mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-11 09:10:13 +01:00
399 lines
13 KiB
JavaScript
399 lines
13 KiB
JavaScript
const { AstraDB: AstraClient } = require("@datastax/astra-db-ts");
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const { TextSplitter } = require("../../TextSplitter");
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const { SystemSettings } = require("../../../models/systemSettings");
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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 { sourceIdentifier } = require("../../chats");
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const AstraDB = {
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name: "AstraDB",
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connect: async function () {
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if (process.env.VECTOR_DB !== "astra")
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throw new Error("AstraDB::Invalid ENV settings");
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const client = new AstraClient(
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process?.env?.ASTRA_DB_APPLICATION_TOKEN,
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process?.env?.ASTRA_DB_ENDPOINT
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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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return { heartbeat: Number(new Date()) };
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},
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// Astra interface will return a valid collection object even if the collection
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// does not actually exist. So we run a simple check which will always throw
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// when the table truly does not exist. Faster than iterating all collections.
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isRealCollection: async function (astraCollection = null) {
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if (!astraCollection) return false;
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return await astraCollection
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.countDocuments()
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.then(() => true)
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.catch(() => false);
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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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const collection = await client.collection(name).catch(() => null);
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const count = await collection.countDocuments().catch(() => 0);
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totalVectors += count ? count : 0;
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}
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return totalVectors;
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},
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namespaceCount: async function (_namespace = null) {
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const { client } = await this.connect();
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const namespace = await this.namespace(client, _namespace);
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return namespace?.vectorCount || 0;
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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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const collection = await client.collection(namespace).catch(() => null);
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if (!(await this.isRealCollection(collection))) return null;
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const count = await collection.countDocuments().catch((e) => {
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console.error("Astra::namespaceExists", e.message);
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return null;
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});
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return {
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name: namespace,
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...collection,
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vectorCount: typeof count === "number" ? count : 0,
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};
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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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return await this.namespaceExists(client, 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 collection = await client.collection(namespace);
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return await this.isRealCollection(collection);
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},
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deleteVectorsInNamespace: async function (client, namespace = null) {
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await client.dropCollection(namespace);
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return true;
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},
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// AstraDB requires a dimension aspect for collection creation
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// we pass this in from the first chunk to infer the dimensions like other
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// providers do.
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getOrCreateCollection: async function (client, namespace, dimensions = null) {
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const isExists = await this.namespaceExists(client, namespace);
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if (!isExists) {
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if (!dimensions)
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throw new Error(
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`AstraDB:getOrCreateCollection Unable to infer vector dimension from input. Open an issue on Github for support.`
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);
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await client.createCollection(namespace, {
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vector: {
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dimension: dimensions,
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metric: "cosine",
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},
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});
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}
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return await client.collection(namespace);
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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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let vectorDimension = null;
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const { pageContent, docId, ...metadata } = 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 { chunks } = cacheResult;
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const documentVectors = [];
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vectorDimension = chunks[0][0].values.length || null;
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const collection = await this.getOrCreateCollection(
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client,
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namespace,
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vectorDimension
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);
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if (!(await this.isRealCollection(collection)))
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throw new Error("Failed to create new AstraDB collection!", {
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namespace,
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});
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for (const chunk of chunks) {
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// Before sending to Astra 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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const newChunks = chunk.map((chunk) => {
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const _id = uuidv4();
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documentVectors.push({ docId, vectorId: _id });
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return {
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_id: _id,
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$vector: chunk.values,
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metadata: chunk.metadata || {},
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};
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});
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await collection.insertMany(newChunks);
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}
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await DocumentVectors.bulkInsert(documentVectors);
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return { vectorized: true, error: null };
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}
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const textSplitter = new TextSplitter({
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chunkSize: TextSplitter.determineMaxChunkSize(
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await SystemSettings.getValueOrFallback({
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label: "text_splitter_chunk_size",
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}),
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getEmbeddingEngineSelection()?.embeddingMaxChunkLength
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),
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chunkOverlap: await SystemSettings.getValueOrFallback(
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{ label: "text_splitter_chunk_overlap" },
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20
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),
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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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if (!!vectorValues && vectorValues.length > 0) {
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for (const [i, vector] of vectorValues.entries()) {
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if (!vectorDimension) vectorDimension = vector.length;
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const vectorRecord = {
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_id: uuidv4(),
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$vector: vector,
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metadata: { ...metadata, text: textChunks[i] },
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};
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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 collection = await this.getOrCreateCollection(
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client,
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namespace,
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vectorDimension
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);
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if (!(await this.isRealCollection(collection)))
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throw new Error("Failed to create new AstraDB collection!", {
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namespace,
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});
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if (vectors.length > 0) {
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const chunks = [];
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console.log("Inserting vectorized chunks into Astra DB.");
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// AstraDB has maximum upsert size of 20 records per-request so we have to use a lower chunk size here
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// in order to do the queries - this takes a lot more time than other providers but there
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// is no way around it. This will save the vector-cache with the same layout, so we don't
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// have to chunk again for cached files.
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for (const chunk of toChunks(vectors, 20)) {
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chunks.push(
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chunk.map((c) => {
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return { id: c._id, values: c.$vector, metadata: c.metadata };
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})
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);
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await collection.insertMany(chunk);
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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 { vectorized: true, error: null };
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} catch (e) {
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console.error("addDocumentToNamespace", e.message);
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return { vectorized: false, error: e.message };
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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)))
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throw new Error(
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"Invalid namespace - has it been collected and populated yet?"
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);
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const collection = await client.collection(namespace);
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const knownDocuments = await DocumentVectors.where({ docId });
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if (knownDocuments.length === 0) return;
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const vectorIds = knownDocuments.map((doc) => doc.vectorId);
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for (const id of vectorIds) {
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await collection.deleteMany({
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_id: id,
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});
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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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topN = 4,
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filterIdentifiers = [],
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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:
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"Invalid query - no namespace found for workspace in vector db!",
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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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topN,
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filterIdentifiers
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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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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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topN = 4,
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filterIdentifiers = []
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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 collection = await client.collection(namespace);
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const responses = await collection
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.find(
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{},
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{
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sort: { $vector: queryVector },
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limit: topN,
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includeSimilarity: true,
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}
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)
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.toArray();
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responses.forEach((response) => {
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if (response.$similarity < similarityThreshold) return;
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if (filterIdentifiers.includes(sourceIdentifier(response.metadata))) {
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console.log(
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"AstraDB: A source was filtered from context as it's parent document is pinned."
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);
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return;
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}
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result.contextTexts.push(response.metadata.text);
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result.sourceDocuments.push(response);
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result.scores.push(response.$similarity);
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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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let header = new Headers();
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header.append("Token", client?.httpClient?.applicationToken);
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header.append("Content-Type", "application/json");
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let raw = JSON.stringify({
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findCollections: {},
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});
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let requestOptions = {
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method: "POST",
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headers: header,
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body: raw,
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redirect: "follow",
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};
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const call = await fetch(client?.httpClient?.baseUrl, requestOptions);
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const resp = await call?.text();
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const collections = resp ? JSON.parse(resp)?.status?.collections : [];
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return collections;
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} catch (e) {
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console.error("Astra::AllNamespace", e);
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return [];
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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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if (!(await this.namespaceExists(client, namespace)))
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throw new Error("Namespace by that name does not exist.");
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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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if (!(await this.namespaceExists(client, namespace)))
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throw new Error("Namespace by that name does not exist.");
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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 ${namespace} was deleted along with ${
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details?.vectorCount || "all"
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} vectors.`,
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};
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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({
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...metadata,
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});
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}
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}
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return documents;
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},
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};
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module.exports.AstraDB = AstraDB;
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