Merge remote-tracking branch 'origin/cz/mergeAll' into fix/edited_component
This commit is contained in:
Lucas Oliveira
commit
98dcadc797
95 changed files with 1389 additions and 716 deletions
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@ -1,13 +1,6 @@
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# syntax=docker/dockerfile:1
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# Keep this syntax directive! It's used to enable Docker BuildKit
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FROM node:20-bookworm-slim as builder-node
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WORKDIR /app
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COPY src/frontend/package.json src/frontend/package-lock.json ./
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RUN npm install
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COPY src/frontend/ ./
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RUN npm run build
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################################
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# BUILDER-BASE
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@ -56,7 +49,6 @@ COPY pyproject.toml poetry.lock README.md ./
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COPY src/ ./src
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COPY scripts/ ./scripts
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RUN python -m pip install requests --user && cd ./scripts && python update_dependencies.py
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COPY --from=builder-node /app/build ./src/backend/base/langflow/frontend
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RUN $POETRY_HOME/bin/poetry lock --no-update \
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&& $POETRY_HOME/bin/poetry build -f wheel \
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&& $POETRY_HOME/bin/poetry run pip install dist/*.whl --force-reinstall
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@ -4,7 +4,7 @@ You can run Langflow in `--backend-only` mode to expose your Langflow app as an
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Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
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The terminal prints ` Welcome to ⛓ Langflow `, and a blank window opens at `http://127.0.0.1:7864/all`.
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The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`.
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Langflow will now serve requests to its API without the frontend running.
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## Prerequisites
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@ -42,7 +42,7 @@ Note the flow ID of `ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef`. You can find this ID
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1. Stop Langflow with Ctrl+C.
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2. Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
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The terminal prints ` Welcome to ⛓ Langflow `, and a blank window opens at `http://127.0.0.1:7864/all`.
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The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`.
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Langflow will now serve requests to its API.
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3. Run the curl code you copied from the UI.
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You should get a result like this:
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67
poetry.lock
generated
67
poetry.lock
generated
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@ -367,13 +367,13 @@ files = [
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|||
|
||||
[[package]]
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||||
name = "bce-python-sdk"
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||||
version = "0.9.11"
|
||||
version = "0.9.14"
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||||
description = "BCE SDK for python"
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,<4,>=2.7"
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||||
files = [
|
||||
{file = "bce_python_sdk-0.9.11-py3-none-any.whl", hash = "sha256:3afb9717f6c0c5f5fe3104a8bea4c111bf2ab3fe87ae73b05492566bc2b5d11a"},
|
||||
{file = "bce_python_sdk-0.9.11.tar.gz", hash = "sha256:d9e977f059fef6466eebdbb34ad1e27b6f76ef90338807ab959693a78a761e7d"},
|
||||
{file = "bce_python_sdk-0.9.14-py3-none-any.whl", hash = "sha256:5704aa454151ee608b01ddda7531457433f9b4bb8afbd00706dd368f3b4339a1"},
|
||||
{file = "bce_python_sdk-0.9.14.tar.gz", hash = "sha256:7cbd182ec1e21034f10d3cdb812f3171d31908f1a783d6cf643039272942d8e8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2556,13 +2556,13 @@ uritemplate = ">=3.0.1,<5"
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|||
|
||||
[[package]]
|
||||
name = "google-auth"
|
||||
version = "2.29.0"
|
||||
version = "2.30.0"
|
||||
description = "Google Authentication Library"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"},
|
||||
{file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"},
|
||||
{file = "google-auth-2.30.0.tar.gz", hash = "sha256:ab630a1320f6720909ad76a7dbdb6841cdf5c66b328d690027e4867bdfb16688"},
|
||||
{file = "google_auth-2.30.0-py2.py3-none-any.whl", hash = "sha256:8df7da660f62757388b8a7f249df13549b3373f24388cb5d2f1dd91cc18180b5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2594,13 +2594,13 @@ httplib2 = ">=0.19.0"
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|||
|
||||
[[package]]
|
||||
name = "google-cloud-aiplatform"
|
||||
version = "1.53.0"
|
||||
version = "1.54.0"
|
||||
description = "Vertex AI API client library"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "google-cloud-aiplatform-1.53.0.tar.gz", hash = "sha256:574cfad8ac5fa5d57ef717f5335ce05636a5fa9b8aeea0f5c325b46b9448e6b1"},
|
||||
{file = "google_cloud_aiplatform-1.53.0-py2.py3-none-any.whl", hash = "sha256:9dfb1f110e6d4795b45afcfab79108fc5c8ed9aa4eaf899e433bc2ca1b76c778"},
|
||||
{file = "google-cloud-aiplatform-1.54.0.tar.gz", hash = "sha256:6f5187d35a32951028465804fbb42b478362bf41e2b634ddd22b150299f6e1d8"},
|
||||
{file = "google_cloud_aiplatform-1.54.0-py2.py3-none-any.whl", hash = "sha256:7b3ed849b9fb59a01bd6f44444ccbb7d18495b867a26f913542f6b2d4c3de252"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2621,7 +2621,7 @@ autologging = ["mlflow (>=1.27.0,<=2.1.1)"]
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|||
cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
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||||
datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
|
||||
endpoint = ["requests (>=2.28.1)"]
|
||||
full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
|
||||
full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)"]
|
||||
langchain = ["langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)"]
|
||||
langchain-testing = ["absl-py", "cloudpickle (>=2.2.1,<4.0)", "langchain (>=0.1.16,<0.2)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"]
|
||||
lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
|
||||
|
|
@ -2631,11 +2631,11 @@ prediction = ["docker (>=5.0.3)", "fastapi (>=0.71.0,<=0.109.1)", "httpx (>=0.23
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|||
preview = ["cloudpickle (<3.0)", "google-cloud-logging (<4.0)"]
|
||||
private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
|
||||
rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"]
|
||||
ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)"]
|
||||
ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "scikit-learn", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
|
||||
ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "setuptools (<70.0.0)"]
|
||||
ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (==2.9.3)", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
|
||||
reasoningengine = ["cloudpickle (>=2.2.1,<4.0)", "pydantic (>=2.6.3,<3)"]
|
||||
tensorboard = ["tensorflow (>=2.3.0,<3.0.0dev)"]
|
||||
testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
|
||||
testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
|
||||
vizier = ["google-vizier (>=0.1.6)"]
|
||||
xai = ["tensorflow (>=2.3.0,<3.0.0dev)"]
|
||||
|
||||
|
|
@ -4025,13 +4025,13 @@ adal = ["adal (>=1.0.2)"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.2.2"
|
||||
version = "0.2.3"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain-0.2.2-py3-none-any.whl", hash = "sha256:58ca0c47bcdd156da66f50a0a4fcedc49bf6950827f4a6b06c8c4842d55805f3"},
|
||||
{file = "langchain-0.2.2.tar.gz", hash = "sha256:9d61e50e9cdc2bea659bc5e6c03650ba048fda63a307490ae368e539f61a0d3a"},
|
||||
{file = "langchain-0.2.3-py3-none-any.whl", hash = "sha256:5dc33cd9c8008693d328b7cb698df69073acecc89ad9c2a95f243b3314f8d834"},
|
||||
{file = "langchain-0.2.3.tar.gz", hash = "sha256:81962cc72cce6515f7bd71e01542727870789bf8b666c6913d85559080c1a201"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4047,20 +4047,6 @@ requests = ">=2,<3"
|
|||
SQLAlchemy = ">=1.4,<3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
|
||||
clarifai = ["clarifai (>=9.1.0)"]
|
||||
cli = ["typer (>=0.9.0,<0.10.0)"]
|
||||
cohere = ["cohere (>=4,<6)"]
|
||||
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,<6)", "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 (>=0.1,<0.2)", "lxml (>=4.9.3,<6.0)", "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)", "rdflib (==7.0.0)", "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,<6)", "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 (>=0.7,<1.0)"]
|
||||
qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
|
||||
text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-anthropic"
|
||||
version = "0.1.15"
|
||||
|
|
@ -4127,13 +4113,13 @@ langchain-core = ">=0.1.42,<0.3"
|
|||
|
||||
[[package]]
|
||||
name = "langchain-community"
|
||||
version = "0.2.3"
|
||||
version = "0.2.4"
|
||||
description = "Community contributed LangChain integrations."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_community-0.2.3-py3-none-any.whl", hash = "sha256:aa895545be2f3f4aa2fea36f6da2e3b4ec50ce61ec986e8f146901a1e9138138"},
|
||||
{file = "langchain_community-0.2.3.tar.gz", hash = "sha256:a3c35af215e47b700e7cb4e548fa8b45c6d46d52b5a5a65af2577c5a0104fc9f"},
|
||||
{file = "langchain_community-0.2.4-py3-none-any.whl", hash = "sha256:8582e9800f4837660dc297cccd2ee1ddc1d8c440d0fe8b64edb07620f0373b0e"},
|
||||
{file = "langchain_community-0.2.4.tar.gz", hash = "sha256:2bb6a1a36b8500a564d25d76469c02457b1a7c3afea6d4a609a47c06b993e3e4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4148,19 +4134,15 @@ requests = ">=2,<3"
|
|||
SQLAlchemy = ">=1.4,<3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
cli = ["typer (>=0.9.0,<0.10.0)"]
|
||||
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)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-search-documents (==11.4.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.6,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpathlib (>=0.18,<0.19)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "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)", "friendli-client (>=1.2.4,<2.0.0)", "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)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "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)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "oracledb (>=2.2.0,<3.0.0)", "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)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "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)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "simsimd (>=4.3.1,<5.0.0)", "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)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.2.4"
|
||||
version = "0.2.5"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_core-0.2.4-py3-none-any.whl", hash = "sha256:5212f7ec78a525e88a178ed3aefe2fd7134b03fb92573dfbab9914f1d92d6ec5"},
|
||||
{file = "langchain_core-0.2.4.tar.gz", hash = "sha256:82bdcc546eb0341cefcf1f4ecb3e49836fff003903afddda2d1312bb8491ef81"},
|
||||
{file = "langchain_core-0.2.5-py3-none-any.whl", hash = "sha256:abe5138f22acff23a079ec538be5268bbf97cf023d51987a0dd474d2a16cae3e"},
|
||||
{file = "langchain_core-0.2.5.tar.gz", hash = "sha256:4a5c2f56b22396a63ef4790043660e393adbfa6832b978f023ca996a04b8e752"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4171,9 +4153,6 @@ pydantic = ">=1,<3"
|
|||
PyYAML = ">=5.3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
extended-testing = ["jinja2 (>=3,<4)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-experimental"
|
||||
version = "0.0.60"
|
||||
|
|
@ -4327,7 +4306,7 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
|
|||
|
||||
[[package]]
|
||||
name = "langflow-base"
|
||||
version = "0.0.59"
|
||||
version = "0.0.60"
|
||||
description = "A Python package with a built-in web application"
|
||||
optional = false
|
||||
python-versions = ">=3.10,<3.13"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "1.0.0a48"
|
||||
version = "1.0.0a49"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Langflow <contact@langflow.org>"]
|
||||
maintainers = [
|
||||
|
|
|
|||
|
|
@ -86,9 +86,9 @@ def update_frontend_node_with_template_values(frontend_node, raw_frontend_node):
|
|||
|
||||
update_template_values(frontend_node["template"], raw_frontend_node["template"])
|
||||
|
||||
old_code = raw_frontend_node['template']['code']['value']
|
||||
new_code = frontend_node['template']['code']['value']
|
||||
frontend_node['edited'] = old_code != new_code
|
||||
old_code = raw_frontend_node["template"]["code"]["value"]
|
||||
new_code = frontend_node["template"]["code"]["value"]
|
||||
frontend_node["edited"] = old_code != new_code
|
||||
|
||||
return frontend_node
|
||||
|
||||
|
|
@ -208,16 +208,18 @@ def format_elapsed_time(elapsed_time: float) -> str:
|
|||
return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}"
|
||||
|
||||
|
||||
async def build_and_cache_graph_from_db(
|
||||
flow_id: str,
|
||||
session: Session,
|
||||
chat_service: "ChatService",
|
||||
):
|
||||
async def build_and_cache_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
|
||||
"""Build and cache the graph."""
|
||||
flow: Optional[Flow] = session.get(Flow, flow_id)
|
||||
if not flow or not flow.data:
|
||||
raise ValueError("Invalid flow ID")
|
||||
graph = Graph.from_payload(flow.data, flow_id)
|
||||
for vertex_id in graph._has_session_id_vertices:
|
||||
vertex = graph.get_vertex(vertex_id)
|
||||
if vertex is None:
|
||||
raise ValueError(f"Vertex {vertex_id} not found")
|
||||
if not vertex._raw_params.get("session_id"):
|
||||
vertex.update_raw_params({"session_id": flow_id})
|
||||
await chat_service.set_cache(flow_id, graph)
|
||||
return graph
|
||||
|
||||
|
|
@ -321,3 +323,4 @@ def parse_exception(exc):
|
|||
if hasattr(exc, "body"):
|
||||
return exc.body["message"]
|
||||
return str(exc)
|
||||
return str(exc)
|
||||
|
|
|
|||
|
|
@ -168,9 +168,9 @@ async def build_vertex(
|
|||
next_runnable_vertices,
|
||||
top_level_vertices,
|
||||
result_dict,
|
||||
log_message,
|
||||
params,
|
||||
valid,
|
||||
log_type,
|
||||
artifacts,
|
||||
vertex,
|
||||
) = await graph.build_vertex(
|
||||
lock=lock,
|
||||
|
|
@ -180,22 +180,22 @@ async def build_vertex(
|
|||
inputs_dict=inputs.model_dump() if inputs else {},
|
||||
files=files,
|
||||
)
|
||||
|
||||
log_obj = Log(message=vertex.artifacts_raw, type=vertex.artifacts_type)
|
||||
result_data_response = ResultDataResponse(**result_dict.model_dump())
|
||||
|
||||
except Exception as exc:
|
||||
logger.exception(f"Error building vertex: {exc}")
|
||||
log_message = format_exception_message(exc)
|
||||
log_type = type(exc).__name__
|
||||
params = format_exception_message(exc)
|
||||
valid = False
|
||||
log_obj = Log(message=params, type="error")
|
||||
result_data_response = ResultDataResponse(results={})
|
||||
log_object = Log(message=log_message, type=log_type)
|
||||
|
||||
artifacts = {}
|
||||
# If there's an error building the vertex
|
||||
# we need to clear the cache
|
||||
await chat_service.clear_cache(flow_id_str)
|
||||
|
||||
result_data_response.logs.append(log_object)
|
||||
result_data_response.message = artifacts
|
||||
result_data_response.logs.append(log_obj)
|
||||
|
||||
# Log the vertex build
|
||||
if not vertex.will_stream:
|
||||
|
|
@ -204,8 +204,9 @@ async def build_vertex(
|
|||
flow_id=flow_id_str,
|
||||
vertex_id=vertex_id,
|
||||
valid=valid,
|
||||
logs=result_data_response.logs,
|
||||
params=params,
|
||||
data=result_data_response,
|
||||
artifacts=artifacts,
|
||||
)
|
||||
|
||||
timedelta = time.perf_counter() - start_time
|
||||
|
|
@ -231,6 +232,7 @@ async def build_vertex(
|
|||
next_vertices_ids=next_runnable_vertices,
|
||||
top_level_vertices=top_level_vertices,
|
||||
valid=valid,
|
||||
params=params,
|
||||
id=vertex.id,
|
||||
data=result_data_response,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -117,6 +117,21 @@ async def get_transactions(
|
|||
dicts = monitor_service.get_transactions(
|
||||
source=source, target=target, status=status, order_by=order_by, flow_id=flow_id
|
||||
)
|
||||
return [TransactionModelResponse(**d) for d in dicts]
|
||||
result = []
|
||||
for d in dicts:
|
||||
d = TransactionModelResponse(
|
||||
index=d["index"],
|
||||
timestamp=d["timestamp"],
|
||||
vertex_id=d["vertex_id"],
|
||||
inputs=d["inputs"],
|
||||
outputs=d["outputs"],
|
||||
status=d["status"],
|
||||
error=d["error"],
|
||||
flow_id=d["flow_id"],
|
||||
source=d["vertex_id"],
|
||||
target=d["target_id"],
|
||||
)
|
||||
result.append(d)
|
||||
return result
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ from datetime import datetime, timezone
|
|||
from enum import Enum
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Union
|
||||
from typing_extensions import TypedDict
|
||||
from uuid import UUID
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serializer
|
||||
|
|
@ -243,11 +244,11 @@ class VerticesOrderResponse(BaseModel):
|
|||
run_id: UUID
|
||||
vertices_to_run: List[str]
|
||||
|
||||
|
||||
class ResultDataResponse(BaseModel):
|
||||
results: Optional[Any] = Field(default_factory=dict)
|
||||
logs: List[Log | None] = Field(default_factory=list)
|
||||
messages: List[ChatOutputResponse | None] = Field(default_factory=list)
|
||||
message: Optional[Any] = Field(default_factory=dict)
|
||||
artifacts: Optional[Any] = Field(default_factory=dict)
|
||||
timedelta: Optional[float] = None
|
||||
duration: Optional[str] = None
|
||||
used_frozen_result: Optional[bool] = False
|
||||
|
|
@ -259,6 +260,8 @@ class VertexBuildResponse(BaseModel):
|
|||
next_vertices_ids: Optional[List[str]] = None
|
||||
top_level_vertices: Optional[List[str]] = None
|
||||
valid: bool
|
||||
params: Optional[Any] = Field(default_factory=dict)
|
||||
"""JSON string of the params."""
|
||||
data: ResultDataResponse
|
||||
"""Mapping of vertex ids to result dict containing the param name and result value."""
|
||||
timestamp: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc))
|
||||
|
|
|
|||
|
|
@ -1,10 +1,13 @@
|
|||
import warnings
|
||||
from typing import Optional, Union
|
||||
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from langchain_core.language_models.llms import LLM
|
||||
from langchain_core.load import load
|
||||
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class LCModelComponent(CustomComponent):
|
||||
|
|
@ -82,7 +85,7 @@ class LCModelComponent(CustomComponent):
|
|||
return status_message
|
||||
|
||||
def get_chat_result(
|
||||
self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
|
||||
self, runnable: BaseChatModel, stream: bool, input_value: str | Record, system_message: Optional[str] = None
|
||||
):
|
||||
messages: list[Union[HumanMessage, SystemMessage]] = []
|
||||
if not input_value and not system_message:
|
||||
|
|
@ -90,7 +93,16 @@ class LCModelComponent(CustomComponent):
|
|||
if system_message:
|
||||
messages.append(SystemMessage(content=system_message))
|
||||
if input_value:
|
||||
messages.append(HumanMessage(content=input_value))
|
||||
if isinstance(input_value, Record):
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
if "prompt" in input_value:
|
||||
prompt = load(input_value.prompt)
|
||||
runnable = prompt | runnable
|
||||
else:
|
||||
messages.append(input_value.to_lc_message())
|
||||
else:
|
||||
messages.append(HumanMessage(content=input_value))
|
||||
if stream:
|
||||
return runnable.stream(messages)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
import base64
|
||||
from copy import deepcopy
|
||||
|
||||
|
||||
from langchain_core.documents import Document
|
||||
|
||||
from langflow.schema import Record
|
||||
from langflow.services.deps import get_storage_service
|
||||
|
||||
|
||||
def record_to_string(record: Record) -> str:
|
||||
|
|
@ -19,7 +20,7 @@ def record_to_string(record: Record) -> str:
|
|||
return record.get_text()
|
||||
|
||||
|
||||
def dict_values_to_string(d: dict) -> dict:
|
||||
async def dict_values_to_string(d: dict) -> dict:
|
||||
"""
|
||||
Converts the values of a dictionary to strings.
|
||||
|
||||
|
|
@ -36,16 +37,43 @@ def dict_values_to_string(d: dict) -> dict:
|
|||
if isinstance(value, list):
|
||||
for i, item in enumerate(value):
|
||||
if isinstance(item, Record):
|
||||
d_copy[key][i] = record_to_string(item)
|
||||
d_copy[key][i] = item.to_lc_message()
|
||||
elif isinstance(item, Document):
|
||||
d_copy[key][i] = document_to_string(item)
|
||||
elif isinstance(value, Record):
|
||||
d_copy[key] = record_to_string(value)
|
||||
if "files" in value and value.files:
|
||||
files = await get_file_paths(value.files)
|
||||
value.files = files
|
||||
d_copy[key] = value.to_lc_message()
|
||||
elif isinstance(value, Document):
|
||||
d_copy[key] = document_to_string(value)
|
||||
return d_copy
|
||||
|
||||
|
||||
async def get_file_paths(files: list[str]):
|
||||
storage_service = get_storage_service()
|
||||
file_paths = []
|
||||
for file in files:
|
||||
flow_id, file_name = file.split("/")
|
||||
file_paths.append(storage_service.build_full_path(flow_id=flow_id, file_name=file_name))
|
||||
return file_paths
|
||||
|
||||
|
||||
async def get_files(
|
||||
file_paths: str,
|
||||
convert_to_base64: bool = False,
|
||||
):
|
||||
storage_service = get_storage_service()
|
||||
file_objects = []
|
||||
for file_path in file_paths:
|
||||
flow_id, file_name = file_path.split("/")
|
||||
file_object = await storage_service.get_file(flow_id=flow_id, file_name=file_name)
|
||||
if convert_to_base64:
|
||||
file_object = base64.b64encode(file_object).decode("utf-8")
|
||||
file_objects.append(file_object)
|
||||
return file_objects
|
||||
|
||||
|
||||
def document_to_string(document: Document) -> str:
|
||||
"""
|
||||
Convert a document to a string.
|
||||
|
|
|
|||
|
|
@ -1,7 +1,9 @@
|
|||
from langchain_core.prompts import PromptTemplate
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
|
||||
from langflow.base.prompts.utils import dict_values_to_string
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import Prompt, TemplateField, Text
|
||||
from langflow.schema.schema import Record
|
||||
|
||||
|
||||
class PromptComponent(CustomComponent):
|
||||
|
|
@ -15,19 +17,14 @@ class PromptComponent(CustomComponent):
|
|||
"code": TemplateField(advanced=True),
|
||||
}
|
||||
|
||||
def build(
|
||||
async def build(
|
||||
self,
|
||||
template: Prompt,
|
||||
**kwargs,
|
||||
) -> Text:
|
||||
from langflow.base.prompts.utils import dict_values_to_string
|
||||
|
||||
prompt_template = PromptTemplate.from_template(Text(template))
|
||||
kwargs = dict_values_to_string(kwargs)
|
||||
kwargs = {k: "\n".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}
|
||||
try:
|
||||
formated_prompt = prompt_template.format(**kwargs)
|
||||
except Exception as exc:
|
||||
raise ValueError(f"Error formatting prompt: {exc}") from exc
|
||||
self.status = f'Prompt:\n"{formated_prompt}"'
|
||||
return formated_prompt
|
||||
) -> Record:
|
||||
prompt_template = ChatPromptTemplate.from_template(Text(template))
|
||||
kwargs = await dict_values_to_string(kwargs)
|
||||
messages = list(kwargs.values())
|
||||
prompt = prompt_template + messages
|
||||
self.status = f'Prompt:\n"{template}"'
|
||||
return Record(data={"prompt": prompt.to_json()})
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ class AmazonBedrockComponent(LCModelComponent):
|
|||
"advanced": True,
|
||||
},
|
||||
"cache": {"display_name": "Cache"},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
|
|
|
|||
|
|
@ -63,7 +63,7 @@ class AnthropicLLM(LCModelComponent):
|
|||
"info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -78,7 +78,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
|
|||
"info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -81,7 +81,7 @@ class QianfanChatEndpointComponent(LCModelComponent):
|
|||
"info": "Endpoint of the Qianfan LLM, required if custom model used.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
|
|||
"required": False,
|
||||
"default": False,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_cohere import ChatCohere
|
||||
from pydantic.v1 import SecretStr
|
||||
from langflow.field_typing import Text
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langchain_cohere import ChatCohere
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class CohereComponent(LCModelComponent):
|
||||
|
|
@ -42,7 +43,7 @@ class CohereComponent(LCModelComponent):
|
|||
"type": "float",
|
||||
"show": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
@ -69,3 +70,4 @@ class CohereComponent(LCModelComponent):
|
|||
temperature=temperature,
|
||||
)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
|
|
|||
|
|
@ -2,9 +2,10 @@ from typing import Optional
|
|||
|
||||
from langchain_community.chat_models.huggingface import ChatHuggingFace
|
||||
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
from langflow.field_typing import Text
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
|
||||
|
||||
class HuggingFaceEndpointsComponent(LCModelComponent):
|
||||
|
|
@ -36,7 +37,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
|
|||
"advanced": True,
|
||||
},
|
||||
"code": {"show": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
@ -72,3 +73,4 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
|
|||
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
|
||||
output = ChatHuggingFace(llm=llm)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
return self.get_chat_result(output, stream, input_value, system_message)
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ class MistralAIModelComponent(LCModelComponent):
|
|||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -194,7 +194,7 @@ class ChatOllamaComponent(LCModelComponent):
|
|||
"info": "Template to use for generating text.",
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ class OpenAIModelComponent(LCModelComponent):
|
|||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from typing import Optional
|
||||
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import Text
|
||||
|
|
@ -74,7 +73,7 @@ class ChatVertexAIComponent(LCModelComponent):
|
|||
"value": False,
|
||||
"advanced": True,
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
|
|
|
|||
|
|
@ -738,7 +738,9 @@ class Graph:
|
|||
# Check the cache for the vertex
|
||||
cached_result = await chat_service.get_cache(key=vertex.id)
|
||||
if isinstance(cached_result, CacheMiss):
|
||||
await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
|
||||
await vertex.build(
|
||||
user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
|
||||
)
|
||||
await chat_service.set_cache(key=vertex.id, data=vertex)
|
||||
else:
|
||||
cached_vertex = cached_result["result"]
|
||||
|
|
@ -752,7 +754,9 @@ class Graph:
|
|||
vertex.result.used_frozen_result = True
|
||||
|
||||
else:
|
||||
await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
|
||||
await vertex.build(
|
||||
user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
|
||||
)
|
||||
|
||||
if vertex.result is not None:
|
||||
params = f"{vertex._built_object_repr()}{params}"
|
||||
|
|
@ -765,11 +769,13 @@ class Graph:
|
|||
next_runnable_vertices, top_level_vertices = await self.get_next_and_top_level_vertices(
|
||||
lock, set_cache_coro, vertex
|
||||
)
|
||||
log_transaction(vertex, status="success")
|
||||
flow_id = self.flow_id
|
||||
log_transaction(flow_id, vertex, status="success")
|
||||
return next_runnable_vertices, top_level_vertices, result_dict, params, valid, artifacts, vertex
|
||||
except Exception as exc:
|
||||
logger.exception(f"Error building vertex: {exc}")
|
||||
log_transaction(vertex, status="failure", error=str(exc))
|
||||
flow_id = self.flow_id
|
||||
log_transaction(flow_id, vertex, status="failure", error=str(exc))
|
||||
raise exc
|
||||
|
||||
async def get_next_and_top_level_vertices(
|
||||
|
|
|
|||
|
|
@ -31,10 +31,10 @@ class ResultData(BaseModel):
|
|||
if not values.get("logs") and values.get("artifacts"):
|
||||
# Build the log from the artifacts
|
||||
message = values["artifacts"]
|
||||
if "stream_url" in message:
|
||||
if "stream_url" in message and "type" in message:
|
||||
stream_url = StreamURL(location=message["stream_url"])
|
||||
values["logs"] = [Log(message=stream_url, type=message["type"])]
|
||||
else:
|
||||
elif "type" in message:
|
||||
values["logs"] = [Log(message=message, type=message["type"])]
|
||||
return values
|
||||
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ from enum import Enum
|
|||
from typing import Any, Generator, Union
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from langflow.schema.schema import Record
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
|
|
|
|||
|
|
@ -10,11 +10,11 @@ from loguru import logger
|
|||
|
||||
from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData
|
||||
from langflow.graph.utils import ArtifactType, UnbuiltObject, UnbuiltResult
|
||||
from langflow.graph.vertex.utils import log_transaction
|
||||
from langflow.interface.initialize import loading
|
||||
from langflow.interface.listing import lazy_load_dict
|
||||
from langflow.schema.schema import INPUT_FIELD_NAME
|
||||
from langflow.services.deps import get_storage_service
|
||||
from langflow.services.monitor.utils import log_transaction
|
||||
from langflow.utils.constants import DIRECT_TYPES
|
||||
from langflow.utils.schemas import ChatOutputResponse
|
||||
from langflow.utils.util import sync_to_async, unescape_string
|
||||
|
|
@ -529,12 +529,13 @@ class Vertex:
|
|||
Returns:
|
||||
The built result if use_result is True, else the built object.
|
||||
"""
|
||||
flow_id = self.graph.flow_id
|
||||
if not self._built:
|
||||
log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error")
|
||||
log_transaction(flow_id, vertex=self, target=requester, status="error")
|
||||
raise ValueError(f"Component {self.display_name} has not been built yet")
|
||||
|
||||
result = self._built_result if self.use_result else self._built_object
|
||||
log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="success")
|
||||
log_transaction(flow_id, vertex=self, target=requester, status="success")
|
||||
return result
|
||||
|
||||
async def _build_vertex_and_update_params(self, key, vertex: "Vertex"):
|
||||
|
|
@ -628,9 +629,8 @@ class Vertex:
|
|||
self._built_object, self.artifacts = result
|
||||
elif len(result) == 3:
|
||||
self._custom_component, self._built_object, self.artifacts = result
|
||||
self.artifacts_raw = self.artifacts.get("raw")
|
||||
self.artifacts_type = self.artifacts.get("type") or ArtifactType.UNKNOWN.value
|
||||
|
||||
self.artifacts_raw = self.artifacts.get("raw", None)
|
||||
self.artifacts_type = self.artifacts.get("type", None) or ArtifactType.UNKNOWN.value
|
||||
else:
|
||||
self._built_object = result
|
||||
|
||||
|
|
|
|||
|
|
@ -116,7 +116,7 @@ class InterfaceVertex(Vertex):
|
|||
sender_name=sender_name,
|
||||
stream_url=stream_url,
|
||||
files=files,
|
||||
type=artifact_type.value,
|
||||
type=artifact_type,
|
||||
)
|
||||
|
||||
self.will_stream = stream_url is not None
|
||||
|
|
@ -213,9 +213,9 @@ class InterfaceVertex(Vertex):
|
|||
flow_id=self.graph.flow_id,
|
||||
vertex_id=self.id,
|
||||
valid=True,
|
||||
logs=self._built_object_repr(),
|
||||
params=self._built_object_repr(),
|
||||
data=self.result,
|
||||
messages=self.artifacts,
|
||||
artifacts=self.artifacts,
|
||||
)
|
||||
|
||||
self._validate_built_object()
|
||||
|
|
|
|||
|
|
@ -1,9 +1,5 @@
|
|||
from typing import TYPE_CHECKING
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from langflow.services.deps import get_monitor_service
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
|
||||
|
|
@ -21,34 +17,3 @@ def build_clean_params(target: "Vertex") -> dict:
|
|||
if isinstance(value, list):
|
||||
params[key] = [item for item in value if isinstance(item, (str, int, bool, float, list, dict))]
|
||||
return params
|
||||
|
||||
|
||||
def log_transaction(source: "Vertex", target: "Vertex", flow_id, status, error=None):
|
||||
"""
|
||||
Logs a transaction between two vertices.
|
||||
|
||||
Args:
|
||||
source (Vertex): The source vertex of the transaction.
|
||||
target (Vertex): The target vertex of the transaction.
|
||||
status: The status of the transaction.
|
||||
error (Optional): Any error associated with the transaction.
|
||||
|
||||
Raises:
|
||||
Exception: If there is an error while logging the transaction.
|
||||
|
||||
"""
|
||||
try:
|
||||
monitor_service = get_monitor_service()
|
||||
clean_params = build_clean_params(target)
|
||||
data = {
|
||||
"source": source.vertex_type,
|
||||
"target": target.vertex_type,
|
||||
"target_args": clean_params,
|
||||
"timestamp": monitor_service.get_timestamp(),
|
||||
"status": status,
|
||||
"error": error,
|
||||
"flow_id": flow_id,
|
||||
}
|
||||
monitor_service.add_row(table_name="transactions", data=data)
|
||||
except Exception as e:
|
||||
logger.error(f"Error logging transaction: {e}")
|
||||
|
|
|
|||
|
|
@ -251,7 +251,7 @@ def get_flow_by_id_or_endpoint_name(
|
|||
flow = db.get(Flow, flow_id)
|
||||
except ValueError:
|
||||
endpoint_name = flow_id_or_name
|
||||
stmt = select(Flow).where(Flow.name == endpoint_name)
|
||||
stmt = select(Flow).where(Flow.endpoint_name == endpoint_name)
|
||||
if user_id:
|
||||
stmt = stmt.where(Flow.user_id == user_id)
|
||||
flow = db.exec(stmt).first()
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -140,7 +140,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -149,7 +149,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -444,7 +444,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -453,7 +453,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -589,7 +589,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -598,7 +598,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -524,7 +524,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -670,7 +670,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -679,7 +679,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -130,7 +130,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -789,7 +789,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -798,7 +798,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -1146,7 +1146,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -1155,7 +1155,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -784,7 +784,7 @@
|
|||
"info": "",
|
||||
"load_from_db": false,
|
||||
"title_case": false,
|
||||
"input_types": ["Text"]
|
||||
"input_types": ["Text", "Record"]
|
||||
},
|
||||
"code": {
|
||||
"type": "code",
|
||||
|
|
@ -793,7 +793,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": MODEL_NAMES,\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float = 0.1,\n model_name: str = \"gpt-4o\",\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens or None,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
@ -1034,7 +1034,7 @@
|
|||
"list": false,
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n",
|
||||
"value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n async def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Record:\n prompt_template = ChatPromptTemplate.from_template(Text(template))\n kwargs = await dict_values_to_string(kwargs)\n messages = list(kwargs.values())\n prompt = prompt_template + messages\n self.status = f'Prompt:\\n\"{template}\"'\n return Record(data={\"prompt\": prompt.to_json()})\n",
|
||||
"fileTypes": [],
|
||||
"file_path": "",
|
||||
"password": false,
|
||||
|
|
|
|||
|
|
@ -125,7 +125,6 @@ async def instantiate_custom_component(params, user_id, vertex, fallback_to_env_
|
|||
custom_repr = build_result
|
||||
if not isinstance(custom_repr, str):
|
||||
custom_repr = str(custom_repr)
|
||||
|
||||
raw = custom_component.repr_value
|
||||
if hasattr(raw, "data"):
|
||||
raw = raw.data
|
||||
|
|
|
|||
|
|
@ -1,11 +1,12 @@
|
|||
import copy
|
||||
import json
|
||||
from typing import Literal, Optional, cast
|
||||
from typing_extensions import TypedDict
|
||||
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
|
||||
from pydantic import BaseModel, model_validator
|
||||
from typing_extensions import TypedDict
|
||||
from langchain_core.prompts.image import ImagePromptTemplate
|
||||
from pydantic import BaseModel, model_serializer, model_validator
|
||||
|
||||
|
||||
class Record(BaseModel):
|
||||
|
|
@ -30,6 +31,11 @@ class Record(BaseModel):
|
|||
values["data"][key] = values[key]
|
||||
return values
|
||||
|
||||
@model_serializer(mode="plain", when_used="json")
|
||||
def serialize_model(self):
|
||||
data = {k: v.to_json() if hasattr(v, "to_json") else v for k, v in self.data.items()}
|
||||
return data
|
||||
|
||||
def get_text(self):
|
||||
"""
|
||||
Retrieves the text value from the data dictionary.
