Nick: prompt option, still need to convert to new structured outputs

This commit is contained in:
Nicolas 2024-08-29 21:00:57 -03:00
parent 49e1cb7ca0
commit 41eb620959
6 changed files with 121 additions and 63 deletions

View File

@ -94,7 +94,7 @@
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View File

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openai: 4.57.0(zod@3.23.8)
languagedetect@2.0.0: {}
@ -8928,16 +8933,19 @@ snapshots:
transitivePeerDependencies:
- debug
openai@4.52.2:
openai@4.57.0(zod@3.23.8):
dependencies:
'@types/node': 18.19.39
'@types/node-fetch': 2.6.11
'@types/qs': 6.9.15
abort-controller: 3.0.0
agentkeepalive: 4.5.0
form-data-encoder: 1.7.2
formdata-node: 4.4.1
node-fetch: 2.7.0
web-streams-polyfill: 3.3.3
qs: 6.12.2
optionalDependencies:
zod: 3.23.8
transitivePeerDependencies:
- encoding

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@ -44,7 +44,8 @@ const strictMessage = "Unrecognized key in body -- please review the v1 API docu
export const extractOptions = z.object({
mode: z.enum(["llm"]).default("llm"),
schema: z.any().optional(),
prompt: z.string().default("Based on the information on the page, extract the information from the schema.")
systemPrompt: z.string().default("Based on the information on the page, extract the information from the schema."),
prompt: z.string().optional()
}).strict(strictMessage);
export type ExtractOptions = z.infer<typeof extractOptions>;
@ -316,6 +317,7 @@ export function legacyExtractorOptions(x: ExtractOptions): ExtractorOptions {
mode: x.mode ? "llm-extraction" : "markdown",
extractionPrompt: x.prompt ?? "Based on the information on the page, extract the information from the schema.",
extractionSchema: x.schema,
userPrompt: x.prompt ?? "",
};
}

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@ -15,7 +15,8 @@ export async function generateCompletions(
// const schema = zodToJsonSchema(options.schema)
const schema = extractionOptions.extractionSchema;
const prompt = extractionOptions.extractionPrompt;
const systemPrompt = extractionOptions.extractionPrompt;
const prompt = extractionOptions.userPrompt;
const switchVariable = "openAI"; // Placholder, want to think more about how we abstract the model provider
@ -31,9 +32,11 @@ export async function generateCompletions(
document: document,
schema: schema,
prompt: prompt,
systemPrompt: systemPrompt,
mode: mode,
});
// Validate the JSON output against the schema using AJV
if(schema){
const validate = ajv.compile(schema);
if (!validate(completionResult.llm_extraction)) {
//TODO: add Custom Error handling middleware that bubbles this up with proper Error code, etc.
@ -43,6 +46,7 @@ export async function generateCompletions(
.join(", ")}\n\nLLM extraction did not match the extraction schema you provided. This could be because of a model hallucination, or an Error on our side. Try adjusting your prompt, and if it doesn't work reach out to support.`
);
}
}
return completionResult;
} catch (error) {

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@ -16,7 +16,6 @@ function prepareOpenAIDoc(
document: Document,
mode: "markdown" | "raw-html"
): [OpenAI.Chat.Completions.ChatCompletionContentPart[], number] | null {
let markdown = document.markdown;
let extractionTarget = document.markdown;
@ -25,7 +24,6 @@ function prepareOpenAIDoc(
extractionTarget = document.rawHtml;
}
// Check if the markdown content exists in the document
if (!extractionTarget) {
return null;
@ -34,33 +32,32 @@ function prepareOpenAIDoc(
// );
}
// count number of tokens
const numTokens = numTokensFromString(extractionTarget, "gpt-4");
if (numTokens > maxTokens) {
// trim the document to the maximum number of tokens, tokens != characters
extractionTarget = extractionTarget.slice(0, (maxTokens * modifier));
extractionTarget = extractionTarget.slice(0, maxTokens * modifier);
}
return [[{ type: "text", text: extractionTarget }], numTokens];
}
export async function generateOpenAICompletions({
client,
model = process.env.MODEL_NAME || "gpt-4o",
model = process.env.MODEL_NAME || "gpt-4o-mini",
document,
schema, //TODO - add zod dynamic type checking
prompt = defaultPrompt,
systemPrompt = defaultPrompt,
prompt,
temperature,
mode
mode,
}: {
client: OpenAI;
model?: string;
document: Document;
schema: any; // This should be replaced with a proper Zod schema type when available
prompt?: string;
systemPrompt?: string;
temperature?: number;
mode: "markdown" | "raw-html";
}): Promise<Document> {
@ -70,17 +67,61 @@ export async function generateOpenAICompletions({
if (preparedDoc === null) {
return {
...document,
warning: "LLM extraction was not performed since the document's content is empty or missing.",
warning:
"LLM extraction was not performed since the document's content is empty or missing.",
};
}
const [content, numTokens] = preparedDoc;
const completion = await openai.chat.completions.create({
let completion;
let llmExtraction;
if (prompt && !schema) {
// If prompt is defined, ask OpenAI to generate a schema based on the prompt
// const schemaCompletion = await openai.chat.completions.create({
// model,
// messages: [
// {
// role: "system",
// content: "You are a helpful assistant that generates JSON schemas based on user prompts.",
// },
// {
// role: "user",
// content: `Generate a JSON schema compatible with openai function calling based on this prompt: ${prompt}`,
// },
// ],
// temperature: 0,
// response_format: { type: "json_object" },
// });
// console.log(schemaCompletion.choices[0].message.content);
// const generatedSchema = JSON.parse(schemaCompletion.choices[0].message.content);
console.log(prompt);
const jsonCompletion = await openai.chat.completions.create({
model,
messages: [
{
role: "system",
content: prompt,
content: systemPrompt,
},
{ role: "user", content },
{ role: "user", content: `Transform the above content into structured json output based on the following user request: ${prompt}` },
],
response_format: { type: "json_object" },
temperature,
});
console.log(jsonCompletion.choices[0].message.content);
llmExtraction = JSON.parse(jsonCompletion.choices[0].message.content.trim());
console.log(llmExtraction);
} else {
completion = await openai.chat.completions.create({
model,
messages: [
{
role: "system",
content: systemPrompt,
},
{ role: "user", content },
],
@ -94,20 +135,22 @@ export async function generateOpenAICompletions({
},
},
],
tool_choice: { "type": "function", "function": {"name": "extract_content"}},
tool_choice: { type: "function", function: { name: "extract_content" } },
temperature,
});
const c = completion.choices[0].message.tool_calls[0].function.arguments;
// Extract the LLM extraction content from the completion response
const llmExtraction = JSON.parse(c);
llmExtraction = JSON.parse(c);
}
// Return the document with the LLM extraction content added
return {
...document,
llm_extraction: llmExtraction,
warning: numTokens > maxTokens ? `Page was trimmed to fit the maximum token limit defined by the LLM model (Max: ${maxTokens} tokens, Attemped: ${numTokens} tokens). If results are not good, email us at help@mendable.ai so we can help you.` : undefined,
warning:
numTokens > maxTokens
? `Page was trimmed to fit the maximum token limit defined by the LLM model (Max: ${maxTokens} tokens, Attemped: ${numTokens} tokens). If results are not good, email us at help@mendable.ai so we can help you.`
: undefined,
};
}

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@ -36,6 +36,7 @@ export type ExtractorOptions = {
mode: "markdown" | "llm-extraction" | "llm-extraction-from-markdown" | "llm-extraction-from-raw-html";
extractionPrompt?: string;
extractionSchema?: Record<string, any>;
userPrompt?: string;
}
export type SearchOptions = {