[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-6642":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":9,"pushedAt":9,"updatedAt":35,"readmeContent":36,"aiSummary":37,"trendingCount":15,"starSnapshotCount":15,"syncStatus":38,"lastSyncTime":39,"discoverSource":40},6642,"FalkorDB","FalkorDB\u002FFalkorDB","A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG).","https:\u002F\u002Fwww.falkordb.com\u002F",null,"C",4546,385,45,566,0,8,34,183,39,29.76,"Other",false,"master",true,[26,27,28,29,30,31,32,33,34],"cloud-database","database","database-as-a-service","developer-tools","devtools","graph-database","graphrag","knowledge-graph","realtime-database","2026-06-12 02:01:27","\u003Cdiv align=\"center\">\n  \u003Cimg width=\"100\" height=\"100\" alt=\"FalkorDB Logo Square B\" src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fec67e5e3-a060-405d-b229-1fe2badcf8ad\" \u002F>\n\u003C\u002Fdiv>\n\u003Cdiv align=\"center\">\n  \u003Ch1>FalkorDB\u003C\u002Fh1>\n  \u003Ch2>Ultra-fast, Multi-tenant Graph Database\u003C\u002Fh2>\n  \u003Ch3>Powering Generative AI, Agent Memory, Cloud Security, and Fraud Detection\u003C\u002Fh3>\n\u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F14787\" target=\"_blank\" rel=\"noopener noreferrer\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F14787\" alt=\"FalkorDB%2FFalkorDB | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\u003C\u002Fdiv>\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fapp.falkordb.cloud\u002Fsignup\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTry%20Free-FalkorDB%20Cloud-FF8101?labelColor=FDE900&style=for-the-badge\" alt=\"Try Free\" \u002F>\n  \u003C\u002Fa>\u003Cbr>\n  \u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002FTJ4SaPTptf\" rel=\"nofollow\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDiscord-%235865F2.svg?&logo=discord&logoColor=white\" alt=\"Discord\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fhub.docker.com\u002Fr\u002Ffalkordb\u002Ffalkordb\u002F\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fdocker\u002Fpulls\u002Ffalkordb\u002Ffalkordb?label=Docker\" alt=\"Dockerhub\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002F6M4QwDXn2w\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1146782921294884966?style=flat-square\" alt=\"Discord\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fcodecov.io\u002Fgh\u002Ffalkordb\u002Ffalkordb\">\n    \u003Cimg src=\"https:\u002F\u002Fcodecov.io\u002Fgh\u002Ffalkordb\u002Ffalkordb\u002Fgraph\u002Fbadge.svg?token=0G4HBEJMW0\" alt=\"codecov\" \u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FFalkorDB\u002FFalkorDB\u002Factions\u002Fworkflows\u002Fbuild.yml\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002FFalkorDB\u002FFalkorDB\u002Factions\u002Fworkflows\u002Fbuild.yml\u002Fbadge.svg?branch=master\" alt=\"Workflow\" \u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n\n![FalkorDB GitHub Repo - Video - 640x365](https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F131867d4-fa2b-41f8-ac72-5c5f6eaf4e12)\n\n\u003C\u002Fdiv>\n\n## UNIQUE FEATURES\n\nOur goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our Graph Database.\n\n🆕 [FalkorDB](https:\u002F\u002Fwww.falkordb.com\u002F) is the first **queryable [Property Graph](https:\u002F\u002Fgithub.com\u002Fopencypher\u002FopenCypher\u002Fblob\u002Fmaster\u002Fdocs\u002Fproperty-graph-model.adoc) database to leverage sparse matrices** for representing the [adjacency matrix](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAdjacency_matrix) in graphs and [linear algebra](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAdjacency_matrix) for querying.\n\n### Key Features\n\n* **Sparse Matrix Representation**: Utilizes sparse matrices to represent adjacency matrices, optimizing storage and performance.\n\n\n* **Linear Algebra Querying**: Employs linear algebra for query execution, enhancing computational efficiency.