added secret and bot_id in config (#1644)

kairon/nlu/classifiers/llm.py

@@ -82,17 +82,17 @@ def load_api_key(self, bot_id: Text):
         if bot_id:
             from kairon.shared.admin.processor import Sysadmin
             llm_secret = Sysadmin.get_llm_secret("openai", bot_id)
-            self.api_key = llm_secret.get('api_key')
+            self.secret = llm_secret.get('api_key')
         elif os.environ.get("LLM_API_KEY"):
-            self.api_key = os.environ.get("LLM_API_KEY")
+            self.secret = {'api_key': os.environ.get("LLM_API_KEY")}
         else:
             raise KeyError(
                 f"either set bot_id'in LLMClassifier config or set LLM_API_KEY in environment variables"
             )
 
     def get_embeddings(self, text):
         embeddings = litellm.embedding(
-            model="text-embedding-3-small", input=text, api_key=self.api_key, max_retries=3
+            model="text-embedding-3-small", input=text, max_retries=3, **self.secret
         )
         return [ embedding['embedding'] for embedding in embeddings['data']]
 
@@ -188,8 +188,8 @@ def predict(self, text):
                 top_p=1,
                 frequency_penalty=0,
                 presence_penalty=0,
-                api_key=self.api_key,
-                max_retries=3
+                max_retries=3,
+                **self.secret
             )
             logger.debug(response)
             responses = json.loads(response.choices[0]["message"]["content"])

kairon/train.py

@@ -38,6 +38,11 @@ def train_model_for_bot(bot: str):
         stories = stories.merge(multiflow_stories[0])
         config = processor.load_config(bot)
         config['assistant_id'] = bot
+
+        index = next((index for (index, d) in enumerate(config['pipeline']) if d["name"] == "kairon.nlu.LLMClassifier"), None)
+        if index:
+            config[index]['bot_id']= bot
+
         rules = processor.get_rules_for_training(bot)
         rules = rules.merge(multiflow_stories[1])
 
@@ -124,3 +129,5 @@ def start_training(bot: str, user: str, token: str = None):
             exception=exception,
         )
     return model_file
+
+