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HeimdaLLM Alternatives
Similar projects and alternatives to HeimdaLLM based on common topics and language
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DB-GPT-Hub
A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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khoj
Your AI second brain. A copilot to get answers to your questions, whether they be from your own notes or from the internet. Use powerful, online (e.g gpt4) or private, local (e.g mistral) LLMs. Self-host locally or use our web app. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.
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OpenDAN-Personal-AI-OS
OpenDAN is an open source Personal AI OS , which consolidates various AI modules in one place for your personal use.
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deeplake
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
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pandas-ai
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better HeimdaLLM alternative or higher similarity.
HeimdaLLM reviews and mentions
Posts with mentions or reviews of HeimdaLLM.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-12.
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Ben Forta – How to Generate SQL Statements with ChatGPT
This is solvable by augmenting the database schema with comments.
When you integrate your database with an LLM, you'll notice the LLM will produce flawed queries based on wrinkles in your database schema. This is because the LLM relies on conventional understanding of how the schema is probably tied together. When you see the flawed queries, you augment the schema with a comment that explains why the schema has a wrinkle. The LLM takes that into consideration and the resulting queries are improved.
A concrete example[1]: I found that when querying the Sakila movie rental database, the generated query would frequently attempt to join the `rental` table to the `film` table through a nonexistent `film_id` column on the rental table. By adding the linked comment, the LLM stopped doing that.
1. https://github.com/amoffat/HeimdaLLM/blob/dev/notebooks/saki...
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My small, no name company has lost its mind with AI
I think we're just scratching the surface of apps. As we figure out how to integrate this technology in novel ways (not just "here's my app + AI!!!!"), it will open new doors.
Shameless self-promotion, I'm trying to build some of those intermediary pieces. I have authored an open source library[1] that lets businesses externalize LLMs to their users, so that users can use natural language to query their data in your database. The goal is to try to simplify UIs to have more natural language components, without needing to send your data to an LLM.
1. https://github.com/amoffat/HeimdaLLM
- Show HN: HeimdaLLM
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A note from our sponsor - InfluxDB
www.influxdata.com | 10 May 2024
Stats
Basic HeimdaLLM repo stats
4
95
8.8
3 months ago
amoffat/HeimdaLLM is an open source project licensed under GNU Affero General Public License v3.0 which is an OSI approved license.
The primary programming language of HeimdaLLM is Python.
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