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--- | ||
"llamaindex": patch | ||
--- | ||
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feat: support jina ai embedding and reranker |
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apps/docs/docs/modules/embeddings/available_embeddings/jinaai.md
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# Jina AI | ||
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To use Jina AI embeddings, you need to import `JinaAIEmbedding` from `llamaindex`. | ||
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```ts | ||
import { JinaAIEmbedding, Settings } from "llamaindex"; | ||
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Settings.embedModel = new JinaAIEmbedding(); | ||
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const document = new Document({ text: essay, id_: "essay" }); | ||
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const index = await VectorStoreIndex.fromDocuments([document]); | ||
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const queryEngine = index.asQueryEngine(); | ||
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const query = "What is the meaning of life?"; | ||
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const results = await queryEngine.query({ | ||
query, | ||
}); | ||
``` |
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apps/docs/docs/modules/node_postprocessors/jinaai_reranker.md
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# Jina AI Reranker | ||
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The Jina AI Reranker is a postprocessor that uses the Jina AI Reranker API to rerank the results of a search query. | ||
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## Setup | ||
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Firstly, you will need to install the `llamaindex` package. | ||
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```bash | ||
pnpm install llamaindex | ||
``` | ||
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Now, you will need to sign up for an API key at [Jina AI](https://jina.ai/reranker). Once you have your API key you can import the necessary modules and create a new instance of the `JinaAIReranker` class. | ||
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```ts | ||
import { | ||
JinaAIReranker, | ||
Document, | ||
OpenAI, | ||
VectorStoreIndex, | ||
Settings, | ||
} from "llamaindex"; | ||
``` | ||
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## Load and index documents | ||
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For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index. | ||
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```ts | ||
const document = new Document({ text: essay, id_: "essay" }); | ||
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Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }); | ||
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const index = await VectorStoreIndex.fromDocuments([document]); | ||
``` | ||
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## Increase similarity topK to retrieve more results | ||
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The default value for `similarityTopK` is 2. This means that only the most similar document will be returned. To retrieve more results, you can increase the value of `similarityTopK`. | ||
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```ts | ||
const retriever = index.asRetriever(); | ||
retriever.similarityTopK = 5; | ||
``` | ||
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## Create a new instance of the JinaAIReranker class | ||
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Then you can create a new instance of the `JinaAIReranker` class and pass in the number of results you want to return. | ||
The Jina AI Reranker API key is set in the `JINAAI_API_KEY` environment variable. | ||
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```bash | ||
export JINAAI_API_KEY=<YOUR API KEY> | ||
``` | ||
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```ts | ||
const nodePostprocessor = new JinaAIReranker({ | ||
topN: 5, | ||
}); | ||
``` | ||
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## Create a query engine with the retriever and node postprocessor | ||
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```ts | ||
const queryEngine = index.asQueryEngine({ | ||
retriever, | ||
nodePostprocessors: [nodePostprocessor], | ||
}); | ||
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// log the response | ||
const response = await queryEngine.query("Where did the author grown up?"); | ||
``` |
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import { getEnv } from "@llamaindex/env"; | ||
import { OpenAIEmbedding } from "./OpenAIEmbedding.js"; | ||
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export class JinaAIEmbedding extends OpenAIEmbedding { | ||
constructor(init?: Partial<OpenAIEmbedding>) { | ||
const { | ||
apiKey = getEnv("JINAAI_API_KEY"), | ||
additionalSessionOptions = {}, | ||
model = "jina-embeddings-v2-base-en", | ||
...rest | ||
} = init ?? {}; | ||
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if (!apiKey) { | ||
throw new Error( | ||
"Set Jina AI API Key in JINAAI_API_KEY env variable. Get one for free or top up your key at https://jina.ai/embeddings", | ||
); | ||
} | ||
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additionalSessionOptions.baseURL = | ||
additionalSessionOptions.baseURL ?? "https://api.jina.ai/v1"; | ||
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super({ | ||
apiKey, | ||
additionalSessionOptions, | ||
model, | ||
...rest, | ||
}); | ||
} | ||
} |
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