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Commit b0b169b

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‎app.py

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import os
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import time
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from langchain_groq import ChatGroq
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.pydantic_v1 import BaseModel, Field
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from langgraph.graph.message import AnyMessage, add_messages
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from langgraph.graph import END, StateGraph
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from langgraph.checkpoint.sqlite import SqliteSaver
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from typing import Annotated, Dict, TypedDict, List
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from operator import itemgetter
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from langchain_core.runnables import RunnablePassthrough
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from langchain_core.prompts import PromptTemplate
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from IPython.display import Image, display
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import uuid
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# Set environment variables
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os.environ['TOKENIZERS_PARALLELISM'] = 'true'
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# mistral_api_key = os.getenv("MISTRAL_API_KEY") # Ensure this is set
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# Set up the LLM
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llm = ChatGroq(temperature=0, groq_api_key="groq_api", model_name="llama3-8b-8192")
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# Define the prompt template
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code_gen_prompt_claude = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"""You are a coding assistant. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer: 1) a prefix describing the code solution, 2) the imports, 3) the functioning code block.
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\n Here is the user question:""",
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),
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("placeholder", "{messages}"),
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]
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)
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# Define the data model
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class code(BaseModel):
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"""Code output"""
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prefix: str = Field(description="Description of the problem and approach")
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imports: str = Field(description="Code block import statements")
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code: str = Field(description="Code block not including import statements")
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description = "Schema for code solutions to questions about LCEL."
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# Set up the structured output
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code_gen_chain = llm.with_structured_output(code, include_raw=False)
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# Define the graph state
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class GraphState(TypedDict):
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"""
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Represents the state of our graph.
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Attributes:
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error : Binary flag for control flow to indicate whether test error was tripped
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messages : With user question, error messages, reasoning
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generation : Code solution
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iterations : Number of tries
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"""
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error: str
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messages: Annotated[list[AnyMessage], add_messages]
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generation: str
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iterations: int
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# Define the nodes
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def generate(state: GraphState):
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"""
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Generate a code solution
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Args:
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state (dict): The current graph state
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Returns:
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state (dict): New key added to state, generation
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"""
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print("---GENERATING CODE SOLUTION---")
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# State
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messages = state["messages"]
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iterations = state["iterations"]
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error = state["error"]
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# Solution
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code_solution = code_gen_chain.invoke(messages)
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messages += [
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(
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"assistant",
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f"Here is my attempt to solve the problem: {code_solution.prefix} \n Imports: {code_solution.imports} \n Code: {code_solution.code}",
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)
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]
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# Increment
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iterations = iterations + 1
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# Add delay to reduce API requests
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time.sleep(1) # Wait for 1 second
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return {"generation": code_solution, "messages": messages, "iterations": iterations}
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def code_check(state: GraphState):
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"""
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Check code
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Args:
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state (dict): The current graph state
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Returns:
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state (dict): New key added to state, error
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"""
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print("---CHECKING CODE---")
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# State
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messages = state["messages"]
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code_solution = state["generation"]
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iterations = state["iterations"]
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# Get solution components
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prefix = code_solution.prefix
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imports = code_solution.imports
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code = code_solution.code
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# Check imports
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try:
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exec(imports)
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except Exception as e:
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print("---CODE IMPORT CHECK: FAILED---")
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error_message = [("user", f"Your solution failed the import test. Here is the error: {e}. Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:")]
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messages += error_message
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return {
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"generation": code_solution,
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"messages": messages,
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"iterations": iterations,
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"error": "yes",
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}
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# Check execution
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try:
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combined_code = f"{imports}\n{code}"
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# Use a shared scope for exec
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global_scope = {}
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exec(combined_code, global_scope)
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except Exception as e:
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print("---CODE BLOCK CHECK: FAILED---")
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error_message = [("user", f"Your solution failed the code execution test: {e}) Reflect on this error and your prior attempt to solve the problem. (1) State what you think went wrong with the prior solution and (2) try to solve this problem again. Return the FULL SOLUTION. Use the code tool to structure the output with a prefix, imports, and code block:")]
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messages += error_message
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return {
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"generation": code_solution,
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"messages": messages,
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"iterations": iterations,
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"error": "yes",
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}
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# No errors
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print("---NO CODE TEST FAILURES---")
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return {
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"generation": code_solution,
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"messages": messages,
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"iterations": iterations,
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"error": "no",
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}
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def decide_to_finish(state: GraphState):
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"""
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Determines whether to finish.
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Args:
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state (dict): The current graph state
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Returns:
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str: Next node to call
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"""
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error = state["error"]
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iterations = state["iterations"]
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if error == "no" or iterations == max_iterations:
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print("---DECISION: FINISH---")
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return "end"
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else:
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print("---DECISION: RE-TRY SOLUTION---")
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return "generate"
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# Define the graph
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builder = StateGraph(GraphState)
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# Add nodes
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builder.add_node("generate", generate) # generation solution
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builder.add_node("check_code", code_check) # check code
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# Build graph
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builder.set_entry_point("generate")
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builder.add_edge("generate", "check_code")
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builder.add_conditional_edges(
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"check_code",
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decide_to_finish,
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{
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"end": END,
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"generate": "generate",
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},
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)
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# Compile the graph
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memory = SqliteSaver.from_conn_string(":memory:")
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graph = builder.compile(checkpointer=memory)
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# Display the graph
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try:
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display(Image(graph.get_graph(xray=True).draw_mermaid_png()))
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except:
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pass
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# Run the graph
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_printed = set()
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thread_id = str(uuid.uuid4())
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config = {
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"configurable": {
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# Checkpoints are accessed by thread_id
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"thread_id": thread_id,
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}
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}
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# Ask user for input
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question = input("Enter your question or search query: ")
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# Run the graph
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max_iterations = 5 # Define the maximum number of iterations
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events = graph.stream(
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{"messages": [("user", question)], "iterations": 0}, config, stream_mode="values"
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)
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def _print_event(event, _printed):
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if str(event) not in _printed:
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print(event)
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_printed.add(str(event))
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for event in events:
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_print_event(event, _printed)
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# Output the final result
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print("Final Result:")
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print(event['generation'])

