diff --git a/src/semantic_search.py b/src/semantic_search.py
new file mode 100644
index 0000000..fbfb9f1
--- /dev/null
+++ b/src/semantic_search.py
@@ -0,0 +1,70 @@
+import numpy as np
+from typing import List, Tuple
+import openai
+
+from settings import EMBEDDING_MODEL, COMPLETIONS_MODEL, MAX_LEN_PROMPT, DATA_PATH
+import config
+
+from dataset import Dataset
+
+
+class AlignmentSearch:
+    def __init__(self,
+            dataset: Dataset,  # Dataset object containing the data.
+        ):
+        self.dataset = dataset
+        
+    def get_embedding(self, text: str) -> np.ndarray:
+        result = openai.Embedding.create(model=EMBEDDING_MODEL, input=text)
+        return result["data"][0]["embedding"]
+    
+    def get_top_k(self, query: str, k: int=10) -> List[Tuple[str, str, str]]:
+        # Receives a query (str) and returns the top k articles (List[Tuple[str, str, str]]) that are most similar to the query.
+        # Each tuple contains the title of an article, its URL, and text.
+        query_embedding = self.get_embedding(query)
+        similarities = np.dot(self.dataset.embeddings, query_embedding)
+        top_k_indices = np.argsort(similarities)[::-1][:k]
+        top_k = [self.dataset.data[i] for i in top_k_indices]
+        return top_k
+    
+    def construct_prompt(self, question: str, texts: List[Tuple[str, str, str]]) -> str:
+        # Receives a question (str) and a list of articles (List[Tuple[str, str, str]]) and returns a prompt (str) to be used for text generation.
+        context = "\n".join(texts)[:MAX_LEN_PROMPT]
+        header = """Answer the question as truthfully as possible using the provided context, and if the answer is not contained within the text below, say "I don't know."\n\nContext:\n"""
+        return header + "".join(context) + "\n\n Q: " + question + "\n A:"
+    
+    def answer_question(self, question: str, texts: List[Tuple[str, str, str]]) -> str:
+        # Receives a question (str) and a list of articles (List[Tuple[str, str, str]]) and returns an answer (str) to the question.
+        prompt = self.construct_prompt(question, texts)
+        COMPLETIONS_API_PARAMS = {
+            "temperature": 0.0,
+            "max_tokens": 500,
+            "model": COMPLETIONS_MODEL,
+        }
+        answer = openai.Completion.create(prompt=prompt, **COMPLETIONS_API_PARAMS)["choices"][0]["text"].strip(" \n")
+        return answer
+    
+    def search_and_answer(self, question: str, k: int=10) -> str:
+        # Receives a question (str) and returns an answer (str) to the question.
+        top_k = self.get_top_k(question, k)
+        answer = self.answer_question(question, top_k)
+        return answer
+    
+    def summarize(self, article: str) -> str:
+        COMPLETIONS_API_PARAMS = {
+            "temperature": 0.0,
+            "max_tokens": 300,
+            "model": COMPLETIONS_MODEL,
+        }
+        response = openai.Completion.create(prompt=article, **COMPLETIONS_API_PARAMS)
+        return response["choices"][0]["text"].strip(" \n")
+    
+
+if __name__ == "__main__":
+    dataset = Dataset(DATA_PATH)
+    dataset.get_alignment_texts()
+    dataset.load_embeddings()
+    search_and_answer = AlignmentSearch(dataset)
+    question = "What is an agent?"
+    answer = search_and_answer.search_and_answer(question)
+    print(answer)
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