#!/bin/bash set -e -E GEMINI_API_KEY="$GEMINI_API_KEY" MODEL_ID="gemini-2.5-pro-exp-03-25" GENERATE_CONTENT_API="streamGenerateContent" upload_to_gemini() { local FILE_PATH="$1" local DISPLAY_NAME="$2" local MIME_TYPE MIME_TYPE=$(file -b --mime-type "${FILE_PATH}") local NUM_BYTES NUM_BYTES=$(wc -c < "${FILE_PATH}") local tmp_header_file tmp_header_file="upload-header.tmp" curl "https://generativelanguage.googleapis.com/upload/v1beta/files?key=${GEMINI_API_KEY}" \ -D "${tmp_header_file}" \ -H "X-Goog-Upload-Protocol: resumable" \ -H "X-Goog-Upload-Command: start" \ -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \ -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \ -H "Content-Type: application/json" \ -d "{\"file\": {\"display_name\": \"${DISPLAY_NAME}\"}}" 2> /dev/null local upload_url upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r") rm "${tmp_header_file}" # Upload the actual bytes. curl "${upload_url}" \ -H "Content-Length: ${NUM_BYTES}" \ -H "X-Goog-Upload-Offset: 0" \ -H "X-Goog-Upload-Command: upload, finalize" \ --data-binary "@${FILE_PATH}" 2> /dev/null > file_info.json local file_uri file_uri=$(jq -r ".file.uri" file_info.json) echo "${file_uri}" rm file_info.json } # Make the file available in local system working directory FILE_0="$(upload_to_gemini "jsonldb.txt" "$(basename jsonldb.txt)")" FILE_1="$(upload_to_gemini "db_operations.py.txt" "$(basename db_operations.py.txt)")" FILE_2="$(upload_to_gemini "config.py.txt" "$(basename config.py.txt)")" FILE_3="$(upload_to_gemini "state_manager.py.txt" "$(basename state_manager.py.txt)")" FILE_4="$(upload_to_gemini "structure.txt" "$(basename structure.txt)")" cat << EOF > request.json { "contents": [ { "role": "user", "parts": [ { "fileData": { "mimeType": "text/plain", "fileUri": "$FILE_0" } }, { "fileData": { "mimeType": "text/plain", "fileUri": "$FILE_1" } }, { "fileData": { "mimeType": "text/plain", "fileUri": "$FILE_2" } }, { "fileData": { "mimeType": "text/plain", "fileUri": "$FILE_3" } }, { "text": "I'm trying to create a knowledge builder for use for offline NLP and text-based physics and character simulation. it's just for fun; i'm trying to figure out how i can exhaustively delineate words provided by a user in a simulation that is so simple that the total meaning needed is handleable. i guess i imagine running it online for a while to generate data, then trying it out offline and adding code while offline to ask the user to go online and build more data for a bit if they do something novel. the idea is it could maybe be used for simplistic multiplayer text adventure games.\nthe idea is also to use some kind of rule inferencing to keep the knowledge graph consistent, and to generate the knowledge and rules using an LLM. but right now i'm just seeing if i can collect data, so rule inferencing would be stubbed.\nan AI generated the attached simple py files but they could be improved.\ncould you draft a new file, or improve an existing one?" }, { "fileData": { "mimeType": "text/plain", "fileUri": "$FILE_4" } }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process that leads to the improved `db_operations.py` and the new `llm_interface.py`:\n\n1. **Understand the Goal:** The user wants to build a knowledge graph using an LLM. The current focus is on data collection. The system should store concepts and their relationships in a file-based database (`jsonldb`). The user provided initial Python files (`config.py`, `db_operations.py`, `state_manager.py`) and the `jsonldb` documentation. The goal is to improve these or add new files to facilitate the data collection process, specifically interacting with the LLM to generate concept data.\n\n2. **Analyze Provided Files:**\n * `jsonldb.txt`: Documentation for `jsonldb`. Key features: folder-based, JSONL files per table, indexing (`.idx`), metadata (`db.meta`), DataFrame/dict interfaces (`get_df`, `get_dict`, `upsert_df`, `upsert_dict`), version control (`commit`, `revert`), linting, visualization. The core operations seem to be `upsert_dict` and `get_dict` for the current task.\n * `db_operations.py.txt`:\n * Uses `jsonldb.FolderDB`.\n * Has `initialize_db` (good practice).\n * Uses a module-level `_db` variable (also good).\n * `_get_db` helper is useful.\n * `_create_placeholder_data`: Defines a basic structure for new concepts. This is important.\n * `concept_exists`: Uses `get_concept`, which is reasonable.\n * `get_concept`: Retrieves a single concept using `db.get_dict` with `lower_key` and `upper_key` set to the same value. This is a correct way to use `jsonldb` for single key lookup. Includes basic error handling (FileNotFoundError).\n * `upsert_concept`: Uses `db.upsert_dict` correctly for a single concept.\n * `ensure_concept_placeholder`: Combines `concept_exists` and `upsert_concept` with the placeholder data. This is a key function for the workflow.\n * `rebuild_metadata`: Calls `db.build_dbmeta`. Useful utility.\n * Commented-out `upsert_concepts_batch`: This is a good idea for efficiency if multiple concepts are processed together.\n * `config.py.txt`:\n * Handles paths using `pathlib`.\n * Loads environment variables (`dotenv`).\n * Defines constants for DB folder, table names, state files, LLM settings (API key, model, retries, timeout), processing limits.\n * Includes a helper `get_state_file_path`.\n * Ensures directories exist.\n * Good structure overall.\n * `state_manager.py.txt`:\n * Manages queues (`high_priority`, `low_priority`) and a processed set (`_processed_concepts`) using `deque` and `set`.\n * Loads/saves state to JSON files using helpers (`_load_json_list`, `_save_json_list`).\n * Includes error handling for file loading.\n * Uses `atexit` to save state automatically (good).\n * Provides functions to add/get items from queues and check/mark processed items.\n * Seems robust for managing the processing workflow.\n * `structure.txt`: Shows a basic project layout.\n\n3. **Identify Missing Pieces/Areas for Improvement:**\n * **LLM Interaction:** The core task is generating knowledge *using an LLM*. There's no code for this yet. A new module is needed to handle communication with the Gemini API.\n * **Data Structure Definition:** The `_create_placeholder_data` in `db_operations.py` defines a *basic* structure. The *actual* structure generated by the LLM needs to be defined or at least anticipated. The prompt for the LLM will be crucial here.\n * **Workflow Integration:** How do `state_manager`, `db_operations`, and the (new) LLM module work together? The `main.py` (mentioned in `structure.txt` but not provided) would orchestrate this. A typical flow: get concept from queue -> ask LLM to generate data -> validate LLM output -> upsert data using `db_operations` -> mark concept as processed using `state_manager`.\n * **Error Handling:** The existing error handling is basic. More specific handling, especially around LLM calls (retries, timeouts, API errors) and data validation, will be needed.\n * **Data Validation:** The LLM output might not be perfect JSON or might not conform to the expected schema. Some validation step is necessary before saving to the DB.\n * **`db_operations.py` Refinements:**\n * The `get_concept` could potentially be slightly simplified if `jsonldb` has a direct single-key lookup (though the current range method is fine). *Self-correction: Looking at `jsonldb.jsonlfile.select_line_jsonl`, there *is* a single-line lookup, but `FolderDB` doesn't expose it directly. The `get_dict` range method is the intended way at the `FolderDB` level.*\n * The placeholder data structure could be more detailed or configurable.\n * Consider adding type hints more rigorously using `typing`. (Already quite good).\n * The batch upsert function should probably be implemented.\n\n4. **Drafting the New File (`llm_interface.py`):**\n * **Purpose:** Encapsulate all logic related to calling the Gemini API.\n * **Dependencies:** `google.generativeai`, `knowledge_graph_builder.config`, potentially `json` for parsing.\n * **Core Function:** A function like `generate_concept_data(concept_name: str) -> Optional[Dict[str, Any]]`.\n * **Inputs:** The concept name.\n * **Outputs:** A dictionary representing the structured data for the concept, or `None` on failure.\n * **Key Steps:**\n * Configure the API client using the key from `config`.\n * Define the prompt. This is *critical*. It needs to instruct the LLM to output JSON matching the desired schema (relationships, metadata, etc.). It should clearly state the input (`concept_name`) and the expected output format.\n * Make the API call (`generate_content`).\n * Handle potential errors during the API call (timeouts, API errors, content filtering). Implement retries based on `config.LLM_MAX_RETRIES`.\n * Extract the text response.\n * Attempt to parse the response as JSON. Handle `JSONDecodeError`.\n * *Optional but recommended:* Validate the parsed JSON against a predefined schema (though this could be done in the calling code).\n * Return the parsed dictionary or `None`.\n * **Refinements:** Add logging, clear error messages, potentially allow passing a custom prompt template.\n\n5. **Improving `db_operations.py`:**\n * **Batch Upsert:** Implement `upsert_concepts_batch` using `db.upsert_dict`. This is straightforward.\n * **Placeholder Data:** Make the placeholder structure slightly more explicit about expected fields, even if empty/None initially. Align it with the potential LLM output structure.\n * **Error Handling in `get_concept`:** Add a more general `Exception` catch, but keep the `FileNotFoundError` specific. Log the errors clearly.\n * **Docstrings:** Ensure docstrings are clear and accurate. (They are already quite good).\n * **Type Hinting:** Review and ensure consistency. (Already good).\n * **Consider `lint_db` integration:** Maybe add a function to lint a specific table after significant updates, or rely on `rebuild_metadata` which might implicitly trigger necessary updates/checks in `jsonldb`. *Self-correction: `lint_db` is separate from `build_dbmeta`. Add a specific `lint_table` function if needed, or just use `lint_db` periodically.* For now, stick to `rebuild_metadata`.\n\n6. **Review and Refine:**\n * Read through the generated code (`llm_interface.py`) and the modified code (`db_operations.py`).\n * Check for consistency with `config.py` and `state_manager.py`.\n * Ensure error handling is reasonable.\n * Verify that the logic aligns with the overall goal of collecting LLM-generated knowledge graph data.\n * Add comments explaining complex parts or design choices (like the prompt structure).\n * Make sure imports are correct (relative imports within the package).\n\n7. **Final Output Generation:** Present the improved `db_operations.py` and the new `llm_interface.py`, explaining the changes and additions, and how they fit into the overall project structure and workflow. Mention the need for `main.py` to orchestrate the process." }, { "text": "Okay, this looks like a fun and interesting project! You're essentially building a pipeline:\n\n1. **Identify Concept:** Get a concept name (e.g., from user input, or seeded list).\n2. **Queue:** Put the concept name into a processing queue (`state_manager`).\n3. **Process:** Pick a concept from the queue.\n4. **Generate Data:** Ask an LLM (Gemini) to generate structured information (relationships, properties) about the concept.\n5. **Store:** Save this structured information into your `jsonldb` database (`db_operations`).\n6. **Mark Done:** Mark the concept as processed (`state_manager`).