c2b47e7645
- Add document ingestion with text chunking, embedding generation, and PostgreSQL persistence - Configure PostgreSQL with pgvector, document schemas, and database migrations - Implement semantic search using Ollama embeddings and pgvector cosine similarity - Add POST /documents, POST /search, and POST /ask endpoints with validation and sanitized error handling - Generate context-grounded answers with source attribution - Filter retrieved chunks using a minimum similarity threshold of 0.5 and return fallback responses when no relevant context is available - Add unit and integration tests for ingestion, search, question answering, validation, and failure handling - Update README with Phase 3 capabilities, setup instructions, and API documentation
74 lines
1.8 KiB
TypeScript
74 lines
1.8 KiB
TypeScript
import { generateResponse } from "./ollama.js";
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import { searchDocuments, type SearchResult } from "./search.js";
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export interface AnswerResult {
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answer: string;
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sources: {
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documentId: string;
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title: string;
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content: string;
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similarity: number;
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}[];
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}
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interface AnswerDependencies {
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search: (query: string, limit?: number) => Promise<SearchResult[]>;
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generate: (prompt: string) => Promise<string>;
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}
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const defaultDependencies: AnswerDependencies = {
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search: searchDocuments,
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generate: generateResponse,
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};
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export const answerQuestion = async (
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question: string,
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dependencies: AnswerDependencies = defaultDependencies,
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): Promise<AnswerResult> => {
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const normalizedQuestion = question.trim();
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if (!normalizedQuestion) {
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throw new Error("Question cannot be empty");
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}
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const results = await dependencies.search(normalizedQuestion, 5);
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const relevantResults = results.filter((result) => result.similarity >= 0.5);
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if (relevantResults.length === 0) {
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return {
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answer:
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"I couldn't find relevant information in the available documents.",
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sources: [],
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};
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}
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const context = relevantResults
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.map(
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(result, index) =>
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`[Source ${index + 1}: ${result.title}]\n${result.content}`,
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)
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.join("\n\n");
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const prompt = `Answer the question using only the context provided below.
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If the context does not contain the answer, say that you don't have enough information.
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Do not invent facts or claim that the context supports something it does not.
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Context:
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${context}
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Question: ${normalizedQuestion}
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Answer:`;
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const answer = await dependencies.generate(prompt);
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return {
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answer,
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sources: relevantResults.map((result) => ({
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documentId: result.documentId,
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title: result.title,
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content: result.content,
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similarity: result.similarity,
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})),
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};
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};
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