mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-09-12 07:10:08 +02:00
463 lines
15 KiB
JavaScript
463 lines
15 KiB
JavaScript
const { default: weaviate } = require("weaviate-ts-client");
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const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
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const { storeVectorResult, cachedVectorInformation } = require("../../files");
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const { v4: uuidv4 } = require("uuid");
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const {
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toChunks,
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getLLMProvider,
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getEmbeddingEngineSelection,
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} = require("../../helpers");
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const { camelCase } = require("../../helpers/camelcase");
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const Weaviate = {
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name: "Weaviate",
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connect: async function () {
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if (process.env.VECTOR_DB !== "weaviate")
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throw new Error("Weaviate::Invalid ENV settings");
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const weaviateUrl = new URL(process.env.WEAVIATE_ENDPOINT);
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const options = {
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scheme: weaviateUrl.protocol?.replace(":", "") || "http",
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host: weaviateUrl?.host,
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...(process.env?.WEAVIATE_API_KEY?.length > 0
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? { apiKey: new weaviate.ApiKey(process.env?.WEAVIATE_API_KEY) }
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: {}),
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};
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const client = weaviate.client(options);
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const isAlive = await await client.misc.liveChecker().do();
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if (!isAlive)
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throw new Error(
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"Weaviate::Invalid Alive signal received - is the service online?"
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);
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return { client };
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},
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heartbeat: async function () {
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await this.connect();
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return { heartbeat: Number(new Date()) };
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},
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totalVectors: async function () {
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const { client } = await this.connect();
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const collectionNames = await this.allNamespaces(client);
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var totalVectors = 0;
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for (const name of collectionNames) {
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totalVectors += await this.namespaceCountWithClient(client, name);
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}
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return totalVectors;
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},
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namespaceCountWithClient: async function (client, namespace) {
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try {
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const response = await client.graphql
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.aggregate()
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.withClassName(camelCase(namespace))
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.withFields("meta { count }")
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.do();
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return (
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response?.data?.Aggregate?.[camelCase(namespace)]?.[0]?.meta?.count || 0
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);
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} catch (e) {
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console.error(`Weaviate:namespaceCountWithClient`, e.message);
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return 0;
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}
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},
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namespaceCount: async function (namespace = null) {
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try {
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const { client } = await this.connect();
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const response = await client.graphql
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.aggregate()
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.withClassName(camelCase(namespace))
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.withFields("meta { count }")
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.do();
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return (
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response?.data?.Aggregate?.[camelCase(namespace)]?.[0]?.meta?.count || 0
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);
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} catch (e) {
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console.error(`Weaviate:namespaceCountWithClient`, e.message);
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return 0;
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}
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},
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similarityResponse: async function (
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client,
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namespace,
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queryVector,
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similarityThreshold = 0.25
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) {
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const result = {
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contextTexts: [],
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sourceDocuments: [],
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scores: [],
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};
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const weaviateClass = await this.namespace(client, namespace);
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const fields = weaviateClass.properties.map((prop) => prop.name).join(" ");
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const queryResponse = await client.graphql
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.get()
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.withClassName(camelCase(namespace))
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.withFields(`${fields} _additional { id certainty }`)
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.withNearVector({ vector: queryVector })
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.withLimit(4)
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.do();
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const responses = queryResponse?.data?.Get?.[camelCase(namespace)];
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responses.forEach((response) => {
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// In Weaviate we have to pluck id from _additional and spread it into the rest
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// of the properties.
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const {
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_additional: { id, certainty },
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...rest
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} = response;
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if (certainty < similarityThreshold) return;
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result.contextTexts.push(rest.text);
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result.sourceDocuments.push({ ...rest, id });
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result.scores.push(certainty);
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});
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return result;
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},
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allNamespaces: async function (client) {
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try {
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const { classes = [] } = await client.schema.getter().do();
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return classes.map((classObj) => classObj.class);
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} catch (e) {
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console.error("Weaviate::AllNamespace", e);
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return [];
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}
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},
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namespace: async function (client, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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if (!(await this.namespaceExists(client, namespace))) return null;
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const weaviateClass = await client.schema
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.classGetter()
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.withClassName(camelCase(namespace))
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.do();
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return {
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...weaviateClass,
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vectorCount: await this.namespaceCount(namespace),
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};
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},
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addVectors: async function (client, vectors = []) {
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const response = { success: true, errors: new Set([]) };
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const results = await client.batch
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.objectsBatcher()
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.withObjects(...vectors)
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.do();
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results.forEach((res) => {
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const { status, errors = [] } = res.result;
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if (status === "SUCCESS" || errors.length === 0) return;
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response.success = false;
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response.errors.add(errors.error?.[0]?.message || null);
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});
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response.errors = [...response.errors];
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return response;
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},
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hasNamespace: async function (namespace = null) {
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if (!namespace) return false;
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const { client } = await this.connect();
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const weaviateClasses = await this.allNamespaces(client);
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return weaviateClasses.includes(camelCase(namespace));
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},
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namespaceExists: async function (client, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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const weaviateClasses = await this.allNamespaces(client);
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return weaviateClasses.includes(camelCase(namespace));
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},
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deleteVectorsInNamespace: async function (client, namespace = null) {
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await client.schema.classDeleter().withClassName(camelCase(namespace)).do();
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return true;
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},
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addDocumentToNamespace: async function (
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namespace,
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documentData = {},
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fullFilePath = null
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) {
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const { DocumentVectors } = require("../../../models/vectors");
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try {
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const {
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pageContent,
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docId,
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id: _id, // Weaviate will abort if `id` is present in properties
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...metadata
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} = documentData;
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if (!pageContent || pageContent.length == 0) return false;
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console.log("Adding new vectorized document into namespace", namespace);
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const cacheResult = await cachedVectorInformation(fullFilePath);
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if (cacheResult.exists) {
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const { client } = await this.connect();
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const weaviateClassExits = await this.hasNamespace(namespace);
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if (!weaviateClassExits) {
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await client.schema
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.classCreator()
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.withClass({
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class: camelCase(namespace),
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description: `Class created by AnythingLLM named ${camelCase(
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namespace
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)}`,
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vectorizer: "none",
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})
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.do();
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}
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const { chunks } = cacheResult;
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const documentVectors = [];
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const vectors = [];
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for (const chunk of chunks) {
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// Before sending to Weaviate and saving the records to our db
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// we need to assign the id of each chunk that is stored in the cached file.
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chunk.forEach((chunk) => {
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const id = uuidv4();
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const flattenedMetadata = this.flattenObjectForWeaviate(
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chunk.properties
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);
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documentVectors.push({ docId, vectorId: id });
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const vectorRecord = {
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id,
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class: camelCase(namespace),
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vector: chunk.vector || chunk.values || [],
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properties: { ...flattenedMetadata },
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};
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vectors.push(vectorRecord);
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});
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const { success: additionResult, errors = [] } =
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await this.addVectors(client, vectors);
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if (!additionResult) {
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console.error("Weaviate::addVectors failed to insert", errors);
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throw new Error("Error embedding into Weaviate");
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}
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}
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await DocumentVectors.bulkInsert(documentVectors);
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return true;
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}
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// If we are here then we are going to embed and store a novel document.
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// We have to do this manually as opposed to using LangChains `Chroma.fromDocuments`
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// because we then cannot atomically control our namespace to granularly find/remove documents
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// from vectordb.
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const textSplitter = new RecursiveCharacterTextSplitter({
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chunkSize:
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getEmbeddingEngineSelection()?.embeddingMaxChunkLength || 1_000,
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chunkOverlap: 20,
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});
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const textChunks = await textSplitter.splitText(pageContent);
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console.log("Chunks created from document:", textChunks.length);
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const LLMConnector = getLLMProvider();
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const documentVectors = [];
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const vectors = [];
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const vectorValues = await LLMConnector.embedChunks(textChunks);
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const submission = {
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ids: [],
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vectors: [],
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properties: [],
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};
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if (!!vectorValues && vectorValues.length > 0) {
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for (const [i, vector] of vectorValues.entries()) {
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const flattenedMetadata = this.flattenObjectForWeaviate(metadata);
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const vectorRecord = {
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class: camelCase(namespace),
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id: uuidv4(),
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vector: vector,
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// [DO NOT REMOVE]
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// LangChain will be unable to find your text if you embed manually and dont include the `text` key.
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// https://github.com/hwchase17/langchainjs/blob/5485c4af50c063e257ad54f4393fa79e0aff6462/langchain/src/vectorstores/weaviate.ts#L133
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properties: { ...flattenedMetadata, text: textChunks[i] },
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};
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submission.ids.push(vectorRecord.id);
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submission.vectors.push(vectorRecord.values);
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submission.properties.push(metadata);
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vectors.push(vectorRecord);
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documentVectors.push({ docId, vectorId: vectorRecord.id });
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}
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} else {
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throw new Error(
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"Could not embed document chunks! This document will not be recorded."
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);
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}
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const { client } = await this.connect();
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const weaviateClassExits = await this.hasNamespace(namespace);
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if (!weaviateClassExits) {
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await client.schema
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.classCreator()
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.withClass({
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class: camelCase(namespace),
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description: `Class created by AnythingLLM named ${camelCase(
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namespace
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)}`,
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vectorizer: "none",
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})
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.do();
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}
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if (vectors.length > 0) {
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const chunks = [];
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for (const chunk of toChunks(vectors, 500)) chunks.push(chunk);
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console.log("Inserting vectorized chunks into Weaviate collection.");
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const { success: additionResult, errors = [] } = await this.addVectors(
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client,
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vectors
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);
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if (!additionResult) {
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console.error("Weaviate::addVectors failed to insert", errors);
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throw new Error("Error embedding into Weaviate");
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}
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await storeVectorResult(chunks, fullFilePath);
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}
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await DocumentVectors.bulkInsert(documentVectors);
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return true;
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} catch (e) {
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console.error(e);
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console.error("addDocumentToNamespace", e.message);
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return false;
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}
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},
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deleteDocumentFromNamespace: async function (namespace, docId) {
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const { DocumentVectors } = require("../../../models/vectors");
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const { client } = await this.connect();
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if (!(await this.namespaceExists(client, namespace))) return;
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const knownDocuments = await DocumentVectors.where({ docId });
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if (knownDocuments.length === 0) return;
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for (const doc of knownDocuments) {
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await client.data
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.deleter()
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.withClassName(camelCase(namespace))
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.withId(doc.vectorId)
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.do();
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}
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const indexes = knownDocuments.map((doc) => doc.id);
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await DocumentVectors.deleteIds(indexes);
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return true;
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},
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performSimilaritySearch: async function ({
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namespace = null,
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input = "",
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LLMConnector = null,
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similarityThreshold = 0.25,
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}) {
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if (!namespace || !input || !LLMConnector)
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throw new Error("Invalid request to performSimilaritySearch.");
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const { client } = await this.connect();
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if (!(await this.namespaceExists(client, namespace))) {
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return {
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contextTexts: [],
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sources: [],
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message: "Invalid query - no documents found for workspace!",
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};
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}
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const queryVector = await LLMConnector.embedTextInput(input);
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const { contextTexts, sourceDocuments } = await this.similarityResponse(
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client,
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namespace,
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queryVector,
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similarityThreshold
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);
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const sources = sourceDocuments.map((metadata, i) => {
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return { ...metadata, text: contextTexts[i] };
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});
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return {
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contextTexts,
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sources: this.curateSources(sources),
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message: false,
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};
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},
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"namespace-stats": async function (reqBody = {}) {
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const { namespace = null } = reqBody;
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if (!namespace) throw new Error("namespace required");
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const { client } = await this.connect();
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const stats = await this.namespace(client, namespace);
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return stats
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? stats
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: { message: "No stats were able to be fetched from DB for namespace" };
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},
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"delete-namespace": async function (reqBody = {}) {
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const { namespace = null } = reqBody;
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const { client } = await this.connect();
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const details = await this.namespace(client, namespace);
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await this.deleteVectorsInNamespace(client, namespace);
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return {
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message: `Namespace ${camelCase(namespace)} was deleted along with ${
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details?.vectorCount
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} vectors.`,
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};
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},
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reset: async function () {
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const { client } = await this.connect();
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const weaviateClasses = await this.allNamespaces(client);
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for (const weaviateClass of weaviateClasses) {
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await client.schema.classDeleter().withClassName(weaviateClass).do();
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}
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return { reset: true };
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},
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curateSources: function (sources = []) {
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const documents = [];
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for (const source of sources) {
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if (Object.keys(source).length > 0) {
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const metadata = source.hasOwnProperty("metadata")
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? source.metadata
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: source;
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documents.push({ ...metadata });
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}
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}
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return documents;
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},
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flattenObjectForWeaviate: function (obj = {}) {
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// Note this function is not generic, it is designed specifically for Weaviate
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// https://weaviate.io/developers/weaviate/config-refs/datatypes#introduction
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// Credit to LangchainJS
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// https://github.com/hwchase17/langchainjs/blob/5485c4af50c063e257ad54f4393fa79e0aff6462/langchain/src/vectorstores/weaviate.ts#L11C1-L50C3
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const flattenedObject = {};
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for (const key in obj) {
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if (!Object.hasOwn(obj, key)) {
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continue;
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}
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const value = obj[key];
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if (typeof obj[key] === "object" && !Array.isArray(value)) {
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const recursiveResult = this.flattenObjectForWeaviate(value);
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for (const deepKey in recursiveResult) {
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if (Object.hasOwn(obj, key)) {
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flattenedObject[`${key}_${deepKey}`] = recursiveResult[deepKey];
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}
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}
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} else if (Array.isArray(value)) {
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if (
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value.length > 0 &&
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typeof value[0] !== "object" &&
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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value.every((el) => typeof el === typeof value[0])
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) {
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// Weaviate only supports arrays of primitive types,
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// where all elements are of the same type
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flattenedObject[key] = value;
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}
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} else {
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flattenedObject[key] = value;
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}
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}
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return flattenedObject;
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},
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};
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module.exports.Weaviate = Weaviate;
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