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Token Management Utility

Helper functions for counting tokens and managing token limits in AI completions.

Overview

The Token utilities help you: - Count tokens in text strings - Select search results that fit within token limits - Manage context window sizes - Optimize token usage for completions

Methods

unique_sdk.utils.token.count_tokens - Count tokens in text

Counts the number of tokens in a text string using tiktoken encoding.

Parameters:

  • text (required) - Text string to count tokens for
  • encoding_model (optional) - Encoding model to use (default: "cl100k_base")

Returns:

  • int - Number of tokens in the text

Example:

from unique_sdk.utils.token import count_tokens

text = "Hello, how are you today?"
token_count = count_tokens(text)
print(f"Tokens: {token_count}")  # Output: Tokens: 6

Example - Count Multiple Texts:

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is Python?"},
    {"role": "assistant", "content": "Python is a programming language."}
]

total_tokens = sum(
    count_tokens(msg["content"]) 
    for msg in messages
)
print(f"Total tokens: {total_tokens}")
unique_sdk.utils.token.pick_search_results_for_token_window - Select results within token limit

Selects search results that fit within a specified token limit.

Parameters:

  • searchResults (required) - List of search result objects with text field
  • tokenLimit (required) - Maximum number of tokens to include
  • encoding_model (optional) - Encoding model (default: "cl100k_base")

Returns:

  • List of search result objects that fit within the token limit

Example:

from unique_sdk.utils.token import pick_search_results_for_token_window
import unique_sdk

# Perform search
search = unique_sdk.Search.create(
    user_id=user_id,
    company_id=company_id,
    searchString="project documentation",
    searchType="COMBINED",
    limit=50  # Get more than needed
)

# Select results that fit in 4000 tokens
selected_results = pick_search_results_for_token_window(
    search["data"],
    tokenLimit=4000
)

print(f"Selected {len(selected_results)} results")

Use Cases

Optimize Context Window Usage 
from unique_sdk.utils.token import count_tokens, pick_search_results_for_token_window

def build_optimized_context(query, max_context_tokens=4000):
    """Build context that fits within token limit."""

    # Search
    search = unique_sdk.Search.create(
        user_id=user_id,
        company_id=company_id,
        searchString=query,
        searchType="COMBINED",
        limit=100  # Get many results
    )

    # Select best results that fit
    context = pick_search_results_for_token_window(
        search["data"],
        tokenLimit=max_context_tokens
    )

    return context
Monitor Token Usage 
from unique_sdk.utils.token import count_tokens

def check_token_usage(messages, max_tokens):
    """Check if messages fit within token limit."""
    total = sum(count_tokens(msg["content"]) for msg in messages)

    if total > max_tokens:
        print(f"Warning: {total} tokens exceeds limit of {max_tokens}")
        return False

    print(f"Using {total}/{max_tokens} tokens ({total/max_tokens*100:.1f}%)")
    return True

messages = [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."}
]

check_token_usage(messages, max_tokens=8000)

Best Practices

Reserve Tokens for Response 
# Always reserve tokens for the AI response
max_tokens = 8000
response_reserve = 1000  # Reserve for response

available_for_context = max_tokens - response_reserve

context = pick_search_results_for_token_window(
    search["data"],
    tokenLimit=available_for_context
)
Combine with Source Processing 
from unique_sdk.utils.sources import merge_sources, sort_sources
from unique_sdk.utils.token import pick_search_results_for_token_window

# 1. Process sources first
merged = merge_sources(search["data"])
sorted_results = sort_sources(merged)

# 2. Then select by token limit
context = pick_search_results_for_token_window(
    sorted_results,
    tokenLimit=4000
)