Sure, here's a condensed version that includes the code examples:
To use Claude and OpenAI API with LangChain, you can follow these steps:

Set up the environment:

pythonCopyimport os

# Set the required environment variables
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "<your-langchain-api-key>"
os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"

Define the tools:

pythonCopyfrom langchain_community.tools.tavily_search import TavilySearchResults
from langchain.tools.retriever import create_retriever_tool
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

# Tavily search tool
search = TavilySearchResults()

# Retriever tool
loader = WebBaseLoader("https://docs.smith.langchain.com/overview")
docs = loader.load()
documents = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200).split_documents(docs)
vector = FAISS.from_documents(documents, OpenAIEmbeddings())
retriever = vector.as_retriever()
retriever_tool = create_retriever_tool(retriever, "langsmith_search", "Search for information about LangSmith.")

tools = [search, retriever_tool]

Create the agent:

pythonCopyfrom langchain_openai import ChatOpenAI
from langchain import hub

# Load the prompt
prompt = hub.pull("hwchase17/openai-functions-agent")

# Create the agent
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

Run the agent:

pythonCopyagent_executor.invoke({"input": "hi!"})
agent_executor.invoke({"input": "how can langsmith help with testing?"})
agent_executor.invoke({"input": "whats the weather in sf?"})
This code creates an agent that can use the Tavily search tool and a retriever tool to search for information and answer queries. The agent can also remember previous interactions by using the chat_history parameter.
To access files and modify them, you can use the window.fs.readFile API provided by LangChain. Here's an example:
pythonCopy# Read a CSV file
response = await window.fs.readFile('monthly_profits.csv')
text = new TextDecoder().decode(response)

# Process the CSV data
import Papa from 'papaparse'
parsedData = Papa.parse(text, {
    header: true,
    dynamicTyping: true,
    skipEmptyLines: true
})
This allows you to read the contents of CSV, XLSX, or other file formats, and then process the data as needed within your agent or other LangChain components.
Remember to handle any errors that may occur when reading or processing files, and to use the appropriate libraries and techniques for working with different file formats.

Creating Tools in LangChain
When constructing an agent in LangChain, you need to provide it with a list of tools that it can use. The tool consists of several components:

name: A unique name within the set of tools.
description: Describes what the tool does, used as context.
args_schema: Optional Pydantic schema for tool parameters.
return_direct: Whether the agent should stop and return the result directly.

LangChain supports creating tools from:

Functions
LangChain Runnables
Subclassing BaseTool

Creating Tools from Functions
The @tool decorator is the simplest way to define a custom tool. It uses the function name as the tool name by default, and the function's docstring as the description.
pythonCopyfrom langchain_core.tools import tool

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b
The StructuredTool.from_function class method provides more configurability, allowing you to specify the name, description, args schema, and sync/async implementations.
Creating Tools from Runnables
LangChain Runnables that accept string or dict input can be converted to tools using the as_tool method, which allows you to specify names, descriptions, and additional schema information.
Creating Tools by Subclassing BaseTool
Subclassing BaseTool provides the most control, but requires more code. You can specify the tool's name, description, args schema, and implement the _run and _arun methods.
Handling Async Tools
LangChain provides an async implementation that assumes the function is expensive to compute, delegating execution to another thread. If you're working in an async codebase, you should create async tools to avoid the overhead.
Handling Tool Errors
To handle errors in tools, you can raise a ToolException and set the handle_tool_error parameter on the tool. This allows the agent to recover from the error and continue execution.
Returning Artifacts from Tools
Sometimes, tools may generate artifacts (e.g., custom objects) that you want to make accessible to downstream components, without exposing the raw output to the model. You can do this by returning a tuple of (content, artifact) and setting response_format="content_and_artifact" on the tool. CopyRetryBLall code examplesEditHere are the code examples from the condensed version:
Creating Tools from Functions:
pythonCopyfrom langchain_core.tools import tool

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b
Using StructuredTool.from_function:
pythonCopyfrom langchain_core.tools import StructuredTool

def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

async def amultiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

calculator = StructuredTool.from_function(func=multiply, coroutine=amultiply)
Creating Tools from Runnables:
pythonCopyfrom langchain_core.language_models import GenericFakeChatModel
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [("human", "Hello. Please respond in the style of {answer_style}.")]
)

llm = GenericFakeChatModel(messages=iter(["hello matey"]))
chain = prompt | llm | StrOutputParser()

as_tool = chain.as_tool(
    name="Style responder", description="Description of when to use tool."
)
Subclassing BaseTool:
pythonCopyfrom typing import Optional, Type
from langchain_core.callbacks import (
    AsyncCallbackManagerForToolRun, CallbackManagerForToolRun
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field

class CalculatorInput(BaseModel):
    a: int = Field(description="first number")
    b: int = Field(description="second number")

class CustomCalculatorTool(BaseTool):
    name: str = "Calculator"
    description: str = "useful for when you need to answer questions about math"
    args_schema: Type[BaseModel] = CalculatorInput
    return_direct: bool = True

    def _run(self, a: int, b: int, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
        return a * b

    async def _arun(self, a: int, b: int, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
        return self._run(a, b, run_manager=run_manager.get_sync())

multiply = CustomCalculatorTool()
Handling Tool Errors:
pythonCopyfrom langchain_core.tools import ToolException

def get_weather(city: str) -> int:
    raise ToolException(f"Error: There is no city by the name of {city}.")

get_weather_tool = StructuredTool.from_function(
    func=get_weather,
    handle_tool_error=True,
)
Returning Artifacts from Tools:
pythonCopyimport random
from typing import List, Tuple
from langchain_core.tools import tool

@tool(response_format="content_and_artifact")
def generate_random_ints(min: int, max: int, size: int) -> Tuple[str, List[int]]:
    array = [random.randint(min, max) for _ in range(size)]
    content = f"Successfully generated array of {size} random ints in [{min}, {max}]."
    return content, array



now ok with all this information, i need you to create full code so that i can use claude and open ai api with langchain so that it have the ability to run the program and see its output, error and access the file directory that the program is in (so it can modify other files if the program is complex) and access to files (and understand its content) such as xslx, mat, csv, etc..