Phase 3: PDF/DOCX extraction, chunking, LLM client with mock interface

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

internal/llm/openai.go

@@ -0,0 +1,119 @@
+package llm
+
+import (
+	"context"
+	"crypto/sha256"
+	"encoding/json"
+	"fmt"
+
+	openai "github.com/sashabaranov/go-openai"
+)
+
+// ParsedQuestion is a question extracted from a document chunk by the LLM.
+type ParsedQuestion struct {
+	Question string
+	Answers  []ParsedAnswer
+}
+
+// ParsedAnswer is one answer choice for a ParsedQuestion.
+type ParsedAnswer struct {
+	Text    string
+	Correct bool
+}
+
+// ChatClient is the interface for creating chat completions.
+// The concrete *openai.Client satisfies this interface.
+type ChatClient interface {
+	CreateChatCompletion(ctx context.Context, req openai.ChatCompletionRequest) (openai.ChatCompletionResponse, error)
+}
+
+// Client wraps a ChatClient with question-extraction logic.
+type Client struct {
+	cc    ChatClient
+	model string
+}
+
+// New creates a Client backed by the real OpenAI API.
+func New(apiKey, model string) *Client {
+	if model == "" {
+		model = "gpt-4o-mini"
+	}
+	return &Client{cc: openai.NewClient(apiKey), model: model}
+}
+
+// NewWithClient creates a Client with an injected ChatClient (useful for tests).
+func NewWithClient(cc ChatClient, model string) *Client {
+	return &Client{cc: cc, model: model}
+}
+
+const systemPrompt = `You extract multiple-choice questions from study material. Return every question found. Exactly one answer per question must be marked correct. If the source doesn't clearly mark a correct answer, omit that question entirely. Do not invent questions not present in the text.
+
+Respond with JSON matching this schema exactly:
+{"questions":[{"question":"<text>","answers":[{"text":"<text>","correct":false},{"text":"<text>","correct":true}]}]}`
+
+type llmResponse struct {
+	Questions []struct {
+		Question string `json:"question"`
+		Answers  []struct {
+			Text    string `json:"text"`
+			Correct bool   `json:"correct"`
+		} `json:"answers"`
+	} `json:"questions"`
+}
+
+// ExtractQuestions sends chunk to the LLM and returns validated, deduplicated questions.
+// Questions that do not have exactly one correct answer are silently dropped.
+func (c *Client) ExtractQuestions(ctx context.Context, chunk string) ([]ParsedQuestion, error) {
+	resp, err := c.cc.CreateChatCompletion(ctx, openai.ChatCompletionRequest{
+		Model: c.model,
+		Messages: []openai.ChatCompletionMessage{
+			{Role: openai.ChatMessageRoleSystem, Content: systemPrompt},
+			{Role: openai.ChatMessageRoleUser, Content: chunk},
+		},
+		ResponseFormat: &openai.ChatCompletionResponseFormat{
+			Type: openai.ChatCompletionResponseFormatTypeJSONObject,
+		},
+	})
+	if err != nil {
+		return nil, fmt.Errorf("openai: %w", err)
+	}
+	if len(resp.Choices) == 0 {
+		return nil, fmt.Errorf("openai: empty response")
+	}
+
+	var raw llmResponse
+	if err := json.Unmarshal([]byte(resp.Choices[0].Message.Content), &raw); err != nil {
+		return nil, fmt.Errorf("parse llm response: %w", err)
+	}
+
+	seen := make(map[string]bool)
+	var out []ParsedQuestion
+	for _, q := range raw.Questions {
+		var nCorrect int
+		for _, a := range q.Answers {
+			if a.Correct {
+				nCorrect++
+			}
+		}
+		if nCorrect != 1 {
+			continue
+		}
+		key := textHash(q.Question)
+		if seen[key] {
+			continue
+		}
+		seen[key] = true
+
+		pq := ParsedQuestion{Question: q.Question}
+		for _, a := range q.Answers {
+			pq.Answers = append(pq.Answers, ParsedAnswer{Text: a.Text, Correct: a.Correct})
+		}
+		out = append(out, pq)
+	}
+	return out, nil
+}
+
+func textHash(s string) string {
+	h := sha256.Sum256([]byte(s))
+	return fmt.Sprintf("%x", h[:8])
+}