LangGraph (1/4): chatbot básico y herramientas

pip install -U langgraph

conda install langgraph

pip install langchain-huggingface

pip install langchain_anthropic

HUGGINGFACE_LANGGRAPH="hf_...."

pip install python-dotenv

	

		

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import os
import dotenv
 
dotenv.load_dotenv()
 
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
	
	
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pip install --upgrade huggingface_hub

conda install -c conda-forge huggingface_hub

	

		

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MODEL = "Qwen/Qwen2.5-72B-Instruct"
	
	
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from huggingface_hub import InferenceClient
 
client = InferenceClient(api_key=HUGGINGFACE_TOKEN, model=MODEL)
client
	
	
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<InferenceClient(model='Qwen/Qwen2.5-72B-Instruct', timeout=None)>
			
		

	

		

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message = [
	{ "role": "user", "content": "Hola, qué tal?" }
]
 
stream = client.chat.completions.create(
	messages=message,
	temperature=0.5,
	max_tokens=1024,
	top_p=0.7,
	stream=False
)
 
response = stream.choices[0].message.content
print(response)
	
	
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¡Hola! Estoy bien, gracias por preguntar. ¿Cómo estás tú? ¿En qué puedo ayudarte hoy?
			
		

	

		

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from typing import Annotated
 
from typing_extensions import TypedDict
 
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
 
class State(TypedDict):
    # Messages have the type "list". The `add_messages` function
    # in the annotation defines how this state key should be updated
    # (in this case, it appends messages to the list, rather than overwriting them)
    messages: Annotated[list, add_messages]
 
graph_builder = StateGraph(State)
	
	
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from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from huggingface_hub import login
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Define the chatbot function
def chatbot_function(state: State):
    return {"messages": [llm.invoke(state["messages"])]}
 
# The first argument is the unique node name
# The second argument is the function or object that will be called whenever
# the node is used.
graph_builder.add_node("chatbot_node", chatbot_function)
	
	
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<langgraph.graph.state.StateGraph at 0x130548440>
			
		

	

		

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from langgraph.graph import START
 
graph_builder.add_edge(START, "chatbot_node")
	
	
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<langgraph.graph.state.StateGraph at 0x130548440>
			
		

	

		

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from langgraph.graph import END
 
graph_builder.add_edge("chatbot_node", END)
	
	
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<langgraph.graph.state.StateGraph at 0x130548440>
			
		

	

		

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graph = graph_builder.compile()
	
	
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from IPython.display import Image, display
 
try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception as e:
    print(f"Error al visualizar el grafo: {e}")
	
	
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<IPython.core.display.Image object>
			
		

	

		

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# Colors for the terminal
COLOR_GREEN = "\033[32m"
COLOR_YELLOW = "\033[33m"
COLOR_RESET = "\033[0m"
 
def stream_graph_updates(user_input: str):
    for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
        for value in event.values():
            print(f"{COLOR_GREEN}User: {COLOR_RESET}{user_input}")
            print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}{value['messages'][-1].content}")
 
while True:
    try:
        user_input = input("User: ")
        if user_input.lower() in ["quit", "exit", "q"]:
            print(f"{COLOR_GREEN}User: {COLOR_RESET}{user_input}")
            print(f"{COLOR_YELLOW}Assistant: {COLOR_RESET}Goodbye!")
            break
        
        events =stream_graph_updates(user_input)
    except:
        # fallback if input() is not available
        user_input = "What do you know about LangGraph?"
        print("User: " + user_input)
        stream_graph_updates(user_input)
        break
	
	
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User: Hello
Assistant: Hello! It's nice to meet you. How can I assist you today? Whether you have questions, need information, or just want to chat, I'm here to help!
User: How are you doing?
Assistant: I'm just a computer program, so I don't have feelings, but I'm here and ready to help you with any questions or tasks you have! How can I assist you today?
User: Me well, I'm making a post about LangGraph, what do you think?
Assistant: LangGraph is an intriguing topic, especially if you're delving into the realm of graph-based models and their applications in natural language processing (NLP). LangGraph, as I understand, is a framework or tool that leverages graph theory to improve or provide a new perspective on NLP tasks such as text classification, information extraction, and semantic analysis. By representing textual information as graphs (nodes for entities and edges for relationships), it can offer a more nuanced understanding of the context and semantics in language data.
If you're making a post about it, here are a few points you might consider:
1. **Introduction to LangGraph**: Start with a brief explanation of what LangGraph is and its core principles. How does it model language or text differently compared to traditional NLP approaches? What unique advantages does it offer by using graph-based methods?
2. **Applications of LangGraph**: Discuss some of the key applications where LangGraph has been or can be applied. This could include improving the accuracy of sentiment analysis, enhancing machine translation, or optimizing chatbot responses to be more contextually aware.
3. **Technical Innovations**: Highlight any technical innovations or advancements that LangGraph brings to the table. This could be about new algorithms, more efficient data structures, or novel ways of training models on graph data.
4. **Challenges and Limitations**: It's also important to address the challenges and limitations of using graph-based methods in NLP. Performance, scalability, and the current state of the technology can be discussed here.
5. **Future Prospects**: Wrap up with a look into the future of LangGraph and graph-based NLP in general. What are the upcoming trends, potential areas of growth, and how might these tools start impacting broader technology landscapes?
Each section can help frame your post in a way that's informative and engaging for your audience, whether they're technical experts or casual readers looking for an introduction to this intriguing area of NLP.
User: q
Assistant: Goodbye!
			
		

	

		

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from typing import Annotated
from typing_extensions import TypedDict
 
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
 
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from huggingface_hub import login
 
from IPython.display import Image, display
 
import os
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
import dotenv
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
# State
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Function
def chatbot_function(state: State):
    return {"messages": [llm.invoke(state["messages"])]}
 
# Start to build the graph
graph_builder = StateGraph(State)
 
# Add nodes to the graph
graph_builder.add_node("chatbot_node", chatbot_function)
 
# Add edges
graph_builder.add_edge(START, "chatbot_node")
graph_builder.add_edge("chatbot_node", END)
 
# Compile the graph
graph = graph_builder.compile()
 
# Display the graph
try:
    display(Image(graph.get_graph().draw_mermaid_png()))
except Exception as e:
    print(f"Error al visualizar el grafo: {e}")
	
	
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<IPython.core.display.Image object>
			
		

from typing_extensions import TypedDict
from typing import Annotated
from langgraph.graph.message import add_messages
from langgraph.graph import StateGraph

class State(TypedDict):
  messages: Annotated[list, add_messages]

from dataclasses import dataclass
from typing import Annotated
from langgraph.graph.message import add_messages
from langgraph.graph import StateGraph

@dataclass
class State:
  messages: Annotated[list, add_messages]

from pydantic import BaseModel, field_validator, ValidationError
from typing import Annotated
from langgraph.graph.message import add_messages

class State(BaseModel):
  messages: Annotated[list, add_messages] # Should start by 'User', 'Assistant' or 'System'

  @field_validator('messages')
  @classmethod
  def validate_messages(cls, value):
    # Ensure the messages start with `User`, `Assistant` or `System`
    if not value.startswith["'User'"] and not value.startswith["'Assistant'"] and not value.startswith["'System'"]:
      raise ValueError("Message must start with 'User', 'Assistant' or 'System'")        return value

try:
  state = PydanticState(messages=["Hello"])
except ValidationError as e:
  print("Validation Error:", e)

	

		

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from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display
 
class State(TypedDict):
    foo: int
 
def node_1(state):
    print("---Node 1---")
    return {"foo": state['foo'] + 1}
 
def node_2(state):
    print("---Node 2---")
    return {"foo": state['foo'] + 1}
 
def node_3(state):
    print("---Node 3---")
    return {"foo": state['foo'] + 1}
 
# Build graph
builder = StateGraph(State)
builder.add_node("node_1", node_1)
builder.add_node("node_2", node_2)
builder.add_node("node_3", node_3)
 
# Logic
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
builder.add_edge("node_1", "node_3")
builder.add_edge("node_2", END)
builder.add_edge("node_3", END)
 
# Add
graph = builder.compile()
 
# View
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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from langgraph.errors import InvalidUpdateError
 
try:
    graph.invoke({"foo" : 1})
except InvalidUpdateError as e:
    print(f"InvalidUpdateError occurred: {e}")
	
	
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---Node 1---
---Node 2---
---Node 3---
InvalidUpdateError occurred: At key 'foo': Can receive only one value per step. Use an Annotated key to handle multiple values.
For troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/INVALID_CONCURRENT_GRAPH_UPDATE
			
		

	

		

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from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display
from operator import add
from typing import Annotated
 
class State(TypedDict):
    foo: Annotated[list[int], add]
 
def node_1(state):
    print("---Node 1---")
    return {"foo": [state['foo'][-1] + 1]}
 
def node_2(state):
    print("---Node 2---")
    return {"foo": [state['foo'][-1] + 1]}
 
def node_3(state):
    print("---Node 3---")
    return {"foo": [state['foo'][-1] + 1]}
 
# Build graph
builder = StateGraph(State)
builder.add_node("node_1", node_1)
builder.add_node("node_2", node_2)
builder.add_node("node_3", node_3)
 
# Logic
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
builder.add_edge("node_1", "node_3")
builder.add_edge("node_2", END)
builder.add_edge("node_3", END)
 
# Add
graph = builder.compile()
 
# View
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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graph.invoke({"foo" : [1]})
	
	
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---Node 1---
---Node 2---
---Node 3---
			
		

			

				
{'foo': [1, 2, 3, 3]}
			
		

	

		

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from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display
from operator import add
from typing import Annotated
 
class State(TypedDict):
    foo: Annotated[list[int], add]
 
def node_1(state):
    print("---Node 1---")
    return {"foo": [state['foo'][-1] + 1]}
 
def node_2_1(state):
    print("---Node 2_1---")
    return {"foo": [state['foo'][-1] + 1]}
 
def node_2_2(state):
    print("---Node 2_2---")
    return {"foo": [state['foo'][-1] + 1]}
 
def node_3(state):
    print("---Node 3---")
    return {"foo": [state['foo'][-1] + 1]}
 
# Build graph
builder = StateGraph(State)
builder.add_node("node_1", node_1)
builder.add_node("node_2_1", node_2_1)
builder.add_node("node_2_2", node_2_2)
builder.add_node("node_3", node_3)
 
# Logic
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2_1")
builder.add_edge("node_1", "node_3")
builder.add_edge("node_2_1", "node_2_2")
builder.add_edge("node_2_2", END)
builder.add_edge("node_3", END)
 
# Add
graph = builder.compile()
 
# View
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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graph.invoke({"foo" : [1]})
	
	
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---Node 1---
---Node 2_1---
---Node 3---
---Node 2_2---
			
		

			

				
{'foo': [1, 2, 3, 3, 4]}
			
		

	

		

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try:
    graph.invoke({"foo" : None})
except TypeError as e:
    print(f"TypeError occurred: {e}")
	
	
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TypeError occurred: can only concatenate list (not "NoneType") to list
			
		

	

		

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from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display
from typing import Annotated
 
def reducer_function(current_list, new_item: list | None):
    if current_list is None:
        current_list = []
 
    if new_item is not None:
        return current_list + new_item
    return current_list
 
class State(TypedDict):
    foo: Annotated[list[int], reducer_function]
 
def node_1(state):
    print("---Node 1---")
    if len(state['foo']) == 0:
        return {'foo': [0]}
    return {"foo": [state['foo'][-1] + 1]}
 
def node_2(state):
    print("---Node 2---")
    return {"foo": [state['foo'][-1] + 1]}
 
def node_3(state):
    print("---Node 3---")
    return {"foo": [state['foo'][-1] + 1]}
 
# Build graph
builder = StateGraph(State)
builder.add_node("node_1", node_1)
builder.add_node("node_2", node_2)
builder.add_node("node_3", node_3)
 
# Logic
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
builder.add_edge("node_1", "node_3")
builder.add_edge("node_2", END)
builder.add_edge("node_3", END)
 
# Add
graph = builder.compile()
 
# View
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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try:
    graph.invoke({"foo" : None})
except TypeError as e:
    print(f"TypeError occurred: {e}")
	
	
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---Node 1---
---Node 2---
---Node 3---
			
		

	

		

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from typing_extensions import TypedDict
from IPython.display import Image, display
from langgraph.graph import StateGraph, START, END
 
class OverallState(TypedDict):
    public_var: int
 
class PrivateState(TypedDict):
    private_var: int
 
def node_1(state: OverallState) -&gt; PrivateState:
    print("---Node 1---")
    return {"private_var": state['public_var'] + 1}
 
def node_2(state: PrivateState) -&gt; OverallState:
    print("---Node 2---")
    return {"public_var": state['private_var'] + 1}
 
# Build graph
builder = StateGraph(OverallState)
builder.add_node("node_1", node_1)
builder.add_node("node_2", node_2)
 
# Logic
builder.add_edge(START, "node_1")
builder.add_edge("node_1", "node_2")
builder.add_edge("node_2", END)
 
# Add
graph = builder.compile()
 
# View
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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graph.invoke({"public_var" : 1})
	
