wehub-resource-sync
129 lines
4.8 KiB
Markdown
129 lines
4.8 KiB
Markdown
# Vertex AI Search accessed via Google Cloud Functions
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This directory contains several versions of approximately the same
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implementation.
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The functions can be deployed to
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[Cloud functions](https://cloud.google.com/functions/) and can be modified to
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supports many different triggers and use cases. Each can also
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[be deployed locally](https://cloud.google.com/functions/docs/running/overview)
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which allows easy experimentation and iteration.
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This example is powered by
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[Vertex AI Search](https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction)
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which does many different things, including **Document & Intranet Search**,
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**Recommendations** and **Grounding and RAG** out-of-the-box (For more
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information, see the blog post
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[Your RAG powered by Google Search](https://cloud.google.com/blog/products/ai-machine-learning/rags-powered-by-google-search-technology-part-1)).
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If you want even more control see
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[Vertex AI Search Component APIs](https://cloud.google.com/generative-ai-app-builder/docs/builder-apis),
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but first explore the out-of-the-box offering because it's easy to setup.
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This example is for the out-of-the-box Vertex AI Search supporting many
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configurations and data types.
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## Pre-requisites
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Before you can use these functions to query Vertex AI Search, you need to create
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and populate a search "data store"; read through instructions in
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[get started with generic search](https://cloud.google.com/generative-ai-app-builder/docs/try-enterprise-search).
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These functions could easily be adapted to other types of Vertex AI Search like
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[generic recommendations](https://cloud.google.com/generative-ai-app-builder/docs/try-generic-recommendations),
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[media search](https://cloud.google.com/generative-ai-app-builder/docs/try-media-search),
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[media recommendations](https://cloud.google.com/generative-ai-app-builder/docs/try-media-recommendations),
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[healthcare search](https://cloud.google.com/generative-ai-app-builder/docs/create-data-store-hc),
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or even
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[retail product discovery](https://cloud.google.com/solutions/retail-product-discovery#documentation).
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You'll need to collect the following details from your search app data store:
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```python
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PROJECT_ID = "YOUR_PROJECT_ID" # alphanumeric
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LOCATION = "global" # or an alternate location
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DATA_STORE_ID = "YOUR_DATA_STORE_ID" # not the app id, alphanumeric
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```
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Additionally you'll need to keep track of some of the choices you make when you
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configure Vertex AI Search.
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### Type of data source
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<!-- textlint-disable -->
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- UNSTRUCTURED
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- STRUCTURED
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- WEBSITE
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- BLENDED
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<!-- textlint-enable -->
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```python
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ENGINE_DATA_TYPE = UNSTRUCTURED
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```
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### Type of chunks to return
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- DOCUMENT_WITH_SNIPPETS
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- DOCUMENT_WITH_EXTRACTIVE_SEGMENTS
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- CHUNK
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- NONE
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```python
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ENGINE_CHUNK_TYPE = DOCUMENT_WITH_EXTRACTIVE_SEGMENTS
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```
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### Type of summarization
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- NONE results only
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- VERTEX_AI_SEARCH LLM add on provided by
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[Vertex AI Search](https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction)
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<!-- NOT ready yet
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- GENERATE_GROUNDED_ANSWERS use the
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[Generate grounded answers with RAG](https://cloud.google.com/generative-ai-app-builder/docs/grounded-gen)
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provided by
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[Vertex AI Search Builder APIs](https://cloud.google.com/generative-ai-app-builder/docs/builder-apis)
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- GEMINI use one of the Gemini models to generate an answer from the results -->
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```python
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SUMMARY_TYPE = VERTEX_AI_SEARCH
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```
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## Architecture
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1. Vertex AI Search is an API hosted on Google Cloud
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2. You will call that API via a Google Cloud Function, which exposes its own API
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3. Your users will the Google Cloud Function API, via your custom app or UI
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```mermaid
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flowchart LR
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A[fa:fa-search Vertex AI Search] --> B(Google Cloud Function)
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B --> C[My App Server]
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C -->|One| D[fa:fa-laptop web]
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C -->|Two| E[fa:fa-mobile mobile]
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```
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## Use case: RAG / Grounding
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Any time you have more source data than can fit into a LLM context window, you
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could benefit from RAG (Retrieval Augmented Generation). The more data you have,
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the more important search is - to get the relevant chunks into the prompt of the
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LLM.
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- **Retrieve** relevant search results, with text chunks (snippets or segments)
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- **Augmented Generation** uses Gemini to generate an answer or summary grounded
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on the relevant search results
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## Use case: Agent Tool (Knowledge Base)
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A natural extension of RAG / Grounding is agentic behavior.
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Whether creating a basic chatbot or a sophisticated tool using multi-agent
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system, you're always going to need search based RAG. The better the search
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quality the better the agent response based on your source data.
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For more on agents, check out
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[Vertex AI Search Use Cases](https://cloud.google.com/products/agent-builder?hl=en#common-uses)
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and
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[https://github.com/GoogleCloudPlatform/generative-ai](https://github.com/GoogleCloudPlatform/generative-ai).
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