diff --git a/learning_resources_search/api.py b/learning_resources_search/api.py
index c15d4a974c..abbbc32d9d 100644
--- a/learning_resources_search/api.py
+++ b/learning_resources_search/api.py
@@ -922,8 +922,9 @@ def _qdrant_similar_results(doc, num_resources):
         list of dict:
             list of serialized resources
     """
-    from vector_search.utils import qdrant_client, vector_point_id
+    from vector_search.utils import dense_encoder, qdrant_client, vector_point_id
 
+    encoder = dense_encoder()
     client = qdrant_client()
     return [
         hit.payload
@@ -931,7 +932,7 @@ def _qdrant_similar_results(doc, num_resources):
             collection_name=f"{settings.QDRANT_BASE_COLLECTION_NAME}.resources",
             query=vector_point_id(doc["readable_id"]),
             limit=num_resources,
-            using=settings.QDRANT_SEARCH_VECTOR_NAME,
+            using=encoder.model_short_name(),
         ).points
     ]
 
diff --git a/main/settings.py b/main/settings.py
index 152c676bc4..d96a9ca26e 100644
--- a/main/settings.py
+++ b/main/settings.py
@@ -795,13 +795,16 @@ def get_all_config_keys():
     name="QDRANT_COLLECTION_NAME", default="resource_embeddings"
 )
 
-QDRANT_SEARCH_VECTOR_NAME = get_string(
-    name="QDRANT_SEARCH_VECTOR_NAME", default="fast-bge-small-en"
-)
 
-QDRANT_DENSE_MODEL = get_string(
-    name="QDRANT_DENSE_MODEL", default="sentence-transformers/all-MiniLM-L6-v2"
-)
+QDRANT_DENSE_MODEL = get_string(name="QDRANT_DENSE_MODEL", default=None)
 QDRANT_SPARSE_MODEL = get_string(
     name="QDRANT_SPARSE_MODEL", default="prithivida/Splade_PP_en_v1"
 )
+QDRANT_ENCODER = get_string(
+    name="QDRANT_ENCODER", default="vector_search.encoders.fastembed.FastEmbedEncoder"
+)
+
+OPENAI_API_KEY = get_string(
+    name="OPENAI_API_KEY",
+    default=None,
+)
diff --git a/main/settings_celery.py b/main/settings_celery.py
index 6eb21387eb..413eea214e 100644
--- a/main/settings_celery.py
+++ b/main/settings_celery.py
@@ -134,6 +134,12 @@
         "schedule": crontab(minute=30, hour=18),  # 2:30pm EST
         "kwargs": {"period": "daily", "subscription_type": "channel_subscription_type"},
     },
+    "daily_embed_new_learning_resources": {
+        "task": "vector_search.tasks.embed_new_learning_resources",
+        "schedule": get_int(
+            "EMBED_NEW_RESOURCES_SCHEDULE_SECONDS", 60 * 30
+        ),  # default is every 30 minutes
+    },
     "send-search-subscription-emails-every-1-days": {
         "task": "learning_resources_search.tasks.send_subscription_emails",
         "schedule": crontab(minute=0, hour=19),  # 3:00pm EST
diff --git a/poetry.lock b/poetry.lock
index dac33d2923..0bff264303 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,127 @@
 # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.4"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.11.9"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.9"
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+
+[package.extras]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
+
+[[package]]
+name = "aiosignal"
+version = "1.3.1"
+description = "aiosignal: a list of registered asynchronous callbacks"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
+    {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
+]
+
+[package.dependencies]
+frozenlist = ">=1.1.0"
+
 [[package]]
 name = "amqp"
 version = "5.3.1"
@@ -38,22 +160,23 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.6.2.post1"
+version = "4.7.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
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+    {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
+    {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
 ]
 
 [package.dependencies]
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -212,17 +335,17 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.35.72"
+version = "1.35.76"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "boto3-1.35.72.tar.gz", hash = "sha256:f9fc94413a959c388b1654c6687a5193293f3c69f8d0af3b86fd48b4096a23f3"},
+    {file = "boto3-1.35.76-py3-none-any.whl", hash = "sha256:69458399f41f57a50770c8974796d96978bcca44915c260319696bb43e47dffd"},
+    {file = "boto3-1.35.76.tar.gz", hash = "sha256:31ddcdb6f15dace2b68f6a0f11bdb58dd3ae79b8a3ccb174ff811ef0bbf938e0"},
 ]
 
 [package.dependencies]
-botocore = ">=1.35.72,<1.36.0"
+botocore = ">=1.35.76,<1.36.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -231,13 +354,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.35.72"
+version = "1.35.76"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "botocore-1.35.76.tar.gz", hash = "sha256:a75a42ae53395796b8300c5fefb2d65a8696dc40dc85e49cf3a769e0c0202b13"},
 ]
 
 [package.dependencies]
@@ -1099,6 +1222,17 @@ wrapt = ">=1.10,<2"
 [package.extras]
 dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+description = "Distro - an OS platform information API"
+optional = false
+python-versions = ">=3.6"
+files = [
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+]
+
 [[package]]
 name = "dj-database-url"
 version = "2.3.0"
@@ -1743,6 +1877,107 @@ files = [
 [package.dependencies]
 python-dateutil = ">=2.7"
 
+[[package]]
+name = "frozenlist"
+version = "1.5.0"
+description = "A list-like structure which implements collections.abc.MutableSequence"
+optional = false
+python-versions = ">=3.8"
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+
 [[package]]
 name = "fsspec"
 version = "2024.10.0"
@@ -2354,13 +2589,13 @@ pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0
 
 [[package]]
 name = "httpx"
-version = "0.28.0"
+version = "0.27.2"
 description = "The next generation HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2369,6 +2604,7 @@ certifi = "*"
 h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
 httpcore = "==1.*"
 idna = "*"
+sniffio = "*"
 
 [package.extras]
 brotli = ["brotli", "brotlicffi"]
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 [package.extras]
 all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
+[[package]]
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+description = "Read metadata from Python packages"
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+python-versions = ">=3.8"
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+
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+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
+perf = ["ipython"]
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+type = ["pytest-mypy"]
+
 [[package]]
 name = "inflection"
 version = "0.5.1"
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 [package.dependencies]
 ansicon = {version = "*", markers = "platform_system == \"Windows\""}
 
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 name = "named-enum"
 version = "1.4.0"
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 name = "onnxruntime"
-version = "1.20.1"
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 optional = false
 python-versions = "*"
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 ]
 
 [package.dependencies]
@@ -3499,6 +3976,30 @@ packaging = "*"
 protobuf = "*"
 sympy = "*"
 
+[[package]]
+name = "openai"
+version = "1.57.0"
+description = "The official Python library for the openai API"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
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+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+jiter = ">=0.4.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+tqdm = ">4"
+typing-extensions = ">=4.11,<5"
+
+[package.extras]
+datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+
 [[package]]
 name = "opensearch-dsl"
 version = "2.1.0"
@@ -3784,6 +4285,97 @@ files = [
 [package.dependencies]
 wcwidth = "*"
 
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 [[package]]
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+version = "2.19.1"
 description = "Python client for Sentry (https://sentry.io)"
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 [package.dependencies]
@@ -5468,13 +6076,13 @@ files = [
 
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 tests = ["memory-profiler (>=0.57.0)", "pytest-benchmark (>=3.2.2)"]
 
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+
+[package.extras]
+blobfile = ["blobfile (>=2)"]
+
 [[package]]
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 version = "1.4.0"
@@ -5785,13 +6440,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
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+version = "0.15.1"
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+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
 [[package]]
 name = "youtube-transcript-api"
 version = "0.6.3"
@@ -6092,6 +6843,25 @@ files = [
 defusedxml = ">=0.7.1,<0.8.0"
 requests = "*"
 
+[[package]]
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+version = "3.21.0"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+optional = false
+python-versions = ">=3.9"
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+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
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+type = ["pytest-mypy"]
+
 [[package]]
 name = "zope-event"
 version = "5.0"
@@ -6167,4 +6937,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = "3.12.6"
-content-hash = "e856da83956784f1dc605a5bb6ae1e04ab4b0e71c15d048f27b4ba62ff11d43c"
+content-hash = "7e7e2b3268212155694fb3d9332327aa5f0bec5f96e2bbb58765624a7a5caf15"
diff --git a/pyproject.toml b/pyproject.toml
index de81fb82c3..fb31aaf382 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -81,6 +81,9 @@ uwsgitop = "^0.12"
 pytest-lazy-fixtures = "^1.1.1"
 pycountry = "^24.6.1"
 qdrant-client = {extras = ["fastembed"], version = "^1.12.0"}
+onnxruntime = "1.19.2"
+openai = "^1.55.3"
+litellm = "^1.53.5"
 
 
 [tool.poetry.group.dev.dependencies]
diff --git a/vector_search/conftest.py b/vector_search/conftest.py
new file mode 100644
index 0000000000..9853b2419c
--- /dev/null
+++ b/vector_search/conftest.py
@@ -0,0 +1,39 @@
+import numpy as np
+import pytest
+
+from vector_search.encoders.base import BaseEncoder
+
+
+class DummyEmbedEncoder(BaseEncoder):
+    """
+    A dummy encoder that returns random vectors
+    """
+
+    def __init__(self, model_name="dummy-embedding"):
+        self.model_name = model_name
+
+    def encode(self, text: str) -> list:  # noqa: ARG002
+        return np.random.random((10, 1))
+
+    def encode_batch(self, texts: list[str]) -> list[list[float]]:
+        return np.random.random((10, len(texts)))
+
+
+@pytest.fixture(autouse=True)
+def _use_dummy_encoder(settings):
+    settings.QDRANT_ENCODER = "vector_search.conftest.DummyEmbedEncoder"
+
+
+@pytest.fixture(autouse=True)
+def _use_test_qdrant_settings(settings, mocker):
+    settings.QDRANT_HOST = "https://test"
+    settings.QDRANT_BASE_COLLECTION_NAME = "test"
+    mock_qdrant = mocker.patch("qdrant_client.QdrantClient")
+    mock_qdrant.scroll.return_value = [
+        [],
+        None,
+    ]
+    mocker.patch(
+        "vector_search.utils.qdrant_client",
+        return_value=mock_qdrant,
+    )
diff --git a/vector_search/encoders/__init__.py b/vector_search/encoders/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/vector_search/encoders/base.py b/vector_search/encoders/base.py
new file mode 100644
index 0000000000..4fe9b7269d
--- /dev/null
+++ b/vector_search/encoders/base.py
@@ -0,0 +1,37 @@
+from abc import ABC, abstractmethod
+
+
+class BaseEncoder(ABC):
+    """
+    Base encoder class
+    """
+
+    def model_short_name(self):
+        """
+        Return the short name of the model
+        used as the vector name in qdrant
+        """
+        split_model_name = self.model_name.split("/")
+        model_name = self.model_name
+        if len(split_model_name) > 1:
+            model_name = split_model_name[1]
+        return model_name
+
+    def encode(self, text):
+        """
+        Embed a single text
+        """
+        return next(iter(self.encode_batch([text])))
+
+    @abstractmethod
+    def encode_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Embed multiple texts
+        """
+        return [self.encode(text) for text in texts]
+
+    def dim(self):
+        """
+        Return the dimension of the embeddings
+        """
+        return len(self.encode("test"))
diff --git a/vector_search/encoders/fastembed.py b/vector_search/encoders/fastembed.py
new file mode 100644
index 0000000000..ccaf21e089
--- /dev/null
+++ b/vector_search/encoders/fastembed.py
@@ -0,0 +1,27 @@
+from fastembed import TextEmbedding
+
+from vector_search.encoders.base import BaseEncoder
+
+
+class FastEmbedEncoder(BaseEncoder):
+    """
+    FastEmbed encoder
+    """
+
+    def __init__(self, model_name="BAAI/bge-small-en-v1.5"):
+        self.model_name = model_name
+        self.model = TextEmbedding(model_name=model_name, lazy_load=True)
+
+    def encode_batch(self, texts: list[str]) -> list[list[float]]:
+        return self.model.embed(texts)
+
+    def dim(self):
+        """
+        Return the dimension of the embeddings
+        """
+        supported_models = [
+            model_config
+            for model_config in self.model.list_supported_models()
+            if model_config["model"] == self.model.model_name
+        ]
+        return supported_models[0]["dim"]
diff --git a/vector_search/encoders/litellm.py b/vector_search/encoders/litellm.py
new file mode 100644
index 0000000000..ccefe68f5f
--- /dev/null
+++ b/vector_search/encoders/litellm.py
@@ -0,0 +1,20 @@
+from litellm import embedding
+
+from vector_search.encoders.base import BaseEncoder
+
+
+class LiteLLMEncoder(BaseEncoder):
+    """
+    LiteLLM encoder
+    """
+
+    def __init__(self, model_name="text-embedding-3-small"):
+        self.model_name = model_name
+
+    def encode_batch(self, texts: list[str]) -> list[list[float]]:
+        return [
+            result["embedding"]
+            for result in embedding(model=self.model_name, input=texts).to_dict()[
+                "data"
+            ]
+        ]
diff --git a/vector_search/encoders/utils.py b/vector_search/encoders/utils.py
new file mode 100644
index 0000000000..09b3ad80ba
--- /dev/null
+++ b/vector_search/encoders/utils.py
@@ -0,0 +1,12 @@
+from django.conf import settings
+from django.utils.module_loading import import_string
+
+
+def dense_encoder():
+    """
+    Return the dense encoder based on settings
+    """
+    Encoder = import_string(settings.QDRANT_ENCODER)
+    if settings.QDRANT_DENSE_MODEL:
+        return Encoder(model_name=settings.QDRANT_DENSE_MODEL)
+    return Encoder()
diff --git a/vector_search/management/commands/generate_embeddings.py b/vector_search/management/commands/generate_embeddings.py
index 20efb8578b..fcde4c176d 100644
--- a/vector_search/management/commands/generate_embeddings.py
+++ b/vector_search/management/commands/generate_embeddings.py
@@ -3,7 +3,7 @@
 from django.core.management.base import BaseCommand, CommandError
 
 from learning_resources_search.constants import LEARNING_RESOURCE_TYPES
-from main.utils import now_in_utc
+from main.utils import clear_search_cache, now_in_utc
 from vector_search.tasks import start_embed_resources
 from vector_search.utils import (
     create_qdrand_collections,
@@ -81,7 +81,7 @@ def handle(self, *args, **options):  # noqa: ARG002
         if error:
             msg = f"Geenerate embeddings errored: {error}"
             raise CommandError(msg)
-
+        clear_search_cache()
         total_seconds = (now_in_utc() - start).total_seconds()
         self.stdout.write(
             f"Embeddings generated and stored, took {total_seconds} seconds"
diff --git a/vector_search/tasks.py b/vector_search/tasks.py
index 73a4ceba2a..c02e85bdcc 100644
--- a/vector_search/tasks.py
+++ b/vector_search/tasks.py
@@ -1,3 +1,4 @@
+import datetime
 import logging
 
 import celery
@@ -27,8 +28,9 @@
 from main.celery import app
 from main.utils import (
     chunks,
+    now_in_utc,
 )
-from vector_search.utils import embed_learning_resources
+from vector_search.utils import embed_learning_resources, filter_existing_qdrant_points
 
 log = logging.getLogger(__name__)
 
@@ -148,3 +150,28 @@ def start_embed_resources(self, indexes, skip_content_files):
     # Use self.replace so that code waiting on this task will also wait on the embedding
     #  and finish tasks
     return self.replace(celery.chain(*index_tasks))
+
+
+@app.task(bind=True)
+def embed_new_learning_resources(self):
+    """
+    Embed new resources from the last day
+    """
+    log.info("Running new resource embedding task")
+    delta = datetime.timedelta(days=1)
+    since = now_in_utc() - delta
+    new_learning_resources = LearningResource.objects.filter(
+        published=True,
+        created_on__gt=since,
+    ).exclude(resource_type=CONTENT_FILE_TYPE)
+    filtered_resources = filter_existing_qdrant_points(new_learning_resources)
+    embed_tasks = celery.group(
+        [
+            generate_embeddings.si(ids, COURSE_TYPE)
+            for ids in chunks(
+                filtered_resources.order_by("id").values_list("id", flat=True),
+                chunk_size=settings.OPENSEARCH_INDEXING_CHUNK_SIZE,
+            )
+        ]
+    )
+    return self.replace(embed_tasks)
diff --git a/vector_search/tasks_test.py b/vector_search/tasks_test.py
index 194c8be14a..1c5deb6edc 100644
--- a/vector_search/tasks_test.py
+++ b/vector_search/tasks_test.py
@@ -1,3 +1,5 @@
+import datetime
+
 import pytest
 from django.conf import settings
 
@@ -5,12 +7,15 @@
 from learning_resources.factories import (
     ContentFileFactory,
     CourseFactory,
+    LearningResourceFactory,
     ProgramFactory,
 )
+from learning_resources.models import LearningResource
 from learning_resources_search.constants import (
     COURSE_TYPE,
 )
-from vector_search.tasks import start_embed_resources
+from main.utils import now_in_utc
+from vector_search.tasks import embed_new_learning_resources, start_embed_resources
 
 pytestmark = pytest.mark.django_db
 
@@ -23,8 +28,7 @@ def test_start_embed_resources(mocker, mocked_celery, index):
     """
     start_embed_resources should generate embeddings for each resource type
     """
-    settings.QDRANT_HOST = "http://test"
-    settings.QDRANT_BASE_COLLECTION_NAME = "test"
+
     mocker.patch("vector_search.tasks.load_course_blocklist", return_value=[])
 
     if index == COURSE_TYPE:
@@ -95,3 +99,42 @@ def test_start_embed_resources_without_settings(mocker, mocked_celery, index):
     start_embed_resources.delay([index], skip_content_files=True)
 
     generate_embeddings_mock.si.assert_not_called()
+
+
+def test_embed_new_learning_resources(mocker, mocked_celery):
+    """
+    embed_new_learning_resources should generate embeddings for new resources
+    based on the period
+    """
+    mocker.patch("vector_search.tasks.load_course_blocklist", return_value=[])
+
+    daily_since = now_in_utc() - datetime.timedelta(hours=5)
+
+    LearningResourceFactory.create_batch(
+        4, created_on=daily_since, resource_type=COURSE_TYPE, published=True
+    )
+    # create resources older than a day
+    LearningResourceFactory.create_batch(
+        4,
+        created_on=now_in_utc() - datetime.timedelta(days=5),
+        resource_type=COURSE_TYPE,
+        published=True,
+    )
+
+    daily_resource_ids = [
+        resource.id
+        for resource in LearningResource.objects.filter(
+            created_on__gt=now_in_utc() - datetime.timedelta(days=1)
+        )
+    ]
+
+    generate_embeddings_mock = mocker.patch(
+        "vector_search.tasks.generate_embeddings", autospec=True
+    )
+
+    with pytest.raises(mocked_celery.replace_exception_class):
+        embed_new_learning_resources.delay()
+    list(mocked_celery.group.call_args[0][0])
+
+    embedded_ids = generate_embeddings_mock.si.mock_calls[0].args[0]
+    assert sorted(daily_resource_ids) == sorted(embedded_ids)
diff --git a/vector_search/utils.py b/vector_search/utils.py
index 8f7312feb0..696141b410 100644
--- a/vector_search/utils.py
+++ b/vector_search/utils.py
@@ -9,6 +9,7 @@
     serialize_bulk_content_files,
     serialize_bulk_learning_resources,
 )
+from vector_search.encoders.utils import dense_encoder
 
 
 def qdrant_client():
@@ -20,6 +21,34 @@ def qdrant_client():
     )
 
