Firebase Genkit provides abstractions that help you build retrieval-augmented generation (RAG) flows, as well as plugins that provide integrations with related tools.
What is RAG?
Retrieval-augmented generation is a technique used to incorporate external sources of information into an LLM’s responses. It's important to be able to do so because, while LLMs are typically trained on a broad body of material, practical use of LLMs often requires specific domain knowledge (for example, you might want to use an LLM to answer customers' questions about your company’s products).
One solution is to fine-tune the model using more specific data. However, this can be expensive both in terms of compute cost and in terms of the effort needed to prepare adequate training data.
In contrast, RAG works by incorporating external data sources into a prompt at the time it's passed to the model. For example, you could imagine the prompt, "What is Bart's relationship to Lisa?" might be expanded ("augmented") by prepending some relevant information, resulting in the prompt, "Homer and Marge's children are named Bart, Lisa, and Maggie. What is Bart's relationship to Lisa?"
This approach has several advantages:
- It can be more cost-effective because you don't have to retrain the model.
- You can continuously update your data source and the LLM can immediately make use of the updated information.
- You now have the potential to cite references in your LLM's responses.
On the other hand, using RAG naturally means longer prompts, and some LLM API services charge for each input token you send. Ultimately, you must evaluate the cost tradeoffs for your applications.
RAG is a very broad area and there are many different techniques used to achieve the best quality RAG. The core Genkit framework offers three main abstractions to help you do RAG:
- Indexers: add documents to an "index".
- Embedders: transforms documents into a vector representation
- Retrievers: retrieve documents from an "index", given a query.
These definitions are broad on purpose because Genkit is un-opinionated about
what an "index" is or how exactly documents are retrieved from it. Genkit only
provides a Document format and everything else is defined by the retriever or
indexer implementation provider.
Indexers
The index is responsible for keeping track of your documents in such a way that you can quickly retrieve relevant documents given a specific query. This is most often accomplished using a vector database, which indexes your documents using multidimensional vectors called embeddings. A text embedding (opaquely) represents the concepts expressed by a passage of text; these are generated using special-purpose ML models. By indexing text using its embedding, a vector database is able to cluster conceptually related text and retrieve documents related to a novel string of text (the query).
Before you can retrieve documents for the purpose of generation, you need to ingest them into your document index. A typical ingestion flow does the following:
Split up large documents into smaller documents so that only relevant portions are used to augment your prompts – "chunking". This is necessary because many LLMs have a limited context window, making it impractical to include entire documents with a prompt.
Genkit doesn't provide built-in chunking libraries; however, there are open source libraries available that are compatible with Genkit.
Generate embeddings for each chunk. Depending on the database you're using, you might explicitly do this with an embedding generation model, or you might use the embedding generator provided by the database.
Add the text chunk and its index to the database.
You might run your ingestion flow infrequently or only once if you are working with a stable source of data. On the other hand, if you are working with data that frequently changes, you might continuously run the ingestion flow (for example, in a Cloud Firestore trigger, whenever a document is updated).
Embedders
An embedder is a function that takes content (text, images, audio, etc.) and creates a numeric vector that encodes the semantic meaning of the original content. As mentioned above, embedders are leveraged as part of the process of indexing, however, they can also be used independently to create embeddings without an index.
Retrievers
A retriever is a concept that encapsulates logic related to any kind of document retrieval. The most popular retrieval cases typically include retrieval from vector stores, however, in Genkit a retriever can be any function that returns data.
To create a retriever, you can use one of the provided implementations or create your own.
Supported indexers, retrievers, and embedders
Genkit provides indexer and retriever support through its plugin system. The following plugins are officially supported:
- Cloud Firestore vector store
- Vertex AI Vector Search
- Chroma DB vector database
- Pinecone cloud vector database
In addition, Genkit supports the following vector stores through predefined code templates, which you can customize for your database configuration and schema:
- PostgreSQL with
pgvector
Embedding model support is provided through the following plugins:
| Plugin | Models |
|---|---|
| Google Generative AI | Gecko text embedding |
| Google Vertex AI | Gecko text embedding |
Defining a RAG Flow
The following examples show how you could ingest a collection of restaurant menu PDF documents into a vector database and retrieve them for use in a flow that determines what food items are available.
