anything-llm/server/models/documents.js
Timothy Carambat 658e7fa390
chore: Better VectorDb and Embedder error messages (#620)
* chore: propogate embedder and vectordb errors during document mutations

* add default value for errors on addDocuments
2024-01-18 11:40:48 -08:00

137 lines
3.7 KiB
JavaScript

const { fileData } = require("../utils/files");
const { v4: uuidv4 } = require("uuid");
const { getVectorDbClass } = require("../utils/helpers");
const prisma = require("../utils/prisma");
const { Telemetry } = require("./telemetry");
const Document = {
forWorkspace: async function (workspaceId = null) {
if (!workspaceId) return [];
return await prisma.workspace_documents.findMany({
where: { workspaceId },
});
},
delete: async function (clause = {}) {
try {
await prisma.workspace_documents.deleteMany({ where: clause });
return true;
} catch (error) {
console.error(error.message);
return false;
}
},
firstWhere: async function (clause = {}) {
try {
const document = await prisma.workspace_documents.findFirst({
where: clause,
});
return document || null;
} catch (error) {
console.error(error.message);
return null;
}
},
addDocuments: async function (workspace, additions = []) {
const VectorDb = getVectorDbClass();
if (additions.length === 0) return { failed: [], embedded: [] };
const embedded = [];
const failedToEmbed = [];
const errors = new Set();
for (const path of additions) {
const data = await fileData(path);
if (!data) continue;
const docId = uuidv4();
const { pageContent, ...metadata } = data;
const newDoc = {
docId,
filename: path.split("/")[1],
docpath: path,
workspaceId: workspace.id,
metadata: JSON.stringify(metadata),
};
const { vectorized, error } = await VectorDb.addDocumentToNamespace(
workspace.slug,
{ ...data, docId },
path
);
if (!vectorized) {
console.error(
"Failed to vectorize",
metadata?.title || newDoc.filename
);
failedToEmbed.push(metadata?.title || newDoc.filename);
errors.add(error);
continue;
}
try {
await prisma.workspace_documents.create({ data: newDoc });
embedded.push(path);
} catch (error) {
console.error(error.message);
}
}
await Telemetry.sendTelemetry("documents_embedded_in_workspace", {
LLMSelection: process.env.LLM_PROVIDER || "openai",
Embedder: process.env.EMBEDDING_ENGINE || "inherit",
VectorDbSelection: process.env.VECTOR_DB || "pinecone",
});
return { failedToEmbed, errors: Array.from(errors), embedded };
},
removeDocuments: async function (workspace, removals = []) {
const VectorDb = getVectorDbClass();
if (removals.length === 0) return;
for (const path of removals) {
const document = await this.firstWhere({
docpath: path,
workspaceId: workspace.id,
});
if (!document) continue;
await VectorDb.deleteDocumentFromNamespace(
workspace.slug,
document.docId
);
try {
await prisma.workspace_documents.delete({
where: { id: document.id, workspaceId: workspace.id },
});
} catch (error) {
console.error(error.message);
}
}
await Telemetry.sendTelemetry("documents_removed_in_workspace", {
LLMSelection: process.env.LLM_PROVIDER || "openai",
Embedder: process.env.EMBEDDING_ENGINE || "inherit",
VectorDbSelection: process.env.VECTOR_DB || "pinecone",
});
return true;
},
count: async function (clause = {}, limit = null) {
try {
const count = await prisma.workspace_documents.count({
where: clause,
...(limit !== null ? { take: limit } : {}),
});
return count;
} catch (error) {
console.error("FAILED TO COUNT DOCUMENTS.", error.message);
return 0;
}
},
};
module.exports = { Document };