[Feature] Add support for vllm as llm source (#1149)

docs/components/llms.mdx

@@ -14,6 +14,7 @@ Embedchain comes with built-in support for various popular large language models
   <Card title="Cohere" href="#cohere"></Card>
   <Card title="Together" href="#together"></Card>
   <Card title="Ollama" href="#ollama"></Card>
+  <Card title="vLLM" href="#vllm"></Card>
   <Card title="GPT4All" href="#gpt4all"></Card>
   <Card title="JinaChat" href="#jinachat"></Card>
   <Card title="Hugging Face" href="#hugging-face"></Card>
@@ -393,6 +394,34 @@ llm:
 
 </CodeGroup>
 
+## Ollama
+
+Setup vLLM by following instructions given in [their docs](https://docs.vllm.ai/en/latest/getting_started/installation.html).
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+# load llm configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+llm:
+  provider: vllm
+  config:
+    model: 'meta-llama/Llama-2-70b-hf'
+    temperature: 0.5
+    top_p: 1
+    top_k: 10
+    stream: true
+    trust_remote_code: true
+```
+
+</CodeGroup>
+
 ## GPT4ALL
 
 Install related dependencies using the following command:
@@ -515,7 +544,7 @@ app = App.from_config(config_path="config.yaml")
 
 ```yaml config.yaml
 llm:
-  provider: huggingface 
+  provider: huggingface
   config:
     endpoint: https://api-inference.huggingface.co/models/gpt2 # replace with your personal endpoint
 ```
@@ -525,7 +554,7 @@ If your endpoint requires additional parameters, you can pass them in the `model
 
 ```
 llm:
-  provider: huggingface 
+  provider: huggingface
   config:
     endpoint: <YOUR_ENDPOINT_URL_HERE>
     model_kwargs: