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ibm: Add support for Embedding Models (#20647)

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---------

Co-authored-by: Erick Friis <erick@langchain.dev>
pull/20674/head
Mateusz Szewczyk 6 months ago committed by GitHub
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10
docs/docs/integrations/providers/ibm.mdx
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@ -37,3 +37,13 @@ See a [usage example](/docs/integrations/llms/ibm_watsonx).
```python
from langchain_ibm import WatsonxLLM
```
## Embedding Models
### WatsonxEmbeddings
See a [usage example](/docs/integrations/text_embedding/ibm_watsonx).
```python
from langchain_ibm import WatsonxEmbeddings
```

243
docs/docs/integrations/text_embedding/ibm_watsonx.ipynb
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@ -0,0 +1,243 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IBM watsonx.ai\n",
"\n",
">WatsonxEmbeddings is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
"\n",
"This example shows how to communicate with `watsonx.ai` models using `LangChain`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setting up\n",
"\n",
"Install the package `langchain-ibm`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -qU langchain-ibm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This cell defines the WML credentials required to work with watsonx Embeddings.\n",
"\n",
"**Action:** Provide the IBM Cloud user API key. For details, see\n",
"[documentation](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"watsonx_api_key = getpass()\n",
"os.environ[\"WATSONX_APIKEY\"] = watsonx_api_key"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Additionaly you are able to pass additional secrets as an environment variable. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n",
"os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the CPD cluster\"\n",
"os.environ[\"WATSONX_PASSWORD\"] = \"your password for accessing the CPD cluster\"\n",
"os.environ[\"WATSONX_USERNAME\"] = \"your username for accessing the CPD cluster\"\n",
"os.environ[\"WATSONX_INSTANCE_ID\"] = \"your instance_id for accessing the CPD cluster\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the model\n",
"\n",
"You might need to adjust model `parameters` for different models."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames\n",
"\n",
"embed_params = {\n",
" EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: 3,\n",
" EmbedTextParamsMetaNames.RETURN_OPTIONS: {\"input_text\": True},\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize the `WatsonxEmbeddings` class with previously set parameters.\n",
"\n",
"\n",
"**Note**: \n",
"\n",
"- To provide context for the API call, you must add `project_id` or `space_id`. For more information see [documentation](https://www.ibm.com/docs/en/watsonx-as-a-service?topic=projects).\n",
"- Depending on the region of your provisioned service instance, use one of the urls described [here](https://ibm.github.io/watsonx-ai-python-sdk/setup_cloud.html#authentication).\n",
"\n",
"In this example, we’ll use the `project_id` and Dallas url.\n",
"\n",
"\n",
"You need to specify `model_id` that will be used for inferencing."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from langchain_ibm import WatsonxEmbeddings\n",
"\n",
"watsonx_embedding = WatsonxEmbeddings(\n",
" model_id=\"ibm/slate-125m-english-rtrvr\",\n",
" url=\"https://us-south.ml.cloud.ibm.com\",\n",
" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
" params=embed_params,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively you can use Cloud Pak for Data credentials. For details, see [documentation](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"watsonx_embedding = WatsonxEmbeddings(\n",
" model_id=\"ibm/slate-125m-english-rtrvr\",\n",
" url=\"PASTE YOUR URL HERE\",\n",
" username=\"PASTE YOUR USERNAME HERE\",\n",
" password=\"PASTE YOUR PASSWORD HERE\",\n",
" instance_id=\"openshift\",\n",
" version=\"5.0\",\n",
" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
" params=embed_params,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Usage\n",
"\n",
"### Embed query"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.0094472, -0.024981909, -0.026013248, -0.040483925, -0.057804465]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = \"This is a test document.\"\n",
"\n",
"query_result = watsonx_embedding.embed_query(text)\n",
"query_result[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Embed documents"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.009447193, -0.024981918, -0.026013244, -0.040483937, -0.057804447]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"texts = [\"This is a content of the document\", \"This is another document\"]\n",
"\n",
"doc_result = watsonx_embedding.embed_documents(texts)\n",
"doc_result[0][:5]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "langchain",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

3
libs/partners/ibm/langchain_ibm/__init__.py
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@ -1,3 +1,4 @@
from langchain_ibm.embeddings import WatsonxEmbeddings
from langchain_ibm.llms import WatsonxLLM
__all__ = ["WatsonxLLM"]
__all__ = ["WatsonxLLM", "WatsonxEmbeddings"]

176
libs/partners/ibm/langchain_ibm/embeddings.py
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@ -0,0 +1,176 @@
import os
from typing import Dict, List, Optional, Union
from ibm_watsonx_ai import APIClient # type: ignore
from ibm_watsonx_ai.foundation_models.embeddings import Embeddings # type: ignore
from langchain_core.embeddings import Embeddings as LangChainEmbeddings
from langchain_core.pydantic_v1 import (
BaseModel,
Extra,
Field,
SecretStr,
root_validator,
)
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
class WatsonxEmbeddings(BaseModel, LangChainEmbeddings):
model_id: str = ""
"""Type of model to use."""
project_id: str = ""
"""ID of the Watson Studio project."""
space_id: str = ""
"""ID of the Watson Studio space."""
url: Optional[SecretStr] = None
"""Url to Watson Machine Learning or CPD instance"""
apikey: Optional[SecretStr] = None
"""Apikey to Watson Machine Learning or CPD instance"""
token: Optional[SecretStr] = None
"""Token to CPD instance"""
password: Optional[SecretStr] = None
"""Password to CPD instance"""
username: Optional[SecretStr] = None
"""Username to CPD instance"""
instance_id: Optional[SecretStr] = None
"""Instance_id of CPD instance"""
version: Optional[SecretStr] = None
"""Version of CPD instance"""
params: Optional[dict] = None
"""Model parameters to use during generate requests."""
verify: Union[str, bool] = ""
"""User can pass as verify one of following:
the path to a CA_BUNDLE file
the path of directory with certificates of trusted CAs
True - default path to truststore will be taken
False - no verification will be made"""
watsonx_embed: Embeddings = Field(default=None) #: :meta private:
watsonx_client: APIClient = Field(default=None) #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that credentials and python package exists in environment."""
if isinstance(values.get("watsonx_client"), APIClient):
watsonx_embed = Embeddings(
model_id=values["model_id"],
params=values["params"],
api_client=values["watsonx_client"],
project_id=values["project_id"],
space_id=values["space_id"],
verify=values["verify"],
)
values["watsonx_embed"] = watsonx_embed
else:
values["url"] = convert_to_secret_str(
get_from_dict_or_env(values, "url", "WATSONX_URL")
)
if "cloud.ibm.com" in values.get("url", "").get_secret_value():
values["apikey"] = convert_to_secret_str(
get_from_dict_or_env(values, "apikey", "WATSONX_APIKEY")
)
else:
if (
not values["token"]
and "WATSONX_TOKEN" not in os.environ
and not values["password"]
and "WATSONX_PASSWORD" not in os.environ
and not values["apikey"]
and "WATSONX_APIKEY" not in os.environ
):
raise ValueError(
"Did not find 'token', 'password' or 'apikey',"
" please add an environment variable"
" `WATSONX_TOKEN`, 'WATSONX_PASSWORD' or 'WATSONX_APIKEY' "
"which contains it,"
" or pass 'token', 'password' or 'apikey'"
" as a named parameter."
)
elif values["token"] or "WATSONX_TOKEN" in os.environ:
values["token"] = convert_to_secret_str(
get_from_dict_or_env(values, "token", "WATSONX_TOKEN")
)
elif values["password"] or "WATSONX_PASSWORD" in os.environ:
values["password"] = convert_to_secret_str(
get_from_dict_or_env(values, "password", "WATSONX_PASSWORD")
)
values["username"] = convert_to_secret_str(
get_from_dict_or_env(values, "username", "WATSONX_USERNAME")
)
elif values["apikey"] or "WATSONX_APIKEY" in os.environ:
values["apikey"] = convert_to_secret_str(
get_from_dict_or_env(values, "apikey", "WATSONX_APIKEY")
)
values["username"] = convert_to_secret_str(
get_from_dict_or_env(values, "username", "WATSONX_USERNAME")
)
if not values["instance_id"] or "WATSONX_INSTANCE_ID" not in os.environ:
values["instance_id"] = convert_to_secret_str(
get_from_dict_or_env(
values, "instance_id", "WATSONX_INSTANCE_ID"
)
)
credentials = {
"url": values["url"].get_secret_value() if values["url"] else None,
"apikey": values["apikey"].get_secret_value()
if values["apikey"]
else None,
"token": values["token"].get_secret_value()
if values["token"]
else None,
"password": values["password"].get_secret_value()
if values["password"]
else None,
"username": values["username"].get_secret_value()
if values["username"]
else None,
"instance_id": values["instance_id"].get_secret_value()
if values["instance_id"]
else None,
"version": values["version"].get_secret_value()
if values["version"]
else None,
}
credentials_without_none_value = {
key: value for key, value in credentials.items() if value is not None
}
watsonx_embed = Embeddings(
model_id=values["model_id"],
params=values["params"],
credentials=credentials_without_none_value,
project_id=values["project_id"],
space_id=values["space_id"],
verify=values["verify"],
)
values["watsonx_embed"] = watsonx_embed
return values
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
return self.watsonx_embed.embed_documents(texts=texts)
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
return self.embed_documents([text])[0]

