Spaces:
Sleeping
Sleeping
File size: 3,594 Bytes
b642bac e1b3737 b642bac 948fcb3 b642bac 948fcb3 e1b3737 b642bac 948fcb3 b642bac e1b3737 b642bac e1b3737 b642bac e1b3737 b642bac |
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 |
"""LangGraph Agent"""
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 HuggingFaceEndpoint, ChatHuggingFace, HuggingFaceEmbeddings
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.globals import set_debug
from langchain_groq import ChatGroq
from tools.search_tools import web_search, arvix_search, wiki_search
from tools.math_tools import multiply, add, subtract, divide
# from supabase.client import Client, create_client
# from langchain.tools.retriever import create_retriever_tool
# from langchain_community.vectorstores import SupabaseVectorStore
import json
from tools.multimodal_tools import extract_text, analyze_image_tool, analyze_audio_tool
from langchain_google_genai import ChatGoogleGenerativeAI
# set_debug(True)
load_dotenv()
tools = [
multiply,
add,
subtract,
divide,
web_search,
wiki_search,
arvix_search,
extract_text,
analyze_image_tool,
analyze_audio_tool
]
def build_graph():
hf_token = os.getenv("HF_TOKEN")
api_key = os.getenv("GEMINI_API_KEY")
# llm = HuggingFaceEndpoint(
# repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
# huggingfacehub_api_token=hf_token,
# )
# chat = ChatHuggingFace(llm=llm, verbose=True)
# llm_with_tools = chat.bind_tools(tools)
# llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
# llm_with_tools = llm.bind_tools(tools)
chat = ChatGoogleGenerativeAI(
model= "gemini-2.5-pro-preview-05-06",
temperature=0,
max_retries=2,
google_api_key=api_key,
thinking_budget= 0
)
chat_with_tools = chat.bind_tools(tools)
def assistant(state: MessagesState):
sys_msg = "You are a helpful assistant with access to tools. Understand user requests accurately. Use your tools when needed to answer effectively. Strictly follow all user instructions and constraints." \
"Pay attention: your output needs to contain only the final answer without any reasoning since it will be strictly evaluated against a dataset which contains only the specific response." \
"Your final output needs to be just the string or integer containing the answer, not an array or technical stuff."
return {
"messages": [chat_with_tools.invoke([sys_msg] + state["messages"])],
}
## The graph
builder = StateGraph(MessagesState)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
"assistant",
# If the latest message requires a tool, route to tools
# Otherwise, provide a direct response
tools_condition,
)
builder.add_edge("tools", "assistant")
return builder.compile()
# test
if __name__ == "__main__":
graph = build_graph()
with open('sample.jsonl', 'r') as jsonl_file:
json_list = list(jsonl_file)
start = 10 #revisit 5, 8,
end = start + 1
for json_str in json_list[start:end]:
json_data = json.loads(json_str)
print(f"Question::::::::: {json_data['Question']}")
print(f"Final answer::::: {json_data['Final answer']}")
question = json_data['Question']
messages = [HumanMessage(content=question)]
messages = graph.invoke({"messages": messages})
for m in messages["messages"]:
m.pretty_print() |