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…rch flow - Migrate all LLM nodes (query generation, reflection, final answer) from Gemini to Groq using llama-3.3-70b-versatile - Keep Gemini exclusively for Google Search grounding to preserve citation metadata - Add graceful handling for Google Search API quota exhaustion (429 / RESOURCE_EXHAUSTED) - Return safe fallback state instead of crashing the graph - Guard against missing or partial API responses - Handle empty candidates and absent grounding metadata - Fall back to plain-text extraction when citations cannot be generated - Improve robustness and documentation - Defensive access to graph state keys - Fix docstring inconsistencies and remove redundant comments
The search component operates on a user-provided directory and retrieves relevant documentation snippets directly from markdown files. Architecture decision: I chose an extended-context, file-based search approach instead of a broad snippet search or vector database. Technical documentation often contains long code examples that are easily truncated by short RAG-style snippets. To preserve full examples while respecting Groq token limits, the search retrieves a small number of highly relevant results (top_k=2) with a larger context window (~100 lines). Relevance is determined using a deterministic keyword and phrase scoring mechanism without external dependencies. This design keeps the agent lightweight, reproducible, and suitable for offline evaluation on local documentation archives.
pesua
reviewed
Jan 22, 2026
| text_lower = text.lower() | ||
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| # Score file path matches (boost) | ||
| path_score = sum(path_lower.count(t) * 3 for t in terms) + sum(path_lower.count(p) * 5 for p in phrases) |
| # Find best match position (prefer phrase matches, then term matches) | ||
| best_idx = len(text) | ||
| best_token = None | ||
| for p in phrases: |
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Дуже заморочений алгоритм пошуку, ти бачив на тестах щоб він працював краще за якийсь примітивний фултекст?
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