‎main.py

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# from langchain.agents import initialize_agent
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# from langchain_experimental.utilities import PythonREPL
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# from langchain_community.tools.ddg_search.tool import DuckDuckGoSearchRun
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# from langchain_core.tools import Tool
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# from langchain_groq import ChatGroq
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# # Create the Python REPL tool
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# python_repl = PythonREPL()
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# python_repl_tool = Tool(
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# name="python_repl",
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# description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
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# func=python_repl.run,
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# )
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# # Create the DuckDuckGo search tool
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# duckduckgo_search = DuckDuckGoSearchRun()
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# duckduckgo_search_tool = Tool(
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# name="duckduckgo_search",
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# description="A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.",
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# func=duckduckgo_search.run,
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# )
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# # Create the list of tools
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# tools = [python_repl_tool, duckduckgo_search_tool]
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# # Initialize the LLM
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# llm = ChatGroq(temperature=0, groq_api_key="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", model_name="llama3-70b-8192")
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# # Initialize the agent
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# agent = initialize_agent(tools, llm, agent_type="zero-shot-react-description")
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# # Run the agent
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# while True:
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# user_input = input("Enter a command or search query (or 'quit' to stop): ")
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# if user_input.lower() == 'quit':
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# break
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# result = agent.run(user_input)
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# print(result)
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from langchain.agents import initialize_agent
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from langchain_experimental.utilities import PythonREPL
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from langchain_community.tools.ddg_search.tool import DuckDuckGoSearchRun
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from langchain_core.tools import Tool
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from langchain_groq import ChatGroq
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from langchain_community.agent_toolkits import FileManagementToolkit
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from tempfile import TemporaryDirectory
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# Create the temporary directory for file operations
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working_directory = TemporaryDirectory()
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# Create the file management toolkit
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file_management_toolkit = FileManagementToolkit(root_dir=str(working_directory.name))
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file_tools = file_management_toolkit.get_tools()
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# Wrap the file operation tools with Tool class
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file_operations_tools = []
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for tool in file_tools:
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file_operations_tools.append(
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Tool(
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name=tool.__class__.__name__,
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description=tool.__doc__,
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func=tool.invoke,
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)
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)
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# Create the Python REPL tool
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python_repl = PythonREPL()
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python_repl_tool = Tool(
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name="python_repl",
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description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
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func=python_repl.run,
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)
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# Create the DuckDuckGo search tool
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duckduckgo_search = DuckDuckGoSearchRun()
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duckduckgo_search_tool = Tool(
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name="duckduckgo_search",
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description="A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events and internet Search related queries. Input should be a search query.",
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func=duckduckgo_search.run,
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)
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# Create the list of tools
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tools = file_operations_tools + [python_repl_tool, duckduckgo_search_tool]
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# Initialize the LLM
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llm = ChatGroq(temperature=0, groq_api_key="groq_api", model_name="llama3-70b-8192")
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# Initialize the agent
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agent = initialize_agent(tools, llm, agent_type="zero-shot-react-description")
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# Run the agent
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while True:
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user_input = input("Enter a command or search query (or 'quit' to stop): ")
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if user_input.lower() == 'quit':
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break
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result = agent.run(user_input)
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print(result)

‎requirements.txt

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langchain
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numpy
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pandas
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tiktoken
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langchain-mistralai
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duckduckgo-search
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langchain_community
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langgraph
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Ipython
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DDGS

‎t.py

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from langchain.agents import initialize_agent,create_structured_chat_agent
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from langchain_experimental.utilities import PythonREPL
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from langchain_community.tools.ddg_search.tool import DuckDuckGoSearchRun
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from langchain_core.tools import Tool
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from langchain_groq import ChatGroq
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# Create the Python REPL tool
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python_repl = PythonREPL()
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python_repl_tool = Tool(
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name="python_repl",
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description="A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
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func=python_repl.run,
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)
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# Create the DuckDuckGo search tool
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duckduckgo_search = DuckDuckGoSearchRun()
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duckduckgo_search_tool = Tool(
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name="duckduckgo_search",
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description="A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.",
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func=duckduckgo_search.run,
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)
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# Create the list of tools
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tools = [python_repl_tool, duckduckgo_search_tool]
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# Initialize the LLM
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llm = ChatGroq(temperature=0, groq_api_key="groq_api", model_name="llama3-70b-8192")
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# Initialize the agent
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agent = initialize_agent(tools, llm, agent_type="zero-shot-react-description")
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# Run the agent
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while True:
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user_input = input("Enter a command or search query (or 'quit' to stop): ")
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if user_input.lower() == 'quit':
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break
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result = agent.run(user_input)
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print(result)

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