init restful api

.gitignore

+.idea/

README.md

+# Vecwise Engine
+
+### Geting started
+
+- Install Miniconda first
+    - `conda create --name vec_engine python=3.6`
+    - `conda activate vec_engine`
+    - `conda install faiss-gpu cuda90 -c pytorch # For CUDA9.0`
+    - `conda install flask`
+    - `pip install flask-restful`

engine/app.py

+from flask import Flask
+from flask_restful import Resource, Api
+
+app = Flask(__name__)
+api = Api(app)
+
+
+from flask_restful import reqparse
+class Vector(Resource):
+    def __init__(self):
+        self.__parser = reqparse.RequestParser()
+        self.__parser.add_argument('groupid', type=str)
+        self.__parser.add_argument('vec', type=str)
+
+    def post(self):
+        # args = self.__parser.parse_args()
+        # vec = args['vec']
+        # groupid = args['groupid']
+        return "vector post"
+
+
+class VectorSearch(Resource):
+    def __init__(self):
+        self.__parser = reqparse.RequestParser()
+        self.__parser.add_argument('groupid', type=str)
+
+    def post(self):
+        return "vectorSearch post"
+
+
+class Index(Resource):
+    def __init__(self):
+        self.__parser = reqparse.RequestParser()
+        self.__parser.add_argument('groupid', type=str)
+
+    def post(self):
+        return "index post"
+
+
+class Group(Resource):
+    def __init__(self):
+        self.__parser = reqparse.RequestParser()
+        self.__parser.add_argument('groupid', type=str)
+
+    def post(self, groupid):
+        return "group post"
+
+    def get(self, groupid):
+        return "group get"
+
+    def delete(self, groupid):
+        return "group delete"
+
+
+class GroupList(Resource):
+    def get(self):
+        return "grouplist get"
+
+
+api.add_resource(Vector, '/vector')
+api.add_resource(Group, '/vector/group/<groupid>')
+api.add_resource(GroupList, '/vector/group')
+api.add_resource(Index, '/vector/index')
+api.add_resource(VectorSearch, '/vector/search')
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

tests/basic_test.py

+import numpy as np
+
+d = 64                           # dimension
+nb = 100000                      # database size
+nq = 10000                       # nb of queries
+np.random.seed(1234)             # make reproducible
+xb = np.random.random((nb, d)).astype('float32')
+xb[:, 0] += np.arange(nb) / 1000.
+xq = np.random.random((nq, d)).astype('float32')
+xq[:, 0] += np.arange(nq) / 1000.
+
+import faiss                    # make faiss available
+
+res = faiss.StandardGpuResources()  # use a single GPU
+
+## Using a flat index
+
+index_flat = faiss.IndexFlatL2(d)  # build a flat (CPU) index
+
+# make it a flat GPU index
+gpu_index_flat = faiss.index_cpu_to_gpu(res, 0, index_flat)
+
+gpu_index_flat.add(xb)         # add vectors to the index
+print(gpu_index_flat.ntotal)
+
+k = 4                          # we want to see 4 nearest neighbors
+D, I = gpu_index_flat.search(xq, k)  # actual search
+print(I[:5])                   # neighbors of the 5 first queries
+print(I[-5:])                  # neighbors of the 5 last queries
+
+
+## Using an IVF index
+
+nlist = 100
+quantizer = faiss.IndexFlatL2(d)  # the other index
+index_ivf = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)
+# here we specify METRIC_L2, by default it performs inner-product search
+
+# make it an IVF GPU index
+gpu_index_ivf = faiss.index_cpu_to_gpu(res, 0, index_ivf)
+
+assert not gpu_index_ivf.is_trained
+gpu_index_ivf.train(xb)        # add vectors to the index
+assert gpu_index_ivf.is_trained
+
+gpu_index_ivf.add(xb)          # add vectors to the index
+print(gpu_index_ivf.ntotal)
+
+k = 4                          # we want to see 4 nearest neighbors
+D, I = gpu_index_ivf.search(xq, k)  # actual search
+print(I[:5])                   # neighbors of the 5 first queries
+print(I[-5:])
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