mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-17 21:25:46 +02:00
Merge branch 'main' into eugene/langgraph_nav
This commit is contained in:
ccurme
@@ -109,9 +109,31 @@ jobs:
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- name: Build
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run: yarn build
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test-js:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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working-directory:
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- "libs/sdk-js"
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defaults:
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run:
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working-directory: ${{ matrix.working-directory }}
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steps:
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- uses: actions/checkout@v3
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- name: Setup Node.js (LTS)
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uses: actions/setup-node@v3
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with:
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node-version: "20"
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cache: "yarn"
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cache-dependency-path: ${{ matrix.working-directory }}/yarn.lock
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- name: Install dependencies
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run: yarn install
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- name: Run tests
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run: yarn test
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ci_success:
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name: "CI Success"
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needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test]
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needs: [lint, lint-js, test, test-langgraph, test-scheduler-kafka, integration-test, test-js]
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if: |
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always()
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runs-on: ubuntu-latest
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@@ -42,10 +42,13 @@ NOTEBOOKS_NO_EXECUTION = [
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"docs/docs/tutorials/storm/storm.ipynb", # issues only when running with VCR
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"docs/docs/tutorials/lats/lats.ipynb", # issues only when running with VCR
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"docs/docs/tutorials/rag/langgraph_crag.ipynb", # flakiness from tavily
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"docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb", # Cannot create a consistent method resolution error from VCR
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"docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb", # flakiness only when running in GHA
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"docs/docs/tutorials/rag/langgraph_self_rag.ipynb", # flakiness only when running in GHA
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"docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb", # flakiness only when running in GHA
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"docs/docs/how-tos/map-reduce.ipynb", # flakiness from structured output, only when running with VCR
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"docs/docs/tutorials/tot/tot.ipynb",
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"docs/docs/how-tos/visualization.ipynb"
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"docs/docs/how-tos/visualization.ipynb",
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"docs/docs/tutorials/llm-compiler/LLMCompiler.ipynb"
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]
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@@ -127,7 +130,18 @@ def add_vcr_to_notebook(
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uses_langsmith = True
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# Add import statement
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vcr_import_lines = [
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vcr_import_lines = []
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if uses_langsmith:
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vcr_import_lines.extend([
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# patch urllib3 to handle vcr errors, see more here:
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# https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
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"import sys",
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f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
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"import _patch as patch_urllib3",
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"patch_urllib3.patch_urllib3()",
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])
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vcr_import_lines.extend([
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"import nest_asyncio",
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"nest_asyncio.apply()",
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"import vcr",
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@@ -157,16 +171,8 @@ def add_vcr_to_notebook(
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"",
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"custom_vcr.register_serializer('advanced_compressed', AdvancedCompressedSerializer())",
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"custom_vcr.serializer = 'advanced_compressed'",
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]
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if uses_langsmith:
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vcr_import_lines.extend(
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# patch urllib3 to handle vcr errors, see more here:
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# https://github.com/langchain-ai/langsmith-sdk/blob/main/python/langsmith/_internal/_patch.py
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"import sys",
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f"sys.path.insert(0, '{os.path.join(DOCS_PATH, '_scripts')}')",
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"import _patch as patch_urllib3",
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"patch_urllib3.patch_urllib3()",
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)
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])
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import_cell = nbformat.v4.new_code_cell(source="\n".join(vcr_import_lines))
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import_cell.pop("id", None)
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notebook.cells.insert(0, import_cell)
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|
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File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -1 +0,0 @@
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|
||||
@@ -1 +0,0 @@
|
||||
eNqdWA1sG9UdT4FsqO00ECAEbOrDgiUtPufsOLaTkNLESZo0cZ3GWdPQFvf5/Gxfcr477t3ZcaoI2gJFsAmZ8bXRtQXSpE0/aEf4KNAV8b0B5UPQrWUNWxkgEGLjU9Mm6P7v7uw4TdgQlhLfvfd//4/f//N541iGaFRU5Dl7RFknGhZ0eKH5jWMaudYgVL9hNE30lBIf6QpHeh4wNPHYopSuq7SuqgqroktRiYxFl6CkqzLuKiGF9Sp4ViVishmJKfHc8Xlt6x1pQilOEuqoQ6vXOwQFZMk6vDgiipQhCCNTGJxBWKZZoolyEumYDrhQG9FgH/6okiYoaYhxIokyoXVrZMShdhnpKYIaKRWpjmUdRQQN60JKxXEnyikGyoqShGJwUMwQGWmEGpJOkZIAkaqEZZMmhUEFMkgEQydxpCu2DiZngxKtghbVc5lSe1KKkUzpSCYkTtkBjWDKdI8phm4eK5oTwxR4Kpaa4Rhwy2ATGyTKyM15ECUMC4FQi3V7UlbAWFHTiEQyzCLLJQXVbaqEyY/5SNSAvygnFC1tMkYiRSoQA1unaTVYCngLIiUoBtrZ7oFlOY5SRFIThlQ0WPl2m0Ekw4p5wpBxTCKM2uZPQTJNgKKKlkMJURZpijnQ4uoENU2oGT3Rv1VxF+omVFVktmgSJRRJUrKMkUUDHl8j29DXoStnYM7ipehhZl02BaELZhV9TI1EQhREgGYaYtM9XjB78Rq50YQeZNk+YPjgAcJ2MliUTBTsjGHhWOleiFpN6ytN/UUZl0KrEd3Q5FKQF9bZi9RcskmZ6haIhBYNY06o9CwEjBiWlRaIDAALaAgx09RpZkE2WvHP3GyiYPoBJ7EIaAfhMca8x9LJzAg5hyQiJ/UU6CXKVNcMAZDMpnIoS1DaoLrtSYcTOTRFIix/aY7qJO0YdqJpad0Lsits7UlahcICZhLm2Ugr0rBIrURj+9VaHKkKGH5VKV8Gzwyuq9dXGJpUUYcqCjUom826soYcJ1pSU+DbrERZghkYVQatEnAVxTKX0DDLAEGpcKIKmyFjEzQgzyAaeq0TLNBQl6IaEtZQUNRFcIALRQCo1gIDJwo2oho/WmPwfIxHrSikUF3KoaCkGPFcPQphGUyHUuREy/uQr2aKsEAREVLgExDWhbUBJ2rvRJU+3u33LUQ1gSnqVixq9agJmCvAKtSIfO6ZrNoUw9rvWYX8JaJcLlfF8FoGp4zTJpw6zohSLkoJ1oRU1E6SaD/ETynoCUM2o3kG8O5Ajbu2upQhxFjqOx3tV0S59GDh/f8e/Dk1mwDLi5gCyQtFLSOCwYWMc6I4EaAbFNPcTDEzwsGLVgK5WNnCkAHfoVq6UB9kSBrnSs8WS0cax9k/SBHwLkS0btZ9oIybvEtZZkVomFCTqCFAWQc9cTwtwqti5qlmFzlGBapjnWROy9uizKQZlKAwnSoZSAD7soo2ADkJdplc4ICL1cbZ8nItrKUV6JlsManqnJeDTIwpnAknyZqugT03fKtYA6yIFNUVRYoK8Mz6dQJLlMAu1AOC0yULJYkNq7zLz9bMkylFFNjaeoeeU6d72VnyzAgKYbEMwiJs6KqhU8fwsM3JHhe+FxOgiRMqaKJqkzma/0e4lLYKhnChVhfLuOU2SlwOGygYi2ByMsWrGoxBGqsWls1WizKfT1OhB/jBkARlUGEd3Joipuo4Kz9MJIXeL7BZBBftLWAAXgBKliwOPAUAxGU4YYE1i8TpFlRZbcYyZLrmBRLLDFG3Qqm7sDqLGkyPQl4x+VMsWOAVWbQyDWbjo8T6wVIr909TJgHDVQwLA7Ph2AjscjDyWTCW1oZCclqpBt0gTeS4PXGx3GQ9sTi2xRg2gyRuYjGlbEHwd7G3qOU0e7uLffJ0Q9fOZFGIlymXrp3l4DAcHUsRKEMavW0kBY0hv2/6BP4ghnoDGQ7TpBIHbfN7k0OiyspkQoIyMw61VSamgPz4ACEqhyUY30atU/n9WFUlUTChqmKNYY9dizmmycztcVaNOJjpZT0/EQYlGturunJgC4y1Lm/Axe8f5GAkF2UJaiAHaCTzo6q5/0TphgrYARPOvobkR63D+0ppFJrfEcJCODKNJetk+R1YS/u8D5Wua4asi2mSHwt2zRRnb06Jq3a5PS7vgWmMaU4W8jvMMvfotMNE13KcAHOGnr+P31fAx5qZ8iOeQCCwsxD/m0bhnG7QjSPgDPLyi2P2Dej+cEfBi5NlF440g2Pyh3pShhN5fChCVOThPV7k9tXx1XVuHi0N9ewJ2nJ6ZvXDgR4YTWgCfNFS8PuYkDLkARIfD87q8UPY7Esc0x/aBUcGVYUSztYqv2cV123d/bj25oes8OIULYllccgUm9/FvGmWsQl7G1KXsQThXJoCEn6Pb5+9VUB6HAzjOTfP8e6DLJsECCymuapoOkfh7qWJei5/zJnGgyyqGqrdNdU+nufroTcKkhEnESPWrKRBKK1n+S4pOP74IAf9B+6D0F856799bYWIccNh/rGZFLoyQOCGO+bhzc/vSyk0wgQwM4p8Rmrh8+TsRAVWbiCpCfCPT6eipESdB3xp+tjMfZvDiNuTpnsGC+ScGM8fuwxeooLbV+0XYn7i8/qx2xvHQozn44mA31NT66+tFR4MtnJBLKQIFzHDLT/W3Le8MdQeHI8A96CiDIjk9uNzzoxGhUQ0lm5Y5hHFQLRXMKKhRsnbUt2VDSY6kk2Z5pWxQU8nr18dodlkW2dHR9TLuf0ef7XPV1MLbnPxLrfLzbUacmZld3t0effS/khLTK9ZMegiq5Y2N1ZH5GaJD6WWD7hIwNO+qiesReVrw226gj3JaLu/Nt02lBCxrMVWuJsDQrZF5nvElqZm6g3zK8CfMOk0VNXDJVyFskgb7ITgICE4Kx2qC+lQj+JmFDS4plc/mIbhThCWJTZis3Ai8A1dOiLqpGG5IpNjdwAGRkaMN2hyW58WbluWre3oDXdq/TTqHfRkwtgTjF095FabdNLc51nWEs9ppSDAA8fbOPh4b8CMninVv6dWj6ziSvObC6vWDzBjskJlMZEYjRANUig/LrCZHwq5RkbB592NffmJgFDr9cf8Pm+t3yMkPB4usiy4v8CtWA1GWBcYwxJkWUbIP5SqbnDUeb3VjnoYZxsCPi/Pmz/TbBi1etxzZ8xdcOvZZebnTPg7dWoi8rZ8fMk5wx9c0fr5/LM333PrO0sWjb/hzR//7LU3Lzju3HTow6Hsw/WuS2++7iP/Fy8tuiT2kzcXH/3x0aEjW2tfoMeyZV173XPuOLiuL3Di3le+PPlu1+ShK1+eDCUXnPy4/Z1fXDN51d0vvaddf+IWvGBb22dX7K5K/8174Hfvn90R3rjrjxf/8sHnn3m9cmtn//b9A29cwNG/PPHJj+7f/dgHV37dM9HgprsvK+/86+oflq2LHr7itkTo1Xue2v6fixdWnpU/92evXF72xcrec8+/dOdTT/OZV7dP3PSHnkdCu4x56wa+XDZ376tPv5B8p27bvMsnrkmPeg+fuPPTTQc/j62/7t5XDi93fPjAXZPuL4+8dZNvRz604erB88KuZ8teu/n+9ydXkU86MpdvnldXNnfbneUX7dvz7uKWI+lfv7cldvvJu7dcv0v9e/ndW5dc9C/nfOeTfHe52Fh+8wuHr+P+/c/2nUsG1s3bct78ry/ecuL2fxzdtrVp18HHfxo6mar7eOzPoRVvPh/p/Grh0Nwjv1lyavVX6lP+8oD+TeabyY/2joUuumFxzdfnX+WNjKTff3J4/oXBDZeQnhd7tzbvvHfThr31/b11D59xl/KDc3776I3vXvD2gvj4DZ+u01/e/EG5eN+vznvxrhvl55b+6VRs79Fnm96KHHc+//p9nmO1z2w/0Heq4qa16xatvIR4PjuyO1Fet/nIAtPhZ5Y11DQZc84qK/svuKhyGQ==
|
||||
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@@ -1 +0,0 @@
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|
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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@@ -1 +0,0 @@
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|
||||
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
|
||||
+1
@@ -0,0 +1 @@
|
||||
eNrtVwtUE1caRqAF+1DWBbWI65DFpbZOSDIhCSDPQHkoBEx4CidMJpNkIJmJMxMhIFZRa320GhXRqq1CeAqohUprfWCprc+26mkrIra1WPrAItStsrawd2LsYu3ZZ/fs7jk750Bm7v/f//3/97vldYtwmiEoclwTQbI4jWIs+GA2ltfR+EILzrArak04a6C09lSFUlVtoYmupwwsa2ZCg4JQM8GnzDiJEnyMMgUtEgZhBpQNAu9mI+4QY9dQWuulcZmlPBPOMKgeZ3ih0IJSHkYBXSQLPngGYjZUBLZBjIGyGLUQAWkpiDUQDFSE44U4qY3kzYZ4NGXEOW4Lg9O8sjywYqK0uJFb0ptZWExxTAxL46gJrOlQI4ODBZaijE6NrNXsEKCzkA4POf6f3kOhUh6JmhwMRooqtJjVnCI1QeocgrU4g9GE2cnLS2fwuxayFMSxQxYzxPFDHD9tQjk+jqbBWRBRCGUYgmHBDtwEFRGsgXsjaMgRXS5IfE6FGaWBAYCdcVhjpkFgaZZwBKy0jPPF6QClKcAxlldWVpZXVmfAUS3Yst5uoBjW1nJ/QvaiGIaD4OAkRmkJUm9r1pcQ5tmQFtcZURZvBEkgcUcAbI2FOG6GUSOxCK+9u8u2DzWbjQTmcCaogKHIJmfSYM6SB8mNXARgkGKStbUpgBHRiUGpVlA5JCTkB4fwpfuKYYZFCdIIKgE2osCeWrOD/uZYghnFCoEQ2FmVttq7m1vG8lCMrSYZxRTK+0SiNGaw1aC0SSJuHbtOW0iWMOG2Onnqg+qcxL+oQ/hCIT9k/32CGSuJ2WocRdV+32acpa0wRgEZtt2ClnvxMeKknjXYqhExUk/jjBmkGF9eC7axFqbcDnKBnzlR5+yHKsXce0m84jLFHgvyYjussoDiFYqhJJSERAJRMCQMCRUKQxEEik9WNcmdalS/mIb9KholGR1IRdy9tNdhBgtZiGsb5b+Y8MNcwoE3nPmgzWC82EwxOOy0ytaUBc+/OwngxNjWu9UFU7QeJYkSh1pbA5dM0PkE2eYkg+rlRALlsImxVUtlkhYn5V6cG4FfAlgogAXCg8UwDdw2EiYCxM7x3zl6QJqFAvC8/iAHS4HRwNjqEcHd58hYFho3AWM47T8JsoeA59AvM92TJeJ4QqTIwfvZGHyMQdUSE/P6g3SniCoB01R8jxkmtLauAPChlulkGgSVaCRCmVSs00gxgU4mFYYIZSJEIEJRyRtgcBEYkMKlzkzRLMzgGJi0rNXWNduEFnNdFY4IgxEJ8DQMTBnMaNHiSosmluJ8YMIgM40bKVS7V/4MLEcxAw4rHdVmq4vNTolOTpQ3KoGRcjCqCHzjpXFuajWmU2tM4SpdDJUTbCkRW9Ks85gEZYoqI80UbVWkL5orVjCpmbF0WnpyobwoJCYbFkoRiUwmlIUgsJAv4Av5QhiZH52hS0eKS7R0RlEWLs7SpSkRS5YsEylUmqXBVjYzdaEyVWXQIiJRjEybjWQVlEjmijIQPmNVq036YqECkckltDBDHJ8pzEhcqMuyBBcBb1DWEB4UBoFKJEB8w539AIN+gO92g/heN4SB04KLQTj//tkXBiWAQ0pBGq1hkJILJg5+wYBVEiwenkKReNdmEAPLIkIbro0mS0w5aabEVE2BRCrCNHoVYkqOy36mQK2gFWpByqK58jgw24wINiYIEpEIFjjjIBGIZY4q/Ivp/6RVB7Lgse0NKxxnDsgjSTEkodPVKnEatJCtETNSFi0Y4zReC3I+Pzrb1hYiEOpwXBOMyDCtWIRJ4LjM+fvuSftpGNi5M6AONYIaW4TZWg1IOC9ULEZ4YZAJDZdJxAKB48xeVsvVJKk/Pu7bGWs9XRyPG/gbHV2nTKa6BY8dvpPpOefd5d9d0x3x6Yzgd/IfOm+a6p5dZXrt1YCEoYP8E8/OMAZu2YR3LBa4RwzZFgdMHO8PPeTtXtzpSjUnDZjOIg1HF//YdPR6+/AF7OTN0jdubb+xsWLfl7+fN7xizo7qK+YLOSujfcPfuuz/6MVW/sBHw2T75cN5j0cVujdGNB3w79nKVjRLiANzW3/wvjj15Dl59JHAFV701JGy1TmfBj2ZcEC2IOfmyx96Hu+N9ZRVlzwqGKiZOM8jK3bDtOmpt9K/SWl4wmtd2makRnN1MOzMlbTAWu/DQZEfdXWOCNvP7H22u3LiQES4WPPGS9emNaivp6Z+SER8hXQGli6sWOf1QdN0TYXw/Z01O+Pja1rO+Hj4d+T2vFYvV0f6vWheNmPTgoEZrSPTM3bqvw44c3LJjs0ZtQfe2zySJKu3bzmye8K2Gb5bv9m6o/Vx6643p/pk6XOmGQTv15deMkVsjMm1e/IrAwVbO1RtfUVrpxlV+fn01t62OTsGH45bt27u26H+Zx9L0O0czTsDf7u/tOGb0d3rXfoMbo8UTH/kZNC0F8kJd5A7ld32wVRBMRs5M9434OASX9enP2oYdO04+qelcLO95Vx24Cpk6anzpne2ZVHI5Sh63ogjqW4uQ1GddRPdXVx+Tdzn+vFfwX3pHGDiQBcEgFQMpYFireAc4UOOde5cYsCgg1LwIiibogtnQynZY3EgY2VY3MQDwOhfBpP3iyAtRuMYlrvYDQUkJ5xUY6jxr2FKQsutcEzqaE28XqDKzJKYUQYpMRc9I08As1Ao+oegJ0rrLSZgGqeSV1rG42Dfz5xWWhwHuA5YbnXgUVz7S4h0rOecJ/d5pP577P4/7v4Z7r7i4vV/5P2fR961mAPZ2LqG/suBzb8Bcjxw65CKpP/YrWPy37h1yP43bx12sRAR/+9cO8SyX/3aoRNLJcFCHMM0OhBsQbAYXEMkEq04WITpBIDy7792/ApwFtyVgrFfD866T/g5nE3vTvk8waus7+mCH8pXuPJy3agOwZlvJ3TZolpjwvbUn+d3r+zOy7CEHfMdHNhDrgow6O1lCVdvNrQo68iR/W4bFr6cF6N76rFa4vpI+oKXBn/MW4mKDt/uqfz8wt7PC3Le2HTy9oV0yRqpdmTeHw71hArOt21xXTstv3rFsqqPB4cHu7Sil3P9AnfZdhgPZRxAR2O/Xz9hfcr3EwPvrNLvWVuxz/+VvLc+CXNxiTxf9Ni5azc35r312ZfX7f4D++39tMl9Q8PF4o27b/W7HdzwgmTXb6acqKmPRHJP+0V8P7nHozhf71nyVcdQ+dLp84jxiqo/etYqfu/pM+t6SdSbb19ZXvlczPEW9xKh31W514mHtNG+L6CJqk2nJ36KzBr3zkTvNvdPKmYObw18drinrOhzt4vdb/pP8YYXxzXf+p7/2e6ZlYGTPC97qXJbn44XqH9c8xbT5/Zk9uLVvf1Rfsu83drrzxaFi6+6eV9M6Nt9ISTUkFb4rJd1PW+grK+hcM6u059NaJ6JDwmXUv0hAepb3qqc/X4fHMufQbSI7faOdPmpL6ZLjn8QF/xKl8e5iJXbc5vhb2cGWs4lrrkme4duLh+eVFpcr30Ce/1Y/lNRWW5VN/Rp/XtH7yRt8bhxe88pWc4f0G6fodjVgwGiK0faMjbnJ+VmMOeSAhRfz9j/4rWh6OM5nhVJmRmfHjjbQSeb62LJk7XlYABtEZd7NATMGlnbjBt/OHRB9UqhT4NpYNIo0TsasLGzmvUNoXpaby7NDz094TWPM1N6Y+oanliw9rkMu9QnW+F5qz545ebc6/59VdDMKR1sf5fNQ/VV7svzKdnN1bdnv7ZdJn+pZcnqniWfWCWzrvls87U/2uktHvSb3Bs6lPp8uc/yyTnnY9qip8gnrQypnDlr67E9qx7p3nK5Ea5TSFffnjMpsm3S42T1qyduLfhNPCkl1n2YeOPJjPGyaK+w002+u9/W9VxVfFnAiyyP9Nt+qW7dXL/z6euW7XvCt2zj8+MPy9rcLE8Nxa+RT672rsytTdzkWhKT9+6GL5ZMVX+8nDopyNnVO5OevHDLFEO7KvCjIO9d/vU7Z39il1X5+SZqlCNXJz3fe+476PRvd40/9GlHb6QxiF9T9d3UW7XfNOXtPNoi3V3xavq2Oz5HIoIu7t+re6n33ea+5W3ZMeoil5HEae2l7bGtGTVE5+yEZH7oIdn0y/h2n6Pj1qe8n2nYsHSRm98rv3thyVVqizLqwJJCvf7h5LjhL77WHcMqW43jwhY0jk577/3FE091VYy+++PDgucuRQ03pZ/qOlR5SKhcs7dDva25dYfre3O+Kn/8rOumd5J6Vfn6Ve3KL0pn1G/z6U86n2EtjvO53t/br1lcvPD5Td8Z+yx9aud1x/1G/R/3eLi4/BkM4jBv
|
||||
@@ -279,6 +279,58 @@
|
||||
}
|
||||
}
|
||||
},
|
||||
"/v1/projects/{project_id}/revisions/{revision_id}/deploy": {
|
||||
"post": {
|
||||
"tags": ["Revisions (v1)"],
|
||||
"summary": "Deploy Revision",
|
||||
"description": "Deploy revision by ID.\n\nThis endpoint redeploys the deployment of a revision without rebuilding the image for the deployment. Redeploying the deployment of a revision may mitigate intermittent issues with a deployment.\n\nThe revision must be in the `DEPLOYED` status and must be the latest revision of the project.",
|
||||
"operationId": "deploy_revision_projects__project_id__revisions__revision_id__deploy_post",
|
||||
"parameters": [
|
||||
{
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string",
|
||||
"format": "uuid",
|
||||
"title": "Project ID"
|
||||
},
|
||||
"name": "project_id",
|
||||
"in": "path"
|
||||
},
|
||||
{
|
||||
"required": true,
|
||||
"schema": {
|
||||
"type": "string",
|
||||
"format": "uuid",
|
||||
"title": "Revision ID"
|
||||
},
|
||||
"name": "revision_id",
|
||||
"in": "path"
|
||||
}
|
||||
],
|
||||
"responses": {
|
||||
"400": {
|
||||
"description": "Revision is not in DEPLOYED status or revision is not the latest revision for the project.",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/ErrorResponse"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"404": {
|
||||
"description": "Revision not found.",
|
||||
"content": {
|
||||
"application/json": {
|
||||
"schema": {
|
||||
"$ref": "#/components/schemas/ErrorResponse"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"/v1/projects/{project_id}/revisions/{revision_id}/interrupt": {
|
||||
"post": {
|
||||
"tags": ["Revisions (v1)"],
|
||||
@@ -319,31 +371,6 @@
|
||||
}
|
||||
},
|
||||
"schemas": {
|
||||
"EnvVar": {
|
||||
"type": "object",
|
||||
"description": "An environment variable or secret.",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "Environment variable or secret name.",
|
||||
"required": true
|
||||
},
|
||||
"value": {
|
||||
"type": "string",
|
||||
"description": "Environment variable or secret value.",
|
||||
"required": true
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"enum": [
|
||||
"default",
|
||||
"secret"
|
||||
],
|
||||
"description": "Field to designate type of the environment variable (default) or secret.",
|
||||
"required": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"ContainerSpec": {
|
||||
"type": "object",
|
||||
"description": "Container specification for a revision's deployment.\n\nIf any field is omitted or set to `null`, the internal default value is used depending on the deployment type (`dev` or `prod`).",
|
||||
@@ -404,6 +431,42 @@
|
||||
}
|
||||
}
|
||||
},
|
||||
"EnvVar": {
|
||||
"type": "object",
|
||||
"description": "An environment variable or secret.",
|
||||
"properties": {
|
||||
"name": {
|
||||
"type": "string",
|
||||
"description": "Environment variable or secret name.",
|
||||
"required": true
|
||||
},
|
||||
"value": {
|
||||
"type": "string",
|
||||
"description": "Environment variable or secret value.",
|
||||
"required": true
|
||||
},
|
||||
"type": {
|
||||
"type": "string",
|
||||
"enum": [
|
||||
"default",
|
||||
"secret"
|
||||
],
|
||||
"description": "Field to designate type of the environment variable (default) or secret.",
|
||||
"required": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"ErrorResponse": {
|
||||
"type": "object",
|
||||
"description": "Error response.",
|
||||
"properties": {
|
||||
"detail": {
|
||||
"type": "string",
|
||||
"description": "Error details.",
|
||||
"required": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"Project": {
|
||||
"type": "object",
|
||||
"description": "A project corresponds to a LangGraph Server deployment and the associated LangSmith tracing project.",
|
||||
|
||||
@@ -289,4 +289,7 @@ RUN set -ex && \
|
||||
RUN PIP_CONFIG_FILE=/pipconfig.txt PYTHONDONTWRITEBYTECODE=1 pip install --no-cache-dir -c /api/constraints.txt -e /deps/*
|
||||
|
||||
ENV LANGSERVE_GRAPHS='{"agent": "/deps/__outer_graphs/src/agent.py:graph", "storm": "/deps/__outer_graphs/src/storm.py:graph"}'
|
||||
```
|
||||
```
|
||||
|
||||
???+ note "Updating your langgraph.json file"
|
||||
The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
|
||||
@@ -14,6 +14,14 @@ Type of authentication for the LangGraph Cloud Server deployment. Valid values:
|
||||
|
||||
For deployments to LangGraph Cloud, this environment variable is set automatically. For local development or deployments where authentication is handled externally (e.g. self-hosted), set this environment variable to `noop`.
|
||||
|
||||
## `LANGSMITH_RUNS_ENDPOINTS`
|
||||
|
||||
For [Bring Your Own Cloud (BYOC)](../../concepts/bring_your_own_cloud.md) deployments with [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) only.
|
||||
|
||||
Set this environment variable to have a BYOC deployment send traces to a self-hosted LangSmith instance. The value of `LANGSMITH_RUNS_ENDPOINTS` is a JSON string: `{"<SELF_HOSTED_LANGSMITH_HOSTNAME>":"<LANGSMITH_API_KEY>"}`.
|
||||
|
||||
`SELF_HOSTED_LANGSMITH_HOSTNAME` is the hostname of the self-hosted LangSmith instance. It must be accessible to the BYOC deployment. `LANGSMITH_API_KEY` is a LangSmith API generated from the self-hosted LangSmith instance.
|
||||
|
||||
## `N_JOBS_PER_WORKER`
|
||||
|
||||
Number of jobs per worker for the LangGraph Cloud task queue. Defaults to `10`.
|
||||
|
||||
@@ -51,5 +51,5 @@ LangChain has no direct access to the resources created in your cloud account, a
|
||||
|
||||
Notes for customers using [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting):
|
||||
|
||||
- Creation of new LangGraph Cloud projects and revisions currently needs to be done on smith.langchain.com.
|
||||
- You can however set up the project to trace to your self-hosted LangSmith instance if desired
|
||||
- Creation of new LangGraph Cloud projects and revisions currently needs to be done on `smith.langchain.com`.
|
||||
- However, you can set up the project to trace to your self-hosted LangSmith instance if desired. See details for [`LANGSMITH_RUNS_ENDPOINTS` environment variable](../cloud/reference/env_var.md#langsmith_runs_endpoints).
|
||||
|
||||
@@ -28,6 +28,10 @@ The guide below will explain the differences between the deployment options.
|
||||
|
||||
The Self-Hosted Enterprise version is only available for the **Enterprise** plan.
|
||||
|
||||
!!! warning "Note"
|
||||
|
||||
The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
|
||||
|
||||
With a Self-Hosted Enterprise deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
|
||||
|
||||
You’ll build a Docker image using the [LangGraph CLI](./langgraph_cli.md), which can then be deployed on your own infrastructure.
|
||||
@@ -43,6 +47,10 @@ For more information, please see:
|
||||
|
||||
The Self-Hosted Lite version is available for all plans.
|
||||
|
||||
!!! warning "Note"
|
||||
|
||||
The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
|
||||
|
||||
The Self-Hosted Lite deployment option is a free (up to 1 million nodes executed), limited version of LangGraph Platform that you can run locally or in a self-hosted manner.
|
||||
|
||||
With a Self-Hosted Lite deployment, you are responsible for managing the infrastructure, including setting up and maintaining required databases and Redis instances.
|
||||
@@ -61,12 +69,11 @@ For more information, please see:
|
||||
|
||||
The Cloud SaaS version of LangGraph Platform is only available for **Plus** and **Enterprise** plans.
|
||||
|
||||
|
||||
The [Cloud SaaS](./langgraph_cloud.md) version of LangGraph Platform is hosted as part of [LangSmith](https://smith.langchain.com/).
|
||||
|
||||
The Cloud SaaS version of LangGraph Platform provides a simple way to deploy and manage your LangGraph applications.
|
||||
|
||||
This deployment option provides an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
|
||||
This deployment option provides access to the LangGraph Platform UI (within LangSmith) and an integration with GitHub, allowing you to deploy code from any of your repositories on GitHub.
|
||||
|
||||
For more information, please see:
|
||||
|
||||
@@ -81,7 +88,7 @@ For more information, please see:
|
||||
The Bring Your Own Cloud version of LangGraph Platform is only available for **Enterprise** plans.
|
||||
|
||||
|
||||
This combines the best of both worlds for Cloud and Self-Hosted. We manage the infrastructure, so you don't have to, but the infrastructure all runs within your cloud. This is currently only available on AWS.
|
||||
This combines the best of both worlds for Cloud and Self-Hosted. Create your deployments through the LangGraph Platform UI (within LangSmith) and we manage the infrastructure so you don't have to. The infrastructure all runs within your cloud. This is currently only available on AWS.
|
||||
|
||||
For more information please see:
|
||||
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 170 KiB After Width: | Height: | Size: 214 KiB |
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|
Before Width: | Height: | Size: 397 KiB |
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|
After Width: | Height: | Size: 214 KiB |
@@ -62,6 +62,9 @@ The server includes all API endpoints for your graph's runs, threads, assistants
|
||||
|
||||
The `langgraph dockerfile` command generates a [Dockerfile](https://docs.docker.com/reference/dockerfile/) that can be used to build images for and deploy instances of the [LangGraph API server](./langgraph_server.md). This is useful if you want to further customize the dockerfile or deploy in a more custom way.
|
||||
|
||||
??? note "Updating your langgraph.json file"
|
||||
The `langgraph dockerfile` command translates all the configuration in your `langgraph.json` file into Dockerfile commands. When using this command, you will have to re-run it whenever you update your `langgraph.json` file. Otherwise, your changes will not be reflected when you build or run the dockerfile.
|
||||
|
||||
## Related
|
||||
|
||||
- [LangGraph CLI API Reference](../cloud/reference/cli.md)
|
||||
|
||||
@@ -112,7 +112,7 @@ In this architecture, agents are defined as graph nodes. Each agent can communic
|
||||
```python
|
||||
from typing import Literal
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langgraph.graph import StateGraph, MessagesState, START
|
||||
from langgraph.graph import StateGraph, MessagesState, START, END
|
||||
|
||||
model = ChatOpenAI()
|
||||
|
||||
|
||||
@@ -147,24 +147,21 @@ In our example, the output of `get_state_history` will look like this:
|
||||
|
||||
### Replay
|
||||
|
||||
It's also possible to play-back a prior graph execution. If we `invoking` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the graph from a checkpoint that corresponds to the `checkpoint_id`.
|
||||
It's also possible to play-back a prior graph execution. If we `invoke` a graph with a `thread_id` and a `checkpoint_id`, then we will *re-play* the previously executed steps _before_ a checkpoint that corresponds to the `checkpoint_id`, and only execute the steps _after_ the checkpoint.
|
||||
|
||||
* `thread_id` is simply the ID of a thread. This is always required.
|
||||
* `checkpoint_id` This identifier refers to a specific checkpoint within a thread.
|
||||
* `thread_id` is the ID of a thread.
|
||||
* `checkpoint_id` is an identifier that refers to a specific checkpoint within a thread.
|
||||
|
||||
You must pass these when invoking the graph as part of the `configurable` portion of the config:
|
||||
|
||||
```python
|
||||
# {"configurable": {"thread_id": "1"}} # valid config
|
||||
# {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}} # also valid config
|
||||
|
||||
config = {"configurable": {"thread_id": "1"}}
|
||||
config = {"configurable": {"thread_id": "1", "checkpoint_id": "0c62ca34-ac19-445d-bbb0-5b4984975b2a"}}
|
||||
graph.invoke(None, config=config)
|
||||
```
|
||||
|
||||
Importantly, LangGraph knows whether a particular checkpoint has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
|
||||
Importantly, LangGraph knows whether a particular step has been executed previously. If it has, LangGraph simply *re-plays* that particular step in the graph and does not re-execute the step, but only for the steps _before_ the provided `checkpoint_id`. All of the steps _after_ `checkpoint_id` will be executed (i.e., a new fork), even if they have been executed previously. See this [how to guide on time-travel to learn more about replaying](../how-tos/human_in_the_loop/time-travel.ipynb).
|
||||
|
||||
|
||||
|
||||
|
||||
### Update state
|
||||
|
||||
|
||||
@@ -32,6 +32,10 @@ To use the Self-Hosted Enterprise version, you must acquire a license key that y
|
||||
- Build the docker image for [LangGraph Server](./langgraph_server.md) using the [LangGraph CLI](./langgraph_cli.md).
|
||||
- Deploy a web server that will run the docker image and pass in the necessary environment variables.
|
||||
|
||||
!!! warning "Note"
|
||||
|
||||
The LangGraph Platform Deployments view (within LangSmith SaaS and self-hosted LangSmith) is not available for Self-Hosted Lite or Self-Hosted Enterprise LangGraph deployments. Self-hosted LangGraph deployments are managed externally from LangSmith (e.g. there is no UI to manage these deployments).
|
||||
|
||||
For step-by-step instructions, see [How to set up a self-hosted deployment of LangGraph](../how-tos/deploy-self-hosted.md).
|
||||
|
||||
## Helm Chart
|
||||
|
||||
@@ -17,17 +17,9 @@ We call these debugging techniques **Time Travel**, composed of two key actions:
|
||||
|
||||
|
||||
|
||||
Replaying allows us to revisit and reproduce an agent's past actions. This can be done either from the current state (or checkpoint) of the graph or from a specific checkpoint.
|
||||
Replaying allows us to revisit and reproduce an agent's past actions, up to and including a specific step (checkpoint).
|
||||
|
||||
To replay from the current state, simply pass `None` as the input along with a `thread`:
|
||||
|
||||
```python
|
||||
thread = {"configurable": {"thread_id": "1"}}
|
||||
for event in graph.stream(None, thread, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
To replay actions from a specific checkpoint, start by retrieving all checkpoints for the thread:
|
||||
To replay actions before a specific checkpoint, start by retrieving all checkpoints for the thread:
|
||||
|
||||
```python
|
||||
all_checkpoints = []
|
||||
@@ -43,7 +35,7 @@ for event in graph.stream(None, config, stream_mode="values"):
|
||||
print(event)
|
||||
```
|
||||
|
||||
The graph efficiently replays previously executed nodes instead of re-executing them, leveraging its awareness of prior checkpoint executions.
|
||||
The graph replays previously executed steps _before_ the provided `checkpoint_id` and executes the steps _after_ `checkpoint_id` (i.e., a new fork), even if they have been executed previously.
|
||||
|
||||
## Forking
|
||||
|
||||
|
||||
@@ -37,7 +37,7 @@ async def authenticate(authorization: str) -> str:
|
||||
detail="Invalid token"
|
||||
)
|
||||
|
||||
# Optional: Add authorization rules
|
||||
# Add authorization rules to actually control access to resources
|
||||
@my_auth.on
|
||||
async def add_owner(
|
||||
ctx: Auth.types.AuthContext,
|
||||
@@ -48,6 +48,13 @@ async def add_owner(
|
||||
metadata = value.setdefault("metadata", {})
|
||||
metadata.update(filters)
|
||||
return filters
|
||||
|
||||
# Assumes you organize information in store like (user_id, resource_type, resource_id)
|
||||
@my_auth.on.store()
|
||||
async def authorize_store(ctx: Auth.types.AuthContext, value: dict):
|
||||
namespace: tuple = value["namespace"]
|
||||
assert namespace[0] == ctx.user.identity, "Not authorized"
|
||||
|
||||
```
|
||||
|
||||
## 2. Update configuration
|
||||
|
||||
@@ -29,6 +29,7 @@ You will eventually need to pass in the following environment variables to the L
|
||||
- `DATABASE_URI`: Postgres connection details. Postgres will be used to store assistants, threads, runs, persist thread state and long term memory, and to manage the state of the background task queue with 'exactly once' semantics.
|
||||
- `LANGSMITH_API_KEY`: (If using [Self-Hosted Lite](../concepts/deployment_options.md#self-hosted-lite)) LangSmith API key. This will be used to authenticate ONCE at server start up.
|
||||
- `LANGGRAPH_CLOUD_LICENSE_KEY`: (If using [Self-Hosted Enterprise](../concepts/deployment_options.md#self-hosted-enterprise)) LangGraph Platform license key. This will be used to authenticate ONCE at server start up.
|
||||
- `LANGCHAIN_ENDPOINT`: To send traces to a [self-hosted LangSmith](https://docs.smith.langchain.com/self_hosting) instance, set `LANGCHAIN_ENDPOINT` to the hostname of the self-hosted LangSmith instance.
|
||||
|
||||
|
||||
## Build the Docker Image
|
||||
|
||||
@@ -208,7 +208,7 @@
|
||||
"from typing import Optional\n",
|
||||
"\n",
|
||||
"from langchain.chat_models import init_chat_model\n",
|
||||
"from langchain_core.tools import InjectedToolArg\n",
|
||||
"from langgraph.prebuilt import InjectedStore\n",
|
||||
"from langgraph.store.base import BaseStore\n",
|
||||
"from typing_extensions import Annotated\n",
|
||||
"\n",
|
||||
@@ -232,7 +232,7 @@
|
||||
" content: str,\n",
|
||||
" *,\n",
|
||||
" memory_id: Optional[uuid.UUID] = None,\n",
|
||||
" store: Annotated[BaseStore, InjectedToolArg],\n",
|
||||
" store: Annotated[BaseStore, InjectedStore],\n",
|
||||
"):\n",
|
||||
" \"\"\"Upsert a memory in the database.\"\"\"\n",
|
||||
" # The LLM can use this tool to store a new memory\n",
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -42,7 +42,10 @@
|
||||
"checkpointer = # postgres checkpointer (see examples below)\n",
|
||||
"graph = builder.compile(checkpointer=checkpointer)\n",
|
||||
"...\n",
|
||||
"```"
|
||||
"```\n",
|
||||
"\n",
|
||||
"!!! info \"Setup\n",
|
||||
" You need to run `.setup()` once on your checkpointer to initialize the database before you can use it."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -123,7 +123,7 @@
|
||||
" return f\"It's sunny in {city}!\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"raw_model = ChatOpenAI()\n",
|
||||
"raw_model = ChatOpenAI(model=\"gpt-4o\")\n",
|
||||
"model = raw_model.with_structured_output(get_weather)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# MULTIPLE_SUBGRAPHS
|
||||
|
||||
You are calling the same subgraph multiple times within a single LangGraph node with checkpointing enabled for each subgraph.
|
||||
You are calling subgraphs multiple times within a single LangGraph node with checkpointing enabled for each subgraph.
|
||||
|
||||
This is currently not allowed due to internal restrictions on how checkpoint namespacing for subgraphs works.
|
||||
|
||||
@@ -9,4 +9,4 @@ This is currently not allowed due to internal restrictions on how checkpoint nam
|
||||
The following may help resolve this error:
|
||||
|
||||
- If you don't need to interrupt/resume from a subgraph, pass `checkpointer=False` when compiling it like this: `.compile(checkpointer=False)`
|
||||
- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.
|
||||
- Don't imperatively call graphs multiple times in the same node, and instead use the [`Send`](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API.
|
||||
|
||||
@@ -275,6 +275,13 @@ async def on_assistants(
|
||||
status_code=403,
|
||||
detail="User lacks the required permissions.",
|
||||
)
|
||||
|
||||
# Assumes you organize information in store like (user_id, resource_type, resource_id)
|
||||
@auth.on.store()
|
||||
async def authorize_store(ctx: Auth.types.AuthContext, value: dict):
|
||||
# The "namespace" field for each store item is a tuple you can think of as the directory of an item.
|
||||
namespace: tuple = value["namespace"]
|
||||
assert namespace[0] == ctx.user.identity, "Not authorized"
|
||||
```
|
||||
|
||||
Notice that instead of one global handler, we now have specific handlers for:
|
||||
|
||||
+1
-1
@@ -582,7 +582,7 @@
|
||||
")\n",
|
||||
"\n",
|
||||
"evaluator = prompt | ChatOpenAI(model=\"gpt-4-turbo-preview\").with_structured_output(\n",
|
||||
" RedTeamingResult\n",
|
||||
" RedTeamingResult, method=\"function_calling\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
|
||||
@@ -246,7 +246,7 @@
|
||||
"\n",
|
||||
"Define the (`fetch_user_flight_information`) tool to let the agent see the current user's flight information. Then define tools to search for flights and manage the passenger's bookings stored in the SQL database.\n",
|
||||
"\n",
|
||||
"We the can [access the RunnableConfig](https://python.langchain.com/docs/how_to/tool_configure/#inferring-by-parameter-type) for a given run to check the `passenger_id` of the user accessing this application. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information.\n",
|
||||
"We then can [access the RunnableConfig](https://python.langchain.com/docs/how_to/tool_configure/#inferring-by-parameter-type) for a given run to check the `passenger_id` of the user accessing this application. The LLM never has to provide these explicitly, they are provided for a given invocation of the graph so that each user cannot access other passengers' booking information.\n",
|
||||
"\n",
|
||||
"<div class=\"admonition warning\">\n",
|
||||
" <p class=\"admonition-title\">Compatibility</p>\n",
|
||||
@@ -3423,7 +3423,7 @@
|
||||
"\n",
|
||||
"#### Utility\n",
|
||||
"\n",
|
||||
"Create a function to make an \"entry\" node for each workflow, stating \"the current assistant ix `assistant_name`\"."
|
||||
"Create a function to make an \"entry\" node for each workflow, stating \"the current assistant is `assistant_name`\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1032,7 +1032,9 @@
|
||||
") # You can optionally add examples\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")\n",
|
||||
"\n",
|
||||
"runnable = joiner_prompt | llm.with_structured_output(JoinOutputs)"
|
||||
"runnable = joiner_prompt | llm.with_structured_output(\n",
|
||||
" JoinOutputs, method=\"function_calling\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@@ -114,7 +114,9 @@ def get_math_tool(llm: ChatOpenAI):
|
||||
MessagesPlaceholder(variable_name="context", optional=True),
|
||||
]
|
||||
)
|
||||
extractor = prompt | llm.with_structured_output(ExecuteCode)
|
||||
extractor = prompt | llm.with_structured_output(
|
||||
ExecuteCode, method="function_calling"
|
||||
)
|
||||
|
||||
def calculate_expression(
|
||||
problem: str,
|
||||
|
||||
@@ -42,13 +42,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 1,
|
||||
"id": "53d1a740-9fea-4a6e-8f95-fb9dbf1c80a1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%capture --no-stderr\n",
|
||||
"! pip install -U langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph tavily-python"
|
||||
"%pip install -U langchain_community tiktoken langchain-openai langchain-cohere langchainhub chromadb langchain langgraph tavily-python"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -68,7 +68,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"_set_env(\"OPENAI_API_KEY\")\n",
|
||||
"_set_env(\"COHERE_API_KEY\")\n",
|
||||
"# _set_env(\"COHERE_API_KEY\")\n",
|
||||
"_set_env(\"TAVILY_API_KEY\")"
|
||||
]
|
||||
},
|
||||
@@ -95,7 +95,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": null,
|
||||
"id": "b224e5ba-50ca-495a-a7fa-0f75a080e03c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -161,7 +161,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 4,
|
||||
"id": "4dec9d98-f3dc-4b7f-abc0-9d01c754f2be",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -196,7 +196,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"# LLM with function call\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
|
||||
"structured_llm_router = llm.with_structured_output(RouteQuery)\n",
|
||||
"\n",
|
||||
"# Prompt\n",
|
||||
@@ -221,7 +221,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 5,
|
||||
"id": "856801cb-f42a-44e7-956f-47845e3664ca",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -229,7 +229,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"binary_score='no'\n"
|
||||
"binary_score='yes'\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -247,7 +247,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"# LLM with function call\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
|
||||
"structured_llm_grader = llm.with_structured_output(GradeDocuments)\n",
|
||||
"\n",
|
||||
"# Prompt\n",
|
||||
@@ -271,7 +271,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 6,
|
||||
"id": "2272333e-50b2-42ab-b472-e1055a3b94a8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -279,7 +279,7 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave based on past experience and interact with other agents. Memory stream is a long-term memory module that records agents' experiences in natural language. The retrieval model surfaces context to inform the agent's behavior based on relevance, recency, and importance.\n"
|
||||
"Agent memory in LLM-powered autonomous systems consists of short-term and long-term memory. Short-term memory utilizes in-context learning for immediate tasks, while long-term memory allows agents to retain and recall information over extended periods, often using external storage for efficient retrieval. This memory structure supports the agent's ability to reflect on past actions and improve future performance.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -293,7 +293,7 @@
|
||||
"prompt = hub.pull(\"rlm/rag-prompt\")\n",
|
||||
"\n",
|
||||
"# LLM\n",
|
||||
"llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
|
||||
"llm = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Post-processing\n",
|
||||
@@ -311,7 +311,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 7,
|
||||
"id": "f0c08d14-77a0-4eed-b882-2d636abb22a3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -321,7 +321,7 @@
|
||||
"GradeHallucinations(binary_score='yes')"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -340,7 +340,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"# LLM with function call\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
|
||||
"structured_llm_grader = llm.with_structured_output(GradeHallucinations)\n",
|
||||
"\n",
|
||||
"# Prompt\n",
|
||||
@@ -359,7 +359,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 8,
|
||||
"id": "ded99680-437a-4c9d-b860-619c88949d84",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -369,7 +369,7 @@
|
||||
"GradeAnswer(binary_score='yes')"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -388,7 +388,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"# LLM with function call\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
|
||||
"structured_llm_grader = llm.with_structured_output(GradeAnswer)\n",
|
||||
"\n",
|
||||
"# Prompt\n",
|
||||
@@ -407,17 +407,17 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"id": "9d75f1d7-a47a-4577-bb0d-84b504b0867e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"What is the role of memory in an agent's functioning?\""
|
||||
"'What are the key concepts and techniques related to agent memory in artificial intelligence?'"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -426,7 +426,7 @@
|
||||
"### Question Re-writer\n",
|
||||
"\n",
|
||||
"# LLM\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n",
|
||||
"\n",
|
||||
"# Prompt\n",
|
||||
"system = \"\"\"You a question re-writer that converts an input question to a better version that is optimized \\n \n",
|
||||
@@ -455,7 +455,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 10,
|
||||
"id": "01d829bb-1074-4976-b650-ead41dcb9788",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -481,7 +481,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 11,
|
||||
"id": "e723fcdb-06e6-402d-912e-899795b78408",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -516,7 +516,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 12,
|
||||
"id": "b76b5ec3-0720-443d-85b1-c0e79659ca0a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -736,7 +736,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 13,
|
||||
"id": "67854e07-9293-4c3c-bf9a-bc9a605570ee",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@@ -796,7 +796,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 14,
|
||||
"id": "29acc541-d726-4b75-84d1-a215845fe88a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -816,11 +816,9 @@
|
||||
"---DECISION: GENERATION ADDRESSES QUESTION---\n",
|
||||
"\"Node 'generate':\"\n",
|
||||
"'\\n---\\n'\n",
|
||||
"('It is expected that the Chicago Bears could have the opportunity to draft '\n",
|
||||
" 'the first defensive player in the 2024 NFL draft. The Bears have the first '\n",
|
||||
" 'overall pick in the draft, giving them a prime position to select top '\n",
|
||||
" 'talent. The top wide receiver Marvin Harrison Jr. from Ohio State is also '\n",
|
||||
" 'mentioned as a potential pick for the Cardinals.')\n"
|
||||
"('The Chicago Bears are expected to draft quarterback Caleb Williams first '\n",
|
||||
" 'overall in the 2024 NFL Draft. They also have a second first-round pick, '\n",
|
||||
" 'where they selected wide receiver Rome Odunze.')\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -843,19 +841,9 @@
|
||||
"pprint(value[\"generation\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "11fddd00-58bf-4910-bf36-be9e5bfba778",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Trace: \n",
|
||||
"\n",
|
||||
"https://smith.langchain.com/public/7e3aa7e5-c51f-45c2-bc66-b34f17ff2263/r"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 15,
|
||||
"id": "69a985dd-03c6-45af-a67b-b15746a2cb5f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@@ -869,7 +857,7 @@
|
||||
"\"Node 'retrieve':\"\n",
|
||||
"'\\n---\\n'\n",
|
||||
"---CHECK DOCUMENT RELEVANCE TO QUESTION---\n",
|
||||
"---GRADE: DOCUMENT RELEVANT---\n",
|
||||
"---GRADE: DOCUMENT NOT RELEVANT---\n",
|
||||
"---GRADE: DOCUMENT RELEVANT---\n",
|
||||
"---GRADE: DOCUMENT NOT RELEVANT---\n",
|
||||
"---GRADE: DOCUMENT RELEVANT---\n",
|
||||
@@ -884,11 +872,11 @@
|
||||
"---DECISION: GENERATION ADDRESSES QUESTION---\n",
|
||||
"\"Node 'generate':\"\n",
|
||||
"'\\n---\\n'\n",
|
||||
"('The types of agent memory include Sensory Memory, Short-Term Memory (STM) or '\n",
|
||||
" 'Working Memory, and Long-Term Memory (LTM) with subtypes of Explicit / '\n",
|
||||
" 'declarative memory and Implicit / procedural memory. Sensory memory retains '\n",
|
||||
" 'sensory information briefly, STM stores information for cognitive tasks, and '\n",
|
||||
" 'LTM stores information for a long time with different types of memories.')\n"
|
||||
"('The types of agent memory include short-term memory, long-term memory, and '\n",
|
||||
" 'sensory memory. Short-term memory is utilized for in-context learning, while '\n",
|
||||
" 'long-term memory allows for the retention and recall of information over '\n",
|
||||
" 'extended periods. Sensory memory involves learning embedding representations '\n",
|
||||
" 'for various raw inputs, such as text and images.')\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -906,16 +894,6 @@
|
||||
"# Final generation\n",
|
||||
"pprint(value[\"generation\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ebf41097-fc4c-4072-95b3-e7e07731ada1",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Trace: \n",
|
||||
"\n",
|
||||
"https://smith.langchain.com/public/fdf0a180-6d15-4d09-bb92-f84f2105ca51/r"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
@@ -934,7 +912,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
"version": "3.12.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -261,7 +261,7 @@
|
||||
" binary_score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
|
||||
"\n",
|
||||
" # LLM\n",
|
||||
" model = ChatOpenAI(temperature=0, model=\"gpt-4-0125-preview\", streaming=True)\n",
|
||||
" model = ChatOpenAI(temperature=0, model=\"gpt-4o\", streaming=True)\n",
|
||||
"\n",
|
||||
" # LLM with tool and validation\n",
|
||||
" llm_with_tool = model.with_structured_output(grade)\n",
|
||||
@@ -376,7 +376,7 @@
|
||||
" prompt = hub.pull(\"rlm/rag-prompt\")\n",
|
||||
"\n",
|
||||
" # LLM\n",
|
||||
" llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n",
|
||||
" llm = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0, streaming=True)\n",
|
||||
"\n",
|
||||
" # Post-processing\n",
|
||||
" def format_docs(docs):\n",
|
||||
@@ -548,7 +548,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.9"
|
||||
"version": "3.12.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user