communty[patch]: Native RAG Support in Prem AI langchain (#22238)

This PR adds native RAG support in langchain premai package. The same
has been added in the docs too.

libs/community/langchain_community/chat_models/premai.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import logging
+import warnings
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -104,7 +105,18 @@ def _response_to_result(
                     text=content, message=ChatMessage(role=role, content=content)
                 )
             )
-    return ChatResult(generations=generations)
+
+    if response.document_chunks is not None:
+        return ChatResult(
+            generations=generations,
+            llm_output={
+                "document_chunks": [
+                    chunk.to_dict() for chunk in response.document_chunks
+                ]
+            },
+        )
+    else:
+        return ChatResult(generations=generations, llm_output={"document_chunks": None})
 
 
 def _convert_delta_response_to_message_chunk(
@@ -118,10 +130,6 @@ def _convert_delta_response_to_message_chunk(
     role = _delta.get("role", "")  # type: ignore
     content = _delta.get("content", "")  # type: ignore
     additional_kwargs: Dict = {}
-
-    if role is None or role == "":
-        raise ChatPremAPIError("Role can not be None. Please check the response")
-
     finish_reasons: Optional[str] = response.choices[0].finish_reason
 
     if role == "user" or default_class == HumanMessageChunk:
@@ -185,17 +193,9 @@ class ChatPremAI(BaseChatModel, BaseModel):
     If model name is other than default model then it will override the calls 
     from the model deployed from launchpad."""
 
-    session_id: Optional[str] = None
-    """The ID of the session to use. It helps to track the chat history."""
-
     temperature: Optional[float] = None
     """Model temperature. Value should be >= 0 and <= 1.0"""
 
-    top_p: Optional[float] = None
-    """top_p adjusts the number of choices for each predicted tokens based on
-        cumulative probabilities. Value should be ranging between 0.0 and 1.0. 
-    """
-
     max_tokens: Optional[int] = None
     """The maximum number of tokens to generate"""
 
@@ -209,30 +209,14 @@ class ChatPremAI(BaseChatModel, BaseModel):
     Changing the system prompt would override the default system prompt.
     """
 
+    repositories: Optional[dict] = None
+    """Add valid repository ids. This will be overriding existing connected 
+    repositories (if any) and will use RAG with the connected repos. 
+    """
+
     streaming: Optional[bool] = False
     """Whether to stream the responses or not."""
 
-    tools: Optional[Dict[str, Any]] = None
-    """A list of tools the model may call. Currently, only functions are 
-    supported as a tool"""
-
-    frequency_penalty: Optional[float] = None
-    """Number between -2.0 and 2.0. Positive values penalize new tokens based"""
-
-    presence_penalty: Optional[float] = None
-    """Number between -2.0 and 2.0. Positive values penalize new tokens based 
-    on whether they appear in the text so far."""
-
-    logit_bias: Optional[dict] = None
-    """JSON object that maps tokens to an associated bias value from -100 to 100."""
-
-    stop: Optional[Union[str, List[str]]] = None
-    """Up to 4 sequences where the API will stop generating further tokens."""
-
-    seed: Optional[int] = None
-    """This feature is in Beta. If specified, our system will make a best effort 
-    to sample deterministically."""
-
     client: Any
 
     class Config:
@@ -268,21 +252,34 @@ class ChatPremAI(BaseChatModel, BaseModel):
 
     @property
     def _default_params(self) -> Dict[str, Any]:
-        # FIXME: n and stop is not supported, so hardcoding to current default value
         return {
             "model": self.model,
             "system_prompt": self.system_prompt,
-            "top_p": self.top_p,
             "temperature": self.temperature,
-            "logit_bias": self.logit_bias,
             "max_tokens": self.max_tokens,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-            "seed": self.seed,
-            "stop": None,
+            "repositories": self.repositories,
         }
 
     def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
+        kwargs_to_ignore = [
+            "top_p",
+            "tools",
+            "frequency_penalty",
+            "presence_penalty",
+            "logit_bias",
+            "stop",
+            "seed",
+        ]
+        keys_to_remove = []
+
+        for key in kwargs:
+            if key in kwargs_to_ignore:
+                warnings.warn(f"WARNING: Parameter {key} is not supported in kwargs.")
+                keys_to_remove.append(key)
+
+        for key in keys_to_remove:
+            kwargs.pop(key)
+
         all_kwargs = {**self._default_params, **kwargs}
         for key in list(self._default_params.keys()):
             if all_kwargs.get(key) is None or all_kwargs.get(key) == "":
@@ -298,7 +295,6 @@ class ChatPremAI(BaseChatModel, BaseModel):
     ) -> ChatResult:
         system_prompt, messages_to_pass = _messages_to_prompt_dict(messages)  # type: ignore
 
-        kwargs["stop"] = stop
         if system_prompt is not None and system_prompt != "":
             kwargs["system_prompt"] = system_prompt
 
@@ -322,7 +318,9 @@ class ChatPremAI(BaseChatModel, BaseModel):
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
         system_prompt, messages_to_pass = _messages_to_prompt_dict(messages)
-        kwargs["stop"] = stop
+
+        if stop is not None:
+            logger.warning("stop is not supported in langchain streaming")
 
         if "system_prompt" not in kwargs:
             if system_prompt is not None and system_prompt != "":