[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

comps/agent/langchain/agent.py

@@ -12,7 +12,7 @@
 comps_path = os.path.join(cur_path, "../../../")
 sys.path.append(comps_path)
 
-from comps import LLMParamsDoc, GeneratedDoc, ServiceType, opea_microservices, register_microservice
+from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 from comps.agent.langchain.src.agent import instantiate_agent
 from comps.agent.langchain.src.utils import get_args
 
@@ -35,19 +35,19 @@ async def llm_generate(input: LLMParamsDoc):
     agent_inst = instantiate_agent(args, args.strategy)
     print(type(agent_inst))
 
-
     # 2. prepare the input for the agent
     if input.streaming:
-        print('-----------STREAMING-------------')
+        print("-----------STREAMING-------------")
         return StreamingResponse(agent_inst.stream_generator(input.query, config), media_type="text/event-stream")
 
     else:
         # TODO: add support for non-streaming mode
-        print('-----------NOT STREAMING-------------')
+        print("-----------NOT STREAMING-------------")
         response = await agent_inst.non_streaming_run(input.query, config)
-        print('-----------Response-------------')
+        print("-----------Response-------------")
         print(response)
         return GeneratedDoc(text=response, prompt=input.query)
 
+
 if __name__ == "__main__":
     opea_microservices["opea_service@comps-react-agent"].start()

comps/agent/langchain/src/strategy/base_agent.py

@@ -19,4 +19,4 @@ def execute(self, state: dict):
         pass
 
     def non_streaming_run(self, query, config):
-        raise NotImplementedError
+        raise NotImplementedError

comps/agent/langchain/src/strategy/docgrader/planner.py

@@ -10,17 +10,17 @@
 from langchain_core.prompts import PromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
+from langchain_openai import ChatOpenAI
 from langgraph.graph import END, START, StateGraph
 from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_openai import ChatOpenAI
 
 from ..base_agent import BaseAgent
 from .prompt import DOC_GRADER_PROMPT, RAGv1_PROMPT
 
+instruction = "Retrieved document is not sufficient or relevant to answer the query. Reformulate the query to search knowledge base again."
+MAX_RETRY = 3
 
-instruction="Retrieved document is not sufficient or relevant to answer the query. Reformulate the query to search knowledge base again."
-MAX_RETRY=3
 
 class AgentStateV1(TypedDict):
     # The add_messages function defines how an update should be processed
@@ -30,6 +30,7 @@ class AgentStateV1(TypedDict):
     doc_score: str
     query_time: str
 
+
 class RagAgent:
     """Invokes the agent model to generate a response based on the current state. Given
     the question, it will decide to retrieve using the retriever tool, or simply end.
@@ -61,6 +62,7 @@ class Retriever:
     def create(cls, tools_descriptions):
         return ToolNode(tools_descriptions)
 
+
 class DocumentGraderV1:
     """Determines whether the retrieved documents are relevant to the question.
 
@@ -93,9 +95,9 @@ class grade(BaseModel):
     def __call__(self, state) -> Literal["generate", "rewrite"]:
         print("---CALL DocumentGrader---")
         messages = state["messages"]
-        last_message = messages[-1] # the latest retrieved doc
+        last_message = messages[-1]  # the latest retrieved doc
 
-        question = messages[0].content # the original query
+        question = messages[0].content  # the original query
         docs = last_message.content
 
         scored_result = self.chain.invoke({"question": question, "context": docs})
@@ -104,12 +106,13 @@ def __call__(self, state) -> Literal["generate", "rewrite"]:
 
         if score.startswith("yes"):
             print("---DECISION: DOCS RELEVANT---")
-            return {"doc_score":"generate"}
+            return {"doc_score": "generate"}
 
         else:
             print(f"---DECISION: DOCS NOT RELEVANT, score is {score}---")
-
-            return {"messages":[HumanMessage(content=instruction)], "doc_score": "rewrite"}
+
+            return {"messages": [HumanMessage(content=instruction)], "doc_score": "rewrite"}
+
 
 class TextGeneratorV1:
     """Generate answer.
@@ -131,11 +134,11 @@ def __call__(self, state):
         print("---GENERATE---")
         messages = state["messages"]
         question = messages[0].content
-        query_time = state['query_time']
+        query_time = state["query_time"]
 
         # find the latest retrieved doc
         # which is a ToolMessage
-        for m in state['messages'][::-1]:
+        for m in state["messages"][::-1]:
             if isinstance(m, ToolMessage):
                 last_message = m
                 break
@@ -144,11 +147,11 @@ def __call__(self, state):
         docs = last_message.content
 
         # Run
-        response = self.rag_chain.invoke({"context": docs, "question": question, "time":query_time})
-        print('@@@@ Used this doc for generation:\n', docs)
-        print('@@@@ Generated response: ', response)
+        response = self.rag_chain.invoke({"context": docs, "question": question, "time": query_time})
+        print("@@@@ Used this doc for generation:\n", docs)
+        print("@@@@ Generated response: ", response)
         return {"messages": [response], "output": response}
-    
+
 
 class RAGAgentDocGraderV1(BaseAgent):
     def __init__(self, args):
@@ -186,28 +189,28 @@ def __init__(self, args):
             "doc_grader",
             self.should_retry,
             {
-                False: "generate",  
-                True: "agent",  
+                False: "generate",
+                True: "agent",
             },
         )
         workflow.add_edge("generate", END)
 
         self.app = workflow.compile()
-    
+
     def should_retry(self, state):
         # first check how many retry attempts have been made
         num_retry = 0
-        for m in state['messages']:
+        for m in state["messages"]:
             if instruction in m.content:
                 num_retry += 1
 
         print("**********Num retry: ", num_retry)
-        
-        if (num_retry <MAX_RETRY) and (state["doc_score"] == "rewrite"):
+
+        if (num_retry < MAX_RETRY) and (state["doc_score"] == "rewrite"):
             return True
         else:
             return False
-    
+
     def prepare_initial_state(self, query):
         return {"messages": [HumanMessage(content=query)]}
 
@@ -225,7 +228,7 @@ async def stream_generator(self, query, config):
             yield "data: [DONE]\n\n"
         except Exception as e:
             yield str(e)
-    
+
     async def non_streaming_run(self, query, config):
         initial_state = self.prepare_initial_state(query)
         try:
@@ -236,13 +239,8 @@ async def non_streaming_run(self, query, config):
                 else:
                     message.pretty_print()
 
-            last_message = s['messages'][-1]
-            print('******Response: ', last_message.content)
+            last_message = s["messages"][-1]
+            print("******Response: ", last_message.content)
             return last_message.content
         except Exception as e:
             return str(e)
-
-
-
-
-

comps/agent/langchain/src/strategy/docgrader/prompt.py

@@ -3,7 +3,7 @@
 
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
 
-DOC_GRADER_PROMPT="""\
+DOC_GRADER_PROMPT = """\
 Given the QUERY, determine if a relevant answer can be derived from the DOCUMENT.\n
 QUERY: {question} \n
 DOCUMENT:\n{context}\n\n

comps/agent/langchain/src/strategy/react/planner.py

@@ -1,16 +1,16 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from langchain.agents import AgentExecutor 
+from langchain.agents import AgentExecutor
 from langchain.agents import create_react_agent as create_react_langchain_agent
-from langgraph.prebuilt import create_react_agent
 from langchain_core.messages import HumanMessage
 from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
 from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+
 from ...utils import has_multi_tool_inputs, tool_renderer
 from ..base_agent import BaseAgent
-from .prompt import hwchase17_react_prompt
-from .prompt import REACT_SYS_MESSAGE
+from .prompt import REACT_SYS_MESSAGE, hwchase17_react_prompt
 
 
 class ReActAgentwithLangchain(BaseAgent):
@@ -48,6 +48,7 @@ async def stream_generator(self, query, config):
             print("---")
         yield "data: [DONE]\n\n"
 
+
 class ReActAgentwithLanggraph(BaseAgent):
     def __init__(self, args):
         super().__init__(args)
@@ -59,11 +60,11 @@ def __init__(self, args):
 
         tools = self.tools_descriptions
 
-        self.app = create_react_agent(self.llm, tools = tools, state_modifier=REACT_SYS_MESSAGE)
+        self.app = create_react_agent(self.llm, tools=tools, state_modifier=REACT_SYS_MESSAGE)
 
     def prepare_initial_state(self, query):
         return {"messages": [HumanMessage(content=query)]}
-    
+
     async def stream_generator(self, query, config):
         initial_state = self.prepare_initial_state(query)
         try:
@@ -78,7 +79,7 @@ async def stream_generator(self, query, config):
             yield "data: [DONE]\n\n"
         except Exception as e:
             yield str(e)
-    
+
     async def non_streaming_run(self, query, config):
         initial_state = self.prepare_initial_state(query)
         try:
@@ -89,8 +90,8 @@ async def non_streaming_run(self, query, config):
                 else:
                     message.pretty_print()
 
-            last_message = s['messages'][-1]
-            print('******Response: ', last_message.content)
+            last_message = s["messages"][-1]
+            print("******Response: ", last_message.content)
             return last_message.content
         except Exception as e:
-            return str(e)
+            return str(e)

comps/agent/langchain/src/strategy/react/prompt.py

@@ -8,11 +8,11 @@
 )
 
 
-REACT_SYS_MESSAGE="""\
+REACT_SYS_MESSAGE = """\
 Decompose the user request into a series of simple tasks when necessary and solve the problem step by step.
 When you cannot get the answer at first, do not give up. Reflect on the info you have from the tools and try to solve the problem in a different way.
 Please follow these guidelines when formulating your answer:
 1. If the question contains a false premise or assumption, answer “invalid question”.
 2. If you are uncertain or do not know the answer, respond with “I don’t know”.
 3. Give concise, factual and relevant answers.
-"""
+"""