Upload config.py

config.py

@@ -1,16 +1,18 @@
-
-class ConfigConstants:
-    # Constants related to datasets and models
-    DATA_SET_NAMES = ['covidqa']#, 'cuad', 'techqa','delucionqa', 'emanual', 'expertqa', 'finqa', 'hagrid', 'hotpotqa', 'msmarco', 'pubmedqa', 'tatqa']
-    EMBEDDING_MODEL_NAME = "sentence-transformers/paraphrase-MiniLM-L3-v2"
-    RE_RANKER_MODEL_NAME = 'cross-encoder/ms-marco-electra-base'
-    GENERATION_MODEL_NAME = 'mixtral-8x7b-32768'
-    VALIDATION_MODEL_NAME = 'llama3-70b-8192'
-    DEFAULT_CHUNK_SIZE = 1000
-    CHUNK_OVERLAP = 200
-
-class AppConfig:
-    def __init__(self, vector_store, gen_llm, val_llm):
-        self.vector_store = vector_store
-        self.gen_llm = gen_llm
+
+class ConfigConstants:
+    # Constants related to datasets and models
+    DATA_SET_NAMES = ['covidqa']#,, 'cuad', 'techqa' 'delucionqa', 'emanual', 'expertqa', 'finqa', 'hagrid', 'hotpotqa', 'msmarco', 'pubmedqa', 'tatqa']
+    EMBEDDING_MODEL_NAME = "sentence-transformers/paraphrase-MiniLM-L3-v2"
+    RE_RANKER_MODEL_NAME = 'cross-encoder/ms-marco-electra-base'
+    GENERATION_MODEL_NAME = 'mixtral-8x7b-32768'
+    VALIDATION_MODEL_NAME = 'llama3-70b-8192'
+    GENERATION_MODELS = ["llama3-8b-8192", "qwen-2.5-32b", "mixtral-8x7b-32768", "gemma2-9b-it" ]
+    VALIDATION_MODELS = ["llama3-70b-8192", "deepseek-r1-distill-llama-70b" ]
+    DEFAULT_CHUNK_SIZE = 1000
+    CHUNK_OVERLAP = 200
+
+class AppConfig:
+    def __init__(self, vector_store, gen_llm, val_llm):
+        self.vector_store = vector_store
+        self.gen_llm = gen_llm
         self.val_llm = val_llm