From de7416b1374989e3935761d7bb4cebc40f600105 Mon Sep 17 00:00:00 2001
From: Lawson Taylor <lawson.j.taylor@gmail.com>
Date: Sat, 8 Jun 2024 19:43:59 +0800
Subject: [PATCH] [FEAT] postgres pgvec backed index implementation
---
poetry.lock | 633 +++++++++++++++++-------------
pyproject.toml | 2 +
semantic_router/index/postgres.py | 238 +++++++++++
3 files changed, 610 insertions(+), 263 deletions(-)
create mode 100644 semantic_router/index/postgres.py
diff --git a/poetry.lock b/poetry.lock
index 3c2e6c6f..e736b08d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
@@ -112,17 +112,17 @@ uvloop = ["uvloop (>=0.15.2)"]
[[package]]
name = "boto3"
-version = "1.34.116"
+version = "1.34.121"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
- {file = "boto3-1.34.116-py3-none-any.whl", hash = "sha256:e7f5ab2d1f1b90971a2b9369760c2c6bae49dae98c084a5c3f5c78e3968ace15"},
- {file = "boto3-1.34.116.tar.gz", hash = "sha256:53cb8aeb405afa1cd2b25421e27a951aeb568026675dec020587861fac96ac87"},
+ {file = "boto3-1.34.121-py3-none-any.whl", hash = "sha256:4e79e400d6d44b4eee5deda6ac0ecd08a3f5a30c45a0d30712795cdc4459fd79"},
+ {file = "boto3-1.34.121.tar.gz", hash = "sha256:ec89f3e0b0dc959c418df29e14d3748c0b05ab7acf7c0b90c839e9f340a659fa"},
]
[package.dependencies]
-botocore = ">=1.34.116,<1.35.0"
+botocore = ">=1.34.121,<1.35.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@@ -131,13 +131,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
-version = "1.34.116"
+version = "1.34.121"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
- {file = "botocore-1.34.116-py3-none-any.whl", hash = "sha256:ec4d42c816e9b2d87a2439ad277e7dda16a4a614ef6839cf66f4c1a58afa547c"},
- {file = "botocore-1.34.116.tar.gz", hash = "sha256:269cae7ba99081519a9f87d7298e238d9e68ba94eb4f8ddfa906224c34cb8b6c"},
+ {file = "botocore-1.34.121-py3-none-any.whl", hash = "sha256:25b05c7646a9f240cde1c8f839552a43f27e71e15c42600275dea93e219f7dd9"},
+ {file = "botocore-1.34.121.tar.gz", hash = "sha256:1a8f94b917c47dfd84a0b531ab607dc53570efb0d073d8686600f2d2be985323"},
]
[package.dependencies]
@@ -149,7 +149,7 @@ urllib3 = [
]
[package.extras]
-crt = ["awscrt (==0.20.9)"]
+crt = ["awscrt (==0.20.11)"]
[[package]]
name = "cachetools"
@@ -164,13 +164,13 @@ files = [
[[package]]
name = "certifi"
-version = "2024.2.2"
+version = "2024.6.2"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
- {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
- {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+ {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
+ {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
]
[[package]]
@@ -743,53 +743,53 @@ typing = ["typing-extensions (>=4.8)"]
[[package]]
name = "fonttools"
-version = "4.52.4"
+version = "4.53.0"
description = "Tools to manipulate font files"
optional = true
python-versions = ">=3.8"
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[[package]]
name = "fsspec"
-version = "2024.5.0"
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description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
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@@ -823,6 +823,7 @@ adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
dev = ["pre-commit", "ruff"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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[[package]]
name = "google-auth"
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description = "Google Authentication Library"
optional = true
python-versions = ">=3.7"
files = [
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name = "google-cloud-aiplatform"
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description = "Vertex AI API client library"
optional = true
python-versions = ">=3.8"
files = [
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optional = true
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[[package]]
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@@ -3997,18 +4094,18 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]]
name = "transformers"
-version = "4.40.2"
+version = "4.41.2"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = true
python-versions = ">=3.8.0"
files = [
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- {file = "transformers-4.40.2.tar.gz", hash = "sha256:657b6054a2097671398d976ad46e60836e7e15f9ea9551631a96e33cb9240649"},
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filelock = "*"
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numpy = ">=1.17"
packaging = ">=20.0"
pyyaml = ">=5.1"
@@ -4021,17 +4118,15 @@ tqdm = ">=4.27"
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flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
ftfy = ["ftfy"]
integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
@@ -4041,7 +4136,7 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"]
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ray = ["ray[tune] (>=2.7.0)"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
sagemaker = ["sagemaker (>=2.31.0)"]
@@ -4050,16 +4145,16 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
timm = ["timm"]
tokenizers = ["tokenizers (>=0.19,<0.20)"]
torch = ["accelerate (>=0.21.0)", "torch"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
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+torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"]
@@ -4113,13 +4208,13 @@ types-urllib3 = "*"
[[package]]
name = "types-requests"
-version = "2.32.0.20240523"
+version = "2.32.0.20240602"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
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- {file = "types_requests-2.32.0.20240523-py3-none-any.whl", hash = "sha256:f19ed0e2daa74302069bbbbf9e82902854ffa780bc790742a810a9aaa52f65ec"},
+ {file = "types-requests-2.32.0.20240602.tar.gz", hash = "sha256:3f98d7bbd0dd94ebd10ff43a7fbe20c3b8528acace6d8efafef0b6a184793f06"},
+ {file = "types_requests-2.32.0.20240602-py3-none-any.whl", hash = "sha256:ed3946063ea9fbc6b5fc0c44fa279188bae42d582cb63760be6cb4b9d06c3de8"},
]
[package.dependencies]
@@ -4138,13 +4233,24 @@ files = [
[[package]]
name = "typing-extensions"
-version = "4.12.0"
+version = "4.12.1"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
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- {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
+ {file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
+ {file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
+]
+
+[[package]]
+name = "tzdata"
+version = "2024.1"
+description = "Provider of IANA time zone data"
+optional = true
+python-versions = ">=2"
+files = [
+ {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+ {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
]
[[package]]
@@ -4203,18 +4309,18 @@ files = [
[[package]]
name = "zipp"
-version = "3.19.0"
+version = "3.19.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
- {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
- {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
+ {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+ {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
]
[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[extras]
bedrock = ["boto3", "botocore"]
@@ -4223,6 +4329,7 @@ hybrid = ["pinecone-text"]
local = ["llama-cpp-python", "tokenizers", "torch", "transformers"]
mistralai = ["mistralai"]
pinecone = ["pinecone-client"]
+postgres = ["psycopg"]
processing = ["matplotlib"]
qdrant = ["qdrant-client"]
vision = ["pillow", "torch", "torchvision", "transformers"]
@@ -4230,4 +4337,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "cb723f43017c81956776fc89703cf4c165c923e46bd0b541c109ccdfe561f2ab"
+content-hash = "277ac37871e99a5e5ed29b5c9baee2aee132b54fa4b8fb5b8c3f32e619155102"
diff --git a/pyproject.toml b/pyproject.toml
index bb832b60..38a44c1f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -41,6 +41,7 @@ google-cloud-aiplatform = {version = "^1.45.0", optional = true}
requests-mock = "^1.12.1"
boto3 = { version = "^1.34.98", optional = true }
botocore = {version = "^1.34.110", optional = true}
+psycopg = { version = "^3.1.19", optional = true, extras = ["binary"] }
[tool.poetry.extras]
hybrid = ["pinecone-text"]
@@ -52,6 +53,7 @@ mistralai = ["mistralai"]
qdrant = ["qdrant-client"]
google = ["google-cloud-aiplatform"]
bedrock = ["boto3", "botocore"]
+postgres = ["psycopg"]
[tool.poetry.group.dev.dependencies]
ipykernel = "^6.25.0"
diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py
new file mode 100644
index 00000000..49cb85fa
--- /dev/null
+++ b/semantic_router/index/postgres.py
@@ -0,0 +1,238 @@
+from semantic_router.index.base import BaseIndex
+import psycopg
+from psycopg.connection import Connection
+from pydantic import BaseModel
+from typing import Any, List, Optional, Tuple, Dict, Union
+from enum import Enum
+from semantic_router.schema import Metric
+import numpy as np
+import os
+import uuid
+
+
+class MetricPgVecOperatorMap(Enum):
+ cosine = "<=>"
+ dotproduct = "<#>" # inner product
+ euclidean = "<->" # L2 distance
+ manhattan = "<+>" # L1 distance
+
+
+def parse_vector(vector_str: Union[str, Any]) -> List[float]:
+ if isinstance(vector_str, str):
+ vector_str = str(vector_str)
+ vector_str = vector_str.strip('()"[]')
+ return list(map(float, vector_str.split(",")))
+ else:
+ return vector_str
+
+
+class PostgresIndexRecord(BaseModel):
+ id: str = ""
+ route: str
+ utterance: str
+ vector: List[float]
+
+ def __init__(self, **data) -> None:
+ super().__init__(**data)
+ clean_route = self.route.strip().replace(" ", "-")
+ route_namespace_uuid = uuid.uuid5(uuid.NAMESPACE_DNS, clean_route)
+ hashed_uuid = uuid.uuid5(route_namespace_uuid, self.utterance)
+ self.id = str(hashed_uuid)
+
+ def to_dict(self) -> Dict:
+ return {
+ "id": self.id,
+ "vector": self.vector,
+ "route": self.route,
+ "utterance": self.utterance,
+ }
+
+
+class PostgresIndex(BaseIndex):
+ """
+ Postgres implementation of Index
+ """
+ connection_string: Optional[str] = None,
+ index_prefix: str = "semantic_router_"
+ index_name: str = "index"
+ dimensions: int = 1536
+ metric: Metric = Metric.COSINE
+ namespace: Optional[str] = ""
+ conn: Optional[Connection] = None
+ type: str = "postgres"
+
+ def __init__(
+ self,
+ connection_string: Optional[str] = None,
+ index_prefix: str = "semantic_router_",
+ index_name: str = "index",
+ dimensions: int = 1536,
+ metric: Metric = Metric.COSINE,
+ namespace: Optional[str] = "",
+ ):
+ super().__init__()
+ if connection_string:
+ self.connection_string = connection_string
+ else:
+ connection_string = os.environ["POSTGRES_CONNECTION_STRING"]
+ if not connection_string:
+ raise ValueError("No connection string provided")
+ else:
+ self.connection_string = str(connection_string)
+ self.index_prefix = index_prefix
+ self.index_name = index_name
+ self.dimensions = dimensions
+ self.metric = metric
+ self.namespace = namespace
+ self.conn = psycopg.connect(conninfo=self.connection_string)
+ self.setup_index()
+
+ def _get_table_name(self) -> str:
+ return f"{self.index_prefix}{self.index_name}"
+
+ def _get_metric_operator(self) -> str:
+ return MetricPgVecOperatorMap[self.metric.value].value
+
+ def _get_score_query(self, embeddings_str: str) -> str:
+ """
+ Creates the select statement required to return the embeddings distance.
+ """
+ opperator = self._get_metric_operator()
+ if self.metric == Metric.COSINE:
+ return f"1 - (vector {opperator} {embeddings_str}) AS score"
+ elif self.metric == Metric.DOTPRODUCT:
+ return f"(vector {opperator} {embeddings_str}) * -1 AS score"
+ elif self.metric == Metric.EUCLIDEAN:
+ return f"vector {opperator} {embeddings_str} AS score"
+ elif self.metric == Metric.MANHATTAN:
+ return f"vector {opperator} {embeddings_str} AS score"
+ else:
+ raise ValueError(f"Unsupported metric: {self.metric}")
+
+ def setup_index(self) -> None:
+ table_name = self._get_table_name()
+ if not self._check_embeddings_dimensions():
+ raise ValueError(
+ f"The length of the vector embeddings in the existing table {table_name} does not match the expected dimensions of {self.dimensions}."
+ )
+ with self.conn.cursor() as cur:
+ cur.execute(
+ f"""
+ CREATE EXTENSION IF NOT EXISTS vector;
+ CREATE TABLE IF NOT EXISTS {table_name} (
+ id uuid PRIMARY KEY,
+ route TEXT,
+ utterance TEXT,
+ vector VECTOR({self.dimensions})
+ );
+ COMMENT ON COLUMN {table_name}.vector IS '{self.dimensions}';
+ """
+ )
+ self.conn.commit()
+
+ def _check_embeddings_dimensions(self) -> bool:
+ """
+ True where the length of the vector embeddings in the table matches the expected dimensions, or no table yet exists.
+ """
+ table_name = self._get_table_name()
+ with self.conn.cursor() as cur:
+ cur.execute(
+ f"SELECT EXISTS(SELECT 1 FROM information_schema.tables WHERE table_name='{table_name}');"
+ )
+ fetch_result = cur.fetchone()
+ exists = fetch_result[0] if fetch_result is not None else None
+ if not exists:
+ return True
+ cur.execute(
+ f"""SELECT col_description('{table_name}':: regclass, attnum) AS column_comment
+ FROM pg_attribute
+ WHERE attrelid = '{table_name}':: regclass
+ AND attname='vector'"""
+ )
+ result = cur.fetchone()
+ dimension_comment = result[0] if result else None
+ if dimension_comment:
+ try:
+ vector_length = int(dimension_comment.split()[-1])
+ print(vector_length)
+ return vector_length == self.dimensions
+ except ValueError:
+ raise ValueError(
+ "The 'vector' column comment does not contain a valid integer."
+ )
+ else:
+ raise ValueError("No comment found for the 'vector' column.")
+
+ def add(
+ self, embeddings: List[List[float]], routes: List[str], utterances: List[Any]
+ ) -> None:
+ table_name = self._get_table_name()
+ new_embeddings_length = len(embeddings[0])
+ if new_embeddings_length != self.dimensions:
+ raise ValueError(
+ f"The vector embeddings being added are of length {new_embeddings_length}, which does not match the expected dimensions of {self.dimensions}."
+ )
+ records = [
+ PostgresIndexRecord(vector=vector, route=route, utterance=utterance)
+ for vector, route, utterance in zip(embeddings, routes, utterances)
+ ]
+ with self.conn.cursor() as cur:
+ cur.executemany(
+ f"INSERT INTO {table_name} (id, route, utterance, vector) VALUES (%s, %s, %s, %s) ON CONFLICT (id) DO NOTHING", # if matching hash exists do nothing.
+ [
+ (record.id, record.route, record.utterance, record.vector)
+ for record in records
+ ],
+ )
+ self.conn.commit()
+
+ def delete(self, route_name: str) -> None:
+ table_name = self._get_table_name()
+ with self.conn.cursor() as cur:
+ cur.execute(f"DELETE FROM {table_name} WHERE route = '{route_name}'")
+ self.conn.commit()
+
+ def describe(self) -> Dict:
+ table_name = self._get_table_name()
+ with self.conn.cursor() as cur:
+ cur.execute(f"SELECT COUNT(*) FROM {table_name}")
+ count = cur.fetchone()[0]
+ return {
+ "type": self.type,
+ "dimensions": self.dimensions,
+ "total_vector_count": count,
+ }
+
+ def query(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ ) -> Tuple[np.ndarray, List[str]]:
+ """
+ Search the index for the query and return top_k results.
+ """
+ table_name = self._get_table_name()
+ with self.conn.cursor() as cur:
+ filter_query = f" AND route = ANY({route_filter})" if route_filter else ""
+ # create the string representation of vector
+ vector_str = f"'[{','.join(map(str, vector.tolist()))}]'"
+ score_query = self._get_score_query(vector_str)
+ opperator = self._get_metric_operator()
+ cur.execute(
+ f"SELECT route, {score_query} FROM {table_name} WHERE true{filter_query} ORDER BY vector {opperator} {vector_str} LIMIT {top_k}"
+ )
+ results = cur.fetchall()
+ print(results)
+ return np.array([result[1] for result in results]), [
+ result[0] for result in results
+ ]
+
+ def delete_index(self) -> None:
+ table_name = self._get_table_name()
+ with self.conn.cursor() as cur:
+ cur.execute(f"DROP TABLE IF EXISTS {table_name}")
+ self.conn.commit()
+
+ class Config:
+ arbitrary_types_allowed = True
--
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