Install dependencies for processing PDFs
npm install llm-chunk pdf-parse @genkit-ai/dev-local-vectorstorenpm i -D --save @types/pdf-parse
Add a local vector store to your configuration
import {
devLocalIndexerRef,
devLocalVectorstore,
} from '@genkit-ai/dev-local-vectorstore';
import { textEmbedding004, vertexAI } from '@genkit-ai/vertexai';
import { z, genkit } from 'genkit';
const ai = genkit({
plugins: [
// vertexAI provides the textEmbedding004 embedder
vertexAI(),
// the local vector store requires an embedder to translate from text to vector
devLocalVectorstore([
{
indexName: 'menuQA',
embedder: textEmbedding004,
},
]),
],
});
Define an Indexer
The following example shows how to create an indexer to ingest a collection of PDF documents and store them in a local vector database.
It uses the local file-based vector similarity retriever that Genkit provides out-of-the-box for simple testing and prototyping (do not use in production)
Create the indexer
export const menuPdfIndexer = devLocalIndexerRef('menuQA');
Create chunking config
This example uses the llm-chunk library which provides a simple text splitter
to break up documents into segments that can be vectorized.
The following definition configures the chunking function to guarantee a document segment of between 1000 and 2000 characters, broken at the end of a sentence, with an overlap between chunks of 100 characters.
const chunkingConfig = {
minLength: 1000,
maxLength: 2000,
splitter: 'sentence',
overlap: 100,
delimiters: '',
} as any;
More chunking options for this library can be found in the llm-chunk documentation.
Define your indexer flow
import { Document } from 'genkit/retriever';
import { chunk } from 'llm-chunk';
import { readFile } from 'fs/promises';
import path from 'path';
import pdf from 'pdf-parse';
async function extractTextFromPdf(filePath: string) {
const pdfFile = path.resolve(filePath);
const dataBuffer = await readFile(pdfFile);
const data = await pdf(dataBuffer);
return data.text;
}
export const indexMenu = ai.defineFlow(
{
name: 'indexMenu',
inputSchema: z.string().describe('PDF file path'),
outputSchema: z.void(),
},
async (filePath: string) => {
filePath = path.resolve(filePath);
// Read the pdf.
const pdfTxt = await run('extract-text', () =>
extractTextFromPdf(filePath)
);
// Divide the pdf text into segments.
const chunks = await run('chunk-it', async () =>
chunk(pdfTxt, chunkingConfig)
);
// Convert chunks of text into documents to store in the index.
const documents = chunks.map((text) => {
return Document.fromText(text, { filePath });
});
// Add documents to the index.
await ai.index({
indexer: menuPdfIndexer,
documents,
});
}
);
Run the indexer flow
genkit flow:run indexMenu "'menu.pdf'"After running the indexMenu flow, the vector database will be seeded with
documents and ready to be used in Genkit flows with retrieval steps.
Define a flow with retrieval
The following example shows how you might use a retriever in a RAG flow. Like the indexer example, this example uses Genkit's file-based vector retriever, which you should not use in production.
import { devLocalRetrieverRef } from '@genkit-ai/dev-local-vectorstore';
// Define the retriever reference
export const menuRetriever = devLocalRetrieverRef('menuQA');
export const menuQAFlow = ai.defineFlow(
{ name: 'menuQA', inputSchema: z.string(), outputSchema: z.string() },
async (input: string) => {
// retrieve relevant documents
const docs = await ai.retrieve({
retriever: menuRetriever,
query: input,
options: { k: 3 },
});
// generate a response
const { text } = await ai.generate({
prompt: `
You are acting as a helpful AI assistant that can answer
questions about the food available on the menu at Genkit Grub Pub.
Use only the context provided to answer the question.
If you don't know, do not make up an answer.
Do not add or change items on the menu.
Question: ${input}`,
docs,
});
return text;
}
);
Write your own indexers and retrievers
It's also possible to create your own retriever. This is useful if your documents are managed in a document store that is not supported in Genkit (eg: MySQL, Google Drive, etc.). The Genkit SDK provides flexible methods that let you provide custom code for fetching documents. You can also define custom retrievers that build on top of existing retrievers in Genkit and apply advanced RAG techniques (such as reranking or prompt extensions) on top.
