Map Chain Example

Mapping Chain Example

This example demonstrates how to create a mapping chain to summarize 3 articles into one summary.

from llama_cpp_agent import AgentChainElement, MapChain
from llama_cpp_agent import LlamaCppAgent
from llama_cpp_agent import MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppServerProvider

model = LlamaCppServerProvider("http://127.0.0.1:8080")

agent = LlamaCppAgent(
    model,
    system_prompt="",
    predefined_messages_formatter_type=MessagesFormatterType.MISTRAL
)

summary_chain = AgentChainElement("out_0", system_prompt="You are an advanced AI agent for summarizing articles",
                                  prompt="Summarize this article into bullet points:\n{item}")

combine_chain = AgentChainElement("out_1", system_prompt="You are an advanced AI agent that summarizes text",
                                  prompt="Please combine the bullet points of different summaries below, into one summary as bullet points:\n{map_output}")

map_chain = MapChain(agent, [summary_chain], [combine_chain])

article_list = [
    """### 1. Quantum Computing: The Next Frontier in Computational Power

**Introduction**
Quantum computing represents a revolutionary approach to information processing, leveraging the principles of quantum mechanics to solve problems that are intractable for classical computers. This article explores the fundamental concepts of quantum computing, its potential applications, and the challenges it faces.

**Quantum Mechanics and Computing**
Quantum computers use quantum bits, or qubits, which can exist in multiple states simultaneously, thanks to superposition. This capability, combined with entanglement—where the state of one qubit can depend on the state of another, no matter the distance between them—allows quantum computers to process a vast number of possibilities concurrently.

**Quantum Algorithms**
Several algorithms have been developed for quantum computers that show significant speed-ups over their classical counterparts. Shor’s Algorithm, for instance, can factorize large integers exponentially faster than the best-known classical algorithms, which has profound implications for cryptography. Grover's Algorithm offers a quadratic speedup for unstructured search problems.

**Applications**
Quantum computing has potential applications across various fields:
- **Cryptography**: Secure communication through quantum key distribution.
- **Drug Discovery**: Modeling molecular interactions at quantum levels to predict drug efficacy and side effects.
- **Optimization Problems**: Enhancing solutions in logistics, finance, and materials science.

**Challenges**
Despite its potential, quantum computing faces several hurdles:
- **Qubit Coherence**: Maintaining the state of qubits for sufficient time is challenging due to decoherence.
- **Error Rates**: Quantum gates are prone to errors significantly higher than conventional binary computing gates.
- **Scalability**: Building machines with enough qubits to be useful for complex problems is currently beyond our reach.

**Conclusion**
Quantum computing is still in its infancy, but it holds the promise of massive computational power. The coming decades are likely to see significant advancements in this field as researchers overcome its current limitations.""",
    """### 2. Machine Learning: Transforming Data into Insights

**Introduction**
Machine Learning (ML) is a branch of artificial intelligence that focuses on building applications that can learn from data and improve their accuracy over time without being explicitly programmed. This article delves into the types of ML, key algorithms, applications, and future prospects.

**Types of Machine Learning**
- **Supervised Learning**: Models predict outputs based on input data, and learning is guided by comparing actual and predicted outputs.
- **Unsupervised Learning**: Algorithms identify patterns in data without reference to known or labeled outcomes.
- **Reinforcement Learning**: Models learn to make sequences of decisions by receiving rewards or penalties.

**Key Algorithms**
- **Linear Regression** and **Logistic Regression** for predictive modeling.
- **Decision Trees** and **Random Forests** for classification and regression tasks.
- **Neural Networks**: Used in deep learning for complex pattern recognition, such as in image and speech recognition.

**Applications**
- **Healthcare**: From diagnosing diseases to personalized medicine.
- **Finance**: For credit scoring, algorithmic trading, and risk assessment.
- **Retail**: Enhancing customer experience through personalized recommendations.

**Challenges and Future Prospects**
The field of ML is not without challenges, including data privacy concerns, the need for large labeled datasets, and the risk of creating biased models. However, ongoing research in areas like unsupervised learning, transfer learning, and the development of more robust models promises to mitigate these issues.

**Conclusion**
Machine learning continues to be a dynamic field of research and application, with the potential to impact numerous sectors profoundly.""",
    """### 3. Blockchain Technology: Decentralizing Trust

**Introduction**
Blockchain technology is best known as the backbone of cryptocurrencies like Bitcoin, but its applications extend far beyond. This article outlines the technology’s fundamentals, applications beyond finance, and the challenges it faces.

**Blockchain Basics**
A blockchain is a decentralized ledger of all transactions across a network. Each transaction is added to a "block" and linked to the previous block, forming a "chain." This structure, combined with cryptographic techniques, makes it secure and immutable.

**Key Features**
- **Decentralization**: No single point of control or failure.
- **Transparency**: Changes to the public blockchain are viewable by all parties creating transparency.
- **Immutability**: Once a transaction is recorded, it cannot be altered, increasing trust.

**Applications**
- **Supply Chain Management**: Enhancing transparency and traceability.
- **Healthcare**: Secure sharing of medical records.
- **Smart Contracts**: Automatically executing contracts when conditions are met.

**Challenges**
- **Scalability**: Current blockchain solutions, like Bitcoin, have limitations on transaction speed and volume.
- **Regulatory Issues**: Balancing the need for regulation with the ethos of decentralization.
- **Energy Consumption**: The energy requirement for "mining" transactions, particularly in networks like Bitcoin, is substantial.

**Conclusion**
Blockchain technology holds great promise for creating a more transparent and efficient world, but significant challenges must be addressed to realize its full potential."""
]

map_chain.run_map_chain(items_to_map=article_list)

Example Output:

* Quantum computing and machine learning are advanced technologies with significant potential in various sectors
* Quantum computing is based on quantum mechanics principles, utilizing qubits that can exist in multiple states simultaneously and entanglement for long-distance interaction
* Quantum algorithms like Shor's and Grover's offer exponential and quadratic speedups over classical algorithms, respectively
* Applications include cryptography, drug discovery, optimization problems, and more
* Challenges include maintaining qubit coherence, reducing error rates, and achieving scalability
* Machine learning is a subfield of artificial intelligence focusing on data-driven models that improve over time
* ML includes three main types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning
* Supervised Learning: models predict outputs based on labeled input data
* Unsupervised Learning: algorithms identify patterns in unlabeled data
* Reinforcement Learning: models learn through rewards or penalties
* Key ML algorithms include Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Neural Networks
* Applications of ML include healthcare, finance, and retail
* Challenges in ML include data privacy, need for large labeled datasets, and creating biased models
* Blockchain technology is a decentralized ledger system for transactions across a network
* A blockchain is made up of a chain of blocks, each containing transactions, which are secure and immutable
* Key features include decentralization, transparency, and immutability
* Applications include supply chain management, healthcare, and smart contracts
* Challenges include scalability, regulatory issues, and energy consumption
* Quantum computing and ML hold great promise but face challenges in coherence, error rates, data privacy, large datasets, and regulation
* Researchers are working to overcome current limitations and advance the fields of quantum computing, machine learning, and blockchain technology.