\n\n\n* **Property Graph Model Compliance**: Supports nodes and relationships with attributes, adhering to the Property Graph Model.\n\n\n* **OpenCypher Support:** Compatible with [OpenCypher](ttps:\u002F\u002Fgithub.com\u002Fopencypher\u002FopenCypher\u002Fblob\u002Fmaster\u002Fdocs\u002Fproperty-graph-model.adoc) query language, including proprietary extensions for advanced querying capabilities.\n\n>Explore FalkorDB in action by visiting the [Demos](https:\u002F\u002Fgithub.com\u002FFalkorDB\u002FFalkorDB\u002Ftree\u002Fmaster\u002Fdemo).\n\n## DOCUMENTATION\n\n[Official Docs](https:\u002F\u002Fdocs.falkordb.com\u002F) | [Clients](https:\u002F\u002Fdocs.falkordb.com\u002Fclients.html) | [Commands](https:\u002F\u002Fdocs.falkordb.com\u002Fcommands\u002F) | 📊 [Latest Performance Benchmarks](https:\u002F\u002Fbenchmark.falkordb.com\u002F)\n\n### Community and Support\n\n* **Discussions**: Join our community discussions on [GitHub Discussions](https:\u002F\u002Fgithub.com\u002FFalkorDB\u002FFalkorDB\u002Fdiscussions) to ask questions, share ideas, and connect with other users.\n\n* **Contributing**: We welcome contributions! Please see our [Contributing Guide](https:\u002F\u002Fgithub.com\u002FFalkorDB\u002FFalkorDB\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md) for more details.\n\n* **License**: This project is licensed under the Server Side Public License v1 (SSPLv1). See the [LICENSE](https:\u002F\u002Fgithub.com\u002FFalkorDB\u002FFalkorDB\u002Fblob\u002Fmaster\u002FLICENSE.txt) file for details.\n\n## GET STARTED\n\n### Step 1\n\nTo quickly try out FalkorDB, launch an instance using docker:\n\n```\ndocker run -p 6379:6379 -p 3000:3000 -it --rm -v .\u002Fdata:\u002Fvar\u002Flib\u002Ffalkordb\u002Fdata falkordb\u002Ffalkordb\n```\n\n### Step 2\n\nThen, open your browser and navigate to `http:\u002F\u002Flocalhost:3000`.\n\n\nYou can also interact with FalkorDB using any of the supported [Client Libraries](https:\u002F\u002Fdocs.falkordb.com\u002Fclients.html)\n\n### MotoGP League Example\n\nIn this example, we'll use the [FalkorDB Python client](https:\u002F\u002Fpypi.org\u002Fproject\u002FFalkorDB\u002F) to create a small graph representing a subset of motorcycle riders and teams participating in the MotoGP league. After creating the graph, we'll query the data to explore its structure and relationships.\n\n```python\nfrom falkordb import FalkorDB\n\n# Connect to FalkorDB\ndb = FalkorDB(host='localhost', port=6379)\n\n# Create the 'MotoGP' graph\ng = db.select_graph('MotoGP')\ng.query(\"\"\"CREATE (:Rider {name:'Valentino Rossi'})-[:rides]->(:Team {name:'Yamaha'}),\n                  (:Rider {name:'Dani Pedrosa'})-[:rides]->(:Team {name:'Honda'}),\n                  (:Rider {name:'Andrea Dovizioso'})-[:rides]->(:Team {name:'Ducati'})\"\"\")\n\n# Query which riders represents Yamaha?\nres = g.query(\"\"\"MATCH (r:Rider)-[:rides]->(t:Team)\n                 WHERE t.name = 'Yamaha'\n                 RETURN r.name\"\"\")\n\nfor row in res.result_set:\n\tprint(row[0])\n\n# Prints: \"Valentino Rossi\"\n\n# Query how many riders represent team Ducati ?\nres = g.query(\"\"\"MATCH (r:Rider)-[:rides]->(t:Team {name:'Ducati'})\n                 RETURN count(r)\"\"\")\n\nprint(res.result_set[0][0])\n# Prints: 1\n```\n\n## START BUILDING\n\n### Compiling\n\nMake sure to complete these requirements:\n\n1️⃣ The FalkorDB repository: `git clone --recurse-submodules -j8 https:\u002F\u002Fgithub.com\u002FFalkorDB\u002FFalkorDB.git`\n\n* Ubuntu, install: `apt-get install build-essential cmake m4 automake peg libtool autoconf python3 python3-pip`\n* Alpine, install: `apk add build-base cmake m4 automake libtool autoconf python3 py3-pip peg git libgomp openssl-dev`\n* OS X, verify that `homebrew` is installed and run: `brew install cmake m4 automake peg libtool autoconf`.