gptcache 0.1.3

GPT Cache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPT Cache works as a memcache for AIGC applications, similar to how Redis works for traditional applications.

GPT Cache

🤠 What is GPT Cache?

Large Language Models (LLMs) are a promising and transformative technology that has rapidly advanced in recent years. These models are capable of generating natural language text and have numerous applications, including chatbots, language translation, and creative writing. However, as the size of these models increases, so do the costs and performance requirements needed to utilize them effectively. This has led to significant challenges in developing on top of large models such as ChatGPT.

To address this issue, we have developed GPT Cache, a project that focuses on caching responses from language models, also known as a semantic cache. The system offers two major benefits:

1. Quick response to user requests: the caching system provides faster response times compared to large model inference, resulting in lower latency and faster response to user requests.
2. Reduced service costs: most LLM services are currently charged based on the number of tokens. If user requests hit the cache, it can reduce the number of requests and lower service costs.

🤔 Why would GPT Cache be helpful?

A good analogy for GptCache is to think of it as a more semantic version of Redis. In GptCache, hits are not limited to exact matches, but rather also include prompts and context similar to previous queries. We believe that the traditional cache design still works for AIGC applications for the following reasons:

• Locality is present everywhere. Like traditional application systems, AIGC applications also face similar hot topics. For instance, ChatGPT itself may be a popular topic among programmers.
• For purpose-built SaaS services, users tend to ask questions within a specific domain, with both temporal and spatial locality.
• By utilizing vector similarity search, it is possible to find a similarity relationship between questions and answers at a relatively low cost.

We provide benchmarks to illustrate the concept. In semantic caching, there are three key measurement dimensions: false positives, false negatives, and hit latency. With the plugin-style implementation, users can easily tradeoff these three measurements according to their needs.

😊 Quick Start

Note:

• You can quickly try GPT cache and put it into a production environment without heavy development. However, please note that the repository is still under heavy development.
• By default, only a limited number of libraries are installed to support the basic cache functionalities. When you need to use additional features, the related libraries will be automatically installed.
• Make sure that the Python version is 3.8.1 or higher, check: python --version
• If you encounter issues installing a library due to a low pip version, run: python -m pip install --upgrade pip.

pip install

pip install gptcache

dev install

# clone gpt cache repo
git clone https://github.com/zilliztech/GPTCache.git
cd GPTCache

# install the repo
pip install -r requirements.txt
python setup.py install

example usage

These examples will help you understand how to use exact and similar matching in caching.

And before running the example, make sure the OPENAI_API_KEY environment variable is set by executing echo $OPENAI_API_KEY.

If it is not already set, it can be set by using OPENAI_API_KEY=YOUR_API_KEY.

It's important to note that this method is only effective temporarily, so if you want a permanent effect, you'll need to modify the environment variable configuration file. For instance, on a Mac, you can modify the file located at /etc/profile.

Click to SHOW example code

OpenAI API original usage

import os
import time

import openai


def response_text(openai_resp):
    return openai_resp['choices'][0]['message']['content']


question = 'what‘s chatgpt'

# OpenAI API original usage
openai.api_key = os.getenv("OPENAI_API_KEY")
start_time = time.time()
response = openai.ChatCompletion.create(
  model='gpt-3.5-turbo',
  messages=[
    {
        'role': 'user',
        'content': question
    }
  ],
)
print(f'Question: {question}')
print("Time consuming: {:.2f}s".format(time.time() - start_time))
print(f'Answer: {response_text(response)}\n')

OpenAI API + GPT Cache, exact match cache

If you ask ChatGPT the exact same two questions, the answer to the second question will be obtained from the cache without requesting ChatGPT again.

import time


def response_text(openai_resp):
    return openai_resp['choices'][0]['message']['content']

print("Cache loading.....")

# To use GPT cache, that's all you need
# -------------------------------------------------
from gptcache.core import cache
from gptcache.adapter import openai

cache.init()
cache.set_openai_key()
# -------------------------------------------------

question = "what's github"
for _ in range(2):
    start_time = time.time()
    response = openai.ChatCompletion.create(
      model='gpt-3.5-turbo',
      messages=[
        {
            'role': 'user',
            'content': question
        }
      ],
    )
    print(f'Question: {question}')
    print("Time consuming: {:.2f}s".format(time.time() - start_time))
    print(f'Answer: {response_text(response)}\n')

OpenAI API + GPT Cache, similar search cache

After obtaining an answer from ChatGPT in response to several similar questions, the answers to subsequent questions can be retrieved from the cache without the need to request ChatGPT again.

import time


def response_text(openai_resp):
    return openai_resp['choices'][0]['message']['content']

from gptcache.core import cache
from gptcache.adapter import openai
from gptcache.embedding import Onnx
from gptcache.cache.factory import get_ss_data_manager
from gptcache.similarity_evaluation.simple import SearchDistanceEvaluation

print("Cache loading.....")

onnx = Onnx()
data_manager = get_ss_data_manager("sqlite", "faiss", dimension=onnx.dimension())
cache.init(
    embedding_func=onnx.to_embeddings,
    data_manager=data_manager,
    similarity_evaluation=SearchDistanceEvaluation(),
    )
cache.set_openai_key()

questions = [
    "what's github",
    "can you explain what GitHub is",
    "can you tell me more about GitHub"
    "what is the purpose of GitHub"
]

for question in questions:
    for _ in range(2):
        start_time = time.time()
        response = openai.ChatCompletion.create(
            model='gpt-3.5-turbo',
            messages=[
                {
                    'role': 'user',
                    'content': question
                }
            ],
        )
        print(f'Question: {question}')
        print("Time consuming: {:.2f}s".format(time.time() - start_time))
        print(f'Answer: {response_text(response)}\n')

To use GPTCache exclusively, only the following lines of code are required, and there is no need to modify any existing code.

from gptcache.core import cache
from gptcache.adapter import openai

cache.init()
cache.set_openai_key()

More Docs：

• System Design, how it was constructed
• Features, all features currently supported by the cache
• Examples, learn better custom caching

🤗 Modules

• LLM Adapter: The LLM Adapter is designed to integrate different LLM models by unifying their APIs and request protocols. GPTCache offers a standardized interface for this purpose, with current support for ChatGPT integration.

  • Support OpenAI chatGPT API.
  • Support other LLMs, such as Hugging Face Hub, Bard, Anthropic, and self-hosted models like LLaMa.

• Embedding Generator: This module is created to extract embeddings from requests for similarity search. GPTCache offers a generic interface that supports multiple embedding APIs, and presents a range of solutions to choose from.

  • Disable embedding. This will turn GPTCache into a keyword-matching cache.
  • Support OpenAI embedding API.
  • Support ONNX with the paraphrase-albert-small-v2-onnx model.
  • Support Hugging Face embedding API.
  • Support Cohere embedding API.
  • Support fastText embedding API.
  • Support SentenceTransformers embedding API.
  • Support other embedding apis

• Cache Storage: Cache Storage is where the response from LLMs, such as ChatGPT, is stored. Cached responses are retrieved to assist in evaluating similarity and are returned to the requester if there is a good semantic match. At present, GPTCache supports SQLite and offers a universally accessible interface for extension of this module.

  • Support SQLite.
  • Support PostgreSQL.
  • Support MySQL.
  • Support MariaDB.
  • Support SQL Server.
  • Support Oracle.
  • Support MongoDB.
  • Support Redis.
  • Support Minio.
  • Support Habse.
  • Support ElasticSearch
  • Support zincsearch
  • Support other storages

• Vector Store: The Vector Store module helps find the K most similar requests from the input request's extracted embedding. The results can help assess similarity. GPTCache provides a user-friendly interface that supports various vector stores, including Milvus, Zilliz Cloud, and FAISS. More options will be available in the future.

  • Support Milvus.
  • Support Zilliz Cloud.
  • Support FAISS.
  • Support Qdrant
  • Support Chroma
  • Support PGVector
  • Support other vector databases

• Cache Manager: The Cache Manager is responsible for controlling the operation of both the Cache Storage and Vector Store.

  • Eviction Policy: Currently, GPTCache makes decisions about evictions based solely on the number of lines. This approach can result in inaccurate resource evaluation and may cause out-of-memory (OOM) errors. We are actively investigating and developing a more sophisticated strategy.
    • LRU eviction policy
    • FIFO eviction policy
    • More complicated eviction policies

• Similarity Evaluator: This module collects data from both the Cache Storage and Vector Store, and uses various strategies to determine the similarity between the input request and the requests from the Vector Store. Based on this similarity, it determines whether a request matches the cache. GPTCache provides a standardized interface for integrating various strategies, along with a collection of implementations to use. The following similarity definitions are currently supported or will be supported in the future:

  • The distance we obtain from the Vector Store.
  • A model-based similarity determined using the albert_duplicate model from ONNX.
  • Exact matches between the input request and the requests obtained from the Vector Store.
  • Distance represented by applying linalg.norm from numpy to the embeddings.
  • BM25 and other similarity measurements
  • Support other models

  Note:Not all combinations of different modules may be compatible with each other. For instance, if we disable the Embedding Extractor, the Vector Store may not function as intended. We are currently working on implementing a combination sanity check for GPTCache.

😆 Contributing

Would you like to contribute to the development of GPT Cache? Take a look at our contribution guidelines.