File size: 5,002 Bytes
0edbbcb 6b1796d 0edbbcb 6b1796d 0edbbcb 6b1796d 0edbbcb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
import os
from dotenv import load_dotenv
from typing import List, Dict, Any, Optional
import tempfile
import re
import json
import requests
from urllib.parse import urlparse
import pytesseract
from PIL import Image, ImageDraw, ImageFont, ImageEnhance, ImageFilter
import cmath
import pandas as pd
import uuid
import numpy as np
from tools.python_interpreter import CodeInterpreter
interpreter_instance = CodeInterpreter()
hf_token = os.environ["HUGGING_FACE_TOKEN"]
from tools.image import *
"""Langraph"""
from langgraph.graph import START, StateGraph, MessagesState
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import (
ChatHuggingFace,
HuggingFaceEndpoint,
HuggingFaceEmbeddings,
)
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
# ------- Tools
from tools.browse import web_search, wiki_search, arxiv_search
from tools.document_process import save_and_read_file, analyze_csv_file, analyze_excel_file, extract_text_from_image, download_file_from_url
from tools.image_tools import analyze_image, generate_simple_image
from tools.simple_math import multiply, add, subtract, divide, modulus, power, square_root
from tools.python_interpreter import execute_code_lang
load_dotenv()
with open("system_prompt.txt", "r", encoding="utf-8") as f:
system_prompt = f.read()
print(system_prompt)
# System message
sys_msg = SystemMessage(content=system_prompt)
# build a retriever
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2",
) # dim=768
supabase: Client = create_client(
os.environ.get("SUPABASE_URL_HUGGING_FACE"), os.environ.get("SUPABASE_SERVICE_ROLE_HUGGING_FACE")
)
vector_store = SupabaseVectorStore(
client=supabase,
embedding=embeddings,
table_name="documents2",
query_name="match_documents_2",
)
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 = [
web_search,
wiki_search,
arxiv_search,
multiply,
add,
subtract,
divide,
modulus,
power,
square_root,
save_and_read_file,
download_file_from_url,
extract_text_from_image,
analyze_csv_file,
analyze_excel_file,
execute_code_lang,
analyze_image,
generate_simple_image,
]
def build_graph(provider: str = "groq"):
if provider == "groq":
# Groq https://console.groq.com/docs/models
llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
elif provider == "huggingface":
llm = ChatHuggingFace(
llm=HuggingFaceEndpoint(
repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
task="text-generation", # for chat‐style use “text-generation”
max_new_tokens=1024,
do_sample=False,
repetition_penalty=1.03,
temperature=0,
),
verbose=True,
)
else:
raise ValueError("Invalid provider. Choose 'groq' or 'huggingface'.")
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.similiarity_search(state['messages'])
if similar_question:
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]}
else:
return {"messages": [sys_msg] + state["messages"]}
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_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")
return builder.compile()
if __name__ == "__main__":
question = "When was the Cyrus Cylinder created?"
graph = build_graph(provider="groq")
messages = [HumanMessage(content=question)]
messages = graph.invoke({"messages": messages})
for m in messages["messages"]:
m.pretty_print()
|