+i/2-s,n)||t0(e,t,l,l,a-r/2+s,o+i/2-s,n))}}},sD.registerNodeShapes=function(){var e=this.nodeShapes={},t=this;this.generateEllipse(),this.generatePolygon("triangle",t6(3,0)),this.generateRoundPolygon("round-triangle",t6(3,0)),this.generatePolygon("rectangle",t6(4,0)),e.square=e.rectangle,this.generateRoundRectangle(),this.generateCutRectangle(),this.generateBarrel(),this.generateBottomRoundrectangle();var n=[0,1,1,0,0,-1,-1,0];this.generatePolygon("diamond",n),this.generateRoundPolygon("round-diamond",n),this.generatePolygon("pentagon",t6(5,0)),this.generateRoundPolygon("round-pentagon",t6(5,0)),this.generatePolygon("hexagon",t6(6,0)),this.generateRoundPolygon("round-hexagon",t6(6,0)),this.generatePolygon("heptagon",t6(7,0)),this.generateRoundPolygon("round-heptagon",t6(7,0)),this.generatePolygon("octagon",t6(8,0)),this.generateRoundPolygon("round-octagon",t6(8,0));var r=Array(20),i=t7(5,0),a=t7(5,Math.PI/5),o=.5*(3-Math.sqrt(5));o*=1.57;for(var s=0;st.className.indexOf(o)&&(t.className=(t.className||"")+" "+o),!s){var l=r.createElement("style");l.id=a,l.textContent="."+o+" { position: relative; }",i.insertBefore(l,i.children[0])}"static"===n.getComputedStyle(t).getPropertyValue("position")&&eJ("A Cytoscape container has style position:static and so can not use UI extensions properly")}this.selection=[void 0,void 0,void 0,void 0,0],this.bezierProjPcts=[.05,.225,.4,.5,.6,.775,.95],this.hoverData={down:null,last:null,downTime:null,triggerMode:null,dragging:!1,initialPan:[null,null],capture:!1},this.dragData={possibleDragElements:[]},this.touchData={start:null,capture:!1,startPosition:[null,null,null,null,null,null],singleTouchStartTime:null,singleTouchMoved:!0,now:[null,null,null,null,null,null],earlier:[null,null,null,null,null,null]},this.redraws=0,this.showFps=e.showFps,this.debug=e.debug,this.hideEdgesOnViewport=e.hideEdgesOnViewport,this.textureOnViewport=e.textureOnViewport,this.wheelSensitivity=e.wheelSensitivity,this.motionBlurEnabled=e.motionBlur,this.forcedPixelRatio=_(e.pixelRatio)?e.pixelRatio:null,this.motionBlur=e.motionBlur,this.motionBlurOpacity=e.motionBlurOpacity,this.motionBlurTransparency=1-this.motionBlurOpacity,this.motionBlurPxRatio=1,this.mbPxRBlurry=1,this.minMbLowQualFrames=4,this.fullQualityMb=!1,this.clearedForMotionBlur=[],this.desktopTapThreshold=e.desktopTapThreshold,this.desktopTapThreshold2=e.desktopTapThreshold*e.desktopTapThreshold,this.touchTapThreshold=e.touchTapThreshold,this.touchTapThreshold2=e.touchTapThreshold*e.touchTapThreshold,this.tapholdDuration=500,this.bindings=[],this.beforeRenderCallbacks=[],this.beforeRenderPriorities={animations:400,eleCalcs:300,eleTxrDeq:200,lyrTxrDeq:150,lyrTxrSkip:100},this.registerNodeShapes(),this.registerArrowShapes(),this.registerCalculationListeners()},sM.notify=function(e,t){var n=this.cy;if(!this.destroyed){if("init"===e){this.load();return}if("destroy"===e){this.destroy();return}("add"===e||"remove"===e||"move"===e&&n.hasCompoundNodes()||"load"===e||"zorder"===e||"mount"===e)&&this.invalidateCachedZSortedEles(),"viewport"===e&&this.redrawHint("select",!0),("load"===e||"resize"===e||"mount"===e)&&(this.invalidateContainerClientCoordsCache(),this.matchCanvasSize(this.container)),this.redrawHint("eles",!0),this.redrawHint("drag",!0),this.startRenderLoop(),this.redraw()}},sM.destroy=function(){this.destroyed=!0,this.cy.stopAnimationLoop();for(var e=0;e=e.deqFastCost*g)break}else if(i){if(p>=e.deqCost*l||p>=e.deqAvgCost*s)break}else if(f>=e.deqNoDrawCost*sB)break;var v=e.deq(t,d,c);if(v.length>0)for(var y=0;y0&&(e.onDeqd(t,u),!i&&e.shouldRedraw(t,u,d,c)&&r())},i(t))}}}},sA=function(){function e(t){var n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:eU;i(this,e),this.idsByKey=new tt,this.keyForId=new tt,this.cachesByLvl=new tt,this.lvls=[],this.getKey=t,this.doesEleInvalidateKey=n}return o(e,[{key:"getIdsFor",value:function(e){null==e&&e$("Can not get id list for null key");var t=this.idsByKey,n=this.idsByKey.get(e);return n||(n=new tr,t.set(e,n)),n}},{key:"addIdForKey",value:function(e,t){null!=e&&this.getIdsFor(e).add(t)}},{key:"deleteIdForKey",value:function(e,t){null!=e&&this.getIdsFor(e).delete(t)}},{key:"getNumberOfIdsForKey",value:function(e){return null==e?0:this.getIdsFor(e).size}},{key:"updateKeyMappingFor",value:function(e){var t=e.id(),n=this.keyForId.get(t),r=this.getKey(e);this.deleteIdForKey(n,t),this.addIdForKey(r,t),this.keyForId.set(t,r)}},{key:"deleteKeyMappingFor",value:function(e){var t=e.id(),n=this.keyForId.get(t);this.deleteIdForKey(n,t),this.keyForId.delete(t)}},{key:"keyHasChangedFor",value:function(e){var t=e.id();return this.keyForId.get(t)!==this.getKey(e)}},{key:"isInvalid",value:function(e){return this.keyHasChangedFor(e)||this.doesEleInvalidateKey(e)}},{key:"getCachesAt",value:function(e){var t=this.cachesByLvl,n=this.lvls,r=t.get(e);return r||(r=new tt,t.set(e,r),n.push(e)),r}},{key:"getCache",value:function(e,t){return this.getCachesAt(t).get(e)}},{key:"get",value:function(e,t){var n=this.getKey(e),r=this.getCache(n,t);return null!=r&&this.updateKeyMappingFor(e),r}},{key:"getForCachedKey",value:function(e,t){var n=this.keyForId.get(e.id());return this.getCache(n,t)}},{key:"hasCache",value:function(e,t){return this.getCachesAt(t).has(e)}},{key:"has",value:function(e,t){var n=this.getKey(e);return this.hasCache(n,t)}},{key:"setCache",value:function(e,t,n){n.key=e,this.getCachesAt(t).set(e,n)}},{key:"set",value:function(e,t,n){var r=this.getKey(e);this.setCache(r,t,n),this.updateKeyMappingFor(e)}},{key:"deleteCache",value:function(e,t){this.getCachesAt(t).delete(e)}},{key:"delete",value:function(e,t){var n=this.getKey(e);this.deleteCache(n,t)}},{key:"invalidateKey",value:function(e){var t=this;this.lvls.forEach(function(n){return t.deleteCache(e,n)})}},{key:"invalidate",value:function(e){var t=e.id(),n=this.keyForId.get(t);this.deleteKeyMappingFor(e);var r=this.doesEleInvalidateKey(e);return r&&this.invalidateKey(n),r||0===this.getNumberOfIdsForKey(n)}}]),e}(),sI={dequeue:"dequeue",downscale:"downscale",highQuality:"highQuality"},sO=e3({getKey:null,doesEleInvalidateKey:eU,drawElement:null,getBoundingBox:null,getRotationPoint:null,getRotationOffset:null,isVisible:eG,allowEdgeTxrCaching:!0,allowParentTxrCaching:!0}),sz=function(e,t){this.renderer=e,this.onDequeues=[];var n=sO(t);K(this,n),this.lookup=new sA(n.getKey,n.doesEleInvalidateKey),this.setupDequeueing()},sL=sz.prototype;sL.reasons=sI,sL.getTextureQueue=function(e){return this.eleImgCaches=this.eleImgCaches||{},this.eleImgCaches[e]=this.eleImgCaches[e]||[]},sL.getRetiredTextureQueue=function(e){var t=this.eleImgCaches.retired=this.eleImgCaches.retired||{};return t[e]=t[e]||[]},sL.getElementQueue=function(){return this.eleCacheQueue=this.eleCacheQueue||new ts(function(e,t){return t.reqs-e.reqs})},sL.getElementKeyToQueue=function(){return this.eleKeyToCacheQueue=this.eleKeyToCacheQueue||{}},sL.getElement=function(e,t,n,r,i){var a,o,s,l=this,u=this.renderer,c=u.cy.zoom(),d=this.lookup;if(!t||0===t.w||0===t.h||isNaN(t.w)||isNaN(t.h)||!e.visible()||e.removed()||!l.allowEdgeTxrCaching&&e.isEdge()||!l.allowParentTxrCaching&&e.isParent())return null;if(null==r&&(r=Math.ceil(tk(c*n))),r<-4)r=-4;else if(c>=7.99||r>3)return null;var h=Math.pow(2,r),p=t.h*h,f=t.w*h,g=u.eleTextBiggerThanMin(e,h);if(!this.isVisible(e,g))return null;var v=d.get(e,r);if(v&&v.invalidated&&(v.invalidated=!1,v.texture.invalidatedWidth-=v.width),v)return v;if(a=p<=25?25:p<=50?50:50*Math.ceil(p/50),p>1024||f>1024)return null;var y=l.getTextureQueue(a),b=y[y.length-2],x=function(){return l.recycleTexture(a,f)||l.addTexture(a,f)};b||(b=y[y.length-1]),b||(b=x()),b.width-b.usedWidthr;_--)T=l.getElement(e,t,n,_,sI.downscale);P()}else{if(!E&&!k&&!C)for(var M=r-1;M>=-4;M--){var B=d.get(e,M);if(B){s=B;break}}if(w(s))return l.queueElement(e,r),s;b.context.translate(b.usedWidth,0),b.context.scale(h,h),this.drawElement(b.context,e,t,g,!1),b.context.scale(1/h,1/h),b.context.translate(-b.usedWidth,0)}return v={x:b.usedWidth,texture:b,level:r,scale:h,width:f,height:p,scaledLabelShown:g},b.usedWidth+=Math.ceil(f+8),b.eleCaches.push(v),d.set(e,r,v),l.checkTextureFullness(b),v},sL.invalidateElements=function(e){for(var t=0;t=.2*e.width&&this.retireTexture(e)},sL.checkTextureFullness=function(e){var t=this.getTextureQueue(e.height);e.usedWidth/e.width>.8&&e.fullnessChecks>=10?e4(t,e):e.fullnessChecks++},sL.retireTexture=function(e){var t=e.height,n=this.getTextureQueue(t),r=this.lookup;e4(n,e),e.retired=!0;for(var i=e.eleCaches,a=0;a=t)return a.retired=!1,a.usedWidth=0,a.invalidatedWidth=0,a.fullnessChecks=0,e9(a.eleCaches),a.context.setTransform(1,0,0,1,0,0),a.context.clearRect(0,0,a.width,a.height),e4(r,a),n.push(a),a}},sL.queueElement=function(e,t){var n=this.getElementQueue(),r=this.getElementKeyToQueue(),i=this.getKey(e),a=r[i];if(a)a.level=Math.max(a.level,t),a.eles.merge(e),a.reqs++,n.updateItem(a);else{var o={eles:e.spawn().merge(e),level:t,reqs:1,key:i};n.push(o),r[i]=o}},sL.dequeue=function(e){for(var t=this.getElementQueue(),n=this.getElementKeyToQueue(),r=[],i=this.lookup,a=0;a<1;a++)if(t.size()>0){var o=t.pop(),s=o.key,l=o.eles[0],u=i.hasCache(l,o.level);if(n[s]=null,u)continue;r.push(o);var c=this.getBoundingBox(l);this.getElement(l,c,e,o.level,sI.dequeue)}else break;return r},sL.removeFromQueue=function(e){var t=this.getElementQueue(),n=this.getElementKeyToQueue(),r=this.getKey(e),i=n[r];null!=i&&(1===i.eles.length?(i.reqs=eH,t.updateItem(i),t.pop(),n[r]=null):i.eles.unmerge(e))},sL.onDequeue=function(e){this.onDequeues.push(e)},sL.offDequeue=function(e){e4(this.onDequeues,e)},sL.setupDequeueing=sN.setupDequeueing({deqRedrawThreshold:100,deqCost:.15,deqAvgCost:.1,deqNoDrawCost:.9,deqFastCost:.9,deq:function(e,t,n){return e.dequeue(t,n)},onDeqd:function(e,t){for(var n=0;n=3.99||n>2)return null}a.validateLayersElesOrdering(n,e);var l=a.layersByLevel,u=Math.pow(2,n),c=l[n]=l[n]||[];if(a.levelIsComplete(n,e))return c;!function(){var t=function(t){if(a.validateLayersElesOrdering(t,e),a.levelIsComplete(t,e))return i=l[t],!0},r=function(e){if(!i)for(var r=n+e;-4<=r&&r<=2&&!t(r);r+=e);};r(1),r(-1);for(var o=c.length-1;o>=0;o--){var s=c[o];s.invalid&&e4(c,s)}}();var d=function(){if(!r){r=tN();for(var t=0;t=p||!tj(h.bb,v.boundingBox()))&&!(h=function(e){var t=(e=e||{}).after;if(d(),r.w*u*(r.h*u)>16e6)return null;var i=a.makeLayer(r,n);if(null!=t){var o=c.indexOf(t)+1;c.splice(o,0,i)}else(void 0===e.insert||e.insert)&&c.unshift(i);return i}({insert:!0,after:h})))return null;i||f?a.queueLayer(h,v):a.drawEleInLayer(h,v,n,t),h.eles.push(v),b[n]=h}return i||(f?null:c)},sV.getEleLevelForLayerLevel=function(e,t){return e},sV.drawEleInLayer=function(e,t,n,r){var i=this.renderer,a=e.context,o=t.boundingBox();0!==o.w&&0!==o.h&&t.visible()&&(n=this.getEleLevelForLayerLevel(n,r),i.setImgSmoothing(a,!1),i.drawCachedElement(a,t,null,null,n,!0),i.setImgSmoothing(a,!0))},sV.levelIsComplete=function(e,t){var n=this.layersByLevel[e];if(!n||0===n.length)return!1;for(var r=0,i=0;i0||a.invalid)return!1;r+=a.eles.length}return r===t.length},sV.validateLayersElesOrdering=function(e,t){var n=this.layersByLevel[e];if(n)for(var r=0;r0){e=!0;break}}return e},sV.invalidateElements=function(e){var t=this;0!==e.length&&(t.lastInvalidationTime=eB(),0!==e.length&&t.haveLayers()&&t.updateElementsInLayers(e,function(e,n,r){t.invalidateLayer(e)}))},sV.invalidateLayer=function(e){if(this.lastInvalidationTime=eB(),!e.invalid){var t=e.level,n=e.eles;e4(this.layersByLevel[t],e),e.elesQueue=[],e.invalid=!0,e.replacement&&(e.replacement.invalid=!0);for(var r=0;r3)||void 0===arguments[3]||arguments[3],a=!(arguments.length>4)||void 0===arguments[4]||arguments[4],o=!(arguments.length>5)||void 0===arguments[5]||arguments[5],s=this,l=t._private.rscratch;if(!(o&&!t.visible()||l.badLine||null==l.allpts||isNaN(l.allpts[0]))){n&&(r=n,e.translate(-r.x1,-r.y1));var u=o?t.pstyle("opacity").value:1,c=o?t.pstyle("line-opacity").value:1,d=t.pstyle("curve-style").value,h=t.pstyle("line-style").value,p=t.pstyle("width").pfValue,f=t.pstyle("line-cap").value,g=u*c,v=u*c,y=function(){var n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:g;"straight-triangle"===d?(s.eleStrokeStyle(e,t,n),s.drawEdgeTrianglePath(t,e,l.allpts)):(e.lineWidth=p,e.lineCap=f,s.eleStrokeStyle(e,t,n),s.drawEdgePath(t,e,l.allpts,h),e.lineCap="butt")},b=function(){var n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:v;s.drawArrowheads(e,t,n)};if(e.lineJoin="round","yes"===t.pstyle("ghost").value){var x=t.pstyle("ghost-offset-x").pfValue,w=t.pstyle("ghost-offset-y").pfValue,E=g*t.pstyle("ghost-opacity").value;e.translate(x,w),y(E),b(E),e.translate(-x,-w)}a&&s.drawEdgeUnderlay(e,t),y(),b(),a&&s.drawEdgeOverlay(e,t),s.drawElementText(e,t,null,i),n&&e.translate(r.x1,r.y1)}};var s1=function(e){if(!["overlay","underlay"].includes(e))throw Error("Invalid state");return function(t,n){if(n.visible()){var r=n.pstyle("".concat(e,"-opacity")).value;if(0!==r){var i=this.usePaths(),a=n._private.rscratch,o=n.pstyle("".concat(e,"-padding")).pfValue,s=n.pstyle("".concat(e,"-color")).value;t.lineWidth=2*o,"self"!==a.edgeType||i?t.lineCap="round":t.lineCap="butt",this.colorStrokeStyle(t,s[0],s[1],s[2],r),this.drawEdgePath(n,t,a.allpts,"solid")}}}};s0.drawEdgeOverlay=s1("overlay"),s0.drawEdgeUnderlay=s1("underlay"),s0.drawEdgePath=function(e,t,n,r){var i=e._private.rscratch,a=t,o=!1,s=this.usePaths(),l=e.pstyle("line-dash-pattern").pfValue,c=e.pstyle("line-dash-offset").pfValue;if(s){var d=n.join("$");i.pathCacheKey&&i.pathCacheKey===d?(f=t=i.pathCache,o=!0):(f=t=new Path2D,i.pathCacheKey=d,i.pathCache=f)}if(a.setLineDash)switch(r){case"dotted":a.setLineDash([1,1]);break;case"dashed":a.setLineDash(l),a.lineDashOffset=c;break;case"solid":a.setLineDash([])}if(!o&&!i.badLine)switch(t.beginPath&&t.beginPath(),t.moveTo(n[0],n[1]),i.edgeType){case"bezier":case"self":case"compound":case"multibezier":for(var h=2;h+3=e.length?{done:!0}:{done:!1,value:e[r++]}},e:function(e){throw e},f:i}}throw TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var a,o=!0,s=!1;return{s:function(){n=n.call(e)},n:function(){var e=n.next();return o=e.done,e},e:function(e){s=!0,a=e},f:function(){try{o||null==n.return||n.return()}finally{if(s)throw a}}}}(i.roundCorners);try{for(v.s();!(g=v.n()).done;){var y=g.value;su(t,y)}}catch(e){v.e(e)}finally{v.f()}t.lineTo(n[n.length-2],n[n.length-1])}else for(var b=2;b+15&&void 0!==arguments[5]?arguments[5]:5,o=arguments.length>6?arguments[6]:void 0;e.beginPath(),e.moveTo(t+a,n),e.lineTo(t+r-a,n),e.quadraticCurveTo(t+r,n,t+r,n+a),e.lineTo(t+r,n+i-a),e.quadraticCurveTo(t+r,n+i,t+r-a,n+i),e.lineTo(t+a,n+i),e.quadraticCurveTo(t,n+i,t,n+i-a),e.lineTo(t,n+a),e.quadraticCurveTo(t,n,t+a,n),e.closePath(),o?e.stroke():e.fill()}s5.eleTextBiggerThanMin=function(e,t){return t||(t=Math.pow(2,Math.ceil(tk(e.cy().zoom()*this.getPixelRatio())))),!(e.pstyle("font-size").pfValue*t5)||void 0===arguments[5]||arguments[5];if(null==r){if(o&&!this.eleTextBiggerThanMin(t))return}else if(!1===r)return;if(t.isNode()){var s=t.pstyle("label");if(!s||!s.value)return;var l=this.getLabelJustification(t);e.textAlign=l,e.textBaseline="bottom"}else{var u=t.element()._private.rscratch.badLine,c=t.pstyle("label"),d=t.pstyle("source-label"),h=t.pstyle("target-label");if(u||(!c||!c.value)&&(!d||!d.value)&&(!h||!h.value))return;e.textAlign="center",e.textBaseline="bottom"}var p=!n;n&&(a=n,e.translate(-a.x1,-a.y1)),null==i?(this.drawText(e,t,null,p,o),t.isEdge()&&(this.drawText(e,t,"source",p,o),this.drawText(e,t,"target",p,o))):this.drawText(e,t,i,p,o),n&&e.translate(a.x1,a.y1)},s5.getFontCache=function(e){var t;this.fontCaches=this.fontCaches||[];for(var n=0;n2)||void 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s=e8(a,"labelX",n),l=e8(a,"labelY",n),u=this.getLabelText(t,n);if(null!=u&&""!==u&&!isNaN(s)&&!isNaN(l)){this.setupTextStyle(e,t,i);var c,d,h,p=n?n+"-":"",f=e8(a,"labelWidth",n),g=e8(a,"labelHeight",n),v=t.pstyle(p+"text-margin-x").pfValue,y=t.pstyle(p+"text-margin-y").pfValue,b=t.isEdge(),x=t.pstyle("text-halign").value,w=t.pstyle("text-valign").value;switch(b&&(x="center",w="center"),s+=v,l+=y,0!==(h=r?this.getTextAngle(t,n):0)&&(c=s,d=l,e.translate(c,d),e.rotate(h),s=0,l=0),w){case"top":break;case"center":l+=g/2;break;case"bottom":l+=g}var E=t.pstyle("text-background-opacity").value,k=t.pstyle("text-border-opacity").value,C=t.pstyle("text-border-width").pfValue,S=t.pstyle("text-background-padding").pfValue,D=0===t.pstyle("text-background-shape").strValue.indexOf("round");if(E>0||C>0&&k>0){var T=s-S;switch(x){case"left":T-=f;break;case"center":T-=f/2}var P=l-g-S,_=f+2*S,M=g+2*S;if(E>0){var 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V=e8(a,"labelWrapCachedLines",n),F=e8(a,"labelLineHeight",n),j=f/2,q=this.getLabelJustification(t);switch("auto"===q||("left"===x?"left"===q?s+=-f:"center"===q&&(s+=-j):"center"===x?"left"===q?s+=-j:"right"===q&&(s+=j):"right"===x&&("center"===q?s+=j:"right"===q&&(s+=f))),w){case"top":case"center":case"bottom":l-=(V.length-1)*F}for(var X=0;X0&&e.strokeText(V[X],s,l),e.fillText(V[X],s,l),l+=F}else R>0&&e.strokeText(u,s,l),e.fillText(u,s,l);0!==h&&(e.rotate(-h),e.translate(-c,-d))}}};var s4={};s4.drawNode=function(e,t,n){var r,i,a,o,s=!(arguments.length>3)||void 0===arguments[3]||arguments[3],l=!(arguments.length>4)||void 0===arguments[4]||arguments[4],u=!(arguments.length>5)||void 0===arguments[5]||arguments[5],c=this,d=t._private,h=d.rscratch,p=t.position();if(_(p.x)&&_(p.y)&&(!u||t.visible())){var f=u?t.effectiveOpacity():1,g=c.usePaths(),v=!1,y=t.padding();r=t.width()+2*y,i=t.height()+2*y,n&&(o=n,e.translate(-o.x1,-o.y1));for(var 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i.MANDATORY_NEWLINE:return"\n";case i.SINGLE_INDENT:return this.indentation.getSingleIndent();default:return e}}}let nX=e=>e===i.SPACE||e===i.SINGLE_INDENT,nK=e=>e===i.SPACE||e===i.SINGLE_INDENT||e===i.NEWLINE,nz="top-level";class nJ{indentTypes=[];constructor(e){this.indent=e}getSingleIndent(){return this.indent}getLevel(){return this.indentTypes.length}increaseTopLevel(){this.indentTypes.push(nz)}increaseBlockLevel(){this.indentTypes.push("block-level")}decreaseTopLevel(){this.indentTypes.length>0&&m(this.indentTypes)===nz&&this.indentTypes.pop()}decreaseBlockLevel(){for(;this.indentTypes.length>0;){let e=this.indentTypes.pop();if(e!==nz)break}}}class nq extends nj{length=0;trailingSpace=!1;constructor(e){super(new nJ("")),this.expressionWidth=e}add(...e){if(e.forEach(e=>this.addToLength(e)),this.length>this.expressionWidth)throw new nQ;super.add(...e)}addToLength(e){if("string"==typeof e)this.length+=e.length,this.trailingSpace=!1;else if(e===i.MANDATORY_NEWLINE||e===i.NEWLINE)throw new nQ;else e===i.INDENT||e===i.SINGLE_INDENT||e===i.SPACE?this.trailingSpace||(this.length++,this.trailingSpace=!0):(e===i.NO_NEWLINE||e===i.NO_SPACE)&&this.trailingSpace&&(this.trailingSpace=!1,this.length--)}}class nQ extends Error{}class n0{inline=!1;nodes=[];index=-1;constructor({cfg:e,dialectCfg:t,params:n,layout:r,inline:o=!1}){this.cfg=e,this.dialectCfg=t,this.inline=o,this.params=n,this.layout=r}format(e){for(this.nodes=e,this.index=0;this.index{this.layout.add(this.showKw(e.nameKw))}),this.formatNode(e.parenthesis)}formatArraySubscript(e){this.withComments(e.array,()=>{this.layout.add(e.array.type===o.keyword?this.showKw(e.array):e.array.text)}),this.formatNode(e.parenthesis)}formatPropertyAccess(e){this.formatNode(e.object),this.layout.add(i.NO_SPACE,"."),this.formatNode(e.property)}formatParenthesis(e){let t=this.formatInlineExpression(e.children);t?(this.layout.add(e.openParen),this.layout.add(...t.getLayoutItems()),this.layout.add(i.NO_SPACE,e.closeParen,i.SPACE)):(this.layout.add(e.openParen,i.NEWLINE),ng(this.cfg)?(this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children)):(this.layout.indentation.increaseBlockLevel(),this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children),this.layout.indentation.decreaseBlockLevel()),this.layout.add(i.NEWLINE,i.INDENT,e.closeParen,i.SPACE))}formatBetweenPredicate(e){this.layout.add(this.showKw(e.betweenKw),i.SPACE),this.layout=this.formatSubExpression(e.expr1),this.layout.add(i.NO_SPACE,i.SPACE,this.showNonTabularKw(e.andKw),i.SPACE),this.layout=this.formatSubExpression(e.expr2),this.layout.add(i.SPACE)}formatCaseExpression(e){this.formatNode(e.caseKw),this.layout.indentation.increaseBlockLevel(),this.layout=this.formatSubExpression(e.expr),this.layout=this.formatSubExpression(e.clauses),this.layout.indentation.decreaseBlockLevel(),this.layout.add(i.NEWLINE,i.INDENT),this.formatNode(e.endKw)}formatCaseWhen(e){this.layout.add(i.NEWLINE,i.INDENT),this.formatNode(e.whenKw),this.layout=this.formatSubExpression(e.condition),this.formatNode(e.thenKw),this.layout=this.formatSubExpression(e.result)}formatCaseElse(e){this.layout.add(i.NEWLINE,i.INDENT),this.formatNode(e.elseKw),this.layout=this.formatSubExpression(e.result)}formatClause(e){this.isOnelineClause(e)?this.formatClauseInOnelineStyle(e):ng(this.cfg)?this.formatClauseInTabularStyle(e):this.formatClauseInIndentedStyle(e)}isOnelineClause(e){return this.dialectCfg.onelineClauses[e.nameKw.text]}formatClauseInIndentedStyle(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.NEWLINE),this.layout.indentation.increaseTopLevel(),this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children),this.layout.indentation.decreaseTopLevel()}formatClauseInOnelineStyle(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.SPACE),this.layout=this.formatSubExpression(e.children)}formatClauseInTabularStyle(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.SPACE),this.layout.indentation.increaseTopLevel(),this.layout=this.formatSubExpression(e.children),this.layout.indentation.decreaseTopLevel()}formatSetOperation(e){this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.nameKw),i.NEWLINE),this.layout.add(i.INDENT),this.layout=this.formatSubExpression(e.children)}formatLimitClause(e){this.withComments(e.limitKw,()=>{this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e.limitKw))}),this.layout.indentation.increaseTopLevel(),ng(this.cfg)?this.layout.add(i.SPACE):this.layout.add(i.NEWLINE,i.INDENT),e.offset&&(this.layout=this.formatSubExpression(e.offset),this.layout.add(i.NO_SPACE,",",i.SPACE)),this.layout=this.formatSubExpression(e.count),this.layout.indentation.decreaseTopLevel()}formatAllColumnsAsterisk(e){this.layout.add("*",i.SPACE)}formatLiteral(e){this.layout.add(e.text,i.SPACE)}formatIdentifier(e){this.layout.add(e.text,i.SPACE)}formatParameter(e){this.layout.add(this.params.get(e),i.SPACE)}formatOperator({text:e}){this.cfg.denseOperators||this.dialectCfg.alwaysDenseOperators.includes(e)?this.layout.add(i.NO_SPACE,e):":"===e?this.layout.add(i.NO_SPACE,e,i.SPACE):this.layout.add(e,i.SPACE)}formatComma(e){this.inline?this.layout.add(i.NO_SPACE,",",i.SPACE):this.layout.add(i.NO_SPACE,",",i.NEWLINE,i.INDENT)}withComments(e,t){this.formatComments(e.leadingComments),t(),this.formatComments(e.trailingComments)}formatComments(e){e&&e.forEach(e=>{e.type===o.line_comment?this.formatLineComment(e):this.formatBlockComment(e)})}formatLineComment(e){P(e.precedingWhitespace||"")?this.layout.add(i.NEWLINE,i.INDENT,e.text,i.MANDATORY_NEWLINE,i.INDENT):this.layout.getLayoutItems().length>0?this.layout.add(i.NO_NEWLINE,i.SPACE,e.text,i.MANDATORY_NEWLINE,i.INDENT):this.layout.add(e.text,i.MANDATORY_NEWLINE,i.INDENT)}formatBlockComment(e){this.isMultilineBlockComment(e)?(this.splitBlockComment(e.text).forEach(e=>{this.layout.add(i.NEWLINE,i.INDENT,e)}),this.layout.add(i.NEWLINE,i.INDENT)):this.layout.add(e.text,i.SPACE)}isMultilineBlockComment(e){return P(e.text)||P(e.precedingWhitespace||"")}isDocComment(e){let t=e.split(/\n/);return/^\/\*\*?$/.test(t[0])&&t.slice(1,t.length-1).every(e=>/^\s*\*/.test(e))&&/^\s*\*\/$/.test(m(t))}splitBlockComment(e){return this.isDocComment(e)?e.split(/\n/).map(e=>/^\s*\*/.test(e)?" "+e.replace(/^\s*/,""):e):e.split(/\n/).map(e=>e.replace(/^\s*/,""))}formatSubExpression(e){return new n0({cfg:this.cfg,dialectCfg:this.dialectCfg,params:this.params,layout:this.layout,inline:this.inline}).format(e)}formatInlineExpression(e){let t=this.params.getPositionalParameterIndex();try{return new n0({cfg:this.cfg,dialectCfg:this.dialectCfg,params:this.params,layout:new nq(this.cfg.expressionWidth),inline:!0}).format(e)}catch(e){if(e instanceof nQ){this.params.setPositionalParameterIndex(t);return}throw e}}formatKeywordNode(e){switch(e.tokenType){case r.RESERVED_JOIN:return this.formatJoin(e);case r.AND:case r.OR:case r.XOR:return this.formatLogicalOperator(e);default:return this.formatKeyword(e)}}formatJoin(e){ng(this.cfg)?(this.layout.indentation.decreaseTopLevel(),this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE),this.layout.indentation.increaseTopLevel()):this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE)}formatKeyword(e){this.layout.add(this.showKw(e),i.SPACE)}formatLogicalOperator(e){"before"===this.cfg.logicalOperatorNewline?ng(this.cfg)?(this.layout.indentation.decreaseTopLevel(),this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE),this.layout.indentation.increaseTopLevel()):this.layout.add(i.NEWLINE,i.INDENT,this.showKw(e),i.SPACE):this.layout.add(this.showKw(e),i.NEWLINE,i.INDENT)}showKw(e){var t;return d(t=e.tokenType)||t===r.RESERVED_CLAUSE||t===r.RESERVED_SELECT||t===r.RESERVED_SET_OPERATION||t===r.RESERVED_JOIN||t===r.LIMIT?function(e,t){if("standard"===t)return e;let n=[];return e.length>=10&&e.includes(" ")&&([e,...n]=e.split(" ")),(e="tabularLeft"===t?e.padEnd(9," "):e.padStart(9," "))+["",...n].join(" ")}(this.showNonTabularKw(e),this.cfg.indentStyle):this.showNonTabularKw(e)}showNonTabularKw(e){switch(this.cfg.keywordCase){case"preserve":return L(e.raw);case"upper":return e.text;case"lower":return e.text.toLowerCase()}}}class n1{constructor(e,t){this.dialect=e,this.cfg=t,this.params=new n_(this.cfg.params)}format(e){let t=this.parse(e),n=this.formatAst(t),r=this.postFormat(n);return r.trimEnd()}parse(e){return(function(e){let t={},n=new nF(n=>[...e.tokenize(n,t).map(nv).map(nP).map(ny).map(nb),c(n.length)]),r=new nk(nW.fromCompiled(nV),{lexer:n});return{parse:(e,n)=>{t=n;let{results:o}=r.feed(e);if(1===o.length)return o[0];if(0===o.length)throw Error("Parse error: Invalid SQL");throw Error(`Parse error: Ambiguous grammar +${JSON.stringify(o,void 0,2)}`)}}})(this.dialect.tokenizer).parse(e,this.cfg.paramTypes||{})}formatAst(e){return e.map(e=>this.formatStatement(e)).join("\n".repeat(this.cfg.linesBetweenQueries+1))}formatStatement(e){let t=new n0({cfg:this.cfg,dialectCfg:this.dialect.formatOptions,params:this.params,layout:new nj(new nJ(nm(this.cfg)))}).format(e.children);return e.hasSemicolon&&(this.cfg.newlineBeforeSemicolon?t.add(i.NEWLINE,";"):t.add(i.NO_NEWLINE,";")),t.toString()}postFormat(e){if(this.cfg.tabulateAlias&&(e=function(e){let t=e.split("\n"),n=[];for(let e=0;e({line:e,matches:e.match(/(^.*?\S) (AS )?(\S+,?$)/i)})).map(({line:e,matches:t})=>t?{precedingText:t[1],as:t[2],alias:t[3]}:{precedingText:e}),i=_(o.map(({precedingText:e})=>e.replace(/\s*,\s*$/,"")));n=[...n,...r=o.map(({precedingText:e,as:t,alias:n})=>e+(n?" 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\ No newline at end of file diff --git a/dbgpt/datasource/conn_tugraph.py b/dbgpt/datasource/conn_tugraph.py index 171f36558..5a6503375 100644 --- a/dbgpt/datasource/conn_tugraph.py +++ b/dbgpt/datasource/conn_tugraph.py @@ -1,6 +1,7 @@ """TuGraph Connector.""" + import json -from typing import Any, Dict, List, cast +from typing import Dict, List, cast from .base import BaseConnector @@ -12,37 +13,60 @@ class TuGraphConnector(BaseConnector): driver: str = "bolt" dialect: str = "tugraph" - def __init__(self, session): + def __init__(self, driver, graph): """Initialize the connector with a Neo4j driver.""" - self._session = session + self._driver = driver self._schema = None + self._graph = graph + self._session = None + + def create_graph(self, graph_name: str) -> None: + """Create a new graph.""" + # run the query to get vertex labels + with self._driver.session(database="default") as session: + graph_list = session.run("CALL dbms.graph.listGraphs()").data() + exists = any(item["graph_name"] == graph_name for item in graph_list) + if not exists: + session.run(f"CALL dbms.graph.createGraph('{graph_name}', '', 2048)") + + def delete_graph(self, graph_name: str) -> None: + """Delete a graph.""" + with self._driver.session(database="default") as session: + graph_list = session.run("CALL dbms.graph.listGraphs()").data() + exists = any(item["graph_name"] == graph_name for item in graph_list) + if exists: + session.run(f"Call dbms.graph.deleteGraph('{graph_name}')") @classmethod def from_uri_db( - cls, host: str, port: int, user: str, pwd: str, db_name: str, **kwargs: Any + cls, host: str, port: int, user: str, pwd: str, db_name: str ) -> "TuGraphConnector": """Create a new TuGraphConnector from host, port, user, pwd, db_name.""" try: from neo4j import GraphDatabase db_url = f"{cls.driver}://{host}:{str(port)}" - with GraphDatabase.driver(db_url, auth=(user, pwd)) as client: - client.verify_connectivity() - session = client.session(database=db_name) - return cast(TuGraphConnector, cls(session=session)) + driver = GraphDatabase.driver(db_url, auth=(user, pwd)) + driver.verify_connectivity() + return cast(TuGraphConnector, cls(driver=driver, graph=db_name)) + except ImportError as err: - raise ImportError("requests package is not installed") from err + raise ImportError( + "neo4j package is not installed, please install it with " + "`pip install neo4j`" + ) from err def get_table_names(self) -> Dict[str, List[str]]: - """Get all table names from the TuGraph database using the Neo4j driver.""" - # Run the query to get vertex labels - v_result = self._session.run("CALL db.vertexLabels()").data() - v_data = [table_name["label"] for table_name in v_result] + """Get all table names from the TuGraph by Neo4j driver.""" + # run the query to get vertex labels + with self._driver.session(database=self._graph) as session: + v_result = session.run("CALL db.vertexLabels()").data() + v_data = [table_name["label"] for table_name in v_result] - # Run the query to get edge labels - e_result = self._session.run("CALL db.edgeLabels()").data() - e_data = [table_name["label"] for table_name in e_result] - return {"vertex_tables": v_data, "edge_tables": e_data} + # run the query to get edge labels + e_result = session.run("CALL db.edgeLabels()").data() + e_data = [table_name["label"] for table_name in e_result] + return {"vertex_tables": v_data, "edge_tables": e_data} def get_grants(self): """Get grants.""" @@ -62,11 +86,13 @@ class TuGraphConnector(BaseConnector): def close(self): """Close the Neo4j driver.""" - self._session.close() + self._driver.close() - def run(self): + def run(self, query: str, fetch: str = "all") -> List: """Run GQL.""" - return [] + with self._driver.session(database=self._graph) as session: + result = session.run(query) + return list(result) def get_columns(self, table_name: str, table_type: str = "vertex") -> List[Dict]: """Get fields about specified graph. @@ -80,27 +106,27 @@ class TuGraphConnector(BaseConnector): eg:[{'name': 'id', 'type': 'int', 'default_expression': '', 'is_in_primary_key': True, 'comment': 'id'}, ...] """ - data = [] - result = None - if table_type == "vertex": - result = self._session.run( - f"CALL db.getVertexSchema('{table_name}')" - ).data() - else: - result = self._session.run(f"CALL db.getEdgeSchema('{table_name}')").data() - schema_info = json.loads(result[0]["schema"]) - for prop in schema_info.get("properties", []): - prop_dict = { - "name": prop["name"], - "type": prop["type"], - "default_expression": "", - "is_in_primary_key": bool( - "primary" in schema_info and prop["name"] == schema_info["primary"] - ), - "comment": prop["name"], - } - data.append(prop_dict) - return data + with self._driver.session(database=self._graph) as session: + data = [] + result = None + if table_type == "vertex": + result = session.run(f"CALL db.getVertexSchema('{table_name}')").data() + else: + result = session.run(f"CALL db.getEdgeSchema('{table_name}')").data() + schema_info = json.loads(result[0]["schema"]) + for prop in schema_info.get("properties", []): + prop_dict = { + "name": prop["name"], + "type": prop["type"], + "default_expression": "", + "is_in_primary_key": bool( + "primary" in schema_info + and prop["name"] == schema_info["primary"] + ), + "comment": prop["name"], + } + data.append(prop_dict) + return data def get_indexes(self, table_name: str, table_type: str = "vertex") -> List[Dict]: """Get table indexes about specified table. @@ -112,14 +138,15 @@ class TuGraphConnector(BaseConnector): List[Dict]:eg:[{'name': 'idx_key', 'column_names': ['id']}] """ # [{'name':'id','column_names':['id']}] - result = self._session.run( - f"CALL db.listLabelIndexes('{table_name}','{table_type}')" - ).data() - transformed_data = [] - for item in result: - new_dict = {"name": item["field"], "column_names": [item["field"]]} - transformed_data.append(new_dict) - return transformed_data + with self._driver.session(database=self._graph) as session: + result = session.run( + f"CALL db.listLabelIndexes('{table_name}','{table_type}')" + ).data() + transformed_data = [] + for item in result: + new_dict = {"name": item["field"], "column_names": [item["field"]]} + transformed_data.append(new_dict) + return transformed_data @classmethod def is_graph_type(cls) -> bool: diff --git a/dbgpt/rag/assembler/base.py b/dbgpt/rag/assembler/base.py index 2dbbfe245..629246860 100644 --- a/dbgpt/rag/assembler/base.py +++ b/dbgpt/rag/assembler/base.py @@ -1,4 +1,5 @@ """Base Assembler.""" + from abc import ABC, abstractmethod from typing import Any, List, Optional @@ -37,13 +38,15 @@ class BaseAssembler(ABC): ) self._chunks: List[Chunk] = [] metadata = { - "knowledge_cls": self._knowledge.__class__.__name__ - if self._knowledge - else None, + "knowledge_cls": ( + self._knowledge.__class__.__name__ if self._knowledge else None + ), "knowledge_type": self._knowledge.type().value if self._knowledge else None, - "path": self._knowledge._path - if self._knowledge and hasattr(self._knowledge, "_path") - else None, + "path": ( + self._knowledge._path + if self._knowledge and hasattr(self._knowledge, "_path") + else None + ), "chunk_parameters": self._chunk_parameters.dict(), } with root_tracer.start_span("BaseAssembler.load_knowledge", metadata=metadata): @@ -70,6 +73,14 @@ class BaseAssembler(ABC): List[str]: List of persisted chunk ids. """ + async def apersist(self) -> List[str]: + """Persist chunks. + + Returns: + List[str]: List of persisted chunk ids. + """ + raise NotImplementedError + def get_chunks(self) -> List[Chunk]: """Return chunks.""" return self._chunks diff --git a/dbgpt/rag/assembler/embedding.py b/dbgpt/rag/assembler/embedding.py index 59725ade4..2f1a6bd9b 100644 --- a/dbgpt/rag/assembler/embedding.py +++ b/dbgpt/rag/assembler/embedding.py @@ -106,6 +106,14 @@ class EmbeddingAssembler(BaseAssembler): """ return self._vector_store_connector.load_document(self._chunks) + async def apersist(self) -> List[str]: + """Persist chunks into store. + + Returns: + List[str]: List of chunk ids. + """ + return await self._vector_store_connector.aload_document(self._chunks) + def _extract_info(self, chunks) -> List[Chunk]: """Extract info from chunks.""" return [] diff --git a/dbgpt/rag/index/__init__.py b/dbgpt/rag/index/__init__.py new file mode 100644 index 000000000..bd4cf4ca2 --- /dev/null +++ b/dbgpt/rag/index/__init__.py @@ -0,0 +1 @@ +"""Module for index.""" diff --git a/dbgpt/rag/index/base.py b/dbgpt/rag/index/base.py new file mode 100644 index 000000000..59f6b49cc --- /dev/null +++ b/dbgpt/rag/index/base.py @@ -0,0 +1,168 @@ +"""Index store base class.""" +import logging +import time +from abc import ABC, abstractmethod +from concurrent.futures import ThreadPoolExecutor +from typing import Any, Dict, List, Optional + +from dbgpt._private.pydantic import BaseModel, ConfigDict, Field, model_to_dict +from dbgpt.core import Chunk, Embeddings +from dbgpt.storage.vector_store.filters import MetadataFilters + +logger = logging.getLogger(__name__) + + +class IndexStoreConfig(BaseModel): + """Index store config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True, extra="allow") + + name: str = Field( + default="dbgpt_collection", + description="The name of index store, if not set, will use the default name.", + ) + embedding_fn: Optional[Embeddings] = Field( + default=None, + description="The embedding function of vector store, if not set, will use the " + "default embedding function.", + ) + max_chunks_once_load: int = Field( + default=10, + description="The max number of chunks to load at once. If your document is " + "large, you can set this value to a larger number to speed up the loading " + "process. Default is 10.", + ) + max_threads: int = Field( + default=1, + description="The max number of threads to use. Default is 1. If you set this " + "bigger than 1, please make sure your vector store is thread-safe.", + ) + + def to_dict(self, **kwargs) -> Dict[str, Any]: + """Convert to dict.""" + return model_to_dict(self, **kwargs) + + +class IndexStoreBase(ABC): + """Index store base class.""" + + @abstractmethod + def load_document(self, chunks: List[Chunk]) -> List[str]: + """Load document in index database. + + Args: + chunks(List[Chunk]): document chunks. + + Return: + List[str]: chunk ids. + """ + + @abstractmethod + def aload_document(self, chunks: List[Chunk]) -> List[str]: + """Load document in index database. + + Args: + chunks(List[Chunk]): document chunks. + + Return: + List[str]: chunk ids. + """ + + @abstractmethod + def similar_search_with_scores( + self, + text, + topk, + score_threshold: float, + filters: Optional[MetadataFilters] = None, + ) -> List[Chunk]: + """Similar search with scores in index database. + + Args: + text(str): The query text. + topk(int): The number of similar documents to return. + score_threshold(int): score_threshold: Optional, a floating point value + between 0 to 1 + filters(Optional[MetadataFilters]): metadata filters. + Return: + List[Chunk]: The similar documents. + """ + + @abstractmethod + def delete_by_ids(self, ids: str): + """Delete docs. + + Args: + ids(str): The vector ids to delete, separated by comma. + """ + + @abstractmethod + def delete_vector_name(self, index_name: str): + """Delete index by name. + + Args: + index_name(str): The name of index to delete. + """ + + def load_document_with_limit( + self, chunks: List[Chunk], max_chunks_once_load: int = 10, max_threads: int = 1 + ) -> List[str]: + """Load document in index database with specified limit. + + Args: + chunks(List[Chunk]): Document chunks. + max_chunks_once_load(int): Max number of chunks to load at once. + max_threads(int): Max number of threads to use. + + Return: + List[str]: Chunk ids. + """ + # Group the chunks into chunks of size max_chunks + chunk_groups = [ + chunks[i : i + max_chunks_once_load] + for i in range(0, len(chunks), max_chunks_once_load) + ] + logger.info( + f"Loading {len(chunks)} chunks in {len(chunk_groups)} groups with " + f"{max_threads} threads." + ) + ids = [] + loaded_cnt = 0 + start_time = time.time() + with ThreadPoolExecutor(max_workers=max_threads) as executor: + tasks = [] + for chunk_group in chunk_groups: + tasks.append(executor.submit(self.load_document, chunk_group)) + for future in tasks: + success_ids = future.result() + ids.extend(success_ids) + loaded_cnt += len(success_ids) + logger.info(f"Loaded {loaded_cnt} chunks, total {len(chunks)} chunks.") + logger.info( + f"Loaded {len(chunks)} chunks in {time.time() - start_time} seconds" + ) + return ids + + def similar_search( + self, text: str, topk: int, filters: Optional[MetadataFilters] = None + ) -> List[Chunk]: + """Similar search in index database. + + Args: + text(str): The query text. + topk(int): The number of similar documents to return. + filters(Optional[MetadataFilters]): metadata filters. + Return: + List[Chunk]: The similar documents. + """ + return self.similar_search_with_scores(text, topk, 1.0, filters) + + async def asimilar_search_with_scores( + self, + doc: str, + topk: int, + score_threshold: float, + filters: Optional[MetadataFilters] = None, + ) -> List[Chunk]: + """Aynsc similar_search_with_score in vector database.""" + return self.similar_search_with_scores(doc, topk, score_threshold, filters) diff --git a/dbgpt/rag/retriever/embedding.py b/dbgpt/rag/retriever/embedding.py index ae53d6f68..c54eb0d09 100644 --- a/dbgpt/rag/retriever/embedding.py +++ b/dbgpt/rag/retriever/embedding.py @@ -229,6 +229,6 @@ class EmbeddingRetriever(BaseRetriever): self, query, score_threshold, filters: Optional[MetadataFilters] = None ) -> List[Chunk]: """Similar search with score.""" - return self._vector_store_connector.similar_search_with_scores( + return await self._vector_store_connector.asimilar_search_with_scores( query, self._top_k, score_threshold, filters ) diff --git a/dbgpt/rag/transformer/__init__.py b/dbgpt/rag/transformer/__init__.py new file mode 100644 index 000000000..c87abce82 --- /dev/null +++ b/dbgpt/rag/transformer/__init__.py @@ -0,0 +1 @@ +"""Module for transformer.""" diff --git a/dbgpt/rag/transformer/base.py b/dbgpt/rag/transformer/base.py new file mode 100644 index 000000000..3c276e689 --- /dev/null +++ b/dbgpt/rag/transformer/base.py @@ -0,0 +1,26 @@ +"""Transformer base class.""" +import logging +from abc import ABC, abstractmethod +from typing import List, Optional + +logger = logging.getLogger(__name__) + + +class TransformerBase: + """Transformer base class.""" + + +class EmbedderBase(TransformerBase, ABC): + """Embedder base class.""" + + +class ExtractorBase(TransformerBase, ABC): + """Extractor base class.""" + + @abstractmethod + async def extract(self, text: str, limit: Optional[int] = None) -> List: + """Extract results from text.""" + + +class TranslatorBase(TransformerBase, ABC): + """Translator base class.""" diff --git a/dbgpt/rag/transformer/keyword_extractor.py b/dbgpt/rag/transformer/keyword_extractor.py new file mode 100644 index 000000000..dec5f14a0 --- /dev/null +++ b/dbgpt/rag/transformer/keyword_extractor.py @@ -0,0 +1,50 @@ +"""KeywordExtractor class.""" +import logging +from typing import List, Optional + +from dbgpt.core import LLMClient +from dbgpt.rag.transformer.llm_extractor import LLMExtractor + +KEYWORD_EXTRACT_PT = ( + "A question is provided below. Given the question, extract up to " + "keywords from the text. Focus on extracting the keywords that we can use " + "to best lookup answers to the question.\n" + "Generate as more as possible synonyms or alias of the keywords " + "considering possible cases of capitalization, pluralization, " + "common expressions, etc.\n" + "Avoid stopwords.\n" + "Provide the keywords and synonyms in comma-separated format." + "Formatted keywords and synonyms text should be separated by a semicolon.\n" + "---------------------\n" + "Example:\n" + "Text: Alice is Bob's mother.\n" + "Keywords:\nAlice,mother,Bob;mummy\n" + "Text: Philz is a coffee shop founded in Berkeley in 1982.\n" + "Keywords:\nPhilz,coffee shop,Berkeley,1982;coffee bar,coffee house\n" + "---------------------\n" + "Text: {text}\n" + "Keywords:\n" +) + +logger = logging.getLogger(__name__) + + +class KeywordExtractor(LLMExtractor): + """KeywordExtractor class.""" + + def __init__(self, llm_client: LLMClient, model_name: str): + """Initialize the KeywordExtractor.""" + super().