RAG-schema-retrieval-automation

Fix Hallucination in Retrieval Augmented Generation AI Applications Using Schema and Output Parser

Tech stack used includes LangChain, Pinecone, Typescript, Openai, and Next.js. LangChain is a framework that makes it easier to build scalable AI/LLM apps and chatbots. Pinecone is a vectorstore for storing embeddings and your PDF in text to later retrieve similar docs.

Development

1. Clone the repo or download the ZIP

git clone https://github.com/hellokellyworld/RAG-schema-retrieval-automation.git

2. Install packages

First run npm install yarn -g to install yarn globally (if you haven't already).

Then run:

yarn install

After installation, you should now see a node_modules folder.

3. Set up your .env file

• Copy .env.example into .env Your .env file should look like this:

OPENAI_API_KEY=

PINECONE_API_KEY=
PINECONE_ENVIRONMENT=

PINECONE_INDEX_NAME=

• Visit openai to retrieve API keys and insert into your .env file.
• Visit pinecone to create and retrieve your API keys, and also retrieve your environment and index name from the dashboard.

Make sure you set the dimension size to be 1536 and use cosine similarity when creating index in Pinecone.

4. In the config folder, replace the PINECONE_NAME_SPACE with a namespace where you'd like to store your embeddings on Pinecone when you run yarn run ingest. This namespace will later be used for queries and retrieval.

5. In utils/makechain.ts chain change the QA_PROMPT for your own usecase. Change modelName in new OpenAI to gpt-4, if you have access to gpt-4 api. Please verify outside this repo that you have access to gpt-4 api, otherwise the application will not work. For gpt-4, you will need to prepay OpenAI before you can activiate it. https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4

Convert your PDF files to embeddings

This repo can load multiple PDF files

1. Inside docs folder, add your pdf files or folders that contain pdf files.

2. Run the script yarn run ingest to 'ingest' and embed your docs. If you run into errors troubleshoot below.

3. Check Pinecone dashboard to verify your namespace and vectors have been added.

Run the app

Once you've verified that the embeddings and content have been successfully added to your Pinecone, you can build the app yarn build. Then, you can run the app yarn start to launch the local dev environment at http://localhost:8081 in browser, and then define fields to extract.

Credit

The source code was inspired by this project here gpt4-pdf-chatbot-langchain.

Frontend of this repo is inspired by langchain-chat-nextjs

Publication

The article about this project can be read here Fix Hallucination in Retrieval Augmented Generation AI Applications Using Schema and Output Parser.