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advanced_rag.py

@@ -25,6 +25,8 @@ import gradio as gr
 import requests
 from pydantic import PrivateAttr
 
+print("Pydantic Version: ")
+print(pydantic.__version__)
 # Add Mistral imports with fallback handling
 try:
     from mistralai import Mistral
@@ -142,14 +144,15 @@ class ElevatedRagChain:
             hf_api_token = os.environ.get("HF_API_TOKEN")
             if not hf_api_token:
                 raise ValueError("Please set the HF_API_TOKEN environment variable to use remote inference.")
-            client = InferenceClient(token=hf_api_token, timeout=180)
+            client = InferenceClient(token=hf_api_token, timeout=240)
             def remote_generate(prompt: str) -> str:
                 response = client.text_generation(
                     prompt,
                     model=repo_id,
                     temperature=self.temperature,
                     top_p=self.top_p,
-                    repetition_penalty=1.1
+                    repetition_penalty=1.1,
+                    wait_for_model=True,
                 )
                 return response
             from langchain.llms.base import LLM
@@ -172,20 +175,25 @@ class ElevatedRagChain:
             if not MISTRAL_AVAILABLE:
                 raise ImportError("Mistral client library not installed. Install with: pip install mistralai")
             from langchain.llms.base import LLM
+            from typing import Any, Optional, List
             class MistralLLM(LLM):
                 temperature: float = 0.7
                 top_p: float = 0.95
-                _client: Any = PrivateAttr()  # Declare _client as a private attribute
-                def __init__(self, api_key: str, temperature: float = 0.7, top_p: float = 0.95):
-                    super().__init__()
+                _client: Any = PrivateAttr(default=None)  # Set default to None
+
+                def __init__(self, api_key: str, temperature: float = 0.7, top_p: float = 0.95, **kwargs: Any):
+                    # Do not pass api_key to super().__init__ since it's not a field
+                    super().__init__(**kwargs)
                     self._client = Mistral(api_key=api_key)
                     self.temperature = temperature
                     self.top_p = top_p
+
                 @property
                 def _llm_type(self) -> str:
                     return "mistral_llm"
+
                 def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-                    response = self._client.chat.complete( 
+                    response = self._client.chat.complete(
                         model="mistral-small-latest",
                         messages=[{"role": "user", "content": prompt}],
                         temperature=self.temperature,
@@ -193,9 +201,11 @@ class ElevatedRagChain:
                         max_tokens=32000
                     )
                     return response.choices[0].message.content
+
                 @property
                 def _identifying_params(self) -> dict:
                     return {"model": "mistral-small-latest"}
+                    
             mistral_llm = MistralLLM(api_key=mistral_api_key, temperature=self.temperature, top_p=self.top_p)
             debug_print("Mistral API pipeline created successfully.")
             return mistral_llm