fix: python lint errors

application/cache.py

@@ -1,29 +1,34 @@
-import redis
-import time
 import json
 import logging
+import time
 from threading import Lock
+
+import redis
+
 from application.core.settings import settings
 from application.utils import get_hash
-import sys
 
 logger = logging.getLogger(__name__)
 
 _redis_instance = None
 _instance_lock = Lock()
 
+
 def get_redis_instance():
     global _redis_instance
     if _redis_instance is None:
         with _instance_lock:
             if _redis_instance is None:
                 try:
-                    _redis_instance = redis.Redis.from_url(settings.CACHE_REDIS_URL, socket_connect_timeout=2)
+                    _redis_instance = redis.Redis.from_url(
+                        settings.CACHE_REDIS_URL, socket_connect_timeout=2
+                    )
                 except redis.ConnectionError as e:
                     logger.error(f"Redis connection error: {e}")
                     _redis_instance = None
     return _redis_instance
 
+
 def gen_cache_key(messages, model="docgpt", tools=None):
     if not all(isinstance(msg, dict) for msg in messages):
         raise ValueError("All messages must be dictionaries.")
@@ -33,6 +38,7 @@ def gen_cache_key(messages, model="docgpt", tools=None):
     cache_key = get_hash(combined)
     return cache_key
 
+
 def gen_cache(func):
     def wrapper(self, model, messages, stream, tools=None, *args, **kwargs):
         try:
@@ -42,7 +48,7 @@ def gen_cache(func):
                 try:
                     cached_response = redis_client.get(cache_key)
                     if cached_response:
-                        return cached_response.decode('utf-8')
+                        return cached_response.decode("utf-8")
                 except redis.ConnectionError as e:
                     logger.error(f"Redis connection error: {e}")
 
@@ -57,20 +63,22 @@ def gen_cache(func):
         except ValueError as e:
             logger.error(e)
             return "Error: No user message found in the conversation to generate a cache key."
+
     return wrapper
 
+
 def stream_cache(func):
     def wrapper(self, model, messages, stream, *args, **kwargs):
         cache_key = gen_cache_key(messages)
         logger.info(f"Stream cache key: {cache_key}")
-        
+
         redis_client = get_redis_instance()
         if redis_client:
             try:
                 cached_response = redis_client.get(cache_key)
                 if cached_response:
                     logger.info(f"Cache hit for stream key: {cache_key}")
-                    cached_response = json.loads(cached_response.decode('utf-8'))
+                    cached_response = json.loads(cached_response.decode("utf-8"))
                     for chunk in cached_response:
                         yield chunk
                         time.sleep(0.03)
@@ -80,16 +88,16 @@ def stream_cache(func):
 
         result = func(self, model, messages, stream, *args, **kwargs)
         stream_cache_data = []
-        
+
         for chunk in result:
             stream_cache_data.append(chunk)
             yield chunk
-        
+
         if redis_client:
             try:
                 redis_client.set(cache_key, json.dumps(stream_cache_data), ex=1800)
                 logger.info(f"Stream cache saved for key: {cache_key}")
             except redis.ConnectionError as e:
                 logger.error(f"Redis connection error: {e}")
-        
-    return wrapper
+
+    return wrapper

application/retriever/classic_rag.py

@@ -1,10 +1,9 @@
-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.retriever.base import BaseRetriever
 from application.tools.agent import Agent
 
 from application.utils import num_tokens_from_string
+from application.vectorstore.vector_creator import VectorCreator
 
 
 class ClassicRAG(BaseRetriever):
@@ -21,7 +20,7 @@ class ClassicRAG(BaseRetriever):
         user_api_key=None,
     ):
         self.question = question
-        self.vectorstore = source['active_docs'] if 'active_docs' in source else None
+        self.vectorstore = source["active_docs"] if "active_docs" in source else None
         self.chat_history = chat_history
         self.prompt = prompt
         self.chunks = chunks
@@ -78,9 +77,9 @@ class ClassicRAG(BaseRetriever):
             # count tokens in history
             for i in self.chat_history:
                 if "prompt" in i and "response" in i:
-                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
-                        i["response"]
-                    )
+                    tokens_batch = num_tokens_from_string(
+                        i["prompt"]
+                    ) + num_tokens_from_string(i["response"])
                     if tokens_current_history + tokens_batch < self.token_limit:
                         tokens_current_history += tokens_batch
                         messages_combine.append(
@@ -95,14 +94,19 @@ class ClassicRAG(BaseRetriever):
         #     settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key
         # )
         # completion = llm.gen_stream(model=self.gpt_model, messages=messages_combine)
-        agent = Agent(llm_name=settings.LLM_NAME,gpt_model=self.gpt_model, api_key=settings.API_KEY, user_api_key=self.user_api_key)
+        agent = Agent(
+            llm_name=settings.LLM_NAME,
+            gpt_model=self.gpt_model,
+            api_key=settings.API_KEY,
+            user_api_key=self.user_api_key,
+        )
         completion = agent.gen(messages_combine)
         for line in completion:
             yield {"answer": str(line)}
 
     def search(self):
         return self._get_data()
-    
+
     def get_params(self):
         return {
             "question": self.question,
@@ -112,5 +116,5 @@ class ClassicRAG(BaseRetriever):
             "chunks": self.chunks,
             "token_limit": self.token_limit,
             "gpt_model": self.gpt_model,
-            "user_api_key": self.user_api_key
+            "user_api_key": self.user_api_key,
         }

application/tools/agent.py

@@ -1,7 +1,6 @@
 import json
 
 from application.core.mongo_db import MongoDB
-from application.core.settings import settings
 from application.llm.llm_creator import LLMCreator
 from application.tools.tool_manager import ToolManager
 

application/tools/tool_manager.py

@@ -27,7 +27,6 @@ class ToolManager:
 
     def load_tool(self, tool_name, tool_config):
         self.config[tool_name] = tool_config
-        tools_dir = os.path.join(os.path.dirname(__file__), "implementations")
         module = importlib.import_module(
             f"application.tools.implementations.{tool_name}"
         )