	
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---Node 1---
---Node 2---
			
		

			

				
{'public_var': 3}
			
		

	

		

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from typing_extensions import TypedDict
from IPython.display import Image, display
from langgraph.graph import StateGraph, START, END
 
class InputState(TypedDict):
    question: str
 
class OutputState(TypedDict):
    answer: str
 
class OverallState(TypedDict):
    question: str
    answer: str
    notes: str
 
def thinking_node(state: InputState):
    return {"answer": "bye", "notes": "... his is name is Lance"}
 
def answer_node(state: OverallState) -&gt; OutputState:
    return {"answer": "bye Lance"}
 
graph = StateGraph(OverallState, input=InputState, output=OutputState)
 
graph.add_node("answer_node", answer_node)
graph.add_node("thinking_node", thinking_node)
graph.add_edge(START, "thinking_node")
graph.add_edge("thinking_node", "answer_node")
graph.add_edge("answer_node", END)
 
graph = graph.compile()
 
# View
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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graph.invoke({"question":"hi"})
	
	
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{'answer': 'bye Lance'}
			
		

	

		

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from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Define the chatbot function
def chatbot_function(state: State):
    return {"messages": [llm.invoke(state["messages"])]}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("chatbot_node", chatbot_function)
 
# Connect nodes
graph_builder.add_edge(START, "chatbot_node")
graph_builder.add_edge("chatbot_node", END)
 
# Compile the graph
graph = graph_builder.compile()
 
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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from langchain_core.messages import AIMessage, HumanMessage
 
messages = [AIMessage(f"So you said you were researching ocean mammals?", name="Bot")]
messages.append(HumanMessage(f"Yes, I know about whales. But what others should I learn about?", name="Lance"))
 
for m in messages:
    m.pretty_print()
	
	
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================================== Ai Message ==================================
Name: Bot
So you said you were researching ocean mammals?
================================ Human Message =================================
Name: Lance
Yes, I know about whales. But what others should I learn about?
			
		

	

		

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output = graph.invoke({'messages': messages})
for m in output['messages']:
    m.pretty_print()
	
	
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================================== Ai Message ==================================
Name: Bot
So you said you were researching ocean mammals?
================================ Human Message =================================
Name: Lance
Yes, I know about whales. But what others should I learn about?
================================== Ai Message ==================================
That's a great topic! Besides whales, there are several other fascinating ocean mammals you might want to learn about. Here are a few:
1. **Dolphins**: Highly intelligent and social, dolphins are found in all oceans of the world. They are known for their playful behavior and communication skills.
2. **Porpoises**: Similar to dolphins but generally smaller and stouter, porpoises are less social and more elusive. They are found in coastal waters around the world.
3. **Seals and Sea Lions**: These are semi-aquatic mammals that can be found in both Arctic and Antarctic regions, as well as in more temperate waters. They are known for their sleek bodies and flippers, and they differ in their ability to walk on land (sea lions can "walk" on their flippers, while seals can only wriggle or slide).
4. **Walruses**: Known for their large tusks and whiskers, walruses are found in the Arctic. They are well-adapted to cold waters and have a thick layer of blubber to keep them warm.
5. **Manatees and Dugongs**: These gentle, herbivorous mammals are often called "sea cows." They live in shallow, coastal areas and are found in tropical and subtropical regions. Manatees are found in the Americas, while dugongs are found in the Indo-Pacific region.
6. **Otters**: While not fully aquatic, sea otters spend most of their lives in the water and are excellent swimmers. They are known for their dense fur, which keeps them warm in cold waters.
7. **Polar Bears**: Although primarily considered land animals, polar bears are excellent swimmers and spend a significant amount of time in the water, especially when hunting for seals.
Each of these mammals has unique adaptations and behaviors that make them incredibly interesting to study. If you have any specific questions or topics you'd like to explore further, feel free to ask!
			
		

	

		

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from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import RemoveMessage
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Nodes
def filter_messages(state: State):
    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"messages": delete_messages}
 
def chat_model_node(state: State):
    return {"messages": [llm.invoke(state["messages"])]}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("filter_messages_node", filter_messages)
graph_builder.add_node("chatbot_node", chat_model_node)
 
# Connecto nodes
graph_builder.add_edge(START, "filter_messages_node")
graph_builder.add_edge("filter_messages_node", "chatbot_node")
graph_builder.add_edge("chatbot_node", END)
 
# Compile the graph
graph = graph_builder.compile()
 
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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<IPython.core.display.Image object>
			
		

	

		

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from langchain_core.messages import AIMessage, HumanMessage
 
messages = [AIMessage(f"So you said you were researching ocean mammals?", name="Bot")]
messages.append(HumanMessage(f"Yes, I know about whales. But what others should I learn about?", name="Lance"))
messages.append(AIMessage(f"I know about sharks too", name="Bot"))
messages.append(HumanMessage(f"What others should I learn about?", name="Lance"))
messages.append(AIMessage(f"I know about dolphins too", name="Bot"))
messages.append(HumanMessage(f"Tell me more about dolphins", name="Lance"))
 
for m in messages:
    m.pretty_print()
	
	
Copied

			

				
================================== Ai Message ==================================
Name: Bot
So you said you were researching ocean mammals?
================================ Human Message =================================
Name: Lance
Yes, I know about whales. But what others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about sharks too
================================ Human Message =================================
Name: Lance
What others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about dolphins too
================================ Human Message =================================
Name: Lance
Tell me more about dolphins
			
		

	

		

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			Input
		
		

			Python
			
				
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output = graph.invoke({'messages': messages})
for m in output['messages']:
    m.pretty_print()
	
	
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================================== Ai Message ==================================
Name: Bot
I know about dolphins too
================================ Human Message =================================
Name: Lance
Tell me more about dolphins
================================== Ai Message ==================================
Dolphins are highly intelligent marine mammals that are part of the family Delphinidae, which includes about 40 species. They are found in oceans worldwide, from tropical to temperate regions, and are known for their agility and playful behavior. Here are some interesting facts about dolphins:
1. **Social Behavior**: Dolphins are highly social animals and often live in groups called pods, which can range from a few individuals to several hundred. Social interactions are complex and include cooperative behaviors, such as hunting and defending against predators.
2. **Communication**: Dolphins communicate using a variety of sounds, including clicks, whistles, and body language. These sounds can be used for navigation (echolocation), communication, and social bonding. Each dolphin has a unique signature whistle that helps identify it to others in the pod.
3. **Intelligence**: Dolphins are considered one of the most intelligent animals on Earth. They have large brains and display behaviors such as problem-solving, mimicry, and even the use of tools. Some studies suggest that dolphins can recognize themselves in mirrors, indicating a level of self-awareness.
4. **Diet**: Dolphins are carnivores and primarily feed on fish and squid. They use echolocation to locate and catch their prey. Some species, like the bottlenose dolphin, have been observed using teamwork to herd fish into tight groups, making them easier to catch.
5. **Reproduction**: Dolphins typically give birth to a single calf after a gestation period of about 10 to 12 months. Calves are born tail-first and are immediately helped to the surface for their first breath by their mother or another dolphin. Calves nurse for up to two years and remain dependent on their mothers for a significant period.
6. **Conservation**: Many dolphin species are threatened by human activities such as pollution, overfishing, and habitat destruction. Some species, like the Indo-Pacific humpback dolphin and the Amazon river dolphin, are endangered. Conservation efforts are crucial to protect these animals and their habitats.
7. **Human Interaction**: Dolphins have a long history of interaction with humans, often appearing in mythology and literature. In some cultures, they are considered sacred or bring good luck. Today, dolphins are popular in marine parks and are often the focus of eco-tourism activities, such as dolphin-watching tours.
Dolphins continue to fascinate scientists and the general public alike, with ongoing research into their behavior, communication, and social structures providing new insights into these remarkable creatures.
			
		

	

		

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from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import trim_messages
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Nodes
def trim_messages_node(state: State):
    # Trim the messages based on the specified parameters
    trimmed_messages = trim_messages(
        state["messages"],
        max_tokens=100, # Maximum tokens allowed in the trimmed list
        strategy="last", # Keep the latest messages
        token_counter=llm, # Use the LLM's tokenizer to count tokens
        allow_partial=True, # Allow cutting messages mid-way if needed
    )
 
    # Print the trimmed messages to see the effect of trim_messages
    print("--- trimmed messages (input to LLM) ---")
    for m in trimmed_messages:
        m.pretty_print()
    print("------------------------------------------------")
 
    # Invoke the LLM with the trimmed messages
    response = llm.invoke(trimmed_messages)
 
    # Return the LLM's response in the correct state format
    return {"messages": [response]}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("trim_messages_node", trim_messages_node)
 
# Connecto nodes
graph_builder.add_edge(START, "trim_messages_node")
graph_builder.add_edge("trim_messages_node", END)
 
# Compile the graph
graph = graph_builder.compile()
 
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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<IPython.core.display.Image object>
			
