This code is a Python script that creates a FAISS index from a log file. The FAISS index is a similarity search index that allows for fast and efficient similarity searches. The script reads the content from a log file, splits it into chunks of 1000 characters, and creates a FAISS index with a dimensionality compatible with OpenAI embeddings. The script also includes commented code that initializes a Kafka consumer and creates a FAISS index if there is a message in the topic.
This script requires the following dependencies:
langchain(https://github.com/LangChain/langchain)numpy(https://numpy.org/)faiss(https://github.com/facebookresearch/faiss)kafka-python(https://github.com/dpkp/kafka-python)
You can install these dependencies by running the following command:
pip install langchain numpy faiss kafka-python
To execute the code, you need to run the main() function in the script. You can do this by running the following command:
python syslog-ng-indexer.py
If you want to contribute to this project, you can fork the repository and submit a pull request with your changes. Please make sure to follow the PEP 8 style guide (https://www.python.org/dev/peps/pep-0008/) and include tests for your changes.
- LangChain. (n.d.). LangChain. GitHub. https://github.com/LangChain/langchain
- NumPy. (2021). NumPy. https://numpy.org/
- Facebook Research. (n.d.). faiss. GitHub. https://github.com/facebookresearch/faiss
- Kafka-Python. (n.d.). kafka-python. GitHub. https://github.com/dpkp/kafka-python