Retrieval Augmented Generation (RAG) can be implemented in JavaScript with LangChain.js and MarkLogic via a “retriever”. The example in this directory demonstrates one kind of retriever that you can consider for your own AI application.

Table of contents

Setup

The only system requirements for running these examples are Node 18.x, 19.x, or 20.x (see [LangChain.js Installation]https://js.langchain.com/v0.1/docs/get_started/installation/) and npm. Minimum versions of npm are dependent on the version of Node. See Node Releases for more information.

For this LangChain.js example, in addition to the environment variables in the .env file described in the setup guide, you’ll also need to add the AZURE_OPENAI_API_INSTANCE_NAME setting to the .env file.

OPENAI_API_VERSION=2023-12-01-preview
AZURE_OPENAI_ENDPOINT=<Your Azure OpenAI endpoint>
AZURE_OPENAI_API_KEY=<Your Azure OpenAI API key>
AZURE_LLM_DEPLOYMENT_NAME=<The name of an LLM deployment in your Azure OpenAI service>
AZURE_OPENAI_API_INSTANCE_NAME=<The host name of your Azure OpenAI endpoint>

Once Node and npm are installed, and your environment is configured, you can run the examples via an IDE such as Visual Code or IntelliJ. To try these examples, you should first use npm to install the required Node modules:

npm install

RAG with a simple word query

A key feature of MarkLogic is its ability to index all text in a document during ingest. Thus, a simple approach to RAG with MarkLogic is to select documents based on the words in a user’s question.

To demonstrate this, you can run the askWordQuery.js module with any question. The module uses a custom LangChain.js retriever that selects documents in the ai-examples-content MarkLogic database containing one or more of the words in the given question. It then includes the top 10 most relevant documents in the request that it sends to Azure OpenAI. For example:

node askWordQuery.js "What disturbances has Jane Doe caused?"

Running this will yield an answer similar to the below (the answer can vary based on the LLM in use and the nature of the configured deployment model):

Jane Doe has caused disturbances such as public intoxication, yelling and banging on doors and windows, smashing windows and graffiti, and disturbing the peace in a residential area. Possible motives for her behavior include personal vendettas, revenge, coping mechanisms, and stealing intellectual property.

RAG with a vector query

MarkLogic 12 has new support for generative AI capabilities via a set of vector operations. With this approach, documents are first selected in a manner similar to the approaches shown above - by leveraging the powerful and flexible set of indexes that have long been available in MarkLogic. The documents are then further filtered and sorted via the following process:

  1. An embedding of the user’s question is generated using LangChain.js and Azure OpenAI.
  2. Using MarkLogic’s new vector API, the generated embedding is compared against the embeddings in each selected crime event document to generate a similarity score for each document.
  3. The documents with the highest similarity scores are sent to the LLM to augment the user’s question.

To try the askVectorQuery.js module, you will need to have installed MarkLogic 12 and also have defined AZURE_EMBEDDING_DEPLOYMENT_NAME in your .env file. Please see the setup guide for more information.

You can now run askVectorQuery.js:

node askVectorQuery.js "What disturbances has Jane Doe caused?"

An example result is shown below:

Jane Doe has caused disturbances at various locations, including shouting, banging on doors and windows, screaming and yelling, and vandalism. The motive for her behavior is unclear, but it may be related to personal issues or mental health challenges. Witnesses have described Jane Doe as a tall woman with long blonde or brown hair, wearing a bright or dark-colored dress, and appearing agitated or upset.”

The results are similar but slightly different to the results shown above for a simple word query. You can compare the document URIs printed by each program to see that a different set of document is selected by each approach.

Summary

The three RAG approaches shown above - a simple word query, a contextual query, and a vector query - demonstrate how easily data can be queried and retrieved from MarkLogic using LangChain.js. Identifying the optimal approach for your own data will require testing the approaches you choose and possibly leveraging additional MarkLogic indexes and/or further enriching your data.