add backends / api key secrets

backend/config.py

@@ -3,8 +3,14 @@ import os
 
 
 def process_config(config):
-    if "HF_TOKEN" not in os.environ:
-        raise ValueError("Please set the HF_TOKEN environment variable.")
+
+    if "CLIENT_TOKEN" not in os.environ:
+        raise ValueError("Please set the CLIENT_TOKEN environment variable.")
+    if "GUIDE_TOKEN" not in os.environ:
+        raise ValueError("Please set the GUIDE_TOKEN environment variable.")
+    if "CLASSIFIER_TOKEN" not in os.environ:
+        raise ValueError("Please set the CLASSIFIER_TOKEN environment variable.")
+    
     client_kwargs = {}
     if "client_llm" in config:
         if "model_id" in config["client_llm"]:
@@ -15,8 +21,11 @@ def process_config(config):
             client_kwargs["inference_server_url"] = config["client_llm"]["url"]
         else:
             raise ValueError("config.yaml is missing client url.")
-        client_kwargs["api_key"] = os.getenv("HF_TOKEN")
-        client_kwargs["llm_backend"] = "HFChat"
+        if "backend" in config["client_llm"]:
+            client_kwargs["llm_backend"] = config["client_llm"]["backend"]
+        else:
+            raise ValueError("config.yaml is missing client backend.")
+        client_kwargs["api_key"] = os.getenv("CLIENT_TOKEN")
         client_kwargs["temperature"] = config["client_llm"].get("temperature",.6)
         client_kwargs["max_tokens"] = config["client_llm"].get("max_tokens",800)
     else:
@@ -32,8 +41,11 @@ def process_config(config):
             guide_kwargs["inference_server_url"] = config["expert_llm"]["url"]
         else:
             raise ValueError("config.yaml is missing expert url.")
-        guide_kwargs["api_key"] = os.getenv("HF_TOKEN")
-        guide_kwargs["llm_backend"] = "HFChat"
+        if "backend" in config["expert_llm"]:
+            guide_kwargs["llm_backend"] = config["expert_llm"]["backend"]
+        else:
+            raise ValueError("config.yaml is missing expert backend.")
+        guide_kwargs["api_key"] = os.getenv("GUIDE_TOKEN")
     else:
         raise ValueError("config.yaml is missing expert_llm settings.")
 
@@ -50,12 +62,9 @@ def process_config(config):
             guide_kwargs["classifier_kwargs"]["batch_size"] = int(config["classifier_llm"]["batch_size"])
         else:
             raise ValueError("config.yaml is missing classifier batch_size.")
-        guide_kwargs["classifier_kwargs"]["api_key"] = os.getenv("HF_TOKEN")  # classifier api key
+        guide_kwargs["classifier_kwargs"]["api_key"] = os.getenv("CLASSIFIER_TOKEN")  # classifier api key
     else:
         raise ValueError("config.yaml is missing classifier_llm settings.")
 
-    logging.info(f"client_kwargs: {client_kwargs}")
-    logging.info(f"guide_kwargs: {guide_kwargs}")
-
     return client_kwargs, guide_kwargs
 

config.yaml

@@ -1,11 +1,13 @@
 client_llm:
   url: ""  # <-- start your own inference endpoint and provide url here (or use https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta)
   model_id: "HuggingFaceH4/zephyr-7b-beta"  # <-- your client llm
+  backend: HFChat
   max_tokens: 800
   temperature: 0.6
 expert_llm:
   url: ""  # <-- start your own inference endpoint and provide url here (or use https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct)
   model_id: "meta-llama/Meta-Llama-3-70B-Instruct"
+  backend: HFChat
 classifier_llm:
   model_id: "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
   url: ""  # <-- start your own inference endpoint of classifier model and provide url here