		

	

		

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			Input
		
		

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from langchain_core.messages import AIMessage, HumanMessage
 
messages = [AIMessage(f"So you said you were researching ocean mammals?", name="Bot")]
messages.append(HumanMessage(f"Yes, I know about whales. But what others should I learn about?", name="Lance"))
messages.append(AIMessage(f"""I know about sharks too. They are very dangerous, but they are also very beautiful.
Sometimes have been seen in the wild, but they are not very common. In the wild, they are very dangerous, but they are also very beautiful.
They live in the sea and in the ocean. They can travel long distances and can be found in many parts of the world.
Often they live in groups of 20 or more, but they are not very common.
They should eat a lot of food. Normally they eat a lot of fish.
The white shark is the largest of the sharks and is the most dangerous.
The great white shark is the most famous of the sharks and is the most dangerous.
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
""", name="Bot"))
messages.append(HumanMessage(f"What others should I learn about?", name="Lance"))
messages.append(AIMessage(f"I know about dolphins too", name="Bot"))
messages.append(HumanMessage(f"Tell me more about dolphins", name="Lance"))
 
for m in messages:
    m.pretty_print()
	
	
Copied

			

				
================================== Ai Message ==================================
Name: Bot
So you said you were researching ocean mammals?
================================ Human Message =================================
Name: Lance
Yes, I know about whales. But what others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about sharks too. They are very dangerous, but they are also very beautiful.
Sometimes have been seen in the wild, but they are not very common. In the wild, they are very dangerous, but they are also very beautiful.
They live in the sea and in the ocean. They can travel long distances and can be found in many parts of the world.
Often they live in groups of 20 or more, but they are not very common.
They should eat a lot of food. Normally they eat a lot of fish.
The white shark is the largest of the sharks and is the most dangerous.
The great white shark is the most famous of the sharks and is the most dangerous.
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
================================ Human Message =================================
Name: Lance
What others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about dolphins too
================================ Human Message =================================
Name: Lance
Tell me more about dolphins
			
		

	

		

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output = graph.invoke({'messages': messages})
	
	
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--- trimmed messages (input to LLM) ---
================================== Ai Message ==================================
Name: Bot
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
================================ Human Message =================================
Name: Lance
What others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about dolphins too
================================ Human Message =================================
Name: Lance
Tell me more about dolphins
------------------------------------------------
			
		

	

		

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for m in output['messages']:
    m.pretty_print()
	
	
Copied

			

				
================================== Ai Message ==================================
Name: Bot
So you said you were researching ocean mammals?
================================ Human Message =================================
Name: Lance
Yes, I know about whales. But what others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about sharks too. They are very dangerous, but they are also very beautiful.
Sometimes have been seen in the wild, but they are not very common. In the wild, they are very dangerous, but they are also very beautiful.
They live in the sea and in the ocean. They can travel long distances and can be found in many parts of the world.
Often they live in groups of 20 or more, but they are not very common.
They should eat a lot of food. Normally they eat a lot of fish.
The white shark is the largest of the sharks and is the most dangerous.
The great white shark is the most famous of the sharks and is the most dangerous.
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
================================ Human Message =================================
Name: Lance
What others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about dolphins too
================================ Human Message =================================
Name: Lance
Tell me more about dolphins
================================== Ai Message ==================================
Certainly! Dolphins are intelligent marine mammals that are part of the family Delphinidae, which includes nearly 40 species. Here are some interesting facts about dolphins:
1. **Intelligence**: Dolphins are known for their high intelligence and have large brains relative to their body size. They exhibit behaviors that suggest social complexity, self-awareness, and problem-solving skills. For example, they can recognize themselves in mirrors, a trait shared by only a few other species.
2. **Communication**: Dolphins communicate using a variety of clicks, whistles, and body language. Each dolphin has a unique "signature whistle" that helps identify it to others, similar to a human name. They use echolocation to navigate and locate prey by emitting clicks and interpreting the echoes that bounce back.
3. **Social Structure**: Dolphins are highly social animals and often live in groups called pods. These pods can vary in size from a few individuals to several hundred. Within these groups, dolphins form complex social relationships and often cooperate to hunt and protect each other from predators.
4. **Habitat**: Dolphins are found in all the world's oceans and in some rivers. Different species have adapted to various environments, from tropical waters to the cooler regions of the open sea. Some species, like the Amazon river dolphin (also known as the boto), live in freshwater rivers.
5. **Diet**: Dolphins are carnivores and primarily eat fish, squid, and crustaceans. Their diet can vary depending on the species and their habitat. Some species, like the killer whale (which is actually a large dolphin), can even hunt larger marine mammals.
6. **Reproduction**: Dolphins have a long gestation period, typically around 10 to 12 months. Calves are born tail-first and are nursed by their mothers for up to two years. Dolphins often form strong bonds with their offspring and other members of their pod.
7. **Conservation**: Many species of dolphins face threats such as pollution, overfishing, and entanglement in fishing nets. Conservation efforts are ongoing to protect these animals and their habitats. Organizations like the International Union for Conservation of Nature (IUCN) and the World Wildlife Fund (WWF) work to raise awareness and implement conservation measures.
8. **Cultural Significance**: Dolphins have been a source of fascination and inspiration for humans for centuries. They appear in myths, legends, and art across many cultures and are often seen as symbols of intelligence, playfulness, and freedom.
Dolphins are truly remarkable creatures with a lot to teach us about social behavior, communication, and the complexities of marine ecosystems. If you have any specific questions or want to know more about a particular species, feel free to ask!
			
		

	

		

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from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import RemoveMessage, trim_messages
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Nodes
def filter_messages(state: State):
    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"messages": delete_messages}
 
def trim_messages_node(state: State):
    # print the messages
    print("--- messages (input to trim_messages) ---")
    for m in state["messages"]:
        m.pretty_print()
    print("------------------------------------------------")
 
    # Trim the messages based on the specified parameters
    trimmed_messages = trim_messages(
        state["messages"],
        max_tokens=100, # Maximum tokens allowed in the trimmed list
        strategy="last", # Keep the latest messages
        token_counter=llm, # Use the LLM's tokenizer to count tokens
        allow_partial=True, # Allow cutting messages mid-way if needed
    )
 
    # Print the trimmed messages to see the effect of trim_messages
    print("--- trimmed messages (input to LLM) ---")
    for m in trimmed_messages:
        m.pretty_print()
    print("------------------------------------------------")
 
    # Invoke the LLM with the trimmed messages
    response = llm.invoke(trimmed_messages)
 
    # Return the LLM's response in the correct state format
    return {"messages": [response]}
 
def chat_model_node(state: State):
    return {"messages": [llm.invoke(state["messages"])]}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("filter_messages_node", filter_messages)
graph_builder.add_node("chatbot_node", chat_model_node)
graph_builder.add_node("trim_messages_node", trim_messages_node)
 
# Connecto nodes
graph_builder.add_edge(START, "filter_messages_node")
graph_builder.add_edge("filter_messages_node", "trim_messages_node")
graph_builder.add_edge("trim_messages_node", "chatbot_node")
graph_builder.add_edge("chatbot_node", END)
 
# Compile the graph
graph = graph_builder.compile()
 
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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<IPython.core.display.Image object>
			
		

	

		

			< >
			Input
		
		

			Python
			
				
				Copy
			
		
	
	

		
from langchain_core.messages import AIMessage, HumanMessage
 
messages = [AIMessage(f"So you said you were researching ocean mammals?", name="Bot")]
messages.append(HumanMessage(f"Yes, I know about whales. But what others should I learn about?", name="Lance"))
messages.append(AIMessage(f"I know about dolphins too", name="Bot"))
messages.append(HumanMessage(f"What others should I learn about?", name="Lance"))
messages.append(AIMessage(f"""I know about sharks too. They are very dangerous, but they are also very beautiful.
Sometimes have been seen in the wild, but they are not very common. In the wild, they are very dangerous, but they are also very beautiful.
They live in the sea and in the ocean. They can travel long distances and can be found in many parts of the world.
Often they live in groups of 20 or more, but they are not very common.
They should eat a lot of food. Normally they eat a lot of fish.
The white shark is the largest of the sharks and is the most dangerous.
The great white shark is the most famous of the sharks and is the most dangerous.
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
""", name="Bot"))
messages.append(HumanMessage(f"What others should I learn about?", name="Lance"))
 
for m in messages:
    m.pretty_print()
	
	
Copied

			

				
================================== Ai Message ==================================
Name: Bot
So you said you were researching ocean mammals?
================================ Human Message =================================
Name: Lance
Yes, I know about whales. But what others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about dolphins too
================================ Human Message =================================
Name: Lance
What others should I learn about?
================================== Ai Message ==================================
Name: Bot
I know about sharks too. They are very dangerous, but they are also very beautiful.
Sometimes have been seen in the wild, but they are not very common. In the wild, they are very dangerous, but they are also very beautiful.
They live in the sea and in the ocean. They can travel long distances and can be found in many parts of the world.
Often they live in groups of 20 or more, but they are not very common.
They should eat a lot of food. Normally they eat a lot of fish.
The white shark is the largest of the sharks and is the most dangerous.
The great white shark is the most famous of the sharks and is the most dangerous.
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
================================ Human Message =================================
Name: Lance
What others should I learn about?
			
		

	

		

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			Input
		
		

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output = graph.invoke({'messages': messages})
	
	
Copied

			

				
--- messages (input to trim_messages) ---
================================== Ai Message ==================================
Name: Bot
I know about sharks too. They are very dangerous, but they are also very beautiful.
Sometimes have been seen in the wild, but they are not very common. In the wild, they are very dangerous, but they are also very beautiful.
They live in the sea and in the ocean. They can travel long distances and can be found in many parts of the world.
Often they live in groups of 20 or more, but they are not very common.
They should eat a lot of food. Normally they eat a lot of fish.
The white shark is the largest of the sharks and is the most dangerous.
The great white shark is the most famous of the sharks and is the most dangerous.
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
================================ Human Message =================================
Name: Lance
What others should I learn about?
------------------------------------------------
--- trimmed messages (input to LLM) ---
================================ Human Message =================================
Name: Lance
What others should I learn about?
------------------------------------------------
			
		

	

		

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for m in output['messages']:
    m.pretty_print()
	
	
Copied

			

				
================================== Ai Message ==================================
Name: Bot
I know about sharks too. They are very dangerous, but they are also very beautiful.
Sometimes have been seen in the wild, but they are not very common. In the wild, they are very dangerous, but they are also very beautiful.
They live in the sea and in the ocean. They can travel long distances and can be found in many parts of the world.
Often they live in groups of 20 or more, but they are not very common.
They should eat a lot of food. Normally they eat a lot of fish.
The white shark is the largest of the sharks and is the most dangerous.
The great white shark is the most famous of the sharks and is the most dangerous.
The tiger shark is the most aggressive of the sharks and is the most dangerous.
The hammerhead shark is the most beautiful of the sharks and is the most dangerous.
The mako shark is the fastest of the sharks and is the most dangerous.
The bull shark is the most common of the sharks and is the most dangerous.
================================ Human Message =================================
Name: Lance
What others should I learn about?
================================== Ai Message ==================================
Certainly! To provide a more tailored response, it would be helpful to know what areas or topics you're interested in. However, here’s a general list of areas that are often considered valuable for personal and professional development:
1. **Technology & Digital Skills**:
   - Programming languages (Python, JavaScript, etc.)
...
   - Goal setting and motivation
   - Personal finance and budgeting
   - Critical thinking and problem solving
8. **Social & Environmental Impact**:
   - Social entrepreneurship
   - Community organizing and activism
   - Sustainable living practices
   - Climate change and environmental policy
If you have a specific area of interest or a particular goal in mind, feel free to share, and I can provide more detailed recommendations!
================================== Ai Message ==================================
			
		

	

		

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from typing import Annotated
from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_core.messages import HumanMessage
from huggingface_hub import login
from IPython.display import Image, display
import os
import dotenv
 
dotenv.load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_LANGGRAPH")
 
class State(TypedDict):
    messages: Annotated[list, add_messages]
 
os.environ["LANGCHAIN_TRACING_V2"] = "false" # Disable LangSmith tracing
 
# Create the LLM model
login(token=HUGGINGFACE_TOKEN) # Login to HuggingFace to use the model
MODEL = "Qwen/Qwen2.5-72B-Instruct"
model = HuggingFaceEndpoint(
    repo_id=MODEL,
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
)
# Create the chat model
llm = ChatHuggingFace(llm=model)
 
# Nodes
def chat_model_node(state: State):
    # Return the LLM's response in the correct state format
    return {"messages": [llm.invoke(state["messages"])]}
 
# Create graph builder
graph_builder = StateGraph(State)
 
# Add nodes
graph_builder.add_node("chatbot_node", chat_model_node)
 
# Connecto nodes
graph_builder.add_edge(START, "chatbot_node")
graph_builder.add_edge("chatbot_node", END)
 
# Compile the graph
graph = graph_builder.compile()
 
display(Image(graph.get_graph().draw_mermaid_png()))
	
	
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