> For the complete documentation index, see [llms.txt](https://docs.clickai.vn/clickai-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clickai.vn/clickai-docs/clickai-docs-en/database/index-method-and-retrieval-settings.md). # Index Method and Retrieval Settings ## Table of Contents · \[Select the Index Method]\(#select-the-index-method) · \[Configure the Retrieval Settings]\(#configure-the-retrieval-settings) · \[Q\&A Mode]\(#qa-mode) ## Select the Index Method ClickAI provides 2 indexing methods: ### High Quality · Calls an Embedding Model to create vectors for each chunk · Enables semantic search (meaning-based retrieval) · Pros: Higher accuracy, better contextual understanding · Cons: Consumes embedding tokens, longer processing time ### Economical · Uses keyword indexing (inverted index) — a standard search engine technique · No embedding model needed, no token cost · Pros: Fast, no embedding cost · Cons: Keyword matching only, no contextual understanding


Criteria	High Quality	Economical
Technology	Embedding vectors + keyword	Keyword indexing (inverted index)
Accuracy	High	Medium
Token cost	Yes (embedding)	No
Processing time	Longer	Faster
Semantic search	✅	❌
Multilingual search	✅	❌

💡 TIP: If you need highly accurate chatbot responses or handle multilingual content, choose \*\*High Quality\*\*. If speed and cost savings are priorities, choose \*\*Economical\*\*. ## Configure the Retrieval Settings The Knowledge Base supports 2 core retrieval techniques: 1\. Semantic Retrieval: Based on vector similarity — text chunks and queries are converted into vectors and matched via similarity scoring. 2\. Keyword Matching: Using an inverted index (a standard search engine technique). ### Weight Settings This feature enables you to set custom weights for semantic priority and keyword priority: #### Semantic Value = 1 (Semantic Search Only) · Activates only semantic search mode · Uses embedding models to search deeper — even if exact query terms don't appear in the knowledge base · Calculates vector distances to return relevant content · Captures meaning across different languages for accurate cross-language results #### Keyword Value = 1 (Keyword Search Only) · Activates only keyword search mode · Performs a full-text match against the knowledge base · Suitable when users know exact terminology · Consumes fewer resources, ideal for quick searches within large document repositories #### Custom Keyword and Semantic Weights Beyond enabling only semantic or keyword search, ClickAI provides flexible custom weight settings: · Continuously adjust weights of both methods · Identify the optimal weight ratio for your business scenario ### Rerank Model · Disabled by default. When enabled, a third-party Rerank model sorts the text chunks returned to optimize results · Helps the LLM access more precise information, improving output quality · Before enabling, go to Settings > Model Providers to configure the Rerank model API key ⚠️ IMPORTANT: If the selected embedding model is multimodal, select a multimodal rerank model (marked with a Vision icon) as well. Otherwise, retrieved images will be excluded from reranking. 📝 NOTE: Enabling Rerank will consume tokens from the Rerank model. Refer to the associated model's pricing page for details. ### TopK and Score Threshold


Setting	Description	Default	Recommended
Top K	Maximum number of most similar chunks to retrieve	3	3-5
Score Threshold	Minimum similarity score for a chunk to be retrieved	0.5	0.5-0.7

· Higher Top K → retrieves more chunks, but may include less relevant content · Higher Score Threshold → requires higher similarity, fewer but more accurate chunks returned ## Q\&A Mode Q\&A Mode is a special option that lets ClickAI automatically analyze documents and generate question-answer pairs. Suitable for: · FAQ documents · Customer support documentation · Documents with clear Q\&A structure *📖 Previous: \[Chunk Settings]\(./03-chunk-settings.md) · Next: \[Knowledge Pipeline]*