diff --git a/docs/docs/integrations/chat/ibm_watsonx.ipynb b/docs/docs/integrations/chat/ibm_watsonx.ipynb new file mode 100644 index 00000000000..5b22b3e7260 --- /dev/null +++ b/docs/docs/integrations/chat/ibm_watsonx.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "1c95cd76", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "---\n", + "sidebar_label: IBM watsonx.ai\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "70996d8a", + "metadata": {}, + "source": [ + "# ChatWatsonx\n", + "\n", + ">ChatWatsonx is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n", + "\n", + "The aim of these examples is to show how to communicate with `watsonx.ai` models using `LangChain` LLMs API." + ] + }, + { + "cell_type": "markdown", + "id": "ef7b088a", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "### Integration details\n", + "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/openai) | Package downloads | Package latest |\n", + "| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n", + "| [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) | ❌ | ❌ | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain-ibm?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain-ibm?style=flat-square&label=%20) |\n", + "\n", + "### Model features\n", + "| [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", + "| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n", + "| ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | " + ] + }, + { + "cell_type": "markdown", + "id": "f406e092", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "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", + "\n", + "### Credentials\n", + "\n", + "The cell below defines the credentials required to work with watsonx Foundation Model inferencing.\n", + "\n", + "**Action:** Provide the IBM Cloud user API key. For details, see\n", + "[Managing user API keys](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "11d572a1", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "watsonx_api_key = getpass()\n", + "os.environ[\"WATSONX_APIKEY\"] = watsonx_api_key" + ] + }, + { + "cell_type": "markdown", + "id": "c59782a7", + "metadata": {}, + "source": [ + "Additionally you are able to pass additional secrets as an environment variable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f98c573c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"WATSONX_URL\"] = \"your service instance url\"\n", + "os.environ[\"WATSONX_TOKEN\"] = \"your token for accessing the CPD cluster\"\n", + "os.environ[\"WATSONX_PASSWORD\"] = \"your password for accessing the CPD cluster\"\n", + "os.environ[\"WATSONX_USERNAME\"] = \"your username for accessing the CPD cluster\"\n", + "os.environ[\"WATSONX_INSTANCE_ID\"] = \"your instance_id for accessing the CPD cluster\"" + ] + }, + { + "cell_type": "markdown", + "id": "b3dc9176", + "metadata": {}, + "source": [ + "### Installation\n", + "\n", + "The LangChain IBM integration lives in the `langchain-ibm` package:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "387eda86", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -qU langchain-ibm" + ] + }, + { + "cell_type": "markdown", + "id": "e36acbef", + "metadata": {}, + "source": [ + "## Instantiation\n", + "\n", + "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)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "407cd500", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = {\n", + " \"decoding_method\": \"sample\",\n", + " \"max_new_tokens\": 100,\n", + " \"min_new_tokens\": 1,\n", + " \"stop_sequences\": [\".\"],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "2b586538", + "metadata": {}, + "source": [ + "Initialize the `WatsonxLLM` class with the previously set parameters.\n", + "\n", + "\n", + "**Note**: \n", + "\n", + "- 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", + "- 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", + "\n", + "In this example, we’ll use the `project_id` and Dallas URL.\n", + "\n", + "\n", + "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)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98371396", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_ibm import ChatWatsonx\n", + "\n", + "chat = ChatWatsonx(\n", + " model_id=\"ibm/granite-13b-chat-v2\",\n", + " url=\"https://us-south.ml.cloud.ibm.com\",\n", + " project_id=\"PASTE YOUR PROJECT_ID HERE\",\n", + " params=parameters,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2202f4e0", + "metadata": {}, + "source": [ + "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). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "243ecccb", + "metadata": {}, + "outputs": [], + "source": [ + "chat = ChatWatsonx(\n", + " model_id=\"ibm/granite-13b-chat-v2\",\n", + " url=\"PASTE YOUR URL HERE\",\n", + " username=\"PASTE YOUR USERNAME HERE\",\n", + " password=\"PASTE YOUR PASSWORD HERE\",\n", + " instance_id=\"openshift\",\n", + " version=\"4.8\",\n", + " project_id=\"PASTE YOUR PROJECT_ID HERE\",\n", + " params=parameters,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "96ed13d4", + "metadata": {}, + "source": [ + "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)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08e66c88", + "metadata": {}, + "outputs": [], + "source": [ + "chat = ChatWatsonx(\n", + " deployment_id=\"PASTE YOUR DEPLOYMENT_ID HERE\",\n", + " url=\"https://us-south.ml.cloud.ibm.com\",\n", + " project_id=\"PASTE YOUR PROJECT_ID HERE\",\n", + " params=parameters,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f571001d", + "metadata": {}, + "source": [ + "## Invocation\n", + "\n", + "To obtain completions, you can call the model directly using a string prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "beea2b5b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Invocation\n", + "\n", + "messages = [\n", + " (\"system\", \"You are a helpful assistant that translates English to French.\"),\n", + " (\n", + " \"human\",\n", + " \"I love you for listening to Rock.\",\n", + " ),\n", + "]\n", + "\n", + "chat.invoke(messages)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "8ab1a25a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Invocation multiple chat\n", + "from langchain_core.messages import (\n", + " HumanMessage,\n", + " SystemMessage,\n", + ")\n", + "\n", + "system_message = SystemMessage(\n", + " content=\"You are a helpful assistant which telling short-info about provided topic.\"\n", + ")\n", + "human_message = HumanMessage(content=\"horse\")\n", + "\n", + "chat.invoke([system_message, human_message])" + ] + }, + { + "cell_type": "markdown", + "id": "20e4b568", + "metadata": {}, + "source": [ + "## Chaining\n", + "Create `ChatPromptTemplate` objects which will be responsible for creating a random question." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "dd919925", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "system = (\n", + " \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n", + ")\n", + "human = \"{input}\"\n", + "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])" + ] + }, + { + "cell_type": "markdown", + "id": "1a013a53", + "metadata": {}, + "source": [ + "Provide a inputs and run the chain." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "68160377", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain = prompt | chat\n", + "chain.invoke(\n", + " {\n", + " \"input_language\": \"English\",\n", + " \"output_language\": \"German\",\n", + " \"input\": \"I love Python\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d2c9da33", + "metadata": {}, + "source": [ + "## Streaming the Model output \n", + "\n", + "You can stream the model output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3f63166a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The moon is a natural satellite of the Earth, and it has been a source of fascination for humans for centuries." + ] + } + ], + "source": [ + "system_message = SystemMessage(\n", + " content=\"You are a helpful assistant which telling short-info about provided topic.\"\n", + ")\n", + "human_message = HumanMessage(content=\"moon\")\n", + "\n", + "for chunk in chat.stream([system_message, human_message]):\n", + " print(chunk.content, end=\"\")" + ] + }, + { + "cell_type": "markdown", + "id": "5a7a2aa1", + "metadata": {}, + "source": [ + "## Batch the Model output \n", + "\n", + "You can batch the model output." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "9e948729", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[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", + " 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')]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "message_1 = [\n", + " SystemMessage(\n", + " content=\"You are a helpful assistant which telling short-info about provided topic.\"\n", + " ),\n", + " 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 +} diff --git a/docs/docs/integrations/providers/ibm.mdx b/docs/docs/integrations/providers/ibm.mdx index 50ee18a895d..bb6f5ef065e 100644 --- a/docs/docs/integrations/providers/ibm.mdx +++ b/docs/docs/integrations/providers/ibm.mdx @@ -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 diff --git a/libs/partners/ibm/langchain_ibm/__init__.py b/libs/partners/ibm/langchain_ibm/__init__.py index 098023d71df..dbda2223fda 100644 --- a/libs/partners/ibm/langchain_ibm/__init__.py +++ b/libs/partners/ibm/langchain_ibm/__init__.py @@ -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"] diff --git a/libs/partners/ibm/langchain_ibm/chat_models.py b/libs/partners/ibm/langchain_ibm/chat_models.py new file mode 100644 index 00000000000..00b7ab2ec2f --- /dev/null +++ b/libs/partners/ibm/langchain_ibm/chat_models.py @@ -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(?:\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]<>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. +<> + +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] <>\n" + message["content"] + "<>\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) diff --git a/libs/partners/ibm/poetry.lock b/libs/partners/ibm/poetry.lock index f76027805df..136b85ab0c6 100644 --- a/libs/partners/ibm/poetry.lock +++ b/libs/partners/ibm/poetry.lock @@ -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 = 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"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 = 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--git a/libs/partners/ibm/pyproject.toml b/libs/partners/ibm/pyproject.toml index 62170ccb2c3..f5a6330ebe7 100644 --- a/libs/partners/ibm/pyproject.toml +++ b/libs/partners/ibm/pyproject.toml @@ -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 diff --git a/libs/partners/ibm/tests/integration_tests/test_chat_models.py b/libs/partners/ibm/tests/integration_tests/test_chat_models.py new file mode 100644 index 00000000000..887ce842760 --- /dev/null +++ b/libs/partners/ibm/tests/integration_tests/test_chat_models.py @@ -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" diff --git a/libs/partners/ibm/tests/unit_tests/test_chat_models.py b/libs/partners/ibm/tests/unit_tests/test_chat_models.py new file mode 100644 index 00000000000..734ab8fe0dc --- /dev/null +++ b/libs/partners/ibm/tests/unit_tests/test_chat_models.py @@ -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__() diff --git a/libs/partners/ibm/tests/unit_tests/test_imports.py b/libs/partners/ibm/tests/unit_tests/test_imports.py index 9fe9fd51e84..7bdeb3246c0 100644 --- a/libs/partners/ibm/tests/unit_tests/test_imports.py +++ b/libs/partners/ibm/tests/unit_tests/test_imports.py @@ -1,6 +1,6 @@ from langchain_ibm import __all__ -EXPECTED_ALL = ["WatsonxLLM", "WatsonxEmbeddings"] +EXPECTED_ALL = ["WatsonxLLM", "WatsonxEmbeddings", "ChatWatsonx"] def test_all_imports() -> None: