Archives
/
langchain
mirror of https://github.com/hwchase17/langchain
2
0
Fork 1
Code Issues Packages Projects Releases Wiki Activity

langchain_ibm[patch] update docstring, dependencies, tests (#18386)

Browse Source 
- **Description:** Update docstring, dependencies, tests, README
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
  - **Tag maintainer:** : 

Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally -> ✅
Please make sure integration_tests passing locally -> ✅

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
pull/18404/head
Mateusz Szewczyk 4 months ago committed by GitHub
parent c2b1abe91b
commit 9298a0b941
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
5 changed files with 101 additions and 56 deletions
Show Stats Download Patch File Download Diff File

22
libs/partners/ibm/README.md
Unescape Escape View File

@ -1,6 +1,6 @@
# langchain-ibm
This package provides the integration between LangChain and IBM Watson AI through the `ibm-watsonx-ai` SDK.
This package provides the integration between LangChain and IBM watsonx.ai through the `ibm-watsonx-ai` SDK.
## Installation
@ -10,10 +10,6 @@ To use the `langchain-ibm` package, follow these installation steps:
pip install langchain-ibm
```
## Usage
### Setting up
@ -44,15 +40,10 @@ In alternative, you can set the environment variable in your terminal.
set WATSONX_APIKEY=your_ibm_api_key
```
### Loading the model
You might need to adjust model parameters for different models or tasks. For more details on the parameters, refer to IBM's [documentation](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames).
```python
parameters = {
"decoding_method": "sample",
@ -83,7 +74,6 @@ watsonx_llm = WatsonxLLM(
- You need to specify the model you want to use for inferencing through `model_id`. You can find the list of available models [here](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#ibm_watsonx_ai.foundation_models.utils.enums.ModelTypes).
Alternatively you can use Cloud Pak for Data credentials. For more details, refer to IBM's [documentation](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html).
```python
@ -99,9 +89,6 @@ watsonx_llm = WatsonxLLM(
)
```
### Create a Chain
Create `PromptTemplate` objects which will be responsible for creating a random question.
@ -123,10 +110,6 @@ response = llm_chain.invoke("dog")
print(response)
```
### Calling the Model Directly
To obtain completions, you can call the model directly using a string prompt.
@ -149,9 +132,6 @@ response = watsonx_llm.generate(
print(response)
```
### Streaming the Model output
You can stream the model output.

14
libs/partners/ibm/langchain_ibm/llms.py
Unescape Escape View File

@ -16,7 +16,7 @@ class WatsonxLLM(BaseLLM):
"""
IBM watsonx.ai large language models.
To use, you should have ``ibm_watsonx_ai`` python package installed,
To use, you should have ``langchain_ibm`` python package installed,
and the environment variable ``WATSONX_APIKEY`` set with your API key, or pass
it as a named parameter to the constructor.
@ -103,6 +103,18 @@ class WatsonxLLM(BaseLLM):
@property
def lc_secrets(self) -> Dict[str, str]:
"""A map of constructor argument names to secret ids.
For example:
{
"url": "WATSONX_URL",
"apikey": "WATSONX_APIKEY",
"token": "WATSONX_TOKEN",
"password": "WATSONX_PASSWORD",
"username": "WATSONX_USERNAME",
"instance_id": "WATSONX_INSTANCE_ID",
}
"""
return {
"url": "WATSONX_URL",
"apikey": "WATSONX_APIKEY",

37
libs/partners/ibm/poetry.lock generated
Unescape Escape View File

@ -245,13 +245,13 @@ ibm-cos-sdk-core = "2.13.4"
[[package]]
name = "ibm-watson-machine-learning"
version = "1.0.348"
version = "1.0.349"
description = "IBM Watson Machine Learning API Client"
optional = false
python-versions = ">=3.10"
files = [
{file = "ibm_watson_machine_learning-1.0.348-py3-none-any.whl", hash = "sha256:46cec3bcc9c6522440290d284845390158b92573d9759b77b6987d22580a1222"},
{file = "ibm_watson_machine_learning-1.0.348.tar.gz", hash = "sha256:9eceaae1c7b01457c9d5e3bdfb24209a828b4c6212b159dbfd9fdb9a74268e14"},
{file = "ibm_watson_machine_learning-1.0.349-py3-none-any.whl", hash = "sha256:b5bc4cdec2a9cda1c9fa6681558f721a7a3058937257ae3040618c7183fe2f55"},
{file = "ibm_watson_machine_learning-1.0.349.tar.gz", hash = "sha256:46dd3d67bee39c3e84b047e651bafa06dfb0cb973354c4e2c582928340b51a17"},
]
[package.dependencies]
@ -274,17 +274,17 @@ fl-rt23-1-py3-10 = ["GPUtil", "cloudpickle (==1.3.0)", "cryptography (==39.0.1)"
[[package]]
name = "ibm-watsonx-ai"
version = "0.1.8"
version = "0.2.0"
description = "IBM watsonx.ai API Client"
optional = false
python-versions = ">=3.10"
files = [
{file = "ibm_watsonx_ai-0.1.8-py3-none-any.whl", hash = "sha256:85536b00aa3c495540480e53a17b56a0990d1340e47fae0e7ea778dcd717e5dc"},
{file = "ibm_watsonx_ai-0.1.8.tar.gz", hash = "sha256:ba4e60091165cb755985f85ef0ece1db76ad1d351dd515a55d739467196dace3"},
{file = "ibm_watsonx_ai-0.2.0-py3-none-any.whl", hash = "sha256:75234916b2cd9accedb401a3c11925845efa08361cff4978589f58e39dad48f6"},
{file = "ibm_watsonx_ai-0.2.0.tar.gz", hash = "sha256:9af9e402c6f0c74e30ac11c0ae1b80e55e21fb2b2d7c546fc506ab1b9be4851e"},
]
[package.dependencies]
ibm-watson-machine-learning = ">=1.0.335"
ibm-watson-machine-learning = ">=1.0.349"
[package.extras]
fl = ["cloudpickle (==1.3.0)", "ddsketch (==1.1.2)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "jsonpickle (==1.4.2)", "lz4", "numcompress (==0.1.2)", "numpy (==1.19.2)", "pandas (==1.3.4)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "psutil", "pyYAML (==5.4.1)", "pytest (==6.2.5)", "requests (==2.27.1)", "scikit-learn (==0.23.2)", "scipy (==1.6.3)", "setproctitle", "skorch (==0.11.0)", "tabulate (==0.8.9)", "tensorflow (==2.4.4)", "torch (==1.7.1)", "websockets (==8.1)"]
@ -372,7 +372,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.1.27"
version = "0.1.28"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
@ -398,13 +398,13 @@ url = "../../core"
[[package]]
name = "langsmith"
version = "0.1.9"
version = "0.1.12"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
{file = "langsmith-0.1.9-py3-none-any.whl", hash = "sha256:f821b3cb07a87eac5cb2181ff0b61051811e4eef09ae4b46e700981f7ae5dfb9"},
{file = "langsmith-0.1.9.tar.gz", hash = "sha256:9bd3e80607722c3d2db84cf3440005491a859b80b5e499bc988032d5c2da91f0"},
{file = "langsmith-0.1.12-py3-none-any.whl", hash = "sha256:4f3d03c365c4d9eb4ed151055e2830ea73235d8c8be0841a63334f9d4fcf8b2b"},
{file = "langsmith-0.1.12.tar.gz", hash = "sha256:cf8d371f92f1035fd98a9692edd8af9c3a60947db4a77864a9c07dec96d3b039"},
]
[package.dependencies]
@ -666,13 +666,13 @@ testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "pydantic"
version = "2.6.2"
version = "2.6.3"
description = "Data validation using Python type hints"
optional = false
python-versions = ">=3.8"
files = [
{file = "pydantic-2.6.2-py3-none-any.whl", hash = "sha256:37a5432e54b12fecaa1049c5195f3d860a10e01bdfd24f1840ef14bd0d3aeab3"},
{file = "pydantic-2.6.2.tar.gz", hash = "sha256:a09be1c3d28f3abe37f8a78af58284b236a92ce520105ddc91a6d29ea1176ba7"},
{file = "pydantic-2.6.3-py3-none-any.whl", hash = "sha256:72c6034df47f46ccdf81869fddb81aade68056003900a8724a4f160700016a2a"},
{file = "pydantic-2.6.3.tar.gz", hash = "sha256:e07805c4c7f5c6826e33a1d4c9d47950d7eaf34868e2690f8594d2e30241f11f"},
]
[package.dependencies]
@ -848,13 +848,13 @@ watchdog = ">=2.0.0"
[[package]]
name = "python-dateutil"
version = "2.8.2"
version = "2.9.0.post0"
description = "Extensions to the standard Python datetime module"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
files = [
{file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
{file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
{file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
{file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
]
[package.dependencies]
@ -896,6 +896,7 @@ files = [
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@ -1152,4 +1153,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<4.0"
content-hash = "82240f0636b69c79784cf3b14d1b73f6df90e4a8e9f9ee4f6b6c0b8653c7ee90"
content-hash = "96cc11c3e73681170c57bf7198ad3a1091620b2272e82dde34bcce4df8721d62"

26
libs/partners/ibm/pyproject.toml
Unescape Escape View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "langchain-ibm"
version = "0.1.0"
version = "0.1.1"
description = "An integration package connecting IBM watsonx.ai and LangChain"
authors = ["IBM"]
readme = "README.md"
@ -12,8 +12,8 @@ license = "MIT"
[tool.poetry.dependencies]
python = ">=3.10,<4.0"
langchain-core = "^0.1.26"
ibm-watsonx-ai = "^0.1.8"
langchain-core = "^0.1.27"
ibm-watsonx-ai = "^0.2.0"
[tool.poetry.group.test]
optional = true
@ -21,12 +21,11 @@ optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
ibm-watsonx-ai = "^0.1.8"
pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
langchain-core = {path = "../../core", develop = true}
langchain-core = { path = "../../core", develop = true }
[tool.poetry.group.codespell]
optional = true
@ -38,7 +37,6 @@ codespell = "^2.2.0"
optional = true
[tool.poetry.group.test_integration.dependencies]
ibm-watsonx-ai = "^0.1.8"
[tool.poetry.group.lint]
optional = true
@ -48,29 +46,27 @@ ruff = "^0.1.5"
[tool.poetry.group.typing.dependencies]
mypy = "^0.991"
langchain-core = {path = "../../core", develop = true}
langchain-core = { path = "../../core", develop = true }
types-requests = "^2"
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.dev.dependencies]
langchain-core = {path = "../../core", develop = true}
langchain-core = { path = "../../core", develop = true }
[tool.ruff]
select = [
"E", # pycodestyle
"F", # pyflakes
"I", # isort
"E", # pycodestyle
"F", # pyflakes
"I", # isort
]
[tool.mypy]
disallow_untyped_defs = "True"
[tool.coverage.run]
omit = [
"tests/*",
]
omit = ["tests/*"]
[build-system]
requires = ["poetry-core>=1.0.0"]

58
libs/partners/ibm/tests/integration_tests/test_llms.py
Unescape Escape View File

@ -6,6 +6,10 @@ You'll need to set WATSONX_APIKEY and WATSONX_PROJECT_ID environment variables.
import os
from ibm_watsonx_ai.foundation_models import Model, ModelInference # type: ignore
from ibm_watsonx_ai.foundation_models.utils.enums import ( # type: ignore
DecodingMethods,
ModelTypes,
)
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames # type: ignore
from langchain_core.outputs import LLMResult
@ -22,6 +26,26 @@ def test_watsonxllm_invoke() -> None:
project_id=WX_PROJECT_ID,
)
response = watsonxllm.invoke("What color sunflower is?")
print(f"\nResponse: {response}")
assert isinstance(response, str)
assert len(response) > 0
def test_watsonxllm_invoke_with_params() -> None:
parameters = {
GenTextParamsMetaNames.DECODING_METHOD: "sample",
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
GenTextParamsMetaNames.MIN_NEW_TOKENS: 5,
}
watsonxllm = WatsonxLLM(
model_id="google/flan-ul2",
url="https://us-south.ml.cloud.ibm.com",
project_id=WX_PROJECT_ID,
params=parameters,
)
response = watsonxllm.invoke("What color sunflower is?")
print(f"\nResponse: {response}")
assert isinstance(response, str)
assert len(response) > 0
@ -33,7 +57,9 @@ def test_watsonxllm_generate() -> None:
project_id=WX_PROJECT_ID,
)
response = watsonxllm.generate(["What color sunflower is?"])
print(f"\nResponse: {response}")
response_text = response.generations[0][0].text
print(f"Response text: {response_text}")
assert isinstance(response, LLMResult)
assert len(response_text) > 0
@ -47,7 +73,9 @@ def test_watsonxllm_generate_with_multiple_prompts() -> None:
response = watsonxllm.generate(
["What color sunflower is?", "What color turtle is?"]
)
print(f"\nResponse: {response}")
response_text = response.generations[0][0].text
print(f"Response text: {response_text}")
assert isinstance(response, LLMResult)
assert len(response_text) > 0
@ -59,7 +87,9 @@ def test_watsonxllm_generate_stream() -> None:
project_id=WX_PROJECT_ID,
)
response = watsonxllm.generate(["What color sunflower is?"], stream=True)
print(f"\nResponse: {response}")
response_text = response.generations[0][0].text
print(f"Response text: {response_text}")
assert isinstance(response, LLMResult)
assert len(response_text) > 0
@ -71,6 +101,7 @@ def test_watsonxllm_stream() -> None:
project_id=WX_PROJECT_ID,
)
response = watsonxllm.invoke("What color sunflower is?")
print(f"\nResponse: {response}")
stream_response = watsonxllm.stream("What color sunflower is?")
@ -80,7 +111,7 @@ def test_watsonxllm_stream() -> None:
chunk, str
), f"chunk expect type '{str}', actual '{type(chunk)}'"
linked_text_stream += chunk
print(f"Linked text stream: {linked_text_stream}")
assert (
response == linked_text_stream
), "Linked text stream are not the same as generated text"
@ -141,3 +172,28 @@ def test_watsonxllm_invoke_from_wx_model_inference_with_params() -> None:
print(f"\nResponse: {response}")
assert isinstance(response, str)
assert len(response) > 0
def test_watsonxllm_invoke_from_wx_model_inference_with_params_as_enum() -> None:
parameters = {
GenTextParamsMetaNames.DECODING_METHOD: DecodingMethods.GREEDY,
GenTextParamsMetaNames.MAX_NEW_TOKENS: 100,
GenTextParamsMetaNames.MIN_NEW_TOKENS: 10,
GenTextParamsMetaNames.TEMPERATURE: 0.5,
GenTextParamsMetaNames.TOP_K: 50,
GenTextParamsMetaNames.TOP_P: 1,
}
model = ModelInference(
model_id=ModelTypes.FLAN_UL2,
credentials={
"apikey": WX_APIKEY,
"url": "https://us-south.ml.cloud.ibm.com",
},
project_id=WX_PROJECT_ID,
params=parameters,
)
watsonxllm = WatsonxLLM(watsonx_model=model)
response = watsonxllm.invoke("What color sunflower is?")
print(f"\nResponse: {response}")
assert isinstance(response, str)
assert len(response) > 0

Write Preview
Loading…
Cancel
Save