File size: 4,449 Bytes
f0743f4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | const fs = require('fs');
const axios = require('axios');
const FormData = require('form-data');
const { logger } = require('@librechat/data-schemas');
const { FileSources } = require('librechat-data-provider');
const { logAxiosError, generateShortLivedToken } = require('@librechat/api');
/**
* Deletes a file from the vector database. This function takes a file object, constructs the full path, and
* verifies the path's validity before deleting the file. If the path is invalid, an error is thrown.
*
* @param {ServerRequest} req - The request object from Express.
* @param {MongoFile} file - The file object to be deleted. It should have a `filepath` property that is
* a string representing the path of the file relative to the publicPath.
*
* @returns {Promise<void>}
* A promise that resolves when the file has been successfully deleted, or throws an error if the
* file path is invalid or if there is an error in deletion.
*/
const deleteVectors = async (req, file) => {
if (!file.embedded || !process.env.RAG_API_URL) {
return;
}
try {
const jwtToken = generateShortLivedToken(req.user.id);
return await axios.delete(`${process.env.RAG_API_URL}/documents`, {
headers: {
Authorization: `Bearer ${jwtToken}`,
'Content-Type': 'application/json',
accept: 'application/json',
},
data: [file.file_id],
});
} catch (error) {
logAxiosError({
error,
message: 'Error deleting vectors',
});
if (
error.response &&
error.response.status !== 404 &&
(error.response.status < 200 || error.response.status >= 300)
) {
logger.warn('Error deleting vectors, file will not be deleted');
throw new Error(error.message || 'An error occurred during file deletion.');
}
}
};
/**
* Uploads a file to the configured Vector database
*
* @param {Object} params - The params object.
* @param {Object} params.req - The request object from Express. It should have a `user` property with an `id` representing the user
* @param {Express.Multer.File} params.file - The file object, which is part of the request. The file object should
* have a `path` property that points to the location of the uploaded file.
* @param {string} params.file_id - The file ID.
* @param {string} [params.entity_id] - The entity ID for shared resources.
* @param {Object} [params.storageMetadata] - Storage metadata for dual storage pattern.
*
* @returns {Promise<{ filepath: string, bytes: number }>}
* A promise that resolves to an object containing:
* - filepath: The path where the file is saved.
* - bytes: The size of the file in bytes.
*/
async function uploadVectors({ req, file, file_id, entity_id, storageMetadata }) {
if (!process.env.RAG_API_URL) {
throw new Error('RAG_API_URL not defined');
}
try {
const jwtToken = generateShortLivedToken(req.user.id);
const formData = new FormData();
formData.append('file_id', file_id);
formData.append('file', fs.createReadStream(file.path));
if (entity_id != null && entity_id) {
formData.append('entity_id', entity_id);
}
// Include storage metadata for RAG API to store with embeddings
if (storageMetadata) {
formData.append('storage_metadata', JSON.stringify(storageMetadata));
}
const formHeaders = formData.getHeaders();
const response = await axios.post(`${process.env.RAG_API_URL}/embed`, formData, {
headers: {
Authorization: `Bearer ${jwtToken}`,
accept: 'application/json',
...formHeaders,
},
});
const responseData = response.data;
logger.debug('Response from embedding file', responseData);
if (responseData.known_type === false) {
throw new Error(`File embedding failed. The filetype ${file.mimetype} is not supported`);
}
if (!responseData.status) {
throw new Error('File embedding failed.');
}
return {
bytes: file.size,
filename: file.originalname,
filepath: FileSources.vectordb,
embedded: Boolean(responseData.known_type),
};
} catch (error) {
logAxiosError({
error,
message: 'Error uploading vectors',
});
throw new Error(error.message || 'An error occurred during file upload.');
}
}
module.exports = {
deleteVectors,
uploadVectors,
};
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