480
libs/partners/ibm/poetry.lock generated
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@ -11,28 +11,6 @@ files = [
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
]
[[package]]
name = "anyio"
version = "4.3.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
files = [
{file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
{file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
trio = ["trio (>=0.23)"]
[[package]]
name = "certifi"
version = "2024.2.2"
@ -173,13 +151,13 @@ files = [
[[package]]
name = "exceptiongroup"
version = "1.2.0"
version = "1.2.1"
description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
files = [
{file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
{file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
{file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"},
{file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
]
[package.extras]
@ -245,22 +223,22 @@ ibm-cos-sdk-core = "2.13.4"
[[package]]
name = "ibm-watson-machine-learning"
version = "1.0.352"
version = "1.0.355"
description = "IBM Watson Machine Learning API Client"
optional = false
python-versions = ">=3.10"
files = [
{file = "ibm_watson_machine_learning-1.0.352-py3-none-any.whl", hash = "sha256:be233468bdd8e11ee57975310febefd013bcae35b7d153053e064f0be6a00242"},
{file = "ibm_watson_machine_learning-1.0.352.tar.gz", hash = "sha256:937b339c76d4ea143439f98ea60cfabacf802102e39873f361ece82174a791d3"},
{file = "ibm_watson_machine_learning-1.0.355-py3-none-any.whl", hash = "sha256:fbd6b06df7b43086483eeab1dbb535d2017191a6a51e2ae1beaa5af5dee01558"},
{file = "ibm_watson_machine_learning-1.0.355.tar.gz", hash = "sha256:b8443a302a50e4e1868e1315bc0a83c8b0de10953780227ea8c0c33909b6ae0c"},
]
[package.dependencies]
certifi = "*"
ibm-cos-sdk = {version = ">=2.12.0,<2.14.0", markers = "python_version >= \"3.10\""}
ibm-cos-sdk = ">=2.12.0,<2.14.0"
importlib-metadata = "*"
lomond = "*"
packaging = "*"
pandas = ">=0.24.2,<1.6.0"
pandas = ">=0.24.2,<2.2.0"
requests = "*"
tabulate = "*"
urllib3 = "*"
@ -274,13 +252,13 @@ fl-rt23-1-py3-10 = ["GPUtil", "cloudpickle (==1.3.0)", "cryptography (==39.0.1)"
[[package]]
name = "ibm-watsonx-ai"
version = "0.2.2"
version = "0.2.6"
description = "IBM watsonx.ai API Client"
optional = false
python-versions = ">=3.10"
files = [
{file = "ibm_watsonx_ai-0.2.2-py3-none-any.whl", hash = "sha256:e2fff3ed3d35be037548a96f6fea211ab1b9cef6a7c1c66c2f5479aafa868e9e"},
{file = "ibm_watsonx_ai-0.2.2.tar.gz", hash = "sha256:00e0d1b46742a6a1b08b2923a8134033f17c2800d347ea06548bc2b649eab78f"},
{file = "ibm_watsonx_ai-0.2.6-py3-none-any.whl", hash = "sha256:94328b2599222737cf8247557815867e962a61603446339e7326afed012410d0"},
{file = "ibm_watsonx_ai-0.2.6.tar.gz", hash = "sha256:76ae8ba5b83cf4e81c800b66844163246ea95f28291048fe4b9582daabdd5fc7"},
]
[package.dependencies]
@ -295,24 +273,24 @@ fl-rt23-1-py3-10 = ["GPUtil", "cloudpickle (==1.3.0)", "cryptography (==39.0.1)"
[[package]]
name = "idna"
version = "3.6"
version = "3.7"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.5"
files = [
{file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
{file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
{file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
{file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
]
[[package]]
name = "importlib-metadata"
version = "7.0.2"
version = "7.1.0"
description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
files = [
{file = "importlib_metadata-7.0.2-py3-none-any.whl", hash = "sha256:f4bc4c0c070c490abf4ce96d715f68e95923320370efb66143df00199bb6c100"},
{file = "importlib_metadata-7.0.2.tar.gz", hash = "sha256:198f568f3230878cb1b44fbd7975f87906c22336dba2e4a7f05278c281fbd792"},
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
]
[package.dependencies]
@ -321,7 +299,7 @@ zipp = ">=0.5"
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
perf = ["ipython"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
[[package]]
name = "iniconfig"
@ -372,7 +350,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.1.31"
version = "0.1.44"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
@ -380,13 +358,11 @@ files = []
develop = true
[package.dependencies]
anyio = ">=3,<5"
jsonpatch = "^1.33"
langsmith = "^0.1.0"
packaging = "^23.2"
pydantic = ">=1,<3"
PyYAML = ">=5.3"
requests = "^2"
tenacity = "^8.1.0"
[package.extras]
@ -398,13 +374,13 @@ url = "../../core"
[[package]]
name = "langsmith"
version = "0.1.24"
version = "0.1.49"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = ">=3.8.1,<4.0"
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.24-py3-none-any.whl", hash = "sha256:898ef5265bca8fc912f7fbf207e1d69cacd86055faecf6811bd42641e6319840"},
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[package.dependencies]
@ -816,17 +814,17 @@ testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy
[[package]]
name = "pytest-mock"
version = "3.12.0"
version = "3.14.0"
description = "Thin-wrapper around the mock package for easier use with pytest"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
pytest = ">=5.0"
pytest = ">=6.2.5"
[package.extras]
dev = ["pre-commit", "pytest-asyncio", "tox"]
@ -988,17 +986,6 @@ files = [
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
]
[[package]]
name = "sniffio"
version = "1.3.1"
description = "Sniff out which async library your code is running under"
optional = false
python-versions = ">=3.7"
files = [
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]
[[package]]
name = "syrupy"
version = "4.6.1"
@ -1054,13 +1041,13 @@ files = [
[[package]]
name = "types-requests"
version = "2.31.0.20240311"
version = "2.31.0.20240406"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
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[package.dependencies]
@ -1068,13 +1055,24 @@ urllib3 = ">=2"
[[package]]
name = "typing-extensions"
version = "4.10.0"
version = "4.11.0"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
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[[package]]
name = "tzdata"
version = "2024.1"
description = "Provider of IANA time zone data"
optional = false
python-versions = ">=2"
files = [
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@ -1136,13 +1134,13 @@ watchmedo = ["PyYAML (>=3.10)"]
[[package]]
name = "zipp"
version = "3.18.0"
version = "3.18.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
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[package.extras]
@ -1152,4 +1150,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<4.0"
content-hash = "84020398bee800f849046e3b88b8501508e2065c120b52823b876d4ed03ad76b"
content-hash = "74145dde7786d332ae06f03a65325b07e35f9be935df97f1258b45ef6332929c"

6
libs/partners/ibm/pyproject.toml
Unescape Escape View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "langchain-ibm"
version = "0.1.3"
version = "0.1.4"
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 = "^0.1.29"
ibm-watsonx-ai = "^0.2.0"
langchain-core = "^0.1.42"
ibm-watsonx-ai = "^0.2.6"
[tool.poetry.group.test]
optional = true

68
libs/partners/ibm/tests/integration_tests/test_embeddings.py
Unescape Escape View File

@ -0,0 +1,68 @@
"""Test WatsonxEmbeddings.
You'll need to set WATSONX_APIKEY and WATSONX_PROJECT_ID environment variables.
"""
import os
from ibm_watsonx_ai import APIClient # type: ignore
from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames # type: ignore
from langchain_ibm import WatsonxEmbeddings
WX_APIKEY = os.environ.get("WATSONX_APIKEY", "")
WX_PROJECT_ID = os.environ.get("WATSONX_PROJECT_ID", "")
URL = "https://us-south.ml.cloud.ibm.com"
MODEL_ID = "ibm/slate-125m-english-rtrvr"
DOCUMENTS = ["What is a generative ai?", "What is a loan and how does it works?"]
def test_01_generate_embed_documents() -> None:
watsonx_embedding = WatsonxEmbeddings(
model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID
)
generate_embedding = watsonx_embedding.embed_documents(texts=DOCUMENTS)
assert len(generate_embedding) == len(DOCUMENTS)
assert all(isinstance(el, float) for el in generate_embedding[0])
def test_02_generate_embed_query() -> None:
watsonx_embedding = WatsonxEmbeddings(
model_id=MODEL_ID,
url=URL,
project_id=WX_PROJECT_ID,
)
generate_embedding = watsonx_embedding.embed_query(text=DOCUMENTS[0])
assert isinstance(generate_embedding, list) and isinstance(
generate_embedding[0], float
)
def test_03_generate_embed_documents_with_param() -> None:
embed_params = {
EmbedTextParamsMetaNames.TRUNCATE_INPUT_TOKENS: 3,
}
watsonx_embedding = WatsonxEmbeddings(
model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID, params=embed_params
)
generate_embedding = watsonx_embedding.embed_documents(texts=DOCUMENTS)
assert len(generate_embedding) == len(DOCUMENTS)
assert all(isinstance(el, float) for el in generate_embedding[0])
def test_10_generate_embed_query_with_client_initialization() -> None:
watsonx_client = APIClient(
wml_credentials={
"url": URL,
"apikey": WX_APIKEY,
}
)
watsonx_embedding = WatsonxEmbeddings(
model_id=MODEL_ID, project_id=WX_PROJECT_ID, watsonx_client=watsonx_client
)
generate_embedding = watsonx_embedding.embed_query(text=DOCUMENTS[0])
assert isinstance(generate_embedding, list) and isinstance(
generate_embedding[0], float
)

62
libs/partners/ibm/tests/unit_tests/test_embeddings.py
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@ -0,0 +1,62 @@
"""Test WatsonxLLM API wrapper."""
import os
from langchain_ibm import WatsonxEmbeddings
os.environ.pop("WATSONX_APIKEY", None)
os.environ.pop("WATSONX_PROJECT_ID", None)
MODEL_ID = "ibm/slate-125m-english-rtrvr"
def test_initialize_watsonx_embeddings_bad_path_without_url() -> None:
try:
WatsonxEmbeddings(
model_id=MODEL_ID,
)
except ValueError as e:
assert "WATSONX_URL" in e.__str__()
def test_initialize_watsonx_embeddings_cloud_bad_path() -> None:
try:
WatsonxEmbeddings(model_id=MODEL_ID, url="https://us-south.ml.cloud.ibm.com")
except ValueError as e:
assert "WATSONX_APIKEY" in e.__str__()
def test_initialize_watsonx_embeddings_cpd_bad_path_without_all() -> None:
try:
WatsonxEmbeddings(
model_id=MODEL_ID,
url="https://cpd-zen.apps.cpd48.cp.fyre.ibm.com",
)
except ValueError as e:
assert (
"WATSONX_APIKEY" in e.__str__()
and "WATSONX_PASSWORD" in e.__str__()
and "WATSONX_TOKEN" in e.__str__()
)
def test_initialize_watsonx_embeddings_cpd_bad_path_password_without_username() -> None:
try:
WatsonxEmbeddings(
model_id=MODEL_ID,
url="https://cpd-zen.apps.cpd48.cp.fyre.ibm.com",
password="test_password",
)
except ValueError as e:
assert "WATSONX_USERNAME" in e.__str__()
def test_initialize_watsonx_embeddings_cpd_bad_path_apikey_without_username() -> None:
try:
WatsonxEmbeddings(
model_id=MODEL_ID,
url="https://cpd-zen.apps.cpd48.cp.fyre.ibm.com",
apikey="test_apikey",
)
except ValueError as e:
assert "WATSONX_USERNAME" in e.__str__()

2
libs/partners/ibm/tests/unit_tests/test_imports.py
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@ -1,6 +1,6 @@
from langchain_ibm import __all__
EXPECTED_ALL = ["WatsonxLLM"]
EXPECTED_ALL = ["WatsonxLLM", "WatsonxEmbeddings"]
def test_all_imports() -> None:

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