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langchain_example.py

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+from typing import List, Literal, Union
+
+import math
+
+from langchain.tools.base import StructuredTool
+from langchain.agents import (
+    Tool,
+    AgentExecutor,
+    LLMSingleActionAgent,
+    AgentOutputParser,
+)
+from langchain.schema import AgentAction, AgentFinish, OutputParserException
+from langchain.prompts import StringPromptTemplate
+from langchain.llms import HuggingFaceTextGenInference
+from langchain.chains import LLMChain
+
+
+##########################################################
+# Step 1: Define the functions you want to articulate. ###
+##########################################################
+
+
+def calculator(
+    input_a: float,
+    input_b: float,
+    operation: Literal["add", "subtract", "multiply", "divide"],
+):
+    """
+    Computes a calculation.
+
+    Args:
+    input_a (float) : Required. The first input.
+    input_b (float) : Required. The second input.
+    operation (string): The operation. Choices include: add to add two numbers, subtract to subtract two numbers, multiply to multiply two numbers, and divide to divide them.
+    """
+    match operation:
+        case "add":
+            return input_a + input_b
+        case "subtract":
+            return input_a - input_b
+        case "multiply":
+            return input_a * input_b
+        case "divide":
+            return input_a / input_b
+
+
+def cylinder_volume(radius, height):
+    """
+    Calculate the volume of a cylinder.
+
+    Parameters:
+    - radius (float): The radius of the base of the cylinder.
+    - height (float): The height of the cylinder.
+
+    Returns:
+    - float: The volume of the cylinder.
+    """
+    if radius < 0 or height < 0:
+        raise ValueError("Radius and height must be non-negative.")
+
+    volume = math.pi * (radius**2) * height
+    return volume
+
+
+#############################################################
+# Step 2: Let's define some utils for building the prompt ###
+#############################################################
+
+
+RAVEN_PROMPT = """
+{raven_tools}
+User Query: Question: {input}
+
+Please pick a function from the above options that best answers the user query and fill in the appropriate arguments.<human_end>"""
+
+
+# Set up a prompt template
+class RavenPromptTemplate(StringPromptTemplate):
+    # The template to use
+    template: str
+    # The list of tools available
+    tools: List[Tool]
+
+    def format(self, **kwargs) -> str:
+        prompt = "<human>:\n"
+        for tool in self.tools:
+            func_signature, func_docstring = tool.description.split(" - ", 1)
+            prompt += f'\nOPTION:\n<func_start>def {func_signature}<func_end>\n<docstring_start>\n"""\n{func_docstring}\n"""\n<docstring_end>\n'
+        kwargs["raven_tools"] = prompt
+        return self.template.format(**kwargs).replace("{{", "{").replace("}}", "}")
+
+
+class RavenOutputParser(AgentOutputParser):
+    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
+        # Check if agent should finish
+        if "Initial Answer:" in llm_output:
+            return AgentFinish(
+                return_values={
+                    "output": llm_output.strip()
+                    .split("\n")[1]
+                    .replace("Initial Answer: ", "")
+                    .strip()
+                },
+                log=llm_output,
+            )
+        else:
+            raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
+
+
+##################################################
+# Step 3: Build the agent with these utilities ###
+##################################################
+
+
+inference_server_url = "<YOUR ENDPOINT URL>"
+assert (
+    inference_server_url is not "<YOUR ENDPOINT URL>"
+), "Please provide your own HF inference endpoint URL!"
+
+llm = HuggingFaceTextGenInference(
+    inference_server_url=inference_server_url,
+    temperature=0.001,
+    max_new_tokens=400,
+    do_sample=False,
+)
+tools = [
+    StructuredTool.from_function(calculator),
+    StructuredTool.from_function(cylinder_volume),
+]
+raven_prompt = RavenPromptTemplate(
+    template=RAVEN_PROMPT, tools=tools, input_variables=["input"]
+)
+llm_chain = LLMChain(llm=llm, prompt=raven_prompt)
+output_parser = RavenOutputParser()
+agent = LLMSingleActionAgent(
+    llm_chain=llm_chain,
+    output_parser=output_parser,
+    stop=["\nReflection:"],
+    allowed_tools=tools,
+)
+agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
+
+call = agent_chain.run(
+    "I have a cake that is about 3 centimenters high and 200 centimeters in radius. How much cake do I have?"
+)
+call = agent_chain.run("What is 1+10?")
+print(exec(call))

non_langchain_example.py

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+from typing import Literal
+
+import math
+
+import inspect
+
+from transformers import pipeline
+
+
+##########################################################
+# Step 1: Define the functions you want to articulate. ###
+##########################################################
+
+
+def calculator(
+    input_a: float,
+    input_b: float,
+    operation: Literal["add", "subtract", "multiply", "divide"],
+):
+    """
+    Computes a calculation.
+
+    Args:
+    input_a (float) : Required. The first input.
+    input_b (float) : Required. The second input.
+    operation (string): The operation. Choices include: add to add two numbers, subtract to subtract two numbers, multiply to multiply two numbers, and divide to divide them.
+    """
+    match operation:
+        case "add":
+            return input_a + input_b
+        case "subtract":
+            return input_a - input_b
+        case "multiply":
+            return input_a * input_b
+        case "divide":
+            return input_a / input_b
+
+
+def cylinder_volume(radius, height):
+    """
+    Calculate the volume of a cylinder.
+
+    Parameters:
+    - radius (float): The radius of the base of the cylinder.
+    - height (float): The height of the cylinder.
+
+    Returns:
+    - float: The volume of the cylinder.
+    """
+    if radius < 0 or height < 0:
+        raise ValueError("Radius and height must be non-negative.")
+
+    volume = math.pi * (radius**2) * height
+    return volume
+
+
+#############################################################
+# Step 2: Let's define some utils for building the prompt ###
+#############################################################
+
+
+def format_functions_for_prompt(*functions):
+    formatted_functions = []
+    for func in functions:
+        source_code = inspect.getsource(func)
+        docstring = inspect.getdoc(func)
+        formatted_functions.append(
+            f"OPTION:\n<func_start>{source_code}<func_end>\n<docstring_start>\n{docstring}\n<docstring_end>"
+        )
+    return "\n".join(formatted_functions)
+
+
+##############################
+# Step 3: Construct Prompt ###
+##############################
+
+
+def construct_prompt(user_query: str):
+    formatted_prompt = format_functions_for_prompt(calculator, cylinder_volume)
+    formatted_prompt += f"\n\nUser Query: Question: {user_query}\n"
+
+    prompt = (
+        "<human>:\n"
+        + formatted_prompt
+        + "Please pick a function from the above options that best answers the user query and fill in the appropriate arguments.<human_end>"
+    )
+    return prompt
+
+
+#######################################
+# Step 4: Execute the function call ###
+#######################################
+
+
+def execute_function_call(model_output):
+    # Ignore everything after "Reflection" since that is not essential.
+    function_call = (
+        model_output[0]["generated_text"]
+        .strip()
+        .split("\n")[1]
+        .replace("Initial Answer:", "")
+        .strip()
+    )
+
+    try:
+        return eval(function_call)
+    except Exception as e:
+        return str(e)
+
+
+if __name__ == "__main__":
+    # Build the model
+    text_gen = pipeline(
+        "text-generation",
+        model="Nexusflow/NexusRaven-13B",
+        device="cuda",
+    )
+
+    # Comp[ute a Simple equation
+    prompt = construct_prompt("What is 1+10?")
+    model_output = text_gen(
+        prompt, do_sample=False, max_new_tokens=400, return_full_text=False
+    )
+    result = execute_function_call(model_output)
+
+    print("Model Output:", model_output)
+    print("Execution Result:", result)
+
+    prompt = construct_prompt(
+        "I have a cake that is about 3 centimenters high and 200 centimeters in diameter. How much cake do I have?"
+    )
+    model_output = text_gen(
+        prompt,
+        do_sample=False,
+        max_new_tokens=400,
+        return_full_text=False,
+        stop=["\nReflection:"],
+    )
+    result = execute_function_call(model_output)
+
+    print("Model Output:", model_output)
+    print("Execution Result:", result)