Integrating Contextual Retrieval into your Q&A generation process for a dataset similar to the Natural Questions (NQ) dataset can enhance the quality and relevance of the generated question and answer (Q&A) pairs. Since you already have a Clojure-based pipeline for scraping websites and producing high-quality semantic chunks, and you’re interested in using a prompt to generate Q&A pairs, incorporating Contextual Retrieval can improve how you leverage your chunks by adding contextual metadata or summaries to ensure questions are more precise and answers are more grounded in the content. This approach complements your existing setup and aligns with your prior interest in advanced retrieval techniques (e.g., RAG systems, Qdrant integration, and Contextual Retrieval vs. Late Chunking, as discussed on April 9, 2025).
Below, I’ll elaborate on how to use the output of Contextual Retrieval prompts to enhance your Q&A generation, modify the existing prompt to incorporate Contextual Retrieval outputs, and provide an updated Clojure workflow to integrate this approach. I’ll assume Contextual Retrieval, as described in Anthropic’s method, involves prepending or appending contextual summaries (e.g., high-level explanations or metadata) to each chunk to improve retrieval relevance, and we’ll adapt this to guide Q&A generation.
Contextual Retrieval enhances document retrieval by augmenting each chunk with a contextual summary or metadata that captures its broader meaning or intent. For example:
- Chunk: "Rising sea levels, driven by melting ice caps and thermal expansion, threaten coastal cities with increased flooding and erosion."
- Contextual Summary: "This chunk discusses the impacts of climate change on coastal regions, focusing on sea level rise and its consequences." This summary is typically generated by a language model (e.g., Grok 3) and helps retrieval systems (e.g., Qdrant, Meilisearch) better match chunks to queries by providing additional semantic context.
In your case, instead of using Contextual Retrieval for retrieval, we’ll use its output (the contextual summary) as an input to the Q&A generation prompt. This ensures that questions are informed by both the chunk’s specific content and its broader context, leading to more relevant and diverse Q&A pairs.
- Improved Question Relevance:
- The contextual summary provides a high-level understanding of the chunk’s role within the document or topic, enabling the model to generate questions that align with the chunk’s broader significance (e.g., asking about impacts of climate change rather than generic weather questions).
- Enhanced Answer Grounding:
- The summary helps the model focus on key themes or entities in the chunk, ensuring short and long answers are precise and faithful to the content.
- Diverse Question Types:
- By understanding the chunk’s context, the model can generate varied questions (e.g., factual, why, how) that reflect the chunk’s role in the document, similar to NQ’s diverse query types.
- Topic Alignment:
- The summary reinforces topic relevance, helping the model select the most appropriate topic from your provided list (e.g., choosing “Climate Change” over “Urban Planning” for sea level rise).
- Compatibility with Your Setup:
- Since you have high-quality semantic chunks and a Clojure pipeline, adding Contextual Retrieval is a natural extension, leveraging your existing LLM calls (e.g., Grok 3 API) to generate summaries.
Here’s how to incorporate Contextual Retrieval outputs into your Q&A generation process:
- Task: For each semantic chunk, generate a contextual summary that captures its main idea, purpose, or role within the document.
- Prompt for Contextual Summary:
You are an expert at summarizing text for contextual understanding. Given a chunk of text, generate a concise summary (1–2 sentences) that captures its main idea, purpose, or role within the broader document. The summary should provide high-level context without repeating the chunk verbatim. **Input**: - **Chunk**: {chunk_text} **Output**: A summary of the chunk in 1–2 sentences. **Example**: - **Chunk**: "Rising sea levels, driven by melting ice caps and thermal expansion, threaten coastal cities with increased flooding and erosion." - **Output**: "This chunk discusses the impacts of climate change on coastal regions, focusing on sea level rise and its consequences." - Implementation:
- Use your existing Clojure setup to call the Grok 3 API (or local model) with this prompt for each chunk.
- Store the summary alongside the chunk and its topics (e.g., in a map like
{:chunk "..." :summary "..." :topics [...]}).
Update the original Q&A prompt to include the contextual summary as an additional input. This ensures the model uses both the chunk and its summary to generate questions and answers, improving specificity and alignment with NQ’s structure.
You are an expert at generating high-quality question and answer pairs for a dataset similar to the Natural Questions dataset. Given a semantic chunk of text, its contextual summary, and a list of possible topics, your task is to:
1. Generate one natural, concise, and relevant question based on the content of the chunk and informed by its contextual summary.
2. Ensure the question aligns with one of the provided topics, selecting the most appropriate topic.
3. Provide a short answer (a concise phrase or sentence within the chunk) that directly answers the question.
4. Provide a long answer (a sentence or paragraph from the chunk or slightly rephrased for clarity) that fully answers the question.
5. Specify the selected topic and the answer type (span, yes/no, or null).
**Input**:
- **Chunk**: {chunk_text}
- **Contextual Summary**: {contextual_summary}
- **Possible Topics**: {topics_list}
**Output Format** (JSON):
```json
{
"question": "<generated question>",
"short_answer": "<concise answer>",
"long_answer": "<detailed answer>",
"selected_topic": "<chosen topic>",
"answer_type": "<span|yes/no|null>"
}
Guidelines:
- Use the contextual summary to understand the chunk’s broader significance and generate a question that reflects both the chunk’s details and its role in the document.
- The question should be natural, as if asked by a curious user, and directly answerable using the chunk’s content.
- The short answer must be a verbatim or near-verbatim excerpt from the chunk.
- The long answer can be a direct excerpt or a slight rephrasing for clarity, but must remain faithful to the chunk.
- Choose the most relevant topic from the provided list, guided by the summary and chunk content.
- If the question is unanswerable within the chunk, set
answer_typeto "null" and provide empty answers. - For yes/no questions, ensure the short answer is "Yes" or "No," and the long answer explains why.
- Avoid generic or overly broad questions; focus on specific details in the chunk and its context.
- Ensure the question is answerable using only the chunk’s content.
Example:
- Chunk: "Rising sea levels, driven by melting ice caps and thermal expansion, threaten coastal cities with increased flooding and erosion."
- Contextual Summary: "This chunk discusses the impacts of climate change on coastal regions, focusing on sea level rise and its consequences."
- Possible Topics: ["Climate Change", "Sustainability", "Urban Planning"]
- Output:
{
"question": "How does climate change contribute to flooding in coastal cities?",
"short_answer": "Rising sea levels",
"long_answer": "Rising sea levels, driven by melting ice caps and thermal expansion, threaten coastal cities with increased flooding and erosion.",
"selected_topic": "Climate Change",
"answer_type": "span"
}
**Changes from Original Prompt**:
- Added `Contextual Summary` as an input field.
- Instructed the model to use the summary to inform question generation, ensuring questions reflect the chunk’s broader context.
- Updated the example to show a question (“How does climate change contribute to flooding...”) that leverages the summary’s focus on climate change impacts, making it more specific than the original (“What threatens coastal cities...”).
#### Step 3: Update the Clojure Workflow
Modify your Clojure script to:
1. Generate contextual summaries for each chunk.
2. Pass both the chunk and its summary to the updated Q&A prompt.
3. Store the results in your dataset.
```plain
(ns dataset.qageneration
(:require [clj-http.client :as http]
[cheshire.core :as json]
[clojure.string :as str]))
;; Function to call a language model API (e.g., Grok 3)
(defn call-llm-api [prompt]
(let [api-url "https://api.x.ai/grok3" ;; Replace with actual API endpoint
api-key "your-api-key" ;; Replace with your API key
request-body (json/generate-string {:prompt prompt :max_tokens 500})
response (http/post api-url
{:headers {"Authorization" (str "Bearer " api-key)
"Content-Type" "application/json"}
:body request-body})]
(json/parse-string (:body response) true)))
;; Prompt for generating contextual summary
(def contextual-summary-prompt
(str "You are an expert at summarizing text for contextual understanding. Given a chunk of text, generate a concise summary (1–2 sentences) that captures its main idea, purpose, or role within the broader document. The summary should provide high-level context without repeating the chunk verbatim.\n\n"
"**Input**:\n- **Chunk**: %s\n\n"
"**Output**:\nA summary of the chunk in 1–2 sentences.\n\n"
"**Example**:\n- **Chunk**: \"Rising sea levels, driven by melting ice caps and thermal expansion, threaten coastal cities with increased flooding and erosion.\"\n- **Output**: \"This chunk discusses the impacts of climate change on coastal regions, focusing on sea level rise and its consequences.\""))
;; Prompt for Q&A generation with contextual retrieval (loaded from artifact)
(def qa-prompt-template
(str "You are an expert at generating high-quality question and answer pairs for a dataset similar to the Natural Questions dataset. Given a semantic chunk of text, its contextual summary, and a list of possible topics, your task is:\n\n"
"1. Generate one natural, concise, and relevant question based on the content of the chunk and informed by its contextual summary.\n"
"2. Ensure the question aligns with one of the provided topics, selecting the most appropriate topic.\n"
"3. Provide a short answer (a concise phrase or sentence within the chunk) that directly answers the question.\n"
"4. Provide a long answer (a sentence or paragraph from the chunk or slightly rephrased for clarity) that fully answers the question.\n"
"5. Specify the selected topic and the answer type (span, yes/no, or null).\n\n"
"**Input**:\n- **Chunk**: %s\n- **Contextual Summary**: %s\n- **Possible Topics**: %s\n\n"
"**Output Format** (JSON):\n`json\n{\n \"question\": \"<generated question>\",\n \"short_answer\": \"<concise answer>\",\n \"long_answer\": \"<detailed answer>\",\n \"selected_topic\": \"<chosen topic>\",\n \"answer_type\": \"<span|yes/no|null>\"\n}\n`\n\n"
"**Guidelines**:\n"
"- Use the contextual summary to understand the chunk’s broader significance and generate a question that reflects both the chunk’s details and its role in the document.\n"
"- The question should be natural, as if asked by a curious user, and directly answerable using the chunk’s content.\n"
"- The short answer must be a verbatim or near-verbatim excerpt from the chunk.\n"
"- The long answer can be a direct excerpt or a slight rephrasing for clarity, but must remain faithful to the chunk.\n"
"- Choose the most relevant topic from the provided list, guided by the summary and chunk content.\n"
"- If the question is unanswerable within the chunk, set `answer_type` to \"null\" and provide empty answers.\n"
"- For yes/no questions, ensure the short answer is \"Yes\" or \"No,\" and the long answer explains why.\n"
"- Avoid generic or overly broad questions; focus on specific details in the chunk and its context.\n"
"- Ensure the question is answerable using only the chunk’s content.\n\n"
"**Example**:\n"
"- **Chunk**: \"Rising sea levels, driven by melting ice caps and thermal expansion, threaten coastal cities with increased flooding and erosion.\"\n"
"- **Contextual Summary**: \"This chunk discusses the impacts of climate change on coastal regions, focusing on sea level rise and its consequences.\"\n"
"- **Possible Topics**: [\"Climate Change\", \"Sustainability\", \"Urban Planning\"]\n"
"- **Output**:\n"
"`json\n{\n \"question\": \"How does climate change contribute to flooding in coastal cities?\",\n \"short_answer\": \"Rising sea levels\",\n \"long_answer\": \"Rising sea levels, driven by melting ice caps and thermal expansion, threaten coastal cities with increased flooding and erosion.\",\n \"selected_topic\": \"Climate Change\",\n \"answer_type\": \"span\"\n}\n`"))
;; Function to generate contextual summary for a chunk
(defn generate-contextual-summary [chunk]
(let [prompt (format contextual-summary-prompt chunk)
response (call-llm-api prompt)]
(:body response)))
;; Function to generate Q&A pair for a chunk with contextual summary
(defn generate-qa-pair [chunk summary topics]
(let [topics-str (str/join ", " topics)
prompt (format qa-prompt-template chunk summary topics-str)
response (call-llm-api prompt)]
(:body response)))
;; Example usage
(def sample-chunk
"Machine learning models require large datasets for training. Overfitting occurs when a model learns noise instead of patterns, leading to poor generalization.")
(def sample-topics ["Machine Learning", "Data Science", "AI Ethics"])
(defn -main []
(let [summary (generate-contextual-summary sample-chunk)
qa-pair (generate-qa-pair sample-chunk summary sample-topics)]
(println "Contextual Summary:" summary)
(println "Generated Q&A Pair:" qa-pair)))
;; Store Q&A pairs in a dataset
(defn store-qa-pairs [qa-pairs output-file]
(spit output-file (json/generate-string qa-pairs {:pretty true})))
;; Process multiple chunks
(defn process-chunks [chunks]
(map (fn [{:keys [chunk topics]}]
(let [summary (generate-contextual-summary chunk)
qa-pair (generate-qa-pair chunk summary topics)]
(merge qa-pair {:chunk chunk :contextual_summary summary})))
chunks))
;; Example: Process multiple chunks
(def chunks-with-topics
[{:chunk "Machine learning models require large datasets for training..."
:topics ["Machine Learning", "Data Science"]}
{:chunk "Sustainable practices can reduce carbon emissions..."
:topics ["Sustainability", "Climate Change"]}])
(comment
;; Run example
(-main)
;; Process and store multiple chunks
(let [qa-pairs (process-chunks chunks-with-topics)]
(store-qa-pairs qa-pairs "qa_dataset.json")))
- Contextual Summary Generation:
- The
generate-contextual-summaryfunction sends each chunk to the LLM with the summary prompt, producing a 1–2 sentence summary (e.g., “This chunk explains overfitting in machine learning...”).
- The
- Q&A Generation:
- The
generate-qa-pairfunction uses the updated prompt, passing the chunk, its summary, and topics to the LLM. - The LLM generates a JSON output with a question, short answer, long answer, selected topic, and answer type, informed by the summary for better context.
- The
- Processing Chunks:
- The
process-chunksfunction iterates over your chunks, generating summaries and Q&A pairs, and stores them with metadata (chunk and summary).
- The
- Storage:
- Results are saved to a JSON file, including the contextual summary for traceability.
Example Output:
{
"question": "What causes poor generalization in machine learning models?",
"short_answer": "Overfitting",
"long_answer": "Overfitting occurs when a model learns noise instead of patterns, leading to poor generalization.",
"selected_topic": "Machine Learning",
"answer_type": "span",
"chunk": "Machine learning models require large datasets for training...",
"contextual_summary": "This chunk explains the concept of overfitting in machine learning and its impact on model performance."
}- Scraping and Chunking: Your existing Clojure code scrapes websites and produces semantic chunks. Ensure chunks are stored with their source URL and topics (e.g.,
{:chunk "..." :topics [...] :url "..."}). - API Calls: Use your Grok 3 API setup (from https://x.ai/api) or local model. Update
call-llm-apiif needed (e.g., adjustmax_tokensfor summaries vs. Q&A). - Scalability: Parallelize
process-chunkswithpmapfor large datasets:(defn process-chunks [chunks] (doall (pmap (fn [{:keys [chunk topics]}] ...) chunks)))
- Error Handling: Add try-catch to handle API failures:
(defn call-llm-api [prompt] (try (let [response (http/post ...)] (json/parse-string (:body response) true)) (catch Exception e (println "API error:" (.getMessage e)) {})))
- RAG and Retrieval (April 8, 2025): Contextual Retrieval aligns with your RAG work, as it enhances chunk relevance, similar to your Qdrant integration. Here, it’s repurposed for Q&A generation rather than retrieval.
- Semantic Chunking (April 9, 2025): Your high-quality chunking routine is ideal for Contextual Retrieval, as it ensures chunks are semantically coherent, making summaries more accurate.
- Question Generation (March 30, 2025): The updated prompt builds on your interest in diverse questions, using the summary to generate questions that reflect the chunk’s role, akin to your perspective-based question generation.
- Summary Quality: If summaries are too generic, refine the summary prompt to focus on specific entities or themes (e.g., “Highlight key concepts like overfitting or sea level rise”).
- Compute Cost: Generating summaries doubles LLM calls. Cache summaries for reused chunks or batch API requests to reduce costs.
- Topic Misalignment: If the model selects incorrect topics, add a guideline to prioritize topics matching keywords in the summary (e.g., “climate” → “Climate Change”).
- Null Answers: If chunks lack answerable content, ensure the prompt’s null handling is robust (e.g., return
{:question "..." :short_answer "" :long_answer "" :answer_type "null"}).
- Pilot Test:
- Run
process-chunkson 5–10 chunks with topics. - Check if summaries capture the chunk’s intent and if Q&A pairs are specific and topic-aligned.
- Example:
(def test-chunk {:chunk "Sustainable practices can reduce carbon emissions..." :topics ["Sustainability", "Climate Change"]}) (process-chunks [test-chunk])
- Run
- Quality Metrics:
- Question Specificity: Are questions tied to the chunk’s details (e.g., “How do sustainable practices reduce emissions?” vs. “What is sustainability?”)?
- Answer Accuracy: Do short and long answers match the chunk’s content?
- Topic Fit: Does the selected topic align with the summary and chunk?
- Manual Review: Sample 10% of outputs to ensure quality, focusing on summary relevance and question diversity.
Using your sample chunk and topics:
(def sample-chunk
"Machine learning models require large datasets for training. Overfitting occurs when a model learns noise instead of patterns, leading to poor generalization.")
(def sample-topics ["Machine Learning", "Data Science", "AI Ethics"])- Summary:
- Prompt output: “This chunk explains the concept of overfitting in machine learning and its impact on model performance.”
- Q&A Pair:
- JSON output (as above): Question about overfitting, answers grounded in the chunk, topic “Machine Learning.”
- Test the Workflow:
- Run the updated script with a few chunks from your dataset.
- Share a sample chunk and topics for a specific example, and I can simulate the output.
- Refine the Prompt:
- If you want more diverse questions (e.g., why/how, as in your March 30, 2025 conversation), add a guideline like: “Generate 50% factual, 25% why, 25% how questions.”
- Example:
{:question "Why does overfitting lead to poor generalization?" :short_answer "Learning noise" :long_answer "..."}.
- Optimize Performance:
- Add batch processing for API calls to handle large datasets.
- Integrate with Qdrant (per your April 5, 2025 interest) to store summaries as metadata for future retrieval tasks.
- Feedback:
- Do you want to see a specific chunk processed with this workflow?
- Should I tweak the prompt for specific question types or add features (e.g., multi-shot prompting, per your April 10, 2025 interest)?
- Need help with Qdrant integration for storing summaries?
Let me know how you’d like to proceed!