diff --git a/poetry.lock b/poetry.lock
index 7da6dff9e..1f26eead9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiofiles"
@@ -192,13 +192,13 @@ files = [
 
 [[package]]
 name = "anthropic"
-version = "0.8.1"
+version = "0.13.0"
 description = "The official Python library for the anthropic API"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "anthropic-0.8.1-py3-none-any.whl", hash = "sha256:4578b99d6e140c84ad5f409e460aabc85acffc6e189dfd91378b9277b4c158b7"},
-    {file = "anthropic-0.8.1.tar.gz", hash = "sha256:736cf85681b8ed066014b0485d8d5be1f2aae65f86e6a25c76a5d5ebb970eee5"},
+    {file = "anthropic-0.13.0-py3-none-any.whl", hash = "sha256:2d4b6a69bf5b31a596669d68820f40f5ed9a9a3333ddaa727166a11ed29275e8"},
+    {file = "anthropic-0.13.0.tar.gz", hash = "sha256:b935d2fee12f7dbfcc80398b3da5f20103ece42aecb97d8ce24459e3c4f8ec8a"},
 ]
 
 [package.dependencies]
@@ -210,6 +210,9 @@ sniffio = "*"
 tokenizers = ">=0.13.0"
 typing-extensions = ">=4.7,<5"
 
+[package.extras]
+vertex = ["google-auth (>=2,<3)"]
+
 [[package]]
 name = "anyio"
 version = "4.2.0"
@@ -243,23 +246,6 @@ files = [
     {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
 ]
 
-[[package]]
-name = "asgiref"
-version = "3.7.2"
-description = "ASGI specs, helper code, and adapters"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "asgiref-3.7.2-py3-none-any.whl", hash = "sha256:89b2ef2247e3b562a16eef663bc0e2e703ec6468e2fa8a5cd61cd449786d4f6e"},
-    {file = "asgiref-3.7.2.tar.gz", hash = "sha256:9e0ce3aa93a819ba5b45120216b23878cf6e8525eb3848653452b4192b92afed"},
-]
-
-[package.dependencies]
-typing-extensions = {version = ">=4", markers = "python_version < \"3.11\""}
-
-[package.extras]
-tests = ["mypy (>=0.800)", "pytest", "pytest-asyncio"]
-
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -428,17 +414,17 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.34.31"
+version = "1.34.32"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "boto3-1.34.31-py3-none-any.whl", hash = "sha256:0d800130e43a5d4e71300cc6f91aabcef6fe6f26bc206bc61374bf695049587a"},
-    {file = "boto3-1.34.31.tar.gz", hash = "sha256:c4dec7ea9bc9210ec783d39b56d332f5a266b0d1e31a96c5092f6bd5252361ba"},
+    {file = "boto3-1.34.32-py3-none-any.whl", hash = "sha256:7bf924c942426839efd7fa5c2c4fe85dba208258393e8017a5ad327c30e5948d"},
+    {file = "boto3-1.34.32.tar.gz", hash = "sha256:9e62f42de2873baab96eb822386d6a3b1d77f6715cb9033d7b4e6e9ebb0cdbe7"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.31,<1.35.0"
+botocore = ">=1.34.32,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -447,13 +433,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.31"
+version = "1.34.32"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">= 3.8"
 files = [
-    {file = "botocore-1.34.31-py3-none-any.whl", hash = "sha256:6ee1ba451ce3d640dccd485906f68a55d9e7f3534553876e4adc75d6060a05ac"},
-    {file = "botocore-1.34.31.tar.gz", hash = "sha256:d5a2153dbe9687f510f179e03913bc9b4e266c865cabebe440c4d05ab923faa7"},
+    {file = "botocore-1.34.32-py3-none-any.whl", hash = "sha256:aa26a74df83eed3db9542c1cf9108138794c344918b36a506c0723717f1acaab"},
+    {file = "botocore-1.34.32.tar.gz", hash = "sha256:466aee158bd0429dbd567c4e2bdf7be9a0a5a74409f8b295c30f34d84c497f9c"},
 ]
 
 [package.dependencies]
@@ -559,30 +545,6 @@ files = [
     {file = "Brotli-1.1.0.tar.gz", hash = "sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724"},
 ]
 
-[[package]]
-name = "build"
-version = "1.0.3"
-description = "A simple, correct Python build frontend"
-optional = false
-python-versions = ">= 3.7"
-files = [
-    {file = "build-1.0.3-py3-none-any.whl", hash = "sha256:589bf99a67df7c9cf07ec0ac0e5e2ea5d4b37ac63301c4986d1acb126aa83f8f"},
-    {file = "build-1.0.3.tar.gz", hash = "sha256:538aab1b64f9828977f84bc63ae570b060a8ed1be419e7870b8b4fc5e6ea553b"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "os_name == \"nt\""}
-importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
-packaging = ">=19.0"
-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 = ["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 = ["importlib-metadata (>=5.1)", "mypy (>=1.5.0,<1.6.0)", "tomli", "typing-extensions (>=3.7.4.3)"]
-virtualenv = ["virtualenv (>=20.0.35)"]
-
 [[package]]
 name = "cachetools"
 version = "5.3.2"
@@ -874,38 +836,28 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.4.22"
+version = "0.4.13"
 description = "Chroma."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "chromadb-0.4.22-py3-none-any.whl", hash = "sha256:ad210b27b4cda2f09d15adc9c83c81bfa66b69f39648a27b637306e40de0680d"},
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+    {file = "chromadb-0.4.13-py3-none-any.whl", hash = "sha256:6959dc4aaa6278c7491dd1911724981a0e46816b19e9f86945b9bd875e6a252a"},
+    {file = "chromadb-0.4.13.tar.gz", hash = "sha256:99d330b9ac8f2ec81f4b34798d34f2ea9f4656bef1da951efa7e93957ef7e706"},
 ]
 
 [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"
-mmh3 = ">=4.0.1"
-numpy = ">=1.22.5"
+numpy = {version = ">=1.22.5", markers = "python_version >= \"3.8\""}
 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"
 overrides = ">=7.3.1"
 posthog = ">=2.4.0"
 pulsar-client = ">=3.1.0"
 pydantic = ">=1.9"
 pypika = ">=0.48.9"
-PyYAML = ">=6.0.0"
 requests = ">=2.28"
-tenacity = ">=8.2.3"
 tokenizers = ">=0.13.2"
 tqdm = ">=4.65.0"
 typer = ">=0.9.0"
@@ -1315,6 +1267,17 @@ files = [
 graph = ["objgraph (>=1.7.2)"]
 profile = ["gprof2dot (>=2022.7.29)"]
 
+[[package]]
+name = "dirtyjson"
+version = "1.0.8"
+description = "JSON decoder for Python that can extract data from the muck"
+optional = false
+python-versions = "*"
+files = [
+    {file = "dirtyjson-1.0.8-py3-none-any.whl", hash = "sha256:125e27248435a58acace26d5c2c4c11a1c0de0a9c5124c5a94ba78e517d74f53"},
+    {file = "dirtyjson-1.0.8.tar.gz", hash = "sha256:90ca4a18f3ff30ce849d100dcf4a003953c79d3a2348ef056f1d9c22231a25fd"},
+]
+
 [[package]]
 name = "diskcache"
 version = "5.6.3"
@@ -1554,13 +1517,13 @@ requests = ["requests (>=2.4.0,<3.0.0)"]
 
 [[package]]
 name = "emoji"
-version = "2.10.0"
+version = "2.10.1"
 description = "Emoji for Python"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
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-    {file = "emoji-2.10.0.tar.gz", hash = "sha256:7e68435eecd2c428c3b4aaa5f72d61a5b1a36c81a5138681cba13d19d94aa3a0"},
+    {file = "emoji-2.10.1-py2.py3-none-any.whl", hash = "sha256:11fb369ea79d20c14efa4362c732d67126df294a7959a2c98bfd7447c12a218e"},
+    {file = "emoji-2.10.1.tar.gz", hash = "sha256:16287283518fb7141bde00198f9ffff4e1c1cb570efb68b2f1ec50975c3a581d"},
 ]
 
 [package.extras]
@@ -2226,13 +2189,13 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4
 
 [[package]]
 name = "google-api-core"
-version = "2.16.0"
+version = "2.16.1"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "google-api-core-2.16.0.tar.gz", hash = "sha256:d1fc42e52aa4042ad812827b7aad858394e2bf73da8329af95ad8efa30bc886b"},
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+    {file = "google-api-core-2.16.1.tar.gz", hash = "sha256:7f668ffa3d5b9f3c6930407e5f5d691c05a376050a5a5fd772b9dc32e70a0c30"},
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 ]
 
 [package.dependencies]
@@ -2684,182 +2647,169 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4
 
 [[package]]
 name = "grpcio"
-version = "1.60.0"
+version = "1.47.5"
 description = "HTTP/2-based RPC framework"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
-grpcio = ">=1.60.0"
-protobuf = ">=4.21.6,<5.0dev"
+grpcio = ">=1.47.5"
+protobuf = ">=3.12.0,<4.0dev"
 setuptools = "*"
 
 [[package]]
@@ -3301,12 +3251,12 @@ testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jina"
-version = "3.8.2"
+version = "3.10.1"
 description = "Build cross-modal and multi-modal applications on the cloud · Neural Search · Creative AI · Cloud Native · MLOps"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -3315,22 +3265,22 @@ aiohttp = "*"
 aiostream = "*"
 cryptography = "*"
 docarray = [
-    {version = ">=0.16.1", extras = ["common"]},
-    {version = ">=0.16.1"},
+    {version = ">=0.16.4"},
+    {version = ">=0.16.3", extras = ["common"]},
 ]
 docker = "*"
 fastapi = ">=0.76.0"
 filelock = "*"
-grpcio = ">=1.46.0"
-grpcio-health-checking = ">=1.46.0"
-grpcio-reflection = ">=1.46.0"
+grpcio = ">=1.46.0,<1.48.1"
+grpcio-health-checking = ">=1.46.0,<1.48.1"
+grpcio-reflection = ">=1.46.0,<1.48.1"
 jcloud = ">=0.0.35"
-jina-hubble-sdk = ">=0.15.1"
+jina-hubble-sdk = ">=0.19.0"
 numpy = "*"
 packaging = ">=20.0"
 pathspec = "*"
 prometheus_client = "*"
-protobuf = ">=3.13.0"
+protobuf = ">=3.20.0"
 pydantic = "*"
 python-multipart = "*"
 pyyaml = ">=5.3.1"
@@ -3343,25 +3293,25 @@ websockets = "*"
 aiofiles = ["aiofiles"]
 aiohttp = ["aiohttp"]
 aiostream = ["aiostream"]
-all = ["Pillow", "aiofiles", "aiohttp", "aiostream", "black (==22.3.0)", "bs4", "coverage (==6.2)", "cryptography", "docarray (>=0.16.1)", "docarray[common] (>=0.16.1)", "docker", "fastapi (>=0.76.0)", "filelock", "flaky", "grpcio (>=1.46.0)", "grpcio-health-checking (>=1.46.0)", "grpcio-reflection (>=1.46.0)", "jcloud (>=0.0.35)", "jina-hubble-sdk (>=0.15.1)", "jsonschema", "kubernetes (>=18.20.0)", "mock", "numpy", "packaging (>=20.0)", "pathspec", "portforward (>=0.2.4)", "prometheus_client", "protobuf (>=3.13.0)", "psutil", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-custom_exit_code", "pytest-kind (==21.1.3)", "pytest-lazy-fixture", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout", "python-multipart", "pyyaml (>=5.3.1)", "requests", "requests-mock", "scipy (>=1.6.1)", "sgqlc", "strawberry-graphql (>=0.96.0)", "tensorflow (>=2.0)", "torch", "uvicorn[standard]", "uvloop", "websockets"]
+all = ["Pillow", "aiofiles", "aiohttp", "aiostream", "black (==22.3.0)", "bs4", "coverage (==6.2)", "cryptography", "docarray (>=0.16.4)", "docarray[common] (>=0.16.3)", "docker", "fastapi (>=0.76.0)", "filelock", "flaky", "grpcio (>=1.46.0,<1.48.1)", "grpcio-health-checking (>=1.46.0,<1.48.1)", "grpcio-reflection (>=1.46.0,<1.48.1)", "jcloud (>=0.0.35)", "jina-hubble-sdk (>=0.19.0)", "jsonschema", "kubernetes (>=18.20.0)", "mock", "numpy", "packaging (>=20.0)", "pathspec", "portforward (>=0.2.4)", "prometheus_client", "protobuf (>=3.20.0)", "psutil", "pydantic", "pytest", "pytest-asyncio", "pytest-cov", "pytest-custom_exit_code", "pytest-kind (==21.1.3)", "pytest-lazy-fixture", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout", "python-multipart", "pyyaml (>=5.3.1)", "requests", "requests-mock", "scipy (>=1.6.1)", "sgqlc", "strawberry-graphql (>=0.96.0)", "tensorflow (>=2.0)", "torch", "uvicorn[standard]", "uvloop", "websockets"]
 black = ["black (==22.3.0)"]
 bs4 = ["bs4"]
 cicd = ["bs4", "jsonschema", "portforward (>=0.2.4)", "sgqlc", "strawberry-graphql (>=0.96.0)", "tensorflow (>=2.0)", "torch"]
-core = ["docarray (>=0.16.1)", "grpcio (>=1.46.0)", "grpcio-health-checking (>=1.46.0)", "grpcio-reflection (>=1.46.0)", "jcloud (>=0.0.35)", "jina-hubble-sdk (>=0.15.1)", "numpy", "packaging (>=20.0)", "protobuf (>=3.13.0)", "pyyaml (>=5.3.1)"]
+core = ["docarray (>=0.16.4)", "grpcio (>=1.46.0,<1.48.1)", "grpcio-health-checking (>=1.46.0,<1.48.1)", "grpcio-reflection (>=1.46.0,<1.48.1)", "jcloud (>=0.0.35)", "jina-hubble-sdk (>=0.19.0)", "numpy", "packaging (>=20.0)", "protobuf (>=3.20.0)", "pyyaml (>=5.3.1)"]
 coverage = ["coverage (==6.2)"]
 cryptography = ["cryptography"]
-devel = ["aiofiles", "aiohttp", "aiostream", "cryptography", "docarray[common] (>=0.16.1)", "docker", "fastapi (>=0.76.0)", "filelock", "pathspec", "prometheus_client", "pydantic", "python-multipart", "requests", "sgqlc", "strawberry-graphql (>=0.96.0)", "uvicorn[standard]", "uvloop", "websockets"]
-docarray = ["docarray (>=0.16.1)"]
-"docarray[common" = ["docarray[common] (>=0.16.1)"]
+devel = ["aiofiles", "aiohttp", "aiostream", "cryptography", "docarray[common] (>=0.16.3)", "docker", "fastapi (>=0.76.0)", "filelock", "pathspec", "prometheus_client", "pydantic", "python-multipart", "requests", "sgqlc", "strawberry-graphql (>=0.96.0)", "uvicorn[standard]", "uvloop", "websockets"]
+docarray = ["docarray (>=0.16.4)"]
+"docarray[common" = ["docarray[common] (>=0.16.3)"]
 docker = ["docker"]
 fastapi = ["fastapi (>=0.76.0)"]
 filelock = ["filelock"]
 flaky = ["flaky"]
-grpcio = ["grpcio (>=1.46.0)"]
-grpcio-health-checking = ["grpcio-health-checking (>=1.46.0)"]
-grpcio-reflection = ["grpcio-reflection (>=1.46.0)"]
+grpcio = ["grpcio (>=1.46.0,<1.48.1)"]
+grpcio-health-checking = ["grpcio-health-checking (>=1.46.0,<1.48.1)"]
+grpcio-reflection = ["grpcio-reflection (>=1.46.0,<1.48.1)"]
 jcloud = ["jcloud (>=0.0.35)"]
-jina-hubble-sdk = ["jina-hubble-sdk (>=0.15.1)"]
+jina-hubble-sdk = ["jina-hubble-sdk (>=0.19.0)"]
 jsonschema = ["jsonschema"]
 kubernetes = ["kubernetes (>=18.20.0)"]
 mock = ["mock"]
@@ -3372,7 +3322,7 @@ perf = ["prometheus_client", "uvloop"]
 pillow = ["Pillow"]
 portforward = ["portforward (>=0.2.4)"]
 prometheus-client = ["prometheus_client"]
-protobuf = ["protobuf (>=3.13.0)"]
+protobuf = ["protobuf (>=3.20.0)"]
 psutil = ["psutil"]
 pydantic = ["pydantic"]
 pytest = ["pytest"]
@@ -3391,7 +3341,7 @@ requests = ["requests"]
 requests-mock = ["requests-mock"]
 scipy = ["scipy (>=1.6.1)"]
 sgqlc = ["sgqlc"]
-standard = ["aiofiles", "aiohttp", "aiostream", "cryptography", "docarray[common] (>=0.16.1)", "docker", "fastapi (>=0.76.0)", "filelock", "pathspec", "prometheus_client", "pydantic", "python-multipart", "requests", "uvicorn[standard]", "uvloop", "websockets"]
+standard = ["aiofiles", "aiohttp", "aiostream", "cryptography", "docarray[common] (>=0.16.3)", "docker", "fastapi (>=0.76.0)", "filelock", "pathspec", "prometheus_client", "pydantic", "python-multipart", "requests", "uvicorn[standard]", "uvloop", "websockets"]
 strawberry-graphql = ["strawberry-graphql (>=0.96.0)"]
 tensorflow = ["tensorflow (>=2.0)"]
 test = ["Pillow", "black (==22.3.0)", "coverage (==6.2)", "flaky", "kubernetes (>=18.20.0)", "mock", "psutil", "pytest", "pytest-asyncio", "pytest-cov", "pytest-custom_exit_code", "pytest-kind (==21.1.3)", "pytest-lazy-fixture", "pytest-mock", "pytest-repeat", "pytest-reraise", "pytest-timeout", "requests-mock", "scipy (>=1.6.1)"]
@@ -3659,41 +3609,15 @@ sqs = ["boto3 (>=1.26.143)", "pycurl (>=7.43.0.5)", "urllib3 (>=1.26.16)"]
 yaml = ["PyYAML (>=3.10)"]
 zookeeper = ["kazoo (>=2.8.0)"]
 
-[[package]]
-name = "kubernetes"
-version = "29.0.0"
-description = "Kubernetes python client"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "kubernetes-29.0.0-py2.py3-none-any.whl", hash = "sha256:ab8cb0e0576ccdfb71886366efb102c6a20f268d817be065ce7f9909c631e43e"},
-    {file = "kubernetes-29.0.0.tar.gz", hash = "sha256:c4812e227ae74d07d53c88293e564e54b850452715a59a927e7e1bc6b9a60459"},
-]
-
-[package.dependencies]
-certifi = ">=14.05.14"
-google-auth = ">=1.0.1"
-oauthlib = ">=3.2.2"
-python-dateutil = ">=2.5.3"
-pyyaml = ">=5.4.1"
-requests = "*"
-requests-oauthlib = "*"
-six = ">=1.9.0"
-urllib3 = ">=1.24.2"
-websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
-
-[package.extras]
-adal = ["adal (>=1.0.2)"]
-
 [[package]]
 name = "langchain"
-version = "0.0.354"
+version = "0.1.4"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain-0.0.354-py3-none-any.whl", hash = "sha256:8d28283a2891422a685b0605dd23b5a1cd6a15ab57a8e359b37a3151a322bad4"},
-    {file = "langchain-0.0.354.tar.gz", hash = "sha256:419c48735b803d70c0dee985e0afcfd7c88528b8c1cd918c57eb23e53d94ea87"},
+    {file = "langchain-0.1.4-py3-none-any.whl", hash = "sha256:6befdd6221f5f326092e31a3c19efdc7ce3d7d1f2e2cab065141071451730ed7"},
+    {file = "langchain-0.1.4.tar.gz", hash = "sha256:8767a9461e2b717ce9a35b1fa20659de89ea86ba9c2a4ff516e05d47ab2d195d"},
 ]
 
 [package.dependencies]
@@ -3701,9 +3625,9 @@ aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
 dataclasses-json = ">=0.5.7,<0.7"
 jsonpatch = ">=1.33,<2.0"
-langchain-community = ">=0.0.8,<0.1"
-langchain-core = ">=0.1.5,<0.2"
-langsmith = ">=0.0.77,<0.1.0"
+langchain-community = ">=0.0.14,<0.1"
+langchain-core = ">=0.1.16,<0.2"
+langsmith = ">=0.0.83,<0.1"
 numpy = ">=1,<2"
 pydantic = ">=1,<3"
 PyYAML = ">=5.3"
@@ -3718,7 +3642,7 @@ cli = ["typer (>=0.9.0,<0.10.0)"]
 cohere = ["cohere (>=4,<5)"]
 docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
 embeddings = ["sentence-transformers (>=2,<3)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
 javascript = ["esprima (>=4.0.1,<5.0.0)"]
 llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
 openai = ["openai (<2)", "tiktoken (>=0.3.2,<0.6.0)"]
@@ -3777,18 +3701,18 @@ extended-testing = ["jinja2 (>=3,<4)"]
 
 [[package]]
 name = "langchain-experimental"
-version = "0.0.47"
+version = "0.0.49"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchain_experimental-0.0.47-py3-none-any.whl", hash = "sha256:d5b6930c4e0a6f280cbb7d327d03d86f555f6168e185a1df64ba4a52b1059f65"},
-    {file = "langchain_experimental-0.0.47.tar.gz", hash = "sha256:0fdba89a438287c14fa0632c6adf87acffd55158a2f66c7a12be7721a7882a0e"},
+    {file = "langchain_experimental-0.0.49-py3-none-any.whl", hash = "sha256:7ddfeab733524785545a09657a482ab3ce866d2bd0984e757768d07ac55fcdcd"},
+    {file = "langchain_experimental-0.0.49.tar.gz", hash = "sha256:b36ef864f8464055e9063f94ee568c0d28cd1f029d23d02a7b4d8ff0f115e69f"},
 ]
 
 [package.dependencies]
-langchain = ">=0.0.350,<0.1"
-langchain-core = ">=0.1,<0.2"
+langchain = ">=0.1,<0.2"
+langchain-core = ">=0.1.7,<0.2.0"
 
 [package.extras]
 extended-testing = ["faker (>=19.3.1,<20.0.0)", "jinja2 (>=3,<4)", "presidio-analyzer (>=2.2.33,<3.0.0)", "presidio-anonymizer (>=2.2.33,<3.0.0)", "sentence-transformers (>=2,<3)", "vowpal-wabbit-next (==0.6.0)"]
@@ -3808,6 +3732,23 @@ files = [
 google-generativeai = ">=0.3.1,<0.4.0"
 langchain-core = ">=0.1,<0.2"
 
+[[package]]
+name = "langchain-openai"
+version = "0.0.2.post1"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = [
+    {file = "langchain_openai-0.0.2.post1-py3-none-any.whl", hash = "sha256:ba468b94c23da9d8ccefe5d5a3c1c65b4b9702292523e53acc689a9110022e26"},
+    {file = "langchain_openai-0.0.2.post1.tar.gz", hash = "sha256:f8e78db4a663feeac71d9f036b9422406c199ea3ef4c97d99ff392c93530e073"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1.7,<0.2"
+numpy = ">=1,<2"
+openai = ">=1.6.1,<2.0.0"
+tiktoken = ">=0.5.2,<0.6.0"
+
 [[package]]
 name = "langdetect"
 version = "1.0.9"
@@ -3882,12 +3823,12 @@ regex = ["regex"]
 
 [[package]]
 name = "llama-cpp-python"
-version = "0.2.37"
+version = "0.2.38"
 description = "Python bindings for the llama.cpp library"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "llama_cpp_python-0.2.37.tar.gz", hash = "sha256:a8f7c8d27334c6b38afac9a33f02a90a8a6c3eed15309e4b9315ac13a3cb3c04"},
+    {file = "llama_cpp_python-0.2.38.tar.gz", hash = "sha256:5acf5dd43e7e36e012924c92cb27612cd116571fe5849528758a2d6b301b7139"},
 ]
 
 [package.dependencies]
@@ -3904,19 +3845,20 @@ test = ["httpx (>=0.24.1)", "pytest (>=7.4.0)", "scipy (>=1.10)"]
 
 [[package]]
 name = "llama-index"
-version = "0.9.39"
+version = "0.9.40"
 description = "Interface between LLMs and your data"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "llama_index-0.9.39-py3-none-any.whl", hash = "sha256:73e19bf664b0643e3c1b88229d4bcaad841f4c6e882a63b27f637386c54d5353"},
-    {file = "llama_index-0.9.39.tar.gz", hash = "sha256:c0d4093cd1c6d6056275f96d6acba56f383ef98925c9ce3fc8cde9fb4dee1f75"},
+    {file = "llama_index-0.9.40-py3-none-any.whl", hash = "sha256:9fd192c574026b3e5eb95c8aed82506c48b46b5acb3401e98e0864d6f485f7a9"},
+    {file = "llama_index-0.9.40.tar.gz", hash = "sha256:bbe8b9584393a90bfb5246333d63df1c34d0989d19737f76f26baed6080b25dc"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.6,<4.0.0"
 dataclasses-json = "*"
 deprecated = ">=1.2.9.3"
+dirtyjson = ">=1.0.8,<2.0.0"
 fsspec = ">=2023.5.0"
 httpx = "*"
 nest-asyncio = ">=1.5.8,<2.0.0"
@@ -4277,98 +4219,6 @@ files = [
 [package.dependencies]
 requests = "*"
 
-[[package]]
-name = "mmh3"
-version = "4.1.0"
-description = "Python extension for MurmurHash (MurmurHash3), a set of fast and robust hash functions."
-optional = false
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 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
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 [[package]]
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@@ -5564,18 +5234,18 @@ grpc = ["googleapis-common-protos (>=1.53.0)", "grpc-gateway-protoc-gen-openapiv
 
 [[package]]
 name = "platformdirs"
-version = "4.1.0"
+version = "4.2.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
 python-versions = ">=3.8"
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-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
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+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
 
 [[package]]
 name = "pluggy"
@@ -5698,22 +5368,33 @@ testing = ["google-api-core[grpc] (>=1.31.5)"]
 
 [[package]]
 name = "protobuf"
-version = "4.25.2"
-description = ""
+version = "3.20.3"
+description = "Protocol Buffers"
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+python-versions = ">=3.7"
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+    {file = "protobuf-3.20.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:daa564862dd0d39c00f8086f88700fdbe8bc717e993a21e90711acfed02f2402"},
+    {file = "protobuf-3.20.3-cp39-cp39-win32.whl", hash = "sha256:819559cafa1a373b7096a482b504ae8a857c89593cf3a25af743ac9ecbd23480"},
+    {file = "protobuf-3.20.3-cp39-cp39-win_amd64.whl", hash = "sha256:03038ac1cfbc41aa21f6afcbcd357281d7521b4157926f30ebecc8d4ea59dcb7"},
+    {file = "protobuf-3.20.3-py2.py3-none-any.whl", hash = "sha256:a7ca6d488aa8ff7f329d4c545b2dbad8ac31464f1d8b1c87ad1346717731e4db"},
+    {file = "protobuf-3.20.3.tar.gz", hash = "sha256:2e3427429c9cffebf259491be0af70189607f365c2f41c7c3764af6f337105f2"},
 ]
 
 [[package]]
@@ -6444,20 +6125,6 @@ files = [
     {file = "PyPika-0.48.9.tar.gz", hash = "sha256:838836a61747e7c8380cd1b7ff638694b7a7335345d0f559b04b2cd832ad5378"},
 ]
 
-[[package]]
-name = "pyproject-hooks"
-version = "1.0.0"
-description = "Wrappers to call pyproject.toml-based build backend hooks."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "pyproject_hooks-1.0.0-py3-none-any.whl", hash = "sha256:283c11acd6b928d2f6a7c73fa0d01cb2bdc5f07c57a2eeb6e83d5e56b97976f8"},
-    {file = "pyproject_hooks-1.0.0.tar.gz", hash = "sha256:f271b298b97f5955d53fb12b72c1fb1948c22c1a6b70b315c54cedaca0264ef5"},
-]
-
-[package.dependencies]
-tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-
 [[package]]
 name = "pyreadline3"
 version = "3.4.1"
@@ -6899,13 +6566,13 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
 
 [[package]]
 name = "qdrant-client"
-version = "1.7.1"
+version = "1.7.2"
 description = "Client library for the Qdrant vector search engine"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "qdrant_client-1.7.1-py3-none-any.whl", hash = "sha256:b6b52007d7dce339007f6fa676a611fbc61609d374aa00d8596b62ea45d831d7"},
-    {file = "qdrant_client-1.7.1.tar.gz", hash = "sha256:7e3660b540e5bddda555a1638dd7905df9963fefe2061712fc5f48bfd78d734d"},
+    {file = "qdrant_client-1.7.2-py3-none-any.whl", hash = "sha256:d00cf0b7b7d6f06847c9bb0126b234151f78f75caf0b920ad976487808bb71ce"},
+    {file = "qdrant_client-1.7.2.tar.gz", hash = "sha256:ec8286cd3a6556d3f033d3b687225af1e715d47767ee27f119aee3c8354c0e07"},
 ]
 
 [package.dependencies]
@@ -7216,24 +6883,6 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
-[[package]]
-name = "requests-oauthlib"
-version = "1.3.1"
-description = "OAuthlib authentication support for Requests."
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-files = [
-    {file = "requests-oauthlib-1.3.1.tar.gz", hash = "sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a"},
-    {file = "requests_oauthlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5"},
-]
-
-[package.dependencies]
-oauthlib = ">=3.0.0"
-requests = ">=2.0.0"
-
-[package.extras]
-rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
-
 [[package]]
 name = "rich"
 version = "13.7.0"
@@ -9031,39 +8680,22 @@ files = [
 
 [[package]]
 name = "weaviate-client"
-version = "4.4b6"
+version = "3.26.2"
 description = "A python native Weaviate client"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "weaviate-client-4.4b6.tar.gz", hash = "sha256:40b4ba7f3300ba537566697ed806b34cefb559285acbd4f0c2602dc71e5794e3"},
-    {file = "weaviate_client-4.4b6-py3-none-any.whl", hash = "sha256:abb6140071ae83c4987176ae928328ad367eedccf7f2d06cb733d12a50ab445e"},
+    {file = "weaviate-client-3.26.2.tar.gz", hash = "sha256:63ec70839b64909810a64aa7b3e5b85088462e93c7e2ed3c32ebefb702f36723"},
+    {file = "weaviate_client-3.26.2-py3-none-any.whl", hash = "sha256:ca43bfb9c06b8ae3fd938dc9158acd93d4cbf4622192e173333e1ff63cf97164"},
 ]
 
 [package.dependencies]
 authlib = ">=1.2.1,<2.0.0"
-grpcio = ">=1.57.0,<2.0.0"
-grpcio-health-checking = ">=1.57.0,<2.0.0"
-grpcio-tools = ">=1.57.0,<2.0.0"
-pydantic = ">=2.1.1,<3.0.0"
 requests = ">=2.30.0,<3.0.0"
 validators = ">=0.21.2,<1.0.0"
 
-[[package]]
-name = "websocket-client"
-version = "1.7.0"
-description = "WebSocket client for Python with low level API options"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
-    {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
-]
-
 [package.extras]
-docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
-optional = ["python-socks", "wsaccel"]
-test = ["websockets"]
+grpc = ["grpcio (>=1.57.0,<2.0.0)", "grpcio-tools (>=1.57.0,<2.0.0)"]
 
 [[package]]
 name = "websockets"
@@ -9484,4 +9116,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.11"
-content-hash = "c6027e40345267e1d1c3100267db82df68931ebc1c64f940efdbfa7db46ba0ae"
+content-hash = "d147aff39506a7f2ca9b8774a8c4340f9221bd9037ef1c484c37840247ff89a4"
diff --git a/pyproject.toml b/pyproject.toml
index cd0831e44..30a0c9dee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.6.5a12"
+version = "0.6.5a13"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [
@@ -25,8 +25,6 @@ documentation = "https://docs.langflow.org"
 langflow = "langflow.__main__:main"
 
 [tool.poetry.dependencies]
-
-
 python = ">=3.9,<3.11"
 fastapi = "^0.108.0"
 uvicorn = "^0.25.0"
@@ -35,9 +33,9 @@ google-search-results = "^2.4.1"
 google-api-python-client = "^2.79.0"
 typer = "^0.9.0"
 gunicorn = "^21.2.0"
-langchain = "~0.0.345"
-openai = "^1.6.1"
-duckdb = "*"
+langchain = "~0.1.0"
+openai = "^1.10.0"
+duckdb = "^0.9.2"
 pandas = "2.0.3"
 chromadb = "^0.4.0"
 huggingface-hub = { version = "^0.19.0", extras = ["inference"] }
@@ -56,7 +54,7 @@ tiktoken = "~0.5.0"
 wikipedia = "^1.4.0"
 qdrant-client = "^1.7.0"
 websockets = "^10.3"
-weaviate-client = { version = "^4.4b6", allow-prereleases = true }
+weaviate-client = "*"
 jina = "*"
 sentence-transformers = { version = "^2.2.2", optional = true }
 ctransformers = { version = "^0.2.10", optional = true }
@@ -64,7 +62,7 @@ cohere = "^4.39.0"
 python-multipart = "^0.0.6"
 sqlmodel = "^0.0.14"
 faiss-cpu = "^1.7.4"
-anthropic = "^0.8.0"
+anthropic = "^0.13.0"
 orjson = "3.9.3"
 multiprocess = "^0.70.14"
 cachetools = "^5.3.1"
@@ -99,7 +97,7 @@ markupsafe = "^2.1.3"
 extract-msg = "^0.45.0"
 # jq is not available for windows
 jq = { version = "^1.6.0", markers = "sys_platform != 'win32'" }
-boto3 = "^1.28.63"
+boto3 = "^1.34.0"
 numexpr = "^2.8.6"
 qianfan = "0.2.0"
 pgvector = "^0.2.3"
@@ -108,12 +106,13 @@ langchain-google-genai = "^0.0.2"
 elasticsearch = "^8.11.1"
 pytube = "^15.0.0"
 llama-index = "^0.9.24"
+langchain-openai = "^0.0.2"
 
 [tool.poetry.group.dev.dependencies]
 pytest-asyncio = "^0.23.1"
 types-redis = "^4.6.0.5"
 ipykernel = "^6.27.0"
-mypy = "^1.7.1"
+mypy = "^1.8.0"
 ruff = "^0.1.5"
 httpx = "*"
 pytest = "^7.4.2"
@@ -155,7 +154,8 @@ exclude = ["src/backend/langflow/alembic/*"]
 line-length = 120
 
 [tool.mypy]
-plugins = "pydantic.mypy"
+plugins = ["pydantic.mypy"]
+follow_imports = "silent"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/src/backend/langflow/alembic/versions/b2fa308044b5_add_unique_constraints.py b/src/backend/langflow/alembic/versions/b2fa308044b5_add_unique_constraints.py
index e24d5a72f..8a2e90abc 100644
--- a/src/backend/langflow/alembic/versions/b2fa308044b5_add_unique_constraints.py
+++ b/src/backend/langflow/alembic/versions/b2fa308044b5_add_unique_constraints.py
@@ -28,7 +28,7 @@ def upgrade() -> None:
             batch_op.add_column(sa.Column('folder', sqlmodel.sql.sqltypes.AutoString(), nullable=True))
             batch_op.add_column(sa.Column('user_id', sqlmodel.sql.sqltypes.GUID(), nullable=True))
             batch_op.create_index(batch_op.f('ix_flow_user_id'), ['user_id'], unique=False)
-            batch_op.create_foreign_key(None, 'user', ['user_id'], ['id'])
+            batch_op.create_foreign_key('fk_flow_user_id_user', 'user', ['user_id'], ['id'])
     except Exception:
         pass
     # ### end Alembic commands ###
@@ -38,7 +38,7 @@ def downgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     try:
         with op.batch_alter_table('flow', schema=None) as batch_op:
-            batch_op.drop_constraint(None, type_='foreignkey')
+            batch_op.drop_constraint('fk_flow_user_id_user', type_='foreignkey')
             batch_op.drop_index(batch_op.f('ix_flow_user_id'))
             batch_op.drop_column('user_id')
             batch_op.drop_column('folder')
diff --git a/src/backend/langflow/components/agents/CSVAgent.py b/src/backend/langflow/components/agents/CSVAgent.py
new file mode 100644
index 000000000..b54e5d90d
--- /dev/null
+++ b/src/backend/langflow/components/agents/CSVAgent.py
@@ -0,0 +1,23 @@
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel, AgentExecutor
+from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
+
+
+class CSVAgentComponent(CustomComponent):
+    display_name = "CSVAgent"
+    description = "Construct a CSV agent from a CSV and tools."
+    documentation = "https://python.langchain.com/docs/modules/agents/toolkits/csv"
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM", "type": BaseLanguageModel},
+            "path": {"display_name": "Path", "field_type": "file", "suffixes": [".csv"], "file_types": [".csv"]},
+        }
+
+    def build(
+        self,
+        llm: BaseLanguageModel,
+        path: str,
+    ) -> AgentExecutor:
+        # Instantiate and return the CSV agent class with the provided llm and path
+        return create_csv_agent(llm=llm, path=path)
diff --git a/src/backend/langflow/components/agents/JsonAgent.py b/src/backend/langflow/components/agents/JsonAgent.py
new file mode 100644
index 000000000..0197b9210
--- /dev/null
+++ b/src/backend/langflow/components/agents/JsonAgent.py
@@ -0,0 +1,24 @@
+from langflow import CustomComponent
+from langchain.agents import AgentExecutor, create_json_agent
+from langflow.field_typing import (
+    BaseLanguageModel,
+)
+from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
+
+
+class JsonAgentComponent(CustomComponent):
+    display_name = "JsonAgent"
+    description = "Construct a json agent from an LLM and tools."
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "toolkit": {"display_name": "Toolkit"},
+        }
+
+    def build(
+        self,
+        llm: BaseLanguageModel,
+        toolkit: JsonToolkit,
+    ) -> AgentExecutor:
+        return create_json_agent(llm=llm, toolkit=toolkit)
diff --git a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
index 66ff0f275..1a06e25fb 100644
--- a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
+++ b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
@@ -3,7 +3,7 @@ from typing import List, Optional
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
-from langchain.chat_models import ChatOpenAI
+from langchain_community.chat_models import ChatOpenAI
 from langchain.memory.token_buffer import ConversationTokenBufferMemory
 from langchain.prompts import SystemMessagePromptTemplate
 from langchain.prompts.chat import MessagesPlaceholder
diff --git a/src/backend/langflow/components/agents/SQLAgent.py b/src/backend/langflow/components/agents/SQLAgent.py
new file mode 100644
index 000000000..42b1b48f3
--- /dev/null
+++ b/src/backend/langflow/components/agents/SQLAgent.py
@@ -0,0 +1,29 @@
+from langflow import CustomComponent
+from typing import Union, Callable
+from langchain.agents import AgentExecutor
+from langflow.field_typing import BaseLanguageModel
+from langchain_community.agent_toolkits.sql.base import create_sql_agent
+from langchain.sql_database import SQLDatabase
+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+
+
+class SQLAgentComponent(CustomComponent):
+    display_name = "SQLAgent"
+    description = "Construct an SQL agent from an LLM and tools."
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "database_uri": {"display_name": "Database URI"},
+            "verbose": {"display_name": "Verbose", "value": False, "advanced": True},
+        }
+
+    def build(
+        self,
+        llm: BaseLanguageModel,
+        database_uri: str,
+        verbose: bool = False,
+    ) -> Union[AgentExecutor, Callable]:
+        db = SQLDatabase.from_uri(database_uri)
+        toolkit = SQLDatabaseToolkit(db=db, llm=llm)
+        return create_sql_agent(llm=llm, toolkit=toolkit)
diff --git a/src/backend/langflow/components/agents/VectorStoreAgent.py b/src/backend/langflow/components/agents/VectorStoreAgent.py
new file mode 100644
index 000000000..b70ea4d59
--- /dev/null
+++ b/src/backend/langflow/components/agents/VectorStoreAgent.py
@@ -0,0 +1,23 @@
+from langflow import CustomComponent
+from langchain.agents import AgentExecutor, create_vectorstore_agent
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
+from typing import Union, Callable
+from langflow.field_typing import BaseLanguageModel
+
+
+class VectorStoreAgentComponent(CustomComponent):
+    display_name = "VectorStoreAgent"
+    description = "Construct an agent from a Vector Store."
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "vector_store_toolkit": {"display_name": "Vector Store Info"},
+        }
+
+    def build(
+        self,
+        llm: BaseLanguageModel,
+        vector_store_toolkit: VectorStoreToolkit,
+    ) -> Union[AgentExecutor, Callable]:
+        return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)
diff --git a/src/backend/langflow/components/agents/VectorStoreRouterAgent.py b/src/backend/langflow/components/agents/VectorStoreRouterAgent.py
new file mode 100644
index 000000000..3174d9513
--- /dev/null
+++ b/src/backend/langflow/components/agents/VectorStoreRouterAgent.py
@@ -0,0 +1,19 @@
+from langflow import CustomComponent
+from langchain_core.language_models.base import BaseLanguageModel
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
+from langchain.agents import create_vectorstore_router_agent
+from typing import Callable
+
+
+class VectorStoreRouterAgentComponent(CustomComponent):
+    display_name = "VectorStoreRouterAgent"
+    description = "Construct an agent from a Vector Store Router."
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "vectorstoreroutertoolkit": {"display_name": "Vector Store Router Toolkit"},
+        }
+
+    def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:
+        return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)
diff --git a/src/backend/langflow/components/chains/LLMChain.py b/src/backend/langflow/components/chains/LLMChain.py
index 8a62a8c55..8596dd0c9 100644
--- a/src/backend/langflow/components/chains/LLMChain.py
+++ b/src/backend/langflow/components/chains/LLMChain.py
@@ -1,7 +1,6 @@
 from typing import Callable, Optional, Union
 
 from langchain.chains import LLMChain
-
 from langflow import CustomComponent
 from langflow.field_typing import BaseLanguageModel, BaseMemory, BasePromptTemplate, Chain, Text
 
diff --git a/src/backend/langflow/components/chains/LLMCheckerChain.py b/src/backend/langflow/components/chains/LLMCheckerChain.py
new file mode 100644
index 000000000..cf5effac8
--- /dev/null
+++ b/src/backend/langflow/components/chains/LLMCheckerChain.py
@@ -0,0 +1,24 @@
+from langflow import CustomComponent
+from langchain.chains import LLMCheckerChain
+from typing import Union, Callable
+from langflow.field_typing import (
+    BaseLanguageModel,
+    Chain,
+)
+
+
+class LLMCheckerChainComponent(CustomComponent):
+    display_name = "LLMCheckerChain"
+    description = ""
+    documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_checker"
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+        }
+
+    def build(
+        self,
+        llm: BaseLanguageModel,
+    ) -> Union[Chain, Callable]:
+        return LLMCheckerChain(llm=llm)
diff --git a/src/backend/langflow/components/chains/LLMMathChain.py b/src/backend/langflow/components/chains/LLMMathChain.py
new file mode 100644
index 000000000..28f430e6d
--- /dev/null
+++ b/src/backend/langflow/components/chains/LLMMathChain.py
@@ -0,0 +1,31 @@
+from typing import Callable, Optional, Union
+
+from langchain.chains import LLMChain, LLMMathChain
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel, BaseMemory, Chain
+
+
+class LLMMathChainComponent(CustomComponent):
+    display_name = "LLMMathChain"
+    description = "Chain that interprets a prompt and executes python code to do math."
+    documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_math"
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "llm_chain": {"display_name": "LLM Chain"},
+            "memory": {"display_name": "Memory"},
+            "input_key": {"display_name": "Input Key"},
+            "output_key": {"display_name": "Output Key"},
+        }
+
+    def build(
+        self,
+        llm: BaseLanguageModel,
+        llm_chain: LLMChain,
+        input_key: str = "question",
+        output_key: str = "answer",
+        memory: Optional[BaseMemory] = None,
+    ) -> Union[LLMMathChain, Callable, Chain]:
+        return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory)
diff --git a/src/backend/langflow/components/chains/RetrievalQA.py b/src/backend/langflow/components/chains/RetrievalQA.py
new file mode 100644
index 000000000..5f1232443
--- /dev/null
+++ b/src/backend/langflow/components/chains/RetrievalQA.py
@@ -0,0 +1,39 @@
+from typing import Callable, Optional, Union
+
+from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+from langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA
+from langflow import CustomComponent
+from langflow.field_typing import BaseMemory, BaseRetriever
+
+
+class RetrievalQAComponent(CustomComponent):
+    display_name = "RetrievalQA"
+    description = "Chain for question-answering against an index."
+
+    def build_config(self):
+        return {
+            "combine_documents_chain": {"display_name": "Combine Documents Chain"},
+            "retriever": {"display_name": "Retriever"},
+            "memory": {"display_name": "Memory", "required": False},
+            "input_key": {"display_name": "Input Key", "advanced": True},
+            "output_key": {"display_name": "Output Key", "advanced": True},
+            "return_source_documents": {"display_name": "Return Source Documents"},
+        }
+
+    def build(
+        self,
+        combine_documents_chain: BaseCombineDocumentsChain,
+        retriever: BaseRetriever,
+        memory: Optional[BaseMemory] = None,
+        input_key: str = "query",
+        output_key: str = "result",
+        return_source_documents: bool = True,
+    ) -> Union[BaseRetrievalQA, Callable]:
+        return RetrievalQA(
+            combine_documents_chain=combine_documents_chain,
+            retriever=retriever,
+            memory=memory,
+            input_key=input_key,
+            output_key=output_key,
+            return_source_documents=return_source_documents,
+        )
diff --git a/src/backend/langflow/components/chains/RetrievalQAWithSourcesChain.py b/src/backend/langflow/components/chains/RetrievalQAWithSourcesChain.py
new file mode 100644
index 000000000..3c46cd8bd
--- /dev/null
+++ b/src/backend/langflow/components/chains/RetrievalQAWithSourcesChain.py
@@ -0,0 +1,42 @@
+from typing import Optional
+
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
+from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever
+
+
+class RetrievalQAWithSourcesChainComponent(CustomComponent):
+    display_name = "RetrievalQAWithSourcesChain"
+    description = "Question-answering with sources over an index."
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "chain_type": {
+                "display_name": "Chain Type",
+                "options": ["stuff", "map_reduce", "map_rerank", "refine"],
+            },
+            "memory": {"display_name": "Memory"},
+            "return_source_documents": {"display_name": "Return Source Documents"},
+        }
+
+    def build(
+        self,
+        retriever: BaseRetriever,
+        llm: BaseLanguageModel,
+        combine_documents_chain: BaseCombineDocumentsChain,
+        chain_type: str,
+        memory: Optional[BaseMemory] = None,
+        return_source_documents: Optional[bool] = True,
+    ) -> BaseQAWithSourcesChain:
+        return RetrievalQAWithSourcesChain.from_chain_type(
+            llm=llm,
+            chain_type=chain_type,
+            combine_documents_chain=combine_documents_chain,
+            memory=memory,
+            return_source_documents=return_source_documents,
+            retriever=retriever,
+        )
diff --git a/src/backend/langflow/components/chains/SQLDatabaseChain.py b/src/backend/langflow/components/chains/SQLDatabaseChain.py
new file mode 100644
index 000000000..56bd433ba
--- /dev/null
+++ b/src/backend/langflow/components/chains/SQLDatabaseChain.py
@@ -0,0 +1,25 @@
+from langflow import CustomComponent
+from typing import Callable, Union
+from langflow.field_typing import BasePromptTemplate, BaseLanguageModel, Chain
+from langchain_community.utilities.sql_database import SQLDatabase
+from langchain_experimental.sql.base import SQLDatabaseChain
+
+
+class SQLDatabaseChainComponent(CustomComponent):
+    display_name = "SQLDatabaseChain"
+    description = ""
+
+    def build_config(self):
+        return {
+            "db": {"display_name": "Database"},
+            "llm": {"display_name": "LLM"},
+            "prompt": {"display_name": "Prompt"},
+        }
+
+    def build(
+        self,
+        db: SQLDatabase,
+        llm: BaseLanguageModel,
+        prompt: BasePromptTemplate,
+    ) -> Union[Chain, Callable, SQLDatabaseChain]:
+        return SQLDatabaseChain.from_llm(llm=llm, db=db, prompt=prompt)
diff --git a/src/backend/langflow/components/documentloaders/DirectoryLoader.py b/src/backend/langflow/components/documentloaders/DirectoryLoader.py
new file mode 100644
index 000000000..0b214dd34
--- /dev/null
+++ b/src/backend/langflow/components/documentloaders/DirectoryLoader.py
@@ -0,0 +1,42 @@
+from langflow import CustomComponent
+from langchain.docstore.document import Document
+from typing import Optional, Dict, Any
+
+
+class DirectoryLoaderComponent(CustomComponent):
+    display_name = "DirectoryLoader"
+    description = "Load from a directory."
+
+    def build_config(self) -> Dict[str, Any]:
+        return {
+            "glob": {"display_name": "Glob Pattern", "value": "**/*.txt"},
+            "load_hidden": {"display_name": "Load Hidden Files", "value": False, "advanced": True},
+            "max_concurrency": {"display_name": "Max Concurrency", "value": 10, "advanced": True},
+            "metadata": {"display_name": "Metadata", "value": {}},
+            "path": {"display_name": "Local Directory"},
+            "recursive": {"display_name": "Recursive", "value": True, "advanced": True},
+            "silent_errors": {"display_name": "Silent Errors", "value": False, "advanced": True},
+            "use_multithreading": {"display_name": "Use Multithreading", "value": True, "advanced": True},
+        }
+
+    def build(
+        self,
+        glob: str,
+        path: str,
+        load_hidden: Optional[bool] = False,
+        max_concurrency: Optional[int] = 10,
+        metadata: Optional[dict] = {},
+        recursive: Optional[bool] = True,
+        silent_errors: Optional[bool] = False,
+        use_multithreading: Optional[bool] = True,
+    ) -> Document:
+        return Document(
+            glob=glob,
+            path=path,
+            load_hidden=load_hidden,
+            max_concurrency=max_concurrency,
+            metadata=metadata,
+            recursive=recursive,
+            silent_errors=silent_errors,
+            use_multithreading=use_multithreading,
+        )
diff --git a/src/backend/langflow/components/embeddings/AzureOpenAIEmbeddings.py b/src/backend/langflow/components/embeddings/AzureOpenAIEmbeddings.py
index e4fdd8738..09b03e17b 100644
--- a/src/backend/langflow/components/embeddings/AzureOpenAIEmbeddings.py
+++ b/src/backend/langflow/components/embeddings/AzureOpenAIEmbeddings.py
@@ -1,7 +1,8 @@
-from langflow import CustomComponent
 from langchain.embeddings.base import Embeddings
 from langchain_community.embeddings import AzureOpenAIEmbeddings
 
+from langflow import CustomComponent
+
 
 class AzureOpenAIEmbeddingsComponent(CustomComponent):
     display_name: str = "AzureOpenAIEmbeddings"
@@ -53,9 +54,9 @@ class AzureOpenAIEmbeddingsComponent(CustomComponent):
         try:
             embeddings = AzureOpenAIEmbeddings(
                 azure_endpoint=azure_endpoint,
-                deployment=azure_deployment,
-                openai_api_version=api_version,
-                openai_api_key=api_key,
+                azure_deployment=azure_deployment,
+                api_version=api_version,
+                api_key=api_key,
             )
 
         except Exception as e:
diff --git a/src/backend/langflow/components/embeddings/CohereEmbeddings.py b/src/backend/langflow/components/embeddings/CohereEmbeddings.py
new file mode 100644
index 000000000..049525b39
--- /dev/null
+++ b/src/backend/langflow/components/embeddings/CohereEmbeddings.py
@@ -0,0 +1,36 @@
+from typing import Optional
+
+from langchain_community.embeddings.cohere import CohereEmbeddings
+from langflow import CustomComponent
+
+
+class CohereEmbeddingsComponent(CustomComponent):
+    display_name = "CohereEmbeddings"
+    description = "Cohere embedding models."
+
+    def build_config(self):
+        return {
+            "cohere_api_key": {"display_name": "Cohere API Key", "password": True},
+            "model": {"display_name": "Model", "default": "embed-english-v2.0", "advanced": True},
+            "truncate": {"display_name": "Truncate", "advanced": True},
+            "max_retries": {"display_name": "Max Retries", "advanced": True},
+            "user_agent": {"display_name": "User Agent", "advanced": True},
+        }
+
+    def build(
+        self,
+        request_timeout: Optional[float] = None,
+        cohere_api_key: str = "",
+        max_retries: Optional[int] = None,
+        model: str = "embed-english-v2.0",
+        truncate: Optional[str] = None,
+        user_agent: str = "langchain",
+    ) -> CohereEmbeddings:
+        return CohereEmbeddings(  # type: ignore
+            max_retries=max_retries,
+            user_agent=user_agent,
+            request_timeout=request_timeout,
+            cohere_api_key=cohere_api_key,
+            model=model,
+            truncate=truncate,
+        )
diff --git a/src/backend/langflow/components/embeddings/HuggingFaceEmbeddings.py b/src/backend/langflow/components/embeddings/HuggingFaceEmbeddings.py
new file mode 100644
index 000000000..6f3540358
--- /dev/null
+++ b/src/backend/langflow/components/embeddings/HuggingFaceEmbeddings.py
@@ -0,0 +1,36 @@
+from langflow import CustomComponent
+from typing import Optional, Dict
+from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
+
+
+class HuggingFaceEmbeddingsComponent(CustomComponent):
+    display_name = "HuggingFaceEmbeddings"
+    description = "HuggingFace sentence_transformers embedding models."
+    documentation = (
+        "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
+    )
+
+    def build_config(self):
+        return {
+            "cache_folder": {"display_name": "Cache Folder", "advanced": True},
+            "encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
+            "model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
+            "model_name": {"display_name": "Model Name"},
+            "multi_process": {"display_name": "Multi Process", "advanced": True},
+        }
+
+    def build(
+        self,
+        cache_folder: Optional[str] = None,
+        encode_kwargs: Optional[Dict] = {},
+        model_kwargs: Optional[Dict] = {},
+        model_name: str = "sentence-transformers/all-mpnet-base-v2",
+        multi_process: bool = False,
+    ) -> HuggingFaceEmbeddings:
+        return HuggingFaceEmbeddings(
+            cache_folder=cache_folder,
+            encode_kwargs=encode_kwargs,
+            model_kwargs=model_kwargs,
+            model_name=model_name,
+            multi_process=multi_process,
+        )
diff --git a/src/backend/langflow/components/embeddings/OpenAIEmbeddings.py b/src/backend/langflow/components/embeddings/OpenAIEmbeddings.py
new file mode 100644
index 000000000..555d45570
--- /dev/null
+++ b/src/backend/langflow/components/embeddings/OpenAIEmbeddings.py
@@ -0,0 +1,117 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+from langchain_openai.embeddings.base import OpenAIEmbeddings
+
+from langflow import CustomComponent
+from langflow.field_typing import NestedDict
+
+
+class OpenAIEmbeddingsComponent(CustomComponent):
+    display_name = "OpenAIEmbeddings"
+    description = "OpenAI embedding models"
+
+    def build_config(self):
+        return {
+            "allowed_special": {
+                "display_name": "Allowed Special",
+                "advanced": True,
+                "field_type": "str",
+                "is_list": True,
+            },
+            "default_headers": {
+                "display_name": "Default Headers",
+                "advanced": True,
+                "field_type": "dict",
+            },
+            "default_query": {
+                "display_name": "Default Query",
+                "advanced": True,
+                "field_type": "NestedDict",
+            },
+            "disallowed_special": {
+                "display_name": "Disallowed Special",
+                "advanced": True,
+                "field_type": "str",
+                "is_list": True,
+            },
+            "chunk_size": {"display_name": "Chunk Size", "advanced": True},
+            "client": {"display_name": "Client", "advanced": True},
+            "deployment": {"display_name": "Deployment", "advanced": True},
+            "embedding_ctx_length": {
+                "display_name": "Embedding Context Length",
+                "advanced": True,
+            },
+            "max_retries": {"display_name": "Max Retries", "advanced": True},
+            "model": {"display_name": "Model", "advanced": True},
+            "model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
+            "openai_api_base": {"display_name": "OpenAI API Base", "password": True, "advanced": True},
+            "openai_api_key": {"display_name": "OpenAI API Key", "password": True},
+            "openai_api_type": {"display_name": "OpenAI API Type", "advanced": True, "password": True},
+            "openai_api_version": {
+                "display_name": "OpenAI API Version",
+                "advanced": True,
+            },
+            "openai_organization": {
+                "display_name": "OpenAI Organization",
+                "advanced": True,
+            },
+            "openai_proxy": {"display_name": "OpenAI Proxy", "advanced": True},
+            "request_timeout": {"display_name": "Request Timeout", "advanced": True},
+            "show_progress_bar": {
+                "display_name": "Show Progress Bar",
+                "advanced": True,
+            },
+            "skip_empty": {"display_name": "Skip Empty", "advanced": True},
+            "tiktoken_model_name": {"display_name": "TikToken Model Name"},
+            "tikToken_enable": {"display_name": "TikToken Enable"},
+        }
+
+    def build(
+        self,
+        default_headers: Optional[Dict[str, str]] = None,
+        default_query: Optional[NestedDict] = {},
+        allowed_special: List[str] = [],
+        disallowed_special: List[str] = ["all"],
+        chunk_size: int = 1000,
+        client: Optional[Any] = None,
+        deployment: str = "text-embedding-ada-002",
+        embedding_ctx_length: int = 8191,
+        max_retries: int = 6,
+        model: str = "text-embedding-ada-002",
+        model_kwargs: NestedDict = {},
+        openai_api_base: Optional[str] = None,
+        openai_api_key: Optional[str] = "",
+        openai_api_type: Optional[str] = None,
+        openai_api_version: Optional[str] = None,
+        openai_organization: Optional[str] = None,
+        openai_proxy: Optional[str] = None,
+        request_timeout: Optional[float] = None,
+        show_progress_bar: bool = False,
+        skip_empty: bool = False,
+        tikToken_enable: bool = True,
+        tiktoken_model_name: Optional[str] = None,
+    ) -> Union[OpenAIEmbeddings, Callable]:
+        return OpenAIEmbeddings(
+            tiktoken_enabled=tikToken_enable,
+            default_headers=default_headers,
+            default_query=default_query,
+            allowed_special=set(allowed_special),
+            disallowed_special=set(disallowed_special),
+            chunk_size=chunk_size,
+            client=client,
+            deployment=deployment,
+            embedding_ctx_length=embedding_ctx_length,
+            max_retries=max_retries,
+            model=model,
+            model_kwargs=model_kwargs,
+            base_url=openai_api_base,
+            api_key=openai_api_key,
+            openai_api_type=openai_api_type,
+            api_version=openai_api_version,
+            organization=openai_organization,
+            openai_proxy=openai_proxy,
+            timeout=request_timeout,
+            show_progress_bar=show_progress_bar,
+            skip_empty=skip_empty,
+            tiktoken_model_name=tiktoken_model_name,
+        )
diff --git a/src/backend/langflow/components/embeddings/VertexAIEmbeddings.py b/src/backend/langflow/components/embeddings/VertexAIEmbeddings.py
new file mode 100644
index 000000000..053fd6c7f
--- /dev/null
+++ b/src/backend/langflow/components/embeddings/VertexAIEmbeddings.py
@@ -0,0 +1,60 @@
+from langflow import CustomComponent
+from langchain.embeddings import VertexAIEmbeddings
+from typing import Optional, List
+
+
+class VertexAIEmbeddingsComponent(CustomComponent):
+    display_name = "VertexAIEmbeddings"
+    description = "Google Cloud VertexAI embedding models."
+
+    def build_config(self):
+        return {
+            "credentials": {"display_name": "Credentials", "value": "", "file_types": [".json"], "field_type": "file"},
+            "instance": {"display_name": "instance", "advanced": True, "field_type": "dict"},
+            "location": {"display_name": "Location", "value": "us-central1", "advanced": True},
+            "max_output_tokens": {"display_name": "Max Output Tokens", "value": 128},
+            "max_retries": {"display_name": "Max Retries", "value": 6, "advanced": True},
+            "model_name": {"display_name": "Model Name", "value": "textembedding-gecko"},
+            "n": {"display_name": "N", "value": 1, "advanced": True},
+            "project": {"display_name": "Project", "advanced": True},
+            "request_parallelism": {"display_name": "Request Parallelism", "value": 5, "advanced": True},
+            "stop": {"display_name": "Stop", "advanced": True},
+            "streaming": {"display_name": "Streaming", "value": False, "advanced": True},
+            "temperature": {"display_name": "Temperature", "value": 0.0},
+            "top_k": {"display_name": "Top K", "value": 40, "advanced": True},
+            "top_p": {"display_name": "Top P", "value": 0.95, "advanced": True},
+        }
+
+    def build(
+        self,
+        instance: Optional[str] = None,
+        credentials: Optional[str] = None,
+        location: str = "us-central1",
+        max_output_tokens: int = 128,
+        max_retries: int = 6,
+        model_name: str = "textembedding-gecko",
+        n: int = 1,
+        project: Optional[str] = None,
+        request_parallelism: int = 5,
+        stop: Optional[List[str]] = None,
+        streaming: bool = False,
+        temperature: float = 0.0,
+        top_k: int = 40,
+        top_p: float = 0.95,
+    ) -> VertexAIEmbeddings:
+        return VertexAIEmbeddings(
+            instance=instance,
+            credentials=credentials,
+            location=location,
+            max_output_tokens=max_output_tokens,
+            max_retries=max_retries,
+            model_name=model_name,
+            n=n,
+            project=project,
+            request_parallelism=request_parallelism,
+            stop=stop,
+            streaming=streaming,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+        )
diff --git a/src/backend/langflow/components/llms/Anthropic.py b/src/backend/langflow/components/llms/Anthropic.py
new file mode 100644
index 000000000..c1b776617
--- /dev/null
+++ b/src/backend/langflow/components/llms/Anthropic.py
@@ -0,0 +1,48 @@
+from typing import Optional
+
+from langchain_community.llms.anthropic import Anthropic
+from pydantic.v1 import SecretStr
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel, NestedDict
+
+
+class AnthropicComponent(CustomComponent):
+    display_name = "Anthropic"
+    description = "Anthropic large language models."
+
+    def build_config(self):
+        return {
+            "anthropic_api_key": {
+                "display_name": "Anthropic API Key",
+                "type": str,
+                "password": True,
+            },
+            "anthropic_api_url": {
+                "display_name": "Anthropic API URL",
+                "type": str,
+            },
+            "model_kwargs": {
+                "display_name": "Model Kwargs",
+                "field_type": "NestedDict",
+                "advanced": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+            },
+        }
+
+    def build(
+        self,
+        anthropic_api_key: str,
+        anthropic_api_url: str,
+        model_kwargs: NestedDict = {},
+        temperature: Optional[float] = None,
+    ) -> BaseLanguageModel:
+        return Anthropic(
+            anthropic_api_key=SecretStr(anthropic_api_key),
+            anthropic_api_url=anthropic_api_url,
+            model_kwargs=model_kwargs,
+            temperature=temperature,
+        )
diff --git a/src/backend/langflow/components/llms/AnthropicLLM.py b/src/backend/langflow/components/llms/AnthropicLLM.py
index 2b9c758e5..94c4ed8f5 100644
--- a/src/backend/langflow/components/llms/AnthropicLLM.py
+++ b/src/backend/langflow/components/llms/AnthropicLLM.py
@@ -1,6 +1,6 @@
 from typing import Optional
 
-from langchain.chat_models.anthropic import ChatAnthropic
+from langchain_community.chat_models.anthropic import ChatAnthropic
 from langchain.llms.base import BaseLanguageModel
 from pydantic.v1 import SecretStr
 
diff --git a/src/backend/langflow/components/llms/AzureChatOpenAI.py b/src/backend/langflow/components/llms/AzureChatOpenAI.py
index 1bce563bf..d43abb3dc 100644
--- a/src/backend/langflow/components/llms/AzureChatOpenAI.py
+++ b/src/backend/langflow/components/llms/AzureChatOpenAI.py
@@ -1,7 +1,7 @@
 from typing import Optional
 from langflow import CustomComponent
 from langchain.llms.base import BaseLanguageModel
-from langchain.chat_models.azure_openai import AzureChatOpenAI
+from langchain_community.chat_models.azure_openai import AzureChatOpenAI
 
 
 class AzureChatOpenAIComponent(CustomComponent):
diff --git a/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py
index 8c828dc64..fd7341e15 100644
--- a/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py
+++ b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py
@@ -1,6 +1,6 @@
 from typing import Optional
 
-from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
+from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
 from langchain.llms.base import BaseLLM
 from pydantic.v1 import SecretStr
 
diff --git a/src/backend/langflow/components/llms/CTransformers.py b/src/backend/langflow/components/llms/CTransformers.py
new file mode 100644
index 000000000..a0668814e
--- /dev/null
+++ b/src/backend/langflow/components/llms/CTransformers.py
@@ -0,0 +1,33 @@
+from typing import Dict, Optional
+
+from langchain_community.llms.ctransformers import CTransformers
+
+from langflow import CustomComponent
+
+
+class CTransformersComponent(CustomComponent):
+    display_name = "CTransformers"
+    description = "C Transformers LLM models"
+    documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
+
+    def build_config(self):
+        return {
+            "model": {"display_name": "Model", "required": True},
+            "model_file": {
+                "display_name": "Model File",
+                "required": False,
+                "field_type": "file",
+                "file_types": [".bin"],
+            },
+            "model_type": {"display_name": "Model Type", "required": True},
+            "config": {
+                "display_name": "Config",
+                "advanced": True,
+                "required": False,
+                "field_type": "dict",
+                "value": '{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}',
+            },
+        }
+
+    def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:
+        return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)  # type: ignore
diff --git a/src/backend/langflow/components/llms/ChatAnthropic.py b/src/backend/langflow/components/llms/ChatAnthropic.py
new file mode 100644
index 000000000..0bce33ab8
--- /dev/null
+++ b/src/backend/langflow/components/llms/ChatAnthropic.py
@@ -0,0 +1,47 @@
+from pydantic import SecretStr
+from langflow import CustomComponent
+from typing import Optional, Union, Callable
+from langflow.field_typing import BaseLanguageModel
+from langchain_community.chat_models.anthropic import ChatAnthropic
+
+
+class ChatAnthropicComponent(CustomComponent):
+    display_name = "ChatAnthropic"
+    description = "`Anthropic` chat large language models."
+    documentation = "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
+
+    def build_config(self):
+        return {
+            "anthropic_api_key": {
+                "display_name": "Anthropic API Key",
+                "field_type": "str",
+                "password": True,
+            },
+            "anthropic_api_url": {
+                "display_name": "Anthropic API URL",
+                "field_type": "str",
+            },
+            "model_kwargs": {
+                "display_name": "Model Kwargs",
+                "field_type": "dict",
+                "advanced": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+            },
+        }
+
+    def build(
+        self,
+        anthropic_api_key: str,
+        anthropic_api_url: Optional[str] = None,
+        model_kwargs: dict = {},
+        temperature: Optional[float] = None,
+    ) -> Union[BaseLanguageModel, Callable]:
+        return ChatAnthropic(
+            anthropic_api_key=SecretStr(anthropic_api_key),
+            anthropic_api_url=anthropic_api_url,
+            model_kwargs=model_kwargs,
+            temperature=temperature,
+        )
diff --git a/src/backend/langflow/components/llms/ChatOllamaEndpoint.py b/src/backend/langflow/components/llms/ChatOllamaEndpoint.py
index 3201ff432..34e9e0bd7 100644
--- a/src/backend/langflow/components/llms/ChatOllamaEndpoint.py
+++ b/src/backend/langflow/components/llms/ChatOllamaEndpoint.py
@@ -1,8 +1,8 @@
 from typing import Any, Dict, List, Optional
 
 # from langchain_community.chat_models import ChatOllama
-from langchain.chat_models import ChatOllama
-from langchain.chat_models.base import BaseChatModel
+from langchain_community.chat_models import ChatOllama
+from langchain_core.language_models.chat_models import BaseChatModel
 
 # from langchain.chat_models import ChatOllama
 from langflow import CustomComponent
diff --git a/src/backend/langflow/components/llms/ChatOpenAI.py b/src/backend/langflow/components/llms/ChatOpenAI.py
new file mode 100644
index 000000000..19ca4f41d
--- /dev/null
+++ b/src/backend/langflow/components/llms/ChatOpenAI.py
@@ -0,0 +1,84 @@
+from typing import Optional, Union
+
+from langchain.llms import BaseLLM
+from langchain_community.chat_models.openai import ChatOpenAI
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel, NestedDict
+
+
+class ChatOpenAIComponent(CustomComponent):
+    display_name = "ChatOpenAI"
+    description = "`OpenAI` Chat large language models API."
+
+    def build_config(self):
+        return {
+            "max_tokens": {
+                "display_name": "Max Tokens",
+                "field_type": "int",
+                "advanced": False,
+                "required": False,
+            },
+            "model_kwargs": {
+                "display_name": "Model Kwargs",
+                "field_type": "NestedDict",
+                "advanced": True,
+                "required": False,
+            },
+            "model_name": {
+                "display_name": "Model Name",
+                "field_type": "str",
+                "advanced": False,
+                "required": False,
+                "options": [
+                    "gpt-4-1106-preview",
+                    "gpt-4",
+                    "gpt-4-32k",
+                    "gpt-3.5-turbo",
+                    "gpt-3.5-turbo-16k",
+                ],
+            },
+            "openai_api_base": {
+                "display_name": "OpenAI API Base",
+                "field_type": "str",
+                "advanced": False,
+                "required": False,
+                "info": (
+                    "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\n"
+                    "You can change this to use other APIs like JinaChat, LocalAI and Prem."
+                ),
+            },
+            "openai_api_key": {
+                "display_name": "OpenAI API Key",
+                "field_type": "str",
+                "advanced": False,
+                "required": False,
+                "password": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+                "advanced": False,
+                "required": False,
+                "value": 0.7,
+            },
+        }
+
+    def build(
+        self,
+        max_tokens: Optional[int] = 256,
+        model_kwargs: NestedDict = {},
+        model_name: str = "gpt-4-1106-preview",
+        openai_api_base: Optional[str] = None,
+        openai_api_key: Optional[str] = None,
+        temperature: float = 0.7,
+    ) -> Union[BaseLanguageModel, BaseLLM]:
+        if not openai_api_base:
+            openai_api_base = "https://api.openai.com/v1"
+        return ChatOpenAI(
+            max_tokens=max_tokens,
+            model_kwargs=model_kwargs,
+            model=model_name,
+            base_url=openai_api_base,
+            api_key=openai_api_key,
+            temperature=temperature,
+        )
diff --git a/src/backend/langflow/components/llms/ChatVertexAI.py b/src/backend/langflow/components/llms/ChatVertexAI.py
new file mode 100644
index 000000000..66235a1d8
--- /dev/null
+++ b/src/backend/langflow/components/llms/ChatVertexAI.py
@@ -0,0 +1,87 @@
+from typing import List, Optional, Union
+
+from langchain.llms import BaseLLM
+from langchain_community.chat_models.vertexai import ChatVertexAI
+from langchain_core.messages.base import BaseMessage
+from langflow import CustomComponent
+from langflow.field_typing import BaseLanguageModel
+
+
+class ChatVertexAIComponent(CustomComponent):
+    display_name = "ChatVertexAI"
+    description = "`Vertex AI` Chat large language models API."
+
+    def build_config(self):
+        return {
+            "credentials": {
+                "display_name": "Credentials",
+                "field_type": "file",
+                "file_types": [".json"],
+                "file_path": None,
+            },
+            "examples": {
+                "display_name": "Examples",
+                "multiline": True,
+            },
+            "location": {
+                "display_name": "Location",
+                "value": "us-central1",
+            },
+            "max_output_tokens": {
+                "display_name": "Max Output Tokens",
+                "value": 128,
+                "advanced": True,
+            },
+            "model_name": {
+                "display_name": "Model Name",
+                "value": "chat-bison",
+            },
+            "project": {
+                "display_name": "Project",
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "value": 0.0,
+            },
+            "top_k": {
+                "display_name": "Top K",
+                "value": 40,
+                "advanced": True,
+            },
+            "top_p": {
+                "display_name": "Top P",
+                "value": 0.95,
+                "advanced": True,
+            },
+            "verbose": {
+                "display_name": "Verbose",
+                "value": False,
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        credentials: Optional[str],
+        project: str,
+        examples: Optional[List[BaseMessage]] = [],
+        location: str = "us-central1",
+        max_output_tokens: int = 128,
+        model_name: str = "chat-bison",
+        temperature: float = 0.0,
+        top_k: int = 40,
+        top_p: float = 0.95,
+        verbose: bool = False,
+    ) -> Union[BaseLanguageModel, BaseLLM]:
+        return ChatVertexAI(
+            credentials=credentials,
+            examples=examples,
+            location=location,
+            max_output_tokens=max_output_tokens,
+            model_name=model_name,
+            project=project,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            verbose=verbose,
+        )
diff --git a/src/backend/langflow/components/llms/Cohere.py b/src/backend/langflow/components/llms/Cohere.py
new file mode 100644
index 000000000..3b74fc9b4
--- /dev/null
+++ b/src/backend/langflow/components/llms/Cohere.py
@@ -0,0 +1,24 @@
+from langchain_community.llms.cohere import Cohere
+from langchain_core.language_models.base import BaseLanguageModel
+from langflow import CustomComponent
+
+
+class CohereComponent(CustomComponent):
+    display_name = "Cohere"
+    description = "Cohere large language models."
+    documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
+
+    def build_config(self):
+        return {
+            "cohere_api_key": {"display_name": "Cohere API Key", "type": "password", "password": True},
+            "max_tokens": {"display_name": "Max Tokens", "default": 256, "type": "int", "show": True},
+            "temperature": {"display_name": "Temperature", "default": 0.75, "type": "float", "show": True},
+        }
+
+    def build(
+        self,
+        cohere_api_key: str,
+        max_tokens: int = 256,
+        temperature: float = 0.75,
+    ) -> BaseLanguageModel:
+        return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)  # type: ignore
diff --git a/src/backend/langflow/components/llms/LlamaCpp.py b/src/backend/langflow/components/llms/LlamaCpp.py
new file mode 100644
index 000000000..831665bfa
--- /dev/null
+++ b/src/backend/langflow/components/llms/LlamaCpp.py
@@ -0,0 +1,129 @@
+from typing import Optional, List, Dict, Any
+from langflow import CustomComponent
+from langchain_community.llms.llamacpp import LlamaCpp
+
+
+class LlamaCppComponent(CustomComponent):
+    display_name = "LlamaCpp"
+    description = "llama.cpp model."
+    documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp"
+
+    def build_config(self):
+        return {
+            "grammar": {"display_name": "Grammar", "advanced": True},
+            "cache": {"display_name": "Cache", "advanced": True},
+            "client": {"display_name": "Client", "advanced": True},
+            "echo": {"display_name": "Echo", "advanced": True},
+            "f16_kv": {"display_name": "F16 KV", "advanced": True},
+            "grammar_path": {"display_name": "Grammar Path", "advanced": True},
+            "last_n_tokens_size": {"display_name": "Last N Tokens Size", "advanced": True},
+            "logits_all": {"display_name": "Logits All", "advanced": True},
+            "logprobs": {"display_name": "Logprobs", "advanced": True},
+            "lora_base": {"display_name": "Lora Base", "advanced": True},
+            "lora_path": {"display_name": "Lora Path", "advanced": True},
+            "max_tokens": {"display_name": "Max Tokens", "advanced": True},
+            "metadata": {"display_name": "Metadata", "advanced": True},
+            "model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
+            "model_path": {
+                "display_name": "Model Path",
+                "field_type": "file",
+                "file_types": [".bin"],
+                "required": True,
+            },
+            "n_batch": {"display_name": "N Batch", "advanced": True},
+            "n_ctx": {"display_name": "N Ctx", "advanced": True},
+            "n_gpu_layers": {"display_name": "N GPU Layers", "advanced": True},
+            "n_parts": {"display_name": "N Parts", "advanced": True},
+            "n_threads": {"display_name": "N Threads", "advanced": True},
+            "repeat_penalty": {"display_name": "Repeat Penalty", "advanced": True},
+            "rope_freq_base": {"display_name": "Rope Freq Base", "advanced": True},
+            "rope_freq_scale": {"display_name": "Rope Freq Scale", "advanced": True},
+            "seed": {"display_name": "Seed", "advanced": True},
+            "stop": {"display_name": "Stop", "advanced": True},
+            "streaming": {"display_name": "Streaming", "advanced": True},
+            "suffix": {"display_name": "Suffix", "advanced": True},
+            "tags": {"display_name": "Tags", "advanced": True},
+            "temperature": {"display_name": "Temperature"},
+            "top_k": {"display_name": "Top K", "advanced": True},
+            "top_p": {"display_name": "Top P", "advanced": True},
+            "use_mlock": {"display_name": "Use Mlock", "advanced": True},
+            "use_mmap": {"display_name": "Use Mmap", "advanced": True},
+            "verbose": {"display_name": "Verbose", "advanced": True},
+            "vocab_only": {"display_name": "Vocab Only", "advanced": True},
+        }
+
+    def build(
+        self,
+        model_path: str,
+        grammar: Optional[str] = None,
+        cache: Optional[bool] = None,
+        client: Optional[Any] = None,
+        echo: Optional[bool] = False,
+        f16_kv: bool = True,
+        grammar_path: Optional[str] = None,
+        last_n_tokens_size: Optional[int] = 64,
+        logits_all: bool = False,
+        logprobs: Optional[int] = None,
+        lora_base: Optional[str] = None,
+        lora_path: Optional[str] = None,
+        max_tokens: Optional[int] = 256,
+        metadata: Optional[Dict] = None,
+        model_kwargs: Dict = {},
+        n_batch: Optional[int] = 8,
+        n_ctx: int = 512,
+        n_gpu_layers: Optional[int] = 1,
+        n_parts: int = -1,
+        n_threads: Optional[int] = 1,
+        repeat_penalty: Optional[float] = 1.1,
+        rope_freq_base: float = 10000.0,
+        rope_freq_scale: float = 1.0,
+        seed: int = -1,
+        stop: Optional[List[str]] = [],
+        streaming: bool = True,
+        suffix: Optional[str] = "",
+        tags: Optional[List[str]] = [],
+        temperature: Optional[float] = 0.8,
+        top_k: Optional[int] = 40,
+        top_p: Optional[float] = 0.95,
+        use_mlock: bool = False,
+        use_mmap: Optional[bool] = True,
+        verbose: bool = True,
+        vocab_only: bool = False,
+    ) -> LlamaCpp:
+        return LlamaCpp(
+            model_path=model_path,
+            grammar=grammar,
+            cache=cache,
+            client=client,
+            echo=echo,
+            f16_kv=f16_kv,
+            grammar_path=grammar_path,
+            last_n_tokens_size=last_n_tokens_size,
+            logits_all=logits_all,
+            logprobs=logprobs,
+            lora_base=lora_base,
+            lora_path=lora_path,
+            max_tokens=max_tokens,
+            metadata=metadata,
+            model_kwargs=model_kwargs,
+            n_batch=n_batch,
+            n_ctx=n_ctx,
+            n_gpu_layers=n_gpu_layers,
+            n_parts=n_parts,
+            n_threads=n_threads,
+            repeat_penalty=repeat_penalty,
+            rope_freq_base=rope_freq_base,
+            rope_freq_scale=rope_freq_scale,
+            seed=seed,
+            stop=stop,
+            streaming=streaming,
+            suffix=suffix,
+            tags=tags,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            use_mlock=use_mlock,
+            use_mmap=use_mmap,
+            verbose=verbose,
+            vocab_only=vocab_only,
+        )
diff --git a/src/backend/langflow/components/llms/OllamaLLM.py b/src/backend/langflow/components/llms/OllamaLLM.py
index fae8e1917..eb5c52975 100644
--- a/src/backend/langflow/components/llms/OllamaLLM.py
+++ b/src/backend/langflow/components/llms/OllamaLLM.py
@@ -1,7 +1,7 @@
-from typing import Optional, List
+from typing import List, Optional
 
-from langchain.llms import Ollama
 from langchain.llms.base import BaseLLM
+from langchain_community.llms.ollama import Ollama
 
 from langflow import CustomComponent
 
@@ -133,29 +133,25 @@ class OllamaLLM(CustomComponent):
             mirostat_eta = None
             mirostat_tau = None
 
-        llm_params = {
-            "base_url": base_url,
-            "model": model,
-            "mirostat": mirostat_value,
-            "mirostat_eta": mirostat_eta,
-            "mirostat_tau": mirostat_tau,
-            "num_ctx": num_ctx,
-            "num_gpu": num_gpu,
-            "num_thread": num_thread,
-            "repeat_last_n": repeat_last_n,
-            "repeat_penalty": repeat_penalty,
-            "temperature": temperature,
-            "stop": stop,
-            "tfs_z": tfs_z,
-            "top_k": top_k,
-            "top_p": top_p,
-        }
-
-        # None Value remove
-        llm_params = {k: v for k, v in llm_params.items() if v is not None}
-
         try:
-            llm = Ollama(**llm_params)
+            llm = Ollama(
+                base_url=base_url,
+                model=model,
+                mirostat=mirostat_value,
+                mirostat_eta=mirostat_eta,
+                mirostat_tau=mirostat_tau,
+                num_ctx=num_ctx,
+                num_gpu=num_gpu,
+                num_thread=num_thread,
+                repeat_last_n=repeat_last_n,
+                repeat_penalty=repeat_penalty,
+                temperature=temperature,
+                stop=stop,
+                tfs_z=tfs_z,
+                top_k=top_k,
+                top_p=top_p,
+            )
+
         except Exception as e:
             raise ValueError("Could not connect to Ollama.") from e
 
diff --git a/src/backend/langflow/components/llms/VertexAI.py b/src/backend/langflow/components/llms/VertexAI.py
new file mode 100644
index 000000000..008451bc8
--- /dev/null
+++ b/src/backend/langflow/components/llms/VertexAI.py
@@ -0,0 +1,147 @@
+from langflow import CustomComponent
+from langchain.llms import BaseLLM
+from typing import Optional, Union, Callable, Dict
+from langchain_community.llms.vertexai import VertexAI
+
+
+class VertexAIComponent(CustomComponent):
+    display_name = "VertexAI"
+    description = "Google Vertex AI large language models"
+
+    def build_config(self):
+        return {
+            "credentials": {
+                "display_name": "Credentials",
+                "field_type": "file",
+                "file_types": [".json"],
+                "required": False,
+                "value": None,
+            },
+            "location": {
+                "display_name": "Location",
+                "type": "str",
+                "advanced": True,
+                "value": "us-central1",
+                "required": False,
+            },
+            "max_output_tokens": {
+                "display_name": "Max Output Tokens",
+                "field_type": "int",
+                "value": 128,
+                "required": False,
+                "advanced": True,
+            },
+            "max_retries": {
+                "display_name": "Max Retries",
+                "type": "int",
+                "value": 6,
+                "required": False,
+                "advanced": True,
+            },
+            "metadata": {
+                "display_name": "Metadata",
+                "field_type": "dict",
+                "required": False,
+                "default": {},
+            },
+            "model_name": {
+                "display_name": "Model Name",
+                "type": "str",
+                "value": "text-bison",
+                "required": False,
+            },
+            "n": {
+                "advanced": True,
+                "display_name": "N",
+                "field_type": "int",
+                "value": 1,
+                "required": False,
+            },
+            "project": {
+                "display_name": "Project",
+                "type": "str",
+                "required": False,
+                "default": None,
+            },
+            "request_parallelism": {
+                "display_name": "Request Parallelism",
+                "field_type": "int",
+                "value": 5,
+                "required": False,
+                "advanced": True,
+            },
+            "streaming": {
+                "display_name": "Streaming",
+                "field_type": "bool",
+                "value": False,
+                "required": False,
+                "advanced": True,
+            },
+            "temperature": {
+                "display_name": "Temperature",
+                "field_type": "float",
+                "value": 0.0,
+                "required": False,
+                "advanced": True,
+            },
+            "top_k": {"display_name": "Top K", "type": "int", "default": 40, "required": False, "advanced": True},
+            "top_p": {
+                "display_name": "Top P",
+                "field_type": "float",
+                "value": 0.95,
+                "required": False,
+                "advanced": True,
+            },
+            "tuned_model_name": {
+                "display_name": "Tuned Model Name",
+                "type": "str",
+                "required": False,
+                "value": None,
+                "advanced": True,
+            },
+            "verbose": {
+                "display_name": "Verbose",
+                "field_type": "bool",
+                "value": False,
+                "required": False,
+            },
+            "name": {"display_name": "Name", "field_type": "str"},
+        }
+
+    def build(
+        self,
+        credentials: Optional[str] = None,
+        location: str = "us-central1",
+        max_output_tokens: int = 128,
+        max_retries: int = 6,
+        metadata: Dict = {},
+        model_name: str = "text-bison",
+        n: int = 1,
+        name: Optional[str] = None,
+        project: Optional[str] = None,
+        request_parallelism: int = 5,
+        streaming: bool = False,
+        temperature: float = 0.0,
+        top_k: int = 40,
+        top_p: float = 0.95,
+        tuned_model_name: Optional[str] = None,
+        verbose: bool = False,
+    ) -> Union[BaseLLM, Callable]:
+        return VertexAI(
+            credentials=credentials,
+            location=location,
+            max_output_tokens=max_output_tokens,
+            max_retries=max_retries,
+            metadata=metadata,
+            model_name=model_name,
+            n=n,
+            name=name,
+            project=project,
+            request_parallelism=request_parallelism,
+            streaming=streaming,
+            temperature=temperature,
+            top_k=top_k,
+            top_p=top_p,
+            tuned_model_name=tuned_model_name,
+            verbose=verbose,
+        )
diff --git a/src/backend/langflow/components/retrievers/MultiQueryRetriever.py b/src/backend/langflow/components/retrievers/MultiQueryRetriever.py
new file mode 100644
index 000000000..d2c12d7a5
--- /dev/null
+++ b/src/backend/langflow/components/retrievers/MultiQueryRetriever.py
@@ -0,0 +1,49 @@
+from typing import Callable, Optional, Union
+
+from langchain.retrievers import MultiQueryRetriever
+from langflow import CustomComponent
+from langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate
+
+
+class MultiQueryRetrieverComponent(CustomComponent):
+    display_name = "MultiQueryRetriever"
+    description = "Initialize from llm using default template."
+    documentation = "https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever"
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "prompt": {
+                "display_name": "Prompt",
+                "default": {
+                    "input_variables": ["question"],
+                    "input_types": {},
+                    "output_parser": None,
+                    "partial_variables": {},
+                    "template": "You are an AI language model assistant. Your task is \n"
+                    "to generate 3 different versions of the given user \n"
+                    "question to retrieve relevant documents from a vector database. \n"
+                    "By generating multiple perspectives on the user question, \n"
+                    "your goal is to help the user overcome some of the limitations \n"
+                    "of distance-based similarity search. Provide these alternative \n"
+                    "questions separated by newlines. Original question: {question}",
+                    "template_format": "f-string",
+                    "validate_template": False,
+                    "_type": "prompt",
+                },
+            },
+            "retriever": {"display_name": "Retriever"},
+            "parser_key": {"display_name": "Parser Key", "default": "lines"},
+        }
+
+    def build(
+        self,
+        llm: BaseLLM,
+        retriever: BaseRetriever,
+        prompt: Optional[PromptTemplate] = None,
+        parser_key: str = "lines",
+    ) -> Union[Callable, MultiQueryRetriever]:
+        if not prompt:
+            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)
+        else:
+            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)
diff --git a/src/backend/langflow/components/textsplitters/CharacterTextSplitter.py b/src/backend/langflow/components/textsplitters/CharacterTextSplitter.py
new file mode 100644
index 000000000..d65c28dab
--- /dev/null
+++ b/src/backend/langflow/components/textsplitters/CharacterTextSplitter.py
@@ -0,0 +1,30 @@
+from langflow import CustomComponent
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_core.documents.base import Document
+from typing import List
+
+
+class CharacterTextSplitterComponent(CustomComponent):
+    display_name = "CharacterTextSplitter"
+    description = "Splitting text that looks at characters."
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "chunk_overlap": {"display_name": "Chunk Overlap", "default": 200},
+            "chunk_size": {"display_name": "Chunk Size", "default": 1000},
+            "separator": {"display_name": "Separator", "default": "\n"},
+        }
+
+    def build(
+        self,
+        documents: List[Document],
+        chunk_overlap: int = 200,
+        chunk_size: int = 1000,
+        separator: str = "\n",
+    ) -> List[Document]:
+        return CharacterTextSplitter(
+            chunk_overlap=chunk_overlap,
+            chunk_size=chunk_size,
+            separator=separator,
+        ).split_documents(documents)
diff --git a/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py b/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
index 636eb427e..1cea7a29b 100644
--- a/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
+++ b/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
@@ -1,7 +1,9 @@
 from typing import Optional
-from langflow import CustomComponent
-from langchain.text_splitter import Language
+
 from langchain.schema import Document
+from langchain.text_splitter import Language
+
+from langflow import CustomComponent
 
 
 class LanguageRecursiveTextSplitterComponent(CustomComponent):
@@ -48,7 +50,7 @@ class LanguageRecursiveTextSplitterComponent(CustomComponent):
         documents: list[Document],
         chunk_size: Optional[int] = 1000,
         chunk_overlap: Optional[int] = 200,
-        separator_type: Optional[str] = "Python",
+        separator_type: str = "Python",
     ) -> list[Document]:
         """
         Split text into chunks of a specified length.
diff --git a/src/backend/langflow/components/toolkits/JsonToolkit.py b/src/backend/langflow/components/toolkits/JsonToolkit.py
new file mode 100644
index 000000000..ec9e49621
--- /dev/null
+++ b/src/backend/langflow/components/toolkits/JsonToolkit.py
@@ -0,0 +1,16 @@
+from langflow import CustomComponent
+from langchain_community.tools.json.tool import JsonSpec
+from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
+
+
+class JsonToolkitComponent(CustomComponent):
+    display_name = "JsonToolkit"
+    description = "Toolkit for interacting with a JSON spec."
+
+    def build_config(self):
+        return {
+            "spec": {"display_name": "Spec", "type": JsonSpec},
+        }
+
+    def build(self, spec: JsonSpec) -> JsonToolkit:
+        return JsonToolkit(spec=spec)
diff --git a/src/backend/langflow/components/toolkits/OpenAPIToolkit.py b/src/backend/langflow/components/toolkits/OpenAPIToolkit.py
new file mode 100644
index 000000000..2d9a860a0
--- /dev/null
+++ b/src/backend/langflow/components/toolkits/OpenAPIToolkit.py
@@ -0,0 +1,23 @@
+from langflow import CustomComponent
+from langflow.field_typing import AgentExecutor
+from typing import Callable
+from langchain_community.utilities.requests import TextRequestsWrapper
+from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
+
+
+class OpenAPIToolkitComponent(CustomComponent):
+    display_name = "OpenAPIToolkit"
+    description = "Toolkit for interacting with an OpenAPI API."
+
+    def build_config(self):
+        return {
+            "json_agent": {"display_name": "JSON Agent"},
+            "requests_wrapper": {"display_name": "Text Requests Wrapper"},
+        }
+
+    def build(
+        self,
+        json_agent: AgentExecutor,
+        requests_wrapper: TextRequestsWrapper,
+    ) -> Callable:
+        return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)
diff --git a/src/backend/langflow/components/toolkits/VectorStoreInfo.py b/src/backend/langflow/components/toolkits/VectorStoreInfo.py
new file mode 100644
index 000000000..ec2323bfd
--- /dev/null
+++ b/src/backend/langflow/components/toolkits/VectorStoreInfo.py
@@ -0,0 +1,26 @@
+from typing import Callable, Union
+
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
+from langchain_community.vectorstores import VectorStore
+
+from langflow import CustomComponent
+
+
+class VectorStoreInfoComponent(CustomComponent):
+    display_name = "VectorStoreInfo"
+    description = "Information about a VectorStore"
+
+    def build_config(self):
+        return {
+            "vectorstore": {"display_name": "VectorStore"},
+            "description": {"display_name": "Description", "multiline": True},
+            "name": {"display_name": "Name"},
+        }
+
+    def build(
+        self,
+        vectorstore: VectorStore,
+        description: str,
+        name: str,
+    ) -> Union[VectorStoreInfo, Callable]:
+        return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)
diff --git a/src/backend/langflow/components/toolkits/VectorStoreRouterToolkit.py b/src/backend/langflow/components/toolkits/VectorStoreRouterToolkit.py
new file mode 100644
index 000000000..ed8797044
--- /dev/null
+++ b/src/backend/langflow/components/toolkits/VectorStoreRouterToolkit.py
@@ -0,0 +1,23 @@
+from langflow import CustomComponent
+from typing import List, Union
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
+from langflow.field_typing import BaseLanguageModel, Tool
+
+
+class VectorStoreRouterToolkitComponent(CustomComponent):
+    display_name = "VectorStoreRouterToolkit"
+    description = "Toolkit for routing between Vector Stores."
+
+    def build_config(self):
+        return {
+            "vectorstores": {"display_name": "Vector Stores"},
+            "llm": {"display_name": "LLM"},
+        }
+
+    def build(
+        self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel
+    ) -> Union[Tool, VectorStoreRouterToolkit]:
+        print("vectorstores", vectorstores)
+        print("llm", llm)
+        return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)
diff --git a/src/backend/langflow/components/toolkits/VectorStoreToolkit.py b/src/backend/langflow/components/toolkits/VectorStoreToolkit.py
new file mode 100644
index 000000000..38b9c9171
--- /dev/null
+++ b/src/backend/langflow/components/toolkits/VectorStoreToolkit.py
@@ -0,0 +1,28 @@
+from langflow import CustomComponent
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
+from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
+from langflow.field_typing import (
+    BaseLanguageModel,
+)
+from langflow.field_typing import (
+    Tool,
+)
+from typing import Union
+
+
+class VectorStoreToolkitComponent(CustomComponent):
+    display_name = "VectorStoreToolkit"
+    description = "Toolkit for interacting with a Vector Store."
+
+    def build_config(self):
+        return {
+            "vectorstore_info": {"display_name": "Vector Store Info"},
+            "llm": {"display_name": "LLM"},
+        }
+
+    def build(
+        self,
+        vectorstore_info: VectorStoreInfo,
+        llm: BaseLanguageModel,
+    ) -> Union[Tool, VectorStoreToolkit]:
+        return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)
diff --git a/src/backend/langflow/components/utilities/BingSearchAPIWrapper.py b/src/backend/langflow/components/utilities/BingSearchAPIWrapper.py
new file mode 100644
index 000000000..b9dc4a2ef
--- /dev/null
+++ b/src/backend/langflow/components/utilities/BingSearchAPIWrapper.py
@@ -0,0 +1,31 @@
+from langflow import CustomComponent
+
+# Assuming `BingSearchAPIWrapper` is a class that exists in the context
+# and has the appropriate methods and attributes.
+# We need to make sure this class is importable from the context where this code will be running.
+from langchain_community.utilities.bing_search import BingSearchAPIWrapper
+
+
+class BingSearchAPIWrapperComponent(CustomComponent):
+    display_name = "BingSearchAPIWrapper"
+    description = "Wrapper for Bing Search API."
+
+    def build_config(self):
+        return {
+            "bing_search_url": {"display_name": "Bing Search URL"},
+            "bing_subscription_key": {
+                "display_name": "Bing Subscription Key",
+                "password": True,
+            },
+            "k": {"display_name": "Number of results", "advanced": True},
+            # 'k' is not included as it is not shown (show=False)
+        }
+
+    def build(
+        self,
+        bing_search_url: str,
+        bing_subscription_key: str,
+        k: int = 10,
+    ) -> BingSearchAPIWrapper:
+        # 'k' has a default value and is not shown (show=False), so it is hardcoded here
+        return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)
diff --git a/src/backend/langflow/components/utilities/GoogleSearchAPIWrapper.py b/src/backend/langflow/components/utilities/GoogleSearchAPIWrapper.py
new file mode 100644
index 000000000..37e9afff6
--- /dev/null
+++ b/src/backend/langflow/components/utilities/GoogleSearchAPIWrapper.py
@@ -0,0 +1,21 @@
+from langflow import CustomComponent
+from typing import Union, Callable
+from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
+
+
+class GoogleSearchAPIWrapperComponent(CustomComponent):
+    display_name = "GoogleSearchAPIWrapper"
+    description = "Wrapper for Google Search API."
+
+    def build_config(self):
+        return {
+            "google_api_key": {"display_name": "Google API Key", "password": True},
+            "google_cse_id": {"display_name": "Google CSE ID", "password": True},
+        }
+
+    def build(
+        self,
+        google_api_key: str,
+        google_cse_id: str,
+    ) -> Union[GoogleSearchAPIWrapper, Callable]:
+        return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)
diff --git a/src/backend/langflow/components/utilities/GoogleSerperAPIWrapper.py b/src/backend/langflow/components/utilities/GoogleSerperAPIWrapper.py
new file mode 100644
index 000000000..84fefedad
--- /dev/null
+++ b/src/backend/langflow/components/utilities/GoogleSerperAPIWrapper.py
@@ -0,0 +1,47 @@
+from langflow import CustomComponent
+from typing import Dict, Optional
+
+# Assuming the existence of GoogleSerperAPIWrapper class in the serper module
+# If this class does not exist, you would need to create it or import the appropriate class from another module
+from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
+
+
+class GoogleSerperAPIWrapperComponent(CustomComponent):
+    display_name = "GoogleSerperAPIWrapper"
+    description = "Wrapper around the Serper.dev Google Search API."
+
+    def build_config(self) -> Dict[str, Dict]:
+        return {
+            "result_key_for_type": {
+                "display_name": "Result Key for Type",
+                "show": True,
+                "multiline": False,
+                "password": False,
+                "name": "result_key_for_type",
+                "advanced": False,
+                "dynamic": False,
+                "info": "",
+                "field_type": "dict",
+                "list": False,
+                "value": {"news": "news", "places": "places", "images": "images", "search": "organic"},
+            },
+            "serper_api_key": {
+                "display_name": "Serper API Key",
+                "show": True,
+                "multiline": False,
+                "password": True,
+                "name": "serper_api_key",
+                "advanced": False,
+                "dynamic": False,
+                "info": "",
+                "type": "str",
+                "list": False,
+            },
+        }
+
+    def build(
+        self,
+        serper_api_key: str,
+        result_key_for_type: Optional[Dict[str, str]] = None,
+    ) -> GoogleSerperAPIWrapper:
+        return GoogleSerperAPIWrapper(result_key_for_type=result_key_for_type, serper_api_key=serper_api_key)
diff --git a/src/backend/langflow/components/utilities/SearxSearchWrapper.py b/src/backend/langflow/components/utilities/SearxSearchWrapper.py
new file mode 100644
index 000000000..b406f3882
--- /dev/null
+++ b/src/backend/langflow/components/utilities/SearxSearchWrapper.py
@@ -0,0 +1,33 @@
+from langflow import CustomComponent
+from typing import Optional, Dict
+from langchain_community.utilities.searx_search import SearxSearchWrapper
+
+
+class SearxSearchWrapperComponent(CustomComponent):
+    display_name = "SearxSearchWrapper"
+    description = "Wrapper for Searx API."
+
+    def build_config(self):
+        return {
+            "headers": {
+                "field_type": "dict",
+                "display_name": "Headers",
+                "multiline": True,
+                "value": '{"Authorization": "Bearer <token>"}',
+            },
+            "k": {"display_name": "k", "advanced": True, "field_type": "int", "value": 10},
+            "searx_host": {
+                "display_name": "Searx Host",
+                "field_type": "str",
+                "value": "https://searx.example.com",
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        k: int = 10,
+        headers: Optional[Dict[str, str]] = None,
+        searx_host: str = "https://searx.example.com",
+    ) -> SearxSearchWrapper:
+        return SearxSearchWrapper(headers=headers, k=k, searx_host=searx_host)
diff --git a/src/backend/langflow/components/utilities/SerpAPIWrapper.py b/src/backend/langflow/components/utilities/SerpAPIWrapper.py
new file mode 100644
index 000000000..924f5628d
--- /dev/null
+++ b/src/backend/langflow/components/utilities/SerpAPIWrapper.py
@@ -0,0 +1,31 @@
+from typing import Callable, Union
+
+from langchain_community.utilities.serpapi import SerpAPIWrapper
+from langflow import CustomComponent
+
+
+class SerpAPIWrapperComponent(CustomComponent):
+    display_name = "SerpAPIWrapper"
+    description = "Wrapper around SerpAPI"
+
+    def build_config(self):
+        return {
+            "serpapi_api_key": {"display_name": "SerpAPI API Key", "type": "str", "password": True},
+            "params": {
+                "display_name": "Parameters",
+                "type": "dict",
+                "advanced": True,
+                "multiline": True,
+                "value": '{"engine": "google","google_domain": "google.com","gl": "us","hl": "en"}',
+            },
+        }
+
+    def build(
+        self,
+        serpapi_api_key: str,
+        params: dict,
+    ) -> Union[SerpAPIWrapper, Callable]:  # Removed quotes around SerpAPIWrapper
+        return SerpAPIWrapper(  # type: ignore
+            serpapi_api_key=serpapi_api_key,
+            params=params,
+        )
diff --git a/src/backend/langflow/components/utilities/WikipediaAPIWrapper.py b/src/backend/langflow/components/utilities/WikipediaAPIWrapper.py
new file mode 100644
index 000000000..00820881b
--- /dev/null
+++ b/src/backend/langflow/components/utilities/WikipediaAPIWrapper.py
@@ -0,0 +1,30 @@
+from typing import Callable, Union
+
+from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
+from langflow import CustomComponent
+
+# Assuming WikipediaAPIWrapper is a class that needs to be imported.
+# The import statement is not included as it is not provided in the JSON
+# and the actual implementation details are unknown.
+
+
+class WikipediaAPIWrapperComponent(CustomComponent):
+    display_name = "WikipediaAPIWrapper"
+    description = "Wrapper around WikipediaAPI."
+
+    def build_config(self):
+        return {}
+
+    def build(
+        self,
+        top_k_results: int = 3,
+        lang: str = "en",
+        load_all_available_meta: bool = False,
+        doc_content_chars_max: int = 4000,
+    ) -> Union[WikipediaAPIWrapper, Callable]:
+        return WikipediaAPIWrapper(  # type: ignore
+            top_k_results=top_k_results,
+            lang=lang,
+            load_all_available_meta=load_all_available_meta,
+            doc_content_chars_max=doc_content_chars_max,
+        )
diff --git a/src/backend/langflow/components/utilities/WolframAlphaAPIWrapper.py b/src/backend/langflow/components/utilities/WolframAlphaAPIWrapper.py
new file mode 100644
index 000000000..2e71a161c
--- /dev/null
+++ b/src/backend/langflow/components/utilities/WolframAlphaAPIWrapper.py
@@ -0,0 +1,18 @@
+from typing import Callable, Union
+
+from langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper
+from langflow import CustomComponent
+
+# Since all the fields in the JSON have show=False, we will only create a basic component
+# without any configurable fields.
+
+
+class WolframAlphaAPIWrapperComponent(CustomComponent):
+    display_name = "WolframAlphaAPIWrapper"
+    description = "Wrapper for Wolfram Alpha."
+
+    def build_config(self):
+        return {"appid": {"display_name": "App ID", "type": "str", "password": True}}
+
+    def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:
+        return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid)  # type: ignore
diff --git a/src/backend/langflow/components/vectorstores/Chroma.py b/src/backend/langflow/components/vectorstores/Chroma.py
index f546eb361..8f25548a8 100644
--- a/src/backend/langflow/components/vectorstores/Chroma.py
+++ b/src/backend/langflow/components/vectorstores/Chroma.py
@@ -3,9 +3,8 @@ from typing import List, Optional, Union
 import chromadb  # type: ignore
 from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever, Document
-from langchain.vectorstores import Chroma
-from langchain.vectorstores.base import VectorStore
-
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.chroma import Chroma
 from langflow import CustomComponent
 
 
diff --git a/src/backend/langflow/components/vectorstores/FAISS.py b/src/backend/langflow/components/vectorstores/FAISS.py
new file mode 100644
index 000000000..dec14f6db
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/FAISS.py
@@ -0,0 +1,26 @@
+from typing import List, Union
+
+from langchain.schema import BaseRetriever
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.faiss import FAISS
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings
+
+
+class FAISSComponent(CustomComponent):
+    display_name = "FAISS"
+    description = "Construct FAISS wrapper from raw documents."
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        documents: List[Document],
+    ) -> Union[VectorStore, FAISS, BaseRetriever]:
+        return FAISS.from_documents(documents=documents, embedding=embedding)
diff --git a/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py b/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
new file mode 100644
index 000000000..d2d215f2b
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
@@ -0,0 +1,47 @@
+from typing import List, Optional
+
+from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+
+from langflow import CustomComponent
+from langflow.field_typing import (
+    Document,
+    Embeddings,
+    NestedDict,
+)
+
+
+class MongoDBAtlasComponent(CustomComponent):
+    display_name = "MongoDB Atlas"
+    description = "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "collection_name": {"display_name": "Collection Name"},
+            "db_name": {"display_name": "Database Name"},
+            "index_name": {"display_name": "Index Name"},
+            "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+        }
+
+    def build(
+        self,
+        documents: List[Document],
+        embedding: Embeddings,
+        collection_name: str = "",
+        db_name: str = "",
+        index_name: str = "",
+        mongodb_atlas_cluster_uri: str = "",
+        search_kwargs: Optional[NestedDict] = None,
+    ) -> MongoDBAtlasVectorSearch:
+        search_kwargs = search_kwargs or {}
+        return MongoDBAtlasVectorSearch(
+            documents=documents,
+            embedding=embedding,
+            collection_name=collection_name,
+            db_name=db_name,
+            index_name=index_name,
+            mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
+            search_kwargs=search_kwargs,
+        )
diff --git a/src/backend/langflow/components/vectorstores/Pinecone.py b/src/backend/langflow/components/vectorstores/Pinecone.py
new file mode 100644
index 000000000..c4613948a
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/Pinecone.py
@@ -0,0 +1,62 @@
+import os
+from typing import List, Optional, Union
+
+import pinecone  # type: ignore
+from langchain.schema import BaseRetriever
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.pinecone import Pinecone
+
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings
+
+
+class PineconeComponent(CustomComponent):
+    display_name = "Pinecone"
+    description = "Construct Pinecone wrapper from raw documents."
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "index_name": {"display_name": "Index Name"},
+            "namespace": {"display_name": "Namespace"},
+            "pinecone_api_key": {"display_name": "Pinecone API Key", "default": "", "password": True, "required": True},
+            "pinecone_env": {"display_name": "Pinecone Environment", "default": "", "required": True},
+            "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"},
+            "pool_threads": {"display_name": "Pool Threads", "default": 1, "advanced": True},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        pinecone_env: str,
+        documents: List[Document],
+        index_name: Optional[str] = None,
+        pinecone_api_key: Optional[str] = None,
+        text_key: Optional[str] = "text",
+        namespace: Optional[str] = "default",
+        pool_threads: Optional[int] = None,
+    ) -> Union[VectorStore, Pinecone, BaseRetriever]:
+        if pinecone_api_key is None or pinecone_env is None:
+            raise ValueError("Pinecone API Key and Environment are required.")
+        if os.getenv("PINECONE_API_KEY") is None and pinecone_api_key is None:
+            raise ValueError("Pinecone API Key is required.")
+
+        pinecone.init(api_key=pinecone_api_key, environment=pinecone_env)  # type: ignore
+        if documents:
+            return Pinecone.from_documents(
+                documents=documents,
+                embedding=embedding,
+                index_name=index_name,
+                pool_threads=pool_threads,
+                namespace=namespace,
+                text_key=text_key,
+            )
+
+        return Pinecone.from_existing_index(
+            index_name=index_name,
+            embedding=embedding,
+            text_key=text_key,
+            namespace=namespace,
+            pool_threads=pool_threads,
+        )
diff --git a/src/backend/langflow/components/vectorstores/Qdrant.py b/src/backend/langflow/components/vectorstores/Qdrant.py
new file mode 100644
index 000000000..ee38406df
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/Qdrant.py
@@ -0,0 +1,76 @@
+from typing import List, Optional, Union
+
+from langchain.schema import BaseRetriever
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.qdrant import Qdrant
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings, NestedDict
+
+
+class QdrantComponent(CustomComponent):
+    display_name = "Qdrant"
+    description = "Construct Qdrant wrapper from a list of texts."
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "api_key": {"display_name": "API Key", "password": True},
+            "collection_name": {"display_name": "Collection Name"},
+            "content_payload_key": {"display_name": "Content Payload Key", "advanced": True},
+            "distance_func": {"display_name": "Distance Function", "advanced": True},
+            "grpc_port": {"display_name": "gRPC Port", "advanced": True},
+            "host": {"display_name": "Host", "advanced": True},
+            "https": {"display_name": "HTTPS", "advanced": True},
+            "location": {"display_name": "Location", "advanced": True},
+            "metadata_payload_key": {"display_name": "Metadata Payload Key", "advanced": True},
+            "path": {"display_name": "Path", "advanced": True},
+            "port": {"display_name": "Port", "advanced": True},
+            "prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True},
+            "prefix": {"display_name": "Prefix", "advanced": True},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+            "timeout": {"display_name": "Timeout", "advanced": True},
+            "url": {"display_name": "URL", "advanced": True},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        documents: List[Document],
+        api_key: Optional[str] = None,
+        collection_name: Optional[str] = None,
+        content_payload_key: str = "page_content",
+        distance_func: str = "Cosine",
+        grpc_port: Optional[int] = 6334,
+        host: Optional[str] = None,
+        https: bool = False,
+        location: str = ":memory:",
+        metadata_payload_key: str = "metadata",
+        path: Optional[str] = None,
+        port: Optional[int] = 6333,
+        prefer_grpc: bool = False,
+        prefix: Optional[str] = None,
+        search_kwargs: Optional[NestedDict] = None,
+        timeout: Optional[float] = None,
+        url: Optional[str] = None,
+    ) -> Union[VectorStore, Qdrant, BaseRetriever]:
+        return Qdrant.from_documents(
+            documents=documents,
+            embedding=embedding,
+            api_key=api_key,
+            collection_name=collection_name,
+            content_payload_key=content_payload_key,
+            distance_func=distance_func,
+            grpc_port=grpc_port,
+            host=host,
+            https=https,
+            location=location,
+            metadata_payload_key=metadata_payload_key,
+            path=path,
+            port=port,
+            prefer_grpc=prefer_grpc,
+            prefix=prefix,
+            search_kwargs=search_kwargs,
+            timeout=timeout,
+            url=url,
+        )
diff --git a/src/backend/langflow/components/vectorstores/Redis.py b/src/backend/langflow/components/vectorstores/Redis.py
index f13428829..6a4e9db2a 100644
--- a/src/backend/langflow/components/vectorstores/Redis.py
+++ b/src/backend/langflow/components/vectorstores/Redis.py
@@ -1,10 +1,10 @@
 from typing import Optional
-from langflow import CustomComponent
 
-from langchain.vectorstores.redis import Redis
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
 from langchain.embeddings.base import Embeddings
+from langchain.schema import Document
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.redis import Redis
+from langflow import CustomComponent
 
 
 class RedisComponent(CustomComponent):
diff --git a/src/backend/langflow/components/vectorstores/SupabaseVectorStore.py b/src/backend/langflow/components/vectorstores/SupabaseVectorStore.py
new file mode 100644
index 000000000..2ec6dfabc
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/SupabaseVectorStore.py
@@ -0,0 +1,44 @@
+from typing import List, Union
+
+from langchain.schema import BaseRetriever
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.supabase import SupabaseVectorStore
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings, NestedDict
+from supabase.client import Client, create_client
+
+
+class SupabaseComponent(CustomComponent):
+    display_name = "Supabase"
+    description = "Return VectorStore initialized from texts and embeddings."
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "query_name": {"display_name": "Query Name"},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+            "supabase_service_key": {"display_name": "Supabase Service Key"},
+            "supabase_url": {"display_name": "Supabase URL"},
+            "table_name": {"display_name": "Table Name", "advanced": True},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        documents: List[Document],
+        query_name: str = "",
+        search_kwargs: NestedDict = {},
+        supabase_service_key: str = "",
+        supabase_url: str = "",
+        table_name: str = "",
+    ) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:
+        supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)
+        return SupabaseVectorStore.from_documents(
+            documents=documents,
+            embedding=embedding,
+            query_name=query_name,
+            search_kwargs=search_kwargs,
+            client=supabase,
+            table_name=table_name,
+        )
diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py
index e3555e6f1..af828c49e 100644
--- a/src/backend/langflow/components/vectorstores/Vectara.py
+++ b/src/backend/langflow/components/vectorstores/Vectara.py
@@ -1,14 +1,13 @@
-from typing import Optional, Union, List
-from langflow import CustomComponent
 import tempfile
-import urllib.request
 import urllib
+import urllib.request
+from typing import List, Optional, Union
 
-from langchain.vectorstores import Vectara
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseRetriever
 from langchain.embeddings import FakeEmbeddings
+from langchain.schema import BaseRetriever, Document
+from langchain_community.vectorstores import Vectara, VectorStore
+
+from langflow import CustomComponent
 
 
 class VectaraComponent(CustomComponent):
diff --git a/src/backend/langflow/components/vectorstores/Weaviate.py b/src/backend/langflow/components/vectorstores/Weaviate.py
index bab1322f7..9b4967c36 100644
--- a/src/backend/langflow/components/vectorstores/Weaviate.py
+++ b/src/backend/langflow/components/vectorstores/Weaviate.py
@@ -1,12 +1,11 @@
-import weaviate  # type: ignore
 from typing import Optional, Union
-from langflow import CustomComponent
 
-from langchain.vectorstores import Weaviate
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseRetriever
+import weaviate  # type: ignore
 from langchain.embeddings.base import Embeddings
+from langchain.schema import BaseRetriever, Document
+from langchain_community.vectorstores import VectorStore, Weaviate
+
+from langflow import CustomComponent
 
 
 class WeaviateVectorStore(CustomComponent):
@@ -45,7 +44,7 @@ class WeaviateVectorStore(CustomComponent):
         search_by_text: bool = False,
         api_key: Optional[str] = None,
         index_name: Optional[str] = None,
-        text_key: Optional[str] = "text",
+        text_key: str = "text",
         embedding: Optional[Embeddings] = None,
         documents: Optional[Document] = None,
         attributes: Optional[list] = None,
diff --git a/src/backend/langflow/components/vectorstores/pgvector.py b/src/backend/langflow/components/vectorstores/pgvector.py
index 4e0c2eb4d..786629dd0 100644
--- a/src/backend/langflow/components/vectorstores/pgvector.py
+++ b/src/backend/langflow/components/vectorstores/pgvector.py
@@ -1,10 +1,10 @@
-from typing import Optional, List
-from langflow import CustomComponent
+from typing import List, Optional
 
-from langchain.vectorstores.pgvector import PGVector
-from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
 from langchain.embeddings.base import Embeddings
+from langchain.schema import Document
+from langchain_community.vectorstores import VectorStore
+from langchain_community.vectorstores.pgvector import PGVector
+from langflow import CustomComponent
 
 
 class PostgresqlVectorComponent(CustomComponent):
diff --git a/src/backend/langflow/field_typing/constants.py b/src/backend/langflow/field_typing/constants.py
index 4a478bfec..ed0c0e442 100644
--- a/src/backend/langflow/field_typing/constants.py
+++ b/src/backend/langflow/field_typing/constants.py
@@ -12,7 +12,7 @@ from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.memory import BaseMemory
 from langchain.text_splitter import TextSplitter
 from langchain.tools import Tool
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
 
 # Type alias for more complex dicts
 NestedDict = Dict[str, Union[str, Dict]]
diff --git a/src/backend/langflow/graph/graph/constants.py b/src/backend/langflow/graph/graph/constants.py
index b18761de8..f85761ed4 100644
--- a/src/backend/langflow/graph/graph/constants.py
+++ b/src/backend/langflow/graph/graph/constants.py
@@ -12,7 +12,6 @@ from langflow.interface.retrievers.base import retriever_creator
 from langflow.interface.text_splitters.base import textsplitter_creator
 from langflow.interface.toolkits.base import toolkits_creator
 from langflow.interface.tools.base import tool_creator
-from langflow.interface.vector_store.base import vectorstore_creator
 from langflow.interface.wrappers.base import wrapper_creator
 from langflow.utils.lazy_load import LazyLoadDictBase
 
@@ -45,7 +44,7 @@ class VertexTypesDict(LazyLoadDictBase):
             **{t: types.LLMVertex for t in llm_creator.to_list()},
             **{t: types.MemoryVertex for t in memory_creator.to_list()},
             **{t: types.EmbeddingVertex for t in embedding_creator.to_list()},
-            **{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
+            # **{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
             **{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()},
             **{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()},
             **{t: types.OutputParserVertex for t in output_parser_creator.to_list()},
diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py
index 5df96e584..608a98a9b 100644
--- a/src/backend/langflow/interface/agents/custom.py
+++ b/src/backend/langflow/interface/agents/custom.py
@@ -2,14 +2,10 @@ from typing import Any, Optional
 
 from langchain.agents import AgentExecutor, ZeroShotAgent
 from langchain.agents.agent_toolkits import (
-    SQLDatabaseToolkit,
     VectorStoreInfo,
     VectorStoreRouterToolkit,
     VectorStoreToolkit,
 )
-from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
-from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
-from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
 from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX as VECTORSTORE_PREFIX
 from langchain.agents.agent_toolkits.vectorstore.prompt import ROUTER_PREFIX as VECTORSTORE_ROUTER_PREFIX
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
@@ -17,9 +13,14 @@ from langchain.base_language import BaseLanguageModel
 from langchain.chains.llm import LLMChain
 from langchain.sql_database import SQLDatabase
 from langchain.tools.sql_database.prompt import QUERY_CHECKER
+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+from langchain_community.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
+from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
+from langchain_community.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
 from langchain_experimental.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFIX
 from langchain_experimental.agents.agent_toolkits.pandas.prompt import SUFFIX_WITH_DF as PANDAS_SUFFIX
 from langchain_experimental.tools.python.tool import PythonAstREPLTool
+
 from langflow.interface.base import CustomAgentExecutor
 
 
diff --git a/src/backend/langflow/interface/custom/directory_reader/directory_reader.py b/src/backend/langflow/interface/custom/directory_reader/directory_reader.py
index 9c50e0181..d6cca5c83 100644
--- a/src/backend/langflow/interface/custom/directory_reader/directory_reader.py
+++ b/src/backend/langflow/interface/custom/directory_reader/directory_reader.py
@@ -66,18 +66,18 @@ class DirectoryReader:
     def filter_loaded_components(self, data: dict, with_errors: bool) -> dict:
         from langflow.interface.custom.utils import build_component
 
-        items = [
-            {
-                "name": menu["name"],
-                "path": menu["path"],
-                "components": [
-                    (*build_component(component), component)
-                    for component in menu["components"]
-                    if (component["error"] if with_errors else not component["error"])
-                ],
-            }
-            for menu in data["menu"]
-        ]
+        items = []
+        for menu in data["menu"]:
+            components = []
+            for component in menu["components"]:
+                try:
+                    if component["error"] if with_errors else not component["error"]:
+                        component_tuple = (*build_component(component), component)
+                        components.append(component_tuple)
+                except Exception as e:
+                    logger.error(f"Error while loading component: {e}")
+                    continue
+            items.append({"name": menu["name"], "path": menu["path"], "components": components})
         filtered = [menu for menu in items if menu["components"]]
         logger.debug(f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}')
         return {"menu": filtered}
diff --git a/src/backend/langflow/interface/custom_lists.py b/src/backend/langflow/interface/custom_lists.py
index 80a46c856..3cff26099 100644
--- a/src/backend/langflow/interface/custom_lists.py
+++ b/src/backend/langflow/interface/custom_lists.py
@@ -1,9 +1,10 @@
 import inspect
 from typing import Any
 
-from langchain import document_loaders, embeddings, llms, memory, requests, text_splitter
-from langchain.agents import agent_toolkits
-from langchain.chat_models import AzureChatOpenAI, ChatAnthropic, ChatOpenAI, ChatVertexAI
+from langchain import llms, memory, requests, text_splitter
+from langchain_community.chat_models import AzureChatOpenAI, ChatAnthropic, ChatOpenAI, ChatVertexAI
+from langchain_community import agent_toolkits, document_loaders, embeddings
+
 from langflow.interface.agents.custom import CUSTOM_AGENTS
 from langflow.interface.chains.custom import CUSTOM_CHAINS
 from langflow.interface.importing.utils import import_class
@@ -24,14 +25,14 @@ llm_type_to_cls_dict["vertexai-chat"] = ChatVertexAI  # type: ignore
 
 # Toolkits
 toolkit_type_to_loader_dict: dict[str, Any] = {
-    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
+    toolkit_name: import_class(f"langchain_community.agent_toolkits.{toolkit_name}")
     # if toolkit_name is lower case it is a loader
     for toolkit_name in agent_toolkits.__all__
     if toolkit_name.islower()
 }
 
 toolkit_type_to_cls_dict: dict[str, Any] = {
-    toolkit_name: import_class(f"langchain.agents.agent_toolkits.{toolkit_name}")
+    toolkit_name: import_class(f"langchain_community.agent_toolkits.{toolkit_name}")
     # if toolkit_name is not lower case it is a class
     for toolkit_name in agent_toolkits.__all__
     if not toolkit_name.islower()
@@ -47,13 +48,14 @@ wrapper_type_to_cls_dict: dict[str, Any] = {wrapper.__name__: wrapper for wrappe
 
 # Embeddings
 embedding_type_to_cls_dict: dict[str, Any] = {
-    embedding_name: import_class(f"langchain.embeddings.{embedding_name}") for embedding_name in embeddings.__all__
+    embedding_name: import_class(f"langchain_community.embeddings.{embedding_name}")
+    for embedding_name in embeddings.__all__
 }
 
 
 # Document Loaders
 documentloaders_type_to_cls_dict: dict[str, Any] = {
-    documentloader_name: import_class(f"langchain.document_loaders.{documentloader_name}")
+    documentloader_name: import_class(f"langchain_community.document_loaders.{documentloader_name}")
     for documentloader_name in document_loaders.__all__
 }
 
diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py
index f3276d952..9d7305f16 100644
--- a/src/backend/langflow/interface/importing/utils.py
+++ b/src/backend/langflow/interface/importing/utils.py
@@ -6,9 +6,9 @@ from typing import Any, Type
 from langchain.agents import Agent
 from langchain.base_language import BaseLanguageModel
 from langchain.chains.base import Chain
-from langchain.chat_models.base import BaseChatModel
 from langchain.prompts import PromptTemplate
 from langchain.tools import BaseTool
+from langchain_core.language_models.chat_models import BaseChatModel
 from langflow.interface.custom.custom_component import CustomComponent
 from langflow.interface.wrappers.base import wrapper_creator
 from langflow.utils import validate
@@ -71,7 +71,7 @@ def import_output_parser(output_parser: str) -> Any:
 
 def import_chat_llm(llm: str) -> BaseChatModel:
     """Import chat llm from llm name"""
-    return import_class(f"langchain.chat_models.{llm}")
+    return import_class(f"langchain_community.chat_models.{llm}")
 
 
 def import_retriever(retriever: str) -> Any:
@@ -148,17 +148,17 @@ def import_chain(chain: str) -> Type[Chain]:
 
 def import_embedding(embedding: str) -> Any:
     """Import embedding from embedding name"""
-    return import_class(f"langchain.embeddings.{embedding}")
+    return import_class(f"langchain_community.embeddings.{embedding}")
 
 
 def import_vectorstore(vectorstore: str) -> Any:
     """Import vectorstore from vectorstore name"""
-    return import_class(f"langchain.vectorstores.{vectorstore}")
+    return import_class(f"langchain_community.vectorstores.{vectorstore}")
 
 
 def import_documentloader(documentloader: str) -> Any:
     """Import documentloader from documentloader name"""
-    return import_class(f"langchain.document_loaders.{documentloader}")
+    return import_class(f"langchain_community.document_loaders.{documentloader}")
 
 
 def import_textsplitter(textsplitter: str) -> Any:
@@ -169,8 +169,8 @@ def import_textsplitter(textsplitter: str) -> Any:
 def import_utility(utility: str) -> Any:
     """Import utility from utility name"""
     if utility == "SQLDatabase":
-        return import_class(f"langchain.sql_database.{utility}")
-    return import_class(f"langchain.utilities.{utility}")
+        return import_class(f"langchain_community.sql_database.{utility}")
+    return import_class(f"langchain_community.utilities.{utility}")
 
 
 def get_function(code):
diff --git a/src/backend/langflow/interface/initialize/loading.py b/src/backend/langflow/interface/initialize/loading.py
index d09c239d6..f273af6ae 100644
--- a/src/backend/langflow/interface/initialize/loading.py
+++ b/src/backend/langflow/interface/initialize/loading.py
@@ -10,7 +10,7 @@ from langchain.agents.tools import BaseTool
 from langchain.chains.base import Chain
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
 from loguru import logger
 from pydantic import ValidationError
 
diff --git a/src/backend/langflow/interface/initialize/vector_store.py b/src/backend/langflow/interface/initialize/vector_store.py
index a0adf85e3..0163cdcc3 100644
--- a/src/backend/langflow/interface/initialize/vector_store.py
+++ b/src/backend/langflow/interface/initialize/vector_store.py
@@ -1,18 +1,18 @@
-from typing import Any, Callable, Dict, Type
-from langchain.vectorstores import (
-    Pinecone,
-    ElasticsearchStore,
-    Qdrant,
-    Chroma,
-    FAISS,
-    Weaviate,
-    SupabaseVectorStore,
-    MongoDBAtlasVectorSearch,
-)
-from langchain.schema import Document
 import os
+from typing import Any, Callable, Dict, Type
 
 import orjson
+from langchain.schema import Document
+from langchain_community.vectorstores import (
+    FAISS,
+    Chroma,
+    ElasticsearchStore,
+    MongoDBAtlasVectorSearch,
+    Pinecone,
+    Qdrant,
+    SupabaseVectorStore,
+    Weaviate,
+)
 
 
 def docs_in_params(params: dict) -> bool:
@@ -27,8 +27,8 @@ def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dic
     MONGODB_ATLAS_CLUSTER_URI = params.pop("mongodb_atlas_cluster_uri")
     if not MONGODB_ATLAS_CLUSTER_URI:
         raise ValueError("Mongodb atlas cluster uri must be provided in the params")
-    from pymongo import MongoClient
     import certifi
+    from pymongo import MongoClient
 
     client: MongoClient = MongoClient(MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where())
     db_name = params.pop("db_name", None)
diff --git a/src/backend/langflow/interface/retrievers/base.py b/src/backend/langflow/interface/retrievers/base.py
index 2439708a3..a6813e76c 100644
--- a/src/backend/langflow/interface/retrievers/base.py
+++ b/src/backend/langflow/interface/retrievers/base.py
@@ -1,14 +1,12 @@
 from typing import Any, ClassVar, Dict, List, Optional, Type
 
-from langchain import retrievers
-
+from langchain_community import retrievers
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
 from langflow.services.deps import get_settings_service
-
 from langflow.template.frontend_node.retrievers import RetrieverFrontendNode
+from langflow.utils.util import build_template_from_class, build_template_from_method
 from loguru import logger
-from langflow.utils.util import build_template_from_method, build_template_from_class
 
 
 class RetrieverCreator(LangChainTypeCreator):
@@ -27,7 +25,7 @@ class RetrieverCreator(LangChainTypeCreator):
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
             self.type_dict: dict[str, Any] = {
-                retriever_name: import_class(f"langchain.retrievers.{retriever_name}")
+                retriever_name: import_class(f"langchain_community.retrievers.{retriever_name}")
                 for retriever_name in retrievers.__all__
             }
         return self.type_dict
diff --git a/src/backend/langflow/interface/tools/constants.py b/src/backend/langflow/interface/tools/constants.py
index dc1bfe0c1..0ac37a0a4 100644
--- a/src/backend/langflow/interface/tools/constants.py
+++ b/src/backend/langflow/interface/tools/constants.py
@@ -1,18 +1,9 @@
 from langchain import tools
 from langchain.agents import Tool
-from langchain.agents.load_tools import (
-    _BASE_TOOLS,
-    _EXTRA_LLM_TOOLS,
-    _EXTRA_OPTIONAL_TOOLS,
-    _LLM_TOOLS,
-)
+from langchain.agents.load_tools import _BASE_TOOLS, _EXTRA_LLM_TOOLS, _EXTRA_OPTIONAL_TOOLS, _LLM_TOOLS
 from langchain.tools.json.tool import JsonSpec
-
 from langflow.interface.importing.utils import import_class
-from langflow.interface.tools.custom import (
-    PythonFunctionTool,
-    PythonFunction,
-)
+from langflow.interface.tools.custom import PythonFunction, PythonFunctionTool
 
 FILE_TOOLS = {"JsonSpec": JsonSpec}
 CUSTOM_TOOLS = {
@@ -21,7 +12,7 @@ CUSTOM_TOOLS = {
     "PythonFunction": PythonFunction,
 }
 
-OTHER_TOOLS = {tool: import_class(f"langchain.tools.{tool}") for tool in tools.__all__}
+OTHER_TOOLS = {tool: import_class(f"langchain_community.tools.{tool}") for tool in tools.__all__}
 
 ALL_TOOLS_NAMES = {
     **_BASE_TOOLS,
diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py
index 7f44be3ec..39c68889f 100644
--- a/src/backend/langflow/interface/types.py
+++ b/src/backend/langflow/interface/types.py
@@ -1,4 +1,5 @@
 from cachetools import LRUCache, cached
+
 from langflow.interface.agents.base import agent_creator
 from langflow.interface.chains.base import chain_creator
 from langflow.interface.custom.directory_reader.utils import merge_nested_dicts_with_renaming
@@ -14,7 +15,6 @@ from langflow.interface.text_splitters.base import textsplitter_creator
 from langflow.interface.toolkits.base import toolkits_creator
 from langflow.interface.tools.base import tool_creator
 from langflow.interface.utilities.base import utility_creator
-from langflow.interface.vector_store.base import vectorstore_creator
 from langflow.interface.wrappers.base import wrapper_creator
 
 
@@ -46,7 +46,7 @@ def build_langchain_types_dict():  # sourcery skip: dict-assign-update-to-union
         toolkits_creator,
         wrapper_creator,
         embedding_creator,
-        vectorstore_creator,
+        # vectorstore_creator,
         documentloader_creator,
         textsplitter_creator,
         utility_creator,
diff --git a/src/backend/langflow/interface/utilities/base.py b/src/backend/langflow/interface/utilities/base.py
index cfebf83a4..0a58ef1bc 100644
--- a/src/backend/langflow/interface/utilities/base.py
+++ b/src/backend/langflow/interface/utilities/base.py
@@ -1,6 +1,6 @@
 from typing import Dict, List, Optional, Type
 
-from langchain import utilities
+from langchain_community import utilities
 from loguru import logger
 
 from langflow.custom.customs import get_custom_nodes
@@ -30,7 +30,7 @@ class UtilityCreator(LangChainTypeCreator):
             self.type_dict = {}
             for utility_name in utilities.__all__:
                 try:
-                    imported = import_class(f"langchain.utilities.{utility_name}")
+                    imported = import_class(f"langchain_community.utilities.{utility_name}")
                     self.type_dict[utility_name] = imported
                 except Exception:
                     pass
diff --git a/src/backend/langflow/interface/vector_store/base.py b/src/backend/langflow/interface/vector_store/base.py
index d04689469..893c78fca 100644
--- a/src/backend/langflow/interface/vector_store/base.py
+++ b/src/backend/langflow/interface/vector_store/base.py
@@ -1,13 +1,12 @@
 from typing import Any, Dict, List, Optional, Type
 
 from langchain import vectorstores
+from loguru import logger
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class
 from langflow.services.deps import get_settings_service
-
 from langflow.template.frontend_node.vectorstores import VectorStoreFrontendNode
-from loguru import logger
 from langflow.utils.util import build_template_from_method
 
 
@@ -22,7 +21,7 @@ class VectorstoreCreator(LangChainTypeCreator):
     def type_to_loader_dict(self) -> Dict:
         if self.type_dict is None:
             self.type_dict: dict[str, Any] = {
-                vectorstore_name: import_class(f"langchain.vectorstores.{vectorstore_name}")
+                vectorstore_name: import_class(f"langchain_community.vectorstores.{vectorstore_name}")
                 for vectorstore_name in vectorstores.__all__
             }
         return self.type_dict
diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py
index 38d61af78..469559ad9 100644
--- a/src/backend/langflow/interface/wrappers/base.py
+++ b/src/backend/langflow/interface/wrappers/base.py
@@ -1,9 +1,9 @@
 from typing import ClassVar, Dict, List, Optional
 
-from langchain.utilities import requests, sql_database
+from langchain_community.utilities import requests, sql_database
+from loguru import logger
 
 from langflow.interface.base import LangChainTypeCreator
-from loguru import logger
 from langflow.utils.util import build_template_from_class, build_template_from_method
 
 
diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py
index 600ca2a0c..6a12fa00e 100644
--- a/src/backend/langflow/processing/process.py
+++ b/src/backend/langflow/processing/process.py
@@ -4,7 +4,7 @@ from typing import Any, Coroutine, Dict, List, Optional, Tuple, Union
 from langchain.agents import AgentExecutor
 from langchain.chains.base import Chain
 from langchain.schema import AgentAction, Document
-from langchain.vectorstores.base import VectorStore
+from langchain_community.vectorstores import VectorStore
 from langchain_core.messages import AIMessage
 from langchain_core.runnables.base import Runnable
 from langflow.graph.graph.base import Graph
diff --git a/src/backend/langflow/utils/constants.py b/src/backend/langflow/utils/constants.py
index b2d00f8e6..0704f2ec9 100644
--- a/src/backend/langflow/utils/constants.py
+++ b/src/backend/langflow/utils/constants.py
@@ -68,110 +68,110 @@ LOADERS_INFO: List[Dict[str, Any]] = [
     {
         "loader": "AirbyteJSONLoader",
         "name": "Airbyte JSON (.jsonl)",
-        "import": "langchain.document_loaders.AirbyteJSONLoader",
+        "import": "langchain_community.document_loaders.AirbyteJSONLoader",
         "defaultFor": ["jsonl"],
         "allowdTypes": ["jsonl"],
     },
     {
         "loader": "JSONLoader",
         "name": "JSON (.json)",
-        "import": "langchain.document_loaders.JSONLoader",
+        "import": "langchain_community.document_loaders.JSONLoader",
         "defaultFor": ["json"],
         "allowdTypes": ["json"],
     },
     {
         "loader": "BSHTMLLoader",
         "name": "BeautifulSoup4 HTML (.html, .htm)",
-        "import": "langchain.document_loaders.BSHTMLLoader",
+        "import": "langchain_community.document_loaders.BSHTMLLoader",
         "allowdTypes": ["html", "htm"],
     },
     {
         "loader": "CSVLoader",
         "name": "CSV (.csv)",
-        "import": "langchain.document_loaders.CSVLoader",
+        "import": "langchain_community.document_loaders.CSVLoader",
         "defaultFor": ["csv"],
         "allowdTypes": ["csv"],
     },
     {
         "loader": "CoNLLULoader",
         "name": "CoNLL-U (.conllu)",
-        "import": "langchain.document_loaders.CoNLLULoader",
+        "import": "langchain_community.document_loaders.CoNLLULoader",
         "defaultFor": ["conllu"],
         "allowdTypes": ["conllu"],
     },
     {
         "loader": "EverNoteLoader",
         "name": "EverNote (.enex)",
-        "import": "langchain.document_loaders.EverNoteLoader",
+        "import": "langchain_community.document_loaders.EverNoteLoader",
         "defaultFor": ["enex"],
         "allowdTypes": ["enex"],
     },
     {
         "loader": "FacebookChatLoader",
         "name": "Facebook Chat (.json)",
-        "import": "langchain.document_loaders.FacebookChatLoader",
+        "import": "langchain_community.document_loaders.FacebookChatLoader",
         "allowdTypes": ["json"],
     },
     {
         "loader": "OutlookMessageLoader",
         "name": "Outlook Message (.msg)",
-        "import": "langchain.document_loaders.OutlookMessageLoader",
+        "import": "langchain_community.document_loaders.OutlookMessageLoader",
         "defaultFor": ["msg"],
         "allowdTypes": ["msg"],
     },
     {
         "loader": "PyPDFLoader",
         "name": "PyPDF (.pdf)",
-        "import": "langchain.document_loaders.PyPDFLoader",
+        "import": "langchain_community.document_loaders.PyPDFLoader",
         "defaultFor": ["pdf"],
         "allowdTypes": ["pdf"],
     },
     {
         "loader": "STRLoader",
         "name": "Subtitle (.str)",
-        "import": "langchain.document_loaders.STRLoader",
+        "import": "langchain_community.document_loaders.STRLoader",
         "defaultFor": ["str"],
         "allowdTypes": ["str"],
     },
     {
         "loader": "TextLoader",
         "name": "Text (.txt)",
-        "import": "langchain.document_loaders.TextLoader",
+        "import": "langchain_community.document_loaders.TextLoader",
         "defaultFor": ["txt"],
         "allowdTypes": ["txt"],
     },
     {
         "loader": "UnstructuredEmailLoader",
         "name": "Unstructured Email (.eml)",
-        "import": "langchain.document_loaders.UnstructuredEmailLoader",
+        "import": "langchain_community.document_loaders.UnstructuredEmailLoader",
         "defaultFor": ["eml"],
         "allowdTypes": ["eml"],
     },
     {
         "loader": "UnstructuredHTMLLoader",
         "name": "Unstructured HTML (.html, .htm)",
-        "import": "langchain.document_loaders.UnstructuredHTMLLoader",
+        "import": "langchain_community.document_loaders.UnstructuredHTMLLoader",
         "defaultFor": ["html", "htm"],
         "allowdTypes": ["html", "htm"],
     },
     {
         "loader": "UnstructuredMarkdownLoader",
         "name": "Unstructured Markdown (.md)",
-        "import": "langchain.document_loaders.UnstructuredMarkdownLoader",
+        "import": "langchain_community.document_loaders.UnstructuredMarkdownLoader",
         "defaultFor": ["md"],
         "allowdTypes": ["md"],
     },
     {
         "loader": "UnstructuredPowerPointLoader",
         "name": "Unstructured PowerPoint (.pptx)",
-        "import": "langchain.document_loaders.UnstructuredPowerPointLoader",
+        "import": "langchain_community.document_loaders.UnstructuredPowerPointLoader",
         "defaultFor": ["pptx"],
         "allowdTypes": ["pptx"],
     },
     {
         "loader": "UnstructuredWordLoader",
         "name": "Unstructured Word (.docx)",
-        "import": "langchain.document_loaders.UnstructuredWordLoader",
+        "import": "langchain_community.document_loaders.UnstructuredWordLoader",
         "defaultFor": ["docx"],
         "allowdTypes": ["docx"],
     },
diff --git a/src/frontend/tests/custom_component_full.ts b/src/frontend/tests/custom_component_full.ts
index 443c3fd85..be24deba3 100644
--- a/src/frontend/tests/custom_component_full.ts
+++ b/src/frontend/tests/custom_component_full.ts
@@ -4,7 +4,7 @@ from langchain.llms.base import BaseLLM
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
 from langchain.schema import Document
-from langchain.field_typing import NestedDict
+from langflow.field_typing import NestedDict
 
 import requests
 
diff --git a/tests/locust/locustfile.py b/tests/locust/locustfile.py
index 1fc91ee2c..856e3f6dd 100644
--- a/tests/locust/locustfile.py
+++ b/tests/locust/locustfile.py
@@ -1,11 +1,12 @@
-from locust import FastHttpUser, task, between
 import random
 import time
-import orjson
-from rich import print
-import httpx
 from pathlib import Path
 
+import httpx
+import orjson
+from locust import FastHttpUser, between, task
+from rich import print
+
 
 class NameTest(FastHttpUser):
     wait_time = between(1, 5)
@@ -13,7 +14,7 @@ class NameTest(FastHttpUser):
     with open("names.txt", "r") as file:
         names = [line.strip() for line in file.readlines()]
 
-    headers = {}
+    headers: dict = {}
 
     def poll_task(self, task_id, sleep_time=1):
         while True:
diff --git a/tests/test_agents_template.py b/tests/test_agents_template.py
deleted file mode 100644
index e354d4a16..000000000
--- a/tests/test_agents_template.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from fastapi.testclient import TestClient
-
-
-def test_zero_shot_agent(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    agents = json_response["agents"]
-
-    zero_shot_agent = agents["ZeroShotAgent"]
-    assert set(zero_shot_agent["base_classes"]) == {
-        "ZeroShotAgent",
-        "BaseSingleActionAgent",
-        "Agent",
-        "function",
-    }
-    template = zero_shot_agent["template"]
-
-    assert template["tools"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "tools",
-        "type": "BaseTool",
-        "list": True,
-        "advanced": False,
-        "info": "",
-    }
-
-    # Additional assertions for other template variables
-    assert template["callback_manager"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "callback_manager",
-        "type": "BaseCallbackManager",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["output_parser"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "output_parser",
-        "type": "AgentOutputParser",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["input_variables"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "input_variables",
-        "type": "str",
-        "list": True,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["prefix"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": True,
-        "value": "Answer the following questions as best you can. You have access to the following tools:",
-        "password": False,
-        "name": "prefix",
-        "type": "str",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["suffix"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": True,
-        "value": "Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}",
-        "password": False,
-        "name": "suffix",
-        "type": "str",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-
-
-def test_json_agent(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    agents = json_response["agents"]
-
-    json_agent = agents["JsonAgent"]
-    assert json_agent["base_classes"] == ["AgentExecutor"]
-    template = json_agent["template"]
-
-    assert template["toolkit"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "toolkit",
-        "type": "BaseToolkit",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "display_name": "LLM",
-        "info": "",
-    }
-
-
-def test_csv_agent(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    agents = json_response["agents"]
-
-    csv_agent = agents["CSVAgent"]
-    assert csv_agent["base_classes"] == ["AgentExecutor"]
-    template = csv_agent["template"]
-
-    assert template["path"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "",
-        "suffixes": [".csv"],
-        "fileTypes": ["csv"],
-        "password": False,
-        "name": "path",
-        "type": "file",
-        "list": False,
-        "file_path": None,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "display_name": "LLM",
-        "info": "",
-    }
-
-
-def test_initialize_agent(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    agents = json_response["agents"]
-
-    initialize_agent = agents["AgentInitializer"]
-    assert initialize_agent["base_classes"] == ["AgentExecutor", "function"]
-    template = initialize_agent["template"]
-
-    assert template["agent"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "zero-shot-react-description",
-        "password": False,
-        "options": [
-            "zero-shot-react-description",
-            "react-docstore",
-            "self-ask-with-search",
-            "conversational-react-description",
-            "openai-functions",
-            "openai-multi-functions",
-        ],
-        "name": "agent",
-        "type": "str",
-        "list": True,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["memory"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "memory",
-        "type": "BaseChatMemory",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["tools"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "tools",
-        "type": "Tool",
-        "list": True,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "display_name": "LLM",
-        "info": "",
-    }
diff --git a/tests/test_chains_template.py b/tests/test_chains_template.py
deleted file mode 100644
index 970c0f315..000000000
--- a/tests/test_chains_template.py
+++ /dev/null
@@ -1,437 +0,0 @@
-from fastapi.testclient import TestClient
-
-# def test_chains_settings(client: TestClient, logged_in_headers):
-#     response = client.get("api/v1/all", headers=logged_in_headers)
-#     assert response.status_code == 200
-#     json_response = response.json()
-#     chains = json_response["chains"]
-#     assert set(chains.keys()) == set(settings.chains)
-
-
-# Test the ConversationChain object
-def test_conversation_chain(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    chains = json_response["chains"]
-
-    chain = chains["ConversationChain"]
-    # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects
-    assert set(chain["base_classes"]) == {"Callable", "Chain"}
-
-    template = chain["template"]
-    assert template["memory"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "memory",
-        "type": "BaseMemory",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["verbose"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "verbose",
-        "type": "bool",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["input_key"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "input",
-        "password": False,
-        "name": "input_key",
-        "type": "str",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["output_key"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "response",
-        "password": False,
-        "name": "output_key",
-        "type": "str",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["_type"] == "ConversationChain"
-
-    # Test the description object
-    assert chain["description"] == "Chain to have a conversation and load context from memory."
-
-
-def test_llm_chain(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    chains = json_response["chains"]
-    chain = chains["LLMChain"]
-
-    # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects
-    assert set(chain["base_classes"]) == {"function", "LLMChain", "Chain", "Text"}
-
-    template = chain["template"]
-    assert template["memory"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "memory",
-        "type": "BaseMemory",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["verbose"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": False,
-        "password": False,
-        "name": "verbose",
-        "type": "bool",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["output_key"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "text",
-        "password": False,
-        "name": "output_key",
-        "type": "str",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-
-
-def test_llm_checker_chain(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    chains = json_response["chains"]
-    chain = chains["LLMCheckerChain"]
-
-    # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects
-    assert set(chain["base_classes"]) == {
-        "function",
-        "LLMCheckerChain",
-        "Chain",
-        "Text",
-    }
-
-    template = chain["template"]
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["_type"] == "LLMCheckerChain"
-
-    # Test the description object
-    assert chain["description"] == ""
-
-
-def test_llm_math_chain(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    chains = json_response["chains"]
-
-    chain = chains["LLMMathChain"]
-    # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects
-    assert set(chain["base_classes"]) == {"function", "LLMMathChain", "Chain", "Text"}
-
-    template = chain["template"]
-    assert template["memory"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "memory",
-        "type": "BaseMemory",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["verbose"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": False,
-        "password": False,
-        "name": "verbose",
-        "type": "bool",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["input_key"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "question",
-        "password": False,
-        "name": "input_key",
-        "type": "str",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["output_key"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "answer",
-        "password": False,
-        "name": "output_key",
-        "type": "str",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["_type"] == "LLMMathChain"
-
-    # Test the description object
-    assert chain["description"] == "Chain that interprets a prompt and executes python code to do math."
-
-
-def test_series_character_chain(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    chains = json_response["chains"]
-
-    chain = chains["SeriesCharacterChain"]
-
-    # Test the base classes, template, memory, verbose, llm, input_key, output_key, and _type objects
-    assert set(chain["base_classes"]) == {
-        "function",
-        "LLMChain",
-        "BaseCustomChain",
-        "Chain",
-        "ConversationChain",
-        "SeriesCharacterChain",
-    }
-    template = chain["template"]
-
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "display_name": "LLM",
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["character"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "character",
-        "type": "str",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["series"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "series",
-        "type": "str",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["_type"] == "SeriesCharacterChain"
-
-    # Test the description object
-    assert (
-        chain["description"]
-        == "SeriesCharacterChain is a chain you can use to have a conversation with a character from a series."
-    )
-
-
-def test_mid_journey_prompt_chain(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    chains = json_response["chains"]
-    chain = chains["MidJourneyPromptChain"]
-    assert isinstance(chain, dict)
-
-    # Test the base_classes object
-    assert set(chain["base_classes"]) == {
-        "LLMChain",
-        "BaseCustomChain",
-        "Chain",
-        "ConversationChain",
-        "MidJourneyPromptChain",
-    }
-
-    # Test the template object
-    template = chain["template"]
-
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "display_name": "LLM",
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    # Test the description object
-    assert chain["description"] == "MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts."
-
-
-def test_time_travel_guide_chain(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    chains = json_response["chains"]
-    chain = chains["TimeTravelGuideChain"]
-    assert isinstance(chain, dict)
-
-    # Test the base_classes object
-    assert set(chain["base_classes"]) == {
-        "LLMChain",
-        "BaseCustomChain",
-        "TimeTravelGuideChain",
-        "Chain",
-        "ConversationChain",
-    }
-
-    # Test the template object
-    template = chain["template"]
-
-    assert template["llm"] == {
-        "required": True,
-        "dynamic": False,
-        "placeholder": "",
-        "display_name": "LLM",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "llm",
-        "type": "BaseLanguageModel",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["memory"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "memory",
-        "type": "BaseChatMemory",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-
-    assert chain["description"] == "Time travel guide chain."
diff --git a/tests/test_llms_template.py b/tests/test_llms_template.py
deleted file mode 100644
index d63d1d016..000000000
--- a/tests/test_llms_template.py
+++ /dev/null
@@ -1,522 +0,0 @@
-from fastapi.testclient import TestClient
-
-
-def test_openai(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    language_models = json_response["llms"]
-
-    model = language_models["OpenAI"]
-    template = model["template"]
-
-    assert template["cache"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "cache",
-        "type": "bool",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["verbose"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "verbose",
-        "type": "bool",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["client"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "client",
-        "type": "Any",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["model_name"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "text-davinci-003",
-        "password": False,
-        "options": [
-            "text-davinci-003",
-            "text-davinci-002",
-            "text-curie-001",
-            "text-babbage-001",
-            "text-ada-001",
-        ],
-        "name": "model_name",
-        "type": "str",
-        "list": True,
-        "advanced": False,
-        "info": "",
-    }
-    # Add more assertions for other properties here
-    assert template["temperature"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": 0.7,
-        "password": False,
-        "name": "temperature",
-        "type": "float",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["max_tokens"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": 256,
-        "password": True,
-        "name": "max_tokens",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["top_p"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 1,
-        "password": False,
-        "name": "top_p",
-        "type": "float",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["frequency_penalty"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 0,
-        "password": False,
-        "name": "frequency_penalty",
-        "type": "float",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["presence_penalty"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 0,
-        "password": False,
-        "name": "presence_penalty",
-        "type": "float",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["n"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 1,
-        "password": False,
-        "name": "n",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["best_of"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 1,
-        "password": False,
-        "name": "best_of",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["model_kwargs"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "model_kwargs",
-        "type": "dict",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["openai_api_key"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "",
-        "password": True,
-        "name": "openai_api_key",
-        "display_name": "OpenAI API Key",
-        "type": "str",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["batch_size"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 20,
-        "password": False,
-        "name": "batch_size",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["request_timeout"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "request_timeout",
-        "type": "float",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["logit_bias"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "logit_bias",
-        "type": "dict",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["max_retries"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 6,
-        "password": False,
-        "name": "max_retries",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["streaming"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": False,
-        "password": False,
-        "name": "streaming",
-        "type": "bool",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-
-
-def test_chat_open_ai(client: TestClient, logged_in_headers):
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    language_models = json_response["llms"]
-
-    model = language_models["ChatOpenAI"]
-    template = model["template"]
-
-    assert template["verbose"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": False,
-        "password": False,
-        "name": "verbose",
-        "type": "bool",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["client"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "client",
-        "type": "Any",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["model_name"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "gpt-3.5-turbo-0613",
-        "password": False,
-        "options": [
-            "gpt-4-1106-preview",
-            "gpt-4",
-            "gpt-4-32k",
-            "gpt-3.5-turbo",
-            "gpt-3.5-turbo-16k",
-        ],
-        "name": "model_name",
-        "type": "str",
-        "list": True,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["temperature"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": 0.7,
-        "password": False,
-        "name": "temperature",
-        "type": "float",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["model_kwargs"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": False,
-        "name": "model_kwargs",
-        "type": "dict",
-        "list": False,
-        "advanced": True,
-        "info": "",
-    }
-    assert template["openai_api_key"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "value": "",
-        "password": True,
-        "name": "openai_api_key",
-        "display_name": "OpenAI API Key",
-        "type": "str",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["request_timeout"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "password": False,
-        "name": "request_timeout",
-        "type": "float",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["max_retries"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 6,
-        "password": False,
-        "name": "max_retries",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["streaming"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": False,
-        "password": False,
-        "name": "streaming",
-        "type": "bool",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["n"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": False,
-        "multiline": False,
-        "value": 1,
-        "password": False,
-        "name": "n",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-
-    assert template["max_tokens"] == {
-        "required": False,
-        "dynamic": False,
-        "placeholder": "",
-        "show": True,
-        "multiline": False,
-        "password": True,
-        "name": "max_tokens",
-        "type": "int",
-        "list": False,
-        "advanced": False,
-        "info": "",
-    }
-    assert template["_type"] == "ChatOpenAI"
-    assert (
-        model["description"] == "`OpenAI` Chat large language models API."  # noqa E501
-    )
-    assert set(model["base_classes"]) == {
-        "BaseLLM",
-        "BaseChatModel",
-        "ChatOpenAI",
-        "BaseLanguageModel",
-    }
-
-
-# Commenting this out for now, as it requires to activate the nodes
-# def test_azure_open_ai(client: TestClient):
-#     response = client.get("/all")
-#     assert response.status_code == 200
-#     json_response = response.json()
-#     language_models = json_response["llms"]
-
-#     model = language_models["AzureOpenAI"]
-#     template = model["template"]
-
-#     assert template["model_name"]["show"] is False
-#     assert template["deployment_name"] == {
-#         "required": False,
-#         "placeholder": "",
-#         "show": True,
-#         "multiline": False,
-#         "value": "",
-#         "password": False,
-#         "name": "deployment_name",
-#         "advanced": False,
-#         "type": "str",
-#         "list": False,
-#     }
-
-
-# def test_azure_chat_open_ai(client: TestClient):
-#     response = client.get("/all")
-#     assert response.status_code == 200
-#     json_response = response.json()
-#     language_models = json_response["llms"]
-
-#     model = language_models["AzureChatOpenAI"]
-#     template = model["template"]
-
-#     assert template["model_name"]["show"] is False
-#     assert template["deployment_name"] == {
-#         "required": False,
-#         "placeholder": "",
-#         "show": True,
-#         "multiline": False,
-#         "value": "",
-#         "password": False,
-#         "name": "deployment_name",
-#         "advanced": False,
-#         "type": "str",
-#         "list": False,
-#     }
-#     assert template["openai_api_type"] == {
-#         "required": False,
-#         "placeholder": "",
-#         "show": False,
-#         "multiline": False,
-#         "value": "azure",
-#         "password": False,
-#         "name": "openai_api_type",
-#         "display_name": "OpenAI API Type",
-#         "advanced": False,
-#         "type": "str",
-#         "list": False,
-#     }
-#     assert template["openai_api_version"] == {
-#         "required": False,
-#         "placeholder": "",
-#         "show": True,
-#         "multiline": False,
-#         "value": "2023-03-15-preview",
-#         "password": False,
-#         "name": "openai_api_version",
-#         "display_name": "OpenAI API Version",
-#         "advanced": False,
-#         "type": "str",
-#         "list": False,
-#     }
diff --git a/tests/test_vectorstore_template.py b/tests/test_vectorstore_template.py
deleted file mode 100644
index 3b5c7ed42..000000000
--- a/tests/test_vectorstore_template.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from fastapi.testclient import TestClient
-from langflow.services.deps import get_settings_service
-
-
-# check that all agents are in settings.agents
-# are in json_response["agents"]
-def test_vectorstores_settings(client: TestClient, logged_in_headers):
-    settings_service = get_settings_service()
-    response = client.get("api/v1/all", headers=logged_in_headers)
-    assert response.status_code == 200
-    json_response = response.json()
-    vectorstores = json_response["vectorstores"]
-    settings_vecs = set(settings_service.settings.VECTORSTORES)
-    assert all(vs in vectorstores for vs in settings_vecs)