diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
new file mode 100644
index 00000000..a51026c9
--- /dev/null
+++ b/.github/workflows/docker.yml
@@ -0,0 +1,32 @@
+#
+name: Create and publish a Docker image
+
+# Configures this workflow to run every time a change is pushed to the branch called `release`.
+on:
+  push:
+    branches: ["feature"]
+
+# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
+env:
+  REGISTRY: registry.cn-beijing.aliyuncs.com
+
+# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here.
+      - uses: aliyun/acr-login@v1
+        with:
+          login-server: "https://${{ env.REGISTRY }}"
+          username: ${{ secrets.ACR_USER }}
+          password: ${{ secrets.ACR_PASSWORD }}
+
+      - name: Build and push image
+        env:
+          IMAGE_TAG: pairag_0.0.1
+        run: |
+          docker build -t ${{ env.REGISTRY }}/mybigpai/aigc_apps:$IMAGE_TAG .
+          docker push ${{ env.REGISTRY }}/mybigpai/aigc_apps:$IMAGE_TAG
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 00000000..549a8a2d
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,24 @@
+FROM python:3.10-slim AS builder
+
+RUN pip3 install poetry
+
+ENV POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_IN_PROJECT=1 \
+    POETRY_VIRTUALENVS_CREATE=1 \
+    POETRY_CACHE_DIR=/tmp/poetry_cache
+
+WORKDIR /app
+COPY . .
+
+RUN poetry install && rm -rf $POETRY_CACHE_DIR
+
+FROM python:3.10-slim AS prod
+ENV VIRTUAL_ENV=/app/.venv \
+    PATH="/app/.venv/bin:$PATH"
+
+RUN apt-get update && apt-get install -y libgl1 libglib2.0-0
+
+WORKDIR /app
+COPY . .
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+ENTRYPOINT ["pai_rag", "run"]
diff --git a/docker/Dockerfile b/docker/Dockerfile
deleted file mode 100644
index 85e699f8..00000000
--- a/docker/Dockerfile
+++ /dev/null
@@ -1,35 +0,0 @@
-FROM filebrowser/filebrowser as filebrowser
-FROM nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu22.04
-
-# 为了避免时区选择交互
-ENV DEBIAN_FRONTEND=noninteractive
-
-# 安装基本工具和编译依赖项
-RUN apt-get update -y && apt-get install -y  \
-    libgl1 libglib2.0-0 \
-    sudo wget tar git git-lfs vim tig \
-    telnet curl net-tools zip\
-    python3-pip python-is-python3 python3.10-venv libpq-dev libffi-dev && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN pip3 install --upgrade pip
-
-# 复制代码到容器中 (根据需要调整或移除此行) [新加坡无法连接gitlab，因此将代码打包上传到OSS再下载]
-# RUN git clone --branch master http://gitlab-ci-token:of-xxx@gitlab.alibaba-inc.com/pai_biz_arch/EasyRAG.git /app/EasyRAG
-
-# 若测试阶段，可以自行通过脚本打包代码上传至OSS，并替换下面的tar包名称
-ADD https://pai-rag.oss-cn-hangzhou.aliyuncs.com/codes/EasyRAG_yyyymmdd-hhmmss-N.tar.gz /app/
-RUN cd /app/ && mkdir EasyRAG && tar -xvf EasyRAG_yyyymmdd-hhmmss-N.tar.gz -C EasyRAG/
-
-# 设置工作目录
-WORKDIR /app/EasyRAG
-
-# 进入工作目录并进行环境安装
-RUN cd /app/EasyRAG
-RUN pip install poetry \
-    && poetry config virtualenvs.create false
-RUN poetry install
-RUN cd src/pai_rag/integrations/llms/paieas && poetry install
-RUN cd /app/EasyRAG/package && pip install pai_llm_trace-0.0.1-py3-none-any.whl
-
-RUN cd /app/EasyRAG
diff --git a/docker/README.md b/docker/README.md
deleted file mode 100644
index 4258524b..00000000
--- a/docker/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# 镜像打包与使用说明
-
-## 1. 镜像构建
-
-### 1.1 EasyRAG镜像
-
-```bash
-cd docker/
-docker build -t rag:test -f Dockerfile .
-```
-
-- 环境建议使用新加坡ECS或本机电脑。由于需要下载基础镜像nvidia/cuda:11.7.1-cudnn8-runtime-ubuntu22.04及安装一系列依赖，弹内开发机网络受限。
-
-- 其中，由于新加坡无法连接Gitlab Repo，因此项目代码部分采用先打包上传至OSS，再下载代码包的方式。
-
-打包命令可参考：
-
-```bash
-sh scripts/package_and_upload_to_oss.sh
-```
-
-- 已经写好Dockerfile，可酌情根据需要调整。【注意替换其中的代码包名称: EasyRAG_yyyymmdd-hhmmss-N.tar.gz】
-
-### 1.2 （可选）Arize-phoenix镜像
-
-- 为了在EAS上部署Arize-phoenix-tracing服务，故采取镜像方式。
-
-- 采用Arize-phoenix官方镜像，地址：**arizephoenix/phoenix:latest**。
-
-## 2. 本地测试
-
-```bash
-docker run -td --network host rag:test
-docker exec -it [id] bash
-# 进入容器后执行 (按需export环境变量)
-pai_rag run
-```
-
-## 3. 镜像上传
-
-测试阶段先使用组内个人镜像 mybigpai:aigc_apps
-[mybigpai:aigc_apps地址](https://cr.console.aliyun.com/repository/cn-beijing/mybigpai/aigc_apps/details)
-
-```bash
-$ docker login --username=xxxx@xxx registry.cn-beijing.aliyuncs.com
-$ docker tag [ImageId] registry.cn-beijing.aliyuncs.com/mybigpai/aigc_apps:[镜像版本号]
-$ docker push registry.cn-beijing.aliyuncs.com/mybigpai/aigc_apps:[镜像版本号]
-```
diff --git a/docker/data_loader/Dockerfile b/docker/data_loader/Dockerfile
deleted file mode 100644
index e69de29b..00000000
diff --git a/poetry.lock b/poetry.lock
index df5f58f4..9bfd357d 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -447,6 +447,23 @@ starlette = ">=0.18"
 [package.extras]
 celery = ["celery"]
 
+[[package]]
+name = "asgiref"
+version = "3.8.1"
+description = "ASGI specs, helper code, and adapters"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47"},
+    {file = "asgiref-3.8.1.tar.gz", hash = "sha256:c343bd80a0bec947a9860adb4c432ffa7db769836c64238fc34bdc3fec84d590"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
+
+[package.extras]
+tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
+
 [[package]]
 name = "async-timeout"
 version = "4.0.3"
@@ -585,6 +602,31 @@ charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
+[[package]]
+name = "build"
+version = "1.2.1"
+description = "A simple, correct Python build frontend"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "build-1.2.1-py3-none-any.whl", hash = "sha256:75e10f767a433d9a86e50d83f418e83efc18ede923ee5ff7df93b6cb0306c5d4"},
+    {file = "build-1.2.1.tar.gz", hash = "sha256:526263f4870c26f26c433545579475377b2b7588b6f1eac76a001e873ae3e19d"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "os_name == \"nt\""}
+importlib-metadata = {version = ">=4.6", markers = "python_full_version < \"3.10.2\""}
+packaging = ">=19.1"
+pyproject_hooks = "*"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+docs = ["furo (>=2023.08.17)", "sphinx (>=7.0,<8.0)", "sphinx-argparse-cli (>=1.5)", "sphinx-autodoc-typehints (>=1.10)", "sphinx-issues (>=3.0.0)"]
+test = ["build[uv,virtualenv]", "filelock (>=3)", "pytest (>=6.2.4)", "pytest-cov (>=2.12)", "pytest-mock (>=2)", "pytest-rerunfailures (>=9.1)", "pytest-xdist (>=1.34)", "setuptools (>=42.0.0)", "setuptools (>=56.0.0)", "setuptools (>=56.0.0)", "setuptools (>=67.8.0)", "wheel (>=0.36.0)"]
+typing = ["build[uv]", "importlib-metadata (>=5.1)", "mypy (>=1.9.0,<1.10.0)", "tomli", "typing-extensions (>=3.7.4.3)"]
+uv = ["uv (>=0.1.18)"]
+virtualenv = ["virtualenv (>=20.0.35)"]
+
 [[package]]
 name = "cachetools"
 version = "5.3.3"
@@ -598,13 +640,13 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2024.2.2"
+version = "2024.6.2"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
-    {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
+    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
+    {file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
 ]
 
 [[package]]
@@ -809,30 +851,33 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.4.17"
+version = "0.5.0"
 description = "Chroma."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "chromadb-0.4.17-py3-none-any.whl", hash = "sha256:8cb88162bc6124441ba5a4b93819463a10e9aaafbe05a3286e876cbdc7a7e11d"},
-    {file = "chromadb-0.4.17.tar.gz", hash = "sha256:120f9d364719b664d5314500f8e6097f0e0b24496bb97a429bc324f8d11f1b52"},
+    {file = "chromadb-0.5.0-py3-none-any.whl", hash = "sha256:8193dc65c143b61d8faf87f02c44ecfa778d471febd70de517f51c5d88a06009"},
+    {file = "chromadb-0.5.0.tar.gz", hash = "sha256:7954af614a9ff7b2902ddbd0a162f33f7ec0669e2429903905c4f7876d1f766f"},
 ]
 
 [package.dependencies]
 bcrypt = ">=4.0.1"
+build = ">=1.0.3"
 chroma-hnswlib = "0.7.3"
 fastapi = ">=0.95.2"
 grpcio = ">=1.58.0"
 importlib-resources = "*"
 kubernetes = ">=28.1.0"
-numpy = {version = ">=1.22.5", markers = "python_version >= \"3.8\""}
+mmh3 = ">=4.0.1"
+numpy = ">=1.22.5"
 onnxruntime = ">=1.14.1"
 opentelemetry-api = ">=1.2.0"
 opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
+opentelemetry-instrumentation-fastapi = ">=0.41b0"
 opentelemetry-sdk = ">=1.2.0"
+orjson = ">=3.9.12"
 overrides = ">=7.3.1"
 posthog = ">=2.4.0"
-pulsar-client = ">=3.1.0"
 pydantic = ">=1.9"
 pypika = ">=0.48.9"
 PyYAML = ">=6.0.0"
@@ -1129,13 +1174,13 @@ typing-inspect = ">=0.4.0,<1"
 
 [[package]]
 name = "datasets"
-version = "2.19.1"
+version = "2.19.2"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.19.1-py3-none-any.whl", hash = "sha256:f7a78d15896f45004ccac1c298f3c7121f92f91f6f2bfbd4e4f210f827e6e411"},
-    {file = "datasets-2.19.1.tar.gz", hash = "sha256:0df9ef6c5e9138cdb996a07385220109ff203c204245578b69cca905eb151d3a"},
+    {file = "datasets-2.19.2-py3-none-any.whl", hash = "sha256:e07ff15d75b1af75c87dd96323ba2a361128d495136652f37fd62f918d17bb4e"},
+    {file = "datasets-2.19.2.tar.gz", hash = "sha256:eccb82fb3bb5ee26ccc6d7a15b7f1f834e2cc4e59b7cff7733a003552bad51ef"},
 ]
 
 [package.dependencies]
@@ -1151,7 +1196,7 @@ pandas = "*"
 pyarrow = ">=12.0.0"
 pyarrow-hotfix = "*"
 pyyaml = ">=5.1"
-requests = ">=2.19.0"
+requests = ">=2.32.1"
 tqdm = ">=4.62.1"
 xxhash = "*"
 
@@ -1159,7 +1204,7 @@ xxhash = "*"
 apache-beam = ["apache-beam (>=2.26.0)"]
 audio = ["librosa", "soundfile (>=0.12.1)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+dev = ["Pillow (>=9.4.0)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"]
 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
 metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
@@ -1167,9 +1212,9 @@ quality = ["ruff (>=0.3.0)"]
 s3 = ["s3fs"]
 tensorflow = ["tensorflow (>=2.6.0)"]
 tensorflow-gpu = ["tensorflow (>=2.6.0)"]
-tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+tests = ["Pillow (>=9.4.0)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 torch = ["torch"]
-vision = ["Pillow (>=6.2.1)"]
+vision = ["Pillow (>=9.4.0)"]
 
 [[package]]
 name = "deprecated"
@@ -1468,13 +1513,14 @@ testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)",
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
-name = "FlagEmbedding"
+name = "flagembedding"
 version = "1.2.10"
 description = "FlagEmbedding"
 optional = false
 python-versions = "*"
-files = []
-develop = false
+files = [
+    {file = "FlagEmbedding-1.2.10.tar.gz", hash = "sha256:0d0a553e5cca935bb80576f031023e8cb1775e6f9b29b0ab6bbe210697755d30"},
+]
 
 [package.dependencies]
 accelerate = ">=0.20.1"
@@ -1483,12 +1529,6 @@ sentence_transformers = "*"
 torch = ">=1.6.0"
 transformers = ">=4.33.0"
 
-[package.source]
-type = "git"
-url = "https://github.com/FlagOpen/FlagEmbedding.git"
-reference = "HEAD"
-resolved_reference = "ba3d2c2bc6b4b9d01ac5eb7655eeae1e30c70c31"
-
 [[package]]
 name = "flatbuffers"
 version = "24.3.25"
@@ -1502,53 +1542,53 @@ files = [
 
 [[package]]
 name = "fonttools"
-version = "4.52.4"
+version = "4.53.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fonttools-4.52.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fb8cd6559f0ae3a8f5e146f80ab2a90ad0325a759be8d48ee82758a0b89fa0aa"},
-    {file = "fonttools-4.52.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5ecb88318ff249bd2a715e7aec36774ce7ae3441128007ef72a39a60601f4a8f"},
-    {file = "fonttools-4.52.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b9a22cf1adaae7b2ba2ed7d8651a4193a4f348744925b4b740e6b38a94599c5b"},
-    {file = "fonttools-4.52.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8873d6edd1dae5c088dd3d61c9fd4dd80c827c486fa224d368233e7f33dc98af"},
-    {file = "fonttools-4.52.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:73ba38b98c012957940a04d9eb5439b42565ac892bba8cfc32e10d88e73921fe"},
-    {file = "fonttools-4.52.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:9725687db3c1cef13c0f40b380c3c15bea0113f4d0231b204d58edd5f2a53d90"},
-    {file = "fonttools-4.52.4-cp310-cp310-win32.whl", hash = "sha256:9180775c9535389a665cae7c5282f8e07754beabf59b66aeba7f6bfeb32a3652"},
-    {file = "fonttools-4.52.4-cp310-cp310-win_amd64.whl", hash = "sha256:46cc5d06ee05fd239c45d7935aaffd060ee773a88b97e901df50478247472643"},
-    {file = "fonttools-4.52.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:d272c7e173c3085308345ccc7fb2ad6ce7f415d777791dd6ce4e8140e354d09c"},
-    {file = "fonttools-4.52.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:21921e5855c399d10ddfc373538b425cabcf8b3258720b51450909e108896450"},
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 [[package]]
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+[[package]]
+name = "nvidia-nccl-cu12"
+version = "2.20.5"
+description = "NVIDIA Collective Communication Library (NCCL) Runtime"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
+]
+
 [[package]]
 name = "nvidia-nvjitlink-cu12"
 version = "12.5.40"
@@ -3973,13 +4148,13 @@ opentelemetry-sdk = ">=1.25.0,<1.26.0"
 
 [[package]]
 name = "opentelemetry-instrumentation"
-version = "0.45b0"
+version = "0.46b0"
 description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "opentelemetry_instrumentation-0.46b0-py3-none-any.whl", hash = "sha256:89cd721b9c18c014ca848ccd11181e6b3fd3f6c7669e35d59c48dc527408c18b"},
+    {file = "opentelemetry_instrumentation-0.46b0.tar.gz", hash = "sha256:974e0888fb2a1e01c38fbacc9483d024bb1132aad92d6d24e2e5543887a7adda"},
 ]
 
 [package.dependencies]
@@ -3987,6 +4162,48 @@ opentelemetry-api = ">=1.4,<2.0"
 setuptools = ">=16.0"
 wrapt = ">=1.0.0,<2.0.0"
 
+[[package]]
+name = "opentelemetry-instrumentation-asgi"
+version = "0.46b0"
+description = "ASGI instrumentation for OpenTelemetry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "opentelemetry_instrumentation_asgi-0.46b0.tar.gz", hash = "sha256:02559f30cf4b7e2a737ab17eb52aa0779bcf4cc06573064f3e2cb4dcc7d3040a"},
+]
+
+[package.dependencies]
+asgiref = ">=3.0,<4.0"
+opentelemetry-api = ">=1.12,<2.0"
+opentelemetry-instrumentation = "0.46b0"
+opentelemetry-semantic-conventions = "0.46b0"
+opentelemetry-util-http = "0.46b0"
+
+[package.extras]
+instruments = ["asgiref (>=3.0,<4.0)"]
+
+[[package]]
+name = "opentelemetry-instrumentation-fastapi"
+version = "0.46b0"
+description = "OpenTelemetry FastAPI Instrumentation"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "opentelemetry_instrumentation_fastapi-0.46b0.tar.gz", hash = "sha256:928a883a36fc89f9702f15edce43d1a7104da93d740281e32d50ffd03dbb4365"},
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+
+[package.dependencies]
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+opentelemetry-instrumentation-asgi = "0.46b0"
+opentelemetry-semantic-conventions = "0.46b0"
+opentelemetry-util-http = "0.46b0"
+
+[package.extras]
+instruments = ["fastapi (>=0.58,<1.0)"]
+
 [[package]]
 name = "opentelemetry-proto"
 version = "1.25.0"
@@ -4031,6 +4248,17 @@ files = [
 [package.dependencies]
 opentelemetry-api = "1.25.0"
 
+[[package]]
+name = "opentelemetry-util-http"
+version = "0.46b0"
+description = "Web util for OpenTelemetry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "opentelemetry_util_http-0.46b0.tar.gz", hash = "sha256:03b6e222642f9c7eae58d9132343e045b50aca9761fcb53709bd2b663571fdf6"},
+]
+
 [[package]]
 name = "orjson"
 version = "3.10.3"
@@ -4537,53 +4765,6 @@ files = [
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-description = "Apache Pulsar Python client library"
-optional = false
-python-versions = "*"
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-
-[package.dependencies]
-certifi = "*"
-
-[package.extras]
-all = ["apache-bookkeeper-client (>=4.16.1)", "fastavro (>=1.9.2)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]
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-functions = ["apache-bookkeeper-client (>=4.16.1)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]
-
 [[package]]
 name = "pyarrow"
 version = "16.1.0"
@@ -5038,6 +5219,17 @@ files = [
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 ]
 
+[[package]]
+name = "pyproject-hooks"
+version = "1.1.0"
+description = "Wrappers to call pyproject.toml-based build backend hooks."
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "pyreadline3"
 version = "3.4.1"
@@ -6232,6 +6424,19 @@ files = [
 [package.dependencies]
 mpmath = ">=1.1.0,<1.4.0"
 
+[[package]]
+name = "tbb"
+version = "2021.12.0"
+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "tenacity"
 version = "8.3.0"
@@ -6540,6 +6745,97 @@ typing-extensions = ">=4.8.0"
 opt-einsum = ["opt-einsum (>=3.3)"]
 optree = ["optree (>=0.9.1)"]
 
+[[package]]
+name = "torch"
+version = "2.3.0"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+typing-extensions = ">=4.8.0"
+
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+optree = ["optree (>=0.9.1)"]
+
+[[package]]
+name = "torch"
+version = "2.3.0+cpu"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "torch-2.3.0+cpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:e3c220702d82c7596924150e0499fbbffcf62a88a59adc860fa357cd8dc1c302"},
+    {file = "torch-2.3.0+cpu-cp310-cp310-win_amd64.whl", hash = "sha256:ab0c05525195b8fecdf2ea75968ed32ccd87dff16381b6e13249babb4a9596ff"},
+    {file = "torch-2.3.0+cpu-cp311-cp311-linux_x86_64.whl", hash = "sha256:97a38b25ee0e3d020691e7846efbca62a3d8a57645c027dcb5ba0adfec36fe55"},
+    {file = "torch-2.3.0+cpu-cp311-cp311-win_amd64.whl", hash = "sha256:a8ac195974be6f067245bae8156b8c06fb0a723b0eed8f2e244b5dd58c7e2a49"},
+    {file = "torch-2.3.0+cpu-cp312-cp312-linux_x86_64.whl", hash = "sha256:a8982e52185771591dad577a124a7770f72f288f8ae5833317b1e329c0d2f07e"},
+    {file = "torch-2.3.0+cpu-cp312-cp312-win_amd64.whl", hash = "sha256:483131a7997995d867313ee902743084e844e830ab2a0c5e079c61ec2da3cd17"},
+    {file = "torch-2.3.0+cpu-cp38-cp38-linux_x86_64.whl", hash = "sha256:8c52484880d5fbe511cffc255dd34847ddeced3f94334c6bf7eb2b0445f10cb4"},
+    {file = "torch-2.3.0+cpu-cp38-cp38-win_amd64.whl", hash = "sha256:28a11bcc0d709b397d675cff689707019b8cc122e6bf328b57b900f47c36f156"},
+    {file = "torch-2.3.0+cpu-cp39-cp39-linux_x86_64.whl", hash = "sha256:1e86e225e472392440ace378ba3165b5e87648e8b5fbf16adc41c0df881c38b8"},
+    {file = "torch-2.3.0+cpu-cp39-cp39-win_amd64.whl", hash = "sha256:5c2afdff80203eaabf4c223a294c2f465020b3360e8e87f76b52ace9c5801ebe"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = "*"
+jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
+networkx = "*"
+sympy = "*"
+typing-extensions = ">=4.8.0"
+
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+optree = ["optree (>=0.9.1)"]
+
+[package.source]
+type = "legacy"
+url = "https://download.pytorch.org/whl/cpu"
+reference = "pytorch_cpu"
+
 [[package]]
 name = "torchvision"
 version = "0.17.2"
@@ -6582,6 +6878,75 @@ torch = "2.2.2"
 [package.extras]
 scipy = ["scipy"]
 
+[[package]]
+name = "torchvision"
+version = "0.18.0"
+description = "image and video datasets and models for torch deep learning"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "torchvision-0.18.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dd61628a3d189c6852a12dc5ed4cd2eece66d2d67f35a866cb16f1dcb06c8c62"},
+    {file = "torchvision-0.18.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:493c45f9937dad37aa1b64b14da17c7a589c72b91adc4837d431009cfe29bd53"},
+    {file = "torchvision-0.18.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:5337f6acfa1fe959d5cb340d01a00614d6b31ce7a4824ccb95435a85c5273b95"},
+    {file = "torchvision-0.18.0-cp310-cp310-win_amd64.whl", hash = "sha256:bd8e6f3b5beb49965f15c461302488edfa3d8c2d01d3bb79b150d6fb62711e3a"},
+    {file = "torchvision-0.18.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:6896a52168befe1105fb3c9335287390ed227e71d1e4ec4d68b62e8a3099fc09"},
+    {file = "torchvision-0.18.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:3d7955398d4ceaad77c487c2c44f6f7813112402c9bab8cd906d346005891048"},
+    {file = "torchvision-0.18.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:e5a24d620cea14a4bb89f24aa2b506230c0a16a3ada57fc53ad80cfd256a2128"},
+    {file = "torchvision-0.18.0-cp311-cp311-win_amd64.whl", hash = "sha256:6ad70ddfa879bda5ed886b2518fe562640e0059787cbd65cb2bffa7674541410"},
+    {file = "torchvision-0.18.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:eb9d83c0e1dbb54ecb0fb04c87f786333e3a6fb8b9c400aca7c31081f9aa5707"},
+    {file = "torchvision-0.18.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:b657d052d146f24cb3b2a78219bfc82ae70a9706671c50f632528907d10cccec"},
+    {file = "torchvision-0.18.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a964afbc7ddf50a46b941477f6c35729b416deedd139756befd488245e2e226d"},
+    {file = "torchvision-0.18.0-cp312-cp312-win_amd64.whl", hash = "sha256:7c770f0f748e0b17f57c0297508d7254f686cdf03fc2e2949f422b20574f4c0f"},
+    {file = "torchvision-0.18.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2115a1906c015f5da9ceedc40a983313b0fd6e2c8a17108a92991706f51f6987"},
+    {file = "torchvision-0.18.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:6323f7e5423ff2594d5891863b919deb9d0de95f01c36bf26fbd879036b6ed08"},
+    {file = "torchvision-0.18.0-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:925d0a82cccf6f986c18b29b4392a942db65cbdb73c13a129c8493822eb9e36f"},
+    {file = "torchvision-0.18.0-cp38-cp38-win_amd64.whl", hash = "sha256:95b42d0dc599b47a01530c7439a5751e67e45b85e3a67113989cf7c7c70f2039"},
+    {file = "torchvision-0.18.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:75e22ecf44a13b8f95b8ad421c0261282d859c61816badaca1959e073ccdd691"},
+    {file = "torchvision-0.18.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:4c334b3e719ba0a9ba6e15d4aff1178f5e6d029174f346163fed525f0ccfffd3"},
+    {file = "torchvision-0.18.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:36efd87001c6bee2383e043e46a025affb03179747c8f4777b9918527ffce756"},
+    {file = "torchvision-0.18.0-cp39-cp39-win_amd64.whl", hash = "sha256:ccc292e093771d5baacf5535ac4416306b6b5f15676341cd4d010d8542eace25"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
+torch = "2.3.0"
+
+[package.extras]
+scipy = ["scipy"]
+
+[[package]]
+name = "torchvision"
+version = "0.18.0+cpu"
+description = "image and video datasets and models for torch deep learning"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "torchvision-0.18.0+cpu-cp310-cp310-linux_x86_64.whl", hash = "sha256:6f285024f8a93598a67fbd2c86b534cd01236e8f6bda8704cd3459cc63d665a3"},
+    {file = "torchvision-0.18.0+cpu-cp310-cp310-win_amd64.whl", hash = "sha256:d09fe6166bca6c3b0bc3f5e87674045af79d9c80bbcf1b88890fc497caa90a98"},
+    {file = "torchvision-0.18.0+cpu-cp311-cp311-linux_x86_64.whl", hash = "sha256:ad2cfbf625f93f6a51bbbb8e993f4e0ba96e84a12a6719cc5f7ed3cc5b0fc09d"},
+    {file = "torchvision-0.18.0+cpu-cp311-cp311-win_amd64.whl", hash = "sha256:09eb510de992fe64fb30d326e0662cbf94ac891a3a616b2a8acf14e6663df6d0"},
+    {file = "torchvision-0.18.0+cpu-cp312-cp312-linux_x86_64.whl", hash = "sha256:d7169c0bc00faee13c1e9eae186cfe19a5b32e1079eec5d47bf494140fe945cf"},
+    {file = "torchvision-0.18.0+cpu-cp312-cp312-win_amd64.whl", hash = "sha256:b9578be98e357d90b508669cd77de8609327fc234d67cfc9455634be436b0bda"},
+    {file = "torchvision-0.18.0+cpu-cp38-cp38-linux_x86_64.whl", hash = "sha256:d20a65db1c6821fd79b8b4678cd21c14c8d8c1a1fd0d5ae2b43528db568ea890"},
+    {file = "torchvision-0.18.0+cpu-cp38-cp38-win_amd64.whl", hash = "sha256:e47a0ccd1e80ced219a2d91250c6edc2901387ea444bfdd16b248d81cc76b171"},
+    {file = "torchvision-0.18.0+cpu-cp39-cp39-linux_x86_64.whl", hash = "sha256:0e764a15c69f51742b09ec17cc2dcd1eee445e87cfd93a9b13b1caa2037f154d"},
+    {file = "torchvision-0.18.0+cpu-cp39-cp39-win_amd64.whl", hash = "sha256:1b83ff7a519036fc63c018b08046d84fceefbcef519fe7f55476e84d1c237e59"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
+torch = "2.3.0"
+
+[package.extras]
+scipy = ["scipy"]
+
+[package.source]
+type = "legacy"
+url = "https://download.pytorch.org/whl/cpu"
+reference = "pytorch_cpu"
+
 [[package]]
 name = "tqdm"
 version = "4.66.4"
@@ -6693,6 +7058,29 @@ build = ["cmake (>=3.20)", "lit"]
 tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
 tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
+[[package]]
+name = "triton"
+version = "2.3.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = false
+python-versions = "*"
+files = [
+    {file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"},
+    {file = "triton-2.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c3d9607f85103afdb279938fc1dd2a66e4f5999a58eb48a346bd42738f986dd"},
+    {file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"},
+    {file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"},
+    {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"},
+    {file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
+tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
+
 [[package]]
 name = "typer"
 version = "0.12.3"
@@ -6723,13 +7111,13 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.0"
+version = "4.12.1"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
-    {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
+    {file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
+    {file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
 ]
 
 [[package]]
@@ -7435,20 +7823,20 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zipp"
-version = "3.19.0"
+version = "3.19.1"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.19.0-py3-none-any.whl", hash = "sha256:96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec"},
-    {file = "zipp-3.19.0.tar.gz", hash = "sha256:952df858fb3164426c976d9338d3961e8e8b3758e2e059e0f754b8c4262625ee"},
+    {file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"},
+    {file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10.0,<3.12"
-content-hash = "6ab5f69acfd6cf7bd5ea1aaee27e6f2fe5b96c97828340f495e7b3d43cc64f8a"
+content-hash = "5c891b42861023d77d87bf4027b2b3e53fe7eaad67d81c893f7f5be7e45a8e69"
diff --git a/pyproject.toml b/pyproject.toml
index d697e109..5398cebc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ readme = "README.md"
 python = ">=3.10.0,<3.12"
 fastapi = "^0.110.1"
 uvicorn = "^0.29.0"
-llama-index-core = "^0.10.29"
+llama-index-core = ">=0.10.29,<=0.10.39"
 llama-index-embeddings-openai = "^0.1.7"
 llama-index-embeddings-azure-openai = "^0.1.7"
 llama-index-embeddings-dashscope = "^0.1.3"
@@ -37,13 +37,22 @@ pytest = "^8.1.1"
 llama-index-retrievers-bm25 = "^0.1.3"
 jieba = "^0.42.1"
 llama-index-embeddings-huggingface = "^0.2.0"
-llama-index-postprocessor-flag-embedding-reranker = "^0.1.2"
-flagembedding = {git = "https://github.com/FlagOpen/FlagEmbedding.git"}
+llama-index-postprocessor-flag-embedding-reranker = "^0.1.3"
+flagembedding = "^1.2.10"
 sentencepiece = "^0.2.0"
 oss2 = "^2.18.5"
 asgi-correlation-id = "^4.3.1"
 openinference-instrumentation-llama-index = "1.3.0"
-torch = "2.2.2"
+torch = [
+  {version = "2.3.0+cpu", source = "pytorch_cpu", platform = "linux"},
+  {version = "2.3.0+cpu", source = "pytorch_cpu", platform = "win32"},
+  {version = "2.2.2", platform = "darwin"}
+]
+torchvision = [
+  {version = "0.18.0+cpu", source = "pytorch_cpu", platform = "linux"},
+  {version = "0.18.0+cpu", source = "pytorch_cpu", platform = "win32"},
+  {version = "0.17.2", platform = "darwin"}
+]
 openpyxl = "^3.1.2"
 pdf2image = "^1.17.0"
 llama-index-storage-chat-store-redis = "^0.1.3"
@@ -67,5 +76,10 @@ load_data = "pai_rag.data.rag_datapipeline:run"
 load_easyocr_model = "pai_rag.utils.download_easyocr_models:download_easyocr_models"
 evaluation = "pai_rag.evaluations.batch_evaluator:run"
 
+[[tool.poetry.source]]
+name = "pytorch_cpu"
+url = "https://download.pytorch.org/whl/cpu"
+priority = "explicit"
+
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
diff --git a/src/pai_rag/app/web/ui.py b/src/pai_rag/app/web/ui.py
index 1d06e420..0a4aede4 100644
--- a/src/pai_rag/app/web/ui.py
+++ b/src/pai_rag/app/web/ui.py
@@ -133,7 +133,6 @@ def respond(input_elements: List[Any]):
         response = rag_client.query_vector(msg)
     else:
         response = rag_client.query(msg, session_id=current_session_id)
-    print("history======:", update_dict["include_history"])
     if update_dict["include_history"]:
         current_session_id = response.session_id
     else:
@@ -384,7 +383,12 @@ def change_llm_src(value):
                             )
                             # similarity_cutoff = gr.Slider(minimum=0, maximum=1, step=0.01,elem_id="similarity_cutoff",value=view_model.similarity_cutoff, label="Similarity Distance Threshold (The more similar the vectors, the smaller the value.)")
                             rerank_model = gr.Radio(
-                                ["No Rerank", "bge-reranker-base", "LLMRerank"],
+                                [
+                                    "no-reranker",
+                                    "bge-reranker-base",
+                                    "bge-reranker-large",
+                                    "llm-reranker",
+                                ],
                                 label="Re-Rank Model (Note: It will take a long time to load the model when using it for the first time.)",
                                 elem_id="rerank_model",
                                 value=view_model.rerank_model,
diff --git a/src/pai_rag/app/web/view_model.py b/src/pai_rag/app/web/view_model.py
index c833c4cd..01f050ca 100644
--- a/src/pai_rag/app/web/view_model.py
+++ b/src/pai_rag/app/web/view_model.py
@@ -81,7 +81,7 @@ class ViewModel(BaseModel):
 
     similarity_top_k: int = 5
     # similarity_cutoff: float = 0.3
-    rerank_model: str = "No Rerank"
+    rerank_model: str = "no-reranker"
     retrieval_mode: str = "hybrid"  # hybrid / embedding / keyword
     query_engine_type: str = "RetrieverQueryEngine"
     BM25_weight: float = 0.5
@@ -181,13 +181,15 @@ def sync_app_config(self, config):
         # if "Similarity" in config["postprocessor"]:
         #     self.similarity_cutoff = config["postprocessor"].get("similarity_cutoff", 0.1)
 
-        rerank_model = config["postprocessor"].get("rerank_model", "No Rerank")
-        if rerank_model == "llm":
-            self.rerank_model = "LLMRerank"
-        elif rerank_model == "bge_reranker_base":
+        rerank_model = config["postprocessor"].get("rerank_model", "no-reranker")
+        if rerank_model == "llm-reranker":
+            self.rerank_model = "llm-reranker"
+        elif rerank_model == "bge-reranker-base":
             self.rerank_model = "bge-reranker-base"
+        elif rerank_model == "bge-reranker-large":
+            self.rerank_model = "bge-reranker-large"
         else:
-            self.rerank_model = "No Rerank"
+            self.rerank_model = "no-reranker"
 
         self.synthesizer_type = config["synthesizer"].get("type", "SimpleSummarize")
         self.text_qa_template = config["synthesizer"].get("text_qa_template", None)
@@ -278,13 +280,15 @@ def to_app_config(self):
             config["retriever"]["retrieval_mode"] = "keyword"
 
         # config["postprocessor"]["similarity_cutoff"] = self.similarity_cutoff
-        if self.rerank_model == "LLMRerank":
-            config["postprocessor"]["rerank_model"] = "llm"
-            config["postprocessor"]["top_n"] = 3
+        if self.rerank_model == "llm-reranker":
+            config["postprocessor"]["rerank_model"] = "llm-reranker"
         elif self.rerank_model == "bge-reranker-base":
             config["postprocessor"]["rerank_model"] = "bge-reranker-base"
+        elif self.rerank_model == "bge-reranker-large":
+            config["postprocessor"]["rerank_model"] = "bge-reranker-large"
         else:
-            config["postprocessor"]["rerank_model"] = "no rerank"
+            config["postprocessor"]["rerank_model"] = "no-reranker"
+        config["postprocessor"]["top_n"] = 3
 
         config["synthesizer"]["type"] = self.synthesizer_type
         config["synthesizer"]["text_qa_template"] = self.text_qa_template
diff --git a/src/pai_rag/config/settings.toml b/src/pai_rag/config/settings.toml
index 49a52513..65d279a3 100644
--- a/src/pai_rag/config/settings.toml
+++ b/src/pai_rag/config/settings.toml
@@ -45,7 +45,7 @@ chunk_size = 500
 chunk_overlap = 10
 
 [rag.postprocessor]
-rerank_model = "no rerank" # [no rerank, llm, bge_reranker_base]
+rerank_model = "no-reranker" # [no-reranker, bge-reranker-base, bge-reranker-large, llm-reranker]
 top_n = 2
 
 [rag.query_engine]
diff --git a/src/pai_rag/core/rag_application.py b/src/pai_rag/core/rag_application.py
index 3ad33e7b..17f4d6e4 100644
--- a/src/pai_rag/core/rag_application.py
+++ b/src/pai_rag/core/rag_application.py
@@ -12,6 +12,7 @@
     ContextDoc,
     RetrievalResponse,
 )
+from llama_index.core.schema import QueryBundle
 
 import logging
 
@@ -64,7 +65,8 @@ async def aquery_retrieval(self, query: RetrievalQuery) -> RetrievalResponse:
 
         session_id = correlation_id.get() or DEFAULT_SESSION_ID
         self.logger.info(f"Get session ID: {session_id}.")
-        node_results = await self.retriever.aretrieve(query.question)
+        query_bundle = QueryBundle(query.question)
+        node_results = await self.query_engine.aretrieve(query_bundle)
 
         docs = [
             ContextDoc(
diff --git a/src/pai_rag/core/rag_configuration.py b/src/pai_rag/core/rag_configuration.py
index be32bbc8..2c0d01eb 100644
--- a/src/pai_rag/core/rag_configuration.py
+++ b/src/pai_rag/core/rag_configuration.py
@@ -38,5 +38,5 @@ def update(self, new_value: Dynaconf):
     def persist(self):
         """Save configuration to file."""
         data = self.config.as_dict()
-        os.makedirs("output", exist_ok=True)
+        os.makedirs("localdata", exist_ok=True)
         loaders.write(GENERATED_CONFIG_FILE_NAME, DynaBox(data).to_dict(), merge=True)
diff --git a/src/pai_rag/modules/postprocessor/postprocessor.py b/src/pai_rag/modules/postprocessor/postprocessor.py
index a11ca187..4060dc00 100644
--- a/src/pai_rag/modules/postprocessor/postprocessor.py
+++ b/src/pai_rag/modules/postprocessor/postprocessor.py
@@ -40,22 +40,24 @@ def _create_new_instance(self, new_params: Dict[str, Any]):
             )
 
         rerank_model = config.get("rerank_model", "")
-        if rerank_model == "llm":
+        if rerank_model == "llm-reranker":
             top_n = config.get("top_n", DEFAULT_RANK_TOP_N)
-            logger.info(f"[PostProcessor]: LLMRerank used with top_n {top_n}.")
+            logger.info(f"[PostProcessor]: Llm reranker used with top_n {top_n}.")
             post_processors.append(LLMRerank(top_n=top_n, llm=llm))
 
-        elif rerank_model == "bge_reranker_base":
+        elif (
+            rerank_model == "bge-reranker-base" or rerank_model == "bge-reranker-large"
+        ):
             model_dir = config.get("rerank_model_dir", DEFAULT_MODEL_DIR)
-            model_name = config.get("rerank_model_name", DEFAULT_RANK_MODEL)
+            model_name = config.get("rerank_model_name", rerank_model)
             model = os.path.join(model_dir, model_name)
             top_n = config.get("top_n", DEFAULT_RANK_TOP_N)
             logger.info(
-                f"[PostProcessor]: Rerank used with top_n {top_n}, model {model_name}."
+                f"[PostProcessor]: Reranker model used with top_n {top_n}, model {model_name}."
             )
             post_processors.append(FlagEmbeddingReranker(model=model, top_n=top_n))
 
         else:
-            logger.info("[PostProcessor]: No Rerank used.")
+            logger.info("[PostProcessor]: No Reranker used.")
 
         return post_processors