LlamaIndex
YouTube ... Quora ...Google search ...Google News ...Bing News
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
- Llamaindex
- Agents ... Robotic Process Automation ... Assistants ... Personal Companions ... Productivity ... Email ... Negotiation ... LangChain
- Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Explainable / Interpretable AI
- Zero to One: A Guide to Building a First PDF Chatbot with LangChain & LlamaIndex — Part 1 | Ryan Nguyen - Medium
- LlamaIndex: Comprehensive guide on storage | Ryan Nguyen - How AI Built This ... Optimizing LLM Applications with Vector Embeddings, affordable alternatives to OpenAI’s API and how we move from LlamaIndex to Langchain
- LlamaIndex: How to use Index correctly. | Ryan Nguyen - How AI Built This ... and understanding what use case for what type of index
LlamaIndex (GPT Index) is a data framework that allows users to connect custom data sources to Large Language Model (LLM)s. It provides tools to structure data, offers data connectors to ingest existing data sources and data formats (APIs, PDFs, docs, SQL, etc.), and provides an advanced retrieval/query interface over the data. LlamaIndex is designed to help developers manage their data for LLM (large language model) applications. It is a simple and flexible framework that can be used to augment LLMs with private data. LlamaIndex is open source and can be downloaded from PyPI. It is also available on GitHub. LlamaIndex is being developed into an enterprise solution that will allow customers to use "protection-grade" data connectors to parse and transport large volumes of data.
- Offers data connectors to ingest your existing data sources and data formats (APIs, PDFs, docs, SQL, etc.)
- Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.
- Infer data structures from unstructured data
- Provides an advanced retrieval/query interface over your data: Feed in any LLMs input prompt, get back retrieved context and knowledge-augmented output.
- Allows easy integrations with your outer application framework (e.g. with LangChain, Flask, Docker, ChatGPT, anything else).
LlamaIndex also offers some general abstractions to convert an index or composed graph into a tool that can be used by an agent. The advanced retrieval/query interface over the data that LlamaIndex provides allows users to feed in any LLM input prompt and get back retrieved context and knowledge-augmented output. Therefore, LlamaIndex works with graphs by allowing users to structure data in a way that can be easily used with LLMs and by providing an interface to query the data in a graph format. Therefore, the graph hierarchy works with the data by allowing users to structure their data in a graph format and use LlamaIndex's tools to easily integrate the data with LLMs.
