Supercharge Your OpenAI Chatgpt API Calls with Langchain Async!

Supercharge Your OpenAI Chatgpt API Calls with Langchain Async!

Table of Contents:

1. Introduction
2. Async Parallel Calls in LLM Chain
3. Demo: Making Regular Calls to LLM
4. Demo: Making Calls to LLM Chain with Input Parameters
5. Demo: Making Agent Calls in Parallel
6. Implementing Async with Lang Syn
7. Requirements and Setup
8. Regular Calls to LLM Using Blank Chain
9. Async Calls to LLM Using Blank Chain
10. Running Agents with Tools in Async Parallel

1. Introduction

In this article, we will explore the concept of async parallel calls in length chain (LLM chain). We will discuss how to make regular calls to LLM, make calls to LLM chain with input parameters, and make agent calls in parallel. This will allow us to achieve a significant speed boost in our language model tasks.

2. Async Parallel Calls in LLM Chain

Async parallel calls in LLM chain were introduced by Chainlink in February 8th. This feature enables the ability to make multiple calls to the language model using the same prompt. It offers incredible speed boost in tasks such as chunk summarization. We will explore the demos of these calls to better understand their benefits.

3. Demo: Making Regular Calls to LLM

In this demo, we will make regular calls to an LLM five times, both serially and concurrently in parallel. We will measure the execution time of both methods and compare the speed boost achieved by async parallel calls.

4. Demo: Making Calls to LLM Chain with Input Parameters

Next, we will explore making calls to an LLM chain with input parameters. This chain allows us to define Prompts with input variables and generate responses accordingly. We will run this chain with both concurrent and serial execution to observe the speed difference. This demo will showcase the benefits of async parallel calls in LLM chain.

5. Demo: Making Agent Calls in Parallel

In this demo, we will learn how to make agent calls in parallel using async parallelization. We will prepare a set of questions and utilize tools such as LLM Math and SERP API for answering these questions. The demo will showcase the improved performance achieved through async parallel calls.

6. Implementing Async with Lang Syn

In this section, we will discuss how to implement async with Lang Syn. We will provide a step-by-step guide for setting up the required environment and installing the necessary packages. We will also provide links to Relevant resources and documentation for further learning.

7. Requirements and Setup

Before we proceed with implementing async in Lang Syn, it is essential to understand the requirements and setup process. We will discuss the necessary packages to install, including Blank Chain, OpenAI, and Google Search Results. Additionally, we will provide instructions on creating a virtual environment and activating it.

8. Regular Calls to LLM Using Blank Chain

To demonstrate regular calls to LLM using Blank Chain, we will define a serial function that generates responses. This function will utilize the OpenAI API and prompt templates. We will run the function five times to showcase the serial execution and measure the elapsed time.

9. Async Calls to LLM Using Blank Chain

Building upon the previous demo, we will explore async calls to LLM using Blank Chain. We will define an async function that generates responses concurrently. We will also define a function to run the async function multiple times concurrently. The elapsed time will be measured and compared with the serial execution.

10. Running Agents with Tools in Async Parallel

In this section, we will focus on running agents with tools in async parallel. We will import the necessary libraries and tools, such as OpenAI, SERP API, and callback managers. We will define functions to generate responses serially and concurrently using agents. The performance difference between serial and concurrent execution will be examined.

Conclusion

In conclusion, async parallel calls in LLM chain offer significant speed boosts in language model tasks. By utilizing async techniques, we can make regular calls, calls with input parameters, and agent calls in parallel. These techniques provide improved performance and efficiency in language model applications. Implementing async with Lang Syn and understanding the requirements and setup process play a crucial role in achieving optimal results.

Highlights:

• Introduction to async parallel calls in LLM chain
• Demos of regular calls to LLM, calls to LLM chain with input parameters, and agent calls in parallel
• Implementing async with Lang Syn
• Requirements and setup process
• Examples of regular and async calls to LLM using Blank Chain
• Running agents with tools in async parallel for improved performance

FAQ:

Q: What are async parallel calls in LLM chain? A: Async parallel calls in LLM chain allow multiple calls to the language model using the same prompt, resulting in a significant speed boost.

Q: How can async be implemented with Lang Syn? A: Async can be implemented with Lang Syn by setting up the required environment, installing the necessary packages, and utilizing async functions.

Q: What tools are used in async parallel calls? A: Tools such as LLM Math and SERP API are commonly used in async parallel calls to enhance the functionalities of the language model.

Q: What are the benefits of running agents in parallel? A: Running agents in parallel improves performance and efficiency in language model applications, resulting in faster response times.

Q: How do regular and async calls differ in LLM? A: Regular calls to LLM are executed serially, while async calls allow for concurrent execution, resulting in faster completion times.

Q: Can async parallel calls be used with all tools? A: Currently, async support is available for tools such as LLM Math and SERP API, but support for other tools may vary. Please refer to the documentation for specific tool compatibility.

Q: What is the AdVantage of using AIO HTTP in async parallel calls? A: AIO HTTP enhances the performance of async parallel calls by optimizing the HTTP requests made to the language model.

Q: How can agents be utilized in parallel execution? A: By leveraging the power of async parallelization, agents can be executed concurrently, allowing for faster processing of queries and responses.