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Merge branch 'master' into harrison/improve-integration-docs

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ccurme 2024-12-03 15:31:38 -05:00 committed by GitHub
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3
.github/workflows/_compile_integration_test.yml vendored
View File

@ -13,7 +13,7 @@ on:
description: "Python version to use"
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:
@ -21,6 +21,7 @@ jobs:
run:
working-directory: ${{ inputs.working-directory }}
runs-on: ubuntu-latest
timeout-minutes: 20
name: "poetry run pytest -m compile tests/integration_tests #${{ inputs.python-version }}"
steps:
- uses: actions/checkout@v4

2
.github/workflows/_integration_test.yml vendored
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@ -12,7 +12,7 @@ on:
description: "Python version to use"
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:

3
.github/workflows/_lint.yml vendored
View File

@ -13,7 +13,7 @@ on:
description: "Python version to use"
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
# This env var allows us to get inline annotations when ruff has complaints.
@ -23,6 +23,7 @@ jobs:
build:
name: "make lint #${{ inputs.python-version }}"
runs-on: ubuntu-latest
timeout-minutes: 20
steps:
- uses: actions/checkout@v4

3
.github/workflows/_release.yml vendored
View File

@ -21,7 +21,7 @@ on:
env:
PYTHON_VERSION: "3.11"
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:
@ -167,6 +167,7 @@ jobs:
- release-notes
- test-pypi-publish
runs-on: ubuntu-latest
timeout-minutes: 20
steps:
- uses: actions/checkout@v4

3
.github/workflows/_test.yml vendored
View File

@ -13,7 +13,7 @@ on:
description: "Python version to use"
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:
@ -21,6 +21,7 @@ jobs:
run:
working-directory: ${{ inputs.working-directory }}
runs-on: ubuntu-latest
timeout-minutes: 20
name: "make test #${{ inputs.python-version }}"
steps:
- uses: actions/checkout@v4

3
.github/workflows/_test_doc_imports.yml vendored
View File

@ -9,11 +9,12 @@ on:
description: "Python version to use"
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:
runs-on: ubuntu-latest
timeout-minutes: 20
name: "check doc imports #${{ inputs.python-version }}"
steps:
- uses: actions/checkout@v4

3
.github/workflows/_test_pydantic.yml vendored
View File

@ -18,7 +18,7 @@ on:
description: "Pydantic version to test."
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:
@ -26,6 +26,7 @@ jobs:
run:
working-directory: ${{ inputs.working-directory }}
runs-on: ubuntu-latest
timeout-minutes: 20
name: "make test # pydantic: ~=${{ inputs.pydantic-version }}, python: ${{ inputs.python-version }}, "
steps:
- uses: actions/checkout@v4

2
.github/workflows/_test_release.yml vendored
View File

@ -14,7 +14,7 @@ on:
description: "Release from a non-master branch (danger!)"
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
PYTHON_VERSION: "3.10"
jobs:

2
.github/workflows/api_doc_build.yml vendored
View File

@ -5,7 +5,7 @@ on:
schedule:
- cron: '0 13 * * *'
env:
POETRY_VERSION: "1.8.1"
POETRY_VERSION: "1.8.4"
PYTHON_VERSION: "3.11"
jobs:

3
.github/workflows/check_diffs.yml vendored
View File

@ -17,7 +17,7 @@ concurrency:
cancel-in-progress: true
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:
@ -119,6 +119,7 @@ jobs:
job-configs: ${{ fromJson(needs.build.outputs.extended-tests) }}
fail-fast: false
runs-on: ubuntu-latest
timeout-minutes: 20
defaults:
run:
working-directory: ${{ matrix.job-configs.working-directory }}

2
.github/workflows/run_notebooks.yml vendored
View File

@ -15,7 +15,7 @@ on:
- cron: '0 13 * * *'
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
jobs:
build:

64
.github/workflows/scheduled_test.yml vendored
View File

@ -2,32 +2,70 @@ name: Scheduled tests
on:
workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI
inputs:
working-directory-force:
type: string
description: "From which folder this pipeline executes - defaults to all in matrix - example value: libs/partners/anthropic"
python-version-force:
type: string
description: "Python version to use - defaults to 3.9 and 3.11 in matrix - example value: 3.9"
schedule:
- cron: '0 13 * * *'
env:
POETRY_VERSION: "1.7.1"
POETRY_VERSION: "1.8.4"
DEFAULT_LIBS: >
[
"libs/partners/openai",
"libs/partners/anthropic",
"libs/partners/fireworks",
"libs/partners/groq",
"libs/partners/mistralai",
"libs/partners/google-vertexai",
"libs/partners/google-genai",
"libs/partners/aws"
]
jobs:
compute-matrix:
if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
runs-on: ubuntu-latest
name: Compute matrix
outputs:
matrix: ${{ steps.set-matrix.outputs.matrix }}
steps:
- name: Set matrix
id: set-matrix
env:
DEFAULT_LIBS: ${{ env.DEFAULT_LIBS }}
WORKING_DIRECTORY_FORCE: ${{ github.event.inputs.working-directory-force || '' }}
PYTHON_VERSION_FORCE: ${{ github.event.inputs.python-version-force || '' }}
run: |
# echo "matrix=..." where matrix is a json formatted str with keys python-version and working-directory
# python-version should default to 3.9 and 3.11, but is overridden to [PYTHON_VERSION_FORCE] if set
# working-directory should default to DEFAULT_LIBS, but is overridden to [WORKING_DIRECTORY_FORCE] if set
python_version='["3.9", "3.11"]'
working_directory="$DEFAULT_LIBS"
if [ -n "$PYTHON_VERSION_FORCE" ]; then
python_version="[\"$PYTHON_VERSION_FORCE\"]"
fi
if [ -n "$WORKING_DIRECTORY_FORCE" ]; then
working_directory="[\"$WORKING_DIRECTORY_FORCE\"]"
fi
matrix="{\"python-version\": $python_version, \"working-directory\": $working_directory}"
echo $matrix
echo "matrix=$matrix" >> $GITHUB_OUTPUT
build:
if: github.repository_owner == 'langchain-ai' || github.event_name != 'schedule'
name: Python ${{ matrix.python-version }} - ${{ matrix.working-directory }}
runs-on: ubuntu-latest
needs: [compute-matrix]
timeout-minutes: 20
strategy:
fail-fast: false
matrix:
python-version:
- "3.9"
- "3.11"
working-directory:
- "libs/partners/openai"
- "libs/partners/anthropic"
- "libs/partners/fireworks"
- "libs/partners/groq"
- "libs/partners/mistralai"
- "libs/partners/google-vertexai"
- "libs/partners/google-genai"
- "libs/partners/aws"
python-version: ${{ fromJSON(needs.compute-matrix.outputs.matrix).python-version }}
working-directory: ${{ fromJSON(needs.compute-matrix.outputs.matrix).working-directory }}
steps:
- uses: actions/checkout@v4

2
docs/docs/integrations/chat/mistralai.ipynb
View File

@ -39,7 +39,7 @@
"### Credentials\n",
"\n",
"\n",
"A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:"
"A valid [API key](https://console.mistral.ai/api-keys/) is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:"
]
},
{

2
docs/docs/integrations/providers/pinecone.mdx
View File

@ -32,7 +32,7 @@ For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook]
### Pinecone Hybrid Search
```bash
pip install pinecone-client pinecone-text
pip install pinecone pinecone-text
```
```python

2
docs/docs/integrations/retrievers/pinecone_hybrid_search.ipynb
View File

@ -24,7 +24,7 @@
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet pinecone-client pinecone-text pinecone-notebooks"
"%pip install --upgrade --quiet pinecone pinecone-text pinecone-notebooks"
]
},
{

2
docs/docs/tutorials/retrievers.ipynb
View File

@ -245,7 +245,7 @@
"\n",
"import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
"\n",
"<EmbeddingTabs customVarName=\"embeddings_model\" />"
"<EmbeddingTabs customVarName=\"embeddings\" />"
]
},
{

14
docs/src/theme/ChatModelTabs.js
View File

@ -15,6 +15,7 @@ import CodeBlock from "@theme-original/CodeBlock";
* @property {string} [googleParams] - Parameters for Google chat model. Defaults to `model="gemini-pro"`
* @property {string} [togetherParams] - Parameters for Together chat model. Defaults to `model="mistralai/Mixtral-8x7B-Instruct-v0.1"`
* @property {string} [nvidiaParams] - Parameters for Nvidia NIM model. Defaults to `model="meta/llama3-70b-instruct"`
* @property {string} [databricksParams] - Parameters for Databricks model. Defaults to `endpoint="databricks-meta-llama-3-1-70b-instruct"`
* @property {string} [awsBedrockParams] - Parameters for AWS Bedrock chat model.
* @property {boolean} [hideOpenai] - Whether or not to hide OpenAI chat model.
* @property {boolean} [hideAnthropic] - Whether or not to hide Anthropic chat model.
@ -27,6 +28,7 @@ import CodeBlock from "@theme-original/CodeBlock";
* @property {boolean} [hideAzure] - Whether or not to hide Microsoft Azure OpenAI chat model.
* @property {boolean} [hideNvidia] - Whether or not to hide NVIDIA NIM model.
* @property {boolean} [hideAWS] - Whether or not to hide AWS models.
* @property {boolean} [hideDatabricks] - Whether or not to hide Databricks models.
* @property {string} [customVarName] - Custom variable name for the model. Defaults to `model`.
*/
@ -46,6 +48,7 @@ export default function ChatModelTabs(props) {
azureParams,
nvidiaParams,
awsBedrockParams,
databricksParams,
hideOpenai,
hideAnthropic,
hideCohere,
@ -57,6 +60,7 @@ export default function ChatModelTabs(props) {
hideAzure,
hideNvidia,
hideAWS,
hideDatabricks,
customVarName,
} = props;
@ -79,6 +83,7 @@ export default function ChatModelTabs(props) {
`\n azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],\n azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],\n openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],\n`;
const nvidiaParamsOrDefault = nvidiaParams ?? `model="meta/llama3-70b-instruct"`
const awsBedrockParamsOrDefault = awsBedrockParams ?? `model="anthropic.claude-3-5-sonnet-20240620-v1:0",\n beta_use_converse_api=True`;
const databricksParamsOrDefault = databricksParams ?? `endpoint="databricks-meta-llama-3-1-70b-instruct"`
const llmVarName = customVarName ?? "model";
@ -182,6 +187,15 @@ export default function ChatModelTabs(props) {
default: false,
shouldHide: hideTogether,
},
{
value: "Databricks",
label: "Databricks",
text: `from databricks_langchain import ChatDatabricks\n\nos.environ["DATABRICKS_HOST"] = "https://example.staging.cloud.databricks.com/serving-endpoints"\n\n${llmVarName} = ChatDatabricks(${databricksParamsOrDefault})`,
apiKeyName: "DATABRICKS_TOKEN",
packageName: "databricks-langchain",
default: false,
shouldHide: hideDatabricks,
},
];
return (

7
libs/community/langchain_community/adapters/openai.py
View File

@ -91,6 +91,8 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
additional_kwargs["function_call"] = dict(function_call)
if tool_calls := _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = tool_calls
if context := _dict.get("context"):
additional_kwargs["context"] = context
return AIMessage(content=content, additional_kwargs=additional_kwargs)
elif role == "system":
return SystemMessage(content=_dict.get("content", ""))
@ -135,6 +137,11 @@ def convert_message_to_dict(message: BaseMessage) -> dict:
# If tool calls only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
if "context" in message.additional_kwargs:
message_dict["context"] = message.additional_kwargs["context"]
# If context only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):

4
libs/community/langchain_community/vectorstores/pinecone.py
View File

@ -31,7 +31,7 @@ def _import_pinecone() -> Any:
except ImportError as e:
raise ImportError(
"Could not import pinecone python package. "
"Please install it with `pip install pinecone-client`."
"Please install it with `pip3 install pinecone`."
) from e
return pinecone
@ -48,7 +48,7 @@ def _is_pinecone_v3() -> bool:
class Pinecone(VectorStore):
"""`Pinecone` vector store.
To use, you should have the ``pinecone-client`` python package installed.
To use, you should have the ``pinecone`` python package installed.
This version of Pinecone is deprecated. Please use `langchain_pinecone.Pinecone`
instead.

27
libs/community/pyproject.toml
View File

@ -1,10 +1,10 @@
[build-system]
requires = ["poetry-core>=1.0.0"]
requires = [ "poetry-core>=1.0.0",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-community"
version = "0.3.8"
version = "0.3.9"
description = "Community contributed LangChain integrations."
authors = []
license = "MIT"
@ -12,15 +12,12 @@ readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
[tool.ruff]
exclude = [
"tests/examples/non-utf8-encoding.py",
"tests/integration_tests/examples/non-utf8-encoding.py",
]
exclude = [ "tests/examples/non-utf8-encoding.py", "tests/integration_tests/examples/non-utf8-encoding.py",]
[tool.mypy]
ignore_missing_imports = "True"
disallow_untyped_defs = "True"
exclude = ["notebooks", "examples", "example_data"]
exclude = [ "notebooks", "examples", "example_data",]
[tool.codespell]
skip = ".git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples,*.trig"
@ -53,24 +50,16 @@ version = ">=1.26.2,<3"
python = ">=3.12"
[tool.ruff.lint]
select = ["E", "F", "I", "T201"]
select = [ "E", "F", "I", "T201",]
[tool.coverage.run]
omit = ["tests/*"]
omit = [ "tests/*",]
[tool.pytest.ini_options]
addopts = "--strict-markers --strict-config --durations=5 --snapshot-warn-unused -vv"
markers = [
"requires: mark tests as requiring a specific library",
"scheduled: mark tests to run in scheduled testing",
"compile: mark placeholder test used to compile integration tests without running them",
]
markers = [ "requires: mark tests as requiring a specific library", "scheduled: mark tests to run in scheduled testing", "compile: mark placeholder test used to compile integration tests without running them",]
asyncio_mode = "auto"
filterwarnings = [
"ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",
"ignore::langchain_core._api.deprecation.LangChainDeprecationWarning:test",
"ignore::langchain_core._api.deprecation.LangChainPendingDeprecationWarning:test",
]
filterwarnings = [ "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning", "ignore::langchain_core._api.deprecation.LangChainDeprecationWarning:test", "ignore::langchain_core._api.deprecation.LangChainPendingDeprecationWarning:test",]
[tool.poetry.group.test]
optional = true

2
libs/core/langchain_core/messages/utils.py
View File

@ -556,6 +556,8 @@ def merge_message_runs(
else:
last_chunk = _msg_to_chunk(last)
curr_chunk = _msg_to_chunk(curr)
if curr_chunk.response_metadata:
curr_chunk.response_metadata.clear()
if (
isinstance(last_chunk.content, str)
and isinstance(curr_chunk.content, str)

18
libs/core/tests/unit_tests/messages/test_utils.py
View File

@ -59,6 +59,24 @@ def test_merge_message_runs_str_without_separator(
assert messages == messages_model_copy
def test_merge_message_runs_response_metadata() -> None:
messages = [
AIMessage("foo", id="1", response_metadata={"input_tokens": 1}),
AIMessage("bar", id="2", response_metadata={"input_tokens": 2}),
]
expected = [
AIMessage(
"foo\nbar",
id="1",
response_metadata={"input_tokens": 1},
)
]
actual = merge_message_runs(messages)
assert actual == expected
# Check it's not mutated
assert messages[1].response_metadata == {"input_tokens": 2}
def test_merge_message_runs_content() -> None:
messages = [
AIMessage("foo", id="1"),

420
libs/partners/mistralai/poetry.lock generated
View File

@ -325,13 +325,13 @@ files = [
[[package]]
name = "huggingface-hub"
version = "0.26.2"
version = "0.26.3"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = false
python-versions = ">=3.8.0"
files = [
{file = "huggingface_hub-0.26.2-py3-none-any.whl", hash = "sha256:98c2a5a8e786c7b2cb6fdeb2740893cba4d53e312572ed3d8afafda65b128c46"},
{file = "huggingface_hub-0.26.2.tar.gz", hash = "sha256:b100d853465d965733964d123939ba287da60a547087783ddff8a323f340332b"},
{file = "huggingface_hub-0.26.3-py3-none-any.whl", hash = "sha256:e66aa99e569c2d5419240a9e553ad07245a5b1300350bfbc5a4945cf7432991b"},
{file = "huggingface_hub-0.26.3.tar.gz", hash = "sha256:90e1fe62ffc26757a073aaad618422b899ccf9447c2bba8c902a90bef5b42e1d"},
]
[package.dependencies]
@ -409,7 +409,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.3.19"
version = "0.3.21"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"
@ -434,7 +434,7 @@ url = "../../core"
[[package]]
name = "langchain-tests"
version = "0.3.2"
version = "0.3.4"
description = "Standard tests for LangChain implementations"
optional = false
python-versions = ">=3.9,<4.0"
@ -443,7 +443,7 @@ develop = true
[package.dependencies]
httpx = "^0.27.0"
langchain-core = "^0.3.15"
langchain-core = "^0.3.19"
pytest = ">=7,<9"
syrupy = "^4"
@ -453,18 +453,18 @@ url = "../../standard-tests"
[[package]]
name = "langsmith"
version = "0.1.143"
version = "0.1.147"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.143-py3-none-any.whl", hash = "sha256:ba0d827269e9b03a90fababe41fa3e4e3f833300b95add10184f7e67167dde6f"},
{file = "langsmith-0.1.143.tar.gz", hash = "sha256:4c5159e5cd84b3f8499433009e72d2076dd2daf6c044ac8a3611b30d0d0161c5"},
{file = "langsmith-0.1.147-py3-none-any.whl", hash = "sha256:7166fc23b965ccf839d64945a78e9f1157757add228b086141eb03a60d699a15"},
{file = "langsmith-0.1.147.tar.gz", hash = "sha256:2e933220318a4e73034657103b3b1a3a6109cc5db3566a7e8e03be8d6d7def7a"},
]
[package.dependencies]
httpx = ">=0.23.0,<1"
orjson = ">=3.9.14,<4.0.0"
orjson = {version = ">=3.9.14,<4.0.0", markers = "platform_python_implementation != \"PyPy\""}
pydantic = [
{version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
{version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
@ -472,6 +472,9 @@ pydantic = [
requests = ">=2,<3"
requests-toolbelt = ">=1.0.0,<2.0.0"
[package.extras]
langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
[[package]]
name = "mypy"
version = "1.13.0"
@ -538,69 +541,86 @@ files = [
[[package]]
name = "orjson"
version = "3.10.11"
version = "3.10.12"
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
optional = false
python-versions = ">=3.8"
files = [
{file = "orjson-3.10.11-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:6dade64687f2bd7c090281652fe18f1151292d567a9302b34c2dbb92a3872f1f"},
{file = "orjson-3.10.11-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82f07c550a6ccd2b9290849b22316a609023ed851a87ea888c0456485a7d196a"},
{file = "orjson-3.10.11-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bd9a187742d3ead9df2e49240234d728c67c356516cf4db018833a86f20ec18c"},
{file = "orjson-3.10.11-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:77b0fed6f209d76c1c39f032a70df2d7acf24b1812ca3e6078fd04e8972685a3"},
{file = "orjson-3.10.11-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:63fc9d5fe1d4e8868f6aae547a7b8ba0a2e592929245fff61d633f4caccdcdd6"},
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[[package]]
name = "tqdm"
version = "4.67.0"
version = "4.67.1"
description = "Fast, Extensible Progress Meter"
optional = false
python-versions = ">=3.7"
files = [
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]
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
dev = ["nbval", "pytest (>=6)", "pytest-asyncio (>=0.24)", "pytest-cov", "pytest-timeout"]
discord = ["requests"]
notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
@ -1117,4 +1167,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "4a86b5a8d678aa583d949272e47d16b9a1ecde22ce590d903bcb1a386dc4ecdf"
content-hash = "a89781c86a72349bba46de58ba88102bf2ae1f58a9b0fe79d952caecb656a585"

15
libs/partners/mistralai/pyproject.toml
View File

@ -1,10 +1,10 @@
[build-system]
requires = ["poetry-core>=1.0.0"]
requires = [ "poetry-core>=1.0.0",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-mistralai"
version = "0.2.2"
version = "0.2.3"
description = "An integration package connecting Mistral and LangChain"
authors = []
readme = "README.md"
@ -20,24 +20,21 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.15"
langchain-core = "^0.3.21"
tokenizers = ">=0.15.1,<1"
httpx = ">=0.25.2,<1"
httpx-sse = ">=0.3.1,<1"
pydantic = ">=2,<3"
[tool.ruff.lint]
select = ["E", "F", "I", "T201"]
select = [ "E", "F", "I", "T201",]
[tool.coverage.run]
omit = ["tests/*"]
omit = [ "tests/*",]
[tool.pytest.ini_options]
addopts = "--strict-markers --strict-config --durations=5"
markers = [
"requires: mark tests as requiring a specific library",
"compile: mark placeholder test used to compile integration tests without running them",
]
markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them",]
asyncio_mode = "auto"
[tool.poetry.group.test]

2
libs/partners/ollama/poetry.lock generated
View File

@ -309,7 +309,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.3.20"
version = "0.3.21"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.9,<4.0"

2
libs/partners/ollama/pyproject.toml
View File

@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-ollama"
version = "0.2.2rc1"
version = "0.2.1"
description = "An integration package connecting Ollama and LangChain"
authors = []
readme = "README.md"

6
libs/partners/openai/langchain_openai/llms/base.py
View File

@ -113,7 +113,7 @@ class BaseOpenAI(BaseLLM):
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None."""
logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
logit_bias: Optional[Dict[str, float]] = None
"""Adjust the probability of specific tokens being generated."""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
@ -205,11 +205,13 @@ class BaseOpenAI(BaseLLM):
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"n": self.n,
"logit_bias": self.logit_bias,
"seed": self.seed,
"logprobs": self.logprobs,
}
if self.logit_bias is not None:
normal_params["logit_bias"] = self.logit_bias
if self.max_tokens is not None:
normal_params["max_tokens"] = self.max_tokens

203
libs/partners/openai/poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
@ -386,84 +386,86 @@ files = [
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version = "0.8.0"
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[[package]]

2
libs/partners/openai/pyproject.toml
View File

@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-openai"
version = "0.2.10"
version = "0.2.11"
description = "An integration package connecting OpenAI and LangChain"
authors = []
readme = "README.md"

28
libs/partners/pinecone/langchain_pinecone/embeddings.py
View File

@ -33,7 +33,7 @@ class PineconeEmbeddings(BaseModel, Embeddings):
# Clients
_client: PineconeClient = PrivateAttr(default=None)
_async_client: aiohttp.ClientSession = PrivateAttr(default=None)
_async_client: Optional[aiohttp.ClientSession] = PrivateAttr(default=None)
model: str
"""Model to use for example 'multilingual-e5-large'."""
# Config
@ -65,6 +65,19 @@ class PineconeEmbeddings(BaseModel, Embeddings):
protected_namespaces=(),
)
@property
def async_client(self) -> aiohttp.ClientSession:
"""Lazily initialize the async client."""
if self._async_client is None:
self._async_client = aiohttp.ClientSession(
headers={
"Api-Key": self.pinecone_api_key.get_secret_value(),
"Content-Type": "application/json",
"X-Pinecone-API-Version": "2024-10",
}
)
return self._async_client
@model_validator(mode="before")
@classmethod
def set_default_config(cls, values: dict) -> Any:
@ -92,15 +105,8 @@ class PineconeEmbeddings(BaseModel, Embeddings):
client = PineconeClient(api_key=api_key_str, source_tag="langchain")
self._client = client
# initialize async client
if not self._async_client:
self._async_client = aiohttp.ClientSession(
headers={
"Api-Key": api_key_str,
"Content-Type": "application/json",
"X-Pinecone-API-Version": "2024-07",
}
)
# Ensure async_client is lazily initialized
_ = self.async_client
return self
def _get_batch_iterator(self, texts: List[str]) -> Iterable:
@ -174,7 +180,7 @@ class PineconeEmbeddings(BaseModel, Embeddings):
"inputs": [{"text": text} for text in texts],
"parameters": parameters,
}
async with self._async_client.post(
async with self.async_client.post(
"https://api.pinecone.io/embed", json=data
) as response:
response_data = await response.json(content_type=None)

1
libs/partners/pinecone/langchain_pinecone/vectorstores.py
View File

@ -74,6 +74,7 @@ class PineconeVectorStore(VectorStore):
dimension=1536,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
deletion_protection="enabled", # Defaults to "disabled"
)
while not pc.describe_index(index_name).status["ready"]:
time.sleep(1)

1843
libs/partners/pinecone/poetry.lock generated
View File

File diff suppressed because it is too large Load Diff

12
libs/partners/pinecone/pyproject.toml
View File

@ -20,16 +20,10 @@ disallow_untyped_defs = "True"
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
langchain-core = "^0.3.15"
pinecone-client = "^5.0.0"
langchain-core = "^0.3.21"
pinecone = "^5.4.0"
aiohttp = ">=3.9.5,<3.10"
[[tool.poetry.dependencies.numpy]]
version = "^1"
python = "<3.12"
[[tool.poetry.dependencies.numpy]]
version = "^1.26.0"
python = ">=3.12"
numpy = ">=1.26.0,<2.0.0"
[tool.ruff.lint]
select = [ "E", "F", "I", "T201",]

2
libs/standard-tests/langchain_tests/integration_tests/__init__.py
View File

@ -20,6 +20,7 @@ from .base_store import BaseStoreAsyncTests, BaseStoreSyncTests
from .cache import AsyncCacheTestSuite, SyncCacheTestSuite
from .chat_models import ChatModelIntegrationTests
from .embeddings import EmbeddingsIntegrationTests
from .retrievers import RetrieversIntegrationTests
from .tools import ToolsIntegrationTests
from .vectorstores import AsyncReadWriteTestSuite, ReadWriteTestSuite
@ -33,4 +34,5 @@ __all__ = [
"SyncCacheTestSuite",
"AsyncReadWriteTestSuite",
"ReadWriteTestSuite",
"RetrieversIntegrationTests",
]

669
libs/standard-tests/langchain_tests/integration_tests/chat_models.py
View File

@ -73,8 +73,46 @@ def _validate_tool_call_message_no_args(message: BaseMessage) -> None:
class ChatModelIntegrationTests(ChatModelTests):
"""Base class for chat model integration tests.
Test subclasses must implement the following two properties:
chat_model_class
The chat model class to test, e.g., ``ChatParrotLink``.
Example:
.. code-block:: python
@property
def chat_model_class(self) -> Type[ChatParrotLink]:
return ChatParrotLink
chat_model_params
Initialization parameters for the chat model.
Example:
.. code-block:: python
@property
def chat_model_params(self) -> dict:
return {"model": "bird-brain-001", "temperature": 0}
.. note::
API references for individual test methods include troubleshooting tips.
.. note::
Test subclasses can control what features are tested (such as tool
calling or multi-modality) by selectively overriding the properties on the
class. Relevant properties are mentioned in the references for each method.
See this page for detail on all properties:
https://python.langchain.com/api_reference/standard_tests/unit_tests/langchain_tests.unit_tests.chat_models.ChatModelTests.html
"""
@property
def standard_chat_model_params(self) -> dict:
""":meta private:"""
return {}
def test_invoke(self, model: BaseChatModel) -> None:
@ -295,8 +333,8 @@ class ChatModelIntegrationTests(ChatModelTests):
.. dropdown:: Configuration
By default, this test is run.
To disable this feature, set `returns_usage_metadata` to False in your test
class:
To disable this feature, set `returns_usage_metadata` to False in your
test class:
.. code-block:: python
@ -435,8 +473,8 @@ class ChatModelIntegrationTests(ChatModelTests):
.. dropdown:: Configuration
By default, this test is run.
To disable this feature, set `returns_usage_metadata` to False in your test
class:
To disable this feature, set `returns_usage_metadata` to False in your
test class:
.. code-block:: python
@ -564,6 +602,28 @@ class ChatModelIntegrationTests(ChatModelTests):
)
def test_stop_sequence(self, model: BaseChatModel) -> None:
"""Test that model does not fail when invoked with the ``stop`` parameter,
which is a standard parameter for stopping generation at a certain token.
More on standard parameters here: https://python.langchain.com/docs/concepts/chat_models/#standard-parameters
This should pass for all integrations.
.. dropdown:: Troubleshooting
If this test fails, check that the function signature for ``_generate``
(as well as ``_stream`` and async variants) accepts the ``stop`` parameter:
.. code-block:: python
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
""" # noqa: E501
result = model.invoke("hi", stop=["you"])
assert isinstance(result, AIMessage)
@ -574,6 +634,44 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(result, AIMessage)
def test_tool_calling(self, model: BaseChatModel) -> None:
"""Test that the model generates tool calls. This test is skipped if the
``has_tool_calling`` property on the test class is set to False.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that ``bind_tools`` is implemented to correctly
translate LangChain tool objects into the appropriate schema for your
chat model.
This test may fail if the chat model does not support a ``tool_choice``
parameter. This parameter can be used to force a tool call. If
``tool_choice`` is not supported and the model consistently fails this
test, you can ``xfail`` the test:
.. code-block:: python
@pytest.mark.xfail(reason=("Does not support tool_choice."))
def test_tool_calling(self, model: BaseChatModel) -> None:
super().test_tool_calling(model)
Otherwise, ensure that the ``tool_choice_value`` property is correctly
specified on the test class.
"""
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
if self.tool_choice_value == "tool_name":
@ -595,6 +693,44 @@ class ChatModelIntegrationTests(ChatModelTests):
_validate_tool_call_message(full)
async def test_tool_calling_async(self, model: BaseChatModel) -> None:
"""Test that the model generates tool calls. This test is skipped if the
``has_tool_calling`` property on the test class is set to False.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that ``bind_tools`` is implemented to correctly
translate LangChain tool objects into the appropriate schema for your
chat model.
This test may fail if the chat model does not support a ``tool_choice``
parameter. This parameter can be used to force a tool call. If
``tool_choice`` is not supported and the model consistently fails this
test, you can ``xfail`` the test:
.. code-block:: python
@pytest.mark.xfail(reason=("Does not support tool_choice."))
async def test_tool_calling_async(self, model: BaseChatModel) -> None:
await super().test_tool_calling_async(model)
Otherwise, ensure that the ``tool_choice_value`` property is correctly
specified on the test class.
"""
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
if self.tool_choice_value == "tool_name":
@ -616,6 +752,46 @@ class ChatModelIntegrationTests(ChatModelTests):
_validate_tool_call_message(full)
def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
"""Test that the model generates tool calls for tools with no arguments.
This test is skipped if the ``has_tool_calling`` property on the test class
is set to False.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that ``bind_tools`` is implemented to correctly
translate LangChain tool objects into the appropriate schema for your
chat model. It should correctly handle the case where a tool has no
arguments.
This test may fail if the chat model does not support a ``tool_choice``
parameter. This parameter can be used to force a tool call. It may also
fail if a provider does not support this form of tool. In these cases,
you can ``xfail`` the test:
.. code-block:: python
@pytest.mark.xfail(reason=("Does not support tool_choice."))
def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
super().test_tool_calling_with_no_arguments(model)
Otherwise, ensure that the ``tool_choice_value`` property is correctly
specified on the test class.
""" # noqa: E501
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
@ -637,6 +813,45 @@ class ChatModelIntegrationTests(ChatModelTests):
_validate_tool_call_message_no_args(full)
def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
"""Test that the model generates tool calls for tools that are derived from
LangChain runnables. This test is skipped if the ``has_tool_calling`` property
on the test class is set to False.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that ``bind_tools`` is implemented to correctly
translate LangChain tool objects into the appropriate schema for your
chat model.
This test may fail if the chat model does not support a ``tool_choice``
parameter. This parameter can be used to force a tool call. If
``tool_choice`` is not supported and the model consistently fails this
test, you can ``xfail`` the test:
.. code-block:: python
@pytest.mark.xfail(reason=("Does not support tool_choice."))
def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
super().test_bind_runnables_as_tools(model)
Otherwise, ensure that the ``tool_choice_value`` property is correctly
specified on the test class.
"""
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
@ -663,7 +878,32 @@ class ChatModelIntegrationTests(ChatModelTests):
assert tool_call["type"] == "tool_call"
def test_structured_output(self, model: BaseChatModel) -> None:
"""Test to verify structured output with a Pydantic model."""
"""Test to verify structured output is generated both on invoke and stream.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, ensure that the model's ``bind_tools`` method
properly handles both JSON Schema and Pydantic V2 models.
``langchain_core`` implements a utility function that will accommodate
most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
""" # noqa: E501
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
@ -690,7 +930,32 @@ class ChatModelIntegrationTests(ChatModelTests):
assert set(chunk.keys()) == {"setup", "punchline"}
async def test_structured_output_async(self, model: BaseChatModel) -> None:
"""Test to verify structured output with a Pydantic model."""
"""Test to verify structured output is generated both on invoke and stream.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, ensure that the model's ``bind_tools`` method
properly handles both JSON Schema and Pydantic V2 models.
``langchain_core`` implements a utility function that will accommodate
most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
""" # noqa: E501
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
@ -718,9 +983,34 @@ class ChatModelIntegrationTests(ChatModelTests):
@pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Test requires pydantic 2.")
def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None:
"""Test to verify compatibility with pydantic.v1.BaseModel.
"""Test to verify we can generate structured output using
pydantic.v1.BaseModel.
pydantic.v1.BaseModel is available in the pydantic 2 package.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, ensure that the model's ``bind_tools`` method
properly handles both JSON Schema and Pydantic V1 models.
``langchain_core`` implements a utility function that will accommodate
most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
"""
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
@ -751,7 +1041,33 @@ class ChatModelIntegrationTests(ChatModelTests):
assert set(chunk.keys()) == {"setup", "punchline"}
def test_structured_output_optional_param(self, model: BaseChatModel) -> None:
"""Test to verify structured output with an optional param."""
"""Test to verify we can generate structured output that includes optional
parameters.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, ensure that the model's ``bind_tools`` method
properly handles Pydantic V2 models with optional parameters.
``langchain_core`` implements a utility function that will accommodate
most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
"""
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
@ -773,10 +1089,42 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(joke_result, Joke)
def test_tool_message_histories_string_content(self, model: BaseChatModel) -> None:
"""
Test that message histories are compatible with string tool contents
(e.g. OpenAI).
"""
"""Test that message histories are compatible with string tool contents
(e.g. OpenAI format). If a model passes this test, it should be compatible
with messages generated from providers following OpenAI format.
This test should be skipped if the model does not support tool calling
(see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that:
1. The model can correctly handle message histories that include AIMessage objects with ``""`` content.
2. The ``tool_calls`` attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format.
3. The model can correctly handle ToolMessage objects with string content and arbitrary string values for ``tool_call_id``.
You can ``xfail`` the test if tool calling is implemented but this format
is not supported.
.. code-block:: python
@pytest.mark.xfail(reason=("Not implemented."))
def test_tool_message_histories_string_content(self, model: BaseChatModel) -> None:
super().test_tool_message_histories_string_content(model)
""" # noqa: E501
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
model_with_tools = model.bind_tools([my_adder_tool])
@ -810,10 +1158,56 @@ class ChatModelIntegrationTests(ChatModelTests):
self,
model: BaseChatModel,
) -> None:
"""
Test that message histories are compatible with list tool contents
(e.g. Anthropic).
"""
"""Test that message histories are compatible with list tool contents
(e.g. Anthropic format).
These message histories will include AIMessage objects with "tool use" and
content blocks, e.g.,
.. code-block:: python
[
{"type": "text", "text": "Hmm let me think about that"},
{
"type": "tool_use",
"input": {"fav_color": "green"},
"id": "foo",
"name": "color_picker",
},
]
This test should be skipped if the model does not support tool calling
(see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that:
1. The model can correctly handle message histories that include AIMessage objects with list content.
2. The ``tool_calls`` attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format.
3. The model can correctly handle ToolMessage objects with string content and arbitrary string values for ``tool_call_id``.
You can ``xfail`` the test if tool calling is implemented but this format
is not supported.
.. code-block:: python
@pytest.mark.xfail(reason=("Not implemented."))
def test_tool_message_histories_list_content(self, model: BaseChatModel) -> None:
super().test_tool_message_histories_list_content(model)
""" # noqa: E501
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
model_with_tools = model.bind_tools([my_adder_tool])
@ -852,9 +1246,48 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(result_list_content, AIMessage)
def test_structured_few_shot_examples(self, model: BaseChatModel) -> None:
"""
Test that model can process few-shot examples with tool calls.
"""
"""Test that the model can process few-shot examples with tool calls.
These are represented as a sequence of messages of the following form:
- ``HumanMessage`` with string content;
- ``AIMessage`` with the ``tool_calls`` attribute populated;
- ``ToolMessage`` with string content;
- ``AIMessage`` with string content (an answer);
- ``HuamnMessage`` with string content (a follow-up question).
This test should be skipped if the model does not support tool calling
(see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
This test uses a utility function in ``langchain_core`` to generate a
sequence of messages representing "few-shot" examples: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.tool_example_to_messages.html
If this test fails, check that the model can correctly handle this
sequence of messages.
You can ``xfail`` the test if tool calling is implemented but this format
is not supported.
.. code-block:: python
@pytest.mark.xfail(reason=("Not implemented."))
def test_structured_few_shot_examples(self, model: BaseChatModel) -> None:
super().test_structured_few_shot_examples(model)
""" # noqa: E501
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
model_with_tools = model.bind_tools([my_adder_tool], tool_choice="any")
@ -874,6 +1307,42 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(result, AIMessage)
def test_image_inputs(self, model: BaseChatModel) -> None:
"""Test that the model can process image inputs.
This test should be skipped (see Configuration below) if the model does not
support image inputs These will take the form of messages with OpenAI-style
image content blocks:
.. code-block:: python
[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
See https://python.langchain.com/docs/concepts/multimodality/
.. dropdown:: Configuration
To disable this test, set ``supports_image_inputs`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def supports_image_inputs(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that the model can correctly handle messages
with image content blocks in OpenAI format, including base64-encoded
images. Otherwise, set the ``supports_image_inputs`` property to False.
"""
if not self.supports_image_inputs:
return
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
@ -890,6 +1359,46 @@ class ChatModelIntegrationTests(ChatModelTests):
model.invoke([message])
def test_image_tool_message(self, model: BaseChatModel) -> None:
"""Test that the model can process ToolMessages with image inputs.
This test should be skipped if the model does not support messages of the
form:
.. code-block:: python
ToolMessage(
content=[
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
],
tool_call_id="1",
name="random_image",
)
This test can be skipped by setting the ``supports_image_tool_message`` property
to False (see Configuration below).
.. dropdown:: Configuration
To disable this test, set ``supports_image_tool_message`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def supports_image_tool_message(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that the model can correctly handle messages
with image content blocks in ToolMessages, including base64-encoded
images. Otherwise, set the ``supports_image_tool_message`` property to
False.
"""
if not self.supports_image_tool_message:
return
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
@ -921,6 +1430,72 @@ class ChatModelIntegrationTests(ChatModelTests):
model.bind_tools([random_image]).invoke(messages)
def test_anthropic_inputs(self, model: BaseChatModel) -> None:
"""Test that model can process Anthropic-style message histories.
These message histories will include ``AIMessage`` objects with ``tool_use``
content blocks, e.g.,
.. code-block:: python
AIMessage(
[
{"type": "text", "text": "Hmm let me think about that"},
{
"type": "tool_use",
"input": {"fav_color": "green"},
"id": "foo",
"name": "color_picker",
},
]
)
as well as ``HumanMessage`` objects containing ``tool_result`` content blocks:
.. code-block:: python
HumanMessage(
[
{
"type": "tool_result",
"tool_use_id": "foo",
"content": [
{
"type": "text",
"text": "green is a great pick! that's my sister's favorite color", # noqa: E501
}
],
"is_error": False,
},
{"type": "text", "text": "what's my sister's favorite color"},
]
)
This test should be skipped if the model does not support messages of this
form (or doesn't support tool calling generally). See Configuration below.
.. dropdown:: Configuration
To disable this test, set ``supports_anthropic_inputs`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def supports_anthropic_inputs(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that:
1. The model can correctly handle message histories that include message objects with list content.
2. The ``tool_calls`` attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format.
3. HumanMessages with "tool_result" content blocks are correctly handled.
Otherwise, if Anthropic tool call and result formats are not supported,
set the ``supports_anthropic_inputs`` property to False.
""" # noqa: E501
if not self.supports_anthropic_inputs:
return
@ -982,7 +1557,45 @@ class ChatModelIntegrationTests(ChatModelTests):
model.bind_tools([color_picker]).invoke(messages)
def test_tool_message_error_status(self, model: BaseChatModel) -> None:
"""Test that ToolMessage with status='error' can be handled."""
"""Test that ToolMessage with ``status="error"`` can be handled.
These messages may take the form:
.. code-block:: python
ToolMessage(
"Error: Missing required argument 'b'.",
name="my_adder_tool",
tool_call_id="abc123",
status="error",
)
If possible, the ``status`` field should be parsed and passed appropriately
to the model.
This test is optional and should be skipped if the model does not support
tool calling (see Configuration below).
.. dropdown:: Configuration
To disable tool calling tests, set ``has_tool_calling`` to False in your
test class:
.. code-block:: python
class TestMyChatModelIntegration(ChatModelIntegrationTests):
@property
def has_tool_calling(self) -> bool:
return False
.. dropdown:: Troubleshooting
If this test fails, check that the ``status`` field on ``ToolMessage``
objects is either ignored or passed to the model appropriately.
Otherwise, ensure that the ``tool_choice_value`` property is correctly
specified on the test class.
"""
if not self.has_tool_calling:
pytest.skip("Test requires tool calling.")
model_with_tools = model.bind_tools([my_adder_tool])
@ -1010,6 +1623,22 @@ class ChatModelIntegrationTests(ChatModelTests):
assert isinstance(result, AIMessage)
def test_message_with_name(self, model: BaseChatModel) -> None:
"""Test that HumanMessage with values for the ``name`` field can be handled.
These messages may take the form:
.. code-block:: python
HumanMessage("hello", name="example_user")
If possible, the ``name`` field should be parsed and passed appropriately
to the model. Otherwise, it should be ignored.
.. dropdown:: Troubleshooting
If this test fails, check that the ``name`` field on ``HumanMessage``
objects is either ignored or passed to the model appropriately.
"""
result = model.invoke([HumanMessage("hello", name="example_user")])
assert result is not None
assert isinstance(result, AIMessage)

78
libs/standard-tests/langchain_tests/integration_tests/retrievers.py Normal file
View File

@ -0,0 +1,78 @@
from abc import abstractmethod
from typing import Type
import pytest
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_tests.base import BaseStandardTests
class RetrieversIntegrationTests(BaseStandardTests):
@property
@abstractmethod
def retriever_constructor(self) -> Type[BaseRetriever]: ...
@property
def retriever_constructor_params(self) -> dict:
return {}
@property
@abstractmethod
def retriever_query_example(self) -> str:
"""
Returns a dictionary representing the "args" of an example retriever call.
"""
...
@pytest.fixture
def retriever(self) -> BaseRetriever:
return self.retriever_constructor(**self.retriever_constructor_params)
def test_k_constructor_param(self) -> None:
"""
Test that the retriever constructor accepts a k parameter.
"""
params = {
k: v for k, v in self.retriever_constructor_params.items() if k != "k"
}
params_3 = {**params, "k": 3}
retriever_3 = self.retriever_constructor(**params_3)
result_3 = retriever_3.invoke(self.retriever_query_example)
assert len(result_3) == 3
assert all(isinstance(doc, Document) for doc in result_3)
params_1 = {**params, "k": 1}
retriever_1 = self.retriever_constructor(**params_1)
result_1 = retriever_1.invoke(self.retriever_query_example)
assert len(result_1) == 1
assert all(isinstance(doc, Document) for doc in result_1)
def test_invoke_with_k_kwarg(self, retriever: BaseRetriever) -> None:
result_1 = retriever.invoke(self.retriever_query_example, k=1)
assert len(result_1) == 1
assert all(isinstance(doc, Document) for doc in result_1)
result_3 = retriever.invoke(self.retriever_query_example, k=3)
assert len(result_3) == 3
assert all(isinstance(doc, Document) for doc in result_3)
def test_invoke_returns_documents(self, retriever: BaseRetriever) -> None:
"""
If invoked with the example params, the retriever should return a list of
Documents.
"""
result = retriever.invoke(self.retriever_query_example)
assert isinstance(result, list)
assert all(isinstance(doc, Document) for doc in result)
async def test_ainvoke_returns_documents(self, retriever: BaseRetriever) -> None:
"""
If ainvoked with the example params, the retriever should return a list of
Documents.
"""
result = await retriever.ainvoke(self.retriever_query_example)
assert isinstance(result, list)
assert all(isinstance(doc, Document) for doc in result)

382
libs/standard-tests/langchain_tests/unit_tests/chat_models.py
View File

@ -1,4 +1,6 @@
"""Unit tests for chat models."""
"""
:autodoc-options: autoproperty
"""
import os
from abc import abstractmethod
@ -77,16 +79,218 @@ def my_adder(a: int, b: int) -> int:
class ChatModelTests(BaseStandardTests):
"""Base class for chat model tests.
Test subclasses must implement the following two properties:
chat_model_class
The chat model class to test, e.g., ``ChatParrotLink``.
Example:
.. code-block:: python
@property
@abstractmethod
def chat_model_class(self) -> Type[BaseChatModel]: ...
def chat_model_class(self) -> Type[ChatParrotLink]:
return ChatParrotLink
chat_model_params
Initialization parameters for the chat model.
Example:
.. code-block:: python
@property
def chat_model_params(self) -> dict:
return {"model": "bird-brain-001", "temperature": 0}
In addition, test subclasses can control what features are tested (such as tool
calling or multi-modality) by selectively overriding the following properties.
Expand to see details:
.. dropdown:: has_tool_calling
Boolean property indicating whether the chat model supports tool calling.
By default, this is determined by whether the chat model's `bind_tools` method
is overridden. It typically does not need to be overridden on the test class.
.. dropdown:: tool_choice_value
Value to use for tool choice when used in tests.
Some tests for tool calling features attempt to force tool calling via a
`tool_choice` parameter. A common value for this parameter is "any". Defaults
to `None`.
Note: if the value is set to "tool_name", the name of the tool used in each
test will be set as the value for `tool_choice`.
Example:
.. code-block:: python
@property
def tool_choice_value(self) -> Optional[str]:
return "any"
.. dropdown:: has_structured_output
Boolean property indicating whether the chat model supports structured
output.
By default, this is determined by whether the chat model's
`with_structured_output` method is overridden. If the base implementation is
intended to be used, this method should be overridden.
See: https://python.langchain.com/docs/concepts/structured_outputs/
Example:
.. code-block:: python
@property
def has_structured_output(self) -> bool:
return True
.. dropdown:: supports_image_inputs
Boolean property indicating whether the chat model supports image inputs.
Defaults to ``False``.
If set to ``True``, the chat model will be tested using content blocks of the
form
.. code-block:: python
[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
See https://python.langchain.com/docs/concepts/multimodality/
Example:
.. code-block:: python
@property
def supports_image_inputs(self) -> bool:
return True
.. dropdown:: supports_video_inputs
Boolean property indicating whether the chat model supports image inputs.
Defaults to ``False``. No current tests are written for this feature.
.. dropdown:: returns_usage_metadata
Boolean property indicating whether the chat model returns usage metadata
on invoke and streaming responses.
``usage_metadata`` is an optional dict attribute on AIMessages that track input
and output tokens: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html
Example:
.. code-block:: python
@property
def returns_usage_metadata(self) -> bool:
return False
.. dropdown:: supports_anthropic_inputs
Boolean property indicating whether the chat model supports Anthropic-style
inputs.
These inputs might feature "tool use" and "tool result" content blocks, e.g.,
.. code-block:: python
[
{"type": "text", "text": "Hmm let me think about that"},
{
"type": "tool_use",
"input": {"fav_color": "green"},
"id": "foo",
"name": "color_picker",
},
]
If set to ``True``, the chat model will be tested using content blocks of this
form.
Example:
.. code-block:: python
@property
def supports_anthropic_inputs(self) -> bool:
return False
.. dropdown:: supports_image_tool_message
Boolean property indicating whether the chat model supports ToolMessages
that include image content, e.g.,
.. code-block:: python
ToolMessage(
content=[
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
],
tool_call_id="1",
name="random_image",
)
If set to ``True``, the chat model will be tested with message sequences that
include ToolMessages of this form.
Example:
.. code-block:: python
@property
def supports_image_tool_message(self) -> bool:
return False
.. dropdown:: supported_usage_metadata_details
Property controlling what usage metadata details are emitted in both invoke
and stream.
``usage_metadata`` is an optional dict attribute on AIMessages that track input
and output tokens: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html
It includes optional keys ``input_token_details`` and ``output_token_details``
that can track usage details associated with special types of tokens, such as
cached, audio, or reasoning.
Only needs to be overridden if these details are supplied.
""" # noqa: E501
@property
@abstractmethod
def chat_model_class(self) -> Type[BaseChatModel]:
"""The chat model class to test, e.g., `ChatParrotLink`."""
...
@property
def chat_model_params(self) -> dict:
"""Initialization parameters for the chat mobdel."""
return {}
@property
def standard_chat_model_params(self) -> dict:
""":meta private:"""
return {
"temperature": 0,
"max_tokens": 100,
@ -97,12 +301,15 @@ class ChatModelTests(BaseStandardTests):
@pytest.fixture
def model(self) -> BaseChatModel:
"""Fixture that returns an instance of the chat model. Should not be
overridden."""
return self.chat_model_class(
**{**self.standard_chat_model_params, **self.chat_model_params}
)
@property
def has_tool_calling(self) -> bool:
"""Boolean property indicating whether the model supports tool calling."""
return self.chat_model_class.bind_tools is not BaseChatModel.bind_tools
@property
@ -112,6 +319,8 @@ class ChatModelTests(BaseStandardTests):
@property
def has_structured_output(self) -> bool:
"""Boolean property indicating whether the chat model supports structured
output."""
return (
self.chat_model_class.with_structured_output
is not BaseChatModel.with_structured_output
@ -119,22 +328,32 @@ class ChatModelTests(BaseStandardTests):
@property
def supports_image_inputs(self) -> bool:
"""Boolean property indicating whether the chat model supports image inputs.
Defaults to ``False``."""
return False
@property
def supports_video_inputs(self) -> bool:
"""Boolean property indicating whether the chat model supports image inputs.
Defaults to ``False``. No current tests are written for this feature."""
return False
@property
def returns_usage_metadata(self) -> bool:
"""Boolean property indicating whether the chat model returns usage metadata
on invoke and streaming responses."""
return True
@property
def supports_anthropic_inputs(self) -> bool:
"""Boolean property indicating whether the chat model supports Anthropic-style
inputs."""
return False
@property
def supports_image_tool_message(self) -> bool:
"""Boolean property indicating whether the chat model supports ToolMessages
that include image content."""
return False
@property
@ -152,31 +371,127 @@ class ChatModelTests(BaseStandardTests):
]
],
]:
"""Property controlling what usage metadata details are emitted in both invoke
and stream. Only needs to be overridden if these details are returned by the
model."""
return {"invoke": [], "stream": []}
class ChatModelUnitTests(ChatModelTests):
"""Base class for chat model unit tests.
Test subclasses must implement the following two properties:
chat_model_class
The chat model class to test, e.g., ``ChatParrotLink``.
Example:
.. code-block:: python
@property
def chat_model_class(self) -> Type[ChatParrotLink]:
return ChatParrotLink
chat_model_params
Initialization parameters for the chat model.
Example:
.. code-block:: python
@property
def chat_model_params(self) -> dict:
return {"model": "bird-brain-001", "temperature": 0}
.. note::
API references for individual test methods include troubleshooting tips.
.. note::
Test subclasses can control what features are tested (such as tool
calling or multi-modality) by selectively overriding the properties on the
class. Relevant properties are mentioned in the references for each method.
See this page for detail on all properties:
https://python.langchain.com/api_reference/standard_tests/unit_tests/langchain_tests.unit_tests.chat_models.ChatModelTests.html
Testing initialization from environment variables
Some unit tests may require testing initialization from environment variables.
These tests can be enabled by overriding the ``init_from_env_params``
property (see below):
.. dropdown:: init_from_env_params
This property is used in unit tests to test initialization from
environment variables. It should return a tuple of three dictionaries
that specify the environment variables, additional initialization args,
and expected instance attributes to check.
Defaults to empty dicts. If not overridden, the test is skipped.
Example:
.. code-block:: python
@property
def init_from_env_params(self) -> Tuple[dict, dict, dict]:
return (
{
"MY_API_KEY": "api_key",
},
{
"model": "bird-brain-001",
},
{
"my_api_key": "api_key",
},
)
""" # noqa: E501
@property
def standard_chat_model_params(self) -> dict:
""":meta private:"""
params = super().standard_chat_model_params
params["api_key"] = "test"
return params
@property
def init_from_env_params(self) -> Tuple[dict, dict, dict]:
"""Return env vars, init args, and expected instance attrs for initializing
from env vars."""
"""This property is used in unit tests to test initialization from environment
variables. It should return a tuple of three dictionaries that specify the
environment variables, additional initialization args, and expected instance
attributes to check."""
return {}, {}, {}
def test_init(self) -> None:
"""Test model initialization. This should pass for all integrations.
.. dropdown:: Troubleshooting
If this test fails, ensure that:
1. ``chat_model_params`` is specified and the model can be initialized from those params;
2. The model accommodates standard parameters: https://python.langchain.com/docs/concepts/chat_models/#standard-parameters
""" # noqa: E501
model = self.chat_model_class(
**{**self.standard_chat_model_params, **self.chat_model_params}
)
assert model is not None
def test_init_from_env(self) -> None:
"""Test initialization from environment variables. Relies on the
``init_from_env_params`` property. Test is skipped if that property is not
set.
.. dropdown:: Troubleshooting
If this test fails, ensure that ``init_from_env_params`` is specified
correctly.
"""
env_params, model_params, expected_attrs = self.init_from_env_params
if env_params:
if not env_params:
pytest.skip("init_from_env_params not specified.")
else:
with mock.patch.dict(os.environ, env_params):
model = self.chat_model_class(**model_params)
assert model is not None
@ -189,6 +504,14 @@ class ChatModelUnitTests(ChatModelTests):
def test_init_streaming(
self,
) -> None:
"""Test that model can be initialized with ``streaming=True``. This is for
backward-compatibility purposes.
.. dropdown:: Troubleshooting
If this test fails, ensure that the model can be initialized with a
boolean ``streaming`` parameter.
"""
model = self.chat_model_class(
**{
**self.standard_chat_model_params,
@ -202,6 +525,18 @@ class ChatModelUnitTests(ChatModelTests):
self,
model: BaseChatModel,
) -> None:
"""Test that chat model correctly handles Pydantic models that are passed
into ``bind_tools``. Test is skipped if the ``has_tool_calling`` property
on the test class is False.
.. dropdown:: Troubleshooting
If this test fails, ensure that the model's ``bind_tools`` method
properly handles Pydantic V2 models. ``langchain_core`` implements
a utility function that will accommodate most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
See example implementation of ``bind_tools`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.bind_tools
""" # noqa: E501
if not self.has_tool_calling:
return
@ -227,12 +562,35 @@ class ChatModelUnitTests(ChatModelTests):
model: BaseChatModel,
schema: Any,
) -> None:
"""Test ``with_structured_output`` method. Test is skipped if the
``has_structured_output`` property on the test class is False.
.. dropdown:: Troubleshooting
If this test fails, ensure that the model's ``bind_tools`` method
properly handles Pydantic V2 models. ``langchain_core`` implements
a utility function that will accommodate most formats: https://python.langchain.com/api_reference/core/utils/langchain_core.utils.function_calling.convert_to_openai_tool.html
See example implementation of ``with_structured_output`` here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output
""" # noqa: E501
if not self.has_structured_output:
return
assert model.with_structured_output(schema) is not None
def test_standard_params(self, model: BaseChatModel) -> None:
"""Test that model properly generates standard parameters. These are used
for tracing purposes.
.. dropdown:: Troubleshooting
If this test fails, check that the model accommodates standard parameters:
https://python.langchain.com/docs/concepts/chat_models/#standard-parameters
Check also that the model class is named according to convention
(e.g., ``ChatProviderName``).
"""
class ExpectedParams(BaseModelV1):
ls_provider: str
ls_model_name: str
@ -260,8 +618,18 @@ class ChatModelUnitTests(ChatModelTests):
pytest.fail(f"Validation error: {e}")
def test_serdes(self, model: BaseChatModel, snapshot: SnapshotAssertion) -> None:
"""Test serialization and deserialization of the model. Test is skipped if the
``is_lc_serializable`` property on the chat model class is not overwritten
to return ``True``.
.. dropdown:: Troubleshooting
If this test fails, check that the ``init_from_env_params`` property is
correctly set on the test class.
"""
if not self.chat_model_class.is_lc_serializable():
return
pytest.skip("Model is not serializable.")
else:
env_params, model_params, expected_attrs = self.init_from_env_params
with mock.patch.dict(os.environ, env_params):
ser = dumpd(model)

32
libs/standard-tests/tests/unit_tests/test_basic_retriever.py Normal file
View File

@ -0,0 +1,32 @@
from typing import Any, Type
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_tests.integration_tests import RetrieversIntegrationTests
class ParrotRetriever(BaseRetriever):
parrot_name: str
k: int = 3
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
) -> list[Document]:
k = kwargs.get("k", self.k)
return [Document(page_content=f"{self.parrot_name} says: {query}")] * k
class TestParrotRetrieverIntegration(RetrieversIntegrationTests):
@property
def retriever_constructor(self) -> Type[ParrotRetriever]:
return ParrotRetriever
@property
def retriever_constructor_params(self) -> dict:
return {"parrot_name": "Polly"}
@property
def retriever_query_example(self) -> str:
return "parrot"