Update

config.py

@@ -1,7 +1,7 @@
 
 class ConfigConstants:
     # Constants related to datasets and models
-    DATA_SET_NAMES = ['covidqa', 'cuad']#, 'delucionqa', 'emanual', 'expertqa', 'finqa', 'hagrid', 'hotpotqa', 'msmarco', 'pubmedqa', 'tatqa', 'techqa']
+    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'

generator/initialize_llm.py

@@ -3,7 +3,7 @@ import os
 from langchain_groq import ChatGroq
 
 def initialize_generation_llm(input_model_name):
-    os.environ["GROQ_API_KEY"] = ""
+    os.environ["GROQ_API_KEY"] = "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"
     
     model_name = input_model_name    
     llm = ChatGroq(model=model_name, temperature=0.7)
@@ -13,7 +13,7 @@ def initialize_generation_llm(input_model_name):
     return llm
 
 def initialize_validation_llm(input_model_name):
-    os.environ["GROQ_API_KEY"] = ""
+    os.environ["GROQ_API_KEY"] = "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"
     
     model_name = input_model_name      
     llm = ChatGroq(model=model_name, temperature=0.7)

retriever/embed_documents.py

@@ -1,4 +1,4 @@
-import os
+'''import os
 import logging
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.vectorstores import FAISS
@@ -16,9 +16,9 @@ def embed_documents(documents, embedding_path="embeddings.faiss"):
         vector_store = FAISS.from_texts([doc['text'] for doc in documents], embedding_model)
         vector_store.save_local(embedding_path)
     
-    return vector_store
+    return vector_store'''
 
-'''import os
+import os
 import logging
 import hashlib
 from typing import List, Dict
@@ -91,6 +91,6 @@ def _save_metadata(metadata_path: str, metadata: Dict[str, bool]):
     """Save metadata to a file."""
     import json
     with open(metadata_path, "w") as f:
-        json.dump(metadata, f)'''
+        json.dump(metadata, f)