anything-llm/server/utils/vectorDbProviders/lance/index.js
Sean Hatfield a126b5f5aa
Replace custom sqlite dbms with prisma (#239)
* WIP converted all sqlite models into prisma calls

* modify db setup and fix ApiKey model calls in admin.js

* renaming function params to be consistent

* converted adminEndpoints to utilize prisma orm

* converted chatEndpoints to utilize prisma orm

* converted inviteEndpoints to utilize prisma orm

* converted systemEndpoints to utilize prisma orm

* converted workspaceEndpoints to utilize prisma orm

* converting sql queries to prisma calls

* fixed default param bug for orderBy and limit

* fixed typo for workspace chats

* fixed order of deletion to account for sql relations

* fix invite CRUD and workspace management CRUD

* fixed CRUD for api keys

* created prisma setup scripts/docs for understanding how to use prisma

* prisma dependency change

* removing unneeded console.logs

* removing unneeded sql escape function

* linting and creating migration script

* migration from depreciated sqlite script update

* removing unneeded migrations in prisma folder

* create backup of old sqlite db and use transactions to ensure all operations complete successfully

* adding migrations to gitignore

* updated PRISMA.md docs for info on how to use sqlite migration script

* comment changes

* adding back migrations folder to repo

* Reviewing SQL and prisma integraiton on fresh repo

* update inline key replacement

* ensure migration script executes and maps foreign_keys regardless of db ordering

* run migration endpoint

* support new prisma backend

* bump version

* change migration call

---------

Co-authored-by: timothycarambat <rambat1010@gmail.com>
2023-09-28 14:00:03 -07:00

354 lines
12 KiB
JavaScript

const lancedb = require("vectordb");
const { toChunks, getLLMProvider } = require("../../helpers");
const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
const { storeVectorResult, cachedVectorInformation } = require("../../files");
const { v4: uuidv4 } = require("uuid");
const { chatPrompt } = require("../../chats");
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 };
},
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) {
const collection = await client.openTable(namespace);
const result = {
contextTexts: [],
sourceDocuments: [],
};
const response = await collection
.search(queryVector)
.metricType("cosine")
.limit(5)
.execute();
response.forEach((item) => {
const { vector: _, ...rest } = item;
result.contextTexts.push(rest.text);
result.sourceDocuments.push(rest);
});
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);
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(",")})`);
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;
console.log("Adding new vectorized document into namespace", namespace);
const cacheResult = await cachedVectorInformation(fullFilePath);
if (cacheResult.exists) {
const { client } = await this.connect();
const { chunks } = cacheResult;
const documentVectors = [];
const submissions = [];
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 });
});
}
await this.updateOrCreateCollection(client, submissions, namespace);
await DocumentVectors.bulkInsert(documentVectors);
return true;
}
// 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: 1000,
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 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({
id: vectorRecord.id,
vector: vectorRecord.values,
...vectorRecord.metadata,
});
documentVectors.push({ docId, vectorId: vectorRecord.id });
}
} else {
console.error(
"Could not use OpenAI to 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 DocumentVectors.bulkInsert(documentVectors);
return true;
} catch (e) {
console.error(e);
console.error("addDocumentToNamespace", e.message);
return false;
}
},
query: async function (reqBody = {}) {
const { namespace = null, input, workspace = {} } = reqBody;
if (!namespace || !input) throw new Error("Invalid request body");
const { client } = await this.connect();
if (!(await this.namespaceExists(client, namespace))) {
return {
response: null,
sources: [],
message: "Invalid query - no documents found for workspace!",
};
}
const LLMConnector = getLLMProvider();
const queryVector = await LLMConnector.embedTextInput(input);
const { contextTexts, sourceDocuments } = await this.similarityResponse(
client,
namespace,
queryVector
);
const prompt = {
role: "system",
content: `${chatPrompt(workspace)}
Context:
${contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")}`,
};
const memory = [prompt, { role: "user", content: input }];
const responseText = await LLMConnector.getChatCompletion(memory, {
temperature: workspace?.openAiTemp ?? 0.7,
});
return {
response: responseText,
sources: this.curateSources(sourceDocuments),
message: false,
};
},
// This implementation of chat uses the chat history and modifies the system prompt at execution
// this is improved over the regular langchain implementation so that chats do not directly modify embeddings
// because then multi-user support will have all conversations mutating the base vector collection to which then
// the only solution is replicating entire vector databases per user - which will very quickly consume space on VectorDbs
chat: async function (reqBody = {}) {
const {
namespace = null,
input,
workspace = {},
chatHistory = [],
} = reqBody;
if (!namespace || !input) throw new Error("Invalid request body");
const { client } = await this.connect();
if (!(await this.namespaceExists(client, namespace))) {
return {
response: null,
sources: [],
message: "Invalid query - no documents found for workspace!",
};
}
const LLMConnector = getLLMProvider();
const queryVector = await LLMConnector.embedTextInput(input);
const { contextTexts, sourceDocuments } = await this.similarityResponse(
client,
namespace,
queryVector
);
const prompt = {
role: "system",
content: `${chatPrompt(workspace)}
Context:
${contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")}`,
};
const memory = [prompt, ...chatHistory, { role: "user", content: input }];
const responseText = await LLMConnector.getChatCompletion(memory, {
temperature: workspace?.openAiTemp ?? 0.7,
});
return {
response: responseText,
sources: this.curateSources(sourceDocuments),
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, ...metadata } = source;
if (Object.keys(metadata).length > 0) {
documents.push({ ...metadata, text });
}
}
return documents;
},
};
module.exports.LanceDb = LanceDb;