thebears/vector_search
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

bump

Browse Source
This commit is contained in:
Ishan S. Patel
2026-07-09 10:26:27 -04:00
parent 79f9a1dbc2
commit da5206c6a6
9 changed files with 170 additions and 60 deletions
Download Patch File Download Diff File Expand all files Collapse all files
clip_endpoint.py
+14 -17
View File
@@ -1,31 +1,28 @@
import open_clip
import torch
import functools
import onnxruntime as ort
do_load = True
if do_load:
#model_name = 'hf-hub:timm/ViT-L-16-SigLIP2-512'
# model, preprocess = open_clip.create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP2-512')
model_name = 'ViT-SO400M-14-SigLIP-384'
pretrained_name = 'webli'
model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained_name)
device = 'cpu'
model.eval()
model_name = 'ViT-SO400M-16-SigLIP2-512'
tokenizer = open_clip.get_tokenizer(model_name)
#onnx_path = "/home/thebears/Source/ml/clip_extract/text/ViT-SO400M-16-SigLIP2-512_text"
onnx_path = "/home/thebears/Source/ml/clip_extract/text/ViT-SO400M-16-SigLIP2-512_text_optimized"
ort_sess = ort.InferenceSession(onnx_path)
from bottle import route, run, template, request, debug
@route('/encode')
def get_matches():
query = request.query.get('query','A large bird eating corn')
query = request.query.get('query',None)
if query is None:
return None
return execute_model(query)
@functools.cache
def execute_model(query):
with torch.no_grad():
text_tokenized = tokenizer(query)
vec = model.encode_text(text_tokenized).detach().cpu().tolist()
vec = ort_sess.run(None, {'tokenized': text_tokenized.numpy() })[0].tolist()
return {'vector':vec}
debug(True)
run(host='0.0.0.0', port=53004, server='bjoern')
clip_endpoint_test.py
+18
View File
@@ -0,0 +1,18 @@
print('hello')
# %%
import open_clip
import torch
do_load = True
if do_load:
model_name = 'hf-hub:timm/ViT-L-16-SigLIP2-512'
# pretrained_name = 'webli'
model, preprocess = open_clip.create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP2-512')
# model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained=pretrained_name)
device = 'cpu'
model.eval()
tokenizer = open_clip.get_tokenizer(model_name)
milvus_migrate/create_collection_v3.py
+24 -18
View File
@@ -1,22 +1,31 @@
from pymilvus import MilvusClient, DataType
import os
from tqdm import tqdm
client = MilvusClient(
uri="http://localhost:19530"
)
#for x in client.list_collections():
# client.drop_collection(x)
paths_scan = ['/srv/ftp_tcc','/srv/ftp','/srv/ftp_local/']
outff = list()
for xpp in paths_scan:
outff.extend([x.path for x in os.scandir(xpp) if x.is_dir()])
out = [x.split('/')[3] for x in outff]
# %%
import os
out = os.listdir('/mergedfs/ftp/')
# %%
for cam in out:
c_collections = set(client.list_collections())
for cam in tqdm(out):
c_col_name = f"nuggets_{cam}_so400m_siglip2"
if c_col_name in c_collections:
print(f"Skipping {c_col_name}")
continue
else:
print(f"Creating {c_col_name}")
schema = MilvusClient.create_schema(
auto_id=False,
nenable_dynamic_field=False,
@@ -25,7 +34,7 @@ for cam in out:
schema.add_field(field_name="filepath", datatype=DataType.VARCHAR, max_length=128)
schema.add_field(field_name="frame_number", datatype=DataType.INT32)
schema.add_field(field_name="date", datatype=DataType.VARCHAR, max_length=len('20241220'), is_partition_key=True)
schema.add_field(field_name="so400m", datatype=DataType.FLOAT16_VECTOR, dim=1024)
schema.add_field(field_name="so400m", datatype=DataType.FLOAT16_VECTOR, dim=1152)
index_params = client.prepare_index_params()
@@ -40,7 +49,7 @@ for cam in out:
index_params.add_index(
field_name="so400m",
index_type="IVF_SQ8",
index_type="DISKANN",
metric_type="COSINE",
params={'nlist':128})
@@ -49,16 +58,13 @@ for cam in out:
index_type='Trie')
client.create_collection(
collection_name=f"nuggets_{cam}_so400m_siglip2",
collection_name=c_col_name,
schema=schema,
index_params=index_params
)
print(cam)
#res = client.get_load_state( collection_name="nuggets_ptz_so400m_siglip2")
#res = client.load_collection(collection_name="nuggets_so400m")
#import numpy
#c = numpy.load('/srv/ftp_tcc/leopards1/2025/07/18/Leopards1_00_20250718170024.oclip_embeds.npz')
milvus_migrate/upload_from_folder.py
+1 -1
View File
@@ -60,7 +60,7 @@ def upload_vector_file(vector_file_to_upload):
root_path = '/srv/ftp/railing/2024'
root_path = '/srv/ftp/railing/2026/'
to_put = list()
for root, dirs, files in os.walk(root_path):
for x in files:
out
View File
search_me.py
+23 -22
View File
@@ -8,51 +8,54 @@ import traceback
from bottle import route, run, template, request, debug
import time
import prettyprinter
# %%
TIMEOUT=2
# %%
TIMEOUT=10
#collection_name = "nuggets_{camera}_so400m_siglip2"
collection_name = "nuggets_{camera}_so400m"
collection_name = "nuggets_{camera}_so400m_siglip2"
client = MilvusClient(
uri="http://localhost:19530"
)
# %%
from bottle import route, run, template
@route('/get_collection_count')
def get_collection_count():
coll_count = dict()
for c in sorted(client.list_collections()):
coll_count[c] = client.get_collection_stats(c)['row_count']
return coll_count
@route('/get_text_match')
def get_matches():
# %%
valid_cameras = {'sidefeeder','ptz','railing','hummingbird','pond'}
query = request.query.get('query','A large bird eating corn')
cameras = request.query.get('cameras','sidefeeder')
num_videos = int(request.query.get('num_videos',5))
cams = set(cameras.split(',')).intersection(valid_cameras)
cams = set(cameras.split(","))
max_age = int(request.query.get('age',5));
print({'Cameras':cams,'Max Age':max_age,'Query':query})
max_date = datetime.now()
min_date = max_date - timedelta(days=(max_age))
min_date = max_date - timedelta(days=(max_age-1))
days_step = (max_date- min_date).days
day_strs = list()
# %%
for x in range(days_step):
# pass
day_strs.append( (min_date + timedelta(days=x)).strftime('%Y%m%d') )
# if days_step > 0:
# day_strs.append( (min_date + timedelta(days=0)).strftime('%Y%m%d') )
day_strs.append( max_date.strftime('%Y%m%d'))
str_insert = ','.join([f'"{x}"'for x in day_strs])
filter_string = 'date in [' + str_insert + ']'
if max_age == 0:
filter_string = ''
# %%
vec_form = requests.get('http://192.168.1.242:53004/encode',params={'query':query}).json()['vector'][0]
vec_search = np.asarray(vec_form).astype(np.float16)
print(f'Doing query for {query}!')
print('Filter string:',filter_string)
all_results=list()
try:
error = ''
@@ -73,11 +76,10 @@ def get_matches():
all_results.extend(results[0])
except Exception as e:
print(traceback.format_exc())
error = traceback.format_exc()
print(error)
results = []
# %%
def linux_to_win_path(form):
form = form.replace('/srv','file://192.168.1.242/thebears/Videos/merged/')
return form
@@ -96,9 +98,8 @@ def get_matches():
pload['winpath'] = linux_to_win_path(pload['filepath'])
pload['frame'] = pload['frame_number']
resul.append(pload)
# %%
return_this = {'query':query,'num_videos':num_videos,'results':resul,'error':error}
prettyprinter.pprint(return_this)
return return_this
search_me_doris.py
View File
search_me_doris_test.py
+19
View File
@@ -0,0 +1,19 @@
from doris_vector_search import DorisVectorClient, AuthOptions
auth = AuthOptions(
host="192.168.1.242",
query_port=9030,
user="thebears",
password="marybear",
)
client = DorisVectorClient(database="nuggets", auth_options=auth)
tbl = client.open_table("video_embeddings")
query = [0.1] * 1152 # Example 128-dimensional vector
# SELECT id FROM sift_1M ORDER BY l2_distance_approximate(embedding, query) LIMIT 10;
result = tbl.search(query, metric_type="inner_product").limit(10).select(["id"]).to_pandas()
print(result)
test_milvus.py
+69
View File
@@ -0,0 +1,69 @@
from pymilvus import MilvusClient, DataType
collection_name = "nuggets_{camera}_so400m_siglip2"
client = MilvusClient(
uri="http://localhost:19530"
)
# %%
coll_count = dict()
for c in sorted(client.list_collections()):
print(c, client.get_load_state(c), client.get_collection_stats(c))
# %%
from pymilvus import utility
# %%
import requests
query = 'A squirrel looking at the camera'
vec_form = requests.get('http://192.168.1.242:53004/encode',params={'query':query}).json()['vector'][0]
# %%
from datetime import datetime, timedelta
max_age = 5
str_insert = ''
days_step = 5
max_date = datetime.now()
min_date = max_date - timedelta(days=(max_age-1))
days_step = (max_date- min_date).days
day_strs = list()
for x in range(days_step):
day_strs.append( (min_date + timedelta(days=x)).strftime('%Y%m%d') )
str_insert = ','.join([f'"{x}"'for x in day_strs])
filter_string = 'date in [' + str_insert + ']'
import numpy as np
col_name = 'nuggets_railing_so400m_siglip2'
vec_search = np.asarray(vec_form).astype(np.float16)
results = client.search(collection_name = col_name,
consistency_level="Eventually",
data = [vec_search],
filter=filter_string,
search_params={'metric_type':'COSINE', 'params':{}},
output_fields=['filepath','frame_number']
)
# %%
for c in client.list_collections():
# ff = client.describe_collection(collection_name = c)
print(c)
# %%
col_target = 'nuggets_leopards1_so400m_siglip2'
import time
# %%
print(client.get_collection_stats(c))
# %%
# %%
st = time.time()
results = client.search(collection_name = col_target,data =[np.random.rand(1152).astype(np.float16)], limit = 100)
print(len(results[0]), time.time() - st)