File size: 6,301 Bytes
8bc446e
8c9df2c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8bc446e
8c9df2c
 
8bc446e
 
 
8c9df2c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
# from langchain_ollama import ChatOllama
from langchain_core.messages import SystemMessage, HumanMessage
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_core.tools import tool
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from math import sqrt

### =============== MATHEMATICAL TOOLS =============== ###


@tool
def multiply(a: float, b: float) -> float:
    """
    Multiplies two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a * b


@tool
def add(a: float, b: float) -> float:
    """
    Adds two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a + b


@tool
def subtract(a: float, b: float) -> int:
    """
    Subtracts two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a - b


@tool
def divide(a: float, b: float) -> float:
    """
    Divides two numbers.
    Args:
        a (float): the first float number
        b (float): the second float number
    """
    if b == 0:
        raise ValueError("Cannot divided by zero.")
    return a / b


@tool
def modulus(a: int, b: int) -> int:
    """
    Get the modulus of two numbers.
    Args:
        a (int): the first number
        b (int): the second number
    """
    return a % b


@tool
def power(a: float, b: float) -> float:
    """
    Get the power of two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a**b


@tool
def square_root(a: float) -> float | complex:
    """
    Get the square root of a number.
    Args:
        a (float): the number to get the square root of
    """
    if a >= 0:
        return a**0.5
    return sqrt(a)


### =============== BROWSER TOOLS =============== ###

search_tool = DuckDuckGoSearchRun()


@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=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}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"wiki_results": formatted_search_docs}


@tool
def web_search(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results.
    Args:
        query: The search query."""
    print(f"I'm running web_search with {query = }")
    search_docs = TavilySearchResults(max_results=3).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 arxiv_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=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[:1000]}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"arxiv_results": formatted_search_docs}


tools = [multiply, add, subtract, divide, modulus, power, square_root, web_search, arxiv_search, wiki_search]


GAIA_SYSTEM_PROMPT = """
You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
"""
sys_msg = SystemMessage(content=GAIA_SYSTEM_PROMPT)


# Build graph function
def build_graph(provider: str = "huggingface"):
    """Build the graph"""
    # Load environment variables from .env file
    # if provider == "ollama":
    #     chat = ChatOllama(model="llama3.1")
    if provider == "huggingface":
        llm = HuggingFaceEndpoint(
            repo_id="Qwen/Qwen2.5-Coder-32B-Instruct"
        )

        chat = ChatHuggingFace(llm=llm, verbose=True)
    else:
        raise ValueError("Invalid provider. Choose 'ollama' or 'huggingface'.")
    # Bind tools to LLM
    chat_with_tools = chat.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        print([sys_msg] + state["messages"])
        return {"messages": [chat_with_tools.invoke([sys_msg] + state["messages"])]}

    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges(
        "assistant",
        tools_condition,
    )
    builder.add_edge("tools", "assistant")

    return builder.compile()


# test
if __name__ == "__main__":
    question = "Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec.\n\nWhat does Teal'c say in response to the question \"Isn't that hot?\""
    # fixed_answer = "extremely"
    graph = build_graph(provider="huggingface")
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()