Integrate Knowledge within Apps

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Create an Application Integrated with Knowledge Base

A knowledge base can be used as an external information source to provide precise answers to user questions via LLM. You can associate an existing knowledge base with any application type in Dify. Taking a chat assistant as an example, the process is as follows:

1. Go to Knowledge > Create Knowledge > Upload file.
2. Go to Studio > Create Application and select Chatbot.
3. Enter Context, click Add, and select one of the knowledge bases you created.
4. In Context Settings, configure the Retrieval Setting.
5. In Add Features, enable Citation and Attribution.
6. In Debug and Preview, input user questions related to the knowledge base for debugging.
7. After debugging, click Publish to create an AI application based on your own knowledge base.

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Connect Knowledge and Set Retrieval Mode

In applications that utilize multiple knowledge bases, it is essential to configure the retrieval mode to enhance the precision of retrieved content. To set the retrieval mode for the knowledge bases, navigate to Context > Retrieval Settings > Rerank Setting.

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Retrieval Setting

The retriever scans all knowledge bases linked to the application for text content relevant to the user’s question. The results are then consolidated. Below is the technical flowchart for the Multi-path Retrieval mode: This method simultaneously queries all knowledge bases connected in Context, seeking relevant text chunks across multiple knowledge bases, collecting all content that aligns with the user’s question, and ultimately applying the Rerank strategy to identify the most appropriate content to respond to the user. This retrieval approach offers more comprehensive and accurate results by leveraging multiple knowledge bases simultaneously. For instance, in application A, with three knowledge bases K1, K2, and K3. When a user sends a question, multiple relevant pieces of content will be retrieved and combined from these knowledge bases. To ensure the most pertinent content is identified, the Rerank strategy is employed to find the content that best relates to the user’s query, enhancing the precision and reliability of the results. In practical Q&A scenarios, the sources of content and retrieval methods for each knowledge base may differ. To manage the mixed content returned from retrieval, the Rerank strategy acts as a refined sorting mechanism. It ensures that the candidate content aligns well with the user’s question, optimizing the ranking of results across multiple knowledge bases to identify the most suitable content, thereby improving answer quality and overall user experience. Considering the costs associated with using Rerank and the needs of the business, the multi-path retrieval mode provides two Rerank settings: Weighted Score This setting uses internal scoring mechanisms and does not require an external Rerank model, thus avoiding any additional processing costs. You can select the most appropriate content matching strategy by adjusting the weight ratio sliders for semantics or keywords.

• Semantic Value of 1 This mode activates semantic retrieval only. By utilizing the Embedding model, the search depth can be enhanced even if the exact words from the query do not appear in the knowledge base, as it calculates vector distances to return the relevant content. Furthermore, when dealing with multilingual content, semantic retrieval can capture meanings across different languages, yielding more accurate cross-language search results.
• Keyword Value of 1 This mode activates keyword retrieval only. It matches the user’s input text against the full text of the knowledge base, making it ideal for scenarios where the user knows the exact information or terminology. This method is resource-efficient, making it suitable for quickly retrieving information from large document repositories.
• Custom Keyword and Semantic Weights In addition to enabling only semantic or keyword retrieval modes, we offer flexible custom Weight Score. You can determine the best weight ratio for your business scenario by continuously adjusting the weights of both.

Rerank Model The Rerank model is an external scoring system that calculates the similarity score between the user’s question and each candidate document provided, improving the results of semantic ranking and returning a list of documents sorted by similarity score from high to low. While this method incurs some additional costs, it is more adept at handling complex knowledge base content, such as content that combines semantic queries and keyword matches, or cases involving multilingual returned content. Dify currently supports multiple Rerank models. To use external Rerank models, you’ll need to provide an API Key. Enter the API Key for the Rerank model (such as Cohere, Jina AI, etc.) on the Model Provider page. Adjustable Parameters

• Top K: Determines how many text chunks, deemed most similar to the user’s query, are retrieved. It also automatically adjusts the number of chunks based on the chosen model’s context window. The default value is 3, and higher numbers will recall more text chunks.
• Score Threshold: Sets the minimum similarity score required for a chunk to be retrieved. Only chunks exceeding this score are retrieved. The default value is 0.5. Higher thresholds demand greater similarity and thus result in fewer chunks being retrieved.