removal of token.

agent/api.py

@@ -30,7 +30,6 @@
 from agent.utils.utility import (
     combine_text_from_list,
     create_tmp_folder,
-    validate_token,
 )
 from agent.utils.vdb import initialize_all_vector_dbs, load_vec_db_conn
 
@@ -116,10 +115,9 @@ async def post_embedd_documents(llm_backend: LLMBackend, files: list[UploadFile]
     """
     logger.info("Embedding Multiple Documents")
 
-    token = validate_token(token=llm_backend.token, llm_backend=llm_backend, aleph_alpha_key=ALEPH_ALPHA_API_KEY, openai_key=OPENAI_API_KEY)
     tmp_dir = create_tmp_folder()
 
-    service = LLMContext(LLMStrategyFactory.get_strategy(strategy_type=LLMProvider.ALEPH_ALPHA, token=token, collection_name=llm_backend.collection_name))
+    service = LLMContext(LLMStrategyFactory.get_strategy(strategy_type=LLMProvider.ALEPH_ALPHA, collection_name=llm_backend.collection_name))
 
     file_names = []
 
@@ -164,10 +162,7 @@ async def embedd_text(embedding: EmbeddTextRequest, llm_backend: LLMBackend) ->
     """
     logger.info("Embedding Text")
 
-    # TODO: REWORK THE TOKEN
-    token = validate_token(token=llm_backend.token, llm_backend=llm_backend, aleph_alpha_key=ALEPH_ALPHA_API_KEY, openai_key=OPENAI_API_KEY)
-
-    service = LLMContext(LLMStrategyFactory.get_strategy(strategy_type=llm_backend.llm_provider, token=token, collection_name=llm_backend.collection_name))
+    service = LLMContext(LLMStrategyFactory.get_strategy(strategy_type=llm_backend.llm_provider, collection_name=llm_backend.collection_name))
 
     # save the string to a txt file in a uuid directory
     tmp_dir = create_tmp_folder()
@@ -243,9 +238,7 @@ def question_answer(rag: RAGRequest, llm_backend: LLMBackend) -> QAResponse:
         msg = "Please provide a Question."
         raise ValueError(msg)
 
-    token = validate_token(token=llm_backend.token, llm_backend=llm_backend, aleph_alpha_key=ALEPH_ALPHA_API_KEY, openai_key=OPENAI_API_KEY)
-
-    service = LLMContext(LLMStrategyFactory.get_strategy(strategy_type=llm_backend.llm_provider, token=token, collection_name=llm_backend.collection_name))
+    service = LLMContext(LLMStrategyFactory.get_strategy(strategy_type=llm_backend.llm_provider, collection_name=llm_backend.collection_name))
     # summarize the history
     if rag.history:
         # combine the texts
@@ -284,13 +277,6 @@ def explain_question_answer(explain_request: ExplainQARequest, llm_backend: LLMB
         msg = "Please provide a Question."
         raise ValueError(msg)
 
-    explain_request.rag_request.search.llm_backend.token = validate_token(
-        token=explain_request.rag_request.search.llm_backend.token,
-        llm_backend=explain_request.rag_request.search.llm_backend.llm_provider,
-        aleph_alpha_key=ALEPH_ALPHA_API_KEY,
-        openai_key=OPENAI_API_KEY,
-    )
-
     service = LLMContext(
         LLMStrategyFactory.get_strategy(strategy_type=llm_backend.llm_provider, token=search.llm_backend.token, collection_name=llm_backend.collection_name)
     )
@@ -324,7 +310,6 @@ def explain_question_answer(explain_request: ExplainQARequest, llm_backend: LLMB
 #         JSONResponse: _description_
 #     """
 #     logger.info("Processing Document")
-#     token = validate_token(token=token, llm_backend=llm_backend, aleph_alpha_key=ALEPH_ALPHA_API_KEY, openai_key=OPENAI_API_KEY)
 
 #     # Create a temporary folder to save the files
 #     tmp_dir = create_tmp_folder()
@@ -347,7 +332,7 @@ def explain_question_answer(explain_request: ExplainQARequest, llm_backend: LLMB
 #         with Path(tmp_dir / file_name).open() as f:
 #             f.write(await file.read())
 
-#     process_documents_aleph_alpha(folder=tmp_dir, token=token, type=document_type)
+#     process_documents_aleph_alpha(folder=tmp_dir, , type=document_type)
 
 #     logger.info(f"Found {len(documents)} documents.")
 #     return documents

agent/backend/LLMStrategy.py

@@ -55,7 +55,7 @@ def get_strategy(strategy_type: str, token: str, collection_name: str) -> LLMBas
         if strategy is None:
             msg = "Unknown Strategy Type"
             raise ValueError(msg)
-        return strategy(token=token, collection_name=collection_name)
+        return strategy(collection_name=collection_name)
 
 
 class LLMContext:

agent/backend/aleph_alpha_service.py

@@ -343,7 +343,7 @@ def process_documents_aleph_alpha(self, folder: str, processing_type: str) -> li
 if __name__ == "__main__":
     query = "Was ist Attention?"
 
-    aa_service = AlephAlphaService(collection_name="", token="")
+    aa_service = AlephAlphaService(collection_name="")
 
     aa_service.embed_documents(directory="tests/resources/")
 

agent/backend/cohere_service.py

@@ -118,7 +118,7 @@ def summarize_text(self, text: str) -> str:
 if __name__ == "__main__":
     query = "Was ist Attention?"
 
-    cohere_service = CohereService(collection_name="", token="")
+    cohere_service = CohereService(collection_name="")
 
     cohere_service.embed_documents(directory="tests/resources/")
 

agent/backend/gpt4all_service.py

@@ -148,7 +148,7 @@ def create_rag_chain(self, rag: RAGRequest, search: SearchParams) -> tuple:
 if __name__ == "__main__":
     query = "Was ist Attention?"
 
-    gpt4all_service = GPT4AllService(collection_name="", token="")
+    gpt4all_service = GPT4AllService(collection_name="")
 
     gpt4all_service.embed_documents(directory="tests/resources/")
 

agent/backend/open_ai_service.py

@@ -1,4 +1,4 @@
-"""Script is used to initialize the Qdrant db backend with Azure OpenAI."""
+"""Script is used to initialize the Qdrant db backend with (Azure) OpenAI."""
 import os
 
 import openai

tests/unit_tests/test_utility.py

@@ -1,5 +1,5 @@
 """Tests for the utility functions."""
-from agent.utils.utility import generate_prompt, set_token
+from agent.utils.utility import generate_prompt
 
 
 def test_generate_prompt() -> None:
@@ -33,34 +33,3 @@ def test_generate_prompt_detect_language_default_parameter() -> None:
 
 def test_combine_text_from_list() -> None:
     """Test that combine_text_from_list returns the correct text."""
-
-
-def test_validate_token() -> None:
-    """Test that validate_token returns the correct token."""
-    token = set_token(token="example_token", llm_backend="openai", aleph_alpha_key="example_key_a", openai_key="example_key_o")
-
-    assert token == "example_token"
-
-    token = set_token(token="", llm_backend="aleph-alpha", aleph_alpha_key="example_key_a", openai_key="example_key_o")
-
-    assert token == "example_key_a"
-
-    token = set_token(token="", llm_backend="openai", aleph_alpha_key="example_key_a", openai_key="example_key_o")
-
-    assert token == "example_key_o"
-
-    token = set_token(token=None, llm_backend="openai", aleph_alpha_key="example_key_a", openai_key="example_key_o")
-
-    assert token == "example_key_o"
-
-    token = set_token(token="", llm_backend="gpt4all", aleph_alpha_key="example_key_a", openai_key="example_key_o")
-
-    assert token == "gpt4all"
-
-    from agent.data_model.request_data_model import LLMProvider
-
-    backend = LLMProvider.ALEPH_ALPHA
-
-    token = set_token(token="", llm_backend=backend, aleph_alpha_key="example_key_a", openai_key="example_key_o")
-
-    assert token == "example_key_a"