redefine tools

langchain-streamlit-demo/app.py

@@ -15,7 +15,6 @@ from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.schema.document import Document
 from langchain.schema.retriever import BaseRetriever
 from langchain.tools import DuckDuckGoSearchRun, WikipediaQueryRun
-from langchain.tools import Tool
 from langchain.utilities import WikipediaAPIWrapper
 from langsmith.client import Client
 from streamlit_feedback import streamlit_feedback
@@ -461,27 +460,30 @@ if st.session_state.llm:
  )
  st_callback = StreamlitCallbackHandler(st.container())
  callbacks.append(st_callback)
- research_assistant_tool = Tool.from_function(
- func=lambda s: research_assistant_chain.invoke(
- {"question": s},
- # config=get_config(callbacks),
- ),
- name="web-research-assistant",
- description="this assistant returns a comprehensive report based on web research. "
- "it's slow and relatively expensive, so use it sparingly. "
- "for quick facts, use duckduckgo instead.",
- )
+
+ from langchain.agents.tools import tool
+ from langchain.callbacks.manager import Callbacks
+
+ @tool("web-research-assistant")
+ def research_assistant_tool(question: str, callbacks: Callbacks = None):
+ """this assistant returns a comprehensive report based on web research.
+ it's slow and relatively expensive, so use it sparingly.
+ for quick facts, use duckduckgo instead.
+ """
+ return research_assistant_chain.invoke(
+ dict(question=question),
+ config=get_config(callbacks),
+ )
 
  python_coder_agent = get_python_agent(st.session_state.llm)
 
- python_coder_tool = Tool.from_function(
- func=lambda s: python_coder_agent.invoke(
- {"input": s},
- # config=get_config(callbacks),
- ),
- name="python-coder-assistant",
- description="this assistant writes Python code. give it clear instructions and requirements.",
- )
+ @tool("python-coder-assistant")
+ def python_coder_tool(input_str: str, callbacks: Callbacks = None):
+ """this assistant writes Python code. give it clear instructions and requirements."""
+ return python_coder_agent.invoke(
+ dict(input=input_str),
+ config=get_config(callbacks),
+ )
 
  TOOLS = [research_assistant_tool, python_coder_tool] + default_tools
 
@@ -496,29 +498,32 @@ if st.session_state.llm:
  prompt,
  )
 
- doc_chain_tool = Tool.from_function(
- func=lambda s: st.session_state.doc_chain.invoke(
- s,
+ @tool("user-document-chat")
+ def doc_chain_tool(input_str: str, callbacks: Callbacks = None):
+ """this assistant returns a response based on the user's custom context."""
+ return st.session_state.doc_chain.invoke(
+ input_str,
  config=get_config(callbacks),
- ),
- name="user-document-chat",
- description="this assistant returns a response based on the user's custom context. ",
- )
+ )
+
  doc_chain_agent = get_doc_agent(
  [doc_chain_tool],
  )
- doc_question_tool = Tool.from_function(
- func=lambda s: doc_chain_agent.invoke(
- s,
+
+ @tool("document-question-tool")
+ def doc_question_tool(input_str: str, callbacks: Callbacks = None):
+ """
+ this assistant answers a question based on the user's custom context.
+ this assistant responds to fully formed questions.
+ Do not send anything besides a question. It already has context.
+ if the user's meaning is unclear, perhaps the answer is here.
+ generally speaking, try this tool before conducting web research.
+ """
+ return doc_chain_agent.invoke(
+ input_str,
  config=get_config(callbacks),
- ),
- name="document-question-tool",
- description="this assistant answers a question based on the user's custom context. "
- "this assistant responds to fully formed questions."
- "Do not send anything besides a question. It already has context."
- "if the user's meaning is unclear, perhaps the answer is here. "
- "generally speaking, try this tool before conducting web research.",
- )
+ )
+
  TOOLS = [doc_question_tool, research_assistant_tool] + default_tools
 
  st.session_state.chain = get_agent(