In the age of data-driven decision-making, leveraging powerful tools like Elasticsearch and BERT can significantly enhance your job search applications. This blog will walk you through the process of integrating Elasticsearch with a pretrained BERT model to improve document indexing and searching capabilities.

Prerequisites

• Docker
• Docker Compose – version 1.22.0

Step 1: Download a Pretrained BERT Model

Start by downloading a suitable pretrained BERT model that best fits your application needs. Below are some options:

• BERT-Base, Uncased – 12-layer, 768-hidden, 12-heads, 110M parameters
• BERT-Large, Uncased – 24-layer, 1024-hidden, 16-heads, 340M parameters
• BERT-Base, Cased – 12-layer, 768-hidden, 12-heads, 110M parameters
• BERT-Large, Cased – 24-layer, 1024-hidden, 16-heads, 340M parameters

Use the following commands to download and unzip the model:

bash
$ wget https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip
$ unzip cased_L-12_H-768_A-12.zip

Step 2: Set Environment Variables

Before proceeding, set the environment variables for your pretrained BERT model and Elasticsearch index name:

bash
$ export PATH_MODEL=./cased_L-12_H-768_A-12
$ export INDEX_NAME=jobsearch

Step 3: Run Docker Containers

Now, run the Docker containers:

bash
$ docker-compose up

CAUTION: Ensure to assign more than 8GB memory to Docker’s configuration, as the BERT container requires high memory.

Step 4: Create an Index

We now need to create an index in the Elasticsearch cluster. Utilize the create index API to define settings, mappings, and aliases for your index:

bash
$ python example/create_index.py --index_file=example/index.json --index_name=jobsearch

Here’s an example of what the index.json might look like:

yaml
{
    "settings": {
        "number_of_shards": 2,
        "number_of_replicas": 1
    },
    "mappings": {
        "dynamic": true,
        "_source": {
            "enabled": true
        },
        "properties": {
            "title": {
                "type": "text"
            },
            "text": {
                "type": "text"
            },
            "text_vector": {
                "type": "dense_vector",
                "dims": 768
            }
        }
    }
}

CAUTION: The dimension of the text_vector must match the dimension of the pretrained BERT model.

Step 5: Create Documents

With the index created, it’s time to convert your documents into vectors using BERT. Prepare your data in CSV format:

bash
$ python example/create_documents.py --data=example/example.csv --index_name=jobsearch

Your example.csv may contain entries like:

csv
Title,Description
Saleswoman,lorem ipsum
Software Developer,lorem ipsum
Chief Financial Officer,lorem ipsum
General Manager,lorem ipsum
Network Administrator,lorem ipsum

After running the script, you’ll receive a JSON document such as:

python
{"_op_type": "index", "_index": "jobsearch", "text": "lorem ipsum", "title": "Saleswoman", "text_vector": [...]}

Step 6: Index Documents

Once your data is in JSON format, index it to the specified Elasticsearch index:

bash
$ python example/index_documents.py

Step 7: Open Browser

Finally, navigate to http://127.0.0.1:5000 to interact with your Elasticsearch instance.

Troubleshooting

If you encounter issues during the integration, consider the following:

• Ensure Docker and Docker Compose are properly installed and running.
• Check memory allocation for Docker; insufficient memory may prevent BERT from functioning correctly.
• Verify that your BERT model path and index name are set properly in the environment variables.
• Ensure your JSON and CSV files are correctly formatted.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following these steps, you can successfully integrate Elasticsearch with BERT for improved job search functionality. This combination of tools allows for effective document indexing and searching, paving the way for seamless user experiences.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.