pathway
llm-app
pathway | llm-app | |
---|---|---|
3 | 12 | |
1,733 | 2,501 | |
8.0% | 12.9% | |
9.6 | 8.9 | |
3 days ago | 6 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pathway
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Choosing Between a Streaming Database and a Stream Processing Framework in Python
We understood how streaming databases differ from traditional databases, stream processing engines, conventional analytics databases, or OLAP databases. Now let’s focus on when and why we can use stream data processing frameworks for Python as an alternative to streaming databases. Python is the go-to language for data science and machine learning. There are some stream-processing libraries and frameworks in Python such as Bytewax, Quix, GlassFlow, Pathway. They have been developed to cope with the challenges Python Engineers face with Apache Kafka or Flink since they do not natively support Python.
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How to build a custom GPT enabled full-stack app for real-time data
Maintain a data snapshot to observe variations in sales prices over time, as Pathway provides a built-in feature to compute differences between two alterations.
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Show HN: LLM App – build a realtime LLM app in 30 lines, with no vector database
Pathway (https://github.com/pathwaycom/pathway) is a data processing framework we are developing that unifies stream and batch processing of large datasets. It lets developers concentrate on writing the data processing logic, without worrying about tracking changes to data and updating the results. The same code can then be run on batch data (e.g. during testing) or on real-time data streams (i.e. online query processing)
In the LLM app, Pathway allows concentrating on prompt building and querying the LLM APIs as if the corpus of documents were static, while all updates to it are handled by the framework itself.
llm-app
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How to use LLMs for real-time alerting
Answering queries and defining alerts: Our application running on Pathway LLM-App exposes the HTTP REST API endpoint to send queries and receive real-time responses. It is used by the Streamlit UI app. Queries are answered by looking up relevant documents in the index, as in the Retrieval-augmented generation (RAG) implementation. Next, queries are categorized for intent: an LLM probes them for natural language commands synonymous with notify or send an alert.
- Open Source Project showcasing Real-time intent detection + alerting via LLMs using Pathway LLM App and Streamlit, both open-source Pythonic frameworks. Can be used in LLMOps for monitoring, PII detection, and in BizOps to notify teams of critical changes by others in their documents.
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Show HN: Alerting in realtime RAG: spot changes to LLM answers, using few tokens
(used in https://github.com/pathwaycom/llm-app/blob/69709a2cf58cdf6ea...)
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How to build a custom GPT enabled full-stack app for real-time data
In this case, you need to build a custom LLM (Language Learning Model) app efficiently to give context to the answer process. A promising approach you find on the internet is utilizing LLMs with vector databases that come with costs like increased prep work, infrastructure, and complexity. Keeping source and vectors in sync is painful. Instead, you can use an open-source LLM App library in Python to implement real-time in-memory data indexing directly reading data from any compatible storage and showing this data on Streamlit UI.
- Python library to build LLM App
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Privacy-preserving LLM App for real-time data
🔗 - Here's the repo link: https://github.com/pathwaycom/llm-app.
- LLM (Large Language Model) App is an innovative AI Chat Bot
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Show HN: LLM App – build a realtime LLM app in 30 lines, with no vector database
To quickly get to the application sources please go to:
- https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the simplest contextless app
- https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the default app that builds a reactive index of context documents
- https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the contextful app reading data from s3
- https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the app using locally available models
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LLM(Large Language Models) for better developer learning of your product
This article explores how LLMs(Large Language Models) and LLM apps such as Pathway can be leveraged for effective and efficient developer education, which can boost the utilization of your product.
What are some alternatives?
makinage - Stream Processing Made Easy
llmflows - LLMFlows - Simple, Explicit and Transparent LLM Apps
SimpleCommand - A simple, standardized way to build and use Service Objects (aka Commands) in Ruby
llm-prompt-testing - Prompt Testing framework for LLMs (specifically OpenAI models). Compute NLP and Responsible AI metrics for each model-generated answer.
Light Service - Series of Actions with an emphasis on simplicity.
falcongpt - Simple GPT app that uses the falcon-7b-instruct model with a Flask front-end.
Interactor - Interactor provides a common interface for performing complex user interactions.
chatgpt-api-python-sales - Find real-time sales with AI-powered Python API using ChatGPT and LLM (Large Language Model) App.
pypath - Python module for prior knowledge integration. Builds databases of signaling pathways, enzyme-substrate interactions, complexes, annotations and intercellular communication roles.
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs