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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+db/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text
+knowledge/Master[[:space:]]List[[:space:]]1-2-25.xlsx filter=lfs diff=lfs merge=lfs -text

app_crew_with_web.py

@@ -0,0 +1,173 @@
+import streamlit as st
+import time
+from crewai import Agent, Task, Crew, Process
+from langchain.llms import OpenAI
+from textwrap import dedent
+from langchain_openai import ChatOpenAI
+from crewai_tools import CSVSearchTool
+from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource
+
+import nest_asyncio
+import os
+from crewai.tools import BaseTool
+from langchain_community.tools import DuckDuckGoSearchRun
+nest_asyncio.apply()
+
+
+class MyCustomDuckDuckGoTool(BaseTool):
+    name: str = "DuckDuckGo Search Tool"
+    description: str = "Search the web for a given query."
+
+    def _run(self, query: str) -> str:
+        # Ensure the DuckDuckGoSearchRun is invoked properly.
+        duckduckgo_tool = DuckDuckGoSearchRun()
+        response = duckduckgo_tool.invoke(query)
+        return response
+
+    def _get_tool(self):
+        # Create an instance of the tool when needed
+        return MyCustomDuckDuckGoTool()
+api_key = os.getenv("YOUR SECRET KEY")
+os.environ["OPENAI_API_KEY"] = api_key
+
+st.set_page_config(
+    page_title="CrewAI Test !", page_icon=":flag-ca:")
+st.title("CrewAI Test ! 🍁")
+st.header("Let's chat :star2:")
+
+# uploaded_file = st.sidebar.file_uploader("Upload a file", key= "uploaded_file")
+# docsearch_structured,docsearch_unstructured = lch.create_db_for_both()
+st.form_submit_button("Please enter your OpenAI API key")
+
+
+@st.cache_resource(show_spinner = "Loading search tools and kb...")
+def prepare_search_tool_and_kb():
+    tool = CSVSearchTool(csv='KB.csv')
+    excel_source = ExcelKnowledgeSource(file_paths=["Master List 1-2-25.xlsx"])
+    Duck_search = MyCustomDuckDuckGoTool()
+    return tool,Duck_search,excel_source
+
+@st.cache_resource(show_spinner = "Loading crew...")
+def prepare_crew():
+    tool,Duck_search,excel_source = prepare_search_tool_and_kb()
+
+    agent_1 = Agent(
+        role=dedent((
+            """
+            Data Knowdledge Agent.
+            """)),
+        backstory=dedent((
+            """
+            An angent with the abiity to search the database return the relevant answer for the question.
+            """)),
+        goal=dedent((
+            """
+            Get relevant answer about the question.
+            """)),
+        allow_delegation=False,
+        verbose=True,
+        # ↑ Whether the agent execution should be in verbose mode
+        max_iter=3,
+        # ↑ maximum number of iterations the agent can perform before being forced to give its best answer
+        llm=ChatOpenAI(model_name="gpt-4o-mini", temperature=0),
+        tools = [tool],
+    )
+
+    agent_2 = Agent(
+        role=dedent((
+            """
+            Web Search Agent.
+            """)),
+        backstory=dedent((
+            """
+            An angent with the abiity to search search the web for the relevant information based on the asked question.
+            """)),
+        goal=dedent((
+            """
+            Get relevant answer about the question.
+            """)),
+        allow_delegation=False,
+        verbose=False,
+        # ↑ Whether the agent execution should be in verbose mode
+        max_iter=3,
+        # ↑ maximum number of iterations the agent can perform before being forced to give its best answer
+        llm=ChatOpenAI(model_name="gpt-4o-mini", temperature=0),
+        tool=[Duck_search]
+    )
+
+
+
+
+    task_1 = Task(
+        description=dedent((
+            """
+            Analyze the csv file and get all the relevant information for the following question.
+
+            Question: {question}
+
+            Make sure to get all the relevant data if there are more than one results.
+
+            Aggerate results into a single output.
+            """)),
+        expected_output=dedent((
+            """
+            A detailed data answer to the question.
+            """)),
+    agent=agent_1,
+    )
+
+    task_2 = Task(
+        description=dedent((
+            """
+            Search for the following question in the web.
+
+            Question: {question}
+
+            Make sure to get all the relevant data if there are more than one results.
+
+            Aggerate results into a single output.
+            """)),
+        expected_output=dedent((
+            """
+            A detailed data answer to the question.
+            """)),
+    agent=agent_2,
+    )
+
+    crew = Crew(agents =[agent_1,agent_2],
+                tasks =[task_1,task_2],verbose=True,  # You can set it to True or False
+            # ↑ indicates the verbosity level for logging during execution.
+            process=Process.sequential,
+            knowledge_sources = [excel_source]
+            # ↑ the process flow that the crew will follow (e.g., sequential, hierarchical).
+        )
+    return crew
+
+crew = prepare_crew()
+
+YES_MESSAGE = "Hello there, how can I help you today? "
+
+if "messages" not in st.session_state.keys():
+    st.session_state.messages = [
+        {"role": "assistant", "content": YES_MESSAGE}
+    ]
+
+for message in st.session_state.messages: # Display the prior chat messages
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+    with st.chat_message("assistant"):
+        with st.spinner("Thinking..., please be patient"):
+            inputs ={"question":prompt}
+            response = crew.kickoff(inputs=inputs)
+            response_str = response.raw
+            # def generate():
+            #     for text in response.response_gen:
+            #         yield text
+            #         time.sleep(0.05)
+            st.write(response_str)
+            message = {"role": "assistant", "content": response_str}
+            st.session_state.messages.append(response_str) 

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db/chroma.sqlite3

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knowledge/Master List 1-2-25.xlsx

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requirements.txt

@@ -0,0 +1,5 @@
+pip==24.0
+streamlit==1.32.2
+python-dotenv==0.21.0
+crewai==0.95.0
+crewai-tools