Simple Retrievers
Simple retrievers let you easily convert existing code into retrievers:
import { z } from "genkit";
import { searchEmails } from "./db";
ai.defineSimpleRetriever(
{
name: "myDatabase",
configSchema: z
.object({
limit: z.number().optional(),
})
.optional(),
// we'll extract "message" from the returned email item
content: "message",
// and several keys to use as metadata
metadata: ["from", "to", "subject"],
},
async (query, config) => {
const result = await searchEmails(query.text, { limit: config.limit });
return result.data.emails;
}
);
Custom Retrievers
import {
CommonRetrieverOptionsSchema,
} from 'genkit/retriever';
import { z } from 'genkit';
export const menuRetriever = devLocalRetrieverRef('menuQA');
const advancedMenuRetrieverOptionsSchema = CommonRetrieverOptionsSchema.extend({
preRerankK: z.number().max(1000),
});
const advancedMenuRetriever = ai.defineRetriever(
{
name: `custom/advancedMenuRetriever`,
configSchema: advancedMenuRetrieverOptionsSchema,
},
async (input, options) => {
const extendedPrompt = await extendPrompt(input);
const docs = await ai.retrieve({
retriever: menuRetriever,
query: extendedPrompt,
options: { k: options.preRerankK || 10 },
});
const rerankedDocs = await rerank(docs);
return rerankedDocs.slice(0, options.k || 3);
}
);
(extendPrompt and rerank is something you would have to implement yourself,
not provided by the framework)
And then you can just swap out your retriever:
const docs = await ai.retrieve({
retriever: advancedRetriever,
query: input,
options: { preRerankK: 7, k: 3 },
});
Rerankers and Two-Stage Retrieval
A reranking model — also known as a cross-encoder — is a type of model that, given a query and document, will output a similarity score. We use this score to reorder the documents by relevance to our query. Reranker APIs take a list of documents (for example the output of a retriever) and reorders the documents based on their relevance to the query. This step can be useful for fine-tuning the results and ensuring that the most pertinent information is used in the prompt provided to a generative model.
Reranker Example
A reranker in Genkit is defined in a similar syntax to retrievers and indexers. Here is an example using a reranker in Genkit. This flow reranks a set of documents based on their relevance to the provided query using a predefined Vertex AI reranker.
const FAKE_DOCUMENT_CONTENT = [
'pythagorean theorem',
'e=mc^2',
'pi',
'dinosaurs',
'quantum mechanics',
'pizza',
'harry potter',
];
export const rerankFlow = ai.defineFlow(
{
name: 'rerankFlow',
inputSchema: z.object({ query: z.string() }),
outputSchema: z.array(
z.object({
text: z.string(),
score: z.number(),
})
),
},
async ({ query }) => {
const documents = FAKE_DOCUMENT_CONTENT.map((text) =>
({ content: text })
);
const rerankedDocuments = await ai.rerank({
reranker: 'vertexai/semantic-ranker-512',
query: ({ content: query }),
documents,
});
return rerankedDocuments.map((doc) => ({
text: doc.content,
score: doc.metadata.score,
}));
}
);
This reranker uses the Vertex AI genkit plugin with semantic-ranker-512 to
score and rank documents. The higher the score, the more relevant the document
is to the query.
Custom Rerankers
You can also define custom rerankers to suit your specific use case. This is helpful when you need to rerank documents using your own custom logic or a custom model. Here’s a simple example of defining a custom reranker:
export const customReranker = ai.defineReranker(
{
name: 'custom/reranker',
configSchema: z.object({
k: z.number().optional(),
}),
},
async (query, documents, options) => {
// Your custom reranking logic here
const rerankedDocs = documents.map((doc) => {
const score = Math.random(); // Assign random scores for demonstration
return {
...doc,
metadata: { ...doc.metadata, score },
};
});
return rerankedDocs.sort((a, b) => b.metadata.score - a.metadata.score).slice(0, options.k || 3);
}
);
Once defined, this custom reranker can be used just like any other reranker in your RAG flows, giving you flexibility to implement advanced reranking strategies.