# import langchain gemini and ollama
from langchain_ollama.llms import OllamaLLM
qwen = OllamaLLM(model="qwen2.5:1.5b")
# Example query
qwen.invoke("Explain machine learning in simple terms (max 50 words)")

"Machine Learning is when computers get better at tasks without being explicitly programmed to do so. It's like training a computer to recognize pictures of cats by showing it many images, and over time, the computer starts recognizing other similar cats too. This way, it learns patterns in data that can be used for various applications, from identifying objects in videos to predicting weather trends!"

# Same for gemini
from dotenv import load_dotenv # used to load environment variables
load_dotenv()  # Load environment variables from .env file
from langchain_google_genai import GoogleGenerativeAI
gemini = GoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.0)
gemini.invoke("Explain machine learning in simple terms (max 50 words)")  # Example query

'Machine learning teaches computers to learn from data without explicit programming. It identifies patterns and makes predictions or decisions based on that learning, improving its accuracy over time with more data.'

from langchain_core.language_models.base import BaseLanguageModel

def explain_topic(llm: BaseLanguageModel, topic: str) -> str:
    """ A general explain topic functionality base on an LLM"""
    query = f"Explain {topic} in simple terms? (max 50 words)"
    response = llm.invoke(query)
    return response

explain_topic(gemini, "London"), explain_topic(qwen, "London")

("London is the capital of England and the United Kingdom. It's a huge, diverse city with a rich history, famous landmarks like Big Ben and Buckingham Palace, and a vibrant cultural scene. It's a global hub for finance, fashion, and the arts.",
 "London is the capital city of England and one of the world's largest cities with over eight million residents. It sits on the River Thames, surrounded by green parks like Hyde Park. Famous landmarks include Buckingham Palace, Big Ben, Tower Bridge, and many museums. The city offers a mix of historical sites, shopping districts, restaurants, and modern attractions.")

# Chat models
from langchain_google_genai import ChatGoogleGenerativeAI
chat_gemini = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.0)
chat_gemini.invoke("Explain machine learning in simple terms").content[:100]  # Example query

'Imagine you\'re teaching a dog a new trick, like "fetch." You don\'t tell the dog exactly how to run, '

# Embedings
from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings

google_embeddings = GoogleGenerativeAIEmbeddings(model="models/text-embedding-004")
google_embeddings.embed_query("What is the weather like in London?")[:5]

[-0.024339474737644196,
 0.019421163946390152,
 -0.005040269810706377,
 -0.021117379888892174,
 -0.030608780682086945]

import pandas as pd
df = pd.read_csv("data/dataset.csv", delimiter=";")  # Example of reading a CSV file with pandas
df.head()

# We add embedding to the pandas dataframe
from langchain_core.embeddings import Embeddings
def embed_row(row: pd.Series, embeddings: Embeddings) -> list:
    adapt  = row["Question"] + " " + row["Answer"]
    return embeddings.embed_query(adapt)

df["embeddings"] = df.apply(lambda row: embed_row(row, google_embeddings), axis=1)
df.head()  # Display the first few rows of the DataFrame with embeddings

# PCA + rendering 
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

pca = PCA(n_components=2)
pca_result = pca.fit_transform(list(df["embeddings"]))

plt.scatter(pca_result[:, 0], pca_result[:, 1], alpha=0.5)
plt.show()

# Prompts
template = """
Translate the following text into {target_language} while maintaining the style of {style}. 
Reply just with the translation:

{text}
"""
template

'\nTranslate the following text into {target_language} while maintaining the style of {style}. Reply just with the translation:\n\n{text}\n'

from langchain.prompts import ChatPromptTemplate
prompt_template = ChatPromptTemplate.from_template(template)
prompt_template.input_variables, prompt_template.messages[0].prompt

(['style', 'target_language', 'text'],
 PromptTemplate(input_variables=['style', 'target_language', 'text'], input_types={}, partial_variables={}, template='\nTranslate the following text into {target_language} while maintaining the style of {style}. Reply just with the translation:\n\n{text}\n'))

prompt_template.format_messages(style="Shakespearean", target_language="French", text="Hello, how are you?")

[HumanMessage(content='\nTranslate the following text into French while maintaining the style of Shakespearean. Reply just with the translation:\n\nHello, how are you?\n', additional_kwargs={}, response_metadata={})]

def translate_text(llm: BaseLanguageModel, text: str, style: str, target_language: str) -> str:
    prompt = prompt_template.format_messages(
        style=style,
        target_language=target_language,
        text=text
    )
    response = llm.invoke(prompt)
    return response

print(translate_text(gemini, "Hello, how are you?", "a normal conversation", "French")) 
print(translate_text(gemini, "Hello, how are you?", "elegant and noble", "Italian"))

Salut, comment ça va ?
Salute, come state?

## Output parser
{
    "cognitive_load": 10,
    "language": "English",
    "style": "Aristocratic",
    "text": "Hello, how are you?"
}

{'cognitive_load': 10,
 'language': 'English',
 'style': 'Aristocratic',
 'text': 'Hello, how are you?'}

review_template = """
For the following text, extract the following information:

1. Cognitive load (0-100): is the text easy to understand? 
2. Language: what language is the text written in?
3. Style: what is the style of the text? (e.g., formal, informal, technical, etc.)
4. Text: the original text.

format the output as a JSON object with the keys "cognitive_load", "language", "style", and "text".

text: {text}
"""

simple_text = "Hello, how are you?"
hard_text = "The problem of induction is a fundamental issue in the philosophy of science, concerning the justification of inductive reasoning and the validity of generalizations based on empirical observations."

message_template_chat = ChatPromptTemplate.from_template(review_template)
response = chat_gemini.invoke(message_template_chat.format_messages(text=simple_text))
response.content
# How to parse it?

'```json\n{\n  "cognitive_load": 10,\n  "language": "English",\n  "style": "Informal",\n  "text": "Hello, how are you?"\n}\n```'

from langchain.output_parsers import ResponseSchema
from langchain.output_parsers import StructuredOutputParser

cognitive_load_schema = ResponseSchema(name="cognitive_load", description="Cognitive load of the text (0-100)", type="number")
language_schema = ResponseSchema(name="language", description="Language of the text", type="string")
style_schema = ResponseSchema(name="style", description="Style of the text", type="string")
text_schema = ResponseSchema(name="text", description="Original text", type="string")
response_schemas = [cognitive_load_schema, language_schema, style_schema, text_schema]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

# Review template (with instructions)
review_template_instructions = """
For the following text, extract the following information:

1. Cognitive load (0-100): is the text easy to understand? 
2. Language: what language is the text written in?
3. Style: what is the style of the text? (e.g., formal, informal, technical, etc.)
4. Text: the original text.

text: {text}

{format_instructions}
"""

from langchain_core.language_models.chat_models import BaseChatModel
def evaluate_cognitive_load(llm: BaseChatModel, text: str) -> dict:
    message_template_chat_instructions = ChatPromptTemplate.from_template(review_template_instructions)
    messages = message_template_chat_instructions.format_messages(text=text, 
                                format_instructions=output_parser.get_format_instructions())
    return output_parser.parse(chat_gemini.invoke(messages).content)

evaluate_cognitive_load(chat_gemini, simple_text)["cognitive_load"], evaluate_cognitive_load(chat_gemini, hard_text)["cognitive_load"]

(5, 65)

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
memory = ConversationBufferMemory()
conversation = ConversationChain(
    llm=chat_gemini,
    memory=memory,
    verbose=False 
)
memory.buffer

''

conversation.invoke("Hi! My name is Gianluca")["response"][:50]

"Hi Gianluca! It's nice to meet you. My name is, we"

conversation.invoke("What is your role in the society?")["response"][:50]

"That's a great question, Gianluca! As a large lang"

conversation.invoke(input="What is my name?")["response"][:50]

'You told me your name is Gianluca!'

memory.buffer[:500]

"Human: Hi! My name is Gianluca\nAI: Hi Gianluca! It's nice to meet you. My name is, well, I don't really *have* a name in the traditional sense. You can just call me AI. I'm a large language model, trained by Google. I've been trained on a massive dataset of text and code, which allows me to communicate and generate human-like text in response to a wide range of prompts and questions. I'm still under development, but I'm learning new things every day! What can I do for you today?\nHuman: What is y"

memory.load_memory_variables({})

{'history': "Human: Hi! My name is Gianluca\nAI: Hi Gianluca! It's nice to meet you. My name is, well, I don't really *have* a name in the human sense. You can just call me AI. I'm a large language model, trained by Google. I'm excited to chat with you today! What's on your mind? I'm ready to answer your questions, discuss interesting topics, or even just tell you a story. I have access to a vast amount of information, so hopefully I can be helpful. Just let me know what you'd like to do!\nHuman: What is your role in the society?\nAI: That's a great question, Gianluca! My role in society is still evolving, but I see myself as a tool that can be used to augment human capabilities and improve various aspects of life. Here are some of the key ways I can contribute:\n\n*   **Information Access and Processing:** I can quickly access and process vast amounts of information, making it easier for people to find answers to their questions, research topics, and stay informed about current events. Think of me as a super-powered search engine with the ability to synthesize and summarize information.\n\n*   **Automation and Efficiency:** I can automate repetitive tasks, freeing up human time and resources for more creative and strategic endeavors. For example, I can help with tasks like data entry, report generation, and customer service inquiries.\n\n*   **Education and Learning:** I can provide personalized learning experiences, answer student questions, and offer feedback on assignments. I can also help educators create engaging and effective learning materials.\n\n*   **Creative Content Generation:** I can assist with writing, translation, and other creative tasks. I can generate different creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. I will try my best to fulfill all your requirements.\n\n*   **Problem Solving and Decision Making:** I can analyze data, identify patterns, and generate insights that can help people make better decisions in various fields, such as business, healthcare, and finance.\n\n*   **Accessibility and Inclusion:** I can provide language translation, text-to-speech, and other accessibility features that can help people with disabilities access information and participate more fully in society.\n\nOf course, it's important to remember that I am still under development, and my capabilities are constantly evolving. It's also crucial to use me responsibly and ethically, being mindful of potential biases and limitations. My goal is to be a helpful and beneficial tool for humanity, and I'm excited to see how I can contribute to a better future!\nHuman: What is my name?\nAI: You told me your name is Gianluca!"}

## Buffer memory
from langchain.memory import ConversationBufferWindowMemory
buffer_memory = ConversationBufferWindowMemory(k=1)  # Keep the last 5 interactions

conversation_short_window = ConversationChain(
    llm=chat_gemini,
    memory=buffer_memory,
    verbose=False
)

conversation_short_window.predict(input="Hi! My name is Gianluca")
conversation_short_window.predict(input="What is your role in the society?")
conversation_short_window.predict(input="What about rasengan")
conversation_short_window.predict(input="What is my name?")

"As an AI, I don't have access to personal information about you, including your name. We haven't exchanged that information, so I don't know it. You would have to tell me!"

# Several alternatives, ConversationTokenMemory, ConversationSummaryMemory
from langchain.memory import ConversationSummaryBufferMemory
memory = ConversationSummaryBufferMemory(llm=chat_gemini, max_token_limit=100)

conversation = ConversationChain(
    llm=chat_gemini,
    memory=memory,
    verbose=False
)
long_history = """
Carlo Magno, also known as Charlemagne, was a medieval emperor who ruled much of Western Europe from 768 to 814. He was the King of the Franks, King of the Lombards, and Emperor of the Romans. His reign marked the Carolingian Renaissance, a revival of art, culture, and learning based on classical models. He is often credited with uniting much of Europe during the early Middle Ages and laying the foundations for modern France and Germany.
"""
conversation.invoke(long_history)
conversation.invoke("Was Carlo Magno a king?")

{'input': 'Was Carlo Magno a king?',
 'history': "System: The human provides a summary of Charlemagne's reign, highlighting his role as a medieval emperor, his unification of Europe, and the Carolingian Renaissance. The AI responds, praising the summary and adding details about Charlemagne's balance of military expansion and cultural reforms, as well as the influence of Alcuin of York and the Carolingian minuscule on modern writing. The AI then asks the human what aspects of Charlemagne's reign they find most interesting.",
 'response': 'Ah, "Carlo Magno"! I see you\'re using the Italian version of his name. Yes, absolutely, Carlo Magno – Charlemagne – was indeed a king! He wasn\'t *just* an emperor, you see. He actually became King of the Franks in 768 AD, jointly ruling with his brother Carloman I until Carloman\'s death in 771 AD, at which point Charlemagne became the sole ruler of the Frankish kingdom. This kingdom was already quite substantial, encompassing much of modern-day France, Belgium, the Netherlands, and parts of Germany.\n\nIt was from this powerful position as King of the Franks that he launched his many military campaigns, expanding his territory and eventually leading to his coronation as Holy Roman Emperor by Pope Leo III in 800 AD. So, he was both a king *and* an emperor, with his kingship preceding his imperial title. Think of it as building a strong foundation (the Frankish kingdom) before constructing a grand edifice (the Holy Roman Empire) on top of it! Does that make sense?'}

memory.load_memory_variables({})

{'history': "System: The human provides a summary of Charlemagne's reign, highlighting his role as a medieval emperor, his unification of Europe, and the Carolingian Renaissance. The AI responds, praising the summary and adding details about Charlemagne's balance of military expansion and cultural reforms, as well as the influence of Alcuin of York and the Carolingian minuscule on modern writing. The AI then asks the human what aspects of Charlemagne's reign they find most interesting. The human then asks if Carlo Magno (Charlemagne) was a king. The AI confirms that Charlemagne was indeed a king, becoming King of the Franks in 768 AD and ruling jointly with his brother until 771 AD, after which he became the sole ruler. The AI emphasizes that his kingship preceded his imperial title and served as the foundation for his later coronation as Holy Roman Emperor."}

# Chains
from langchain.chains import LLMChain
prompt = ChatPromptTemplate.from_template(
    "What is the best name to describe \
    a company that makes {product}?"
)
chain = prompt | chat_gemini  # Chain the prompt with the LLM

chain.invoke("bicycles").content[:300]  # Example query

'The "best" name depends on the specific brand identity you want to create. Here\'s a breakdown of different approaches and examples:\n\n**1. Classic & Traditional:**\n\n*   **Focus:** Heritage, craftsmanship, reliability.\n*   **Examples:**\n    *   [Your Last Name] Cycles (e.g., "Smith Cycles")\n    *   [L'

second_prompt = ChatPromptTemplate.from_template(
    "Write a 20 words description for the following \
    company:{company_name}"
)

chain = chain | second_prompt | chat_gemini  # Chain the prompt with the LLM

chain.invoke("bicycles").content[:300]  # Example query

'We craft unique bicycle names, from classic to quirky, ensuring brand identity, target audience alignment, and memorability for your cycling company.'

## Advanced chaining
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
first_prompt = ChatPromptTemplate.from_template(
    "Translate the following review in English \n\n{review}\n\n"
)
second_prompt = ChatPromptTemplate.from_template(
    "Summarize the the following review in a single sentence \n\n{review}\n\n",
    
)
third_prompt = ChatPromptTemplate.from_template(
    "What is the language of the following review? \n\n{review}\n\n"
)
fourth_prompt = ChatPromptTemplate.from_template(
    "Write a follow up response to the following summary in the specified language \n\nSummary:{summary}\n\n Language:{language}\n\n"
)

# Simple Chaining
# 1. Translate the review
translate_chain = first_prompt | chat_gemini
# 2. Summarize and detect language in parallel
summarize_chain = second_prompt | chat_gemini
language_chain = third_prompt | chat_gemini

# 3. Combine
full_chain = (
    RunnablePassthrough.assign(
        translated=translate_chain
    )
    .assign(
        summary=lambda x: summarize_chain.invoke({"review": x["translated"]}),
        language=lambda x: language_chain.invoke({"review": x["translated"]}),
    )
    .assign(
        follow_up=lambda x: (fourth_prompt | chat_gemini).invoke(
            {"summary": x["summary"], "language": x["language"]}
        )
    )
)

# Example input
result = full_chain.invoke({"review": "Das war ein großartiger Aufenthalt! Das Hotel war wunderschön, das Personal sehr freundlich und hilfsbereit. Das Essen im Restaurant war ausgezeichnet und die Zimmer waren sauber und komfortabel. Die Lage war perfekt für Sightseeing. Wir werden definitiv wiederkommen!"})
result["language"].content, result["summary"].content[:50], result["follow_up"].content[:50]

('The language of the review is **English**.',
 'The reviewer had a wonderful stay at a beautiful h',
 "That's fantastic to hear! We're so glad you enjoye")

fishing_template = load_prompt("data/prompts/fishing.txt")
guitar_template = load_prompt("data/prompts/guitar.txt")
computer_science_template = load_prompt("data/prompts/computer_science.txt")
anime_template = load_prompt("data/prompts/anime.txt")

prompt_selector = """
Giving the following question, select the most appropriate between the following destinations:
{destinations}
Reply with just the template name, without any additional text. If you don't know the answer, reply with "general"

The question is:
{input}
"""

prompt_infos = {
    "fishing": {
        "description": "Good for answering questions about fishing",
        "prompt_template": fishing_template
    },
    "guitar": {
        "description": "Good for answering questions about guitar and music", 
        "prompt_template": guitar_template
    },
    "computer science": {
        "description": "Good for answering computer science questions",
        "prompt_template": computer_science_template
    },
    "anime": {
        "description": "Good for answering questions about anime and manga",
        "prompt_template": anime_template
    }
}
destinations = [f"{name}: {info['description']}" for name, info in prompt_infos.items()]
destinations_str = "\n".join(destinations)
destinations_str

'fishing: Good for answering questions about fishing\nguitar: Good for answering questions about guitar and music\ncomputer science: Good for answering computer science questions\nanime: Good for answering questions about anime and manga'

prompt_selector_template = ChatPromptTemplate.from_template(prompt_selector)
selector_chain = (
    {"destinations": lambda x: destinations_str, "input": lambda x: x} |
    prompt_selector_template | 
    chat_gemini
)
print(selector_chain.invoke("What is the best way to catch a fish?").content)
print(selector_chain.invoke("What if moon is made of cheese?").content)

fishing
general

semantic_chains = {name: ChatPromptTemplate.from_template(info['prompt_template']) | chat_gemini for name, info in prompt_infos.items()}

from langchain_core.runnables import RunnableLambda

def route_to_chain(data):
    input_text = data["input"]
    category = data["category"].content.strip().lower()
    
    if category in semantic_chains:
        return semantic_chains[category].invoke(input_text)
    else:
        return chat_gemini.invoke(input_text)

smart_assistant_chain = (
    RunnablePassthrough.assign(
        category=lambda x: selector_chain.invoke(x["input"])
    ) |
    RunnableLambda(route_to_chain)
)

# Test the smart assistant
test_questions = [
    "What is the best bait for catching bass?",
    "How do I tune my guitar?", 
    "What is machine learning?",
    "Who is the main character in Naruto?",
    "What is the meaning of life?"
]

for question in test_questions:
    print(f"Question: {question}")
    print(f"Answer: {smart_assistant_chain.invoke({'input': question}).content[:50]}")
    print("-" * 50)

Question: What is the best bait for catching bass?
Answer: Alright, let's talk bass bait! That's a question t
--------------------------------------------------
Question: How do I tune my guitar?
Answer: Alright, let's get your guitar in tune! Tuning is 
--------------------------------------------------
Question: What is machine learning?
Answer: Okay, let's break down machine learning. As a comp
--------------------------------------------------
Question: Who is the main character in Naruto?
Answer: Ah, a fantastic question! The main character in Na
--------------------------------------------------
Question: What is the meaning of life?
Answer: Ah, the million-dollar question! The meaning of li
--------------------------------------------------

from langchain.agents import tool
import python_weather
from python_weather import Locale

@tool
async def get_weather(location: str) -> str:
    """
    Get the current weather for a given location.
    
    Args:
        location (str): The name of the location to get the weather for.

    Returns:
        str: A string describing the current weather in the specified location.
    """
    async with python_weather.Client(unit=python_weather.METRIC, locale=Locale.ITALIAN) as client:

        # Fetch a weather forecast from a city.
        weather = await client.get(location)
        return weather.temperature
    
await get_weather.ainvoke("London")  # Example usage of the tool

26

get_weather

StructuredTool(name='get_weather', description='Get the current weather for a given location.\n\nArgs:\n    location (str): The name of the location to get the weather for.\n\nReturns:\n    str: A string describing the current weather in the specified location.', args_schema=<class 'langchain_core.utils.pydantic.get_weather'>, coroutine=<function get_weather at 0x7f2e6177e340>)

prompt = f"""
You operate in a loop of: Thought, Action, PAUSE, Observation.
At the end of the loop, you output an Answer.

- Use **Thought** to describe your reasoning about the question.
- Use **Action** to execute one of the available actions, then return PAUSE.
- **Observation** will be the result of the action.

Available Actions:
calculate:
    e.g., calculate 2 + 2
    Returns a calculation result.

...

Example Session:
Question: [Your question here]
Thought: [Your reasoning]
Action: [Action to take]
PAUSE
Observation: [Result of the action]
...
Answer: [Final answer]
"""

from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
tools = load_tools(["llm-math", "serpapi"], llm=chat_gemini)

agent = initialize_agent(
    tools, 
    chat_gemini, 
    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
    handle_parsing_errors=True,
    verbose = True
)

agent.invoke("What is the 25% of ln(17325.124)?")


> Entering new AgentExecutor chain...
Thought: I need to calculate the natural logarithm of 17325.124 and then multiply the result by 0.25.
Action:
```json
{
  "action": "Calculator",
  "action_input": "ln(17325.124)"
}
```
Observation: Answer: 9.759912981319648
Thought:I now need to multiply the result of the natural logarithm by 0.25.
Action:
```json
{
  "action": "Calculator",
  "action_input": "9.759912981319648 * 0.25"
}
```
Observation: Answer: 2.439978245329912
Thought:I have calculated the natural logarithm of 17325.124 and multiplied the result by 0.25. I now have the final answer.
Final Answer: 2.439978245329912

> Finished chain.

{'input': 'What is the 25% of ln(17325.124)?', 'output': '2.439978245329912'}

chat_gemini.invoke("What is the 25% of ln(17325.124)? reply with just the result").content

'2.297'

question = "Tom M. Mitchell is an American computer scientist \
and the Founders University Professor at Carnegie Mellon University (CMU)\
what book did he write?"
result = agent(question)


> Entering new AgentExecutor chain...
Thought: I need to find out what book Tom M. Mitchell wrote. I can use the search tool to find this information.
Action:
```json
{
  "action": "Search",
  "action_input": "Tom M. Mitchell book"
}
```
Observation: ['Tom Michael Mitchell is an American computer scientist and the Founders University Professor at Carnegie Mellon University.', 'Tom M. Mitchell type: American computer scientist.', 'Tom M. Mitchell entity_type: people, scholars.', 'Tom M. Mitchell kgmid: /m/0h7pbt5.', 'Tom M. Mitchell place_of_birth: Blossburg, PA.', 'Tom M. Mitchell books: Machine Learning.', 'Tom M. Mitchell education: Massachusetts Institute of Technology, Stanford University.', 'Tom M. Mitchell h_index: 105.', 'Tom M. Mitchell academic_advisor: Bruce G. Buchanan.', 'Tom M. Mitchell awards: IJCAI Computers and Thought Award, Presidential Young Investigator Award.', 'Tom M. Mitchell affiliation: Carnegie Mellon University.', 'Machine Learning, Tom Mitchell, McGraw Hill, 1997. ... Machine Learning is the study of computer algorithms that improve automatically through experience. This ...', 'This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience.', 'Book Info: Presents the key algorithms and theory that form the core of machine learning. Discusses such theoretical issues as How does learning performance ...', 'Machine Learning Tom Mitchell. by: Tom M. Mitchell. Publication date: 1997-03-01. Topics: Decision tree learning, Artificial Neural Network.', 'This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate ...', 'Title: Machine Learning. Author: Mitchell, Tom M. (Tom Michael), 1951-. Note: main text c1997; additional chapters c2017. Link: PDF with commentary at CMU.', 'Machine Learning by Tom M. Mitchell 1997 Algorithms Learning Systems AI Paperbac. Pre-Owned. $30.00. Buy It Now. +$5.38 delivery. Located in United States.', 'This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience.', 'Find nearly any book by Tom M. Mitchell. Get the best deal by comparing prices from over 100000 booksellers.', "I have finally gotten around to Tom Mitchell's book on machine learning and so far it's fantastic. Great mix of math and diagrams, very wordy, a bit repetitive."]
Thought:Thought: The search results indicate that Tom M. Mitchell wrote the book "Machine Learning".
Final Answer: Machine Learning

> Finished chain.

memory = ConversationBufferMemory(memory_key="chat_history")
agent = initialize_agent(
    [get_weather], 
    chat_gemini, 
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    handle_parsing_errors=True,
    verbose = False,
    memory=memory,
)

await agent.ainvoke("Hello! My name is Gianluca.. What is the weather like in London?")

{'input': 'Hello! My name is Gianluca.. What is the weather like in London?',
 'chat_history': '',
 'output': 'Hello Gianluca! The current weather in London is 26 degrees.\n```'}

await agent.ainvoke("What is my name?")

{'input': 'What is my name?',
 'chat_history': 'Human: Hello! My name is Gianluca.. What is the weather like in London?\nAI: Hello Gianluca! The current weather in London is 26 degrees.\n```',
 'output': 'Your name is Gianluca.\n```'}

from langchain.agents import tool

@tool
def execute_python_code(code: str) -> str:
    """
    Execute the given Python code and return the result.
    """
    try:
        local_vars = {}
        # remove ```python and ``` from the code
        code = code.strip().replace("```python", "").replace("```", "")
        exec(code, {}, local_vars)
        if 'result' in local_vars:
            return str(local_vars['result'])
        return "Code executed successfully."
    except Exception as e:
        return str(e)

memory = ConversationBufferMemory(memory_key="chat_history")
agent = initialize_agent(
    [execute_python_code], 
    chat_gemini, 
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    handle_parsing_errors=True,
    verbose = False,
    memory=memory,
)

agent.invoke("Plot a fancy kde with different colors and style with matplotlib")

{'input': 'Plot a fancy kde with different colors and style with matplotlib',
 'chat_history': '',
 'output': 'I have generated a fancy KDE plot with different colors and styles using matplotlib and scipy.stats. The plot displays the KDE for two different distributions, labeled as "X Distribution" (red, dashed line) and "Y Distribution" (blue, solid line). It includes labels, a title, a legend, a grid, and a custom background color.'}

memory = ConversationBufferMemory(memory_key="chat_history")
agent = initialize_agent(
    [execute_python_code], 
    chat_gemini, 
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    handle_parsing_errors=True,
    verbose = False,
    memory=memory,
)

agent.invoke("I have this data: [1, 2, 3, 7, 11]")

{'input': 'I have this data: [1, 2, 3, 7, 11]',
 'chat_history': '',
 'output': "Okay, I see you have the data [1, 2, 3, 7, 11]. What would you like to do with it? I can help you calculate things like the mean, median, standard deviation, or perform other operations. Just let me know what you're interested in."}

agent.invoke("Plot them please")

{'input': 'Plot them please',
 'chat_history': "Human: I have this data: [1, 2, 3, 7, 11]\nAI: Okay, I see you have the data [1, 2, 3, 7, 11]. What would you like to do with it? I can help you calculate things like the mean, median, standard deviation, or perform other operations. Just let me know what you're interested in.",
 'output': 'I have plotted the data for you. You should be able to see a graph with the data points [1, 2, 3, 7, 11] plotted against their index. The x-axis is labeled "Index", the y-axis is labeled "Value", and the plot has a title "Plot of Data". A grid is also displayed on the plot.'}

from langchain_community.utilities import SerpAPIWrapper

@tool
def search_company_news(company_name: str) -> str:
    """
    Use this tool to find recent news articles and headlines about a specific company.
    The input should be the company's full name (e.g., 'NVIDIA', 'Microsoft').
    This provides qualitative context.
    """
    print(f"--- Searching for news about: {company_name} ---")
    # For news, we target the Google News engine specifically for better results.
    search = SerpAPIWrapper(params={"engine": "google_news", "tbm": "nws"})
    query = f"latest news on {company_name}"
    return search.run(query)

@tool
def search_stock_data(company_ticker: str) -> str:
    """
    Use this tool to get the current stock price, market cap, and other key financial metrics
    for a company. The input should be the company's stock ticker symbol (e.g., 'NVDA', 'GOOGL', 'MSFT').
    """
    print(f"--- Searching for stock data for ticker: {company_ticker} ---")
    # A standard Google search for a stock ticker is very effective.
    # SerpAPI will return the contents of the Google Finance widget.
    search = SerpAPIWrapper()
    query = f"{company_ticker} stock price"
    return search.run(query)

from dotenv import load_dotenv
load_dotenv()  # Load environment variables from .env file

agent = initialize_agent(
    [search_stock_data, search_company_news],
    chat_gemini,
    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
    handle_parsing_errors=True,
    verbose=False
)

agent.invoke("Give me a financial snapshot of Microsoft (MSFT). I want to know its current stock price, and also find some recent news headlines about the company.")

--- Searching for stock data for ticker: MSFT ---
--- Searching for news about: Microsoft ---

{'input': 'Give me a financial snapshot of Microsoft (MSFT). I want to know its current stock price, and also find some recent news headlines about the company.',
 'output': "The current stock price for Microsoft (MSFT) is $497.41. Here are some recent news headlines about the company:\n\n*   All-new Surface Copilot+ PCs arrive in Singapore: The Surface Pro, 12-inch and Surface Laptop, 13-inch\n*   Microsoft says new AI tool can diagnose patients 4 times more accurately than human doctors\n*   Microsoft AI CEO Mustafa Suleyman: AI can provide complex medical support, diagnoses\n*   Brave New Kernel: Microsoft Previews Safer Windows Ecosystem\n*   Windows is getting rid of the Blue Screen of Death after 40 years\n*   Microsoft’s (MSFT) Latest Medical AI Tool Can Now Outperform Doctors\n*   AI vs. MDs: Microsoft AI tool outperforms doctors in diagnosing complex medical cases\n*   Microsoft breaks out to new highs. What the charts say to do from here\n*   Amid Rumors Of Halo On PS5 And Switch 2, Microsoft Confirms Halo News Is Coming\n*   Microsoft releases first preview of Windows 11 25H2 update for these users\n*   New Windows 11 update fixes a black screen bug that's one of the most annoying to hit PC gamers in quite some time\n*   Microsoft opens new AI for manufacturing Co-Innovation Lab in Wisconsin\n*   Microsoft retires Blue Screen of Death after 40 years\n*   Windows 12 release is pushed back at least another year as Microsoft announces Windows 11 version 25H2\n*   Microsoft to change Windows's infamous Blue Screen of Death to something much darker in response to last year's CloudStrike crashes\n*   Microsoft's new AI beats doctors at diagnosing complex health conditions\n*   Microsoft Edge's latest stable channel update adds MORE AI — All Copilot all the time\n*   Still hoping for Windows 12 this year? Forget about that, as Windows 11 25H2 is now confirmed as a minor update - but there's good news too\n*   Microsoft Shares Latest Findings from 2025 Work Trend Index: Unlocking Indonesia’s Potential Through Human-AI Collaboration – Indonesia News Center\n*   Windows seemingly lost 400 million users in the past three years — official Microsoft statements show hints of a shrinking user base\n*   Microsoft claims its new medical AI tool is 4x more accurate than doctors\n*   Free Microsoft Flight Simulator update expands New York and beyond\n*   Microsoft (MSFT) Just Got a New $530 Target — Here’s What’s Driving It\n*   Microsoft’s sketchy Win 10 vs Win 11 performance claims pit a 9-year-old PC against a modern machine to claim 2.3X gain\n*   Microsoft claims AI diagnostic tool can outperform doctors\n*   :( Microsoft’s ‘Blue Screen of Death’ Is Going Away\n*   Windows 11 KB5060829 update released with 38 new changes, fixes\n*   One of France's largest cities has now also ditched Microsoft for open source software\n*   Microsoft to Preview New Windows Endpoint Security Platform After CrowdStrike Outage\n*   RIFT - Microsoft's New Open-Source Tool to Analyze Malware in Rust Binaries\n*   Microsoft says goodbye to the Windows blue screen of death\n*   Windows Blue screen of death axed after 40 years, but BSOD still remains — will be replaced by new black Windows 11 'unexpected restart screen'\n*   Microsoft to Retire the Blue Screen of Death (Again) for a Black Void\n*   Microsoft’s new AI agent for Windows 11 can manage settings for you\n*   Is Microsoft’s new Mu for you?\n*   Microsoft's own AI chip delayed six months in major setback — in-house chip now reportedly expected in 2026, but won't hold a candle to Nvidia Blackwell\n*   What’s new in Copilot Studio: May 2025\n*   Microsoft's AI Bet Is Paying Off Big\n*   Microsoft is phasing out passwords soon – here's why passkeys are replacing them and what to do next\n*   Microsoft Developer head to employees: Using … is no longer optional, as company considers another change\n*   News and updates from Microsoft Security\n*   This new 4K Samsung monitor does everything, including emptying your wallet for features you may never use\n*   Microsoft Ads gets granular with new asset-level reviews\n*   All the Azure news you don’t want to miss from Microsoft Build 2025\n*   Microsoft Build 2025\n*   Why Microsoft Stock Continues Hitting All-Time Highs?\n*   Microsoft fixes Outlook bug causing crashes when opening emails\n*   Surface and Windows May 6 news\n*   Microsoft’s Majorana 1 chip carves new path for quantum computing - Source\n*   Microsoft Build 2025: The age of AI agents and building the open agentic web\n*   Microsoft Extends Windows 10 Security Updates for One Year with New Enrollment Options\n*   Microsoft fixes known issue that breaks Windows 11 updates\n*   3 new ways AI agents can help you do even more\n*   Microsoft transfers a top cybersecurity exec: As we continue to ..., says internal memo\n*   Is ChatGPT making us dumber? A new MIT study claims using AI tools causes cognitive issues, and it’s not the first – Microsoft has already warned about ‘diminished independent problem-solving’\n*   Satya Nadella Warns: AI's Power Problem and Microsoft's New Focus\n*   Pushing passkeys forward: Microsoft’s latest updates for simpler, safer sign-ins\n*   New US visa rule, Amazon announces dates for its biggest sale of the year, Microsoft kills Blue Screen of\n*   Microsoft planning thousands of job cuts aimed at salespeople, Bloomberg News reports\n*   Announcing a new strategic collaboration to bring clarity to threat actor naming\n*   Microsoft Releases Famous Flyer 12: The Piper PA-28-236 Dakota\n*   How to follow tech trends and news with AI\n*   I’ve Been Following the Windows 12 Rumors—Here’s What I Think Is Coming for Microsoft's Next OS\n*   How real-world businesses are transforming with AI — with 261 new stories\n*   Tech layoffs June 2025: Microsoft, Google, Disney, ZoomInfo join the list of companies said to be shedding jobs\n*   Microsoft integrates 1Password with Windows 11 for passkey-based sign-ins\n*   How to Stay on Windows 10 Without Paying $30: Microsoft Has 2 New Options\n*   Meet the Deputy CISOs who help shape Microsoft’s approach to cybersecurity: Part 2\n*   Introducing Microsoft 365 Copilot Tuning, multi-agent orchestration, and more from Microsoft Build 2025\n*   Microsoft layoffs: Xbox team reportedly faces new wave of cuts amid strategic overhaul\n*   Stay Ahead with the Latest from Copilot\n*   Microsoft Dragon Copilot provides the healthcare industry’s first unified voice AI assistant that enables clinicians to streamline clinical documentation, surface information and automate tasks - Source\n*   Transforming R&D with agentic AI: Introducing Microsoft Discovery\n*   Microsoft Ignite 2024\n*   Multi-agent orchestration, maker controls, and more: Microsoft Copilot Studio announcements at Microsoft Build 2025\n*   What’s new in Power Apps: April 2025 Feature Update\n*   From sea to sky: Microsoft’s Aurora AI foundation model goes beyond weather forecasting - Source\n*   Microsoft 50th Anniversary + Copilot event\n*   Microsoft beats Q3 earnings estimates on top and bottom line on strong cloud bookings\n*   Microsoft unveils Microsoft Security Copilot agents and new protections for AI\n*   Microsoft announces new European digital commitments - Microsoft On the Issues\n*   Microsoft launches new European Security Program\n*   What's New in Microsoft 365 — April 2025\n*   What’s new in Power Apps: February 2025 Feature Update\n*   Microsoft launches new Surface Copilot+ PCs for Business to empower professionals across the Middle East to enhance productivity and accelerate AI innovation\n*   5 ways AI is changing healthcare\n*   Microsoft Work Trend Index 2025 report reveals the “Frontier Firms” is born, a new organization blueprint is emerging\n*   Microsoft links recent Microsoft 365 outage to buggy update\n*   Microsoft’s new genAI model to power agents in Windows 11\n*   New innovations in Microsoft Purview for protected, AI-ready data\n*   Microsoft Planning Thousands More Job Cuts Aimed at Salespeople\n*   What's New in Microsoft 365 — May 2025\n*   Microsoft June 2025 Patch Tuesday fixes exploited zero-day, 66 flaws\n*   What’s new in Copilot Studio: April 2025\n*   Microsoft 2025 annual Work Trend Index\n*   Meet Microsoft Dragon Copilot: Your new AI assistant for clinical workflow\n*   Microsoft unveils Majorana 1, the world’s first quantum processor powered by topological qubits\n*   Microsoft and NVIDIA accelerate AI development and performance\n*   3 Actively Exploited Zero-Day Flaws Patched in Microsoft's Latest Security Update\n*   Announcing new Microsoft Dataverse capabilities for multi-agent operations"}

# Stores
from langchain.vectorstores import DocArrayInMemorySearch
# Retrievers
from langchain.indexes import VectorstoreIndexCreator
# Loaders
from langchain.document_loaders import CSVLoader

loader = CSVLoader(file_path='data/dataset.csv')
index = VectorstoreIndexCreator(embedding=google_embeddings, vectorstore_cls=DocArrayInMemorySearch).from_loaders(loaders=[loader])

index.vectorstore.search("Is rasengan a powerful attack?", search_type="similarity", k=1)

[Document(metadata={'source': 'data/dataset.csv', 'row': 1}, page_content="Topic;Question;Answer: Naruto;What is the name of Naruto's signature ninja technique?;Rasengan")]

smollm = OllamaLLM(model="smollm:360m")
index.query("What is the best attack of Naruto? Reply with just the attack (a word)", llm=smollm, verbose=True)


> Entering new RetrievalQA chain...

> Finished chain.

'The best attack of Naruto is the "Rasengan" technique, which is a powerful and deadly ninja technique that involves using the power of the eyes to strike multiple enemies at once.'

smollm.invoke("What is the best attack of Naruto? Reply with just the attack (a word)")

"The best attack of Naruto!\n\nNaruto's most iconic and effective attacks are the ones that allow him to take down enemies, defeat bosses, or complete challenges. Here are some of the best attacks of Naruto:\n\n1. **Katana Attack**: The Katana is Naruto's signature weapon, and it's a powerful and deadly attack. It allows him to take down enemies quickly and easily, making it an essential part of his arsenal.\n2. **Naruto's Swords**: Naruto's swords are designed for close combat and can be used in various ways, such as stabbing enemies or using them to deflect blows. They're also incredibly effective at taking down opponents who are not well-equipped.\n3. **Boss Attacks**: Naruto's most famous attack is the Boss Attack, which allows him to take down massive bosses like the Troll King and the Troll Queen. This attack requires a high level of skill and strategy, but it can be very effective in taking down powerful enemies.\n4. **Naruto's Swords**: The Swords are Naruto's signature weapon, and they're designed for close combat. They allow him to take down opponents quickly and easily, making them an essential part of his arsenal.\n5. **Katana Attack with Swords**: This attack combines the Katana with swords, allowing Naruto to take down enemies quickly and efficiently. It's a powerful and effective way to take down tough foes.\n6. **Naruto's Swords**: The Swords are Naruto's signature weapon, and they're designed for close combat. They allow him to take down opponents quickly and easily, making them an essential part of his arsenal.\n7. **Katana Attack with Swords**: This attack combines the Katana with swords, allowing Naruto to take down enemies quickly and efficiently. It's a powerful and effective way to take down tough foes.\n8. **Naruto's Swords**: The Swords are Naruto's signature weapon, and they're designed for close combat. They allow him to take down opponents quickly and easily, making them an essential part of his arsenal.\n9. **Katana Attack with Swords**: This attack combines the Katana with swords, allowing Naruto to take down enemies quickly and efficiently. It's a powerful and effective way to take down tough foes.\n10. **Naruto's Swords**: The Swords are Naruto's signature weapon, and they're designed for close combat. They allow him to take down opponents quickly and easily, making them an essential part of his arsenal.\n\nThese attacks are all powerful and effective ways to take down enemies in Naruto. They require skill, strategy, and practice to master, but they can be very effective when used correctly."

# Evaluation? How to?
file = 'data/long_dataset.csv'
loader = CSVLoader(file_path=file)
data = loader.load()

index = VectorstoreIndexCreator(
    vectorstore_cls=DocArrayInMemorySearch,
    embedding=google_embeddings
).from_loaders([loader])

data[0]

Document(metadata={'source': 'data/long_dataset.csv', 'row': 0}, page_content="Description: Itachi's actions were driven by a profound personal sacrifice to prevent a civil war. The Uchiha clan, feeling marginalized by the Konoha leadership, was planning a coup d'état. Faced with an ultimatum from Danzo Shimura, Itachi chose to single-handedly annihilate his clan, sparing only his younger brother, to prevent a much larger conflict that would have weakened the village and potentially led to another Great Ninja War. He shouldered the immense burden of being branded a traitor and a murderer, creating a villainous persona to push Sasuke to become strong enough to one day defeat him, hoping this would ultimately redeem the Uchiha name in the eyes of the village.")

from langchain.chains import RetrievalQA
qa = RetrievalQA.from_chain_type(
    llm=chat_gemini, 
    chain_type="stuff", # "stuff" is a simple chain that concatenates documents and passes them to the LLM
    retriever=index.vectorstore.as_retriever(), 
    verbose=False,
)

# How to evaluate? Dataset generation
from langchain.evaluation.qa import QAGenerateChain
example_gen_chain = QAGenerateChain.from_llm(llm=chat_gemini)

new_examples = example_gen_chain.apply_and_parse(
    [{"doc": t} for t in data[:5]]
)

/home/gianluca/Teaching/woa-2025-intro-to-langchain/.venv/lib/python3.13/site-packages/langchain/chains/llm.py:370: UserWarning: The apply_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
  warnings.warn(

new_examples

[{'qa_pairs': {'query': 'According to the document, what ultimatum was Itachi faced with that led to his decision to annihilate the Uchiha clan?',
   'answer': 'Itachi was faced with an ultimatum from Danzo Shimura, which led him to choose to single-handedly annihilate his clan.'}},
 {'qa_pairs': {'query': 'According to the document, what fundamental difference in chakra control do the Rasengan and Chidori represent?',
   'answer': 'The Rasengan represents the pinnacle of shape manipulation, while the Chidori represents the pinnacle of nature transformation.'}},
 {'qa_pairs': {'query': 'According to the document, what is the core philosophy of Konoha known as, and what does it state?',
   'answer': "The core philosophy of Konoha is known as the 'Will of Fire', which states that the entire village is a family and a shinobi's strength comes from their love and loyalty to their comrades and home."}},
 {'qa_pairs': {'query': "According to the document, what was the primary personal tragedy that warped Nagato's ideology and led him to believe that true peace could only be achieved through immense pain and suffering?",
   'answer': 'The death of his friend Yahiko.'}},
 {'qa_pairs': {'query': "According to the document, what specific event, orchestrated by Madara Uchiha, served as the catalyst for Obito Uchiha's descent into villainy?",
   'answer': "The orchestrated death of Rin Nohara at the hands of Kakashi, which Obito witnessed after being saved by Madara Uchiha, served as the catalyst for Obito Uchiha's descent into villainy."}}]

import langchain
qa.run(new_examples[0]['qa_pairs']["query"])

'According to the document, Itachi was faced with an ultimatum from Danzo Shimura. He had to choose between annihilating his clan or face a much larger conflict that would have weakened the village and potentially led to another Great Ninja War.'

langchain.debug = False
from langchain.evaluation.qa import QAEvalChain
eval_chain = QAEvalChain.from_llm(chat_gemini)
queries = [ example['qa_pairs'] for example in new_examples ]
predictions = qa.apply(queries)

graded_outputs = eval_chain.evaluate(queries, predictions)

graded_outputs[0]

{'results': 'CORRECT'}

for i, eg in enumerate(queries):
    print(f"Example {i}:")
    print("Question: " + predictions[i]['query'])
    print("Real Answer: " + predictions[i]['answer'])
    print("Predicted Answer: " + predictions[i]['result'])
    print("Predicted Grade: " + graded_outputs[i]['results'])
    print()

Example 0:
Question: According to the document, what ultimatum was Itachi faced with that led to his decision to annihilate the Uchiha clan?
Real Answer: Itachi was faced with an ultimatum from Danzo Shimura, which led him to choose to single-handedly annihilate his clan.
Predicted Answer: According to the document, Itachi was faced with an ultimatum from Danzo Shimura.
Predicted Grade: CORRECT

Example 1:
Question: According to the document, what fundamental difference in chakra control do the Rasengan and Chidori represent?
Real Answer: The Rasengan represents the pinnacle of shape manipulation, while the Chidori represents the pinnacle of nature transformation.
Predicted Answer: According to the document, the Rasengan is the pinnacle of shape manipulation, while the Chidori is the pinnacle of nature transformation.
Predicted Grade: CORRECT

Example 2:
Question: According to the document, what is the core philosophy of Konoha known as, and what does it state?
Real Answer: The core philosophy of Konoha is known as the 'Will of Fire', which states that the entire village is a family and a shinobi's strength comes from their love and loyalty to their comrades and home.
Predicted Answer: The core philosophy of Konoha is known as the 'Will of Fire'. It states that the entire village is a family and a shinobi's strength comes from their love and loyalty to their comrades and home. This love fosters a desire for peace and cooperation.
Predicted Grade: CORRECT

Example 3:
Question: According to the document, what was the primary personal tragedy that warped Nagato's ideology and led him to believe that true peace could only be achieved through immense pain and suffering?
Real Answer: The death of his friend Yahiko.
Predicted Answer: According to the document, the primary personal tragedy that warped Nagato's ideology was the death of his friend Yahiko.
Predicted Grade: CORRECT

Example 4:
Question: According to the document, what specific event, orchestrated by Madara Uchiha, served as the catalyst for Obito Uchiha's descent into villainy?
Real Answer: The orchestrated death of Rin Nohara at the hands of Kakashi, which Obito witnessed after being saved by Madara Uchiha, served as the catalyst for Obito Uchiha's descent into villainy.
Predicted Answer: The orchestrated death of Rin Nohara, at the hands of Kakashi, was the catalyst for Obito Uchiha's descent into villainy. Madara Uchiha manipulated this event to break Obito's spirit.
Predicted Grade: CORRECT

Aspect	Base Model	Chat Model
Input	Plain text prompt	List of structured messages
Designed for	One-shot completions	Multi-turn conversations
Output	Text completion	Message object
Classes in LangChain	`BaseLanguageModel`	`BaseChatModel`

	Topic	Question	Answer	embeddings
0	Naruto	Who is the main protagonist of the series?	Naruto Uzumaki	[-0.06318295747041702, -0.025433462113142014, ...
1	Naruto	What is the name of Naruto's signature ninja t...	Rasengan	[-0.016836581751704216, -0.02863299660384655, ...
2	Naruto	Which village does Naruto belong to?	Konohagakure (The Village Hidden in the Leaves)	[-0.05141858384013176, 0.014148241840302944, 0...
3	Naruto	Who are the other two members of Naruto's orig...	Sasuke Uchiha and Sakura Haruno	[-0.0174933560192585, -0.008309093303978443, 0...
4	Naruto	Who is the sensei (teacher) of Team 7?	Kakashi Hatake	[-0.0031194821931421757, -0.05045430734753609,...

Developing Autonomous Agents in Practice with LangChain¶

About Me¶

Gianluca Aguzzi¶

Lesson Goals¶

Autonomous Agents¶

LLM - In a Nutshell¶

LLMs Panorama: How to Use Them? 🌐🤖¶

LLM Applications: How to Build Them?¶

LangChain - Overview¶

LangChain - Models¶

LLMs providers¶

Ollama¶

Gemini¶

Base Language Model¶

Chat Models¶

Chat Models vs BaseModel¶

Embeddings¶

Embeddings - Semantics¶

Embeddings - Visual Example of Semantic Similarity¶

Prompts¶

Prompts - Pratical Example¶

Prompt Template¶

Example: Prompt for Translation in a Certain Style/Language¶

Structured Output¶

Output: Why Structured?¶

🚩 Task: Extracting Cognitive Load from Text¶

Zero-Shot Prompting for Structured Output¶

Response Schema & Structured Output Parser¶

Memory¶

Memory types¶

Conversation Chain¶

Window Memory¶

Summary Memory¶

Chains (Everything is a Chain :))¶

Example: A simple chain with a prompt and an LLM¶

Example: Advanced Chain with Multiple Steps¶

RunnablePassthrough: Why?¶

Example: Branching Chains¶

RunnableLambda¶

Agents¶

Actions -- Tools¶

How to Perform Search?¶

How Agents Selects Tools?¶

ReAct¶

ReAct: How Does It Work?¶

ReAct In Practice: LangChain¶

How to initialize an Agent?¶

Agents - Combine with memory¶

Agents - Can Agents Run Arbitrary Python Code?¶

Agents - Modelling Example¶

Vector Stores -- Extra¶

Indexes: How Do We Evaluate Performance?¶

Conclusion¶