\n\t* The version of Clang that ships with the OS X toolchain does not support OpenMP, which is a requirement for FalkorDB. One way to resolve this is to run `brew install gcc g++` and follow the on-screen instructions to update the symbolic links. Note that this is a system-wide change - setting the environment variables for `CC` and `CXX` will work if that is not an option.\n\n2️⃣ Build by running `make` in the project's directory.\n\nCongratulations! You can find the compiled binary at `bin\u002F\u003Carch>\u002Fsrc\u002Ffalkordb.so`.\n\n### Running tests\n\nStart by installing the required Python packages by running ```pip install -r requirements.txt``` from the ```tests``` directory.\n\n> Note: If you've got ```redis-server``` in PATH, just invoke ```make test```. Otherwise, invoke ```REDIS_SERVER=\u003Credis-server-location> make test```. For a more verbose output, run ```make test V=1```.\n\n### Building in a docker\n\nThe FalkorDB build system runs within docker. For detailed instructions on building, please [see here](https:\u002F\u002Fdocs.falkordb.com\u002Fdocker-examples\u002FREADME.html).\n\n## LOADING FALKORDB INTO REDIS\n\nFalkorDB is hosted by [Redis](https:\u002F\u002Fredis.io), so you'll first have to load it as a Module to a Redis server. \n> Note: [Redis 7.4](https:\u002F\u002Fredis.io\u002Fdownload) is required for the latest FalkorDB version.\n\n💡 We recommend having Redis load FalkorDB during startup by adding the following to your redis.conf file:\n\n```\nloadmodule \u002Fpath\u002Fto\u002Fmodule\u002Fsrc\u002Ffalkordb.so\n```\n\nIn the line above, replace `\u002Fpath\u002Fto\u002Fmodule\u002Fsrc\u002Ffalkordb.so` with the actual path to FalkorDB's library.\nIf Redis is running as a service, you must ensure that the `redis` user (default) has the necessary file\u002Ffolder permissions\nto access `falkordb.so`.\n\nAlternatively, you can have Redis load FalkorDB using the following command line argument syntax:\n\n```sh\n~\u002F$ redis-server --loadmodule \u002Fpath\u002Fto\u002Fmodule\u002Fsrc\u002Ffalkordb.so\n```\n\nLastly, you can also use the [`MODULE LOAD`](http:\u002F\u002Fredis.io\u002Fcommands\u002Fmodule-load) command. Note, however, that `MODULE LOAD` is a dangerous command and may be blocked\u002Fdeprecated in the future due to security considerations.\n\nOnce you've successfully loaded FalkorDB your Redis log should see lines similar to:\n\n```\n...\n30707:M 20 Jun 02:08:12.314 * Module 'graph' loaded from \u003Credacted>\u002Fsrc\u002Ffalkordb.so\n...\n```\n\nIf the server fails to launch with output similar to:\n\n```\n# Module \u002Fusr\u002Flib\u002Fredis\u002Fmodules\u002Ffalkordb.so failed to load: libgomp.so.1: cannot open shared object file: No such file or directory\n# Can't load module from \u002Fusr\u002Flib\u002Fredis\u002Fmodules\u002Ffalkordb.so: server aborting\n```\n\nThe system is missing the run-time dependency OpenMP. This can be installed on Ubuntu with `apt-get install libgomp1`, on RHEL\u002FCentOS with `yum install libgomp`, and on OSX with `brew install libomp`.\n\n## USING FALKORDB\n\nYou can call FalkorDB's commands from any Redis client. Here are several methods:\n\n### With `redis-cli`\n\n```sh\n$ redis-cli\n127.0.0.1:6379> GRAPH.QUERY social \"CREATE (:person {name: 'roi', age: 33, gender: 'male', status: 'married'})\"\n```\n\n### With any other client\n\nYou can interact with FalkorDB using your client's ability to send raw Redis commands.\n\n>Note: Depending on your client of choice, the exact method for doing that may vary.