Merge branch 'add_extra_fields_documentloaders' of https://github.com/logspace-ai/langflow into add_extra_fields_documentloaders
This commit is contained in:
Alexandre Henrique
commit
5862f19c20
86 changed files with 1915 additions and 773 deletions
1
Makefile
1
Makefile
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@ -43,6 +43,7 @@ install_backend:
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poetry install
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backend:
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make install_backend
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poetry run uvicorn langflow.main:app --port 7860 --reload --log-level debug
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build_frontend:
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164
poetry.lock
generated
164
poetry.lock
generated
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@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand.
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[[package]]
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name = "aiofiles"
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@ -148,6 +148,27 @@ files = [
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{file = "aiostream-0.4.5.tar.gz", hash = "sha256:3ecbf87085230fbcd9605c32ca20c4fb41af02c71d076eab246ea22e35947d88"},
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]
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[[package]]
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name = "anthropic"
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version = "0.2.10"
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description = "Library for accessing the anthropic API"
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category = "main"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "anthropic-0.2.10-py3-none-any.whl", hash = "sha256:a007496207fd186b0bcb9592b00ca130069d2a427f3d6f602a61dbbd1ac6316e"},
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{file = "anthropic-0.2.10.tar.gz", hash = "sha256:e4da061a86d8ffb86072c0b0feaf219a3a4f7dfddd4224df9ba769e469498c19"},
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]
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[package.dependencies]
|
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aiohttp = "*"
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httpx = "*"
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requests = "*"
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tokenizers = "*"
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[package.extras]
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dev = ["black (>=22.3.0)", "pytest"]
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[[package]]
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name = "anyio"
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version = "3.7.0"
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@ -719,14 +740,14 @@ superset = ["apache-superset (>=1.4.1)"]
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[[package]]
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name = "cohere"
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version = "4.6.0"
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version = "4.7.0"
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description = ""
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||||
category = "main"
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optional = false
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||||
python-versions = ">=3.7,<4.0"
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files = [
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{file = "cohere-4.6.0-py3-none-any.whl", hash = "sha256:fc60fa73a2d96bdb9f70da4a290d3ede320b74ac01a24c229011049d7cb3511f"},
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||||
{file = "cohere-4.6.0.tar.gz", hash = "sha256:43218a0a40f6fc023e068732994fb631ce5d160a0bc9f9a3a22524b5932f34ea"},
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||||
{file = "cohere-4.7.0-py3-none-any.whl", hash = "sha256:ed15621bd271b941110a572cbf6187a4db78cc8d7d9d35a881bffcaeeea21d7c"},
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||||
{file = "cohere-4.7.0.tar.gz", hash = "sha256:46c674545ad36133a555c6db7f25bdc299e05ec4f77dee707b661a57eb06aea6"},
|
||||
]
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[package.dependencies]
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@ -862,31 +883,31 @@ toml = ["tomli"]
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[[package]]
|
||||
name = "cryptography"
|
||||
version = "40.0.2"
|
||||
version = "41.0.1"
|
||||
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
python-versions = ">=3.7"
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||||
files = [
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||||
{file = "cryptography-40.0.2-cp36-abi3-macosx_10_12_universal2.whl", hash = "sha256:8f79b5ff5ad9d3218afb1e7e20ea74da5f76943ee5edb7f76e56ec5161ec782b"},
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||||
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|
||||
{file = "cryptography-40.0.2-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4df2af28d7bedc84fe45bd49bc35d710aede676e2a4cb7fc6d103a2adc8afe4d"},
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||||
{file = "cryptography-40.0.2-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0dcca15d3a19a66e63662dc8d30f8036b07be851a8680eda92d079868f106288"},
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{file = "cryptography-40.0.2-cp36-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:adc0d980fd2760c9e5de537c28935cc32b9353baaf28e0814df417619c6c8c3b"},
|
||||
{file = "cryptography-40.0.2-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:d5a1bd0e9e2031465761dfa920c16b0065ad77321d8a8c1f5ee331021fda65e9"},
|
||||
{file = "cryptography-40.0.2-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:a95f4802d49faa6a674242e25bfeea6fc2acd915b5e5e29ac90a32b1139cae1c"},
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{file = "cryptography-40.0.2-cp36-abi3-win32.whl", hash = "sha256:aecbb1592b0188e030cb01f82d12556cf72e218280f621deed7d806afd2113f9"},
|
||||
{file = "cryptography-40.0.2-cp36-abi3-win_amd64.whl", hash = "sha256:b12794f01d4cacfbd3177b9042198f3af1c856eedd0a98f10f141385c809a14b"},
|
||||
{file = "cryptography-40.0.2-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:142bae539ef28a1c76794cca7f49729e7c54423f615cfd9b0b1fa90ebe53244b"},
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||||
{file = "cryptography-40.0.2-pp38-pypy38_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:956ba8701b4ffe91ba59665ed170a2ebbdc6fc0e40de5f6059195d9f2b33ca0e"},
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||||
{file = "cryptography-40.0.2-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:4f01c9863da784558165f5d4d916093737a75203a5c5286fde60e503e4276c7a"},
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||||
{file = "cryptography-40.0.2-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:3daf9b114213f8ba460b829a02896789751626a2a4e7a43a28ee77c04b5e4958"},
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{file = "cryptography-40.0.2-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:48f388d0d153350f378c7f7b41497a54ff1513c816bcbbcafe5b829e59b9ce5b"},
|
||||
{file = "cryptography-40.0.2-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c0764e72b36a3dc065c155e5b22f93df465da9c39af65516fe04ed3c68c92636"},
|
||||
{file = "cryptography-40.0.2-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:cbaba590180cba88cb99a5f76f90808a624f18b169b90a4abb40c1fd8c19420e"},
|
||||
{file = "cryptography-40.0.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7a38250f433cd41df7fcb763caa3ee9362777fdb4dc642b9a349721d2bf47404"},
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||||
{file = "cryptography-40.0.2.tar.gz", hash = "sha256:c33c0d32b8594fa647d2e01dbccc303478e16fdd7cf98652d5b3ed11aa5e5c99"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:f73bff05db2a3e5974a6fd248af2566134d8981fd7ab012e5dd4ddb1d9a70699"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:1a5472d40c8f8e91ff7a3d8ac6dfa363d8e3138b961529c996f3e2df0c7a411a"},
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||||
{file = "cryptography-41.0.1-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7fa01527046ca5facdf973eef2535a27fec4cb651e4daec4d043ef63f6ecd4ca"},
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||||
{file = "cryptography-41.0.1-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b46e37db3cc267b4dea1f56da7346c9727e1209aa98487179ee8ebed09d21e43"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:d198820aba55660b4d74f7b5fd1f17db3aa5eb3e6893b0a41b75e84e4f9e0e4b"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:948224d76c4b6457349d47c0c98657557f429b4e93057cf5a2f71d603e2fc3a3"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:059e348f9a3c1950937e1b5d7ba1f8e968508ab181e75fc32b879452f08356db"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:b4ceb5324b998ce2003bc17d519080b4ec8d5b7b70794cbd2836101406a9be31"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-win32.whl", hash = "sha256:8f4ab7021127a9b4323537300a2acfb450124b2def3756f64dc3a3d2160ee4b5"},
|
||||
{file = "cryptography-41.0.1-cp37-abi3-win_amd64.whl", hash = "sha256:1fee5aacc7367487b4e22484d3c7e547992ed726d14864ee33c0176ae43b0d7c"},
|
||||
{file = "cryptography-41.0.1-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:9a6c7a3c87d595608a39980ebaa04d5a37f94024c9f24eb7d10262b92f739ddb"},
|
||||
{file = "cryptography-41.0.1-pp38-pypy38_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:5d092fdfedaec4cbbffbf98cddc915ba145313a6fdaab83c6e67f4e6c218e6f3"},
|
||||
{file = "cryptography-41.0.1-pp38-pypy38_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:1a8e6c2de6fbbcc5e14fd27fb24414507cb3333198ea9ab1258d916f00bc3039"},
|
||||
{file = "cryptography-41.0.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:cb33ccf15e89f7ed89b235cff9d49e2e62c6c981a6061c9c8bb47ed7951190bc"},
|
||||
{file = "cryptography-41.0.1-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:5f0ff6e18d13a3de56f609dd1fd11470918f770c6bd5d00d632076c727d35485"},
|
||||
{file = "cryptography-41.0.1-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:7bfc55a5eae8b86a287747053140ba221afc65eb06207bedf6e019b8934b477c"},
|
||||
{file = "cryptography-41.0.1-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:eb8163f5e549a22888c18b0d53d6bb62a20510060a22fd5a995ec8a05268df8a"},
|
||||
{file = "cryptography-41.0.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:8dde71c4169ec5ccc1087bb7521d54251c016f126f922ab2dfe6649170a3b8c5"},
|
||||
{file = "cryptography-41.0.1.tar.gz", hash = "sha256:d34579085401d3f49762d2f7d6634d6b6c2ae1242202e860f4d26b046e3a1006"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -895,23 +916,23 @@ cffi = ">=1.12"
|
|||
[package.extras]
|
||||
docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
|
||||
docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
|
||||
pep8test = ["black", "check-manifest", "mypy", "ruff"]
|
||||
sdist = ["setuptools-rust (>=0.11.4)"]
|
||||
nox = ["nox"]
|
||||
pep8test = ["black", "check-sdist", "mypy", "ruff"]
|
||||
sdist = ["build"]
|
||||
ssh = ["bcrypt (>=3.1.5)"]
|
||||
test = ["iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-shard (>=0.1.2)", "pytest-subtests", "pytest-xdist"]
|
||||
test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
|
||||
test-randomorder = ["pytest-randomly"]
|
||||
tox = ["tox"]
|
||||
|
||||
[[package]]
|
||||
name = "ctransformers"
|
||||
version = "0.2.2"
|
||||
version = "0.2.5"
|
||||
description = "Python bindings for the Transformer models implemented in C/C++ using GGML library."
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "ctransformers-0.2.2-py3-none-any.whl", hash = "sha256:bf682dd0293dd87911c9a9a1169a4873ff55baebc16d465c6029c77f11b18cf6"},
|
||||
{file = "ctransformers-0.2.2.tar.gz", hash = "sha256:1fc36b3fde36d9fd3cb69e48993315bb1f5f54ae552720eb909dc4b3a131c743"},
|
||||
{file = "ctransformers-0.2.5-py3-none-any.whl", hash = "sha256:5e0ee7d2be2cb1d627a702acdbf1f4f3c9c97d706e9d7f59a13079c1836a1432"},
|
||||
{file = "ctransformers-0.2.5.tar.gz", hash = "sha256:b813f19d5c2249b75422ae3188b1c834aeb8a095800df32328559a740acdb404"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1044,14 +1065,14 @@ weaviate = ["weaviate-client (>=3.9.0,<3.10.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "docker"
|
||||
version = "6.1.2"
|
||||
version = "6.1.3"
|
||||
description = "A Python library for the Docker Engine API."
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "docker-6.1.2-py3-none-any.whl", hash = "sha256:134cd828f84543cbf8e594ff81ca90c38288df3c0a559794c12f2e4b634ea19e"},
|
||||
{file = "docker-6.1.2.tar.gz", hash = "sha256:dcc088adc2ec4e7cfc594e275d8bd2c9738c56c808de97476939ef67db5af8c2"},
|
||||
{file = "docker-6.1.3-py3-none-any.whl", hash = "sha256:aecd2277b8bf8e506e484f6ab7aec39abe0038e29fa4a6d3ba86c3fe01844ed9"},
|
||||
{file = "docker-6.1.3.tar.gz", hash = "sha256:aa6d17830045ba5ef0168d5eaa34d37beeb113948c413affe1d5991fc11f9a20"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1096,9 +1117,7 @@ files = [
|
|||
{file = "duckdb-0.8.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:b2707096d6df4321044fcde2c9f04da632d11a8be60957fd09d49a42fae71a29"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b27df1b70ae74d2c88efb5ffca8490954fdc678099509a9c4404ca30acc53426"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:75a97c800271b52dd0f37696d074c50576dcb4b2750b6115932a98696a268070"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:804cac261a5e016506a6d67838a65d19b06a237f7949f1704f0e800eb708286a"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c6b9abca7fa6713e1d031c18485343b4de99742c7e1b85c10718aa2f31a4e2c6"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:51aa6d606d49072abcfeb3be209eb559ac94c1b5e70f58ac3adbb94aca9cd69f"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:7c8dc769aaf2be0a1c57995ca657e5b92c1c56fc8437edb720ca6cab571adf14"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-win32.whl", hash = "sha256:c4207d18b42387c4a035846d8878eb967070198be8ac26fd77797ce320d1a400"},
|
||||
{file = "duckdb-0.8.0-cp311-cp311-win_amd64.whl", hash = "sha256:0c392257547c20794c3072fcbca99a49ef0a49974005d755e93893e2b4875267"},
|
||||
|
|
@ -1194,6 +1213,41 @@ files = [
|
|||
[package.extras]
|
||||
tests = ["asttokens", "littleutils", "pytest", "rich"]
|
||||
|
||||
[[package]]
|
||||
name = "faiss-cpu"
|
||||
version = "1.7.4"
|
||||
description = "A library for efficient similarity search and clustering of dense vectors."
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "faiss-cpu-1.7.4.tar.gz", hash = "sha256:265dc31b0c079bf4433303bf6010f73922490adff9188b915e2d3f5e9c82dd0a"},
|
||||
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|
||||
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|
||||
{file = "faiss_cpu-1.7.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:17b7fa7194a228a84929d9e6619d0e7dbf00cc0f717e3462253766f5e3d07de8"},
|
||||
{file = "faiss_cpu-1.7.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dca531952a2e3eac56f479ff22951af4715ee44788a3fe991d208d766d3f95f3"},
|
||||
{file = "faiss_cpu-1.7.4-cp310-cp310-win_amd64.whl", hash = "sha256:7173081d605e74766f950f2e3d6568a6f00c53f32fd9318063e96728c6c62821"},
|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d0bbd6f55d7940cc0692f79e32a58c66106c3c950cee2341b05722de9da23ea3"},
|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e13c14280376100f143767d0efe47dcb32618f69e62bbd3ea5cd38c2e1755926"},
|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c521cb8462f3b00c0c7dfb11caff492bb67816528b947be28a3b76373952c41d"},
|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afdd9fe1141117fed85961fd36ee627c83fc3b9fd47bafb52d3c849cc2f088b7"},
|
||||
{file = "faiss_cpu-1.7.4-cp311-cp311-win_amd64.whl", hash = "sha256:2ff7f57889ea31d945e3b87275be3cad5d55b6261a4e3f51c7aba304d76b81fb"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:eeaf92f27d76249fb53c1adafe617b0f217ab65837acf7b4ec818511caf6e3d8"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:102b1bd763e9b0c281ac312590af3eaf1c8b663ccbc1145821fe6a9f92b8eaaf"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5512da6707c967310c46ff712b00418b7ae28e93cb609726136e826e9f2f14fa"},
|
||||
{file = "faiss_cpu-1.7.4-cp37-cp37m-win_amd64.whl", hash = "sha256:0c2e5b9d8c28c99f990e87379d5bbcc6c914da91ebb4250166864fd12db5755b"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:43f67f325393145d360171cd98786fcea6120ce50397319afd3bb78be409fb8a"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:6a4e4af194b8fce74c4b770cad67ad1dd1b4673677fc169723e4c50ba5bd97a8"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:31bfb7b9cffc36897ae02a983e04c09fe3b8c053110a287134751a115334a1df"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:52d7de96abef2340c0d373c1f5cbc78026a3cebb0f8f3a5920920a00210ead1f"},
|
||||
{file = "faiss_cpu-1.7.4-cp38-cp38-win_amd64.whl", hash = "sha256:699feef85b23c2c729d794e26ca69bebc0bee920d676028c06fd0e0becc15c7e"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:559a0133f5ed44422acb09ee1ac0acffd90c6666d1bc0d671c18f6e93ad603e2"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ea1d71539fe3dc0f1bed41ef954ca701678776f231046bf0ca22ccea5cf5bef6"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:12d45e0157024eb3249842163162983a1ac8b458f1a8b17bbf86f01be4585a99"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2f0eab359e066d32c874f51a7d4bf6440edeec068b7fe47e6d803c73605a8b4c"},
|
||||
{file = "faiss_cpu-1.7.4-cp39-cp39-win_amd64.whl", hash = "sha256:98459ceeeb735b9df1a5b94572106ffe0a6ce740eb7e4626715dd218657bb4dc"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "fake-useragent"
|
||||
version = "1.1.3"
|
||||
|
|
@ -1211,14 +1265,14 @@ importlib-resources = {version = ">=5.0", markers = "python_version < \"3.10\""}
|
|||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.95.2"
|
||||
version = "0.96.0"
|
||||
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "fastapi-0.95.2-py3-none-any.whl", hash = "sha256:d374dbc4ef2ad9b803899bd3360d34c534adc574546e25314ab72c0c4411749f"},
|
||||
{file = "fastapi-0.95.2.tar.gz", hash = "sha256:4d9d3e8c71c73f11874bcf5e33626258d143252e329a01002f767306c64fb982"},
|
||||
{file = "fastapi-0.96.0-py3-none-any.whl", hash = "sha256:b8e11fe81e81eab4e1504209917338e0b80f783878a42c2b99467e5e1019a1e9"},
|
||||
{file = "fastapi-0.96.0.tar.gz", hash = "sha256:71232d47c2787446991c81c41c249f8a16238d52d779c0e6b43927d3773dbe3c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1387,14 +1441,14 @@ uritemplate = ">=3.0.1,<5"
|
|||
|
||||
[[package]]
|
||||
name = "google-auth"
|
||||
version = "2.19.0"
|
||||
version = "2.19.1"
|
||||
description = "Google Authentication Library"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "google-auth-2.19.0.tar.gz", hash = "sha256:f39d528077ac540793dd3c22a8706178f157642a67d874db25c640b7fead277e"},
|
||||
{file = "google_auth-2.19.0-py2.py3-none-any.whl", hash = "sha256:be617bfaf77774008e9d177573f782e109188c8a64ae6e744285df5cea3e7df6"},
|
||||
{file = "google-auth-2.19.1.tar.gz", hash = "sha256:a9cfa88b3e16196845e64a3658eb953992129d13ac7337b064c6546f77c17183"},
|
||||
{file = "google_auth-2.19.1-py2.py3-none-any.whl", hash = "sha256:ea165e014c7cbd496558796b627c271aa8c18b4cba79dc1cc962b24c5efdfb85"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2037,14 +2091,14 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
|
|||
|
||||
[[package]]
|
||||
name = "ipython"
|
||||
version = "8.13.2"
|
||||
version = "8.14.0"
|
||||
description = "IPython: Productive Interactive Computing"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "ipython-8.13.2-py3-none-any.whl", hash = "sha256:ffca270240fbd21b06b2974e14a86494d6d29290184e788275f55e0b55914926"},
|
||||
{file = "ipython-8.13.2.tar.gz", hash = "sha256:7dff3fad32b97f6488e02f87b970f309d082f758d7b7fc252e3b19ee0e432dbb"},
|
||||
{file = "ipython-8.14.0-py3-none-any.whl", hash = "sha256:248aca623f5c99a6635bc3857677b7320b9b8039f99f070ee0d20a5ca5a8e6bf"},
|
||||
{file = "ipython-8.14.0.tar.gz", hash = "sha256:1d197b907b6ba441b692c48cf2a3a2de280dc0ac91a3405b39349a50272ca0a1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2376,13 +2430,13 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain-serve"
|
||||
version = "0.0.40"
|
||||
version = "0.0.41"
|
||||
description = "Langchain Serve - serve your langchain apps on Jina AI Cloud."
|
||||
category = "main"
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "langchain-serve-0.0.40.tar.gz", hash = "sha256:c60b173fcf0b682fbb70d34e8f485ce168e2229f55cb5c4ffbc26a5206af1c06"},
|
||||
{file = "langchain-serve-0.0.41.tar.gz", hash = "sha256:fcf0d3ac9e48b5b24825ae2e1d8a383795c5744b18ef61cb74a99020a9e5d46a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4679,14 +4733,14 @@ idna2008 = ["idna"]
|
|||
|
||||
[[package]]
|
||||
name = "rich"
|
||||
version = "13.3.5"
|
||||
version = "13.4.1"
|
||||
description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "rich-13.3.5-py3-none-any.whl", hash = "sha256:69cdf53799e63f38b95b9bf9c875f8c90e78dd62b2f00c13a911c7a3b9fa4704"},
|
||||
{file = "rich-13.3.5.tar.gz", hash = "sha256:2d11b9b8dd03868f09b4fffadc84a6a8cda574e40dc90821bd845720ebb8e89c"},
|
||||
{file = "rich-13.4.1-py3-none-any.whl", hash = "sha256:d204aadb50b936bf6b1a695385429d192bc1fdaf3e8b907e8e26f4c4e4b5bf75"},
|
||||
{file = "rich-13.4.1.tar.gz", hash = "sha256:76f6b65ea7e5c5d924ba80e322231d7cb5b5981aa60bfc1e694f1bc097fe6fe1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5100,14 +5154,14 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"]
|
|||
|
||||
[[package]]
|
||||
name = "textual"
|
||||
version = "0.26.0"
|
||||
version = "0.27.0"
|
||||
description = "Modern Text User Interface framework"
|
||||
category = "main"
|
||||
optional = true
|
||||
python-versions = ">=3.7,<4.0"
|
||||
files = [
|
||||
{file = "textual-0.26.0-py3-none-any.whl", hash = "sha256:1efd04e9f61b3e95fd1c65436d3262f99e3f86cdeb524d13045bb551eb615c02"},
|
||||
{file = "textual-0.26.0.tar.gz", hash = "sha256:78094c83017d2836b726513abdf434cc034a0e68cc45e63b3b056c9b8b7fa673"},
|
||||
{file = "textual-0.27.0-py3-none-any.whl", hash = "sha256:dc45eaf7da330686c56d6f76f59d05fd216ce6aad90fa44ee269881efc622151"},
|
||||
{file = "textual-0.27.0.tar.gz", hash = "sha256:8bdcb09dc35a706ef939b1276ccfdec10eaaee6147b41cb7587cf33298a8dd33"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6180,4 +6234,4 @@ deploy = ["langchain-serve"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.12"
|
||||
content-hash = "2bf357ad30f79c68751b34c991b4a73767ceb628657f4133228d4eb487d8a6fb"
|
||||
content-hash = "4cac7dea0c1222711ba7eed82d5716d5e361d454edb6e0299b387fbb115d2c3d"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "0.0.79"
|
||||
version = "0.0.80"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
maintainers = [
|
||||
|
|
@ -22,7 +22,7 @@ langflow = "langflow.__main__:main"
|
|||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9,<3.12"
|
||||
fastapi = "^0.95.0"
|
||||
fastapi = "^0.96.0"
|
||||
uvicorn = "^0.20.0"
|
||||
beautifulsoup4 = "^4.11.2"
|
||||
google-search-results = "^2.4.1"
|
||||
|
|
@ -57,6 +57,8 @@ jina = "3.15.2"
|
|||
sentence-transformers = "^2.2.2"
|
||||
ctransformers = "^0.2.2"
|
||||
cohere = "^4.6.0"
|
||||
faiss-cpu = "^1.7.4"
|
||||
anthropic = "^0.2.9"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
|
@ -76,6 +78,15 @@ types-pillow = "^9.5.0.2"
|
|||
[tool.poetry.extras]
|
||||
deploy = ["langchain-serve"]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
minversion = "6.0"
|
||||
addopts = "-ra"
|
||||
testpaths = ["tests", "integration"]
|
||||
console_output_style = "progress"
|
||||
filterwarnings = ["ignore::DeprecationWarning"]
|
||||
log_cli = true
|
||||
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 120
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from langflow.cache import cache_manager
|
||||
from langflow.interface.loading import load_flow_from_json
|
||||
from langflow.processing.process import load_flow_from_json
|
||||
|
||||
__all__ = ["load_flow_from_json", "cache_manager"]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,3 @@
|
|||
from langflow.api.router import router
|
||||
|
||||
__all__ = ["router"]
|
||||
8
src/backend/langflow/api/router.py
Normal file
8
src/backend/langflow/api/router.py
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
# Router for base api
|
||||
from fastapi import APIRouter
|
||||
from langflow.api.v1 import chat_router, endpoints_router, validate_router
|
||||
|
||||
router = APIRouter(prefix="/api/v1", tags=["api"])
|
||||
router.include_router(chat_router)
|
||||
router.include_router(endpoints_router)
|
||||
router.include_router(validate_router)
|
||||
5
src/backend/langflow/api/v1/__init__.py
Normal file
5
src/backend/langflow/api/v1/__init__.py
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
from langflow.api.v1.endpoints import router as endpoints_router
|
||||
from langflow.api.v1.validate import router as validate_router
|
||||
from langflow.api.v1.chat import router as chat_router
|
||||
|
||||
__all__ = ["chat_router", "endpoints_router", "validate_router"]
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from pydantic import BaseModel, validator
|
||||
|
||||
from langflow.graph.utils import extract_input_variables_from_prompt
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
|
||||
|
||||
class CacheResponse(BaseModel):
|
||||
|
|
@ -3,7 +3,7 @@ from typing import Any
|
|||
|
||||
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
|
||||
|
||||
from langflow.api.schemas import ChatResponse
|
||||
from langflow.api.v1.schemas import ChatResponse
|
||||
|
||||
|
||||
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py
|
||||
|
|
@ -6,7 +6,7 @@ from fastapi import (
|
|||
status,
|
||||
)
|
||||
|
||||
from langflow.api.chat_manager import ChatManager
|
||||
from langflow.chat.manager import ChatManager
|
||||
from langflow.utils.logger import logger
|
||||
|
||||
router = APIRouter()
|
||||
|
|
@ -3,13 +3,13 @@ from importlib.metadata import version
|
|||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
|
||||
from langflow.api.schemas import (
|
||||
from langflow.api.v1.schemas import (
|
||||
ExportedFlow,
|
||||
GraphData,
|
||||
PredictRequest,
|
||||
PredictResponse,
|
||||
)
|
||||
from langflow.interface.run import process_graph_cached
|
||||
|
||||
from langflow.interface.types import build_langchain_types_dict
|
||||
|
||||
# build router
|
||||
|
|
@ -25,6 +25,8 @@ def get_all():
|
|||
@router.post("/predict", response_model=PredictResponse)
|
||||
async def get_load(predict_request: PredictRequest):
|
||||
try:
|
||||
from langflow.processing.process import process_graph_cached
|
||||
|
||||
exported_flow: ExportedFlow = predict_request.exported_flow
|
||||
graph_data: GraphData = exported_flow.data
|
||||
data = graph_data.dict()
|
||||
|
|
@ -40,8 +42,3 @@ async def get_load(predict_request: PredictRequest):
|
|||
@router.get("/version")
|
||||
def get_version():
|
||||
return {"version": version("langflow")}
|
||||
|
||||
|
||||
@router.get("/health")
|
||||
def get_health():
|
||||
return {"status": "OK"}
|
||||
|
|
@ -2,15 +2,15 @@ import json
|
|||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
|
||||
from langflow.api.base import (
|
||||
from langflow.api.v1.base import (
|
||||
Code,
|
||||
CodeValidationResponse,
|
||||
Prompt,
|
||||
PromptValidationResponse,
|
||||
validate_prompt,
|
||||
)
|
||||
from langflow.graph.node.types import VectorStoreNode
|
||||
from langflow.interface.run import build_graph
|
||||
from langflow.graph.vertex.types import VectorStoreVertex
|
||||
from langflow.graph import Graph
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.utils.validate import validate_code
|
||||
|
||||
|
|
@ -44,12 +44,12 @@ def post_validate_prompt(prompt: Prompt):
|
|||
def post_validate_node(node_id: str, data: dict):
|
||||
try:
|
||||
# build graph
|
||||
graph = build_graph(data)
|
||||
graph = Graph.from_payload(data)
|
||||
# validate node
|
||||
node = graph.get_node(node_id)
|
||||
if node is None:
|
||||
raise ValueError(f"Node {node_id} not found")
|
||||
if not isinstance(node, VectorStoreNode):
|
||||
if not isinstance(node, VectorStoreVertex):
|
||||
node.build()
|
||||
return json.dumps({"valid": True, "params": str(node._built_object_repr())})
|
||||
except Exception as e:
|
||||
|
|
@ -1,21 +1,18 @@
|
|||
import asyncio
|
||||
import json
|
||||
from collections import defaultdict
|
||||
from typing import Dict, List
|
||||
|
||||
from fastapi import WebSocket, status
|
||||
|
||||
from langflow.api.schemas import ChatMessage, ChatResponse, FileResponse
|
||||
from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse
|
||||
from langflow.cache import cache_manager
|
||||
from langflow.cache.manager import Subject
|
||||
from langflow.interface.run import (
|
||||
get_result_and_steps,
|
||||
load_or_build_langchain_object,
|
||||
)
|
||||
from langflow.interface.utils import pil_to_base64, try_setting_streaming_options
|
||||
from langflow.chat.utils import process_graph
|
||||
from langflow.interface.utils import pil_to_base64
|
||||
from langflow.utils.logger import logger
|
||||
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
from typing import Dict, List
|
||||
|
||||
|
||||
class ChatHistory(Subject):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
|
@ -191,33 +188,3 @@ class ChatManager:
|
|||
except Exception as e:
|
||||
logger.exception(e)
|
||||
self.disconnect(client_id)
|
||||
|
||||
|
||||
async def process_graph(
|
||||
graph_data: Dict,
|
||||
is_first_message: bool,
|
||||
chat_message: ChatMessage,
|
||||
websocket: WebSocket,
|
||||
):
|
||||
langchain_object = load_or_build_langchain_object(graph_data, is_first_message)
|
||||
langchain_object = try_setting_streaming_options(langchain_object, websocket)
|
||||
logger.debug("Loaded langchain object")
|
||||
|
||||
if langchain_object is None:
|
||||
# Raise user facing error
|
||||
raise ValueError(
|
||||
"There was an error loading the langchain_object. Please, check all the nodes and try again."
|
||||
)
|
||||
|
||||
# Generate result and thought
|
||||
try:
|
||||
logger.debug("Generating result and thought")
|
||||
result, intermediate_steps = await get_result_and_steps(
|
||||
langchain_object, chat_message.message or "", websocket=websocket
|
||||
)
|
||||
logger.debug("Generated result and intermediate_steps")
|
||||
return result, intermediate_steps
|
||||
except Exception as e:
|
||||
# Log stack trace
|
||||
logger.exception(e)
|
||||
raise e
|
||||
41
src/backend/langflow/chat/utils.py
Normal file
41
src/backend/langflow/chat/utils.py
Normal file
|
|
@ -0,0 +1,41 @@
|
|||
from fastapi import WebSocket
|
||||
from langflow.api.v1.schemas import ChatMessage
|
||||
from langflow.processing.process import (
|
||||
load_or_build_langchain_object,
|
||||
)
|
||||
from langflow.processing.base import get_result_and_steps
|
||||
from langflow.interface.utils import try_setting_streaming_options
|
||||
from langflow.utils.logger import logger
|
||||
|
||||
|
||||
from typing import Dict
|
||||
|
||||
|
||||
async def process_graph(
|
||||
graph_data: Dict,
|
||||
is_first_message: bool,
|
||||
chat_message: ChatMessage,
|
||||
websocket: WebSocket,
|
||||
):
|
||||
langchain_object = load_or_build_langchain_object(graph_data, is_first_message)
|
||||
langchain_object = try_setting_streaming_options(langchain_object, websocket)
|
||||
logger.debug("Loaded langchain object")
|
||||
|
||||
if langchain_object is None:
|
||||
# Raise user facing error
|
||||
raise ValueError(
|
||||
"There was an error loading the langchain_object. Please, check all the nodes and try again."
|
||||
)
|
||||
|
||||
# Generate result and thought
|
||||
try:
|
||||
logger.debug("Generating result and thought")
|
||||
result, intermediate_steps = await get_result_and_steps(
|
||||
langchain_object, chat_message.message or "", websocket=websocket
|
||||
)
|
||||
logger.debug("Generated result and intermediate_steps")
|
||||
return result, intermediate_steps
|
||||
except Exception as e:
|
||||
# Log stack trace
|
||||
logger.exception(e)
|
||||
raise e
|
||||
|
|
@ -51,10 +51,13 @@ embeddings:
|
|||
llms:
|
||||
- OpenAI
|
||||
# - AzureOpenAI
|
||||
# - AzureChatOpenAI
|
||||
- ChatOpenAI
|
||||
- LlamaCpp
|
||||
- CTransformers
|
||||
- Cohere
|
||||
- Anthropic
|
||||
- ChatAnthropic
|
||||
memories:
|
||||
- ConversationBufferMemory
|
||||
- ConversationSummaryMemory
|
||||
|
|
@ -73,13 +76,14 @@ toolkits:
|
|||
- JsonToolkit
|
||||
- VectorStoreInfo
|
||||
- VectorStoreRouterToolkit
|
||||
- VectorStoreToolkit
|
||||
tools:
|
||||
- Search
|
||||
- PAL-MATH
|
||||
- Calculator
|
||||
- Serper Search
|
||||
- Tool
|
||||
- PythonFunction
|
||||
- PythonFunctionTool
|
||||
- JsonSpec
|
||||
- News API
|
||||
- TMDB API
|
||||
|
|
@ -118,6 +122,7 @@ vectorstores:
|
|||
- Chroma
|
||||
- Qdrant
|
||||
- Weaviate
|
||||
- FAISS
|
||||
wrappers:
|
||||
- RequestsWrapper
|
||||
# - ChatPromptTemplate
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langflow.template import frontend_node
|
|||
CUSTOM_NODES = {
|
||||
"prompts": {"ZeroShotPrompt": frontend_node.prompts.ZeroShotPromptNode()},
|
||||
"tools": {
|
||||
"PythonFunction": frontend_node.tools.PythonFunctionNode(),
|
||||
"PythonFunctionTool": frontend_node.tools.PythonFunctionToolNode(),
|
||||
"Tool": frontend_node.tools.ToolNode(),
|
||||
},
|
||||
"agents": {
|
||||
|
|
|
|||
|
|
@ -1,35 +1,35 @@
|
|||
from langflow.graph.edge.base import Edge
|
||||
from langflow.graph.graph.base import Graph
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.node.types import (
|
||||
AgentNode,
|
||||
ChainNode,
|
||||
DocumentLoaderNode,
|
||||
EmbeddingNode,
|
||||
LLMNode,
|
||||
MemoryNode,
|
||||
PromptNode,
|
||||
TextSplitterNode,
|
||||
ToolNode,
|
||||
ToolkitNode,
|
||||
VectorStoreNode,
|
||||
WrapperNode,
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.vertex.types import (
|
||||
AgentVertex,
|
||||
ChainVertex,
|
||||
DocumentLoaderVertex,
|
||||
EmbeddingVertex,
|
||||
LLMVertex,
|
||||
MemoryVertex,
|
||||
PromptVertex,
|
||||
TextSplitterVertex,
|
||||
ToolVertex,
|
||||
ToolkitVertex,
|
||||
VectorStoreVertex,
|
||||
WrapperVertex,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"Graph",
|
||||
"Node",
|
||||
"Vertex",
|
||||
"Edge",
|
||||
"AgentNode",
|
||||
"ChainNode",
|
||||
"DocumentLoaderNode",
|
||||
"EmbeddingNode",
|
||||
"LLMNode",
|
||||
"MemoryNode",
|
||||
"PromptNode",
|
||||
"TextSplitterNode",
|
||||
"ToolNode",
|
||||
"ToolkitNode",
|
||||
"VectorStoreNode",
|
||||
"WrapperNode",
|
||||
"AgentVertex",
|
||||
"ChainVertex",
|
||||
"DocumentLoaderVertex",
|
||||
"EmbeddingVertex",
|
||||
"LLMVertex",
|
||||
"MemoryVertex",
|
||||
"PromptVertex",
|
||||
"TextSplitterVertex",
|
||||
"ToolVertex",
|
||||
"ToolkitVertex",
|
||||
"VectorStoreVertex",
|
||||
"WrapperVertex",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -2,13 +2,13 @@ from langflow.utils.logger import logger
|
|||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
|
||||
|
||||
class Edge:
|
||||
def __init__(self, source: "Node", target: "Node"):
|
||||
self.source: "Node" = source
|
||||
self.target: "Node" = target
|
||||
def __init__(self, source: "Vertex", target: "Vertex"):
|
||||
self.source: "Vertex" = source
|
||||
self.target: "Vertex" = target
|
||||
self.validate_edge()
|
||||
|
||||
def validate_edge(self) -> None:
|
||||
|
|
@ -41,7 +41,7 @@ class Edge:
|
|||
logger.debug(self.target_reqs)
|
||||
if no_matched_type:
|
||||
raise ValueError(
|
||||
f"Edge between {self.source.node_type} and {self.target.node_type} "
|
||||
f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
|
||||
f"has no matched type"
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
from typing import Dict, List, Type, Union
|
||||
|
||||
from langflow.graph.edge.base import Edge
|
||||
from langflow.graph.graph.constants import NODE_TYPE_MAP
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.node.types import (
|
||||
FileToolNode,
|
||||
LLMNode,
|
||||
ToolkitNode,
|
||||
from langflow.graph.graph.constants import VERTEX_TYPE_MAP
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.vertex.types import (
|
||||
FileToolVertex,
|
||||
LLMVertex,
|
||||
ToolkitVertex,
|
||||
)
|
||||
from langflow.interface.tools.constants import FILE_TOOLS
|
||||
from langflow.utils import payload
|
||||
|
|
@ -24,9 +24,30 @@ class Graph:
|
|||
self._edges = edges
|
||||
self._build_graph()
|
||||
|
||||
@classmethod
|
||||
@classmethod
|
||||
def from_payload(cls, payload: Dict) -> "Graph":
|
||||
"""
|
||||
Creates a graph from a payload.
|
||||
|
||||
Args:
|
||||
payload (Dict): The payload to create the graph from.
|
||||
|
||||
Returns:
|
||||
Graph: The created graph.
|
||||
"""
|
||||
if "data" in payload:
|
||||
payload = payload["data"]
|
||||
try:
|
||||
nodes = payload["nodes"]
|
||||
edges = payload["edges"]
|
||||
return cls(nodes, edges)
|
||||
except KeyError as exc:
|
||||
raise ValueError("Invalid payload") from exc
|
||||
|
||||
def _build_graph(self) -> None:
|
||||
"""Builds the graph from the nodes and edges."""
|
||||
self.nodes = self._build_nodes()
|
||||
self.nodes = self._build_vertices()
|
||||
self.edges = self._build_edges()
|
||||
for edge in self.edges:
|
||||
edge.source.add_edge(edge)
|
||||
|
|
@ -43,12 +64,12 @@ class Graph:
|
|||
llm_node = None
|
||||
for node in self.nodes:
|
||||
node._build_params()
|
||||
if isinstance(node, LLMNode):
|
||||
if isinstance(node, LLMVertex):
|
||||
llm_node = node
|
||||
|
||||
if llm_node:
|
||||
for node in self.nodes:
|
||||
if isinstance(node, ToolkitNode):
|
||||
if isinstance(node, ToolkitVertex):
|
||||
node.params["llm"] = llm_node
|
||||
|
||||
def _remove_invalid_nodes(self) -> None:
|
||||
|
|
@ -60,23 +81,23 @@ class Graph:
|
|||
or (len(self.nodes) == 1 and len(self.edges) == 0)
|
||||
]
|
||||
|
||||
def _validate_node(self, node: Node) -> bool:
|
||||
def _validate_node(self, node: Vertex) -> bool:
|
||||
"""Validates a node."""
|
||||
# All nodes that do not have edges are invalid
|
||||
return len(node.edges) > 0
|
||||
|
||||
def get_node(self, node_id: str) -> Union[None, Node]:
|
||||
def get_node(self, node_id: str) -> Union[None, Vertex]:
|
||||
"""Returns a node by id."""
|
||||
return next((node for node in self.nodes if node.id == node_id), None)
|
||||
|
||||
def get_nodes_with_target(self, node: Node) -> List[Node]:
|
||||
def get_nodes_with_target(self, node: Vertex) -> List[Vertex]:
|
||||
"""Returns the nodes connected to a node."""
|
||||
connected_nodes: List[Node] = [
|
||||
connected_nodes: List[Vertex] = [
|
||||
edge.source for edge in self.edges if edge.target == node
|
||||
]
|
||||
return connected_nodes
|
||||
|
||||
def build(self) -> List[Node]:
|
||||
def build(self) -> List[Vertex]:
|
||||
"""Builds the graph."""
|
||||
# Get root node
|
||||
root_node = payload.get_root_node(self)
|
||||
|
|
@ -84,9 +105,9 @@ class Graph:
|
|||
raise ValueError("No root node found")
|
||||
return root_node.build()
|
||||
|
||||
def get_node_neighbors(self, node: Node) -> Dict[Node, int]:
|
||||
def get_node_neighbors(self, node: Vertex) -> Dict[Vertex, int]:
|
||||
"""Returns the neighbors of a node."""
|
||||
neighbors: Dict[Node, int] = {}
|
||||
neighbors: Dict[Vertex, int] = {}
|
||||
for edge in self.edges:
|
||||
if edge.source == node:
|
||||
neighbor = edge.target
|
||||
|
|
@ -117,28 +138,30 @@ class Graph:
|
|||
edges.append(Edge(source, target))
|
||||
return edges
|
||||
|
||||
def _get_node_class(self, node_type: str, node_lc_type: str) -> Type[Node]:
|
||||
def _get_vertex_class(self, node_type: str, node_lc_type: str) -> Type[Vertex]:
|
||||
"""Returns the node class based on the node type."""
|
||||
if node_type in FILE_TOOLS:
|
||||
return FileToolNode
|
||||
if node_type in NODE_TYPE_MAP:
|
||||
return NODE_TYPE_MAP[node_type]
|
||||
return NODE_TYPE_MAP[node_lc_type] if node_lc_type in NODE_TYPE_MAP else Node
|
||||
return FileToolVertex
|
||||
if node_type in VERTEX_TYPE_MAP:
|
||||
return VERTEX_TYPE_MAP[node_type]
|
||||
return (
|
||||
VERTEX_TYPE_MAP[node_lc_type] if node_lc_type in VERTEX_TYPE_MAP else Vertex
|
||||
)
|
||||
|
||||
def _build_nodes(self) -> List[Node]:
|
||||
"""Builds the nodes of the graph."""
|
||||
nodes: List[Node] = []
|
||||
def _build_vertices(self) -> List[Vertex]:
|
||||
"""Builds the vertices of the graph."""
|
||||
nodes: List[Vertex] = []
|
||||
for node in self._nodes:
|
||||
node_data = node["data"]
|
||||
node_type: str = node_data["type"] # type: ignore
|
||||
node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore
|
||||
|
||||
NodeClass = self._get_node_class(node_type, node_lc_type)
|
||||
nodes.append(NodeClass(node))
|
||||
VertexClass = self._get_vertex_class(node_type, node_lc_type)
|
||||
nodes.append(VertexClass(node))
|
||||
|
||||
return nodes
|
||||
|
||||
def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]:
|
||||
def get_children_by_node_type(self, node: Vertex, node_type: str) -> List[Vertex]:
|
||||
"""Returns the children of a node based on the node type."""
|
||||
children = []
|
||||
node_types = [node.data["type"]]
|
||||
|
|
|
|||
|
|
@ -1,17 +1,17 @@
|
|||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.node.types import (
|
||||
AgentNode,
|
||||
ChainNode,
|
||||
DocumentLoaderNode,
|
||||
EmbeddingNode,
|
||||
LLMNode,
|
||||
MemoryNode,
|
||||
PromptNode,
|
||||
TextSplitterNode,
|
||||
ToolNode,
|
||||
ToolkitNode,
|
||||
VectorStoreNode,
|
||||
WrapperNode,
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.vertex.types import (
|
||||
AgentVertex,
|
||||
ChainVertex,
|
||||
DocumentLoaderVertex,
|
||||
EmbeddingVertex,
|
||||
LLMVertex,
|
||||
MemoryVertex,
|
||||
PromptVertex,
|
||||
TextSplitterVertex,
|
||||
ToolVertex,
|
||||
ToolkitVertex,
|
||||
VectorStoreVertex,
|
||||
WrapperVertex,
|
||||
)
|
||||
from langflow.interface.agents.base import agent_creator
|
||||
from langflow.interface.chains.base import chain_creator
|
||||
|
|
@ -33,17 +33,17 @@ from typing import Dict, Type
|
|||
DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"]
|
||||
|
||||
|
||||
NODE_TYPE_MAP: Dict[str, Type[Node]] = {
|
||||
**{t: PromptNode for t in prompt_creator.to_list()},
|
||||
**{t: AgentNode for t in agent_creator.to_list()},
|
||||
**{t: ChainNode for t in chain_creator.to_list()},
|
||||
**{t: ToolNode for t in tool_creator.to_list()},
|
||||
**{t: ToolkitNode for t in toolkits_creator.to_list()},
|
||||
**{t: WrapperNode for t in wrapper_creator.to_list()},
|
||||
**{t: LLMNode for t in llm_creator.to_list()},
|
||||
**{t: MemoryNode for t in memory_creator.to_list()},
|
||||
**{t: EmbeddingNode for t in embedding_creator.to_list()},
|
||||
**{t: VectorStoreNode for t in vectorstore_creator.to_list()},
|
||||
**{t: DocumentLoaderNode for t in documentloader_creator.to_list()},
|
||||
**{t: TextSplitterNode for t in textsplitter_creator.to_list()},
|
||||
VERTEX_TYPE_MAP: Dict[str, Type[Vertex]] = {
|
||||
**{t: PromptVertex for t in prompt_creator.to_list()},
|
||||
**{t: AgentVertex for t in agent_creator.to_list()},
|
||||
**{t: ChainVertex for t in chain_creator.to_list()},
|
||||
**{t: ToolVertex for t in tool_creator.to_list()},
|
||||
**{t: ToolkitVertex for t in toolkits_creator.to_list()},
|
||||
**{t: WrapperVertex for t in wrapper_creator.to_list()},
|
||||
**{t: LLMVertex for t in llm_creator.to_list()},
|
||||
**{t: MemoryVertex for t in memory_creator.to_list()},
|
||||
**{t: EmbeddingVertex for t in embedding_creator.to_list()},
|
||||
**{t: VectorStoreVertex for t in vectorstore_creator.to_list()},
|
||||
**{t: DocumentLoaderVertex for t in documentloader_creator.to_list()},
|
||||
**{t: TextSplitterVertex for t in textsplitter_creator.to_list()},
|
||||
}
|
||||
|
|
|
|||
0
src/backend/langflow/graph/graph/utils.py
Normal file
0
src/backend/langflow/graph/graph/utils.py
Normal file
|
|
@ -1,4 +1,6 @@
|
|||
import re
|
||||
from typing import Any, Union
|
||||
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
|
||||
|
||||
def validate_prompt(prompt: str):
|
||||
|
|
@ -14,6 +16,12 @@ def fix_prompt(prompt: str):
|
|||
return prompt + " {input}"
|
||||
|
||||
|
||||
def extract_input_variables_from_prompt(prompt: str) -> list[str]:
|
||||
"""Extract input variables from prompt."""
|
||||
return re.findall(r"{(.*?)}", prompt)
|
||||
def flatten_list(list_of_lists: list[Union[list, Any]]) -> list:
|
||||
"""Flatten list of lists."""
|
||||
new_list = []
|
||||
for item in list_of_lists:
|
||||
if isinstance(item, list):
|
||||
new_list.extend(item)
|
||||
else:
|
||||
new_list.append(item)
|
||||
return new_list
|
||||
|
|
|
|||
0
src/backend/langflow/graph/vertex/__init__.py
Normal file
0
src/backend/langflow/graph/vertex/__init__.py
Normal file
|
|
@ -1,5 +1,5 @@
|
|||
from langflow.cache import base as cache_utils
|
||||
from langflow.graph.node.constants import DIRECT_TYPES
|
||||
from langflow.graph.vertex.constants import DIRECT_TYPES
|
||||
from langflow.interface import loading
|
||||
from langflow.interface.listing import ALL_TYPES_DICT
|
||||
from langflow.utils.logger import logger
|
||||
|
|
@ -17,7 +17,7 @@ if TYPE_CHECKING:
|
|||
from langflow.graph.edge.base import Edge
|
||||
|
||||
|
||||
class Node:
|
||||
class Vertex:
|
||||
def __init__(self, data: Dict, base_type: Optional[str] = None) -> None:
|
||||
self.id: str = data["id"]
|
||||
self._data = data
|
||||
|
|
@ -48,12 +48,12 @@ class Node:
|
|||
]
|
||||
|
||||
template_dict = self.data["node"]["template"]
|
||||
self.node_type = (
|
||||
self.vertex_type = (
|
||||
self.data["type"] if "Tool" not in self.output else template_dict["_type"]
|
||||
)
|
||||
if self.base_type is None:
|
||||
for base_type, value in ALL_TYPES_DICT.items():
|
||||
if self.node_type in value:
|
||||
if self.vertex_type in value:
|
||||
self.base_type = base_type
|
||||
break
|
||||
|
||||
|
|
@ -113,7 +113,7 @@ class Node:
|
|||
if value["required"] and not edges:
|
||||
# If a required parameter is not found, raise an error
|
||||
raise ValueError(
|
||||
f"Required input {key} for module {self.node_type} not found"
|
||||
f"Required input {key} for module {self.vertex_type} not found"
|
||||
)
|
||||
elif value["list"]:
|
||||
# If this is a list parameter, append all sources to a list
|
||||
|
|
@ -128,7 +128,7 @@ class Node:
|
|||
# so we need to check if value has value
|
||||
new_value = value.get("value")
|
||||
if new_value is None:
|
||||
warnings.warn(f"Value for {key} in {self.node_type} is None. ")
|
||||
warnings.warn(f"Value for {key} in {self.vertex_type} is None. ")
|
||||
if value.get("type") == "int":
|
||||
with contextlib.suppress(TypeError, ValueError):
|
||||
new_value = int(new_value) # type: ignore
|
||||
|
|
@ -148,12 +148,12 @@ class Node:
|
|||
# and continue
|
||||
# Another aspect is that the node_type is the class that we need to import
|
||||
# and instantiate with these built params
|
||||
logger.debug(f"Building {self.node_type}")
|
||||
logger.debug(f"Building {self.vertex_type}")
|
||||
# Build each node in the params dict
|
||||
for key, value in self.params.copy().items():
|
||||
# Check if Node or list of Nodes and not self
|
||||
# to avoid recursion
|
||||
if isinstance(value, Node):
|
||||
if isinstance(value, Vertex):
|
||||
if value == self:
|
||||
del self.params[key]
|
||||
continue
|
||||
|
|
@ -174,10 +174,16 @@ class Node:
|
|||
# turn result which is a function into a coroutine
|
||||
# so that it can be awaited
|
||||
self.params["coroutine"] = sync_to_async(result)
|
||||
if isinstance(result, list):
|
||||
# If the result is a list, then we need to extend the list
|
||||
# with the result but first check if the key exists
|
||||
# if it doesn't, then we need to create a new list
|
||||
if isinstance(self.params[key], list):
|
||||
self.params[key].extend(result)
|
||||
|
||||
self.params[key] = result
|
||||
elif isinstance(value, list) and all(
|
||||
isinstance(node, Node) for node in value
|
||||
isinstance(node, Vertex) for node in value
|
||||
):
|
||||
self.params[key] = []
|
||||
for node in value:
|
||||
|
|
@ -193,17 +199,17 @@ class Node:
|
|||
|
||||
try:
|
||||
self._built_object = loading.instantiate_class(
|
||||
node_type=self.node_type,
|
||||
node_type=self.vertex_type,
|
||||
base_type=self.base_type,
|
||||
params=self.params,
|
||||
)
|
||||
except Exception as exc:
|
||||
raise ValueError(
|
||||
f"Error building node {self.node_type}: {str(exc)}"
|
||||
f"Error building node {self.vertex_type}: {str(exc)}"
|
||||
) from exc
|
||||
|
||||
if self._built_object is None:
|
||||
raise ValueError(f"Node type {self.node_type} not found")
|
||||
raise ValueError(f"Node type {self.vertex_type} not found")
|
||||
|
||||
self._built = True
|
||||
|
||||
|
|
@ -220,7 +226,7 @@ class Node:
|
|||
return f"Node(id={self.id}, data={self.data})"
|
||||
|
||||
def __eq__(self, __o: object) -> bool:
|
||||
return self.id == __o.id if isinstance(__o, Node) else False
|
||||
return self.id == __o.id if isinstance(__o, Vertex) else False
|
||||
|
||||
def __hash__(self) -> int:
|
||||
return id(self)
|
||||
|
|
@ -1,22 +1,23 @@
|
|||
from typing import Any, Dict, List, Optional, Union
|
||||
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.utils import extract_input_variables_from_prompt
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
from langflow.graph.utils import flatten_list
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
|
||||
|
||||
class AgentNode(Node):
|
||||
class AgentVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="agents")
|
||||
|
||||
self.tools: List[ToolNode] = []
|
||||
self.chains: List[ChainNode] = []
|
||||
self.tools: List[Union[ToolkitVertex, ToolVertex]] = []
|
||||
self.chains: List[ChainVertex] = []
|
||||
|
||||
def _set_tools_and_chains(self) -> None:
|
||||
for edge in self.edges:
|
||||
source_node = edge.source
|
||||
if isinstance(source_node, ToolNode):
|
||||
if isinstance(source_node, (ToolVertex, ToolkitVertex)):
|
||||
self.tools.append(source_node)
|
||||
elif isinstance(source_node, ChainNode):
|
||||
elif isinstance(source_node, ChainVertex):
|
||||
self.chains.append(source_node)
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
|
|
@ -32,25 +33,130 @@ class AgentNode(Node):
|
|||
|
||||
self._build()
|
||||
|
||||
#! Cannot deepcopy VectorStore, VectorStoreRouter, or SQL agents
|
||||
if self.node_type in ["VectorStoreAgent", "VectorStoreRouterAgent", "SQLAgent"]:
|
||||
return self._built_object
|
||||
return self._built_object
|
||||
|
||||
|
||||
class ToolNode(Node):
|
||||
class ToolVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="tools")
|
||||
|
||||
|
||||
class PromptNode(Node):
|
||||
class LLMVertex(Vertex):
|
||||
built_node_type = None
|
||||
class_built_object = None
|
||||
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="llms")
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
# LLM is different because some models might take up too much memory
|
||||
# or time to load. So we only load them when we need them.ß
|
||||
if self.vertex_type == self.built_node_type:
|
||||
return self.class_built_object
|
||||
if not self._built or force:
|
||||
self._build()
|
||||
self.built_node_type = self.vertex_type
|
||||
self.class_built_object = self._built_object
|
||||
# Avoid deepcopying the LLM
|
||||
# that are loaded from a file
|
||||
return self._built_object
|
||||
|
||||
|
||||
class ToolkitVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="toolkits")
|
||||
|
||||
|
||||
class FileToolVertex(ToolVertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data)
|
||||
|
||||
|
||||
class WrapperVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="wrappers")
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
if not self._built or force:
|
||||
if "headers" in self.params:
|
||||
self.params["headers"] = eval(self.params["headers"])
|
||||
self._build()
|
||||
return self._built_object
|
||||
|
||||
|
||||
class DocumentLoaderVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="documentloaders")
|
||||
|
||||
def _built_object_repr(self):
|
||||
# This built_object is a list of documents. Maybe we should
|
||||
# show how many documents are in the list?
|
||||
if self._built_object:
|
||||
return f"""{self.vertex_type}({len(self._built_object)} documents)
|
||||
Documents: {self._built_object[:3]}..."""
|
||||
return f"{self.vertex_type}()"
|
||||
|
||||
|
||||
class EmbeddingVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="embeddings")
|
||||
|
||||
|
||||
class VectorStoreVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="vectorstores")
|
||||
|
||||
def _built_object_repr(self):
|
||||
return "Vector stores can take time to build. It will build on the first query."
|
||||
|
||||
|
||||
class MemoryVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="memory")
|
||||
|
||||
|
||||
class TextSplitterVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="textsplitters")
|
||||
|
||||
def _built_object_repr(self):
|
||||
# This built_object is a list of documents. Maybe we should
|
||||
# show how many documents are in the list?
|
||||
if self._built_object:
|
||||
return f"""{self.vertex_type}({len(self._built_object)} documents)
|
||||
\nDocuments: {self._built_object[:3]}..."""
|
||||
return f"{self.vertex_type}()"
|
||||
|
||||
|
||||
class ChainVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="chains")
|
||||
|
||||
def build(
|
||||
self,
|
||||
force: bool = False,
|
||||
tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None,
|
||||
) -> Any:
|
||||
if not self._built or force:
|
||||
# Check if the chain requires a PromptVertex
|
||||
for key, value in self.params.items():
|
||||
if isinstance(value, PromptVertex):
|
||||
# Build the PromptVertex, passing the tools if available
|
||||
self.params[key] = value.build(tools=tools, force=force)
|
||||
|
||||
self._build()
|
||||
|
||||
return self._built_object
|
||||
|
||||
|
||||
class PromptVertex(Vertex):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="prompts")
|
||||
|
||||
def build(
|
||||
self,
|
||||
force: bool = False,
|
||||
tools: Optional[Union[List[Node], List[ToolNode]]] = None,
|
||||
tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None,
|
||||
) -> Any:
|
||||
if not self._built or force:
|
||||
if (
|
||||
|
|
@ -59,12 +165,16 @@ class PromptNode(Node):
|
|||
):
|
||||
self.params["input_variables"] = []
|
||||
# Check if it is a ZeroShotPrompt and needs a tool
|
||||
if "ShotPrompt" in self.node_type:
|
||||
if "ShotPrompt" in self.vertex_type:
|
||||
tools = (
|
||||
[tool_node.build() for tool_node in tools]
|
||||
if tools is not None
|
||||
else []
|
||||
)
|
||||
# flatten the list of tools if it is a list of lists
|
||||
# first check if it is a list
|
||||
if tools and isinstance(tools, list) and isinstance(tools[0], list):
|
||||
tools = flatten_list(tools)
|
||||
self.params["tools"] = tools
|
||||
prompt_params = [
|
||||
key
|
||||
|
|
@ -81,113 +191,3 @@ class PromptNode(Node):
|
|||
|
||||
self._build()
|
||||
return self._built_object
|
||||
|
||||
|
||||
class ChainNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="chains")
|
||||
|
||||
def build(
|
||||
self,
|
||||
force: bool = False,
|
||||
tools: Optional[Union[List[Node], List[ToolNode]]] = None,
|
||||
) -> Any:
|
||||
if not self._built or force:
|
||||
# Check if the chain requires a PromptNode
|
||||
for key, value in self.params.items():
|
||||
if isinstance(value, PromptNode):
|
||||
# Build the PromptNode, passing the tools if available
|
||||
self.params[key] = value.build(tools=tools, force=force)
|
||||
|
||||
self._build()
|
||||
|
||||
#! Cannot deepcopy SQLDatabaseChain
|
||||
if self.node_type in ["SQLDatabaseChain"]:
|
||||
return self._built_object
|
||||
return self._built_object
|
||||
|
||||
|
||||
class LLMNode(Node):
|
||||
built_node_type = None
|
||||
class_built_object = None
|
||||
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="llms")
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
# LLM is different because some models might take up too much memory
|
||||
# or time to load. So we only load them when we need them.ß
|
||||
if self.node_type == self.built_node_type:
|
||||
return self.class_built_object
|
||||
if not self._built or force:
|
||||
self._build()
|
||||
self.built_node_type = self.node_type
|
||||
self.class_built_object = self._built_object
|
||||
# Avoid deepcopying the LLM
|
||||
# that are loaded from a file
|
||||
return self._built_object
|
||||
|
||||
|
||||
class ToolkitNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="toolkits")
|
||||
|
||||
|
||||
class FileToolNode(ToolNode):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data)
|
||||
|
||||
|
||||
class WrapperNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="wrappers")
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
if not self._built or force:
|
||||
if "headers" in self.params:
|
||||
self.params["headers"] = eval(self.params["headers"])
|
||||
self._build()
|
||||
return self._built_object
|
||||
|
||||
|
||||
class DocumentLoaderNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="documentloaders")
|
||||
|
||||
def _built_object_repr(self):
|
||||
# This built_object is a list of documents. Maybe we should
|
||||
# show how many documents are in the list?
|
||||
if self._built_object:
|
||||
return f"""{self.node_type}({len(self._built_object)} documents)
|
||||
Documents: {self._built_object[:3]}..."""
|
||||
return f"{self.node_type}()"
|
||||
|
||||
|
||||
class EmbeddingNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="embeddings")
|
||||
|
||||
|
||||
class VectorStoreNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="vectorstores")
|
||||
|
||||
def _built_object_repr(self):
|
||||
return "Vector stores can take time to build. It will build on the first query."
|
||||
|
||||
|
||||
class MemoryNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="memory")
|
||||
|
||||
|
||||
class TextSplitterNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="textsplitters")
|
||||
|
||||
def _built_object_repr(self):
|
||||
# This built_object is a list of documents. Maybe we should
|
||||
# show how many documents are in the list?
|
||||
if self._built_object:
|
||||
return f"""{self.node_type}({len(self._built_object)} documents)\nDocuments: {self._built_object[:3]}..."""
|
||||
return f"{self.node_type}()"
|
||||
|
|
@ -69,7 +69,7 @@ class JsonAgent(CustomAgentExecutor):
|
|||
|
||||
@classmethod
|
||||
def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel):
|
||||
tools = toolkit.get_tools()
|
||||
tools = toolkit if isinstance(toolkit, list) else toolkit.get_tools()
|
||||
tool_names = {tool.name for tool in tools}
|
||||
prompt = ZeroShotAgent.create_prompt(
|
||||
tools,
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from langchain.memory.buffer import ConversationBufferMemory
|
|||
from langchain.schema import BaseMemory
|
||||
from pydantic import Field, root_validator
|
||||
|
||||
from langflow.graph.utils import extract_input_variables_from_prompt
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
|
||||
DEFAULT_SUFFIX = """"
|
||||
Current conversation:
|
||||
|
|
|
|||
|
|
@ -11,12 +11,15 @@ from langchain import (
|
|||
text_splitter,
|
||||
)
|
||||
from langchain.agents import agent_toolkits
|
||||
from langchain.chat_models import ChatOpenAI
|
||||
from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
|
||||
from langchain.chat_models import ChatAnthropic
|
||||
|
||||
from langflow.interface.importing.utils import import_class
|
||||
|
||||
## LLMs
|
||||
llm_type_to_cls_dict = llms.type_to_cls_dict
|
||||
llm_type_to_cls_dict["anthropic-chat"] = ChatAnthropic # type: ignore
|
||||
llm_type_to_cls_dict["azure-chat"] = AzureChatOpenAI # type: ignore
|
||||
llm_type_to_cls_dict["openai-chat"] = ChatOpenAI # type: ignore
|
||||
|
||||
## Chains
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ from langchain.base_language import BaseLanguageModel
|
|||
from langchain.chains.base import Chain
|
||||
from langchain.chat_models.base import BaseChatModel
|
||||
from langchain.tools import BaseTool
|
||||
from langflow.utils import validate
|
||||
|
||||
|
||||
def import_module(module_path: str) -> Any:
|
||||
|
|
@ -147,3 +148,10 @@ def import_utility(utility: str) -> Any:
|
|||
if utility == "SQLDatabase":
|
||||
return import_class(f"langchain.sql_database.{utility}")
|
||||
return import_class(f"langchain.utilities.{utility}")
|
||||
|
||||
|
||||
def get_function(code):
|
||||
"""Get the function"""
|
||||
function_name = validate.extract_function_name(code)
|
||||
|
||||
return validate.create_function(code, function_name)
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@ from langchain.agents.load_tools import (
|
|||
_LLM_TOOLS,
|
||||
)
|
||||
from langchain.agents.loading import load_agent_from_config
|
||||
from langflow.graph import Graph
|
||||
from langchain.agents.tools import Tool
|
||||
from langchain.base_language import BaseLanguageModel
|
||||
from langchain.callbacks.base import BaseCallbackManager
|
||||
|
|
@ -21,12 +20,11 @@ from langchain.llms.loading import load_llm_from_config
|
|||
from pydantic import ValidationError
|
||||
|
||||
from langflow.interface.agents.custom import CUSTOM_AGENTS
|
||||
from langflow.interface.importing.utils import import_by_type
|
||||
from langflow.interface.run import fix_memory_inputs
|
||||
from langflow.interface.importing.utils import get_function, import_by_type
|
||||
from langflow.interface.toolkits.base import toolkits_creator
|
||||
from langflow.interface.types import get_type_list
|
||||
from langflow.interface.utils import load_file_into_dict
|
||||
from langflow.utils import util, validate
|
||||
from langflow.utils import util
|
||||
|
||||
|
||||
def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
|
||||
|
|
@ -100,11 +98,9 @@ def instantiate_tool(node_type, class_object, params):
|
|||
if node_type == "JsonSpec":
|
||||
params["dict_"] = load_file_into_dict(params.pop("path"))
|
||||
return class_object(**params)
|
||||
elif node_type == "PythonFunction":
|
||||
function_string = params["code"]
|
||||
if isinstance(function_string, str):
|
||||
return validate.eval_function(function_string)
|
||||
raise ValueError("Function should be a string")
|
||||
elif node_type == "PythonFunctionTool":
|
||||
params["func"] = get_function(params.get("code"))
|
||||
return class_object(**params)
|
||||
elif node_type.lower() == "tool":
|
||||
return class_object(**params)
|
||||
return class_object(**params)
|
||||
|
|
@ -112,8 +108,11 @@ def instantiate_tool(node_type, class_object, params):
|
|||
|
||||
def instantiate_toolkit(node_type, class_object, params):
|
||||
loaded_toolkit = class_object(**params)
|
||||
if toolkits_creator.has_create_function(node_type):
|
||||
return load_toolkits_executor(node_type, loaded_toolkit, params)
|
||||
# Commenting this out for now to use toolkits as normal tools
|
||||
# if toolkits_creator.has_create_function(node_type):
|
||||
# return load_toolkits_executor(node_type, loaded_toolkit, params)
|
||||
if isinstance(loaded_toolkit, BaseToolkit):
|
||||
return loaded_toolkit.get_tools()
|
||||
return loaded_toolkit
|
||||
|
||||
|
||||
|
|
@ -162,37 +161,6 @@ def instantiate_utility(node_type, class_object, params):
|
|||
return class_object(**params)
|
||||
|
||||
|
||||
def load_flow_from_json(path: str, build=True):
|
||||
"""Load flow from json file"""
|
||||
# This is done to avoid circular imports
|
||||
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
flow_graph = json.load(f)
|
||||
data_graph = flow_graph["data"]
|
||||
nodes = data_graph["nodes"]
|
||||
# Substitute ZeroShotPrompt with PromptTemplate
|
||||
# nodes = replace_zero_shot_prompt_with_prompt_template(nodes)
|
||||
# Add input variables
|
||||
# nodes = payload.extract_input_variables(nodes)
|
||||
|
||||
# Nodes, edges and root node
|
||||
edges = data_graph["edges"]
|
||||
graph = Graph(nodes, edges)
|
||||
if build:
|
||||
langchain_object = graph.build()
|
||||
if hasattr(langchain_object, "verbose"):
|
||||
langchain_object.verbose = True
|
||||
|
||||
if hasattr(langchain_object, "return_intermediate_steps"):
|
||||
# https://github.com/hwchase17/langchain/issues/2068
|
||||
# Deactivating until we have a frontend solution
|
||||
# to display intermediate steps
|
||||
langchain_object.return_intermediate_steps = False
|
||||
fix_memory_inputs(langchain_object)
|
||||
return langchain_object
|
||||
return graph
|
||||
|
||||
|
||||
def replace_zero_shot_prompt_with_prompt_template(nodes):
|
||||
"""Replace ZeroShotPrompt with PromptTemplate"""
|
||||
for node in nodes:
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from typing import Dict, List, Optional, Type
|
|||
from langchain.prompts import PromptTemplate
|
||||
from pydantic import root_validator
|
||||
|
||||
from langflow.graph.utils import extract_input_variables_from_prompt
|
||||
from langflow.interface.utils import extract_input_variables_from_prompt
|
||||
|
||||
# Steps to create a BaseCustomPrompt:
|
||||
# 1. Create a prompt template that endes with:
|
||||
|
|
|
|||
|
|
@ -1,10 +1,3 @@
|
|||
import contextlib
|
||||
import io
|
||||
from typing import Any, Dict, List, Tuple
|
||||
|
||||
from langchain.schema import AgentAction
|
||||
|
||||
from langflow.api.callback import AsyncStreamingLLMCallbackHandler, StreamingLLMCallbackHandler # type: ignore
|
||||
from langflow.cache.base import compute_dict_hash, load_cache, memoize_dict
|
||||
from langflow.graph import Graph
|
||||
from langflow.utils.logger import logger
|
||||
|
|
@ -24,15 +17,6 @@ def load_langchain_object(data_graph, is_first_message=False):
|
|||
return computed_hash, langchain_object
|
||||
|
||||
|
||||
def load_or_build_langchain_object(data_graph, is_first_message=False):
|
||||
"""
|
||||
Load langchain object from cache if it exists, otherwise build it.
|
||||
"""
|
||||
if is_first_message:
|
||||
build_langchain_object_with_caching.clear_cache()
|
||||
return build_langchain_object_with_caching(data_graph)
|
||||
|
||||
|
||||
@memoize_dict(maxsize=10)
|
||||
def build_langchain_object_with_caching(data_graph):
|
||||
"""
|
||||
|
|
@ -40,16 +24,10 @@ def build_langchain_object_with_caching(data_graph):
|
|||
"""
|
||||
|
||||
logger.debug("Building langchain object")
|
||||
graph = build_graph(data_graph)
|
||||
graph = Graph.from_payload(data_graph)
|
||||
return graph.build()
|
||||
|
||||
|
||||
def build_graph(data_graph):
|
||||
nodes = data_graph["nodes"]
|
||||
edges = data_graph["edges"]
|
||||
return Graph(nodes, edges)
|
||||
|
||||
|
||||
def build_langchain_object(data_graph):
|
||||
"""
|
||||
Build langchain object from data_graph.
|
||||
|
|
@ -66,29 +44,6 @@ def build_langchain_object(data_graph):
|
|||
return graph.build()
|
||||
|
||||
|
||||
def process_graph_cached(data_graph: Dict[str, Any], message: str):
|
||||
"""
|
||||
Process graph by extracting input variables and replacing ZeroShotPrompt
|
||||
with PromptTemplate,then run the graph and return the result and thought.
|
||||
"""
|
||||
# Load langchain object
|
||||
is_first_message = len(data_graph.get("chatHistory", [])) == 0
|
||||
langchain_object = load_or_build_langchain_object(data_graph, is_first_message)
|
||||
logger.debug("Loaded langchain object")
|
||||
|
||||
if langchain_object is None:
|
||||
# Raise user facing error
|
||||
raise ValueError(
|
||||
"There was an error loading the langchain_object. Please, check all the nodes and try again."
|
||||
)
|
||||
|
||||
# Generate result and thought
|
||||
logger.debug("Generating result and thought")
|
||||
result, thought = get_result_and_thought(langchain_object, message)
|
||||
logger.debug("Generated result and thought")
|
||||
return {"result": str(result), "thought": thought.strip()}
|
||||
|
||||
|
||||
def get_memory_key(langchain_object):
|
||||
"""
|
||||
Given a LangChain object, this function retrieves the current memory key from the object's memory attribute.
|
||||
|
|
@ -124,147 +79,3 @@ def update_memory_keys(langchain_object, possible_new_mem_key):
|
|||
langchain_object.memory.input_key = input_key
|
||||
langchain_object.memory.output_key = output_key
|
||||
langchain_object.memory.memory_key = possible_new_mem_key
|
||||
|
||||
|
||||
def fix_memory_inputs(langchain_object):
|
||||
"""
|
||||
Given a LangChain object, this function checks if it has a memory attribute and if that memory key exists in the
|
||||
object's input variables. If so, it does nothing. Otherwise, it gets a possible new memory key using the
|
||||
get_memory_key function and updates the memory keys using the update_memory_keys function.
|
||||
"""
|
||||
if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
|
||||
try:
|
||||
if langchain_object.memory.memory_key in langchain_object.input_variables:
|
||||
return
|
||||
except AttributeError:
|
||||
input_variables = (
|
||||
langchain_object.prompt.input_variables
|
||||
if hasattr(langchain_object, "prompt")
|
||||
else langchain_object.input_keys
|
||||
)
|
||||
if langchain_object.memory.memory_key in input_variables:
|
||||
return
|
||||
|
||||
possible_new_mem_key = get_memory_key(langchain_object)
|
||||
if possible_new_mem_key is not None:
|
||||
update_memory_keys(langchain_object, possible_new_mem_key)
|
||||
|
||||
|
||||
async def get_result_and_steps(langchain_object, message: str, **kwargs):
|
||||
"""Get result and thought from extracted json"""
|
||||
|
||||
try:
|
||||
if hasattr(langchain_object, "verbose"):
|
||||
langchain_object.verbose = True
|
||||
chat_input = None
|
||||
memory_key = ""
|
||||
if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
|
||||
memory_key = langchain_object.memory.memory_key
|
||||
|
||||
if hasattr(langchain_object, "input_keys"):
|
||||
for key in langchain_object.input_keys:
|
||||
if key not in [memory_key, "chat_history"]:
|
||||
chat_input = {key: message}
|
||||
else:
|
||||
chat_input = message # type: ignore
|
||||
|
||||
if hasattr(langchain_object, "return_intermediate_steps"):
|
||||
# https://github.com/hwchase17/langchain/issues/2068
|
||||
# Deactivating until we have a frontend solution
|
||||
# to display intermediate steps
|
||||
langchain_object.return_intermediate_steps = True
|
||||
|
||||
fix_memory_inputs(langchain_object)
|
||||
try:
|
||||
async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)]
|
||||
output = await langchain_object.acall(chat_input, callbacks=async_callbacks)
|
||||
except Exception as exc:
|
||||
# make the error message more informative
|
||||
logger.debug(f"Error: {str(exc)}")
|
||||
sync_callbacks = [StreamingLLMCallbackHandler(**kwargs)]
|
||||
output = langchain_object(chat_input, callbacks=sync_callbacks)
|
||||
|
||||
intermediate_steps = (
|
||||
output.get("intermediate_steps", []) if isinstance(output, dict) else []
|
||||
)
|
||||
|
||||
result = (
|
||||
output.get(langchain_object.output_keys[0])
|
||||
if isinstance(output, dict)
|
||||
else output
|
||||
)
|
||||
thought = format_actions(intermediate_steps) if intermediate_steps else ""
|
||||
except Exception as exc:
|
||||
raise ValueError(f"Error: {str(exc)}") from exc
|
||||
return result, thought
|
||||
|
||||
|
||||
def get_result_and_thought(langchain_object, message: str):
|
||||
"""Get result and thought from extracted json"""
|
||||
try:
|
||||
if hasattr(langchain_object, "verbose"):
|
||||
langchain_object.verbose = True
|
||||
chat_input = None
|
||||
memory_key = ""
|
||||
if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
|
||||
memory_key = langchain_object.memory.memory_key
|
||||
|
||||
if hasattr(langchain_object, "input_keys"):
|
||||
for key in langchain_object.input_keys:
|
||||
if key not in [memory_key, "chat_history"]:
|
||||
chat_input = {key: message}
|
||||
else:
|
||||
chat_input = message # type: ignore
|
||||
|
||||
if hasattr(langchain_object, "return_intermediate_steps"):
|
||||
# https://github.com/hwchase17/langchain/issues/2068
|
||||
# Deactivating until we have a frontend solution
|
||||
# to display intermediate steps
|
||||
langchain_object.return_intermediate_steps = False
|
||||
|
||||
fix_memory_inputs(langchain_object)
|
||||
|
||||
with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer):
|
||||
try:
|
||||
# if hasattr(langchain_object, "acall"):
|
||||
# output = await langchain_object.acall(chat_input)
|
||||
# else:
|
||||
output = langchain_object(chat_input)
|
||||
except ValueError as exc:
|
||||
# make the error message more informative
|
||||
logger.debug(f"Error: {str(exc)}")
|
||||
output = langchain_object.run(chat_input)
|
||||
|
||||
intermediate_steps = (
|
||||
output.get("intermediate_steps", []) if isinstance(output, dict) else []
|
||||
)
|
||||
|
||||
result = (
|
||||
output.get(langchain_object.output_keys[0])
|
||||
if isinstance(output, dict)
|
||||
else output
|
||||
)
|
||||
if intermediate_steps:
|
||||
thought = format_actions(intermediate_steps)
|
||||
else:
|
||||
thought = output_buffer.getvalue()
|
||||
|
||||
except Exception as exc:
|
||||
raise ValueError(f"Error: {str(exc)}") from exc
|
||||
return result, thought
|
||||
|
||||
|
||||
def format_actions(actions: List[Tuple[AgentAction, str]]) -> str:
|
||||
"""Format a list of (AgentAction, answer) tuples into a string."""
|
||||
output = []
|
||||
for action, answer in actions:
|
||||
log = action.log
|
||||
tool = action.tool
|
||||
tool_input = action.tool_input
|
||||
output.append(f"Log: {log}")
|
||||
if "Action" not in log and "Action Input" not in log:
|
||||
output.append(f"Tool: {tool}")
|
||||
output.append(f"Tool Input: {tool_input}")
|
||||
output.append(f"Answer: {answer}")
|
||||
output.append("") # Add a blank line
|
||||
return "\n".join(output)
|
||||
|
|
|
|||
|
|
@ -42,24 +42,27 @@ class ToolkitCreator(LangChainTypeCreator):
|
|||
|
||||
def get_signature(self, name: str) -> Optional[Dict]:
|
||||
try:
|
||||
return build_template_from_class(name, self.type_to_loader_dict)
|
||||
template = build_template_from_class(name, self.type_to_loader_dict)
|
||||
# add Tool to base_classes
|
||||
if "toolkit" in name.lower() and template:
|
||||
template["base_classes"].append("Tool")
|
||||
return template
|
||||
except ValueError as exc:
|
||||
raise ValueError("Prompt not found") from exc
|
||||
raise ValueError("Toolkit not found") from exc
|
||||
except AttributeError as exc:
|
||||
logger.error(f"Prompt {name} not loaded: {exc}")
|
||||
logger.error(f"Toolkit {name} not loaded: {exc}")
|
||||
return None
|
||||
|
||||
def to_list(self) -> List[str]:
|
||||
return list(self.type_to_loader_dict.keys())
|
||||
|
||||
def get_create_function(self, name: str) -> Callable:
|
||||
if loader_name := self.create_functions.get(name, None):
|
||||
# import loader
|
||||
if loader_name := self.create_functions.get(name):
|
||||
return import_module(
|
||||
f"from langchain.agents.agent_toolkits import {loader_name[0]}"
|
||||
)
|
||||
else:
|
||||
raise ValueError("Loader not found")
|
||||
raise ValueError("Toolkit not found")
|
||||
|
||||
def has_create_function(self, name: str) -> bool:
|
||||
# check if the function list is not empty
|
||||
|
|
|
|||
|
|
@ -71,7 +71,8 @@ class ToolCreator(LangChainTypeCreator):
|
|||
|
||||
for tool, tool_fcn in ALL_TOOLS_NAMES.items():
|
||||
tool_params = get_tool_params(tool_fcn)
|
||||
tool_name = tool_params.get("name", tool)
|
||||
|
||||
tool_name = tool_params.get("name") or tool
|
||||
|
||||
if tool_name in settings.tools or settings.dev:
|
||||
if tool_name == "JsonSpec":
|
||||
|
|
|
|||
|
|
@ -9,10 +9,10 @@ from langchain.agents.load_tools import (
|
|||
from langchain.tools.json.tool import JsonSpec
|
||||
|
||||
from langflow.interface.importing.utils import import_class
|
||||
from langflow.interface.tools.custom import PythonFunction
|
||||
from langflow.interface.tools.custom import PythonFunctionTool
|
||||
|
||||
FILE_TOOLS = {"JsonSpec": JsonSpec}
|
||||
CUSTOM_TOOLS = {"Tool": Tool, "PythonFunction": PythonFunction}
|
||||
CUSTOM_TOOLS = {"Tool": Tool, "PythonFunctionTool": PythonFunctionTool}
|
||||
|
||||
OTHER_TOOLS = {tool: import_class(f"langchain.tools.{tool}") for tool in tools.__all__}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,13 +1,14 @@
|
|||
from typing import Callable, Optional
|
||||
from typing import Optional
|
||||
from langflow.interface.importing.utils import get_function
|
||||
|
||||
from pydantic import BaseModel, validator
|
||||
|
||||
from langflow.utils import validate
|
||||
from langchain.agents.tools import Tool
|
||||
|
||||
|
||||
class Function(BaseModel):
|
||||
code: str
|
||||
function: Optional[Callable] = None
|
||||
imports: Optional[str] = None
|
||||
|
||||
# Eval code and store the function
|
||||
|
|
@ -24,14 +25,17 @@ class Function(BaseModel):
|
|||
|
||||
return v
|
||||
|
||||
def get_function(self):
|
||||
"""Get the function"""
|
||||
function_name = validate.extract_function_name(self.code)
|
||||
|
||||
return validate.create_function(self.code, function_name)
|
||||
|
||||
|
||||
class PythonFunction(Function):
|
||||
class PythonFunctionTool(Function, Tool):
|
||||
"""Python function"""
|
||||
|
||||
name: str = "Custom Tool"
|
||||
description: str
|
||||
code: str
|
||||
|
||||
def ___init__(self, name: str, description: str, code: str):
|
||||
self.name = name
|
||||
self.description = description
|
||||
self.code = code
|
||||
self.func = get_function(self.code)
|
||||
super().__init__(name=name, description=description, func=self.func)
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import base64
|
|||
import json
|
||||
import os
|
||||
from io import BytesIO
|
||||
import re
|
||||
|
||||
import yaml
|
||||
from langchain.base_language import BaseLanguageModel
|
||||
|
|
@ -48,3 +49,8 @@ def try_setting_streaming_options(langchain_object, websocket):
|
|||
llm.streaming = True
|
||||
|
||||
return langchain_object
|
||||
|
||||
|
||||
def extract_input_variables_from_prompt(prompt: str) -> list[str]:
|
||||
"""Extract input variables from prompt."""
|
||||
return re.findall(r"{(.*?)}", prompt)
|
||||
|
|
|
|||
|
|
@ -1,9 +1,7 @@
|
|||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
|
||||
from langflow.api.chat import router as chat_router
|
||||
from langflow.api.endpoints import router as endpoints_router
|
||||
from langflow.api.validate import router as validate_router
|
||||
from langflow.api import router
|
||||
|
||||
|
||||
def create_app():
|
||||
|
|
@ -14,6 +12,10 @@ def create_app():
|
|||
"*",
|
||||
]
|
||||
|
||||
@app.get("/health")
|
||||
def get_health():
|
||||
return {"status": "OK"}
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=origins,
|
||||
|
|
@ -22,9 +24,7 @@ def create_app():
|
|||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
app.include_router(endpoints_router)
|
||||
app.include_router(validate_router)
|
||||
app.include_router(chat_router)
|
||||
app.include_router(router)
|
||||
return app
|
||||
|
||||
|
||||
|
|
|
|||
0
src/backend/langflow/processing/__init__.py
Normal file
0
src/backend/langflow/processing/__init__.py
Normal file
55
src/backend/langflow/processing/base.py
Normal file
55
src/backend/langflow/processing/base.py
Normal file
|
|
@ -0,0 +1,55 @@
|
|||
from langflow.api.v1.callback import (
|
||||
AsyncStreamingLLMCallbackHandler,
|
||||
StreamingLLMCallbackHandler,
|
||||
)
|
||||
from langflow.processing.process import fix_memory_inputs, format_actions
|
||||
from langflow.utils.logger import logger
|
||||
|
||||
|
||||
async def get_result_and_steps(langchain_object, message: str, **kwargs):
|
||||
"""Get result and thought from extracted json"""
|
||||
|
||||
try:
|
||||
if hasattr(langchain_object, "verbose"):
|
||||
langchain_object.verbose = True
|
||||
chat_input = None
|
||||
memory_key = ""
|
||||
if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
|
||||
memory_key = langchain_object.memory.memory_key
|
||||
|
||||
if hasattr(langchain_object, "input_keys"):
|
||||
for key in langchain_object.input_keys:
|
||||
if key not in [memory_key, "chat_history"]:
|
||||
chat_input = {key: message}
|
||||
else:
|
||||
chat_input = message # type: ignore
|
||||
|
||||
if hasattr(langchain_object, "return_intermediate_steps"):
|
||||
# https://github.com/hwchase17/langchain/issues/2068
|
||||
# Deactivating until we have a frontend solution
|
||||
# to display intermediate steps
|
||||
langchain_object.return_intermediate_steps = True
|
||||
|
||||
fix_memory_inputs(langchain_object)
|
||||
try:
|
||||
async_callbacks = [AsyncStreamingLLMCallbackHandler(**kwargs)]
|
||||
output = await langchain_object.acall(chat_input, callbacks=async_callbacks)
|
||||
except Exception as exc:
|
||||
# make the error message more informative
|
||||
logger.debug(f"Error: {str(exc)}")
|
||||
sync_callbacks = [StreamingLLMCallbackHandler(**kwargs)]
|
||||
output = langchain_object(chat_input, callbacks=sync_callbacks)
|
||||
|
||||
intermediate_steps = (
|
||||
output.get("intermediate_steps", []) if isinstance(output, dict) else []
|
||||
)
|
||||
|
||||
result = (
|
||||
output.get(langchain_object.output_keys[0])
|
||||
if isinstance(output, dict)
|
||||
else output
|
||||
)
|
||||
thought = format_actions(intermediate_steps) if intermediate_steps else ""
|
||||
except Exception as exc:
|
||||
raise ValueError(f"Error: {str(exc)}") from exc
|
||||
return result, thought
|
||||
172
src/backend/langflow/processing/process.py
Normal file
172
src/backend/langflow/processing/process.py
Normal file
|
|
@ -0,0 +1,172 @@
|
|||
import contextlib
|
||||
import io
|
||||
from langchain.schema import AgentAction
|
||||
import json
|
||||
from langflow.interface.run import (
|
||||
build_langchain_object_with_caching,
|
||||
get_memory_key,
|
||||
update_memory_keys,
|
||||
)
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.graph import Graph
|
||||
|
||||
|
||||
from typing import Any, Dict, List, Tuple
|
||||
|
||||
|
||||
def fix_memory_inputs(langchain_object):
|
||||
"""
|
||||
Given a LangChain object, this function checks if it has a memory attribute and if that memory key exists in the
|
||||
object's input variables. If so, it does nothing. Otherwise, it gets a possible new memory key using the
|
||||
get_memory_key function and updates the memory keys using the update_memory_keys function.
|
||||
"""
|
||||
if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
|
||||
try:
|
||||
if langchain_object.memory.memory_key in langchain_object.input_variables:
|
||||
return
|
||||
except AttributeError:
|
||||
input_variables = (
|
||||
langchain_object.prompt.input_variables
|
||||
if hasattr(langchain_object, "prompt")
|
||||
else langchain_object.input_keys
|
||||
)
|
||||
if langchain_object.memory.memory_key in input_variables:
|
||||
return
|
||||
|
||||
possible_new_mem_key = get_memory_key(langchain_object)
|
||||
if possible_new_mem_key is not None:
|
||||
update_memory_keys(langchain_object, possible_new_mem_key)
|
||||
|
||||
|
||||
def format_actions(actions: List[Tuple[AgentAction, str]]) -> str:
|
||||
"""Format a list of (AgentAction, answer) tuples into a string."""
|
||||
output = []
|
||||
for action, answer in actions:
|
||||
log = action.log
|
||||
tool = action.tool
|
||||
tool_input = action.tool_input
|
||||
output.append(f"Log: {log}")
|
||||
if "Action" not in log and "Action Input" not in log:
|
||||
output.append(f"Tool: {tool}")
|
||||
output.append(f"Tool Input: {tool_input}")
|
||||
output.append(f"Answer: {answer}")
|
||||
output.append("") # Add a blank line
|
||||
return "\n".join(output)
|
||||
|
||||
|
||||
def get_result_and_thought(langchain_object, message: str):
|
||||
"""Get result and thought from extracted json"""
|
||||
try:
|
||||
if hasattr(langchain_object, "verbose"):
|
||||
langchain_object.verbose = True
|
||||
chat_input = None
|
||||
memory_key = ""
|
||||
if hasattr(langchain_object, "memory") and langchain_object.memory is not None:
|
||||
memory_key = langchain_object.memory.memory_key
|
||||
|
||||
if hasattr(langchain_object, "input_keys"):
|
||||
for key in langchain_object.input_keys:
|
||||
if key not in [memory_key, "chat_history"]:
|
||||
chat_input = {key: message}
|
||||
else:
|
||||
chat_input = message # type: ignore
|
||||
|
||||
if hasattr(langchain_object, "return_intermediate_steps"):
|
||||
# https://github.com/hwchase17/langchain/issues/2068
|
||||
# Deactivating until we have a frontend solution
|
||||
# to display intermediate steps
|
||||
langchain_object.return_intermediate_steps = False
|
||||
|
||||
fix_memory_inputs(langchain_object)
|
||||
|
||||
with io.StringIO() as output_buffer, contextlib.redirect_stdout(output_buffer):
|
||||
try:
|
||||
# if hasattr(langchain_object, "acall"):
|
||||
# output = await langchain_object.acall(chat_input)
|
||||
# else:
|
||||
output = langchain_object(chat_input)
|
||||
except ValueError as exc:
|
||||
# make the error message more informative
|
||||
logger.debug(f"Error: {str(exc)}")
|
||||
output = langchain_object.run(chat_input)
|
||||
|
||||
intermediate_steps = (
|
||||
output.get("intermediate_steps", []) if isinstance(output, dict) else []
|
||||
)
|
||||
|
||||
result = (
|
||||
output.get(langchain_object.output_keys[0])
|
||||
if isinstance(output, dict)
|
||||
else output
|
||||
)
|
||||
if intermediate_steps:
|
||||
thought = format_actions(intermediate_steps)
|
||||
else:
|
||||
thought = output_buffer.getvalue()
|
||||
|
||||
except Exception as exc:
|
||||
raise ValueError(f"Error: {str(exc)}") from exc
|
||||
return result, thought
|
||||
|
||||
|
||||
def load_or_build_langchain_object(data_graph, is_first_message=False):
|
||||
"""
|
||||
Load langchain object from cache if it exists, otherwise build it.
|
||||
"""
|
||||
if is_first_message:
|
||||
build_langchain_object_with_caching.clear_cache()
|
||||
return build_langchain_object_with_caching(data_graph)
|
||||
|
||||
|
||||
def process_graph_cached(data_graph: Dict[str, Any], message: str):
|
||||
"""
|
||||
Process graph by extracting input variables and replacing ZeroShotPrompt
|
||||
with PromptTemplate,then run the graph and return the result and thought.
|
||||
"""
|
||||
# Load langchain object
|
||||
is_first_message = len(data_graph.get("chatHistory", [])) == 0
|
||||
langchain_object = load_or_build_langchain_object(data_graph, is_first_message)
|
||||
logger.debug("Loaded langchain object")
|
||||
|
||||
if langchain_object is None:
|
||||
# Raise user facing error
|
||||
raise ValueError(
|
||||
"There was an error loading the langchain_object. Please, check all the nodes and try again."
|
||||
)
|
||||
|
||||
# Generate result and thought
|
||||
logger.debug("Generating result and thought")
|
||||
result, thought = get_result_and_thought(langchain_object, message)
|
||||
logger.debug("Generated result and thought")
|
||||
return {"result": str(result), "thought": thought.strip()}
|
||||
|
||||
|
||||
def load_flow_from_json(path: str, build=True):
|
||||
"""Load flow from json file"""
|
||||
# This is done to avoid circular imports
|
||||
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
flow_graph = json.load(f)
|
||||
data_graph = flow_graph["data"]
|
||||
nodes = data_graph["nodes"]
|
||||
# Substitute ZeroShotPrompt with PromptTemplate
|
||||
# nodes = replace_zero_shot_prompt_with_prompt_template(nodes)
|
||||
# Add input variables
|
||||
# nodes = payload.extract_input_variables(nodes)
|
||||
|
||||
# Nodes, edges and root node
|
||||
edges = data_graph["edges"]
|
||||
graph = Graph(nodes, edges)
|
||||
if build:
|
||||
langchain_object = graph.build()
|
||||
if hasattr(langchain_object, "verbose"):
|
||||
langchain_object.verbose = True
|
||||
|
||||
if hasattr(langchain_object, "return_intermediate_steps"):
|
||||
# https://github.com/hwchase17/langchain/issues/2068
|
||||
# Deactivating until we have a frontend solution
|
||||
# to display intermediate steps
|
||||
langchain_object.return_intermediate_steps = False
|
||||
fix_memory_inputs(langchain_object)
|
||||
return langchain_object
|
||||
return graph
|
||||
|
|
@ -146,7 +146,7 @@ class CSVAgentNode(FrontendNode):
|
|||
),
|
||||
],
|
||||
)
|
||||
description: str = """Construct a json agent from a CSV and tools."""
|
||||
description: str = """Construct a CSV agent from a CSV and tools."""
|
||||
base_classes: list[str] = ["AgentExecutor"]
|
||||
|
||||
def to_dict(self):
|
||||
|
|
@ -194,7 +194,7 @@ class InitializeAgentNode(FrontendNode):
|
|||
),
|
||||
],
|
||||
)
|
||||
description: str = """Construct a json agent from an LLM and tools."""
|
||||
description: str = """Construct a zero shot agent from an LLM and tools."""
|
||||
base_classes: list[str] = ["AgentExecutor", "function"]
|
||||
|
||||
def to_dict(self):
|
||||
|
|
|
|||
|
|
@ -117,14 +117,30 @@ class FrontendNode(BaseModel):
|
|||
) -> None:
|
||||
"""Handles specific field values for certain fields."""
|
||||
if key == "headers":
|
||||
field.value = """{'Authorization':
|
||||
'Bearer <token>'}"""
|
||||
if name == "OpenAI" and key == "model_name":
|
||||
field.options = constants.OPENAI_MODELS
|
||||
field.is_list = True
|
||||
elif name == "ChatOpenAI" and key == "model_name":
|
||||
field.options = constants.CHAT_OPENAI_MODELS
|
||||
field.value = """{'Authorization': 'Bearer <token>'}"""
|
||||
FrontendNode._handle_model_specific_field_values(field, key, name)
|
||||
FrontendNode._handle_api_key_specific_field_values(field, key, name)
|
||||
|
||||
@staticmethod
|
||||
def _handle_model_specific_field_values(
|
||||
field: TemplateField, key: str, name: Optional[str] = None
|
||||
) -> None:
|
||||
"""Handles specific field values related to models."""
|
||||
model_dict = {
|
||||
"OpenAI": constants.OPENAI_MODELS,
|
||||
"ChatOpenAI": constants.CHAT_OPENAI_MODELS,
|
||||
"Anthropic": constants.ANTHROPIC_MODELS,
|
||||
"ChatAnthropic": constants.ANTHROPIC_MODELS,
|
||||
}
|
||||
if name in model_dict and key == "model_name":
|
||||
field.options = model_dict[name]
|
||||
field.is_list = True
|
||||
|
||||
@staticmethod
|
||||
def _handle_api_key_specific_field_values(
|
||||
field: TemplateField, key: str, name: Optional[str] = None
|
||||
) -> None:
|
||||
"""Handles specific field values related to API keys."""
|
||||
if "api_key" in key and "OpenAI" in str(name):
|
||||
field.display_name = "OpenAI API Key"
|
||||
field.required = False
|
||||
|
|
|
|||
|
|
@ -12,6 +12,18 @@ class LLMFrontendNode(FrontendNode):
|
|||
field.name.title().replace("Openai", "OpenAI").replace("_", " ")
|
||||
).replace("Api", "API")
|
||||
|
||||
@staticmethod
|
||||
def format_azure_field(field: TemplateField):
|
||||
if field.name == "model_name":
|
||||
field.show = False # Azure uses deployment_name instead of model_name.
|
||||
if field.name == "openai_api_type":
|
||||
field.show = False
|
||||
field.password = False
|
||||
field.value = "azure"
|
||||
if field.name == "openai_api_version":
|
||||
field.password = False
|
||||
field.value = "2023-03-15-preview"
|
||||
|
||||
@staticmethod
|
||||
def format_field(field: TemplateField, name: Optional[str] = None) -> None:
|
||||
display_names_dict = {
|
||||
|
|
@ -43,8 +55,16 @@ class LLMFrontendNode(FrontendNode):
|
|||
field.field_type = "code"
|
||||
field.advanced = True
|
||||
field.show = True
|
||||
elif field.name in ["model_name", "temperature", "model_file", "model_type"]:
|
||||
elif field.name in [
|
||||
"model_name",
|
||||
"temperature",
|
||||
"model_file",
|
||||
"model_type",
|
||||
"deployment_name",
|
||||
]:
|
||||
field.advanced = False
|
||||
field.show = True
|
||||
|
||||
LLMFrontendNode.format_openai_field(field)
|
||||
if "azure" in name.lower():
|
||||
LLMFrontendNode.format_azure_field(field)
|
||||
|
|
|
|||
|
|
@ -59,11 +59,33 @@ class ToolNode(FrontendNode):
|
|||
return super().to_dict()
|
||||
|
||||
|
||||
class PythonFunctionNode(FrontendNode):
|
||||
name: str = "PythonFunction"
|
||||
class PythonFunctionToolNode(FrontendNode):
|
||||
name: str = "PythonFunctionTool"
|
||||
template: Template = Template(
|
||||
type_name="python_function",
|
||||
type_name="PythonFunctionTool",
|
||||
fields=[
|
||||
TemplateField(
|
||||
field_type="str",
|
||||
required=True,
|
||||
placeholder="",
|
||||
is_list=False,
|
||||
show=True,
|
||||
multiline=False,
|
||||
value="",
|
||||
name="name",
|
||||
advanced=False,
|
||||
),
|
||||
TemplateField(
|
||||
field_type="str",
|
||||
required=True,
|
||||
placeholder="",
|
||||
is_list=False,
|
||||
show=True,
|
||||
multiline=False,
|
||||
value="",
|
||||
name="description",
|
||||
advanced=False,
|
||||
),
|
||||
TemplateField(
|
||||
field_type="code",
|
||||
required=True,
|
||||
|
|
@ -73,11 +95,11 @@ class PythonFunctionNode(FrontendNode):
|
|||
value=DEFAULT_PYTHON_FUNCTION,
|
||||
name="code",
|
||||
advanced=False,
|
||||
)
|
||||
),
|
||||
],
|
||||
)
|
||||
description: str = "Python function to be executed."
|
||||
base_classes: list[str] = ["function"]
|
||||
base_classes: list[str] = ["Tool"]
|
||||
|
||||
def to_dict(self):
|
||||
return super().to_dict()
|
||||
|
|
|
|||
|
|
@ -7,6 +7,20 @@ OPENAI_MODELS = [
|
|||
]
|
||||
CHAT_OPENAI_MODELS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"]
|
||||
|
||||
ANTHROPIC_MODELS = [
|
||||
"claude-v1", # largest model, ideal for a wide range of more complex tasks.
|
||||
"claude-v1-100k", # An enhanced version of claude-v1 with a 100,000 token (roughly 75,000 word) context window.
|
||||
"claude-instant-v1", # A smaller model with far lower latency, sampling at roughly 40 words/sec!
|
||||
"claude-instant-v1-100k", # Like claude-instant-v1 with a 100,000 token context window but retains its performance.
|
||||
# Specific sub-versions of the above models:
|
||||
"claude-v1.3", # Vs claude-v1.2: better instruction-following, code, and non-English dialogue and writing.
|
||||
"claude-v1.3-100k", # An enhanced version of claude-v1.3 with a 100,000 token (roughly 75,000 word) context window.
|
||||
"claude-v1.2", # Vs claude-v1.1: small adv in general helpfulness, instruction following, coding, and other tasks.
|
||||
"claude-v1.0", # An earlier version of claude-v1.
|
||||
"claude-instant-v1.1", # Latest version of claude-instant-v1. Better than claude-instant-v1.0 at most tasks.
|
||||
"claude-instant-v1.1-100k", # Version of claude-instant-v1.1 with a 100K token context window.
|
||||
"claude-instant-v1.0", # An earlier version of claude-instant-v1.
|
||||
]
|
||||
|
||||
DEFAULT_PYTHON_FUNCTION = """
|
||||
def python_function(text: str) -> str:
|
||||
|
|
|
|||
|
|
@ -302,7 +302,9 @@ def format_dict(d, name: Optional[str] = None):
|
|||
elif name == "ChatOpenAI" and key == "model_name":
|
||||
value["options"] = constants.CHAT_OPENAI_MODELS
|
||||
value["list"] = True
|
||||
|
||||
elif (name == "Anthropic" or name == "ChatAnthropic") and key == "model_name":
|
||||
value["options"] = constants.ANTHROPIC_MODELS
|
||||
value["list"] = True
|
||||
return d
|
||||
|
||||
|
||||
|
|
|
|||
467
src/frontend/package-lock.json
generated
467
src/frontend/package-lock.json
generated
|
|
@ -13,6 +13,7 @@
|
|||
"@headlessui/react": "^1.7.10",
|
||||
"@heroicons/react": "^2.0.15",
|
||||
"@mui/material": "^5.11.9",
|
||||
"@radix-ui/react-tooltip": "^1.0.6",
|
||||
"@tabler/icons-react": "^2.18.0",
|
||||
"@tailwindcss/forms": "^0.5.3",
|
||||
"@tailwindcss/line-clamp": "^0.4.4",
|
||||
|
|
@ -20,7 +21,10 @@
|
|||
"ansi-to-html": "^0.7.2",
|
||||
"axios": "^1.3.2",
|
||||
"base64-js": "^1.5.1",
|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
|
|
@ -37,6 +41,8 @@
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|||
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||||
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|
||||
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||||
|
|
@ -911,6 +917,18 @@
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|||
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|
||||
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||||
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|
@ -1274,6 +1292,407 @@
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|
||||
"peerDependencies": {
|
||||
"@types/react": "*",
|
||||
"react": "^16.8 || ^17.0 || ^18.0"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"@types/react": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/@radix-ui/react-use-rect": {
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/@radix-ui/react-use-rect/-/react-use-rect-1.0.1.tgz",
|
||||
"integrity": "sha512-Cq5DLuSiuYVKNU8orzJMbl15TXilTnJKUCltMVQg53BQOF1/C5toAaGrowkgksdBQ9H+SRL23g0HDmg9tvmxXw==",
|
||||
"dependencies": {
|
||||
"@babel/runtime": "^7.13.10",
|
||||
"@radix-ui/rect": "1.0.1"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@types/react": "*",
|
||||
"react": "^16.8 || ^17.0 || ^18.0"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"@types/react": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/@radix-ui/react-use-size": {
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/@radix-ui/react-use-size/-/react-use-size-1.0.1.tgz",
|
||||
"integrity": "sha512-ibay+VqrgcaI6veAojjofPATwledXiSmX+C0KrBk/xgpX9rBzPV3OsfwlhQdUOFbh+LKQorLYT+xTXW9V8yd0g==",
|
||||
"dependencies": {
|
||||
"@babel/runtime": "^7.13.10",
|
||||
"@radix-ui/react-use-layout-effect": "1.0.1"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@types/react": "*",
|
||||
"react": "^16.8 || ^17.0 || ^18.0"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"@types/react": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/@radix-ui/react-visually-hidden": {
|
||||
"version": "1.0.3",
|
||||
"resolved": "https://registry.npmjs.org/@radix-ui/react-visually-hidden/-/react-visually-hidden-1.0.3.tgz",
|
||||
"integrity": "sha512-D4w41yN5YRKtu464TLnByKzMDG/JlMPHtfZgQAu9v6mNakUqGUI9vUrfQKz8NK41VMm/xbZbh76NUTVtIYqOMA==",
|
||||
"dependencies": {
|
||||
"@babel/runtime": "^7.13.10",
|
||||
"@radix-ui/react-primitive": "1.0.3"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@types/react": "*",
|
||||
"@types/react-dom": "*",
|
||||
"react": "^16.8 || ^17.0 || ^18.0",
|
||||
"react-dom": "^16.8 || ^17.0 || ^18.0"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"@types/react": {
|
||||
"optional": true
|
||||
},
|
||||
"@types/react-dom": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/@radix-ui/rect": {
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/@radix-ui/rect/-/rect-1.0.1.tgz",
|
||||
"integrity": "sha512-fyrgCaedtvMg9NK3en0pnOYJdtfwxUcNolezkNPUsoX57X8oQk+NkqcvzHXD2uKNij6GXmWU9NDru2IWjrO4BQ==",
|
||||
"dependencies": {
|
||||
"@babel/runtime": "^7.13.10"
|
||||
}
|
||||
},
|
||||
"node_modules/@reactflow/background": {
|
||||
"version": "11.1.7",
|
||||
"resolved": "https://registry.npmjs.org/@reactflow/background/-/background-11.1.7.tgz",
|
||||
|
|
@ -2422,7 +2841,7 @@
|
|||
"version": "18.2.4",
|
||||
"resolved": "https://registry.npmjs.org/@types/react-dom/-/react-dom-18.2.4.tgz",
|
||||
"integrity": "sha512-G2mHoTMTL4yoydITgOGwWdWMVd8sNgyEP85xVmMKAPUBwQWm9wBPQUmvbeF4V3WBY1P7mmL4BkjQ0SqUpf1snw==",
|
||||
"dev": true,
|
||||
"devOptional": true,
|
||||
"dependencies": {
|
||||
"@types/react": "*"
|
||||
}
|
||||
|
|
@ -2947,6 +3366,25 @@
|
|||
"node": ">= 6"
|
||||
}
|
||||
},
|
||||
"node_modules/class-variance-authority": {
|
||||
"version": "0.6.0",
|
||||
"resolved": "https://registry.npmjs.org/class-variance-authority/-/class-variance-authority-0.6.0.tgz",
|
||||
"integrity": "sha512-qdRDgfjx3GRb9fpwpSvn+YaidnT7IUJNe4wt5/SWwM+PmUwJUhQRk/8zAyNro0PmVfmen2635UboTjIBXXxy5A==",
|
||||
"dependencies": {
|
||||
"clsx": "1.2.1"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://joebell.co.uk"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"typescript": ">= 4.5.5 < 6"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"typescript": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/classcat": {
|
||||
"version": "5.0.4",
|
||||
"resolved": "https://registry.npmjs.org/classcat/-/classcat-5.0.4.tgz",
|
||||
|
|
@ -4728,6 +5166,14 @@
|
|||
"yallist": "^3.0.2"
|
||||
}
|
||||
},
|
||||
"node_modules/lucide-react": {
|
||||
"version": "0.233.0",
|
||||
"resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-0.233.0.tgz",
|
||||
"integrity": "sha512-r0jMHF0vPDq2wBbZ0B3rtIcBjDyWDKpHu+vAjD2OHn2WLUr3HN5IHovtO0EMgQXuSI7YrMZbjsEZWC2uBHr8nQ==",
|
||||
"peerDependencies": {
|
||||
"react": "^16.5.1 || ^17.0.0 || ^18.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/lz-string": {
|
||||
"version": "1.4.4",
|
||||
"resolved": "https://registry.npmjs.org/lz-string/-/lz-string-1.4.4.tgz",
|
||||
|
|
@ -7001,6 +7447,15 @@
|
|||
"resolved": "https://registry.npmjs.org/symbol-tree/-/symbol-tree-3.2.4.tgz",
|
||||
"integrity": "sha512-9QNk5KwDF+Bvz+PyObkmSYjI5ksVUYtjW7AU22r2NKcfLJcXp96hkDWU3+XndOsUb+AQ9QhfzfCT2O+CNWT5Tw=="
|
||||
},
|
||||
"node_modules/tailwind-merge": {
|
||||
"version": "1.13.0",
|
||||
"resolved": "https://registry.npmjs.org/tailwind-merge/-/tailwind-merge-1.13.0.tgz",
|
||||
"integrity": "sha512-mUTmDbcU+IhOvJ0c42eLQ/nRkvolTqfpVaVQRSxfJAv9TabS6Y2zW/1wKpKLdKzyL3Gh8j6NTLl6MWNmvOM6kA==",
|
||||
"funding": {
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/dcastil"
|
||||
}
|
||||
},
|
||||
"node_modules/tailwindcss": {
|
||||
"version": "3.3.2",
|
||||
"resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.3.2.tgz",
|
||||
|
|
@ -7038,6 +7493,14 @@
|
|||
"node": ">=14.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/tailwindcss-animate": {
|
||||
"version": "1.0.5",
|
||||
"resolved": "https://registry.npmjs.org/tailwindcss-animate/-/tailwindcss-animate-1.0.5.tgz",
|
||||
"integrity": "sha512-UU3qrOJ4lFQABY+MVADmBm+0KW3xZyhMdRvejwtXqYOL7YjHYxmuREFAZdmVG5LPe5E9CAst846SLC4j5I3dcw==",
|
||||
"peerDependencies": {
|
||||
"tailwindcss": ">=3.0.0 || insiders"
|
||||
}
|
||||
},
|
||||
"node_modules/terser": {
|
||||
"version": "5.16.3",
|
||||
"resolved": "https://registry.npmjs.org/terser/-/terser-5.16.3.tgz",
|
||||
|
|
@ -7156,7 +7619,7 @@
|
|||
"version": "5.0.4",
|
||||
"resolved": "https://registry.npmjs.org/typescript/-/typescript-5.0.4.tgz",
|
||||
"integrity": "sha512-cW9T5W9xY37cc+jfEnaUvX91foxtHkza3Nw3wkoF4sSlKn0MONdkdEndig/qPBWXNkmplh3NzayQzCiHM4/hqw==",
|
||||
"dev": true,
|
||||
"devOptional": true,
|
||||
"bin": {
|
||||
"tsc": "bin/tsc",
|
||||
"tsserver": "bin/tsserver"
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@
|
|||
"@headlessui/react": "^1.7.10",
|
||||
"@heroicons/react": "^2.0.15",
|
||||
"@mui/material": "^5.11.9",
|
||||
"@radix-ui/react-tooltip": "^1.0.6",
|
||||
"@tabler/icons-react": "^2.18.0",
|
||||
"@tailwindcss/forms": "^0.5.3",
|
||||
"@tailwindcss/line-clamp": "^0.4.4",
|
||||
|
|
@ -15,7 +16,10 @@
|
|||
"ansi-to-html": "^0.7.2",
|
||||
"axios": "^1.3.2",
|
||||
"base64-js": "^1.5.1",
|
||||
"class-variance-authority": "^0.6.0",
|
||||
"clsx": "^1.2.1",
|
||||
"lodash": "^4.17.21",
|
||||
"lucide-react": "^0.233.0",
|
||||
"react": "^18.2.0",
|
||||
"react-ace": "^10.1.0",
|
||||
"react-cookie": "^4.1.1",
|
||||
|
|
@ -32,6 +36,8 @@
|
|||
"rehype-mathjax": "^4.0.2",
|
||||
"remark-gfm": "^3.0.1",
|
||||
"remark-math": "^5.1.1",
|
||||
"tailwind-merge": "^1.13.0",
|
||||
"tailwindcss-animate": "^1.0.5",
|
||||
"uuid": "^9.0.0",
|
||||
"vite-plugin-svgr": "^3.2.0",
|
||||
"web-vitals": "^2.1.4"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,11 @@
|
|||
import { Handle, Position, useUpdateNodeInternals } from "reactflow";
|
||||
import Tooltip from "../../../../components/TooltipComponent";
|
||||
import { classNames, isValidConnection } from "../../../../utils";
|
||||
import {
|
||||
classNames,
|
||||
groupByFamily,
|
||||
isValidConnection,
|
||||
toFirstUpperCase,
|
||||
} from "../../../../utils";
|
||||
import { useContext, useEffect, useRef, useState } from "react";
|
||||
import InputComponent from "../../../../components/inputComponent";
|
||||
import ToggleComponent from "../../../../components/toggleComponent";
|
||||
|
|
@ -15,6 +20,10 @@ import InputFileComponent from "../../../../components/inputFileComponent";
|
|||
import { TabsContext } from "../../../../contexts/tabsContext";
|
||||
import IntComponent from "../../../../components/intComponent";
|
||||
import PromptAreaComponent from "../../../../components/promptComponent";
|
||||
import { nodeNames, nodeIcons } from "../../../../utils";
|
||||
import React from "react";
|
||||
import { nodeColors } from "../../../../utils";
|
||||
import ShadTooltip from "../../../../components/ShadTooltipComponent";
|
||||
|
||||
export default function ParameterComponent({
|
||||
left,
|
||||
|
|
@ -28,6 +37,7 @@ export default function ParameterComponent({
|
|||
required = false,
|
||||
}: ParameterComponentType) {
|
||||
const ref = useRef(null);
|
||||
const refHtml = useRef(null);
|
||||
const updateNodeInternals = useUpdateNodeInternals();
|
||||
const [position, setPosition] = useState(0);
|
||||
useEffect(() => {
|
||||
|
|
@ -48,6 +58,48 @@ export default function ParameterComponent({
|
|||
let disabled =
|
||||
reactFlowInstance?.getEdges().some((e) => e.targetHandle === id) ?? false;
|
||||
const { save } = useContext(TabsContext);
|
||||
const [myData, setMyData] = useState(useContext(typesContext).data);
|
||||
|
||||
useEffect(() => {
|
||||
const groupedObj = groupByFamily(myData, tooltipTitle);
|
||||
|
||||
refHtml.current = groupedObj.map((item, i) => (
|
||||
<span
|
||||
key={item}
|
||||
className={classNames(
|
||||
i > 0 ? "items-center flex mt-3" : "items-center flex"
|
||||
)}
|
||||
>
|
||||
<div
|
||||
className="h-5 w-5"
|
||||
style={{
|
||||
color: nodeColors[item.family],
|
||||
}}
|
||||
>
|
||||
{React.createElement(nodeIcons[item.family])}
|
||||
</div>
|
||||
<span className="ps-2 text-gray-950">
|
||||
{nodeNames[item.family] ?? ""}{" "}
|
||||
<span className={classNames(left ? "hidden" : "")}>
|
||||
{" "}
|
||||
-
|
||||
{item.type.split(", ").length > 2
|
||||
? item.type.split(", ").map((el, i) => (
|
||||
<>
|
||||
<span key={el}>
|
||||
{i == item.type.split(", ").length - 1
|
||||
? el
|
||||
: (el += `, `)}
|
||||
</span>
|
||||
{i % 2 == 0 && i > 0 && <br></br>}
|
||||
</>
|
||||
))
|
||||
: item.type}
|
||||
</span>
|
||||
</span>
|
||||
</span>
|
||||
));
|
||||
}, [tooltipTitle]);
|
||||
|
||||
return (
|
||||
<div
|
||||
|
|
@ -69,7 +121,11 @@ export default function ParameterComponent({
|
|||
type === "int") ? (
|
||||
<></>
|
||||
) : (
|
||||
<Tooltip title={tooltipTitle + (required ? " (required)" : "")}>
|
||||
<ShadTooltip
|
||||
delayDuration={0}
|
||||
content={refHtml.current}
|
||||
side={left ? "left" : "right"}
|
||||
>
|
||||
<Handle
|
||||
type={left ? "target" : "source"}
|
||||
position={left ? Position.Left : Position.Right}
|
||||
|
|
@ -86,7 +142,7 @@ export default function ParameterComponent({
|
|||
top: position,
|
||||
}}
|
||||
></Handle>
|
||||
</Tooltip>
|
||||
</ShadTooltip>
|
||||
)}
|
||||
|
||||
{left === true &&
|
||||
|
|
|
|||
|
|
@ -28,8 +28,8 @@ import NodeModal from "../../modals/NodeModal";
|
|||
import { useCallback } from "react";
|
||||
import { TabsContext } from "../../contexts/tabsContext";
|
||||
import { debounce } from "../../utils";
|
||||
import TooltipReact from "../../components/ReactTooltipComponent";
|
||||
import Tooltip from "../../components/TooltipComponent";
|
||||
import ShadTooltip from "../../components/ShadTooltipComponent";
|
||||
export default function GenericNode({
|
||||
data,
|
||||
selected,
|
||||
|
|
@ -115,14 +115,9 @@ export default function GenericNode({
|
|||
}}
|
||||
/>
|
||||
<div className="ml-2 truncate">
|
||||
<TooltipReact
|
||||
delayShow={1000}
|
||||
selector={`node-selector-${data.type}`}
|
||||
htmlContent={data.type}
|
||||
position="top"
|
||||
>
|
||||
<ShadTooltip delayDuration={1500} content={data.type}>
|
||||
<div className="ml-2 truncate">{data.type}</div>
|
||||
</TooltipReact>
|
||||
</ShadTooltip>
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex gap-3">
|
||||
|
|
@ -253,11 +248,7 @@ export default function GenericNode({
|
|||
: toTitleCase(t)
|
||||
}
|
||||
name={t}
|
||||
tooltipTitle={
|
||||
"Type: " +
|
||||
data.node.template[t].type +
|
||||
(data.node.template[t].list ? " list" : "")
|
||||
}
|
||||
tooltipTitle={data.node.template[t].type}
|
||||
required={data.node.template[t].required}
|
||||
id={data.node.template[t].type + "|" + t + "|" + data.id}
|
||||
left={true}
|
||||
|
|
@ -283,7 +274,7 @@ export default function GenericNode({
|
|||
data={data}
|
||||
color={nodeColors[types[data.type]] ?? nodeColors.unknown}
|
||||
title={data.type}
|
||||
tooltipTitle={`Type: ${data.node.base_classes.join(" | ")}`}
|
||||
tooltipTitle={`${data.node.base_classes.join("\n")}`}
|
||||
id={[data.type, data.id, ...data.node.base_classes].join("|")}
|
||||
type={data.node.base_classes.join("|")}
|
||||
left={false}
|
||||
|
|
|
|||
|
|
@ -37,13 +37,15 @@ const TooltipReact: FC<TooltipProps> = ({
|
|||
id={selector}
|
||||
content={content}
|
||||
className={classNames(
|
||||
"!bg-white !text-xs !font-normal !text-gray-700 !shadow-md !opacity-100 z-20",
|
||||
"!bg-white !text-xs !font-normal !text-gray-700 !shadow-md !opacity-100 z-[9999]",
|
||||
className
|
||||
)}
|
||||
place={position}
|
||||
clickable={clickable}
|
||||
isOpen={disabled ? false : undefined}
|
||||
delayShow={delayShow}
|
||||
positionStrategy="absolute"
|
||||
float={true}
|
||||
>
|
||||
{htmlContent && htmlContent}
|
||||
</ReactTooltip>
|
||||
|
|
|
|||
25
src/frontend/src/components/ShadTooltipComponent/index.tsx
Normal file
25
src/frontend/src/components/ShadTooltipComponent/index.tsx
Normal file
|
|
@ -0,0 +1,25 @@
|
|||
import {
|
||||
Tooltip,
|
||||
TooltipContent,
|
||||
TooltipProvider,
|
||||
TooltipTrigger,
|
||||
} from "../ui/tooltip";
|
||||
|
||||
const ShadTooltip = (props) => {
|
||||
return (
|
||||
<TooltipProvider>
|
||||
<Tooltip delayDuration={props.delayDuration}>
|
||||
<TooltipTrigger asChild>{props.children}</TooltipTrigger>
|
||||
<TooltipContent
|
||||
side={props.side}
|
||||
avoidCollisions={false}
|
||||
sticky="always"
|
||||
>
|
||||
{props.content}
|
||||
</TooltipContent>
|
||||
</Tooltip>
|
||||
</TooltipProvider>
|
||||
);
|
||||
};
|
||||
|
||||
export default ShadTooltip;
|
||||
|
|
@ -25,7 +25,9 @@ export default function Dropdown({
|
|||
<>
|
||||
<div className="relative mt-1 w-full">
|
||||
<Listbox.Button className="relative w-full cursor-default rounded-md border border-gray-300 bg-white dark:bg-gray-900 py-2 pl-3 pr-10 text-left shadow-sm focus:border-indigo-500 focus:outline-none focus:ring-1 focus:ring-indigo-500 sm:text-sm">
|
||||
<span className="block truncate w-full">{internalValue}</span>
|
||||
<span className="block truncate w-full dark:text-gray-300">
|
||||
{internalValue}
|
||||
</span>
|
||||
<span className="pointer-events-none absolute inset-y-0 right-0 flex items-center pr-2">
|
||||
<ChevronUpDownIcon
|
||||
className="h-5 w-5 text-gray-400"
|
||||
|
|
|
|||
29
src/frontend/src/components/ui/tooltip.tsx
Normal file
29
src/frontend/src/components/ui/tooltip.tsx
Normal file
|
|
@ -0,0 +1,29 @@
|
|||
"use client";
|
||||
|
||||
import * as React from "react";
|
||||
import * as TooltipPrimitive from "@radix-ui/react-tooltip";
|
||||
import { cn } from "../../utils";
|
||||
|
||||
const TooltipProvider = TooltipPrimitive.Provider;
|
||||
|
||||
const Tooltip = TooltipPrimitive.Root;
|
||||
|
||||
const TooltipTrigger = TooltipPrimitive.Trigger;
|
||||
|
||||
const TooltipContent = React.forwardRef<
|
||||
React.ElementRef<typeof TooltipPrimitive.Content>,
|
||||
React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content>
|
||||
>(({ className, sideOffset = 4, ...props }, ref) => (
|
||||
<TooltipPrimitive.Content
|
||||
ref={ref}
|
||||
sideOffset={sideOffset}
|
||||
className={cn(
|
||||
"z-50 overflow-hidden rounded-md border bg-popover px-3 py-1.5 text-sm text-popover-foreground shadow-md animate-in fade-in-50 data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1",
|
||||
className
|
||||
)}
|
||||
{...props}
|
||||
/>
|
||||
));
|
||||
TooltipContent.displayName = TooltipPrimitive.Content.displayName;
|
||||
|
||||
export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider };
|
||||
|
|
@ -13,13 +13,16 @@ const initialValue = {
|
|||
export const darkContext = createContext<darkContextType>(initialValue);
|
||||
|
||||
export function DarkProvider({ children }) {
|
||||
const [dark, setDark] = useState(false);
|
||||
const [dark, setDark] = useState(
|
||||
JSON.parse(window.localStorage.getItem("isDark")) ?? false
|
||||
);
|
||||
useEffect(() => {
|
||||
if (dark) {
|
||||
document.getElementById("body").classList.add("dark");
|
||||
} else {
|
||||
document.getElementById("body").classList.remove("dark");
|
||||
}
|
||||
window.localStorage.setItem("isDark", dark.toString());
|
||||
}, [dark]);
|
||||
return (
|
||||
<darkContext.Provider
|
||||
|
|
|
|||
|
|
@ -47,7 +47,7 @@ export const TabsContext = createContext<TabsContextType>(
|
|||
);
|
||||
|
||||
export function TabsProvider({ children }: { children: ReactNode }) {
|
||||
const { setNoticeData } = useContext(alertContext);
|
||||
const { setErrorData, setNoticeData } = useContext(alertContext);
|
||||
const [tabIndex, setTabIndex] = useState(0);
|
||||
const [flows, setFlows] = useState<Array<FlowType>>([]);
|
||||
const [id, setId] = useState(uuidv4());
|
||||
|
|
@ -95,25 +95,25 @@ export function TabsProvider({ children }: { children: ReactNode }) {
|
|||
edge.style = { stroke: "#555555" };
|
||||
});
|
||||
flow.data.nodes.forEach((node) => {
|
||||
if (Object.keys(templates[node.data.type]["template"]).length > 0) {
|
||||
node.data.node.base_classes =
|
||||
templates[node.data.type]["base_classes"];
|
||||
const template = templates[node.data.type];
|
||||
if (!template) {
|
||||
setErrorData({ title: `Unknown node type: ${node.data.type}` });
|
||||
return;
|
||||
}
|
||||
if (Object.keys(template["template"]).length > 0) {
|
||||
node.data.node.base_classes = template["base_classes"];
|
||||
flow.data.edges.forEach((edge) => {
|
||||
if (edge.source === node.id) {
|
||||
edge.sourceHandle = edge.sourceHandle
|
||||
.split("|")
|
||||
.slice(0, 2)
|
||||
.concat(templates[node.data.type]["base_classes"])
|
||||
.concat(template["base_classes"])
|
||||
.join("|");
|
||||
}
|
||||
});
|
||||
node.data.node.description =
|
||||
templates[node.data.type]["description"];
|
||||
node.data.node.description = template["description"];
|
||||
node.data.node.template = updateTemplate(
|
||||
templates[node.data.type][
|
||||
"template"
|
||||
] as unknown as APITemplateType,
|
||||
|
||||
template["template"] as unknown as APITemplateType,
|
||||
node.data.node.template as APITemplateType
|
||||
);
|
||||
}
|
||||
|
|
@ -316,21 +316,25 @@ export function TabsProvider({ children }: { children: ReactNode }) {
|
|||
edge.animated = edge.targetHandle.split("|")[0] === "Text";
|
||||
});
|
||||
data.nodes.forEach((node) => {
|
||||
if (Object.keys(templates[node.data.type]["template"]).length > 0) {
|
||||
node.data.node.base_classes =
|
||||
templates[node.data.type]["base_classes"];
|
||||
const template = templates[node.data.type];
|
||||
if (!template) {
|
||||
setErrorData({ title: `Unknown node type: ${node.data.type}` });
|
||||
return;
|
||||
}
|
||||
if (Object.keys(template["template"]).length > 0) {
|
||||
node.data.node.base_classes = template["base_classes"];
|
||||
flow.data.edges.forEach((edge) => {
|
||||
if (edge.source === node.id) {
|
||||
edge.sourceHandle = edge.sourceHandle
|
||||
.split("|")
|
||||
.slice(0, 2)
|
||||
.concat(templates[node.data.type]["base_classes"])
|
||||
.concat(template["base_classes"])
|
||||
.join("|");
|
||||
}
|
||||
});
|
||||
node.data.node.description = templates[node.data.type]["description"];
|
||||
node.data.node.description = template["description"];
|
||||
node.data.node.template = updateTemplate(
|
||||
templates[node.data.type]["template"] as unknown as APITemplateType,
|
||||
template["template"] as unknown as APITemplateType,
|
||||
node.data.node.template as APITemplateType
|
||||
);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -14,13 +14,13 @@ export async function sendAll(data: sendAllProps) {
|
|||
export async function checkCode(
|
||||
code: string
|
||||
): Promise<AxiosResponse<errorsTypeAPI>> {
|
||||
return await axios.post("/validate/code", { code });
|
||||
return await axios.post("api/v1/validate/code", { code });
|
||||
}
|
||||
|
||||
export async function checkPrompt(
|
||||
template: string
|
||||
): Promise<AxiosResponse<PromptTypeAPI>> {
|
||||
return await axios.post("/validate/prompt", { template });
|
||||
return await axios.post("api/v1/validate/prompt", { template });
|
||||
}
|
||||
|
||||
export async function getExamples(): Promise<FlowType[]> {
|
||||
|
|
|
|||
9
src/frontend/src/icons/Anthropic/anthropic.svg
Normal file
9
src/frontend/src/icons/Anthropic/anthropic.svg
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" viewBox="-170.333 113.047 600 67.4" width="600" height="67.4">
|
||||
<defs>
|
||||
<style type="text/css">
|
||||
.st0{fill:#1F1F1E;}
|
||||
</style>
|
||||
</defs>
|
||||
<path class="st0" d="M -23.533 126.747 L -1.633 126.747 L -1.633 179.347 L 12.367 179.347 L 12.367 126.747 L 34.267 126.747 L 34.267 114.147 L -23.533 114.147 L -23.533 126.747 Z M -48.133 159.747 L -77.433 114.147 L -93.233 114.147 L -93.233 179.247 L -79.733 179.247 L -79.733 133.647 L -50.433 179.347 L -34.633 179.347 L -34.633 114.247 L -48.133 114.247 L -48.133 159.747 Z M 90.067 140.147 L 59.367 140.147 L 59.367 114.147 L 45.367 114.147 L 45.367 179.247 L 59.367 179.247 L 59.367 152.647 L 90.067 152.647 L 90.067 179.247 L 104.067 179.247 L 104.067 114.147 L 90.067 114.147 L 90.067 140.147 Z M -144.333 114.147 L -170.333 179.247 L -155.833 179.247 L -150.533 165.547 L -123.333 165.547 L -118.033 179.247 L -103.533 179.247 L -129.533 114.147 L -144.333 114.147 Z M -145.833 153.547 L -136.933 130.647 L -128.033 153.547 L -145.833 153.547 Z M 219.667 113.047 C 200.867 113.047 187.567 127.047 187.567 146.847 C 187.567 166.447 200.867 180.447 219.667 180.447 C 238.367 180.447 251.567 166.447 251.567 146.847 C 251.567 127.047 238.367 113.047 219.667 113.047 Z M 219.667 167.447 C 208.667 167.447 201.967 159.647 201.967 146.847 C 201.967 133.947 208.667 126.047 219.667 126.047 C 230.567 126.047 237.167 133.847 237.167 146.847 C 237.167 159.547 230.567 167.447 219.667 167.447 Z M 414.767 157.447 C 412.367 163.747 407.467 167.447 400.867 167.447 C 389.867 167.447 383.167 159.647 383.167 146.847 C 383.167 133.947 389.867 126.047 400.867 126.047 C 407.467 126.047 412.267 129.647 414.767 136.047 L 429.567 136.047 C 425.967 122.047 415.067 113.047 400.867 113.047 C 382.067 113.047 368.767 127.047 368.767 146.847 C 368.767 166.447 382.067 180.447 400.867 180.447 C 415.067 180.447 425.967 171.347 429.667 157.447 L 414.767 157.447 Z M 325.867 114.147 L 351.867 179.247 L 366.067 179.247 L 340.067 114.147 L 325.867 114.147 Z M 296.367 114.147 L 264.567 114.147 L 264.567 179.247 L 278.567 179.247 L 278.567 155.647 L 296.467 155.647 C 311.267 155.647 320.267 147.847 320.267 134.847 C 320.267 121.947 311.167 114.147 296.367 114.147 Z M 295.767 143.147 L 278.567 143.147 L 278.567 126.747 L 295.767 126.747 C 302.667 126.747 306.267 129.547 306.267 134.947 C 306.267 140.347 302.667 143.147 295.767 143.147 Z M 176.867 134.047 C 176.867 121.747 167.867 114.247 153.067 114.247 L 121.267 114.247 L 121.267 179.347 L 135.267 179.347 L 135.267 153.847 L 150.767 153.847 L 164.767 179.347 L 180.167 179.347 L 164.667 151.947 C 172.367 148.847 176.867 142.647 176.867 134.047 Z M 135.167 126.747 L 152.367 126.747 C 159.267 126.747 162.867 129.247 162.867 134.047 C 162.867 138.747 159.267 141.347 152.367 141.347 L 135.167 141.347 L 135.167 126.747 Z"></path>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 2.9 KiB |
11
src/frontend/src/icons/Anthropic/anthropic_box.svg
Normal file
11
src/frontend/src/icons/Anthropic/anthropic_box.svg
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" viewBox="-126.9 247.9 207.161 212.728" width="207.161" height="212.728">
|
||||
<defs>
|
||||
<style type="text/css">
|
||||
.st0{fill:#1F1F1E;}
|
||||
</style>
|
||||
</defs>
|
||||
<path class="st0" d="M 19.9 260.5 L 41.8 260.5 L 41.8 313.1 L 55.8 313.1 L 55.8 260.5 L 77.7 260.5 L 77.7 247.9 L 19.9 247.9 L 19.9 260.5 Z M -4.7 293.5 L -34 247.9 L -49.8 247.9 L -49.8 313 L -36.3 313 L -36.3 267.4 L -7 313.1 L 8.8 313.1 L 8.8 248 L -4.7 248 L -4.7 293.5 Z M -100.9 247.9 L -126.9 313 L -112.4 313 L -107.1 299.3 L -79.9 299.3 L -74.6 313 L -60.1 313 L -86.1 247.9 L -100.9 247.9 Z M -102.4 287.3 L -93.5 264.4 L -84.6 287.3 L -102.4 287.3 Z"></path>
|
||||
<path class="st0" d="M 38.246 437.628 C 35.846 443.928 30.946 447.628 24.346 447.628 C 13.346 447.628 6.646 439.828 6.646 427.028 C 6.646 414.128 13.346 406.228 24.346 406.228 C 30.946 406.228 35.746 409.828 38.246 416.228 L 53.046 416.228 C 49.446 402.228 38.546 393.228 24.346 393.228 C 5.546 393.228 -7.754 407.228 -7.754 427.028 C -7.754 446.628 5.546 460.628 24.346 460.628 C 38.546 460.628 49.446 451.528 53.146 437.628 L 38.246 437.628 Z M -50.654 394.328 L -24.654 459.428 L -10.454 459.428 L -36.454 394.328 L -50.654 394.328 Z M -80.154 394.328 L -111.954 394.328 L -111.954 459.428 L -97.954 459.428 L -97.954 435.828 L -80.054 435.828 C -65.254 435.828 -56.254 428.028 -56.254 415.028 C -56.254 402.128 -65.354 394.328 -80.154 394.328 Z M -80.754 423.328 L -97.954 423.328 L -97.954 406.928 L -80.754 406.928 C -73.854 406.928 -70.254 409.728 -70.254 415.128 C -70.254 420.528 -73.854 423.328 -80.754 423.328 Z"></path>
|
||||
<path class="st0" d="M -81.239 347.704 L -111.939 347.704 L -111.939 321.704 L -125.939 321.704 L -125.939 386.804 L -111.939 386.804 L -111.939 360.204 L -81.239 360.204 L -81.239 386.804 L -67.239 386.804 L -67.239 321.704 L -81.239 321.704 L -81.239 347.704 Z M 48.361 320.604 C 29.561 320.604 16.261 334.604 16.261 354.404 C 16.261 374.004 29.561 388.004 48.361 388.004 C 67.061 388.004 80.261 374.004 80.261 354.404 C 80.261 334.604 67.061 320.604 48.361 320.604 Z M 48.361 375.004 C 37.361 375.004 30.661 367.204 30.661 354.404 C 30.661 341.504 37.361 333.604 48.361 333.604 C 59.261 333.604 65.861 341.404 65.861 354.404 C 65.861 367.104 59.261 375.004 48.361 375.004 Z M 5.561 341.604 C 5.561 329.304 -3.439 321.804 -18.239 321.804 L -50.039 321.804 L -50.039 386.904 L -36.039 386.904 L -36.039 361.404 L -20.539 361.404 L -6.539 386.904 L 8.861 386.904 L -6.639 359.504 C 1.061 356.404 5.561 350.204 5.561 341.604 Z M -36.139 334.304 L -18.939 334.304 C -12.039 334.304 -8.439 336.804 -8.439 341.604 C -8.439 346.304 -12.039 348.904 -18.939 348.904 L -36.139 348.904 L -36.139 334.304 Z"></path>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 2.7 KiB |
9
src/frontend/src/icons/Anthropic/index.tsx
Normal file
9
src/frontend/src/icons/Anthropic/index.tsx
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
import React, { forwardRef } from "react";
|
||||
import { ReactComponent as AnthropicSVG } from "./anthropic_box.svg";
|
||||
|
||||
export const AnthropicIcon = forwardRef<
|
||||
SVGSVGElement,
|
||||
React.PropsWithChildren<{}>
|
||||
>((props, ref) => {
|
||||
return <AnthropicSVG ref={ref} {...props} />;
|
||||
});
|
||||
|
|
@ -2,22 +2,88 @@
|
|||
@tailwind components;
|
||||
@tailwind utilities;
|
||||
|
||||
@layer base {
|
||||
:root {
|
||||
--background: 0 0% 100%;
|
||||
--foreground: 222.2 47.4% 11.2%;
|
||||
--muted: 210 40% 96.1%;
|
||||
--muted-foreground: 215.4 16.3% 46.9%;
|
||||
--popover: 0 0% 100%;
|
||||
--popover-foreground: 222.2 47.4% 11.2%;
|
||||
--card: 0 0% 100%;
|
||||
--card-foreground: 222.2 47.4% 11.2%;
|
||||
--border: 214.3 31.8% 91.4%;
|
||||
--input: 214.3 31.8% 91.4%;
|
||||
--primary: 222.2 47.4% 11.2%;
|
||||
--primary-foreground: 210 40% 98%;
|
||||
--secondary: 210 40% 96.1%;
|
||||
--secondary-foreground: 222.2 47.4% 11.2%;
|
||||
--accent: 210 40% 96.1%;
|
||||
--accent-foreground: 222.2 47.4% 11.2%;
|
||||
--destructive: 0 100% 50%;
|
||||
--destructive-foreground: 210 40% 98%;
|
||||
--ring: 215 20.2% 65.1%;
|
||||
--radius: 0.5rem;
|
||||
}
|
||||
|
||||
.dark {
|
||||
-background: 224 71% 4%;
|
||||
-foreground: 213 31% 91%;
|
||||
-muted: 223 47% 11%;
|
||||
-muted-foreground: 215.4 16.3% 56.9%;
|
||||
-popover: 224 71% 4%;
|
||||
-popover-foreground: 215 20.2% 65.1%;
|
||||
-card: 224 71% 4%;
|
||||
-card-foreground: 213 31% 91%;
|
||||
-border: 216 34% 17%;
|
||||
-input: 216 34% 17%;
|
||||
-primary: 210 40% 98%;
|
||||
-primary-foreground: 222.2 47.4% 1.2%;
|
||||
-secondary: 222.2 47.4% 11.2%;
|
||||
-secondary-foreground: 210 40% 98%;
|
||||
-accent: 216 34% 17%;
|
||||
-accent-foreground: 210 40% 98%;
|
||||
-destructive: 0 63% 31%;
|
||||
-destructive-foreground: 210 40% 98%;
|
||||
-ring: 216 34% 17%;
|
||||
-radius: 0.5rem;
|
||||
}
|
||||
}
|
||||
|
||||
@layer base {
|
||||
* {
|
||||
@apply border-border;
|
||||
}
|
||||
body {
|
||||
@apply bg-background text-foreground;
|
||||
font-feature-settings: "rlig" 1, "calt" 1;
|
||||
}
|
||||
}
|
||||
|
||||
@layer base {
|
||||
* {
|
||||
@apply border-border;
|
||||
}
|
||||
body {
|
||||
@apply bg-background text-foreground;
|
||||
font-feature-settings: "rlig" 1, "calt" 1;
|
||||
}
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Roboto", "Oxygen",
|
||||
"Ubuntu", "Cantarell", "Fira Sans", "Droid Sans", "Helvetica Neue",
|
||||
sans-serif;
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
margin: 0;
|
||||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Roboto", "Oxygen",
|
||||
"Ubuntu", "Cantarell", "Fira Sans", "Droid Sans", "Helvetica Neue",
|
||||
sans-serif;
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
}
|
||||
|
||||
code {
|
||||
font-family: source-code-pro, Menlo, Monaco, Consolas, "Courier New",
|
||||
monospace;
|
||||
font-family: source-code-pro, Menlo, Monaco, Consolas, "Courier New",
|
||||
monospace;
|
||||
}
|
||||
|
||||
/* The style below sets the cursor property of the element with the class .react-flow__pane to the default cursor.
|
||||
The cursor: default; property value restores the browser's default cursor style for the targeted element. By applying this style, the element will no longer have a custom cursor appearance such as "grab" or any other custom cursor defined elsewhere in the application. Instead, it will revert to the default cursor style determined by the browser, typically an arrow-shaped cursor. */
|
||||
.react-flow__pane {
|
||||
cursor: default;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -182,10 +182,10 @@ export default function ChatModal({
|
|||
try {
|
||||
const urlWs =
|
||||
process.env.NODE_ENV === "development"
|
||||
? `ws://localhost:7860/chat/${id.current}`
|
||||
? `ws://localhost:7860/api/v1/chat/${id.current}`
|
||||
: `${window.location.protocol === "https:" ? "wss" : "ws"}://${
|
||||
window.location.host
|
||||
}/chat/${id.current}`;
|
||||
}api/v1/chat/${id.current}`;
|
||||
const newWs = new WebSocket(urlWs);
|
||||
newWs.onopen = () => {
|
||||
console.log("WebSocket connection established!");
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import { DisclosureComponentType } from "../../../../types/components";
|
|||
export default function DisclosureComponent({
|
||||
button: { title, Icon, buttons = [] },
|
||||
children,
|
||||
openDisc,
|
||||
}: DisclosureComponentType) {
|
||||
return (
|
||||
<Disclosure as="div" key={title}>
|
||||
|
|
@ -27,14 +28,14 @@ export default function DisclosureComponent({
|
|||
<div>
|
||||
<ChevronRightIcon
|
||||
className={`${
|
||||
open ? "rotate-90 transform" : ""
|
||||
open || openDisc ? "rotate-90 transform" : ""
|
||||
} h-4 w-4 text-gray-800 dark:text-white`}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</Disclosure.Button>
|
||||
</div>
|
||||
<Disclosure.Panel as="div" className="-mt-px">
|
||||
<Disclosure.Panel as="div" className="-mt-px" static={openDisc}>
|
||||
{children}
|
||||
</Disclosure.Panel>
|
||||
</>
|
||||
|
|
|
|||
|
|
@ -9,12 +9,13 @@ import {
|
|||
import { useContext, useEffect, useState, useRef } from "react";
|
||||
import { typesContext } from "../../../../contexts/typesContext";
|
||||
import { APIClassType, APIObjectType } from "../../../../types/api";
|
||||
import TooltipReact from "../../../../components/ReactTooltipComponent";
|
||||
import { MagnifyingGlassIcon } from "@heroicons/react/24/outline";
|
||||
import ShadTooltip from "../../../../components/ShadTooltipComponent";
|
||||
|
||||
export default function ExtraSidebar() {
|
||||
const { data } = useContext(typesContext);
|
||||
const [dataFilter, setFilterData] = useState(data);
|
||||
const [search, setSearch] = useState("");
|
||||
|
||||
function onDragStart(
|
||||
event: React.DragEvent<any>,
|
||||
|
|
@ -58,6 +59,7 @@ export default function ExtraSidebar() {
|
|||
className="dark:text-white focus:outline-none block w-full rounded-md py-1.5 ps-3 pr-9 text-gray-900 shadow-sm ring-1 ring-inset ring-gray-300 placeholder:text-gray-400 sm:text-sm sm:leading-6 dark:ring-0 dark:bg-[#2d3747] dark:focus:outline-none"
|
||||
onChange={(e) => {
|
||||
handleSearchInput(e.target.value);
|
||||
setSearch(e.target.value);
|
||||
}}
|
||||
/>
|
||||
<div className="absolute inset-y-0 right-0 flex py-1.5 pr-3 items-center">
|
||||
|
|
@ -71,6 +73,7 @@ export default function ExtraSidebar() {
|
|||
.map((d: keyof APIObjectType, i) =>
|
||||
Object.keys(dataFilter[d]).length > 0 ? (
|
||||
<DisclosureComponent
|
||||
openDisc={search.length == 0 ? false : true}
|
||||
key={i}
|
||||
button={{
|
||||
title: nodeNames[d] ?? nodeNames.unknown,
|
||||
|
|
@ -81,12 +84,10 @@ export default function ExtraSidebar() {
|
|||
{Object.keys(dataFilter[d])
|
||||
.sort()
|
||||
.map((t: string, k) => (
|
||||
<TooltipReact
|
||||
selector={t}
|
||||
htmlContent={t}
|
||||
position="right"
|
||||
delayShow={1500}
|
||||
key={k}
|
||||
<ShadTooltip
|
||||
content={t}
|
||||
delayDuration={1500}
|
||||
side="right"
|
||||
>
|
||||
<div key={k} data-tooltip-id={t}>
|
||||
<div
|
||||
|
|
@ -118,7 +119,7 @@ export default function ExtraSidebar() {
|
|||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</TooltipReact>
|
||||
</ShadTooltip>
|
||||
))}
|
||||
</div>
|
||||
</DisclosureComponent>
|
||||
|
|
|
|||
|
|
@ -116,7 +116,7 @@ export default function TabsManagerComponent() {
|
|||
</button>
|
||||
</div>
|
||||
</div>
|
||||
<div className="w-full h-full">
|
||||
<div className="w-full h-full dark:bg-gray-800">
|
||||
<ReactFlowProvider>
|
||||
{flows[tabIndex] ? (
|
||||
<FlowPage flow={flows[tabIndex]}></FlowPage>
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import {
|
|||
ReactElement,
|
||||
ReactFragment,
|
||||
ReactNode,
|
||||
SVGProps,
|
||||
} from "react";
|
||||
import { NodeDataType } from "../flow/index";
|
||||
export type InputComponentType = {
|
||||
|
|
@ -56,6 +57,7 @@ export type FileComponentType = {
|
|||
|
||||
export type DisclosureComponentType = {
|
||||
children: ReactNode;
|
||||
openDisc: boolean;
|
||||
button: {
|
||||
title: string;
|
||||
Icon: ForwardRefExoticComponent<React.SVGProps<SVGSVGElement>>;
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ import { FlowType, NodeType } from "./types/flow";
|
|||
import { APITemplateType, TemplateVariableType } from "./types/api";
|
||||
import _ from "lodash";
|
||||
import { ChromaIcon } from "./icons/ChromaIcon";
|
||||
import { AnthropicIcon } from "./icons/Anthropic";
|
||||
import { AirbyteIcon } from "./icons/Airbyte";
|
||||
import { AzIcon } from "./icons/AzLogo";
|
||||
import { BingIcon } from "./icons/Bing";
|
||||
|
|
@ -47,6 +48,12 @@ import { WolframIcon } from "./icons/Wolfram";
|
|||
import { WordIcon } from "./icons/Word";
|
||||
import { SerperIcon } from "./icons/Serper";
|
||||
import { v4 as uuidv4 } from "uuid";
|
||||
import { clsx, type ClassValue } from "clsx";
|
||||
import { twMerge } from "tailwind-merge";
|
||||
|
||||
export function cn(...inputs: ClassValue[]) {
|
||||
return twMerge(clsx(inputs));
|
||||
}
|
||||
|
||||
export function classNames(...classes: Array<string>) {
|
||||
return classes.filter(Boolean).join(" ");
|
||||
|
|
@ -153,6 +160,8 @@ export const nodeIcons: {
|
|||
AirbyteJSONLoader: AirbyteIcon,
|
||||
// SerpAPIWrapper: SerperIcon,
|
||||
// AZLyricsLoader: AzIcon,
|
||||
Anthropic: AnthropicIcon,
|
||||
ChatAnthropic: AnthropicIcon,
|
||||
BingSearchAPIWrapper: BingIcon,
|
||||
BingSearchRun: BingIcon,
|
||||
Cohere: CohereIcon,
|
||||
|
|
@ -632,3 +641,58 @@ export function updateIds(newFlow, getNodeId) {
|
|||
e.targetHandle;
|
||||
});
|
||||
}
|
||||
|
||||
export function groupByFamily(data, baseClasses) {
|
||||
let arrOfParent: string[] = [];
|
||||
let arrOfType: { family: string; type: string }[] = [];
|
||||
|
||||
Object.keys(data).map((d) => {
|
||||
Object.keys(data[d]).map((n) => {
|
||||
if (
|
||||
data[d][n].base_classes.some((r) => baseClasses.split("\n").includes(r))
|
||||
) {
|
||||
arrOfParent.push(d);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
let uniq = arrOfParent.filter(
|
||||
(item, index) => arrOfParent.indexOf(item) === index
|
||||
);
|
||||
|
||||
Object.keys(data).map((d) => {
|
||||
Object.keys(data[d]).map((n) => {
|
||||
baseClasses.split("\n").forEach((tol) => {
|
||||
data[d][n].base_classes.forEach((data) => {
|
||||
if (tol == data) {
|
||||
arrOfType.push({
|
||||
family: d,
|
||||
type: data,
|
||||
});
|
||||
}
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
let groupedBy = arrOfType.filter((object, index, self) => {
|
||||
const foundIndex = self.findIndex(
|
||||
(o) => o.family === object.family && o.type === object.type
|
||||
);
|
||||
return foundIndex === index;
|
||||
});
|
||||
|
||||
let groupedObj = groupedBy.reduce((result, item) => {
|
||||
const existingGroup = result.find((group) => group.family === item.family);
|
||||
|
||||
if (existingGroup) {
|
||||
existingGroup.type += `, ${item.type}`;
|
||||
} else {
|
||||
result.push({ family: item.family, type: item.type });
|
||||
}
|
||||
|
||||
return result;
|
||||
}, []);
|
||||
|
||||
return groupedObj;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,11 +1,83 @@
|
|||
/** @type {import('tailwindcss').Config} */
|
||||
const { fontFamily } = require("tailwindcss/defaultTheme")
|
||||
|
||||
import plugin from "tailwindcss/plugin";
|
||||
module.exports = {
|
||||
content: ["./index.html", "./src/**/*.{js,ts,tsx,jsx}"],
|
||||
darkMode: "class",
|
||||
important: true,
|
||||
theme: {
|
||||
container: {
|
||||
center: true,
|
||||
padding: "2rem",
|
||||
screens: {
|
||||
"2xl": "1400px",
|
||||
},
|
||||
},
|
||||
extend: {
|
||||
colors: {
|
||||
border: "hsl(var(--border))",
|
||||
input: "hsl(var(--input))",
|
||||
ring: "hsl(var(--ring))",
|
||||
background: "hsl(var(--background))",
|
||||
foreground: "hsl(var(--foreground))",
|
||||
primary: {
|
||||
DEFAULT: "hsl(var(--primary))",
|
||||
foreground: "hsl(var(--primary-foreground))",
|
||||
},
|
||||
secondary: {
|
||||
DEFAULT: "hsl(var(--secondary))",
|
||||
foreground: "hsl(var(--secondary-foreground))",
|
||||
},
|
||||
destructive: {
|
||||
DEFAULT: "hsl(var(--destructive))",
|
||||
foreground: "hsl(var(--destructive-foreground))",
|
||||
},
|
||||
muted: {
|
||||
DEFAULT: "hsl(var(--muted))",
|
||||
foreground: "hsl(var(--muted-foreground))",
|
||||
},
|
||||
accent: {
|
||||
DEFAULT: "hsl(var(--accent))",
|
||||
foreground: "hsl(var(--accent-foreground))",
|
||||
},
|
||||
popover: {
|
||||
DEFAULT: "hsl(var(--popover))",
|
||||
foreground: "hsl(var(--popover-foreground))",
|
||||
},
|
||||
card: {
|
||||
DEFAULT: "hsl(var(--card))",
|
||||
foreground: "hsl(var(--card-foreground))",
|
||||
},
|
||||
},
|
||||
borderRadius: {
|
||||
lg: `var(--radius)`,
|
||||
md: `calc(var(--radius) - 2px)`,
|
||||
sm: "calc(var(--radius) - 4px)",
|
||||
},
|
||||
fontFamily: {
|
||||
sans: ["var(--font-sans)", ...fontFamily.sans],
|
||||
},
|
||||
keyframes: {
|
||||
"accordion-down": {
|
||||
from: { height: 0 },
|
||||
to: { height: "var(--radix-accordion-content-height)" },
|
||||
},
|
||||
"accordion-up": {
|
||||
from: { height: "var(--radix-accordion-content-height)" },
|
||||
to: { height: 0 },
|
||||
},
|
||||
pulseGreen: {
|
||||
"0%": { boxShadow: "0 0 0 0 rgba(72, 187, 120, 0.7)" },
|
||||
"100%": { boxShadow: "0 0 0 10px rgba(72, 187, 120, 0)" },
|
||||
},
|
||||
},
|
||||
animation: {
|
||||
"accordion-down": "accordion-down 0.2s ease-out",
|
||||
"accordion-up": "accordion-up 0.2s ease-out",
|
||||
"pulse-green": "pulseGreen 1s linear",
|
||||
'spin-once': 'spin 1s linear 0.7'
|
||||
},
|
||||
borderColor: {
|
||||
"red-outline": "rgba(255, 0, 0, 0.8)",
|
||||
"green-outline": "rgba(72, 187, 120, 0.7)",
|
||||
|
|
@ -14,17 +86,6 @@ module.exports = {
|
|||
"red-outline": "0 0 5px rgba(255, 0, 0, 0.5)",
|
||||
"green-outline": "0 0 5px rgba(72, 187, 120, 0.7)",
|
||||
},
|
||||
|
||||
animation: {
|
||||
"pulse-green": "pulseGreen 1s linear",
|
||||
'spin-once': 'spin 1s linear 0.7'
|
||||
},
|
||||
keyframes: {
|
||||
pulseGreen: {
|
||||
"0%": { boxShadow: "0 0 0 0 rgba(72, 187, 120, 0.7)" },
|
||||
"100%": { boxShadow: "0 0 0 10px rgba(72, 187, 120, 0)" },
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
plugins: [
|
||||
|
|
@ -96,4 +157,4 @@ module.exports = {
|
|||
}),
|
||||
require("@tailwindcss/typography"),
|
||||
],
|
||||
};
|
||||
};
|
||||
|
|
@ -19,7 +19,8 @@
|
|||
"isolatedModules": true,
|
||||
"noEmit": true,
|
||||
"jsx": "react-jsx",
|
||||
"noImplicitAny": false
|
||||
"noImplicitAny": false,
|
||||
"baseUrl": "."
|
||||
},
|
||||
"include": [
|
||||
"src"
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ const apiRoutes = [
|
|||
];
|
||||
|
||||
// Use environment variable to determine the target.
|
||||
const target = process.env.VITE_PROXY_TARGET || "http://127.0.0.1:7860";
|
||||
const target = process.env.VITE_PROXY_TARGET || "http://127.0.0.1:7860/api/v1";
|
||||
|
||||
const proxyTargets = apiRoutes.reduce((proxyObj, route) => {
|
||||
proxyObj[route] = {
|
||||
|
|
|
|||
|
|
@ -197,7 +197,7 @@
|
|||
"y": 136.29836646158452
|
||||
},
|
||||
"data": {
|
||||
"type": "PythonFunction",
|
||||
"type": "PythonFunctionTool",
|
||||
"node": {
|
||||
"template": {
|
||||
"code": {
|
||||
|
|
@ -210,6 +210,26 @@
|
|||
"type": "str",
|
||||
"list": false
|
||||
},
|
||||
"description": {
|
||||
"required": true,
|
||||
"placeholder": "",
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "My description",
|
||||
"name": "description",
|
||||
"type": "str",
|
||||
"list": false
|
||||
},
|
||||
"name": {
|
||||
"required": true,
|
||||
"placeholder": "",
|
||||
"show": true,
|
||||
"multiline": true,
|
||||
"value": "My Tool",
|
||||
"name": "name",
|
||||
"type": "str",
|
||||
"list": false
|
||||
},
|
||||
"_type": "python_function"
|
||||
},
|
||||
"description": "Python function to be executed.",
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from langflow.settings import settings
|
|||
# check that all agents are in settings.agents
|
||||
# are in json_response["agents"]
|
||||
def test_agents_settings(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
agents = json_response["agents"]
|
||||
|
|
@ -13,7 +13,7 @@ def test_agents_settings(client: TestClient):
|
|||
|
||||
|
||||
def test_zero_shot_agent(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
agents = json_response["agents"]
|
||||
|
|
@ -52,7 +52,7 @@ def test_zero_shot_agent(client: TestClient):
|
|||
|
||||
|
||||
def test_json_agent(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
agents = json_response["agents"]
|
||||
|
|
@ -87,7 +87,7 @@ def test_json_agent(client: TestClient):
|
|||
|
||||
|
||||
def test_csv_agent(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
agents = json_response["agents"]
|
||||
|
|
@ -126,7 +126,7 @@ def test_csv_agent(client: TestClient):
|
|||
|
||||
|
||||
def test_initialize_agent(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
agents = json_response["agents"]
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
import json
|
||||
from langflow.graph import Graph
|
||||
from langflow.processing.process import load_or_build_langchain_object
|
||||
|
||||
import pytest
|
||||
from langflow.interface.run import (
|
||||
build_graph,
|
||||
build_langchain_object_with_caching,
|
||||
load_or_build_langchain_object,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -62,7 +62,7 @@ def test_build_langchain_object_with_caching(basic_data_graph):
|
|||
|
||||
# Test build_graph
|
||||
def test_build_graph(basic_data_graph):
|
||||
graph = build_graph(basic_data_graph)
|
||||
graph = Graph.from_payload(basic_data_graph)
|
||||
assert graph is not None
|
||||
assert len(graph.nodes) == len(basic_data_graph["nodes"])
|
||||
assert len(graph.edges) == len(basic_data_graph["edges"])
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from langflow.settings import settings
|
|||
|
||||
|
||||
def test_chains_settings(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
@ -12,7 +12,7 @@ def test_chains_settings(client: TestClient):
|
|||
|
||||
# Test the ConversationChain object
|
||||
def test_conversation_chain(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
@ -94,7 +94,7 @@ def test_conversation_chain(client: TestClient):
|
|||
|
||||
|
||||
def test_llm_chain(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
@ -152,7 +152,7 @@ def test_llm_chain(client: TestClient):
|
|||
|
||||
|
||||
def test_llm_checker_chain(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
@ -228,7 +228,7 @@ def test_llm_checker_chain(client: TestClient):
|
|||
|
||||
|
||||
def test_llm_math_chain(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
@ -306,7 +306,7 @@ def test_llm_math_chain(client: TestClient):
|
|||
|
||||
|
||||
def test_series_character_chain(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
@ -368,7 +368,7 @@ def test_series_character_chain(client: TestClient):
|
|||
|
||||
|
||||
def test_mid_journey_prompt_chain(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
@ -407,7 +407,7 @@ def test_mid_journey_prompt_chain(client: TestClient):
|
|||
|
||||
|
||||
def test_time_travel_guide_chain(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
chains = json_response["chains"]
|
||||
|
|
|
|||
|
|
@ -1,16 +1,23 @@
|
|||
# Test this:
|
||||
from langflow.interface.importing.utils import get_function
|
||||
import pytest
|
||||
from langflow.interface.tools.custom import PythonFunction
|
||||
from langflow.interface.tools.custom import PythonFunctionTool
|
||||
from langflow.utils import constants
|
||||
|
||||
|
||||
def test_python_function():
|
||||
"""Test Python function"""
|
||||
func = PythonFunction(code=constants.DEFAULT_PYTHON_FUNCTION)
|
||||
assert func.get_function()("text") == "text"
|
||||
code = constants.DEFAULT_PYTHON_FUNCTION
|
||||
func = get_function(code)
|
||||
func = PythonFunctionTool(name="Test", description="Testing", code=code, func=func)
|
||||
assert func("text") == "text"
|
||||
# the tool decorator should raise an error if
|
||||
# the function is not str -> str
|
||||
|
||||
# This raises ValidationError
|
||||
with pytest.raises(SyntaxError):
|
||||
func = PythonFunction(code=pytest.CODE_WITH_SYNTAX_ERROR)
|
||||
code = pytest.CODE_WITH_SYNTAX_ERROR
|
||||
func = get_function(code)
|
||||
func = PythonFunctionTool(
|
||||
name="Test", description="Testing", code=code, func=func
|
||||
)
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from langflow.interface.tools.constants import CUSTOM_TOOLS
|
|||
|
||||
|
||||
def test_get_all(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
# We need to test the custom nodes
|
||||
|
|
@ -21,7 +21,7 @@ import math
|
|||
def square(x):
|
||||
return x ** 2
|
||||
"""
|
||||
response1 = client.post("/validate/code", json={"code": code1})
|
||||
response1 = client.post("api/v1/validate/code", json={"code": code1})
|
||||
assert response1.status_code == 200
|
||||
assert response1.json() == {"imports": {"errors": []}, "function": {"errors": []}}
|
||||
|
||||
|
|
@ -32,7 +32,7 @@ import non_existent_module
|
|||
def square(x):
|
||||
return x ** 2
|
||||
"""
|
||||
response2 = client.post("/validate/code", json={"code": code2})
|
||||
response2 = client.post("api/v1/validate/code", json={"code": code2})
|
||||
assert response2.status_code == 200
|
||||
assert response2.json() == {
|
||||
"imports": {"errors": ["No module named 'non_existent_module'"]},
|
||||
|
|
@ -46,7 +46,7 @@ import math
|
|||
def square(x)
|
||||
return x ** 2
|
||||
"""
|
||||
response3 = client.post("/validate/code", json={"code": code3})
|
||||
response3 = client.post("api/v1/validate/code", json={"code": code3})
|
||||
assert response3.status_code == 200
|
||||
assert response3.json() == {
|
||||
"imports": {"errors": []},
|
||||
|
|
@ -54,11 +54,11 @@ def square(x)
|
|||
}
|
||||
|
||||
# Test case with invalid JSON payload
|
||||
response4 = client.post("/validate/code", json={"invalid_key": code1})
|
||||
response4 = client.post("api/v1/validate/code", json={"invalid_key": code1})
|
||||
assert response4.status_code == 422
|
||||
|
||||
# Test case with an empty code string
|
||||
response5 = client.post("/validate/code", json={"code": ""})
|
||||
response5 = client.post("api/v1/validate/code", json={"code": ""})
|
||||
assert response5.status_code == 200
|
||||
assert response5.json() == {"imports": {"errors": []}, "function": {"errors": []}}
|
||||
|
||||
|
|
@ -69,7 +69,7 @@ import math
|
|||
def square(x)
|
||||
return x ** 2
|
||||
"""
|
||||
response6 = client.post("/validate/code", json={"code": code6})
|
||||
response6 = client.post("api/v1/validate/code", json={"code": code6})
|
||||
assert response6.status_code == 200
|
||||
assert response6.json() == {
|
||||
"imports": {"errors": []},
|
||||
|
|
@ -95,13 +95,13 @@ INVALID_PROMPT = "This is an invalid prompt without any input variable."
|
|||
|
||||
|
||||
def test_valid_prompt(client: TestClient):
|
||||
response = client.post("/validate/prompt", json={"template": VALID_PROMPT})
|
||||
response = client.post("api/v1/validate/prompt", json={"template": VALID_PROMPT})
|
||||
assert response.status_code == 200
|
||||
assert response.json() == {"input_variables": ["product"]}
|
||||
|
||||
|
||||
def test_invalid_prompt(client: TestClient):
|
||||
response = client.post("/validate/prompt", json={"template": INVALID_PROMPT})
|
||||
response = client.post("api/v1/validate/prompt", json={"template": INVALID_PROMPT})
|
||||
assert response.status_code == 200
|
||||
assert response.json() == {"input_variables": []}
|
||||
|
||||
|
|
@ -116,7 +116,7 @@ def test_invalid_prompt(client: TestClient):
|
|||
],
|
||||
)
|
||||
def test_various_prompts(client, prompt, expected_input_variables):
|
||||
response = client.post("/validate/prompt", json={"template": prompt})
|
||||
response = client.post("api/v1/validate/prompt", json={"template": prompt})
|
||||
assert response.status_code == 200
|
||||
assert response.json() == {
|
||||
"input_variables": expected_input_variables,
|
||||
|
|
|
|||
|
|
@ -1,22 +1,22 @@
|
|||
from typing import Type, Union
|
||||
from langflow.graph.edge.base import Edge
|
||||
from langflow.graph.node.base import Node
|
||||
from langflow.graph.vertex.base import Vertex
|
||||
|
||||
import pytest
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.llms.fake import FakeListLLM
|
||||
from langflow.graph import Graph
|
||||
from langflow.graph.node.types import (
|
||||
AgentNode,
|
||||
ChainNode,
|
||||
FileToolNode,
|
||||
LLMNode,
|
||||
PromptNode,
|
||||
ToolkitNode,
|
||||
ToolNode,
|
||||
WrapperNode,
|
||||
from langflow.graph.vertex.types import (
|
||||
AgentVertex,
|
||||
ChainVertex,
|
||||
FileToolVertex,
|
||||
LLMVertex,
|
||||
PromptVertex,
|
||||
ToolkitVertex,
|
||||
ToolVertex,
|
||||
WrapperVertex,
|
||||
)
|
||||
from langflow.interface.run import get_result_and_thought
|
||||
from langflow.processing.process import get_result_and_thought
|
||||
from langflow.utils.payload import get_root_node
|
||||
|
||||
# Test cases for the graph module
|
||||
|
|
@ -25,7 +25,7 @@ from langflow.utils.payload import get_root_node
|
|||
# BASIC_EXAMPLE_PATH, COMPLEX_EXAMPLE_PATH, OPENAPI_EXAMPLE_PATH
|
||||
|
||||
|
||||
def get_node_by_type(graph, node_type: Type[Node]) -> Union[Node, None]:
|
||||
def get_node_by_type(graph, node_type: Type[Vertex]) -> Union[Vertex, None]:
|
||||
"""Get a node by type"""
|
||||
return next((node for node in graph.nodes if isinstance(node, node_type)), None)
|
||||
|
||||
|
|
@ -35,7 +35,7 @@ def test_graph_structure(basic_graph):
|
|||
assert len(basic_graph.nodes) > 0
|
||||
assert len(basic_graph.edges) > 0
|
||||
for node in basic_graph.nodes:
|
||||
assert isinstance(node, Node)
|
||||
assert isinstance(node, Vertex)
|
||||
for edge in basic_graph.edges:
|
||||
assert isinstance(edge, Edge)
|
||||
assert edge.source in basic_graph.nodes
|
||||
|
|
@ -158,14 +158,16 @@ def test_get_node_neighbors_complex(complex_graph):
|
|||
tool_neighbors = complex_graph.get_nodes_with_target(tool)
|
||||
assert tool_neighbors is not None
|
||||
# Check if there is a PythonFunction in the tool's neighbors
|
||||
assert any("PythonFunction" in neighbor.data["type"] for neighbor in tool_neighbors)
|
||||
assert any(
|
||||
"PythonFunctionTool" in neighbor.data["type"] for neighbor in tool_neighbors
|
||||
)
|
||||
|
||||
|
||||
def test_get_node(basic_graph):
|
||||
"""Test getting a single node"""
|
||||
node_id = basic_graph.nodes[0].id
|
||||
node = basic_graph.get_node(node_id)
|
||||
assert isinstance(node, Node)
|
||||
assert isinstance(node, Vertex)
|
||||
assert node.id == node_id
|
||||
|
||||
|
||||
|
|
@ -174,7 +176,7 @@ def test_build_nodes(basic_graph):
|
|||
|
||||
assert len(basic_graph.nodes) == len(basic_graph._nodes)
|
||||
for node in basic_graph.nodes:
|
||||
assert isinstance(node, Node)
|
||||
assert isinstance(node, Vertex)
|
||||
|
||||
|
||||
def test_build_edges(basic_graph):
|
||||
|
|
@ -182,8 +184,8 @@ def test_build_edges(basic_graph):
|
|||
assert len(basic_graph.edges) == len(basic_graph._edges)
|
||||
for edge in basic_graph.edges:
|
||||
assert isinstance(edge, Edge)
|
||||
assert isinstance(edge.source, Node)
|
||||
assert isinstance(edge.target, Node)
|
||||
assert isinstance(edge.source, Vertex)
|
||||
assert isinstance(edge.target, Vertex)
|
||||
|
||||
|
||||
def test_get_root_node(basic_graph, complex_graph):
|
||||
|
|
@ -191,13 +193,13 @@ def test_get_root_node(basic_graph, complex_graph):
|
|||
assert isinstance(basic_graph, Graph)
|
||||
root = get_root_node(basic_graph)
|
||||
assert root is not None
|
||||
assert isinstance(root, Node)
|
||||
assert isinstance(root, Vertex)
|
||||
assert root.data["type"] == "TimeTravelGuideChain"
|
||||
# For complex example, the root node is a ZeroShotAgent too
|
||||
assert isinstance(complex_graph, Graph)
|
||||
root = get_root_node(complex_graph)
|
||||
assert root is not None
|
||||
assert isinstance(root, Node)
|
||||
assert isinstance(root, Vertex)
|
||||
assert root.data["type"] == "ZeroShotAgent"
|
||||
|
||||
|
||||
|
|
@ -239,11 +241,10 @@ def test_build_params(basic_graph):
|
|||
assert "memory" in root.params
|
||||
|
||||
|
||||
def test_build(basic_graph, complex_graph, openapi_graph):
|
||||
def test_build(basic_graph, complex_graph):
|
||||
"""Test Node's build method"""
|
||||
assert_agent_was_built(basic_graph)
|
||||
assert_agent_was_built(complex_graph)
|
||||
assert_agent_was_built(openapi_graph)
|
||||
|
||||
|
||||
def assert_agent_was_built(graph):
|
||||
|
|
@ -257,14 +258,14 @@ def assert_agent_was_built(graph):
|
|||
|
||||
|
||||
def test_agent_node_build(complex_graph):
|
||||
agent_node = get_node_by_type(complex_graph, AgentNode)
|
||||
agent_node = get_node_by_type(complex_graph, AgentVertex)
|
||||
assert agent_node is not None
|
||||
built_object = agent_node.build()
|
||||
assert built_object is not None
|
||||
|
||||
|
||||
def test_tool_node_build(complex_graph):
|
||||
tool_node = get_node_by_type(complex_graph, ToolNode)
|
||||
tool_node = get_node_by_type(complex_graph, ToolVertex)
|
||||
assert tool_node is not None
|
||||
built_object = tool_node.build()
|
||||
assert built_object is not None
|
||||
|
|
@ -272,7 +273,7 @@ def test_tool_node_build(complex_graph):
|
|||
|
||||
|
||||
def test_chain_node_build(complex_graph):
|
||||
chain_node = get_node_by_type(complex_graph, ChainNode)
|
||||
chain_node = get_node_by_type(complex_graph, ChainVertex)
|
||||
assert chain_node is not None
|
||||
built_object = chain_node.build()
|
||||
assert built_object is not None
|
||||
|
|
@ -280,7 +281,7 @@ def test_chain_node_build(complex_graph):
|
|||
|
||||
|
||||
def test_prompt_node_build(complex_graph):
|
||||
prompt_node = get_node_by_type(complex_graph, PromptNode)
|
||||
prompt_node = get_node_by_type(complex_graph, PromptVertex)
|
||||
assert prompt_node is not None
|
||||
built_object = prompt_node.build()
|
||||
assert built_object is not None
|
||||
|
|
@ -288,7 +289,7 @@ def test_prompt_node_build(complex_graph):
|
|||
|
||||
|
||||
def test_llm_node_build(basic_graph):
|
||||
llm_node = get_node_by_type(basic_graph, LLMNode)
|
||||
llm_node = get_node_by_type(basic_graph, LLMVertex)
|
||||
assert llm_node is not None
|
||||
built_object = llm_node.build()
|
||||
assert built_object is not None
|
||||
|
|
@ -296,7 +297,7 @@ def test_llm_node_build(basic_graph):
|
|||
|
||||
|
||||
def test_toolkit_node_build(openapi_graph):
|
||||
toolkit_node = get_node_by_type(openapi_graph, ToolkitNode)
|
||||
toolkit_node = get_node_by_type(openapi_graph, ToolkitVertex)
|
||||
assert toolkit_node is not None
|
||||
built_object = toolkit_node.build()
|
||||
assert built_object is not None
|
||||
|
|
@ -304,7 +305,7 @@ def test_toolkit_node_build(openapi_graph):
|
|||
|
||||
|
||||
def test_file_tool_node_build(openapi_graph):
|
||||
file_tool_node = get_node_by_type(openapi_graph, FileToolNode)
|
||||
file_tool_node = get_node_by_type(openapi_graph, FileToolVertex)
|
||||
assert file_tool_node is not None
|
||||
built_object = file_tool_node.build()
|
||||
assert built_object is not None
|
||||
|
|
@ -312,7 +313,7 @@ def test_file_tool_node_build(openapi_graph):
|
|||
|
||||
|
||||
def test_wrapper_node_build(openapi_graph):
|
||||
wrapper_node = get_node_by_type(openapi_graph, WrapperNode)
|
||||
wrapper_node = get_node_by_type(openapi_graph, WrapperVertex)
|
||||
assert wrapper_node is not None
|
||||
built_object = wrapper_node.build()
|
||||
assert built_object is not None
|
||||
|
|
@ -327,7 +328,7 @@ def test_get_result_and_thought(basic_graph):
|
|||
message = "Hello"
|
||||
# Find the node that is an LLMNode and change the
|
||||
# _built_object to a FakeListLLM
|
||||
llm_node = get_node_by_type(basic_graph, LLMNode)
|
||||
llm_node = get_node_by_type(basic_graph, LLMVertex)
|
||||
assert llm_node is not None
|
||||
llm_node._built_object = FakeListLLM(responses=responses)
|
||||
llm_node._built = True
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from langflow.settings import settings
|
|||
|
||||
|
||||
def test_llms_settings(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
llms = json_response["llms"]
|
||||
|
|
@ -11,7 +11,7 @@ def test_llms_settings(client: TestClient):
|
|||
|
||||
|
||||
# def test_hugging_face_hub(client: TestClient):
|
||||
# response = client.get("/all")
|
||||
# response = client.get("api/v1/all")
|
||||
# assert response.status_code == 200
|
||||
# json_response = response.json()
|
||||
# language_models = json_response["llms"]
|
||||
|
|
@ -103,7 +103,7 @@ def test_llms_settings(client: TestClient):
|
|||
|
||||
|
||||
def test_openai(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
language_models = json_response["llms"]
|
||||
|
|
@ -333,7 +333,7 @@ def test_openai(client: TestClient):
|
|||
|
||||
|
||||
def test_chat_open_ai(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
language_models = json_response["llms"]
|
||||
|
|
@ -482,3 +482,77 @@ def test_chat_open_ai(client: TestClient):
|
|||
"ChatOpenAI",
|
||||
"BaseLanguageModel",
|
||||
}
|
||||
|
||||
|
||||
def test_azure_open_ai(client: TestClient):
|
||||
response = client.get("/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
language_models = json_response["llms"]
|
||||
|
||||
model = language_models["AzureOpenAI"]
|
||||
template = model["template"]
|
||||
|
||||
assert template["model_name"].show is False
|
||||
assert template["deployment_name"] == {
|
||||
"required": False,
|
||||
"placeholder": "",
|
||||
"show": True,
|
||||
"multiline": False,
|
||||
"value": "",
|
||||
"password": False,
|
||||
"name": "deployment_name",
|
||||
"advanced": False,
|
||||
"type": "str",
|
||||
"list": False,
|
||||
}
|
||||
|
||||
|
||||
def test_azure_chat_open_ai(client: TestClient):
|
||||
response = client.get("/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
language_models = json_response["llms"]
|
||||
|
||||
model = language_models["AzureChatOpenAI"]
|
||||
template = model["template"]
|
||||
|
||||
assert template["model_name"].show is False
|
||||
assert template["deployment_name"] == {
|
||||
"required": False,
|
||||
"placeholder": "",
|
||||
"show": True,
|
||||
"multiline": False,
|
||||
"value": "",
|
||||
"password": False,
|
||||
"name": "deployment_name",
|
||||
"advanced": False,
|
||||
"type": "str",
|
||||
"list": False,
|
||||
}
|
||||
assert template["openai_api_type"] == {
|
||||
"required": False,
|
||||
"placeholder": "",
|
||||
"show": False,
|
||||
"multiline": False,
|
||||
"value": "azure",
|
||||
"password": False,
|
||||
"name": "openai_api_type",
|
||||
"display_name": "OpenAI API Type",
|
||||
"advanced": False,
|
||||
"type": "str",
|
||||
"list": False,
|
||||
}
|
||||
assert template["openai_api_version"] == {
|
||||
"required": False,
|
||||
"placeholder": "",
|
||||
"show": True,
|
||||
"multiline": False,
|
||||
"value": "2023-03-15-preview",
|
||||
"password": False,
|
||||
"name": "openai_api_version",
|
||||
"display_name": "OpenAI API Version",
|
||||
"advanced": False,
|
||||
"type": "str",
|
||||
"list": False,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import json
|
|||
|
||||
import pytest
|
||||
from langchain.chains.base import Chain
|
||||
from langflow import load_flow_from_json
|
||||
from langflow.processing.process import load_flow_from_json
|
||||
from langflow.graph import Graph
|
||||
from langflow.utils.payload import get_root_node
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from langflow.settings import settings
|
|||
|
||||
|
||||
def test_prompts_settings(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
prompts = json_response["prompts"]
|
||||
|
|
@ -11,7 +11,7 @@ def test_prompts_settings(client: TestClient):
|
|||
|
||||
|
||||
def test_prompt_template(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
prompts = json_response["prompts"]
|
||||
|
|
@ -89,7 +89,7 @@ def test_prompt_template(client: TestClient):
|
|||
|
||||
|
||||
def test_few_shot_prompt_template(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
prompts = json_response["prompts"]
|
||||
|
|
@ -168,7 +168,7 @@ def test_few_shot_prompt_template(client: TestClient):
|
|||
|
||||
|
||||
def test_zero_shot_prompt(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
prompts = json_response["prompts"]
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ from langflow.settings import settings
|
|||
# check that all agents are in settings.agents
|
||||
# are in json_response["agents"]
|
||||
def test_vectorstores_settings(client: TestClient):
|
||||
response = client.get("/all")
|
||||
response = client.get("api/v1/all")
|
||||
assert response.status_code == 200
|
||||
json_response = response.json()
|
||||
vectorstores = json_response["vectorstores"]
|
||||
|
|
|
|||
|
|
@ -5,17 +5,17 @@ from fastapi.testclient import TestClient
|
|||
|
||||
|
||||
def test_websocket_connection(client: TestClient):
|
||||
with client.websocket_connect("/chat/test_client") as websocket:
|
||||
with client.websocket_connect("api/v1/chat/test_client") as websocket:
|
||||
assert websocket.scope["client"] == ["testclient", 50000]
|
||||
assert websocket.scope["path"] == "/chat/test_client"
|
||||
assert websocket.scope["path"] == "/api/v1/chat/test_client"
|
||||
|
||||
|
||||
def test_chat_history(client: TestClient):
|
||||
# Mock the process_graph function to return a specific value
|
||||
with patch("langflow.api.chat_manager.process_graph") as mock_process_graph:
|
||||
with patch("langflow.chat.manager.process_graph") as mock_process_graph:
|
||||
mock_process_graph.return_value = ("Hello, I'm a mock response!", "")
|
||||
|
||||
with client.websocket_connect("/chat/test_client") as websocket:
|
||||
with client.websocket_connect("api/v1/chat/test_client") as websocket:
|
||||
# First message should be the history
|
||||
history = websocket.receive_json()
|
||||
assert history == [] # Empty history
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue