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Fixing the Limitation of Large Language Models with GraphQL

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    Andrew Blase


In the world of large language models, efficient data handling is not just a luxury—it's a necessity. This blog post explores how GraphQL, a powerful data query and manipulation language, can serve as the optimal data source for these models.

Thinking about Large Language Models

Understanding the Basics

Before we dive into the heart of the matter, let's familiarize ourselves with some key terms:

  • Large Language Models: These are AI models trained on a vast amount of text data. They can generate human-like text and are used in a variety of applications, from drafting emails to writing code. Examples include OpenAI's GPT-3.5 and GPT-4.

  • GraphQL: This is a query language for APIs and a runtime for executing those queries with your existing data. It provides an efficient and powerful alternative to REST.

  • Token Limitation: In the context of language models, a token can be as short as one character or as long as one word. Large language models read text in chunks called tokens. However, these models have a maximum limit to the number of tokens they can process at once, known as token limitation.

The Power of GraphQL

GraphQL stands out for its ability to fetch exactly the data you need. In traditional REST APIs, you often end up over-fetching or under-fetching data. GraphQL solves this problem, allowing you to specify precisely what data you want—no more, no less. This feature not only makes GraphQL efficient but also reduces the need for data transformation and cleanup.

Large Language Models and Their Limitations

Large language models like GPT-3.5 and GPT-4 have revolutionized many fields. However, they come with their own set of challenges, one of which is token limitation. These models can only process a certain number of tokens at once. If the data exceeds this limit, it can lead to data overload and impact the model's performance.

GraphQL and Large Language Models: A Perfect Match

Given the token limitations of large language models, GraphQL's precise data retrieval is a game-changer. By fetching only the necessary data, GraphQL ensures that these models operate within their token limits, thereby preventing data overload and enhancing performance.

Woman uses GraphQL and Large Language Models

LangChain: A Practical Implementation

LangChain is an open-source library that equips developers with the necessary tools to create applications powered by large language models (LLMs). It stands as a practical example of implementing GraphQL with large language models. LangChain is an orchestration tool for prompts, making it easier for developers to chain different prompts interactively. This allows for more complex tasks to be broken down into smaller sub-tasks, introducing context and memory into completions. By leveraging GraphQL, LangChain ensures efficient data handling, making it an excellent resource for anyone interested in this field.

LangChain's GraphQL Implementation

from langchain import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
from langchain.utilities import GraphQLAPIWrapper

llm = OpenAI(temperature=0)

tools = load_tools(

agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True

graphql_fields = """allFilms {
    films {
      speciesConnection {
        species {
          homeworld {


suffix = "Search for the titles of all the stawars films stored in the graphql database that has this schema " + graphql_fields)


In conclusion, GraphQL's ability to fetch precise data makes it an optimal data source for large language models. By preventing data overload and ensuring efficient data handling, GraphQL helps these models operate within their token limits, thereby enhancing their performance. To learn more about this fascinating intersection of technologies, sign up for our newsletter.

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