Merge branch 'dev' into add_extra_fields_documentloaders
This commit is contained in:
Alexandre Henrique Pereira Tavares
GitHub
commit
f31f0a5a35
107 changed files with 2964 additions and 608 deletions
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@ -1,2 +1,6 @@
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#!/bin/sh
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make format
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added_files=$(git diff --name-only --cached --diff-filter=d)
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make format
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git add ${added_files}
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2
Makefile
2
Makefile
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@ -59,7 +59,7 @@ lcserve_push:
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make build_frontend
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@version=$$(poetry version --short); \
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lc-serve push --app langflow.lcserve:app --app-dir . \
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--image-name langflow --image-tag $${version} --verbose
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--image-name langflow --image-tag $${version} --verbose --public
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lcserve_deploy:
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@:$(if $(uses),,$(error `uses` is not set. Please run `make uses=... lcserve_deploy`))
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5
package.json
Normal file
5
package.json
Normal file
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@ -0,0 +1,5 @@
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{
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"dependencies": {
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"vite-plugin-svgr": "^3.2.0"
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}
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}
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156
poetry.lock
generated
156
poetry.lock
generated
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@ -1,4 +1,4 @@
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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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# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
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[[package]]
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name = "aiofiles"
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@ -717,6 +717,23 @@ pandas = ["pandas"]
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sqlalchemy = ["sqlalchemy (>1.3.21,<1.4)"]
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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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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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]
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[package.dependencies]
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aiohttp = ">=3.0,<4.0"
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backoff = ">=2.0,<3.0"
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requests = ">=2.0,<3.0"
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[[package]]
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name = "colorama"
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version = "0.4.6"
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@ -769,63 +786,72 @@ typing = ["mypy (>=0.990)"]
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[[package]]
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name = "coverage"
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version = "7.2.6"
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||||
version = "7.2.7"
|
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description = "Code coverage measurement for Python"
|
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category = "dev"
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "coverage-7.2.7-cp37-cp37m-win_amd64.whl", hash = "sha256:b1c546aca0ca4d028901d825015dc8e4d56aac4b541877690eb76490f1dc8ed0"},
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{file = "coverage-7.2.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5e330fc79bd7207e46c7d7fd2bb4af2963f5f635703925543a70b99574b0fea9"},
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{file = "coverage-7.2.7-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1e9d683426464e4a252bf70c3498756055016f99ddaec3774bf368e76bbe02b6"},
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||||
{file = "coverage-7.2.7-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d13c64ee2d33eccf7437961b6ea7ad8673e2be040b4f7fd4fd4d4d28d9ccb1e"},
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{file = "coverage-7.2.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:537891ae8ce59ef63d0123f7ac9e2ae0fc8b72c7ccbe5296fec45fd68967b6c9"},
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{file = "coverage-7.2.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:06fb182e69f33f6cd1d39a6c597294cff3143554b64b9825d1dc69d18cc2fff2"},
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{file = "coverage-7.2.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:201e7389591af40950a6480bd9edfa8ed04346ff80002cec1a66cac4549c1ad7"},
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||||
