langchain
Version:
Typescript bindings for langchain
119 lines (118 loc) • 4.31 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.VectorStoreRetrieverMemory = void 0;
const documents_1 = require("@langchain/core/documents");
const memory_1 = require("@langchain/core/memory");
const document_js_1 = require("../util/document.cjs");
/**
* Class for managing long-term memory in Large Language Model (LLM)
* applications. It provides a way to persist and retrieve relevant
* documents from a vector store database, which can be useful for
* maintaining conversation history or other types of memory in an LLM
* application.
* @example
* ```typescript
* const vectorStore = new MemoryVectorStore(new OpenAIEmbeddings());
* const memory = new VectorStoreRetrieverMemory({
* vectorStoreRetriever: vectorStore.asRetriever(1),
* memoryKey: "history",
* });
*
* // Saving context to memory
* await memory.saveContext(
* { input: "My favorite food is pizza" },
* { output: "thats good to know" },
* );
* await memory.saveContext(
* { input: "My favorite sport is soccer" },
* { output: "..." },
* );
* await memory.saveContext({ input: "I don't the Celtics" }, { output: "ok" });
*
* // Loading memory variables
* console.log(
* await memory.loadMemoryVariables({ prompt: "what sport should i watch?" }),
* );
* ```
*/
class VectorStoreRetrieverMemory extends memory_1.BaseMemory {
constructor(fields) {
super();
Object.defineProperty(this, "vectorStoreRetriever", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "inputKey", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "memoryKey", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "returnDocs", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "metadata", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
this.vectorStoreRetriever = fields.vectorStoreRetriever;
this.inputKey = fields.inputKey;
this.memoryKey = fields.memoryKey ?? "memory";
this.returnDocs = fields.returnDocs ?? false;
this.metadata = fields.metadata;
}
get memoryKeys() {
return [this.memoryKey];
}
/**
* Method to load memory variables. It uses the vectorStoreRetriever to
* get relevant documents based on the query obtained from the input
* values.
* @param values An InputValues object.
* @returns A Promise that resolves to a MemoryVariables object.
*/
async loadMemoryVariables(values) {
const query = (0, memory_1.getInputValue)(values, this.inputKey);
const results = await this.vectorStoreRetriever.getRelevantDocuments(query);
return {
[this.memoryKey]: this.returnDocs
? results
: (0, document_js_1.formatDocumentsAsString)(results),
};
}
/**
* Method to save context. It constructs a document from the input and
* output values (excluding the memory key) and adds it to the vector
* store database using the vectorStoreRetriever.
* @param inputValues An InputValues object.
* @param outputValues An OutputValues object.
* @returns A Promise that resolves to void.
*/
async saveContext(inputValues, outputValues) {
const metadata = typeof this.metadata === "function"
? this.metadata(inputValues, outputValues)
: this.metadata;
const text = Object.entries(inputValues)
.filter(([k]) => k !== this.memoryKey)
.concat(Object.entries(outputValues))
.map(([k, v]) => `${k}: ${v}`)
.join("\n");
await this.vectorStoreRetriever.addDocuments([
new documents_1.Document({ pageContent: text, metadata }),
]);
}
}
exports.VectorStoreRetrieverMemory = VectorStoreRetrieverMemory;