ea4all-gradio-agents-mcp-hackathon-tools-refactoring-apm

ea4all/ea4all_mcp.py

@@ -25,7 +25,8 @@ from PIL import Image
 
 from ea4all.utils.utils import (
     UIUtils,
-    ea4all_agent_init, get_image, filter_page, init_dbr
+    ea4all_agent_init, get_image, filter_page,
+    get_question_diagram_from_example,
 )
 
 TITLE =  """
@@ -40,7 +41,7 @@ TITLE =  """
 tracer = LangChainTracer(project_name=os.getenv('LANGCHAIN_PROJECT'))
 
 config = RunnableConfig(
-        run_name = os.getenv('LANGCHAIN_RUNNAME', "ea4all-mcp"), 
+        run_name = os.getenv('LANGCHAIN_RUNNAME', "ea4all-gradio-agent-mcp-hackathon-run"), 
         tags = [os.getenv('EA4ALL_ENV', "MCP")],
         callbacks = [tracer],
         recursion_limit = 25,
@@ -63,7 +64,7 @@ async def run_qna_agentic_system(question: str) -> AsyncGenerator[list, None]:
     format_response = ""
     chat_memory = []
     if not question:
-        format_response = "Hi, how are you today? To start our conversation, please chat your message!"
+        format_response = "Hi, how are you today? To start using the EA4ALL MCP Tool, provide the required Inputs!"
         chat_memory.append(ChatMessage(role="assistant", content=format_response))
         yield chat_memory
 
@@ -505,7 +506,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
     qna_prompt.submit(run_qna_agentic_system,[qna_prompt],ea4all_chatbot, api_name="landscape_answering_agent")
     #qna_prompt.submit(lambda: "", None, [qna_prompt])
     #ea4all_chatbot.like(fn=get_user_feedback)
-    #qna_examples.input(lambda value: value, qna_examples, qna_prompt)
+    qna_examples.input(lambda value: value, qna_examples, qna_prompt, show_api=False)
 
     #Execute Reference Architecture
     dbr_run.click(run_reference_architecture_agentic_system,show_progress='full',inputs=[dbr_text],outputs=[togaf_vision,tabs_togaf,tabs_reference_architecture, architecture_runway, diagram_header, tab_diagram], api_name="togaf_blueprint_generation")
@@ -515,7 +516,7 @@ with gr.Blocks(title="Your ArchitectGPT",fill_height=True, fill_width=True) as e
     vqa_prompt.submit(run_vqa_agentic_system,[vqa_prompt, vqa_image], ea4all_vqa, api_name="diagram_answering_agent")
     
     #ea4all_vqa.like(fn=get_user_feedback)
-    vqa_examples.input(lambda value: [value['text'], value['files'][-1]], vqa_examples, outputs=[vqa_prompt, vqa_image])
+    vqa_examples.input(get_question_diagram_from_example, vqa_examples, outputs=[vqa_prompt, vqa_image], show_api=False)
 
     #Invoke CrewAI PMO Agentic System
     pmo_prompt.submit(run_pmo_agentic_system,[pmo_prompt],pmo_chatbot, api_name="architect_demand_agent")

ea4all/src/ea4all_apm/graph.py

@@ -6,8 +6,6 @@ and key functions for processing & routing user queries, generating answer to
 Enterprise Architecture related user questions
 about an IT Landscape or Websearch.
 """
-import json
-import tempfile
 import os
 
 from langgraph.graph import END, StateGraph
@@ -19,7 +17,7 @@ from langchain_core.prompts import PromptTemplate, FewShotChatMessagePromptTempl
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers.json import JsonOutputParser
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables.history import RunnableLambda
+from langchain_core.runnables import RunnableLambda
 from langchain_core.runnables import RunnablePassthrough, RunnableConfig
 from langchain_core.runnables import RunnableGenerator
 from langchain_core.documents import Document
@@ -27,19 +25,15 @@ from langchain_core.documents import Document
 from langchain.load import dumps, loads
 from langchain.hub import pull
 
-##Utils and tools
-from langchain_community.document_loaders import JSONLoader
-from langchain_community.utilities import BingSearchAPIWrapper
-from langchain_community.tools.bing_search.tool import BingSearchResults
-
 from operator import itemgetter
+from typing import AsyncGenerator, AsyncIterator
 
 #compute amount of tokens used
 import tiktoken
 
 #import APMGraph packages
 from ea4all.src.ea4all_apm.configuration import AgentConfiguration
-from ea4all.src.ea4all_apm.state import APMState, InputState
+from ea4all.src.ea4all_apm.state import InputState, OutputState, OverallState
 import ea4all.src.ea4all_apm.prompts as e4p
 from ea4all.src.shared.utils import (
     load_mock_content, 
@@ -50,12 +44,15 @@ from ea4all.src.shared.utils import (
     _join_paths,
 )
 from ea4all.src.shared import vectorstore
+from ea4all.src.tools.tools import (
+    websearch,
+)
 
 # This file contains sample APM QUESTIONS
 APM_MOCK_QNA = "apm_qna_mock.txt"
 
 async def retrieve_documents(
-    state: APMState, *, config: RunnableConfig
+    state: OverallState, *, config: RunnableConfig
 ) -> dict[str, list[Document]]:
     """Retrieve documents based on a given query.
 
@@ -223,6 +220,7 @@ async def get_retrieval_chain(rag_input, ea4all_user, question, retriever, confi
         decomposition_prompt = ChatPromptTemplate.from_template(e4p.decomposition_answer_recursevely_template)
 
         # Answer each question and return final answer
+        answer = ""
         q_a_pairs = ""
         for q in questions:    
             rag_chain = (
@@ -257,7 +255,7 @@ async def get_retrieval_chain(rag_input, ea4all_user, question, retriever, confi
         retrieval_chain = (
             {
                 # Retrieve context using the normal question
-                "normal_context": RunnableLambda(lambda x: x["standalone_question"]) | retriever,
+                "normal_context": RunnableLambda(lambda x: getattr(x, "standalone_question")) | retriever,
                 # Retrieve context using the step-back question
                 "step_back_context": generate_queries_step_back | retriever,
                 # Pass on the question
@@ -287,14 +285,14 @@ async def get_retrieval_chain(rag_input, ea4all_user, question, retriever, confi
 
 #Get relevant asnwers to user query
 ##get_relevant_documents "deprecated" - replaced by invoke : 2024-06-07
-def get_relevant_answers(query, config: RunnableConfig):
+async def get_relevant_answers(state: OverallState, query, config: RunnableConfig):
 
     if query != "":
         #retriever.vectorstore.index.ntotal
         #retriever = retriever_faiss(user_ip)
         #response = retriever.invoke({"standalone_question": query})
         
-        response = retrieve_documents(query, config)
+        response = await retrieve_documents(state, config=config)
         return response
     else:
         return []
@@ -346,7 +344,7 @@ def get_relevant_questions():
 
 #Rephrase the original user question based on system prompt to lead a better LLM answer
 def user_query_rephrasing(
-        state: APMState, _prompt=None, *, config: RunnableConfig
+        state: OverallState, _prompt=None, *, config: RunnableConfig
     ) -> dict[str,str]:
 
     question = getattr(state,'question')
@@ -475,7 +473,7 @@ async def grade_documents(state, config: RunnableConfig):
         score = retrieval_grader(llm).ainvoke(
             {"user_question": question, "document": d.page_content}
         )
-        grade = score["score"]
+        grade = getattr(score,"score", "no")
         # Document relevant
         if grade.lower() == "yes":
             print("---GRADE: DOCUMENT RELEVANT---")
@@ -518,7 +516,7 @@ def decide_to_generate(state):
         return "generate"
 
 def grade_generation_v_documents_and_question(
-    state:APMState, config: RunnableConfig):
+    state:OverallState, config: RunnableConfig):
     """
     Determines whether the generation is grounded in the document and answers question.
 
@@ -571,7 +569,7 @@ def grade_generation_v_documents_and_question(
         yield "not supported"
 
 async def apm_query_router(
-    state: APMState, config: RunnableConfig
+    state: OverallState, config: RunnableConfig
 ) -> str:
     
     configuration = AgentConfiguration.from_runnable_config(config)
@@ -595,12 +593,16 @@ async def apm_query_router(
 
     response = await route.ainvoke({"user_question": user_query})
 
-    datasource = extract_structured_output(response.content)['datasource']
-
+    extracted = extract_structured_output(response.content)
+    if extracted is not None:
+        datasource = extracted.get('datasource', 'vectorstore')
+    else:
+        datasource = 'vectorstore'
+    
     return datasource
 
 async def retrieve(
-    state: APMState, config: RunnableConfig
+    state: OverallState, config: RunnableConfig
 ):
     """
     Retrieve documents
@@ -654,49 +656,8 @@ async def retrieve(
 
     return {"documents": format_docs(documents['cdocs']), "question": question, "rag":getattr(state,'rag')}
 
-async def websearch(
-        state: APMState, config: RunnableConfig
-    ) -> dict[str,any]:
-    """
-    Web search based on the re-phrased question.
-
-    Args:
-        state (dict): The current graph state
-        config (RunnableConfig): Configuration with the model used for query analysis.
-
-    Returns:
-        state (dict): Updates documents key with appended web results
-    """
-
-    # print("---WEB SEARCH---")
-    ##Rephrase user question to lead bettern LLM response
-    question = user_query_rephrasing(state=state, config=config)['question']
-
-    ##API Wrapper
-    search = BingSearchAPIWrapper()
-
-    ##Bing Search Results
-    web_results = BingSearchResults(k=3, api_wrapper=search)
-    result = await web_results.ainvoke(
-        {"query": question},
-    )
-    fixed_string = result.replace("'", "\"")
-    result_json = json.loads(fixed_string)
-
-    # Create a temporary file
-    with tempfile.NamedTemporaryFile(mode='w', delete=False) as temp_file:
-        # Write the JSON data to the temporary file
-        json.dump(result_json, temp_file)
-        temp_file.flush()
-
-        # Load the JSON data from the temporary file
-        loader = JSONLoader(file_path=temp_file.name, jq_schema=".[]", text_content=False)
-        docs = loader.load()
-   
-    return {"documents": format_docs(docs), "question": question, "web_search": "Yes", "generation": None}
-
 ### Edges ###
-def route_to_node(state:APMState):
+def route_to_node(state:OverallState):
     
     if state.source == "websearch":
         #print("---ROUTE QUESTION TO WEB SEARCH---")
@@ -706,8 +667,8 @@ def route_to_node(state:APMState):
         return "vectorstore"
 
 async def route_question(
-    state: APMState, config: RunnableConfig
-) -> dict[str, any]:
+    state: OverallState, config: RunnableConfig
+) -> dict[str, str]:
     """
     Route question to web search or RAG.
 
@@ -724,12 +685,16 @@ async def route_question(
     return {"source":source}
     
 async def stream_generation(
-    state: APMState, config: RunnableConfig
-):
+    state: OverallState, config: RunnableConfig
+) -> AsyncGenerator[str, None]:
     configuration = AgentConfiguration.from_runnable_config(config)
 
     llm = get_llm_client(model=configuration.query_model, api_base_url=configuration.api_base_url,streaming=configuration.streaming)
 
+    documents = None
+    web_search = None
+    question = None
+    chat_memory = None
     async for s in state:
         documents = getattr(s,"documents")
         web_search = getattr(s,"web_search")
@@ -759,8 +724,8 @@ async def stream_generation(
         yield(output)
     
 async def generate(
-        state: APMState, config: RunnableConfig
-    ) -> dict[str, any]:
+        state: OverallState, config: RunnableConfig
+    ) -> dict[str, str]:
     """
     Generate answer
 
@@ -797,7 +762,7 @@ async def generate(
 
 #ea4all-qna-agent-conversational-with-memory
 async def apm_agentic_qna(
-    state:APMState, config: RunnableConfig):
+    state:OverallState, config: RunnableConfig):
 
     configuration = AgentConfiguration.from_runnable_config(config)
 
@@ -845,14 +810,17 @@ async def apm_agentic_qna(
 
     return {"documents": format_docs(documents['cdocs']), "question": question, "rag":5, "web_search": "No", "generation": None}
 
-async def final(state: APMState):
+async def final(state: OverallState):
     return {"safety_status": state}
 
-async def choose_next(state: APMState):
-    return "exit" if state.safety_status[0] == 'no' else "route"
+async def choose_next(state: OverallState):
+    if state.safety_status is not None and len(state.safety_status) > 0 and state.safety_status[0] == 'no':
+        return "exit"
+    else:
+        return "route"
 
 class SafetyCheck:
-    def apm_safety_check(self,state: APMState, config: RunnableConfig):
+    def apm_safety_check(self,state: OverallState, config: RunnableConfig):
 
         configuration = AgentConfiguration.from_runnable_config(config)
         question = state.question
@@ -877,7 +845,7 @@ class SafetyCheck:
     def __init__(self):
         self._safety_run = self.apm_safety_check
 
-    def __call__(self, state: APMState, config: RunnableConfig) -> dict[str, list]:
+    def __call__(self, state: OverallState, config: RunnableConfig) -> dict[str, list]:
         try:
             response = getattr(self, '_safety_run')(state, config)
             return {"safety_status": [response['safety_status'][0], "", state.question]}
@@ -886,7 +854,7 @@ class SafetyCheck:
 
 ##BUILD APM Graph
 # Build graph
-workflow = StateGraph(APMState, input=InputState, config_schema=AgentConfiguration)
+workflow = StateGraph(OverallState, input=InputState, output=OutputState, config_schema=AgentConfiguration)
 
 # Define the nodes
 workflow.add_node("safety_check",SafetyCheck())

ea4all/src/ea4all_apm/state.py

@@ -8,6 +8,11 @@ from dataclasses import dataclass, field
 from typing import Optional, Literal, List, Tuple
 from typing_extensions import TypedDict
 
+class Router(TypedDict):
+    """Classify a user query."""
+    logic: str
+    datasource: Optional[Literal["vectorstore", "websearch"]]
+
 # Optional, the InputState is a restricted version of the State that is used to
 # define a narrower interface to the outside world vs. what is maintained
 # internally.
@@ -25,58 +30,31 @@ class InputState:
         question: user question
     """
     question: str
-    safety_status: Optional[Tuple[str, str, str]] = None
-
-    """Messages track the primary execution state of the agent.
-
-    Typically accumulates a pattern of Human/AI/Human/AI messages; if
-    you were to combine this template with a tool-calling ReAct agent pattern,
-    it may look like this:
-
-    1. HumanMessage - user input
-    2. AIMessage with .tool_calls - agent picking tool(s) to use to collect
-         information
-    3. ToolMessage(s) - the responses (or errors) from the executed tools
-    
-        (... repeat steps 2 and 3 as needed ...)
-    4. AIMessage without .tool_calls - agent responding in unstructured
-        format to the user.
-
-    5. HumanMessage - user responds with the next conversational turn.
-
-        (... repeat steps 2-5 as needed ... )
-    
-    Merges two lists of messages, updating existing messages by ID.
-
-    By default, this ensures the state is "append-only", unless the
-    new message has the same ID as an existing message.
-
-    Returns:
-        A new list of messages with the messages from `right` merged into `left`.
-        If a message in `right` has the same ID as a message in `left`, the
-        message from `right` will replace the message from `left`."""
 
+@dataclass(kw_only=True)
+class OutputState:
+    """Represents the output schema for the APM agent.
+    """
 
-class Router(TypedDict):
-    """Classify a user query."""
-    logic: str
-    datasource: Optional[Literal["vectorstore", "websearch"]] = None
+    answer: str
+    """Answer to user's Architecture IT Landscape question about ."""
 
 @dataclass(kw_only=True)
-class APMState(InputState):
+class OverallState(InputState):
     """State of the APM graph / agent."""
     
     """
-        safety_status: user question's safeguarding status, justification, rephrased question
-        router: classification of the user's query
-        source: RAG or websearch
-        web_search: whether to add search
-        retrieved: list of documents retrieved by the retriever
-        rag: last RAG approach used
-        chat_memory: user chat memory
-        generation: should the agent generate a response
-        documents: list of documents retrieved by the retriever
+    safety_status: user question's safeguarding status, justification, rephrased question
+    router: classification of the user's query
+    source: RAG or websearch
+    web_search: whether to add search
+    retrieved: list of documents retrieved by the retriever
+    rag: last RAG approach used
+    chat_memory: user chat memory
+    generation: should the agent generate a response
+    documents: list of documents retrieved by the retriever
     """
+    safety_status: Optional[Tuple[str, str, str]] = None
     router: Optional[Router] = None
     source: Optional[str] = None
     rag: Optional[str] = None

ea4all/src/graph.py

@@ -83,7 +83,7 @@ def make_supervisor_node(model: BaseChatModel, members: list[str]):
             if _goto not in ["portfolio_team", "diagram_team", "blueprint_team", "websearch_team"]:
                 _goto = "__end__"
 
-        print(f"---Supervisor got a request--- Routing to {_goto} \n User Question: {state['messages'][-1].content}")
+        print(f"---Supervisor got a request--- Question: {state['messages'][-1].content} ==> Routing to {_goto}\n")
 
         return Command(
                 #update={"next": _goto}, 
@@ -98,7 +98,7 @@ async def call_landscape_agentic(state: State, config: RunnableConfig) -> Comman
         update={
             "messages": [
                 AIMessage(
-                    content=response.get('generation', response['safety_status']), name="landscape_agentic"
+                    content=str(response), name="landscape_agentic"
                 )
             ]
         },
@@ -151,7 +151,7 @@ async def call_togaf_agentic(state: State, config: RunnableConfig) -> Command[Li
 
 # Wrap-up websearch answer to user's question
 async def call_generate_websearch(state:State, config: RunnableConfig) -> Command[Literal["__end__"]]:
-    from ea4all.src.ea4all_apm.state import APMState
+    from ea4all.src.ea4all_apm.state import OverallState
 
     if config is not None:
         source = config.get('metadata', {}).get('langgraph_node', 'unknown')
@@ -164,7 +164,7 @@ async def call_generate_websearch(state:State, config: RunnableConfig) -> Comman
         "source": source
     }
 
-    apm_state = APMState(**state_dict)
+    apm_state = OverallState(**state_dict)
     generation = await apm_graph.nodes["generate"].ainvoke(apm_state, config)
 
     return Command(

ea4all/src/tools/tools.py

@@ -2,8 +2,9 @@ from typing import Literal, Annotated
 from typing_extensions import TypedDict
 import json
 import tempfile
+import os
 
-from langchain_core.runnables import RunnableConfig
+from langchain_core.runnables import RunnableLambda, RunnableConfig
 
 from langgraph.graph import END
 from langgraph.types import Command
@@ -21,14 +22,14 @@ from ea4all.src.shared.configuration import (
 
 from ea4all.src.shared.state import (
     State
-)
+    )
 
 from ea4all.src.shared.utils import (
     get_llm_client,
     format_docs,
 )
 
-def make_supervisor_node(config: RunnableConfig, members: list[str]) -> str:
+def make_supervisor_node(config: RunnableConfig, members: list[str]) -> RunnableLambda:
     options = ["FINISH"] + members
     system_prompt = (
         "You are a supervisor tasked with managing a conversation between the"
@@ -61,9 +62,9 @@ def make_supervisor_node(config: RunnableConfig, members: list[str]) -> str:
 
         return Command(goto=goto, update={"next": goto})
 
-    return supervisor_node
+    return RunnableLambda(supervisor_node)
 
-async def websearch(state: State):
+async def websearch(state: State) -> dict[str,dict[str,str]]:
     """
     Web search based on the re-phrased question.
 
@@ -76,15 +77,20 @@ async def websearch(state: State):
     """
 
     ##API Wrapper
-    search = BingSearchAPIWrapper()
+    bing_subscription_key = os.environ.get("BING_SUBSCRIPTION_KEY", "")
+    bing_search_url = os.environ.get("BING_SEARCH_URL", "https://api.bing.microsoft.com/v7.0/search")
+    search = BingSearchAPIWrapper(
+        bing_subscription_key=bing_subscription_key,
+        bing_search_url=bing_search_url
+    )
 
     question = state.get('messages')[-1].content
 
     ##Bing Search Results
     web_results = BingSearchResults(
-        k=5, 
         api_wrapper=search,
         handle_tool_error=True,
+        args_schema={"k":"5"}, 
     )
     
     result = await web_results.ainvoke({"query": question})

ea4all/utils/utils.py

@@ -8,7 +8,6 @@ from ea4all.src.shared.configuration import BaseConfiguration
 from ea4all.src.ea4all_indexer.configuration import IndexConfiguration
 from ea4all.src.ea4all_indexer.graph import indexer_graph
 
-
 from langchain_community.document_loaders import ConfluenceLoader
 from langchain_core.messages import ChatMessage
 from langsmith import Client
@@ -164,3 +163,11 @@ def on_dbrtext(file):
 
 def unload_dbr():
     return gr.TextArea(visible=False)
+
+def get_question_diagram_from_example(value) -> list:
+    """
+    Extracts the question and diagram from the selected example.
+    """
+    if value:
+        return [value['text'], value['files'][-1]] if 'files' in value else [value['text'], None]
+    return ["", None]

pyproject.toml

@@ -4,4 +4,6 @@ version = "0.1.0"
 description = "EA4ALL Agentic System MCP Server"
 readme = "README.md"
 requires-python = ">=3.12"
-dependencies = []
+dependencies = [
+    "jq>=1.8.0",
+]

requirements.txt

@@ -4,6 +4,7 @@ gradio==5.32.1
 gradio_client==1.10.2
 graphviz
 huggingface-hub
+jq>=1.8.0
 langchain==0.3.25
 langchain-community==0.3.24
 langchain-core==0.3.61

uv.lock

@@ -0,0 +1,42 @@
+version = 1
+revision = 2
+requires-python = ">=3.12"
+
+[[package]]
+name = "ea4all-gradio-agent-mcp-hackathon"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "jq" },
+]
+
+[package.metadata]
+requires-dist = [{ name = "jq", specifier = ">=1.8.0" }]
+
+[[package]]
+name = "jq"
+version = "1.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/32/3eaca3ac81c804d6849da2e9f536ac200f4ad46a696890854c1f73b2f749/jq-1.8.0.tar.gz", hash = "sha256:53141eebca4bf8b4f2da5e44271a8a3694220dfd22d2b4b2cfb4816b2b6c9057", size = 2058265, upload-time = "2024-08-17T08:13:36.301Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/45/b3/dd0d41cecb0d8712bc792b3c40b42a36c355d814d61f6bda4d61cbb188e5/jq-1.8.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:14f5988ae3604ebfdba2da398f9bd941bb3a72144a2831cfec2bc22bd23d5563", size = 415943, upload-time = "2024-08-17T08:14:38.437Z" },
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