docs: document OpenLIT integration (#2386)

Signed-off-by: patcher9 <patcher99@dokulabs.com>
Co-authored-by: Jared Van Bortel <jared@nomic.ai>

README.md

@@ -67,6 +67,7 @@ An alternative way to install GPT4All is to use one of the offline installers av
 
 * :parrot::link: [Langchain](https://python.langchain.com/en/latest/modules/models/llms/integrations/gpt4all.html)
 * :card_file_box: [Weaviate Vector Database](https://github.com/weaviate/weaviate) - [module docs](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-gpt4all)
+* :telescope: [OpenLIT (OTel-native Monitoring)](https://github.com/openlit/openlit) - [Docs](https://docs.openlit.io/latest/integrations/gpt4all)
 
 
 ## Contributing

gpt4all-bindings/python/docs/gpt4all_monitoring.md

@@ -0,0 +1,47 @@
+# Monitoring
+
+Leverage OpenTelemetry to perform real-time monitoring of your LLM application using [OpenLIT](https://github.com/openlit/openlit). This tool helps you easily collect data on user interactions, performance metrics, and other key information, which can assist in enhancing the functionality and dependability of your GPT4All based LLM application.
+
+## How it works?
+
+OpenLIT adds automatic OTel instrumentation to the GPT4all SDK. It covers the `generate` and `embedding` functions, helping to track LLM usage by gathering inputs and outputs. This allows users to monitor and evaluate the performance and behavior of their LLM application in different environments. Additionally, you have the flexibility to view and analyze the generated traces and metrics either in the OpenLIT UI or by exporting them to widely used observability tools like Grafana and DataDog for more comprehensive analysis and visualization.
+
+## Getting Started
+
+Here’s a straightforward guide to help you set up and start monitoring your application:
+
+### 1. Install the OpenLIT SDK
+Open your terminal and run:
+
+```shell
+pip install openlit
+```
+
+### 2. Setup Monitoring for your Application
+In your application, initiate OpenLIT as outlined below:
+
+```python
+from gpt4all import GPT4All
+import openlit
+
+openlit.init()  # Initialize OpenLIT monitoring
+
+model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf')
+
+# Start a chat session and send queries
+with model.chat_session():
+    response1 = model.generate(prompt='hello', temp=0)
+    response2 = model.generate(prompt='write me a short poem', temp=0)
+    response3 = model.generate(prompt='thank you', temp=0)
+
+    print(model.current_chat_session)
+```
+This setup wraps your gpt4all model interactions, capturing valuable data about each request and response.
+
+### Visualize
+
+Once you've set up data collection with OpenLIT, you can visualize and analyze this information to better understand your application's performance:
+
+- **Using OpenLIT UI:** Connect to OpenLIT's UI to start exploring performance metrics. Visit the OpenLIT [Quickstart Guide](https://docs.openlit.io/latest/quickstart) for step-by-step details.
+
+- **Integrate with existing Observability Tools:** If you use tools like Grafana or DataDog, you can integrate the data collected by OpenLIT. For instructions on setting up these connections, check the OpenLIT [Connections Guide](https://docs.openlit.io/latest/connections/intro).

gpt4all-bindings/python/mkdocs.yml

@@ -14,6 +14,7 @@ nav:
       - 'GPT4All in Python':
         - 'Generation': 'gpt4all_python.md'
         - 'Embedding': 'gpt4all_python_embedding.md'
+        - 'Monitoring with OpenLIT': 'gpt4all_monitoring.md'
       - 'GPT4ALL in NodeJs': 'gpt4all_nodejs.md'
       - 'gpt4all_cli.md'
     - 'Wiki':