anything-llm/server/utils/vectorDbProviders/lance/index.js

const lancedb = require("vectordb");
const {
  toChunks,
  getLLMProvider,
  getEmbeddingEngineSelection,
} = require("../../helpers");
const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
const { storeVectorResult, cachedVectorInformation } = require("../../files");
const { v4: uuidv4, v5: uuidv5 } = require("uuid");
const { GraphManager } = require("../../graphManager");

const LanceDb = {
  uri: `${
    !!process.env.STORAGE_DIR ? `${process.env.STORAGE_DIR}/` : "./storage/"
  }lancedb`,
  name: "LanceDb",
  connect: async function () {
    if (process.env.VECTOR_DB !== "lancedb")
      throw new Error("LanceDB::Invalid ENV settings");

    const client = await lancedb.connect(this.uri);
    return { client };
  },
  distanceToSimilarity: function (distance = null) {
    if (distance === null || typeof distance !== "number") return 0.0;
    if (distance >= 1.0) return 1;
    if (distance <= 0) return 0;
    return 1 - distance;
  },
  heartbeat: async function () {
    await this.connect();
    return { heartbeat: Number(new Date()) };
  },
  tables: async function () {
    const fs = require("fs");
    const { client } = await this.connect();
    const dirs = fs.readdirSync(client.uri);
    return dirs.map((folder) => folder.replace(".lance", ""));
  },
  totalVectors: async function () {
    const { client } = await this.connect();
    const tables = await this.tables();
    let count = 0;
    for (const tableName of tables) {
      const table = await client.openTable(tableName);
      count += await table.countRows();
    }
    return count;
  },
  namespaceCount: async function (_namespace = null) {
    const { client } = await this.connect();
    const exists = await this.namespaceExists(client, _namespace);
    if (!exists) return 0;

    const table = await client.openTable(_namespace);
    return (await table.countRows()) || 0;
  },
  embedder: function () {
    return new OpenAIEmbeddings({ openAIApiKey: process.env.OPEN_AI_KEY });
  },
  similarityResponse: async function ({
    client,
    namespace,
    queryVector,
    similarityThreshold = 0.25,
    topN = 4,
    textQuery = null,
    useKGExpansion = false,
  }) {
    const collection = await client.openTable(namespace);
    const result = {
      allTexts: [],
      contextTexts: [],
      sourceDocuments: [],
      scores: [],
    };

    const response = await collection
      .search(queryVector)
      .metricType("cosine")
      .limit(topN)
      .execute();

    response.forEach((item) => {
      item.text ? result.allTexts.push(item.text) : null;
      if (this.distanceToSimilarity(item.score) < similarityThreshold) return;
      const { vector: _, ...rest } = item;
      result.contextTexts.push(rest.text);
      result.sourceDocuments.push(rest);
      result.scores.push(this.distanceToSimilarity(item.score));
    });

    // Only attempt to expand the original question if we found at least _something_ from the vectorDB
    // even if it was filtered out by score - because then there is a chance we can expand on it and save the query.
    if (useKGExpansion && result.allTexts.length > 0) {
      const expansionTexts = textQuery
        ? [textQuery, ...result.allTexts]
        : result.allTexts;
      result.contextTexts = await new GraphManager().knowledgeGraphSearch(
        namespace,
        expansionTexts
      );
    }

    return result;
  },
  namespace: async function (client, namespace = null) {
    if (!namespace) throw new Error("No namespace value provided.");
    const collection = await client.openTable(namespace).catch(() => false);
    if (!collection) return null;

    return {
      ...collection,
    };
  },
  updateOrCreateCollection: async function (client, data = [], namespace) {
    const hasNamespace = await this.hasNamespace(namespace);
    if (hasNamespace) {
      const collection = await client.openTable(namespace);
      await collection.add(data);
      return true;
    }

    await client.createTable(namespace, data);
    return true;
  },
  hasNamespace: async function (namespace = null) {
    if (!namespace) return false;
    const { client } = await this.connect();
    const exists = await this.namespaceExists(client, namespace);
    return exists;
  },
  namespaceExists: async function (_client, namespace = null) {
    if (!namespace) throw new Error("No namespace value provided.");
    const collections = await this.tables();
    return collections.includes(namespace);
  },
  deleteVectorsInNamespace: async function (client, namespace = null) {
    const fs = require("fs");
    fs.rm(`${client.uri}/${namespace}.lance`, { recursive: true }, () => null);
    new GraphManager().deleteGraph(namespace);
    return true;
  },
  deleteDocumentFromNamespace: async function (namespace, docId) {
    const { client } = await this.connect();
    const exists = await this.namespaceExists(client, namespace);
    if (!exists) {
      console.error(
        `LanceDB:deleteDocumentFromNamespace - namespace ${namespace} does not exist.`
      );
      return;
    }

    const { DocumentVectors } = require("../../../models/vectors");
    const table = await client.openTable(namespace);
    const vectorIds = (await DocumentVectors.where({ docId })).map(
      (record) => record.vectorId
    );

    if (vectorIds.length === 0) return;
    await table.delete(`id IN (${vectorIds.map((v) => `'${v}'`).join(",")})`);
    await new GraphManager({ namespace }).$deleteByDocumentId(docId);
    return true;
  },
  addDocumentToNamespace: async function (
    namespace,
    documentData = {},
    fullFilePath = null
  ) {
    const { DocumentVectors } = require("../../../models/vectors");
    try {
      const { pageContent, docId, ...metadata } = documentData;
      if (!pageContent || pageContent.length == 0) return false;

      const cacheResult = await cachedVectorInformation(fullFilePath);
      const cacheKey = uuidv5(fullFilePath, uuidv5.URL);
      if (cacheResult.exists) {
        const { client } = await this.connect();
        const { chunks } = cacheResult;
        const documentVectors = [];
        const submissions = [];
        const metadatas = [];

        for (const chunk of chunks) {
          chunk.forEach((chunk) => {
            const id = uuidv4();
            const { id: _id, ...metadata } = chunk.metadata;
            documentVectors.push({ docId, vectorId: id });
            submissions.push({ id: id, vector: chunk.values, ...metadata });
            metadatas.push({ ...metadata, vectorId: id, docId });
          });
        }

        await this.updateOrCreateCollection(client, submissions, namespace);
        await DocumentVectors.bulkInsert(documentVectors);
        await new GraphManager().insertIntoGraph(
          namespace,
          metadatas,
          cacheKey
        );
        return { vectorized: true, error: null };
      }

      // If we are here then we are going to embed and store a novel document.
      // We have to do this manually as opposed to using LangChains `xyz.fromDocuments`
      // because we then cannot atomically control our namespace to granularly find/remove documents
      // from vectordb.
      const textSplitter = new RecursiveCharacterTextSplitter({
        chunkSize:
          getEmbeddingEngineSelection()?.embeddingMaxChunkLength || 1_000,
        chunkOverlap: 20,
      });
      const textChunks = await textSplitter.splitText(pageContent);

      console.log("Chunks created from document:", textChunks.length);
      const LLMConnector = getLLMProvider();
      const documentVectors = [];
      const vectors = [];
      const submissions = [];
      const metadatas = [];
      const vectorValues = await LLMConnector.embedChunks(textChunks);

      if (!!vectorValues && vectorValues.length > 0) {
        for (const [i, vector] of vectorValues.entries()) {
          const vectorRecord = {
            id: uuidv4(),
            values: vector,
            // [DO NOT REMOVE]
            // LangChain will be unable to find your text if you embed manually and dont include the `text` key.
            // https://github.com/hwchase17/langchainjs/blob/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L64
            metadata: { ...metadata, text: textChunks[i] },
          };

          vectors.push(vectorRecord);
          submissions.push({
            ...vectorRecord.metadata,
            id: vectorRecord.id,
            vector: vectorRecord.values,
          });
          documentVectors.push({ docId, vectorId: vectorRecord.id });
          metadatas.push({
            ...vectorRecord.metadata,
            vectorId: vectorRecord.id,
            docId,
          });
        }
      } else {
        throw new Error(
          "Could not embed document chunks! This document will not be recorded."
        );
      }

      if (vectors.length > 0) {
        const chunks = [];
        for (const chunk of toChunks(vectors, 500)) chunks.push(chunk);

        console.log("Inserting vectorized chunks into LanceDB collection.");
        const { client } = await this.connect();
        await this.updateOrCreateCollection(client, submissions, namespace);
        await storeVectorResult(chunks, fullFilePath);
        await new GraphManager().insertIntoGraph(
          namespace,
          metadatas,
          cacheKey
        );
      }

      await DocumentVectors.bulkInsert(documentVectors);
      return { vectorized: true, error: null };
    } catch (e) {
      console.error("addDocumentToNamespace", e.message);
      return { vectorized: false, error: e.message };
    }
  },
  performSimilaritySearch: async function ({
    namespace = null,
    input = "",
    LLMConnector = null,
    similarityThreshold = 0.25,
    topN = 4,
  }) {
    if (!namespace || !input || !LLMConnector)
      throw new Error("Invalid request to performSimilaritySearch.");

    const { client } = await this.connect();
    if (!(await this.namespaceExists(client, namespace))) {
      return {
        contextTexts: [],
        sources: [],
        message: "Invalid query - no documents found for workspace!",
      };
    }

    const queryVector = await LLMConnector.embedTextInput(input);
    const { contextTexts, sourceDocuments } = await this.similarityResponse({
      client,
      namespace,
      queryVector,
      similarityThreshold,
      topN,
      textQuery: input,
    });

    const sources = sourceDocuments.map((metadata, i) => {
      return { metadata: { ...metadata, text: contextTexts[i] } };
    });
    return {
      contextTexts,
      sources: this.curateSources(sources),
      message: false,
    };
  },
  "namespace-stats": async function (reqBody = {}) {
    const { namespace = null } = reqBody;
    if (!namespace) throw new Error("namespace required");
    const { client } = await this.connect();
    if (!(await this.namespaceExists(client, namespace)))
      throw new Error("Namespace by that name does not exist.");
    const stats = await this.namespace(client, namespace);
    return stats
      ? stats
      : { message: "No stats were able to be fetched from DB for namespace" };
  },
  "delete-namespace": async function (reqBody = {}) {
    const { namespace = null } = reqBody;
    const { client } = await this.connect();
    if (!(await this.namespaceExists(client, namespace)))
      throw new Error("Namespace by that name does not exist.");

    await this.deleteVectorsInNamespace(client, namespace);
    return {
      message: `Namespace ${namespace} was deleted.`,
    };
  },
  reset: async function () {
    const { client } = await this.connect();
    const fs = require("fs");
    fs.rm(`${client.uri}`, { recursive: true }, () => null);
    return { reset: true };
  },
  curateSources: function (sources = []) {
    const documents = [];
    for (const source of sources) {
      const { text, vector: _v, score: _s, ...rest } = source;
      const metadata = rest.hasOwnProperty("metadata") ? rest.metadata : rest;
      if (Object.keys(metadata).length > 0) {
        documents.push({
          ...metadata,
          ...(text ? { text } : {}),
        });
      }
    }

    return documents;
  },
};

module.exports.LanceDb = LanceDb;