formatting and merging
This commit is contained in:
commit
95f7a42569
27 changed files with 418 additions and 422 deletions
1
.dockerignore
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.dockerignore
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@ -0,0 +1 @@
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.venv/
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@ -15,4 +15,4 @@ COPY ./ ./
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# Install dependencies
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RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi
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CMD ["uvicorn","--factory", "langflow.main:create_app", "--host", "0.0.0.0", "--port", "5003", "--reload", "log-level", "debug"]
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CMD ["uvicorn", "--factory", "src.backend.langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug"]
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@ -11,7 +11,7 @@ services:
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[
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"sh",
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"-c",
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"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",
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"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",
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]
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ports:
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- 7860:7860
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@ -9,7 +9,7 @@ services:
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- "7860:7860"
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volumes:
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- ./:/app
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command: bash -c "uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload"
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command: bash -c "uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload"
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frontend:
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build:
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@ -73,3 +73,25 @@ Used to load [OpenAI’s](https://openai.com/) embedding models.
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- **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.
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- **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.
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---
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### VertexAIEmbeddings
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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).
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:::info
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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.
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:::
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- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
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- **location:** The default location to use when making API calls – defaults to `us-central1`.
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- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.
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- **model_name:** The name of the Vertex AI large language model – defaults to `text-bison`.
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- **project:** The default GCP project to use when making Vertex API calls.
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- **request_parallelism:** The amount of parallelism allowed for requests issued to VertexAI models – defaults to `5`.
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- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.
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- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
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- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
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- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
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- **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`.
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@ -185,6 +185,28 @@ Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large lang
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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.
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:::
|
||||
|
||||
- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
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- **location:** The default location to use when making API calls – defaults to `us-central1`.
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- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.
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- **model_name:** The name of the Vertex AI large language model – defaults to `text-bison`.
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- **project:** The default GCP project to use when making Vertex API calls.
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- **request_parallelism:** The amount of parallelism allowed for requests issued to VertexAI models – defaults to `5`.
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- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.
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- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
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- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
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- **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`.
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---
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### ChatVertexAI
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||||
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||||
Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large language models.
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||||
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||||
:::info
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||||
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.
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||||
:::
|
||||
|
||||
- **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`.
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192
poetry.lock
generated
192
poetry.lock
generated
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@ -165,13 +165,13 @@ tz = ["python-dateutil"]
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||||
[[package]]
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||||
name = "anthropic"
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||||
version = "0.3.8"
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||||
version = "0.3.9"
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||||
description = "Client library for the anthropic API"
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optional = false
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||||
python-versions = ">=3.7,<4.0"
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||||
files = [
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||||
{file = "anthropic-0.3.8-py3-none-any.whl", hash = "sha256:97ffe1bacc4214dc89b19f496cf2769746971e86f7c835a05aa21b76f260d279"},
|
||||
{file = "anthropic-0.3.8.tar.gz", hash = "sha256:6651099807456c3b95b3879f5ad7d00f7e7e4f7649a2394d18032ab8be54ef16"},
|
||||
{file = "anthropic-0.3.9-py3-none-any.whl", hash = "sha256:23e8daf266c707faa0b85328ada03dcf885e62c2f236eb0159352e29b9c4e2e3"},
|
||||
{file = "anthropic-0.3.9.tar.gz", hash = "sha256:c19b75308c07cb1ecbea03ffcdde9ba68e5ca22a22479217712396385678bde3"},
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||||
]
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||||
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||||
[package.dependencies]
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||||
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@ -180,7 +180,7 @@ distro = ">=1.7.0,<2"
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httpx = ">=0.23.0,<1"
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pydantic = ">=1.9.0,<2.0.0"
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tokenizers = ">=0.13.0"
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typing-extensions = ">=4.1.1,<5"
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||||
typing-extensions = ">=4.5,<5"
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||||
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||||
[[package]]
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||||
name = "anyio"
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||||
|
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@ -792,13 +792,13 @@ sqlalchemy = ["sqlalchemy (>1.3.21,<2.0)"]
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|||
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||||
[[package]]
|
||||
name = "cohere"
|
||||
version = "4.19.3"
|
||||
version = "4.20.0"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.7,<4.0"
|
||||
files = [
|
||||
{file = "cohere-4.19.3-py3-none-any.whl", hash = "sha256:6c98f1e58b93b6316c824385c1d2032ed352280e9efa5695ba98306258abf84f"},
|
||||
{file = "cohere-4.19.3.tar.gz", hash = "sha256:c3aaa716c4da7d7a8ed68705fcdc92f1b1a2260b737cee6bd27af5c347f31496"},
|
||||
{file = "cohere-4.20.0-py3-none-any.whl", hash = "sha256:bebe4b1d21da0719aaa7db2cc180b5f3d0a802c19ad2d893d627971286e50497"},
|
||||
{file = "cohere-4.20.0.tar.gz", hash = "sha256:a16e86981945c201bab67ad6d47e57f42a37bf2a774f6ac6b8b12233aae6409a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
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@ -858,71 +858,63 @@ typing = ["mypy (>=0.990)"]
|
|||
|
||||
[[package]]
|
||||
name = "coverage"
|
||||
version = "7.2.7"
|
||||
version = "7.3.0"
|
||||
description = "Code coverage measurement for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -978,13 +970,13 @@ test-randomorder = ["pytest-randomly"]
|
|||
|
||||
[[package]]
|
||||
name = "ctransformers"
|
||||
version = "0.2.21"
|
||||
version = "0.2.22"
|
||||
description = "Python bindings for the Transformer models implemented in C/C++ using GGML library."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "ctransformers-0.2.21-py3-none-any.whl", hash = "sha256:18a0555d02f55a3935f5544b885038562f80e497a6197d8e871941a087dba546"},
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||||
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|
||||
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||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1309,13 +1301,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.1.2"
|
||||
version = "1.1.3"
|
||||
description = "Backport of PEP 654 (exception groups)"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "exceptiongroup-1.1.2-py3-none-any.whl", hash = "sha256:e346e69d186172ca7cf029c8c1d16235aa0e04035e5750b4b95039e65204328f"},
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|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -1697,13 +1689,13 @@ six = "*"
|
|||
|
||||
[[package]]
|
||||
name = "google-cloud-aiplatform"
|
||||
version = "1.30.0"
|
||||
version = "1.30.1"
|
||||
description = "Vertex AI API client library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "google-cloud-aiplatform-1.30.0.tar.gz", hash = "sha256:26c16069553d177c3277a4371279871c38e31eab1134906cf83ea905e4203ec4"},
