bedrock docs

docs/my-website/docs/providers/bedrock.md

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+# AWS Bedrock
+
+### API KEYS
+```python
+!pip install boto3 
+
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+```
+
+### Usage
+```python
+import os 
+from litellm import completion
+
+os.environ["AWS_ACCESS_KEY_ID"] = ""
+os.environ["AWS_SECRET_ACCESS_KEY"] = ""
+os.environ["AWS_REGION_NAME"] = ""
+
+response = completion(
+            model="bedrock/amazon.titan-tg1-large", 
+            messages=[{ "content": "Hello, how are you?","role": "user"}],
+            temperature=0.2,
+            max_tokens=80,
+)
+```
+
+### Supported AWS Bedrock Models
+Here's an example of using a bedrock model with LiteLLM 
+
+| Model Name       | Function Call                                  | Required OS Variables              |
+|------------------|--------------------------------------------|------------------------------------|
+| Llama2 7B        | `completion(model='sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b, messages=messages)`   | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`     |
+| Custom LLM Endpoint        | `completion(model='sagemaker/your-endpoint, messages=messages)`   | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']`     |