Initial commit

Author: seldo

.gitignore

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+.env
+.DS_Store

1_basic_agent.py

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+from dotenv import load_dotenv
+load_dotenv()
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.openai import OpenAI
+from llama_index.core.tools import FunctionTool
+
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers and returns the product"""
+    return a * b
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+
+def add(a: float, b: float) -> float:
+    """Add two numbers and returns the sum"""
+    return a + b
+
+add_tool = FunctionTool.from_defaults(fn=add)
+
+llm = OpenAI(model="gpt-3.5-turbo",temperature=0)
+agent = ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True)
+
+response = agent.chat("What is 20+(2*4)? Use a tool to calculate every step.")
+
+print(response)

2_local_agent.py

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+from dotenv import load_dotenv
+load_dotenv()
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.ollama import Ollama
+from llama_index.core.tools import FunctionTool
+
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers and returns the product"""
+    return a * b
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+
+def add(a: float, b: float) -> float:
+    """Add two numbers and returns the sum"""
+    return a + b
+
+add_tool = FunctionTool.from_defaults(fn=add)
+
+llm = Ollama(model="mixtral:8x7b", request_timeout=120.0)
+agent = ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True)
+
+response = agent.chat("What is 20+(2*4)? Calculate step by step.")
+
+print(response)

3_rag_agent.py

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+from dotenv import load_dotenv
+load_dotenv()
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.openai import OpenAI
+from llama_index.core.tools import FunctionTool
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
+from llama_index.core.tools import QueryEngineTool
+
+# settings
+Settings.llm = OpenAI(model="gpt-3.5-turbo",temperature=0)
+
+# function tools
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers and returns the product"""
+    return a * b
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+
+def add(a: float, b: float) -> float:
+    """Add two numbers and returns the sum"""
+    return a + b
+
+add_tool = FunctionTool.from_defaults(fn=add)
+
+# rag pipeline
+documents = SimpleDirectoryReader("./data").load_data()
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+
+# response = query_engine.query("What was the total amount of the 2023 Canadian federal budget?")
+# print(response)
+
+# rag pipeline as a tool
+budget_tool = QueryEngineTool.from_defaults(
+    query_engine, 
+    name="canadian_budget_2023",
+    description="A RAG engine with some basic facts about the 2023 Canadian federal budget."
+)
+
+agent = ReActAgent.from_tools([multiply_tool, add_tool, budget_tool], verbose=True)
+
+response = agent.chat("What is the total amount of the 2023 Canadian federal budget multiplied by 3? Go step by step, using a tool to do any math.")
+
+print(response)

4_llamaparse.py

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+from dotenv import load_dotenv
+load_dotenv()
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.openai import OpenAI
+from llama_index.core.tools import FunctionTool
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
+from llama_parse import LlamaParse
+
+# settings
+Settings.llm = OpenAI(model="gpt-3.5-turbo",temperature=0)
+
+# function tools
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers and returns the product"""
+    return a * b
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+
+def add(a: float, b: float) -> float:
+    """Add two numbers and returns the sum"""
+    return a + b
+
+add_tool = FunctionTool.from_defaults(fn=add)
+
+# rag pipeline
+documents = SimpleDirectoryReader("./data").load_data()
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+
+response = query_engine.query("How much exactly was allocated to a tax credit to promote investment in green technologies in the 2023 Canadian federal budget?")
+print(response)
+
+documents2 = LlamaParse(result_type="markdown").load_data("./data/2023_canadian_budget.pdf")
+index2 = VectorStoreIndex.from_documents(documents2)
+query_engine2 = index2.as_query_engine()
+
+response2 = query_engine2.query("How much exactly was allocated to a tax credit to promote investment in green technologies in the 2023 Canadian federal budget?")
+print(response2)

5_memory.py

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+from dotenv import load_dotenv
+load_dotenv()
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.openai import OpenAI
+from llama_index.core.tools import FunctionTool
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
+from llama_parse import LlamaParse
+from llama_index.core.tools import QueryEngineTool
+
+# settings
+Settings.llm = OpenAI(model="gpt-3.5-turbo",temperature=0)
+
+# function tools
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers and returns the product"""
+    return a * b
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+
+def add(a: float, b: float) -> float:
+    """Add two numbers and returns the sum"""
+    return a + b
+
+add_tool = FunctionTool.from_defaults(fn=add)
+
+# rag pipeline
+documents = LlamaParse(result_type="markdown").load_data("./data/2023_canadian_budget.pdf")
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+
+budget_tool = QueryEngineTool.from_defaults(
+    query_engine, 
+    name="canadian_budget_2023",
+    description="A RAG engine with some basic facts about the 2023 Canadian federal budget. Ask natural-language questions about the budget."
+)
+
+agent = ReActAgent.from_tools([multiply_tool, add_tool, budget_tool], verbose=True)
+
+response = agent.chat("How much exactly was allocated to a tax credit to promote investment in green technologies in the 2023 Canadian federal budget?")
+
+print(response)
+
+response = agent.chat("How much was allocated to a implement a means-tested dental care program in the 2023 Canadian federal budget?")
+
+print(response)
+
+response = agent.chat("How much was the total of those two allocations added together? Use a tool to answer any questions.")
+
+print(response)

6_tools.py

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+from dotenv import load_dotenv
+load_dotenv()
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.openai import OpenAI
+from llama_index.core.tools import FunctionTool
+from llama_index.core import Settings
+from llama_index.tools.yahoo_finance import YahooFinanceToolSpec
+
+# settings
+Settings.llm = OpenAI(model="gpt-4o",temperature=0)
+
+# function tools
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers and returns the product"""
+    return a * b
+
+multiply_tool = FunctionTool.from_defaults(fn=multiply)
+
+def add(a: float, b: float) -> float:
+    """Add two numbers and returns the sum"""
+    return a + b
+
+add_tool = FunctionTool.from_defaults(fn=add)
+
+finance_tools = YahooFinanceToolSpec().to_tool_list()
+finance_tools.extend([multiply_tool, add_tool])
+
+agent = ReActAgent.from_tools(finance_tools, verbose=True)
+
+response = agent.chat("What is the current price of NVDA?")
+
+print(response)

data/2023_canadian_budget.pdf

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+[tool.poetry]
+name = "agents-tutorial"
+version = "0.1.0"
+description = ""
+authors = ["Laurie Voss <[email protected]>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"