Teaching Study Teacher of the project project that focuses on Semantic Chunking, Medical Token Management, and Certification Contact Credit Count of the Counting Counting Center of LLM
Controlling the significant context is the challenge of multilingualism, especially in areas such as Google Colab, where resources are on data problems and long documents can pass the Token windows. In this lesson, we guide you in the effective implementation of the Model Manufacturer (MCP) Delivery ModelConteeXtExteeXtExteeXTEXTER. You will learn how to combine the manager about the face-to-face model, shown here with Flan-T5, add, and get appropriate context pieces. On the way, we will cover the tokens to calculate GPT-2 Tokenzer, strategies to use the Composition-Windows context, which allows you to ask and see your moving context during real time.
import torch
import numpy as np
from typing import List, Dict, Any, Optional, Union, Tuple
from dataclasses import dataclass
import time
import gc
from tqdm.notebook import tqdmWe import important libraries to create a dynamic manager: Tormel and Numel Handle Tensor and Number Activities, while typing and typaphlasse provide formal types and data containers. Help modules, such as a Time and GC, supporting cleanliness and memory cleaning, and the TQDM.NoteBook offering active Chunk Development bars.
@dataclass
class ContextChunk:
"""A chunk of text with metadata for the Model Context Protocol."""
text: str
embedding: Optional[torch.Tensor] = None
importance: float = 1.0
timestamp: float = 0.0
metadata: Dict[str, Any] = None
def __post_init__(self):
if self.metadata is None:
self.metadata = {}
if self.timestamp == 0.0:
self.timestamp = time.time()Compressuk data data enables one part of the text and its prevention, the specified user score, timingshop, and metadatary metadata. Its of its OT_Init__ approach confirms that each chunk time is the present time with the funny metadata in the empty dictionary if no offer.
class ModelContextManager:
"""
Manager for implementing Model Context Protocol in LLMs on Google Colab.
Handles context window optimization, token management, and relevance scoring.
"""
def __init__(
self,
max_context_length: int = 8192,
embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2",
relevance_threshold: float = 0.7,
recency_weight: float = 0.3,
importance_weight: float = 0.3,
semantic_weight: float = 0.4,
device: str = "cuda" if torch.cuda.is_available() else "cpu"
):
"""
Initialize the Model Context Manager.
Args:
max_context_length: Maximum number of tokens in context window
embedding_model: Model to use for text embeddings
relevance_threshold: Threshold for chunk relevance to be included
recency_weight: Weight for recency in relevance calculation
importance_weight: Weight for importance in relevance calculation
semantic_weight: Weight for semantic similarity in relevance calculation
device: Device to run computations on
"""
self.max_context_length = max_context_length
self.device = device
self.chunks = []
self.current_token_count = 0
self.relevance_threshold = relevance_threshold
self.recency_weight = recency_weight
self.importance_weight = importance_weight
self.semantic_weight = semantic_weight
try:
from sentence_transformers import SentenceTransformer
print(f"Loading embedding model {embedding_model}...")
self.embedding_model = SentenceTransformer(embedding_model).to(self.device)
print(f"Embedding model loaded successfully on {self.device}")
except ImportError:
print("Installing sentence-transformers...")
import subprocess
subprocess.run(["pip", "install", "sentence-transformers"])
from sentence_transformers import SentenceTransformer
self.embedding_model = SentenceTransformer(embedding_model).to(self.device)
print(f"Embedding model loaded successfully on {self.device}")
try:
from transformers import GPT2Tokenizer
self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
except ImportError:
print("Installing transformers...")
import subprocess
subprocess.run(["pip", "install", "transformers"])
from transformers import GPT2Tokenizer
self.tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
def add_chunk(self, text: str, importance: float = 1.0, metadata: Dict[str, Any] = None) -> None:
"""
Add a new chunk of text to the context manager.
Args:
text: The text content to add
importance: Importance score (0-1)
metadata: Additional metadata for the chunk
"""
with torch.no_grad():
embedding = self.embedding_model.encode(text, convert_to_tensor=True)
chunk = ContextChunk(
text=text,
embedding=embedding,
importance=importance,
timestamp=time.time(),
metadata=metadata or {}
)
self.chunks.append(chunk)
self.current_token_count += len(self.tokenizer.encode(text))
if self.current_token_count > self.max_context_length:
self.optimize_context()
def optimize_context(self) -> None:
"""Optimize context by removing less relevant chunks to fit within token limit."""
if not self.chunks:
return
print("Optimizing context window...")
scores = self.score_chunks()
sorted_indices = np.argsort(scores)[::-1]
new_chunks = []
new_token_count = 0
for idx in sorted_indices:
chunk = self.chunks[idx]
chunk_tokens = len(self.tokenizer.encode(chunk.text))
if new_token_count + chunk_tokens <= self.max_context_length:
new_chunks.append(chunk)
new_token_count += chunk_tokens
else:
if scores[idx] > self.relevance_threshold * 1.5:
for i, included_chunk in enumerate(new_chunks):
included_idx = sorted_indices[i]
if scores[included_idx] < self.relevance_threshold:
included_tokens = len(self.tokenizer.encode(included_chunk.text))
if new_token_count - included_tokens + chunk_tokens <= self.max_context_length:
new_chunks.remove(included_chunk)
new_token_count -= included_tokens
new_chunks.append(chunk)
new_token_count += chunk_tokens
break
removed_count = len(self.chunks) - len(new_chunks)
self.chunks = new_chunks
self.current_token_count = new_token_count
print(f"Context optimized: Removed {removed_count} chunks, {len(new_chunks)} remaining, using {new_token_count}/{self.max_context_length} tokens")
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
def score_chunks(self, query: str = None) -> np.ndarray:
"""
Score chunks based on recency, importance, and semantic relevance.
Args:
query: Optional query to calculate semantic relevance against
Returns:
Array of scores for each chunk
"""
if not self.chunks:
return np.array([])
current_time = time.time()
max_age = max(current_time - chunk.timestamp for chunk in self.chunks) or 1.0
recency_scores = np.array([
1.0 - ((current_time - chunk.timestamp) / max_age)
for chunk in self.chunks
])
importance_scores = np.array([chunk.importance for chunk in self.chunks])
if query is not None:
query_embedding = self.embedding_model.encode(query, convert_to_tensor=True)
similarity_scores = np.array([
torch.cosine_similarity(chunk.embedding, query_embedding, dim=0).item()
for chunk in self.chunks
])
similarity_scores = (similarity_scores - similarity_scores.min()) / (similarity_scores.max() - similarity_scores.min() + 1e-8)
else:
similarity_scores = np.ones(len(self.chunks))
final_scores = (
self.recency_weight * recency_scores +
self.importance_weight * importance_scores +
self.semantic_weight * similarity_scores
)
return final_scores
def retrieve_context(self, query: str = None, k: int = None) -> str:
"""
Retrieve the most relevant context for a given query.
Args:
query: The query to retrieve context for
k: The maximum number of chunks to return (None = all relevant chunks)
Returns:
String containing the combined relevant context
"""
if not self.chunks:
return ""
scores = self.score_chunks(query)
relevant_indices = np.where(scores >= self.relevance_threshold)[0]
relevant_indices = relevant_indices[np.argsort(scores[relevant_indices])[::-1]]
if k is not None:
relevant_indices = relevant_indices[:k]
relevant_texts = [self.chunks[i].text for i in relevant_indices]
return "nn".join(relevant_texts)
def get_stats(self) -> Dict[str, Any]:
"""Get statistics about the current context state."""
return {
"chunk_count": len(self.chunks),
"token_count": self.current_token_count,
"max_tokens": self.max_context_length,
"usage_percentage": self.current_token_count / self.max_context_length * 100 if self.max_context_length else 0,
"avg_chunk_size": self.current_token_count / len(self.chunks) if self.chunks else 0,
"oldest_chunk_age": time.time() - min(chunk.timestamp for chunk in self.chunks) if self.chunks else 0,
}
def visualize_context(self):
"""Visualize the current context window distribution."""
try:
import matplotlib.pyplot as plt
import pandas as pd
if not self.chunks:
print("No chunks to visualize")
return
scores = self.score_chunks()
chunk_sizes = [len(self.tokenizer.encode(chunk.text)) for chunk in self.chunks]
timestamps = [chunk.timestamp for chunk in self.chunks]
relative_times = [time.time() - ts for ts in timestamps]
importance = [chunk.importance for chunk in self.chunks]
df = pd.DataFrame({
'Size (tokens)': chunk_sizes,
'Age (seconds)': relative_times,
'Importance': importance,
'Score': scores
})
fig, axs = plt.subplots(2, 2, figsize=(14, 10))
axs[0, 0].bar(range(len(chunk_sizes)), chunk_sizes)
axs[0, 0].set_title('Token Distribution by Chunk')
axs[0, 0].set_ylabel('Tokens')
axs[0, 0].set_xlabel('Chunk Index')
axs[0, 1].scatter(chunk_sizes, scores)
axs[0, 1].set_title('Score vs Chunk Size')
axs[0, 1].set_xlabel('Tokens')
axs[0, 1].set_ylabel('Score')
axs[1, 0].scatter(relative_times, scores)
axs[1, 0].set_title('Score vs Chunk Age')
axs[1, 0].set_xlabel('Age (seconds)')
axs[1, 0].set_ylabel('Score')
axs[1, 1].scatter(importance, scores)
axs[1, 1].set_title('Score vs Importance')
axs[1, 1].set_xlabel('Importance')
axs[1, 1].set_ylabel('Score')
plt.tight_layout()
plt.show()
except ImportError:
print("Please install matplotlib and pandas for visualization")
print('!pip install matplotlib pandas')ModelConteExt.com Class Orchestrates final final management of llms with the installation text, to produce embodding, and tracking the Token against the limited limit. Using goals associated with (and recruitment, importance, and semantic matches), the default contexts), to return the most appropriate chunks, and simple monitoring activities and the viewing of the context.
class MCPColabDemo:
"""Demonstration of Model Context Protocol in Google Colab with a Language Model."""
def __init__(
self,
model_name: str = "google/flan-t5-base",
max_context_length: int = 2048,
device: str = "cuda" if torch.cuda.is_available() else "cpu"
):
"""
Initialize the MCP Colab demo with a specified model.
Args:
model_name: Hugging Face model name
max_context_length: Maximum context length for the MCP manager
device: Device to run the model on
"""
self.device = device
self.context_manager = ModelContextManager(
max_context_length=max_context_length,
device=device
)
try:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
print(f"Loading model {model_name}...")
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
print(f"Model loaded successfully on {device}")
except ImportError:
print("Installing transformers...")
import subprocess
subprocess.run(["pip", "install", "transformers"])
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
print(f"Model loaded successfully on {device}")
def add_document(self, text: str, chunk_size: int = 512, overlap: int = 50) -> None:
"""
Add a document to the context by chunking it appropriately.
Args:
text: Document text
chunk_size: Size of each chunk in characters
overlap: Overlap between chunks in characters
"""
chunks = []
for i in range(0, len(text), chunk_size - overlap):
chunk = text[i:i + chunk_size]
if len(chunk) > 20:
chunks.append(chunk)
print(f"Adding {len(chunks)} chunks to context...")
for i, chunk in enumerate(tqdm(chunks)):
pos = i / len(chunks)
importance = 1.0 - 0.5 * min(pos, 1 - pos)
self.context_manager.add_chunk(
text=chunk,
importance=importance,
metadata={"source": "document", "position": i, "total_chunks": len(chunks)}
)
def process_query(self, query: str, max_new_tokens: int = 256) -> str:
"""
Process a query using the context manager and model.
Args:
query: The query to process
max_new_tokens: Maximum number of tokens in response
Returns:
Model response
"""
self.context_manager.add_chunk(query, importance=1.0, metadata={"type": "query"})
relevant_context = self.context_manager.retrieve_context(query=query)
prompt = f"Context: {relevant_context}nnQuestion: {query}nnAnswer:"
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
print("Generating response...")
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.7,
top_p=0.9,
)
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
self.context_manager.add_chunk(
response,
importance=0.9,
metadata={"type": "response", "query": query}
)
return response
def interactive_session(self):
"""Run an interactive session in the notebook."""
from IPython.display import clear_output
print("Starting interactive MCP session. Type 'exit' to end.")
conversation_history = []
while True:
query = input("nYour query: ")
if query.lower() == 'exit':
break
if query.lower() == 'stats':
print("nContext Statistics:")
stats = self.context_manager.get_stats()
for key, value in stats.items():
print(f"{key}: {value}")
self.context_manager.visualize_context()
continue
if query.lower() == 'clear':
self.context_manager.chunks = []
self.context_manager.current_token_count = 0
conversation_history = []
clear_output(wait=True)
print("Context cleared!")
continue
response = self.process_query(query)
conversation_history.append((query, response))
print("nResponse:")
print(response)
print("n" + "-"*50)
stats = self.context_manager.get_stats()
print(f"Context usage: {stats['token_count']}/{stats['max_tokens']} tokens ({stats['usage_percentage']:.1f}%)")The Symbolabdemo binds the boss of the context in the SEQ2seQ llm, loading the Flan face model appears the content window.
def run_mcp_demo():
"""Run a simple demo of the Model Context Protocol."""
print("Running Model Context Protocol Demo...")
context_manager = ModelContextManager(max_context_length=4096)
print("Adding sample chunks...")
context_manager.add_chunk(
"The Model Context Protocol (MCP) is a framework for managing context "
"windows in large language models. It helps optimize token usage and improve relevance.",
importance=1.0
)
context_manager.add_chunk(
"Context management involves techniques like sliding windows, chunking, "
"and relevance filtering to handle large documents efficiently.",
importance=0.8
)
for i in range(10):
context_manager.add_chunk(
f"This is test chunk {i} with some filler content to simulate a larger context "
f"window that needs optimization. This helps demonstrate the MCP functionality "
f"for context window management in language models on Google Colab.",
importance=0.5 - (i * 0.02)
)
stats = context_manager.get_stats()
print("nInitial Statistics:")
for key, value in stats.items():
print(f"{key}: {value}")
query = "How does the Model Context Protocol work?"
print(f"nRetrieving context for: '{query}'")
context = context_manager.retrieve_context(query)
print(f"nRelevant context:n{context}")
print("nVisualizing context:")
context_manager.visualize_context()
print("nDemo complete!")The work of the run_mcp_demo binds everything together on one piece of script: Intheatates ModelConteeXtManager, showing a comprehensive, complete manifestation of the Movel Contactor status in action.
if __name__ == "__main__":
run_mcp_demo()
Finally, the standard Python Center is an ordinary entry process
In conclusion, we will have a valid MCP functional program that is not only the use of the Curbs Turaway Token but also prioritizing your essential bodies. ModelConteeXtManager equips you with tools to measure semantic compatibility, new temporary, and user-assigned importance. At the same time, the accompanying category of this SMCPCOLABDEMO provides an accessible framework for real-time assessment and mental view. Interested in these ways, you can add important principles by repairing compliance media, testing various embryos, or combining other llm viewers to comply with your specific domain functioning. Finally, this method allows you to create the NCAs, which is relevant to the correct, leading to more accurate and practical answers from your Language models.
Here is the Colab Notebook. Also, don't forget to follow Sane and join ours Telegraph station including LinkedIn Grtopic. Don't forget to join ours 90k + ml subreddit.
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