mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2025-05-02 17:07:13 +00:00
Move OpenAI api calls into its own interface/Class (#162)
* Move OpenAI api calls into its own interface/Class move curate sources to be specific for each vectorDBs response for chat/query * remove comment
This commit is contained in:
Timothy Carambat
GitHub
parent
0a2f837fb2
commit
8929d96ed0
5 changed files with 115 additions and 184 deletions
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@ -22,30 +22,7 @@ function toChunks(arr, size) {
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);
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}
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function curateSources(sources = []) {
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const documents = [];
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// Sometimes the source may or may not have a metadata property
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// in the response so we search for it explicitly or just spread the entire
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// source and check to see if at least title exists.
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for (const source of sources) {
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if (source.hasOwnProperty("metadata")) {
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const { metadata = {} } = source;
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if (Object.keys(metadata).length > 0) {
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documents.push({ ...metadata });
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}
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} else {
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if (Object.keys(source).length > 0) {
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documents.push({ ...source });
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}
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}
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}
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return documents;
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}
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module.exports = {
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getVectorDbClass,
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toChunks,
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curateSources,
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};
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@ -1,4 +1,5 @@
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const { Configuration, OpenAIApi } = require("openai");
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class OpenAi {
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constructor() {
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const config = new Configuration({
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@ -7,6 +8,7 @@ class OpenAi {
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const openai = new OpenAIApi(config);
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this.openai = openai;
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}
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isValidChatModel(modelName = "") {
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const validModels = ["gpt-4", "gpt-3.5-turbo"];
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return validModels.includes(modelName);
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@ -79,6 +81,37 @@ class OpenAi {
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return textResponse;
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}
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async getChatCompletion(messages = [], { temperature = 0.7 }) {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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const { data } = await this.openai.createChatCompletion({
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model,
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messages,
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temperature,
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});
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if (!data.hasOwnProperty("choices")) return null;
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return data.choices[0].message.content;
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}
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async embedTextInput(textInput) {
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const result = await this.embedChunks(textInput);
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return result?.[0] || [];
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}
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async embedChunks(textChunks = []) {
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const {
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data: { data },
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} = await this.openai.createEmbedding({
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model: "text-embedding-ada-002",
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input: textChunks,
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});
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return data.length > 0 &&
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data.every((embd) => embd.hasOwnProperty("embedding"))
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? data.map((embd) => embd.embedding)
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: null;
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}
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}
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module.exports = {
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@ -5,10 +5,10 @@ const { VectorDBQAChain } = require("langchain/chains");
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const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
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const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
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const { storeVectorResult, cachedVectorInformation } = require("../../files");
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const { Configuration, OpenAIApi } = require("openai");
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const { v4: uuidv4 } = require("uuid");
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const { toChunks, curateSources } = require("../../helpers");
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const { toChunks } = require("../../helpers");
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const { chatPrompt } = require("../../chats");
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const { OpenAi } = require("../../openAi");
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const Chroma = {
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name: "Chroma",
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@ -57,26 +57,6 @@ const Chroma = {
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embedder: function () {
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return new OpenAIEmbeddings({ openAIApiKey: process.env.OPEN_AI_KEY });
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},
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openai: function () {
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const config = new Configuration({ apiKey: process.env.OPEN_AI_KEY });
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const openai = new OpenAIApi(config);
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return openai;
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},
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getChatCompletion: async function (
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openai,
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messages = [],
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{ temperature = 0.7 }
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) {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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const { data } = await openai.createChatCompletion({
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model,
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messages,
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temperature,
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});
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if (!data.hasOwnProperty("choices")) return null;
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return data.choices[0].message.content;
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},
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llm: function ({ temperature = 0.7 }) {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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return new OpenAI({
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@ -85,22 +65,6 @@ const Chroma = {
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temperature,
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});
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},
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embedTextInput: async function (openai, textInput) {
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const result = await this.embedChunks(openai, textInput);
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return result?.[0] || [];
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},
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embedChunks: async function (openai, chunks = []) {
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const {
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data: { data },
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} = await openai.createEmbedding({
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model: "text-embedding-ada-002",
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input: chunks,
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});
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return data.length > 0 &&
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data.every((embd) => embd.hasOwnProperty("embedding"))
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? data.map((embd) => embd.embedding)
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: null;
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},
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similarityResponse: async function (client, namespace, queryVector) {
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const collection = await client.getCollection({ name: namespace });
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const result = {
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@ -212,10 +176,10 @@ const Chroma = {
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const textChunks = await textSplitter.splitText(pageContent);
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console.log("Chunks created from document:", textChunks.length);
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const openAiConnector = new OpenAi();
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const documentVectors = [];
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const vectors = [];
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const openai = this.openai();
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const vectorValues = await this.embedChunks(openai, textChunks);
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const vectorValues = await openAiConnector.embedChunks(textChunks);
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const submission = {
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ids: [],
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embeddings: [],
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@ -322,7 +286,7 @@ const Chroma = {
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const response = await chain.call({ query: input });
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return {
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response: response.text,
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sources: curateSources(response.sourceDocuments),
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sources: this.curateSources(response.sourceDocuments),
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message: false,
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};
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},
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@ -348,7 +312,8 @@ const Chroma = {
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};
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}
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const queryVector = await this.embedTextInput(this.openai(), input);
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const openAiConnector = new OpenAi();
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const queryVector = await openAiConnector.embedTextInput(input);
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const { contextTexts, sourceDocuments } = await this.similarityResponse(
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client,
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namespace,
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@ -359,19 +324,24 @@ const Chroma = {
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content: `${chatPrompt(workspace)}
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Context:
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${contextTexts
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.map((text, i) => {
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return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
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})
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.join("")}`,
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.map((text, i) => {
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return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
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})
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.join("")}`,
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};
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const memory = [prompt, ...chatHistory, { role: "user", content: input }];
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const responseText = await this.getChatCompletion(this.openai(), memory, {
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const responseText = await openAiConnector.getChatCompletion(memory, {
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temperature: workspace?.openAiTemp ?? 0.7,
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});
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// When we roll out own response we have separate metadata and texts,
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// so for source collection we need to combine them.
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const sources = sourceDocuments.map((metadata, i) => {
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return { metadata: { ...metadata, text: contextTexts[i] } };
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});
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return {
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response: responseText,
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sources: curateSources(sourceDocuments),
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sources: this.curateSources(sources),
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message: false,
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};
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},
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@ -403,6 +373,22 @@ const Chroma = {
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await client.reset();
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return { reset: true };
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},
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curateSources: function (sources = []) {
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const documents = [];
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for (const source of sources) {
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const { metadata = {} } = source;
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if (Object.keys(metadata).length > 0) {
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documents.push({
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...metadata,
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...(source.hasOwnProperty("pageContent")
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? { text: source.pageContent }
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: {}),
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});
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}
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}
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return documents;
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},
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};
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module.exports.Chroma = Chroma;
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@ -3,23 +3,9 @@ const { toChunks } = require("../../helpers");
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const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
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const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
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const { storeVectorResult, cachedVectorInformation } = require("../../files");
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const { Configuration, OpenAIApi } = require("openai");
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const { v4: uuidv4 } = require("uuid");
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const { chatPrompt } = require("../../chats");
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// Since we roll our own results for prompting we
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// have to manually curate sources as well.
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function curateLanceSources(sources = []) {
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const documents = [];
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for (const source of sources) {
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const { text, vector: _v, score: _s, ...metadata } = source;
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if (Object.keys(metadata).length > 0) {
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documents.push({ ...metadata, text });
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}
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}
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return documents;
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}
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const { OpenAi } = require("../../openAi");
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const LanceDb = {
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uri: `${
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@ -61,51 +47,9 @@ const LanceDb = {
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const table = await client.openTable(_namespace);
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return (await table.countRows()) || 0;
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},
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embeddingFunc: function () {
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return new lancedb.OpenAIEmbeddingFunction(
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"context",
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process.env.OPEN_AI_KEY
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);
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},
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embedTextInput: async function (openai, textInput) {
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const result = await this.embedChunks(openai, textInput);
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return result?.[0] || [];
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},
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embedChunks: async function (openai, chunks = []) {
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const {
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data: { data },
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} = await openai.createEmbedding({
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model: "text-embedding-ada-002",
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input: chunks,
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});
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return data.length > 0 &&
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data.every((embd) => embd.hasOwnProperty("embedding"))
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? data.map((embd) => embd.embedding)
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: null;
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},
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embedder: function () {
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return new OpenAIEmbeddings({ openAIApiKey: process.env.OPEN_AI_KEY });
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},
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openai: function () {
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const config = new Configuration({ apiKey: process.env.OPEN_AI_KEY });
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const openai = new OpenAIApi(config);
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return openai;
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},
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getChatCompletion: async function (
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openai,
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messages = [],
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{ temperature = 0.7 }
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) {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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const { data } = await openai.createChatCompletion({
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model,
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messages,
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temperature,
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});
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if (!data.hasOwnProperty("choices")) return null;
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return data.choices[0].message.content;
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},
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similarityResponse: async function (client, namespace, queryVector) {
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const collection = await client.openTable(namespace);
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const result = {
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@ -225,11 +169,11 @@ const LanceDb = {
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const textChunks = await textSplitter.splitText(pageContent);
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console.log("Chunks created from document:", textChunks.length);
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const openAiConnector = new OpenAi();
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const documentVectors = [];
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const vectors = [];
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const submissions = [];
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const openai = this.openai();
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const vectorValues = await this.embedChunks(openai, textChunks);
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const vectorValues = await openAiConnector.embedChunks(textChunks);
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if (!!vectorValues && vectorValues.length > 0) {
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for (const [i, vector] of vectorValues.entries()) {
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@ -287,7 +231,8 @@ const LanceDb = {
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}
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// LanceDB does not have langchainJS support so we roll our own here.
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const queryVector = await this.embedTextInput(this.openai(), input);
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const openAiConnector = new OpenAi();
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const queryVector = await openAiConnector.embedTextInput(input);
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const { contextTexts, sourceDocuments } = await this.similarityResponse(
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client,
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namespace,
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@ -304,13 +249,13 @@ const LanceDb = {
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.join("")}`,
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};
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const memory = [prompt, { role: "user", content: input }];
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const responseText = await this.getChatCompletion(this.openai(), memory, {
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const responseText = await openAiConnector.getChatCompletion(memory, {
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temperature: workspace?.openAiTemp ?? 0.7,
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});
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return {
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response: responseText,
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sources: curateLanceSources(sourceDocuments),
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sources: this.curateSources(sourceDocuments),
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message: false,
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};
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},
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@ -336,7 +281,8 @@ const LanceDb = {
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};
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}
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const queryVector = await this.embedTextInput(this.openai(), input);
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const openAiConnector = new OpenAi();
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const queryVector = await openAiConnector.embedTextInput(input);
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const { contextTexts, sourceDocuments } = await this.similarityResponse(
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client,
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namespace,
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@ -353,13 +299,13 @@ const LanceDb = {
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.join("")}`,
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};
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const memory = [prompt, ...chatHistory, { role: "user", content: input }];
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const responseText = await this.getChatCompletion(this.openai(), memory, {
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const responseText = await openAiConnector.getChatCompletion(memory, {
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temperature: workspace?.openAiTemp ?? 0.7,
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});
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return {
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response: responseText,
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sources: curateLanceSources(sourceDocuments),
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sources: this.curateSources(sourceDocuments),
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message: false,
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};
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},
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@ -391,6 +337,17 @@ const LanceDb = {
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fs.rm(`${client.uri}`, { recursive: true }, () => null);
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return { reset: true };
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},
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curateSources: function (sources = []) {
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const documents = [];
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for (const source of sources) {
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const { text, vector: _v, score: _s, ...metadata } = source;
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if (Object.keys(metadata).length > 0) {
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documents.push({ ...metadata, text });
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}
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}
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return documents;
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},
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};
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module.exports.LanceDb = LanceDb;
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@ -1,16 +1,14 @@
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const { PineconeClient } = require("@pinecone-database/pinecone");
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const { PineconeStore } = require("langchain/vectorstores/pinecone");
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const { OpenAI } = require("langchain/llms/openai");
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const { VectorDBQAChain, LLMChain } = require("langchain/chains");
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const { VectorDBQAChain } = require("langchain/chains");
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const { OpenAIEmbeddings } = require("langchain/embeddings/openai");
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const { VectorStoreRetrieverMemory } = require("langchain/memory");
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const { PromptTemplate } = require("langchain/prompts");
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const { RecursiveCharacterTextSplitter } = require("langchain/text_splitter");
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const { storeVectorResult, cachedVectorInformation } = require("../../files");
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const { Configuration, OpenAIApi } = require("openai");
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const { v4: uuidv4 } = require("uuid");
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const { toChunks, curateSources } = require("../../helpers");
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const { toChunks } = require("../../helpers");
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const { chatPrompt } = require("../../chats");
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const { OpenAi } = require("../../openAi");
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const Pinecone = {
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name: "Pinecone",
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@ -34,42 +32,6 @@ const Pinecone = {
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embedder: function () {
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return new OpenAIEmbeddings({ openAIApiKey: process.env.OPEN_AI_KEY });
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},
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openai: function () {
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const config = new Configuration({ apiKey: process.env.OPEN_AI_KEY });
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const openai = new OpenAIApi(config);
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return openai;
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},
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getChatCompletion: async function (
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openai,
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messages = [],
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{ temperature = 0.7 }
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) {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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const { data } = await openai.createChatCompletion({
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model,
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messages,
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temperature,
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});
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if (!data.hasOwnProperty("choices")) return null;
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return data.choices[0].message.content;
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},
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embedTextInput: async function (openai, textInput) {
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const result = await this.embedChunks(openai, textInput);
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return result?.[0] || [];
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},
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embedChunks: async function (openai, chunks = []) {
|
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const {
|
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data: { data },
|
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} = await openai.createEmbedding({
|
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model: "text-embedding-ada-002",
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input: chunks,
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});
|
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return data.length > 0 &&
|
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data.every((embd) => embd.hasOwnProperty("embedding"))
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? data.map((embd) => embd.embedding)
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: null;
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},
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llm: function ({ temperature = 0.7 }) {
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const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
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return new OpenAI({
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|
|
@ -182,10 +144,10 @@ const Pinecone = {
|
|||
const textChunks = await textSplitter.splitText(pageContent);
|
||||
|
||||
console.log("Chunks created from document:", textChunks.length);
|
||||
const openAiConnector = new OpenAi();
|
||||
const documentVectors = [];
|
||||
const vectors = [];
|
||||
const openai = this.openai();
|
||||
const vectorValues = await this.embedChunks(openai, textChunks);
|
||||
const vectorValues = await openAiConnector.embedChunks(textChunks);
|
||||
|
||||
if (!!vectorValues && vectorValues.length > 0) {
|
||||
for (const [i, vector] of vectorValues.entries()) {
|
||||
|
|
@ -299,7 +261,7 @@ const Pinecone = {
|
|||
const response = await chain.call({ query: input });
|
||||
return {
|
||||
response: response.text,
|
||||
sources: curateSources(response.sourceDocuments),
|
||||
sources: this.curateSources(response.sourceDocuments),
|
||||
message: false,
|
||||
};
|
||||
},
|
||||
|
|
@ -322,7 +284,8 @@ const Pinecone = {
|
|||
"Invalid namespace - has it been collected and seeded yet?"
|
||||
);
|
||||
|
||||
const queryVector = await this.embedTextInput(this.openai(), input);
|
||||
const openAiConnector = new OpenAi();
|
||||
const queryVector = await openAiConnector.embedTextInput(input);
|
||||
const { contextTexts, sourceDocuments } = await this.similarityResponse(
|
||||
pineconeIndex,
|
||||
namespace,
|
||||
|
|
@ -340,17 +303,32 @@ const Pinecone = {
|
|||
};
|
||||
|
||||
const memory = [prompt, ...chatHistory, { role: "user", content: input }];
|
||||
|
||||
const responseText = await this.getChatCompletion(this.openai(), memory, {
|
||||
const responseText = await openAiConnector.getChatCompletion(memory, {
|
||||
temperature: workspace?.openAiTemp ?? 0.7,
|
||||
});
|
||||
|
||||
return {
|
||||
response: responseText,
|
||||
sources: curateSources(sourceDocuments),
|
||||
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;
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue