Merge remote-tracking branch 'origin/dev' into celery

This commit is contained in:
Gabriel Luiz Freitas Almeida 2023-08-16 19:26:06 -03:00
commit 97ad9889be
123 changed files with 7000 additions and 3272 deletions

1
.dockerignore Normal file
View file

@ -0,0 +1 @@
.venv/

View file

@ -32,7 +32,10 @@ lint:
poetry run ruff . --fix
install_frontend:
cd src/frontend && npm install
cd src/frontend && npm install;
install_frontendc:
cd src/frontend && npm ci;
run_frontend:
cd src/frontend && npm start
@ -40,6 +43,10 @@ run_frontend:
frontend:
make install_frontend
make run_frontend
frontendc:
make install_frontendc
make run_frontend
install_backend:
poetry install --extras deploy

View file

@ -15,4 +15,4 @@ COPY ./ ./
# Install dependencies
RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi
CMD ["uvicorn","--factory", "langflow.main:create_app", "--host", "0.0.0.0", "--port", "5003", "--reload", "log-level", "debug"]
CMD ["uvicorn", "--factory", "src.backend.langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug"]

View file

@ -11,7 +11,7 @@ services:
[
"sh",
"-c",
"pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload",
"pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload",
]
ports:
- 7860:7860

View file

@ -9,7 +9,7 @@ services:
- "7860:7860"
volumes:
- ./:/app
command: bash -c "uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload"
command: bash -c "uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload"
frontend:
build:

View file

@ -73,3 +73,25 @@ Used to load [OpenAI’s](https://openai.com/) embedding models.
- **request_timeout:** Used to specify the maximum amount of time, in milliseconds, to wait for a response from the OpenAI API when generating embeddings for a given text.
- **tiktoken_model_name:** Used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name.
---
### VertexAIEmbeddings
Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
:::info
Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
:::
- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
- **location:** The default location to use when making API calls – defaults to `us-central1`.
- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.
- **model_name:** The name of the Vertex AI large language model – defaults to `text-bison`.
- **project:** The default GCP project to use when making Vertex API calls.
- **request_parallelism:** The amount of parallelism allowed for requests issued to VertexAI models – defaults to `5`.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.
- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.

View file

@ -185,6 +185,28 @@ Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large lang
Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
:::
- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
- **location:** The default location to use when making API calls – defaults to `us-central1`.
- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.
- **model_name:** The name of the Vertex AI large language model – defaults to `text-bison`.
- **project:** The default GCP project to use when making Vertex API calls.
- **request_parallelism:** The amount of parallelism allowed for requests issued to VertexAI models – defaults to `5`.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.
- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.
---
### ChatVertexAI
Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large language models.
:::info
Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
:::
- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
- **location:** The default location to use when making API calls – defaults to `us-central1`.
- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.

2
package-lock.json generated
View file

@ -1,5 +1,5 @@
{
"name": "langflow",
"name": "reactFlow",
"lockfileVersion": 3,
"requires": true,
"packages": {

141
poetry.lock generated
View file

@ -146,13 +146,13 @@ files = [
[[package]]
name = "alembic"
version = "1.11.2"
version = "1.11.3"
description = "A database migration tool for SQLAlchemy."
optional = false
python-versions = ">=3.7"
files = [
{file = "alembic-1.11.2-py3-none-any.whl", hash = "sha256:7981ab0c4fad4fe1be0cf183aae17689fe394ff874fd2464adb774396faf0796"},
{file = "alembic-1.11.2.tar.gz", hash = "sha256:678f662130dc540dac12de0ea73de9f89caea9dbea138f60ef6263149bf84657"},
{file = "alembic-1.11.3-py3-none-any.whl", hash = "sha256:d6c96c2482740592777c400550a523bc7a9aada4e210cae2e733354ddae6f6f8"},
{file = "alembic-1.11.3.tar.gz", hash = "sha256:3db4ce81a9072e1b5aa44c2d202add24553182672a12daf21608d6f62a8f9cf9"},
]
[package.dependencies]
@ -179,20 +179,20 @@ vine = ">=5.0.0"
[[package]]
name = "anthropic"
version = "0.3.9"
version = "0.3.10"
description = "Client library for the anthropic API"
optional = false
python-versions = ">=3.7,<4.0"
files = [
{file = "anthropic-0.3.9-py3-none-any.whl", hash = "sha256:23e8daf266c707faa0b85328ada03dcf885e62c2f236eb0159352e29b9c4e2e3"},
{file = "anthropic-0.3.9.tar.gz", hash = "sha256:c19b75308c07cb1ecbea03ffcdde9ba68e5ca22a22479217712396385678bde3"},
{file = "anthropic-0.3.10-py3-none-any.whl", hash = "sha256:95cd73168296a4f91a5899a28660991e044322cf94442d07b99901d4ca74acd6"},
{file = "anthropic-0.3.10.tar.gz", hash = "sha256:d1f66efc541fbff0ecfd37fd4d3690f9daaa748fc42d9ded5863a10815a5d97b"},
]
[package.dependencies]
anyio = ">=3.5.0,<4"
distro = ">=1.7.0,<2"
httpx = ">=0.23.0,<1"
pydantic = ">=1.9.0,<2.0.0"
pydantic = ">=1.9.0,<3"
tokenizers = ">=0.13.0"
typing-extensions = ">=4.5,<5"
@ -349,6 +349,40 @@ files = [
{file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
]
[[package]]
name = "bcrypt"
version = "4.0.1"
description = "Modern password hashing for your software and your servers"
optional = false
python-versions = ">=3.6"
files = [
{file = "bcrypt-4.0.1-cp36-abi3-macosx_10_10_universal2.whl", hash = "sha256:b1023030aec778185a6c16cf70f359cbb6e0c289fd564a7cfa29e727a1c38f8f"},
{file = "bcrypt-4.0.1-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_24_aarch64.whl", hash = "sha256:08d2947c490093a11416df18043c27abe3921558d2c03e2076ccb28a116cb6d0"},
{file = "bcrypt-4.0.1-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0eaa47d4661c326bfc9d08d16debbc4edf78778e6aaba29c1bc7ce67214d4410"},
{file = "bcrypt-4.0.1-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ae88eca3024bb34bb3430f964beab71226e761f51b912de5133470b649d82344"},
{file = "bcrypt-4.0.1-cp36-abi3-manylinux_2_24_x86_64.whl", hash = "sha256:a522427293d77e1c29e303fc282e2d71864579527a04ddcfda6d4f8396c6c36a"},
{file = "bcrypt-4.0.1-cp36-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:fbdaec13c5105f0c4e5c52614d04f0bca5f5af007910daa8b6b12095edaa67b3"},
{file = "bcrypt-4.0.1-cp36-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:ca3204d00d3cb2dfed07f2d74a25f12fc12f73e606fcaa6975d1f7ae69cacbb2"},