__init__(llm_client, model_name, KEYWORD_EXTRACT_PT) + + def _parse_response(self, text: str, limit: Optional[int] = None) -> List[str]: + keywords = set() + + for part in text.split(";"): + for s in part.strip().split(","): + keyword = s.strip() + if keyword: + keywords.add(keyword) + if limit and len(keywords) >= limit: + return list(keywords) + + return list(keywords) diff --git a/dbgpt/rag/transformer/llm_extractor.py b/dbgpt/rag/transformer/llm_extractor.py new file mode 100644 index 000000000..8ec06804a --- /dev/null +++ b/dbgpt/rag/transformer/llm_extractor.py @@ -0,0 +1,50 @@ +"""TripletExtractor class.""" +import logging +from abc import ABC, abstractmethod +from typing import List, Optional + +from dbgpt.core import HumanPromptTemplate, LLMClient, ModelMessage, ModelRequest +from dbgpt.rag.transformer.base import ExtractorBase + +logger = logging.getLogger(__name__) + + +class LLMExtractor(ExtractorBase, ABC): + """LLMExtractor class.""" + + def __init__(self, llm_client: LLMClient, model_name: str, prompt_template: str): + """Initialize the LLMExtractor.""" + self._llm_client = llm_client + self._model_name = model_name + self._prompt_template = prompt_template + + async def extract(self, text: str, limit: Optional[int] = None) -> List: + """Extract by LLm.""" + template = HumanPromptTemplate.from_template(self._prompt_template) + messages = template.format_messages(text=text) + + # use default model if needed + if not self._model_name: + models = await self._llm_client.models() + if not models: + raise Exception("No models available") + self._model_name = models[0].model + logger.info(f"Using model {self._model_name} to extract") + + model_messages = ModelMessage.from_base_messages(messages) + request = ModelRequest(model=self._model_name, messages=model_messages) + response = await self._llm_client.generate(request=request) + + if not response.success: + code = str(response.error_code) + reason = response.text + logger.error(f"request llm failed ({code}) {reason}") + return [] + + if limit and limit < 1: + ValueError("optional argument limit >= 1") + return self._parse_response(response.text, limit) + + @abstractmethod + def _parse_response(self, text: str, limit: Optional[int] = None) -> List: + """Parse llm response.""" diff --git a/dbgpt/rag/transformer/text2cypher.py b/dbgpt/rag/transformer/text2cypher.py new file mode 100644 index 000000000..939fe2d94 --- /dev/null +++ b/dbgpt/rag/transformer/text2cypher.py @@ -0,0 +1,10 @@ +"""Text2Cypher class.""" +import logging + +from dbgpt.rag.transformer.base import TranslatorBase + +logger = logging.getLogger(__name__) + + +class Text2Cypher(TranslatorBase): + """Text2Cypher class.""" diff --git a/dbgpt/rag/transformer/text2gql.py b/dbgpt/rag/transformer/text2gql.py new file mode 100644 index 000000000..62b6d587f --- /dev/null +++ b/dbgpt/rag/transformer/text2gql.py @@ -0,0 +1,10 @@ +"""Text2GQL class.""" +import logging + +from dbgpt.rag.transformer.base import TranslatorBase + +logger = logging.getLogger(__name__) + + +class Text2GQL(TranslatorBase): + """Text2GQL class.""" diff --git a/dbgpt/rag/transformer/text2vector.py b/dbgpt/rag/transformer/text2vector.py new file mode 100644 index 000000000..d7257001c --- /dev/null +++ b/dbgpt/rag/transformer/text2vector.py @@ -0,0 +1,10 @@ +"""Text2Vector class.""" +import logging + +from dbgpt.rag.transformer.base import EmbedderBase + +logger = logging.getLogger(__name__) + + +class Text2Vector(EmbedderBase): + """Text2Vector class.""" diff --git a/dbgpt/rag/transformer/triplet_extractor.py b/dbgpt/rag/transformer/triplet_extractor.py new file mode 100644 index 000000000..7a591560f --- /dev/null +++ b/dbgpt/rag/transformer/triplet_extractor.py @@ -0,0 +1,71 @@ +"""TripletExtractor class.""" +import logging +import re +from typing import Any, List, Optional, Tuple + +from dbgpt.core import LLMClient +from dbgpt.rag.transformer.llm_extractor import LLMExtractor + +logger = logging.getLogger(__name__) + +TRIPLET_EXTRACT_PT = ( + "Some text is provided below. Given the text, " + "extract up to knowledge triplets as more as possible " + "in the form of (subject, predicate, object).\n" + "Avoid stopwords.\n" + "---------------------\n" + "Example:\n" + "Text: Alice is Bob's mother.\n" + "Triplets:\n(Alice, is mother of, Bob)\n" + "Text: Alice has 2 apples.\n" + "Triplets:\n(Alice, has 2, apple)\n" + "Text: Alice was given 1 apple by Bob.\n" + "Triplets:(Bob, gives 1 apple, Bob)\n" + "Text: Alice was pushed by Bob.\n" + "Triplets:(Bob, pushes, Alice)\n" + "Text: Bob's mother Alice has 2 apples.\n" + "Triplets:\n(Alice, is mother of, Bob)\n(Alice, has 2, apple)\n" + "Text: A Big monkey climbed up the tall fruit tree and picked 3 peaches.\n" + "Triplets:\n(monkey, climbed up, fruit tree)\n(monkey, picked 3, peach)\n" + "Text: Alice has 2 apples, she gives 1 to Bob.\n" + "Triplets:\n" + "(Alice, has 2, apple)\n(Alice, gives 1 apple, Bob)\n" + "Text: Philz is a coffee shop founded in Berkeley in 1982.\n" + "Triplets:\n" + "(Philz, is, coffee shop)\n(Philz, founded in, Berkeley)\n" + "(Philz, founded in, 1982)\n" + "---------------------\n" + "Text: {text}\n" + "Triplets:\n" +) + + +class TripletExtractor(LLMExtractor): + """TripletExtractor class.""" + + def __init__(self, llm_client: LLMClient, model_name: str): + """Initialize the TripletExtractor.""" + super().__init__(llm_client, model_name, TRIPLET_EXTRACT_PT) + + def _parse_response( + self, text: str, limit: Optional[int] = None + ) -> List[Tuple[Any, ...]]: + triplets = [] + + for line in text.split("\n"): + for match in re.findall(r"\((.*?)\)", line): + splits = match.split(",") + parts = [split.strip() for split in splits if split.strip()] + if len(parts) == 3: + parts = [ + p.strip( + "`~!@#$%^&*()-=+[]\\{}|;':\",./<>?" + "·！￥&*（）—【】、「」；‘’：“”，。、《》？" + ) + for p in parts + ] + triplets.append(tuple(parts)) + if limit and len(triplets) >= limit: + return triplets + + return triplets diff --git a/dbgpt/serve/rag/service/service.py b/dbgpt/serve/rag/service/service.py index ff2a17133..02a39a683 100644 --- a/dbgpt/serve/rag/service/service.py +++ b/dbgpt/serve/rag/service/service.py @@ -1,3 +1,4 @@ +import asyncio import json import logging import os @@ -21,8 +22,10 @@ from dbgpt.configs.model_config import ( EMBEDDING_MODEL_CONFIG, KNOWLEDGE_UPLOAD_ROOT_PATH, ) -from dbgpt.core import Chunk +from dbgpt.core import Chunk, LLMClient from dbgpt.core.awel.dag.dag_manager import DAGManager +from dbgpt.model import DefaultLLMClient +from dbgpt.model.cluster import WorkerManagerFactory from dbgpt.rag.assembler import EmbeddingAssembler from dbgpt.rag.chunk_manager import ChunkParameters from dbgpt.rag.embedding import EmbeddingFactory @@ -71,7 +74,7 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes document_dao: Optional[KnowledgeDocumentDao] = None, chunk_dao: Optional[DocumentChunkDao] = None, ): - self._system_app = None + self._system_app = system_app self._dao: KnowledgeSpaceDao = dao self._document_dao: KnowledgeDocumentDao = document_dao self._chunk_dao: DocumentChunkDao = chunk_dao @@ -112,6 +115,13 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes """Returns the internal ServeConfig.""" return self._serve_config + @property + def llm_client(self) -> LLMClient: + worker_manager = self._system_app.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + return DefaultLLMClient(worker_manager, True) + def create_space(self, request: SpaceServeRequest) -> SpaceServeResponse: """Create a new Space entity @@ -198,7 +208,7 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes raise Exception(f"create document failed, {request.doc_name}") return doc_id - def sync_document(self, requests: List[KnowledgeSyncRequest]) -> List: + async def sync_document(self, requests: List[KnowledgeSyncRequest]) -> List: """Create a new document entity Args: @@ -236,7 +246,7 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes if space_context is None else int(space_context["embedding"]["chunk_overlap"]) ) - self._sync_knowledge_document(space_id, doc, chunk_parameters) + await self._sync_knowledge_document(space_id, doc, chunk_parameters) doc_ids.append(doc.id) return doc_ids @@ -284,10 +294,11 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes space = self.get(query_request) if space is None: raise HTTPException(status_code=400, detail=f"Space {space_id} not found") - config = VectorStoreConfig(name=space.name) + config = VectorStoreConfig( + name=space.name, llm_client=self.llm_client, model_name=None + ) vector_store_connector = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, - vector_store_config=config, + vector_store_type=space.vector_type, vector_store_config=config ) # delete vectors vector_store_connector.delete_vector_name(space.name) @@ -316,12 +327,22 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes docuemnt = self._document_dao.get_one(query_request) if docuemnt is None: raise Exception(f"there are no or more than one document {document_id}") + + # get space by name + spaces = self._dao.get_knowledge_space( + KnowledgeSpaceEntity(name=docuemnt.space) + ) + if len(spaces) != 1: + raise Exception(f"invalid space name: {docuemnt.space}") + space = spaces[0] + vector_ids = docuemnt.vector_ids if vector_ids is not None: - config = VectorStoreConfig(name=docuemnt.space) + config = VectorStoreConfig( + name=space.name, llm_client=self.llm_client, model_name=None + ) vector_store_connector = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, - vector_store_config=config, + vector_store_type=space.vector_type, vector_store_config=config ) # delete vector by ids vector_store_connector.delete_by_ids(vector_ids) @@ -375,7 +396,7 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes """ return self._document_dao.get_list_page(request, page, page_size) - def _batch_document_sync( + async def _batch_document_sync( self, space_id, sync_requests: List[KnowledgeSyncRequest] ) -> List[int]: """batch sync knowledge document chunk into vector store @@ -413,11 +434,11 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes if space_context is None else int(space_context["embedding"]["chunk_overlap"]) ) - self._sync_knowledge_document(space_id, doc, chunk_parameters) + await self._sync_knowledge_document(space_id, doc, chunk_parameters) doc_ids.append(doc.id) return doc_ids - def _sync_knowledge_document( + async def _sync_knowledge_document( self, space_id, doc_vo: DocumentVO, @@ -439,10 +460,11 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes name=space.name, embedding_fn=embedding_fn, max_chunks_once_load=CFG.KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD, + llm_client=self.llm_client, + model_name=None, ) vector_store_connector = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, - vector_store_config=config, + vector_store_type=space.vector_type, vector_store_config=config ) knowledge = KnowledgeFactory.create( datasource=doc.content, @@ -458,15 +480,16 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes doc.chunk_size = len(chunk_docs) doc.gmt_modified = datetime.now() self._document_dao.update_knowledge_document(doc) - executor = CFG.SYSTEM_APP.get_component( - ComponentType.EXECUTOR_DEFAULT, ExecutorFactory - ).create() - executor.submit(self.async_doc_embedding, assembler, chunk_docs, doc) + # executor = CFG.SYSTEM_APP.get_component( + # ComponentType.EXECUTOR_DEFAULT, ExecutorFactory + # ).create() + # executor.submit(self.async_doc_embedding, assembler, chunk_docs, doc) + asyncio.create_task(self.async_doc_embedding(assembler, chunk_docs, doc)) logger.info(f"begin save document chunks, doc:{doc.doc_name}") return chunk_docs @trace("async_doc_embedding") - def async_doc_embedding(self, assembler, chunk_docs, doc): + async def async_doc_embedding(self, assembler, chunk_docs, doc): """async document embedding into vector db Args: - client: EmbeddingEngine Client @@ -475,14 +498,19 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes """ logger.info( - f"async doc embedding sync, doc:{doc.doc_name}, chunks length is {len(chunk_docs)}, begin embedding to vector store-{CFG.VECTOR_STORE_TYPE}" + f"async doc embedding sync, doc:{doc.doc_name}, chunks length is {len(chunk_docs)}" ) try: with root_tracer.start_span( "app.knowledge.assembler.persist", metadata={"doc": doc.doc_name, "chunks": len(chunk_docs)}, ): - vector_ids = assembler.persist() + # vector_ids = assembler.persist() + space = self.get({"name": doc.space}) + if space and space.vector_type == "KnowledgeGraph": + vector_ids = await assembler.apersist() + else: + vector_ids = assembler.persist() doc.status = SyncStatus.FINISHED.name doc.result = "document embedding success" if vector_ids is not None: diff --git a/dbgpt/storage/graph_store/__init__.py b/dbgpt/storage/graph_store/__init__.py new file mode 100644 index 000000000..f9eba922d --- /dev/null +++ b/dbgpt/storage/graph_store/__init__.py @@ -0,0 +1,37 @@ +"""Graph Store Module.""" +from typing import Tuple, Type + + +def _import_memgraph() -> Tuple[Type, Type]: + from dbgpt.storage.graph_store.memgraph_store import ( + MemoryGraphStore, + MemoryGraphStoreConfig, + ) + + return MemoryGraphStore, MemoryGraphStoreConfig + + +def _import_tugraph() -> Tuple[Type, Type]: + from dbgpt.storage.graph_store.tugraph_store import TuGraphStore, TuGraphStoreConfig + + return TuGraphStore, TuGraphStoreConfig + + +def _import_neo4j() -> Tuple[Type, Type]: + from dbgpt.storage.graph_store.neo4j_store import Neo4jStore, Neo4jStoreConfig + + return Neo4jStore, Neo4jStoreConfig + + +def __getattr__(name: str) -> Tuple[Type, Type]: + if name == "Memory": + return _import_memgraph() + elif name == "TuGraph": + return _import_tugraph() + elif name == "Neo4j": + return _import_neo4j() + else: + raise AttributeError(f"Could not find: {name}") + + +__all__ = ["Memory", "TuGraph", "Neo4j"] diff --git a/dbgpt/storage/graph_store/base.py b/dbgpt/storage/graph_store/base.py new file mode 100644 index 000000000..d68ca9154 --- /dev/null +++ b/dbgpt/storage/graph_store/base.py @@ -0,0 +1,68 @@ +"""Graph store base class.""" +import logging +from abc import ABC, abstractmethod +from typing import List, Optional, Tuple + +from dbgpt._private.pydantic import BaseModel, ConfigDict, Field +from dbgpt.core import Embeddings +from dbgpt.storage.graph_store.graph import Direction, Graph + +logger = logging.getLogger(__name__) + + +class GraphStoreConfig(BaseModel): + """Graph store config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True, extra="allow") + + name: str = Field( + default="dbgpt_collection", + description="The name of graph store, inherit from index store.", + ) + embedding_fn: Optional[Embeddings] = Field( + default=None, + description="The embedding function of graph store, optional.", + ) + + +class GraphStoreBase(ABC): + """Graph store base class.""" + + @abstractmethod + def insert_triplet(self, sub: str, rel: str, obj: str): + """Add triplet.""" + + @abstractmethod + def get_triplets(self, sub: str) -> List[Tuple[str, str]]: + """Get triplets.""" + + @abstractmethod + def delete_triplet(self, sub: str, rel: str, obj: str): + """Delete triplet.""" + + @abstractmethod + def drop(self): + """Drop graph.""" + + @abstractmethod + def get_schema(self, refresh: bool = False) -> str: + """Get schema.""" + + @abstractmethod + def get_full_graph(self, limit: Optional[int] = None) -> Graph: + """Get full graph.""" + + @abstractmethod + def explore( + self, + subs: List[str], + direct: Direction = Direction.BOTH, + depth: Optional[int] = None, + fan: Optional[int] = None, + limit: Optional[int] = None, + ) -> Graph: + """Explore on graph.""" + + @abstractmethod + def query(self, query: str, **args) -> Graph: + """Execute a query.""" diff --git a/dbgpt/storage/graph_store/factory.py b/dbgpt/storage/graph_store/factory.py new file mode 100644 index 000000000..1e3d70804 --- /dev/null +++ b/dbgpt/storage/graph_store/factory.py @@ -0,0 +1,42 @@ +"""Connector for vector store.""" +import logging +from typing import Tuple, Type + +from dbgpt.storage import graph_store +from dbgpt.storage.graph_store.base import GraphStoreBase, GraphStoreConfig + +logger = logging.getLogger(__name__) + + +class GraphStoreFactory: + """Factory for graph store.""" + + @staticmethod + def create(graph_store_type: str, graph_store_configure=None) -> GraphStoreBase: + """Create a GraphStore instance. + + Args: + - graph_store_type: graph store type Memory, TuGraph, Neo4j + - graph_store_config: graph store config + """ + store_cls, cfg_cls = GraphStoreFactory.__find_type(graph_store_type) + + try: + config = cfg_cls() + if graph_store_configure: + graph_store_configure(config) + return store_cls(config) + except Exception as e: + logger.error("create graph store failed: %s", e) + raise e + + @staticmethod + def __find_type(graph_store_type: str) -> Tuple[Type, Type]: + for t in graph_store.__all__: + if t.lower() == graph_store_type.lower(): + store_cls, cfg_cls = getattr(graph_store, t) + if issubclass(store_cls, GraphStoreBase) and issubclass( + cfg_cls, GraphStoreConfig + ): + return store_cls, cfg_cls + raise Exception(f"Graph store {graph_store_type} not supported") diff --git a/dbgpt/storage/graph_store/graph.py b/dbgpt/storage/graph_store/graph.py new file mode 100644 index 000000000..dedb3576b --- /dev/null +++ b/dbgpt/storage/graph_store/graph.py @@ -0,0 +1,477 @@ +"""Graph store base class.""" +import itertools +import json +import logging +import re +from abc import ABC, abstractmethod +from collections import defaultdict +from enum import Enum +from typing import Any, Dict, Iterator, List, Optional, Set, Tuple + +import networkx as nx + +logger = logging.getLogger(__name__) + + +class Direction(Enum): + """Direction class.""" + + OUT = 0 + IN = 1 + BOTH = 2 + + +class Elem(ABC): + """Elem class.""" + + def __init__(self): + """Initialize Elem.""" + self._props = {} + + @property + def props(self) -> Dict[str, Any]: + """Get all the properties of Elem.""" + return self._props + + def set_prop(self, key: str, value: Any): + """Set a property of ELem.""" + self._props[key] = value + + def get_prop(self, key: str): + """Get one of the properties of Elem.""" + return self._props.get(key) + + def del_prop(self, key: str): + """Delete a property of ELem.""" + self._props.pop(key, None) + + def has_props(self, **props): + """Check if the element has the specified properties with the given values.""" + return all(self._props.get(k) == v for k, v in props.items()) + + @abstractmethod + def format(self, label_key: Optional[str] = None): + """Format properties into a string.""" + formatted_props = [ + f"{k}:{json.dumps(v)}" for k, v in self._props.items() if k != label_key + ] + return f"{{{';'.join(formatted_props)}}}" + + +class Vertex(Elem): + """Vertex class.""" + + def __init__(self, vid: str, **props): + """Initialize Vertex.""" + super().__init__() + self._vid = vid + for k, v in props.items(): + self.set_prop(k, v) + + @property + def vid(self) -> str: + """Return the vertex ID.""" + return self._vid + + def format(self, label_key: Optional[str] = None): + """Format vertex properties into a string.""" + label = self.get_prop(label_key) if label_key else self._vid + props_str = super().format(label_key) + if props_str == "{}": + return f"({label})" + else: + return f"({label}:{props_str})" + + def __str__(self): + """Return the vertex ID as its string representation.""" + return f"({self._vid})" + + +class Edge(Elem): + """Edge class.""" + + def __init__(self, sid: str, tid: str, **props): + """Initialize Edge.""" + super().__init__() + self._sid = sid + self._tid = tid + for k, v in props.items(): + self.set_prop(k, v) + + @property + def sid(self) -> str: + """Return the source vertex ID of the edge.""" + return self._sid + + @property + def tid(self) -> str: + """Return the target vertex ID of the edge.""" + return self._tid + + def nid(self, vid): + """Return neighbor id.""" + if vid == self._sid: + return self._tid + elif vid == self._tid: + return self._sid + else: + raise ValueError(f"Get nid of {vid} on {self} failed") + + def format(self, label_key: Optional[str] = None): + """Format the edge properties into a string.""" + label = self.get_prop(label_key) if label_key else "" + props_str = super().format(label_key) + if props_str == "{}": + return f"-[{label}]->" if label else "->" + else: + return f"-[{label}:{props_str}]->" if label else f"-[{props_str}]->" + + def triplet(self, label_key: str) -> Tuple[str, str, str]: + """Return a triplet.""" + assert label_key, "label key is needed" + return self._sid, str(self.get_prop(label_key)), self._tid + + def __str__(self): + """Return the edge '(sid)->(tid)'.""" + return f"({self._sid})->({self._tid})" + + +class Graph(ABC): + """Graph class.""" + + @abstractmethod + def upsert_vertex(self, vertex: Vertex): + """Add a vertex.""" + + @abstractmethod + def append_edge(self, edge: Edge): + """Add an edge.""" + + @abstractmethod + def has_vertex(self, vid: str) -> bool: + """Check vertex exists.""" + + @abstractmethod + def get_vertex(self, vid: str) -> Vertex: + """Get a vertex.""" + + @abstractmethod + def get_neighbor_edges( + self, + vid: str, + direction: Direction = Direction.OUT, + limit: Optional[int] = None, + ) -> Iterator[Edge]: + """Get neighbor edges.""" + + @abstractmethod + def vertices(self) -> Iterator[Vertex]: + """Get vertex iterator.""" + + @abstractmethod + def edges(self) -> Iterator[Edge]: + """Get edge iterator.""" + + @abstractmethod + def del_vertices(self, *vids: str): + """Delete vertices and their neighbor edges.""" + + @abstractmethod + def del_edges(self, sid: str, tid: str, **props): + """Delete edges(sid -> tid) matches props.""" + + @abstractmethod + def del_neighbor_edges(self, vid: str, direction: Direction = Direction.OUT): + """Delete neighbor edges.""" + + @abstractmethod + def search( + self, + vids: List[str], + direct: Direction = Direction.OUT, + depth: Optional[int] = None, + fan: Optional[int] = None, + limit: Optional[int] = None, + ) -> "Graph": + """Search on graph.""" + + @abstractmethod + def schema(self) -> Dict[str, Any]: + """Get schema.""" + + @abstractmethod + def format(self) -> str: + """Format graph data to string.""" + + +class MemoryGraph(Graph): + """Graph class.""" + + def __init__(self, vertex_label: Optional[str] = None, edge_label: str = "label"): + """Initialize MemoryGraph with vertex label and edge label.""" + assert edge_label, "Edge label is needed" + + # metadata + self._vertex_label = vertex_label + self._edge_label = edge_label + self._vertex_prop_keys = {vertex_label} if vertex_label else set() + self._edge_prop_keys = {edge_label} + self._edge_count = 0 + + # init vertices, out edges, in edges index + self._vs: Any = defaultdict() + self._oes: Any = defaultdict(lambda: defaultdict(set)) + self._ies: Any = defaultdict(lambda: defaultdict(set)) + + @property + def vertex_label(self): + """Return the label for vertices.""" + return self._vertex_label + + @property + def edge_label(self): + """Return the label for edges.""" + return self._edge_label + + @property + def vertex_prop_keys(self): + """Return a set of property keys for vertices.""" + return self._vertex_prop_keys + + @property + def edge_prop_keys(self): + """Return a set of property keys for edges.""" + return self._edge_prop_keys + + @property + def vertex_count(self): + """Return the number of vertices in the graph.""" + return len(self._vs) + + @property + def edge_count(self): + """Return the count of edges in the graph.""" + return self._edge_count + + def upsert_vertex(self, vertex: Vertex): + """Insert or update a vertex based on its ID.""" + if vertex.vid in self._vs: + self._vs[vertex.vid].props.update(vertex.props) + else: + self._vs[vertex.vid] = vertex + + # update metadata + self._vertex_prop_keys.update(vertex.props.keys()) + + def append_edge(self, edge: Edge): + """Append an edge if it doesn't exist; requires edge label.""" + if self.edge_label not in edge.props.keys(): + raise ValueError(f"Edge prop '{self.edge_label}' is needed") + + sid = edge.sid + tid = edge.tid + + if edge in self._oes[sid][tid]: + return False + + # init vertex index + self._vs.setdefault(sid, Vertex(sid)) + self._vs.setdefault(tid, Vertex(tid)) + + # update edge index + self._oes[sid][tid].add(edge) + self._ies[tid][sid].add(edge) + + # update metadata + self._edge_prop_keys.update(edge.props.keys()) + self._edge_count += 1 + return True + + def has_vertex(self, vid: str) -> bool: + """Retrieve a vertex by ID.""" + return vid in self._vs + + def get_vertex(self, vid: str) -> Vertex: + """Retrieve a vertex by ID.""" + return self._vs[vid] + + def get_neighbor_edges( + self, + vid: str, + direction: Direction = Direction.OUT, + limit: Optional[int] = None, + ) -> Iterator[Edge]: + """Get edges connected to a vertex by direction.""" + if direction == Direction.OUT: + es = (e for es in self._oes[vid].values() for e in es) + + elif direction == Direction.IN: + es = iter(e for es in self._ies[vid].values() for e in es) + + elif direction == Direction.BOTH: + oes = (e for es in self._oes[vid].values() for e in es) + ies = (e for es in self._ies[vid].values() for e in es) + + # merge + tuples = itertools.zip_longest(oes, ies) + es = (e for t in tuples for e in t if e is not None) + + # distinct + seen = set() + + # es = (e for e in es if e not in seen and not seen.add(e)) + def unique_elements(elements): + for element in elements: + if element not in seen: + seen.add(element) + yield element + + es = unique_elements(es) + else: + raise ValueError(f"Invalid direction: {direction}") + + return itertools.islice(es, limit) if limit else es + + def vertices(self) -> Iterator[Vertex]: + """Return vertices.""" + return iter(self._vs.values()) + + def edges(self) -> Iterator[Edge]: + """Return edges.""" + return iter(e for nbs in self._oes.values() for es in nbs.values() for e in es) + + def del_vertices(self, *vids: str): + """Delete specified vertices.""" + for vid in vids: + self.del_neighbor_edges(vid, Direction.BOTH) + self._vs.pop(vid, None) + + def del_edges(self, sid: str, tid: str, **props): + """Delete edges.""" + old_edge_cnt = len(self._oes[sid][tid]) + + if not props: + self._edge_count -= old_edge_cnt + self._oes[sid].pop(tid, None) + self._ies[tid].pop(sid, None) + return + + def remove_matches(es): + return set(filter(lambda e: not e.has_props(**props), es)) + + self._oes[sid][tid] = remove_matches(self._oes[sid][tid]) + self._ies[tid][sid] = remove_matches(self._ies[tid][sid]) + + self._edge_count -= old_edge_cnt - len(self._oes[sid][tid]) + + def del_neighbor_edges(self, vid: str, direction: Direction = Direction.OUT): + """Delete all neighbor edges.""" + + def del_index(idx, i_idx): + for nid in idx[vid].keys(): + self._edge_count -= len(i_idx[nid][vid]) + i_idx[nid].pop(vid, None) + idx.pop(vid, None) + + if direction in [Direction.OUT, Direction.BOTH]: + del_index(self._oes, self._ies) + + if direction in [Direction.IN, Direction.BOTH]: + del_index(self._ies, self._oes) + + def search( + self, + vids: List[str], + direct: Direction = Direction.OUT, + depth: Optional[int] = None, + fan: Optional[int] = None, + limit: Optional[int] = None, + ) -> "MemoryGraph": + """Search the graph from a vertex with specified parameters.""" + subgraph = MemoryGraph() + + for vid in vids: + self.__search(vid, direct, depth, fan, limit, 0, set(), subgraph) + + return subgraph + + def __search( + self, + vid: str, + direct: Direction, + depth: Optional[int], + fan: Optional[int], + limit: Optional[int], + _depth: int, + _visited: Set, + _subgraph: "MemoryGraph", + ): + if vid in _visited or depth and _depth >= depth: + return + + # visit vertex + if not self.has_vertex(vid): + return + _subgraph.upsert_vertex(self.get_vertex(vid)) + _visited.add(vid) + + # visit edges + nids = set() + for edge in self.get_neighbor_edges(vid, direct, fan): + if limit and _subgraph.edge_count >= limit: + return + + # append edge success then visit new vertex + if _subgraph.append_edge(edge): + nid = edge.nid(vid) + if nid not in _visited: + nids.add(nid) + + # next hop + for nid in nids: + self.__search( + nid, direct, depth, fan, limit, _depth + 1, _visited, _subgraph + ) + + def schema(self) -> Dict[str, Any]: + """Return schema.""" + return { + "schema": [ + { + "type": "VERTEX", + "label": f"{self._vertex_label}", + "properties": [{"name": k} for k in self._vertex_prop_keys], + }, + { + "type": "EDGE", + "label": f"{self._edge_label}", + "properties": [{"name": k} for k in self._edge_prop_keys], + }, + ] + } + + def format(self) -> str: + """Format graph to string.""" + vs_str = "\n".join(v.format(self.vertex_label) for v in self.vertices()) + es_str = "\n".join( + f"{self.get_vertex(e.sid).format(self.vertex_label)}" + f"{e.format(self.edge_label)}" + f"{self.get_vertex(e.tid).format(self.vertex_label)}" + for e in self.edges() + ) + return f"Vertices:\n{vs_str}\n\nEdges:\n{es_str}" + + def graphviz(self, name="g"): + """View graphviz graph: https://dreampuf.github.io/GraphvizOnline.""" + g = nx.MultiDiGraph() + for vertex in self.vertices(): + g.add_node(vertex.vid) + + for edge in self.edges(): + triplet = edge.triplet(self.edge_label) + g.add_edge(triplet[0], triplet[2], label=triplet[1]) + + digraph = nx.nx_agraph.to_agraph(g).to_string() + digraph = digraph.replace('digraph ""', f"digraph {name}") + digraph = re.sub(r"key=\d+,?\s*", "", digraph) + return digraph diff --git a/dbgpt/storage/graph_store/memgraph_store.py b/dbgpt/storage/graph_store/memgraph_store.py new file mode 100644 index 000000000..610b59535 --- /dev/null +++ b/dbgpt/storage/graph_store/memgraph_store.py @@ -0,0 +1,81 @@ +"""Graph store base class.""" +import json +import logging +from typing import List, Optional, Tuple + +from dbgpt._private.pydantic import ConfigDict, Field +from dbgpt.storage.graph_store.base import GraphStoreBase, GraphStoreConfig +from dbgpt.storage.graph_store.graph import Direction, Edge, Graph, MemoryGraph + +logger = logging.getLogger(__name__) + + +class MemoryGraphStoreConfig(GraphStoreConfig): + """Memory graph store config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + edge_name_key: str = Field( + default="label", + description="The label of edge name, `label` by default.", + ) + + +class MemoryGraphStore(GraphStoreBase): + """Memory graph store.""" + + def __init__(self, graph_store_config: MemoryGraphStoreConfig): + """Initialize MemoryGraphStore with a memory graph.""" + self._edge_name_key = graph_store_config.edge_name_key + self._graph = MemoryGraph(edge_label=self._edge_name_key) + + def insert_triplet(self, sub: str, rel: str, obj: str): + """Insert a triplet into the graph.""" + self._graph.append_edge(Edge(sub, obj, **{self._edge_name_key: rel})) + + def get_triplets(self, sub: str) -> List[Tuple[str, str]]: + """Retrieve triplets originating from a subject.""" + subgraph = self.explore([sub], direct=Direction.OUT, depth=1) + return [(e.get_prop(self._edge_name_key), e.tid) for e in subgraph.edges()] + + def delete_triplet(self, sub: str, rel: str, obj: str): + """Delete a specific triplet from the graph.""" + self._graph.del_edges(sub, obj, **{self._edge_name_key: rel}) + + def drop(self): + """Drop graph.""" + self._graph = None + + def get_schema(self, refresh: bool = False) -> str: + """Return the graph schema as a JSON string.""" + return json.dumps(self._graph.schema()) + + def get_full_graph(self, limit: Optional[int] = None) -> MemoryGraph: + """Return self.""" + if not limit: + return self._graph + + subgraph = MemoryGraph() + for count, edge in enumerate(self._graph.edges()): + if count >= limit: + break + subgraph.upsert_vertex(self._graph.get_vertex(edge.sid)) + subgraph.upsert_vertex(self._graph.get_vertex(edge.tid)) + subgraph.append_edge(edge) + count += 1 + return subgraph + + def explore( + self, + subs: List[str], + direct: Direction = Direction.BOTH, + depth: Optional[int] = None, + fan: Optional[int] = None, + limit: Optional[int] = None, + ) -> MemoryGraph: + """Explore the graph from given subjects up to a depth.""" + return self._graph.search(subs, direct, depth, fan, limit) + + def query(self, query: str, **args) -> Graph: + """Execute a query on graph.""" + raise NotImplementedError("Query memory graph not allowed") diff --git a/dbgpt/storage/graph_store/neo4j_store.py b/dbgpt/storage/graph_store/neo4j_store.py new file mode 100644 index 000000000..151a98865 --- /dev/null +++ b/dbgpt/storage/graph_store/neo4j_store.py @@ -0,0 +1,64 @@ +"""Neo4j vector store.""" +import logging +from typing import List, Optional, Tuple + +from dbgpt._private.pydantic import ConfigDict +from dbgpt.storage.graph_store.base import GraphStoreBase, GraphStoreConfig +from dbgpt.storage.graph_store.graph import Direction, Graph, MemoryGraph + +logger = logging.getLogger(__name__) + + +class Neo4jStoreConfig(GraphStoreConfig): + """Neo4j store config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + +class Neo4jStore(GraphStoreBase): + """Neo4j graph store.""" + + # todo: add neo4j implementation + + def __init__(self, graph_store_config: Neo4jStoreConfig): + """Initialize the Neo4jStore with connection details.""" + pass + + def insert_triplet(self, sub: str, rel: str, obj: str): + """Insert triplets.""" + pass + + def get_triplets(self, sub: str) -> List[Tuple[str, str]]: + """Get triplets.""" + return [] + + def delete_triplet(self, sub: str, rel: str, obj: str): + """Delete triplets.""" + pass + + def drop(self): + """Drop graph.""" + pass + + def get_schema(self, refresh: bool = False) -> str: + """Get schema.""" + return "" + + def get_full_graph(self, limit: Optional[int] = None) -> Graph: + """Get full graph.""" + return MemoryGraph() + + def explore( + self, + subs: List[str], + direct: Direction = Direction.BOTH, + depth: Optional[int] = None, + fan: Optional[int] = None, + limit: Optional[int] = None, + ) -> Graph: + """Explore the graph from given subjects up to a depth.""" + return MemoryGraph() + + def query(self, query: str, **args) -> Graph: + """Execute a query on graph.""" + return MemoryGraph() diff --git a/dbgpt/storage/graph_store/tugraph_store.py b/dbgpt/storage/graph_store/tugraph_store.py new file mode 100644 index 000000000..5ffa95925 --- /dev/null +++ b/dbgpt/storage/graph_store/tugraph_store.py @@ -0,0 +1,239 @@ +"""TuGraph vector store.""" +import logging +import os +from typing import List, Optional, Tuple + +from dbgpt._private.pydantic import ConfigDict, Field +from dbgpt.datasource.conn_tugraph import TuGraphConnector +from dbgpt.storage.graph_store.base import GraphStoreBase, GraphStoreConfig +from dbgpt.storage.graph_store.graph import Direction, Edge, MemoryGraph, Vertex + +logger = logging.getLogger(__name__) + + +class TuGraphStoreConfig(GraphStoreConfig): + """TuGraph store config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + host: str = Field( + default="127.0.0.1", + description="TuGraph host", + ) + port: int = Field( + default=7687, + description="TuGraph port", + ) + username: str = Field( + default="admin", + description="login username", + ) + password: str = Field( + default="123456", + description="login password", + ) + vertex_type: str = Field( + default="entity", + description="The type of graph vertex, `entity` by default.", + ) + edge_type: str = Field( + default="relation", + description="The type of graph edge, `relation` by default.", + ) + edge_name_key: str = Field( + default="label", + description="The label of edge name, `label` by default.", + ) + + +class TuGraphStore(GraphStoreBase): + """TuGraph graph store.""" + + def __init__(self, config: TuGraphStoreConfig) -> None: + """Initialize the TuGraphStore with connection details.""" + self._host = os.getenv("TUGRAPH_HOST", "127.0.0.1") or config.host + self._port = int(os.getenv("TUGRAPH_PORT", 7687)) or config.port + self._username = os.getenv("TUGRAPH_USERNAME", "admin") or config.username + self._password = os.getenv("TUGRAPH_PASSWORD", "73@TuGraph") or config.password + self._node_label = ( + os.getenv("TUGRAPH_VERTEX_TYPE", "entity") or config.vertex_type + ) + self._edge_label = ( + os.getenv("TUGRAPH_EDGE_TYPE", "relation") or config.edge_type + ) + self.edge_name_key = ( + os.getenv("TUGRAPH_EDGE_NAME_KEY", "label") or config.edge_name_key + ) + self._graph_name = config.name + self.conn = TuGraphConnector.from_uri_db( + host=self._host, + port=self._port, + user=self._username, + pwd=self._password, + db_name=config.name, + ) + self.conn.create_graph(graph_name=config.name) + + self._create_schema() + + def _check_label(self, elem_type: str): + result = self.conn.get_table_names() + if elem_type == "vertex": + return self._node_label in result["vertex_tables"] + if elem_type == "edge": + return self._edge_label in result["edge_tables"] + + def _create_schema(self): + if not self._check_label("vertex"): + create_vertex_gql = ( + f"CALL db.createLabel(" + f"'vertex', '{self._node_label}', " + f"'id', ['id',string,false])" + ) + self.conn.run(create_vertex_gql) + if not self._check_label("edge"): + create_edge_gql = f"""CALL db.createLabel( + 'edge', '{self._edge_label}', '[["{self._node_label}", + "{self._node_label}"]]', ["id",STRING,false])""" + self.conn.run(create_edge_gql) + + def get_triplets(self, subj: str) -> List[Tuple[str, str]]: + """Get triplets.""" + query = ( + f"MATCH (n1:{self._node_label})-[r]->(n2:{self._node_label}) " + f'WHERE n1.id = "{subj}" RETURN r.id as rel, n2.id as obj;' + ) + data = self.conn.run(query) + return [(record["rel"], record["obj"]) for record in data] + + def insert_triplet(self, subj: str, rel: str, obj: str) -> None: + """Add triplet.""" + + def escape_quotes(value: str) -> str: + """Escape single and double quotes in a string for queries.""" + return value.replace("'", "\\'").replace('"', '\\"') + + subj_escaped = escape_quotes(subj) + rel_escaped = escape_quotes(rel) + obj_escaped = escape_quotes(obj) + + subj_query = f"MERGE (n1:{self._node_label} {{id:'{subj_escaped}'}})" + obj_query = f"MERGE (n1:{self._node_label} {{id:'{obj_escaped}'}})" + rel_query = ( + f"MERGE (n1:{self._node_label} {{id:'{subj_escaped}'}})" + f"-[r:{self._edge_label} {{id:'{rel_escaped}'}}]->" + f"(n2:{self._node_label} {{id:'{obj_escaped}'}})" + ) + self.conn.run(query=subj_query) + self.conn.run(query=obj_query) + self.conn.run(query=rel_query) + + def drop(self): + """Delete Graph.""" + self.conn.delete_graph(self._graph_name) + + def delete_triplet(self, sub: str, rel: str, obj: str) -> None: + """Delete triplet.""" + del_query = ( + f"MATCH (n1:{self._node_label} {{id:'{sub}'}})" + f"-[r:{self._edge_label} {{id:'{rel}'}}]->" + f"(n2:{self._node_label} {{id:'{obj}'}}) DELETE n1,n2,r" + ) + self.conn.run(query=del_query) + + def get_schema(self, refresh: bool = False) -> str: + """Get the schema of the graph store.""" + query = "CALL dbms.graph.getGraphSchema()" + data = self.conn.run(query=query) + schema = data[0]["schema"] + return schema + + def get_full_graph(self, limit: Optional[int] = None) -> MemoryGraph: + """Get full graph.""" + if not limit: + raise Exception("limit must be set") + return self.query(f"MATCH (n)-[r]-(m) RETURN n,m,r LIMIT {limit}") + + def explore( + self, + subs: List[str], + direct: Direction = Direction.BOTH, + depth: Optional[int] = None, + fan: Optional[int] = None, + limit: Optional[int] = None, + ) -> MemoryGraph: + """Explore the graph from given subjects up to a depth.""" + if fan is not None: + raise ValueError("Fan functionality is not supported at this time.") + else: + depth_string = f"1..{depth}" + if depth is None: + depth_string = ".." + + limit_string = f"LIMIT {limit}" + if limit is None: + limit_string = "" + + query = ( + f"MATCH p=(n:{self._node_label})" + f"-[r:{self._edge_label}*{depth_string}]-(m:{self._node_label}) " + f"WHERE n.id IN {subs} RETURN p {limit_string}" + ) + return self.query(query) + + def query(self, query: str, **args) -> MemoryGraph: + """Execute a query on graph.""" + + def _format_paths(paths): + formatted_paths = [] + for path in paths: + formatted_path = [] + nodes = list(path["p"].nodes) + rels = list(path["p"].relationships) + for i in range(len(nodes)): + formatted_path.append(nodes[i]._properties["id"]) + if i < len(rels): + formatted_path.append(rels[i]._properties["id"]) + formatted_paths.append(formatted_path) + return formatted_paths + + def _format_query_data(data): + node_ids_set = set() + rels_set = set() + from neo4j import graph + + for record in data: + for key in record.keys(): + value = record[key] + if isinstance(value, graph.Node): + node_id = value._properties["id"] + node_ids_set.add(node_id) + elif isinstance(value, graph.Relationship): + rel_nodes = value.nodes + prop_id = value._properties["id"] + src_id = rel_nodes[0]._properties["id"] + dst_id = rel_nodes[1]._properties["id"] + rels_set.add((src_id, dst_id, prop_id)) + elif isinstance(value, graph.Path): + formatted_paths = _format_paths(data) + for path in formatted_paths: + for i in range(0, len(path), 2): + node_ids_set.add(path[i]) + if i + 2 < len(path): + rels_set.add((path[i], path[i + 2], path[i + 1])) + + nodes = [Vertex(node_id) for node_id in node_ids_set] + rels = [ + Edge(src_id, dst_id, label=prop_id) + for (src_id, dst_id, prop_id) in rels_set + ] + return {"nodes": nodes, "edges": rels} + + result = self.conn.run(query=query) + graph = _format_query_data(result) + mg = MemoryGraph() + for vertex in graph["nodes"]: + mg.upsert_vertex(vertex) + for edge in graph["edges"]: + mg.append_edge(edge) + return mg diff --git a/dbgpt/storage/knowledge_graph/__init__.py b/dbgpt/storage/knowledge_graph/__init__.py new file mode 100644 index 000000000..d654f79f5 --- /dev/null +++ b/dbgpt/storage/knowledge_graph/__init__.py @@ -0,0 +1 @@ +"""Module for KG.""" diff --git a/dbgpt/storage/knowledge_graph/base.py b/dbgpt/storage/knowledge_graph/base.py new file mode 100644 index 000000000..55dda8a35 --- /dev/null +++ b/dbgpt/storage/knowledge_graph/base.py @@ -0,0 +1,28 @@ +"""Knowledge graph base class.""" +import logging +from abc import ABC, abstractmethod +from typing import Optional + +from dbgpt._private.pydantic import ConfigDict +from dbgpt.rag.index.base import IndexStoreBase, IndexStoreConfig +from dbgpt.storage.graph_store.graph import Graph + +logger = logging.getLogger(__name__) + + +class KnowledgeGraphConfig(IndexStoreConfig): + """Knowledge graph config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True, extra="allow") + + +class KnowledgeGraphBase(IndexStoreBase, ABC): + """Knowledge graph base class.""" + + @abstractmethod + def query_graph(self, limit: Optional[int] = None) -> Graph: + """Get graph data.""" + + def delete_by_ids(self, ids: str): + """Delete document by ids.""" + raise Exception("Delete document not supported by knowledge graph") diff --git a/dbgpt/storage/knowledge_graph/knowledge_graph.py b/dbgpt/storage/knowledge_graph/knowledge_graph.py new file mode 100644 index 000000000..5bcddf4fd --- /dev/null +++ b/dbgpt/storage/knowledge_graph/knowledge_graph.py @@ -0,0 +1,147 @@ +"""Knowledge graph class.""" +import asyncio +import logging +import os +from typing import List, Optional + +from dbgpt._private.pydantic import ConfigDict, Field +from dbgpt.core import Chunk, LLMClient +from dbgpt.rag.transformer.keyword_extractor import KeywordExtractor +from dbgpt.rag.transformer.triplet_extractor import TripletExtractor +from dbgpt.storage.graph_store.base import GraphStoreBase, GraphStoreConfig +from dbgpt.storage.graph_store.factory import GraphStoreFactory +from dbgpt.storage.graph_store.graph import Graph +from dbgpt.storage.knowledge_graph.base import KnowledgeGraphBase, KnowledgeGraphConfig +from dbgpt.storage.vector_store.filters import MetadataFilters + +logger = logging.getLogger(__name__) + + +class BuiltinKnowledgeGraphConfig(KnowledgeGraphConfig): + """Builtin knowledge graph config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + llm_client: LLMClient = Field(default=None, description="The default llm client.") + + model_name: str = Field(default=None, description="The name of llm model.") + + graph_store_type: str = Field( + default="TuGraph", description="The type of graph store." + ) + + +class BuiltinKnowledgeGraph(KnowledgeGraphBase): + """Builtin knowledge graph class.""" + + def __init__(self, config: BuiltinKnowledgeGraphConfig): + """Create builtin knowledge graph instance.""" + self._config = config + + self._llm_client = config.llm_client + if not self._llm_client: + raise ValueError("No llm client provided.") + + self._model_name = config.model_name + self._triplet_extractor = TripletExtractor(self._llm_client, self._model_name) + self._keyword_extractor = KeywordExtractor(self._llm_client, self._model_name) + self._graph_store_type = ( + os.getenv("GRAPH_STORE_TYPE", "TuGraph") or config.graph_store_type + ) + + def configure(cfg: GraphStoreConfig): + cfg.name = self._config.name + cfg.embedding_fn = self._config.embedding_fn + + self._graph_store: GraphStoreBase = GraphStoreFactory.create( + self._graph_store_type, configure + ) + + def load_document(self, chunks: List[Chunk]) -> List[str]: + """Extract and persist triplets to graph store.""" + + async def process_chunk(chunk): + triplets = await self._triplet_extractor.extract(chunk.content) + for triplet in triplets: + self._graph_store.insert_triplet(*triplet) + logger.info(f"load {len(triplets)} triplets from chunk {chunk.chunk_id}") + return chunk.chunk_id + + # wait async tasks completed + tasks = [process_chunk(chunk) for chunk in chunks] + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + result = loop.run_until_complete(asyncio.gather(*tasks)) + loop.close() + return result + + async def aload_document(self, chunks: List[Chunk]) -> List[str]: # type: ignore + """Extract and persist triplets to graph store. + + Args: + chunks: List[Chunk]: document chunks. + Return: + List[str]: chunk ids. + """ + for chunk in chunks: + triplets = await self._triplet_extractor.extract(chunk.content) + for triplet in triplets: + self._graph_store.insert_triplet(*triplet) + logger.info(f"load {len(triplets)} triplets from chunk {chunk.chunk_id}") + return [chunk.chunk_id for chunk in chunks] + + def similar_search_with_scores( + self, + text, + topk, + score_threshold: float, + filters: Optional[MetadataFilters] = None, + ) -> List[Chunk]: + """Search neighbours on knowledge graph.""" + raise Exception("Sync similar_search_with_scores not supported") + + async def asimilar_search_with_scores( + self, + text, + topk, + score_threshold: float, + filters: Optional[MetadataFilters] = None, + ) -> List[Chunk]: + """Search neighbours on knowledge graph.""" + if not filters: + logger.info("Filters on knowledge graph not supported yet") + + # extract keywords and explore graph store + keywords = await self._keyword_extractor.extract(text) + subgraph = self._graph_store.explore(keywords, limit=topk) + logger.info(f"Search subgraph from {len(keywords)} keywords") + + content = ( + "The following vertices and edges data after [Subgraph Data] " + "are retrieved from the knowledge graph based on the keywords:\n" + f"Keywords:\n{','.join(keywords)}\n" + "---------------------\n" + "You can refer to the sample vertices and edges to understand " + "the real knowledge graph data provided by [Subgraph Data].\n" + "Sample vertices:\n" + "(alice)\n" + "(bob:{age:28})\n" + '(carry:{age:18;role:"teacher"})\n\n' + "Sample edges:\n" + "(alice)-[reward]->(alice)\n" + '(alice)-[notify:{method:"email"}]->' + '(carry:{age:18;role:"teacher"})\n' + '(bob:{age:28})-[teach:{course:"math";hour:180}]->(alice)\n' + "---------------------\n" + f"Subgraph Data:\n{subgraph.format()}\n" + ) + return [Chunk(content=content, metadata=subgraph.schema())] + + def query_graph(self, limit: Optional[int] = None) -> Graph: + """Query graph.""" + return self._graph_store.get_full_graph(limit) + + def delete_vector_name(self, index_name: str): + """Delete vector name.""" + logger.info(f"Remove graph index {index_name}") + self._graph_store.drop() diff --git a/dbgpt/storage/knowledge_graph/open_spg.py b/dbgpt/storage/knowledge_graph/open_spg.py new file mode 100644 index 000000000..589e0cc32 --- /dev/null +++ b/dbgpt/storage/knowledge_graph/open_spg.py @@ -0,0 +1,49 @@ +"""OpenSPG class.""" +import logging +from typing import List, Optional + +from dbgpt._private.pydantic import ConfigDict +from dbgpt.core import Chunk +from dbgpt.storage.graph_store.graph import Graph, MemoryGraph +from dbgpt.storage.knowledge_graph.base import KnowledgeGraphBase, KnowledgeGraphConfig +from dbgpt.storage.vector_store.filters import MetadataFilters + +logger = logging.getLogger(__name__) + + +class OpenSPGConfig(KnowledgeGraphConfig): + """OpenSPG config.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + +class OpenSPG(KnowledgeGraphBase): + """OpenSPG class.""" + + # todo: add OpenSPG implementation + + def __init__(self, config: OpenSPGConfig): + """Initialize the OpenSPG with config details.""" + pass + + def load_document(self, chunks: List[Chunk]) -> List[str]: + """Load document.""" + return [] + + def similar_search_with_scores( + self, + text, + topk, + score_threshold: float, + filters: Optional[MetadataFilters] = None, + ) -> List[Chunk]: + """Similar with scores.""" + return [] + + def query_graph(self, limit: Optional[int] = None) -> Graph: + """Query graph.""" + return MemoryGraph() + + def delete_vector_name(self, index_name: str): + """Delete vector name.""" + pass diff --git a/dbgpt/storage/vector_store/__init__.py b/dbgpt/storage/vector_store/__init__.py index ab354eacb..8314e4a4f 100644 --- a/dbgpt/storage/vector_store/__init__.py +++ b/dbgpt/storage/vector_store/__init__.py @@ -1,44 +1,68 @@ """Vector Store Module.""" -from typing import Any +from typing import Tuple, Type -def _import_pgvector() -> Any: - from dbgpt.storage.vector_store.pgvector_store import PGVectorStore +def _import_pgvector() -> Tuple[Type, Type]: + from dbgpt.storage.vector_store.pgvector_store import PGVectorConfig, PGVectorStore - return PGVectorStore + return PGVectorStore, PGVectorConfig -def _import_milvus() -> Any: - from dbgpt.storage.vector_store.milvus_store import MilvusStore +def _import_milvus() -> Tuple[Type, Type]: + from dbgpt.storage.vector_store.milvus_store import MilvusStore, MilvusVectorConfig - return MilvusStore + return MilvusStore, MilvusVectorConfig -def _import_chroma() -> Any: - from dbgpt.storage.vector_store.chroma_store import ChromaStore +def _import_chroma() -> Tuple[Type, Type]: + from dbgpt.storage.vector_store.chroma_store import ChromaStore, ChromaVectorConfig - return ChromaStore + return ChromaStore, ChromaVectorConfig -def _import_weaviate() -> Any: - from dbgpt.storage.vector_store.weaviate_store import WeaviateStore +def _import_weaviate() -> Tuple[Type, Type]: + from dbgpt.storage.vector_store.weaviate_store import ( + WeaviateStore, + WeaviateVectorConfig, + ) - return WeaviateStore + return WeaviateStore, WeaviateVectorConfig -def _import_oceanbase() -> Any: - from dbgpt.storage.vector_store.oceanbase_store import OceanBaseStore +def _import_oceanbase() -> Tuple[Type, Type]: + from dbgpt.storage.vector_store.oceanbase_store import ( + OceanBaseConfig, + OceanBaseStore, + ) - return OceanBaseStore + return OceanBaseStore, OceanBaseConfig -def _import_elastic() -> Any: - from dbgpt.storage.vector_store.elastic_store import ElasticStore +def _import_elastic() -> Tuple[Type, Type]: + from dbgpt.storage.vector_store.elastic_store import ( + ElasticsearchVectorConfig, + ElasticStore, + ) - return ElasticStore + return ElasticStore, ElasticsearchVectorConfig -def __getattr__(name: str) -> Any: +def _import_builtin_knowledge_graph() -> Tuple[Type, Type]: + from dbgpt.storage.knowledge_graph.knowledge_graph import ( + BuiltinKnowledgeGraph, + BuiltinKnowledgeGraphConfig, + ) + + return BuiltinKnowledgeGraph, BuiltinKnowledgeGraphConfig + + +def _import_openspg() -> Tuple[Type, Type]: + from dbgpt.storage.knowledge_graph.open_spg import OpenSPG, OpenSPGConfig + + return OpenSPG, OpenSPGConfig + + +def __getattr__(name: str) -> Tuple[Type, Type]: if name == "Chroma": return _import_chroma() elif name == "Milvus": @@ -51,8 +75,23 @@ def __getattr__(name: str) -> Any: return _import_oceanbase() elif name == "ElasticSearch": return _import_elastic() + elif name == "KnowledgeGraph": + return _import_builtin_knowledge_graph() + elif name == "OpenSPG": + return _import_openspg() else: raise AttributeError(f"Could not find: {name}") -__all__ = ["Chroma", "Milvus", "Weaviate", "OceanBase", "PGVector", "ElasticSearch"] +__vector_store__ = [ + "Chroma", + "Milvus", + "Weaviate", + "OceanBase", + "PGVector", + "ElasticSearch", +] + +__knowledge_graph__ = ["KnowledgeGraph", "OpenSPG"] + +__all__ = __vector_store__ + __knowledge_graph__ diff --git a/dbgpt/storage/vector_store/base.py b/dbgpt/storage/vector_store/base.py index b65f389a5..5046504b5 100644 --- a/dbgpt/storage/vector_store/base.py +++ b/dbgpt/storage/vector_store/base.py @@ -1,27 +1,25 @@ """Vector store base class.""" import logging import math -import time from abc import ABC, abstractmethod -from concurrent.futures import ThreadPoolExecutor -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional -from dbgpt._private.pydantic import BaseModel, ConfigDict, Field, model_to_dict +from dbgpt._private.pydantic import ConfigDict, Field from dbgpt.core import Chunk, Embeddings from dbgpt.core.awel.flow import Parameter +from dbgpt.rag.index.base import IndexStoreBase, IndexStoreConfig from dbgpt.storage.vector_store.filters import MetadataFilters from dbgpt.util.i18n_utils import _ logger = logging.getLogger(__name__) - _COMMON_PARAMETERS = [ Parameter.build_from( _("Collection Name"), "name", str, description=_( - "The name of vector store, if not set, will use the default " "name." + "The name of vector store, if not set, will use the default name." ), optional=True, default="dbgpt_collection", @@ -31,7 +29,7 @@ _COMMON_PARAMETERS = [ "user", str, description=_( - "The user of vector store, if not set, will use the default " "user." + "The user of vector store, if not set, will use the default user." ), optional=True, default=None, @@ -84,99 +82,26 @@ _COMMON_PARAMETERS = [ ] -class VectorStoreConfig(BaseModel): +class VectorStoreConfig(IndexStoreConfig): """Vector store config.""" - model_config = ConfigDict(arbitrary_types_allowed=True) + model_config = ConfigDict(arbitrary_types_allowed=True, extra="allow") - name: str = Field( - default="dbgpt_collection", - description="The name of vector store, if not set, will use the default name.", - ) user: Optional[str] = Field( default=None, description="The user of vector store, if not set, will use the default user.", ) password: Optional[str] = Field( default=None, - description="The password of vector store, if not set, will use the default " - "password.", - ) - embedding_fn: Optional[Embeddings] = Field( - default=None, - description="The embedding function of vector store, if not set, will use the " - "default embedding function.", - ) - max_chunks_once_load: int = Field( - default=10, - description="The max number of chunks to load at once. If your document is " - "large, you can set this value to a larger number to speed up the loading " - "process. Default is 10.", - ) - max_threads: int = Field( - default=1, - description="The max number of threads to use. Default is 1. If you set this " - "bigger than 1, please make sure your vector store is thread-safe.", + description=( + "The password of vector store, if not set, will use the default password." + ), ) - def to_dict(self, **kwargs) -> Dict[str, Any]: - """Convert to dict.""" - return model_to_dict(self, **kwargs) - -class VectorStoreBase(ABC): +class VectorStoreBase(IndexStoreBase, ABC): """Vector store base class.""" - @abstractmethod - def load_document(self, chunks: List[Chunk]) -> List[str]: - """Load document in vector database. - - Args: - chunks(List[Chunk]): document chunks. - - Return: - List[str]: chunk ids. - """ - - def load_document_with_limit( - self, chunks: List[Chunk], max_chunks_once_load: int = 10, max_threads: int = 1 - ) -> List[str]: - """Load document in vector database with specified limit. - - Args: - chunks(List[Chunk]): Document chunks. - max_chunks_once_load(int): Max number of chunks to load at once. - max_threads(int): Max number of threads to use. - - Return: - List[str]: Chunk ids. - """ - # Group the chunks into chunks of size max_chunks - chunk_groups = [ - chunks[i : i + max_chunks_once_load] - for i in range(0, len(chunks), max_chunks_once_load) - ] - logger.info( - f"Loading {len(chunks)} chunks in {len(chunk_groups)} groups with " - f"{max_threads} threads." - ) - ids = [] - loaded_cnt = 0 - start_time = time.time() - with ThreadPoolExecutor(max_workers=max_threads) as executor: - tasks = [] - for chunk_group in chunk_groups: - tasks.append(executor.submit(self.load_document, chunk_group)) - for future in tasks: - success_ids = future.result() - ids.extend(success_ids) - loaded_cnt += len(success_ids) - logger.info(f"Loaded {loaded_cnt} chunks, total {len(chunks)} chunks.") - logger.info( - f"Loaded {len(chunks)} chunks in {time.time() - start_time} seconds" - ) - return ids - def filter_by_score_threshold( self, chunks: List[Chunk], score_threshold: float ) -> List[Chunk]: @@ -207,63 +132,11 @@ class VectorStoreBase(ABC): ) return candidates_chunks - @abstractmethod - def similar_search( - self, text: str, topk: int, filters: Optional[MetadataFilters] = None - ) -> List[Chunk]: - """Similar search in vector database. - - Args: - text(str): The query text. - topk(int): The number of similar documents to return. - filters(Optional[MetadataFilters]): metadata filters. - Return: - List[Chunk]: The similar documents. - """ - pass - - @abstractmethod - def similar_search_with_scores( - self, - text, - topk, - score_threshold: float, - filters: Optional[MetadataFilters] = None, - ) -> List[Chunk]: - """Similar search with scores in vector database. - - Args: - text(str): The query text. - topk(int): The number of similar documents to return. - score_threshold(int): score_threshold: Optional, a floating point value - between 0 to 1 - filters(Optional[MetadataFilters]): metadata filters. - Return: - List[Chunk]: The similar documents. - """ - @abstractmethod def vector_name_exists(self) -> bool: """Whether vector name exists.""" return False - @abstractmethod - def delete_by_ids(self, ids: str): - """Delete vectors by ids. - - Args: - ids(str): The ids of vectors to delete, separated by comma. - """ - - @abstractmethod - def delete_vector_name(self, vector_name: str): - """Delete vector by name. - - Args: - vector_name(str): The name of vector to delete. - """ - pass - def convert_metadata_filters(self, filters: MetadataFilters) -> Any: """Convert metadata filters to vector store filters. @@ -285,3 +158,14 @@ class VectorStoreBase(ABC): def _default_relevance_score_fn(self, distance: float) -> float: """Return a similarity score on a scale [0, 1].""" return 1.0 - distance / math.sqrt(2) + + async def aload_document(self, chunks: List[Chunk]) -> List[str]: # type: ignore + """Load document in index database. + + Args: + chunks(List[Chunk]): document chunks. + + Return: + List[str]: chunk ids. + """ + raise NotImplementedError diff --git a/dbgpt/storage/vector_store/connector.py b/dbgpt/storage/vector_store/connector.py index 5ba0a8a39..84f84d2e6 100644 --- a/dbgpt/storage/vector_store/connector.py +++ b/dbgpt/storage/vector_store/connector.py @@ -1,8 +1,9 @@ """Connector for vector store.""" import copy +import logging import os -from typing import Any, Dict, List, Optional, Type, cast +from typing import Any, Dict, List, Optional, Tuple, Type, cast from dbgpt.core import Chunk, Embeddings from dbgpt.core.awel.flow import ( @@ -12,11 +13,14 @@ from dbgpt.core.awel.flow import ( ResourceCategory, register_resource, ) -from dbgpt.storage.vector_store.base import VectorStoreBase, VectorStoreConfig +from dbgpt.rag.index.base import IndexStoreBase, IndexStoreConfig +from dbgpt.storage.vector_store.base import VectorStoreConfig from dbgpt.storage.vector_store.filters import MetadataFilters from dbgpt.util.i18n_utils import _ -connector: Dict[str, Type] = {} +logger = logging.getLogger(__name__) + +connector: Dict[str, Tuple[Type, Type]] = {} def _load_vector_options() -> List[OptionValue]: @@ -25,7 +29,7 @@ def _load_vector_options() -> List[OptionValue]: return [ OptionValue(label=cls, name=cls, value=cls) for cls in vector_store.__all__ - if issubclass(getattr(vector_store, cls), VectorStoreBase) + if issubclass(getattr(vector_store, cls)[0], IndexStoreBase) ] @@ -73,7 +77,7 @@ class VectorStoreConnector: def __init__( self, vector_store_type: str, - vector_store_config: Optional[VectorStoreConfig] = None, + vector_store_config: Optional[IndexStoreConfig] = None, ) -> None: """Create a VectorStoreConnector instance. @@ -81,20 +85,41 @@ class VectorStoreConnector: - vector_store_type: vector store type Milvus, Chroma, Weaviate - ctx: vector store config params. """ - self._vector_store_config = vector_store_config + self._index_store_config = vector_store_config self._register() if self._match(vector_store_type): - self.connector_class = connector[vector_store_type] + self.connector_class, self.config_class = connector[vector_store_type] else: - raise Exception(f"Vector Store Type Not support. {0}", vector_store_type) + raise Exception(f"Vector store {vector_store_type} not supported") - print(self.connector_class) + logger.info(f"VectorStore:{self.connector_class}") self._vector_store_type = vector_store_type self._embeddings = ( vector_store_config.embedding_fn if vector_store_config else None ) - self.client = self.connector_class(vector_store_config) + + try: + if vector_store_config is not None: + config: IndexStoreConfig = self.config_class() + config.name = getattr(vector_store_config, "name", "default_name") + config.embedding_fn = getattr(vector_store_config, "embedding_fn", None) + config.max_chunks_once_load = getattr( + vector_store_config, "max_chunks_once_load", 5 + ) + config.max_threads = getattr(vector_store_config, "max_threads", 4) + config.user = getattr(vector_store_config, "user", None) + config.password = getattr(vector_store_config, "password", None) + + # extra + config_dict = vector_store_config.dict() + config.llm_client = config_dict.get("llm_client", None) + config.model_name = config_dict.get("model_name", None) + + self.client = self.connector_class(config) + except Exception as e: + logger.error("connect vector store failed: %s", e) + raise e @classmethod def from_default( @@ -122,12 +147,12 @@ class VectorStoreConnector: Return chunk ids. """ max_chunks_once_load = ( - self._vector_store_config.max_chunks_once_load - if self._vector_store_config + self._index_store_config.max_chunks_once_load + if self._index_store_config else 10 ) max_threads = ( - self._vector_store_config.max_threads if self._vector_store_config else 1 + self._index_store_config.max_threads if self._index_store_config else 1 ) return self.client.load_document_with_limit( chunks, @@ -135,6 +160,17 @@ class VectorStoreConnector: max_threads, ) + async def aload_document(self, chunks: List[Chunk]) -> List[str]: + """Load document in vector database. + + Args: + - chunks: document chunks. + Return chunk ids. + """ + return await self.client.aload_document( + chunks, + ) + def similar_search( self, doc: str, topk: int, filters: Optional[MetadataFilters] = None ) -> List[Chunk]: @@ -174,12 +210,24 @@ class VectorStoreConnector: doc, topk, score_threshold, filters ) + async def asimilar_search_with_scores( + self, + doc: str, + topk: int, + score_threshold: float, + filters: Optional[MetadataFilters] = None, + ) -> List[Chunk]: + """Async similar_search_with_score in vector database.""" + return await self.client.asimilar_search_with_scores( + doc, topk, score_threshold, filters + ) + @property - def vector_store_config(self) -> VectorStoreConfig: + def vector_store_config(self) -> IndexStoreConfig: """Return the vector store config.""" - if not self._vector_store_config: + if not self._index_store_config: raise ValueError("vector store config not set.") - return self._vector_store_config + return self._index_store_config def vector_name_exists(self): """Whether vector name exists.""" @@ -226,6 +274,8 @@ class VectorStoreConnector: from dbgpt.storage import vector_store for cls in vector_store.__all__: - if issubclass(getattr(vector_store, cls), VectorStoreBase): - _k, _v = cls, getattr(vector_store, cls) - connector.update({_k: _v}) + store_cls, config_cls = getattr(vector_store, cls) + if issubclass(store_cls, IndexStoreBase) and issubclass( + config_cls, IndexStoreConfig + ): + connector[cls] = (store_cls, config_cls) diff --git a/docs/docs/cookbook/rag/graph_rag_app_develop.md b/docs/docs/cookbook/rag/graph_rag_app_develop.md new file mode 100644 index 000000000..54d65a2bb --- /dev/null +++ b/docs/docs/cookbook/rag/graph_rag_app_develop.md @@ -0,0 +1,210 @@ +# Graph RAG User Manual + +In this example, we will show how to use the Graph RAG framework in DB-GPT. Using a graph database to implement RAG can, to some extent, alleviate the uncertainty and interpretability issues brought about by vector database retrieval. + +You can refer to the python example file `DB-GPT/examples/rag/graph_rag_example.py` in the source code. This example demonstrates how to load knowledge from a document and store it in a graph store. Subsequently, it recalls knowledge relevant to your question by searching for triplets in the graph store. + + +### Install Dependencies + +First, you need to install the `dbgpt` library. + +```bash +pip install "dbgpt[rag]>=0.5.6" +```` + +### Prepare Graph Database + +To store the knowledge in graph, we need an graph database, [TuGraph](https://github.com/TuGraph-family/tugraph-db) is the first graph database supported by DB-GPT. + +Visit github repository of TuGraph to view [Quick Start](https://tugraph-db.readthedocs.io/zh-cn/latest/3.quick-start/1.preparation.html#id5) document, follow the instructions to pull the TuGraph database docker image (latest / version >= 4.3.0) and launch it. + +``` +docker pull tugraph/tugraph-runtime-centos7:latest +docker run -it -d -p 7001:7001 -p 7070:7070 -p 7687:7687 -p 8000:8000 -p 8888:8888 -p 8889:8889 -p 9090:9090 \ + -v /root/tugraph/data:/var/lib/lgraph/data -v /root/tugraph/log:/var/log/lgraph_log \ + --name tugraph_demo tugraph/tugraph-runtime-centos7:latest /bin/bash +``` + +The default port for the bolt protocol is `7687`, and DB-GPT accesses TuGraph through this port via `neo4j` python client. + +``` + pip install "neo4j>=5.20.0" +``` + +### Prepare LLM + +To build a Graph RAG program, we need a LLM, here are some of the LLMs that DB-GPT supports: + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + + + + +First, you should install the `openai` library. + +```bash +pip install openai +``` +Then set your API key in the environment `OPENAI_API_KEY`. + +```python +from dbgpt.model.proxy import OpenAILLMClient + +llm_client = OpenAILLMClient() +``` + + + + +You should have a YI account and get the API key from the YI official website. + +First, you should install the `openai` library. + +```bash +pip install openai +``` + +Then set your API key in the environment variable `YI_API_KEY`. + +```python +from dbgpt.model.proxy import YiLLMClient + +llm_client = YiLLMClient() +``` + + + + +If you have deployed [DB-GPT cluster](/docs/installation/model_service/cluster) and +[API server](/docs/installation/advanced_usage/OpenAI_SDK_call) +, you can connect to the API server to get the LLM model. + +The API is compatible with the OpenAI API, so you can use the OpenAILLMClient to +connect to the API server. + +First you should install the `openai` library. +```bash +pip install openai +``` + +```python +from dbgpt.model.proxy import OpenAILLMClient + +llm_client = OpenAILLMClient(api_base="http://localhost:8100/api/v1/", api_key="{your_api_key}") +``` + + + + + + +### TuGraph Configuration + +Set variables below in `.env` file, let DB-GPT know how to connect to TuGraph. + +``` +GRAPH_STORE_TYPE=TuGraph +TUGRAPH_HOST=127.0.0.1 +TUGRAPH_PORT=7687 +TUGRAPH_USERNAME=admin +TUGRAPH_PASSWORD=73@TuGraph +``` + + + +### Load into Knowledge Graph + +When using a graph database as the underlying knowledge storage platform, it is necessary to build a knowledge graph to facilitate the archiving and retrieval of documents. DB-GPT leverages the capabilities of large language models to implement an integrated knowledge graph, while still maintaining the flexibility to freely connect to other knowledge graph systems and graph database systems. + +To maintain compatibility with existing conventional RAG frameworks, we continue to access the knowledge graph through the `VectorStoreConnector` interface. Simply set the `vector_store_type` to `KnowledgeGraph` to enable this connection. + +```python +from dbgpt.model.proxy.llms.chatgpt import OpenAILLMClient +from dbgpt.storage.vector_store.base import VectorStoreConfig +from dbgpt.storage.vector_store.connector import VectorStoreConnector + +def _create_vector_connector(): + """Create vector connector.""" + return VectorStoreConnector( + vector_store_type="KnowledgeGraph", + vector_store_config=VectorStoreConfig( + name="graph_rag_test_kg", + embedding_fn=None, + llm_client=OpenAILLMClient(), + model_name="gpt-4" + ) + ) +``` + + + +### Retrieve from Knowledge Graph + +Then you can retrieve the knowledge from the knowledge graph, which is the same with vector store. + +```python +import os + +from dbgpt.configs.model_config import ROOT_PATH +from dbgpt.rag import ChunkParameters +from dbgpt.rag.assembler import EmbeddingAssembler +from dbgpt.rag.knowledge import KnowledgeFactory + +async def main(): + file_path = os.path.join(ROOT_PATH, "examples/test_files/tranformers_story.md") + knowledge = KnowledgeFactory.from_file_path(file_path) + vector_connector = _create_kg_connector() + chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE") + # get embedding assembler + assembler = EmbeddingAssembler.load_from_knowledge( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + vector_store_connector=vector_connector, + ) + assembler.persist() + # get embeddings retriever + retriever = assembler.as_retriever(3) + chunks = await retriever.aretrieve_with_scores( + "What actions has Megatron taken?", + score_threshold=0.3 + ) + print(f"embedding rag example results:{chunks}") + vector_connector.delete_vector_name("graph_rag_test") +``` + + + + +### Chat Knowledge via GraphRAG + +Here we demonstrate how to achieve chat knowledge through Graph RAG on web page. + +First, create a knowledge base using the `Knowledge Graph` type. Upload the knowledge documents and wait for the slicing to complete. + + +

+ +

+ +Then, view the knowledge graph data. +

+ +

+ +The graph data may look like this. +

+ +

+ +Start chat to knowledge based on Graph RAG. +

+ +

diff --git a/docs/sidebars.js b/docs/sidebars.js index 9fa03854b..07d7b0c93 100755 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -470,6 +470,16 @@ const sidebars = { } ], }, + { + type: 'category', + label: 'RAG', + items: [ + { + type: 'doc', + id: 'cookbook/rag/graph_rag_app_develop', + } + ], + }, { type: 'category', label: 'App', @@ -479,7 +489,7 @@ const sidebars = { id: 'cookbook/app/data_analysis_app_develop', } ], - }, + }, ], }, diff --git a/docs/static/img/chat_knowledge/graph_rag/create_knowledge_graph.jpg b/docs/static/img/chat_knowledge/graph_rag/create_knowledge_graph.jpg new file mode 100644 index 0000000000000000000000000000000000000000..292e91f702ae8443c658e20afde9d37d7a10a92f GIT binary patch literal 252193 zcmeEv2_ThSxA1cu9P>;Fr!r4TX5kn@l4QN(GP_Opkz)?Rz>wbmXGJ`;uky3-n38UO?W z01v=_0AU31R`++X1ppl#KnMT;GJqJu4v>Hl7zI#*9Q*}qKm-8jUN{i|ggOAkKjNGP zpSu^B+g_UQPofl}pE0N-Q=mU#Si){p!U7VU!l5rKVx}vyA+*C*?(WV+bO7Mu>g}no 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