initial commit

.gitignore

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+.env

app.py

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+from live_search import Search_Class
+import gradio as gr
+
+bot = Search_Class()
+
+with gr.Blocks() as app:
+    with gr.Tab("ChatBot"):
+        gr.Markdown("""## 🤖 Real-time AI Assitant for:
+                        - 📰 News Updates
+                        - 💹 Stock Market Updates 
+                        - ☁️ Weather Updates
+                    """)
+        chat = gr.Chatbot()
+        text = gr.Textbox(placeholder="➡️ Enter your query here", show_label= False)
+        text.submit(fn = bot.run,
+                    inputs=text,
+                    outputs=chat)
+        submit = gr.Button(value = "Submit")
+        submit.click(fn = bot.run,
+                    inputs=text,
+                    outputs=chat)
+        clear = gr.ClearButton([chat, text], value= "Clear and Delete Memory")
+        clear.click(fn = bot.setup_memory)
+
+app.launch()

live_search.py

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+from langchain_community.tools import WikipediaQueryRun
+from langchain_community.utilities import WikipediaAPIWrapper
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.vectorstores import FAISS
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.tools.retriever import create_retriever_tool
+from dotenv import load_dotenv
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain.agents import create_react_agent, create_tool_calling_agent
+from langchain.agents import AgentExecutor
+from langchain import hub
+from langchain.agents import Tool
+from langchain_community.utilities import SerpAPIWrapper
+from langchain_core.runnables.history import RunnableWithMessageHistory
+
+class Search_Class:
+    def __init__(self):
+        self.setup_env()
+        self.setup_llm()
+        self.setup_embeddings()
+        self.setup_vector_store()
+        self.setup_tools()
+        self.setup_memory()
+        self.setup_agent()
+        
+    def setup_env(self):
+        load_dotenv()
+
+    def setup_llm(self):
+        self.llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0)
+        # self.llm = ChatGroq(model="llama3-70b-8192", temperature=0)
+    def setup_embeddings(self):
+        self.loader = WebBaseLoader("https://www.etmoney.com/stocks/list-of-stocks")
+        self.docs = self.loader.load()
+        self.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+        self.documents = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200).split_documents(self.docs)
+
+    def setup_vector_store(self):
+        self.vectordb = FAISS.from_documents(self.documents, self.embeddings)
+        self.retriever = self.vectordb.as_retriever()
+
+    def setup_tools(self):
+        self.search_tool = Tool(
+            name="Search",
+            description="A search engine. Useful for when you need to answer questions about current events. Input should be a search query.",
+            func=SerpAPIWrapper().run
+        )
+        api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+        api_wrapper = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
+        self.wiki_tool = Tool(
+            name = "Wikipedia",
+            description = "A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.",
+            func = api_wrapper.run
+        )
+        self.retriever_tool = create_retriever_tool(
+            self.retriever, "stock_search", "For any information related to stock prices use this tool"
+        )
+        self.tools = [self.search_tool, self.wiki_tool]
+        self.names = ["Wikipedia","stock_search"]
+
+    def setup_memory(self):
+        self.memory = ChatMessageHistory(session_id="test-session")
+        self.chat_history = []
+
+    def setup_agent(self):
+        self.prompt = hub.pull("satvikjain/react_smaller")
+        self.agent = create_tool_calling_agent(self.llm, self.tools, self.prompt)
+        self.agent_executor = AgentExecutor(agent=self.agent, tools=self.tools, verbose=True, handle_parsing_errors=True)
+        self.agent_executor.return_intermediate_steps = True
+        self.agent_with_chat_history = RunnableWithMessageHistory(
+                                                            self.agent_executor,
+                                                            lambda session_id: self.memory,
+                                                            input_messages_key="input",
+                                                            history_messages_key="chat_history"
+                                                        )
+
+    def run(self, user_input = "Hi"):
+        response = self.agent_with_chat_history.invoke({
+            "input": user_input, "tools": self.tools, "tool_names":self.names},
+            config={"configurable": {"session_id": "test-session"}
+                    }
+        )
+        self.chat_history.append([user_input, response["output"]])
+        return self.chat_history
+    

requirements.txt

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+langchain
+langchain_community
+langchain_google_genai
+langchain_text_splitters
+langchain_core
+gradio