|
||||
|
|
@ -103,7 +109,9 @@ class Record(BaseModel):
|
|||
text = self.data.pop(self.text_key, self.default_value)
|
||||
return Document(page_content=text, metadata=self.data)
|
||||
|
||||
def to_lc_message(self) -> BaseMessage:
|
||||
def to_lc_message(
|
||||
self,
|
||||
) -> BaseMessage:
|
||||
"""
|
||||
Converts the Record to a BaseMessage.
|
||||
|
||||
|
|
@ -119,8 +127,22 @@ class Record(BaseModel):
|
|||
raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}")
|
||||
sender = self.data.get("sender", "Machine")
|
||||
text = self.data.get("text", "")
|
||||
files = self.data.get("files", [])
|
||||
if sender == "User":
|
||||
return HumanMessage(content=text)
|
||||
if files:
|
||||
contents = [{"type": "text", "text": text}]
|
||||
for file_path in files:
|
||||
image_template = ImagePromptTemplate()
|
||||
image_prompt_value = image_template.invoke(input={"path": file_path})
|
||||
contents.append({"type": "image_url", "image_url": image_prompt_value.image_url})
|
||||
human_message = HumanMessage(content=contents)
|
||||
else:
|
||||
human_message = HumanMessage(
|
||||
content=[{"type": "text", "text": text}],
|
||||
)
|
||||
|
||||
return human_message
|
||||
|
||||
return AIMessage(content=text)
|
||||
|
||||
def __getattr__(self, key):
|
||||
|
|
@ -170,8 +192,14 @@ class Record(BaseModel):
|
|||
|
||||
def __str__(self) -> str:
|
||||
# return a JSON string representation of the Record atributes
|
||||
try:
|
||||
data = {k: v.to_json() if hasattr(v, "to_json") else v for k, v in self.data.items()}
|
||||
return json.dumps(data, indent=4)
|
||||
except Exception:
|
||||
return str(self.data)
|
||||
|
||||
return json.dumps(self.data)
|
||||
def __contains__(self, key):
|
||||
return key in self.data
|
||||
|
||||
|
||||
INPUT_FIELD_NAME = "input_value"
|
||||
|
|
|
|||
|
|
@ -23,6 +23,9 @@ class CacheMiss:
|
|||
def __repr__(self):
|
||||
return "<CACHE_MISS>"
|
||||
|
||||
def __bool__(self):
|
||||
return False
|
||||
|
||||
|
||||
def create_cache_folder(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
|
|
|
|||
|
|
@ -11,25 +11,26 @@ if TYPE_CHECKING:
|
|||
class TransactionModel(BaseModel):
|
||||
index: Optional[int] = Field(default=None)
|
||||
timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp")
|
||||
flow_id: str
|
||||
source: str
|
||||
target: str
|
||||
target_args: dict
|
||||
vertex_id: str
|
||||
target_id: str | None = None
|
||||
inputs: dict
|
||||
outputs: Optional[dict] = None
|
||||
status: str
|
||||
error: Optional[str] = None
|
||||
flow_id: Optional[str] = Field(default=None, alias="flow_id")
|
||||
|
||||
class Config:
|
||||
from_attributes = True
|
||||
populate_by_name = True
|
||||
|
||||
# validate target_args in case it is a JSON
|
||||
@field_validator("target_args", mode="before")
|
||||
@field_validator("outputs", "inputs", mode="before")
|
||||
def validate_target_args(cls, v):
|
||||
if isinstance(v, str):
|
||||
return json.loads(v)
|
||||
return v
|
||||
|
||||
@field_serializer("target_args")
|
||||
@field_serializer("outputs", "inputs")
|
||||
def serialize_target_args(v):
|
||||
if isinstance(v, dict):
|
||||
return json.dumps(v)
|
||||
|
|
@ -39,19 +40,21 @@ class TransactionModel(BaseModel):
|
|||
class TransactionModelResponse(BaseModel):
|
||||
index: Optional[int] = Field(default=None)
|
||||
timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp")
|
||||
flow_id: str
|
||||
source: str
|
||||
target: str
|
||||
target_args: dict
|
||||
vertex_id: str
|
||||
inputs: dict
|
||||
outputs: Optional[dict] = None
|
||||
status: str
|
||||
error: Optional[str] = None
|
||||
flow_id: Optional[str] = Field(default=None, alias="flow_id")
|
||||
source: Optional[str] = None
|
||||
target: Optional[str] = None
|
||||
|
||||
class Config:
|
||||
from_attributes = True
|
||||
populate_by_name = True
|
||||
|
||||
# validate target_args in case it is a JSON
|
||||
@field_validator("target_args", mode="before")
|
||||
@field_validator("outputs", "inputs", mode="before")
|
||||
def validate_target_args(cls, v):
|
||||
if isinstance(v, str):
|
||||
return json.loads(v)
|
||||
|
|
@ -129,15 +132,16 @@ class VertexBuildModel(BaseModel):
|
|||
id: Optional[str] = Field(default=None, alias="id")
|
||||
flow_id: str
|
||||
valid: bool
|
||||
logs: Any
|
||||
params: Any
|
||||
data: dict
|
||||
artifacts: dict
|
||||
timestamp: datetime = Field(default_factory=datetime.now)
|
||||
|
||||
class Config:
|
||||
from_attributes = True
|
||||
populate_by_name = True
|
||||
|
||||
@field_serializer("data")
|
||||
@field_serializer("data", "artifacts")
|
||||
def serialize_dict(v):
|
||||
if isinstance(v, dict):
|
||||
# check if the value of each key is a BaseModel or a list of BaseModels
|
||||
|
|
@ -151,8 +155,8 @@ class VertexBuildModel(BaseModel):
|
|||
return v.model_dump_json()
|
||||
return v
|
||||
|
||||
@field_validator("logs", mode="before")
|
||||
def validate_logs(cls, v):
|
||||
@field_validator("params", mode="before")
|
||||
def validate_params(cls, v):
|
||||
if isinstance(v, str):
|
||||
try:
|
||||
return json.loads(v)
|
||||
|
|
@ -160,7 +164,7 @@ class VertexBuildModel(BaseModel):
|
|||
return v
|
||||
return v
|
||||
|
||||
@field_serializer("logs")
|
||||
@field_serializer("params")
|
||||
def serialize_params(v):
|
||||
if isinstance(v, list) and all(isinstance(i, BaseModel) for i in v):
|
||||
return json.dumps([i.model_dump() for i in v])
|
||||
|
|
@ -172,11 +176,17 @@ class VertexBuildModel(BaseModel):
|
|||
return json.loads(v)
|
||||
return v
|
||||
|
||||
@field_validator("artifacts", mode="before")
|
||||
def validate_artifacts(cls, v):
|
||||
if isinstance(v, str):
|
||||
return json.loads(v)
|
||||
elif isinstance(v, BaseModel):
|
||||
return v.model_dump()
|
||||
return v
|
||||
|
||||
|
||||
class VertexBuildResponseModel(VertexBuildModel):
|
||||
messages: list[MessageModel] = []
|
||||
|
||||
@field_serializer("data")
|
||||
@field_serializer("data", "artifacts")
|
||||
def serialize_dict(v):
|
||||
return v
|
||||
|
||||
|
|
|
|||
|
|
@ -168,7 +168,9 @@ class MonitorService(Service):
|
|||
order_by: Optional[str] = "timestamp",
|
||||
flow_id: Optional[str] = None,
|
||||
):
|
||||
query = "SELECT index,flow_id, source, target, target_args, status, error, timestamp FROM transactions"
|
||||
query = (
|
||||
"SELECT index,flow_id, status, error, timestamp, vertex_id, inputs, outputs, target_id FROM transactions"
|
||||
)
|
||||
conditions = []
|
||||
if source:
|
||||
conditions.append(f"source = '{source}'")
|
||||
|
|
@ -183,7 +185,7 @@ class MonitorService(Service):
|
|||
query += " WHERE " + " AND ".join(conditions)
|
||||
|
||||
if order_by:
|
||||
query += f" ORDER BY {order_by}"
|
||||
query += f" ORDER BY {order_by} DESC"
|
||||
with duckdb.connect(str(self.db_path)) as conn:
|
||||
df = conn.execute(query).df()
|
||||
|
||||
|
|
|
|||
|
|
@ -119,21 +119,16 @@ async def log_message(
|
|||
sender_name: str,
|
||||
message: str,
|
||||
session_id: str,
|
||||
artifacts: Optional[dict] = None,
|
||||
files: Optional[list] = None,
|
||||
flow_id: Optional[str] = None,
|
||||
):
|
||||
try:
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
|
||||
if isinstance(session_id, Vertex):
|
||||
session_id = await session_id.build() # type: ignore
|
||||
|
||||
monitor_service = get_monitor_service()
|
||||
row = {
|
||||
"sender": sender,
|
||||
"sender_name": sender_name,
|
||||
"message": message,
|
||||
"artifacts": artifacts or {},
|
||||
"files": files or [],
|
||||
"session_id": session_id,
|
||||
"timestamp": monitor_service.get_timestamp(),
|
||||
"flow_id": flow_id,
|
||||
|
|
@ -147,9 +142,9 @@ async def log_vertex_build(
|
|||
flow_id: str,
|
||||
vertex_id: str,
|
||||
valid: bool,
|
||||
logs: Any,
|
||||
params: Any,
|
||||
data: "ResultDataResponse",
|
||||
messages: Optional[dict] = None,
|
||||
artifacts: Optional[dict] = None,
|
||||
):
|
||||
try:
|
||||
monitor_service = get_monitor_service()
|
||||
|
|
@ -158,9 +153,9 @@ async def log_vertex_build(
|
|||
"flow_id": flow_id,
|
||||
"id": vertex_id,
|
||||
"valid": valid,
|
||||
"logs": logs,
|
||||
"params": params,
|
||||
"data": data.model_dump(),
|
||||
"messages": messages or {},
|
||||
"artifacts": artifacts or {},
|
||||
"timestamp": monitor_service.get_timestamp(),
|
||||
}
|
||||
monitor_service.add_row(table_name="vertex_builds", data=row)
|
||||
|
|
@ -183,17 +178,19 @@ def build_clean_params(target: "Vertex") -> dict:
|
|||
return params
|
||||
|
||||
|
||||
def log_transaction(vertex: "Vertex", status, error=None):
|
||||
def log_transaction(flow_id, vertex: "Vertex", status, target: Optional["Vertex"] = None, error=None):
|
||||
try:
|
||||
monitor_service = get_monitor_service()
|
||||
clean_params = build_clean_params(vertex)
|
||||
data = {
|
||||
"vertex_id": vertex.id,
|
||||
"vertex_id": str(vertex.id),
|
||||
"target_id": str(target.id) if target else None,
|
||||
"inputs": clean_params,
|
||||
"output": str(vertex.result),
|
||||
"outputs": vertex.result.model_dump_json() if vertex.result else None,
|
||||
"timestamp": monitor_service.get_timestamp(),
|
||||
"status": status,
|
||||
"error": error,
|
||||
"flow_id": flow_id,
|
||||
}
|
||||
monitor_service.add_row(table_name="transactions", data=data)
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -90,9 +90,9 @@ async def build_vertex(
|
|||
flow_id=flow_id,
|
||||
vertex_id=vertex_id,
|
||||
valid=valid,
|
||||
logs=params,
|
||||
params=params,
|
||||
data=result_dict,
|
||||
messages=artifacts,
|
||||
artifacts=artifacts,
|
||||
)
|
||||
|
||||
# Emit the vertex build response
|
||||
|
|
|
|||
|
|
@ -159,7 +159,10 @@ def create_class(code, class_name):
|
|||
|
||||
# Replace from langflow import CustomComponent with from langflow.custom import CustomComponent
|
||||
code = code.replace("from langflow import CustomComponent", "from langflow.custom import CustomComponent")
|
||||
|
||||
code = code.replace(
|
||||
"from langflow.interface.custom.custom_component import CustomComponent",
|
||||
"from langflow.custom import CustomComponent",
|
||||
)
|
||||
module = ast.parse(code)
|
||||
exec_globals = prepare_global_scope(code, module)
|
||||
|
||||
|
|
|
|||
39
src/backend/base/poetry.lock
generated
39
src/backend/base/poetry.lock
generated
|
|
@ -1159,13 +1159,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.2.2"
|
||||
version = "0.2.3"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain-0.2.2-py3-none-any.whl", hash = "sha256:58ca0c47bcdd156da66f50a0a4fcedc49bf6950827f4a6b06c8c4842d55805f3"},
|
||||
{file = "langchain-0.2.2.tar.gz", hash = "sha256:9d61e50e9cdc2bea659bc5e6c03650ba048fda63a307490ae368e539f61a0d3a"},
|
||||
{file = "langchain-0.2.3-py3-none-any.whl", hash = "sha256:5dc33cd9c8008693d328b7cb698df69073acecc89ad9c2a95f243b3314f8d834"},
|
||||
{file = "langchain-0.2.3.tar.gz", hash = "sha256:81962cc72cce6515f7bd71e01542727870789bf8b666c6913d85559080c1a201"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1181,29 +1181,15 @@ requests = ">=2,<3"
|
|||
SQLAlchemy = ">=1.4,<3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
|
||||
clarifai = ["clarifai (>=9.1.0)"]
|
||||
cli = ["typer (>=0.9.0,<0.10.0)"]
|
||||
cohere = ["cohere (>=4,<6)"]
|
||||
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,<6)", "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 (>=0.1,<0.2)", "lxml (>=4.9.3,<6.0)", "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)", "rdflib (==7.0.0)", "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,<6)", "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 (>=0.7,<1.0)"]
|
||||
qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
|
||||
text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-community"
|
||||
version = "0.2.3"
|
||||
version = "0.2.4"
|
||||
description = "Community contributed LangChain integrations."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_community-0.2.3-py3-none-any.whl", hash = "sha256:aa895545be2f3f4aa2fea36f6da2e3b4ec50ce61ec986e8f146901a1e9138138"},
|
||||
{file = "langchain_community-0.2.3.tar.gz", hash = "sha256:a3c35af215e47b700e7cb4e548fa8b45c6d46d52b5a5a65af2577c5a0104fc9f"},
|
||||
{file = "langchain_community-0.2.4-py3-none-any.whl", hash = "sha256:8582e9800f4837660dc297cccd2ee1ddc1d8c440d0fe8b64edb07620f0373b0e"},
|
||||
{file = "langchain_community-0.2.4.tar.gz", hash = "sha256:2bb6a1a36b8500a564d25d76469c02457b1a7c3afea6d4a609a47c06b993e3e4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1218,19 +1204,15 @@ requests = ">=2,<3"
|
|||
SQLAlchemy = ">=1.4,<3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
cli = ["typer (>=0.9.0,<0.10.0)"]
|
||||
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)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-search-documents (==11.4.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.6,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpathlib (>=0.18,<0.19)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "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)", "friendli-client (>=1.2.4,<2.0.0)", "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)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "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)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "oracledb (>=2.2.0,<3.0.0)", "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)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "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)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "simsimd (>=4.3.1,<5.0.0)", "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)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.2.4"
|
||||
version = "0.2.5"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_core-0.2.4-py3-none-any.whl", hash = "sha256:5212f7ec78a525e88a178ed3aefe2fd7134b03fb92573dfbab9914f1d92d6ec5"},
|
||||
{file = "langchain_core-0.2.4.tar.gz", hash = "sha256:82bdcc546eb0341cefcf1f4ecb3e49836fff003903afddda2d1312bb8491ef81"},
|
||||
{file = "langchain_core-0.2.5-py3-none-any.whl", hash = "sha256:abe5138f22acff23a079ec538be5268bbf97cf023d51987a0dd474d2a16cae3e"},
|
||||
{file = "langchain_core-0.2.5.tar.gz", hash = "sha256:4a5c2f56b22396a63ef4790043660e393adbfa6832b978f023ca996a04b8e752"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1241,9 +1223,6 @@ pydantic = ">=1,<3"
|
|||
PyYAML = ">=5.3"
|
||||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
extended-testing = ["jinja2 (>=3,<4)"]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-experimental"
|
||||
version = "0.0.60"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow-base"
|
||||
version = "0.0.59"
|
||||
version = "0.0.60"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Langflow <contact@langflow.org>"]
|
||||
maintainers = [
|
||||
|
|
|
|||
|
|
@ -44,9 +44,9 @@ import useFetchDataOnMount from "../../../hooks/use-fetch-data-on-mount";
|
|||
import useHandleOnNewValue from "../../../hooks/use-handle-new-value";
|
||||
import useHandleNodeClass from "../../../hooks/use-handle-node-class";
|
||||
import useHandleRefreshButtonPress from "../../../hooks/use-handle-refresh-buttons";
|
||||
import OutputModal from "../outputModal";
|
||||
import TooltipRenderComponent from "../tooltipRenderComponent";
|
||||
import { TEXT_FIELD_TYPES } from "./constants";
|
||||
import OutputModal from "../outputModal";
|
||||
|
||||
export default function ParameterComponent({
|
||||
left,
|
||||
|
|
@ -274,7 +274,9 @@ export default function ParameterComponent({
|
|||
: "Please build the component first"
|
||||
}
|
||||
>
|
||||
<button
|
||||
<Button
|
||||
variant="none"
|
||||
size="none"
|
||||
disabled={!displayOutputPreview || unknownOutput}
|
||||
onClick={() => setOpenOutputModal(true)}
|
||||
data-testid={`output-inspection-${title.toLowerCase()}`}
|
||||
|
|
@ -288,7 +290,7 @@ export default function ParameterComponent({
|
|||
)}
|
||||
name={"ScanEye"}
|
||||
/>
|
||||
</button>
|
||||
</Button>
|
||||
</ShadTooltip>
|
||||
)}
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,10 +1,12 @@
|
|||
import TableAutoCellRender from "../tableComponent/components/tableAutoCellRender";
|
||||
|
||||
export default function ArrayReader({ array }: { array: any[] }): JSX.Element {
|
||||
//TODO check array type
|
||||
return (
|
||||
<div>
|
||||
<ul>
|
||||
{array.map((item, index) => (
|
||||
<li key={index}>{item}</li>
|
||||
<li key={index}>{<TableAutoCellRender value={item} />}</li>
|
||||
))}
|
||||
</ul>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -115,6 +115,7 @@ function CsvOutputComponent({
|
|||
style={{ height: "100%", width: "100%" }}
|
||||
>
|
||||
<TableComponent
|
||||
key={"csv-output"}
|
||||
rowData={rowData}
|
||||
columnDefs={colDefs}
|
||||
defaultColDef={defaultColDef}
|
||||
|
|
|
|||
|
|
@ -13,7 +13,9 @@ function RecordsOutputComponent({
|
|||
rows: any;
|
||||
columnMode?: "intersection" | "union";
|
||||
}) {
|
||||
console.log("rows", rows);
|
||||
const columns = extractColumnsFromRows(rows, columnMode);
|
||||
console.log("columns", columns);
|
||||
|
||||
const columnDefs = columns.map((col, idx) => ({
|
||||
...col,
|
||||
|
|
@ -23,6 +25,7 @@ function RecordsOutputComponent({
|
|||
|
||||
return (
|
||||
<TableComponent
|
||||
key={"recordsOutputComponent"}
|
||||
overlayNoRowsTemplate="No data available"
|
||||
suppressRowClickSelection={true}
|
||||
pagination={pagination}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,95 @@
|
|||
import { cn } from "../../../../utils/utils";
|
||||
import ShadTooltip from "../../../shadTooltipComponent";
|
||||
import { Button } from "../../../ui/button";
|
||||
import IconComponent from "../../../genericIconComponent";
|
||||
|
||||
export default function TableOptions({
|
||||
resetGrid,
|
||||
duplicateRow,
|
||||
deleteRow,
|
||||
hasSelection,
|
||||
stateChange,
|
||||
}: {
|
||||
resetGrid: () => void;
|
||||
duplicateRow?: () => void;
|
||||
deleteRow?: () => void;
|
||||
hasSelection: boolean;
|
||||
stateChange: boolean;
|
||||
}): JSX.Element {
|
||||
return (
|
||||
<div className={cn("absolute bottom-3 left-6")}>
|
||||
<div className="flex items-center gap-2">
|
||||
<div>
|
||||
<ShadTooltip content="Reset Columns">
|
||||
<Button
|
||||
variant="none"
|
||||
size="none"
|
||||
onClick={() => {
|
||||
resetGrid();
|
||||
}}
|
||||
disabled={!stateChange}
|
||||
>
|
||||
<IconComponent
|
||||
name="RotateCcw"
|
||||
className={cn("h-5 w-5 text-primary transition-all")}
|
||||
/>
|
||||
</Button>
|
||||
</ShadTooltip>
|
||||
</div>
|
||||
{duplicateRow && (
|
||||
<div>
|
||||
<ShadTooltip
|
||||
content={
|
||||
!hasSelection ? (
|
||||
<span>Select items to duplicate</span>
|
||||
) : (
|
||||
<span>Duplicate selected items</span>
|
||||
)
|
||||
}
|
||||
>
|
||||
<Button
|
||||
variant="none"
|
||||
size="none"
|
||||
onClick={duplicateRow}
|
||||
disabled={!hasSelection}
|
||||
>
|
||||
<IconComponent
|
||||
name="Copy"
|
||||
className={cn("h-5 w-5 text-primary transition-all")}
|
||||
/>
|
||||
</Button>
|
||||
</ShadTooltip>
|
||||
</div>
|
||||
)}
|
||||
{deleteRow && (
|
||||
<div>
|
||||
<ShadTooltip
|
||||
content={
|
||||
!hasSelection ? (
|
||||
<span>Select items to delete</span>
|
||||
) : (
|
||||
<span>Delete selected items</span>
|
||||
)
|
||||
}
|
||||
>
|
||||
<Button
|
||||
variant="none"
|
||||
size="none"
|
||||
onClick={deleteRow}
|
||||
disabled={!hasSelection}
|
||||
>
|
||||
<IconComponent
|
||||
name="Trash2"
|
||||
className={cn(
|
||||
"h-5 w-5 text-primary transition-all",
|
||||
!hasSelection ? "" : "hover:text-destructive",
|
||||
)}
|
||||
/>
|
||||
</Button>
|
||||
</ShadTooltip>
|
||||
</div>
|
||||
)}{" "}
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
|
@ -9,20 +9,17 @@ import { Badge } from "../../../ui/badge";
|
|||
|
||||
export default function TableAutoCellRender({
|
||||
value,
|
||||
}: CustomCellRendererProps) {
|
||||
}: CustomCellRendererProps | { value: any }) {
|
||||
function getCellType() {
|
||||
switch (typeof value) {
|
||||
case "object":
|
||||
if (value === null) {
|
||||
return String(value);
|
||||
} else if (Array.isArray(value)) {
|
||||
return <ArrayReader array={value} />;
|
||||
} else if (value.definitions) {
|
||||
// use a custom render defined by the sender
|
||||
return <ObjectRender object={value} />;
|
||||
} else {
|
||||
return <ObjectRender object={value} />;
|
||||
}
|
||||
break;
|
||||
case "string":
|
||||
if (isTimeStampString(value)) {
|
||||
return <DateReader date={value} />;
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import "ag-grid-community/styles/ag-grid.css"; // Mandatory CSS required by the grid
|
||||
import "ag-grid-community/styles/ag-theme-quartz.css"; // Optional Theme applied to the grid
|
||||
import { AgGridReact, AgGridReactProps } from "ag-grid-react";
|
||||
import { ElementRef, forwardRef, useRef } from "react";
|
||||
import { ElementRef, forwardRef, useRef, useState } from "react";
|
||||
import {
|
||||
DEFAULT_TABLE_ALERT_MSG,
|
||||
DEFAULT_TABLE_ALERT_TITLE,
|
||||
|
|
@ -11,7 +11,8 @@ import "../../style/ag-theme-shadcn.css"; // Custom CSS applied to the grid
|
|||
import { cn, toTitleCase } from "../../utils/utils";
|
||||
import ForwardedIconComponent from "../genericIconComponent";
|
||||
import { Alert, AlertDescription, AlertTitle } from "../ui/alert";
|
||||
import ResetColumns from "./components/ResetColumns";
|
||||
import TableOptions from "./components/TableOptions";
|
||||
import { useParams } from "react-router-dom";
|
||||
import resetGrid from "./utils/reset-grid-columns";
|
||||
|
||||
interface TableComponentProps extends AgGridReactProps {
|
||||
|
|
@ -20,8 +21,8 @@ interface TableComponentProps extends AgGridReactProps {
|
|||
alertTitle?: string;
|
||||
alertDescription?: string;
|
||||
editable?: boolean | string[];
|
||||
onDelete?: (selectedRows: any) => void;
|
||||
onDuplicate?: (selectedRows: any) => void;
|
||||
onDelete?: () => void;
|
||||
onDuplicate?: () => void;
|
||||
}
|
||||
|
||||
const TableComponent = forwardRef<
|
||||
|
|
@ -69,9 +70,12 @@ const TableComponent = forwardRef<
|
|||
});
|
||||
const gridRef = useRef(null);
|
||||
// @ts-ignore
|
||||
const realRef = ref?.current ? ref : gridRef;
|
||||
const realRef: React.MutableRefObject<AgGridReact> = ref?.current
|
||||
? ref
|
||||
: gridRef;
|
||||
const dark = useDarkStore((state) => state.dark);
|
||||
const initialColumnDefs = useRef(colDef);
|
||||
const [columnStateChange, setColumnStateChange] = useState(false);
|
||||
|
||||
const makeLastColumnNonResizable = (columnDefs) => {
|
||||
columnDefs.forEach((colDef, index) => {
|
||||
|
|
@ -87,6 +91,9 @@ const TableComponent = forwardRef<
|
|||
params.api.setGridOption("columnDefs", updatedColumnDefs);
|
||||
initialColumnDefs.current = params.api.getColumnDefs();
|
||||
if (props.onGridReady) props.onGridReady(params);
|
||||
setTimeout(() => {
|
||||
setColumnStateChange(false);
|
||||
}, 50);
|
||||
};
|
||||
|
||||
const onColumnMoved = (params) => {
|
||||
|
|
@ -121,7 +128,7 @@ const TableComponent = forwardRef<
|
|||
>
|
||||
<AgGridReact
|
||||
{...props}
|
||||
className={cn(props.className, "custom-scroll")}
|
||||
className={cn(props.className, "cusm-scroll")}
|
||||
defaultColDef={{
|
||||
minWidth: 100,
|
||||
autoHeight: true,
|
||||
|
|
@ -131,8 +138,26 @@ const TableComponent = forwardRef<
|
|||
pagination={true}
|
||||
onGridReady={onGridReady}
|
||||
onColumnMoved={onColumnMoved}
|
||||
onStateUpdated={(e) => {
|
||||
console.log(e);
|
||||
if (e.sources.some((source) => source.includes("column"))) {
|
||||
setColumnStateChange(true);
|
||||
}
|
||||
}}
|
||||
/>
|
||||
<TableOptions
|
||||
stateChange={columnStateChange}
|
||||
hasSelection={realRef.current?.api.getSelectedRows().length > 0}
|
||||
duplicateRow={props.onDuplicate ? props.onDuplicate : undefined}
|
||||
deleteRow={props.onDelete ? props.onDelete : undefined}
|
||||
resetGrid={() => {
|
||||
console.log("teste");
|
||||
resetGrid(realRef, initialColumnDefs);
|
||||
setTimeout(() => {
|
||||
setColumnStateChange(false);
|
||||
}, 100);
|
||||
}}
|
||||
/>
|
||||
<ResetColumns resetGrid={() => resetGrid(realRef, initialColumnDefs)} />
|
||||
</div>
|
||||
);
|
||||
},
|
||||
|
|
|
|||
|
|
@ -982,9 +982,7 @@ export async function postBuildVertex(
|
|||
files?: string[],
|
||||
): Promise<AxiosResponse<VertexBuildTypeAPI>> {
|
||||
// input_value is optional and is a query parameter
|
||||
const data = input_value
|
||||
? { inputs: { input_value: input_value } }
|
||||
: undefined;
|
||||
const data = { inputs: { input_value: input_value ?? "" } };
|
||||
if (data && files) {
|
||||
data["files"] = files;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -44,43 +44,23 @@ export default function SessionView({ rows }: { rows: Array<any> }) {
|
|||
}
|
||||
|
||||
return (
|
||||
<div className="flex h-full w-full flex-col justify-between gap-6">
|
||||
<>
|
||||
<div className="flex w-full items-center justify-end gap-4 space-y-0.5">
|
||||
<div className="flex flex-shrink-0 items-center gap-2">
|
||||
<Button
|
||||
data-testid="api-key-button-store"
|
||||
variant="primary"
|
||||
className="group px-2"
|
||||
disabled={selectedRows.length === 0}
|
||||
onClick={handleRemoveMessages}
|
||||
>
|
||||
<ForwardedIconComponent
|
||||
name="Trash2"
|
||||
className={cn(
|
||||
"h-5 w-5 text-destructive group-disabled:text-primary",
|
||||
)}
|
||||
/>
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
</>
|
||||
<TableComponent
|
||||
readOnlyEdit
|
||||
onCellEditRequest={(event) => {
|
||||
handleUpdateMessage(event);
|
||||
}}
|
||||
editable={["Sender Name", "Message"]}
|
||||
overlayNoRowsTemplate="No data available"
|
||||
onSelectionChanged={(event: SelectionChangedEvent) => {
|
||||
setSelectedRows(event.api.getSelectedRows().map((row) => row.index));
|
||||
}}
|
||||
rowSelection="multiple"
|
||||
suppressRowClickSelection={true}
|
||||
pagination={true}
|
||||
columnDefs={columns}
|
||||
rowData={rows}
|
||||
/>
|
||||
</div>
|
||||
<TableComponent
|
||||
key={"sessionView"}
|
||||
onDelete={handleRemoveMessages}
|
||||
readOnlyEdit
|
||||
onCellEditRequest={(event) => {
|
||||
handleUpdateMessage(event);
|
||||
}}
|
||||
editable={["Sender Name", "Message"]}
|
||||
overlayNoRowsTemplate="No data available"
|
||||
onSelectionChanged={(event: SelectionChangedEvent) => {
|
||||
setSelectedRows(event.api.getSelectedRows().map((row) => row.index));
|
||||
}}
|
||||
rowSelection="multiple"
|
||||
suppressRowClickSelection={true}
|
||||
pagination={true}
|
||||
columnDefs={columns}
|
||||
rowData={rows}
|
||||
/>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,52 @@
|
|||
import IconComponent from "../../../../../../../components/genericIconComponent";
|
||||
import { Case } from "../../../../../../../shared/components/caseComponent";
|
||||
import { classNames } from "../../../../../../../utils/utils";
|
||||
|
||||
const ButtonSendWrapper = ({
|
||||
send,
|
||||
lockChat,
|
||||
noInput,
|
||||
saveLoading,
|
||||
chatValue,
|
||||
}) => {
|
||||
return (
|
||||
<button
|
||||
className={classNames(
|
||||
"form-modal-send-button",
|
||||
noInput
|
||||
? "bg-high-indigo text-background"
|
||||
: chatValue === ""
|
||||
? "text-primary"
|
||||
: "bg-chat-send text-background",
|
||||
)}
|
||||
disabled={lockChat || saveLoading}
|
||||
onClick={(): void => send()}
|
||||
>
|
||||
<Case condition={lockChat || saveLoading}>
|
||||
<IconComponent
|
||||
name="Lock"
|
||||
className="form-modal-lock-icon"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</Case>
|
||||
|
||||
<Case condition={noInput}>
|
||||
<IconComponent
|
||||
name="Zap"
|
||||
className="form-modal-play-icon"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</Case>
|
||||
|
||||
<Case condition={!(lockChat || saveLoading) && !noInput}>
|
||||
<IconComponent
|
||||
name="LucideSend"
|
||||
className="form-modal-send-icon "
|
||||
aria-hidden="true"
|
||||
/>
|
||||
</Case>
|
||||
</button>
|
||||
);
|
||||
};
|
||||
|
||||
export default ButtonSendWrapper;
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
import { Textarea } from "../../../../../../../components/ui/textarea";
|
||||
import { classNames } from "../../../../../../../utils/utils";
|
||||
|
||||
const TextAreaWrapper = ({
|
||||
checkSendingOk,
|
||||
send,
|
||||
lockChat,
|
||||
noInput,
|
||||
saveLoading,
|
||||
chatValue,
|
||||
setChatValue,
|
||||
CHAT_INPUT_PLACEHOLDER,
|
||||
CHAT_INPUT_PLACEHOLDER_SEND,
|
||||
inputRef,
|
||||
setInputFocus,
|
||||
files,
|
||||
isDragging,
|
||||
}) => {
|
||||
const getPlaceholderText = (
|
||||
isDragging: boolean,
|
||||
noInput: boolean,
|
||||
): string => {
|
||||
if (isDragging) {
|
||||
return "Drop here";
|
||||
} else if (noInput) {
|
||||
return CHAT_INPUT_PLACEHOLDER;
|
||||
} else {
|
||||
return CHAT_INPUT_PLACEHOLDER_SEND;
|
||||
}
|
||||
};
|
||||
|
||||
const lockClass =
|
||||
lockChat || saveLoading
|
||||
? "form-modal-lock-true bg-input"
|
||||
: noInput
|
||||
? "form-modal-no-input bg-input"
|
||||
: "form-modal-lock-false bg-background";
|
||||
|
||||
const fileClass =
|
||||
files.length > 0
|
||||
? "rounded-b-lg ring-0 focus:ring-0 focus:border-2 rounded-t-none border-t-0 border-border focus:border-t-0 focus:border-ring"
|
||||
: "rounded-md border-t border-border focus:ring-0 focus:border-2 focus:border-ring";
|
||||
|
||||
const additionalClassNames = "form-modal-lockchat pl-14";
|
||||
|
||||
return (
|
||||
<Textarea
|
||||
onFocus={(e) => {
|
||||
setInputFocus(true);
|
||||
e.target.style.borderTopWidth = "0";
|
||||
}}
|
||||
onBlur={() => setInputFocus(false)}
|
||||
onKeyDown={(event) => {
|
||||
if (checkSendingOk(event)) {
|
||||
send();
|
||||
}
|
||||
}}
|
||||
rows={1}
|
||||
ref={inputRef}
|
||||
disabled={lockChat || noInput || saveLoading}
|
||||
style={{
|
||||
resize: "none",
|
||||
bottom: `${inputRef?.current?.scrollHeight}px`,
|
||||
maxHeight: "150px",
|
||||
overflow: `${
|
||||
inputRef.current && inputRef.current.scrollHeight > 150
|
||||
? "auto"
|
||||
: "hidden"
|
||||
}`,
|
||||
}}
|
||||
value={lockChat ? "Thinking..." : saveLoading ? "Saving..." : chatValue}
|
||||
onChange={(event): void => {
|
||||
setChatValue(event.target.value);
|
||||
}}
|
||||
className={classNames(lockClass, fileClass, additionalClassNames)}
|
||||
placeholder={getPlaceholderText(isDragging, noInput)}
|
||||
/>
|
||||
);
|
||||
};
|
||||
|
||||
export default TextAreaWrapper;
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
import ForwardedIconComponent from "../../../../../../../components/genericIconComponent";
|
||||
import { Button } from "../../../../../../../components/ui/button";
|
||||
|
||||
const UploadFileButton = ({
|
||||
fileInputRef,
|
||||
handleFileChange,
|
||||
handleButtonClick,
|
||||
lockChat,
|
||||
}) => {
|
||||
return (
|
||||
<div>
|
||||
<input
|
||||
disabled={lockChat}
|
||||
type="file"
|
||||
ref={fileInputRef}
|
||||
style={{ display: "none" }}
|
||||
onChange={handleFileChange}
|
||||
/>
|
||||
<Button
|
||||
disabled={lockChat}
|
||||
className={`font-bold text-white transition-all ${lockChat ? "cursor-not-allowed" : "hover:text-muted-foreground"}`}
|
||||
onClick={handleButtonClick}
|
||||
variant="none"
|
||||
size="none"
|
||||
>
|
||||
<ForwardedIconComponent name="PaperclipIcon" />
|
||||
</Button>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
export default UploadFileButton;
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
export const getClassNamesFilePreview = (inputFocus) => {
|
||||
return `flex w-full items-center gap-4 rounded-t-lg bg-background px-14 py-5 overflow-auto custom-scroll ${
|
||||
inputFocus
|
||||
? "border border-b-0 border-ring border-2"
|
||||
: "border border-b-0 border-border"
|
||||
}`;
|
||||
};
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
import { useEffect } from "react";
|
||||
|
||||
const useAutoResizeTextArea = (value, inputRef) => {
|
||||
useEffect(() => {
|
||||
if (inputRef.current && inputRef.current.scrollHeight! !== 0) {
|
||||
inputRef.current.style!.height = "inherit"; // Reset the height
|
||||
inputRef.current.style!.height = `${inputRef.current.scrollHeight!}px`; // Set it to the scrollHeight
|
||||
}
|
||||
}, [value]);
|
||||
|
||||
return inputRef;
|
||||
};
|
||||
|
||||
export default useAutoResizeTextArea;
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
import ShortUniqueId from "short-unique-id";
|
||||
import useFileUpload from "./use-file-upload";
|
||||
|
||||
const useDragAndDrop = (setIsDragging, setFiles, currentFlowId) => {
|
||||
const dragOver = (e) => {
|
||||
e.preventDefault();
|
||||
if (e.dataTransfer.types.some((type) => type === "Files")) {
|
||||
setIsDragging(true);
|
||||
}
|
||||
};
|
||||
|
||||
const dragEnter = (e) => {
|
||||
if (e.dataTransfer.types.some((type) => type === "Files")) {
|
||||
setIsDragging(true);
|
||||
}
|
||||
e.preventDefault();
|
||||
};
|
||||
|
||||
const dragLeave = (e) => {
|
||||
e.preventDefault();
|
||||
setIsDragging(false);
|
||||
};
|
||||
|
||||
const onDrop = (e) => {
|
||||
e.preventDefault();
|
||||
if (e.dataTransfer.files && e.dataTransfer.files.length > 0) {
|
||||
handleFiles(e.dataTransfer.files, setFiles, currentFlowId);
|
||||
e.dataTransfer.clearData();
|
||||
}
|
||||
setIsDragging(false);
|
||||
};
|
||||
return {
|
||||
dragOver,
|
||||
dragEnter,
|
||||
dragLeave,
|
||||
onDrop,
|
||||
};
|
||||
};
|
||||
|
||||
const handleFiles = (files, setFiles, currentFlowId) => {
|
||||
if (files) {
|
||||
const uid = new ShortUniqueId({ length: 3 });
|
||||
const id = uid();
|
||||
const type = files[0].type.split("/")[0];
|
||||
const blob = files[0];
|
||||
|
||||
setFiles((prevFiles) => [
|
||||
...prevFiles,
|
||||
{ file: blob, loading: true, error: false, id, type },
|
||||
]);
|
||||
|
||||
useFileUpload(blob, currentFlowId, setFiles, id);
|
||||
}
|
||||
};
|
||||
export default useDragAndDrop;
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
import { uploadFile } from "../../../../../../controllers/API";
|
||||
|
||||
const useFileUpload = (blob, currentFlowId, setFiles, id) => {
|
||||
uploadFile(blob, currentFlowId)
|
||||
.then((res) => {
|
||||
setFiles((prev) => {
|
||||
const newFiles = [...prev];
|
||||
const updatedIndex = newFiles.findIndex((file) => file.id === id);
|
||||
newFiles[updatedIndex].loading = false;
|
||||
newFiles[updatedIndex].path = res.data.file_path;
|
||||
return newFiles;
|
||||
});
|
||||
})
|
||||
.catch(() => {
|
||||
setFiles((prev) => {
|
||||
const newFiles = [...prev];
|
||||
const updatedIndex = newFiles.findIndex((file) => file.id === id);
|
||||
newFiles[updatedIndex].loading = false;
|
||||
newFiles[updatedIndex].error = true;
|
||||
return newFiles;
|
||||
});
|
||||
});
|
||||
|
||||
return null;
|
||||
};
|
||||
|
||||
export default useFileUpload;
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
import { useEffect } from "react";
|
||||
|
||||
const useFocusOnUnlock = (lockChat, inputRef) => {
|
||||
useEffect(() => {
|
||||
if (!lockChat && inputRef.current) {
|
||||
inputRef.current.focus();
|
||||
}
|
||||
}, [lockChat, inputRef]);
|
||||
|
||||
return inputRef;
|
||||
};
|
||||
|
||||
export default useFocusOnUnlock;
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
import ShortUniqueId from "short-unique-id";
|
||||
import useFileUpload from "./use-file-upload";
|
||||
import useAlertStore from "../../../../../../stores/alertStore";
|
||||
|
||||
const fsErrorText =
|
||||
"Please ensure your file has one of the following extensions:";
|
||||
const snErrorTxt = "png, jpg, jpeg, gif, bmp, webp";
|
||||
|
||||
export const useHandleFileChange = (setFiles, currentFlowId) => {
|
||||
const setErrorData = useAlertStore((state) => state.setErrorData);
|
||||
const handleFileChange = async (
|
||||
event: React.ChangeEvent<HTMLInputElement>,
|
||||
) => {
|
||||
const fileInput = event.target;
|
||||
const file = fileInput.files?.[0];
|
||||
if (file) {
|
||||
const allowedExtensions = ["png", "jpg", "jpeg", "gif", "bmp", "webp"];
|
||||
const fileExtension = file.name.split(".").pop()?.toLowerCase();
|
||||
|
||||
if (!fileExtension || !allowedExtensions.includes(fileExtension)) {
|
||||
setErrorData({
|
||||
title: "Error uploading file",
|
||||
list: [fsErrorText, snErrorTxt],
|
||||
});
|
||||
return;
|
||||
}
|
||||
|
||||
const uid = new ShortUniqueId({ length: 10 }); // Increase the length to ensure uniqueness
|
||||
const id = uid();
|
||||
const type = file.type.split("/")[0];
|
||||
const blob = file;
|
||||
|
||||
setFiles((prevFiles) => [
|
||||
...prevFiles,
|
||||
{ file: blob, loading: true, error: false, id, type },
|
||||
]);
|
||||
|
||||
useFileUpload(blob, currentFlowId, setFiles, id);
|
||||
}
|
||||
|
||||
// Clear the file input value to ensure the change event is triggered even for the same file
|
||||
fileInput.value = "";
|
||||
};
|
||||
|
||||
return {
|
||||
handleFileChange,
|
||||
};
|
||||
};
|
||||
|
||||
export default useHandleFileChange;
|
||||
|
|
@ -0,0 +1,61 @@
|
|||
import { useEffect } from "react";
|
||||
import ShortUniqueId from "short-unique-id";
|
||||
import useFileUpload from "./use-file-upload";
|
||||
import useAlertStore from "../../../../../../stores/alertStore";
|
||||
|
||||
const fsErrorText =
|
||||
"Please ensure your file has one of the following extensions:";
|
||||
const snErrorTxt = "png, jpg, jpeg, gif, bmp, webp";
|
||||
|
||||
const useUpload = (uploadFile, currentFlowId, setFiles, lockChat) => {
|
||||
const setErrorData = useAlertStore((state) => state.setErrorData);
|
||||
useEffect(() => {
|
||||
const handlePaste = (event: ClipboardEvent): void => {
|
||||
if (lockChat) {
|
||||
return;
|
||||
}
|
||||
const items = event.clipboardData?.items;
|
||||
if (items) {
|
||||
for (let i = 0; i < items.length; i++) {
|
||||
const type = items[0].type.split("/")[0];
|
||||
const uid = new ShortUniqueId({ length: 3 });
|
||||
const blob = items[i].getAsFile();
|
||||
if (blob) {
|
||||
const allowedExtensions = [
|
||||
"png",
|
||||
"jpg",
|
||||
"jpeg",
|
||||
"gif",
|
||||
"bmp",
|
||||
"webp",
|
||||
];
|
||||
const fileExtension = blob.name.split(".").pop()?.toLowerCase();
|
||||
|
||||
if (!fileExtension || !allowedExtensions.includes(fileExtension)) {
|
||||
setErrorData({
|
||||
title: "Error uploading file",
|
||||
list: [fsErrorText, snErrorTxt],
|
||||
});
|
||||
return;
|
||||
}
|
||||
const id = uid();
|
||||
setFiles((prevFiles) => [
|
||||
...prevFiles,
|
||||
{ file: blob, loading: true, error: false, id, type },
|
||||
]);
|
||||
useFileUpload(blob, currentFlowId, setFiles, id);
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
document.addEventListener("paste", handlePaste);
|
||||
return () => {
|
||||
document.removeEventListener("paste", handlePaste);
|
||||
};
|
||||
}, [uploadFile, currentFlowId, lockChat]);
|
||||
|
||||
return null;
|
||||
};
|
||||
|
||||
export default useUpload;
|
||||
|
|
@ -1,7 +1,4 @@
|
|||
import { useEffect, useState } from "react";
|
||||
import ShortUniqueId from "short-unique-id";
|
||||
import IconComponent from "../../../../../components/genericIconComponent";
|
||||
import { Textarea } from "../../../../../components/ui/textarea";
|
||||
import { useRef, useState } from "react";
|
||||
import {
|
||||
CHAT_INPUT_PLACEHOLDER,
|
||||
CHAT_INPUT_PLACEHOLDER_SEND,
|
||||
|
|
@ -9,11 +6,18 @@ import {
|
|||
import { uploadFile } from "../../../../../controllers/API";
|
||||
import useFlowsManagerStore from "../../../../../stores/flowsManagerStore";
|
||||
import {
|
||||
ChatInputType,
|
||||
FilePreviewType,
|
||||
chatInputType,
|
||||
} from "../../../../../types/components";
|
||||
import { classNames } from "../../../../../utils/utils";
|
||||
import FilePreview from "../filePreviewChat";
|
||||
import ButtonSendWrapper from "./components/buttonSendWrapper";
|
||||
import TextAreaWrapper from "./components/textAreaWrapper";
|
||||
import UploadFileButton from "./components/uploadFileButton";
|
||||
import { getClassNamesFilePreview } from "./helpers/get-class-file-preview";
|
||||
import useAutoResizeTextArea from "./hooks/use-auto-resize-text-area";
|
||||
import useFocusOnUnlock from "./hooks/use-focus-unlock";
|
||||
import useHandleFileChange from "./hooks/use-handle-file-change";
|
||||
import useUpload from "./hooks/use-upload";
|
||||
export default function ChatInput({
|
||||
lockChat,
|
||||
chatValue,
|
||||
|
|
@ -21,199 +25,102 @@ export default function ChatInput({
|
|||
setChatValue,
|
||||
inputRef,
|
||||
noInput,
|
||||
}: chatInputType): JSX.Element {
|
||||
files,
|
||||
setFiles,
|
||||
isDragging,
|
||||
}: ChatInputType): JSX.Element {
|
||||
const [repeat, setRepeat] = useState(1);
|
||||
const saveLoading = useFlowsManagerStore((state) => state.saveLoading);
|
||||
const currentFlowId = useFlowsManagerStore((state) => state.currentFlowId);
|
||||
const uid = new ShortUniqueId({ length: 3 });
|
||||
const [files, setFiles] = useState<FilePreviewType[]>([]);
|
||||
const [inputFocus, setInputFocus] = useState<boolean>(false);
|
||||
const fileInputRef = useRef<HTMLInputElement>(null);
|
||||
|
||||
useEffect(() => {
|
||||
if (!lockChat && inputRef.current) {
|
||||
inputRef.current.focus();
|
||||
}
|
||||
}, [lockChat, inputRef]);
|
||||
useFocusOnUnlock(lockChat, inputRef);
|
||||
useAutoResizeTextArea(chatValue, inputRef);
|
||||
useUpload(uploadFile, currentFlowId, setFiles, lockChat || saveLoading);
|
||||
const { handleFileChange } = useHandleFileChange(setFiles, currentFlowId);
|
||||
|
||||
useEffect(() => {
|
||||
if (inputRef.current && inputRef.current.scrollHeight !== 0) {
|
||||
inputRef.current.style.height = "inherit"; // Reset the height
|
||||
inputRef.current.style.height = `${inputRef.current.scrollHeight}px`; // Set it to the scrollHeight
|
||||
}
|
||||
}, [chatValue]);
|
||||
|
||||
//listen to ctrl-v to paste images
|
||||
useEffect(() => {
|
||||
const handlePaste = (event: ClipboardEvent): void => {
|
||||
const items = event.clipboardData?.items;
|
||||
if (items) {
|
||||
for (let i = 0; i < items.length; i++) {
|
||||
if (items[i].type.indexOf("image") !== -1) {
|
||||
const blob = items[i].getAsFile();
|
||||
if (blob) {
|
||||
const id = uid();
|
||||
setFiles([
|
||||
...files,
|
||||
{ file: blob, loading: true, error: false, id },
|
||||
]);
|
||||
uploadFile(blob, currentFlowId)
|
||||
.then((res) => {
|
||||
setFiles((prev) => {
|
||||
const newFiles = [...prev];
|
||||
const updatedIndex = newFiles.findIndex(
|
||||
(file) => file.id === id,
|
||||
);
|
||||
newFiles[updatedIndex].loading = false;
|
||||
newFiles[updatedIndex].path = res.data.file_path;
|
||||
return newFiles;
|
||||
});
|
||||
})
|
||||
.catch((err) => {
|
||||
setFiles((prev) => {
|
||||
const newFiles = [...prev];
|
||||
const updatedIndex = newFiles.findIndex(
|
||||
(file) => file.id === id,
|
||||
);
|
||||
newFiles[updatedIndex].loading = false;
|
||||
newFiles[updatedIndex].error = true;
|
||||
return newFiles;
|
||||
});
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
document.addEventListener("paste", handlePaste);
|
||||
return () => {
|
||||
document.removeEventListener("paste", handlePaste);
|
||||
};
|
||||
}, []);
|
||||
|
||||
function send() {
|
||||
const send = () => {
|
||||
sendMessage({
|
||||
repeat,
|
||||
files: files.map((file) => file.path ?? "").filter((file) => file !== ""),
|
||||
});
|
||||
setFiles([]);
|
||||
}
|
||||
};
|
||||
|
||||
const checkSendingOk = (event: React.KeyboardEvent<HTMLTextAreaElement>) => {
|
||||
return (
|
||||
event.key === "Enter" &&
|
||||
!lockChat &&
|
||||
!saveLoading &&
|
||||
!event.shiftKey &&
|
||||
!event.nativeEvent.isComposing
|
||||
);
|
||||
};
|
||||
|
||||
const classNameFilePreview = getClassNamesFilePreview(inputFocus);
|
||||
|
||||
const handleButtonClick = () => {
|
||||
fileInputRef.current!.click();
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="flex w-full flex-col-reverse">
|
||||
<div className="relative w-full">
|
||||
<Textarea
|
||||
onKeyDown={(event) => {
|
||||
if (
|
||||
event.key === "Enter" &&
|
||||
!lockChat &&
|
||||
!saveLoading &&
|
||||
!event.shiftKey &&
|
||||
!event.nativeEvent.isComposing
|
||||
) {
|
||||
send();
|
||||
}
|
||||
}}
|
||||
rows={1}
|
||||
ref={inputRef}
|
||||
disabled={lockChat || noInput || saveLoading}
|
||||
style={{
|
||||
resize: "none",
|
||||
bottom: `${inputRef?.current?.scrollHeight}px`,
|
||||
maxHeight: "150px",
|
||||
overflow: `${
|
||||
inputRef.current && inputRef.current.scrollHeight > 150
|
||||
? "auto"
|
||||
: "hidden"
|
||||
}`,
|
||||
}}
|
||||
value={
|
||||
lockChat ? "Thinking..." : saveLoading ? "Saving..." : chatValue
|
||||
}
|
||||
onChange={(event): void => {
|
||||
setChatValue(event.target.value);
|
||||
}}
|
||||
className={classNames(
|
||||
lockChat || saveLoading
|
||||
? " form-modal-lock-true bg-input"
|
||||
: noInput
|
||||
? "form-modal-no-input bg-input"
|
||||
: " form-modal-lock-false bg-background",
|
||||
|
||||
"form-modal-lockchat",
|
||||
)}
|
||||
placeholder={
|
||||
noInput ? CHAT_INPUT_PLACEHOLDER : CHAT_INPUT_PLACEHOLDER_SEND
|
||||
}
|
||||
<TextAreaWrapper
|
||||
checkSendingOk={checkSendingOk}
|
||||
send={send}
|
||||
lockChat={lockChat}
|
||||
noInput={noInput}
|
||||
saveLoading={saveLoading}
|
||||
chatValue={chatValue}
|
||||
setChatValue={setChatValue}
|
||||
CHAT_INPUT_PLACEHOLDER={CHAT_INPUT_PLACEHOLDER}
|
||||
CHAT_INPUT_PLACEHOLDER_SEND={CHAT_INPUT_PLACEHOLDER_SEND}
|
||||
inputRef={inputRef}
|
||||
setInputFocus={setInputFocus}
|
||||
files={files}
|
||||
isDragging={isDragging}
|
||||
/>
|
||||
<div className="form-modal-send-icon-position">
|
||||
<button
|
||||
className={classNames(
|
||||
"form-modal-send-button",
|
||||
noInput
|
||||
? "bg-high-indigo text-background"
|
||||
: chatValue === ""
|
||||
? "text-primary"
|
||||
: "bg-chat-send text-background",
|
||||
)}
|
||||
disabled={lockChat || saveLoading}
|
||||
onClick={(): void => send()}
|
||||
>
|
||||
{lockChat || saveLoading ? (
|
||||
<IconComponent
|
||||
name="Lock"
|
||||
className="form-modal-lock-icon"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
) : noInput ? (
|
||||
<IconComponent
|
||||
name="Zap"
|
||||
className="form-modal-play-icon"
|
||||
aria-hidden="true"
|
||||
/>
|
||||
) : (
|
||||
<IconComponent
|
||||
name="LucideSend"
|
||||
className="form-modal-send-icon "
|
||||
aria-hidden="true"
|
||||
/>
|
||||
)}
|
||||
</button>
|
||||
<ButtonSendWrapper
|
||||
send={send}
|
||||
lockChat={lockChat}
|
||||
noInput={noInput}
|
||||
saveLoading={saveLoading}
|
||||
chatValue={chatValue}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div
|
||||
className={`absolute bottom-2 left-4 ${lockChat || saveLoading ? "cursor-not-allowed" : ""}`}
|
||||
>
|
||||
<UploadFileButton
|
||||
lockChat={lockChat || saveLoading}
|
||||
fileInputRef={fileInputRef}
|
||||
handleFileChange={handleFileChange}
|
||||
handleButtonClick={handleButtonClick}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex w-full gap-2 pb-2">
|
||||
{files.map((file) => (
|
||||
<FilePreview
|
||||
error={file.error}
|
||||
file={file.file}
|
||||
loading={file.loading}
|
||||
key={file.id}
|
||||
onDelete={() => {
|
||||
setFiles((prev) => prev.filter((f) => f.id !== file.id));
|
||||
// TODO: delete file on backend
|
||||
}}
|
||||
/>
|
||||
))}
|
||||
</div>
|
||||
{/*
|
||||
<Popover>
|
||||
<PopoverTrigger asChild>
|
||||
<Button variant="primary" className="h-13 px-4">
|
||||
<IconComponent name="Repeat" className="" aria-hidden="true" />
|
||||
</Button>
|
||||
</PopoverTrigger>
|
||||
<PopoverContent className="w-fit">
|
||||
<div className="flex flex-col items-center justify-center gap-2">
|
||||
<span className="text-sm">Repetitions: </span>
|
||||
<Input
|
||||
onChange={(e) => {
|
||||
handleChange(parseInt(e.target.value));
|
||||
{files.length > 0 && (
|
||||
<div className={classNameFilePreview}>
|
||||
{files.map((file) => (
|
||||
<FilePreview
|
||||
error={file.error}
|
||||
file={file.file}
|
||||
loading={file.loading}
|
||||
key={file.id}
|
||||
onDelete={() => {
|
||||
setFiles((prev: FilePreviewType[]) =>
|
||||
prev.filter((f) => f.id !== file.id),
|
||||
);
|
||||
// TODO: delete file on backend
|
||||
}}
|
||||
className="w-16"
|
||||
type="number"
|
||||
min={0}
|
||||
/>
|
||||
</div>
|
||||
</PopoverContent>
|
||||
</Popover> */}
|
||||
))}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,35 @@
|
|||
import { useState } from "react";
|
||||
import ForwardedIconComponent from "../../../../../../../components/genericIconComponent";
|
||||
import formatFileName from "../../../filePreviewChat/utils/format-file-name";
|
||||
import FileCard from "../../../fileComponent";
|
||||
|
||||
export default function FileCardWrapper({
|
||||
index,
|
||||
name,
|
||||
type,
|
||||
path,
|
||||
}: {
|
||||
index: number;
|
||||
name: string;
|
||||
type: string;
|
||||
path: string;
|
||||
}) {
|
||||
const [show, setShow] = useState<boolean>(true);
|
||||
return (
|
||||
<div key={index} className="flex flex-col gap-2">
|
||||
<span
|
||||
onClick={() => setShow(!show)}
|
||||
className="flex cursor-pointer gap-2 text-sm text-muted-foreground"
|
||||
>
|
||||
{formatFileName(name, 50)}
|
||||
<ForwardedIconComponent name={show ? "ChevronDown" : "ChevronRight"} />
|
||||
</span>
|
||||
<FileCard
|
||||
showFile={show}
|
||||
fileName={name}
|
||||
fileType={type}
|
||||
content={path}
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
|
@ -7,13 +7,17 @@ import remarkMath from "remark-math";
|
|||
import MaleTechnology from "../../../../../assets/male-technologist.png";
|
||||
import Robot from "../../../../../assets/robot.png";
|
||||
import CodeTabsComponent from "../../../../../components/codeTabsComponent";
|
||||
import IconComponent from "../../../../../components/genericIconComponent";
|
||||
import IconComponent, {
|
||||
ForwardedIconComponent,
|
||||
} from "../../../../../components/genericIconComponent";
|
||||
import SanitizedHTMLWrapper from "../../../../../components/sanitizedHTMLWrapper";
|
||||
import useAlertStore from "../../../../../stores/alertStore";
|
||||
import useFlowStore from "../../../../../stores/flowStore";
|
||||
import { chatMessagePropsType } from "../../../../../types/components";
|
||||
import { classNames, cn } from "../../../../../utils/utils";
|
||||
import FileCard from "../fileComponent";
|
||||
import formatFileName from "../filePreviewChat/utils/format-file-name";
|
||||
import FileCardWrapper from "./components/fileCardWrapper";
|
||||
|
||||
export default function ChatMessage({
|
||||
chat,
|
||||
|
|
@ -22,6 +26,7 @@ export default function ChatMessage({
|
|||
updateChat,
|
||||
setLockChat,
|
||||
}: chatMessagePropsType): JSX.Element {
|
||||
const [showFile, setShowFile] = useState<boolean>(true);
|
||||
const convert = new Convert({ newline: true });
|
||||
const [hidden, setHidden] = useState(true);
|
||||
const template = chat.template;
|
||||
|
|
@ -308,34 +313,35 @@ dark:prose-invert"
|
|||
</span>
|
||||
</>
|
||||
) : (
|
||||
<span
|
||||
className="prose text-primary word-break-break-word dark:prose-invert"
|
||||
data-testid={
|
||||
"chat-message-" + chat.sender_name + "-" + chatMessage
|
||||
}
|
||||
>
|
||||
{chatMessage}
|
||||
</span>
|
||||
<div className="flex flex-col">
|
||||
<span
|
||||
className="prose text-primary word-break-break-word dark:prose-invert"
|
||||
data-testid={
|
||||
"chat-message-" + chat.sender_name + "-" + chatMessage
|
||||
}
|
||||
>
|
||||
{chatMessage}
|
||||
</span>
|
||||
{chat.files && (
|
||||
<div className="my-2 flex flex-col gap-5">
|
||||
{chat.files.map((file, index) => {
|
||||
return (
|
||||
<FileCardWrapper
|
||||
index={index}
|
||||
name={file.name}
|
||||
type={file.type}
|
||||
path={file.path}
|
||||
/>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
<div id={lastMessage ? "last-chat-message" : ""}></div>
|
||||
{chat.files && (
|
||||
<div className="my-2 w-full">
|
||||
{chat.files.map((file, index) => {
|
||||
return (
|
||||
<div key={index} className="my-2 w-full">
|
||||
<FileCard
|
||||
fileName={file.name}
|
||||
fileType={file.type}
|
||||
content={file.path}
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
)}
|
||||
</>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,30 @@
|
|||
import ForwardedIconComponent from "../../../../../../../components/genericIconComponent";
|
||||
import { Button } from "../../../../../../../components/ui/button";
|
||||
|
||||
export default function DownloadButton({
|
||||
isHovered,
|
||||
handleDownload,
|
||||
}: {
|
||||
isHovered: boolean;
|
||||
handleDownload: () => void;
|
||||
}): JSX.Element | undefined {
|
||||
if (isHovered) {
|
||||
return (
|
||||
<div
|
||||
className={`absolute right-1 top-1 rounded-md bg-muted text-sm font-bold text-foreground `}
|
||||
>
|
||||
<Button
|
||||
variant={"none"}
|
||||
className="bg-transparent px-2 py-1 text-ring"
|
||||
onClick={handleDownload}
|
||||
>
|
||||
<ForwardedIconComponent
|
||||
name="DownloadCloud"
|
||||
className="h-5 w-5 bg-transparent text-current hover:scale-110"
|
||||
/>
|
||||
</Button>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
return undefined;
|
||||
}
|
||||
|
|
@ -1,19 +1,21 @@
|
|||
import { useState } from "react";
|
||||
import IconComponent from "../../../../../components/genericIconComponent";
|
||||
import { ForwardedIconComponent } from "../../../../../components/genericIconComponent";
|
||||
import { BACKEND_URL, BASE_URL_API } from "../../../../../constants/constants";
|
||||
import useFlowsManagerStore from "../../../../../stores/flowsManagerStore";
|
||||
import { fileCardPropsType } from "../../../../../types/components";
|
||||
import formatFileName from "../filePreviewChat/utils/format-file-name";
|
||||
import DownloadButton from "./components/downloadButton/downloadButton";
|
||||
import getClasses from "./utils/get-classes";
|
||||
import handleDownload from "./utils/handle-download";
|
||||
|
||||
const imgTypes = new Set(["png", "jpg"]);
|
||||
const imgTypes = new Set(["png", "jpg", "jpeg", "gif", "webp", "image"]);
|
||||
|
||||
export default function FileCard({
|
||||
fileName,
|
||||
content,
|
||||
fileType,
|
||||
}: fileCardPropsType): JSX.Element {
|
||||
const handleDownload = (): void => {
|
||||
//TODO: update download function
|
||||
};
|
||||
showFile = true,
|
||||
}: fileCardPropsType): JSX.Element | undefined {
|
||||
const [isHovered, setIsHovered] = useState(false);
|
||||
const currentFlowId = useFlowsManagerStore((state) => state.currentFlowId);
|
||||
function handleMouseEnter(): void {
|
||||
|
|
@ -23,64 +25,57 @@ export default function FileCard({
|
|||
setIsHovered(false);
|
||||
}
|
||||
|
||||
if (imgTypes.has(fileType)) {
|
||||
const fileWrapperClasses = getClasses(isHovered);
|
||||
|
||||
const imgSrc = `${BACKEND_URL.slice(
|
||||
0,
|
||||
BACKEND_URL.length - 1,
|
||||
)}${BASE_URL_API}files/images/${content}`;
|
||||
|
||||
if (showFile) {
|
||||
if (imgTypes.has(fileType)) {
|
||||
return (
|
||||
<div
|
||||
className="inline-block w-full rounded-lg transition-all"
|
||||
onMouseEnter={handleMouseEnter}
|
||||
onMouseLeave={handleMouseLeave}
|
||||
style={{ display: "inline-block" }}
|
||||
>
|
||||
<div className="relative w-[50%] rounded-lg border border-border">
|
||||
<img
|
||||
src={imgSrc}
|
||||
alt="generated image"
|
||||
className="m-0 h-auto w-auto rounded-lg border border-border p-0 transition-all"
|
||||
/>
|
||||
<DownloadButton
|
||||
isHovered={isHovered}
|
||||
handleDownload={() => handleDownload({ fileName, content })}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<div
|
||||
className="relative h-1/4 w-1/4"
|
||||
className={fileWrapperClasses}
|
||||
onClick={() => handleDownload({ fileName, content })}
|
||||
onMouseEnter={handleMouseEnter}
|
||||
onMouseLeave={handleMouseLeave}
|
||||
>
|
||||
<img
|
||||
src={`${BACKEND_URL.slice(
|
||||
0,
|
||||
BACKEND_URL.length - 1,
|
||||
)}${BASE_URL_API}files/images/${content}`}
|
||||
alt="generated image"
|
||||
className="h-full w-full rounded-lg"
|
||||
/>
|
||||
{isHovered && (
|
||||
<div className={`file-card-modal-image-div `}>
|
||||
<button
|
||||
className="file-card-modal-image-button "
|
||||
onClick={handleDownload}
|
||||
>
|
||||
<IconComponent
|
||||
name="DownloadCloud"
|
||||
className="h-5 w-5 text-current hover:scale-110"
|
||||
/>
|
||||
</button>
|
||||
<div className="ml-3 flex h-full w-full items-center gap-2 text-sm">
|
||||
<ForwardedIconComponent name="File" className="h-8 w-8" />
|
||||
<div className="flex flex-col">
|
||||
<span className="font-bold">{formatFileName(fileName, 20)}</span>
|
||||
<span>File</span>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
<DownloadButton
|
||||
isHovered={isHovered}
|
||||
handleDownload={() => handleDownload({ fileName, content })}
|
||||
/>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<button onClick={handleDownload} className="file-card-modal-button">
|
||||
<div className="file-card-modal-div">
|
||||
{" "}
|
||||
{imgTypes.has(fileType) ? (
|
||||
<img
|
||||
src={`${BACKEND_URL.slice(
|
||||
0,
|
||||
BACKEND_URL.length - 1,
|
||||
)}${BASE_URL_API}files/images/${content}`}
|
||||
alt=""
|
||||
className="h-8 w-8"
|
||||
/>
|
||||
) : (
|
||||
<IconComponent name="File" className="h-8 w-8" />
|
||||
)}
|
||||
<div className="file-card-modal-footer">
|
||||
{" "}
|
||||
<div className="file-card-modal-name">{fileName}</div>
|
||||
<div className="file-card-modal-type">{fileType}</div>
|
||||
</div>
|
||||
<IconComponent
|
||||
name="DownloadCloud"
|
||||
className="ml-auto h-6 w-6 text-current"
|
||||
/>
|
||||
</div>
|
||||
</button>
|
||||
);
|
||||
return undefined;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,5 @@
|
|||
export default function getClasses(isHovered: boolean): string {
|
||||
return `relative ${false ? "h-20 w-20" : "h-20 w-80"} cursor-pointer rounded-lg border border-ring bg-muted shadow transition duration-300 hover:drop-shadow-lg ${
|
||||
isHovered ? "shadow-md" : ""
|
||||
}`;
|
||||
}
|
||||
|
|
@ -0,0 +1,43 @@
|
|||
import {
|
||||
BACKEND_URL,
|
||||
BASE_URL_API,
|
||||
} from "../../../../../../constants/constants";
|
||||
|
||||
let isDownloading = false;
|
||||
|
||||
export default async function handleDownload({
|
||||
fileName,
|
||||
content,
|
||||
}: {
|
||||
fileName: string;
|
||||
content: string;
|
||||
}): Promise<void> {
|
||||
if (isDownloading) return;
|
||||
|
||||
try {
|
||||
isDownloading = true;
|
||||
|
||||
const response = await fetch(
|
||||
`${BACKEND_URL.slice(0, BACKEND_URL.length - 1)}${BASE_URL_API}files/download/${content}`,
|
||||
);
|
||||
if (!response.ok) {
|
||||
throw new Error("Network response was not ok");
|
||||
}
|
||||
|
||||
const blob = await response.blob();
|
||||
const url = URL.createObjectURL(blob);
|
||||
|
||||
const link = document.createElement("a");
|
||||
link.href = url;
|
||||
link.setAttribute("download", fileName); // Set the filename
|
||||
document.body.appendChild(link);
|
||||
link.click();
|
||||
document.body.removeChild(link);
|
||||
|
||||
URL.revokeObjectURL(url); // Clean up the URL object
|
||||
} catch (error) {
|
||||
console.error("Failed to download file:", error);
|
||||
} finally {
|
||||
isDownloading = false;
|
||||
}
|
||||
}
|
||||
|
|
@ -1,6 +1,11 @@
|
|||
import { useState } from "react";
|
||||
import IconComponent from "../../../../../components/genericIconComponent";
|
||||
import LoadingComponent from "../../../../../components/loadingComponent";
|
||||
import IconComponent, {
|
||||
ForwardedIconComponent,
|
||||
} from "../../../../../components/genericIconComponent";
|
||||
import { Skeleton } from "../../../../../components/ui/skeleton";
|
||||
import formatFileName from "./utils/format-file-name";
|
||||
|
||||
const supImgFiles = ["png", "jpg", "jpeg", "gif", "bmp", "webp", "image"];
|
||||
|
||||
export default function FilePreview({
|
||||
error,
|
||||
|
|
@ -13,35 +18,93 @@ export default function FilePreview({
|
|||
error: boolean;
|
||||
onDelete: () => void;
|
||||
}) {
|
||||
const fileType = file.type.toLowerCase();
|
||||
const isImage = supImgFiles.some((type) => fileType.includes(type));
|
||||
|
||||
const [isHovered, setIsHovered] = useState(false);
|
||||
|
||||
return (
|
||||
<div className="relative inline-block w-56">
|
||||
{loading && <LoadingComponent remSize={5} />}
|
||||
{error && <div>Error...</div>}
|
||||
<div
|
||||
className={`relative overflow-hidden rounded-md bg-background p-4 transition duration-300 ${
|
||||
isHovered ? "shadow-md" : ""
|
||||
}`}
|
||||
onMouseEnter={() => setIsHovered(true)}
|
||||
onMouseLeave={() => setIsHovered(false)}
|
||||
>
|
||||
<img
|
||||
src={URL.createObjectURL(file)}
|
||||
alt="file"
|
||||
className="block h-auto w-full"
|
||||
/>
|
||||
{isHovered && (
|
||||
<div className="absolute inset-0 flex items-center justify-center bg-black bg-opacity-30">
|
||||
<div
|
||||
className="cursor-pointer rounded-full bg-white bg-opacity-80 p-2"
|
||||
onClick={onDelete}
|
||||
<div className="relative inline-block">
|
||||
{loading ? (
|
||||
isImage ? (
|
||||
<div className="flex h-20 w-20 items-center justify-center rounded-md border border-ring bg-background ">
|
||||
<svg
|
||||
aria-hidden="true"
|
||||
className={`h-10 w-10 animate-spin fill-black text-muted`}
|
||||
viewBox="0 0 100 101"
|
||||
fill="none"
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
>
|
||||
<IconComponent name="trash" className="stroke-red-500" />
|
||||
<path
|
||||
d="M100 50.5908C100 78.2051 77.6142 100.591 50 100.591C22.3858 100.591 0 78.2051 0 50.5908C0 22.9766 22.3858 0.59082 50 0.59082C77.6142 0.59082 100 22.9766 100 50.5908ZM9.08144 50.5908C9.08144 73.1895 27.4013 91.5094 50 91.5094C72.5987 91.5094 90.9186 73.1895 90.9186 50.5908C90.9186 27.9921 72.5987 9.67226 50 9.67226C27.4013 9.67226 9.08144 27.9921 9.08144 50.5908Z"
|
||||
fill="currentColor"
|
||||
/>
|
||||
<path
|
||||
d="M93.9676 39.0409C96.393 38.4038 97.8624 35.9116 97.0079 33.5539C95.2932 28.8227 92.871 24.3692 89.8167 20.348C85.8452 15.1192 80.8826 10.7238 75.2124 7.41289C69.5422 4.10194 63.2754 1.94025 56.7698 1.05124C51.7666 0.367541 46.6976 0.446843 41.7345 1.27873C39.2613 1.69328 37.813 4.19778 38.4501 6.62326C39.0873 9.04874 41.5694 10.4717 44.0505 10.1071C47.8511 9.54855 51.7191 9.52689 55.5402 10.0491C60.8642 10.7766 65.9928 12.5457 70.6331 15.2552C75.2735 17.9648 79.3347 21.5619 82.5849 25.841C84.9175 28.9121 86.7997 32.2913 88.1811 35.8758C89.083 38.2158 91.5421 39.6781 93.9676 39.0409Z"
|
||||
fill="currentFill"
|
||||
/>
|
||||
</svg>
|
||||
</div>
|
||||
) : (
|
||||
<div
|
||||
className={`relative ${
|
||||
isImage ? "h-20 w-20" : "h-20 w-80"
|
||||
} cursor-wait rounded-lg border border-ring bg-background transition duration-300 ${
|
||||
isHovered ? "shadow-md" : ""
|
||||
}`}
|
||||
>
|
||||
<div className="ml-3 flex h-full w-full items-center gap-2 text-sm">
|
||||
<Skeleton className="h-10 w-10 rounded-lg" />
|
||||
<div className="flex flex-col gap-1">
|
||||
<Skeleton className="h-3 w-48" />
|
||||
<Skeleton className="h-3 w-10" />
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)
|
||||
) : error ? (
|
||||
<div>Error...</div>
|
||||
) : (
|
||||
<div
|
||||
className={`relative mt-2 ${
|
||||
isImage ? "h-20 w-20" : "h-20 w-80"
|
||||
} cursor-pointer rounded-lg border border-ring bg-background transition duration-300 ${
|
||||
isHovered ? "shadow-md" : ""
|
||||
}`}
|
||||
onMouseEnter={() => setIsHovered(true)}
|
||||
onMouseLeave={() => setIsHovered(false)}
|
||||
>
|
||||
{isImage ? (
|
||||
<img
|
||||
src={URL.createObjectURL(file)}
|
||||
alt="file"
|
||||
className="block h-full w-full rounded-md border border-border"
|
||||
/>
|
||||
) : (
|
||||
<div className="ml-3 flex h-full w-full items-center gap-2 text-sm">
|
||||
<ForwardedIconComponent name="File" className="h-8 w-8" />
|
||||
<div className="flex flex-col">
|
||||
<span className="font-bold">{formatFileName(file.name)}</span>
|
||||
<span>File</span>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
{isHovered && (
|
||||
<div
|
||||
className={`absolute ${
|
||||
isImage ? "bottom-16 left-16" : "bottom-16 left-[19em]"
|
||||
} flex h-5 w-5 items-center justify-center`}
|
||||
>
|
||||
<div
|
||||
className="flex h-7 w-7 cursor-pointer items-center justify-center rounded-full bg-gray-200 p-2 transition-all"
|
||||
onClick={onDelete}
|
||||
>
|
||||
<IconComponent name="X" className="stroke-slate-950 stroke-2" />
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,14 @@
|
|||
export default function formatFileName(
|
||||
name: string,
|
||||
numberToTruncate: number = 25,
|
||||
): string {
|
||||
if (name[numberToTruncate] === undefined) {
|
||||
return name;
|
||||
}
|
||||
const fileExtension = name.split(".").pop(); // Get the file extension
|
||||
const baseName = name.slice(0, name.lastIndexOf(".")); // Get the base name without the extension
|
||||
if (baseName.length > 6) {
|
||||
return `${baseName.slice(0, numberToTruncate)}...${fileExtension}`;
|
||||
}
|
||||
return name;
|
||||
}
|
||||
|
|
@ -11,9 +11,10 @@ import useFlowStore from "../../../../stores/flowStore";
|
|||
import useFlowsManagerStore from "../../../../stores/flowsManagerStore";
|
||||
import { VertexBuildTypeAPI, sendAllProps } from "../../../../types/api";
|
||||
import { ChatMessageType, ChatOutputType } from "../../../../types/chat";
|
||||
import { chatViewProps } from "../../../../types/components";
|
||||
import { FilePreviewType, chatViewProps } from "../../../../types/components";
|
||||
import { classNames } from "../../../../utils/utils";
|
||||
import ChatInput from "./chatInput";
|
||||
import useDragAndDrop from "./chatInput/hooks/use-drag-and-drop";
|
||||
import ChatMessage from "./chatMessage";
|
||||
|
||||
export default function ChatView({
|
||||
|
|
@ -61,13 +62,12 @@ export default function ChatView({
|
|||
const chatMessages: ChatMessageType[] = chatOutputResponses
|
||||
.sort((a, b) => Date.parse(a.timestamp) - Date.parse(b.timestamp))
|
||||
//
|
||||
.filter(
|
||||
(output) => output.data.messages && output.data.messages.length > 0,
|
||||
)
|
||||
.filter((output) => output.data.message)
|
||||
.map((output, index) => {
|
||||
try {
|
||||
const { sender, message, sender_name, stream_url, files } = output
|
||||
.data.messages[0] as ChatOutputType;
|
||||
.data.message as ChatOutputType;
|
||||
|
||||
const is_ai = sender === "Machine" || sender === null;
|
||||
return {
|
||||
isSend: !is_ai,
|
||||
|
|
@ -154,9 +154,23 @@ export default function ChatView({
|
|||
// return newChatHistory;
|
||||
// });
|
||||
}
|
||||
const [files, setFiles] = useState<FilePreviewType[]>([]);
|
||||
const [isDragging, setIsDragging] = useState(false);
|
||||
|
||||
const { dragOver, dragEnter, dragLeave, onDrop } = useDragAndDrop(
|
||||
setIsDragging,
|
||||
setFiles,
|
||||
currentFlowId,
|
||||
);
|
||||
|
||||
return (
|
||||
<div className="eraser-column-arrangement">
|
||||
<div
|
||||
className="eraser-column-arrangement"
|
||||
onDragOver={dragOver}
|
||||
onDragEnter={dragEnter}
|
||||
onDragLeave={dragLeave}
|
||||
onDrop={onDrop}
|
||||
>
|
||||
<div className="eraser-size">
|
||||
<div className="eraser-position">
|
||||
<Button
|
||||
|
|
@ -258,6 +272,9 @@ export default function ChatView({
|
|||
setChatValue(value);
|
||||
}}
|
||||
inputRef={ref}
|
||||
files={files}
|
||||
setFiles={setFiles}
|
||||
isDragging={isDragging}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -3,33 +3,29 @@ import AccordionComponent from "../../components/accordionComponent";
|
|||
import IconComponent from "../../components/genericIconComponent";
|
||||
import ShadTooltip from "../../components/shadTooltipComponent";
|
||||
import { Badge } from "../../components/ui/badge";
|
||||
import { Button } from "../../components/ui/button";
|
||||
import {
|
||||
Tabs,
|
||||
TabsContent,
|
||||
TabsList,
|
||||
TabsTrigger,
|
||||
} from "../../components/ui/tabs";
|
||||
import {
|
||||
CHAT_FORM_DIALOG_SUBTITLE,
|
||||
OUTPUTS_MODAL_TITLE,
|
||||
TEXT_INPUT_MODAL_TITLE,
|
||||
} from "../../constants/constants";
|
||||
import { CHAT_FORM_DIALOG_SUBTITLE } from "../../constants/constants";
|
||||
import { InputOutput } from "../../constants/enums";
|
||||
import { getMessagesTable } from "../../controllers/API";
|
||||
import useAlertStore from "../../stores/alertStore";
|
||||
import useFlowStore from "../../stores/flowStore";
|
||||
import useFlowsManagerStore from "../../stores/flowsManagerStore";
|
||||
import { useMessagesStore } from "../../stores/messagesStore";
|
||||
import { IOModalPropsType } from "../../types/components";
|
||||
import { NodeDataType, NodeType } from "../../types/flow";
|
||||
import { updateVerticesOrder } from "../../utils/buildUtils";
|
||||
import { cn } from "../../utils/utils";
|
||||
import BaseModal from "../baseModal";
|
||||
import IOFieldView from "./components/IOFieldView";
|
||||
import ChatView from "./components/chatView";
|
||||
import { getMessagesTable } from "../../controllers/API";
|
||||
import { useMessagesStore } from "../../stores/messagesStore";
|
||||
import SessionView from "./components/SessionView";
|
||||
import useRemoveSession from "./components/SessionView/hooks";
|
||||
import useAlertStore from "../../stores/alertStore";
|
||||
import { Button } from "../../components/ui/button";
|
||||
import ChatView from "./components/chatView";
|
||||
|
||||
export default function IOModal({
|
||||
children,
|
||||
|
|
@ -93,12 +89,6 @@ export default function IOModal({
|
|||
return updateVerticesOrder(currentFlow!.id, null);
|
||||
}
|
||||
|
||||
// useEffect(() => {
|
||||
// if (open) {
|
||||
// updateVertices();
|
||||
// }
|
||||
// }, [open, currentFlow]);
|
||||
|
||||
async function sendMessage({
|
||||
repeat = 1,
|
||||
files,
|
||||
|
|
@ -121,10 +111,14 @@ export default function IOModal({
|
|||
setLockChat(false);
|
||||
});
|
||||
}
|
||||
const { rows, columns } = await getMessagesTable("union", currentFlow!.id);
|
||||
setMessages(rows);
|
||||
setColumns(columns);
|
||||
setLockChat(false);
|
||||
if (chatInput) {
|
||||
setNode(chatInput.id, (node: NodeType) => {
|
||||
const newNode = { ...node };
|
||||
|
||||
newNode.data.node!.template["input_value"].value = chatValue;
|
||||
return newNode;
|
||||
});
|
||||
|
|
|
|||
|
|
@ -23,8 +23,6 @@ const useColumnDefs = (
|
|||
: templateParam.name) ?? params.data.key
|
||||
);
|
||||
},
|
||||
tooltipField: "display_name",
|
||||
tooltipComponent: TableTooltipRender,
|
||||
wrapText: true,
|
||||
autoHeight: true,
|
||||
flex: 1,
|
||||
|
|
@ -35,7 +33,6 @@ const useColumnDefs = (
|
|||
headerName: "Description",
|
||||
field: "info",
|
||||
tooltipField: "info",
|
||||
tooltipComponent: TableTooltipRender,
|
||||
wrapText: true,
|
||||
autoHeight: true,
|
||||
flex: 2,
|
||||
|
|
|
|||
|
|
@ -92,6 +92,7 @@ const EditNodeModal = forwardRef(
|
|||
<div className="h-full">
|
||||
{nodeLength > 0 && (
|
||||
<TableComponent
|
||||
key={"editNode"}
|
||||
onGridReady={(params) => {
|
||||
setGridApi(params.api);
|
||||
}}
|
||||
|
|
|
|||
|
|
@ -86,6 +86,7 @@ export default function FlowLogsModal({
|
|||
</TabsList>
|
||||
</Tabs>
|
||||
<TableComponent
|
||||
key={activeTab}
|
||||
readOnlyEdit
|
||||
className="h-max-full h-full w-full"
|
||||
pagination={rows.length === 0 ? false : true}
|
||||
|
|
|
|||
|
|
@ -6,13 +6,11 @@ import { cn } from "../../../../../../utils/utils";
|
|||
|
||||
type ApiKeyHeaderComponentProps = {
|
||||
selectedRows: string[];
|
||||
handleDeleteKey: () => void;
|
||||
fetchApiKeys: () => void;
|
||||
userId: string;
|
||||
};
|
||||
const ApiKeyHeaderComponent = ({
|
||||
selectedRows,
|
||||
handleDeleteKey,
|
||||
fetchApiKeys,
|
||||
userId,
|
||||
}: ApiKeyHeaderComponentProps) => {
|
||||
|
|
@ -30,20 +28,6 @@ const ApiKeyHeaderComponent = ({
|
|||
<p className="text-sm text-muted-foreground">{API_PAGE_PARAGRAPH}</p>
|
||||
</div>
|
||||
<div className="flex flex-shrink-0 items-center gap-2">
|
||||
<Button
|
||||
data-testid="api-key-button-store"
|
||||
variant="primary"
|
||||
className="group px-2"
|
||||
disabled={selectedRows.length === 0}
|
||||
onClick={handleDeleteKey}
|
||||
>
|
||||
<ForwardedIconComponent
|
||||
name="Trash2"
|
||||
className={cn(
|
||||
"h-5 w-5 text-destructive group-disabled:text-primary",
|
||||
)}
|
||||
/>
|
||||
</Button>
|
||||
<SecretKeyModal data={userId} onCloseModal={fetchApiKeys}>
|
||||
<Button data-testid="api-key-button-store" variant="primary">
|
||||
<ForwardedIconComponent name="Plus" className="mr-2 w-4" />
|
||||
|
|
|
|||
|
|
@ -45,13 +45,14 @@ export default function ApiKeysPage() {
|
|||
<div className="flex h-full w-full flex-col justify-between gap-6">
|
||||
<ApiKeyHeaderComponent
|
||||
selectedRows={selectedRows}
|
||||
handleDeleteKey={handleDeleteKey}
|
||||
fetchApiKeys={fetchApiKeys}
|
||||
userId={userId}
|
||||
/>
|
||||
|
||||
<div className="flex h-full w-full flex-col justify-between">
|
||||
<TableComponent
|
||||
key={"apiKeys"}
|
||||
onDelete={handleDeleteKey}
|
||||
overlayNoRowsTemplate="No data available"
|
||||
onSelectionChanged={(event: SelectionChangedEvent) => {
|
||||
setSelectedRows(event.api.getSelectedRows().map((row) => row.id));
|
||||
|
|
|
|||
|
|
@ -143,20 +143,6 @@ export default function GlobalVariablesPage() {
|
|||
</p>
|
||||
</div>
|
||||
<div className="flex flex-shrink-0 items-center gap-2">
|
||||
<Button
|
||||
data-testid="api-key-button-store"
|
||||
variant="primary"
|
||||
className="group px-2"
|
||||
disabled={selectedRows.length === 0}
|
||||
onClick={removeVariables}
|
||||
>
|
||||
<IconComponent
|
||||
name="Trash2"
|
||||
className={cn(
|
||||
"h-5 w-5 text-destructive group-disabled:text-primary",
|
||||
)}
|
||||
/>
|
||||
</Button>
|
||||
<AddNewVariableButton>
|
||||
<Button data-testid="api-key-button-store" variant="primary">
|
||||
<IconComponent name="Plus" className="mr-2 w-4" />
|
||||
|
|
@ -168,6 +154,7 @@ export default function GlobalVariablesPage() {
|
|||
|
||||
<div className="flex h-full w-full flex-col justify-between">
|
||||
<TableComponent
|
||||
key={"globalVariables"}
|
||||
overlayNoRowsTemplate="No data available"
|
||||
onSelectionChanged={(event: SelectionChangedEvent) => {
|
||||
setSelectedRows(event.api.getSelectedRows().map((row) => row.name));
|
||||
|
|
@ -177,6 +164,7 @@ export default function GlobalVariablesPage() {
|
|||
pagination={true}
|
||||
columnDefs={colDefs}
|
||||
rowData={rowData}
|
||||
onDelete={removeVariables}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -113,6 +113,7 @@ export default function ShortcutsPage() {
|
|||
</div>
|
||||
<div className="flex h-full w-full flex-col justify-between">
|
||||
<TableComponent
|
||||
key={"shortcuts"}
|
||||
pagination={false}
|
||||
columnDefs={colDefs}
|
||||
rowData={nodesRowData}
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ type HeaderMessagesComponentProps = {
|
|||
};
|
||||
const HeaderMessagesComponent = ({
|
||||
selectedRows,
|
||||
handleRemoveMessages,
|
||||
}: HeaderMessagesComponentProps) => {
|
||||
return (
|
||||
<>
|
||||
|
|
@ -26,22 +25,6 @@ const HeaderMessagesComponent = ({
|
|||
behaviors.
|
||||
</p>
|
||||
</div>
|
||||
<div className="flex flex-shrink-0 items-center gap-2">
|
||||
<Button
|
||||
data-testid="api-key-button-store"
|
||||
variant="primary"
|
||||
className="group px-2"
|
||||
disabled={selectedRows.length === 0}
|
||||
onClick={handleRemoveMessages}
|
||||
>
|
||||
<ForwardedIconComponent
|
||||
name="Trash2"
|
||||
className={cn(
|
||||
"h-5 w-5 text-destructive group-disabled:text-primary",
|
||||
)}
|
||||
/>
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
</>
|
||||
);
|
||||
|
|
|
|||
|
|
@ -53,6 +53,8 @@ export default function MessagesPage() {
|
|||
|
||||
<div className="flex h-full w-full flex-col justify-between">
|
||||
<TableComponent
|
||||
key={"sessionView"}
|
||||
onDelete={handleRemoveMessages}
|
||||
readOnlyEdit
|
||||
onCellEditRequest={(event) => {
|
||||
handleUpdateMessage(event);
|
||||
|
|
|
|||
|
|
@ -16,18 +16,14 @@ import {
|
|||
import { BuildStatus } from "../constants/enums";
|
||||
import { getFlowPool } from "../controllers/API";
|
||||
import { VertexBuildTypeAPI } from "../types/api";
|
||||
import { ChatInputType, ChatOutputType } from "../types/chat";
|
||||
import {
|
||||
NodeDataType,
|
||||
NodeType,
|
||||
sourceHandleType,
|
||||
targetHandleType,
|
||||
} from "../types/flow";
|
||||
import {
|
||||
ChatOutputType,
|
||||
FlowStoreType,
|
||||
VertexLayerElementType,
|
||||
chatInputType,
|
||||
} from "../types/zustand/flow";
|
||||
import { FlowStoreType, VertexLayerElementType } from "../types/zustand/flow";
|
||||
import { buildVertices } from "../utils/buildUtils";
|
||||
import {
|
||||
checkChatInput,
|
||||
|
|
@ -77,7 +73,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
|
|||
},
|
||||
updateFlowPool: (
|
||||
nodeId: string,
|
||||
data: VertexBuildTypeAPI | ChatOutputType | chatInputType,
|
||||
data: VertexBuildTypeAPI | ChatOutputType | ChatInputType,
|
||||
buildId?: string,
|
||||
) => {
|
||||
let newFlowPool = cloneDeep({ ...get().flowPool });
|
||||
|
|
@ -94,9 +90,9 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
|
|||
}
|
||||
//update data results
|
||||
else {
|
||||
newFlowPool[nodeId][index].data.messages[0] = data as
|
||||
newFlowPool[nodeId][index].data.message = data as
|
||||
| ChatOutputType
|
||||
| chatInputType;
|
||||
| ChatInputType;
|
||||
}
|
||||
}
|
||||
get().setFlowPool(newFlowPool);
|
||||
|
|
@ -492,8 +488,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
|
|||
(id) => !vertexBuildData.inactivated_vertices?.includes(id),
|
||||
);
|
||||
const top_level_vertices = vertexBuildData.top_level_vertices.filter(
|
||||
(vertex) =>
|
||||
!vertexBuildData.inactivated_vertices?.includes(vertex.id),
|
||||
(vertex) => !vertexBuildData.inactivated_vertices?.includes(vertex),
|
||||
);
|
||||
const nextVertices: VertexLayerElementType[] = zip(
|
||||
next_vertices_ids,
|
||||
|
|
@ -518,7 +513,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
|
|||
}
|
||||
|
||||
get().addDataToFlowPool(
|
||||
{ ...vertexBuildData, buildId: runId },
|
||||
{ ...vertexBuildData, run_id: runId },
|
||||
vertexBuildData.id,
|
||||
);
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import { Edge, Node, Viewport } from "reactflow";
|
||||
import { ChatOutputType, chatInputType } from "../chat";
|
||||
import { ChatInputType, ChatOutputType } from "../chat";
|
||||
import { FlowType } from "../flow";
|
||||
//kind and class are just representative names to represent the actual structure of the object received by the API
|
||||
export type APIDataType = { [key: string]: APIKindType };
|
||||
|
|
@ -158,19 +158,20 @@ export type VertexBuildTypeAPI = {
|
|||
data: VertexDataTypeAPI;
|
||||
timestamp: string;
|
||||
params: any;
|
||||
messages: ChatOutputType[] | chatInputType[];
|
||||
messages: ChatOutputType[] | ChatInputType[];
|
||||
};
|
||||
|
||||
// data is the object received by the API
|
||||
// it has results, artifacts, timedelta, duration
|
||||
export type VertexDataTypeAPI = {
|
||||
results: { [key: string]: string };
|
||||
logs: any[];
|
||||
messages: ChatOutputType[] | chatInputType[];
|
||||
logs: { message: any; type: string }[];
|
||||
messages: ChatOutputType[] | ChatInputType[];
|
||||
inactive?: boolean;
|
||||
timedelta?: number;
|
||||
duration?: string;
|
||||
artifacts?: any;
|
||||
message: ChatOutputType | ChatInputType;
|
||||
};
|
||||
|
||||
export type CodeErrorDataTypeAPI = {
|
||||
|
|
|
|||
|
|
@ -22,15 +22,19 @@ export type ChatOutputType = {
|
|||
files?: Array<{ path: string; type: string; name: string }>;
|
||||
};
|
||||
|
||||
export type chatInputType = {
|
||||
result: string;
|
||||
export type ChatInputType = {
|
||||
message: string;
|
||||
sender: string;
|
||||
sender_name: string;
|
||||
stream_url?: string;
|
||||
files?: Array<{ path: string; type: string; name: string }>;
|
||||
};
|
||||
|
||||
export type FlowPoolObjectType = {
|
||||
timestamp: string;
|
||||
valid: boolean;
|
||||
// list of chat outputs or list of chat inputs
|
||||
messages: Array<ChatOutputType | chatInputType> | [];
|
||||
data: { artifacts?: any; results: any | ChatOutputType | chatInputType };
|
||||
messages: Array<ChatOutputType | ChatInputType> | [];
|
||||
data: { artifacts: any; results: any | ChatOutputType | ChatInputType };
|
||||
id: string;
|
||||
};
|
||||
|
|
|
|||
|
|
@ -487,7 +487,12 @@ export type headerFlowsType = {
|
|||
style?: FlowStyleType;
|
||||
};
|
||||
|
||||
export type chatInputType = {
|
||||
export type ChatInputType = {
|
||||
isDragging: boolean;
|
||||
files: FilePreviewType[];
|
||||
setFiles: (
|
||||
files: FilePreviewType[] | ((prev: FilePreviewType[]) => FilePreviewType[]),
|
||||
) => void;
|
||||
chatValue: string;
|
||||
inputRef: {
|
||||
current: any;
|
||||
|
|
@ -526,6 +531,7 @@ export type fileCardPropsType = {
|
|||
fileName: string;
|
||||
content: string;
|
||||
fileType: string;
|
||||
showFile?: boolean;
|
||||
};
|
||||
|
||||
export type nodeToolbarPropsType = {
|
||||
|
|
|
|||
|
|
@ -9,28 +9,16 @@ import {
|
|||
} from "reactflow";
|
||||
import { BuildStatus } from "../../../constants/enums";
|
||||
import { VertexBuildTypeAPI } from "../../api";
|
||||
import { ChatInputType, ChatOutputType } from "../../chat";
|
||||
import { FlowState } from "../../tabs";
|
||||
|
||||
export type chatInputType = {
|
||||
result: string;
|
||||
files?: string[];
|
||||
};
|
||||
|
||||
export type ChatOutputType = {
|
||||
message: string;
|
||||
sender: string;
|
||||
sender_name: string;
|
||||
stream_url?: string;
|
||||
files?: string[];
|
||||
};
|
||||
|
||||
export type FlowPoolObjectType = {
|
||||
timestamp: string;
|
||||
valid: boolean;
|
||||
messages: Array<ChatOutputType | chatInputType> | [];
|
||||
messages: Array<ChatOutputType | ChatInputType> | [];
|
||||
data: {
|
||||
artifacts: any | ChatOutputType | chatInputType;
|
||||
results: any | ChatOutputType | chatInputType;
|
||||
artifacts: any | ChatOutputType | ChatInputType;
|
||||
results: any | ChatOutputType | ChatInputType;
|
||||
};
|
||||
duration?: string;
|
||||
progress?: number;
|
||||
|
|
@ -45,8 +33,8 @@ export type FlowPoolObjectTypeNew = {
|
|||
timestamp: string;
|
||||
valid: boolean;
|
||||
data: {
|
||||
logs?: any | ChatOutputType | chatInputType;
|
||||
results: any | ChatOutputType | chatInputType;
|
||||
logs?: any | ChatOutputType | ChatInputType;
|
||||
results: any | ChatOutputType | ChatInputType;
|
||||
};
|
||||
duration?: string;
|
||||
progress?: number;
|
||||
|
|
@ -167,7 +155,7 @@ export type FlowStoreType = {
|
|||
};
|
||||
updateFlowPool: (
|
||||
nodeId: string,
|
||||
data: VertexBuildTypeAPI | ChatOutputType | chatInputType,
|
||||
data: VertexBuildTypeAPI | ChatOutputType | ChatInputType,
|
||||
buildId?: string,
|
||||
) => void;
|
||||
getNodePosition: (nodeId: string) => { x: number; y: number };
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
export type chatInputType = {
|
||||
export type ChatInputType = {
|
||||
result: string;
|
||||
};
|
||||
|
||||
|
|
@ -12,7 +12,7 @@ export type FlowPoolObjectType = {
|
|||
timestamp: string;
|
||||
valid: boolean;
|
||||
params: any;
|
||||
data: { artifacts: any; results: any | ChatOutputType | chatInputType };
|
||||
data: { artifacts: any; results: any | ChatOutputType | ChatInputType };
|
||||
id: string;
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -93,6 +93,7 @@ import {
|
|||
Package2,
|
||||
Palette,
|
||||
Paperclip,
|
||||
PaperclipIcon,
|
||||
Pencil,
|
||||
PencilLine,
|
||||
Pin,
|
||||
|
|
@ -535,6 +536,7 @@ export const nodeIconsLucide: iconsType = {
|
|||
FolderPlusIcon,
|
||||
FolderIcon,
|
||||
Discord: FaDiscord,
|
||||
PaperclipIcon,
|
||||
RotateCcw,
|
||||
Settings,
|
||||
Streamlit,
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import { ColDef, ColGroupDef } from "ag-grid-community";
|
||||
import clsx, { ClassValue } from "clsx";
|
||||
import { twMerge } from "tailwind-merge";
|
||||
import TableAutoCellRender from "../components/tableComponent/components/tableAutoCellRender";
|
||||
import { APIDataType, TemplateVariableType } from "../types/api";
|
||||
import {
|
||||
groupedObjType,
|
||||
|
|
@ -10,6 +9,7 @@ import {
|
|||
} from "../types/components";
|
||||
import { NodeType } from "../types/flow";
|
||||
import { FlowState } from "../types/tabs";
|
||||
import TableAutoCellRender from "../components/tableComponent/components/tableAutoCellRender";
|
||||
|
||||
export function classNames(...classes: Array<string>): string {
|
||||
return classes.filter(Boolean).join(" ");
|
||||
|
|
|
|||
BIN
src/frontend/tests/end-to-end/assets/chain.png
Normal file
BIN
src/frontend/tests/end-to-end/assets/chain.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 31 KiB |
|
|
@ -1,4 +1,5 @@
|
|||
import { expect, test } from "@playwright/test";
|
||||
import { readFileSync } from "fs";
|
||||
|
||||
test("user must interact with chat with Input/Output", async ({ page }) => {
|
||||
if (!process.env.CI) {
|
||||
|
|
@ -147,3 +148,87 @@ test("user must be able to see output inspection", async ({ page }) => {
|
|||
await page.getByText("Type", { exact: true }).isVisible();
|
||||
await page.getByText("User", { exact: true }).last().isVisible();
|
||||
});
|
||||
|
||||
test("user must be able to send an image on chat", async ({ page }) => {
|
||||
if (!process.env.CI) {
|
||||
dotenv.config();
|
||||
dotenv.config({ path: path.resolve(__dirname, "../../.env") });
|
||||
}
|
||||
|
||||
await page.goto("/");
|
||||
|
||||
await page.waitForTimeout(1000);
|
||||
|
||||
let modalCount = 0;
|
||||
try {
|
||||
const modalTitleElement = await page?.getByTestId("modal-title");
|
||||
if (modalTitleElement) {
|
||||
modalCount = await modalTitleElement.count();
|
||||
}
|
||||
} catch (error) {
|
||||
modalCount = 0;
|
||||
}
|
||||
|
||||
while (modalCount === 0) {
|
||||
await page.getByText("New Project", { exact: true }).click();
|
||||
await page.waitForTimeout(5000);
|
||||
modalCount = await page.getByTestId("modal-title")?.count();
|
||||
}
|
||||
|
||||
await page.getByRole("heading", { name: "Basic Prompting" }).click();
|
||||
await page.waitForTimeout(1000);
|
||||
|
||||
await page.getByTitle("fit view").click();
|
||||
await page.getByTitle("zoom out").click();
|
||||
await page.getByTitle("zoom out").click();
|
||||
await page.getByTitle("zoom out").click();
|
||||
|
||||
if (!process.env.OPENAI_API_KEY) {
|
||||
//You must set the OPENAI_API_KEY on .env file to run this test
|
||||
expect(false).toBe(true);
|
||||
}
|
||||
|
||||
await page
|
||||
.getByTestId("popover-anchor-input-openai_api_key")
|
||||
.fill(process.env.OPENAI_API_KEY ?? "");
|
||||
|
||||
await page.getByText("Chat Input", { exact: true }).click();
|
||||
await page.getByTestId("more-options-modal").click();
|
||||
await page.getByTestId("edit-button-modal").click();
|
||||
await page.getByTestId("toggle-edit-return_record").click();
|
||||
expect(
|
||||
await page.getByTestId("toggle-edit-return_record").isChecked(),
|
||||
).toBeTruthy();
|
||||
await page.getByText("Save Changes").click();
|
||||
|
||||
await page.getByText("Playground", { exact: true }).click();
|
||||
|
||||
const jsonContent = readFileSync(
|
||||
"src/frontend/tests/end-to-end/assets/chain.png",
|
||||
"utf-8",
|
||||
);
|
||||
|
||||
// Create the DataTransfer and File
|
||||
const dataTransfer = await page.evaluateHandle((data) => {
|
||||
const dt = new DataTransfer();
|
||||
// Convert the buffer to a hex array
|
||||
const file = new File([data], "chain.png", {
|
||||
type: "application/json",
|
||||
});
|
||||
dt.items.add(file);
|
||||
return dt;
|
||||
}, jsonContent);
|
||||
|
||||
const element = await page.getByPlaceholder("Send a message...");
|
||||
|
||||
await element.dispatchEvent("drop", { dataTransfer });
|
||||
|
||||
await page.getByText("chain.png").isVisible();
|
||||
await page.getByTestId("icon-LucideSend").click();
|
||||
await page.waitForTimeout(2000);
|
||||
await page.getByText("chain.png").isVisible();
|
||||
|
||||
await page.getByText("Close", { exact: true }).click();
|
||||
await page.getByTestId("icon-ScanEye").last().click();
|
||||
await page.getByText("Restart").isHidden();
|
||||
});
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ from uuid import UUID, uuid4
|
|||
import pytest
|
||||
from fastapi import status
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
from langflow.custom.directory_reader.directory_reader import DirectoryReader
|
||||
from langflow.services.deps import get_settings_service
|
||||
|
||||
|
|
@ -269,7 +270,9 @@ def test_get_all(client: TestClient, logged_in_headers):
|
|||
all_names = [component_name for _, components in response.json().items() for component_name in components]
|
||||
json_response = response.json()
|
||||
# We need to test the custom nodes
|
||||
assert len(all_names) == len(files)
|
||||
assert len(all_names) <= len(
|
||||
files
|
||||
) # Less or equal because we might have some files that don't have the dependencies installed
|
||||
assert "ChatInput" in json_response["inputs"]
|
||||
assert "Prompt" in json_response["inputs"]
|
||||
assert "ChatOutput" in json_response["outputs"]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue