diff --git a/application/api/answer/routes.py b/application/api/answer/routes.py
index b122eac1..1b3c9b97 100644
--- a/application/api/answer/routes.py
+++ b/application/api/answer/routes.py
@@ -8,13 +8,14 @@ import traceback
 
 from pymongo import MongoClient
 from bson.objectid import ObjectId
-from transformers import GPT2TokenizerFast
+from application.utils import count_tokens
 
 
 
 from application.core.settings import settings
 from application.vectorstore.vector_creator import VectorCreator
 from application.llm.llm_creator import LLMCreator
+from application.retriever.retriever_creator import RetrieverCreator
 from application.error import bad_request
 
 
@@ -62,9 +63,6 @@ async def async_generate(chain, question, chat_history):
     return result
 
 
-def count_tokens(string):
-    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
-    return len(tokenizer(string)['input_ids'])
 
 
 def run_async_chain(chain, question, chat_history):
@@ -104,61 +102,11 @@ def get_vectorstore(data):
 def is_azure_configured():
     return settings.OPENAI_API_BASE and settings.OPENAI_API_VERSION and settings.AZURE_DEPLOYMENT_NAME
 
-
-def complete_stream(question, docsearch, chat_history, prompt_id, conversation_id, chunks=2):
-    llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
-    if prompt_id == 'default':
-        prompt = chat_combine_template
-    elif prompt_id == 'creative':
-        prompt = chat_combine_creative
-    elif prompt_id == 'strict':
-        prompt = chat_combine_strict
-    else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
-        
-    if chunks == 0:
-        docs = []
-    else:
-        docs = docsearch.search(question, k=chunks)
-    if settings.LLM_NAME == "llama.cpp":
-        docs = [docs[0]]
-    # join all page_content together with a newline
-    docs_together = "\n".join([doc.page_content for doc in docs])
-    p_chat_combine = prompt.replace("{summaries}", docs_together)
-    messages_combine = [{"role": "system", "content": p_chat_combine}]
-    source_log_docs = []
-    for doc in docs:
-        if doc.metadata:
-            source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
-        else:
-            source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-
-    if len(chat_history) > 1:
-        tokens_current_history = 0
-        # count tokens in history
-        chat_history.reverse()
-        for i in chat_history:
-            if "prompt" in i and "response" in i:
-                tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
-                if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
-                    tokens_current_history += tokens_batch
-                    messages_combine.append({"role": "user", "content": i["prompt"]})
-                    messages_combine.append({"role": "system", "content": i["response"]})
-    messages_combine.append({"role": "user", "content": question})
-
-    response_full = ""
-    completion = llm.gen_stream(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                                messages=messages_combine)
-    for line in completion:
-        data = json.dumps({"answer": str(line)})
-        response_full += str(line)
-        yield f"data: {data}\n\n"
-
-    # save conversation to database
+def save_conversation(conversation_id, question, response, source_log_docs, llm):
     if conversation_id is not None:
         conversations_collection.update_one(
             {"_id": ObjectId(conversation_id)},
-            {"$push": {"queries": {"prompt": question, "response": response_full, "sources": source_log_docs}}},
+            {"$push": {"queries": {"prompt": question, "response": response, "sources": source_log_docs}}},
         )
 
     else:
@@ -168,20 +116,50 @@ def complete_stream(question, docsearch, chat_history, prompt_id, conversation_i
                                                              "words, respond ONLY with the summary, use the same "
                                                              "language as the system \n\nUser: " + question + "\n\n" +
                                                              "AI: " +
-                                                             response_full},
+                                                             response},
                             {"role": "user", "content": "Summarise following conversation in no more than 3 words, "
                                                         "respond ONLY with the summary, use the same language as the "
                                                         "system"}]
 
-        completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
+        completion = llm.gen(model=gpt_model,
                              messages=messages_summary, max_tokens=30)
         conversation_id = conversations_collection.insert_one(
             {"user": "local",
              "date": datetime.datetime.utcnow(),
              "name": completion,
-             "queries": [{"prompt": question, "response": response_full, "sources": source_log_docs}]}
+             "queries": [{"prompt": question, "response": response, "sources": source_log_docs}]}
         ).inserted_id
 
+def get_prompt(prompt_id):
+    if prompt_id == 'default':
+        prompt = chat_combine_template
+    elif prompt_id == 'creative':
+        prompt = chat_combine_creative
+    elif prompt_id == 'strict':
+        prompt = chat_combine_strict
+    else:
+        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
+    return prompt
+
+
+def complete_stream(question, retriever, conversation_id):
+    
+    
+    response_full = ""
+    source_log_docs = []
+    answer = retriever.gen()
+    for line in answer:
+        if "answer" in line:
+            response_full += str(line["answer"])
+            data = json.dumps(line)
+            yield f"data: {data}\n\n"
+        elif "source" in line:
+            source_log_docs.append(line["source"])
+
+
+    llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
+    conversation_id = save_conversation(conversation_id, question, response_full, source_log_docs, llm)
+
     # send data.type = "end" to indicate that the stream has ended as json
     data = json.dumps({"type": "id", "id": str(conversation_id)})
     yield f"data: {data}\n\n"
@@ -213,25 +191,26 @@ def stream():
         chunks = int(data["chunks"])
     else:
         chunks = 2
+    
+    prompt = get_prompt(prompt_id)
 
     # check if active_docs is set
 
     if "api_key" in data:
         data_key = get_data_from_api_key(data["api_key"])
-        vectorstore = get_vectorstore({"active_docs": data_key["source"]})
+        source = {"active_docs": data_key["source"]}
     elif "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
+        source = {"active_docs": data["active_docs"]}
     else:
-        vectorstore = ""
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, settings.EMBEDDINGS_KEY)
+        source = {}
+
+    retriever = RetrieverCreator.create_retriever("classic", question=question, 
+        source=source, chat_history=history, prompt=prompt, chunks=chunks, gpt_model=gpt_model
+        )
 
     return Response(
-        complete_stream(question, docsearch,
-                        chat_history=history,
-                        prompt_id=prompt_id,
-                        conversation_id=conversation_id,
-                        chunks=chunks), mimetype="text/event-stream"
-    )
+        complete_stream(question=question, retriever=retriever,
+                        conversation_id=conversation_id), mimetype="text/event-stream")
 
 
 @answer.route("/api/answer", methods=["POST"])
@@ -255,110 +234,35 @@ def api_answer():
         chunks = int(data["chunks"])
     else:
         chunks = 2
-
-    if prompt_id == 'default':
-        prompt = chat_combine_template
-    elif prompt_id == 'creative':
-        prompt = chat_combine_creative
-    elif prompt_id == 'strict':
-        prompt = chat_combine_strict
-    else:
-        prompt = prompts_collection.find_one({"_id": ObjectId(prompt_id)})["content"]
+    
+    prompt = get_prompt(prompt_id)
 
     # use try and except  to check for exception
     try:
         # check if the vectorstore is set
         if "api_key" in data:
             data_key = get_data_from_api_key(data["api_key"])
-            vectorstore = get_vectorstore({"active_docs": data_key["source"]})
+            source = {"active_docs": data_key["source"]}
         else:
-            vectorstore = get_vectorstore(data)
-        # loading the index and the store and the prompt template
-        # Note if you have used other embeddings than OpenAI, you need to change the embeddings
-        docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, settings.EMBEDDINGS_KEY)
+            source = {data}
 
-
-        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
-
-
-
-        if chunks == 0:
-            docs = []
-        else:
-            docs = docsearch.search(question, k=chunks)
-        # join all page_content together with a newline
-        docs_together = "\n".join([doc.page_content for doc in docs])
-        p_chat_combine = prompt.replace("{summaries}", docs_together)
-        messages_combine = [{"role": "system", "content": p_chat_combine}]
+        retriever = RetrieverCreator.create_retriever("classic", question=question, 
+            source=source, chat_history=history, prompt=prompt, chunks=chunks, gpt_model=gpt_model
+            )
         source_log_docs = []
-        for doc in docs:
-            if doc.metadata:
-                source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
-            else:
-                source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-        # join all page_content together with a newline
+        response_full = ""
+        for line in retriever.gen():
+            if "source" in line:
+                source_log_docs.append(line["source"])
+            elif "answer" in line:
+                response_full += line["answer"]
+            
+        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
+            
 
+        result = {"answer": response_full, "sources": source_log_docs}
+        result["conversation_id"] = save_conversation(conversation_id, question, response_full, source_log_docs, llm)
 
-        if len(history) > 1:
-            tokens_current_history = 0
-            # count tokens in history
-            history.reverse()
-            for i in history:
-                if "prompt" in i and "response" in i:
-                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
-                    if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
-                        tokens_current_history += tokens_batch
-                        messages_combine.append({"role": "user", "content": i["prompt"]})
-                        messages_combine.append({"role": "system", "content": i["response"]})
-        messages_combine.append({"role": "user", "content": question})
-
-
-        completion = llm.gen(model=gpt_model, engine=settings.AZURE_DEPLOYMENT_NAME,
-                                    messages=messages_combine)
-
-
-        result = {"answer": completion, "sources": source_log_docs}
-        logger.debug(result)
-
-        # generate conversationId
-        if conversation_id is not None:
-            conversations_collection.update_one(
-                {"_id": ObjectId(conversation_id)},
-                {"$push": {"queries": {"prompt": question,
-                                       "response": result["answer"], "sources": result['sources']}}},
-            )
-
-        else:
-            # create new conversation
-            # generate summary
-            messages_summary = [
-                {"role": "assistant", "content": "Summarise following conversation in no more than 3 words, "
-                    "respond ONLY with the summary, use the same language as the system \n\n"
-                    "User: " + question + "\n\n" + "AI: " + result["answer"]},
-                {"role": "user", "content": "Summarise following conversation in no more than 3 words, "
-                    "respond ONLY with the summary, use the same language as the system"}
-            ]
-
-            completion = llm.gen(
-                model=gpt_model,
-                engine=settings.AZURE_DEPLOYMENT_NAME,
-                messages=messages_summary,
-                max_tokens=30
-            )
-            conversation_id = conversations_collection.insert_one(
-                {"user": "local",
-                "date": datetime.datetime.utcnow(),
-                "name": completion,
-                "queries": [{"prompt": question, "response": result["answer"], "sources": source_log_docs}]}
-            ).inserted_id
-
-        result["conversation_id"] = str(conversation_id)
-
-        # mock result
-        # result = {
-        #     "answer": "The answer is 42",
-        #     "sources": ["https://en.wikipedia.org/wiki/42_(number)", "https://en.wikipedia.org/wiki/42_(number)"]
-        # }
         return result
     except Exception as e:
         # print whole traceback
@@ -375,20 +279,20 @@ def api_search():
 
     if "api_key" in data:
         data_key = get_data_from_api_key(data["api_key"])
-        vectorstore = data_key["source"]
+        source = {"active_docs": data_key["source"]}
     elif "active_docs" in data:
-        vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
+        source = {"active_docs": data["active_docs"]}
     else:
-        vectorstore = ""
+        source = {}
     if 'chunks' in data:
         chunks = int(data["chunks"])
     else:
         chunks = 2
-    docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, settings.EMBEDDINGS_KEY)
-    if chunks == 0:
-        docs = []
-    else:
-        docs = docsearch.search(question, k=chunks)
+
+    retriever = RetrieverCreator.create_retriever("classic", question=question, 
+            source=source, chat_history=[], prompt="default", chunks=chunks, gpt_model=gpt_model
+            )
+    docs = retriever.search()
 
     source_log_docs = []
     for doc in docs:
@@ -396,6 +300,5 @@ def api_search():
             source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
         else:
             source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
-        #yield f"data:{data}\n\n"
     return source_log_docs
 
diff --git a/application/llm/anthropic.py b/application/llm/anthropic.py
index a64d71e9..6b0d6467 100644
--- a/application/llm/anthropic.py
+++ b/application/llm/anthropic.py
@@ -10,7 +10,7 @@ class AnthropicLLM(BaseLLM):
         self.HUMAN_PROMPT = HUMAN_PROMPT
         self.AI_PROMPT = AI_PROMPT
 
-    def gen(self, model, messages, engine=None, max_tokens=300, stream=False, **kwargs):
+    def gen(self, model, messages, max_tokens=300, stream=False, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Context \n {context} \n ### Question \n {user_question}"
@@ -25,7 +25,7 @@ class AnthropicLLM(BaseLLM):
         )
         return completion.completion
 
-    def gen_stream(self, model, messages, engine=None, max_tokens=300, **kwargs):
+    def gen_stream(self, model, messages, max_tokens=300, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Context \n {context} \n ### Question \n {user_question}"
diff --git a/application/llm/docsgpt_provider.py b/application/llm/docsgpt_provider.py
index e0c5dbad..d540a911 100644
--- a/application/llm/docsgpt_provider.py
+++ b/application/llm/docsgpt_provider.py
@@ -8,7 +8,7 @@ class DocsGPTAPILLM(BaseLLM):
         self.endpoint =  "https://llm.docsgpt.co.uk"
 
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
+    def gen(self, model, messages, stream=False, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -24,7 +24,7 @@ class DocsGPTAPILLM(BaseLLM):
 
         return response_clean
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+    def gen_stream(self, model, messages, stream=True, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
diff --git a/application/llm/huggingface.py b/application/llm/huggingface.py
index ef3b1fbc..554bee2f 100644
--- a/application/llm/huggingface.py
+++ b/application/llm/huggingface.py
@@ -29,7 +29,7 @@ class HuggingFaceLLM(BaseLLM):
         )
         hf = HuggingFacePipeline(pipeline=pipe)
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
+    def gen(self, model, messages, stream=False, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -38,7 +38,7 @@ class HuggingFaceLLM(BaseLLM):
 
         return result.content
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+    def gen_stream(self, model, messages, stream=True, **kwargs):
 
         raise NotImplementedError("HuggingFaceLLM Streaming is not implemented yet.")
 
diff --git a/application/llm/llama_cpp.py b/application/llm/llama_cpp.py
index f18d4379..be34d4ff 100644
--- a/application/llm/llama_cpp.py
+++ b/application/llm/llama_cpp.py
@@ -12,7 +12,7 @@ class LlamaCpp(BaseLLM):
 
         llama = Llama(model_path=llm_name, n_ctx=2048)
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
+    def gen(self, model, messages, stream=False, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -24,7 +24,7 @@ class LlamaCpp(BaseLLM):
         
         return result['choices'][0]['text'].split('### Answer \n')[-1]
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+    def gen_stream(self, model, messages, stream=True, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
diff --git a/application/llm/openai.py b/application/llm/openai.py
index a132399a..4b0ed25a 100644
--- a/application/llm/openai.py
+++ b/application/llm/openai.py
@@ -18,7 +18,7 @@ class OpenAILLM(BaseLLM):
         
         return openai
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
+    def gen(self, model, messages, stream=False, engine=settings.AZURE_DEPLOYMENT_NAME, **kwargs):
         response = self.client.chat.completions.create(model=model,
             messages=messages,
             stream=stream,
@@ -26,7 +26,7 @@ class OpenAILLM(BaseLLM):
 
         return response.choices[0].message.content
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+    def gen_stream(self, model, messages, stream=True, engine=settings.AZURE_DEPLOYMENT_NAME, **kwargs):
         response = self.client.chat.completions.create(model=model,
             messages=messages,
             stream=stream,
diff --git a/application/llm/premai.py b/application/llm/premai.py
index 4bc8a898..5faa5fee 100644
--- a/application/llm/premai.py
+++ b/application/llm/premai.py
@@ -12,7 +12,7 @@ class PremAILLM(BaseLLM):
         self.api_key = api_key
         self.project_id = settings.PREMAI_PROJECT_ID
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
+    def gen(self, model, messages, stream=False, **kwargs):
         response = self.client.chat.completions.create(model=model,
             project_id=self.project_id,
             messages=messages,
@@ -21,7 +21,7 @@ class PremAILLM(BaseLLM):
 
         return response.choices[0].message["content"]
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+    def gen_stream(self, model, messages, stream=True, **kwargs):
         response = self.client.chat.completions.create(model=model,
             project_id=self.project_id,
             messages=messages,
diff --git a/application/llm/sagemaker.py b/application/llm/sagemaker.py
index 84ae09ad..b81f6385 100644
--- a/application/llm/sagemaker.py
+++ b/application/llm/sagemaker.py
@@ -74,7 +74,7 @@ class SagemakerAPILLM(BaseLLM):
         self.runtime = runtime
 
 
-    def gen(self, model, engine, messages, stream=False, **kwargs):
+    def gen(self, model, messages, stream=False, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
@@ -103,7 +103,7 @@ class SagemakerAPILLM(BaseLLM):
         print(result[0]['generated_text'], file=sys.stderr)
         return result[0]['generated_text'][len(prompt):]
 
-    def gen_stream(self, model, engine, messages, stream=True, **kwargs):
+    def gen_stream(self, model, messages, stream=True, **kwargs):
         context = messages[0]['content']
         user_question = messages[-1]['content']
         prompt = f"### Instruction \n {user_question} \n ### Context \n {context} \n ### Answer \n"
diff --git a/application/retriever/__init__.py b/application/retriever/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/application/retriever/base.py b/application/retriever/base.py
new file mode 100644
index 00000000..3bfaa5e1
--- /dev/null
+++ b/application/retriever/base.py
@@ -0,0 +1,10 @@
+from abc import ABC, abstractmethod
+
+
+class BaseRetriever(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def gen(self, *args, **kwargs):
+        pass
diff --git a/application/retriever/classic_rag.py b/application/retriever/classic_rag.py
new file mode 100644
index 00000000..dc757946
--- /dev/null
+++ b/application/retriever/classic_rag.py
@@ -0,0 +1,83 @@
+import os
+import json
+from application.retriever.base import BaseRetriever
+from application.core.settings import settings
+from application.vectorstore.vector_creator import VectorCreator
+from application.llm.llm_creator import LLMCreator
+
+from application.utils import count_tokens
+
+
+
+class ClassicRAG(BaseRetriever):
+
+    def __init__(self, question, source, chat_history, prompt, chunks=2, gpt_model='docsgpt'):
+        self.question = question
+        self.vectorstore = self._get_vectorstore(source=source)
+        self.chat_history = chat_history
+        self.prompt = prompt
+        self.chunks = chunks
+        self.gpt_model = gpt_model
+
+    def _get_vectorstore(self, source):
+        if "active_docs" in source:
+            if source["active_docs"].split("/")[0] == "default":
+                    vectorstore = ""
+            elif source["active_docs"].split("/")[0] == "local":
+                vectorstore = "indexes/" + source["active_docs"]
+            else:
+                vectorstore = "vectors/" + source["active_docs"]
+            if source["active_docs"] == "default":
+                vectorstore = ""
+        else:
+            vectorstore = ""
+        vectorstore = os.path.join("application", vectorstore)
+        return vectorstore
+
+    
+    def _get_data(self):
+        if self.chunks == 0:
+            docs = []
+        else:
+            docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, self.vectorstore, settings.EMBEDDINGS_KEY)
+            docs = docsearch.search(self.question, k=self.chunks)
+        if settings.LLM_NAME == "llama.cpp":
+            docs = [docs[0]]
+        return docs
+    
+    def gen(self):
+        docs = self._get_data()
+        
+        # join all page_content together with a newline
+        docs_together = "\n".join([doc.page_content for doc in docs])
+        p_chat_combine = self.prompt.replace("{summaries}", docs_together)
+        messages_combine = [{"role": "system", "content": p_chat_combine}]
+        for doc in docs:
+            if doc.metadata:
+                yield {"source": {"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content}} 
+            else:
+                yield {"source": {"title": doc.page_content, "text": doc.page_content}}
+
+        if len(self.chat_history) > 1:
+            tokens_current_history = 0
+            # count tokens in history
+            self.chat_history.reverse()
+            for i in self.chat_history:
+                if "prompt" in i and "response" in i:
+                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(i["response"])
+                    if tokens_current_history + tokens_batch < settings.TOKENS_MAX_HISTORY:
+                        tokens_current_history += tokens_batch
+                        messages_combine.append({"role": "user", "content": i["prompt"]})
+                        messages_combine.append({"role": "system", "content": i["response"]})
+        messages_combine.append({"role": "user", "content": self.question})
+
+        llm = LLMCreator.create_llm(settings.LLM_NAME, api_key=settings.API_KEY)
+
+        completion = llm.gen_stream(model=self.gpt_model,
+                                    messages=messages_combine)
+        for line in completion:
+            yield {"answer": str(line)}
+    
+    def search(self):
+        return self._get_data()
+        
diff --git a/application/retriever/retriever_creator.py b/application/retriever/retriever_creator.py
new file mode 100644
index 00000000..9255f4e6
--- /dev/null
+++ b/application/retriever/retriever_creator.py
@@ -0,0 +1,15 @@
+from application.retriever.classic_rag import ClassicRAG
+
+
+
+class RetrieverCreator:
+    retievers = {
+        'classic': ClassicRAG,
+    }
+
+    @classmethod
+    def create_retriever(cls, type, *args, **kwargs):
+        retiever_class = cls.retievers.get(type.lower())
+        if not retiever_class:
+            raise ValueError(f"No retievers class found for type {type}")
+        return retiever_class(*args, **kwargs)
\ No newline at end of file
diff --git a/application/utils.py b/application/utils.py
new file mode 100644
index 00000000..ac98efc6
--- /dev/null
+++ b/application/utils.py
@@ -0,0 +1,6 @@
+from transformers import GPT2TokenizerFast
+
+
+def count_tokens(string):
+    tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
+    return len(tokenizer(string)['input_ids'])
\ No newline at end of file