Helixdevelopment Helixagent Langchain Cost Tuning

LangChain Cost Tuning

Overview

Strategies for reducing LLM API costs while maintaining quality in LangChain applications.

Prerequisites

• LangChain application in production
• Access to API usage dashboard
• Understanding of token pricing

Instructions

Step 1: Understand Token Pricing

# Current approximate pricing (check provider for current rates)
PRICING = {
    "openai": {
        "gpt-4o": {"input": 0.005, "output": 0.015},      # per 1K tokens
        "gpt-4o-mini": {"input": 0.00015, "output": 0.0006},
        "gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
    },
    "anthropic": {
        "claude-3-5-sonnet": {"input": 0.003, "output": 0.015},
        "claude-3-haiku": {"input": 0.00025, "output": 0.00125},
    },
    "google": {
        "gemini-1.5-pro": {"input": 0.00125, "output": 0.005},
        "gemini-1.5-flash": {"input": 0.000075, "output": 0.0003},
    }
}

def estimate_cost(
    input_tokens: int,
    output_tokens: int,
    model: str = "gpt-4o-mini"
) -> float:
    """Estimate API cost for a request."""
    provider, model_name = model.split("/") if "/" in model else ("openai", model)
    rates = PRICING.get(provider, {}).get(model_name, {"input": 0.001, "output": 0.002})
    return (input_tokens / 1000 * rates["input"]) + (output_tokens / 1000 * rates["output"])

Step 2: Implement Token Counting

import tiktoken
from langchain_core.callbacks import BaseCallbackHandler

class CostTrackingCallback(BaseCallbackHandler):
    """Track token usage and costs."""

    def __init__(self, model: str = "gpt-4o-mini"):
        self.model = model
        self.total_input_tokens = 0
        self.total_output_tokens = 0
        self.requests = 0

    def on_llm_end(self, response, **kwargs) -> None:
        """Track tokens from LLM response."""
        if response.llm_output and "token_usage" in response.llm_output:
            usage = response.llm_output["token_usage"]
            self.total_input_tokens += usage.get("prompt_tokens", 0)
            self.total_output_tokens += usage.get("completion_tokens", 0)
            self.requests += 1

    @property
    def total_cost(self) -> float:
        return estimate_cost(
            self.total_input_tokens,
            self.total_output_tokens,
            self.model
        )

    def report(self) -> dict:
        return {
            "requests": self.requests,
            "input_tokens": self.total_input_tokens,
            "output_tokens": self.total_output_tokens,
            "total_tokens": self.total_input_tokens + self.total_output_tokens,
            "estimated_cost": f"${self.total_cost:.4f}"
        }

# Usage
tracker = CostTrackingCallback()
llm = ChatOpenAI(model="gpt-4o-mini", callbacks=[tracker])

# After operations
print(tracker.report())

Step 3: Optimize Prompt Length

import tiktoken

def optimize_prompt(
    text: str,
    max_tokens: int = 2000,
    model: str = "gpt-4o-mini"
) -> str:
    """Truncate text to fit within token budget."""
    encoding = tiktoken.encoding_for_model(model)
    tokens = encoding.encode(text)

    if len(tokens) <= max_tokens:
        return text

    # Truncate and add indicator
    truncated = encoding.decode(tokens[:max_tokens - 10])
    return truncated + "... [truncated]"

def summarize_context(long_text: str, llm) -> str:
    """Summarize long context to reduce tokens."""
    if count_tokens(long_text) < 2000:
        return long_text

    summary_prompt = ChatPromptTemplate.from_template(
        "Summarize this text in 500 words or less, preserving key facts:\n\n{text}"
    )
    chain = summary_prompt | llm | StrOutputParser()
    return chain.invoke({"text": long_text})

Step 4: Model Tiering Strategy

from langchain_openai import ChatOpenAI
from langchain_core.runnables import RunnableBranch

# Define model tiers
llm_cheap = ChatOpenAI(model="gpt-4o-mini", temperature=0)    # $0.15/1M tokens
llm_medium = ChatOpenAI(model="gpt-4o", temperature=0)         # $5/1M tokens
llm_powerful = ChatOpenAI(model="o1", temperature=0)           # $15/1M tokens

def select_model(input_data: dict) -> str:
    """Route to appropriate model based on task."""
    task_type = input_data.get("task_type", "simple")

    if task_type in ["chat", "faq", "simple"]:
        return "cheap"
    elif task_type in ["analysis", "summary", "medium"]:
        return "medium"
    else:
        return "powerful"

router = RunnableBranch(
    (lambda x: select_model(x) == "cheap", prompt | llm_cheap),
    (lambda x: select_model(x) == "medium", prompt | llm_medium),
    prompt | llm_powerful
)

# Simple chat: ~$0.0001 per request
# Complex analysis: ~$0.01 per request
# Cost reduction: 100x for simple tasks

Step 5: Implement Caching

from langchain_core.globals import set_llm_cache
from langchain_community.cache import RedisSemanticCache
from langchain_openai import OpenAIEmbeddings

# Semantic caching - finds similar queries
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
set_llm_cache(RedisSemanticCache(
    redis_url="redis://localhost:6379",
    embedding=embeddings,
    score_threshold=0.95  # High similarity required
))

# Example savings:
# - "What is Python?" and "What's Python?" -> Same cached response
# - 100 similar queries -> 1 API call + 99 cache hits
# - Potential 99% cost reduction for repetitive queries

Step 6: Set Budget Limits

class BudgetLimitCallback(BaseCallbackHandler):
    """Enforce budget limits."""

    def __init__(self, daily_budget: float = 10.0, model: str = "gpt-4o-mini"):
        self.daily_budget = daily_budget
        self.model = model
        self.daily_spend = 0.0
        self.last_reset = datetime.now().date()

    def on_llm_start(self, serialized, prompts, **kwargs) -> None:
        """Check budget before request."""
        today = datetime.now().date()
        if today != self.last_reset:
            self.daily_spend = 0.0
            self.last_reset = today

        if self.daily_spend >= self.daily_budget:
            raise RuntimeError(f"Daily budget of ${self.daily_budget} exceeded")

    def on_llm_end(self, response, **kwargs) -> None:
        """Update spend after request."""
        if response.llm_output and "token_usage" in response.llm_output:
            usage = response.llm_output["token_usage"]
            cost = estimate_cost(
                usage.get("prompt_tokens", 0),
                usage.get("completion_tokens", 0),
                self.model
            )
            self.daily_spend += cost

# Usage
budget_callback = BudgetLimitCallback(daily_budget=50.0)
llm = ChatOpenAI(model="gpt-4o-mini", callbacks=[budget_callback])

Cost Optimization Summary

Output

• Token counting and cost tracking
• Prompt optimization utilities
• Model routing for cost efficiency
• Budget enforcement callbacks

Resources

• OpenAI Pricing
• Anthropic Pricing
• tiktoken

Next Steps

Use langchain-reference-architecture for scalable production patterns.

Source: HelixDevelopment/HelixAgent — distributed by TomeVault.