https://github.com/danielfrg/gcp-llm-retrieval-augmentation
A retrieval augmentation LLM demo in GCP
https://github.com/danielfrg/gcp-llm-retrieval-augmentation
gcp langchain llm matching-engine vertex-ai
Last synced: 8 months ago
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A retrieval augmentation LLM demo in GCP
- Host: GitHub
- URL: https://github.com/danielfrg/gcp-llm-retrieval-augmentation
- Owner: danielfrg
- Archived: true
- Created: 2023-07-27T16:38:32.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-23T07:09:46.000Z (over 1 year ago)
- Last Synced: 2024-10-04T13:35:26.799Z (about 1 year ago)
- Topics: gcp, langchain, llm, matching-engine, vertex-ai
- Language: Jupyter Notebook
- Homepage: https://llmops-demos-frg.web.app
- Size: 323 KB
- Stars: 19
- Watchers: 3
- Forks: 7
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# LLM retrieval augmentation in Google Cloud
This demo features
[GCP Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) and
[VertexAI PaLM](https://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/api-quickstart)
to combine the functionality of retrieval augmentation and conversational engines
to create a question answering system where the user can ask a question and the
LLM will use it's given context to answer the question.
The Dataset used is the
[Stanford Question Answering Dataset (SQuAD)](https://huggingface.co/datasets/squad)
, a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles.
The demo can be accessed [here](https://llmops-demos-frg.web.app).
## Services used
- [VertexAI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview): ANN Similarity Seach
- [VertexAI PaLM](https://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/api-quickstart): Conversational Engine
- [Cloud Run](https://cloud.google.com/run): Hosting of the API
- [Firestore](https://cloud.google.com/firestore): Document Database
- [Firebase](https://firebase.google.com): Frontend hosting
- [Cloud Build](https://cloud.google.com/build): CI/CD
Frameworks:
- [LangChain](https://python.langchain.com/): Framework for creating conversational agent and retrieval augmentation
- [Tensorflow Hub](https://www.tensorflow.org/hub):
Embeddings
## Prerequisites
- [Terraform](https://cloud.google.com/docs/terraform)
- A GCP project created
## Docs
1. [Infrastructure and Vector Search Setup](/docs/1_matching_engine.md):
Setup the required infrastructure using Terraform and create
the Vector Search index
2. [Create embeddings](/docs/2_create_embeddings.md): Generate the embeddings for the documents and index them in
Vector Search
3. [Firestore](/docs/3_firestore.md): Index the documents in Firestore
4. [LangChain Retriever and Agent](/docs/4_agent.md): Create a LangChain retriever and conversational agent
5. [Cloud Run](/docs/5_cloud_run.md): Grab all the code, package it and deploy the API to Cloud Run
6. [Firebase WebUI](/docs/6_firebase.md): Create the Web app