infiniflow/infinity
Fork: 252 Star: 2382 (更新于 2024-09-01 19:54:20)
license: Apache-2.0
Language: C++ .
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
最后发布版本: v0.3.0-dev4 ( 2024-08-08 01:14:30)
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text
Document | Benchmark | Twitter | Discord
Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as dense vector, sparse vector, tensor, full-text, and structured data. It provides robust support for various LLM applications, including search, recommenders, question-answering, conversational AI, copilot, content generation, and many more RAG (Retrieval-augmented Generation) applications.
⚡️ Performance
🌟 Key Features
Infinity comes with high performance, flexibility, ease-of-use, and many features designed to address the challenges facing the next-generation AI applications:
🚀 Incredibly fast
- Achieves 0.1 milliseconds query latency and 15K+ QPS on million-scale vector datasets.
- Achieves 1 millisecond latency and 12K+ QPS in full-text search on 33M documents.
See the Benchmark report for more information.
🔮 Powerful search
- Supports a hybrid search of dense embedding, sparse embedding, tensor, and full text, in addition to filtering.
- Supports several types of rerankers including RRF, weighted sum and ColBERT.
🍔 Rich data types
Supports a wide range of data types including strings, numerics, vectors, and more.
🎁 Ease-of-use
- Intuitive Python API. See the Python API
- A single-binary architecture with no dependencies, making deployment a breeze.
- Embedded in Python as a module and friendly to AI developers.
🎮 Get Started
Infinity, also available as a Python module, eliminates the need for a separate back-end server and all the complex communication settings. Using pip install
and import infinity
, you can quickly build a local AI application in Python, leveraging the world's fastest and the most powerful RAG database:
pip install infinity-sdk==0.3.0.dev7
import infinity
# Connect to infinity
infinity_obj = infinity.connect("/path/to/save/to")
db = infinity_obj.get_database("default_db")
table = db.create_table("my_table", {"num": {"type": "integer"}, "body": {"type": "varchar"}, "vec": {"type": "vector, 4, float"}})
table.insert([{"num": 1, "body": "unnecessary and harmful", "vec": [1.0, 1.2, 0.8, 0.9]}])
table.insert([{"num": 2, "body": "Office for Harmful Blooms", "vec": [4.0, 4.2, 4.3, 4.5]}])
res = table.output(["*"]).match_dense("vec", [3.0, 2.8, 2.7, 3.1], "float", "ip", 2).to_pl()
print(res)
🛠️ Deploy Infinity as a separate server
If you wish to deploy a standalone Infinity server and access it remotely:
🛠️ Build from Source
See Build from Source.
💡 For more information about Infinity's Python API, see the Python API Reference.
📚 Document
📜 Roadmap
See the Infinity Roadmap 2024
🙌 Community
最近版本更新:(数据更新于 2024-08-11 12:46:38)
2024-08-08 01:14:30 v0.3.0-dev4
2024-08-05 14:16:53 v0.3.0-dev3
2024-07-25 12:38:26 v0.3.0-dev2
2024-07-22 00:43:16 v0.3.0-dev1
2024-07-15 18:18:28 v0.2.1
2024-07-15 14:35:42 v0.2.1-dev6
2024-07-11 23:03:56 nightly
2024-07-13 14:04:37 v0.2.1-dev5
2024-06-26 12:08:08 v0.2.0
2024-05-10 12:56:53 v0.1.1
主题(topics):
ai-native, approximate-nearest-neighbor-search, bm25, cpp20, cpp20-modules, embedding, full-text-search, hnsw, hybrid-search, information-retrival, nearest-neighbor-search, rag, search-engine, tensor-database, vector, vector-database, vector-search, vectordatabase
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