{file = "bcrypt-4.0.1-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:089098effa1bc35dc055366740a067a2fc76987e8ec75349eb9484061c54f535"},
{file = "bcrypt-4.0.1-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:e9a51bbfe7e9802b5f3508687758b564069ba937748ad7b9e890086290d2f79e"},
{file = "bcrypt-4.0.1-cp36-abi3-win32.whl", hash = "sha256:2caffdae059e06ac23fce178d31b4a702f2a3264c20bfb5ff541b338194d8fab"},
{file = "bcrypt-4.0.1-cp36-abi3-win_amd64.whl", hash = "sha256:8a68f4341daf7522fe8d73874de8906f3a339048ba406be6ddc1b3ccb16fc0d9"},
{file = "bcrypt-4.0.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf4fa8b2ca74381bb5442c089350f09a3f17797829d958fad058d6e44d9eb83c"},
{file = "bcrypt-4.0.1-pp37-pypy37_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:67a97e1c405b24f19d08890e7ae0c4f7ce1e56a712a016746c8b2d7732d65d4b"},
{file = "bcrypt-4.0.1-pp37-pypy37_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:b3b85202d95dd568efcb35b53936c5e3b3600c7cdcc6115ba461df3a8e89f38d"},
{file = "bcrypt-4.0.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cbb03eec97496166b704ed663a53680ab57c5084b2fc98ef23291987b525cb7d"},
{file = "bcrypt-4.0.1-pp38-pypy38_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:5ad4d32a28b80c5fa6671ccfb43676e8c1cc232887759d1cd7b6f56ea4355215"},
{file = "bcrypt-4.0.1-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:b57adba8a1444faf784394de3436233728a1ecaeb6e07e8c22c8848f179b893c"},
{file = "bcrypt-4.0.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:705b2cea8a9ed3d55b4491887ceadb0106acf7c6387699fca771af56b1cdeeda"},
{file = "bcrypt-4.0.1-pp39-pypy39_pp73-manylinux_2_24_x86_64.whl", hash = "sha256:2b3ac11cf45161628f1f3733263e63194f22664bf4d0c0f3ab34099c02134665"},
{file = "bcrypt-4.0.1-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:3100851841186c25f127731b9fa11909ab7b1df6fc4b9f8353f4f1fd952fbf71"},
{file = "bcrypt-4.0.1.tar.gz", hash = "sha256:27d375903ac8261cfe4047f6709d16f7d18d39b1ec92aaf72af989552a650ebd"},
]
[package.extras]
tests = ["pytest (>=3.2.1,!=3.3.0)"]
typecheck = ["mypy"]
[[package]]
name = "beautifulsoup4"
version = "4.12.2"
@ -2552,13 +2586,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
[[package]]
name = "humanize"
version = "4.7.0"
version = "4.8.0"
description = "Python humanize utilities"
optional = true
python-versions = ">=3.8"
files = [
{file = "humanize-4.7.0-py3-none-any.whl", hash = "sha256:df7c429c2d27372b249d3f26eb53b07b166b661326e0325793e0a988082e3889"},
{file = "humanize-4.7.0.tar.gz", hash = "sha256:7ca0e43e870981fa684acb5b062deb307218193bca1a01f2b2676479df849b3a"},
{file = "humanize-4.8.0-py3-none-any.whl", hash = "sha256:8bc9e2bb9315e61ec06bf690151ae35aeb65651ab091266941edf97c90836404"},
{file = "humanize-4.8.0.tar.gz", hash = "sha256:9783373bf1eec713a770ecaa7c2d7a7902c98398009dfa3d8a2df91eec9311e8"},
]
[package.extras]
@ -3140,13 +3174,13 @@ test = ["psutil", "pytest", "pytest-asyncio"]
[[package]]
name = "langsmith"
version = "0.0.22"
version = "0.0.23"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
{file = "langsmith-0.0.22-py3-none-any.whl", hash = "sha256:1bc94a2e5bfa355ca15d9e658c2c2d04c8cc45c61892a1be08a7c3b40f2fd3f4"},
{file = "langsmith-0.0.22.tar.gz", hash = "sha256:5726c7841294db2a9e5863e20718878d16e28722bdaf3169a278ff3bda2f0be7"},
{file = "langsmith-0.0.23-py3-none-any.whl", hash = "sha256:68a48ecd5b389d14de55e9b6ac237a280ed3720814907277e25026b18e037103"},
{file = "langsmith-0.0.23.tar.gz", hash = "sha256:2d26d78bef5ab928da632d0dfbae8985afa6ffdaec5a895dfa277ed24c6936fd"},
]
[package.dependencies]
@ -3723,33 +3757,38 @@ dill = ">=0.3.7"
[[package]]
name = "mypy"
version = "1.5.0"
version = "1.5.1"
description = "Optional static typing for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "mypy-1.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ad3109bec37cc33654de8db30fe8ff3a1bb57ea65144167d68185e6dced9868d"},
{file = "mypy-1.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b4ea3a0241cb005b0ccdbd318fb99619b21ae51bcf1660b95fc22e0e7d3ba4a1"},
{file = "mypy-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1fe816e26e676c1311b9e04fd576543b873576d39439f7c24c8e5c7728391ecf"},
{file = "mypy-1.5.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:42170e68adb1603ccdc55a30068f72bcfcde2ce650188e4c1b2a93018b826735"},
{file = "mypy-1.5.0-cp310-cp310-win_amd64.whl", hash = "sha256:d145b81a8214687cfc1f85c03663a5bbe736777410e5580e54d526e7e904f564"},
{file = "mypy-1.5.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c36011320e452eb30bec38b9fd3ba20569dc9545d7d4540d967f3ea1fab9c374"},
{file = "mypy-1.5.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f3940cf5845b2512b3ab95463198b0cdf87975dfd17fdcc6ce9709a9abe09e69"},
{file = "mypy-1.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9166186c498170e1ff478a7f540846b2169243feb95bc228d39a67a1a450cdc6"},
{file = "mypy-1.5.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:725b57a19b7408ef66a0fd9db59b5d3e528922250fb56e50bded27fea9ff28f0"},
{file = "mypy-1.5.0-cp311-cp311-win_amd64.whl", hash = "sha256:eec5c927aa4b3e8b4781840f1550079969926d0a22ce38075f6cfcf4b13e3eb4"},
{file = "mypy-1.5.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:79c520aa24f21852206b5ff2cf746dc13020113aa73fa55af504635a96e62718"},
{file = "mypy-1.5.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:769ddb6bfe55c2bd9c7d6d7020885a5ea14289619db7ee650e06b1ef0852c6f4"},
{file = "mypy-1.5.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cbf18f8db7e5f060d61c91e334d3b96d6bb624ddc9ee8a1cde407b737acbca2c"},
{file = "mypy-1.5.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:a2500ad063413bc873ae102cf655bf49889e0763b260a3a7cf544a0cbbf7e70a"},
{file = "mypy-1.5.0-cp38-cp38-win_amd64.whl", hash = "sha256:84cf9f7d8a8a22bb6a36444480f4cbf089c917a4179fbf7eea003ea931944a7f"},
{file = "mypy-1.5.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a551ed0fc02455fe2c1fb0145160df8336b90ab80224739627b15ebe2b45e9dc"},
{file = "mypy-1.5.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:372fd97293ed0076d52695849f59acbbb8461c4ab447858cdaeaf734a396d823"},
{file = "mypy-1.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8a7444d6fcac7e2585b10abb91ad900a576da7af8f5cffffbff6065d9115813"},
{file = "mypy-1.5.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:35b13335c6c46a386577a51f3d38b2b5d14aa619e9633bb756bd77205e4bd09f"},
{file = "mypy-1.5.0-cp39-cp39-win_amd64.whl", hash = "sha256:2c9d570f53908cbea326ad8f96028a673b814d9dca7515bf71d95fa662c3eb6f"},
{file = "mypy-1.5.0-py3-none-any.whl", hash = "sha256:69b32d0dedd211b80f1b7435644e1ef83033a2af2ac65adcdc87c38db68a86be"},
{file = "mypy-1.5.0.tar.gz", hash = "sha256:f3460f34b3839b9bc84ee3ed65076eb827cd99ed13ed08d723f9083cada4a212"},
{file = "mypy-1.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f33592ddf9655a4894aef22d134de7393e95fcbdc2d15c1ab65828eee5c66c70"},
{file = "mypy-1.5.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:258b22210a4a258ccd077426c7a181d789d1121aca6db73a83f79372f5569ae0"},
{file = "mypy-1.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a9ec1f695f0c25986e6f7f8778e5ce61659063268836a38c951200c57479cc12"},
{file = "mypy-1.5.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:abed92d9c8f08643c7d831300b739562b0a6c9fcb028d211134fc9ab20ccad5d"},
{file = "mypy-1.5.1-cp310-cp310-win_amd64.whl", hash = "sha256:a156e6390944c265eb56afa67c74c0636f10283429171018446b732f1a05af25"},
{file = "mypy-1.5.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6ac9c21bfe7bc9f7f1b6fae441746e6a106e48fc9de530dea29e8cd37a2c0cc4"},
{file = "mypy-1.5.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:51cb1323064b1099e177098cb939eab2da42fea5d818d40113957ec954fc85f4"},
{file = "mypy-1.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:596fae69f2bfcb7305808c75c00f81fe2829b6236eadda536f00610ac5ec2243"},
{file = "mypy-1.5.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:32cb59609b0534f0bd67faebb6e022fe534bdb0e2ecab4290d683d248be1b275"},
{file = "mypy-1.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:159aa9acb16086b79bbb0016145034a1a05360626046a929f84579ce1666b315"},
{file = "mypy-1.5.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f6b0e77db9ff4fda74de7df13f30016a0a663928d669c9f2c057048ba44f09bb"},
{file = "mypy-1.5.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:26f71b535dfc158a71264e6dc805a9f8d2e60b67215ca0bfa26e2e1aa4d4d373"},
{file = "mypy-1.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2fc3a600f749b1008cc75e02b6fb3d4db8dbcca2d733030fe7a3b3502902f161"},
{file = "mypy-1.5.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:26fb32e4d4afa205b24bf645eddfbb36a1e17e995c5c99d6d00edb24b693406a"},
{file = "mypy-1.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:82cb6193de9bbb3844bab4c7cf80e6227d5225cc7625b068a06d005d861ad5f1"},
{file = "mypy-1.5.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:4a465ea2ca12804d5b34bb056be3a29dc47aea5973b892d0417c6a10a40b2d65"},
{file = "mypy-1.5.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:9fece120dbb041771a63eb95e4896791386fe287fefb2837258925b8326d6160"},
{file = "mypy-1.5.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d28ddc3e3dfeab553e743e532fb95b4e6afad51d4706dd22f28e1e5e664828d2"},
{file = "mypy-1.5.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:57b10c56016adce71fba6bc6e9fd45d8083f74361f629390c556738565af8eeb"},
{file = "mypy-1.5.1-cp38-cp38-win_amd64.whl", hash = "sha256:ff0cedc84184115202475bbb46dd99f8dcb87fe24d5d0ddfc0fe6b8575c88d2f"},
{file = "mypy-1.5.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8f772942d372c8cbac575be99f9cc9d9fb3bd95c8bc2de6c01411e2c84ebca8a"},
{file = "mypy-1.5.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5d627124700b92b6bbaa99f27cbe615c8ea7b3402960f6372ea7d65faf376c14"},
{file = "mypy-1.5.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:361da43c4f5a96173220eb53340ace68cda81845cd88218f8862dfb0adc8cddb"},
{file = "mypy-1.5.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:330857f9507c24de5c5724235e66858f8364a0693894342485e543f5b07c8693"},
{file = "mypy-1.5.1-cp39-cp39-win_amd64.whl", hash = "sha256:c543214ffdd422623e9fedd0869166c2f16affe4ba37463975043ef7d2ea8770"},
{file = "mypy-1.5.1-py3-none-any.whl", hash = "sha256:f757063a83970d67c444f6e01d9550a7402322af3557ce7630d3c957386fa8f5"},
{file = "mypy-1.5.1.tar.gz", hash = "sha256:b031b9601f1060bf1281feab89697324726ba0c0bae9d7cd7ab4b690940f0b92"},
]
[package.dependencies]
@ -4451,6 +4490,23 @@ files = [
qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
testing = ["docopt", "pytest (<6.0.0)"]
[[package]]
name = "passlib"
version = "1.7.4"
description = "comprehensive password hashing framework supporting over 30 schemes"
optional = false
python-versions = "*"
files = [
{file = "passlib-1.7.4-py2.py3-none-any.whl", hash = "sha256:aa6bca462b8d8bda89c70b382f0c298a20b5560af6cbfa2dce410c0a2fb669f1"},
{file = "passlib-1.7.4.tar.gz", hash = "sha256:defd50f72b65c5402ab2c573830a6978e5f202ad0d984793c8dde2c4152ebe04"},
]
[package.extras]
argon2 = ["argon2-cffi (>=18.2.0)"]
bcrypt = ["bcrypt (>=3.1.0)"]
build-docs = ["cloud-sptheme (>=1.10.1)", "sphinx (>=1.6)", "sphinxcontrib-fulltoc (>=1.2.0)"]
totp = ["cryptography"]
[[package]]
name = "pathspec"
version = "0.11.2"
@ -7351,6 +7407,17 @@ files = [
{file = "validators-0.21.0.tar.gz", hash = "sha256:245b98ab778ed9352a7269c6a8f6c2a839bed5b2a7e3e60273ce399d247dd4b3"},
]
[[package]]
name = "vine"
version = "5.0.0"
description = "Promises, promises, promises."
optional = true
python-versions = ">=3.6"
files = [
{file = "vine-5.0.0-py2.py3-none-any.whl", hash = "sha256:4c9dceab6f76ed92105027c49c823800dd33cacce13bdedc5b914e3514b7fb30"},
{file = "vine-5.0.0.tar.gz", hash = "sha256:7d3b1624a953da82ef63462013bbd271d3eb75751489f9807598e8f340bd637e"},
]
[[package]]
name = "watchfiles"
version = "0.19.0"
@ -7842,4 +7909,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.11"
content-hash = "00a3de3cc8916619f8434289b23a51e768b7b7c74f33e29c0ea3c61688ea3874"
content-hash = "c1581fd2c650cc1365793ca33278917dd86bb4527cc956106a82a5c513ecc483"

View file

@ -45,7 +45,7 @@ unstructured = "^0.7.0"
pypdf = "^3.11.0"
lxml = "^4.9.2"
pysrt = "^1.1.2"
fake-useragent = "^1.1.3"
fake-useragent = "^1.2.1"
docstring-parser = "^0.15"
psycopg2-binary = "^2.9.6"
pyarrow = "^12.0.0"
@ -81,6 +81,9 @@ celery = { extras = ["redis"], version = "^5.3.1", optional = true }
redis = { version = "^4.6.0", optional = true }
flower = { version = "^2.0.0", optional = true }
alembic = "^1.11.2"
passlib = "^1.7.4"
bcrypt = "^4.0.1"
python-jose = "^3.3.0"
metaphor-python = "^0.1.11"

View file

@ -11,4 +11,4 @@ RUN rm *.whl
EXPOSE 80
CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "80", "langflow.backend.app:app" ]
CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "7860", "--factory", "langflow.main:create_app" ]

View file

View file

@ -0,0 +1,177 @@
from uuid import UUID
from typing import Annotated
from jose import JWTError, jwt
from sqlalchemy.orm import Session
from passlib.context import CryptContext
from fastapi.security import OAuth2PasswordBearer
from fastapi import Depends, HTTPException, status
from datetime import datetime, timedelta, timezone
from langflow.services.utils import get_settings_manager
from langflow.services.utils import get_session
from langflow.database.models.user import (
User,
get_user_by_id,
get_user_by_username,
update_user_last_login_at,
)
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="login")
async def get_current_user(
token: Annotated[str, Depends(oauth2_scheme)], db: Session = Depends(get_session)
) -> User:
settings_manager = get_settings_manager()
credentials_exception = HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Could not validate credentials",
headers={"WWW-Authenticate": "Bearer"},
)
try:
payload = jwt.decode(
token,
settings_manager.settings.SECRET_KEY,
algorithms=[settings_manager.settings.ALGORITHM],
)
user_id: UUID = payload.get("sub") # type: ignore
token_type: str = payload.get("type") # type: ignore
if user_id is None or token_type:
raise credentials_exception
except JWTError as e:
raise credentials_exception from e
user = get_user_by_id(db, user_id) # type: ignore
if user is None:
raise credentials_exception
return user
async def get_current_active_user(
current_user: Annotated[User, Depends(get_current_user)]
):
if not current_user.is_active:
raise HTTPException(status_code=400, detail="Inactive user")
return current_user
def verify_password(plain_password, hashed_password):
return pwd_context.verify(plain_password, hashed_password)
def get_password_hash(password):
return pwd_context.hash(password)
def create_token(data: dict, expires_delta: timedelta):
settings_manager = get_settings_manager()
to_encode = data.copy()
expire = datetime.now(timezone.utc) + expires_delta
to_encode["exp"] = expire
return jwt.encode(
to_encode,
settings_manager.settings.SECRET_KEY,
algorithm=settings_manager.settings.ALGORITHM,
)
def create_user_longterm_token(
user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False
) -> dict:
access_token_expires_longterm = timedelta(days=365)
access_token = create_token(
data={"sub": str(user_id)},
expires_delta=access_token_expires_longterm,
)
# Update: last_login_at
if update_last_login:
update_user_last_login_at(user_id, db)
return {
"access_token": access_token,
"refresh_token": None,
"token_type": "bearer",
}
def create_user_tokens(
user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False
) -> dict:
settings_manager = get_settings_manager()
access_token_expires = timedelta(
minutes=settings_manager.settings.ACCESS_TOKEN_EXPIRE_MINUTES
)
access_token = create_token(
data={"sub": str(user_id)},
expires_delta=access_token_expires,
)
refresh_token_expires = timedelta(
minutes=settings_manager.settings.REFRESH_TOKEN_EXPIRE_MINUTES
)
refresh_token = create_token(
data={"sub": str(user_id), "type": "rf"},
expires_delta=refresh_token_expires,
)
# Update: last_login_at
if update_last_login:
update_user_last_login_at(user_id, db)
return {
"access_token": access_token,
"refresh_token": refresh_token,
"token_type": "bearer",
}
def create_refresh_token(refresh_token: str, db: Session = Depends(get_session)):
settings_manager = get_settings_manager()
try:
payload = jwt.decode(
refresh_token,
settings_manager.settings.SECRET_KEY,
algorithms=[settings_manager.settings.ALGORITHM],
)
user_id: UUID = payload.get("sub") # type: ignore
token_type: str = payload.get("type") # type: ignore
if user_id is None or token_type is None:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token"
)
return create_user_tokens(user_id, db)
except JWTError as e:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid refresh token",
) from e
def authenticate_user(
username: str, password: str, db: Session = Depends(get_session)
) -> User | None:
user = get_user_by_username(db, username)
if not user:
return None
if not user.is_active:
if not user.last_login_at:
raise HTTPException(status_code=400, detail="Waiting for approval")
raise HTTPException(status_code=400, detail="Inactive user")
return user if verify_password(password, user.password) else None

View file

@ -104,6 +104,8 @@ embeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
CohereEmbeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/cohere"
VertexAIEmbeddings:
documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/google_vertex_ai_palm"
llms:
OpenAI:
documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai"
@ -127,8 +129,8 @@ llms:
# There's a bug in this component deactivating until we get it sorted: _language_models.py", line 804, in send_message
# is_blocked=safety_attributes.get("blocked", False),
# AttributeError: 'list' object has no attribute 'get'
# ChatVertexAI:
# documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
ChatVertexAI:
documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
###
memories:
# https://github.com/supabase-community/supabase-py/issues/482

View file

@ -0,0 +1,7 @@
from pydantic import BaseModel
class Token(BaseModel):
access_token: str
refresh_token: str
token_type: str

View file

@ -0,0 +1,94 @@
from sqlmodel import Field
from uuid import UUID, uuid4
from pydantic import BaseModel
from typing import Optional, List
from sqlalchemy.orm import Session
from datetime import timezone, datetime
from sqlalchemy.exc import IntegrityError
from fastapi import HTTPException, Depends
from langflow.services.utils import get_session
from langflow.services.database.models.base import SQLModelSerializable, SQLModel
class User(SQLModelSerializable, table=True):
id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
username: str = Field(index=True, unique=True)
password: str = Field()
is_active: bool = Field(default=False)
is_superuser: bool = Field(default=False)
create_at: datetime = Field(default_factory=datetime.utcnow)
updated_at: datetime = Field(default_factory=datetime.utcnow)
last_login_at: Optional[datetime] = Field()
class UserAddModel(SQLModel):
username: str = Field()
password: str = Field()
class UserListModel(SQLModel):
id: UUID = Field(default_factory=uuid4)
username: str = Field()
is_active: bool = Field()
is_superuser: bool = Field()
create_at: datetime = Field()
updated_at: datetime = Field()
last_login_at: Optional[datetime] = Field()
class UserPatchModel(SQLModel):
username: Optional[str] = Field()
is_active: Optional[bool] = Field()
is_superuser: Optional[bool] = Field()
last_login_at: Optional[datetime] = Field()
class UsersResponse(BaseModel):
total_count: int
users: List[UserListModel]
def get_user_by_username(db: Session, username: str) -> User:
db_user = db.query(User).filter(User.username == username).first()
return User.from_orm(db_user) if db_user else None # type: ignore
def get_user_by_id(db: Session, id: UUID) -> User:
db_user = db.query(User).filter(User.id == id).first()
return User.from_orm(db_user) if db_user else None # type: ignore
def update_user(
user_id: UUID, user: UserPatchModel, db: Session = Depends(get_session)
) -> User:
user_db = get_user_by_username(db, user.username) # type: ignore
if user_db and user_db.id != user_id:
raise HTTPException(status_code=409, detail="Username already exists")
user_db = get_user_by_id(db, user_id)
if not user_db:
raise HTTPException(status_code=404, detail="User not found")
try:
user_data = user.dict(exclude_unset=True)
for key, value in user_data.items():
setattr(user_db, key, value)
user_db.updated_at = datetime.now(timezone.utc)
user_db = db.merge(user_db)
db.commit()
if db.identity_key(instance=user_db) is not None:
db.refresh(user_db)
except IntegrityError as e:
db.rollback()
raise HTTPException(status_code=400, detail=str(e)) from e
return user_db
def update_user_last_login_at(user_id: UUID, db: Session = Depends(get_session)):
user_data = UserPatchModel(last_login_at=datetime.now(timezone.utc)) # type: ignore
return update_user(user_id, user_data, db)

View file

@ -88,7 +88,7 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
elif base_type == "toolkits":
return instantiate_toolkit(node_type, class_object, params)
elif base_type == "embeddings":
return instantiate_embedding(class_object, params)
return instantiate_embedding(node_type, class_object, params)
elif base_type == "vectorstores":
return instantiate_vectorstore(class_object, params)
elif base_type == "documentloaders":
@ -147,7 +147,7 @@ def instantiate_llm(node_type, class_object, params: Dict):
# This is a workaround so JinaChat works until streaming is implemented
# if "openai_api_base" in params and "jina" in params["openai_api_base"]:
# False if condition is True
if node_type == "VertexAI":
if "VertexAI" in node_type:
return initialize_vertexai(class_object=class_object, params=params)
# max_tokens sometimes is a string and should be an int
if "max_tokens" in params:
@ -261,9 +261,13 @@ def instantiate_toolkit(node_type, class_object: Type[BaseToolkit], params: Dict
return loaded_toolkit
def instantiate_embedding(class_object, params: Dict):
def instantiate_embedding(node_type, class_object, params: Dict):
params.pop("model", None)
params.pop("headers", None)
if "VertexAI" in node_type:
return initialize_vertexai(class_object=class_object, params=params)
try:
return class_object(**params)
except ValidationError:

View file

@ -6,6 +6,8 @@ from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from langflow.api import router
from langflow.routers import login, users, health
from langflow.interface.utils import setup_llm_caching
from langflow.services.database.utils import initialize_database
from langflow.services.manager import initialize_services
@ -19,13 +21,7 @@ def create_app():
app = FastAPI()
origins = [
"*",
]
@app.get("/health")
def get_health():
return {"status": "OK"}
origins = ["*"]
app.add_middleware(
CORSMiddleware,
@ -34,6 +30,11 @@ def create_app():
allow_methods=["*"],
allow_headers=["*"],
)
app.include_router(login.router)
app.include_router(users.router)
app.include_router(health.router)
app.include_router(router)
app.on_event("startup")(initialize_services)

View file

View file

@ -0,0 +1,8 @@
from fastapi import APIRouter
router = APIRouter()
@router.get("/health")
def get_health():
return {"status": "OK"}

Some files were not shown because too many files have changed in this diff Show more