second try

agent/agent_1.py

@@ -25,7 +25,7 @@ from sklearn.metrics.pairwise import cosine_similarity
 load_dotenv()
 
 # Load QA pairs and compute embeddings once
-qa_df = pd.read_csv("/statics/qa_pairs.csv")
+qa_df = pd.read_csv("statics/qa_pairs.csv")
 embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 qa_embeddings = embeddings_model.embed_documents(qa_df["question"].tolist())
 
@@ -143,7 +143,7 @@ def qa_reference(query: str) -> str:
     top_idx = int(np.argmax(sims))
     return f"Similar question: {qa_df.question[top_idx]}\nAnswer: {qa_df.answer[top_idx]}"
 
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
+with open("statics/system_prompt.txt", "r", encoding="utf-8") as f:
     system_prompt = f.read()
 
 sys_msg = SystemMessage(content=system_prompt)