{file = "coverage-7.2.7-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f6951407391b639504e3b3be51b7ba5f3528adbf1a8ac3302b687ecababf929e"},
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{file = "coverage-7.2.7-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6f48351d66575f535669306aa7d6d6f71bc43372473b54a832222803eb956fd1"},
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||||
{file = "coverage-7.2.7-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:b29019c76039dc3c0fd815c41392a044ce555d9bcdd38b0fb60fb4cd8e475ba9"},
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||||
{file = "coverage-7.2.7-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:81c13a1fc7468c40f13420732805a4c38a105d89848b7c10af65a90beff25250"},
|
||||
{file = "coverage-7.2.7-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:975d70ab7e3c80a3fe86001d8751f6778905ec723f5b110aed1e450da9d4b7f2"},
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||||
{file = "coverage-7.2.7-cp39-cp39-win32.whl", hash = "sha256:7ee7d9d4822c8acc74a5e26c50604dff824710bc8de424904c0982e25c39c6cb"},
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||||
{file = "coverage-7.2.7-cp39-cp39-win_amd64.whl", hash = "sha256:eb393e5ebc85245347950143969b241d08b52b88a3dc39479822e073a1a8eb27"},
|
||||
{file = "coverage-7.2.7-pp37.pp38.pp39-none-any.whl", hash = "sha256:b7b4c971f05e6ae490fef852c218b0e79d4e52f79ef0c8475566584a8fb3e01d"},
|
||||
{file = "coverage-7.2.7.tar.gz", hash = "sha256:924d94291ca674905fe9481f12294eb11f2d3d3fd1adb20314ba89e94f44ed59"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1070,7 +1096,9 @@ 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"},
|
||||
|
|
@ -1340,14 +1368,14 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
|
|||
|
||||
[[package]]
|
||||
name = "google-api-python-client"
|
||||
version = "2.87.0"
|
||||
version = "2.88.0"
|
||||
description = "Google API Client Library for Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "google-api-python-client-2.87.0.tar.gz", hash = "sha256:bbea5869877c822d12d318943833d988497b3a18b9ca2386967118074db676f3"},
|
||||
{file = "google_api_python_client-2.87.0-py2.py3-none-any.whl", hash = "sha256:29b52232b159be72a79890b6d9f703cf6d8ebbec0ef6371c5670c1abeca5a9f9"},
|
||||
{file = "google-api-python-client-2.88.0.tar.gz", hash = "sha256:37068453f79ea28e5394a8fe20a4ba620594e7f8541068bea2e844dacdcc9d33"},
|
||||
{file = "google_api_python_client-2.88.0-py2.py3-none-any.whl", hash = "sha256:d003008400a779524ea21b5a3ddc6fc59327d401fb8c37c466d413694c279cae"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2311,14 +2339,14 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.0.184"
|
||||
version = "0.0.186"
|
||||
description = "Building applications with LLMs through composability"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "langchain-0.0.184-py3-none-any.whl", hash = "sha256:819513b3acbd32ad24a55e48e0cabc8f60e09bb4293ca5b655d18e6d24353940"},
|
||||
{file = "langchain-0.0.184.tar.gz", hash = "sha256:e02a73b5cac889e761fc11c12297d4479a9727287c51a251ca2669a8a79ae348"},
|
||||
{file = "langchain-0.0.186-py3-none-any.whl", hash = "sha256:c53ac8943351602dbde84759d32d57fe2e6599279576935a004688e43ee8ffbf"},
|
||||
{file = "langchain-0.0.186.tar.gz", hash = "sha256:36d6d3872727a6f7d6db1b05b13caac35fed19a0d395d2264ed82aae53cfddfd"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2335,12 +2363,12 @@ SQLAlchemy = ">=1.4,<3"
|
|||
tenacity = ">=8.1.0,<9.0.0"
|
||||
|
||||
[package.extras]
|
||||
all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.2.6,<0.3.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=3,<4)", "deeplake (>=3.3.0,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=2.8.6,<3.0.0)", "elasticsearch (>=8,<9)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jina (>=3.14,<4.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.1.dev3,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "momento (>=1.5.0,<2.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.1.2,<2.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "spacy (>=3,<4)", "steamship (>=2.16.9,<3.0.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
|
||||
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|
||||
azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "openai (>=0,<1)"]
|
||||
cohere = ["cohere (>=3,<4)"]
|
||||
docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
|
||||
embeddings = ["sentence-transformers (>=2,<3)"]
|
||||
extended-testing = ["atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "chardet (>=5.1.0,<6.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "psychicapi (>=0.2,<0.3)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "telethon (>=1.28.5,<2.0.0)", "tqdm (>=4.48.0)", "zep-python (>=0.30,<0.31)"]
|
||||
extended-testing = ["atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "chardet (>=5.1.0,<6.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "psychicapi (>=0.2,<0.3)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "telethon (>=1.28.5,<2.0.0)", "tqdm (>=4.48.0)", "zep-python (>=0.30,<0.31)"]
|
||||
llms = ["anthropic (>=0.2.6,<0.3.0)", "cohere (>=3,<4)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
|
||||
openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.4.0)"]
|
||||
qdrant = ["qdrant-client (>=1.1.2,<2.0.0)"]
|
||||
|
|
@ -2348,13 +2376,13 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "langchain-serve"
|
||||
version = "0.0.38"
|
||||
version = "0.0.40"
|
||||
description = "Langchain Serve - serve your langchain apps on Jina AI Cloud."
|
||||
category = "main"
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "langchain-serve-0.0.38.tar.gz", hash = "sha256:649b8e26eebe6b33960c081b388fb7118acbfdc00f97dd935a580ab88aca53d6"},
|
||||
{file = "langchain-serve-0.0.40.tar.gz", hash = "sha256:c60b173fcf0b682fbb70d34e8f485ce168e2229f55cb5c4ffbc26a5206af1c06"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6152,4 +6180,4 @@ deploy = ["langchain-serve"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.9,<3.12"
|
||||
content-hash = "c51c9c7aa21fbe949ea9218412f9979064c4dd8a7fa23da76347a35333306a6e"
|
||||
content-hash = "2bf357ad30f79c68751b34c991b4a73767ceb628657f4133228d4eb487d8a6fb"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "0.0.78"
|
||||
version = "0.0.79"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
maintainers = [
|
||||
|
|
@ -29,7 +29,7 @@ google-search-results = "^2.4.1"
|
|||
google-api-python-client = "^2.79.0"
|
||||
typer = "^0.7.0"
|
||||
gunicorn = "^20.1.0"
|
||||
langchain = "^0.0.184"
|
||||
langchain = "^0.0.186"
|
||||
openai = "^0.27.7"
|
||||
types-pyyaml = "^6.0.12.8"
|
||||
dill = "^0.3.6"
|
||||
|
|
@ -49,13 +49,14 @@ psycopg2-binary = "^2.9.6"
|
|||
pyarrow = "^11.0.0"
|
||||
tiktoken = "^0.3.3"
|
||||
wikipedia = "^1.4.0"
|
||||
langchain-serve = { version = "^0.0.38", optional = true }
|
||||
langchain-serve = { version = ">0.0.39", optional = true }
|
||||
qdrant-client = "^1.2.0"
|
||||
websockets = "^11.0.3"
|
||||
weaviate-client = "^3.19.2"
|
||||
jina = "3.15.2"
|
||||
sentence-transformers = "^2.2.2"
|
||||
ctransformers = "^0.2.2"
|
||||
cohere = "^4.6.0"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from langflow.api.base import (
|
|||
PromptValidationResponse,
|
||||
validate_prompt,
|
||||
)
|
||||
from langflow.graph.nodes import VectorStoreNode
|
||||
from langflow.graph.node.types import VectorStoreNode
|
||||
from langflow.interface.run import build_graph
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.utils.validate import validate_code
|
||||
|
|
|
|||
|
|
@ -42,17 +42,19 @@ documentloaders:
|
|||
- IMSDbLoader
|
||||
- GitbookLoader
|
||||
- ReadTheDocsLoader
|
||||
- SlackDirectoryLoader
|
||||
- NotionDirectoryLoader
|
||||
embeddings:
|
||||
- OpenAIEmbeddings
|
||||
- HuggingFaceEmbeddings
|
||||
- CohereEmbeddings
|
||||
llms:
|
||||
- OpenAI
|
||||
# - AzureOpenAI
|
||||
- ChatOpenAI
|
||||
- HuggingFaceHub
|
||||
- LlamaCpp
|
||||
- CTransformers
|
||||
- Cohere
|
||||
memories:
|
||||
- ConversationBufferMemory
|
||||
- ConversationSummaryMemory
|
||||
|
|
@ -63,9 +65,9 @@ prompts:
|
|||
- ZeroShotPrompt
|
||||
textsplitters:
|
||||
- CharacterTextSplitter
|
||||
- RecursiveCharacterTextSplitter
|
||||
- LatexTextSplitter
|
||||
- PythonCodeTextSplitter
|
||||
# - RecursiveCharacterTextSplitter
|
||||
# - LatexTextSplitter
|
||||
# - PythonCodeTextSplitter
|
||||
toolkits:
|
||||
- OpenAPIToolkit
|
||||
- JsonToolkit
|
||||
|
|
|
|||
|
|
@ -1,4 +1,35 @@
|
|||
from langflow.graph.base import Edge, Node
|
||||
from langflow.graph.graph import Graph
|
||||
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,
|
||||
)
|
||||
|
||||
__all__ = ["Graph", "Node", "Edge"]
|
||||
__all__ = [
|
||||
"Graph",
|
||||
"Node",
|
||||
"Edge",
|
||||
"AgentNode",
|
||||
"ChainNode",
|
||||
"DocumentLoaderNode",
|
||||
"EmbeddingNode",
|
||||
"LLMNode",
|
||||
"MemoryNode",
|
||||
"PromptNode",
|
||||
"TextSplitterNode",
|
||||
"ToolNode",
|
||||
"ToolkitNode",
|
||||
"VectorStoreNode",
|
||||
"WrapperNode",
|
||||
]
|
||||
|
|
|
|||
0
src/backend/langflow/graph/edge/__init__.py
Normal file
0
src/backend/langflow/graph/edge/__init__.py
Normal file
52
src/backend/langflow/graph/edge/base.py
Normal file
52
src/backend/langflow/graph/edge/base.py
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
from langflow.utils.logger import logger
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.node.base import Node
|
||||
|
||||
|
||||
class Edge:
|
||||
def __init__(self, source: "Node", target: "Node"):
|
||||
self.source: "Node" = source
|
||||
self.target: "Node" = target
|
||||
self.validate_edge()
|
||||
|
||||
def validate_edge(self) -> None:
|
||||
# Validate that the outputs of the source node are valid inputs
|
||||
# for the target node
|
||||
self.source_types = self.source.output
|
||||
self.target_reqs = self.target.required_inputs + self.target.optional_inputs
|
||||
# Both lists contain strings and sometimes a string contains the value we are
|
||||
# looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"]
|
||||
# so we need to check if any of the strings in source_types is in target_reqs
|
||||
self.valid = any(
|
||||
output in target_req
|
||||
for output in self.source_types
|
||||
for target_req in self.target_reqs
|
||||
)
|
||||
# Get what type of input the target node is expecting
|
||||
|
||||
self.matched_type = next(
|
||||
(
|
||||
output
|
||||
for output in self.source_types
|
||||
for target_req in self.target_reqs
|
||||
if output in target_req
|
||||
),
|
||||
None,
|
||||
)
|
||||
no_matched_type = self.matched_type is None
|
||||
if no_matched_type:
|
||||
logger.debug(self.source_types)
|
||||
logger.debug(self.target_reqs)
|
||||
if no_matched_type:
|
||||
raise ValueError(
|
||||
f"Edge between {self.source.node_type} and {self.target.node_type} "
|
||||
f"has no matched type"
|
||||
)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return (
|
||||
f"Edge(source={self.source.id}, target={self.target.id}, valid={self.valid}"
|
||||
f", matched_type={self.matched_type})"
|
||||
)
|
||||
0
src/backend/langflow/graph/graph/__init__.py
Normal file
0
src/backend/langflow/graph/graph/__init__.py
Normal file
149
src/backend/langflow/graph/graph/base.py
Normal file
149
src/backend/langflow/graph/graph/base.py
Normal file
|
|
@ -0,0 +1,149 @@
|
|||
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.interface.tools.constants import FILE_TOOLS
|
||||
from langflow.utils import payload
|
||||
|
||||
|
||||
class Graph:
|
||||
"""A class representing a graph of nodes and edges."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]],
|
||||
edges: List[Dict[str, str]],
|
||||
) -> None:
|
||||
self._nodes = nodes
|
||||
self._edges = edges
|
||||
self._build_graph()
|
||||
|
||||
def _build_graph(self) -> None:
|
||||
"""Builds the graph from the nodes and edges."""
|
||||
self.nodes = self._build_nodes()
|
||||
self.edges = self._build_edges()
|
||||
for edge in self.edges:
|
||||
edge.source.add_edge(edge)
|
||||
edge.target.add_edge(edge)
|
||||
|
||||
# This is a hack to make sure that the LLM node is sent to
|
||||
# the toolkit node
|
||||
self._build_node_params()
|
||||
# remove invalid nodes
|
||||
self._remove_invalid_nodes()
|
||||
|
||||
def _build_node_params(self) -> None:
|
||||
"""Identifies and handles the LLM node within the graph."""
|
||||
llm_node = None
|
||||
for node in self.nodes:
|
||||
node._build_params()
|
||||
if isinstance(node, LLMNode):
|
||||
llm_node = node
|
||||
|
||||
if llm_node:
|
||||
for node in self.nodes:
|
||||
if isinstance(node, ToolkitNode):
|
||||
node.params["llm"] = llm_node
|
||||
|
||||
def _remove_invalid_nodes(self) -> None:
|
||||
"""Removes invalid nodes from the graph."""
|
||||
self.nodes = [
|
||||
node
|
||||
for node in self.nodes
|
||||
if self._validate_node(node)
|
||||
or (len(self.nodes) == 1 and len(self.edges) == 0)
|
||||
]
|
||||
|
||||
def _validate_node(self, node: Node) -> 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]:
|
||||
"""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]:
|
||||
"""Returns the nodes connected to a node."""
|
||||
connected_nodes: List[Node] = [
|
||||
edge.source for edge in self.edges if edge.target == node
|
||||
]
|
||||
return connected_nodes
|
||||
|
||||
def build(self) -> List[Node]:
|
||||
"""Builds the graph."""
|
||||
# Get root node
|
||||
root_node = payload.get_root_node(self)
|
||||
if root_node is None:
|
||||
raise ValueError("No root node found")
|
||||
return root_node.build()
|
||||
|
||||
def get_node_neighbors(self, node: Node) -> Dict[Node, int]:
|
||||
"""Returns the neighbors of a node."""
|
||||
neighbors: Dict[Node, int] = {}
|
||||
for edge in self.edges:
|
||||
if edge.source == node:
|
||||
neighbor = edge.target
|
||||
if neighbor not in neighbors:
|
||||
neighbors[neighbor] = 0
|
||||
neighbors[neighbor] += 1
|
||||
elif edge.target == node:
|
||||
neighbor = edge.source
|
||||
if neighbor not in neighbors:
|
||||
neighbors[neighbor] = 0
|
||||
neighbors[neighbor] += 1
|
||||
return neighbors
|
||||
|
||||
def _build_edges(self) -> List[Edge]:
|
||||
"""Builds the edges of the graph."""
|
||||
# Edge takes two nodes as arguments, so we need to build the nodes first
|
||||
# and then build the edges
|
||||
# if we can't find a node, we raise an error
|
||||
|
||||
edges: List[Edge] = []
|
||||
for edge in self._edges:
|
||||
source = self.get_node(edge["source"])
|
||||
target = self.get_node(edge["target"])
|
||||
if source is None:
|
||||
raise ValueError(f"Source node {edge['source']} not found")
|
||||
if target is None:
|
||||
raise ValueError(f"Target node {edge['target']} not found")
|
||||
edges.append(Edge(source, target))
|
||||
return edges
|
||||
|
||||
def _get_node_class(self, node_type: str, node_lc_type: str) -> Type[Node]:
|
||||
"""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
|
||||
|
||||
def _build_nodes(self) -> List[Node]:
|
||||
"""Builds the nodes of the graph."""
|
||||
nodes: List[Node] = []
|
||||
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))
|
||||
|
||||
return nodes
|
||||
|
||||
def get_children_by_node_type(self, node: Node, node_type: str) -> List[Node]:
|
||||
"""Returns the children of a node based on the node type."""
|
||||
children = []
|
||||
node_types = [node.data["type"]]
|
||||
if "node" in node.data:
|
||||
node_types += node.data["node"]["base_classes"]
|
||||
if node_type in node_types:
|
||||
children.append(node)
|
||||
return children
|
||||
49
src/backend/langflow/graph/graph/constants.py
Normal file
49
src/backend/langflow/graph/graph/constants.py
Normal file
|
|
@ -0,0 +1,49 @@
|
|||
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.interface.agents.base import agent_creator
|
||||
from langflow.interface.chains.base import chain_creator
|
||||
from langflow.interface.document_loaders.base import documentloader_creator
|
||||
from langflow.interface.embeddings.base import embedding_creator
|
||||
from langflow.interface.llms.base import llm_creator
|
||||
from langflow.interface.memories.base import memory_creator
|
||||
from langflow.interface.prompts.base import prompt_creator
|
||||
from langflow.interface.text_splitters.base import textsplitter_creator
|
||||
from langflow.interface.toolkits.base import toolkits_creator
|
||||
from langflow.interface.tools.base import tool_creator
|
||||
from langflow.interface.vector_store.base import vectorstore_creator
|
||||
from langflow.interface.wrappers.base import wrapper_creator
|
||||
|
||||
|
||||
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()},
|
||||
}
|
||||
0
src/backend/langflow/graph/node/__init__.py
Normal file
0
src/backend/langflow/graph/node/__init__.py
Normal file
229
src/backend/langflow/graph/node/base.py
Normal file
229
src/backend/langflow/graph/node/base.py
Normal file
|
|
@ -0,0 +1,229 @@
|
|||
from langflow.cache import base as cache_utils
|
||||
from langflow.graph.node.constants import DIRECT_TYPES
|
||||
from langflow.interface import loading
|
||||
from langflow.interface.listing import ALL_TYPES_DICT
|
||||
from langflow.utils.logger import logger
|
||||
from langflow.utils.util import sync_to_async
|
||||
|
||||
|
||||
import contextlib
|
||||
import inspect
|
||||
import types
|
||||
import warnings
|
||||
from typing import Any, Dict, List, Optional
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.graph.edge.base import Edge
|
||||
|
||||
|
||||
class Node:
|
||||
def __init__(self, data: Dict, base_type: Optional[str] = None) -> None:
|
||||
self.id: str = data["id"]
|
||||
self._data = data
|
||||
self.edges: List["Edge"] = []
|
||||
self.base_type: Optional[str] = base_type
|
||||
self._parse_data()
|
||||
self._built_object = None
|
||||
self._built = False
|
||||
|
||||
def _parse_data(self) -> None:
|
||||
self.data = self._data["data"]
|
||||
self.output = self.data["node"]["base_classes"]
|
||||
template_dicts = {
|
||||
key: value
|
||||
for key, value in self.data["node"]["template"].items()
|
||||
if isinstance(value, dict)
|
||||
}
|
||||
|
||||
self.required_inputs = [
|
||||
template_dicts[key]["type"]
|
||||
for key, value in template_dicts.items()
|
||||
if value["required"]
|
||||
]
|
||||
self.optional_inputs = [
|
||||
template_dicts[key]["type"]
|
||||
for key, value in template_dicts.items()
|
||||
if not value["required"]
|
||||
]
|
||||
|
||||
template_dict = self.data["node"]["template"]
|
||||
self.node_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:
|
||||
self.base_type = base_type
|
||||
break
|
||||
|
||||
def _build_params(self):
|
||||
# Some params are required, some are optional
|
||||
# but most importantly, some params are python base classes
|
||||
# like str and others are LangChain objects like LLMChain, BasePromptTemplate
|
||||
# so we need to be able to distinguish between the two
|
||||
|
||||
# The dicts with "type" == "str" are the ones that are python base classes
|
||||
# and most likely have a "value" key
|
||||
|
||||
# So for each key besides "_type" in the template dict, we have a dict
|
||||
# with a "type" key. If the type is not "str", then we need to get the
|
||||
# edge that connects to that node and get the Node with the required data
|
||||
# and use that as the value for the param
|
||||
# If the type is "str", then we need to get the value of the "value" key
|
||||
# and use that as the value for the param
|
||||
template_dict = {
|
||||
key: value
|
||||
for key, value in self.data["node"]["template"].items()
|
||||
if isinstance(value, dict)
|
||||
}
|
||||
params = {}
|
||||
for key, value in template_dict.items():
|
||||
if key == "_type":
|
||||
continue
|
||||
# If the type is not transformable to a python base class
|
||||
# then we need to get the edge that connects to this node
|
||||
if value.get("type") == "file":
|
||||
# Load the type in value.get('suffixes') using
|
||||
# what is inside value.get('content')
|
||||
# value.get('value') is the file name
|
||||
file_name = value.get("value")
|
||||
content = value.get("content")
|
||||
type_to_load = value.get("suffixes")
|
||||
file_path = cache_utils.save_binary_file(
|
||||
content=content, file_name=file_name, accepted_types=type_to_load
|
||||
)
|
||||
|
||||
params[key] = file_path
|
||||
|
||||
elif value.get("type") not in DIRECT_TYPES:
|
||||
# Get the edge that connects to this node
|
||||
edges = [
|
||||
edge
|
||||
for edge in self.edges
|
||||
if edge.target == self and edge.matched_type in value["type"]
|
||||
]
|
||||
|
||||
# Get the output of the node that the edge connects to
|
||||
# if the value['list'] is True, then there will be more
|
||||
# than one time setting to params[key]
|
||||
# so we need to append to a list if it exists
|
||||
# or create a new list if it doesn't
|
||||
|
||||
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"
|
||||
)
|
||||
elif value["list"]:
|
||||
# If this is a list parameter, append all sources to a list
|
||||
params[key] = [edge.source for edge in edges]
|
||||
elif edges:
|
||||
# If a single parameter is found, use its source
|
||||
params[key] = edges[0].source
|
||||
|
||||
elif value["required"] or value.get("value"):
|
||||
# If value does not have value this still passes
|
||||
# but then gives a keyError
|
||||
# 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. ")
|
||||
if value.get("type") == "int":
|
||||
with contextlib.suppress(TypeError, ValueError):
|
||||
new_value = int(new_value) # type: ignore
|
||||
params[key] = new_value
|
||||
|
||||
# Add _type to params
|
||||
self.params = params
|
||||
|
||||
def _build(self):
|
||||
# The params dict is used to build the module
|
||||
# it contains values and keys that point to nodes which
|
||||
# have their own params dict
|
||||
# When build is called, we iterate through the params dict
|
||||
# and if the value is a node, we call build on that node
|
||||
# and use the output of that build as the value for the param
|
||||
# if the value is not a node, then we use the value as the param
|
||||
# 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}")
|
||||
# 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 value == self:
|
||||
del self.params[key]
|
||||
continue
|
||||
result = value.build()
|
||||
# If the key is "func", then we need to use the run method
|
||||
if key == "func":
|
||||
if not isinstance(result, types.FunctionType):
|
||||
# func can be
|
||||
# PythonFunction(code='\ndef upper_case(text: str) -> str:\n return text.upper()\n')
|
||||
# so we need to check if there is an attribute called run
|
||||
if hasattr(result, "run"):
|
||||
result = result.run # type: ignore
|
||||
elif hasattr(result, "get_function"):
|
||||
result = result.get_function() # type: ignore
|
||||
elif inspect.iscoroutinefunction(result):
|
||||
self.params["coroutine"] = result
|
||||
else:
|
||||
# turn result which is a function into a coroutine
|
||||
# so that it can be awaited
|
||||
self.params["coroutine"] = sync_to_async(result)
|
||||
|
||||
self.params[key] = result
|
||||
elif isinstance(value, list) and all(
|
||||
isinstance(node, Node) for node in value
|
||||
):
|
||||
self.params[key] = []
|
||||
for node in value:
|
||||
built = node.build()
|
||||
if isinstance(built, list):
|
||||
self.params[key].extend(built)
|
||||
else:
|
||||
self.params[key].append(built)
|
||||
|
||||
# Get the class from LANGCHAIN_TYPES_DICT
|
||||
# and instantiate it with the params
|
||||
# and return the instance
|
||||
|
||||
try:
|
||||
self._built_object = loading.instantiate_class(
|
||||
node_type=self.node_type,
|
||||
base_type=self.base_type,
|
||||
params=self.params,
|
||||
)
|
||||
except Exception as exc:
|
||||
raise ValueError(
|
||||
f"Error building node {self.node_type}: {str(exc)}"
|
||||
) from exc
|
||||
|
||||
if self._built_object is None:
|
||||
raise ValueError(f"Node type {self.node_type} not found")
|
||||
|
||||
self._built = True
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
if not self._built or force:
|
||||
self._build()
|
||||
|
||||
return self._built_object
|
||||
|
||||
def add_edge(self, edge: "Edge") -> None:
|
||||
self.edges.append(edge)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
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
|
||||
|
||||
def __hash__(self) -> int:
|
||||
return id(self)
|
||||
|
||||
def _built_object_repr(self):
|
||||
return repr(self._built_object)
|
||||
1
src/backend/langflow/graph/node/constants.py
Normal file
1
src/backend/langflow/graph/node/constants.py
Normal file
|
|
@ -0,0 +1 @@
|
|||
DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"]
|
||||
193
src/backend/langflow/graph/node/types.py
Normal file
193
src/backend/langflow/graph/node/types.py
Normal file
|
|
@ -0,0 +1,193 @@
|
|||
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
|
||||
|
||||
|
||||
class AgentNode(Node):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="agents")
|
||||
|
||||
self.tools: List[ToolNode] = []
|
||||
self.chains: List[ChainNode] = []
|
||||
|
||||
def _set_tools_and_chains(self) -> None:
|
||||
for edge in self.edges:
|
||||
source_node = edge.source
|
||||
if isinstance(source_node, ToolNode):
|
||||
self.tools.append(source_node)
|
||||
elif isinstance(source_node, ChainNode):
|
||||
self.chains.append(source_node)
|
||||
|
||||
def build(self, force: bool = False) -> Any:
|
||||
if not self._built or force:
|
||||
self._set_tools_and_chains()
|
||||
# First, build the tools
|
||||
for tool_node in self.tools:
|
||||
tool_node.build()
|
||||
|
||||
# Next, build the chains and the rest
|
||||
for chain_node in self.chains:
|
||||
chain_node.build(tools=self.tools)
|
||||
|
||||
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):
|
||||
def __init__(self, data: Dict):
|
||||
super().__init__(data, base_type="tools")
|
||||
|
||||
|
||||
class PromptNode(Node):
|
||||
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,
|
||||
) -> Any:
|
||||
if not self._built or force:
|
||||
if (
|
||||
"input_variables" not in self.params
|
||||
or self.params["input_variables"] is None
|
||||
):
|
||||
self.params["input_variables"] = []
|
||||
# Check if it is a ZeroShotPrompt and needs a tool
|
||||
if "ShotPrompt" in self.node_type:
|
||||
tools = (
|
||||
[tool_node.build() for tool_node in tools]
|
||||
if tools is not None
|
||||
else []
|
||||
)
|
||||
self.params["tools"] = tools
|
||||
prompt_params = [
|
||||
key
|
||||
for key, value in self.params.items()
|
||||
if isinstance(value, str) and key != "format_instructions"
|
||||
]
|
||||
else:
|
||||
prompt_params = ["template"]
|
||||
for param in prompt_params:
|
||||
prompt_text = self.params[param]
|
||||
variables = extract_input_variables_from_prompt(prompt_text)
|
||||
self.params["input_variables"].extend(variables)
|
||||
self.params["input_variables"] = list(set(self.params["input_variables"]))
|
||||
|
||||
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}()"
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import Dict, List, Optional
|
||||
|
||||
from langchain.agents import loading
|
||||
from langchain.agents import types
|
||||
|
||||
from langflow.custom.customs import get_custom_nodes
|
||||
from langflow.interface.agents.custom import CUSTOM_AGENTS
|
||||
|
|
@ -16,7 +16,7 @@ class AgentCreator(LangChainTypeCreator):
|
|||
@property
|
||||
def type_to_loader_dict(self) -> Dict:
|
||||
if self.type_dict is None:
|
||||
self.type_dict = loading.AGENT_TO_CLASS
|
||||
self.type_dict = types.AGENT_TO_CLASS
|
||||
# Add JsonAgent to the list of agents
|
||||
for name, agent in CUSTOM_AGENTS.items():
|
||||
# TODO: validate AgentType
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ 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
|
||||
|
|
@ -31,7 +32,7 @@ from langflow.utils import util, validate
|
|||
def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
|
||||
"""Instantiate class from module type and key, and params"""
|
||||
params = convert_params_to_sets(params)
|
||||
|
||||
params = convert_kwargs(params)
|
||||
if node_type in CUSTOM_AGENTS:
|
||||
custom_agent = CUSTOM_AGENTS.get(node_type)
|
||||
if custom_agent:
|
||||
|
|
@ -50,6 +51,16 @@ def convert_params_to_sets(params):
|
|||
return params
|
||||
|
||||
|
||||
def convert_kwargs(params):
|
||||
# if *kwargs are passed as a string, convert to dict
|
||||
# first find any key that has kwargs in it
|
||||
kwargs_keys = [key for key in params.keys() if "kwargs" in key]
|
||||
for key in kwargs_keys:
|
||||
if isinstance(params[key], str):
|
||||
params[key] = json.loads(params[key])
|
||||
return params
|
||||
|
||||
|
||||
def instantiate_based_on_type(class_object, base_type, node_type, params):
|
||||
if base_type == "agents":
|
||||
return instantiate_agent(class_object, params)
|
||||
|
|
@ -154,7 +165,6 @@ def instantiate_utility(node_type, class_object, params):
|
|||
def load_flow_from_json(path: str, build=True):
|
||||
"""Load flow from json file"""
|
||||
# This is done to avoid circular imports
|
||||
from langflow.graph import Graph
|
||||
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
flow_graph = json.load(f)
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ 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.graph import Graph
|
||||
from langflow.graph import Graph
|
||||
from langflow.utils.logger import logger
|
||||
|
||||
|
||||
|
|
|
|||
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Reference in a new issue