\n\n#### Example: Using FalkorDB with a Python client\n\nThis code snippet shows how to use FalkorDB with from Python using [falkordb-py](https:\u002F\u002Fgithub.com\u002FFalkorDB\u002Ffalkordb-py):\n\n```Python\nfrom falkordb import FalkorDB\n\n# Connect to FalkorDB\ndb = FalkorDB(host='localhost', port=6379)\n\n# Select the social graph\ng = db.select_graph('social')\n\nreply = g.query(\"CREATE (:person {name:'roi', age:33, gender:'male', status:'married'})\")\n```\n\n## CLIENT LIBRARIES\n\n>Note: Some languages have client libraries that provide support for FalkorDB's commands:\n\n### Official Clients\n\n\n| Project                                                   | Language   | License | Author                                      | Stars                                                             | Package | Comment    |\n| --------------------------------------------------------- | ---------- | ------- | ------------------------------------------- | ----------------------------------------------------------------- | ------- | ---------- |\n| [jfalkordb][jfalkordb-url] | Java | BSD | [FalkorDB][falkordb-url] | [![jfalkordb-stars]][jfalkordb-url] | [Maven][jfalkordb-package]||\n| [falkordb-py][falkordb-py-url] | Python | MIT | [FalkorDB][falkordb-url] | [![falkordb-py-stars]][falkordb-py-url] | [pypi][falkordb-py-package]||\n| [falkordb-ts][falkordb-ts-url] | Node.JS | MIT | [FalkorDB][falkordb-url] | [![falkordb-ts-stars]][falkordb-ts-url] | [npm][falkordb-ts-package]||\n| [falkordb-rs][falkordb-rs-url] | Rust | MIT | [FalkorDB][falkordb-url] | [![falkordb-rs-stars]][falkordb-rs-url] | [Crate][falkordb-rs-package]||\n| [falkordb-go][falkordb-go-url] | Go | BSD | [FalkorDB][falkordb-url] | [![falkordb-go-stars]][falkordb-go-url] | [GitHub][falkordb-go-url]||\n| [NFalkorDB][nfalkordb-url]  |  C# | Apache-2.0 | [FalkorDB][falkordb-url] | [![nfalkordb-stars]][nfalkordb-url] | [nuget][nfalkordb-package] | |\n\n\n### Additional Clients\n\n| Project                                                   | Language   | License | Author                                      | Stars                                                             | Package | Comment    |\n| --------------------------------------------------------- | ---------- | ------- | ------------------------------------------- | ----------------------------------------------------------------- | ------- | ---------- |\n| [nredisstack][nredisstack-url] | .NET | MIT | [Redis][redis-url] | [![nredisstack-stars]][nredisstack-url] | [nuget][nredisstack-package]||\n| [redisgraph-rb][redisgraph-rb-url]                        | Ruby       | BSD     | [Redis][redisgraph-rb-author]          | [![redisgraph-rb-stars]][redisgraph-rb-url]                       | [GitHub][redisgraph-rb-url] ||\n| [redgraph][redgraph-url]                                  | Ruby       | MIT     | [pzac][redgraph-author]                | [![redgraph-stars]][redgraph-url]                                 | [GitHub][redgraph-url] ||\n| [redisgraph-go][redisgraph-go-url]                        | Go         | BSD     | [Redis][redisgraph-go-author]          | [![redisgraph-go-stars]][redisgraph-go-url]                       | [GitHub][redisgraph-go-url]||\n| [rueidis][rueidis-url]                                    | Go         | Apache 2.0 | [Rueian][rueidis-author]          | [![rueidis-stars]][rueidis-url]                       | [GitHub][rueidis-url] ||\n| [ioredisgraph][ioredisgraph-url]                          | JavaScript | ISC     | [Jonah][ioredisgraph-author]                | [![ioredisgraph-stars]][ioredisgraph-url]                         | [GitHub][ioredisgraph-url] ||\n| [@hydre\u002Frgraph][rgraph-url]                               | JavaScript | MIT     | [Sceat][rgraph-author]                      | [![rgraph-stars]][rgraph-url]                                     | [GitHub][rgraph-url] ||\n| [php-redis-graph][php-redis-graph-url]                    | PHP        | MIT     | [KJDev][php-redis-graph-author]             | [![php-redis-graph-stars]][php-redis-graph-url]                   | [GitHub][php-redis-graph-url] ||\n| [redisgraph_php][redisgraph_php-url]                      | PHP        | MIT     | [jpbourbon][redisgraph_php-author]          | [![redisgraph_php-stars]][redisgraph_php-url]                     | [GitHub][redisgraph_php-url] ||\n| [redisgraph-ex][redisgraph-ex-url]                        | Elixir     | MIT     | [crflynn][redisgraph-ex-author]             | [![redisgraph-ex-stars]][redisgraph-ex-url]                       | [GitHub][redisgraph-ex-url] ||\n| [redisgraph-rs][redisgraph-rs-url]                        | Rust       | MIT     | [malte-v][redisgraph-rs-author]             | [![redisgraph-rs-stars]][redisgraph-rs-url]                       | [GitHub][redisgraph-rs-url] ||\n| [redis_graph][redis_graph-url]                            | Rust       | BSD     | [tompro][redis_graph-author]                | [![redis_graph-stars]][redis_graph-url]                           | [GitHub][redis_graph-url] ||\n| [rustis][rustis-url]                                     | Rust       | MIT          | [Dahomey Technologies][rustis-author]       | [![rustis-stars]][rustis-url]    | [Crate](https:\u002F\u002Fcrates.io\u002Fcrates\u002Frustis) | [Documentation](https:\u002F\u002Fdocs.rs\u002Frustis\u002Flatest\u002Frustis\u002Fcommands\u002Ftrait.GraphCommands.html) |\n| [NRedisGraph][NRedisGraph-url]                            | C#         | BSD     | [tombatron][NRedisGraph-author]             | [![NRedisGraph-stars]][NRedisGraph-url]                           | [GitHub][NRedisGraph-url] ||\n| [RedisGraph.jl][RedisGraph.jl-url]                        | Julia      | MIT     | [xyxel][RedisGraph.jl-author]               | [![RedisGraph.jl-stars]][RedisGraph.jl-url]                       | [GitHub][RedisGraph.jl-url] ||\n\n[redis-url]: https:\u002F\u002Fredis.com\n[falkordb-url]: https:\u002F\u002Fwww.falkordb.com\n\n[falkordb-py-url]: http:\u002F\u002Fgithub.com\u002FfalkorDB\u002Ffalkordb-py\n[falkordb-py-stars]: https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002FfalkorDB\u002Ffalkordb-py.svg?style=social&label=Star&maxAge=2592000\n[falkordb-py-package]: https:\u002F\u002Fpypi.org\u002Fproject\u002FFalkorDB\n\n[jfalkordb-url]: https:\u002F\u002Fgithub.com\u002Ffalkordb\u002Fjfalkordb\n[jfalkordb-stars]: https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Ffalkordb\u002Fjfalkordb.svg?style=social&label=Star&maxAge=2592000\n[jfalkordb-package]: https:\u002F\u002Fsearch.maven.org\u002Fartifact\u002Fcom.falkordb\u002Fjfalkordb\n\n[nfalkordb-url]: https:\u002F\u002Fgithub.com\u002Ffalkordb\u002FNFalkorDB\n[nfalkordb-stars]: https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Ffalkordb\u002Fnfalkordb.svg?style=social&label=Star&maxAge=2592000\n[nfalkordb-package]: https:\u002F\u002Fwww.nuget.org\u002Fpackages\u002FNFalkorDB\n\n[nredisstack-url]: https:\u002F\u002Fgithub.com\u002Fredis\u002Fnredisstack\n[nredisstack-stars]: https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fredis\u002Fnredisstack.svg?style=social&label=Star&maxAge=2592000\n[nredisstack-package]: https:\u002F\u002Fwww.nuget.org\u002Fpackages\u002Fnredisstack\u002F\n\n[falkordb-ts-url]: https:\u002F\u002Fgithub.com\u002Ffalkordb\u002Ffalkordb-ts\n[falkordb-ts-stars]: https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Ffalkordb\u002Ffalkordb-ts.svg?style=social&label=Star&maxAge=2592000\n[falkordb-ts-package]: 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https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fdahomey-technologies\u002Frustis.svg?style=social&label=Star&maxAge=2592000\n\n## LICENSE\n\nLicensed under the Server Side Public License v1 (SSPLv1). See [LICENSE](LICENSE.txt).\n\n### Support our work\n\n⭐️ If you find this repository helpful, please consider giving it a star!\n\n↗️ Graph, graph database, RAG, graphrag, Retrieval-Augmented Generation,Information Retrieval, Natural Language Processing, LLM, Embeddings, Semantic Search\n","FalkorDB 是一个超高速的图数据库，它利用 GraphBLAS 技术在底层实现稀疏邻接矩阵的图表示，旨在为大型语言模型（LLM）提供最佳的知识图谱。其核心功能包括使用稀疏矩阵优化存储和性能、基于线性代数进行查询以提高计算效率，并且支持属性图模型和 OpenCypher 查询语言。这些特性使得 FalkorDB 非常适合需要快速处理复杂关系数据的应用场景，如生成式人工智能、代理记忆、云安全以及欺诈检测等领域。",2,"2026-06-11 03:08:02","top_language"]