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WatsonxLLM updates/enhancements (#14598)
- **Description:** updates/enhancements to IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider (prompt tuned models and prompt templates deployments support) - **Dependencies:** [ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/), - **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan - **Twitter handle:** details in comment below. Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally. ✅ --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
Mateusz Szewczyk
GitHub
parent
7a0feba9f7
commit
cbfaccc424
@ -5,9 +5,9 @@
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"id": "70996d8a",
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"metadata": {},
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"source": [
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"# WatsonxLLM\n",
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"# IBM watsonx.ai\n",
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"\n",
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"[WatsonxLLM](https://ibm.github.io/watson-machine-learning-sdk/fm_extensions.html) is wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
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"[WatsonxLLM](https://ibm.github.io/watsonx-ai-python-sdk/fm_extensions.html#langchain) is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
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"This example shows how to communicate with watsonx.ai models using LangChain."
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]
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},
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@ -16,7 +16,7 @@
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"id": "ea35b2b7",
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"metadata": {},
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"source": [
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"Install the package [`ibm_watson_machine_learning`](https://ibm.github.io/watson-machine-learning-sdk/install.html)."
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"Install the package [`ibm-watsonx-ai`](https://ibm.github.io/watsonx-ai-python-sdk/install.html)."
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]
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},
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{
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@ -26,7 +26,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install ibm_watson_machine_learning"
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"%pip install ibm-watsonx-ai"
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]
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},
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{
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@ -60,7 +60,7 @@
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"metadata": {},
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"source": [
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"## Load the model\n",
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"You might need to adjust model `parameters` for different models or tasks, to do so please refer to [documentation](https://ibm.github.io/watson-machine-learning-sdk/model.html#metanames.GenTextParamsMetaNames)."
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"You might need to adjust model `parameters` for different models or tasks. For details, refer to [documentation](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames)."
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]
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},
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{
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@ -70,7 +70,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams\n",
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"from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams\n",
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"\n",
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"parameters = {\n",
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" GenParams.DECODING_METHOD: \"sample\",\n",
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@ -87,7 +87,15 @@
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"id": "2b586538",
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"metadata": {},
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"source": [
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"Initialize the `WatsonxLLM` class with previous set params."
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"Initialize the `WatsonxLLM` class with previously set parameters.\n",
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"\n",
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"\n",
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"**Note**: \n",
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"\n",
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"- 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",
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"- 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",
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"\n",
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"In this example, we’ll use the `project_id` and Dallas url."
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]
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},
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{
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@ -102,7 +110,7 @@
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"watsonx_llm = WatsonxLLM(\n",
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" model_id=\"google/flan-ul2\",\n",
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" url=\"https://us-south.ml.cloud.ibm.com\",\n",
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" project_id=\"***\",\n",
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" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
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" params=parameters,\n",
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")"
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]
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@ -112,19 +120,49 @@
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"id": "2202f4e0",
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"metadata": {},
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"source": [
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"Alternatively you can use Cloud Pak for Data credentials. For details, see [documentation](https://ibm.github.io/watson-machine-learning-sdk/setup_cpd.html).\n",
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"```\n",
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"Alternatively you can use Cloud Pak for Data credentials. For details, see [documentation](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html). "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "243ecccb",
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"metadata": {},
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"outputs": [],
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"source": [
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"watsonx_llm = WatsonxLLM(\n",
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" model_id='google/flan-ul2',\n",
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" url=\"***\",\n",
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" username=\"***\",\n",
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" password=\"***\",\n",
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" model_id=\"google/flan-ul2\",\n",
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" url=\"PASTE YOUR URL HERE\",\n",
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" username=\"PASTE YOUR USERNAME HERE\",\n",
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" password=\"PASTE YOUR PASSWORD HERE\",\n",
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" instance_id=\"openshift\",\n",
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" version=\"4.8\",\n",
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" project_id='***',\n",
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" params=parameters\n",
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")\n",
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"``` "
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" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
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" params=parameters,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "96ed13d4",
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"metadata": {},
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"source": [
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"Instead of `model_id`, you can also pass the `deployment_id` of the previously tuned model. The entire model tuning workflow is described [here](https://ibm.github.io/watsonx-ai-python-sdk/pt_working_with_class_and_prompt_tuner.html)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "08e66c88",
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"metadata": {},
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"outputs": [],
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"source": [
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"watsonx_llm = WatsonxLLM(\n",
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" deployment_id=\"PASTE YOUR DEPLOYMENT_ID HERE\",\n",
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" url=\"https://us-south.ml.cloud.ibm.com\",\n",
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" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
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" params=parameters,\n",
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")"
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]
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},
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{
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@ -187,7 +225,7 @@
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"metadata": {},
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"source": [
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"## Calling the Model Directly\n",
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"To obtain completions, you can can the model directly using string prompt."
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"To obtain completions, you can call the model directly using a string prompt."
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]
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},
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{
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@ -15,7 +15,7 @@ class WatsonxLLM(BaseLLM):
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"""
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IBM watsonx.ai large language models.
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To use, you should have ``ibm_watson_machine_learning`` python package installed,
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To use, you should have ``ibm_watsonx_ai`` python package installed,
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and the environment variable ``WATSONX_APIKEY`` set with your API key, or pass
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it as a named parameter to the constructor.
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@ -23,7 +23,7 @@ class WatsonxLLM(BaseLLM):
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Example:
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.. code-block:: python
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from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames
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from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
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parameters = {
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GenTextParamsMetaNames.DECODING_METHOD: "sample",
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GenTextParamsMetaNames.MAX_NEW_TOKENS: 100,
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@ -34,7 +34,7 @@ class WatsonxLLM(BaseLLM):
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}
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from langchain_community.llms import WatsonxLLM
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llm = WatsonxLLM(
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watsonx_llm = WatsonxLLM(
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model_id="google/flan-ul2",
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url="https://us-south.ml.cloud.ibm.com",
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apikey="*****",
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@ -46,6 +46,9 @@ class WatsonxLLM(BaseLLM):
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model_id: str = ""
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"""Type of model to use."""
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deployment_id: str = ""
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"""Type of deployed model to use."""
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project_id: str = ""
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"""ID of the Watson Studio project."""
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@ -159,7 +162,7 @@ class WatsonxLLM(BaseLLM):
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)
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try:
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from ibm_watson_machine_learning.foundation_models import Model
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from ibm_watsonx_ai.foundation_models import ModelInference
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credentials = {
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"url": values["url"].get_secret_value() if values["url"] else None,
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@ -186,8 +189,9 @@ class WatsonxLLM(BaseLLM):
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key: value for key, value in credentials.items() if value is not None
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}
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watsonx_model = Model(
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watsonx_model = ModelInference(
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model_id=values["model_id"],
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deployment_id=values["deployment_id"],
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credentials=credentials_without_none_value,
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params=values["params"],
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project_id=values["project_id"],
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@ -198,8 +202,8 @@ class WatsonxLLM(BaseLLM):
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except ImportError:
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raise ImportError(
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"Could not import ibm_watson_machine_learning python package. "
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"Please install it with `pip install ibm_watson_machine_learning`."
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"Could not import ibm_watsonx_ai python package. "
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"Please install it with `pip install ibm_watsonx_ai`."
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)
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return values
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@ -208,6 +212,7 @@ class WatsonxLLM(BaseLLM):
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"""Get the identifying parameters."""
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return {
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"model_id": self.model_id,
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"deployment_id": self.deployment_id,
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"params": self.params,
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"project_id": self.project_id,
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"space_id": self.space_id,
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@ -257,9 +262,34 @@ class WatsonxLLM(BaseLLM):
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)
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generations.append([gen])
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final_token_usage = self._extract_token_usage(response)
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llm_output = {"token_usage": final_token_usage, "model_id": self.model_id}
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llm_output = {
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"token_usage": final_token_usage,
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"model_id": self.model_id,
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"deployment_id": self.deployment_id,
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}
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return LLMResult(generations=generations, llm_output=llm_output)
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def _stream_response_to_generation_chunk(
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self,
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stream_response: Dict[str, Any],
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) -> GenerationChunk:
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"""Convert a stream response to a generation chunk."""
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if not stream_response["results"]:
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return GenerationChunk(text="")
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return GenerationChunk(
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text=stream_response["results"][0]["generated_text"],
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generation_info=dict(
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finish_reason=stream_response["results"][0].get("stop_reason", None),
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llm_output={
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"generated_token_count": stream_response["results"][0].get(
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"generated_token_count", None
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),
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"model_id": self.model_id,
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"deployment_id": self.deployment_id,
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},
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),
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)
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def _call(
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self,
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prompt: str,
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@ -277,7 +307,7 @@ class WatsonxLLM(BaseLLM):
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Example:
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.. code-block:: python
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response = watsonxllm("What is a molecule")
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response = watsonx_llm("What is a molecule")
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"""
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result = self._generate(
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prompts=[prompt], stop=stop, run_manager=run_manager, **kwargs
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@ -302,8 +332,13 @@ class WatsonxLLM(BaseLLM):
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Example:
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.. code-block:: python
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response = watsonxllm.generate(["What is a molecule"])
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response = watsonx_llm.generate(["What is a molecule"])
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"""
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if stop:
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if self.params:
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self.params.update({"stop_sequences": stop})
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else:
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self.params = {"stop_sequences": stop}
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should_stream = stream if stream is not None else self.streaming
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if should_stream:
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if len(prompts) > 1:
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@ -320,9 +355,12 @@ class WatsonxLLM(BaseLLM):
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else:
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generation += chunk
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assert generation is not None
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if isinstance(generation.generation_info, dict):
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llm_output = generation.generation_info.pop("llm_output")
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return LLMResult(generations=[[generation]], llm_output=llm_output)
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return LLMResult(generations=[[generation]])
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else:
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response = self.watsonx_model.generate(prompt=prompts)
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response = self.watsonx_model.generate(prompt=prompts, params=self.params)
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return self._create_llm_result(response)
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def _stream(
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@ -342,12 +380,20 @@ class WatsonxLLM(BaseLLM):
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Example:
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.. code-block:: python
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response = watsonxllm.stream("What is a molecule")
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response = watsonx_llm.stream("What is a molecule")
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for chunk in response:
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print(chunk, end='')
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"""
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for chunk in self.watsonx_model.generate_text_stream(prompt=prompt):
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if chunk:
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yield GenerationChunk(text=chunk)
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if stop:
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if self.params:
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self.params.update({"stop_sequences": stop})
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else:
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self.params = {"stop_sequences": stop}
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for stream_resp in self.watsonx_model.generate_text_stream(
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prompt=prompt, raw_response=True, params=self.params
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):
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chunk = self._stream_response_to_generation_chunk(stream_resp)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(chunk)
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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