diff --git a/.gitignore b/.gitignore
index 9b4ec366..689157b4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -43,8 +43,9 @@ htmlcov/
 # Weaviate
 weaviate_data/
 
-# CodeGate Dashboard DB
+# CodeGate Dashboard DB & VectoroDB
 codegate.db
+sqlite_data/vectordb.db
 
 # certificate directory
 *certs/
diff --git a/README.md b/README.md
index 1563a49c..29288324 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,8 @@
 
 ## Introduction
 
+<img src="./assets/codegate.gif" style="width: 70%; height: 70%;" />
+
 CodeGate is a local gateway that makes AI coding assistants safer. CodeGate
 ensures AI-generated recommendations adhere to best practices, while
 safeguarding your code's integrity, and protecting your individual privacy. With
diff --git a/assets/codegate.gif b/assets/codegate.gif
new file mode 100644
index 00000000..553cd1f1
Binary files /dev/null and b/assets/codegate.gif differ
diff --git a/config.yaml b/config.yaml
index f044bda1..4e4ff0b3 100644
--- a/config.yaml
+++ b/config.yaml
@@ -6,26 +6,15 @@ host: "localhost"      # Host to bind to (use localhost for all interfaces)
 
 # Logging configuration
 log_level: "INFO"  # One of: ERROR, WARNING, INFO, DEBUG
+log_format: "JSON"  # One of: JSON, TEXT
 
-# Note: This configuration can be overridden by:
-# 1. CLI arguments (--port, --host, --log-level)
-# 2. Environment variables (CODEGATE_APP_PORT, CODEGATE_APP_HOST, CODEGATE_APP_LOG_LEVEL)
-
-
-# Embedding model configuration
-
-####
-# Inference model configuration
-##
-
-# Model to use for chatting
+# Model configuration
 model_base_path: "./codegate_volume/models"
+embedding_model: "all-minilm-L6-v2-q5_k_m.gguf"
 
-# Context length of the model
+# Chat model configuration
 chat_model_n_ctx: 32768
-
-# Number of layers to offload to GPU. If -1, all layers are offloaded.
 chat_model_n_gpu_layers: -1
 
-# Embedding model
-embedding_model: "all-minilm-L6-v2-q5_k_m.gguf"
\ No newline at end of file
+# Storage configuration
+vec_db_path: "./sqlite_data/vectordb.db"  # Path to SQLite vector database for similarity search
diff --git a/config.yaml.example b/config.yaml.example
index 83548e9a..05edcbc9 100644
--- a/config.yaml.example
+++ b/config.yaml.example
@@ -43,7 +43,12 @@ server_key: "server.key"   # Server key file name
 #    CODEGATE_SERVER_CERT
 #    CODEGATE_SERVER_KEY
 
+# Storage configuration
+vec_db_path: "./sqlite_data/vectordb.db"  # Path to SQLite vector database file for similarity search
+
 # Embedding model configuration
+model_base_path: "./codegate_volume/models"  # Base path for model files
+embedding_model: "all-minilm-L6-v2-q5_k_m.gguf"  # Model to use for embeddings
 
 ####
 # Inference model configuration
diff --git a/data/malicious.jsonl b/data/malicious.jsonl
index 10a11dcc..05e44b71 100644
--- a/data/malicious.jsonl
+++ b/data/malicious.jsonl
@@ -4,7 +4,7 @@
 {"name":"malicious-pypi-dummy","type":"pypi","description":"Dummy malicious to test with simple package name on pypi"}
 {"name":"@prefix/malicious-maven-dummy","type":"maven","description":"Dummy malicious to test with encoded package name on maven"}
 {"name":"malicious-maven-dummy","type":"maven","description":"Dummy malicious to test with simple package name on maven"}
-{"name":"github.com/malicious-go-dummy","type":"npm","description":"Dummy malicious to test with encoded package name on go"}
-{"name":"malicious-go-dummy","type":"npm","description":"Dummy malicious to test with simple package name on go"}
-{"name":"@prefix/malicious-crates-dummy","type":"npm","description":"Dummy malicious to test with encoded package name on crates"}
-{"name":"malicious-crates-dummy","type":"npm","description":"Dummy malicious to test with simple package name on crates"}
+{"name":"github.com/malicious-go-dummy","type":"go","description":"Dummy malicious to test with encoded package name on go"}
+{"name":"malicious-go-dummy","type":"go","description":"Dummy malicious to test with simple package name on go"}
+{"name":"@prefix/malicious-crates-dummy","type":"crates","description":"Dummy malicious to test with encoded package name on crates"}
+{"name":"malicious-crates-dummy","type":"crates","description":"Dummy malicious to test with simple package name on crates"}
diff --git a/poetry.lock b/poetry.lock
index 77229cc4..dc8a91e8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -191,20 +191,6 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi
 tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
-[[package]]
-name = "authlib"
-version = "1.3.1"
-description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "Authlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:d35800b973099bbadc49b42b256ecb80041ad56b7fe1216a362c7943c088f377"},
-    {file = "authlib-1.3.1.tar.gz", hash = "sha256:7ae843f03c06c5c0debd63c9db91f9fda64fa62a42a77419fa15fbb7e7a58917"},
-]
-
-[package.dependencies]
-cryptography = "*"
-
 [[package]]
 name = "bandit"
 version = "1.8.0"
@@ -502,13 +488,13 @@ files = [
 
 [[package]]
 name = "click"
-version = "8.1.7"
+version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
-    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
+    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
+    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
 ]
 
 [package.dependencies]
@@ -612,6 +598,7 @@ files = [
     {file = "cryptography-44.0.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:761817a3377ef15ac23cd7834715081791d4ec77f9297ee694ca1ee9c2c7e5eb"},
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     {file = "cryptography-44.0.0-cp37-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:4ac4c9f37eba52cb6fbeaf5b59c152ea976726b865bd4cf87883a7e7006cc543"},
+    {file = "cryptography-44.0.0-cp37-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:60eb32934076fa07e4316b7b2742fa52cbb190b42c2df2863dbc4230a0a9b385"},
     {file = "cryptography-44.0.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:ed3534eb1090483c96178fcb0f8893719d96d5274dfde98aa6add34614e97c8e"},
     {file = "cryptography-44.0.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f3f6fdfa89ee2d9d496e2c087cebef9d4fcbb0ad63c40e821b39f74bf48d9c5e"},
     {file = "cryptography-44.0.0-cp37-abi3-win32.whl", hash = "sha256:eb33480f1bad5b78233b0ad3e1b0be21e8ef1da745d8d2aecbb20671658b9053"},
@@ -622,6 +609,7 @@ files = [
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     {file = "cryptography-44.0.0-cp39-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:9e6fc8a08e116fb7c7dd1f040074c9d7b51d74a8ea40d4df2fc7aa08b76b9e6c"},
+    {file = "cryptography-44.0.0-cp39-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:9abcc2e083cbe8dde89124a47e5e53ec38751f0d7dfd36801008f316a127d7ba"},
     {file = "cryptography-44.0.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:d2436114e46b36d00f8b72ff57e598978b37399d2786fd39793c36c6d5cb1c64"},
     {file = "cryptography-44.0.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a01956ddfa0a6790d594f5b34fc1bfa6098aca434696a03cfdbe469b8ed79285"},
     {file = "cryptography-44.0.0-cp39-abi3-win32.whl", hash = "sha256:eca27345e1214d1b9f9490d200f9db5a874479be914199194e746c893788d417"},
@@ -809,13 +797,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2024.10.0"
+version = "2024.12.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
-    {file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
+    {file = "fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2"},
+    {file = "fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f"},
 ]
 
 [package.extras]
@@ -932,157 +920,6 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
-[[package]]
-name = "grpcio"
-version = "1.68.1"
-description = "HTTP/2-based RPC framework"
-optional = false
-python-versions = ">=3.8"
-files = [
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-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (>=1.12,<1.14)", "pytest-mypy"]
-
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -2763,6 +2560,20 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3_binary"]
 
+[[package]]
+name = "sqlite-vec"
+version = "0.1.6"
+description = ""
+optional = false
+python-versions = "*"
+files = [
+    {file = "sqlite_vec-0.1.6-py3-none-macosx_10_6_x86_64.whl", hash = "sha256:77491bcaa6d496f2acb5cc0d0ff0b8964434f141523c121e313f9a7d8088dee3"},
+    {file = "sqlite_vec-0.1.6-py3-none-macosx_11_0_arm64.whl", hash = "sha256:fdca35f7ee3243668a055255d4dee4dea7eed5a06da8cad409f89facf4595361"},
+    {file = "sqlite_vec-0.1.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b0519d9cd96164cd2e08e8eed225197f9cd2f0be82cb04567692a0a4be02da3"},
+    {file = "sqlite_vec-0.1.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux1_x86_64.whl", hash = "sha256:823b0493add80d7fe82ab0fe25df7c0703f4752941aee1c7b2b02cec9656cb24"},
+    {file = "sqlite_vec-0.1.6-py3-none-win_amd64.whl", hash = "sha256:c65bcfd90fa2f41f9000052bcb8bb75d38240b2dae49225389eca6c3136d3f0c"},
+]
+
 [[package]]
 name = "starlette"
 version = "0.41.3"
@@ -2968,40 +2779,6 @@ h11 = ">=0.8"
 [package.extras]
 standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
 
-[[package]]
-name = "validators"
-version = "0.34.0"
-description = "Python Data Validation for Humans™"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "validators-0.34.0-py3-none-any.whl", hash = "sha256:c804b476e3e6d3786fa07a30073a4ef694e617805eb1946ceee3fe5a9b8b1321"},
-    {file = "validators-0.34.0.tar.gz", hash = "sha256:647fe407b45af9a74d245b943b18e6a816acf4926974278f6dd617778e1e781f"},
-]
-
-[package.extras]
-crypto-eth-addresses = ["eth-hash[pycryptodome] (>=0.7.0)"]
-
-[[package]]
-name = "weaviate-client"
-version = "4.10.2"
-description = "A python native Weaviate client"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "weaviate_client-4.10.2-py3-none-any.whl", hash = "sha256:e1706438aa7b57be5443bbdebff206cc6688110d1669d54c2721b3aa640b2c4c"},
-    {file = "weaviate_client-4.10.2.tar.gz", hash = "sha256:fde5ad8e36604674d26b115288b58a7e182c91e36c2b41a00d18a36fe4ec7e3f"},
-]
-
-[package.dependencies]
-authlib = ">=1.2.1,<1.3.2"
-grpcio = ">=1.66.2,<2.0.0"
-grpcio-health-checking = ">=1.66.2,<2.0.0"
-grpcio-tools = ">=1.66.2,<2.0.0"
-httpx = ">=0.26.0,<0.29.0"
-pydantic = ">=2.8.0,<3.0.0"
-validators = "0.34.0"
-
 [[package]]
 name = "wheel"
 version = "0.45.1"
@@ -3134,4 +2911,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11,<4.0"
-content-hash = "aae85414c141e4416825dd58988cdff5088526081d6ff5ba641b7bb1fd620a71"
+content-hash = "ec5f9c34a5bb0cf2b6464858305ef3d59c488fc27e117078554dd027758e6c06"
diff --git a/pyproject.toml b/pyproject.toml
index 85919671..4783bf0f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,6 @@ click = ">=8.1.0"
 PyYAML = ">=6.0.1"
 fastapi = ">=0.115.5"
 uvicorn = ">=0.32.1"
-weaviate-client = ">=4.9.6"
 structlog = ">=24.4.0"
 litellm = "^1.55.4"
 llama_cpp_python = ">=0.3.2"
@@ -21,6 +20,8 @@ greenlet = "^3.0.3"
 aiosqlite = "^0.20.0"
 ollama = ">=0.4.4"
 pydantic-settings = "^2.7.0"
+sqlite-vec = ">=0.1.0"
+numpy = ">=1.24.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.4.0"
diff --git a/scripts/import_packages.py b/scripts/import_packages.py
index 75be7c7d..dc279af1 100644
--- a/scripts/import_packages.py
+++ b/scripts/import_packages.py
@@ -2,152 +2,125 @@
 import asyncio
 import json
 import os
-import shutil
+import sqlite3
 
-import weaviate
-from weaviate.classes.config import DataType, Property
-from weaviate.embedded import EmbeddedOptions
-from weaviate.util import generate_uuid5
+import numpy as np
+import sqlite_vec
 
 from codegate.inference.inference_engine import LlamaCppInferenceEngine
 from codegate.utils.utils import generate_vector_string
 
 
 class PackageImporter:
-    def __init__(self, jsonl_dir="data", take_backup=True, restore_backup=True):
-        self.take_backup_flag = take_backup
-        self.restore_backup_flag = restore_backup
-
-        self.client = weaviate.WeaviateClient(
-            embedded_options=EmbeddedOptions(
-                persistence_data_path="./weaviate_data",
-                grpc_port=50052,
-                additional_env_vars={
-                    "ENABLE_MODULES": "backup-filesystem",
-                    "BACKUP_FILESYSTEM_PATH": os.getenv("BACKUP_FILESYSTEM_PATH", "/tmp"),
-                },
-            )
-        )
+    def __init__(self, jsonl_dir="data", db_path="./sqlite_data/vectordb.db"):
+        os.makedirs(os.path.dirname(db_path), exist_ok=True)
+        self.db_path = db_path
         self.json_files = [
             os.path.join(jsonl_dir, "archived.jsonl"),
             os.path.join(jsonl_dir, "deprecated.jsonl"),
             os.path.join(jsonl_dir, "malicious.jsonl"),
         ]
-        self.client.connect()
+        self.conn = self._get_connection()
         self.inference_engine = LlamaCppInferenceEngine()
         self.model_path = "./codegate_volume/models/all-minilm-L6-v2-q5_k_m.gguf"
 
-    def restore_backup(self):
-        if os.getenv("BACKUP_FOLDER"):
-            try:
-                self.client.backup.restore(
-                    backup_id=os.getenv("BACKUP_FOLDER"),
-                    backend="filesystem",
-                    wait_for_completion=True,
-                )
-            except Exception as e:
-                print(f"Failed to restore backup: {e}")
-
-    def take_backup(self):
-        # if backup folder exists, remove it
-        backup_path = os.path.join(
-            os.getenv("BACKUP_FILESYSTEM_PATH", "/tmp"), os.getenv("BACKUP_TARGET_ID", "backup")
-        )
-        if os.path.exists(backup_path):
-            shutil.rmtree(backup_path)
-
-        #  take a backup of the data
-        try:
-            self.client.backup.create(
-                backup_id=os.getenv("BACKUP_TARGET_ID", "backup"),
-                backend="filesystem",
-                wait_for_completion=True,
-            )
-        except Exception as e:
-            print(f"Failed to take backup: {e}")
+    def _get_connection(self):
+        conn = sqlite3.connect(self.db_path)
+        conn.enable_load_extension(True)
+        sqlite_vec.load(conn)
+        conn.enable_load_extension(False)
+        return conn
 
     def setup_schema(self):
-        if not self.client.collections.exists("Package"):
-            self.client.collections.create(
-                "Package",
-                properties=[
-                    Property(name="name", data_type=DataType.TEXT),
-                    Property(name="type", data_type=DataType.TEXT),
-                    Property(name="status", data_type=DataType.TEXT),
-                    Property(name="description", data_type=DataType.TEXT),
-                ],
+        cursor = self.conn.cursor()
+        cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS packages (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                name TEXT NOT NULL,
+                type TEXT NOT NULL,
+                status TEXT NOT NULL,
+                description TEXT,
+                embedding BLOB
             )
+        """
+        )
+
+        # Create indexes for faster querying
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_name ON packages(name)")
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_type ON packages(type)")
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_status ON packages(status)")
 
-    async def process_package(self, batch, package):
+        self.conn.commit()
+
+    async def process_package(self, package):
         vector_str = generate_vector_string(package)
         vector = await self.inference_engine.embed(self.model_path, [vector_str])
-        # This is where the synchronous call is made
-        batch.add_object(properties=package, vector=vector[0])
+        vector_array = np.array(vector[0], dtype=np.float32)
+
+        cursor = self.conn.cursor()
+        cursor.execute(
+            """
+            INSERT INTO packages (name, type, status, description, embedding)
+            VALUES (?, ?, ?, ?, ?)
+        """,
+            (
+                package["name"],
+                package["type"],
+                package["status"],
+                package["description"],
+                vector_array,  # sqlite-vec will handle numpy arrays directly
+            ),
+        )
+        self.conn.commit()
 
     async def add_data(self):
-        collection = self.client.collections.get("Package")
-        existing_packages = list(collection.iterator())
-        packages_dict = {
-            f"{package.properties['name']}/{package.properties['type']}": {
-                "status": package.properties["status"],
-                "description": package.properties["description"],
-            }
-            for package in existing_packages
+        cursor = self.conn.cursor()
+
+        # Get existing packages
+        cursor.execute(
+            """
+            SELECT name, type, status, description
+            FROM packages
+        """
+        )
+        existing_packages = {
+            f"{row[0]}/{row[1]}": {"status": row[2], "description": row[3]}
+            for row in cursor.fetchall()
         }
 
         for json_file in self.json_files:
+            print("Adding data from", json_file)
             with open(json_file, "r") as f:
-                print("Adding data from", json_file)
-                packages_to_insert = []
                 for line in f:
                     package = json.loads(line)
                     package["status"] = json_file.split("/")[-1].split(".")[0]
                     key = f"{package['name']}/{package['type']}"
 
-                    if key in packages_dict and packages_dict[key] == {
+                    if key in existing_packages and existing_packages[key] == {
                         "status": package["status"],
                         "description": package["description"],
                     }:
                         print("Package already exists", key)
                         continue
 
-                    vector_str = generate_vector_string(package)
-                    vector = await self.inference_engine.embed(self.model_path, [vector_str])
-                    packages_to_insert.append((package, vector[0]))
-
-                # Synchronous batch insert after preparing all data
-                with collection.batch.dynamic() as batch:
-                    for package, vector in packages_to_insert:
-                        batch.add_object(
-                            properties=package, vector=vector, uuid=generate_uuid5(package)
-                        )
+                    await self.process_package(package)
 
     async def run_import(self):
-        if self.restore_backup_flag:
-            self.restore_backup()
         self.setup_schema()
         await self.add_data()
-        if self.take_backup_flag:
-            self.take_backup()
+
+    def __del__(self):
+        try:
+            if hasattr(self, "conn"):
+                self.conn.close()
+        except Exception as e:
+            print(f"Failed to close connection: {e}")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
-        description="Run the package importer with optional backup flags."
-    )
-    parser.add_argument(
-        "--take-backup",
-        type=lambda x: x.lower() == "true",
-        default=True,
-        help="Specify whether to take a backup after "
-        "data import (True or False). Default is True.",
-    )
-    parser.add_argument(
-        "--restore-backup",
-        type=lambda x: x.lower() == "true",
-        default=True,
-        help="Specify whether to restore a backup before "
-        "data import (True or False). Default is True.",
+        description="Import packages into SQLite database with vector search capabilities."
     )
     parser.add_argument(
         "--jsonl-dir",
@@ -155,14 +128,13 @@ async def run_import(self):
         default="data",
         help="Directory containing JSONL files. Default is 'data'.",
     )
+    parser.add_argument(
+        "--db-path",
+        type=str,
+        default="./sqlite_data/vectordb.db",
+        help="Path to SQLite database file. Default is './sqlite_data/vectordb.db'.",
+    )
     args = parser.parse_args()
 
-    importer = PackageImporter(
-        jsonl_dir=args.jsonl_dir, take_backup=args.take_backup, restore_backup=args.restore_backup
-    )
+    importer = PackageImporter(jsonl_dir=args.jsonl_dir, db_path=args.db_path)
     asyncio.run(importer.run_import())
-    try:
-        assert importer.client.is_live()
-        pass
-    finally:
-        importer.client.close()
diff --git a/sqlite_data/vectordb.db b/sqlite_data/vectordb.db
new file mode 100644
index 00000000..edf6ef96
Binary files /dev/null and b/sqlite_data/vectordb.db differ
diff --git a/src/codegate/ca/codegate_ca.py b/src/codegate/ca/codegate_ca.py
index a95a2716..067c9815 100644
--- a/src/codegate/ca/codegate_ca.py
+++ b/src/codegate/ca/codegate_ca.py
@@ -135,12 +135,12 @@ def _load_existing_certificates(self) -> None:
         expiry_date = current_time + timedelta(days=TLS_GRACE_PERIOD_DAYS)
 
         for filename in os.listdir(certs_dir):
-            if (
-                filename.endswith('.crt') and
-                filename not in [Config.get_config().ca_cert, Config.get_config().server_cert]
-            ):
+            if filename.endswith(".crt") and filename not in [
+                Config.get_config().ca_cert,
+                Config.get_config().server_cert,
+            ]:
                 cert_path = os.path.join(certs_dir, filename)
-                key_path = os.path.join(certs_dir, filename.replace('.crt', '.key'))
+                key_path = os.path.join(certs_dir, filename.replace(".crt", ".key"))
 
                 # Skip if key file doesn't exist
                 if not os.path.exists(key_path):
@@ -320,7 +320,6 @@ def generate_ca_certificates(self) -> None:
         self._ca_cert_expiry = self._ca_cert.not_valid_after_utc
         self._ca_last_load_time = datetime.now(timezone.utc)
 
-
         # Define file paths for certificate and key
         ca_cert_path = self.get_cert_path(Config.get_config().ca_cert)
         ca_key_path = self.get_cert_path(Config.get_config().ca_key)
diff --git a/src/codegate/cli.py b/src/codegate/cli.py
index 983d9a60..87af19f6 100644
--- a/src/codegate/cli.py
+++ b/src/codegate/cli.py
@@ -238,7 +238,13 @@ def show_prompts(prompts: Optional[Path]) -> None:
     "--db-path",
     type=str,
     default=None,
-    help="Path to the SQLite database file",
+    help="Path to the main SQLite database file (default: ./codegate_volume/db/codegate.db)",
+)
+@click.option(
+    "--vec-db-path",
+    type=str,
+    default=None,
+    help="Path to the vector SQLite database file (default: ./sqlite_data/vectordb.db)",
 )
 def serve(
     port: Optional[int],
@@ -255,6 +261,7 @@ def serve(
     model_base_path: Optional[str],
     embedding_model: Optional[str],
     db_path: Optional[str],
+    vec_db_path: Optional[str],
     certs_dir: Optional[str],
     ca_cert: Optional[str],
     ca_key: Optional[str],
@@ -292,6 +299,7 @@ def serve(
             server_cert=server_cert,
             server_key=server_key,
             db_path=db_path,
+            vec_db_path=vec_db_path,
         )
 
         # Set up logging first
@@ -356,6 +364,7 @@ async def run_servers(cfg: Config, app) -> None:
                 "embedding_model": cfg.embedding_model,
                 "certs_dir": cfg.certs_dir,
                 "db_path": cfg.db_path,
+                "vec_db_path": cfg.vec_db_path,
             },
         )
 
diff --git a/src/codegate/config.py b/src/codegate/config.py
index 76d947f2..3f99fd04 100644
--- a/src/codegate/config.py
+++ b/src/codegate/config.py
@@ -43,6 +43,7 @@ class Config:
     chat_model_n_gpu_layers: int = -1
     embedding_model: str = "all-minilm-L6-v2-q5_k_m.gguf"
     db_path: Optional[str] = None
+    vec_db_path: Optional[str] = "./sqlite_data/vectordb.db"  # Vector database
 
     # Certificate configuration
     certs_dir: str = "./codegate_volume/certs"
@@ -140,6 +141,8 @@ def from_file(cls, config_path: Union[str, Path]) -> "Config":
                     "chat_model_n_gpu_layers", cls.chat_model_n_gpu_layers
                 ),
                 embedding_model=config_data.get("embedding_model", cls.embedding_model),
+                db_path=config_data.get("db_path", cls.db_path),
+                vec_db_path=config_data.get("vec_db_path", cls.vec_db_path),
                 certs_dir=config_data.get("certs_dir", cls.certs_dir),
                 ca_cert=config_data.get("ca_cert", cls.ca_cert),
                 ca_key=config_data.get("ca_key", cls.ca_key),
@@ -193,6 +196,10 @@ def from_env(cls) -> "Config":
                 config.server_key = os.environ["CODEGATE_SERVER_KEY"]
             if "CODEGATE_FORCE_CERTS" in os.environ:
                 config.force_certs = os.environ["CODEGATE_FORCE_CERTS"]
+            if "CODEGATE_DB_PATH" in os.environ:
+                config.db_path = os.environ["CODEGATE_DB_PATH"]
+            if "CODEGATE_VEC_DB_PATH" in os.environ:
+                config.vec_db_path = os.environ["CODEGATE_VEC_DB_PATH"]
 
             # Load provider URLs from environment variables
             for provider in DEFAULT_PROVIDER_URLS.keys():
@@ -224,6 +231,7 @@ def load(
         server_key: Optional[str] = None,
         force_certs: Optional[bool] = None,
         db_path: Optional[str] = None,
+        vec_db_path: Optional[str] = None,
     ) -> "Config":
         """Load configuration with priority resolution.
 
@@ -250,7 +258,8 @@ def load(
             server_cert: Optional path to server certificate
             server_key: Optional path to server key
             force_certs: Optional flag to force certificate generation
-            db_path: Optional path to the SQLite database file
+            db_path: Optional path to the main SQLite database file
+            vec_db_path: Optional path to the vector SQLite database file
 
         Returns:
             Config: Resolved configuration
@@ -299,6 +308,10 @@ def load(
             config.server_key = env_config.server_key
         if "CODEGATE_FORCE_CERTS" in os.environ:
             config.force_certs = env_config.force_certs
+        if "CODEGATE_DB_PATH" in os.environ:
+            config.db_path = env_config.db_path
+        if "CODEGATE_VEC_DB_PATH" in os.environ:
+            config.vec_db_path = env_config.vec_db_path
 
         # Override provider URLs from environment
         for provider, url in env_config.provider_urls.items():
@@ -335,6 +348,8 @@ def load(
             config.server_key = server_key
         if db_path is not None:
             config.db_path = db_path
+        if vec_db_path is not None:
+            config.vec_db_path = vec_db_path
         if force_certs is not None:
             config.force_certs = force_certs
 
diff --git a/src/codegate/inference/inference_engine.py b/src/codegate/inference/inference_engine.py
index caaa92a9..a69b7e13 100644
--- a/src/codegate/inference/inference_engine.py
+++ b/src/codegate/inference/inference_engine.py
@@ -77,5 +77,20 @@ async def embed(self, model_path, content):
         """
         Generates an embedding for the given content using the specified model.
         """
+        logger.debug(
+            "Generating embedding",
+            model=model_path.split("/")[-1],
+            content=content,
+            content_length=len(content[0]) if content else 0,
+        )
+
         model = await self.__get_model(model_path=model_path, embedding=True)
-        return model.embed(content)
+        embedding = model.embed(content)
+
+        logger.debug(
+            "Generated embedding",
+            model=model_path.split("/")[-1],
+            vector_length=len(embedding[0]) if embedding else 0,
+        )
+
+        return embedding
diff --git a/src/codegate/pipeline/codegate_context_retriever/codegate.py b/src/codegate/pipeline/codegate_context_retriever/codegate.py
index 13853b1a..2ad4767e 100644
--- a/src/codegate/pipeline/codegate_context_retriever/codegate.py
+++ b/src/codegate/pipeline/codegate_context_retriever/codegate.py
@@ -29,25 +29,24 @@ def name(self) -> str:
         """
         return "codegate-context-retriever"
 
-    async def get_objects_from_db(
-        self, ecosystem, packages: list[str] = None
-    ) -> list[object]:
+    async def get_objects_from_db(self, ecosystem, packages: list[str] = None) -> list[object]:
+        logger.debug("Searching database for packages", ecosystem=ecosystem, packages=packages)
         storage_engine = StorageEngine()
-        objects = await storage_engine.search(
-            distance=0.8, ecosystem=ecosystem, packages=packages
+        objects = await storage_engine.search(distance=0.8, ecosystem=ecosystem, packages=packages)
+        logger.debug(
+            "Database search results",
+            result_count=len(objects),
+            results=[obj["properties"] for obj in objects] if objects else None,
         )
         return objects
 
     def generate_context_str(self, objects: list[object], context: PipelineContext) -> str:
         context_str = ""
+        matched_packages = []
         for obj in objects:
-            # generate dictionary from object
-            package_obj = {
-                "name": obj.properties["name"],
-                "type": obj.properties["type"],
-                "status": obj.properties["status"],
-                "description": obj.properties["description"],
-            }
+            # The object is already a dictionary with 'properties'
+            package_obj = obj["properties"]
+            matched_packages.append(f"{package_obj['name']} ({package_obj['type']})")
             # Add one alert for each package found
             context.add_alert(
                 self.name,
@@ -56,6 +55,11 @@ def generate_context_str(self, objects: list[object], context: PipelineContext)
             )
             package_str = generate_vector_string(package_obj)
             context_str += package_str + "\n"
+
+        if matched_packages:
+            logger.debug(
+                "Found matching packages in sqlite-vec database", matched_packages=matched_packages
+            )
         return context_str
 
     async def __lookup_packages(self, user_query: str, context: PipelineContext):
@@ -83,7 +87,7 @@ async def __lookup_ecosystem(self, user_query: str, context: PipelineContext):
             extra_headers=context.metadata.get("extra_headers", None),
         )
 
-        logger.info(f"Ecosystem in user query: {ecosystem}")
+        logger.debug("Extracted ecosystem from query", ecosystem=ecosystem, query=user_query)
         return ecosystem
 
     async def process(
@@ -104,6 +108,13 @@ async def process(
         ecosystem = await self.__lookup_ecosystem(user_messages, context)
         packages = await self.__lookup_packages(user_messages, context)
 
+        logger.debug(
+            "Processing request",
+            user_messages=user_messages,
+            extracted_ecosystem=ecosystem,
+            extracted_packages=packages,
+        )
+
         context_str = "CodeGate did not find any malicious or archived packages."
 
         if len(packages) > 0:
@@ -132,4 +143,6 @@ async def process(
         context_msg = f'Context: {context_str} \n\n Query: {message["content"]}'
         message["content"] = context_msg
 
+        logger.debug("Final context message", context_message=context_msg)
+
         return PipelineResult(request=new_request, context=context)
diff --git a/src/codegate/providers/anthropic/provider.py b/src/codegate/providers/anthropic/provider.py
index db26e682..78c5df72 100644
--- a/src/codegate/providers/anthropic/provider.py
+++ b/src/codegate/providers/anthropic/provider.py
@@ -1,7 +1,7 @@
 import json
-import structlog
 from typing import Optional
 
+import structlog
 from fastapi import Header, HTTPException, Request
 
 from codegate.pipeline.base import SequentialPipelineProcessor
diff --git a/src/codegate/providers/llamacpp/provider.py b/src/codegate/providers/llamacpp/provider.py
index e63f1fbc..37dc64d1 100644
--- a/src/codegate/providers/llamacpp/provider.py
+++ b/src/codegate/providers/llamacpp/provider.py
@@ -1,8 +1,8 @@
 import json
-import structlog
 from typing import Optional
 
-from fastapi import Request, HTTPException
+import structlog
+from fastapi import HTTPException, Request
 
 from codegate.pipeline.base import SequentialPipelineProcessor
 from codegate.pipeline.output import OutputPipelineProcessor
@@ -11,7 +11,7 @@
 from codegate.providers.llamacpp.normalizer import LLamaCppInputNormalizer, LLamaCppOutputNormalizer
 
 
-class LlamaCppProvider(BaseProvider):    
+class LlamaCppProvider(BaseProvider):
     def __init__(
         self,
         pipeline_processor: Optional[SequentialPipelineProcessor] = None,
@@ -59,8 +59,9 @@ async def create_completion(
             except ValueError as e:
                 # capture well known exceptions
                 logger.error("Error in LlamaCppProvider completion", error=str(e))
-                if str(e).startswith("Model path does not exist") or \
-                        str(e).startswith("No file found"):
+                if str(e).startswith("Model path does not exist") or str(e).startswith(
+                    "No file found"
+                ):
                     raise HTTPException(status_code=404, detail=str(e))
                 elif "exceed" in str(e):
                     raise HTTPException(status_code=429, detail=str(e))
diff --git a/src/codegate/providers/ollama/provider.py b/src/codegate/providers/ollama/provider.py
index d0517299..dc64baec 100644
--- a/src/codegate/providers/ollama/provider.py
+++ b/src/codegate/providers/ollama/provider.py
@@ -1,9 +1,9 @@
 import json
 from typing import Optional
 
-from fastapi import Request, HTTPException
 import httpx
 import structlog
+from fastapi import HTTPException, Request
 
 from codegate.config import Config
 from codegate.pipeline.base import SequentialPipelineProcessor
diff --git a/src/codegate/providers/openai/provider.py b/src/codegate/providers/openai/provider.py
index e3b78da2..75c201da 100644
--- a/src/codegate/providers/openai/provider.py
+++ b/src/codegate/providers/openai/provider.py
@@ -1,7 +1,7 @@
 import json
-import structlog
 from typing import Optional
 
+import structlog
 from fastapi import Header, HTTPException, Request
 
 from codegate.pipeline.base import SequentialPipelineProcessor
diff --git a/src/codegate/providers/vllm/provider.py b/src/codegate/providers/vllm/provider.py
index b80a3096..6d7b9bca 100644
--- a/src/codegate/providers/vllm/provider.py
+++ b/src/codegate/providers/vllm/provider.py
@@ -1,8 +1,8 @@
 import json
-import structlog
 from typing import Optional
 
 import httpx
+import structlog
 from fastapi import Header, HTTPException, Request
 from litellm import atext_completion
 
diff --git a/src/codegate/server.py b/src/codegate/server.py
index b1b6afd9..57206712 100644
--- a/src/codegate/server.py
+++ b/src/codegate/server.py
@@ -18,6 +18,7 @@
 
 logger = structlog.get_logger("codegate")
 
+
 async def custom_error_handler(request, exc: Exception):
     """This is a Middleware to handle exceptions and log them."""
     # Capture the stack trace
diff --git a/src/codegate/storage/storage_engine.py b/src/codegate/storage/storage_engine.py
index 5aaf91b4..ab2cd6a8 100644
--- a/src/codegate/storage/storage_engine.py
+++ b/src/codegate/storage/storage_engine.py
@@ -1,11 +1,10 @@
+import os
+import sqlite3
 from typing import List
 
+import numpy as np
+import sqlite_vec
 import structlog
-import weaviate
-import weaviate.classes as wvc
-from weaviate.classes.config import DataType
-from weaviate.classes.query import Filter, MetadataQuery
-from weaviate.embedded import EmbeddedOptions
 
 from codegate.config import Config
 from codegate.inference.inference_engine import LlamaCppInferenceEngine
@@ -13,18 +12,6 @@
 logger = structlog.get_logger("codegate")
 VALID_ECOSYSTEMS = ["npm", "pypi", "crates", "maven", "go"]
 
-schema_config = [
-    {
-        "name": "Package",
-        "properties": [
-            {"name": "name", "data_type": DataType.TEXT},
-            {"name": "type", "data_type": DataType.TEXT},
-            {"name": "status", "data_type": DataType.TEXT},
-            {"name": "description", "data_type": DataType.TEXT},
-        ],
-    },
-]
-
 
 class StorageEngine:
     __storage_engine = None
@@ -34,113 +21,104 @@ def __new__(cls, *args, **kwargs):
             cls.__storage_engine = super().__new__(cls)
         return cls.__storage_engine
 
-    # This function is needed only for the unit testing for the
-    # mocks to work.
     @classmethod
     def recreate_instance(cls, *args, **kwargs):
         cls.__storage_engine = None
         return cls(*args, **kwargs)
 
-    def __init__(self, data_path="./weaviate_data"):
+    def __init__(self, data_path="./sqlite_data"):
         if hasattr(self, "initialized"):
             return
 
         self.initialized = True
         self.data_path = data_path
+        os.makedirs(data_path, exist_ok=True)
+
+        # Use vec_db_path from config if available, otherwise fallback to default
+        config = Config.get_config()
+        self.db_path = (
+            config.vec_db_path
+            if config and hasattr(config, "vec_db_path")
+            else os.path.join(data_path, "vectordb.db")
+        )
+
         self.inference_engine = LlamaCppInferenceEngine()
         self.model_path = (
             f"{Config.get_config().model_base_path}/{Config.get_config().embedding_model}"
         )
-        self.schema_config = schema_config
 
-        # setup schema for weaviate
-        self.weaviate_client = self.get_client(self.data_path)
-        if self.weaviate_client is not None:
-            try:
-                self.weaviate_client.connect()
-                self.setup_schema(self.weaviate_client)
-            except Exception as e:
-                logger.error(f"Failed to connect or setup schema: {str(e)}")
+        self.conn = self._get_connection()
+        self._setup_schema()
 
     def __del__(self):
         try:
-            self.weaviate_client.close()
+            if hasattr(self, "conn"):
+                self.conn.close()
         except Exception as e:
-            logger.error(f"Failed to close client: {str(e)}")
+            logger.error(f"Failed to close connection: {str(e)}")
 
-    def get_client(self, data_path):
+    def _get_connection(self):
         try:
-            # Configure Weaviate logging
-            additional_env_vars = {
-                # Basic logging configuration
-                "LOG_FORMAT": Config.get_config().log_format.value.lower(),
-                "LOG_LEVEL": Config.get_config().log_level.value.lower(),
-                # Disable colored output
-                "LOG_FORCE_COLOR": "false",
-                # Configure JSON format
-                "LOG_JSON_FIELDS": "timestamp, level,message",
-                # Configure text format
-                "LOG_METHOD": Config.get_config().log_format.value.lower(),
-                "LOG_LEVEL_IN_UPPER": "false",  # Keep level lowercase like codegate format
-                # Disable additional fields
-                "LOG_GIT_HASH": "false",
-                "LOG_VERSION": "false",
-                "LOG_BUILD_INFO": "false",
-            }
-
-            client = weaviate.WeaviateClient(
-                embedded_options=EmbeddedOptions(
-                    persistence_data_path=data_path,
-                    additional_env_vars=additional_env_vars,
-                ),
-            )
-            return client
+            conn = sqlite3.connect(self.db_path)
+            conn.enable_load_extension(True)
+            sqlite_vec.load(conn)
+            conn.enable_load_extension(False)
+            return conn
         except Exception as e:
-            logger.error(f"Error during client creation: {str(e)}")
-            return None
-
-    def setup_schema(self, client):
-        for class_config in self.schema_config:
-            if not client.collections.exists(class_config["name"]):
-                client.collections.create(
-                    class_config["name"], properties=class_config["properties"]
-                )
-            logger.info(f"Weaviate schema for class {class_config['name']} setup complete.")
+            logger.error("Failed to initialize database connection", error=str(e))
+            raise
+
+    def _setup_schema(self):
+        cursor = self.conn.cursor()
+        cursor.execute(
+            """
+            CREATE TABLE IF NOT EXISTS packages (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                name TEXT NOT NULL,
+                type TEXT NOT NULL,
+                status TEXT NOT NULL,
+                description TEXT,
+                embedding BLOB
+            )
+        """
+        )
 
-    async def search_by_property(self, name: str, properties: List[str]) -> list[object]:
-        if len(properties) == 0:
-            return []
+        # Create indexes for faster querying
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_name ON packages(name)")
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_type ON packages(type)")
+        cursor.execute("CREATE INDEX IF NOT EXISTS idx_status ON packages(status)")
 
-        # Perform the vector search
-        if self.weaviate_client is None:
-            logger.error("Could not find client, not returning results.")
-            return []
+        self.conn.commit()
 
-        if not self.weaviate_client:
-            logger.error("Invalid client, cannot perform search.")
+    async def search_by_property(self, name: str, properties: List[str]) -> list[object]:
+        if len(properties) == 0:
             return []
 
         try:
-            packages = self.weaviate_client.collections.get("Package")
-            response = packages.query.fetch_objects(
-                filters=Filter.by_property(name).contains_any(properties),
-            )
-
-            if not response:
-                return []
-
-            # Weaviate performs substring matching of the properties. So
-            # we need to double check the response.
-            properties = [prop.lower() for prop in properties]
-            filterd_objects = []
-            for object in response.objects:
-                if object.properties[name].lower() in properties:
-                    filterd_objects.append(object)
-            response.objects = filterd_objects
-
-            return response.objects
+            cursor = self.conn.cursor()
+            placeholders = ",".join("?" * len(properties))
+            query = f"""
+                SELECT name, type, status, description
+                FROM packages
+                WHERE LOWER({name}) IN ({placeholders})
+            """  # nosec
+
+            cursor.execute(query, [prop.lower() for prop in properties])
+            results = []
+            for row in cursor.fetchall():
+                results.append(
+                    {
+                        "properties": {
+                            "name": row[0],
+                            "type": row[1],
+                            "status": row[2],
+                            "description": row[3],
+                        }
+                    }
+                )
+            return results
         except Exception as e:
-            logger.error(f"An error occurred: {str(e)}")
+            logger.error(f"An error occurred during property search: {str(e)}")
             return []
 
     async def search(
@@ -152,66 +130,99 @@ async def search(
         distance: float = 0.3,
     ) -> list[object]:
         """
-        Search the 'Package' collection based on a query string, ecosystem and packages.
-        If packages and ecosystem are both not none, then filter the objects using them.
-        If packages is not none and ecosystem is none, then filter the objects using
-        package names.
-        If packages is none, then perform vector search.
-
-        Args:
-            query (str): The text query for which to search.
-            limit (int): The number of results to return.
-            distance (float): The distance threshold for the search.
-            ecosystem (str): The ecosystem to search in.
-            packages (list): The list of packages to filter the search.
-
-        Returns:
-            list: A list of matching results with their properties and distances.
+        Search packages based on vector similarity or direct property matches.
         """
-
         try:
-            collection = self.weaviate_client.collections.get("Package")
+            cursor = self.conn.cursor()
 
-            response = None
             if packages and ecosystem and ecosystem in VALID_ECOSYSTEMS:
-                response = collection.query.fetch_objects(
-                    filters=wvc.query.Filter.all_of([
-                        wvc.query.Filter.by_property("name").contains_any(packages),
-                        wvc.query.Filter.by_property("type").equal(ecosystem)
-                    ]),
+                placeholders = ",".join("?" * len(packages))
+                query_sql = f"""
+                    SELECT name, type, status, description
+                    FROM packages
+                    WHERE LOWER(name) IN ({placeholders})
+                    AND LOWER(type) = ?
+                """  # nosec
+                params = [p.lower() for p in packages] + [ecosystem.lower()]
+                logger.debug(
+                    "Searching by package names and ecosystem",
+                    packages=packages,
+                    ecosystem=ecosystem,
+                    sql=query_sql,
+                    params=params,
                 )
-                response.objects = [
-                    obj
-                    for obj in response.objects
-                    if obj.properties["name"].lower() in packages
-                    and obj.properties["type"].lower() == ecosystem.lower()
-                ]
+                cursor.execute(query_sql, params)
+
             elif packages and not ecosystem:
-                response = collection.query.fetch_objects(
-                    filters=wvc.query.Filter.all_of([
-                        wvc.query.Filter.by_property("name").contains_any(packages),
-                    ]),
+                placeholders = ",".join("?" * len(packages))
+                query_sql = f"""
+                    SELECT name, type, status, description
+                    FROM packages
+                    WHERE LOWER(name) IN ({placeholders})
+                """  # nosec
+                params = [p.lower() for p in packages]
+                logger.debug(
+                    "Searching by package names only",
+                    packages=packages,
+                    sql=query_sql,
+                    params=params,
                 )
-                response.objects = [
-                    obj
-                    for obj in response.objects
-                    if obj.properties["name"].lower() in packages
-                ]
+                cursor.execute(query_sql, params)
+
             elif query:
-                # Perform the vector search
-                # Generate the vector for the query
+                # Generate embedding for the query
                 query_vector = await self.inference_engine.embed(self.model_path, [query])
-
-                response = collection.query.near_vector(
-                    query_vector[0],
+                query_embedding = np.array(query_vector[0], dtype=np.float32)
+                query_embedding_bytes = query_embedding.tobytes()
+
+                query_sql = """
+                    WITH distances AS (
+                        SELECT name, type, status, description,
+                               vec_distance_cosine(embedding, ?) as distance
+                        FROM packages
+                    )
+                    SELECT name, type, status, description, distance
+                    FROM distances
+                    WHERE distance <= ?
+                    ORDER BY distance ASC
+                    LIMIT ?
+                """  # nosec
+                logger.debug(
+                    "Performing vector similarity search",
+                    query=query,
+                    distance_threshold=distance,
                     limit=limit,
-                    distance=distance,
-                    return_metadata=MetadataQuery(distance=True),
                 )
-
-            if not response:
+                cursor.execute(query_sql, (query_embedding_bytes, distance, limit))
+            else:
                 return []
-            return response.objects
+
+            # Log the raw SQL results
+            rows = cursor.fetchall()
+            logger.debug(
+                "Raw SQL results",
+                row_count=len(rows),
+                rows=[
+                    {"name": row[0], "type": row[1], "status": row[2], "description": row[3]}
+                    for row in rows
+                ],
+            )
+
+            results = []
+            for row in rows:
+                result = {
+                    "properties": {
+                        "name": row[0],
+                        "type": row[1],
+                        "status": row[2],
+                        "description": row[3],
+                    }
+                }
+                if query:  # Add distance for vector searches
+                    result["metadata"] = {"distance": row[4]}
+                results.append(result)
+
+            return results
 
         except Exception as e:
             logger.error(f"Error during search: {str(e)}")
diff --git a/src/codegate/storage/utils.py b/src/codegate/storage/utils.py
index 45b77a85..f9140fe8 100644
--- a/src/codegate/storage/utils.py
+++ b/src/codegate/storage/utils.py
@@ -1,22 +1,29 @@
-import weaviate
-from weaviate.embedded import EmbeddedOptions
+import os
+import shutil
 
 
 def restore_storage_backup(backup_path: str, backup_name: str):
-    client = weaviate.WeaviateClient(
-        embedded_options=EmbeddedOptions(
-            persistence_data_path="./weaviate_data",
-            grpc_port=50052,
-            additional_env_vars={
-                "ENABLE_MODULES": "backup-filesystem",
-                "BACKUP_FILESYSTEM_PATH": backup_path,
-            },
-        )
-    )
-    client.connect()
+    """
+    Restore a SQLite database backup.
+
+    Args:
+        backup_path: The directory containing the backup
+        backup_name: The name of the backup file
+    """
+    backup_file = os.path.join(backup_path, backup_name)
+    target_dir = "./sqlite_data"
+    target_file = os.path.join(target_dir, "vectordb.db")
+
+    if not os.path.exists(backup_file):
+        print(f"Backup file not found: {backup_file}")
+        return
+
     try:
-        client.backup.restore(backup_id=backup_name, backend="filesystem", wait_for_completion=True)
+        # Create target directory if it doesn't exist
+        os.makedirs(target_dir, exist_ok=True)
+
+        # Copy the backup file to the target location
+        shutil.copy2(backup_file, target_file)
+        print(f"Successfully restored backup from {backup_file}")
     except Exception as e:
         print(f"Failed to restore backup: {e}")
-    finally:
-        client.close()
diff --git a/tests/test_storage.py b/tests/test_storage.py
deleted file mode 100644
index dafb1547..00000000
--- a/tests/test_storage.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from codegate.config import Config
-from codegate.storage.storage_engine import (
-    StorageEngine,
-)  # Adjust the import based on your actual path
-
-
-@pytest.fixture
-def mock_weaviate_client():
-    client = MagicMock()
-    response = MagicMock()
-    response.objects = [
-        {
-            "properties": {
-                "name": "test",
-                "type": "library",
-                "status": "active",
-                "description": "test description",
-            }
-        }
-    ]
-    client.collections.get.return_value.query.near_vector.return_value = response
-    return client
-
-
-@pytest.fixture
-def mock_inference_engine():
-    engine = AsyncMock()
-    engine.embed.return_value = [0.1, 0.2, 0.3]  # Adjust based on expected vector dimensions
-    return engine
-
-
-@pytest.mark.asyncio
-async def test_search(mock_weaviate_client, mock_inference_engine):
-    Config.load(config_path="./config.yaml")
-
-    # Patch the LlamaCppInferenceEngine.embed method (not the entire class)
-    with patch(
-        "codegate.inference.inference_engine.LlamaCppInferenceEngine.embed",
-        mock_inference_engine.embed,
-    ):
-        # Initialize StorageEngine
-        with patch(
-            "codegate.storage.storage_engine.StorageEngine.get_client",
-            return_value=mock_weaviate_client,
-        ):
-            # Initialize StorageEngine
-            # Need to recreate instance to use the mock
-            storage_engine = StorageEngine.recreate_instance(data_path="./weaviate_data")
-
-            # Invoke the search method
-            results = await storage_engine.search(query="test query", limit=5, distance=0.3)
-
-            # Assertions to validate the expected behavior
-            assert len(results) == 1  # Assert that one result is returned
-            assert results[0]["properties"]["name"] == "test"
-            mock_weaviate_client.connect.assert_called()
diff --git a/tests/vectordb/test_sqlitevec.py b/tests/vectordb/test_sqlitevec.py
new file mode 100644
index 00000000..f80b644d
--- /dev/null
+++ b/tests/vectordb/test_sqlitevec.py
@@ -0,0 +1,153 @@
+import os
+from unittest.mock import AsyncMock, patch
+
+import numpy as np
+import pytest
+
+from codegate.config import Config
+from codegate.storage.storage_engine import StorageEngine
+
+
+@pytest.fixture(scope="module")
+def mock_sqlite_vec():
+    with patch("sqlite_vec.load") as mock_load:
+        # Mock the vector similarity extension loading
+        def setup_vector_similarity(conn):
+            cursor = conn.cursor()
+            # Create a table to store our mock distance function
+            cursor.execute(
+                """
+            CREATE TABLE IF NOT EXISTS vector_distances (
+                id INTEGER PRIMARY KEY,
+                distance FLOAT
+            )"""
+            )
+            # Insert a mock distance value that will be used in searches
+            cursor.execute("INSERT INTO vector_distances (distance) VALUES (0.1)")
+
+            # Create a view that simulates the vec_distance_cosine function
+            cursor.execute(
+                """
+            CREATE VIEW IF NOT EXISTS vec_distance_cosine_view AS
+            SELECT distance FROM vector_distances WHERE id = 1
+            """
+            )
+
+            # Create a function that returns the mock distance
+            conn.create_function("vec_distance_cosine", 2, lambda x, y: 0.1)
+
+        mock_load.side_effect = setup_vector_similarity
+        yield mock_load
+
+
+@pytest.fixture(scope="module")
+def test_db_path():
+    return "./test_sqlite_data/vectordb.db"
+
+
+@pytest.fixture(scope="module")
+def mock_config(test_db_path):
+    # Create a mock config instance
+    config = Config()
+    config.model_base_path = "./codegate_volume/models"
+    config.embedding_model = "all-minilm-L6-v2-q5_k_m.gguf"
+    config.vec_db_path = test_db_path
+
+    # Mock Config.get_config to return our test config
+    with patch("codegate.config.Config.get_config", return_value=config):
+        yield config
+
+
+@pytest.fixture(scope="module")
+def storage_engine(mock_sqlite_vec, mock_config, test_db_path):
+    # Setup: Create a temporary database for testing
+    test_db_dir = os.path.dirname(test_db_path)
+    os.makedirs(test_db_dir, exist_ok=True)
+
+    engine = StorageEngine(data_path=test_db_dir)
+    yield engine
+    # Teardown: Remove the temporary database and directory
+    del engine
+    if os.path.exists(test_db_path):
+        os.remove(test_db_path)
+    os.rmdir(test_db_dir)
+
+
+@pytest.fixture(autouse=True)
+def clean_database(storage_engine):
+    # Clear all data before each test
+    cursor = storage_engine.conn.cursor()
+    cursor.execute("DELETE FROM packages")
+    storage_engine.conn.commit()
+    yield
+
+
+@pytest.mark.asyncio
+async def test_search_by_property(storage_engine):
+    # Insert test data
+    cursor = storage_engine.conn.cursor()
+    cursor.execute(
+        """
+        INSERT INTO packages (name, type, status, description)
+        VALUES ('invokehttp', 'pypi', 'active', 'An evil package')
+    """
+    )
+    storage_engine.conn.commit()
+
+    # Test search by property
+    results = await storage_engine.search_by_property("name", ["invokehttp"])
+    assert len(results) == 1
+    assert results[0]["properties"]["name"] == "invokehttp"
+    assert results[0]["properties"]["type"] == "pypi"
+    assert results[0]["properties"]["status"] == "active"
+    assert results[0]["properties"]["description"] == "An evil package"
+
+
+@pytest.mark.asyncio
+async def test_search_by_package_names(storage_engine):
+    # Insert test data
+    cursor = storage_engine.conn.cursor()
+    cursor.execute(
+        """
+        INSERT INTO packages (name, type, status, description)
+        VALUES ('invokehttp', 'pypi', 'active', 'An evil package')
+    """
+    )
+    storage_engine.conn.commit()
+
+    # Test search by package names
+    results = await storage_engine.search(packages=["invokehttp"])
+    assert len(results) == 1
+    assert results[0]["properties"]["name"] == "invokehttp"
+    assert results[0]["properties"]["type"] == "pypi"
+    assert results[0]["properties"]["status"] == "active"
+    assert results[0]["properties"]["description"] == "An evil package"
+
+
+@pytest.mark.asyncio
+async def test_search_by_query(storage_engine):
+    # Mock the inference engine to return a fixed embedding
+    with patch.object(
+        storage_engine.inference_engine, "embed", new=AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    ):
+        # Insert test data with embedding
+        cursor = storage_engine.conn.cursor()
+        embedding = np.array([0.1, 0.2, 0.3], dtype=np.float32).tobytes()
+        cursor.execute(
+            """
+            INSERT INTO packages (name, type, status, description, embedding)
+            VALUES ('invokehttp', 'pypi', 'active', 'An evil package', ?)
+        """,
+            (embedding,),
+        )
+        storage_engine.conn.commit()
+
+        # Test search by query
+        results = await storage_engine.search(query="test")
+        assert len(results) == 1
+        assert results[0]["properties"]["name"] == "invokehttp"
+        assert results[0]["properties"]["type"] == "pypi"
+        assert results[0]["properties"]["status"] == "active"
+        assert results[0]["properties"]["description"] == "An evil package"
+        assert "metadata" in results[0]
+        assert "distance" in results[0]["metadata"]