Tutorial de Langgraph: una guía paso a paso para crear una tubería de análisis de texto

# Install required packages
!pip install langgraph langchain langchain-openai python-dotenv

import os
from dotenv import load_dotenv

# Load environment variables from .env file (create this with your API key)
load_dotenv()

# Set OpenAI API key
os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')

from langchain_openai import ChatOpenAI

# Initialize the ChatOpenAI instance
llm = ChatOpenAI(model="gpt-4o-mini")

# Test the setup
response = llm.invoke("Hello! Are you working?")
print(response.content)

import os
from typing import TypedDict, List, Annotated
from langgraph.graph import StateGraph, END
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage
from langchain_core.runnables.graph import MermaidDrawMethod
from IPython.display import display, Image

class State(TypedDict):
    text: str
    classification: str
    entities: List[str]
    summary: str

# Initialize our language model with temperature=0 for more deterministic outputs
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

def classification_node(state: State):
    '''Classify the text into one of the categories: News, Blog, Research, or Other'''
    prompt = PromptTemplate(
        input_variables=["text"],
        template="Classify the following text into one of the categories: News, Blog, Research, or Other.\n\nText:{text}\n\nCategory:"
    )
    message = HumanMessage(content=prompt.format(text=state["text"]))
    classification = llm.invoke([message]).content.strip()
    return {"classification": classification}

def entity_extraction_node(state: State):
    '''Extract all the entities (Person, Organization, Location) from the text'''
    prompt = PromptTemplate(
        input_variables=["text"],
        template="Extract all the entities (Person, Organization, Location) from the following text. Provide the result as a comma-separated list.\n\nText:{text}\n\nEntities:"
    )
    message = HumanMessage(content=prompt.format(text=state["text"]))
    entities = llm.invoke([message]).content.strip().split(", ")
    return {"entities": entities}

def summarization_node(state: State):
    '''Summarize the text in one short sentence'''
    prompt = PromptTemplate(
        input_variables=["text"],
        template="Summarize the following text in one short sentence.\n\nText:{text}\n\nSummary:"
    )
    message = HumanMessage(content=prompt.format(text=state["text"]))
    summary = llm.invoke([message]).content.strip()
    return {"summary": summary}

# Create our StateGraph
workflow = StateGraph(State)

# Add nodes to the graph
workflow.add_node("classification_node", classification_node)
workflow.add_node("entity_extraction", entity_extraction_node)
workflow.add_node("summarization", summarization_node)

# Add edges to the graph
workflow.set_entry_point("classification_node")  # Set the entry point of the graph
workflow.add_edge("classification_node", "entity_extraction")
workflow.add_edge("entity_extraction", "summarization")
workflow.add_edge("summarization", END)

# Compile the graph
app = workflow.compile()

sample_text = """ OpenAI has announced the GPT-4 model, which is a large multimodal model that exhibits human-level performance on various professional benchmarks. It is developed to improve the alignment and safety of AI systems. Additionally, the model is designed to be more efficient and scalable than its predecessor, GPT-3. The GPT-4 model is expected to be released in the coming months and will be available to the public for research and development purposes. """ 
state_input = {"text": sample_text} 
result = app.invoke(state_input) 
print("Classification:", result["classification"]) 
print("\nEntities:", result["entities"]) 
print("\nSummary:", result["summary"])
Classification: News Entities: ['OpenAI', 'GPT-4', 'GPT-3'] Summary: OpenAI's upcoming GPT-4 model is a multimodal AI that aims for human-level performance and improved safety, efficiency, and scalability compared to GPT-3.

# Replace this with your own text to analyze your_text = """ The recent advancements in quantum computing have opened new possibilities for cryptography and data security. Researchers at MIT and Google have demonstrated quantum algorithms that could potentially break current encryption methods. However, they are also developing new quantum-resistant encryption techniques to protect data in the future. """ 

# Process the text through our pipeline your_result = app.invoke({"text": your_text}) print("Classification:", your_result["classification"]) 

print("\nEntities:", your_result["entities"]) 
print("\nSummary:", your_result["summary"])

Classification: Research Entities: ['MIT', 'Google'] Summary: Recent advancements in quantum computing may threaten current encryption methods while also prompting the development of new quantum-resistant techniques.

# First, let's update our State to include sentiment
class EnhancedState(TypedDict):
    text: str
    classification: str
    entities: List[str]
    summary: str
    sentiment: str

# Create our sentiment analysis node
def sentiment_node(state: EnhancedState):
    '''Analyze the sentiment of the text: Positive, Negative, or Neutral'''
    prompt = PromptTemplate(
        input_variables=["text"],
        template="Analyze the sentiment of the following text. Is it Positive, Negative, or Neutral?\n\nText:{text}\n\nSentiment:"
    )
    message = HumanMessage(content=prompt.format(text=state["text"]))
    sentiment = llm.invoke([message]).content.strip()
    return {"sentiment": sentiment}

# Create a new workflow with the enhanced state
enhanced_workflow = StateGraph(EnhancedState)

# Add the existing nodes
enhanced_workflow.add_node("classification_node", classification_node)
enhanced_workflow.add_node("entity_extraction", entity_extraction_node)
enhanced_workflow.add_node("summarization", summarization_node)

# Add our new sentiment node
enhanced_workflow.add_node("sentiment_analysis", sentiment_node)

# Create a more complex workflow with branches
enhanced_workflow.set_entry_point("classification_node")
enhanced_workflow.add_edge("classification_node", "entity_extraction")
enhanced_workflow.add_edge("entity_extraction", "summarization")
enhanced_workflow.add_edge("summarization", "sentiment_analysis")
enhanced_workflow.add_edge("sentiment_analysis", END)

# Compile the enhanced graph
enhanced_app = enhanced_workflow.compile()

# Try the enhanced pipeline with the same text
enhanced_result = enhanced_app.invoke({"text": sample_text})

print("Classification:", enhanced_result["classification"])
print("\nEntities:", enhanced_result["entities"])
print("\nSummary:", enhanced_result["summary"])
print("\nSentiment:", enhanced_result["sentiment"])

# Route after classification
def route_after_classification(state: EnhancedState) -> str:
    category = state["classification"].lower() # returns: "news", "blog", "research", "other"
    return category in ["news", "research"]

from langgraph.graph import StateGraph, END

conditional_workflow = StateGraph(EnhancedState)

# Add nodes
conditional_workflow.add_node("classification_node", classification_node)
conditional_workflow.add_node("entity_extraction", entity_extraction_node)
conditional_workflow.add_node("summarization", summarization_node)
conditional_workflow.add_node("sentiment_analysis", sentiment_node)

# Set entry point
conditional_workflow.set_entry_point("classification_node")

# Add conditional edge
conditional_workflow.add_conditional_edges("classification_node", route_after_classification, path_map={
    True: "entity_extraction",
    False: "summarization"
})

# Add remaining static edges
conditional_workflow.add_edge("entity_extraction", "summarization")
conditional_workflow.add_edge("summarization", "sentiment_analysis")
conditional_workflow.add_edge("sentiment_analysis", END)

# Compile
conditional_app = conditional_workflow.compile()

test_text = """
OpenAI released the GPT-4 model with enhanced performance on academic and professional tasks. It's seen as a major breakthrough in alignment and reasoning capabilities.
"""

result = conditional_app.invoke({"text": test_text})

print("Classification:", result["classification"])
print("Entities:", result.get("entities", "Skipped"))
print("Summary:", result["summary"])
print("Sentiment:", result["sentiment"])

blog_text = """
Here's what I learned from a week of meditating in silence. No phones, no talking—just me, my breath, and some deep realizations.
"""

result = conditional_app.invoke({"text": blog_text})

print("Classification:", result["classification"])
print("Entities:", result.get("entities", "Skipped (not applicable)"))
print("Summary:", result["summary"])
print("Sentiment:", result["sentiment"])