Updates

Agent/tools/kg_search.py

@@ -21,7 +21,7 @@ examples = example_pairs["examples"]
 
 # LLM for choose the best similar examples
 load_dotenv()
-os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY")
+os.environ["GEMINI_API_KEY"] = os.getenv("GEMINI_API_KEY")
 
 embedding_model = GoogleGenerativeAIEmbeddings(
     model= "models/text-embedding-004"
@@ -63,7 +63,7 @@ def generate_cypher(question: str) -> str:
     os.environ["NEO4J_URI"] = os.getenv("NEO4J_URI")
     os.environ["NEO4J_USERNAME"] = os.getenv("NEO4J_USERNAME")
     os.environ["NEO4J_PASSWORD"] = os.getenv("NEO4J_PASSWORD")
-    os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY")
+    os.environ["GEMINI_API_KEY"] = os.getenv("GEMINI_API_KEY")
 
     gemini_chat = ChatGoogleGenerativeAI(
         model= "gemini-1.5-flash-latest"

Agent/tools/tavily_search_v2.py

@@ -7,7 +7,7 @@ from langchain.tools import BaseTool, StructuredTool, tool
 load_dotenv()
 
 os.environ["TAVILY_API_KEY"] = os.getenv("TAVILY_API_KEY")
-os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY")
+os.environ["GEMINI_API_KEY"] = os.getenv("GEMINI_API_KEY")
 
 def tavily_search(question: str) -> str:
     """

Agent/utils.py

@@ -51,13 +51,7 @@ def get_llm_response(query):
     return chain.invoke({"query": query})["result"]
 
 def llm_answer(message, history):
-    # history_langchain_format = []
-    #
-    # for human, ai in history:
-    #     history_langchain_format.append(HumanMessage(content= human))
-    #     history_langchain_format.append(AIMessage(content= ai))
-    #
-    # history_langchain_format.append(HumanMessage(content= message["text"]))
+
 
     try:
         response = get_llm_response(message["text"])