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+Metadata-Version: 2.1
+Name: nano-vectordb
+Version: 0.0.4.3
+Summary: A simple, easy-to-hack Vector Database implementation
+Home-page: https://github.com/gusye1234/nano-vectordb
+Author: JianbaiYe
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.9
+Description-Content-Type: text/markdown
+Requires-Dist: numpy
+
+<div align="center">
+  <h1>nano-VectorDB</h1>
+  <p><strong>A simple, easy-to-hack Vector Database</strong></p>
+  <p>
+    <img src="https://img.shields.io/badge/python->=3.9.11-blue">
+    <a href="https://pypi.org/project/nano-vectordb/">
+      <img src="https://img.shields.io/pypi/v/nano-vectordb.svg">
+    </a>
+    <a href="https://codecov.io/github/gusye1234/nano-vectordb" > 
+ <img src="https://codecov.io/github/gusye1234/nano-vectordb/graph/badge.svg?token=3ACScwuv4h"/> 
+ </a>
+  </p>
+</div>
+
+
+
+
+🌬️ A vector database implementation with single-dependency (`numpy`).
+
+🎁 It can handle a query from `100,000` vectors and return in 100 milliseconds.
+
+🏃 It's okay for your prototypes, maybe even more.
+
+🏃 Support naive [multi-tenancy](#Multi-Tenancy).
+
+
+
+## Install
+
+**Install from PyPi**
+
+```shell
+pip install nano-vectordb
+```
+
+**Install from source**
+
+```shell
+# clone this repo first
+cd nano-vectordb
+pip install -e .
+```
+
+
+
+## Quick Start
+
+**Faking your data**:
+
+```python
+from nano_vectordb import NanoVectorDB
+import numpy as np
+
+data_len = 100_000
+fake_dim = 1024
+fake_embeds = np.random.rand(data_len, fake_dim)    
+
+fakes_data = [{"__vector__": fake_embeds[i], **ANYFIELDS} for i in range(data_len)]
+```
+
+You can add any fields to a data. But there are two keywords:
+
+- `__id__`: If passed, `NanoVectorDB` will use your id, otherwise a generated id will be used.
+- `__vector__`: must pass, your embedding `np.ndarray`.
+
+### Init a DB
+
+```python
+vdb = NanoVectorDB(fake_dim, storage_file="fool.json")
+```
+
+Next time you init `vdb` from `fool.json`, `NanoVectorDB` will load the index automatically.
+
+### Upsert
+
+```python
+r = vdb.upsert(fakes_data)
+print(r["update"], r["insert"])
+```
+
+### Query
+
+```python
+# query with embedding 
+vdb.query(np.random.rand(fake_dim))
+
+# arguments:
+vdb.query(np.random.rand(fake_dim), top_k=5, better_than_threshold=0.01)
+```
+
+#### Conditional filter
+
+```python
+vdb.query(np.random.rand(fake_dim), filter_lambda=lambda x: x["any_field"] == "any_value")
+```
+
+### Save
+
+```python
+# will create/overwrite 'fool.json'
+vdb.save()
+```
+
+### Get, Delete
+
+```python
+# get and delete the inserted data
+print(vdb.get(r["insert"]))
+vdb.delete(r["insert"])
+```
+
+### Additional Data
+
+```python
+vdb.store_additional_data(a=1, b=2, c=3)
+print(vdb.get_additional_data())
+```
+
+
+
+## Multi-Tenancy
+
+If you have multiple vectorDB to use, you can use `MultiTenantNanoVDB` to manage:
+
+```python
+from nano_vectordb import NanoVectorDB, MultiTenantNanoVDB
+
+multi_tenant = MultiTenantNanoVDB(1024)
+tenant_id = multi_tenant.create_tenant()
+
+# tenant is a NanoVectorDB, you can upsert, query, get... on this.
+tenant: NanoVectorDB = multi_tenant.get_tenant(tenant_id)
+
+# some chores:
+multi_tenant.delete_tenant(tenant_id)
+multi_tenant.contain_tenant(tenant_id)
+
+# save it
+multi_tenant.save()
+```
+
+`MultiTenantNanoVDB` use a queue to manage the total vector dbs in memory, you can adjust the parameter:
+
+```python
+# There will be only `max_capacity` NanoVectorDB in the memory.
+multi_tenant = MultiTenantNanoVDB(1024, max_capacity=1)
+```
+
+
+
+## Benchmark
+
+> Embedding Dim: 1024. Device: MacBook M3 Pro
+
+- Save a index with `100,000` vectors will generate a roughly 520M json file.
+- Insert `100,000` vectors will cost roughly `2`s
+- Query from `100,000` vectors will cost roughly `0.1`s