43 lines
1.5 KiB
TypeScript
43 lines
1.5 KiB
TypeScript
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import { LlamaModel, LlamaContext } from "node-llama-cpp";
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import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings";
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import { LlamaBaseCppInputs } from "../utils/llama_cpp.js";
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/**
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* Note that the modelPath is the only required parameter. For testing you
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* can set this in the environment variable `LLAMA_PATH`.
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*/
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export interface LlamaCppEmbeddingsParams extends LlamaBaseCppInputs, EmbeddingsParams {
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}
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/**
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* @example
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* ```typescript
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* // Initialize LlamaCppEmbeddings with the path to the model file
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* const embeddings = new LlamaCppEmbeddings({
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* modelPath: "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin",
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* });
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*
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* // Embed a query string using the Llama embeddings
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* const res = embeddings.embedQuery("Hello Llama!");
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*
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* // Output the resulting embeddings
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* console.log(res);
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*
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* ```
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*/
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export declare class LlamaCppEmbeddings extends Embeddings {
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_model: LlamaModel;
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_context: LlamaContext;
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constructor(inputs: LlamaCppEmbeddingsParams);
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/**
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* Generates embeddings for an array of texts.
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* @param texts - An array of strings to generate embeddings for.
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* @returns A Promise that resolves to an array of embeddings.
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*/
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embedDocuments(texts: string[]): Promise<number[][]>;
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/**
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* Generates an embedding for a single text.
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* @param text - A string to generate an embedding for.
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* @returns A Promise that resolves to an array of numbers representing the embedding.
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*/
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embedQuery(text: string): Promise<number[]>;
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}
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