ToxyBorg/llama_langchain_documents_embeddings
just testing langchain with llama cpp documents embeddings
This project helps developers and data scientists transform unstructured documents like PDFs into a format suitable for advanced search and question-answering systems. It takes a directory of documents, breaks them into smaller pieces, and then processes these pieces to create numerical representations (embeddings) and a searchable index. The output is a highly efficient system that can quickly find relevant document sections and answer specific questions based on the content.
No commits in the last 6 months.
Use this if you are a developer or data scientist building an application that needs to perform semantic search or question answering over a collection of private or specialized documents using local language models.
Not ideal if you are looking for an end-user application to search documents without needing to write code, or if you plan to use cloud-based language models and embedding services.
Stars
16
Forks
1
Language
Python
License
Unlicense
Category
Last pushed
Jun 18, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/ToxyBorg/llama_langchain_documents_embeddings"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Azure-Samples/azure-ai-document-processing-samples
A collection of samples demonstrating techniques for processing documents with Azure AI...
artitw/text2text
Text2Text Language Modeling Toolkit
aiplanethub/beyondllm
Build, evaluate and observe LLM apps
build-on-aws/langchain-embeddings
This repository demonstrates the construction of a state-of-the-art multimodal search engine,...
qianniuspace/llm_notebooks
AI 应用示例合集