mirror of
https://github.com/hwchase17/langchain.git
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langchain[patch],community[patch]: Move unit tests that depend on community to community (#21685)
This commit is contained in:
Eugene Yurtsev
GitHub
parent
97a4ae50d2
commit
8607735b80
@ -2,18 +2,16 @@
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Optional
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from typing import Any, Optional
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.tools import Tool
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from langchain_community.chains.openapi.chain import OpenAPIEndpointChain
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from langchain_community.tools.openapi.utils.api_models import APIOperation
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from langchain_community.tools.openapi.utils.openapi_utils import OpenAPISpec
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from langchain_community.utilities.requests import Requests
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if TYPE_CHECKING:
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from langchain.chains.api.openapi.chain import OpenAPIEndpointChain
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class NLATool(Tool):
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"""Natural Language API Tool."""
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16
libs/community/poetry.lock
generated
16
libs/community/poetry.lock
generated
@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
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[[package]]
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name = "aenum"
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@ -3455,6 +3455,7 @@ files = [
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{file = "jq-1.6.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:227b178b22a7f91ae88525810441791b1ca1fc71c86f03190911793be15cec3d"},
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@ -4031,7 +4032,7 @@ url = "../core"
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[[package]]
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name = "langchain-text-splitters"
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version = "0.0.1"
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version = "0.0.2"
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description = "LangChain text splitting utilities"
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optional = false
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python-versions = ">=3.8.1,<4.0"
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@ -6104,8 +6105,6 @@ files = [
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{file = "psycopg2-2.9.9-cp37-cp37m-win32.whl", hash = "sha256:5e0d98cade4f0e0304d7d6f25bbfbc5bd186e07b38eac65379309c4ca3193efa"},
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{file = "psycopg2-2.9.9-cp37-cp37m-win_amd64.whl", hash = "sha256:7e2dacf8b009a1c1e843b5213a87f7c544b2b042476ed7755be813eaf4e8347a"},
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{file = "psycopg2-2.9.9-cp38-cp38-win32.whl", hash = "sha256:ff432630e510709564c01dafdbe996cb552e0b9f3f065eb89bdce5bd31fabf4c"},
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@ -6148,7 +6147,6 @@ files = [
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{file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2d423c8d8a3c82d08fe8af900ad5b613ce3632a1249fd6a223941d0735fce493"},
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{file = "psycopg2_binary-2.9.9-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e5afae772c00980525f6d6ecf7cbca55676296b580c0e6abb407f15f3706996"},
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@ -6157,8 +6155,6 @@ files = [
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{file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:911dda9c487075abd54e644ccdf5e5c16773470a6a5d3826fda76699410066fb"},
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{file = "psycopg2_binary-2.9.9-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:57fede879f08d23c85140a360c6a77709113efd1c993923c59fde17aa27599fe"},
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{file = "psycopg2_binary-2.9.9-cp312-cp312-win32.whl", hash = "sha256:64cf30263844fa208851ebb13b0732ce674d8ec6a0c86a4e160495d299ba3c93"},
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{file = "psycopg2_binary-2.9.9-cp312-cp312-win_amd64.whl", hash = "sha256:81ff62668af011f9a48787564ab7eded4e9fb17a4a6a74af5ffa6a457400d2ab"},
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{file = "psycopg2_binary-2.9.9-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:2293b001e319ab0d869d660a704942c9e2cce19745262a8aba2115ef41a0a42a"},
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{file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:03ef7df18daf2c4c07e2695e8cfd5ee7f748a1d54d802330985a78d2a5a6dca9"},
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{file = "psycopg2_binary-2.9.9-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a602ea5aff39bb9fac6308e9c9d82b9a35c2bf288e184a816002c9fae930b77"},
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@ -6691,26 +6687,31 @@ python-versions = ">=3.8"
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files = [
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{file = "PyMuPDF-1.23.26-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:645a05321aecc8c45739f71f0eb574ce33138d19189582ffa5241fea3a8e2549"},
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{file = "PyMuPDF-1.23.26-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:2dfc9e010669ae92fade6fb72aaea49ebe3b8dcd7ee4dcbbe50115abcaa4d3fe"},
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{file = "PyMuPDF-1.23.26-cp310-none-manylinux2014_aarch64.whl", hash = "sha256:734ee380b3abd038602be79114194a3cb74ac102b7c943bcb333104575922c50"},
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{file = "PyMuPDF-1.23.26-cp310-none-manylinux2014_x86_64.whl", hash = "sha256:b22f8d854f8196ad5b20308c1cebad3d5189ed9f0988acbafa043947ea7e6c55"},
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{file = "PyMuPDF-1.23.26-cp310-none-win32.whl", hash = "sha256:cc0f794e3466bc96b5bf79d42fbc1551428751e3fef38ebc10ac70396b676144"},
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{file = "PyMuPDF-1.23.26-cp310-none-win_amd64.whl", hash = "sha256:2eb701247d8e685a24e45899d1175f01a3ce5fc792a4431c91fbb68633b29298"},
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{file = "PyMuPDF-1.23.26-cp311-none-macosx_10_9_x86_64.whl", hash = "sha256:e2804a64bb57da414781e312fb0561f6be67658ad57ed4a73dce008b23fc70a6"},
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{file = "PyMuPDF-1.23.26-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:97b40bb22e3056874634617a90e0ed24a5172cf71791b9e25d1d91c6743bc567"},
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{file = "PyMuPDF-1.23.26-cp311-none-manylinux2014_aarch64.whl", hash = "sha256:fab8833559bc47ab26ce736f915b8fc1dd37c108049b90396f7cd5e1004d7593"},
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{file = "PyMuPDF-1.23.26-cp311-none-manylinux2014_x86_64.whl", hash = "sha256:f25aafd3e7fb9d7761a22acf2b67d704f04cc36d4dc33a3773f0eb3f4ec3606f"},
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{file = "PyMuPDF-1.23.26-cp311-none-win32.whl", hash = "sha256:05e672ed3e82caca7ef02a88ace30130b1dd392a1190f03b2b58ffe7aa331400"},
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{file = "PyMuPDF-1.23.26-cp311-none-win_amd64.whl", hash = "sha256:92b3c4dd4d0491d495f333be2d41f4e1c155a409bc9d04b5ff29655dccbf4655"},
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{file = "PyMuPDF-1.23.26-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:42ad2b819b90ce1947e11b90ec5085889df0a2e3aa0207bc97ecacfc6157cabc"},
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{file = "PyMuPDF-1.23.26-cp312-none-manylinux2014_x86_64.whl", hash = "sha256:bb42d4b8407b4de7cb58c28f01449f16f32a6daed88afb41108f1aeb3552bdd4"},
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{file = "PyMuPDF-1.23.26-cp312-none-win32.whl", hash = "sha256:c40d044411615e6f0baa7d3d933b3032cf97e168c7fa77d1be8a46008c109aee"},
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{file = "PyMuPDF-1.23.26-cp312-none-win_amd64.whl", hash = "sha256:3f876533aa7f9a94bcd9a0225ce72571b7808260903fec1d95c120bc842fb52d"},
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{file = "PyMuPDF-1.23.26-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:0bbb0cf6593e53524f3fc26fb5e6ead17c02c64791caec7c4afe61b677dedf80"},
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{file = "PyMuPDF-1.23.26-cp38-none-manylinux2014_aarch64.whl", hash = "sha256:5ef4360f20015673c20cf59b7e19afc97168795188c584254ed3778cde43ce77"},
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{file = "PyMuPDF-1.23.26-cp38-none-manylinux2014_x86_64.whl", hash = "sha256:d7cd88842b2e7f4c71eef4d87c98c35646b80b60e6375392d7ce40e519261f59"},
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{file = "PyMuPDF-1.23.26-cp38-none-win32.whl", hash = "sha256:6577e2f473625e2d0df5f5a3bf1e4519e94ae749733cc9937994d1b256687bfa"},
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{file = "PyMuPDF-1.23.26-cp38-none-win_amd64.whl", hash = "sha256:fbe1a3255b2cd0d769b2da2c4efdd0c0f30d4961a1aac02c0f75cf951b337aa4"},
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{file = "PyMuPDF-1.23.26-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:73fce034f2afea886a59ead2d0caedf27e2b2a8558b5da16d0286882e0b1eb82"},
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{file = "PyMuPDF-1.23.26-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:b3de8618b7cb5b36db611083840b3bcf09b11a893e2d8262f4e042102c7e65de"},
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{file = "PyMuPDF-1.23.26-cp39-none-manylinux2014_aarch64.whl", hash = "sha256:879e7f5ad35709d8760ab6103c3d5dac8ab8043a856ab3653fd324af7358ee87"},
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{file = "PyMuPDF-1.23.26-cp39-none-manylinux2014_x86_64.whl", hash = "sha256:deee96c2fd415ded7b5070d8d5b2c60679aee6ed0e28ac0d2cb998060d835c2c"},
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{file = "PyMuPDF-1.23.26-cp39-none-win32.whl", hash = "sha256:9f7f4ef99dd8ac97fb0b852efa3dcbee515798078b6c79a6a13c7b1e7c5d41a4"},
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{file = "PyMuPDF-1.23.26-cp39-none-win_amd64.whl", hash = "sha256:ba9a54552c7afb9ec85432c765e2fa9a81413acfaa7d70db7c9b528297749e5b"},
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@ -7151,7 +7152,6 @@ files = [
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{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
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{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
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{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
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{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
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{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
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{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
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{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
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@ -1,14 +1,10 @@
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from pathlib import Path
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from tempfile import TemporaryDirectory
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import pytest
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from langchain_core.language_models import FakeListLLM
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from langchain_core.tools import Tool
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from langchain.agents.agent_types import AgentType
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from langchain.agents.initialize import initialize_agent, load_agent
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pytest.importorskip("langchain_community")
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from langchain_core.language_models import FakeListLLM
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from langchain_core.tools import Tool
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def test_mrkl_serialization() -> None:
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@ -2,10 +2,10 @@
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from typing import List
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import pytest
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from langchain_core.embeddings import FakeEmbeddings
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from langchain.evaluation.loading import EvaluatorType, load_evaluators
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from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
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from langchain_core.embeddings import FakeEmbeddings
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from tests.unit_tests.llms.fake_chat_model import FakeChatModel
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from tests.unit_tests.llms.fake_llm import FakeLLM
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@ -26,7 +26,6 @@ def test_load_evaluators(evaluator_type: EvaluatorType) -> None:
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)
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@pytest.mark.community
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@pytest.mark.parametrize(
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"evaluator_types",
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[
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0
libs/community/tests/unit_tests/imports/__init__.py
Normal file
0
libs/community/tests/unit_tests/imports/__init__.py
Normal file
@ -0,0 +1,24 @@
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"""Verify proxy imports from langchain to community are behaving as expected."""
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def test_all_proxy_llms_are_llm_subclasses() -> None:
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"""Simple test to make sure all things are subclasses of BaseLLM."""
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from langchain import llms
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from langchain_core.language_models import BaseLLM
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for cls in llms.__all__:
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assert issubclass(getattr(llms, cls), BaseLLM)
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def test_vectorstores() -> None:
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"""Simple test to make sure all things can be imported."""
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from langchain import vectorstores
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from langchain_core.vectorstores import VectorStore
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for cls in vectorstores.__all__:
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if cls not in [
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"AlibabaCloudOpenSearchSettings",
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"ClickhouseSettings",
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"MyScaleSettings",
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]:
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assert issubclass(getattr(vectorstores, cls), VectorStore)
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0
libs/community/tests/unit_tests/load/__init__.py
Normal file
0
libs/community/tests/unit_tests/load/__init__.py
Normal file
@ -0,0 +1,875 @@
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# serializer version: 1
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# name: test_person
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'''
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{
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"lc": 1,
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"type": "constructor",
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"id": [
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"tests",
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"unit_tests",
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"load",
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"test_dump",
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"Person"
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],
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"kwargs": {
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"secret": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"SECRET"
|
||||
]
|
||||
},
|
||||
"you_can_see_me": "hello"
|
||||
}
|
||||
}
|
||||
'''
|
||||
# ---
|
||||
# name: test_person.1
|
||||
'''
|
||||
{
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"my",
|
||||
"special",
|
||||
"namespace",
|
||||
"SpecialPerson"
|
||||
],
|
||||
"kwargs": {
|
||||
"secret": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"SECRET"
|
||||
]
|
||||
},
|
||||
"you_can_see_me": "hello",
|
||||
"another_secret": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"ANOTHER_SECRET"
|
||||
]
|
||||
},
|
||||
"another_visible": "bye"
|
||||
}
|
||||
}
|
||||
'''
|
||||
# ---
|
||||
# name: test_person_with_kwargs
|
||||
'{"lc":1,"type":"constructor","id":["tests","unit_tests","load","test_dump","Person"],"kwargs":{"secret":{"lc":1,"type":"secret","id":["SECRET"]},"you_can_see_me":"hello"}}'
|
||||
# ---
|
||||
# name: test_serialize_llmchain
|
||||
'''
|
||||
{
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"chains",
|
||||
"llm",
|
||||
"LLMChain"
|
||||
],
|
||||
"kwargs": {
|
||||
"prompt": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"kwargs": {
|
||||
"input_variables": [
|
||||
"name"
|
||||
],
|
||||
"template": "hello {name}!",
|
||||
"template_format": "f-string"
|
||||
},
|
||||
"name": "PromptTemplate",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "PromptInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"name": "PromptTemplate"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "PromptTemplateOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"llms",
|
||||
"openai",
|
||||
"OpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model_name": "davinci",
|
||||
"temperature": 0.5,
|
||||
"max_tokens": 256,
|
||||
"top_p": 1,
|
||||
"n": 1,
|
||||
"best_of": 1,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
},
|
||||
"openai_proxy": "",
|
||||
"batch_size": 20,
|
||||
"max_retries": 2,
|
||||
"disallowed_special": "all"
|
||||
},
|
||||
"name": "OpenAI",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "OpenAIInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"llms",
|
||||
"openai",
|
||||
"OpenAI"
|
||||
],
|
||||
"name": "OpenAI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "OpenAIOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"output_key": "text",
|
||||
"output_parser": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"kwargs": {},
|
||||
"name": "StrOutputParser",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"name": "StrOutputParser"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"return_final_only": true
|
||||
},
|
||||
"name": "LLMChain",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "ChainInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"chains",
|
||||
"llm",
|
||||
"LLMChain"
|
||||
],
|
||||
"name": "LLMChain"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "ChainOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
'''
|
||||
# ---
|
||||
# name: test_serialize_llmchain_chat
|
||||
'''
|
||||
{
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"chains",
|
||||
"llm",
|
||||
"LLMChain"
|
||||
],
|
||||
"kwargs": {
|
||||
"prompt": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"chat",
|
||||
"ChatPromptTemplate"
|
||||
],
|
||||
"kwargs": {
|
||||
"input_variables": [
|
||||
"name"
|
||||
],
|
||||
"messages": [
|
||||
{
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"chat",
|
||||
"HumanMessagePromptTemplate"
|
||||
],
|
||||
"kwargs": {
|
||||
"prompt": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"kwargs": {
|
||||
"input_variables": [
|
||||
"name"
|
||||
],
|
||||
"template": "hello {name}!",
|
||||
"template_format": "f-string"
|
||||
},
|
||||
"name": "PromptTemplate",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "PromptInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"name": "PromptTemplate"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "PromptTemplateOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"name": "ChatPromptTemplate",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "PromptInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"chat",
|
||||
"ChatPromptTemplate"
|
||||
],
|
||||
"name": "ChatPromptTemplate"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "ChatPromptTemplateOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"chat_models",
|
||||
"openai",
|
||||
"ChatOpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model_name": "davinci",
|
||||
"temperature": 0.5,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
},
|
||||
"openai_proxy": "",
|
||||
"max_retries": 2,
|
||||
"n": 1
|
||||
},
|
||||
"name": "ChatOpenAI",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "ChatOpenAIInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"chat_models",
|
||||
"openai",
|
||||
"ChatOpenAI"
|
||||
],
|
||||
"name": "ChatOpenAI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "ChatOpenAIOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"output_key": "text",
|
||||
"output_parser": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"kwargs": {},
|
||||
"name": "StrOutputParser",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"name": "StrOutputParser"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"return_final_only": true
|
||||
},
|
||||
"name": "LLMChain",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "ChainInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"chains",
|
||||
"llm",
|
||||
"LLMChain"
|
||||
],
|
||||
"name": "LLMChain"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "ChainOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
'''
|
||||
# ---
|
||||
# name: test_serialize_llmchain_with_non_serializable_arg
|
||||
'''
|
||||
{
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"chains",
|
||||
"llm",
|
||||
"LLMChain"
|
||||
],
|
||||
"kwargs": {
|
||||
"prompt": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"kwargs": {
|
||||
"input_variables": [
|
||||
"name"
|
||||
],
|
||||
"template": "hello {name}!",
|
||||
"template_format": "f-string"
|
||||
},
|
||||
"name": "PromptTemplate",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "PromptInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"name": "PromptTemplate"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "PromptTemplateOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"llms",
|
||||
"openai",
|
||||
"OpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model_name": "davinci",
|
||||
"temperature": 0.5,
|
||||
"max_tokens": 256,
|
||||
"top_p": 1,
|
||||
"n": 1,
|
||||
"best_of": 1,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
},
|
||||
"openai_proxy": "",
|
||||
"batch_size": 20,
|
||||
"max_retries": 2,
|
||||
"disallowed_special": "all"
|
||||
},
|
||||
"name": "OpenAI",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "OpenAIInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"llms",
|
||||
"openai",
|
||||
"OpenAI"
|
||||
],
|
||||
"name": "OpenAI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "OpenAIOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"output_key": "text",
|
||||
"output_parser": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"kwargs": {},
|
||||
"name": "StrOutputParser",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"name": "StrOutputParser"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"return_final_only": true
|
||||
},
|
||||
"name": "LLMChain",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "ChainInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"chains",
|
||||
"llm",
|
||||
"LLMChain"
|
||||
],
|
||||
"name": "LLMChain"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "ChainOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
'''
|
||||
# ---
|
||||
# name: test_serialize_openai_llm
|
||||
'''
|
||||
{
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"llms",
|
||||
"openai",
|
||||
"OpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model_name": "davinci",
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 256,
|
||||
"top_p": 1,
|
||||
"n": 1,
|
||||
"best_of": 1,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
},
|
||||
"openai_proxy": "",
|
||||
"batch_size": 20,
|
||||
"max_retries": 2,
|
||||
"disallowed_special": "all"
|
||||
},
|
||||
"name": "OpenAI",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "OpenAIInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"llms",
|
||||
"openai",
|
||||
"OpenAI"
|
||||
],
|
||||
"name": "OpenAI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "OpenAIOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
'''
|
||||
# ---
|
||||
255
libs/community/tests/unit_tests/load/test_dump.py
Normal file
255
libs/community/tests/unit_tests/load/test_dump.py
Normal file
@ -0,0 +1,255 @@
|
||||
"""Test for Serializable base class"""
|
||||
|
||||
import json
|
||||
import os
|
||||
from typing import Any, Dict, List
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytest
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain_core.load.dump import dumps
|
||||
from langchain_core.load.serializable import Serializable
|
||||
from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
|
||||
from langchain_core.prompts.prompt import PromptTemplate
|
||||
from langchain_core.pydantic_v1 import Field, root_validator
|
||||
from langchain_core.tracers.langchain import LangChainTracer
|
||||
|
||||
|
||||
class Person(Serializable):
|
||||
secret: str
|
||||
|
||||
you_can_see_me: str = "hello"
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
return True
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"secret": "SECRET"}
|
||||
|
||||
@property
|
||||
def lc_attributes(self) -> Dict[str, str]:
|
||||
return {"you_can_see_me": self.you_can_see_me}
|
||||
|
||||
|
||||
class SpecialPerson(Person):
|
||||
another_secret: str
|
||||
|
||||
another_visible: str = "bye"
|
||||
|
||||
@classmethod
|
||||
def get_lc_namespace(cls) -> List[str]:
|
||||
return ["my", "special", "namespace"]
|
||||
|
||||
# Gets merged with parent class's secrets
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {"another_secret": "ANOTHER_SECRET"}
|
||||
|
||||
# Gets merged with parent class's attributes
|
||||
@property
|
||||
def lc_attributes(self) -> Dict[str, str]:
|
||||
return {"another_visible": self.another_visible}
|
||||
|
||||
|
||||
class NotSerializable:
|
||||
pass
|
||||
|
||||
|
||||
def test_person(snapshot: Any) -> None:
|
||||
p = Person(secret="hello")
|
||||
assert dumps(p, pretty=True) == snapshot
|
||||
sp = SpecialPerson(another_secret="Wooo", secret="Hmm")
|
||||
assert dumps(sp, pretty=True) == snapshot
|
||||
assert Person.lc_id() == ["tests", "unit_tests", "load", "test_dump", "Person"]
|
||||
assert SpecialPerson.lc_id() == ["my", "special", "namespace", "SpecialPerson"]
|
||||
|
||||
|
||||
def test_typeerror() -> None:
|
||||
assert (
|
||||
dumps({(1, 2): 3})
|
||||
== """{"lc": 1, "type": "not_implemented", "id": ["builtins", "dict"], "repr": "{(1, 2): 3}"}""" # noqa: E501
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_openai_llm(snapshot: Any) -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
with patch.dict(os.environ, {"LANGCHAIN_API_KEY": "test-api-key"}):
|
||||
llm = OpenAI( # type: ignore[call-arg]
|
||||
model="davinci",
|
||||
temperature=0.5,
|
||||
openai_api_key="hello",
|
||||
# This is excluded from serialization
|
||||
callbacks=[LangChainTracer()],
|
||||
)
|
||||
llm.temperature = 0.7 # this is reflected in serialization
|
||||
assert dumps(llm, pretty=True) == snapshot
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain(snapshot: Any) -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
assert dumps(chain, pretty=True) == snapshot
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain_env() -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
|
||||
import os
|
||||
|
||||
has_env = "OPENAI_API_KEY" in os.environ
|
||||
if not has_env:
|
||||
os.environ["OPENAI_API_KEY"] = "env_variable"
|
||||
|
||||
llm_2 = OpenAI(model="davinci", temperature=0.5) # type: ignore[call-arg]
|
||||
prompt_2 = PromptTemplate.from_template("hello {name}!")
|
||||
chain_2 = LLMChain(llm=llm_2, prompt=prompt_2)
|
||||
|
||||
assert dumps(chain_2, pretty=True) == dumps(chain, pretty=True)
|
||||
|
||||
if not has_env:
|
||||
del os.environ["OPENAI_API_KEY"]
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain_chat(snapshot: Any) -> None:
|
||||
from langchain_community.chat_models.openai import ChatOpenAI
|
||||
|
||||
llm = ChatOpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[HumanMessagePromptTemplate.from_template("hello {name}!")]
|
||||
)
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
assert dumps(chain, pretty=True) == snapshot
|
||||
|
||||
import os
|
||||
|
||||
has_env = "OPENAI_API_KEY" in os.environ
|
||||
if not has_env:
|
||||
os.environ["OPENAI_API_KEY"] = "env_variable"
|
||||
|
||||
llm_2 = ChatOpenAI(model="davinci", temperature=0.5) # type: ignore[call-arg]
|
||||
prompt_2 = ChatPromptTemplate.from_messages(
|
||||
[HumanMessagePromptTemplate.from_template("hello {name}!")]
|
||||
)
|
||||
chain_2 = LLMChain(llm=llm_2, prompt=prompt_2)
|
||||
|
||||
assert dumps(chain_2, pretty=True) == dumps(chain, pretty=True)
|
||||
|
||||
if not has_env:
|
||||
del os.environ["OPENAI_API_KEY"]
|
||||
|
||||
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain_with_non_serializable_arg(snapshot: Any) -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
llm = OpenAI( # type: ignore[call-arg]
|
||||
model="davinci",
|
||||
temperature=0.5,
|
||||
openai_api_key="hello",
|
||||
client=NotSerializable,
|
||||
)
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
assert dumps(chain, pretty=True) == snapshot
|
||||
|
||||
|
||||
def test_person_with_kwargs(snapshot: Any) -> None:
|
||||
person = Person(secret="hello")
|
||||
assert dumps(person, separators=(",", ":")) == snapshot
|
||||
|
||||
|
||||
def test_person_with_invalid_kwargs() -> None:
|
||||
person = Person(secret="hello")
|
||||
with pytest.raises(TypeError):
|
||||
dumps(person, invalid_kwarg="hello")
|
||||
|
||||
|
||||
class TestClass(Serializable):
|
||||
my_favorite_secret: str = Field(alias="my_favorite_secret_alias")
|
||||
my_other_secret: str = Field()
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def get_from_env(cls, values: Dict) -> Dict:
|
||||
"""Get the values from the environment."""
|
||||
if "my_favorite_secret" not in values:
|
||||
values["my_favorite_secret"] = os.getenv("MY_FAVORITE_SECRET")
|
||||
if "my_other_secret" not in values:
|
||||
values["my_other_secret"] = os.getenv("MY_OTHER_SECRET")
|
||||
return values
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
return True
|
||||
|
||||
@classmethod
|
||||
def get_lc_namespace(cls) -> List[str]:
|
||||
return ["my", "special", "namespace"]
|
||||
|
||||
@property
|
||||
def lc_secrets(self) -> Dict[str, str]:
|
||||
return {
|
||||
"my_favorite_secret": "MY_FAVORITE_SECRET",
|
||||
"my_other_secret": "MY_OTHER_SECRET",
|
||||
}
|
||||
|
||||
|
||||
def test_aliases_hidden() -> None:
|
||||
test_class = TestClass(my_favorite_secret="hello", my_other_secret="world") # type: ignore[call-arg]
|
||||
dumped = json.loads(dumps(test_class, pretty=True))
|
||||
expected_dump = {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": ["my", "special", "namespace", "TestClass"],
|
||||
"kwargs": {
|
||||
"my_favorite_secret": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": ["MY_FAVORITE_SECRET"],
|
||||
},
|
||||
"my_other_secret": {"lc": 1, "type": "secret", "id": ["MY_OTHER_SECRET"]},
|
||||
},
|
||||
}
|
||||
assert dumped == expected_dump
|
||||
# Check while patching the os environment
|
||||
with patch.dict(
|
||||
os.environ, {"MY_FAVORITE_SECRET": "hello", "MY_OTHER_SECRET": "world"}
|
||||
):
|
||||
test_class = TestClass() # type: ignore[call-arg]
|
||||
dumped = json.loads(dumps(test_class, pretty=True))
|
||||
|
||||
# Check by alias
|
||||
test_class = TestClass(my_favorite_secret_alias="hello", my_other_secret="world") # type: ignore[call-arg]
|
||||
dumped = json.loads(dumps(test_class, pretty=True))
|
||||
expected_dump = {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": ["my", "special", "namespace", "TestClass"],
|
||||
"kwargs": {
|
||||
"my_favorite_secret": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": ["MY_FAVORITE_SECRET"],
|
||||
},
|
||||
"my_other_secret": {"lc": 1, "type": "secret", "id": ["MY_OTHER_SECRET"]},
|
||||
},
|
||||
}
|
||||
assert dumped == expected_dump
|
||||
@ -3,7 +3,6 @@ import inspect
|
||||
import pkgutil
|
||||
from types import ModuleType
|
||||
|
||||
import pytest
|
||||
from langchain_core.load.mapping import SERIALIZABLE_MAPPING
|
||||
|
||||
|
||||
@ -55,7 +54,6 @@ def import_all_modules(package_name: str) -> dict:
|
||||
return classes
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
def test_import_all_modules() -> None:
|
||||
"""Test import all modules works as expected"""
|
||||
all_modules = import_all_modules("langchain")
|
||||
@ -79,7 +77,6 @@ def test_import_all_modules() -> None:
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
def test_serializable_mapping() -> None:
|
||||
to_skip = {
|
||||
# This should have had a different namespace, as it was never
|
||||
@ -1,11 +1,8 @@
|
||||
import pytest
|
||||
from langchain.retrievers.ensemble import EnsembleRetriever
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.embeddings import FakeEmbeddings
|
||||
|
||||
from langchain.retrievers.ensemble import EnsembleRetriever
|
||||
|
||||
pytest.importorskip("langchain_community")
|
||||
|
||||
|
||||
@pytest.mark.requires("rank_bm25")
|
||||
def test_ensemble_retriever_get_relevant_docs() -> None:
|
||||
@ -4,20 +4,26 @@ from typing import Dict, Generator, List, Union
|
||||
|
||||
import pytest
|
||||
from _pytest.fixtures import FixtureRequest
|
||||
from langchain.globals import get_llm_cache, set_llm_cache
|
||||
from langchain_core.caches import InMemoryCache
|
||||
from langchain_core.language_models import FakeListChatModel, FakeListLLM
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from langchain_core.language_models.llms import BaseLLM
|
||||
from langchain_core.load import dumps
|
||||
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
|
||||
from langchain_core.outputs import ChatGeneration, Generation
|
||||
from sqlalchemy import create_engine
|
||||
from langchain_core.outputs import ChatGeneration
|
||||
from sqlalchemy import Column, Integer, Sequence, String, create_engine
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
pytest.importorskip("langchain_community")
|
||||
try:
|
||||
from sqlalchemy.orm import declarative_base
|
||||
except ImportError:
|
||||
from sqlalchemy.ext.declarative import declarative_base
|
||||
|
||||
from langchain.globals import get_llm_cache, set_llm_cache
|
||||
from langchain_core.outputs import Generation, LLMResult
|
||||
|
||||
from langchain_community.cache import SQLAlchemyCache # noqa: E402
|
||||
from tests.unit_tests.llms.fake_llm import FakeLLM
|
||||
|
||||
|
||||
def get_sqlite_cache() -> SQLAlchemyCache:
|
||||
@ -210,3 +216,44 @@ def create_llm_string(llm: Union[BaseLLM, BaseChatModel]) -> str:
|
||||
_dict: Dict = llm.dict()
|
||||
_dict["stop"] = None
|
||||
return str(sorted([(k, v) for k, v in _dict.items()]))
|
||||
|
||||
|
||||
def test_sql_alchemy_cache() -> None:
|
||||
"""Test custom_caching behavior."""
|
||||
Base = declarative_base()
|
||||
|
||||
class FulltextLLMCache(Base): # type: ignore
|
||||
"""Postgres table for fulltext-indexed LLM Cache."""
|
||||
|
||||
__tablename__ = "llm_cache_fulltext"
|
||||
id = Column(Integer, Sequence("cache_id"), primary_key=True)
|
||||
prompt = Column(String, nullable=False)
|
||||
llm = Column(String, nullable=False)
|
||||
idx = Column(Integer)
|
||||
response = Column(String)
|
||||
|
||||
engine = create_engine("sqlite://")
|
||||
|
||||
from langchain_community.cache import SQLAlchemyCache
|
||||
|
||||
set_llm_cache(SQLAlchemyCache(engine, FulltextLLMCache))
|
||||
llm = FakeLLM()
|
||||
params = llm.dict()
|
||||
params["stop"] = None
|
||||
llm_string = str(sorted([(k, v) for k, v in params.items()]))
|
||||
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
|
||||
output = llm.generate(["foo", "bar", "foo"])
|
||||
expected_cache_output = [Generation(text="foo")]
|
||||
cache_output = get_llm_cache().lookup("bar", llm_string)
|
||||
assert cache_output == expected_cache_output
|
||||
set_llm_cache(None)
|
||||
expected_generations = [
|
||||
[Generation(text="fizz")],
|
||||
[Generation(text="foo")],
|
||||
[Generation(text="fizz")],
|
||||
]
|
||||
expected_output = LLMResult(
|
||||
generations=expected_generations,
|
||||
llm_output=None,
|
||||
)
|
||||
assert output == expected_output
|
||||
|
||||
@ -23,7 +23,7 @@ from langchain._api import create_importer
|
||||
|
||||
_module_lookup = {
|
||||
"APIChain": "langchain.chains.api.base",
|
||||
"OpenAPIEndpointChain": "langchain.chains.api.openapi.chain",
|
||||
"OpenAPIEndpointChain": "langchain_community.chains.openapi.chain",
|
||||
"AnalyzeDocumentChain": "langchain.chains.combine_documents.base",
|
||||
"MapReduceDocumentsChain": "langchain.chains.combine_documents.map_reduce",
|
||||
"MapRerankDocumentsChain": "langchain.chains.combine_documents.map_rerank",
|
||||
@ -36,23 +36,23 @@ _module_lookup = {
|
||||
"ConversationalRetrievalChain": "langchain.chains.conversational_retrieval.base",
|
||||
"generate_example": "langchain.chains.example_generator",
|
||||
"FlareChain": "langchain.chains.flare.base",
|
||||
"ArangoGraphQAChain": "langchain.chains.graph_qa.arangodb",
|
||||
"GraphQAChain": "langchain.chains.graph_qa.base",
|
||||
"GraphCypherQAChain": "langchain.chains.graph_qa.cypher",
|
||||
"FalkorDBQAChain": "langchain.chains.graph_qa.falkordb",
|
||||
"HugeGraphQAChain": "langchain.chains.graph_qa.hugegraph",
|
||||
"KuzuQAChain": "langchain.chains.graph_qa.kuzu",
|
||||
"NebulaGraphQAChain": "langchain.chains.graph_qa.nebulagraph",
|
||||
"NeptuneOpenCypherQAChain": "langchain.chains.graph_qa.neptune_cypher",
|
||||
"NeptuneSparqlQAChain": "langchain.chains.graph_qa.neptune_sparql",
|
||||
"OntotextGraphDBQAChain": "langchain.chains.graph_qa.ontotext_graphdb",
|
||||
"GraphSparqlQAChain": "langchain.chains.graph_qa.sparql",
|
||||
"ArangoGraphQAChain": "langchain_community.chains.graph_qa.arangodb",
|
||||
"GraphQAChain": "langchain_community.chains.graph_qa.base",
|
||||
"GraphCypherQAChain": "langchain_community.chains.graph_qa.cypher",
|
||||
"FalkorDBQAChain": "langchain_community.chains.graph_qa.falkordb",
|
||||
"HugeGraphQAChain": "langchain_community.chains.graph_qa.hugegraph",
|
||||
"KuzuQAChain": "langchain_community.chains.graph_qa.kuzu",
|
||||
"NebulaGraphQAChain": "langchain_community.chains.graph_qa.nebulagraph",
|
||||
"NeptuneOpenCypherQAChain": "langchain_community.chains.graph_qa.neptune_cypher",
|
||||
"NeptuneSparqlQAChain": "langchain_community.chains.graph_qa.neptune_sparql",
|
||||
"OntotextGraphDBQAChain": "langchain_community.chains.graph_qa.ontotext_graphdb",
|
||||
"GraphSparqlQAChain": "langchain_community.chains.graph_qa.sparql",
|
||||
"create_history_aware_retriever": "langchain.chains.history_aware_retriever",
|
||||
"HypotheticalDocumentEmbedder": "langchain.chains.hyde.base",
|
||||
"LLMChain": "langchain.chains.llm",
|
||||
"LLMCheckerChain": "langchain.chains.llm_checker.base",
|
||||
"LLMMathChain": "langchain.chains.llm_math.base",
|
||||
"LLMRequestsChain": "langchain.chains.llm_requests",
|
||||
"LLMRequestsChain": "langchain_community.chains.llm_requests",
|
||||
"LLMSummarizationCheckerChain": "langchain.chains.llm_summarization_checker.base",
|
||||
"load_chain": "langchain.chains.loading",
|
||||
"MapReduceChain": "langchain.chains.mapreduce",
|
||||
|
||||
@ -343,7 +343,6 @@ addopts = "--strict-markers --strict-config --durations=5 --snapshot-warn-unused
|
||||
markers = [
|
||||
"requires: mark tests as requiring a specific library",
|
||||
"scheduled: mark tests to run in scheduled testing",
|
||||
"community: mark tests that require langchain-community to be installed",
|
||||
"compile: mark placeholder test used to compile integration tests without running them"
|
||||
]
|
||||
asyncio_mode = "auto"
|
||||
|
||||
@ -1,14 +1,4 @@
|
||||
"""Test base LLM functionality."""
|
||||
import importlib
|
||||
|
||||
import pytest
|
||||
from sqlalchemy import Column, Integer, Sequence, String, create_engine
|
||||
|
||||
try:
|
||||
from sqlalchemy.orm import declarative_base
|
||||
except ImportError:
|
||||
from sqlalchemy.ext.declarative import declarative_base
|
||||
|
||||
from langchain_core.caches import InMemoryCache
|
||||
from langchain_core.outputs import Generation, LLMResult
|
||||
|
||||
@ -50,48 +40,3 @@ def test_caching() -> None:
|
||||
llm_output=None,
|
||||
)
|
||||
assert output == expected_output
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
importlib.util.find_spec("langchain_community") is None,
|
||||
reason="langchain_community not installed",
|
||||
)
|
||||
def test_custom_caching() -> None:
|
||||
"""Test custom_caching behavior."""
|
||||
Base = declarative_base()
|
||||
|
||||
class FulltextLLMCache(Base): # type: ignore
|
||||
"""Postgres table for fulltext-indexed LLM Cache."""
|
||||
|
||||
__tablename__ = "llm_cache_fulltext"
|
||||
id = Column(Integer, Sequence("cache_id"), primary_key=True)
|
||||
prompt = Column(String, nullable=False)
|
||||
llm = Column(String, nullable=False)
|
||||
idx = Column(Integer)
|
||||
response = Column(String)
|
||||
|
||||
engine = create_engine("sqlite://")
|
||||
|
||||
from langchain_community.cache import SQLAlchemyCache
|
||||
|
||||
set_llm_cache(SQLAlchemyCache(engine, FulltextLLMCache))
|
||||
llm = FakeLLM()
|
||||
params = llm.dict()
|
||||
params["stop"] = None
|
||||
llm_string = str(sorted([(k, v) for k, v in params.items()]))
|
||||
get_llm_cache().update("foo", llm_string, [Generation(text="fizz")])
|
||||
output = llm.generate(["foo", "bar", "foo"])
|
||||
expected_cache_output = [Generation(text="foo")]
|
||||
cache_output = get_llm_cache().lookup("bar", llm_string)
|
||||
assert cache_output == expected_cache_output
|
||||
set_llm_cache(None)
|
||||
expected_generations = [
|
||||
[Generation(text="fizz")],
|
||||
[Generation(text="foo")],
|
||||
[Generation(text="fizz")],
|
||||
]
|
||||
expected_output = LLMResult(
|
||||
generations=expected_generations,
|
||||
llm_output=None,
|
||||
)
|
||||
assert output == expected_output
|
||||
|
||||
@ -1,6 +1,3 @@
|
||||
import pytest
|
||||
from langchain_core.language_models import BaseLLM
|
||||
|
||||
from langchain import llms
|
||||
|
||||
EXPECT_ALL = [
|
||||
@ -91,10 +88,3 @@ EXPECT_ALL = [
|
||||
def test_all_imports() -> None:
|
||||
"""Simple test to make sure all things can be imported."""
|
||||
assert set(llms.__all__) == set(EXPECT_ALL)
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
def test_all_subclasses() -> None:
|
||||
"""Simple test to make sure all things are subclasses of BaseLLM."""
|
||||
for cls in llms.__all__:
|
||||
assert issubclass(getattr(llms, cls), BaseLLM)
|
||||
|
||||
@ -8,12 +8,7 @@ from unittest.mock import patch
|
||||
import pytest
|
||||
from langchain_core.load.dump import dumps
|
||||
from langchain_core.load.serializable import Serializable
|
||||
from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
|
||||
from langchain_core.prompts.prompt import PromptTemplate
|
||||
from langchain_core.pydantic_v1 import Field, root_validator
|
||||
from langchain_core.tracers.langchain import LangChainTracer
|
||||
|
||||
from langchain.chains.llm import LLMChain
|
||||
|
||||
|
||||
class Person(Serializable):
|
||||
@ -74,105 +69,6 @@ def test_typeerror() -> None:
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_openai_llm(snapshot: Any) -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
with patch.dict(os.environ, {"LANGCHAIN_API_KEY": "test-api-key"}):
|
||||
llm = OpenAI( # type: ignore[call-arg]
|
||||
model="davinci",
|
||||
temperature=0.5,
|
||||
openai_api_key="hello",
|
||||
# This is excluded from serialization
|
||||
callbacks=[LangChainTracer()],
|
||||
)
|
||||
llm.temperature = 0.7 # this is reflected in serialization
|
||||
assert dumps(llm, pretty=True) == snapshot
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain(snapshot: Any) -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
assert dumps(chain, pretty=True) == snapshot
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain_env() -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
|
||||
import os
|
||||
|
||||
has_env = "OPENAI_API_KEY" in os.environ
|
||||
if not has_env:
|
||||
os.environ["OPENAI_API_KEY"] = "env_variable"
|
||||
|
||||
llm_2 = OpenAI(model="davinci", temperature=0.5) # type: ignore[call-arg]
|
||||
prompt_2 = PromptTemplate.from_template("hello {name}!")
|
||||
chain_2 = LLMChain(llm=llm_2, prompt=prompt_2)
|
||||
|
||||
assert dumps(chain_2, pretty=True) == dumps(chain, pretty=True)
|
||||
|
||||
if not has_env:
|
||||
del os.environ["OPENAI_API_KEY"]
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain_chat(snapshot: Any) -> None:
|
||||
from langchain_community.chat_models.openai import ChatOpenAI
|
||||
|
||||
llm = ChatOpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[HumanMessagePromptTemplate.from_template("hello {name}!")]
|
||||
)
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
assert dumps(chain, pretty=True) == snapshot
|
||||
|
||||
import os
|
||||
|
||||
has_env = "OPENAI_API_KEY" in os.environ
|
||||
if not has_env:
|
||||
os.environ["OPENAI_API_KEY"] = "env_variable"
|
||||
|
||||
llm_2 = ChatOpenAI(model="davinci", temperature=0.5) # type: ignore[call-arg]
|
||||
prompt_2 = ChatPromptTemplate.from_messages(
|
||||
[HumanMessagePromptTemplate.from_template("hello {name}!")]
|
||||
)
|
||||
chain_2 = LLMChain(llm=llm_2, prompt=prompt_2)
|
||||
|
||||
assert dumps(chain_2, pretty=True) == dumps(chain, pretty=True)
|
||||
|
||||
if not has_env:
|
||||
del os.environ["OPENAI_API_KEY"]
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
@pytest.mark.requires("openai")
|
||||
def test_serialize_llmchain_with_non_serializable_arg(snapshot: Any) -> None:
|
||||
from langchain_community.llms.openai import OpenAI
|
||||
|
||||
llm = OpenAI( # type: ignore[call-arg]
|
||||
model="davinci",
|
||||
temperature=0.5,
|
||||
openai_api_key="hello",
|
||||
client=NotSerializable,
|
||||
)
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
assert dumps(chain, pretty=True) == snapshot
|
||||
|
||||
|
||||
def test_person_with_kwargs(snapshot: Any) -> None:
|
||||
person = Person(secret="hello")
|
||||
assert dumps(person, separators=(",", ":")) == snapshot
|
||||
|
||||
@ -7,12 +7,10 @@ from langchain_core.prompts.prompt import PromptTemplate
|
||||
|
||||
from langchain.chains.llm import LLMChain
|
||||
|
||||
pytest.importorskip(
|
||||
"langchain_community",
|
||||
)
|
||||
pytest.importorskip("langchain_openai", reason="langchain_openai not installed")
|
||||
pytest.importorskip("langchain_community", reason="langchain_community not installed")
|
||||
|
||||
|
||||
from langchain_community.llms.openai import ( # noqa: E402, # ignore: community-import
|
||||
from langchain_community.llms.openai import ( # noqa: E402 # ignore: community-import
|
||||
OpenAI as CommunityOpenAI,
|
||||
)
|
||||
|
||||
|
||||
@ -92,24 +92,3 @@ def test_test_group_dependencies(poetry_conf: Mapping[str, Any]) -> None:
|
||||
"requests-mock",
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
def test_imports() -> None:
|
||||
"""Test that you can import all top level things okay."""
|
||||
from langchain_community.callbacks import OpenAICallbackHandler # noqa: F401
|
||||
from langchain_community.chat_models import ChatOpenAI # noqa: F401
|
||||
from langchain_community.document_loaders import BSHTMLLoader # noqa: F401
|
||||
from langchain_community.embeddings import OpenAIEmbeddings # noqa: F401
|
||||
from langchain_community.llms import OpenAI # noqa: F401
|
||||
from langchain_community.retrievers import VespaRetriever # noqa: F401
|
||||
from langchain_community.tools import DuckDuckGoSearchResults # noqa: F401
|
||||
from langchain_community.utilities import (
|
||||
SearchApiAPIWrapper, # noqa: F401
|
||||
SerpAPIWrapper, # noqa: F401
|
||||
)
|
||||
from langchain_community.vectorstores import FAISS # noqa: F401
|
||||
from langchain_core.prompts import BasePromptTemplate # noqa: F401
|
||||
|
||||
from langchain.agents import OpenAIFunctionsAgent # noqa: F401
|
||||
from langchain.chains import LLMChain # noqa: F401
|
||||
|
||||
@ -1,13 +1,12 @@
|
||||
import importlib
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
# Attempt to recursively import all modules in langchain
|
||||
PKG_ROOT = Path(__file__).parent.parent.parent
|
||||
|
||||
COMMUNITY_NOT_INSTALLED = importlib.util.find_spec("langchain_community") is None
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
def test_import_all() -> None:
|
||||
"""Generate the public API for this package."""
|
||||
library_code = PKG_ROOT / "langchain"
|
||||
@ -26,8 +25,13 @@ def test_import_all() -> None:
|
||||
|
||||
for name in all:
|
||||
# Attempt to import the name from the module
|
||||
obj = getattr(mod, name)
|
||||
assert obj is not None
|
||||
try:
|
||||
obj = getattr(mod, name)
|
||||
assert obj is not None
|
||||
except ModuleNotFoundError as e:
|
||||
# If the module is not installed, we suppress the error
|
||||
if "Module langchain_community" in str(e) and COMMUNITY_NOT_INSTALLED:
|
||||
pass
|
||||
|
||||
|
||||
def test_import_all_using_dir() -> None:
|
||||
@ -42,6 +46,9 @@ def test_import_all_using_dir() -> None:
|
||||
# Without init
|
||||
module_name = module_name.rsplit(".", 1)[0]
|
||||
|
||||
if module_name.startswith("langchain_community.") and COMMUNITY_NOT_INSTALLED:
|
||||
continue
|
||||
|
||||
try:
|
||||
mod = importlib.import_module(module_name)
|
||||
except ModuleNotFoundError as e:
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
import pytest
|
||||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from langchain import vectorstores
|
||||
|
||||
|
||||
@pytest.mark.community
|
||||
def test_all_imports() -> None:
|
||||
"""Simple test to make sure all things can be imported."""
|
||||
for cls in vectorstores.__all__:
|
||||
if cls not in [
|
||||
"AlibabaCloudOpenSearchSettings",
|
||||
"ClickhouseSettings",
|
||||
"MyScaleSettings",
|
||||
]:
|
||||
assert issubclass(getattr(vectorstores, cls), VectorStore)
|
||||
Loading…
Reference in New Issue
Block a user