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https://github.com/Mintplex-Labs/anything-llm.git
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be9d8b0397
* WIP on continuous prompt window summary * wip * Move chat out of VDB simplify chat interface normalize LLM model interface have compression abstraction Cleanup compressor TODO: Anthropic stuff * Implement compression for Anythropic Fix lancedb sources * cleanup vectorDBs and check that lance, chroma, and pinecone are returning valid metadata sources * Resolve Weaviate citation sources not working with schema * comment cleanup
58 lines
1.6 KiB
JavaScript
58 lines
1.6 KiB
JavaScript
const { getEncodingNameForModel, getEncoding } = require("js-tiktoken");
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class TokenManager {
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constructor(model = "gpt-3.5-turbo") {
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this.model = model;
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this.encoderName = this.getEncodingFromModel(model);
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this.encoder = getEncoding(this.encoderName);
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this.buffer = 50;
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}
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getEncodingFromModel(model) {
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try {
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return getEncodingNameForModel(model);
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} catch {
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return "cl100k_base";
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}
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}
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tokensFromString(input = "") {
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const tokens = this.encoder.encode(input);
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return tokens;
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}
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bytesFromTokens(tokens = []) {
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const bytes = this.encoder.decode(tokens);
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return bytes;
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}
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countFromString(input = "") {
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const tokens = this.encoder.encode(input);
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return tokens.length;
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}
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statsFrom(input) {
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if (typeof input === "string") return this.countFromString(input);
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// What is going on here?
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// https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb Item 6.
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// The only option is to estimate. From repeated testing using the static values in the code we are always 2 off,
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// which means as of Nov 1, 2023 the additional factor on ln: 476 changed from 3 to 5.
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if (Array.isArray(input)) {
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const perMessageFactorTokens = input.length * 3;
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const tokensFromContent = input.reduce(
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(a, b) => a + this.countFromString(b.content),
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0
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);
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const diffCoefficient = 5;
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return perMessageFactorTokens + tokensFromContent + diffCoefficient;
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}
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throw new Error("Not a supported tokenized format.");
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}
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}
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module.exports = {
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TokenManager,
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};
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