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Top 23 Python langchain Projects
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DB-GPT
AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
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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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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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InternGPT
InternGPT (iGPT) is an open source demo platform where you can easily showcase your AI models. Now it supports DragGAN, ChatGPT, ImageBind, multimodal chat like GPT-4, SAM, interactive image editing, etc. Try it at igpt.opengvlab.com (支持DragGAN、ChatGPT、ImageBind、SAM的在线Demo系统)
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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argilla
Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
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Ask-Anything
[CVPR2024 Highlight][VideoChatGPT] ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS.
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RasaGPT
💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
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api-for-open-llm
Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, Xverse, SqlCoder, CodeLLaMA, ChatGLM, ChatGLM2, ChatGLM3 etc. 开源大模型的统一后端接口
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DemoGPT
Create 🦜️🔗 LangChain apps by just using prompts🌟 Star to support our work! | 只需使用句子即可创建 LangChain 应用程序。 给个star支持我们的工作吧!
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agenta
The all-in-one LLM developer platform: prompt management, evaluation, human feedback, and deployment all in one place.
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SaaSHub
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Interact your data and environment using the local GPT (https://github.com/csunny/DB-GPT)
Project mention: Ask HN: What are the drawbacks of caching LLM responses? | news.ycombinator.com | 2024-03-15Just found this: https://github.com/zilliztech/GPTCache which seems to address this idea/issue.
Project mention: Ask HN: Tell us about your project that's not done yet but you want feedback on | news.ycombinator.com | 2023-08-16This is very cool! I just downloaded the extension and tried a hello world program. Will tinker a bit more.
If you can make a library to run python code inside a JS project (like Next JS) then we could build a code interpreter in Next JS. Would be super cool. Here is a python implementation - https://github.com/shroominic/codeinterpreter-api - for your reference.
You can also create an issue and ask the developers for help.
Project mention: Open-Source Data Collection Platform for LLM Fine-Tuning and RLHF | news.ycombinator.com | 2023-06-05I'm Dani, CEO and co-founder of Argilla.
Happy to answer any questions you might have and excited to hear your thoughts!
More about Argilla
GitHub: https://github.com/argilla-io/argilla
Project mention: Show HN: Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community | news.ycombinator.com | 2023-09-30Hey friends!
We have built an app for getting insights about your favorite GitHub community using large language models.
The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.
This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.
Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!
We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.
We’ve been working on this app for a month now and are excited to open source it today :)
Some useful links:
* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...
* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/
* EvaDB - https://github.com/georgia-tech-db/evadb
Please let us know what you think!
RasaGPT: headless LLM chatbot platform built on top of Rasa and Langchain (https://github.com/paulpierre/RasaGPT)
Another thing to try is one of the repositories like SolidGPT: https://github.com/AI-Citizen/SolidGPT
Try this: https://github.com/refuel-ai/autolabel
Then the main challenge just becomes prompt design, which can sometimes be nebulous for NLP annotation.
Project mention: Open-interpreter: OpenAI's Code Interpreter in your terminal, running locally | news.ycombinator.com | 2023-11-12I wrote a similar thing for myself: https://github.com/ricklamers/shell-ai
It currently has 882 stars and I think a few people at least are enjoying using it.
Project mention: Lanarky: Deploy LLM applications in production, built on FastAPI | news.ycombinator.com | 2023-06-10
Project mention: Ask HN: How are you testing your LLM applications? | news.ycombinator.com | 2024-02-06I am biased, but I would use a platform and not roll your own solution. You will tend to underestimate the depth of capabilities needed for an eval framework.
Now for solutions, shameless plug here, we are building an open-source platform for experimenting and evaluating complex LLM apps (https://github.com/agenta-ai/agenta). We offer automatic evaluators as well as human annotation capabilities. Currently, we only provide testing before deployment, but we have plans to include post-production evaluations as well.
Other tools I would look at in the space are promptfoo (also open-source, more dev oriented), humanloop (one of the most feature complete tools in the space, enterprise oriented), however more enterprise oriented / costly) and vellum (YC company, more focused towards product teams)
Python langchain related posts
- Show HN: Tiger – Function Hub for LLM Agents
- News DataStax just bought our startup Langflow
- Ask HN: How are you testing your LLM applications?
- Using Large Language Models for Hyperparameter Optimization, Zhang et al. 2023 [GPT-4 is quite good at finding the optimal hyperparameters for machine learning tasks]
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langchain VS agenta - a user suggested alternative
2 projects | 22 Nov 2023
- Open-interpreter: OpenAI's Code Interpreter in your terminal, running locally
- Node-based AutoGen with local LLMs inside ComfyUI
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A note from our sponsor - SaaSHub
www.saashub.com | 27 Apr 2024
Index
What are some of the best open-source langchain projects in Python? This list will help you:
Project | Stars | |
---|---|---|
1 | DB-GPT | 10,943 |
2 | awesome-chatgpt-zh | 9,867 |
3 | deeplake | 7,708 |
4 | GPTCache | 6,406 |
5 | TaskingAI | 4,233 |
6 | codeinterpreter-api | 3,642 |
7 | InternGPT | 3,121 |
8 | argilla | 3,108 |
9 | Ask-Anything | 2,663 |
10 | evadb | 2,569 |
11 | RasaGPT | 2,168 |
12 | api-for-open-llm | 1,952 |
13 | SolidGPT | 1,948 |
14 | autolabel | 1,778 |
15 | Chrome-GPT | 1,589 |
16 | DemoGPT | 1,566 |
17 | shell-ai | 950 |
18 | lanarky | 939 |
19 | autollm | 908 |
20 | llm_agents | 877 |
21 | agenta | 823 |
22 | langcorn | 812 |
23 | langchain-visualizer | 692 |
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