anything-llm/server/utils/vectorDbProviders/pinecone/index.js
Timothy Carambat dc4ad6b5a9
[BETA] Live document sync (#1719)
* wip bg workers for live document sync

* Add ability to re-embed specific documents across many workspaces via background queue
bgworkser is gated behind expieremental system setting flag that needs to be explictly enabled
UI for watching/unwatching docments that are embedded.
TODO: UI to easily manage all bg tasks and see run results
TODO: UI to enable this feature and background endpoints to manage it

* create frontend views and paths
Move elements to correct experimental scope

* update migration to delete runs on removal of watched document

* Add watch support to YouTube transcripts (#1716)

* Add watch support to YouTube transcripts
refactor how sync is done for supported types

* Watch specific files in Confluence space (#1718)

Add failure-prune check for runs

* create tmp workflow modifications for beta image

* create tmp workflow modifications for beta image

* create tmp workflow modifications for beta image

* dual build
update copy of alert modals

* update job interval

* Add support for live-sync of Github files

* update copy for document sync feature

* hide Experimental features from UI

* update docs links

* [FEAT] Implement new settings menu for experimental features (#1735)

* implement new settings menu for experimental features

* remove unused context save bar

---------

Co-authored-by: timothycarambat <rambat1010@gmail.com>

* dont run job on boot

* unset workflow changes

* Add persistent encryption service
Relay key to collector so persistent encryption can be used
Encrypt any private data in chunkSources used for replay during resync jobs

* update jsDOC

* Linting and organization

* update modal copy for feature

---------

Co-authored-by: Sean Hatfield <seanhatfield5@gmail.com>
2024-06-21 13:38:50 -07:00

297 lines
11 KiB
JavaScript

const { Pinecone } = require("@pinecone-database/pinecone");
const { TextSplitter } = require("../../TextSplitter");
const { SystemSettings } = require("../../../models/systemSettings");
const { storeVectorResult, cachedVectorInformation } = require("../../files");
const { v4: uuidv4 } = require("uuid");
const { toChunks, getEmbeddingEngineSelection } = require("../../helpers");
const { sourceIdentifier } = require("../../chats");
const PineconeDB = {
name: "Pinecone",
connect: async function () {
if (process.env.VECTOR_DB !== "pinecone")
throw new Error("Pinecone::Invalid ENV settings");
const client = new Pinecone({
apiKey: process.env.PINECONE_API_KEY,
});
const pineconeIndex = client.Index(process.env.PINECONE_INDEX);
const { status } = await client.describeIndex(process.env.PINECONE_INDEX);
if (!status.ready) throw new Error("Pinecone::Index not ready.");
return { client, pineconeIndex, indexName: process.env.PINECONE_INDEX };
},
totalVectors: async function () {
const { pineconeIndex } = await this.connect();
const { namespaces } = await pineconeIndex.describeIndexStats();
return Object.values(namespaces).reduce(
(a, b) => a + (b?.recordCount || 0),
0
);
},
namespaceCount: async function (_namespace = null) {
const { pineconeIndex } = await this.connect();
const namespace = await this.namespace(pineconeIndex, _namespace);
return namespace?.recordCount || 0;
},
similarityResponse: async function (
index,
namespace,
queryVector,
similarityThreshold = 0.25,
topN = 4,
filterIdentifiers = []
) {
const result = {
contextTexts: [],
sourceDocuments: [],
scores: [],
};
const pineconeNamespace = index.namespace(namespace);
const response = await pineconeNamespace.query({
vector: queryVector,
topK: topN,
includeMetadata: true,
});
response.matches.forEach((match) => {
if (match.score < similarityThreshold) return;
if (filterIdentifiers.includes(sourceIdentifier(match.metadata))) {
console.log(
"Pinecone: A source was filtered from context as it's parent document is pinned."
);
return;
}
result.contextTexts.push(match.metadata.text);
result.sourceDocuments.push(match);
result.scores.push(match.score);
});
return result;
},
namespace: async function (index, namespace = null) {
if (!namespace) throw new Error("No namespace value provided.");
const { namespaces } = await index.describeIndexStats();
return namespaces.hasOwnProperty(namespace) ? namespaces[namespace] : null;
},
hasNamespace: async function (namespace = null) {
if (!namespace) return false;
const { pineconeIndex } = await this.connect();
return await this.namespaceExists(pineconeIndex, namespace);
},
namespaceExists: async function (index, namespace = null) {
if (!namespace) throw new Error("No namespace value provided.");
const { namespaces } = await index.describeIndexStats();
return namespaces.hasOwnProperty(namespace);
},
deleteVectorsInNamespace: async function (index, namespace = null) {
const pineconeNamespace = index.namespace(namespace);
await pineconeNamespace.deleteAll();
return true;
},
addDocumentToNamespace: async function (
namespace,
documentData = {},
fullFilePath = null,
skipCache = false
) {
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);
if (!skipCache) {
const cacheResult = await cachedVectorInformation(fullFilePath);
if (cacheResult.exists) {
const { pineconeIndex } = await this.connect();
const pineconeNamespace = pineconeIndex.namespace(namespace);
const { chunks } = cacheResult;
const documentVectors = [];
for (const chunk of chunks) {
// Before sending to Pinecone and saving the records to our db
// we need to assign the id of each chunk that is stored in the cached file.
const newChunks = chunk.map((chunk) => {
const id = uuidv4();
documentVectors.push({ docId, vectorId: id });
return { ...chunk, id };
});
await pineconeNamespace.upsert([...newChunks]);
}
await DocumentVectors.bulkInsert(documentVectors);
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 `PineconeStore.fromDocuments`
// because we then cannot atomically control our namespace to granularly find/remove documents
// from vectordb.
// https://github.com/hwchase17/langchainjs/blob/2def486af734c0ca87285a48f1a04c057ab74bdf/langchain/src/vectorstores/pinecone.ts#L167
const EmbedderEngine = getEmbeddingEngineSelection();
const textSplitter = new TextSplitter({
chunkSize: TextSplitter.determineMaxChunkSize(
await SystemSettings.getValueOrFallback({
label: "text_splitter_chunk_size",
}),
EmbedderEngine?.embeddingMaxChunkLength
),
chunkOverlap: await SystemSettings.getValueOrFallback(
{ label: "text_splitter_chunk_overlap" },
20
),
chunkHeaderMeta: {
sourceDocument: metadata?.title,
published: metadata?.published || "unknown",
},
});
const textChunks = await textSplitter.splitText(pageContent);
console.log("Chunks created from document:", textChunks.length);
const documentVectors = [];
const vectors = [];
const vectorValues = await EmbedderEngine.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);
documentVectors.push({ docId, vectorId: vectorRecord.id });
}
} else {
throw new Error(
"Could not embed document chunks! This document will not be recorded."
);
}
if (vectors.length > 0) {
const chunks = [];
const { pineconeIndex } = await this.connect();
const pineconeNamespace = pineconeIndex.namespace(namespace);
console.log("Inserting vectorized chunks into Pinecone.");
for (const chunk of toChunks(vectors, 100)) {
chunks.push(chunk);
await pineconeNamespace.upsert([...chunk]);
}
await storeVectorResult(chunks, fullFilePath);
}
await DocumentVectors.bulkInsert(documentVectors);
return { vectorized: true, error: null };
} catch (e) {
console.error("addDocumentToNamespace", e.message);
return { vectorized: false, error: e.message };
}
},
deleteDocumentFromNamespace: async function (namespace, docId) {
const { DocumentVectors } = require("../../../models/vectors");
const { pineconeIndex } = await this.connect();
if (!(await this.namespaceExists(pineconeIndex, namespace))) return;
const knownDocuments = await DocumentVectors.where({ docId });
if (knownDocuments.length === 0) return;
const vectorIds = knownDocuments.map((doc) => doc.vectorId);
const pineconeNamespace = pineconeIndex.namespace(namespace);
for (const batchOfVectorIds of toChunks(vectorIds, 1000)) {
await pineconeNamespace.deleteMany(batchOfVectorIds);
}
const indexes = knownDocuments.map((doc) => doc.id);
await DocumentVectors.deleteIds(indexes);
return true;
},
"namespace-stats": async function (reqBody = {}) {
const { namespace = null } = reqBody;
if (!namespace) throw new Error("namespace required");
const { pineconeIndex } = await this.connect();
if (!(await this.namespaceExists(pineconeIndex, namespace)))
throw new Error("Namespace by that name does not exist.");
const stats = await this.namespace(pineconeIndex, namespace);
return stats
? stats
: { message: "No stats were able to be fetched from DB" };
},
"delete-namespace": async function (reqBody = {}) {
const { namespace = null } = reqBody;
const { pineconeIndex } = await this.connect();
if (!(await this.namespaceExists(pineconeIndex, namespace)))
throw new Error("Namespace by that name does not exist.");
const details = await this.namespace(pineconeIndex, namespace);
await this.deleteVectorsInNamespace(pineconeIndex, namespace);
return {
message: `Namespace ${namespace} was deleted along with ${details.vectorCount} vectors.`,
};
},
performSimilaritySearch: async function ({
namespace = null,
input = "",
LLMConnector = null,
similarityThreshold = 0.25,
topN = 4,
filterIdentifiers = [],
}) {
if (!namespace || !input || !LLMConnector)
throw new Error("Invalid request to performSimilaritySearch.");
const { pineconeIndex } = await this.connect();
if (!(await this.namespaceExists(pineconeIndex, namespace)))
throw new Error(
"Invalid namespace - has it been collected and populated yet?"
);
const queryVector = await LLMConnector.embedTextInput(input);
const { contextTexts, sourceDocuments } = await this.similarityResponse(
pineconeIndex,
namespace,
queryVector,
similarityThreshold,
topN,
filterIdentifiers
);
const sources = sourceDocuments.map((metadata, i) => {
return { ...metadata, text: contextTexts[i] };
});
return {
contextTexts,
sources: this.curateSources(sources),
message: false,
};
},
curateSources: function (sources = []) {
const documents = [];
for (const source of sources) {
const { metadata = {} } = source;
if (Object.keys(metadata).length > 0) {
documents.push({
...metadata,
...(source.hasOwnProperty("pageContent")
? { text: source.pageContent }
: {}),
});
}
}
return documents;
},
};
module.exports.Pinecone = PineconeDB;