The Supervisor is working

README.md

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-# multi-source-multi-agent-finance-assistant
+# Multi-Source Multi-Agent Finance Assistant

agents/api_agent.py

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+from langchain_community.tools.yahoo_finance_news import YahooFinanceNewsTool
+from langgraph.prebuilt import create_react_agent
+from langchain_google_genai import ChatGoogleGenerativeAI
+from dotenv import load_dotenv
+import os
+import google.generativeai as genai
+
+load_dotenv()
+
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+def get_api_agent():
+    return create_react_agent(
+        model=ChatGoogleGenerativeAI(model="gemini-2.0-flash"),
+        tools=[YahooFinanceNewsTool()],
+        prompt=(
+            "You are a Financial agent.\n\n"
+            "INSTRUCTIONS:\n"
+            "- You polls real-time & historical market data.\n"
+            "- After you're done with your tasks, respond to the supervisor directly\n"
+            "- Respond ONLY with the results of your work, do NOT include ANY other text.\n"
+            "- You can use the tools provided to you to get the data."
+        ),
+        name="Financial_agent",
+    )
+
+# api_agent = get_api_agent()
+
+# result = api_agent.invoke({"messages": ["Latest news about Apple?"]})
+
+# for i in result["messages"]:
+#     i.pretty_print()

agents/retriever_agent.py

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+from langgraph.prebuilt import create_react_agent
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.tools.retriever import create_retriever_tool
+from data_ingestion.get_data import get_vector_store
+from dotenv import load_dotenv
+import google.generativeai as genai
+import os
+
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+
+def get_retriever_agent():
+    vectorstore = get_vector_store()
+    return create_react_agent(
+    model=ChatGoogleGenerativeAI(model="gemini-2.0-flash"),
+    tools=[create_retriever_tool(vectorstore.as_retriever(), "retrieve_blog_posts", "Search and return information about Lilian Weng blog posts.",)],
+    prompt=(
+        "You are a retriever agent.\n\n"
+        "INSTRUCTIONS:\n"
+        "- Get the data from the vector store.\n"
+        "- After you're done with your tasks, respond to the supervisor directly\n"
+        "- Respond ONLY with the results of your work, do NOT include ANY other text."
+    ),
+    name="retriever_agent",
+)
+
+retriever_agent = get_retriever_agent()
+
+result = retriever_agent.invoke({"messages": ["Latest news about Apple?"]})
+
+for i in result["messages"]:
+    i.pretty_print()

agents/scraping_agent.py

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+from langgraph.prebuilt import create_react_agent
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.tools import tool
+from langchain_community.document_loaders import WebBaseLoader, PyPDFLoader
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from dotenv import load_dotenv
+import google.generativeai as genai
+import os
+
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+@tool
+def web_loader(url: str) -> str:
+    """Provides the docs of the web of the url provided."""
+    doc = WebBaseLoader(url).load()
+    s = ''
+    for i in doc:
+        s += '\n' + i.page_content.strip()
+    return s
+
+@tool
+def pdf_loader(file_path: str) -> str:
+    """Provides the pdf docs of the file_path provided."""
+    doc = PyPDFLoader(file_path).load()
+    s = ''
+    for i in doc:
+        s += '\n' + i.page_content.strip()
+    return s
+
+@tool
+def csv_loader(file_path: str) -> str:
+    """Provides the csv docs of the file_path provided."""
+    doc = CSVLoader(file_path).load()
+    s = ''
+    for i in doc:
+        s += '\n' + i.page_content.strip()
+    return s
+
+def get_scraping_agent():
+    return create_react_agent(
+    model=ChatGoogleGenerativeAI(model="gemini-2.0-flash"),
+    tools=[web_loader, pdf_loader, csv_loader],
+    prompt=(
+        "You are a scraping agent.\n\n"
+        "INSTRUCTIONS:\n"
+        "- Get the data from the web, pdf, csv, etc.\n"
+        "- After you're done with your tasks, respond to the supervisor directly\n"
+        "- Respond ONLY with the results of your work, do NOT include ANY other text."
+    ),
+    name="scraping_agent",
+)

data_ingestion/get_data.py

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+from langchain_community.document_loaders import WebBaseLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from pypdf import PdfReader
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import os
+
+def get_pdf_text(pdf):
+    text=""
+    pdf_reader= PdfReader(pdf)
+    for page in pdf_reader.pages:
+        text+= page.extract_text()
+    return  text
+
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=50)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+
+def create_vector_store(text:str = "Hello world!"):
+    chunks = get_text_chunks(text)
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/gemini-embedding-exp-03-07")
+    vector_store = FAISS.from_texts(chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+    return vector_store
+    
+def get_vector_store():
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/gemini-embedding-exp-03-07")
+    if not os.path.exists("faiss_index"):
+        return create_vector_store()
+    vectorstore = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    return vectorstore

orchestrator/supervisor.py

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+import sys, os
+top_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(top_dir)
+
+from langgraph_supervisor import create_supervisor
+from agents.retriever_agent import get_retriever_agent
+from agents.scraping_agent import get_scraping_agent
+from agents.api_agent import get_api_agent
+from langchain_google_genai import ChatGoogleGenerativeAI
+from dotenv import load_dotenv
+import google.generativeai as genai
+
+
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+
+def get_supervisor():
+    retriever_agent = get_retriever_agent()
+    scraping_agent = get_scraping_agent()
+    api_agent = get_api_agent()
+    return create_supervisor(
+        model = ChatGoogleGenerativeAI(model="gemini-2.0-flash"),
+        agents=[scraping_agent, retriever_agent, api_agent],
+        prompt=(
+            "You are a supervisor managing three agents:\n"
+            "- a scraping agent. Assign scraping-related tasks to this agent\n"
+            "- a retriever agent. Provide the link or path to  fetch any documment and it will help you with it's content\n"
+            "- a api agent. Assign finance-related tasks to this agent\n"
+            "Assign work to one agent at a time, do not call agents in parallel.\n"
+            "Do not do any work yourself."
+        ),
+        add_handoff_back_messages=True,
+        output_mode="full_history",
+    ).compile()
+
+# supervisor = get_supervisor()
+
+# result = supervisor.invoke({"messages": ["Latest news about Apple?"]})
+
+# for i in result["messages"]:
+#     i.pretty_print()

requirements.txt

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+langchain 
+langgraph 
+google-generativeai 
+google-ai-generativelanguage 
+yfinance
+dotenv
+langchain-community
+langchain-google-genai
+langgraph_supervisor
+faiss-cpu
+pypdf
+streamlit