Advanced Features

Purpose:
RAG enhances the LLM's responses by retrieving relevant documents or context from an external source. This is especially useful when the LLM's internal context is insufficient.

Implementation Details:

• Module: rag.py
• Libraries Used:
  • SentenceTransformers for computing embeddings.
  • FAISS for fast similarity search.

Key Methods:

• index_documents(documents: List[str]): Processes and indexes a list of documents.
• retrieve(query: str, top_k: int = 3): Retrieves the most relevant documents based on the query.

from rag import RetrievalAugmentedGeneration

# Initialize RAG module
rag = RetrievalAugmentedGeneration()

# Index a set of documents
documents = [
    "Python is a versatile programming language.",
    "FAISS is used for efficient similarity search.",
    "SentenceTransformers provide robust embeddings for text."
]
rag.index_documents(documents)

# Retrieve context for a query
relevant_docs = rag.retrieve("How do I search efficiently?", top_k=2)
print("Retrieved Documents:", relevant_docs)

Purpose:
Enables messaging and coordination between multiple agents, supporting more complex multi-agent systems.

Implementation Details:

• Module: agent_communication.py
• Functionality:
  • Sending messages to other agents.
  • Receiving messages from a communication queue.

Key Methods:

• send_message(agent_id: str, message: Dict[str, Any]): Sends a message to a designated agent.
• receive_messages(): Retrieves and clears the current message queue.

from agent_communication import InterAgentCommunicator

# Create a communicator instance
communicator = InterAgentCommunicator()

# Send a message to agent "AgentX"
communicator.send_message("AgentX", {"text": "Hello, AgentX!"})

# Retrieve messages
messages = communicator.receive_messages()
print("Inter-Agent Messages:", messages)

Purpose:
Provides a mechanism to log performance metrics and assess the efficiency and responsiveness of the agent. This is useful for monitoring and iterative improvements.

Implementation Details:

• Module: evaluation_framework.py
• Functionality:
  • Logging custom metrics.
  • Evaluating response time.
  • Reporting current performance metrics.

Key Methods:

• log_metric(name: str, value: Any): Records a performance metric.
• evaluate_response_time(start_time: float): Computes and logs the response time.
• report(): Returns a dictionary of logged metrics.

import time
from evaluation_framework import EvaluationFramework

# Initialize evaluation framework
eval_framework = EvaluationFramework()

# Start a timer and simulate a process
start_time = time.time()
# ... perform some operations ...
time.sleep(0.5)

# Evaluate and log response time
response_time = eval_framework.evaluate_response_time(start_time)
print("Response Time:", response_time)

# Retrieve and print all metrics
metrics_report = eval_framework.report()
print("Metrics Report:", metrics_report)

Purpose:
Adds the capability to process and analyze non-text data such as images and audio. This can be used to build richer interactive applications.

Implementation Details:

• Module: multimodal_support.py
• Libraries Used:
  • Pillow for image processing.

Key Methods:

• process_image(image_path: str): Processes an image and returns descriptive metadata (e.g., dimensions).
• process_audio(audio_path: str): (Stub) Processes audio and returns a placeholder message or transcription.

from multimodal_support import MultimodalProcessor

# Create a processor instance
processor = MultimodalProcessor()

# Process an image file
image_info = processor.process_image("path/to/image.jpg")
print("Image Info:", image_info)

# Process an audio file (stub)
audio_info = processor.process_audio("path/to/audio.mp3")
print("Audio Info:", audio_info)

Purpose:
Enhances user interaction by streaming the LLM's response incrementally, rather than waiting for a full output. This is especially useful for long responses.

Implementation Details:

• Module: streaming_responses.py
• Functionality:
  • Implements a generator to yield chunks of text from the complete response.

Key Methods:

• stream_response(response: str, chunk_size: int = 20): Yields segments of the response text.

from streaming_responses import stream_response

full_response = "This is a very long response generated by the agent, which we will stream in small chunks."
print("Streaming Response:")
for chunk in stream_response(full_response, chunk_size=20):
    print(chunk)

Purpose:
Ensures that functions receive the correct types of inputs and produce expected types of outputs. This helps catch errors early and improves code robustness.

Implementation Details:

• Module: type_safety.py
• Functionality:
  • Validates inputs and outputs using helper functions.

Key Methods:

• validate_input(value: Any, expected_type: Type, param_name: str = "parameter"): Raises a TypeError if the input type does not match.
• validate_output(value: Any, expected_type: Type, param_name: str = "output"): Raises a TypeError if the output type does not match.

from type_safety import validate_input, validate_output

# Validate input parameter
validate_input(123, int, "example_param")

# Validate output type
validate_output("hello", str, "example_output")

print("Type safety checks passed.")