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fix: fully separate chunkconcurrency from chunk length

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This commit is contained in:
timothycarambat 2023-12-20 11:20:40 -08:00
parent 7bee849c65
commit 049bfa14cb
4 changed files with 12 additions and 8 deletions
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7
server/utils/EmbeddingEngines/azureOpenAi/index.js
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@ -16,7 +16,8 @@ class AzureOpenAiEmbedder {
// Limit of how many strings we can process in a single pass to stay with resource or network limits
// https://learn.microsoft.com/en-us/azure/ai-services/openai/faq#i-am-trying-to-use-embeddings-and-received-the-error--invalidrequesterror--too-many-inputs--the-max-number-of-inputs-is-1---how-do-i-fix-this-:~:text=consisting%20of%20up%20to%2016%20inputs%20per%20API%20request
this.embeddingMaxChunkLength = 16;
this.maxConcurrentChunks = 16;
this.embeddingMaxChunkLength = 1_000;
}
async embedTextInput(textInput) {
@ -34,9 +35,9 @@ class AzureOpenAiEmbedder {
// Because there is a limit on how many chunks can be sent at once to Azure OpenAI
// we concurrently execute each max batch of text chunks possible.
// Refer to constructor embeddingMaxChunkLength for more info.
// Refer to constructor maxConcurrentChunks for more info.
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.embeddingMaxChunkLength)) {
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai

3
server/utils/EmbeddingEngines/localAi/index.js
View File

@ -18,6 +18,7 @@ class LocalAiEmbedder {
this.openai = new OpenAIApi(config);
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.maxConcurrentChunks = 50;
this.embeddingMaxChunkLength = maximumChunkLength();
}
@ -28,7 +29,7 @@ class LocalAiEmbedder {
async embedChunks(textChunks = []) {
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.embeddingMaxChunkLength)) {
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai

5
server/utils/EmbeddingEngines/native/index.js
View File

@ -14,7 +14,8 @@ class NativeEmbedder {
this.modelPath = path.resolve(this.cacheDir, "Xenova", "all-MiniLM-L6-v2");
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.embeddingMaxChunkLength = 50;
this.maxConcurrentChunks = 50;
this.embeddingMaxChunkLength = 1_000;
// Make directory when it does not exist in existing installations
if (!fs.existsSync(this.cacheDir)) fs.mkdirSync(this.cacheDir);
@ -63,7 +64,7 @@ class NativeEmbedder {
async embedChunks(textChunks = []) {
const Embedder = await this.embedderClient();
const embeddingResults = [];
for (const chunk of toChunks(textChunks, this.embeddingMaxChunkLength)) {
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
const output = await Embedder(chunk, {
pooling: "mean",
normalize: true,

5
server/utils/EmbeddingEngines/openAi/index.js
View File

@ -11,6 +11,7 @@ class OpenAiEmbedder {
this.openai = openai;
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.maxConcurrentChunks = 500;
this.embeddingMaxChunkLength = 1_000;
}
@ -22,9 +23,9 @@ class OpenAiEmbedder {
async embedChunks(textChunks = []) {
// Because there is a hard POST limit on how many chunks can be sent at once to OpenAI (~8mb)
// we concurrently execute each max batch of text chunks possible.
// Refer to constructor embeddingMaxChunkLength for more info.
// Refer to constructor maxConcurrentChunks for more info.
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.embeddingMaxChunkLength)) {
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai