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Aug 26, 2025
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updates for 0.4.1 #26

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8408613
updates for 0.4.1
rbs333 Aug 26, 2025
263f700
fix tests
rbs333 Aug 26, 2025
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1,586 changes: 415 additions & 1,171 deletions docs/examples/bayesian_optimization/00_bayes_study.ipynb
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12,466 changes: 1,266 additions & 11,200 deletions docs/examples/grid_study/00_grid_study.ipynb
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222 changes: 72 additions & 150 deletions docs/examples/grid_study/01_custom_grid_study.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,37 @@
"%pip install redis-retrieval-optimizer"
]
},
{
"cell_type": "markdown",
"id": "a498afe9",
"metadata": {},
"source": [
"## Check version"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5eea1c17",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.4.1'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import redis_retrieval_optimizer\n",
"\n",
"redis_retrieval_optimizer.__version__"
]
},
{
"cell_type": "markdown",
"id": "270a4f1b",
Expand All @@ -45,7 +76,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"id": "b66894d7",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -246,12 +277,14 @@
"def gather_pre_filter_results(search_method_input: SearchMethodInput) -> SearchMethodOutput:\n",
" redis_res_vector = {}\n",
"\n",
" for key in search_method_input.raw_queries:\n",
" query_info = search_method_input.raw_queries[key]\n",
" query = pre_filter_query(query_info, 10, search_method_input.emb_model)\n",
" for key, query_info in search_method_input.raw_queries.items():\n",
"\n",
" query = pre_filter_query(query_info, search_method_input.ret_k, search_method_input.emb_model)\n",
"\n",
" res = run_search_w_time(\n",
" search_method_input.index, query, search_method_input.query_metrics\n",
" )\n",
"\n",
" score_dict = make_score_dict_vec(res, id_field_name=\"_id\")\n",
"\n",
" redis_res_vector[key] = score_dict\n",
Expand All @@ -265,12 +298,16 @@
"def gather_vector_results(search_method_input: SearchMethodInput) -> SearchMethodOutput:\n",
" redis_res_vector = {}\n",
"\n",
" for key in search_method_input.raw_queries:\n",
" text_query = search_method_input.raw_queries[key]\n",
" vec_query = vector_query(text_query, 10, search_method_input.emb_model)\n",
" for key, text_query in search_method_input.raw_queries.items():\n",
" # create query\n",
" vec_query = vector_query(text_query, search_method_input.ret_k, search_method_input.emb_model)\n",
"\n",
" # run with timing helper\n",
" res = run_search_w_time(\n",
" search_method_input.index, vec_query, search_method_input.query_metrics\n",
" )\n",
"\n",
" # format scores dict for ranx evaluation\n",
" score_dict = make_score_dict_vec(res, id_field_name=\"_id\")\n",
" redis_res_vector[key] = score_dict\n",
" \n",
Expand Down Expand Up @@ -335,133 +372,18 @@
"id": "cc56171b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/tyler.hutcherson/Library/Caches/pypoetry/virtualenvs/redis-retrieval-optimizer-Z5sMIYJj-py3.11/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:56:39 datasets INFO PyTorch version 2.7.0 available.\n",
"09:56:40 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: mps\n",
"09:56:40 sentence_transformers.SentenceTransformer INFO Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 4.18it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recreating: loading corpus from file\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"09:56:43 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: mps\n",
"09:56:43 sentence_transformers.SentenceTransformer INFO Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 55.47it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running search method: basic_vector\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Batches: 100%|██████████| 1/1 [00:00<00:00, 9.15it/s]\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running search method: pre_filter_vector\n"
"14:59:00 datasets INFO PyTorch version 2.3.0 available.\n",
"14:59:00 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: mps\n",
"14:59:00 sentence_transformers.SentenceTransformer INFO Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n",
"Recreating: loading corpus from file\n",
"14:59:08 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: mps\n",
"14:59:08 sentence_transformers.SentenceTransformer INFO Load pretrained SentenceTransformer: sentence-transformers/all-MiniLM-L6-v2\n",
"Running search method: basic_vector with dtype: float32\n",
"Running search method: pre_filter_vector with dtype: float32\n"
]
}
],
Expand Down Expand Up @@ -490,7 +412,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"id": "47ef7edc",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -518,57 +440,57 @@
" <th>search_method</th>\n",
" <th>model</th>\n",
" <th>avg_query_time</th>\n",
" <th>recall@k</th>\n",
" <th>recall</th>\n",
" <th>precision</th>\n",
" <th>ndcg@k</th>\n",
" <th>f1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>pre_filter_vector</td>\n",
" <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
" <td>0.001590</td>\n",
" <td>1.0</td>\n",
" <td>0.25</td>\n",
" <td>0.914903</td>\n",
" <td>0.000536</td>\n",
" <td>1.000000</td>\n",
" <td>0.416667</td>\n",
" <td>0.553810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>basic_vector</td>\n",
" <td>sentence-transformers/all-MiniLM-L6-v2</td>\n",
" <td>0.002136</td>\n",
" <td>0.9</td>\n",
" <td>0.23</td>\n",
" <td>0.717676</td>\n",
" <td>0.001578</td>\n",
" <td>0.866667</td>\n",
" <td>0.350000</td>\n",
" <td>0.470476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" search_method model avg_query_time \\\n",
"1 pre_filter_vector sentence-transformers/all-MiniLM-L6-v2 0.001590 \n",
"0 basic_vector sentence-transformers/all-MiniLM-L6-v2 0.002136 \n",
"1 pre_filter_vector sentence-transformers/all-MiniLM-L6-v2 0.000536 \n",
"0 basic_vector sentence-transformers/all-MiniLM-L6-v2 0.001578 \n",
"\n",
" recall@k precision ndcg@k \n",
"1 1.0 0.25 0.914903 \n",
"0 0.9 0.23 0.717676 "
" recall precision f1 \n",
"1 1.000000 0.416667 0.553810 \n",
"0 0.866667 0.350000 0.470476 "
]
},
"execution_count": 9,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"metrics[[\"search_method\", \"model\", \"avg_query_time\", \"recall\", \"precision\", \"ndcg\"]].sort_values(by=\"ndcg\", ascending=False)"
"metrics[[\"search_method\", \"model\", \"avg_query_time\", \"recall\", \"precision\", \"f1\"]].sort_values(by=\"f1\", ascending=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "redis-retrieval-optimizer-Z5sMIYJj-py3.11",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
Expand All @@ -582,7 +504,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
"version": "3.11.9"
}
},
"nbformat": 4,
Expand Down
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