mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-09 00:10:10 +01:00
304 lines
11 KiB
JavaScript
304 lines
11 KiB
JavaScript
const { PineconeClient } = require("@pinecone-database/pinecone");
|
|
const { PineconeStore } = require("langchain/vectorstores/pinecone");
|
|
const { OpenAI } = require("langchain/llms/openai");
|
|
const { ChatOpenAI } = require("langchain/chat_models/openai");
|
|
const { VectorDBQAChain, LLMChain } = require("langchain/chains");
|
|
const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
|
|
const { VectorStoreRetrieverMemory } = require("langchain/memory");
|
|
const { PromptTemplate } = require("langchain/prompts");
|
|
const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
|
|
const { storeVectorResult, cachedVectorInformation } = require("../../files");
|
|
const { Configuration, OpenAIApi } = require("openai");
|
|
const { v4: uuidv4 } = require("uuid");
|
|
const { toChunks, curateSources } = require("../../helpers");
|
|
|
|
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 });
|
|
},
|
|
openai: function () {
|
|
const config = new Configuration({ apiKey: process.env.OPEN_AI_KEY });
|
|
const openai = new OpenAIApi(config);
|
|
return openai;
|
|
},
|
|
embedChunk: async function (openai, textChunk) {
|
|
const {
|
|
data: { data },
|
|
} = await openai.createEmbedding({
|
|
model: "text-embedding-ada-002",
|
|
input: textChunk,
|
|
});
|
|
return data.length > 0 && data[0].hasOwnProperty("embedding")
|
|
? data[0].embedding
|
|
: null;
|
|
},
|
|
llm: function () {
|
|
const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
|
|
return new OpenAI({
|
|
openAIApiKey: process.env.OPEN_AI_KEY,
|
|
temperature: 0.7,
|
|
modelName: model,
|
|
});
|
|
},
|
|
chatLLM: function () {
|
|
const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
|
|
return new ChatOpenAI({
|
|
openAIApiKey: process.env.OPEN_AI_KEY,
|
|
temperature: 0.7,
|
|
modelName: model,
|
|
});
|
|
},
|
|
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
|
|
);
|
|
},
|
|
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 documentVectors = [];
|
|
const vectors = [];
|
|
const openai = this.openai();
|
|
for (const textChunk of textChunks) {
|
|
const vectorValues = await this.embedChunk(openai, textChunk);
|
|
|
|
if (!!vectorValues) {
|
|
const vectorRecord = {
|
|
id: uuidv4(),
|
|
values: vectorValues,
|
|
// [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: textChunk },
|
|
};
|
|
vectors.push(vectorRecord);
|
|
documentVectors.push({ docId, vectorId: vectorRecord.id });
|
|
} else {
|
|
console.error(
|
|
"Could not use OpenAI to embed document chunk! 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 } = 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();
|
|
const chain = VectorDBQAChain.fromLLM(model, vectorStore, {
|
|
k: 5,
|
|
returnSourceDocuments: true,
|
|
});
|
|
const response = await chain.call({ query: input });
|
|
return {
|
|
response: response.text,
|
|
sources: curateSources(response.sourceDocuments),
|
|
message: false,
|
|
};
|
|
},
|
|
// This implementation of chat also expands the memory of the chat itself
|
|
// and adds more tokens to the PineconeDB instance namespace
|
|
chat: async function (reqBody = {}) {
|
|
const { namespace = null, input } = 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 vectorStore = await PineconeStore.fromExistingIndex(this.embedder(), {
|
|
pineconeIndex,
|
|
namespace,
|
|
});
|
|
|
|
const memory = new VectorStoreRetrieverMemory({
|
|
vectorStoreRetriever: vectorStore.asRetriever(1),
|
|
memoryKey: "history",
|
|
});
|
|
|
|
const model = this.llm();
|
|
const prompt =
|
|
PromptTemplate.fromTemplate(`The following is a friendly conversation between a human and an AI. The AI is very casual and talkative and responds with a friendly tone. If the AI does not know the answer to a question, it truthfully says it does not know.
|
|
Relevant pieces of previous conversation:
|
|
{history}
|
|
|
|
Current conversation:
|
|
Human: {input}
|
|
AI:`);
|
|
|
|
const chain = new LLMChain({ llm: model, prompt, memory });
|
|
const response = await chain.call({ input });
|
|
return { response: response.text, sources: [], message: false };
|
|
},
|
|
};
|
|
|
|
module.exports.Pinecone = Pinecone;
|