56 lines
2.3 KiB
TypeScript
56 lines
2.3 KiB
TypeScript
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import type { BaseLanguageModelInterface } from "@langchain/core/language_models/base";
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import { Document } from "@langchain/core/documents";
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import { BasePromptTemplate } from "@langchain/core/prompts";
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import { VectorStore, VectorStoreRetriever, VectorStoreRetrieverInput } from "@langchain/core/vectorstores";
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import { CallbackManagerForRetrieverRun } from "@langchain/core/callbacks/manager";
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/**
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* A string that corresponds to a specific prompt template.
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*/
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export type PromptKey = "websearch" | "scifact" | "arguana" | "trec-covid" | "fiqa" | "dbpedia-entity" | "trec-news" | "mr-tydi";
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/**
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* Options for the HydeRetriever class, which includes a BaseLanguageModel
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* instance, a VectorStore instance, and an optional promptTemplate which
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* can either be a BasePromptTemplate instance or a PromptKey.
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*/
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export type HydeRetrieverOptions<V extends VectorStore> = VectorStoreRetrieverInput<V> & {
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llm: BaseLanguageModelInterface;
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promptTemplate?: BasePromptTemplate | PromptKey;
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};
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/**
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* A class for retrieving relevant documents based on a given query. It
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* extends the VectorStoreRetriever class and uses a BaseLanguageModel to
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* generate a hypothetical answer to the query, which is then used to
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* retrieve relevant documents.
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* @example
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* ```typescript
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* const retriever = new HydeRetriever({
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* vectorStore: new MemoryVectorStore(new OpenAIEmbeddings()),
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* llm: new ChatOpenAI(),
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* k: 1,
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* });
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* await vectorStore.addDocuments(
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* [
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* "My name is John.",
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* "My name is Bob.",
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* "My favourite food is pizza.",
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* "My favourite food is pasta.",
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* ].map((pageContent) => new Document({ pageContent })),
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* );
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* const results = await retriever.getRelevantDocuments(
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* "What is my favourite food?",
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* );
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* ```
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*/
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export declare class HydeRetriever<V extends VectorStore = VectorStore> extends VectorStoreRetriever<V> {
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static lc_name(): string;
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get lc_namespace(): string[];
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llm: BaseLanguageModelInterface;
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promptTemplate?: BasePromptTemplate;
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constructor(fields: HydeRetrieverOptions<V>);
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_getRelevantDocuments(query: string, runManager?: CallbackManagerForRetrieverRun): Promise<Document[]>;
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}
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/**
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* Returns a BasePromptTemplate instance based on a given PromptKey.
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*/
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export declare function getPromptTemplateFromKey(key: PromptKey): BasePromptTemplate;
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