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import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_community.tools import DuckDuckGoSearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from supabase.client import Client, create_client
load_dotenv()
@tool
def multiply(a: int, b: int) -> int:
"""Multiply two numbers.
Args:
a: first int
b: second int
"""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Add two numbers.
Args:
a: first int
b: second int
"""
return a+b
@tool
def subtract(a: int, b:int) -> int:
"""Subtract two numbers.
Args:
a: first int
b: second int
"""
return a-b
@tool
def divide(a: int, b: int) -> int:
"""Divide two numbers.
Args:
a: first int
b: second int
"""
if b == 0:
raise ValueError("Cannot divide by zero.")
return a / b
@tool
def modulus(a: int, b:int) -> int:
"""Get the modulus of two numbers.
Args:
a: first int
b: second int
"""
return a%b
@tool
def wiki_search(query: str) -> str:
"""Search Wikipedia for a query and return maximum 2 results.
Args:
query: The search query.
"""
search_docs = WikipediaLoader(query=query, load_max_docs=3).load()
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
for doc in search_docs
])
return {"wiki_results": formatted_search_docs}
@tool
def web_search(query: str) -> str:
"""Search Duck2DuckGo for a query and return maximum 3 results.
Args:
query: The search query."""
search_docs = DuckDuckGoSearchResults(max_results=4).invoke(query=query)
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
for doc in search_docs
])
return {"web_results": formatted_search_docs}
@tool
def arvix_search(query: str) -> str:
"""Search Arxiv for a query and return maximum 3 result.
Args:
query: The search query."""
search_docs = ArxivLoader(query=query, load_max_docs=2).load()
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
for doc in search_docs
])
return {"arvix_results": formatted_search_docs}
with open("system_prompt.txt","r",encoding="utf-8") as f:
system_prompt = f.read()
# System message
sys_msg = SystemMessage(content=system_prompt)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # dim=768
supabase: Client = create_client(
os.environ.get("SUPABASE_URL"),
os.environ.get("SUPABASE_SERVICE_KEY"))
vector_store = SupabaseVectorStore(
client=supabase,
embedding= embeddings,
table_name="documents",
query_name="match_documents_langchain",
)
create_retriever_tool = create_retriever_tool(
retriever=vector_store.as_retriever(),
name="Question Search",
description="A tool to retrieve similar questions from a vector store.",
)
tools = [
multiply,
add,
subtract,
divide,
modulus,
wiki_search,
web_search,
arvix_search,
]
def build_graph():
llm = ChatHuggingFace(
llm=HuggingFaceEndpoint(
repo_id="meta-llama/Llama-2-7b-chat-hf",
temperature=0,
)
)
llm_with_tools = llm.bind_tools(tools)
def assistant(state: MessagesState):
"""Assistant node"""
return{"messages":[llm_with_tools.invoke(state["messages"])]}
def retriever(state: MessagesState):
"""Retriever node"""
similar_question = vector_store.similarity_search(state["messages"][0].content)
example_msg = HumanMessage(
content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
)
return {"messages": [sys_msg] + state["messages"] + [example_msg]}
builder = StateGraph(MessagesState)
builder.add_node("retriever", retriever)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START,"retriever")
builder.add_edge("retriever","assistant")
builder.add_edge("retriever","assistant")
builder.add_conditional_edges(
"assistant",
tools_condition,
)
builder.add_edge("tools","assistant")
return builder.compile()
if __name__ == "__main__":
question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
# Build the graph
graph = build_graph()
messages = [HumanMessage(content=question)]
messages = graph.invoke({"messages":messages})
for m in messages["messages"]:
m.preetty_print() |