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

335 lines
12 KiB
JavaScript

const { PineconeClient } = require("@pinecone-database/pinecone");
const { PineconeStore } = require("langchain/vectorstores/pinecone");
const { OpenAI } = require("langchain/llms/openai");
const { VectorDBQAChain } = require("langchain/chains");
const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
const { storeVectorResult, cachedVectorInformation } = require("../../files");
const { v4: uuidv4 } = require("uuid");
const { toChunks } = require("../../helpers");
const { chatPrompt } = require("../../chats");
const { OpenAi } = require("../../AiProviders/openAi");
const Pinecone = {
name: "Pinecone",
connect: async function () {
if (process.env.VECTOR_DB !== "pinecone")
throw new Error("Pinecone::Invalid ENV settings");
const client = new PineconeClient();
await client.init({
apiKey: process.env.PINECONE_API_KEY,
environment: process.env.PINECONE_ENVIRONMENT,
});
const pineconeIndex = client.Index(process.env.PINECONE_INDEX);
const { status } = await client.describeIndex({
indexName: process.env.PINECONE_INDEX,
});
if (!status.ready) throw new Error("Pinecode::Index not ready.");
return { client, pineconeIndex, indexName: process.env.PINECONE_INDEX };
},
embedder: function () {
return new OpenAIEmbeddings({ openAIApiKey: process.env.OPEN_AI_KEY });
},
llm: function ({ temperature = 0.7 }) {
const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
return new OpenAI({
openAIApiKey: process.env.OPEN_AI_KEY,
modelName: model,
temperature,
});
},
totalIndicies: async function () {
const { pineconeIndex } = await this.connect();
const { namespaces } = await pineconeIndex.describeIndexStats1();
return Object.values(namespaces).reduce(
(a, b) => a + (b?.vectorCount || 0),
0
);
},
namespaceCount: async function (_namespace = null) {
const { pineconeIndex } = await this.connect();
const namespace = await this.namespace(pineconeIndex, _namespace);
return namespace?.vectorCount || 0;
},
similarityResponse: async function (index, namespace, queryVector) {
const result = {
contextTexts: [],
sourceDocuments: [],
};
const response = await index.query({
queryRequest: {
namespace,
vector: queryVector,
topK: 4,
includeMetadata: true,
},
});
response.matches.forEach((match) => {
result.contextTexts.push(match.metadata.text);
result.sourceDocuments.push(match);
});
return result;
},
namespace: async function (index, namespace = null) {
if (!namespace) throw new Error("No namespace value provided.");
const { namespaces } = await index.describeIndexStats1();
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.describeIndexStats1();
return namespaces.hasOwnProperty(namespace);
},
deleteVectorsInNamespace: async function (index, namespace = null) {
await index.delete1({ namespace, deleteAll: true });
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 { pineconeIndex } = await this.connect();
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 };
});
// Push chunks with new ids to pinecone.
await pineconeIndex.upsert({
upsertRequest: {
vectors: [...newChunks],
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 `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 textSplitter = new RecursiveCharacterTextSplitter({
chunkSize: 1000,
chunkOverlap: 20,
});
const textChunks = await textSplitter.splitText(pageContent);
console.log("Chunks created from document:", textChunks.length);
const openAiConnector = new OpenAi();
const documentVectors = [];
const vectors = [];
const vectorValues = await openAiConnector.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 {
console.error(
"Could not use OpenAI to embed document chunks! This document will not be recorded."
);
}
if (vectors.length > 0) {
const chunks = [];
const { pineconeIndex } = await this.connect();
console.log("Inserting vectorized chunks into Pinecone.");
for (const chunk of toChunks(vectors, 100)) {
chunks.push(chunk);
await pineconeIndex.upsert({
upsertRequest: {
vectors: [...chunk],
namespace,
},
});
}
await storeVectorResult(chunks, fullFilePath);
}
await DocumentVectors.bulkInsert(documentVectors);
return true;
} catch (e) {
console.error("addDocumentToNamespace", e.message);
return false;
}
},
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 = '${docId}'`);
if (knownDocuments.length === 0) return;
const vectorIds = knownDocuments.map((doc) => doc.vectorId);
await pineconeIndex.delete1({
ids: vectorIds,
namespace,
});
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 ${details.vectorCount} vectors.`,
};
},
query: async function (reqBody = {}) {
const { namespace = null, input, workspace = {} } = reqBody;
if (!namespace || !input) throw new Error("Invalid request body");
const { pineconeIndex } = await this.connect();
if (!(await this.namespaceExists(pineconeIndex, namespace))) {
return {
response: null,
sources: [],
message: "Invalid query - no documents found for workspace!",
};
}
const vectorStore = await PineconeStore.fromExistingIndex(this.embedder(), {
pineconeIndex,
namespace,
});
const model = this.llm({
temperature: workspace?.openAiTemp ?? 0.7,
});
const chain = VectorDBQAChain.fromLLM(model, vectorStore, {
k: 5,
returnSourceDocuments: true,
});
const response = await chain.call({ query: input });
return {
response: response.text,
sources: this.curateSources(response.sourceDocuments),
message: false,
};
},
// This implementation of chat uses the chat history and modifies the system prompt at execution
// this is improved over the regular langchain implementation so that chats do not directly modify embeddings
// because then multi-user support will have all conversations mutating the base vector collection to which then
// the only solution is replicating entire vector databases per user - which will very quickly consume space on VectorDbs
chat: async function (reqBody = {}) {
const {
namespace = null,
input,
workspace = {},
chatHistory = [],
} = reqBody;
if (!namespace || !input) throw new Error("Invalid request body");
const { pineconeIndex } = await this.connect();
if (!(await this.namespaceExists(pineconeIndex, namespace)))
throw new Error(
"Invalid namespace - has it been collected and seeded yet?"
);
const openAiConnector = new OpenAi();
const queryVector = await openAiConnector.embedTextInput(input);
const { contextTexts, sourceDocuments } = await this.similarityResponse(
pineconeIndex,
namespace,
queryVector
);
const prompt = {
role: "system",
content: `${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 openAiConnector.getChatCompletion(memory, {
temperature: workspace?.openAiTemp ?? 0.7,
});
return {
response: responseText,
sources: this.curateSources(sourceDocuments),
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 = Pinecone;