1package llm
  2
  3import (
  4	"context"
  5	"fmt"
  6	"os"
  7	"strings"
  8	"time"
  9
 10	"llm_aggregator/internal/defaults"
 11	"llm_aggregator/internal/progress"
 12
 13	"github.com/openai/openai-go/v3"
 14	"github.com/openai/openai-go/v3/option"
 15)
 16
 17// LLMClient is a client for interacting with LLM API.
 18type LLMClient struct {
 19	client      openai.Client
 20	model       string
 21	maxTokens   int
 22	temperature float64
 23	logger      *progress.Context
 24}
 25
 26// NewLLMClient creates a new LLM client.
 27// apiKey: LLM API key (or read from LLM_AGGREGATOR_API_KEY env var)
 28// baseURL: API base URL (defaults to "https://api.deepseek.com")
 29// model: Model to use (defaults to "deepseek-chat")
 30// maxTokens: Maximum tokens in response (defaults to 4000)
 31// temperature: Sampling temperature (0.0 to 1.0, defaults to 0.7)
 32func NewLLMClient(apiKey, baseURL, model string, maxTokens int, temperature float64) (*LLMClient, error) {
 33	// Get API key from parameter or environment variable
 34	if apiKey == "" {
 35		apiKey = os.Getenv("LLM_AGGREGATOR_API_KEY")
 36	}
 37	if apiKey == "" || strings.TrimSpace(apiKey) == "" {
 38		return nil, fmt.Errorf(
 39			"LLM API key is required. " +
 40				"Set LLM_AGGREGATOR_API_KEY environment variable or pass apiKey parameter",
 41		)
 42	}
 43
 44	// Set defaults using central constants
 45	if baseURL == "" {
 46		baseURL = defaults.DefaultBaseURL
 47	}
 48	if model == "" {
 49		model = defaults.DefaultModel
 50	}
 51	if maxTokens == 0 {
 52		maxTokens = defaults.DefaultMaxTokens
 53	}
 54	if temperature == 0 {
 55		temperature = defaults.DefaultTemperature
 56	}
 57
 58	// Create OpenAI client configured for LLM
 59	client := openai.NewClient(
 60		option.WithAPIKey(apiKey),
 61		option.WithBaseURL(baseURL),
 62	)
 63
 64	return &LLMClient{
 65		client:      client,
 66		model:       model,
 67		maxTokens:   maxTokens,
 68		temperature: temperature,
 69	}, nil
 70}
 71
 72// SetLogger sets the logger for the LLM client
 73func (dc *LLMClient) SetLogger(logger *progress.Context) {
 74	dc.logger = logger
 75}
 76
 77// SummariseArticles summarises a list of articles based on user prompt.
 78// articles: List of article maps
 79// userPrompt: User's query/summarisation request
 80// systemPrompt: Optional system prompt (defaults to helpful assistant)
 81func (dc *LLMClient) SummariseArticles(
 82	articles []map[string]any,
 83	userPrompt string,
 84	systemPrompt string,
 85) (string, error) {
 86	if len(articles) == 0 {
 87		return "No articles to summarise.", nil
 88	}
 89
 90	// Prepare context from articles
 91	context := dc.prepareContext(articles)
 92
 93	// Create messages for chat completion API
 94	messages := dc.createMessages(context, userPrompt, systemPrompt)
 95
 96	// Call API with messages
 97	return dc.callAPIWithMessages(messages)
 98}
 99
100func (dc *LLMClient) prepareContext(articles []map[string]any) string {
101	contextParts := []string{}
102
103	for i, article := range articles {
104		contextParts = append(contextParts, fmt.Sprintf("--- ARTICLE %d ---", i+1))
105		contextParts = append(contextParts, fmt.Sprintf("Title: %s", article["title"]))
106
107		if source, ok := article["source_feed"].(string); ok && source != "" {
108			contextParts = append(contextParts, fmt.Sprintf("Source: %s", source))
109		}
110
111		if published, ok := article["published"]; ok {
112			switch pub := published.(type) {
113			case time.Time:
114				if !pub.IsZero() {
115					contextParts = append(contextParts, fmt.Sprintf("Published: %s", pub.Format(time.RFC3339)))
116				}
117			case string:
118				contextParts = append(contextParts, fmt.Sprintf("Published: %s", pub))
119			default:
120				contextParts = append(contextParts, fmt.Sprintf("Published: %v", pub))
121			}
122		}
123
124		if author, ok := article["author"].(string); ok && author != "" {
125			contextParts = append(contextParts, fmt.Sprintf("Author: %s", author))
126		}
127
128		if link, ok := article["link"].(string); ok && link != "" {
129			contextParts = append(contextParts, fmt.Sprintf("Link: %s", link))
130		}
131
132		if content, ok := article["content"].(string); ok && content != "" {
133			// Truncate very long content
134			maxContentLen := 3000
135			if len(content) > maxContentLen {
136				content = content[:maxContentLen] + "... [truncated]"
137			}
138			contextParts = append(contextParts, fmt.Sprintf("Content: %s", content))
139		}
140
141		contextParts = append(contextParts, "") // Empty line between articles
142	}
143
144	return strings.Join(contextParts, "\n")
145}
146
147func (dc *LLMClient) createMessages(context, userPrompt, systemPrompt string) []openai.ChatCompletionMessageParamUnion {
148	if systemPrompt == "" {
149		systemPrompt = `You are an expert analyst and summariser.
150You analyse content from multiple sources and provide
151concise, insightful summaries based on user requests.
152Focus on key points, trends, and important information.`
153	}
154
155	// Combine context with user prompt
156	fullUserContent := fmt.Sprintf(`Here are articles from various RSS feeds:
157
158%s
159
160User request: %s
161
162Please provide a comprehensive summary/analysis addressing the user's request.
163Focus on key insights, trends, and important information from the articles.
164If relevant, note any patterns, contradictions, or notable developments.`,
165		context, userPrompt)
166
167	messages := []openai.ChatCompletionMessageParamUnion{
168		openai.SystemMessage(systemPrompt),
169		openai.UserMessage(fullUserContent),
170	}
171
172	return messages
173}
174
175func (dc *LLMClient) callAPIWithMessages(messages []openai.ChatCompletionMessageParamUnion) (string, error) {
176	ctx := context.Background()
177
178	if dc.logger != nil {
179		dc.logger.Logf("Calling LLM API with model: %s", dc.model)
180	}
181
182	response, err := dc.client.Chat.Completions.New(ctx, openai.ChatCompletionNewParams{
183		Model:       dc.model,
184		Messages:    messages,
185		MaxTokens:   openai.Int(int64(dc.maxTokens)),
186		Temperature: openai.Float(dc.temperature),
187	})
188
189	if err != nil {
190		// Provide more specific error messages
191		errStr := err.Error()
192		if strings.Contains(errStr, "401") {
193			return "", fmt.Errorf("invalid API key. Please check your LLM API key")
194		} else if strings.Contains(errStr, "429") {
195			return "", fmt.Errorf("rate limit exceeded. Please try again later")
196		} else if strings.Contains(errStr, "500") {
197			return "", fmt.Errorf("LLM API server error. Please try again later")
198		} else if strings.Contains(errStr, "404") {
199			return "", fmt.Errorf("API endpoint not found. Please check the base URL and endpoint. OpenAI API uses /chat/completions")
200		}
201		return "", fmt.Errorf("failed to connect to LLM API: %w", err)
202	}
203
204	// Extract text from response
205	if len(response.Choices) == 0 {
206		return "", fmt.Errorf("no response choices returned from API")
207	}
208
209	outputText := response.Choices[0].Message.Content
210
211	// Print token usage
212	if dc.logger != nil {
213		dc.logger.Logf(
214			"LLM API response: %d prompt tokens, %d completion tokens",
215			response.Usage.PromptTokens,
216			response.Usage.CompletionTokens,
217		)
218	}
219
220	return outputText, nil
221}
222
223// AnalyseWithCustomPrompt analyses articles with custom system and user prompts.
224func (dc *LLMClient) AnalyseWithCustomPrompt(
225	articles []map[string]any,
226	systemPrompt string,
227	userPrompt string,
228) (string, error) {
229	context := dc.prepareContext(articles)
230
231	// Create messages for chat completion API
232	messages := []openai.ChatCompletionMessageParamUnion{
233		openai.SystemMessage(systemPrompt),
234		openai.UserMessage(fmt.Sprintf(`Here are articles from various RSS feeds:
235
236%s
237
238%s`, context, userPrompt)),
239	}
240
241	return dc.callAPIWithMessages(messages)
242}