docs(dspy): removed session id and support for prem repositories

docs/docs/deep-dive/language_model_clients/remote_models/PremAI.mdx

@@ -13,7 +13,6 @@ The constructor initializes the base class `LM` to support prompting requests to
 - `model` (_str_): Models supported by PremAI. Example: `mistral-tiny`. We recommend using the model selected in [project launchpad](https://docs.premai.io/get-started/launchpad).
 - `project_id` (_int_): The [project id](https://docs.premai.io/get-started/projects) which contains the model of choice.
 - `api_key` (_Optional[str]_, _optional_): API provider from PremAI. Defaults to None.
-- `session_id` (_Optional[int]_, _optional_): The ID of the session to use. It helps to track the chat history.
 - `**kwargs`: Additional language model arguments will be passed to the API provider.
 
 Example of PremAI constructor:
@@ -37,7 +36,7 @@ class PremAI(LM):
 
 **Parameters:**
 - `prompt` (_str_): Prompt to send to PremAI.
-- `**kwargs`: Additional keyword arguments for completion request.
+- `**kwargs`: Additional keyword arguments for completion request. You can find all the additional kwargs [here](https://docs.premai.io/get-started/sdk#optional-parameters).
 
 **Returns:**
 - `str`: Completions string from the chosen LLM provider 
@@ -67,4 +66,34 @@ print(response.answer)
 ```python
 response = premai_client(prompt='What is the capital of Paris?')
 print(response)
+```
+
+### Native RAG Support
+
+Prem Repositories which allows users to upload documents (.txt, .pdf etc) and connect those repositories to the LLMs. You can think Prem repositories as native RAG, where each repository can be considered as a vector database. You can connect multiple repositories. You can learn more about repositories [here](https://docs.premai.io/get-started/repositories).
+
+Repositories are also supported in dspy premai. Here is how you can do it. 
+
+```python
+query = "what is the diameter of individual Galaxy"
+repository_ids = [1991, ]
+repositories = dict(
+    ids=repository_ids,
+    similarity_threshold=0.3,
+    limit=3
+)
+```
+
+First we start by defining our repository with some repository ids. Make sure that the ids are valid repository ids. You can learn more about how to get the repository id [here](https://docs.premai.io/get-started/repositories). 
+
+> Please note: Similar like `model` when you invoke the argument `repositories`, then you are potentially overriding the repositories connected in the launchpad. 
+
+Now, we connect the repository with our chat object to invoke RAG based generations. 
+
+```python 
+response = premai_client(prompt=query, max_tokens=100, repositories=repositories)
+
+print(response)
+print("---")
+print(json.dumps(premai_client.history, indent=4))
 ```