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ibm: Add support for Chat Models (#22979)
This commit is contained in:
Mateusz Szewczyk
GitHub
parent
16c59118eb
commit
a78ccb993c
585
docs/docs/integrations/chat/ibm_watsonx.ipynb
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585
docs/docs/integrations/chat/ibm_watsonx.ipynb
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "1c95cd76",
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"metadata": {
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"vscode": {
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"languageId": "raw"
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}
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},
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"source": [
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"---\n",
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"sidebar_label: IBM watsonx.ai\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": "70996d8a",
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"metadata": {},
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"source": [
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"# ChatWatsonx\n",
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"\n",
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">ChatWatsonx is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
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"\n",
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"The aim of these examples is to show how to communicate with `watsonx.ai` models using `LangChain` LLMs API."
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]
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},
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{
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"cell_type": "markdown",
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"id": "ef7b088a",
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"metadata": {},
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"source": [
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"## Overview\n",
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"\n",
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"### Integration details\n",
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"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/openai) | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [ChatWatsonx](https://api.python.langchain.com/en/latest/ibm_api_reference.html) | [langchain-ibm](https://api.python.langchain.com/en/latest/ibm_api_reference.html) | ❌ | ❌ | ❌ |  |  |\n",
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"\n",
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"### Model features\n",
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"| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | Image input | Audio input | Video input | [Token-level streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
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"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\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": "f406e092",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"To access IBM watsonx.ai models you'll need to create an IBM watsonx.ai account, get an API key, and install the `langchain-ibm` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"The cell below defines the credentials required to work with watsonx Foundation Model inferencing.\n",
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"\n",
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"**Action:** Provide the IBM Cloud user API key. For details, see\n",
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"[Managing user API keys](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui)."
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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": 2,
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"id": "11d572a1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"watsonx_api_key = getpass()\n",
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"os.environ[\"WATSONX_APIKEY\"] = watsonx_api_key"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c59782a7",
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"metadata": {},
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"source": [
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"Additionally you are able to pass additional secrets as an environment variable. "
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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": "f98c573c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n",
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"os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the CPD cluster\"\n",
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"os.environ[\"WATSONX_PASSWORD\"] = \"your password for accessing the CPD cluster\"\n",
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"os.environ[\"WATSONX_USERNAME\"] = \"your username for accessing the CPD cluster\"\n",
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"os.environ[\"WATSONX_INSTANCE_ID\"] = \"your instance_id for accessing the CPD cluster\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3dc9176",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain IBM integration lives in the `langchain-ibm` package:"
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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": "387eda86",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -qU langchain-ibm"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e36acbef",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"You might need to adjust model `parameters` for different models or tasks. For details, refer to [Available MetaNames](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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"cell_type": "code",
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"execution_count": 1,
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"id": "407cd500",
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"metadata": {},
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"outputs": [],
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"source": [
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"parameters = {\n",
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" \"decoding_method\": \"sample\",\n",
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" \"max_new_tokens\": 100,\n",
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" \"min_new_tokens\": 1,\n",
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" \"stop_sequences\": [\".\"],\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": "2b586538",
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"metadata": {},
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"source": [
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"Initialize the `WatsonxLLM` class with the 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 pass the `project_id` or `space_id`. To get your project or space ID, open your project or space, go to the **Manage** tab, and click **General**. For more information see: [Project documentation](https://www.ibm.com/docs/en/watsonx-as-a-service?topic=projects) or [Deployment space documentation](https://www.ibm.com/docs/en/watsonx/saas?topic=spaces-creating-deployment).\n",
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"- Depending on the region of your provisioned service instance, use one of the urls listed in [watsonx.ai API Authentication](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.\n",
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"\n",
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"\n",
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"You need to specify the `model_id` that will be used for inferencing. You can find the list of all the available models in [Supported foundation models](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#ibm_watsonx_ai.foundation_models.utils.enums.ModelTypes)."
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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": "98371396",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_ibm import ChatWatsonx\n",
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"\n",
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"chat = ChatWatsonx(\n",
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" model_id=\"ibm/granite-13b-chat-v2\",\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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"cell_type": "markdown",
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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 [watsonx.ai software setup](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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"chat = ChatWatsonx(\n",
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" model_id=\"ibm/granite-13b-chat-v2\",\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=\"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 in [Working with TuneExperiment and PromptTuner](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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"chat = ChatWatsonx(\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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"cell_type": "markdown",
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"id": "f571001d",
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"metadata": {},
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"source": [
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"## Invocation\n",
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"\n",
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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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"cell_type": "code",
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"execution_count": 22,
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"id": "beea2b5b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\"Je t'aime pour écouter la Rock.\", response_metadata={'token_usage': {'generated_token_count': 12, 'input_token_count': 28}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-05b305ce-5401-4a10-b557-41a4b15c7f6f-0')"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Invocation\n",
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"\n",
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"messages = [\n",
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" (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
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" (\n",
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" \"human\",\n",
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" \"I love you for listening to Rock.\",\n",
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" ),\n",
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"]\n",
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"\n",
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"chat.invoke(messages)"
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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": 41,
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"id": "8ab1a25a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Sure, I can help you with that! Horses are large, powerful mammals that belong to the family Equidae.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-391776ff-3b38-4768-91e8-ff64177149e5-0')"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Invocation multiple chat\n",
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"from langchain_core.messages import (\n",
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" HumanMessage,\n",
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" SystemMessage,\n",
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")\n",
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"\n",
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"system_message = SystemMessage(\n",
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" content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
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")\n",
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"human_message = HumanMessage(content=\"horse\")\n",
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"\n",
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"chat.invoke([system_message, human_message])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "20e4b568",
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"metadata": {},
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"source": [
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"## Chaining\n",
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"Create `ChatPromptTemplate` objects which will be responsible for creating a random question."
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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": 17,
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"id": "dd919925",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"\n",
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"system = (\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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")\n",
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"human = \"{input}\"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a013a53",
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"metadata": {},
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"source": [
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"Provide a inputs and run the chain."
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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": 18,
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"id": "68160377",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Ich liebe Python.', response_metadata={'token_usage': {'generated_token_count': 5, 'input_token_count': 23}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-1b1ccf5d-0e33-46f2-a087-e2a136ba1fb7-0')"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain = prompt | chat\n",
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"chain.invoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"German\",\n",
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" \"input\": \"I love Python\",\n",
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" }\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": "d2c9da33",
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"metadata": {},
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"source": [
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"## Streaming the Model output \n",
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"\n",
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"You can stream the model output."
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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": 4,
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"id": "3f63166a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The moon is a natural satellite of the Earth, and it has been a source of fascination for humans for centuries."
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]
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}
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],
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"source": [
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"system_message = SystemMessage(\n",
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" content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
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")\n",
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"human_message = HumanMessage(content=\"moon\")\n",
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"\n",
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"for chunk in chat.stream([system_message, human_message]):\n",
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" print(chunk.content, end=\"\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5a7a2aa1",
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"metadata": {},
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"source": [
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"## Batch the Model output \n",
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"\n",
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"You can batch the model output."
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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": 32,
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"id": "9e948729",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content='Cats are domestic animals that belong to the Felidae family.', response_metadata={'token_usage': {'generated_token_count': 13, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-71a8bd7a-a1aa-497b-9bdd-a4d6fe1d471a-0'),\n",
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" AIMessage(content='Dogs are domesticated mammals of the family Canidae, characterized by their adaptability to various environments and social structures.', response_metadata={'token_usage': {'generated_token_count': 24, 'input_token_count': 24}, 'model_name': 'ibm/granite-13b-chat-v2', 'system_fingerprint': '', 'finish_reason': 'stop_sequence'}, id='run-22b7a0cb-e44a-4b68-9921-872f82dcd82b-0')]"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"message_1 = [\n",
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" SystemMessage(\n",
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" content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
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" ),\n",
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" HumanMessage(content=\"cat\"),\n",
|
||||
"]\n",
|
||||
"message_2 = [\n",
|
||||
" SystemMessage(\n",
|
||||
" content=\"You are a helpful assistant which telling short-info about provided topic.\"\n",
|
||||
" ),\n",
|
||||
" HumanMessage(content=\"dog\"),\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"chat.batch([message_1, message_2])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c739e1fe",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Tool calling\n",
|
||||
"\n",
|
||||
"### ChatWatsonx.bind_tools()\n",
|
||||
"\n",
|
||||
"Please note that `ChatWatsonx.bind_tools` is on beta state, so right now we only support `mistralai/mixtral-8x7b-instruct-v01` model.\n",
|
||||
"\n",
|
||||
"You should also redefine `max_new_tokens` parameter to get the entire model response. By default `max_new_tokens` is set ot 20."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "328fce76",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_ibm import ChatWatsonx\n",
|
||||
"\n",
|
||||
"parameters = {\"max_new_tokens\": 200}\n",
|
||||
"\n",
|
||||
"chat = ChatWatsonx(\n",
|
||||
" model_id=\"mistralai/mixtral-8x7b-instruct-v01\",\n",
|
||||
" url=\"https://us-south.ml.cloud.ibm.com\",\n",
|
||||
" project_id=\"PASTE YOUR PROJECT_ID HERE\",\n",
|
||||
" params=parameters,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "e1633a73",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class GetWeather(BaseModel):\n",
|
||||
" \"\"\"Get the current weather in a given location\"\"\"\n",
|
||||
"\n",
|
||||
" location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"llm_with_tools = chat.bind_tools([GetWeather])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "3bf9b8ab",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='', additional_kwargs={'function_call': {'type': 'function'}, 'tool_calls': [{'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"Los Angeles\"}'}, 'id': None}, {'type': 'function', 'function': {'name': 'GetWeather', 'arguments': '{\"location\": \"New York\"}'}, 'id': None}]}, response_metadata={'token_usage': {'generated_token_count': 99, 'input_token_count': 320}, 'model_name': 'mistralai/mixtral-8x7b-instruct-v01', 'system_fingerprint': '', 'finish_reason': 'eos_token'}, id='run-38627104-f2ac-4edb-8390-d5425fb65979-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None}, {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ai_msg = llm_with_tools.invoke(\n",
|
||||
" \"Which city is hotter today: LA or NY?\",\n",
|
||||
")\n",
|
||||
"ai_msg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ba03dbf4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### AIMessage.tool_calls\n",
|
||||
"Notice that the AIMessage has a `tool_calls` attribute. This contains in a standardized ToolCall format that is model-provider agnostic."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "38f10ba7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'name': 'GetWeather', 'args': {'location': 'Los Angeles'}, 'id': None},\n",
|
||||
" {'name': 'GetWeather', 'args': {'location': 'New York'}, 'id': None}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ai_msg.tool_calls"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9ee72a59",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## API reference\n",
|
||||
"\n",
|
||||
"For detailed documentation of all IBM watsonx.ai features and configurations head to the API reference: https://api.python.langchain.com/en/latest/ibm_api_reference.html"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": 5
|
||||
}
|
||||
@ -28,6 +28,16 @@ import os
|
||||
os.environ["WATSONX_APIKEY"] = "your IBM watsonx.ai api key"
|
||||
```
|
||||
|
||||
## Chat Model
|
||||
|
||||
### ChatWatsonx
|
||||
|
||||
See a [usage example](/docs/integrations/chat/ibm_watsonx).
|
||||
|
||||
```python
|
||||
from langchain_ibm import ChatWatsonx
|
||||
```
|
||||
|
||||
## LLMs
|
||||
|
||||
### WatsonxLLM
|
||||
|
||||
@ -1,4 +1,5 @@
|
||||
from langchain_ibm.chat_models import ChatWatsonx
|
||||
from langchain_ibm.embeddings import WatsonxEmbeddings
|
||||
from langchain_ibm.llms import WatsonxLLM
|
||||
|
||||
__all__ = ["WatsonxLLM", "WatsonxEmbeddings"]
|
||||
__all__ = ["WatsonxLLM", "WatsonxEmbeddings", "ChatWatsonx"]
|
||||
|
||||
869
libs/partners/ibm/langchain_ibm/chat_models.py
Normal file
869
libs/partners/ibm/langchain_ibm/chat_models.py
Normal file
@ -0,0 +1,869 @@
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
from operator import itemgetter
|
||||
from typing import (
|
||||
Any,
|
||||
Callable,
|
||||
Dict,
|
||||
Iterator,
|
||||
List,
|
||||
Literal,
|
||||
Mapping,
|
||||
Optional,
|
||||
Sequence,
|
||||
Tuple,
|
||||
Type,
|
||||
TypedDict,
|
||||
Union,
|
||||
cast,
|
||||
)
|
||||
|
||||
from ibm_watsonx_ai import Credentials # type: ignore
|
||||
from ibm_watsonx_ai.foundation_models import ModelInference # type: ignore
|
||||
from langchain_core.callbacks import CallbackManagerForLLMRun
|
||||
from langchain_core.language_models import LanguageModelInput
|
||||
from langchain_core.language_models.chat_models import (
|
||||
BaseChatModel,
|
||||
generate_from_stream,
|
||||
)
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
AIMessageChunk,
|
||||
BaseMessage,
|
||||
BaseMessageChunk,
|
||||
ChatMessage,
|
||||
ChatMessageChunk,
|
||||
FunctionMessage,
|
||||
FunctionMessageChunk,
|
||||
HumanMessage,
|
||||
HumanMessageChunk,
|
||||
SystemMessage,
|
||||
SystemMessageChunk,
|
||||
ToolMessage,
|
||||
ToolMessageChunk,
|
||||
convert_to_messages,
|
||||
)
|
||||
from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
|
||||
from langchain_core.output_parsers.base import OutputParserLike
|
||||
from langchain_core.output_parsers.openai_tools import (
|
||||
JsonOutputKeyToolsParser,
|
||||
PydanticToolsParser,
|
||||
make_invalid_tool_call,
|
||||
parse_tool_call,
|
||||
)
|
||||
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
||||
from langchain_core.prompt_values import ChatPromptValue
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
|
||||
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
|
||||
from langchain_core.tools import BaseTool
|
||||
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
|
||||
from langchain_core.utils.function_calling import (
|
||||
convert_to_openai_function,
|
||||
convert_to_openai_tool,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
|
||||
"""Convert a dictionary to a LangChain message.
|
||||
|
||||
Args:
|
||||
_dict: The dictionary.
|
||||
|
||||
Returns:
|
||||
The LangChain message.
|
||||
"""
|
||||
role = _dict.get("role")
|
||||
if role == "user":
|
||||
return HumanMessage(content=_dict.get("generated_text", ""))
|
||||
else:
|
||||
additional_kwargs: Dict = {}
|
||||
tool_calls = []
|
||||
invalid_tool_calls = []
|
||||
try:
|
||||
content = ""
|
||||
|
||||
raw_tool_calls = _dict.get("generated_text")
|
||||
if raw_tool_calls:
|
||||
json_parts = re.split(r"\n\n(?:<blank line>\n\n)?", raw_tool_calls)
|
||||
parsed_raw_tool_calls = [
|
||||
json.loads(part) for part in json_parts if part.strip()
|
||||
]
|
||||
additional_kwargs["tool_calls"] = parsed_raw_tool_calls
|
||||
additional_kwargs["function_call"] = dict(parsed_raw_tool_calls)
|
||||
|
||||
for obj in parsed_raw_tool_calls:
|
||||
b = json.dumps(obj["function"]["arguments"])
|
||||
obj["function"]["arguments"] = b
|
||||
|
||||
for raw_tool_call in parsed_raw_tool_calls:
|
||||
try:
|
||||
raw_tool_call["id"] = "None"
|
||||
tool_calls.append(
|
||||
parse_tool_call(raw_tool_call, return_id=True)
|
||||
)
|
||||
except Exception as e:
|
||||
invalid_tool_calls.append(
|
||||
dict(make_invalid_tool_call(raw_tool_call, str(e)))
|
||||
)
|
||||
except: # noqa: E722
|
||||
content = _dict.get("generated_text", "") or ""
|
||||
|
||||
return AIMessage(
|
||||
content=content,
|
||||
additional_kwargs=additional_kwargs,
|
||||
tool_calls=tool_calls,
|
||||
invalid_tool_calls=invalid_tool_calls,
|
||||
)
|
||||
|
||||
|
||||
def _convert_message_to_dict(message: BaseMessage) -> dict:
|
||||
"""Convert a LangChain message to a dictionary.
|
||||
|
||||
Args:
|
||||
message: The LangChain message.
|
||||
|
||||
Returns:
|
||||
The dictionary.
|
||||
"""
|
||||
message_dict: Dict[str, Any]
|
||||
if isinstance(message, ChatMessage):
|
||||
message_dict = {"role": message.role, "content": message.content}
|
||||
elif isinstance(message, HumanMessage):
|
||||
message_dict = {"role": "user", "content": message.content}
|
||||
elif isinstance(message, AIMessage):
|
||||
message_dict = {"role": "assistant", "content": message.content}
|
||||
if "function_call" in message.additional_kwargs:
|
||||
message_dict["function_call"] = message.additional_kwargs["function_call"]
|
||||
# If function call only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
if "tool_calls" in message.additional_kwargs:
|
||||
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
|
||||
# If tool calls 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):
|
||||
message_dict = {
|
||||
"role": "function",
|
||||
"content": message.content,
|
||||
"name": message.name,
|
||||
}
|
||||
elif isinstance(message, ToolMessage):
|
||||
message_dict = {
|
||||
"role": "tool",
|
||||
"content": message.content,
|
||||
"tool_call_id": "None",
|
||||
}
|
||||
else:
|
||||
raise TypeError(f"Got unknown type {message}")
|
||||
if "name" in message.additional_kwargs:
|
||||
message_dict["name"] = message.additional_kwargs["name"]
|
||||
return message_dict
|
||||
|
||||
|
||||
def _convert_delta_to_message_chunk(
|
||||
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
|
||||
) -> BaseMessageChunk:
|
||||
role = cast(str, _dict.get("role"))
|
||||
content = cast(str, _dict.get("content") or "")
|
||||
additional_kwargs: Dict = {}
|
||||
if _dict.get("function_call"):
|
||||
function_call = dict(_dict["function_call"])
|
||||
if "name" in function_call and function_call["name"] is None:
|
||||
function_call["name"] = ""
|
||||
additional_kwargs["function_call"] = function_call
|
||||
if raw_tool_calls := _dict.get("tool_calls"):
|
||||
additional_kwargs["tool_calls"] = raw_tool_calls
|
||||
try:
|
||||
tool_call_chunks = [
|
||||
{
|
||||
"name": rtc["function"].get("name"),
|
||||
"args": rtc["function"].get("arguments"),
|
||||
"id": rtc.get("id"),
|
||||
"index": rtc["index"],
|
||||
}
|
||||
for rtc in raw_tool_calls
|
||||
]
|
||||
except KeyError:
|
||||
pass
|
||||
else:
|
||||
tool_call_chunks = []
|
||||
|
||||
if role == "user" or default_class == HumanMessageChunk:
|
||||
return HumanMessageChunk(content=content)
|
||||
elif role == "assistant" or default_class == AIMessageChunk:
|
||||
return AIMessageChunk(
|
||||
content=content,
|
||||
additional_kwargs=additional_kwargs,
|
||||
tool_call_chunks=tool_call_chunks,
|
||||
)
|
||||
elif role == "system" or default_class == SystemMessageChunk:
|
||||
return SystemMessageChunk(content=content)
|
||||
elif role == "function" or default_class == FunctionMessageChunk:
|
||||
return FunctionMessageChunk(content=content, name=_dict["name"])
|
||||
elif role == "tool" or default_class == ToolMessageChunk:
|
||||
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
|
||||
elif role or default_class == ChatMessageChunk:
|
||||
return ChatMessageChunk(content=content, role=role)
|
||||
else:
|
||||
return default_class(content=content) # type: ignore
|
||||
|
||||
|
||||
class _FunctionCall(TypedDict):
|
||||
name: str
|
||||
|
||||
|
||||
class ChatWatsonx(BaseChatModel):
|
||||
"""
|
||||
IBM watsonx.ai large language chat models.
|
||||
|
||||
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.
|
||||
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
|
||||
parameters = {
|
||||
GenTextParamsMetaNames.DECODING_METHOD: "sample",
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 100,
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 1,
|
||||
GenTextParamsMetaNames.TEMPERATURE: 0.5,
|
||||
GenTextParamsMetaNames.TOP_K: 50,
|
||||
GenTextParamsMetaNames.TOP_P: 1,
|
||||
}
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
watsonx_llm = ChatWatsonx(
|
||||
model_id="meta-llama/llama-3-70b-instruct",
|
||||
url="https://us-south.ml.cloud.ibm.com",
|
||||
apikey="*****",
|
||||
project_id="*****",
|
||||
params=parameters,
|
||||
)
|
||||
"""
|
||||
|
||||
model_id: str = ""
|
||||
"""Type of model to use."""
|
||||
|
||||
deployment_id: str = ""
|
||||
"""Type of deployed 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
|
||||
"""Chat 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"""
|
||||
|
||||
streaming: bool = False
|
||||
""" Whether to stream the results or not. """
|
||||
|
||||
watsonx_model: ModelInference = Field(default=None, exclude=True) #: :meta private:
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
allow_population_by_field_name = True
|
||||
|
||||
@classmethod
|
||||
def is_lc_serializable(cls) -> bool:
|
||||
return False
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "watsonx-chat"
|
||||
|
||||
@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",
|
||||
"token": "WATSONX_TOKEN",
|
||||
"password": "WATSONX_PASSWORD",
|
||||
"username": "WATSONX_USERNAME",
|
||||
"instance_id": "WATSONX_INSTANCE_ID",
|
||||
}
|
||||
|
||||
@root_validator()
|
||||
def validate_environment(cls, values: Dict) -> Dict:
|
||||
"""Validate that credentials and python package exists in environment."""
|
||||
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 = Credentials(
|
||||
url=values["url"].get_secret_value() if values["url"] else None,
|
||||
api_key=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,
|
||||
verify=values["verify"],
|
||||
)
|
||||
|
||||
watsonx_chat = ModelInference(
|
||||
model_id=values["model_id"],
|
||||
deployment_id=values["deployment_id"],
|
||||
credentials=credentials,
|
||||
params=values["params"],
|
||||
project_id=values["project_id"],
|
||||
space_id=values["space_id"],
|
||||
)
|
||||
values["watsonx_model"] = watsonx_chat
|
||||
|
||||
return values
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
should_stream = stream if stream is not None else self.streaming
|
||||
if should_stream:
|
||||
stream_iter = self._stream(
|
||||
messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
)
|
||||
return generate_from_stream(stream_iter)
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop, **kwargs)
|
||||
chat_prompt = self._create_chat_prompt(message_dicts)
|
||||
|
||||
tools = kwargs.get("tools")
|
||||
|
||||
if tools:
|
||||
chat_prompt = f"""[AVAILABLE_TOOLS]
|
||||
{json.dumps(tools[0], indent=2)}
|
||||
[/AVAILABLE_TOOLS]
|
||||
[INST]<<SYS>>You are Mixtral Chat function calling, an AI language model developed by
|
||||
Mistral AI. You are a cautious assistant. You carefully follow instructions. You are
|
||||
helpful and harmless and you follow ethical guidelines and promote positive behavior.
|
||||
<</SYS>>
|
||||
|
||||
To use these tools you must always respond in JSON format containing `"type"` and
|
||||
`"function"` key-value pairs. Also `"function"` key-value pair always containing
|
||||
`"name"` and `"arguments"` key-value pairs.
|
||||
|
||||
Between subsequent JSONs should be one blank line.
|
||||
|
||||
Remember, even when answering to the user, you must still use this only JSON format!
|
||||
|
||||
{chat_prompt}[/INST]"""
|
||||
|
||||
if "tools" in kwargs:
|
||||
del kwargs["tools"]
|
||||
if "tool_choice" in kwargs:
|
||||
del kwargs["tool_choice"]
|
||||
|
||||
if "params" in kwargs:
|
||||
del kwargs["params"]
|
||||
|
||||
response = self.watsonx_model.generate(
|
||||
prompt=chat_prompt, params=params, **kwargs
|
||||
)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
chat_prompt = self._create_chat_prompt(message_dicts)
|
||||
|
||||
for chunk in self.watsonx_model.generate_text_stream(
|
||||
prompt=chat_prompt, raw_response=True, params=params, **kwargs
|
||||
):
|
||||
if not isinstance(chunk, dict):
|
||||
chunk = chunk.dict()
|
||||
if len(chunk["results"]) == 0:
|
||||
continue
|
||||
choice = chunk["results"][0]
|
||||
|
||||
chunk = AIMessageChunk(
|
||||
content=choice["generated_text"],
|
||||
)
|
||||
generation_info = {}
|
||||
if finish_reason := choice.get("stop_reason"):
|
||||
generation_info["finish_reason"] = finish_reason
|
||||
logprobs = choice.get("logprobs")
|
||||
if logprobs:
|
||||
generation_info["logprobs"] = logprobs
|
||||
chunk = ChatGenerationChunk(
|
||||
message=chunk, generation_info=generation_info or None
|
||||
)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
|
||||
|
||||
yield chunk
|
||||
|
||||
def _create_chat_prompt(self, messages: List[Dict[str, Any]]) -> str:
|
||||
prompt = ""
|
||||
|
||||
if self.model_id in ["ibm/granite-13b-chat-v1", "ibm/granite-13b-chat-v2"]:
|
||||
for message in messages:
|
||||
if message["role"] == "system":
|
||||
prompt += "<|system|>\n" + message["content"] + "\n\n"
|
||||
elif message["role"] == "assistant":
|
||||
prompt += "<|assistant|>\n" + message["content"] + "\n\n"
|
||||
elif message["role"] == "function":
|
||||
prompt += "<|function|>\n" + message["content"] + "\n\n"
|
||||
elif message["role"] == "tool":
|
||||
prompt += "<|tool|>\n" + message["content"] + "\n\n"
|
||||
else:
|
||||
prompt += "<|user|>:\n" + message["content"] + "\n\n"
|
||||
|
||||
prompt += "<|assistant|>\n"
|
||||
|
||||
elif self.model_id in [
|
||||
"meta-llama/llama-2-13b-chat",
|
||||
"meta-llama/llama-2-70b-chat",
|
||||
]:
|
||||
for message in messages:
|
||||
if message["role"] == "system":
|
||||
prompt += "[INST] <<SYS>>\n" + message["content"] + "<</SYS>>\n\n"
|
||||
elif message["role"] == "assistant":
|
||||
prompt += message["content"] + "\n[INST]\n\n"
|
||||
else:
|
||||
prompt += message["content"] + "\n[/INST]\n"
|
||||
|
||||
else:
|
||||
prompt = ChatPromptValue(messages=convert_to_messages(messages)).to_string()
|
||||
|
||||
return prompt
|
||||
|
||||
def _create_message_dicts(
|
||||
self, messages: List[BaseMessage], stop: Optional[List[str]], **kwargs: Any
|
||||
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
||||
params = {**self.params} if self.params else {}
|
||||
params = params | {**kwargs.get("params", {})}
|
||||
if stop is not None:
|
||||
if params and "stop_sequences" in params:
|
||||
raise ValueError(
|
||||
"`stop_sequences` found in both the input and default params."
|
||||
)
|
||||
params = (params or {}) | {"stop_sequences": stop}
|
||||
message_dicts = [_convert_message_to_dict(m) for m in messages]
|
||||
return message_dicts, params
|
||||
|
||||
def _create_chat_result(self, response: Union[dict]) -> ChatResult:
|
||||
generations = []
|
||||
sum_of_total_generated_tokens = 0
|
||||
sum_of_total_input_tokens = 0
|
||||
|
||||
if response.get("error"):
|
||||
raise ValueError(response.get("error"))
|
||||
|
||||
for res in response["results"]:
|
||||
message = _convert_dict_to_message(res)
|
||||
generation_info = dict(finish_reason=res.get("stop_reason"))
|
||||
if "logprobs" in res:
|
||||
generation_info["logprobs"] = res["logprobs"]
|
||||
if "generated_token_count" in res:
|
||||
sum_of_total_generated_tokens += res["generated_token_count"]
|
||||
if "input_token_count" in res:
|
||||
sum_of_total_input_tokens += res["input_token_count"]
|
||||
gen = ChatGeneration(
|
||||
message=message,
|
||||
generation_info=generation_info,
|
||||
)
|
||||
generations.append(gen)
|
||||
token_usage = {
|
||||
"generated_token_count": sum_of_total_generated_tokens,
|
||||
"input_token_count": sum_of_total_input_tokens,
|
||||
}
|
||||
llm_output = {
|
||||
"token_usage": token_usage,
|
||||
"model_name": self.model_id,
|
||||
"system_fingerprint": response.get("system_fingerprint", ""),
|
||||
}
|
||||
return ChatResult(generations=generations, llm_output=llm_output)
|
||||
|
||||
def bind_functions(
|
||||
self,
|
||||
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
||||
function_call: Optional[
|
||||
Union[_FunctionCall, str, Literal["auto", "none"]]
|
||||
] = None,
|
||||
**kwargs: Any,
|
||||
) -> Runnable[LanguageModelInput, BaseMessage]:
|
||||
"""Bind functions (and other objects) to this chat model.
|
||||
|
||||
Assumes model is compatible with IBM watsonx.ai function-calling API.
|
||||
|
||||
Args:
|
||||
functions: A list of function definitions to bind to this chat model.
|
||||
Can be a dictionary, pydantic model, or callable. Pydantic
|
||||
models and callables will be automatically converted to
|
||||
their schema dictionary representation.
|
||||
function_call: Which function to require the model to call.
|
||||
Must be the name of the single provided function or
|
||||
"auto" to automatically determine which function to call
|
||||
(if any).
|
||||
**kwargs: Any additional parameters to pass to the
|
||||
:class:`~langchain.runnable.Runnable` constructor.
|
||||
"""
|
||||
|
||||
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
|
||||
if function_call is not None:
|
||||
function_call = (
|
||||
{"name": function_call}
|
||||
if isinstance(function_call, str)
|
||||
and function_call not in ("auto", "none")
|
||||
else function_call
|
||||
)
|
||||
if isinstance(function_call, dict) and len(formatted_functions) != 1:
|
||||
raise ValueError(
|
||||
"When specifying `function_call`, you must provide exactly one "
|
||||
"function."
|
||||
)
|
||||
if (
|
||||
isinstance(function_call, dict)
|
||||
and formatted_functions[0]["name"] != function_call["name"]
|
||||
):
|
||||
raise ValueError(
|
||||
f"Function call {function_call} was specified, but the only "
|
||||
f"provided function was {formatted_functions[0]['name']}."
|
||||
)
|
||||
kwargs = {**kwargs, "function_call": function_call}
|
||||
return super().bind(
|
||||
functions=formatted_functions,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def bind_tools(
|
||||
self,
|
||||
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
||||
**kwargs: Any,
|
||||
) -> Runnable[LanguageModelInput, BaseMessage]:
|
||||
"""Bind tool-like objects to this chat model.
|
||||
|
||||
Args:
|
||||
tools: A list of tool definitions to bind to this chat model.
|
||||
Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
|
||||
models, callables, and BaseTools will be automatically converted to
|
||||
their schema dictionary representation.
|
||||
**kwargs: Any additional parameters to pass to the
|
||||
:class:`~langchain.runnable.Runnable` constructor.
|
||||
"""
|
||||
bind_tools_supported_models = ["mistralai/mixtral-8x7b-instruct-v01"]
|
||||
if self.model_id not in bind_tools_supported_models:
|
||||
raise Warning(
|
||||
f"bind_tools() method for ChatWatsonx support only "
|
||||
f"following models: {bind_tools_supported_models}"
|
||||
)
|
||||
|
||||
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
||||
|
||||
return super().bind(tools=formatted_tools, **kwargs)
|
||||
|
||||
def with_structured_output(
|
||||
self,
|
||||
schema: Optional[Union[Dict, Type[BaseModel]]] = None,
|
||||
*,
|
||||
method: Literal["function_calling", "json_mode"] = "function_calling",
|
||||
include_raw: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
|
||||
"""Model wrapper that returns outputs formatted to match the given schema.
|
||||
|
||||
Args:
|
||||
schema: The output schema as a dict or a Pydantic class. If a Pydantic class
|
||||
then the model output will be an object of that class. If a dict then
|
||||
the model output will be a dict. With a Pydantic class the returned
|
||||
attributes will be validated, whereas with a dict they will not be. If
|
||||
`method` is "function_calling" and `schema` is a dict, then the dict
|
||||
must match the IBM watsonx.ai function-calling spec.
|
||||
method: The method for steering model generation, either "function_calling"
|
||||
or "json_mode". If "function_calling" then the schema will be converted
|
||||
to an IBM watsonx.ai function and the returned model will make use of the
|
||||
function-calling API. If "json_mode" then IBM watsonx.ai's JSON mode will be
|
||||
used. Note that if using "json_mode" then you must include instructions
|
||||
for formatting the output into the desired schema into the model call.
|
||||
include_raw: If False then only the parsed structured output is returned. If
|
||||
an error occurs during model output parsing it will be raised. If True
|
||||
then both the raw model response (a BaseMessage) and the parsed model
|
||||
response will be returned. If an error occurs during output parsing it
|
||||
will be caught and returned as well. The final output is always a dict
|
||||
with keys "raw", "parsed", and "parsing_error".
|
||||
|
||||
Returns:
|
||||
A Runnable that takes any ChatModel input and returns as output:
|
||||
|
||||
If include_raw is True then a dict with keys:
|
||||
raw: BaseMessage
|
||||
parsed: Optional[_DictOrPydantic]
|
||||
parsing_error: Optional[BaseException]
|
||||
|
||||
If include_raw is False then just _DictOrPydantic is returned,
|
||||
where _DictOrPydantic depends on the schema:
|
||||
|
||||
If schema is a Pydantic class then _DictOrPydantic is the Pydantic
|
||||
class.
|
||||
|
||||
If schema is a dict then _DictOrPydantic is a dict.
|
||||
|
||||
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
class AnswerWithJustification(BaseModel):
|
||||
'''An answer to the user question along with justification for the answer.'''
|
||||
answer: str
|
||||
justification: str
|
||||
|
||||
llm = ChatWatsonx(...)
|
||||
structured_llm = llm.with_structured_output(AnswerWithJustification)
|
||||
|
||||
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
||||
|
||||
# -> AnswerWithJustification(
|
||||
# answer='They weigh the same',
|
||||
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
|
||||
# )
|
||||
|
||||
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
class AnswerWithJustification(BaseModel):
|
||||
'''An answer to the user question along with justification for the answer.'''
|
||||
answer: str
|
||||
justification: str
|
||||
|
||||
llm = ChatWatsonx(...)
|
||||
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
|
||||
|
||||
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
||||
# -> {
|
||||
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
|
||||
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
|
||||
# 'parsing_error': None
|
||||
# }
|
||||
|
||||
Example: Function-calling, dict schema (method="function_calling", include_raw=False):
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
from langchain_core.utils.function_calling import convert_to_openai_tool
|
||||
|
||||
class AnswerWithJustification(BaseModel):
|
||||
'''An answer to the user question along with justification for the answer.'''
|
||||
answer: str
|
||||
justification: str
|
||||
|
||||
dict_schema = convert_to_openai_tool(AnswerWithJustification)
|
||||
llm = ChatWatsonx(...)
|
||||
structured_llm = llm.with_structured_output(dict_schema)
|
||||
|
||||
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
||||
# -> {
|
||||
# 'answer': 'They weigh the same',
|
||||
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
|
||||
# }
|
||||
|
||||
Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True):
|
||||
.. code-block::
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
class AnswerWithJustification(BaseModel):
|
||||
answer: str
|
||||
justification: str
|
||||
|
||||
llm = ChatWatsonx(...)
|
||||
structured_llm = llm.with_structured_output(
|
||||
AnswerWithJustification,
|
||||
method="json_mode",
|
||||
include_raw=True
|
||||
)
|
||||
|
||||
structured_llm.invoke(
|
||||
"Answer the following question. "
|
||||
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
|
||||
"What's heavier a pound of bricks or a pound of feathers?"
|
||||
)
|
||||
# -> {
|
||||
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
|
||||
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
|
||||
# 'parsing_error': None
|
||||
# }
|
||||
|
||||
Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True):
|
||||
.. code-block::
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
|
||||
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
|
||||
|
||||
structured_llm.invoke(
|
||||
"Answer the following question. "
|
||||
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
|
||||
"What's heavier a pound of bricks or a pound of feathers?"
|
||||
)
|
||||
# -> {
|
||||
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
|
||||
# 'parsed': {
|
||||
# 'answer': 'They are both the same weight.',
|
||||
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
|
||||
# },
|
||||
# 'parsing_error': None
|
||||
# }
|
||||
""" # noqa: E501
|
||||
if kwargs:
|
||||
raise ValueError(f"Received unsupported arguments {kwargs}")
|
||||
is_pydantic_schema = _is_pydantic_class(schema)
|
||||
if method == "function_calling":
|
||||
if schema is None:
|
||||
raise ValueError(
|
||||
"schema must be specified when method is 'function_calling'. "
|
||||
"Received None."
|
||||
)
|
||||
llm = self.bind_tools([schema], tool_choice=True)
|
||||
if is_pydantic_schema:
|
||||
output_parser: OutputParserLike = PydanticToolsParser(
|
||||
tools=[schema], first_tool_only=True
|
||||
)
|
||||
else:
|
||||
key_name = convert_to_openai_tool(schema)["function"]["name"]
|
||||
output_parser = JsonOutputKeyToolsParser(
|
||||
key_name=key_name, first_tool_only=True
|
||||
)
|
||||
elif method == "json_mode":
|
||||
llm = self.bind(response_format={"type": "json_object"})
|
||||
output_parser = (
|
||||
PydanticOutputParser(pydantic_object=schema)
|
||||
if is_pydantic_schema
|
||||
else JsonOutputParser()
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unrecognized method argument. Expected one of 'function_calling' or "
|
||||
f"'json_format'. Received: '{method}'"
|
||||
)
|
||||
|
||||
if include_raw:
|
||||
parser_assign = RunnablePassthrough.assign(
|
||||
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
|
||||
)
|
||||
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
|
||||
parser_with_fallback = parser_assign.with_fallbacks(
|
||||
[parser_none], exception_key="parsing_error"
|
||||
)
|
||||
return RunnableMap(raw=llm) | parser_with_fallback
|
||||
else:
|
||||
return llm | output_parser
|
||||
|
||||
|
||||
def _is_pydantic_class(obj: Any) -> bool:
|
||||
return isinstance(obj, type) and issubclass(obj, BaseModel)
|
||||
472
libs/partners/ibm/poetry.lock
generated
472
libs/partners/ibm/poetry.lock
generated
@ -2,24 +2,24 @@
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.6.0"
|
||||
version = "0.7.0"
|
||||
description = "Reusable constraint types to use with typing.Annotated"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
|
||||
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
|
||||
{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
|
||||
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2024.2.2"
|
||||
version = "2024.6.2"
|
||||
description = "Python package for providing Mozilla's CA Bundle."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
|
||||
{file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
|
||||
{file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
|
||||
{file = "certifi-2024.6.2.tar.gz", hash = "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -123,13 +123,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "codespell"
|
||||
version = "2.2.6"
|
||||
version = "2.3.0"
|
||||
description = "Codespell"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "codespell-2.2.6-py3-none-any.whl", hash = "sha256:9ee9a3e5df0990604013ac2a9f22fa8e57669c827124a2e961fe8a1da4cacc07"},
|
||||
{file = "codespell-2.2.6.tar.gz", hash = "sha256:a8c65d8eb3faa03deabab6b3bbe798bea72e1799c7e9e955d57eca4096abcff9"},
|
||||
{file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"},
|
||||
{file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -179,57 +179,57 @@ python-dateutil = ">=2.7"
|
||||
|
||||
[[package]]
|
||||
name = "ibm-cos-sdk"
|
||||
version = "2.13.4"
|
||||
version = "2.13.5"
|
||||
description = "IBM SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">= 3.6"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "ibm-cos-sdk-2.13.4.tar.gz", hash = "sha256:ee06bb89205e2bd031967e7a0d3fc47b39363be03badc49442202394a791d24a"},
|
||||
{file = "ibm-cos-sdk-2.13.5.tar.gz", hash = "sha256:1aff7f9863ac9072a3db2f0053bec99478b26f3fb5fa797ce96a15bbb13cd40e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
ibm-cos-sdk-core = "2.13.4"
|
||||
ibm-cos-sdk-s3transfer = "2.13.4"
|
||||
ibm-cos-sdk-core = "2.13.5"
|
||||
ibm-cos-sdk-s3transfer = "2.13.5"
|
||||
jmespath = ">=0.10.0,<=1.0.1"
|
||||
|
||||
[[package]]
|
||||
name = "ibm-cos-sdk-core"
|
||||
version = "2.13.4"
|
||||
version = "2.13.5"
|
||||
description = "Low-level, data-driven core of IBM SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "ibm-cos-sdk-core-2.13.4.tar.gz", hash = "sha256:c0f3c03b6c21bb69d3dedd2a1bb647621e0d99e0e1c0929d2c36bd45cfd4166c"},
|
||||
{file = "ibm-cos-sdk-core-2.13.5.tar.gz", hash = "sha256:d3a99d8b06b3f8c00b1a9501f85538d592463e63ddf8cec32672ab5a0b107b83"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
jmespath = ">=0.10.0,<=1.0.1"
|
||||
python-dateutil = ">=2.8.2,<3.0.0"
|
||||
requests = ">=2.31.0,<3.0"
|
||||
python-dateutil = ">=2.9.0,<3.0.0"
|
||||
requests = ">=2.32.3,<3.0"
|
||||
urllib3 = {version = ">=1.26.18,<2.2", markers = "python_version >= \"3.10\""}
|
||||
|
||||
[[package]]
|
||||
name = "ibm-cos-sdk-s3transfer"
|
||||
version = "2.13.4"
|
||||
version = "2.13.5"
|
||||
description = "IBM S3 Transfer Manager"
|
||||
optional = false
|
||||
python-versions = ">= 3.6"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "ibm-cos-sdk-s3transfer-2.13.4.tar.gz", hash = "sha256:3c93feaf66254803b2b8523720efaf90e7374b544ac0a411099617f3b4689279"},
|
||||
{file = "ibm-cos-sdk-s3transfer-2.13.5.tar.gz", hash = "sha256:9649b1f2201c6de96ff5a6b5a3686de3a809e6ef3b8b12c7c4f2f7ce72da7749"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
ibm-cos-sdk-core = "2.13.4"
|
||||
ibm-cos-sdk-core = "2.13.5"
|
||||
|
||||
[[package]]
|
||||
name = "ibm-watsonx-ai"
|
||||
version = "1.0.2"
|
||||
version = "1.0.9"
|
||||
description = "IBM watsonx.ai API Client"
|
||||
optional = false
|
||||
python-versions = ">=3.10"
|
||||
files = [
|
||||
{file = "ibm_watsonx_ai-1.0.2-py3-none-any.whl", hash = "sha256:784b37f06d2f82712ab621380c7ec2e2ef7ea082cdd9e839390ae86f616764bc"},
|
||||
{file = "ibm_watsonx_ai-1.0.2.tar.gz", hash = "sha256:2e557e0857ed2038b7350d0246d27cef7d6654f57ef8dd113f5c8fed66c43af8"},
|
||||
{file = "ibm_watsonx_ai-1.0.9-py3-none-any.whl", hash = "sha256:3b9d42a60418430ddb37b1f9d7e98ade241089518903ebd937d77ff16b07e20b"},
|
||||
{file = "ibm_watsonx_ai-1.0.9.tar.gz", hash = "sha256:c0733a028d34ac75904812a6e3d213feffa401498a9e02a3e3d8d4d2d6d83d32"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -245,8 +245,9 @@ urllib3 = "*"
|
||||
|
||||
[package.extras]
|
||||
fl-crypto = ["pyhelayers (==1.5.0.3)"]
|
||||
fl-rt22-2-py3-10 = ["GPUtil", "cloudpickle (==1.3.0)", "cryptography (==39.0.1)", "ddsketch (==1.1.2)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.1.1)", "jsonpickle (==1.4.2)", "lz4", "numcompress (==0.1.2)", "numpy (==1.23.1)", "pandas (==1.4.3)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==3.19.5)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.27.1)", "scikit-learn (==1.1.1)", "scipy (==1.8.1)", "setproctitle", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.9.3)", "torch (==1.12.1)", "websockets (==10.1)"]
|
||||
fl-rt23-1-py3-10 = ["GPUtil", "cloudpickle (==1.3.0)", "cryptography (==39.0.1)", "ddsketch (==1.1.2)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.1.1)", "jsonpickle (==1.4.2)", "lz4", "numcompress (==0.1.2)", "numpy (==1.23.5)", "pandas (==1.5.3)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==4.22.1)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.27.1)", "scikit-learn (==1.1.1)", "scipy (==1.10.1)", "setproctitle", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.12.0)", "torch (==2.0.0)", "websockets (==10.1)"]
|
||||
fl-crypto-rt24-1 = ["pyhelayers (==1.5.3.1)"]
|
||||
fl-rt23-1-py3-10 = ["GPUtil", "cryptography (==42.0.5)", "ddsketch (==2.0.4)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.1.1)", "lz4", "msgpack (==1.0.7)", "msgpack-numpy (==0.4.8)", "numcompress (==0.1.2)", "numpy (==1.23.5)", "pandas (==1.5.3)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==4.22.1)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.31.0)", "scikit-learn (==1.1.1)", "scipy (==1.10.1)", "setproctitle", "skops (==0.9.0)", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.12.0)", "torch (==2.0.1)", "websockets (==10.1)"]
|
||||
fl-rt24-1-py3-11 = ["GPUtil", "cryptography (==42.0.5)", "ddsketch (==2.0.4)", "diffprivlib (==0.5.1)", "environs (==9.5.0)", "gym", "image (==1.5.33)", "joblib (==1.3.2)", "lz4", "msgpack (==1.0.7)", "msgpack-numpy (==0.4.8)", "numcompress (==0.1.2)", "numpy (==1.26.4)", "pandas (==2.1.4)", "parse (==1.19.0)", "pathlib2 (==2.3.6)", "protobuf (==4.22.1)", "psutil", "pyYAML (==6.0.1)", "pytest (==6.2.5)", "requests (==2.31.0)", "scikit-learn (==1.3.2)", "scipy (==1.11.4)", "setproctitle", "skops (==0.9.0)", "skorch (==0.12.0)", "tabulate (==0.8.9)", "tensorflow (==2.14.1)", "torch (==2.1.2)", "websockets (==10.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
@ -261,22 +262,22 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "7.1.0"
|
||||
version = "8.0.0"
|
||||
description = "Read metadata from Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
|
||||
{file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
|
||||
{file = "importlib_metadata-8.0.0-py3-none-any.whl", hash = "sha256:15584cf2b1bf449d98ff8a6ff1abef57bf20f3ac6454f431736cd3e660921b2f"},
|
||||
{file = "importlib_metadata-8.0.0.tar.gz", hash = "sha256:188bd24e4c346d3f0a933f275c2fec67050326a856b9a359881d7c2a697e8812"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
zipp = ">=0.5"
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
doc = ["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)", "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)"]
|
||||
test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "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"
|
||||
@ -316,18 +317,18 @@ jsonpointer = ">=1.9"
|
||||
|
||||
[[package]]
|
||||
name = "jsonpointer"
|
||||
version = "2.4"
|
||||
version = "3.0.0"
|
||||
description = "Identify specific nodes in a JSON document (RFC 6901)"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
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||||
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|
||||
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||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.2.0rc1"
|
||||
version = "0.2.10"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@ -336,14 +337,14 @@ develop = true
|
||||
|
||||
[package.dependencies]
|
||||
jsonpatch = "^1.33"
|
||||
langsmith = "^0.1.0"
|
||||
packaging = "^23.2"
|
||||
pydantic = ">=1,<3"
|
||||
langsmith = "^0.1.75"
|
||||
packaging = ">=23.2,<25"
|
||||
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\""},
|
||||
]
|
||||
PyYAML = ">=5.3"
|
||||
tenacity = "^8.1.0"
|
||||
|
||||
[package.extras]
|
||||
extended-testing = ["jinja2 (>=3,<4)"]
|
||||
tenacity = "^8.1.0,!=8.4.0"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
@ -351,18 +352,21 @@ url = "../../core"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.1.59"
|
||||
version = "0.1.82"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
orjson = ">=3.9.14,<4.0.0"
|
||||
pydantic = ">=1,<3"
|
||||
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\""},
|
||||
]
|
||||
requests = ">=2,<3"
|
||||
|
||||
[[package]]
|
||||
@ -487,68 +491,68 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
version = "3.10.3"
|
||||
version = "3.10.5"
|
||||
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
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optional = false
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python-versions = ">=3.8"
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@ -903,13 +906,13 @@ files = [
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||||
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||||
[[package]]
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{file = "watchdog-4.0.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:093b23e6906a8b97051191a4a0c73a77ecc958121d42346274c6af6520dec175"},
|
||||
{file = "watchdog-4.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:611be3904f9843f0529c35a3ff3fd617449463cb4b73b1633950b3d97fa4bfb7"},
|
||||
{file = "watchdog-4.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:62c613ad689ddcb11707f030e722fa929f322ef7e4f18f5335d2b73c61a85c28"},
|
||||
{file = "watchdog-4.0.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:d4925e4bf7b9bddd1c3de13c9b8a2cdb89a468f640e66fbfabaf735bd85b3e35"},
|
||||
{file = "watchdog-4.0.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:cad0bbd66cd59fc474b4a4376bc5ac3fc698723510cbb64091c2a793b18654db"},
|
||||
{file = "watchdog-4.0.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:a3c2c317a8fb53e5b3d25790553796105501a235343f5d2bf23bb8649c2c8709"},
|
||||
{file = "watchdog-4.0.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c9904904b6564d4ee8a1ed820db76185a3c96e05560c776c79a6ce5ab71888ba"},
|
||||
{file = "watchdog-4.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:667f3c579e813fcbad1b784db7a1aaa96524bed53437e119f6a2f5de4db04235"},
|
||||
{file = "watchdog-4.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:d10a681c9a1d5a77e75c48a3b8e1a9f2ae2928eda463e8d33660437705659682"},
|
||||
{file = "watchdog-4.0.1-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0144c0ea9997b92615af1d94afc0c217e07ce2c14912c7b1a5731776329fcfc7"},
|
||||
{file = "watchdog-4.0.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:998d2be6976a0ee3a81fb8e2777900c28641fb5bfbd0c84717d89bca0addcdc5"},
|
||||
{file = "watchdog-4.0.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e7921319fe4430b11278d924ef66d4daa469fafb1da679a2e48c935fa27af193"},
|
||||
{file = "watchdog-4.0.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:f0de0f284248ab40188f23380b03b59126d1479cd59940f2a34f8852db710625"},
|
||||
{file = "watchdog-4.0.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:bca36be5707e81b9e6ce3208d92d95540d4ca244c006b61511753583c81c70dd"},
|
||||
{file = "watchdog-4.0.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:ab998f567ebdf6b1da7dc1e5accfaa7c6992244629c0fdaef062f43249bd8dee"},
|
||||
{file = "watchdog-4.0.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:dddba7ca1c807045323b6af4ff80f5ddc4d654c8bce8317dde1bd96b128ed253"},
|
||||
{file = "watchdog-4.0.1-py3-none-manylinux2014_armv7l.whl", hash = "sha256:4513ec234c68b14d4161440e07f995f231be21a09329051e67a2118a7a612d2d"},
|
||||
{file = "watchdog-4.0.1-py3-none-manylinux2014_i686.whl", hash = "sha256:4107ac5ab936a63952dea2a46a734a23230aa2f6f9db1291bf171dac3ebd53c6"},
|
||||
{file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64.whl", hash = "sha256:6e8c70d2cd745daec2a08734d9f63092b793ad97612470a0ee4cbb8f5f705c57"},
|
||||
{file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:f27279d060e2ab24c0aa98363ff906d2386aa6c4dc2f1a374655d4e02a6c5e5e"},
|
||||
{file = "watchdog-4.0.1-py3-none-manylinux2014_s390x.whl", hash = "sha256:f8affdf3c0f0466e69f5b3917cdd042f89c8c63aebdb9f7c078996f607cdb0f5"},
|
||||
{file = "watchdog-4.0.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ac7041b385f04c047fcc2951dc001671dee1b7e0615cde772e84b01fbf68ee84"},
|
||||
{file = "watchdog-4.0.1-py3-none-win32.whl", hash = "sha256:206afc3d964f9a233e6ad34618ec60b9837d0582b500b63687e34011e15bb429"},
|
||||
{file = "watchdog-4.0.1-py3-none-win_amd64.whl", hash = "sha256:7577b3c43e5909623149f76b099ac49a1a01ca4e167d1785c76eb52fa585745a"},
|
||||
{file = "watchdog-4.0.1-py3-none-win_ia64.whl", hash = "sha256:d7b9f5f3299e8dd230880b6c55504a1f69cf1e4316275d1b215ebdd8187ec88d"},
|
||||
{file = "watchdog-4.0.1.tar.gz", hash = "sha256:eebaacf674fa25511e8867028d281e602ee6500045b57f43b08778082f7f8b44"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -1108,20 +1114,20 @@ watchmedo = ["PyYAML (>=3.10)"]
|
||||
|
||||
[[package]]
|
||||
name = "zipp"
|
||||
version = "3.18.1"
|
||||
version = "3.19.2"
|
||||
description = "Backport of pathlib-compatible object wrapper for zip files"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
|
||||
{file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
|
||||
{file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
|
||||
{file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
|
||||
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<4.0"
|
||||
content-hash = "4ee7eb72c61697f46434280fe94002e6d419d0394d334407d0edfbf963370d48"
|
||||
content-hash = "a6472e2dca29fdda0169f09cbd92622406d9da95ca5f5d13ed6c2f2c750e968a"
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-ibm"
|
||||
version = "0.1.7"
|
||||
version = "0.1.8"
|
||||
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 = ">=0.1.50,<0.3"
|
||||
ibm-watsonx-ai = "^1.0.1"
|
||||
langchain-core = ">=0.2.2,<0.3"
|
||||
ibm-watsonx-ai = "^1.0.8"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
optional = true
|
||||
|
||||
193
libs/partners/ibm/tests/integration_tests/test_chat_models.py
Normal file
193
libs/partners/ibm/tests/integration_tests/test_chat_models.py
Normal file
@ -0,0 +1,193 @@
|
||||
import json
|
||||
import os
|
||||
|
||||
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames # type: ignore
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
BaseMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_core.pydantic_v1 import BaseModel
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
|
||||
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 = "mistralai/mixtral-8x7b-instruct-v01"
|
||||
|
||||
|
||||
def test_01_generate_chat() -> None:
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID)
|
||||
messages = [
|
||||
("system", "You are a helpful assistant that translates English to French."),
|
||||
(
|
||||
"human",
|
||||
"Translate this sentence from English to French. I love programming.",
|
||||
),
|
||||
]
|
||||
response = chat.invoke(messages)
|
||||
assert response
|
||||
|
||||
|
||||
def test_01a_generate_chat_with_invoke_params() -> None:
|
||||
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
|
||||
|
||||
params = {
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 1,
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
}
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID)
|
||||
messages = [
|
||||
("system", "You are a helpful assistant that translates English to French."),
|
||||
(
|
||||
"human",
|
||||
"Translate this sentence from English to French. I love programming.",
|
||||
),
|
||||
]
|
||||
response = chat.invoke(messages, params=params)
|
||||
assert response
|
||||
|
||||
|
||||
def test_02_generate_chat_with_few_inputs() -> None:
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat.generate([[message], [message]])
|
||||
assert response
|
||||
|
||||
|
||||
def test_03_generate_chat_with_few_various_inputs() -> None:
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID)
|
||||
system_message = SystemMessage(content="You are to chat with the user.")
|
||||
human_message = HumanMessage(content="Hello")
|
||||
response = chat.invoke([system_message, human_message])
|
||||
assert isinstance(response, BaseMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_05_generate_chat_with_stream() -> None:
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID)
|
||||
response = chat.stream("What's the weather in san francisco")
|
||||
for chunk in response:
|
||||
assert isinstance(chunk.content, str)
|
||||
|
||||
|
||||
def test_10_chaining() -> None:
|
||||
chat = ChatWatsonx(model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID)
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
(
|
||||
"system",
|
||||
"You are a helpful assistant that "
|
||||
"translates {input_language} to {output_language}.",
|
||||
),
|
||||
("human", "{input}"),
|
||||
]
|
||||
)
|
||||
chain = prompt | chat
|
||||
|
||||
response = chain.invoke(
|
||||
{
|
||||
"input_language": "English",
|
||||
"output_language": "German",
|
||||
"input": "I love programming.",
|
||||
}
|
||||
)
|
||||
assert response
|
||||
|
||||
|
||||
def test_11_chaining_with_params() -> None:
|
||||
parameters = {
|
||||
GenTextParamsMetaNames.DECODING_METHOD: "sample",
|
||||
GenTextParamsMetaNames.MIN_NEW_TOKENS: 5,
|
||||
GenTextParamsMetaNames.MAX_NEW_TOKENS: 10,
|
||||
}
|
||||
chat = ChatWatsonx(
|
||||
model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID, params=parameters
|
||||
)
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
[
|
||||
(
|
||||
"system",
|
||||
"You are a helpful assistant that translates "
|
||||
"{input_language} to {output_language}.",
|
||||
),
|
||||
("human", "{input}"),
|
||||
]
|
||||
)
|
||||
chain = prompt | chat
|
||||
|
||||
response = chain.invoke(
|
||||
{
|
||||
"input_language": "English",
|
||||
"output_language": "German",
|
||||
"input": "I love programming.",
|
||||
}
|
||||
)
|
||||
assert response
|
||||
|
||||
|
||||
def test_20_tool_choice() -> None:
|
||||
"""Test that tool choice is respected."""
|
||||
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
|
||||
|
||||
params = {GenTextParamsMetaNames.MAX_NEW_TOKENS: 500}
|
||||
chat = ChatWatsonx(
|
||||
model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID, params=params
|
||||
)
|
||||
|
||||
class MyTool(BaseModel):
|
||||
name: str
|
||||
age: int
|
||||
|
||||
with_tool = chat.bind_tools([MyTool], tool_choice="MyTool")
|
||||
|
||||
resp = with_tool.invoke("Who was the 27 year old named Erick?")
|
||||
assert isinstance(resp, AIMessage)
|
||||
assert resp.content == "" # should just be tool call
|
||||
tool_calls = resp.additional_kwargs["tool_calls"]
|
||||
assert len(tool_calls) == 1
|
||||
tool_call = tool_calls[0]
|
||||
assert tool_call["function"]["name"] == "MyTool"
|
||||
assert json.loads(tool_call["function"]["arguments"]) == {
|
||||
"age": 27,
|
||||
"name": "Erick",
|
||||
}
|
||||
assert tool_call["type"] == "function"
|
||||
assert isinstance(resp.tool_calls, list)
|
||||
assert len(resp.tool_calls) == 1
|
||||
tool_call = resp.tool_calls[0]
|
||||
assert tool_call["name"] == "MyTool"
|
||||
assert tool_call["args"] == {"age": 27, "name": "Erick"}
|
||||
|
||||
|
||||
def test_21_tool_choice_bool() -> None:
|
||||
"""Test that tool choice is respected just passing in True."""
|
||||
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
|
||||
|
||||
params = {GenTextParamsMetaNames.MAX_NEW_TOKENS: 500}
|
||||
chat = ChatWatsonx(
|
||||
model_id=MODEL_ID, url=URL, project_id=WX_PROJECT_ID, params=params
|
||||
)
|
||||
|
||||
class MyTool(BaseModel):
|
||||
name: str
|
||||
age: int
|
||||
|
||||
with_tool = chat.bind_tools([MyTool], tool_choice=True)
|
||||
|
||||
resp = with_tool.invoke("Who was the 27 year old named Erick?")
|
||||
assert isinstance(resp, AIMessage)
|
||||
assert resp.content == "" # should just be tool call
|
||||
tool_calls = resp.additional_kwargs["tool_calls"]
|
||||
assert len(tool_calls) == 1
|
||||
tool_call = tool_calls[0]
|
||||
assert tool_call["function"]["name"] == "MyTool"
|
||||
assert json.loads(tool_call["function"]["arguments"]) == {
|
||||
"age": 27,
|
||||
"name": "Erick",
|
||||
}
|
||||
assert tool_call["type"] == "function"
|
||||
62
libs/partners/ibm/tests/unit_tests/test_chat_models.py
Normal file
62
libs/partners/ibm/tests/unit_tests/test_chat_models.py
Normal file
@ -0,0 +1,62 @@
|
||||
"""Test ChatWatsonx API wrapper."""
|
||||
|
||||
import os
|
||||
|
||||
from langchain_ibm import ChatWatsonx
|
||||
|
||||
os.environ.pop("WATSONX_APIKEY", None)
|
||||
os.environ.pop("WATSONX_PROJECT_ID", None)
|
||||
|
||||
MODEL_ID = "mistralai/mixtral-8x7b-instruct-v01"
|
||||
|
||||
|
||||
def test_initialize_chat_watsonx_bad_path_without_url() -> None:
|
||||
try:
|
||||
ChatWatsonx(
|
||||
model_id=MODEL_ID,
|
||||
)
|
||||
except ValueError as e:
|
||||
assert "WATSONX_URL" in e.__str__()
|
||||
|
||||
|
||||
def test_initialize_chat_watsonx_cloud_bad_path() -> None:
|
||||
try:
|
||||
ChatWatsonx(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_chat_watsonx_cpd_bad_path_without_all() -> None:
|
||||
try:
|
||||
ChatWatsonx(
|
||||
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_chat_watsonx_cpd_bad_path_password_without_username() -> None:
|
||||
try:
|
||||
ChatWatsonx(
|
||||
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_chat_watsonx_cpd_bad_path_apikey_without_username() -> None:
|
||||
try:
|
||||
ChatWatsonx(
|
||||
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__()
|
||||
@ -1,6 +1,6 @@
|
||||
from langchain_ibm import __all__
|
||||
|
||||
EXPECTED_ALL = ["WatsonxLLM", "WatsonxEmbeddings"]
|
||||
EXPECTED_ALL = ["WatsonxLLM", "WatsonxEmbeddings", "ChatWatsonx"]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user