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||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2987,17 +2979,17 @@ test = ["psutil", "pytest", "pytest-asyncio"]
|
|||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.0.21"
|
||||
version = "0.0.22"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
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||||
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|
||||
{file = "langsmith-0.0.22.tar.gz", hash = "sha256:5726c7841294db2a9e5863e20718878d16e28722bdaf3169a278ff3bda2f0be7"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pydantic = ">=1,<2"
|
||||
pydantic = ">=1,<3"
|
||||
requests = ">=2,<3"
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3400,13 +3392,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "metaphor-python"
|
||||
version = "0.1.12"
|
||||
version = "0.1.14"
|
||||
description = "A Python package for the Metaphor API."
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "metaphor-python-0.1.12.tar.gz", hash = "sha256:fb3c33326ad1862954d2a77e5d81a6304219314b958283620cef757df331312c"},
|
||||
{file = "metaphor_python-0.1.12-py3-none-any.whl", hash = "sha256:86210ba825c047461221833c6fe3c010651457b2c733804bca4cf0e5eef89f95"},
|
||||
{file = "metaphor-python-0.1.14.tar.gz", hash = "sha256:9c9be35d1270e1e2984637711c15b614ad852fbd2b51d428b4cc478e95018fab"},
|
||||
{file = "metaphor_python-0.1.14-py3-none-any.whl", hash = "sha256:9435fb702d62032e4affbf7825fe4b1f0c6099d33b4867f4238c07f4515eb9a2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4281,17 +4273,17 @@ xml = ["lxml (>=4.6.3)"]
|
|||
|
||||
[[package]]
|
||||
name = "pandas-stubs"
|
||||
version = "2.0.2.230605"
|
||||
version = "2.0.3.230814"
|
||||
description = "Type annotations for pandas"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pandas_stubs-2.0.2.230605-py3-none-any.whl", hash = "sha256:39106b602f3cb6dc5f728b84e1b32bde6ecf41ee34ee714c66228009609fbada"},
|
||||
{file = "pandas_stubs-2.0.2.230605.tar.gz", hash = "sha256:624c7bb06d38145a44b61be459ccd19b038e0bf20364a025ecaab78fea65e858"},
|
||||
{file = "pandas_stubs-2.0.3.230814-py3-none-any.whl", hash = "sha256:4b3dfc027d49779176b7daa031a3405f7b839bcb6e312f4b9f29fea5feec5b4f"},
|
||||
{file = "pandas_stubs-2.0.3.230814.tar.gz", hash = "sha256:1d5cc09e36e3d9f9a1ed9dceae4e03eeb26d1b898dd769996925f784365c8769"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.24.3"
|
||||
numpy = {version = ">=1.25.0", markers = "python_version >= \"3.9\""}
|
||||
types-pytz = ">=2022.1.1"
|
||||
|
||||
[[package]]
|
||||
|
|
@ -5193,13 +5185,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
|
|||
|
||||
[[package]]
|
||||
name = "pypdf"
|
||||
version = "3.15.0"
|
||||
version = "3.15.1"
|
||||
description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "pypdf-3.15.0-py3-none-any.whl", hash = "sha256:2e29ddb62561ec91157c784783714703ddd3ce08f070ecbc57404fb86cd9fc97"},
|
||||
{file = "pypdf-3.15.0.tar.gz", hash = "sha256:8a6264e1c47c63dc2484e29bdfa76b121435896a84e94b7c5ae82c6ae96354bb"},
|
||||
{file = "pypdf-3.15.1-py3-none-any.whl", hash = "sha256:99b337af7da8046d1e2e94354846e8c56753e1cdc817ac0fbe770c1e2281902b"},
|
||||
{file = "pypdf-3.15.1.tar.gz", hash = "sha256:d0dfaf4f10dfb06ac39e1d6a9cbffd63e77621d1e89c0ef08f346fd902df7b4b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6508,13 +6500,13 @@ widechars = ["wcwidth"]
|
|||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.2.2"
|
||||
version = "8.2.3"
|
||||
description = "Retry code until it succeeds"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tenacity-8.2.2-py3-none-any.whl", hash = "sha256:2f277afb21b851637e8f52e6a613ff08734c347dc19ade928e519d7d2d8569b0"},
|
||||
{file = "tenacity-8.2.2.tar.gz", hash = "sha256:43af037822bd0029025877f3b2d97cc4d7bb0c2991000a3d59d71517c5c969e0"},
|
||||
{file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"},
|
||||
{file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -6960,13 +6952,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "types-pytz"
|
||||
version = "2023.3.0.0"
|
||||
version = "2023.3.0.1"
|
||||
description = "Typing stubs for pytz"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "types-pytz-2023.3.0.0.tar.gz", hash = "sha256:ecdc70d543aaf3616a7e48631543a884f74205f284cefd6649ddf44c6a820aac"},
|
||||
{file = "types_pytz-2023.3.0.0-py3-none-any.whl", hash = "sha256:4fc2a7fbbc315f0b6630e0b899fd6c743705abe1094d007b0e612d10da15e0f3"},
|
||||
{file = "types-pytz-2023.3.0.1.tar.gz", hash = "sha256:1a7b8d4aac70981cfa24478a41eadfcd96a087c986d6f150d77e3ceb3c2bdfab"},
|
||||
{file = "types_pytz-2023.3.0.1-py3-none-any.whl", hash = "sha256:65152e872137926bb67a8fe6cc9cfd794365df86650c5d5fdc7b167b0f38892e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -7701,4 +7693,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.11"
|
||||
content-hash = "18156a4c5ecf05bf430bf2a00da1771c6e16e6408cfb05fe36fc3ff0ad29e213"
|
||||
content-hash = "0f7d3f932eb130e7f3b6d234fc36e403ef409d63cfc825ba9f4c470203425ad2"
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
|
|
|
|||
|
|
@ -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" ]
|
||||
|
|
|
|||
|
|
@ -7,6 +7,8 @@ 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,
|
||||
|
|
@ -16,12 +18,6 @@ from langflow.database.models.user import (
|
|||
)
|
||||
|
||||
|
||||
# TODO: Move to env - JUST FOR TEST!!!!!
|
||||
SECRET_KEY = "698619adad2d916f1f32d264540976964b3c0d3828e0870a65add5800a8cc6b9"
|
||||
ALGORITHM = "HS256"
|
||||
ACCESS_TOKEN_EXPIRE_MINUTES = 60
|
||||
REFRESH_TOKEN_EXPIRE_MINUTES = 70
|
||||
|
||||
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
|
||||
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="login")
|
||||
|
||||
|
|
@ -29,6 +25,8 @@ 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",
|
||||
|
|
@ -36,7 +34,11 @@ async def get_current_user(
|
|||
)
|
||||
|
||||
try:
|
||||
payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
|
||||
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
|
||||
|
||||
|
|
@ -68,29 +70,63 @@ def get_password_hash(password):
|
|||
|
||||
|
||||
def create_token(data: dict, expires_delta: timedelta):
|
||||
to_encode = data.copy()
|
||||
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, SECRET_KEY, algorithm=ALGORITHM)
|
||||
return jwt.encode(
|
||||
to_encode,
|
||||
settings_manager.settings.SECRET_KEY,
|
||||
algorithm=settings_manager.settings.ALGORITHM,
|
||||
)
|
||||
|
||||
|
||||
def create_user_tokens(user_id: UUID, db: Session = Depends(get_session)) -> dict:
|
||||
access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
|
||||
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=REFRESH_TOKEN_EXPIRE_MINUTES)
|
||||
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
|
||||
update_user_last_login_at(user_id, db)
|
||||
if update_last_login:
|
||||
update_user_last_login_at(user_id, db)
|
||||
|
||||
return {
|
||||
"access_token": access_token,
|
||||
|
|
@ -99,9 +135,15 @@ def create_user_tokens(user_id: UUID, db: Session = Depends(get_session)) -> dic
|
|||
}
|
||||
|
||||
|
||||
def create_refresh_token(refresh_token: str):
|
||||
def create_refresh_token(refresh_token: str, db: Session = Depends(get_session)):
|
||||
settings_manager = get_settings_manager()
|
||||
|
||||
try:
|
||||
payload = jwt.decode(refresh_token, SECRET_KEY, algorithms=[ALGORITHM])
|
||||
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
|
||||
|
||||
|
|
@ -110,7 +152,7 @@ def create_refresh_token(refresh_token: str):
|
|||
status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token"
|
||||
)
|
||||
|
||||
return create_user_tokens(user_id)
|
||||
return create_user_tokens(user_id, db)
|
||||
|
||||
except JWTError as e:
|
||||
raise HTTPException(
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
from uuid import UUID
|
||||
from sqlalchemy.orm import Session
|
||||
from fastapi import APIRouter, Depends, HTTPException, status
|
||||
from fastapi.security import OAuth2PasswordRequestForm
|
||||
|
|
@ -8,8 +9,11 @@ from langflow.auth.auth import (
|
|||
authenticate_user,
|
||||
create_user_tokens,
|
||||
create_refresh_token,
|
||||
create_user_longterm_token,
|
||||
)
|
||||
|
||||
from langflow.services.utils import get_settings_manager
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
|
|
@ -20,7 +24,7 @@ async def login_to_get_access_token(
|
|||
# _: Session = Depends(get_current_active_user)
|
||||
):
|
||||
if user := authenticate_user(form_data.username, form_data.password, db):
|
||||
return create_user_tokens(user_id=user.id, db=db)
|
||||
return create_user_tokens(user_id=user.id, db=db, update_last_login=True)
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
|
|
@ -29,6 +33,23 @@ async def login_to_get_access_token(
|
|||
)
|
||||
|
||||
|
||||
@router.get("/auto_login")
|
||||
async def auto_login(db: Session = Depends(get_session)):
|
||||
settings_manager = get_settings_manager()
|
||||
|
||||
if settings_manager.settings.AUTO_LOGIN:
|
||||
user_id = UUID("3fa85f64-5717-4562-b3fc-2c963f66afa6")
|
||||
return create_user_longterm_token(user_id, db)
|
||||
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail={
|
||||
"message": "Auto login is disabled. Please enable it in the settings",
|
||||
"auto_login": False,
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@router.post("/refresh")
|
||||
async def refresh_token(token: str):
|
||||
if token:
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import contextlib
|
||||
import json
|
||||
import os
|
||||
import secrets
|
||||
from typing import Optional, List
|
||||
from pathlib import Path
|
||||
|
||||
|
|
@ -35,6 +36,15 @@ class Settings(BaseSettings):
|
|||
REMOVE_API_KEYS: bool = False
|
||||
COMPONENTS_PATH: List[str] = []
|
||||
|
||||
# Login settings
|
||||
SECRET_KEY: str = secrets.token_hex(32)
|
||||
ALGORITHM: str = "HS256"
|
||||
ACCESS_TOKEN_EXPIRE_MINUTES: int = 60
|
||||
REFRESH_TOKEN_EXPIRE_MINUTES: int = 70
|
||||
# If AUTO_LOGIN = True
|
||||
# > The application does not request login and logs in automatically as a super user.
|
||||
AUTO_LOGIN: bool = True
|
||||
|
||||
@validator("DATABASE_URL", pre=True)
|
||||
def set_database_url(cls, value):
|
||||
if not value:
|
||||
|
|
|
|||
|
|
@ -1,171 +0,0 @@
|
|||
import contextlib
|
||||
import json
|
||||
import os
|
||||
from typing import Optional, List
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
from pydantic import BaseSettings, root_validator, validator
|
||||
from langflow.utils.logger import logger
|
||||
|
||||
BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components")
|
||||
|
||||
|
||||
class Settings(BaseSettings):
|
||||
CHAINS: dict = {}
|
||||
AGENTS: dict = {}
|
||||
PROMPTS: dict = {}
|
||||
LLMS: dict = {}
|
||||
TOOLS: dict = {}
|
||||
MEMORIES: dict = {}
|
||||
EMBEDDINGS: dict = {}
|
||||
VECTORSTORES: dict = {}
|
||||
DOCUMENTLOADERS: dict = {}
|
||||
WRAPPERS: dict = {}
|
||||
RETRIEVERS: dict = {}
|
||||
TOOLKITS: dict = {}
|
||||
TEXTSPLITTERS: dict = {}
|
||||
UTILITIES: dict = {}
|
||||
OUTPUT_PARSERS: dict = {}
|
||||
CUSTOM_COMPONENTS: dict = {}
|
||||
|
||||
DEV: bool = False
|
||||
DATABASE_URL: Optional[str] = None
|
||||
CACHE: str = "InMemoryCache"
|
||||
REMOVE_API_KEYS: bool = False
|
||||
COMPONENTS_PATH: List[str] = []
|
||||
|
||||
@validator("DATABASE_URL", pre=True)
|
||||
def set_database_url(cls, value):
|
||||
if not value:
|
||||
logger.debug(
|
||||
"No database_url provided, trying LANGFLOW_DATABASE_URL env variable"
|
||||
)
|
||||
if langflow_database_url := os.getenv("LANGFLOW_DATABASE_URL"):
|
||||
value = langflow_database_url
|
||||
logger.debug("Using LANGFLOW_DATABASE_URL env variable.")