 
+def points_generator(
+    ids,
+    metadata,
+    encoded_docs,
+    vector_name,
+):
+    """
+    Get a generator for embedding points to store in Qdrant
+
+    Args:
+        ids (list): list of unique point ids
+        metadata (list): list of metadata dictionaries
+        encoded_docs (list): list of vectorized documents
+        vector_name (str): name of the vector in qdrant
+    Returns:
+        generator:
+            A generator of PointStruct objects
+    """
+    if ids is None:
+        ids = iter(lambda: uuid.uuid4().hex, None)
+    if metadata is None:
+        metadata = iter(dict, None)
+    for idx, meta, vector in zip(ids, metadata, encoded_docs):
+        payload = meta
+        point_vector: dict[str, models.Vector] = {vector_name: vector}
+        yield models.PointStruct(id=idx, payload=payload, vector=point_vector)
+
+
 def create_qdrand_collections(force_recreate):
     """
     Create or recreate QDrant collections
@@ -33,6 +62,7 @@ def create_qdrand_collections(force_recreate):
     content_files_collection_name = (
         f"{settings.QDRANT_BASE_COLLECTION_NAME}.content_files"
     )
+    encoder = dense_encoder()
     if (
         not client.collection_exists(collection_name=resources_collection_name)
         or force_recreate
@@ -41,7 +71,11 @@ def create_qdrand_collections(force_recreate):
         client.recreate_collection(
             collection_name=resources_collection_name,
             on_disk_payload=True,
-            vectors_config=client.get_fastembed_vector_params(),
+            vectors_config={
+                encoder.model_short_name(): models.VectorParams(
+                    size=encoder.dim(), distance=models.Distance.COSINE
+                )
+            },
             sparse_vectors_config=client.get_fastembed_sparse_vector_params(),
             optimizers_config=models.OptimizersConfigDiff(default_segment_number=2),
             quantization_config=models.ScalarQuantization(
@@ -60,7 +94,11 @@ def create_qdrand_collections(force_recreate):
         client.recreate_collection(
             collection_name=content_files_collection_name,
             on_disk_payload=True,
-            vectors_config=client.get_fastembed_vector_params(),
+            vectors_config={
+                encoder.model_short_name(): models.VectorParams(
+                    size=encoder.dim(), distance=models.Distance.COSINE
+                )
+            },
             sparse_vectors_config=client.get_fastembed_sparse_vector_params(),
             optimizers_config=models.OptimizersConfigDiff(default_segment_number=2),
             quantization_config=models.ScalarQuantization(
@@ -123,12 +161,11 @@ def embed_learning_resources(ids, resource_type):
                 f"{doc['key']}.{doc['run_readable_id']}.{doc['resource_readable_id']}"
             )
         ids.append(vector_point_id(vector_point_key))
-    client.add(
-        collection_name=collection_name,
-        ids=ids,
-        documents=docs,
-        metadata=metadata,
-    )
+    encoder = dense_encoder()
+    embeddings = encoder.encode_batch(docs)
+    vector_name = encoder.model_short_name()
+    points = points_generator(ids, metadata, embeddings, vector_name)
+    client.upload_points(collection_name, points=points, wait=False)
 
 
 def vector_search(
@@ -150,10 +187,11 @@ def vector_search(
     """
     if query_string:
         client = qdrant_client()
-
-        search_result = client.query(
+        encoder = dense_encoder()
+        search_result = client.query_points(
             collection_name=f"{settings.QDRANT_BASE_COLLECTION_NAME}.resources",
-            query_text=query_string,
+            using=encoder.model_short_name(),
+            query=encoder.encode(query_string),
             query_filter=models.Filter(
                 must=[
                     models.FieldCondition(
@@ -164,17 +202,16 @@ def vector_search(
             limit=limit,
             offset=offset,
         )
-        # Select and return metadata
         hits = [
             {
-                "id": hit.metadata["id"],
-                "readable_id": hit.metadata["readable_id"],
-                "resource_type": hit.metadata["resource_type"],
-                "title": hit.metadata["title"],
-                "description": hit.metadata["description"],
-                "platform": hit.metadata["platform"],
+                "id": hit.payload["id"],
+                "readable_id": hit.payload["readable_id"],
+                "resource_type": hit.payload["resource_type"],
+                "title": hit.payload["title"],
+                "description": hit.payload["description"],
+                "platform": hit.payload["platform"],
             }
-            for hit in search_result
+            for hit in search_result.points
         ]
     else:
         results = serialize_bulk_learning_resources(
@@ -195,3 +232,50 @@ def vector_search(
             for resource in results
         ]
     return {"hits": hits, "total": {"value": 10000}}
+
+
+def filter_existing_qdrant_points(learning_resources):
+    """
+    Filter learning resources that already have embeddings
+    Args:
+        learning_resources (QuerySet): Learning resources to check
+    Returns:
+        Queryset of learning resources that do not have embeddings in Qdrant
+
+    """
+    readable_ids = [
+        learning_resource.readable_id for learning_resource in learning_resources
+    ]
+    client = qdrant_client()
+    results = client.scroll(
+        collection_name=f"{settings.QDRANT_BASE_COLLECTION_NAME}.resources",
+        scroll_filter=models.Filter(
+            must=models.FieldCondition(
+                key="readable_id", match=models.MatchAny(any=readable_ids)
+            )
+        ),
+    )
+    next_page_offset = results[1]
+    existing_readable_ids = [point.payload["readable_id"] for point in results[0]]
+    # go page by page to fetch all existing readable ids
+    while next_page_offset:
+        results = client.scroll(
+            collection_name=f"{settings.QDRANT_BASE_COLLECTION_NAME}.resources",
+            scroll_filter=models.Filter(
+                must=models.FieldCondition(
+                    key="readable_id", match=models.MatchAny(any=readable_ids)
+                )
+            ),
+            offset=next_page_offset,
+        )
+        existing_readable_ids.extend(
+            [point.payload["readable_id"] for point in results[0]]
+        )
+        next_page_offset = results[1]
+    return LearningResource.objects.filter(
+        readable_id__in=[
+            readable_id
+            for readable_id in readable_ids
+            if readable_id not in existing_readable_ids
+        ]
+    )
diff --git a/vector_search/utils_test.py b/vector_search/utils_test.py
index 43abb88e43..13ec1bb411 100644
--- a/vector_search/utils_test.py
+++ b/vector_search/utils_test.py
@@ -1,9 +1,12 @@
 import pytest
+from qdrant_client.models import PointStruct
 
 from learning_resources.factories import ContentFileFactory, LearningResourceFactory
 from learning_resources_search.serializers import serialize_bulk_content_files
 from vector_search.utils import (
+    create_qdrand_collections,
     embed_learning_resources,
+    filter_existing_qdrant_points,
     vector_point_id,
 )
 
@@ -18,7 +21,6 @@ def test_vector_point_id_used_for_embed(mocker, content_type):
     else:
         resources = ContentFileFactory.create_batch(5)
     mock_qdrant = mocker.patch("qdrant_client.QdrantClient")
-    mock_qdrant.query.return_value = []
     mocker.patch(
         "vector_search.utils.qdrant_client",
         return_value=mock_qdrant,
@@ -35,4 +37,135 @@ def test_vector_point_id_used_for_embed(mocker, content_type):
             )
             for resource in serialize_bulk_content_files([r.id for r in resources])
         ]
-    assert sorted(mock_qdrant.add.mock_calls[0].kwargs["ids"]) == sorted(point_ids)
+
+    assert sorted(
+        [p.id for p in mock_qdrant.upload_points.mock_calls[0].kwargs["points"]]
+    ) == sorted(point_ids)
+
+
+def test_filter_existing_qdrant_points(mocker):
+    """
+    Test that filter_existing_qdrant_points filters out
+    resources that are already embedded in Qdrant
+    """
+    mock_qdrant = mocker.patch("qdrant_client.QdrantClient")
+    mocker.patch(
+        "vector_search.utils.qdrant_client",
+        return_value=mock_qdrant,
+    )
+    resources = LearningResourceFactory.create_batch(10)
+    already_embedded = resources[:3]
+    mock_qdrant.scroll.return_value = [
+        [
+            PointStruct(
+                id=resource.id,
+                payload={"readable_id": resource.readable_id},
+                vector=[0, 0, 0, 0],
+            )
+            for resource in already_embedded
+        ],
+        None,
+    ]
+    filtered_resources = filter_existing_qdrant_points(resources)
+
+    assert (
+        len(
+            [
+                res.id
+                for res in already_embedded
+                if res.id in filtered_resources.values_list("id", flat=True)
+            ]
+        )
+        == 0
+    )
+    assert filtered_resources.count() == 7
+
+
+def test_force_create_qdrand_collections(mocker):
+    """
+    Test that the force flag will recreate collections
+    even if they exist
+    """
+    mock_qdrant = mocker.patch("qdrant_client.QdrantClient")
+    mocker.patch(
+        "vector_search.utils.qdrant_client",
+        return_value=mock_qdrant,
+    )
+    mock_qdrant.collection_exists.return_value = True
+    create_qdrand_collections(force_recreate=True)
+    assert (
+        mock_qdrant.recreate_collection.mock_calls[0].kwargs["collection_name"]
+        == "test.resources"
+    )
+    assert (
+        mock_qdrant.recreate_collection.mock_calls[1].kwargs["collection_name"]
+        == "test.content_files"
+    )
+    assert (
+        "dummy-embedding"
+        in mock_qdrant.recreate_collection.mock_calls[0].kwargs["vectors_config"]
+    )
+    assert (
+        "dummy-embedding"
+        in mock_qdrant.recreate_collection.mock_calls[1].kwargs["vectors_config"]
+    )
+
+
+def test_auto_create_qdrand_collections(mocker):
+    """
+    Test that collections will get autocreated if they
+    don't exist
+    """
+    mock_qdrant = mocker.patch("qdrant_client.QdrantClient")
+    mocker.patch(
+        "vector_search.utils.qdrant_client",
+        return_value=mock_qdrant,
+    )
+    mock_qdrant.collection_exists.return_value = False
+    create_qdrand_collections(force_recreate=False)
+    assert (
+        mock_qdrant.recreate_collection.mock_calls[0].kwargs["collection_name"]
+        == "test.resources"
+    )
+    assert (
+        mock_qdrant.recreate_collection.mock_calls[1].kwargs["collection_name"]
+        == "test.content_files"
+    )
+    assert (
+        "dummy-embedding"
+        in mock_qdrant.recreate_collection.mock_calls[0].kwargs["vectors_config"]
+    )
+    assert (
+        "dummy-embedding"
+        in mock_qdrant.recreate_collection.mock_calls[1].kwargs["vectors_config"]
+    )
+
+
+def test_skip_creating_qdrand_collections(mocker):
+    """
+    Test collections do not get recreated
+    if they exist and force_recreate is False
+    """
+    mock_qdrant = mocker.patch("qdrant_client.QdrantClient")
+    mocker.patch(
+        "vector_search.utils.qdrant_client",
+        return_value=mock_qdrant,
+    )
+    mock_qdrant.collection_exists.return_value = False
+    create_qdrand_collections(force_recreate=False)
+    assert (
+        mock_qdrant.recreate_collection.mock_calls[0].kwargs["collection_name"]
+        == "test.resources"
+    )
+    assert (
+        mock_qdrant.recreate_collection.mock_calls[1].kwargs["collection_name"]
+        == "test.content_files"
+    )
+    assert (
+        "dummy-embedding"
+        in mock_qdrant.recreate_collection.mock_calls[0].kwargs["vectors_config"]
+    )
+    assert (
+        "dummy-embedding"
+        in mock_qdrant.recreate_collection.mock_calls[1].kwargs["vectors_config"]
+    )
diff --git a/vector_search/views_test.py b/vector_search/views_test.py
index 5847e6bc1c..351dc854b8 100644
--- a/vector_search/views_test.py
+++ b/vector_search/views_test.py
@@ -8,7 +8,6 @@
 
 def test_vector_search_returns_all_resources_for_empty_query(mocker, client):
     """Test vector search endpoint returns all resources when 'q' is empty"""
-
     LearningResourceFactory.create_batch(5)
     mock_qdrant = mocker.patch("qdrant_client.QdrantClient")
     mock_qdrant.query.return_value = []