#!/bin/bash # in this one gemini quickly itself broke really badly set -e -E GEMINI_API_KEY="$GEMINI_API_KEY" MODEL_ID="gemini-2.5-pro-exp-03-25" GENERATE_CONTENT_API="streamGenerateContent" cat << EOF > request.json { "contents": [ { "role": "user", "parts": [ { "text": "let's draft a document for querying an LLM for exhaustive data on a list of sensed words or concepts. this is part of a larger knowledge builder. draft a prompt." }, ] }, { "role": "model", "parts": [ { "text": "WARN: Failed to process high-priority '{concept_to_process}'.\") # Decide how to handle fails\n else:\n # Process low priority if high is empty\n low_priority_processed_count = 0\n while low_priority_processed_count < config.MAX_LOW_PRIORITY_PER_CYCLE:\n concept_to_process =" }, { "text": "Here's a breakdown of the thinking process behind crafting that LLM prompt for exhaustive" }, { "text": " self.state_manager.get_next_low_priority()\n " }, { "text": " data gathering:\n\n1. **Deconstruct the Request:**\n *" }, { "text": "if concept_to_process is None:\n break # Low queue empty" }, { "text": " **Goal:** Query an LLM for *exhaustive data* on a list" }, { "text": "\n\n if self.state_manager.has_been_processed(concept" }, { "text": " of *sensed words or concepts*.\n * **Context:** Part" }, { "text": "_to_process):\n print(f\"INFO: Skipping already processed" }, { "text": " of a *larger knowledge builder*. This implies the output needs to be structured, detailed" }, { "text": " low-priority: '{concept_to_process}'\")\n continue\n\n" }, { "text": ", and suitable for integration into a knowledge base.\n * **Input" }, { "text": " if self._process_concept(concept_to_process):\n " }, { "text": ":** A *list* of words/concepts.\n * **Output:** A document (the prompt itself) that will instruct the LLM.\n\n2. " }, { "text": "processed_in_cycle += 1\n low_priority_processed_count += " }, { "text": "**Identify Key Challenges & Requirements:**\n * **Exhaustiveness:** How" }, { "text": "1\n processed_this_iteration = True\n else:\n " }, { "text": " do you define \"exhaustive\"? It's relative. We need to guide the LLM towards covering many facets without asking for *literally everything* (which is impossible" }, { "text": "print(f\"WARN: Failed to process low-priority '{concept_to_process}'.\") # Handle fails\n\n # Check exit condition\n if not" }, { "text": "). This means specifying *types* of information.\n * **Structure:** The output needs to be machine-readable or at least easily parseable for the" }, { "text": " processed_this_iteration:\n consecutive_empty_cycles += " }, { "text": " knowledge builder. JSON or a well-defined structured format is ideal.\n * **Clarity for LLM:** The prompt must be unambiguous. LLMs can" }, { "text": "1\n print(f\"INFO: No concepts processed this iteration ({consecutive_empty" }, { "text": " misinterpret vague requests.\n * **Scalability:** The prompt should work well whether the input list has one item or many.\n * **Consistency" }, { "text": "_cycles} consecutive empty).\")\n if consecutive_empty_cycles >= 2: # Exit after 2 cycles with nothing to do\n print(\"INFO:" }, { "text": ":** The output format should be consistent across all concepts.\n * **Handling Ambiguity:** Words can have multiple meanings (polysemy). The prompt needs to address this.\n * **Source/Confidence (Optional but Good" }, { "text": " Queues appear empty. Exiting loop.\")\n break\n # Optional: Add a longer sleep if queues are temporarily empty\n time.sleep(5" }, { "text": "):** For a knowledge builder, knowing the source or confidence level of information can be valuable, though often hard for current LLMs to provide reliably. We can ask, but manage expectations.\n * **Avoiding Hallucinations:** While hard to prevent" }, { "text": ")\n else:\n consecutive_empty_cycles = 0 # Reset counter if something was processed\n\n # Optional: Periodic state saving (atexit handles final save)\n # if processed_in_cycle > 0 and processed" }, { "text": " entirely, structuring the request and asking for specific *types* of factual information can help mitigate this.\n\n3. **Brainstorm Prompt Components:**\n * **Role/Persona:** Setting a role helps the LLM focus (e.g.," }, { "text": "_in_cycle % 20 == 0:\n # self.state_manager.save_state()\n\n # Optional: Small delay between concepts\n # time.sleep(0.5)\n\n except KeyboardInterrupt:\n print(\"\\nINFO: Keyboard interrupt received. Shutting down gracefully...\")\n finally:" }, { "text": " \"You are a knowledge extraction expert\").\n * **Objective:** Clearly state the goal (e.g., \"Extract comprehensive information...\").\n * **Input:** Define how the input list will be provided.\n * **Output Format" }, { "text": "\n # State saving is handled by atexit in StateManager\n print(\"Knowledge Builder run finished.\")\n\n```\n\n**(f) `main.py` (Updated Entry Point)**\n\n```python\n# knowledge_graph_" }, { "text": ":** Specify the desired structure (JSON preferred for knowledge builders). Define the schema.\n * **Information Categories (The \"Exhaustive\" Part):** This is crucial. List the *types* of data needed for each concept. Examples:\n" }, { "text": "builder/main.py\n\n# Ensure the script can find the package modules when run directly\n# This might be needed depending on how you run it (python main.py vs python -m ...)\nimport sys\nimport os\nfrom pathlib import Path\n" }, { "text": " * Definition(s)\n * Etymology\n * Key Properties/Attributes\n * Examples\n * Contexts of Use\n * Related Concepts (Synonyms, Antonyms, Hyponyms, Hypernyms, Meronyms, Holonyms)\n * Common Misconceptions\n" }, { "text": "# Add project root to path if necessary\nproject_root = Path(__file__).parent.parent\nif str(project_root) not in sys.path:\n" }, { "text": " * Historical Significance (if applicable)\n * Cultural Significance (if applicable)\n * Applications/Uses (if applicable)\n * Disambiguation (handling multiple meanings)\n * **Instructions:** Provide specific guidance on how to fill the structure.\n * Be thorough.\n *" }, { "text": " sys.path.insert(0, str(project_root))\n\n\nfrom knowledge_graph_builder.knowledge_builder import KnowledgeBuilder\n\ndef run_builder():\n \"\"\"Instantiates and runs the KnowledgeBuilder.\"\"\"\n try" }, { "text": " Handle ambiguity.\n * Stick to the format.\n * Indicate missing information.\n * Process each item independently.\n * **Example (Optional but Recommended):** Show the LLM *exactly* what a good output looks like for one concept. This drastically improves compliance.\n * " }, { "text": ":\n builder = KnowledgeBuilder()\n builder.run()\n except Exception as e:\n print(f\"\\nFATAL ERROR in" }, { "text": "**Placeholder for Input:** Clearly mark where the list of concepts will be inserted.\n\n4. **Drafting - Iteration 1 (Mental Outline):**\n * \"Okay, LLM, I need info on these words: [list]. Give me everything you know, like definitions, examples, related words" }, { "text": " Knowledge Builder: {e}\")\n # Consider more detailed logging here\n sys.exit(1)\n\nif __name__ == \"__main__\":\n # If running `python knowledge_graph_builder/main.py`\n run_builder()\n\n # If you prefer running as a module `" }, { "text": ". Put it in JSON.\"\n * *Critique:* Too vague. \"Everything\" is undefined. \"Related words\" is ambiguous. JSON structure isn't specified.\n\n5. **Drafting - Iteration 2 (Adding Structure & Detail):**\n * \"Act as a knowledge bot. For each word in [list], give me a JSON object with keys: '" }, { "text": "python -m knowledge_graph_builder.main`\n # the imports should work without sys.path modification if your project root\n # is in the PYTHONPATH or you run from the project root directory.\n # The `if __name__ == \"__main__\":` block still works correctly.\n```\n\n**4. How" }, { "text": "definition', 'examples', 'synonyms', 'antonyms'.\"\n * *Critique:* Better, but not \"exhaustive.\" Missing many important facets like context, properties, disambiguation. Still needs more guidance.\n\n6. **Drafting - Iteration 3 (Expanding Categories, Specifying Format - Closer to Final):**\n * \"You are a knowledge extraction assistant. Your task is" }, { "text": " to Run**\n\n1. Make sure you have `google-generativeai`, `python-dotenv`, and `jsonldb` installed (`pip install ...`).\n2. Create a `.env` file in your `your_project_root` directory with your `GEMINI_API_KEY=" }, { "text": " to generate comprehensive data for each concept in the provided list. Output MUST be a JSON array, where each element corresponds to a concept from the input list. Each element should be a JSON object with the following keys: `concept_name`, `definitions` (array of objects with `sense` and `definition`), `etymology`, `key" }, { "text": "YOUR_API_KEY_HERE`.\n3. Navigate to your `your_project_root` directory in the terminal.\n4. Run the builder: `python -m knowledge_graph_builder.main` or `python knowledge_graph_builder/main.py`.\n\n**Key Changes and Benefits:**\n\n* " }, { "text": "_properties` (array of strings), `examples` (array of strings), `contexts` (array of strings), `related_concepts` (object with `synonyms`, `antonyms`, etc.), `disambiguation_notes`, `common_misconceptions` (array of strings). Process this list: [list].\"\n * *Critique:* Much better. Covers key areas. JSON structure is defined. Still" }, { "text": "**Object-Oriented:** Code is organized into classes (`DatabaseManager`, `LLMClient`, `StateManager`, `KnowledgeBuilder`, `PromptLoader`) with clear responsibilities.\n* **Triple-Based:** The LLM is now prompted to generate SPO triples, offering more flexibility than the fixed structure. `DatabaseManager` handles storing these as lists within the concept's data.\n* **Externalized Prompts:**" }, { "text": " needs refinement on instructions, handling missing data, and potentially an example.\n\n7. **Drafting - Iteration 4 (Refining Instructions, Adding Example, Final Polish - Leading to the Provided Example):**\n * Add clear instructions about thoroughness, handling multiple senses explicitly within `definitions`, specifying how to handle missing info (`null` or empty arrays).\n * Include the `disambiguation_notes`" }, { "text": " The LLM prompt is in a separate `.txt` file, making it easy to modify without changing Python code. `PromptLoader` handles loading it.\n* **Clearer Orchestration:** `KnowledgeBuilder` manages the overall workflow, coordinating the other components.\n* **Improved Maintainability:** This structure is easier to understand, test, and extend. For example, adding support for a different LLM would" }, { "text": " field to explicitly address polysemy.\n * Add fields like `historical_significance`, `cultural_significance`, `applications` where relevant.\n * Refine the `related_concepts` structure.\n * Add a clear \"Instructions\" section.\n * Create a concrete example for one concept (e.g., \"Algorithm\") to illustrate the desired output format and level of detail.\n * Use clear placeholders like `[Your List of Concepts Here" }, { "text": " primarily involve modifying `LLMClient`. Adding new database operations belongs in `DatabaseManager`.\n\nRemember to refine the prompt in `prompts/generate_triples.txt` and potentially the logic for identifying related concepts in `KnowledgeBuilder._process_concept` based on the specific needs and patterns you observe in your generated data." }, { "text": "]`.\n * Add constraints like \"Focus on factual information\" and \"Avoid personal opinions.\"\n * Ensure the overall tone is clear, direct, and task-oriented.\n\n8. **Self-Correction/Review:**\n * Is the JSON structure robust? Yes, uses arrays and objects appropriately.\n * Is \"exhaustive\" addressed reasonably? Yes, by breaking it down into specific categories.\n * Is it clear how to handle ambiguity? Yes, via the `definitions` array structure and `disambiguation_notes`.\n * Is the output format suitable for a knowledge builder? Yes, structured JSON is ideal.\n * Is the prompt clear for the LLM? Yes, includes role, objective" }, ] }, { "role": "user", "parts": [ { "text": "INSERT_INPUT_HERE" }, ] }, ], "generationConfig": { "responseMimeType": "text/plain", }, } EOF curl \ -X POST \ -H "Content-Type: application/json" \ "https://generativelanguage.googleapis.com/v1beta/models/${MODEL_ID}:${GENERATE_CONTENT_API}?key=${GEMINI_API_KEY}" -d '@request.json'