|
||||
else:
|
||||
logger.debug("No DATABASE_URL env variable, using sqlite database")
|
||||
value = "sqlite:///./langflow.db"
|
||||
|
||||
return value
|
||||
|
||||
@validator("COMPONENTS_PATH", pre=True)
|
||||
def set_components_path(cls, value):
|
||||
if os.getenv("LANGFLOW_COMPONENTS_PATH"):
|
||||
logger.debug("Adding LANGFLOW_COMPONENTS_PATH to components_path")
|
||||
langflow_component_path = os.getenv("LANGFLOW_COMPONENTS_PATH")
|
||||
if (
|
||||
Path(langflow_component_path).exists()
|
||||
and langflow_component_path not in value
|
||||
):
|
||||
if isinstance(langflow_component_path, list):
|
||||
for path in langflow_component_path:
|
||||
if path not in value:
|
||||
value.append(path)
|
||||
logger.debug(
|
||||
f"Extending {langflow_component_path} to components_path"
|
||||
)
|
||||
elif langflow_component_path not in value:
|
||||
value.append(langflow_component_path)
|
||||
logger.debug(
|
||||
f"Appending {langflow_component_path} to components_path"
|
||||
)
|
||||
|
||||
if not value:
|
||||
value = [BASE_COMPONENTS_PATH]
|
||||
logger.debug("Setting default components path to components_path")
|
||||
elif BASE_COMPONENTS_PATH not in value:
|
||||
value.append(BASE_COMPONENTS_PATH)
|
||||
logger.debug("Adding default components path to components_path")
|
||||
|
||||
logger.debug(f"Components path: {value}")
|
||||
return value
|
||||
|
||||
class Config:
|
||||
validate_assignment = True
|
||||
extra = "ignore"
|
||||
env_prefix = "LANGFLOW_"
|
||||
|
||||
@root_validator(allow_reuse=True)
|
||||
def validate_lists(cls, values):
|
||||
for key, value in values.items():
|
||||
if key != "dev" and not value:
|
||||
values[key] = []
|
||||
return values
|
||||
|
||||
def update_from_yaml(self, file_path: str, dev: bool = False):
|
||||
new_settings = load_settings_from_yaml(file_path)
|
||||
self.CHAINS = new_settings.CHAINS or {}
|
||||
self.AGENTS = new_settings.AGENTS or {}
|
||||
self.PROMPTS = new_settings.PROMPTS or {}
|
||||
self.LLMS = new_settings.LLMS or {}
|
||||
self.TOOLS = new_settings.TOOLS or {}
|
||||
self.MEMORIES = new_settings.MEMORIES or {}
|
||||
self.WRAPPERS = new_settings.WRAPPERS or {}
|
||||
self.TOOLKITS = new_settings.TOOLKITS or {}
|
||||
self.TEXTSPLITTERS = new_settings.TEXTSPLITTERS or {}
|
||||
self.UTILITIES = new_settings.UTILITIES or {}
|
||||
self.EMBEDDINGS = new_settings.EMBEDDINGS or {}
|
||||
self.VECTORSTORES = new_settings.VECTORSTORES or {}
|
||||
self.DOCUMENTLOADERS = new_settings.DOCUMENTLOADERS or {}
|
||||
self.RETRIEVERS = new_settings.RETRIEVERS or {}
|
||||
self.OUTPUT_PARSERS = new_settings.OUTPUT_PARSERS or {}
|
||||
self.CUSTOM_COMPONENTS = new_settings.CUSTOM_COMPONENTS or {}
|
||||
self.COMPONENTS_PATH = new_settings.COMPONENTS_PATH or []
|
||||
self.DEV = dev
|
||||
|
||||
def update_settings(self, **kwargs):
|
||||
logger.debug("Updating settings")
|
||||
for key, value in kwargs.items():
|
||||
# value may contain sensitive information, so we don't want to log it
|
||||
if not hasattr(self, key):
|
||||
logger.debug(f"Key {key} not found in settings")
|
||||
continue
|
||||
logger.debug(f"Updating {key}")
|
||||
if isinstance(getattr(self, key), list):
|
||||
# value might be a '[something]' string
|
||||
with contextlib.suppress(json.decoder.JSONDecodeError):
|
||||
value = json.loads(str(value))
|
||||
if isinstance(value, list):
|
||||
for item in value:
|
||||
if item not in getattr(self, key):
|
||||
getattr(self, key).append(item)
|
||||
logger.debug(f"Extended {key}")
|
||||
else:
|
||||
getattr(self, key).append(value)
|
||||
logger.debug(f"Appended {key}")
|
||||
|
||||
else:
|
||||
setattr(self, key, value)
|
||||
logger.debug(f"Updated {key}")
|
||||
logger.debug(f"{key}: {getattr(self, key)}")
|
||||
|
||||
|
||||
def save_settings_to_yaml(settings: Settings, file_path: str):
|
||||
with open(file_path, "w") as f:
|
||||
settings_dict = settings.dict()
|
||||
yaml.dump(settings_dict, f)
|
||||
|
||||
|
||||
def load_settings_from_yaml(file_path: str) -> Settings:
|
||||
# Check if a string is a valid path or a file name
|
||||
if "/" not in file_path:
|
||||
# Get current path
|
||||
current_path = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
file_path = os.path.join(current_path, file_path)
|
||||
|
||||
with open(file_path, "r") as f:
|
||||
settings_dict = yaml.safe_load(f)
|
||||
settings_dict = {k.upper(): v for k, v in settings_dict.items()}
|
||||
|
||||
for key in settings_dict:
|
||||
if key not in Settings.__fields__.keys():
|
||||
raise KeyError(f"Key {key} not found in settings")
|
||||
logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}")
|
||||
|
||||
return Settings(**settings_dict)
|
||||
|
||||
|
||||
settings = load_settings_from_yaml("config.yaml")
|
||||
|
|
@ -5,6 +5,47 @@ from langflow.template.frontend_node.base import FrontendNode
|
|||
|
||||
|
||||
class EmbeddingFrontendNode(FrontendNode):
|
||||
def add_extra_fields(self) -> None:
|
||||
if "VertexAI" in self.template.type_name:
|
||||
# Add credentials field which should of type file.
|
||||
self.template.add_field(
|
||||
TemplateField(
|
||||
field_type="file",
|
||||
required=False,
|
||||
show=True,
|
||||
name="credentials",
|
||||
value="",
|
||||
suffixes=[".json"],
|
||||
file_types=["json"],
|
||||
)
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def format_vertex_field(field: TemplateField, name: str):
|
||||
if "VertexAI" in name:
|
||||
advanced_fields = [
|
||||
"verbose",
|
||||
"top_p",
|
||||
"top_k",
|
||||
"max_output_tokens",
|
||||
]
|
||||
if field.name in advanced_fields:
|
||||
field.advanced = True
|
||||
show_fields = [
|
||||
"verbose",
|
||||
"project",
|
||||
"location",
|
||||
"credentials",
|
||||
"max_output_tokens",
|
||||
"model_name",
|
||||
"temperature",
|
||||
"top_p",
|
||||
"top_k",
|
||||
]
|
||||
|
||||
if field.name in show_fields:
|
||||
field.show = True
|
||||
|
||||
@staticmethod
|
||||
def format_jina_fields(field: TemplateField):
|
||||
if "jina" in field.name:
|
||||
|
|
@ -41,10 +82,36 @@ class EmbeddingFrontendNode(FrontendNode):
|
|||
@staticmethod
|
||||
def format_field(field: TemplateField, name: Optional[str] = None) -> None:
|
||||
FrontendNode.format_field(field, name)
|
||||
if name and "vertex" in name.lower():
|
||||
EmbeddingFrontendNode.format_vertex_field(field, name)
|
||||
field.advanced = not field.required
|
||||
field.show = True
|
||||
if field.name == "headers":
|
||||
field.show = False
|
||||
if field.name == "model_kwargs":
|
||||
field.field_type = "code"
|
||||
field.advanced = True
|
||||
field.show = True
|
||||
elif field.name in [
|
||||
"model_name",
|
||||
"temperature",
|
||||
"model_file",
|
||||
"model_type",
|
||||
"deployment_name",
|
||||
"credentials",
|
||||
]:
|
||||
field.advanced = False
|
||||
field.show = True
|
||||
if field.name == "credentials":
|
||||
field.field_type = "file"
|
||||
if name == "VertexAI" and field.name not in [
|
||||
"callbacks",
|
||||
"client",
|
||||
"stop",
|
||||
"tags",
|
||||
"cache",
|
||||
]:
|
||||
field.show = True
|
||||
|
||||
# Format Jina fields
|
||||
EmbeddingFrontendNode.format_jina_fields(field)
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ export const CatchAllRoute = () => {
|
|||
|
||||
// Redirect to the root ("/") when the catch-all route is matched
|
||||
useEffect(() => {
|
||||
navigate('/');
|
||||
navigate("/");
|
||||
}, []);
|
||||
|
||||
return null;
|
||||
|
|
|
|||
|
|
@ -611,4 +611,4 @@ export function tabsArray(codes: string[], method: number) {
|
|||
|
||||
export const BASE_URL_API = "http://localhost:7860/";
|
||||
|
||||
export let LOCALHOST_JWT = true;
|
||||
export let LOCALHOST_JWT = false;
|
||||
|
|
@ -47,14 +47,11 @@ function ApiInterceptor() {
|
|||
navigate("/login");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if(!refreshToken && error?.config?.url?.includes(
|
||||
"login"
|
||||
) ){
|
||||
return Promise.reject(error);
|
||||
}
|
||||
else{
|
||||
|
||||
if (!refreshToken && error?.config?.url?.includes("login")) {
|
||||
return Promise.reject(error);
|
||||
} else {
|
||||
logout();
|
||||
navigate("/login");
|
||||
}
|
||||
|
|
@ -63,7 +60,6 @@ function ApiInterceptor() {
|
|||
return Promise.reject(error);
|
||||
// }
|
||||
}
|
||||
|
||||
}
|
||||
);
|
||||
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ import {
|
|||
getPythonApiCode,
|
||||
getPythonCode,
|
||||
getWidgetCode,
|
||||
tabsArray,
|
||||
} from "../../utils/utils";
|
||||
import BaseModal from "../baseModal";
|
||||
|
||||
|
|
@ -48,39 +49,14 @@ const ApiModal = forwardRef(
|
|||
const pythonCode = getPythonCode(flow, tweak.current, tabsState);
|
||||
const widgetCode = getWidgetCode(flow, tabsState);
|
||||
const tweaksCode = buildTweaks(flow);
|
||||
const [tabs, setTabs] = useState([
|
||||
{
|
||||
name: "cURL",
|
||||
mode: "bash",
|
||||
image: "https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: "sh",
|
||||
code: curl_code,
|
||||
},
|
||||
{
|
||||
name: "Python API",
|
||||
mode: "python",
|
||||
image:
|
||||
"https://images.squarespace-cdn.com/content/v1/5df3d8c5d2be5962e4f87890/1628015119369-OY4TV3XJJ53ECO0W2OLQ/Python+API+Training+Logo.png?format=1000w",
|
||||
language: "py",
|
||||
code: pythonApiCode,
|
||||
},
|
||||
{
|
||||
name: "Python Code",
|
||||
mode: "python",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: pythonCode,
|
||||
},
|
||||
{
|
||||
name: "Chat Widget HTML",
|
||||
description:
|
||||
"Insert this code anywhere in your <body> tag. To use with react and other libs, check our <a class='link-color' href='https://docs.langflow.org/guidelines/widget'>documentation</a>.",
|
||||
mode: "html",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: widgetCode,
|
||||
},
|
||||
]);
|
||||
const codesArray = [
|
||||
curl_code,
|
||||
pythonApiCode,
|
||||
pythonCode,
|
||||
widgetCode,
|
||||
pythonCode,
|
||||
];
|
||||
const [tabs, setTabs] = useState(tabsArray(codesArray, 0));
|
||||
|
||||
function startState() {
|
||||
tweak.current = [];
|
||||
|
|
@ -101,80 +77,9 @@ const ApiModal = forwardRef(
|
|||
|
||||
if (Object.keys(tweaksCode).length > 0) {
|
||||
setActiveTab("0");
|
||||
setTabs([
|
||||
{
|
||||
name: "cURL",
|
||||
mode: "bash",
|
||||
image: "https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: "sh",
|
||||
code: curl_code,
|
||||
},
|
||||
{
|
||||
name: "Python API",
|
||||
mode: "python",
|
||||
image:
|
||||
"https://images.squarespace-cdn.com/content/v1/5df3d8c5d2be5962e4f87890/1628015119369-OY4TV3XJJ53ECO0W2OLQ/Python+API+Training+Logo.png?format=1000w",
|
||||
language: "py",
|
||||
code: pythonApiCode,
|
||||
},
|
||||
{
|
||||
name: "Python Code",
|
||||
mode: "python",
|
||||
language: "py",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
code: pythonCode,
|
||||
},
|
||||
{
|
||||
name: "Chat Widget HTML",
|
||||
description:
|
||||
"Insert this code anywhere in your <body> tag. To use with react and other libs, check our <a class='link-color' href='https://docs.langflow.org/guidelines/widget'>documentation</a>.",
|
||||
mode: "html",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: widgetCode,
|
||||
},
|
||||
{
|
||||
name: "Tweaks",
|
||||
mode: "python",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: pythonCode,
|
||||
},
|
||||
]);
|
||||
setTabs(tabsArray(codesArray, 1));
|
||||
} else {
|
||||
setTabs([
|
||||
{
|
||||
name: "cURL",
|
||||
mode: "bash",
|
||||
image: "https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: "sh",
|
||||
code: curl_code,
|
||||
},
|
||||
{
|
||||
name: "Python API",
|
||||
mode: "python",
|
||||
image:
|
||||
"https://images.squarespace-cdn.com/content/v1/5df3d8c5d2be5962e4f87890/1628015119369-OY4TV3XJJ53ECO0W2OLQ/Python+API+Training+Logo.png?format=1000w",
|
||||
language: "py",
|
||||
code: pythonApiCode,
|
||||
},
|
||||
{
|
||||
name: "Python Code",
|
||||
mode: "python",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: pythonCode,
|
||||
},
|
||||
{
|
||||
name: "Chat Widget HTML",
|
||||
description:
|
||||
"Insert this code anywhere in your <body> tag. To use with react and other libs, check our <a class='link-color' href='https://docs.langflow.org/guidelines/widget'>documentation</a>.",
|
||||
mode: "html",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: widgetCode,
|
||||
},
|
||||
]);
|
||||
setTabs(tabsArray(codesArray, 1));
|
||||
}
|
||||
}, [flow["data"]["nodes"], open]);
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@ import {
|
|||
} from "../../types/components";
|
||||
import { nodeIconsLucide } from "../../utils/styleUtils";
|
||||
import BaseModal from "../baseModal";
|
||||
import InputComponent from "../../components/inputComponent";
|
||||
|
||||
export default function UserManagementModal({
|
||||
title,
|
||||
|
|
|
|||
|
|
@ -160,7 +160,8 @@ export default function FormModal({
|
|||
}
|
||||
|
||||
function getWebSocketUrl(chatId, isDevelopment = false) {
|
||||
const isSecureProtocol = window.location.protocol === "https:";
|
||||
const isSecureProtocol =
|
||||
window.location.protocol === "https:" || window.location.port === "443";
|
||||
const webSocketProtocol = isSecureProtocol ? "wss" : "ws";
|
||||
const host = isDevelopment ? "localhost:7860" : window.location.host;
|
||||
const chatEndpoint = `/api/v1/chat/${chatId}`;
|
||||
|
|
|
|||
|
|
@ -251,7 +251,7 @@ export default function AdminPage() {
|
|||
)}
|
||||
<div
|
||||
className={
|
||||
"max-h-[26rem] overflow-scroll overflow-x-hidden rounded-md border-2 bg-muted custom-scroll" +
|
||||
"max-h-[26rem] min-h-[26rem] overflow-scroll overflow-x-hidden rounded-md border-2 bg-muted custom-scroll" +
|
||||
(loadingUsers ? " border-0" : "")
|
||||
}
|
||||
>
|
||||
|
|
@ -362,7 +362,7 @@ export default function AdminPage() {
|
|||
titleHeader={`${user.id}`}
|
||||
cancelText="Cancel"
|
||||
confirmationText="Edit"
|
||||
icon={"UserCog2"}
|
||||
icon={"UserPlus2"}
|
||||
data={user}
|
||||
index={index}
|
||||
onConfirm={(index, editUser) => {
|
||||
|
|
|
|||
|
|
@ -1,5 +1,8 @@
|
|||
import { Route, Routes } from "react-router-dom";
|
||||
import { ProtectedAdminRoute } from "./components/authAdminGuard";
|
||||
import { ProtectedRoute } from "./components/authGuard";
|
||||
import { ProtectedLoginRoute } from "./components/authLoginGuard";
|
||||
import { CatchAllRoute } from "./components/catchAllRoutes";
|
||||
import AdminPage from "./pages/AdminPage";
|
||||
import LoginAdminPage from "./pages/AdminPage/LoginPage";
|
||||
import CommunityPage from "./pages/CommunityPage";
|
||||
|
|
@ -8,9 +11,6 @@ import HomePage from "./pages/MainPage";
|
|||
import DeleteAccountPage from "./pages/deleteAccountPage";
|
||||
import LoginPage from "./pages/loginPage";
|
||||
import SignUp from "./pages/signUpPage";
|
||||
import { CatchAllRoute } from "./components/catchAllRoutes";
|
||||
import { ProtectedRoute } from "./components/authGuard";
|
||||
import { ProtectedAdminRoute } from "./components/authAdminGuard";
|
||||
|
||||
const Router = () => {
|
||||
return (
|
||||
|
|
|
|||
|
|
@ -214,6 +214,7 @@ export const nodeIconsLucide = {
|
|||
SupabaseVectorStore: SupabaseIcon,
|
||||
VertexAI: VertexAIIcon,
|
||||
ChatVertexAI: VertexAIIcon,
|
||||
VertexAIEmbeddings: VertexAIIcon,
|
||||
agents: Rocket,
|
||||
WikipediaAPIWrapper: SvgWikipedia,
|
||||
chains: Link,
|
||||
|
|
|
|||
|
|
@ -410,3 +410,82 @@ chat_input_field: Input key that you want the chat to send the user message with
|
|||
}host_url="http://localhost:7860"
|
||||
></langflow-chat>`;
|
||||
}
|
||||
|
||||
export function tabsArray(codes: string[], method: number) {
|
||||
if (!method) return;
|
||||
if (method === 0) {
|
||||
return [
|
||||
{
|
||||
name: "cURL",
|
||||
mode: "bash",
|
||||
image: "https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: "sh",
|
||||
code: codes[0],
|
||||
},
|
||||
{
|
||||
name: "Python API",
|
||||
mode: "python",
|
||||
image:
|
||||
"https://images.squarespace-cdn.com/content/v1/5df3d8c5d2be5962e4f87890/1628015119369-OY4TV3XJJ53ECO0W2OLQ/Python+API+Training+Logo.png?format=1000w",
|
||||
language: "py",
|
||||
code: codes[1],
|
||||
},
|
||||
{
|
||||
name: "Python Code",
|
||||
mode: "python",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: codes[2],
|
||||
},
|
||||
{
|
||||
name: "Chat Widget HTML",
|
||||
description:
|
||||
"Insert this code anywhere in your <body> tag. To use with react and other libs, check our <a class='link-color' href='https://langflow.org/guidelines/widget'>documentation</a>.",
|
||||
mode: "html",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: codes[3],
|
||||
},
|
||||
];
|
||||
}
|
||||
return [
|
||||
{
|
||||
name: "cURL",
|
||||
mode: "bash",
|
||||
image: "https://curl.se/logo/curl-symbol-transparent.png",
|
||||
language: "sh",
|
||||
code: codes[0],
|
||||
},
|
||||
{
|
||||
name: "Python API",
|
||||
mode: "python",
|
||||
image:
|
||||
"https://images.squarespace-cdn.com/content/v1/5df3d8c5d2be5962e4f87890/1628015119369-OY4TV3XJJ53ECO0W2OLQ/Python+API+Training+Logo.png?format=1000w",
|
||||
language: "py",
|
||||
code: codes[1],
|
||||
},
|
||||
{
|
||||
name: "Python Code",
|
||||
mode: "python",
|
||||
language: "py",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
code: codes[2],
|
||||
},
|
||||
{
|
||||
name: "Chat Widget HTML",
|
||||
description:
|
||||
"Insert this code anywhere in your <body> tag. To use with react and other libs, check our <a class='link-color' href='https://langflow.org/guidelines/widget'>documentation</a>.",
|
||||
mode: "html",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: codes[3],
|
||||
},
|
||||
{
|
||||
name: "Tweaks",
|
||||
mode: "python",
|
||||
image: "https://cdn-icons-png.flaticon.com/512/5968/5968350.png",
|
||||
language: "py",
|
||||
code: codes[4],
|
||||
},
|
||||
];
|
||||
}
|
||||
|
|
|
|||
|
|
@ -6,6 +6,9 @@ const apiRoutes = ["^/api/v1/", "/health"];
|
|||
// Use environment variable to determine the target.
|
||||
const target = process.env.VITE_PROXY_TARGET || "http://127.0.0.1:7860";
|
||||
|
||||
// Use environment variable to determine the UI server port
|
||||
const port = process.env.VITE_PORT || 3000;
|
||||
|
||||
const proxyTargets = apiRoutes.reduce((proxyObj, route) => {
|
||||
proxyObj[route] = {
|
||||
target: target,
|
||||
|
|
@ -22,7 +25,7 @@ export default defineConfig(() => {
|
|||
},
|
||||
plugins: [react(), svgr()],
|
||||
server: {
|
||||
port: 3000,
|
||||
port: port,
|
||||
proxy: {
|
||||
...proxyTargets,
|
||||
},
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue