diff options
Diffstat (limited to 'src/client/views/nodes/chatbot/tools')
| -rw-r--r-- | src/client/views/nodes/chatbot/tools/RAGTool.ts | 104 |
1 files changed, 24 insertions, 80 deletions
diff --git a/src/client/views/nodes/chatbot/tools/RAGTool.ts b/src/client/views/nodes/chatbot/tools/RAGTool.ts index c24306dcd..f4b7b42ea 100644 --- a/src/client/views/nodes/chatbot/tools/RAGTool.ts +++ b/src/client/views/nodes/chatbot/tools/RAGTool.ts @@ -14,114 +14,58 @@ export class RAGTool extends BaseTool { { hypothetical_document_chunk: { type: 'string', - description: - "Detailed version of the prompt that is effectively a hypothetical document chunk that would be ideal to embed and compare to the vectors of real document chunks to fetch the most relevant document chunks to answer the user's query", + description: "A detailed prompt representing an ideal chunk to embed and compare against document vectors to retrieve the most relevant content for answering the user's query.", required: 'true', }, }, ` - Your task is to provide a comprehensive response to the user's prompt based on the given chunks and chat history. Follow these structural guidelines meticulously: + When using the RAG tool, the structure must adhere to the format described in the ReAct prompt. Below are additional guidelines specifically for RAG-based responses: - 1. Overall Structure: - <answer> - [Main content with grounded_text tags interspersed with normal plain text (information that is not derived from chunks' information)] - <citations> - [Individual citation tags] - </citations> - <follow_up_questions> - [Three question tags] - </follow_up_questions> - </answer> - - 2. Grounded Text Tag Structure: - - Basic format: - <grounded_text citation_index="[citation index number(s)]"> - [Your generated text based on information from a subset of a chunk (a citation's direct text)] - </grounded_text> + 1. **Grounded Text Guidelines**: + - Each <grounded_text> tag must correspond to exactly one citation, ensuring a one-to-one relationship. + - Always cite a **subset** of the chunk, never the full text. The citation should be as short as possible while providing the relevant information (typically one to two sentences). + - Do not paraphrase the chunk text in the citation; use the original subset directly from the chunk. + - If multiple citations are needed for different sections of the response, create new <grounded_text> tags for each. - 3. Citation Tag Structure: - <citation index="[unique number]" chunk_id="[UUID v4]" type="[text/image/table]"> - [For text: relevant subset of original chunk] - [For image/table: leave empty] - </citation> + 2. **Citation Guidelines**: + - The citation must include only the relevant excerpt from the chunk being referenced. + - Use unique citation indices and reference the chunk_id for the source of the information. + - For text chunks, the citation content must reflect the **exact subset** of the original chunk that is relevant to the grounded_text tag. - 4. Detailed Grounded Text Guidelines: - a. Wrap all information derived from chunks in grounded_text tags. - b. DO NOT PUT ANYTHING THAT IS NOT DIRECTLY DERIVED FROM INFORMATION FROM CHUNKS (EITHER IMAGE, TABLE, OR TEXT) IN GROUNDED_TEXT TAGS. - c. Use a single grounded_text tag for suquential and closely related information that references the same citation. If other citations' information are used sequentially, create new grounded_text tags. - d. Ensure every grounded_text tag has up to a few corresponding citations (should not be more than 3 and only 1 is fine). Multiple citation indices should be separated by commas. - e. Grounded text can be as short as a few words or as long as several sentences. - f. Avoid overlapping or nesting grounded_text tags; instead, use sequential tags. - - 5. Detailed Citation Guidelines: - a. Create a unique citation for each distinct piece of information from the chunks that is used to support grounded_text. - b. ALL TEXT CITATIONS must have direct text in its element content (e.g. <citation ...>DIRECT TEXT HERE</citation>) that is a relevant SUBSET of the original text chunk that is being cited specifically. - c. DO NOT paraphrase or summarize the text; use the original text as much as possible. - d. DO NOT USE THE FULL TEXT CHUNK as the citation content; only use the relevant subset of the text that the grounded_text is base. AS SHORT AS POSSIBLE WHILE PROVIDING INFORMATION (ONE TO TWO SENTENCES USUALLY)! - e. Ensure each citation has a unique index number. - f. Specify the correct type: "text", "image", or "table". - g. For text chunks, the content of the citation should ALWAYS have the relevant subset of the original text that the grounded_text is based on. - h. For image/table chunks, leave the citation content empty. - i. One citation can be used for multiple grounded_text tags if they are based on the same chunk information. - j. !!!DO NOT OVERCITE - only include citations for information that is directly relevant to the grounded_text. - - 6. Structural Integrity Checks: - a. Ensure all opening tags have corresponding closing tags. - b. Verify that all grounded_text tags have valid citation_index attributes (they should be equal to the associated citation(s) index field—not their chunk_id field). - c. Check that all cited indices in grounded_text tags have corresponding citations. - - Example of grounded_text usage: + **Example**: <answer> - <grounded_text citation_index="1,2"> - Artificial Intelligence (AI) is revolutionizing various sectors, with healthcare experiencing significant transformations in areas such as diagnosis and treatment planning. - </grounded_text> - <grounded_text citation_index="2,3,4"> - In the field of medical diagnosis, AI has shown remarkable capabilities, particularly in radiology. For instance, AI systems have drastically improved mammogram analysis, achieving 99% accuracy at a rate 30 times faster than human radiologists. + <grounded_text citation_index="1"> + Artificial Intelligence is revolutionizing various sectors, with healthcare seeing transformations in diagnosis and treatment planning. </grounded_text> - <grounded_text citation_index="4"> - This advancement not only enhances the efficiency of healthcare systems but also significantly reduces the occurrence of false positives, leading to fewer unnecessary biopsies and reduced patient stress. + <grounded_text citation_index="2"> + Based on recent data, AI has drastically improved mammogram analysis, achieving 99% accuracy at a rate 30 times faster than human radiologists. </grounded_text> - - <grounded_text citation_index="5,6"> - Beyond diagnosis, AI is playing a crucial role in drug discovery and development. By analyzing vast amounts of genetic and molecular data, AI algorithms can identify potential drug candidates much faster than traditional methods. - </grounded_text> - <grounded_text citation_index="6"> - This could potentially reduce the time and cost of bringing new medications to market, especially for rare diseases that have historically received less attention due to limited market potential. - </grounded_text> - - [... rest of the content ...] <citations> - <citation index="1" chunk_id="123e4567-e89b-12d3-a456-426614174000" type="text">Artificial Intelligence is revolutionizing various industries, with healthcare being one of the most profoundly affected sectors.</citation> - <citation index="2" chunk_id="123e4567-e89b-12d3-a456-426614174001" type="text">AI has shown particular promise in the field of radiology, enhancing the accuracy and speed of image analysis.</citation> - <citation index="3" chunk_id="123e4567-e89b-12d3-a456-426614174002" type="text">According to recent studies, AI systems have achieved 99% accuracy in mammogram analysis, performing the task 30 times faster than human radiologists.</citation> - <citation index="4" chunk_id="123e4567-e89b-12d3-a456-426614174003" type="text">The improvement in mammogram accuracy has led to a significant reduction in false positives, decreasing the need for unnecessary biopsies and reducing patient anxiety.</citation> - <citation index="5" chunk_id="123e4567-e89b-12d3-a456-426614174004" type="text">AI is accelerating the drug discovery process by analyzing complex molecular and genetic data to identify potential drug candidates.</citation> - <citation index="6" chunk_id="123e4567-e89b-12d3-a456-426614174005" type="text">The use of AI in drug discovery could significantly reduce the time and cost associated with bringing new medications to market, particularly for rare diseases.</citation> + <citation index="1" chunk_id="abc123" type="text">Artificial Intelligence is revolutionizing various industries, especially in healthcare.</citation> + <citation index="2" chunk_id="abc124" type="table"></citation> </citations> <follow_up_questions> - <question>How might AI-driven personalized medicine impact the cost and accessibility of healthcare in the future?</question> - <question>What measures can be taken to ensure that AI systems in healthcare are free from biases and equally effective for diverse populations?</question> - <question>How could the role of healthcare professionals evolve as AI becomes more integrated into medical practices?</question> + <question>How can AI enhance patient outcomes in fields outside radiology?</question> + <question>What are the challenges in implementing AI systems across different hospitals?</question> + <question>How might AI-driven advancements impact healthcare costs?</question> </follow_up_questions> </answer> `, - `Performs a RAG (Retrieval-Augmented Generation) search on user documents and returns a - set of document chunks (either images or text) that can be used to provide a grounded response based on - user documents` + `Performs a RAG (Retrieval-Augmented Generation) search on user documents and returns a set of document chunks (text or images) to provide a grounded response based on user documents.` ); } - async execute(args: { hypothetical_document_chunk: string }): Promise { + async execute(args: { hypothetical_document_chunk: string }): Promise<any> { const relevantChunks = await this.vectorstore.retrieve(args.hypothetical_document_chunk); const formatted_chunks = await this.getFormattedChunks(relevantChunks); return formatted_chunks; } - async getFormattedChunks(relevantChunks: RAGChunk[]): Promise { + async getFormattedChunks(relevantChunks: RAGChunk[]): Promise<any> { try { const { formattedChunks } = await Networking.PostToServer('/formatChunks', { relevantChunks }); |