diff --git a/Dockerfile b/Dockerfile
index ddac4117..d45f03da 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,6 +11,8 @@ WORKDIR /app
 COPY . .
 
 RUN poetry install && rm -rf $POETRY_CACHE_DIR
+
+ENV PYTHON_AGENT_PATH="https://python-agent.oss-rg-china-mainland.aliyuncs.com/1.1.0.rc/aliyun-python-agent.tar.gz"
 RUN poetry run aliyun-bootstrap -a install
 
 FROM python:3.11-slim AS prod
diff --git a/Dockerfile_gpu b/Dockerfile_gpu
index e8203f82..b42c00ab 100644
--- a/Dockerfile_gpu
+++ b/Dockerfile_gpu
@@ -13,6 +13,8 @@ RUN mv pyproject_gpu.toml pyproject.toml \
     && rm poetry.lock
 
 RUN poetry install && rm -rf $POETRY_CACHE_DIR
+
+ENV PYTHON_AGENT_PATH="https://python-agent.oss-rg-china-mainland.aliyuncs.com/1.1.0.rc/aliyun-python-agent.tar.gz"
 RUN poetry run aliyun-bootstrap -a install
 
 FROM python:3.11-slim AS prod
diff --git a/Dockerfile_ui b/Dockerfile_ui
index 0e5d30e5..9a6037e9 100644
--- a/Dockerfile_ui
+++ b/Dockerfile_ui
@@ -11,6 +11,8 @@ WORKDIR /app
 COPY . .
 
 RUN poetry install && rm -rf $POETRY_CACHE_DIR
+ENV PYTHON_AGENT_PATH="https://python-agent.oss-rg-china-mainland.aliyuncs.com/1.1.0.rc/aliyun-python-agent.tar.gz"
+RUN poetry run aliyun-bootstrap -a install
 
 FROM python:3.11-slim AS prod
 
diff --git a/poetry.lock b/poetry.lock
index ae024583..7146bd1c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -175,21 +175,6 @@ yarl = ">=1.12.0,<2.0"
 [package.extras]
 speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
-[[package]]
-name = "aioitertools"
-version = "0.12.0"
-description = "itertools and builtins for AsyncIO and mixed iterables"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "aioitertools-0.12.0-py3-none-any.whl", hash = "sha256:fc1f5fac3d737354de8831cbba3eb04f79dd649d8f3afb4c5b114925e662a796"},
-    {file = "aioitertools-0.12.0.tar.gz", hash = "sha256:c2a9055b4fbb7705f561b9d86053e8af5d10cc845d22c32008c43490b2d8dd6b"},
-]
-
-[package.extras]
-dev = ["attribution (==1.8.0)", "black (==24.8.0)", "build (>=1.2)", "coverage (==7.6.1)", "flake8 (==7.1.1)", "flit (==3.9.0)", "mypy (==1.11.2)", "ufmt (==2.7.1)", "usort (==1.0.8.post1)"]
-docs = ["sphinx (==8.0.2)", "sphinx-mdinclude (==0.6.2)"]
-
 [[package]]
 name = "aiosignal"
 version = "1.3.1"
@@ -204,24 +189,6 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
-[[package]]
-name = "aiosqlite"
-version = "0.20.0"
-description = "asyncio bridge to the standard sqlite3 module"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
-    {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
-]
-
-[package.dependencies]
-typing_extensions = ">=4.0"
-
-[package.extras]
-dev = ["attribution (==1.7.0)", "black (==24.2.0)", "coverage[toml] (==7.4.1)", "flake8 (==7.0.0)", "flake8-bugbear (==24.2.6)", "flit (==3.9.0)", "mypy (==1.8.0)", "ufmt (==2.3.0)", "usort (==1.0.8.post1)"]
-docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
-
 [[package]]
 name = "albucore"
 version = "0.0.17"
@@ -262,25 +229,6 @@ scipy = ">=1.10.0"
 hub = ["huggingface-hub"]
 text = ["pillow"]
 
-[[package]]
-name = "alembic"
-version = "1.13.3"
-description = "A database migration tool for SQLAlchemy."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "alembic-1.13.3-py3-none-any.whl", hash = "sha256:908e905976d15235fae59c9ac42c4c5b75cfcefe3d27c0fbf7ae15a37715d80e"},
-    {file = "alembic-1.13.3.tar.gz", hash = "sha256:203503117415561e203aa14541740643a611f641517f0209fcae63e9fa09f1a2"},
-]
-
-[package.dependencies]
-Mako = "*"
-SQLAlchemy = ">=1.3.0"
-typing-extensions = ">=4"
-
-[package.extras]
-tz = ["backports.zoneinfo"]
-
 [[package]]
 name = "alibabacloud-credentials"
 version = "0.3.5"
@@ -617,104 +565,6 @@ doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)",
 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)"]
 trio = ["trio (>=0.26.1)"]
 
-[[package]]
-name = "arize-phoenix"
-version = "5.5.2"
-description = "AI Observability and Evaluation"
-optional = false
-python-versions = "<3.13,>=3.9"
-files = [
-    {file = "arize_phoenix-5.5.2-py3-none-any.whl", hash = "sha256:63c1ab857843305f5dd5499e0b425e862c7b68811199eb8497805f822850458b"},
-    {file = "arize_phoenix-5.5.2.tar.gz", hash = "sha256:137926748bbf55bcc56c573906c5218a53faa474eed05cfe35ff3cd004958270"},
-]
-
-[package.dependencies]
-aioitertools = "*"
-aiosqlite = "*"
-alembic = ">=1.3.0,<2"
-arize-phoenix-evals = ">=0.13.1"
-arize-phoenix-otel = ">=0.5.1"
-authlib = "*"
-cachetools = "*"
-fast-hdbscan = ">=0.2.0"
-fastapi = "*"
-grpc-interceptor = "*"
-grpcio = "*"
-httpx = "*"
-jinja2 = "*"
-numba = ">=0.60.0"
-numpy = "!=2.0.0"
-openinference-instrumentation = ">=0.1.12"
-openinference-semantic-conventions = ">=0.1.9"
-opentelemetry-exporter-otlp = "*"
-opentelemetry-proto = ">=1.12.0"
-opentelemetry-sdk = "*"
-opentelemetry-semantic-conventions = "*"
-pandas = ">=1.0"
-protobuf = ">=3.20.2,<6.0"
-psutil = "*"
-pyarrow = "*"
-pydantic = ">=1.0,<2.0.dev0 || >=2.1.dev0,<3"
-python-multipart = "*"
-scikit-learn = "*"
-scipy = "*"
-sqlalchemy = {version = ">=2.0.4,<3", extras = ["asyncio"]}
-sqlean-py = ">=3.45.1"
-starlette = "*"
-strawberry-graphql = "0.243.1"
-tqdm = "*"
-typing-extensions = ">=4.6"
-umap-learn = "*"
-uvicorn = "*"
-websockets = "*"
-wrapt = "*"
-
-[package.extras]
-container = ["anthropic", "fast-hdbscan (>=0.2.0)", "numba (>=0.60.0)", "openai (>=1.0.0)", "opentelemetry-exporter-otlp", "opentelemetry-instrumentation-fastapi", "opentelemetry-instrumentation-grpc", "opentelemetry-instrumentation-sqlalchemy", "opentelemetry-proto (>=1.12.0)", "opentelemetry-sdk", "opentelemetry-semantic-conventions", "prometheus-client", "py-grpc-prometheus", "strawberry-graphql[opentelemetry] (==0.243.1)", "umap-learn", "uvloop"]
-dev = ["anthropic", "arize[autoembeddings,llm-evaluation]", "asgi-lifespan", "asyncpg", "faker (>=30.1.0)", "gcsfs", "google-cloud-aiplatform (>=1.3)", "grpc-interceptor[testing]", "hatch", "jupyter", "langchain (>=0.0.334)", "litellm (>=1.0.3)", "llama-index (>=0.10.3)", "mypy (==1.11.2)", "nbqa", "pandas (>=1.0)", "pandas-stubs (==2.2.2.240603)", "portpicker", "pre-commit", "prometheus-client", "psycopg[binary,pool]", "pytest (==8.3.3)", "pytest-asyncio", "pytest-cov", "pytest-postgresql", "pytest-xdist", "ruff (==0.6.9)", "strawberry-graphql[debug-server,opentelemetry] (==0.243.1)", "tabulate", "tox (==4.18.1)", "tox-uv (==1.11.3)", "types-tabulate", "uv (==0.4.8)", "uvloop"]
-embeddings = ["fast-hdbscan (>=0.2.0)", "numba (>=0.60.0)", "umap-learn"]
-llama-index = ["llama-index (==0.11.0)", "llama-index-agent-openai (>=0.2.7)", "llama-index-embeddings-openai (>=0.1.10)", "llama-index-llms-openai (>=0.1.24)", "llama-index-readers-file (>=0.1.25)"]
-pg = ["asyncpg", "psycopg[binary,pool]"]
-
-[[package]]
-name = "arize-phoenix-evals"
-version = "0.17.2"
-description = "LLM Evaluations"
-optional = false
-python-versions = "<3.14,>=3.8"
-files = [
-    {file = "arize_phoenix_evals-0.17.2-py3-none-any.whl", hash = "sha256:32c05c5c7684a92dd94dff6d8b41f486d21012ea696283724a73e5762f3b0903"},
-    {file = "arize_phoenix_evals-0.17.2.tar.gz", hash = "sha256:ac6b9002b02fc616fc6b1be1b181df712b6022ecb113021a4fa102cf881328d3"},
-]
-
-[package.dependencies]
-pandas = "*"
-tqdm = "*"
-typing-extensions = ">=4.5,<5"
-
-[package.extras]
-dev = ["anthropic (>0.18.0)", "boto3", "litellm (>=1.28.9)", "mistralai (<1)", "openai (>=1.0.0)", "vertexai"]
-test = ["anthropic (>=0.18.0)", "boto3", "litellm (>=1.28.9)", "mistralai (<1)", "nest-asyncio", "openai (>=1.0.0)", "openinference-semantic-conventions", "pandas", "pandas-stubs (<=2.0.2.230605)", "respx", "tqdm", "types-tqdm", "typing-extensions (>=4.5,<5)", "vertexai"]
-
-[[package]]
-name = "arize-phoenix-otel"
-version = "0.6.1"
-description = "LLM Observability"
-optional = false
-python-versions = "<3.14,>=3.8"
-files = [
-    {file = "arize_phoenix_otel-0.6.1-py3-none-any.whl", hash = "sha256:fc00dd847a248cece5f20dffeb8fabfab72c1c38f198bcee4260618c6af1d8a5"},
-    {file = "arize_phoenix_otel-0.6.1.tar.gz", hash = "sha256:f5f7a257159f49b398dfe2ba45bb70e22766cdf61bb365c0c47dc55f329bfe95"},
-]
-
-[package.dependencies]
-openinference-semantic-conventions = ">=0.1.9"
-opentelemetry-exporter-otlp = "*"
-opentelemetry-proto = ">=1.12.0"
-opentelemetry-sdk = "*"
-opentelemetry-semantic-conventions = "*"
-typing-extensions = ">=4.5,<5"
-
 [[package]]
 name = "asgi-correlation-id"
 version = "4.3.4"
@@ -733,20 +583,6 @@ 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.extras]
-tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
-
 [[package]]
 name = "aspose-slides"
 version = "24.10.0"
@@ -873,20 +709,6 @@ 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 = "1.3.2"
-description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "Authlib-1.3.2-py2.py3-none-any.whl", hash = "sha256:ede026a95e9f5cdc2d4364a52103f5405e75aa156357e831ef2bfd0bc5094dfc"},
-    {file = "authlib-1.3.2.tar.gz", hash = "sha256:4b16130117f9eb82aa6eec97f6dd4673c3f960ac0283ccdae2897ee4bc030ba2"},
-]
-
-[package.dependencies]
-cryptography = "*"
-
 [[package]]
 name = "azure-core"
 version = "1.31.0"
@@ -938,17 +760,6 @@ files = [
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
-[[package]]
-name = "backoff"
-version = "2.2.1"
-description = "Function decoration for backoff and retry"
-optional = false
-python-versions = ">=3.7,<4.0"
-files = [
-    {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
-    {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
-]
-
 [[package]]
 name = "bce-python-sdk"
 version = "0.9.23"
@@ -965,46 +776,6 @@ future = ">=0.6.0"
 pycryptodome = ">=3.8.0"
 six = ">=1.4.0"
 
-[[package]]
-name = "bcrypt"
-version = "4.2.0"
-description = "Modern password hashing for your software and your servers"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "bcrypt-4.2.0-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:096a15d26ed6ce37a14c1ac1e48119660f21b24cba457f160a4b830f3fe6b5cb"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c02d944ca89d9b1922ceb8a46460dd17df1ba37ab66feac4870f6862a1533c00"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d84cf6d877918620b687b8fd1bf7781d11e8a0998f576c7aa939776b512b98d"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:1bb429fedbe0249465cdd85a58e8376f31bb315e484f16e68ca4c786dcc04291"},
-    {file = "bcrypt-4.2.0-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:655ea221910bcac76ea08aaa76df427ef8625f92e55a8ee44fbf7753dbabb328"},
-    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:1ee38e858bf5d0287c39b7a1fc59eec64bbf880c7d504d3a06a96c16e14058e7"},
-    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:0da52759f7f30e83f1e30a888d9163a81353ef224d82dc58eb5bb52efcabc399"},
-    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3698393a1b1f1fd5714524193849d0c6d524d33523acca37cd28f02899285060"},
-    {file = "bcrypt-4.2.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:762a2c5fb35f89606a9fde5e51392dad0cd1ab7ae64149a8b935fe8d79dd5ed7"},
-    {file = "bcrypt-4.2.0-cp37-abi3-win32.whl", hash = "sha256:5a1e8aa9b28ae28020a3ac4b053117fb51c57a010b9f969603ed885f23841458"},
-    {file = "bcrypt-4.2.0-cp37-abi3-win_amd64.whl", hash = "sha256:8f6ede91359e5df88d1f5c1ef47428a4420136f3ce97763e31b86dd8280fbdf5"},
-    {file = "bcrypt-4.2.0-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:c52aac18ea1f4a4f65963ea4f9530c306b56ccd0c6f8c8da0c06976e34a6e841"},
-    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3bbbfb2734f0e4f37c5136130405332640a1e46e6b23e000eeff2ba8d005da68"},
-    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3413bd60460f76097ee2e0a493ccebe4a7601918219c02f503984f0a7ee0aebe"},
-    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:8d7bb9c42801035e61c109c345a28ed7e84426ae4865511eb82e913df18f58c2"},
-    {file = "bcrypt-4.2.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3d3a6d28cb2305b43feac298774b997e372e56c7c7afd90a12b3dc49b189151c"},
-    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:9c1c4ad86351339c5f320ca372dfba6cb6beb25e8efc659bedd918d921956bae"},
-    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:27fe0f57bb5573104b5a6de5e4153c60814c711b29364c10a75a54bb6d7ff48d"},
-    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:8ac68872c82f1add6a20bd489870c71b00ebacd2e9134a8aa3f98a0052ab4b0e"},
-    {file = "bcrypt-4.2.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:cb2a8ec2bc07d3553ccebf0746bbf3d19426d1c6d1adbd4fa48925f66af7b9e8"},
-    {file = "bcrypt-4.2.0-cp39-abi3-win32.whl", hash = "sha256:77800b7147c9dc905db1cba26abe31e504d8247ac73580b4aa179f98e6608f34"},
-    {file = "bcrypt-4.2.0-cp39-abi3-win_amd64.whl", hash = "sha256:61ed14326ee023917ecd093ee6ef422a72f3aec6f07e21ea5f10622b735538a9"},
-    {file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:39e1d30c7233cfc54f5c3f2c825156fe044efdd3e0b9d309512cc514a263ec2a"},
-    {file = "bcrypt-4.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:f4f4acf526fcd1c34e7ce851147deedd4e26e6402369304220250598b26448db"},
-    {file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:1ff39b78a52cf03fdf902635e4c81e544714861ba3f0efc56558979dd4f09170"},
-    {file = "bcrypt-4.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:373db9abe198e8e2c70d12b479464e0d5092cc122b20ec504097b5f2297ed184"},
-    {file = "bcrypt-4.2.0.tar.gz", hash = "sha256:cf69eaf5185fd58f268f805b505ce31f9b9fc2d64b376642164e9244540c1221"},
-]
-
-[package.extras]
-tests = ["pytest (>=3.2.1,!=3.3.0)"]
-typecheck = ["mypy"]
-
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -1236,29 +1007,6 @@ files = [
 [package.dependencies]
 beautifulsoup4 = "*"
 
-[[package]]
-name = "build"
-version = "1.2.2.post1"
-description = "A simple, correct Python build frontend"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "build-1.2.2.post1-py3-none-any.whl", hash = "sha256:1d61c0887fa860c01971625baae8bdd338e517b836a2f70dd1f7aa3a6b2fc5b5"},
-    {file = "build-1.2.2.post1.tar.gz", hash = "sha256:b36993e92ca9375a219c99e606a122ff365a760a2d4bba0caa09bd5278b608b7"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "os_name == \"nt\""}
-packaging = ">=19.1"
-pyproject_hooks = "*"
-
-[package.extras]
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-
 [[package]]
 name = "modelscope"
 version = "1.19.1"
@@ -5872,17 +5203,6 @@ nlp = ["Pillow", "addict", "attrs", "boto3", "datasets (>=3.0.0)", "einops", "em
 science = ["Pillow", "addict", "attrs", "biopython", "datasets (>=3.0.0)", "einops", "iopath", "ipdb", "lmdb", "ml-collections", "oss2", "python-dateutil (>=2.1)", "scipy", "setuptools (==69.5.1)", "simplejson (>=3.3.0)", "sortedcontainers (>=1.5.9)", "tensorboardX", "tokenizers", "transformers", "urllib3 (>=1.26)"]
 server = ["fastapi", "sse-starlette", "uvicorn"]
 
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 [[package]]
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 version = "10.5.0"
@@ -6847,48 +6167,6 @@ opentelemetry-api = ">=1.4,<2.0"
 setuptools = ">=16.0"
 wrapt = ">=1.0.0,<2.0.0"
 
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-[package.dependencies]
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-
-[package.extras]
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-
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-
-[package.extras]
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-
 [[package]]
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 version = "1.27.0"
@@ -6934,17 +6212,6 @@ files = [
 deprecated = ">=1.2.6"
 opentelemetry-api = "1.27.0"
 
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-optional = false
-python-versions = ">=3.8"
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 [[package]]
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 version = "3.3.0"
@@ -7062,17 +6329,6 @@ files = [
 [package.dependencies]
 attrs = ">=19.2.0"
 
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 [[package]]
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@@ -7577,29 +6833,6 @@ docs = ["sphinx (>=1.7.1)"]
 redis = ["redis"]
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-
-[package.extras]
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-
 [[package]]
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@@ -8324,20 +7557,6 @@ typing-extensions = "*"
 [package.extras]
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-[package.extras]
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-
 [[package]]
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 version = "2.9.0"
@@ -8516,27 +7735,6 @@ files = [
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-description = "A SQL query builder API for Python"
-optional = false
-python-versions = "*"
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-    {file = "watchfiles-0.24.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49fb58bcaa343fedc6a9e91f90195b20ccb3135447dc9e4e2570c3a39565853e"},
-    {file = "watchfiles-0.24.0.tar.gz", hash = "sha256:afb72325b74fa7a428c009c1b8be4b4d7c2afedafb2982827ef2156646df2fe1"},
-]
-
-[package.dependencies]
-anyio = ">=3.0.0"
-
 [[package]]
 name = "wcwidth"
 version = "0.2.13"
@@ -12146,4 +11072,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.11.0,<3.12"
-content-hash = "43a4e0032bd0f4ba2bd3ef566d5df828d54269760568bd0158d31bbf575662de"
+content-hash = "303aa5656280e280748582594c9652228f0c24fe3fdba9063d939153a64c658d"
diff --git a/pyproject.toml b/pyproject.toml
index 145e3040..6310404f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,6 @@ llama-index-llms-openai = "^0.1.27"
 llama-index-llms-azure-openai = "^0.1.6"
 llama-index-llms-dashscope = "^0.1.2"
 llama-index-readers-database = "^0.1.3"
-llama-index-vector-stores-chroma = "^0.1.6"
 llama-index-vector-stores-faiss = "^0.1.2"
 llama-index-vector-stores-analyticdb = "^0.1.1"
 llama-index-vector-stores-elasticsearch = "^0.2.0"
@@ -98,7 +97,6 @@ detectron2 = [
   {markers = "sys_platform != 'win32' and sys_platform != 'linux' ", url = "https://pai-rag.oss-cn-hangzhou.aliyuncs.com/packages/python_wheels/detectron2-0.6%2B864913fpt2.2.2cpu-cp311-cp311-macosx_10_9_universal2.whl"}
 ]
 magic-pdf = {version = "0.7.0b1", extras = ["full"]}
-llama-index-callbacks-arize-phoenix = "0.1.6"
 peft = "^0.12.0"
 duckduckgo-search = "6.2.12"
 aliyun-bootstrap = "^1.0.1"
diff --git a/pyproject_gpu.toml b/pyproject_gpu.toml
index b0a6ad28..3523941c 100644
--- a/pyproject_gpu.toml
+++ b/pyproject_gpu.toml
@@ -21,7 +21,6 @@ llama-index-llms-openai = "^0.1.27"
 llama-index-llms-azure-openai = "^0.1.6"
 llama-index-llms-dashscope = "^0.1.2"
 llama-index-readers-database = "^0.1.3"
-llama-index-vector-stores-chroma = "^0.1.6"
 llama-index-vector-stores-faiss = "^0.1.2"
 llama-index-vector-stores-analyticdb = "^0.1.1"
 llama-index-vector-stores-elasticsearch = "^0.2.0"
@@ -92,7 +91,6 @@ detectron2 = [
   {markers = "sys_platform != 'win32' and sys_platform != 'linux' ", url = "https://pai-rag.oss-cn-hangzhou.aliyuncs.com/packages/python_wheels/detectron2-0.6%2B864913fpt2.2.2cpu-cp311-cp311-macosx_10_9_universal2.whl"}
 ]
 magic-pdf = {version = "0.7.0b1", extras = ["full"]}
-llama-index-callbacks-arize-phoenix = "0.1.6"
 peft = "^0.12.0"
 aliyun-bootstrap = "^1.0.1"
 docx = "^0.2.4"
diff --git a/src/pai_rag/app/api/service.py b/src/pai_rag/app/api/service.py
index a6d6dd6a..c4c162d3 100644
--- a/src/pai_rag/app/api/service.py
+++ b/src/pai_rag/app/api/service.py
@@ -2,6 +2,7 @@
 from pai_rag.core.rag_config_manager import RagConfigManager
 from pai_rag.core.rag_service import rag_service
 from pai_rag.app.api import query
+from pai_rag.app.api.v1.chat import router_v1
 from pai_rag.app.api import agent_demo
 from pai_rag.app.api.middleware import init_middleware
 from pai_rag.app.api.error_handler import config_app_errors
@@ -9,6 +10,7 @@
 
 def init_router(app: FastAPI):
     app.include_router(query.router, prefix="/service", tags=["RAG"])
+    app.include_router(router_v1, prefix="/v1", tags=["v1"])
     app.include_router(agent_demo.demo_router, tags=["AgentDemo"], prefix="/demo/api")
 
 
diff --git a/src/pai_rag/app/api/v1/chat.py b/src/pai_rag/app/api/v1/chat.py
new file mode 100644
index 00000000..9eaad722
--- /dev/null
+++ b/src/pai_rag/app/api/v1/chat.py
@@ -0,0 +1,274 @@
+import traceback
+from typing import Any, List
+from fastapi import APIRouter, Body, BackgroundTasks, UploadFile, Form
+import uuid
+import hashlib
+import os
+import tempfile
+import shutil
+import pandas as pd
+from pai_rag.core.models.errors import UserInputError
+from pai_rag.core.rag_index_manager import RagIndexEntry, index_manager
+from pai_rag.core.rag_service import rag_service
+from pai_rag.app.api.models import (
+    RagQuery,
+    RetrievalQuery,
+)
+from fastapi.responses import StreamingResponse
+from loguru import logger
+
+from pai_rag.integrations.nodeparsers.pai.pai_node_parser import (
+    COMMON_FILE_PATH_FODER_NAME,
+)
+
+router_v1 = APIRouter()
+
+
+@router_v1.post("/query")
+async def aquery_v1(query: RagQuery):
+    response = await rag_service.aquery_v1(query)
+    if not query.stream:
+        return response
+    else:
+        return StreamingResponse(
+            response,
+            media_type="text/event-stream",
+        )
+
+
+@router_v1.post("/query/llm")
+async def aquery_llm_v1(query: RagQuery):
+    response = await rag_service.aquery_llm_v1(query)
+    if not query.stream:
+        return response
+    else:
+        return StreamingResponse(
+            response,
+            media_type="text/event-stream",
+        )
+
+
+@router_v1.post("/query/search")
+async def aquery_search_v1(query: RagQuery):
+    response = await rag_service.aquery_search_v1(query)
+    if not query.stream:
+        return response
+    else:
+        return StreamingResponse(
+            response,
+            media_type="text/event-stream",
+        )
+
+
+@router_v1.post("/query/retrieval")
+async def aquery_retrieval(query: RetrievalQuery):
+    return await rag_service.aquery_retrieval(query)
+
+
+@router_v1.post("/query/agent")
+async def aquery_agent(query: RagQuery):
+    response = await rag_service.aquery_agent(query)
+    if not query.stream:
+        return response
+    else:
+        return StreamingResponse(
+            response,
+            media_type="text/event-stream",
+        )
+
+
+@router_v1.post("/config/agent")
+async def aload_agent_config(file: UploadFile):
+    fn = file.filename
+    data = await file.read()
+    file_hash = hashlib.md5(data).hexdigest()
+    save_file = os.path.join("localdata", f"{file_hash}_{fn}")
+
+    with open(save_file, "wb") as f:
+        f.write(data)
+        f.close()
+    return await rag_service.aload_agent_config(save_file)
+
+
+@router_v1.patch("/config")
+async def aupdate(new_config: Any = Body(None)):
+    rag_service.reload(new_config)
+    return {"msg": "Update RAG configuration successfully."}
+
+
+@router_v1.get("/config")
+async def aconfig():
+    return rag_service.get_config()
+
+
+@router_v1.get("/indexes/{index_name}")
+async def get_index(index_name: str):
+    try:
+        return index_manager.get_index_by_name(index_name=index_name)
+    except Exception as ex:
+        logger.error(f"Get index '{index_name}' failed: {ex} {traceback.format_exc()}")
+        raise UserInputError(f"Get index '{index_name}' failed: {ex}")
+
+
+@router_v1.post("/indexes/{index_name}")
+async def add_index(index_name: str, index_entry: RagIndexEntry):
+    try:
+        index_manager.add_index(index_entry)
+        return {"msg": f"Add index '{index_name}' successfully."}
+    except Exception as ex:
+        logger.error(f"Add index '{index_name}' failed: {ex} {traceback.format_exc()}")
+        raise UserInputError(f"Add index '{index_name}' failed: {ex}")
+
+
+@router_v1.patch("/indexes/{index_name}")
+async def update_index(index_name: str, index_entry: RagIndexEntry):
+    try:
+        index_manager.update_index(index_entry)
+        return {"msg": f"Update index '{index_name}' successfully."}
+    except Exception as ex:
+        logger.error(
+            f"Update index '{index_name}' failed: {ex} {traceback.format_exc()}"
+        )
+        raise UserInputError(f"Update index '{index_name}' failed: {ex}")
+
+
+@router_v1.delete("/indexes/{index_name}")
+async def delete_index(index_name: str):
+    try:
+        index_manager.delete_index(index_name)
+        return {"msg": f"Delete index '{index_name}' successfully."}
+    except Exception as ex:
+        logger.error(
+            f"Delete index '{index_name}' failed: {ex} {traceback.format_exc()}"
+        )
+        raise UserInputError(f"Delete index '{index_name}' failed: {ex}")
+
+
+@router_v1.get("/indexes")
+async def list_indexes():
+    return index_manager.list_indexes()
+
+
+@router_v1.get("/get_upload_state")
+def task_status(task_id: str):
+    status, detail = rag_service.get_task_status(task_id)
+    return {"task_id": task_id, "status": status, "detail": detail}
+
+
+@router_v1.post("/upload_data")
+async def upload_data(
+    files: List[UploadFile] = Body(None),
+    oss_path: str = Form(None),
+    index_name: str = Form(None),
+    enable_raptor: bool = Form(False),
+    enable_multimodal: bool = Form(False),
+    background_tasks: BackgroundTasks = BackgroundTasks(),
+):
+    task_id = uuid.uuid4().hex
+    logger.info(
+        f"Upload data task_id: {task_id} index_name: {index_name} enable_multimodal: {enable_multimodal}"
+    )
+    if oss_path:
+        background_tasks.add_task(
+            rag_service.add_knowledge,
+            task_id=task_id,
+            filter_pattern=None,
+            oss_path=oss_path,
+            from_oss=True,
+            index_name=index_name,
+            enable_raptor=enable_raptor,
+            enable_multimodal=enable_multimodal,
+        )
+    else:
+        if not files:
+            return {"message": "No upload file sent"}
+        tmpdir = tempfile.mkdtemp()
+        input_files = []
+        for file in files:
+            fn = file.filename
+            data = await file.read()
+            file_hash = hashlib.md5(data).hexdigest()
+            tmp_file_dir = os.path.join(
+                tmpdir, f"{COMMON_FILE_PATH_FODER_NAME}/{file_hash}"
+            )
+            os.makedirs(tmp_file_dir, exist_ok=True)
+            save_file = os.path.join(tmp_file_dir, fn)
+
+            with open(save_file, "wb") as f:
+                f.write(data)
+                f.close()
+            input_files.append(save_file)
+
+        background_tasks.add_task(
+            rag_service.add_knowledge,
+            task_id=task_id,
+            input_files=input_files,
+            filter_pattern=None,
+            index_name=index_name,
+            oss_path=None,
+            enable_raptor=enable_raptor,
+            temp_file_dir=tmpdir,
+            enable_multimodal=enable_multimodal,
+        )
+
+    return {"task_id": task_id}
+
+
+@router_v1.post("/upload_datasheet")
+async def upload_datasheet(
+    file: UploadFile,
+):
+    task_id = uuid.uuid4().hex
+    if not file:
+        return None
+
+    persist_path = "./localdata/data_analysis"
+
+    os.makedirs(name=persist_path, exist_ok=True)
+
+    # 清空目录中的文件
+    for filename in os.listdir(persist_path):
+        file_path = os.path.join(persist_path, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+        except Exception as e:
+            logger.info(f"Failed to delete {file_path}. Reason: {e}")
+
+    # 指定持久化存储位置
+    file_name = os.path.basename(file.filename)  # 获取文件名
+    destination_path = os.path.join(persist_path, file_name)
+    # 写入文件
+    try:
+        # shutil.copy(file.filename, destination_path)
+        with open(destination_path, "wb") as f:
+            shutil.copyfileobj(file.file, f)
+        logger.info("data analysis file saved successfully")
+
+        if destination_path.endswith(".csv"):
+            df = pd.read_csv(destination_path)
+        elif destination_path.endswith(".xlsx"):
+            df = pd.read_excel(destination_path)
+        else:
+            raise TypeError("Unsupported file type.")
+
+    except Exception as e:
+        return StreamingResponse(status_code=500, content={"message": str(e)})
+
+    return {
+        "task_id": task_id,
+        "destination_path": destination_path,
+        "data_preview": df.head(10).to_json(orient="records", lines=False),
+    }
+
+
+@router_v1.post("/query/data_analysis")
+async def aquery_analysis(query: RagQuery):
+    response = await rag_service.aquery_analysis(query)
+    if not query.stream:
+        return response
+    else:
+        return StreamingResponse(
+            response,
+            media_type="text/event-stream",
+        )
diff --git a/src/pai_rag/app/web/event_listeners.py b/src/pai_rag/app/web/event_listeners.py
index b8a39e3d..d64e58ec 100644
--- a/src/pai_rag/app/web/event_listeners.py
+++ b/src/pai_rag/app/web/event_listeners.py
@@ -66,14 +66,18 @@ def change_emb_source(source, model):
         EMBEDDING_DIM_DICT.get(source, DEFAULT_EMBED_SIZE)
         if source.lower() == "huggingface"
         else DEFAULT_EMBED_SIZE,
-        EMBEDDING_TYPE_DICT.get(model, "Default")
-        if source.lower() == "huggingface"
-        else "Default",
+        gr.update(
+            value=EMBEDDING_TYPE_DICT.get(model, "Default")
+            if source.lower() == "huggingface"
+            else "Default",
+            visible=True if source.lower() == "huggingface" else False,
+        ),
         gr.update(
             value=f"Model Introduction: [{model}]({EMBEDDING_MODEL_LINK_DICT[model]})"
             if source.lower() == "huggingface"
             else ""
         ),
+        gr.update(visible=True if source.lower() != "huggingface" else False),
     ]
 
 
diff --git a/src/pai_rag/app/web/index_utils.py b/src/pai_rag/app/web/index_utils.py
index 7e010013..03e3a71a 100644
--- a/src/pai_rag/app/web/index_utils.py
+++ b/src/pai_rag/app/web/index_utils.py
@@ -35,6 +35,7 @@
     "embed_type",
     "embed_link",
     "embed_batch_size",
+    "embed_api_key",
     "vectordb_type",
     "faiss_path",
     "adb_ak",
@@ -121,7 +122,8 @@ def index_to_components_settings(
     embed_type_setting = {
         "value": EMBEDDING_TYPE_DICT.get(embed_model, "Default")
         if embed_source == "huggingface"
-        else "Default"
+        else "Default",
+        "visible": True if embed_source == "huggingface" else False,
     }
     embed_link_setting = {
         "value": f"Model Introduction: [{embed_model}]({EMBEDDING_MODEL_LINK_DICT[embed_model]})"
@@ -129,6 +131,13 @@ def index_to_components_settings(
         else ""
     }
     embed_batch_size_setting = {"value": index_entry.embedding_config.embed_batch_size}
+    if index_entry.embedding_config.source.value == "huggingface":
+        embed_api_key_setting = {"value": "", "visible": False}
+    else:
+        embed_api_key_setting = {
+            "value": index_entry.embedding_config.api_key,
+            "visible": True,
+        }
 
     embed_component_settings = [
         {"value": embed_source},
@@ -137,6 +146,7 @@ def index_to_components_settings(
         embed_type_setting,
         embed_link_setting,
         embed_batch_size_setting,
+        embed_api_key_setting,
     ]
 
     vector_store_config = index_entry.vector_store_config
@@ -313,6 +323,7 @@ def components_to_index(
     embed_source,
     embed_model,
     embed_batch_size,
+    embed_api_key,
     vectordb_type,
     hologres_host,
     hologres_port,
@@ -363,6 +374,8 @@ def components_to_index(
         "model": embed_model,
         "embed_batch_size": int(embed_batch_size),
     }
+    if embed_source != "huggingface":
+        embedding["api_key"] = embed_api_key
 
     if vectordb_type.lower() == "hologres":
         vector_store = {
diff --git a/src/pai_rag/app/web/rag_client.py b/src/pai_rag/app/web/rag_client.py
index e6946c72..9b11d38b 100644
--- a/src/pai_rag/app/web/rag_client.py
+++ b/src/pai_rag/app/web/rag_client.py
@@ -45,63 +45,63 @@ def clear_history(self):
 
     @property
     def query_url(self):
-        return f"{self.endpoint}service/query"
+        return f"{self.endpoint}v1/query"
 
     @property
     def search_url(self):
-        return f"{self.endpoint}service/query/search"
+        return f"{self.endpoint}v1/query/search"
 
     @property
     def data_analysis_url(self):
-        return f"{self.endpoint}service/query/data_analysis"
+        return f"{self.endpoint}v1/query/data_analysis"
 
     @property
     def llm_url(self):
-        return f"{self.endpoint}service/query/llm"
+        return f"{self.endpoint}v1/query/llm"
 
     @property
     def retrieval_url(self):
-        return f"{self.endpoint}service/query/retrieval"
+        return f"{self.endpoint}v1/query/retrieval"
 
     @property
     def config_url(self):
-        return f"{self.endpoint}service/config"
+        return f"{self.endpoint}v1/config"
 
     @property
     def load_data_url(self):
-        return f"{self.endpoint}service/upload_data"
+        return f"{self.endpoint}v1/upload_data"
 
     @property
     def load_datasheet_url(self):
-        return f"{self.endpoint}service/upload_datasheet"
+        return f"{self.endpoint}v1/upload_datasheet"
 
     @property
     def load_agent_cfg_url(self):
-        return f"{self.endpoint}service/config/agent"
+        return f"{self.endpoint}v1/config/agent"
 
     @property
     def get_load_state_url(self):
-        return f"{self.endpoint}service/get_upload_state"
+        return f"{self.endpoint}v1/get_upload_state"
 
     @property
     def get_evaluate_generate_url(self):
-        return f"{self.endpoint}service/evaluate/generate"
+        return f"{self.endpoint}v1/evaluate/generate"
 
     @property
     def get_evaluate_retrieval_url(self):
-        return f"{self.endpoint}service/evaluate/retrieval"
+        return f"{self.endpoint}v1/evaluate/retrieval"
 
     @property
     def get_evaluate_response_url(self):
-        return f"{self.endpoint}service/evaluate/response"
+        return f"{self.endpoint}v1/evaluate/response"
 
     @property
     def index_url(self):
-        return f"{self.endpoint}service/indexes/"
+        return f"{self.endpoint}v1/indexes/"
 
     @property
     def list_index_url(self):
-        return f"{self.endpoint}service/indexes"
+        return f"{self.endpoint}v1/indexes"
 
     def _format_rag_response(
         self, question, response, with_history: bool = False, stream: bool = False
diff --git a/src/pai_rag/app/web/tabs/data_analysis_tab.py b/src/pai_rag/app/web/tabs/data_analysis_tab.py
index 3712c87a..82208786 100644
--- a/src/pai_rag/app/web/tabs/data_analysis_tab.py
+++ b/src/pai_rag/app/web/tabs/data_analysis_tab.py
@@ -9,9 +9,6 @@
 )
 
 
-DEFAULT_IS_INTERACTIVE = True
-
-
 def upload_file_fn(input_file):
     if input_file is None:
         return None
diff --git a/src/pai_rag/app/web/tabs/settings_tab.py b/src/pai_rag/app/web/tabs/settings_tab.py
index 1a1ef36d..0e7286f0 100644
--- a/src/pai_rag/app/web/tabs/settings_tab.py
+++ b/src/pai_rag/app/web/tabs/settings_tab.py
@@ -6,8 +6,6 @@
 from pai_rag.app.web.tabs.vector_db_panel import create_vector_db_panel
 import pai_rag.app.web.event_listeners as ev_listeners
 
-DEFAULT_IS_INTERACTIVE = True
-
 
 def create_setting_tab() -> Dict[str, Any]:
     components = []
@@ -37,7 +35,7 @@ def create_setting_tab() -> Dict[str, Any]:
                     EMBEDDING_API_KEY_DICT.keys(),
                     label="Embedding Type",
                     elem_id="embed_source",
-                    interactive=DEFAULT_IS_INTERACTIVE.lower() != "false",
+                    interactive=True,
                 )
                 embed_model = gr.Dropdown(
                     label="Embedding Model Name",
@@ -57,6 +55,12 @@ def create_setting_tab() -> Dict[str, Any]:
                         label="Embedding Type",
                         elem_id="embed_type",
                     )
+                    embed_api_key = gr.Textbox(
+                        label="API KEY",
+                        elem_id="embed_api_key",
+                        visible=False,
+                        type="password",
+                    )
                     embed_link = gr.Markdown(
                         label="Model URL Link",
                         elem_id="embed_link",
@@ -85,7 +89,7 @@ def create_setting_tab() -> Dict[str, Any]:
             embed_source.input(
                 fn=ev_listeners.change_emb_source,
                 inputs=[embed_source, embed_model],
-                outputs=[embed_model, embed_dim, embed_type, embed_link],
+                outputs=[embed_model, embed_dim, embed_type, embed_link, embed_api_key],
             )
             embed_model.input(
                 fn=ev_listeners.change_emb_model,
@@ -99,6 +103,7 @@ def create_setting_tab() -> Dict[str, Any]:
                     embed_type,
                     embed_link,
                     embed_model,
+                    embed_api_key,
                     embed_batch_size,
                     vector_index,
                     new_index_name,
@@ -155,7 +160,7 @@ def create_setting_tab() -> Dict[str, Any]:
                     ["paieas", "dashscope"],
                     label="LLM Model Source",
                     elem_id="llm",
-                    interactive=DEFAULT_IS_INTERACTIVE.lower() != "false",
+                    interactive=True,
                 )
                 llm_eas_url = gr.Textbox(
                     label="EAS Url",
@@ -198,7 +203,7 @@ def create_setting_tab() -> Dict[str, Any]:
                         ["paieas", "dashscope"],
                         label="LLM Model Source",
                         elem_id="mllm",
-                        interactive=DEFAULT_IS_INTERACTIVE.lower() != "false",
+                        interactive=True,
                     )
                     with gr.Row(
                         visible=(mllm == "paieas"), elem_id="m_eas_col"
diff --git a/src/pai_rag/app/web/tabs/vector_db_panel.py b/src/pai_rag/app/web/tabs/vector_db_panel.py
index e01f140d..78f7a105 100644
--- a/src/pai_rag/app/web/tabs/vector_db_panel.py
+++ b/src/pai_rag/app/web/tabs/vector_db_panel.py
@@ -3,8 +3,6 @@
 from pai_rag.app.web.utils import components_to_dict
 import pai_rag.app.web.event_listeners as ev_listeners
 
-DEFAULT_IS_INTERACTIVE = True
-
 
 def create_vector_db_panel() -> Dict[str, Any]:
     components = []
@@ -22,7 +20,7 @@ def create_vector_db_panel() -> Dict[str, Any]:
                 ],
                 label="Which VectorStore do you want to use?",
                 elem_id="vectordb_type",
-                interactive=DEFAULT_IS_INTERACTIVE.lower() != "false",
+                interactive=True,
             )
             # Adb
             with gr.Column(visible=(vectordb_type == "analyticdb")) as adb_col:
diff --git a/src/pai_rag/app/web/ui_constants.py b/src/pai_rag/app/web/ui_constants.py
index 0bff82d1..886c928a 100644
--- a/src/pai_rag/app/web/ui_constants.py
+++ b/src/pai_rag/app/web/ui_constants.py
@@ -93,7 +93,7 @@
     "paraphrase-multilingual-MiniLM-L12-v2": "https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
 }
 
-EMBEDDING_API_KEY_DICT = {"huggingface": False, "openai": True, "dashscope": True}
+EMBEDDING_API_KEY_DICT = {"huggingface": False, "dashscope": True}
 
 LLM_MODEL_KEY_DICT = {
     "dashscope": [
diff --git a/src/pai_rag/app/web/view_model.py b/src/pai_rag/app/web/view_model.py
index 9af4c462..5a325ffe 100644
--- a/src/pai_rag/app/web/view_model.py
+++ b/src/pai_rag/app/web/view_model.py
@@ -77,8 +77,6 @@ class ViewModel(BaseModel):
 
     config_file: str = None
 
-    vectordb_type: str = "faiss"
-
     # retriever
     similarity_top_k: int = 5
     image_similarity_top_k: int = 2
diff --git a/src/pai_rag/app/web/webui.py b/src/pai_rag/app/web/webui.py
index 6e23a0f5..18aa9a61 100644
--- a/src/pai_rag/app/web/webui.py
+++ b/src/pai_rag/app/web/webui.py
@@ -21,8 +21,6 @@
 
 from loguru import logger
 
-DEFAULT_IS_INTERACTIVE = True
-
 
 def resume_ui():
     outputs = {}
diff --git a/src/pai_rag/core/rag_application.py b/src/pai_rag/core/rag_application.py
index ac280a24..ad3ce45b 100644
--- a/src/pai_rag/core/rag_application.py
+++ b/src/pai_rag/core/rag_application.py
@@ -44,15 +44,33 @@ class RagChatType(str, Enum):
     WEB = "web"
 
 
+class SseVersion(int, Enum):
+    V0 = 0  # Backward compatibility
+    V1 = 1  # New V1 version
+
+
+def _event_chunk_wrapper(chunk_content, sse_version: SseVersion = SseVersion.V0):
+    if sse_version == sse_version.V1:
+        return f"data: {chunk_content}\n\n"
+    else:
+        return f"{chunk_content}\n"
+
+
 async def event_generator_async(
-    response, extra_info=None, chat_store=None, session_id=None
+    response,
+    extra_info=None,
+    chat_store=None,
+    session_id=None,
+    sse_version: SseVersion = SseVersion.V0,
 ):
     content = ""
     async for token in response.async_response_gen():
         if token and token != DEFAULT_EMPTY_RESPONSE_GEN:
             chunk = {"delta": token, "is_finished": False}
             content += token
-            yield "data: " + json.dumps(chunk, ensure_ascii=False) + "\n\n"
+            yield _event_chunk_wrapper(
+                json.dumps(chunk, ensure_ascii=False), sse_version
+            )
 
     if chat_store:
         chat_store.add_message(
@@ -65,9 +83,10 @@ async def event_generator_async(
     else:
         last_chunk = {"delta": "", "is_finished": True}
 
-    yield "data: " + json.dumps(
+    last_chunk_data = json.dumps(
         last_chunk, default=lambda x: x.dict(), ensure_ascii=False
-    ) + "\n\n"
+    )
+    yield _event_chunk_wrapper(last_chunk_data, sse_version)
 
 
 class RagApplication:
@@ -143,7 +162,12 @@ async def aretrieve(self, query: RetrievalQuery) -> RetrievalResponse:
 
         return RetrievalResponse(docs=docs)
 
-    async def aquery(self, query: RagQuery, chat_type: RagChatType = RagChatType.RAG):
+    async def aquery(
+        self,
+        query: RagQuery,
+        chat_type: RagChatType = RagChatType.RAG,
+        sse_version: SseVersion = SseVersion.V0,
+    ):
         session_id = query.session_id or uuid_generator()
         logger.debug(f"Get session ID: {session_id}.")
         session_config = self.config.model_copy()
@@ -233,6 +257,7 @@ async def aquery(self, query: RagQuery, chat_type: RagChatType = RagChatType.RAG
                 extra_info=result_info,
                 chat_store=chat_store,
                 session_id=session_id,
+                sse_version=sse_version,
             )
 
     async def aquery_agent(self, query: RagQuery) -> RagResponse:
diff --git a/src/pai_rag/core/rag_service.py b/src/pai_rag/core/rag_service.py
index 6e561d44..66f9ae4a 100644
--- a/src/pai_rag/core/rag_service.py
+++ b/src/pai_rag/core/rag_service.py
@@ -3,7 +3,7 @@
 import traceback
 from asgi_correlation_id import correlation_id
 from pai_rag.core.models.errors import UserInputError
-from pai_rag.core.rag_application import RagApplication, RagChatType
+from pai_rag.core.rag_application import RagApplication, RagChatType, SseVersion
 from pai_rag.core.rag_config_manager import RagConfigManager
 from pai_rag.utils.oss_utils import get_oss_auth
 from pai_rag.app.api.models import (
@@ -116,6 +116,33 @@ def get_task_status(self, task_id: str) -> str:
 
         return status, detail
 
+    async def aquery_v1(self, query: RagQuery):
+        try:
+            return await self.rag.aquery(
+                query, RagChatType.RAG, sse_version=SseVersion.V1
+            )
+        except Exception as ex:
+            logger.error(traceback.format_exc())
+            raise UserInputError(f"Query RAG failed: {ex}")
+
+    async def aquery_search_v1(self, query: RagQuery):
+        try:
+            return await self.rag.aquery(
+                query, RagChatType.WEB, sse_version=SseVersion.V1
+            )
+        except Exception as ex:
+            logger.error(traceback.format_exc())
+            raise UserInputError(f"Query Search failed: {ex}")
+
+    async def aquery_llm_v1(self, query: RagQuery):
+        try:
+            return await self.rag.aquery(
+                query, RagChatType.LLM, sse_version=SseVersion.V1
+            )
+        except Exception as ex:
+            logger.error(traceback.format_exc())
+            raise UserInputError(f"Query RAG failed: {ex}")
+
     async def aquery(self, query: RagQuery):
         try:
             return await self.rag.aquery(query, RagChatType.RAG)
@@ -123,7 +150,9 @@ async def aquery(self, query: RagQuery):
             logger.error(traceback.format_exc())
             raise UserInputError(f"Query RAG failed: {ex}")
 
-    async def aquery_search(self, query: RagQuery):
+    async def aquery_search(
+        self, query: RagQuery, sse_version: SseVersion = SseVersion.V0
+    ):
         try:
             return await self.rag.aquery(query, RagChatType.WEB)
         except Exception as ex:
diff --git a/src/pai_rag/integrations/nodeparsers/pai/pai_node_parser.py b/src/pai_rag/integrations/nodeparsers/pai/pai_node_parser.py
index 2377032c..2df5ce45 100644
--- a/src/pai_rag/integrations/nodeparsers/pai/pai_node_parser.py
+++ b/src/pai_rag/integrations/nodeparsers/pai/pai_node_parser.py
@@ -48,6 +48,19 @@ class NodeParserConfig(BaseModel):
 )
 
 COMMON_FILE_PATH_FODER_NAME = "__pairag__knowledgebase__"
+DEFAULT_EXCLUDED_METADATA_KEYS = [
+    "file_name",
+    "file_type",
+    "file_size",
+    "creation_date",
+    "last_modified_date",
+    "last_accessed_date",
+    "file_path",
+    "image_url",
+    "total_pages",
+    "source",
+    "row_number",
+]
 
 
 def format_temp_file_path(temp_file_path):
@@ -124,7 +137,7 @@ def get_nodes_from_documents(
         self, nodes: List[BaseNode], **kwargs: Any
     ) -> List[BaseNode]:
         # Accumulate node index for doc
-        splitted_nodes = []
+        splitted_nodes: List[BaseNode] = []
         self._doc_cnt_map = {}
 
         for doc_node in nodes:
@@ -173,11 +186,13 @@ def get_nodes_from_documents(
                         )
                         splitted_nodes.append(tmp_node)
 
-        for node in nodes:
-            node.excluded_embed_metadata_keys.append("file_path")
-            node.excluded_embed_metadata_keys.append("image_url")
-            node.excluded_embed_metadata_keys.append("total_pages")
-            node.excluded_embed_metadata_keys.append("source")
+        for node in splitted_nodes:
+            node.excluded_embed_metadata_keys = list(
+                set(node.excluded_embed_metadata_keys + DEFAULT_EXCLUDED_METADATA_KEYS)
+            )
+            node.excluded_llm_metadata_keys = list(
+                set(node.excluded_llm_metadata_keys + DEFAULT_EXCLUDED_METADATA_KEYS)
+            )
 
         logger.info(
             f"[DataReader] Split {len(nodes)} documents into {len(splitted_nodes)} nodes."
diff --git a/src/pai_rag/integrations/synthesizer/pai_synthesizer.py b/src/pai_rag/integrations/synthesizer/pai_synthesizer.py
index 68adcfad..82abdc29 100644
--- a/src/pai_rag/integrations/synthesizer/pai_synthesizer.py
+++ b/src/pai_rag/integrations/synthesizer/pai_synthesizer.py
@@ -379,24 +379,25 @@ async def aget_response(
 
         logger.info("Synthsize using LLM with no image inputs.")
         text_qa_template = self._text_qa_template.partial_format(query_str=query_str)
-        single_text_chunk = "\n".join(text_chunks)
         truncated_chunks = self._prompt_helper.truncate(
             prompt=text_qa_template,
-            text_chunks=[single_text_chunk],
+            text_chunks=text_chunks,
         )
 
+        context_str = "\n".join(truncated_chunks)
+
         response: RESPONSE_TEXT_TYPE
         if not streaming:
             response = await self._llm.apredict(
                 text_qa_template,
-                context_str=truncated_chunks,
+                context_str=context_str,
                 **response_kwargs,
             )
         else:
             # customized modify [will be removed]
             response = await self._llm.astream(
                 text_qa_template,
-                context_str=truncated_chunks,
+                context_str=context_str,
                 **response_kwargs,
             )
 
@@ -428,23 +429,23 @@ def get_response(
             )
 
         text_qa_template = self._text_qa_template.partial_format(query_str=query_str)
-        single_text_chunk = "\n".join(text_chunks)
         truncated_chunks = self._prompt_helper.truncate(
             prompt=text_qa_template,
-            text_chunks=[single_text_chunk],
+            text_chunks=text_chunks,
         )
 
+        context_str = "\n".join(truncated_chunks)
         response: RESPONSE_TEXT_TYPE
         if not streaming:
             response = self._llm.predict(
                 text_qa_template,
-                context_str=truncated_chunks,
+                context_str=context_str,
                 **kwargs,
             )
         else:
             response = self._llm.stream(
                 text_qa_template,
-                context_str=truncated_chunks,
+                context_str=context_str,
                 **kwargs,
             )
 
diff --git a/src/pai_rag/main.py b/src/pai_rag/main.py
index b4cad535..5f0583b1 100644
--- a/src/pai_rag/main.py
+++ b/src/pai_rag/main.py
@@ -1,3 +1,7 @@
+import os
+os.environ["ENABLE_FASTAPI"] = "false"
+os.environ["ENABLE_REQUESTS"] = "false"
+os.environ["ENABLE_AIOHTTPCLIENT"] = "false"
 from aliyun.opentelemetry.instrumentation.auto_instrumentation import sitecustomize # ByPass unused import: use for arms trace initialization
 
 import click
@@ -5,7 +9,6 @@
 from fastapi import FastAPI
 from pai_rag.core.rag_config_manager import RagConfigManager
 from pai_rag.utils.constants import DEFAULT_MODEL_DIR
-import os
 from pathlib import Path
 
 _BASE_DIR = Path(__file__).parent