mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-19 12:40:09 +01:00
04e29203a5
* Add header static class for metadata assembly * update comments * patch header parsing for links
294 lines
10 KiB
JavaScript
294 lines
10 KiB
JavaScript
const { Pinecone } = require("@pinecone-database/pinecone");
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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 { toChunks, getEmbeddingEngineSelection } = require("../../helpers");
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const { sourceIdentifier } = require("../../chats");
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const PineconeDB = {
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name: "Pinecone",
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connect: async function () {
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if (process.env.VECTOR_DB !== "pinecone")
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throw new Error("Pinecone::Invalid ENV settings");
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const client = new Pinecone({
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apiKey: process.env.PINECONE_API_KEY,
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});
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const pineconeIndex = client.Index(process.env.PINECONE_INDEX);
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const { status } = await client.describeIndex(process.env.PINECONE_INDEX);
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if (!status.ready) throw new Error("Pinecone::Index not ready.");
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return { client, pineconeIndex, indexName: process.env.PINECONE_INDEX };
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},
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totalVectors: async function () {
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const { pineconeIndex } = await this.connect();
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const { namespaces } = await pineconeIndex.describeIndexStats();
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return Object.values(namespaces).reduce(
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(a, b) => a + (b?.recordCount || 0),
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0
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);
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},
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namespaceCount: async function (_namespace = null) {
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const { pineconeIndex } = await this.connect();
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const namespace = await this.namespace(pineconeIndex, _namespace);
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return namespace?.recordCount || 0;
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},
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similarityResponse: async function (
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index,
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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 pineconeNamespace = index.namespace(namespace);
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const response = await pineconeNamespace.query({
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vector: queryVector,
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topK: topN,
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includeMetadata: true,
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});
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response.matches.forEach((match) => {
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if (match.score < similarityThreshold) return;
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if (filterIdentifiers.includes(sourceIdentifier(match.metadata))) {
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console.log(
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"Pinecone: 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(match.metadata.text);
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result.sourceDocuments.push(match);
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result.scores.push(match.score);
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});
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return result;
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},
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namespace: async function (index, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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const { namespaces } = await index.describeIndexStats();
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return namespaces.hasOwnProperty(namespace) ? namespaces[namespace] : null;
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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 { pineconeIndex } = await this.connect();
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return await this.namespaceExists(pineconeIndex, namespace);
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},
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namespaceExists: async function (index, namespace = null) {
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if (!namespace) throw new Error("No namespace value provided.");
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const { namespaces } = await index.describeIndexStats();
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return namespaces.hasOwnProperty(namespace);
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},
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deleteVectorsInNamespace: async function (index, namespace = null) {
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const pineconeNamespace = index.namespace(namespace);
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await pineconeNamespace.deleteAll();
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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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skipCache = false
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) {
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const { DocumentVectors } = require("../../../models/vectors");
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try {
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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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if (!skipCache) {
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const cacheResult = await cachedVectorInformation(fullFilePath);
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if (cacheResult.exists) {
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const { pineconeIndex } = await this.connect();
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const pineconeNamespace = pineconeIndex.namespace(namespace);
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const { chunks } = cacheResult;
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const documentVectors = [];
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for (const chunk of chunks) {
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// Before sending to Pinecone 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 { ...chunk, id };
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});
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await pineconeNamespace.upsert([...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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}
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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 `PineconeStore.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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// https://github.com/hwchase17/langchainjs/blob/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L167
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const EmbedderEngine = getEmbeddingEngineSelection();
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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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EmbedderEngine?.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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chunkHeaderMeta: TextSplitter.buildHeaderMeta(metadata),
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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 documentVectors = [];
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const vectors = [];
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const vectorValues = await EmbedderEngine.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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const vectorRecord = {
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id: uuidv4(),
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values: 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/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L64
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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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if (vectors.length > 0) {
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const chunks = [];
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const { pineconeIndex } = await this.connect();
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const pineconeNamespace = pineconeIndex.namespace(namespace);
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console.log("Inserting vectorized chunks into Pinecone.");
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for (const chunk of toChunks(vectors, 100)) {
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chunks.push(chunk);
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await pineconeNamespace.upsert([...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 { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, 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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const vectorIds = knownDocuments.map((doc) => doc.vectorId);
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const pineconeNamespace = pineconeIndex.namespace(namespace);
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for (const batchOfVectorIds of toChunks(vectorIds, 1000)) {
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await pineconeNamespace.deleteMany(batchOfVectorIds);
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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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"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 { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, namespace)))
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throw new Error("Namespace by that name does not exist.");
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const stats = await this.namespace(pineconeIndex, 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" };
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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 { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, namespace)))
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throw new Error("Namespace by that name does not exist.");
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const details = await this.namespace(pineconeIndex, namespace);
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await this.deleteVectorsInNamespace(pineconeIndex, namespace);
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return {
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message: `Namespace ${namespace} was deleted along with ${details.vectorCount} vectors.`,
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};
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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 { pineconeIndex } = await this.connect();
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if (!(await this.namespaceExists(pineconeIndex, 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 queryVector = await LLMConnector.embedTextInput(input);
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const { contextTexts, sourceDocuments } = await this.similarityResponse(
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pineconeIndex,
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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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curateSources: function (sources = []) {
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const documents = [];
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for (const source of sources) {
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const { metadata = {} } = source;
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if (Object.keys(metadata).length > 0) {
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documents.push({
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...metadata,
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...(source.hasOwnProperty("pageContent")
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? { text: source.pageContent }
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: {}),
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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.Pinecone = PineconeDB;
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