Updated partners/ibm README (#18268)

## PR title
partners: changed the README file for the IBM Watson AI integration in
the libs/partners/ibm folder.

## PR message
Description: Changed the README file of partners/ibm following the docs
on https://python.langchain.com/docs/integrations/llms/ibm_watsonx

The README includes:

- Brief description
- Installation
- Setting-up instructions (API key, project id, ...)
- Basic usage:
  - Loading the model
  - Direct inference
  - Chain invoking
  - Streaming the model output
  
Issue: #17545

Dependencies: None

Twitter handle: None

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>

libs/partners/ibm/README.md

@@ -1,18 +1,164 @@
 # langchain-ibm
 
-This package contains the LangChain integrations for IBM watsonx.ai through their `ibm-watsonx-ai` SDK.
+This package provides the integration between LangChain and IBM Watson AI through the `ibm-watsonx-ai` SDK.
 
-## Installation and Setup
+## Installation
+
+To use the `langchain-ibm` package, follow these installation steps:
 
-- Install the LangChain partner package
 ```bash
 pip install langchain-ibm
 ```
 
-## LLM
 
-See a [usage example](https://python.langchain.com/docs/integrations/llms/watsonxllm).
+
+
+
+## Usage
+
+### Setting up
+
+To use IBM's models, you must have an IBM Cloud user API key. Here's how to obtain and set up your API key:
+
+1. **Obtain an API Key:** For more details on how to create and manage an API key, refer to IBM's [documentation](https://cloud.ibm.com/docs/account?topic=account-userapikey&interface=ui).
+2. **Set the API Key as an Environment Variable:** For security reasons, it's recommended to not hard-code your API key directly in your scripts. Instead, set it up as an environment variable. You can use the following code to prompt for the API key and set it as an environment variable:
+
+```python
+import os
+from getpass import getpass
+
+watsonx_api_key = getpass()
+os.environ["WATSONX_APIKEY"] = watsonx_api_key
+```
+
+In alternative, you can set the environment variable in your terminal.
+
+- **Linux/macOS:** Open your terminal and execute the following command:
+     ```bash
+     export WATSONX_APIKEY='your_ibm_api_key'
+     ```
+     To make this environment variable persistent across terminal sessions, add the above line to your `~/.bashrc`, `~/.bash_profile`, or `~/.zshrc` file.
+
+- **Windows:** For Command Prompt, use:
+    ```cmd
+    set WATSONX_APIKEY=your_ibm_api_key
+    ```
+
+
+
+
+
+### Loading the model
+
+You might need to adjust model parameters for different models or tasks. For more details on the parameters, refer to IBM's [documentation](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames).
+
+
+```python
+parameters = {
+    "decoding_method": "sample",
+    "max_new_tokens": 100,
+    "min_new_tokens": 1,
+    "temperature": 0.5,
+    "top_k": 50,
+    "top_p": 1,
+}
+```
+
+Initialize the WatsonxLLM class with the previously set parameters.
 
 ```python
 from langchain_ibm import WatsonxLLM
+
+watsonx_llm = WatsonxLLM(
+    model_id="PASTE THE CHOSEN MODEL_ID HERE",
+    url="PASTE YOUR URL HERE",
+    project_id="PASTE YOUR PROJECT_ID HERE",
+    params=parameters,
+)
+```
+
+**Note:**
+- You must provide a `project_id` or `space_id`. For more information refer to IBM's [documentation](https://www.ibm.com/docs/en/watsonx-as-a-service?topic=projects).
+- Depending on the region of your provisioned service instance, use one of the urls described [here](https://ibm.github.io/watsonx-ai-python-sdk/setup_cloud.html#authentication).
+- You need to specify the model you want to use for inferencing through `model_id`. You can find the list of available models [here](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#ibm_watsonx_ai.foundation_models.utils.enums.ModelTypes).
+
+
+
+Alternatively you can use Cloud Pak for Data credentials. For more details, refer to IBM's [documentation](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html).
+
+```python
+watsonx_llm = WatsonxLLM(
+    model_id="ibm/granite-13b-instruct-v2",
+    url="PASTE YOUR URL HERE",
+    username="PASTE YOUR USERNAME HERE",
+    password="PASTE YOUR PASSWORD HERE",
+    instance_id="openshift",
+    version="4.8",
+    project_id="PASTE YOUR PROJECT_ID HERE",
+    params=parameters,
+)
+```
+
+
+
+
+### Create a Chain
+
+Create `PromptTemplate` objects which will be responsible for creating a random question.
+
+```python
+from langchain.prompts import PromptTemplate
+
+template = "Generate a random question about {topic}: Question: "
+prompt = PromptTemplate.from_template(template)
+```
+
+Provide a topic and run the LLMChain.
+
+```python
+from langchain.chains import LLMChain
+
+llm_chain = LLMChain(prompt=prompt, llm=watsonx_llm)
+response = llm_chain.invoke("dog")
+print(response)
+```
+
+
+
+
+
+### Calling the Model Directly
+To obtain completions, you can call the model directly using a string prompt.
+
+```python
+# Calling a single prompt
+
+response = watsonx_llm.invoke("Who is man's best friend?")
+print(response)
+```
+
+```python
+# Calling multiple prompts
+
+response = watsonx_llm.generate(
+    [
+        "The fastest dog in the world?",
+        "Describe your chosen dog breed",
+    ]
+)
+print(response)
+```
+
+
+
+
+### Streaming the Model output
+
+You can stream the model output.
+
+```python
+for chunk in watsonx_llm.stream(
+    "Describe your favorite breed of dog and why it is your favorite."
+):
+    print(chunk, end="")
 ```