Last updated: March 31, 2023
Large language models (LLMs) have become a cornerstone of natural language processing, offering unparalleled capabilities for knowledge generation and reasoning. The past few weeks have seen a number of high-profile releases of models and interfaces. However, despite their immense potential, incorporating custom, private data into these models remains a challenge. This is where tools like LangChain and LlamaIndex (formerly 'GPT Index') come into play, offering innovative solutions for data ingestion and indexing, enabling developers to augment LLMs with their unique datasets.
LangChain and LlamaIndex facilitate in-context learning, an emerging paradigm that allows developers to insert context into input prompts, leveraging LLM's reasoning capabilities for generating more relevant and accurate responses. This differs from finetuning, which requires retraining models using custom datasets, often demanding significant computational resources and time.
By addressing data ingestion and indexing, LangChain and LlamaIndex provide a streamlined framework for integrating custom data into LLMs. Their flexible design simplifies incorporating external data sources, enabling developers to focus on creating powerful applications that harness LLMs' full potential.
These tools bridge the gap between external data and LLMs, ensuring seamless integration while maintaining performance. By utilizing LangChain and LlamaIndex, developers can unlock LLMs' true potential and build cutting-edge applications tailored to specific use cases and datasets.
🛣️ The project we built uses both langchain and llama_index as well as some extra code for the Slack bot itself. If you want to get your hands dirty and try out a simpler version, feel free to check out our Generative Chat example that was released previously.
Stack and Components
This project uses a simple stack to train the pipeline:
Local Stack with GCP Artifact Store:
View the DAG and steps used in this pipeline on our demo dashboard.