\n7. **Repeat.**\n\nThe provided files (`config.py`, `db_operations.py`, `state_manager.py`) set up the configuration, database interaction layer, and the queuing/state tracking nicely. The `jsonldb` library seems suitable for this file-based approach with potential versioning.\n\nThe key missing piece is the interaction with the LLM (Step 4). Let's create a new file for that, `llm_interface.py`, and also make some minor improvements and additions to `db_operations.py`.\n\n---\n\n**1. New File: `knowledge_graph_builder/llm_interface.py`**\n\nThis module will handle communication with the Gemini API.\n\n```python\n# knowledge_graph_builder/llm_interface.py\n\nimport google.generativeai as genai\nimport json\nimport time\nimport datetime\nfrom typing import Optional, Dict, Any, List\n\nfrom . import config # Relative import\n\n# Configure the Gemini client (do this once)\ntry:\n genai.configure(api_key=config.GEMINI_API_KEY)\n # Safety settings can be adjusted if needed\n safety_settings = [\n {\"category\": \"HARM_CATEGORY_HARASSMENT\", \"threshold\": \"BLOCK_MEDIUM_AND_ABOVE\"},\n {\"category\": \"HARM_CATEGORY_HATE_SPEECH\", \"threshold\": \"BLOCK_MEDIUM_AND_ABOVE\"},\n {\"category\": \"HARM_CATEGORY_SEXUALLY_EXPLICIT\", \"threshold\": \"BLOCK_MEDIUM_AND_ABOVE\"},\n {\"category\": \"HARM_CATEGORY_DANGEROUS_CONTENT\", \"threshold\": \"BLOCK_MEDIUM_AND_ABOVE\"},\n ]\n # Set up the model generation configuration\n generation_config = genai.GenerationConfig(\n # Ensure JSON output if the model supports it directly,\n # otherwise, we'll rely on prompt engineering.\n # response_mime_type=\"application/json\", # Uncomment if model supports direct JSON\n temperature=0.7, # Adjust creativity vs. factuality\n )\n model = genai.GenerativeModel(\n model_name=config.MODEL_NAME,\n safety_settings=safety_settings,\n generation_config=generation_config\n )\n print(f\"INFO: Gemini model '{config.MODEL_NAME}' initialized.\")\nexcept Exception as e:\n print(f\"ERROR: Failed to configure Gemini API: {e}. Check API key and model name.\")\n # Depending on your application's needs, you might want to exit here\n # or allow it to run without LLM capabilities.\n model = None # Ensure model is None if setup fails\n\ndef _create_llm_prompt(concept_name: str) -> str:\n \"\"\"Creates the prompt for the LLM to generate knowledge graph data.\"\"\"\n\n # --- This is the CRITICAL part: Define your desired output structure ---\n # Be very specific about the JSON format you want.\n # Include examples if possible.\n prompt = f\"\"\"\nAnalyze the concept: \"{concept_name}\"\n\nGenerate a JSON object representing its core properties and relationships relevant to a text-based simulation or knowledge graph. The JSON object should strictly follow this structure:\n\n{{\n \"concept_name\": \"{concept_name}\",\n \"description\": \"A concise definition or description of the concept.\",\n \"properties\": {{\n \"category\": \"e.g., 'object', 'character', 'location', 'action', 'abstract'\",\n \"physical_state\": \"e.g., 'solid', 'liquid', 'gaseous', 'none', 'variable'\",\n \"is_animate\": \"boolean (true/false)\",\n \"can_contain\": [\"list\", \"of\", \"concept\", \"names\", \"it\", \"can\", \"hold\"],\n \"typical_location\": \"concept name of a typical location\"\n // Add other relevant properties as needed\n }},\n \"relationships\": [\n {{\n \"type\": \"e.g., 'is_a', 'part_of', 'made_of', 'used_for', 'causes', 'requires', 'located_in'\",\n \"target_concept\": \"Name of the related concept\",\n \"strength\": \"Optional: numeric value (0.0-1.0) or qualitative ('high', 'medium', 'low')\"\n }}\n // Add more relationships as needed\n ],\n \"validation_notes\": [\n \"Any ambiguities, assumptions made, or areas needing further clarification.\"\n ]\n}}\n\nRules:\n- Output *only* the JSON object, starting with `{{` and ending with `}}`. Do not include any introductory text, explanations, or markdown formatting like ```json ... ```.\n- All keys and string values must be enclosed in double quotes.\n- Boolean values must be lowercase `true` or `false`.\n- Lists should use square brackets `[]`.\n- If a property or relationship is not applicable or unknown, use `null` or an empty list `[]` as appropriate for the structure. For example, if `can_contain` is not applicable, use `[]`. If `typical_location` is unknown, use `null`.\n- Ensure the `target_concept` in relationships refers to other potential concepts in the knowledge graph.\n\nConcept to analyze: \"{concept_name}\"\n\nJSON Output:\n\"\"\"\n return prompt\n\ndef _parse_llm_response(response_text: str, concept_name: str) -> Optional[Dict[str, Any]]:\n \"\"\"Attempts to parse the LLM response text into a JSON dictionary.\"\"\"\n try:\n # Sometimes LLMs wrap the JSON in markdown, try to strip it\n if response_text.strip().startswith(\"```json\"):\n response_text = response_text.strip()[7:]\n if response_text.strip().endswith(\"```\"):\n response_text = response_text.strip()[:-3]\n\n data = json.loads(response_text.strip())\n\n # Basic validation: Check if it's a dictionary\n if not isinstance(data, dict):\n print(f\"ERROR: LLM response for '{concept_name}' was not a JSON object.\")\n return None\n\n # Optional: Add more schema validation here if needed\n # e.g., check for required keys like 'description', 'properties', 'relationships'\n\n return data\n except json.JSONDecodeError as e:\n print(f\"ERROR: Failed to decode LLM JSON response for '{concept_name}': {e}\")\n print(f\"--- LLM Response Text ---:\\n{response_text}\\n-------------------------\")\n return None\n except Exception as e:\n print(f\"ERROR: Unexpected error parsing LLM response for '{concept_name}': {e}\")\n return None\n\ndef generate_concept_data(concept_name: str) -> Optional[Dict[str, Any]]:\n \"\"\"\n Uses the configured LLM to generate structured data for a given concept.\n Handles retries and basic parsing.\n\n Args:\n concept_name: The name of the concept to generate data for.\n\n Returns:\n A dictionary containing the structured data, or None if generation failed.\n \"\"\"\n if model is None:\n print(\"ERROR: LLM model not initialized. Cannot generate concept data.\")\n return None\n\n prompt = _create_llm_prompt(concept_name)\n retries = config.LLM_MAX_RETRIES\n delay = 2 # Initial delay in seconds for retries\n\n for attempt in range(retries):\n try:\n print(f\"INFO: Requesting LLM data for '{concept_name}' (Attempt {attempt + 1}/{retries})...\")\n response = model.generate_content(\n prompt,\n request_options={'timeout': config.LLM_TIMEOUT}\n )\n\n # Check for safety blocks or empty responses\n if not response.candidates or not response.candidates[0].content.parts:\n # Handle cases where the response might be blocked or empty\n block_reason = response.prompt_feedback.block_reason if response.prompt_feedback else 'Unknown'\n safety_ratings = response.prompt_feedback.safety_ratings if response.prompt_feedback else 'N/A'\n print(f\"WARN: LLM response for '{concept_name}' was blocked or empty. Reason: {block_reason}. Safety: {safety_ratings}\")\n # Decide if this specific block reason warrants a retry or immediate failure\n if block_reason != 'Unknown': # Example: Don't retry if blocked for safety\n return None # Failed permanently for this concept due to blocking\n # Otherwise, continue to retry logic below\n\n else:\n raw_response_text = response.text\n parsed_data = _parse_llm_response(raw_response_text, concept_name)\n\n if parsed_data:\n # Add metadata about the generation process\n now = datetime.datetime.now(datetime.timezone.utc).isoformat()\n metadata = {\n \"created_at\": parsed_data.get(\"metadata\", {}).get(\"created_at\"), # Preserve if exists\n \"processed_at\": now,\n \"status\": \"processed\",\n \"source_model\": config.MODEL_NAME,\n \"generation_prompt_hash\": hash(prompt) # Optional: track prompt changes\n }\n # Merge LLM generated data with our metadata\n # Ensure concept_name consistency\n final_data = {\n \"concept_name\": concept_name, # Enforce consistency\n \"description\": parsed_data.get(\"description\", \"N/A\"),\n \"properties\": parsed_data.get(\"properties\", {}),\n \"relationships\": parsed_data.get(\"relationships\", []),\n \"validation_notes\": parsed_data.get(\"validation_notes\", []),\n \"metadata\": metadata # Overwrite/add our metadata\n }\n print(f\"INFO: Successfully generated and parsed data for '{concept_name}'.\")\n return final_data\n else:\n # Parsing failed, will proceed to retry logic if attempts remain\n print(f\"WARN: Parsing failed for '{concept_name}'.\")\n # No data to return even if retries are exhausted\n\n # If we reached here, it means either the response was empty/blocked (and retryable)\n # or parsing failed. Prepare for next attempt.\n\n except Exception as e:\n # Includes API errors, timeouts, etc.\n print(f\"ERROR: LLM API call failed for '{concept_name}' (Attempt {attempt + 1}/{retries}): {e}\")\n # Consider specific error types for different retry logic if needed\n\n # Wait before retrying\n if attempt < retries - 1:\n print(f\"INFO: Retrying in {delay} seconds...\")\n time.sleep(delay)\n delay *= 2 # Exponential backoff\n else:\n print(f\"ERROR: Max retries reached for '{concept_name}'. Generation failed.\")\n return None # Failed after all retries\n\n return None # Should technically be unreachable if loop logic is correct, but acts as safeguard\n```\n\n**Key points in `llm_interface.py`:**\n\n1. **Configuration:** Initializes the Gemini client using settings from `config.py`.\n2. **Prompt Engineering (`_create_llm_prompt`):** This is *crucial*. You need to tell the LLM *exactly* what JSON structure you want. I've included a detailed example structure. You **must adapt this structure** to fit the specific kinds of information you need for your physics/character simulation. Be precise about keys, value types (string, boolean, list, null), and expected content. Explicitly telling it *not* to add extra text or markdown is important.\n3. **API Call:** Uses `model.generate_content` with a timeout.\n4. **Error Handling & Retries:** Implements basic retry logic with exponential backoff for API errors or timeouts.\n5. **Response Parsing (`_parse_llm_response`):** Attempts to clean up potential markdown formatting and parse the response string into a Python dictionary using `json.loads`. Includes error handling for invalid JSON.\n6. **Metadata Addition:** Adds processing timestamp, status, and source model information to the final dictionary before returning it. It tries to preserve any existing `created_at` timestamp if the concept was a placeholder.\n7. **Safety/Blocking:** Includes basic checks for blocked responses.\n\n---\n\n**2. Improvements to `knowledge_graph_builder/db_operations.py`**\n\nLet's refine the existing database operations file slightly.\n\n```python\n# knowledge_graph_builder/db_operations.py\n\nimport datetime\nimport time # For potential delays if needed\nfrom typing import Dict, Any, Optional, List\n\nfrom jsonldb import FolderDB\nfrom . import config # Relative import\n\n# Module-level variable to hold the FolderDB instance\n_db: Optional[FolderDB] = None\n\ndef initialize_db() -> FolderDB:\n \"\"\"\n Initializes the FolderDB instance, ensuring the concepts file exists\n and building initial metadata if the db.meta file is missing.\n Should be called once at application startup.\n \"\"\"\n global _db\n if _db is None:\n try:\n # config.py ensures DB_FOLDER exists\n db_path_str = str(config.DB_FOLDER)\n _db = FolderDB(db_path_str)\n\n # Ensure the concepts file exists for jsonldb initialization\n concepts_file = config.DB_FOLDER / f\"{config.CONCEPTS_TABLE_NAME}.jsonl\"\n if not concepts_file.exists():\n concepts_file.touch()\n print(f\"INFO: Created empty concepts file: {concepts_file}\")\n # Give jsonldb a moment if the file was *just* created\n time.sleep(0.1)\n\n # Check if db.meta exists, build if not\n meta_file = config.DB_FOLDER / \"db.meta\"\n if not meta_file.exists():\n print(\"INFO: db.meta not found. Building initial database metadata...\")\n try:\n _db.build_dbmeta()\n print(\"INFO: Initial db.meta created.\")\n except Exception as meta_e:\n # Log error but maybe continue if only metadata build failed initially\n print(f\"WARN: Failed to build initial db.meta: {meta_e}\")\n\n print(f\"INFO: Database connection initialized for folder: {db_path_str}\")\n except FileNotFoundError as fnf_e:\n print(f\"ERROR: Database folder '{config.DB_FOLDER}' not found or inaccessible during DB init: {fnf_e}\")\n raise # Propagate critical init error\n except Exception as e:\n print(f\"ERROR: Failed to initialize database connection: {e}\")\n raise # Propagate critical init error\n return _db\n\ndef _get_db() -> FolderDB:\n \"\"\"Internal helper to get the initialized DB instance.\"\"\"\n if _db is None:\n # Option 1: Attempt initialization if not done explicitly (current behavior)\n print(\"WARN: Database accessed before explicit initialization. Attempting implicit init.\")\n return initialize_db()\n # Option 2: Enforce explicit initialization (safer in complex apps)\n # raise RuntimeError(\"Database not initialized. Call initialize_db() first.\")\n return _db\n\ndef _create_placeholder_data() -> Dict[str, Any]:\n \"\"\"\n Creates a default data structure for a new concept placeholder.\n Align this structure with the expected final structure from the LLM,\n but mark it clearly as unprocessed.\n \"\"\"\n now = datetime.datetime.now(datetime.timezone.utc).isoformat()\n return {\n # Keep concept_name out here, it's the key in the DB dict\n \"description\": None,\n \"properties\": {},\n \"relationships\": [],\n \"metadata\": {\n \"created_at\": now,\n \"processed_at\": None, # Not processed yet\n \"status\": \"placeholder\", # Clearly mark as placeholder\n \"source_model\": None, # Source unknown until processed\n },\n \"validation_notes\": [\"Initial placeholder created.\"] # Add a note\n }\n\ndef concept_exists(concept_name: str) -> bool:\n \"\"\"\n Checks if a concept exists in the database by trying to retrieve it.\n More robustly handles potential errors during lookup.\n \"\"\"\n db = _get_db()\n table_name = config.CONCEPTS_TABLE_NAME\n try:\n # Use get_dict which is efficient for checking existence via range\n result = db.get_dict(names=[table_name], lower_key=concept_name, upper_key=concept_name)\n # Check if the table and the specific key exist in the result\n return table_name in result and concept_name in result[table_name]\n except FileNotFoundError:\n # This specific error suggests the .jsonl or .idx file might be missing.\n # This is less likely an \"entry not found\" and more a structural issue.\n print(f\"WARN: File/Index missing for table '{table_name}' when checking existence of '{concept_name}'. Assuming not found.\")\n # Optionally trigger metadata rebuild here if this becomes frequent\n # try:\n # print(f\"Attempting metadata rebuild due to FileNotFoundError...\")\n # db.build_dbmeta()\n # except Exception as rebuild_e:\n # print(f\"ERROR: Failed to rebuild metadata: {rebuild_e}\")\n return False\n except Exception as e:\n # Catch other potential errors during the lookup (e.g., permissions, disk errors)\n print(f\"ERROR: Failed to check existence for concept '{concept_name}': {e}\")\n return False # Treat errors during check as \"not found\" or inaccessible\n\ndef get_concept(concept_name: str) -> Optional[Dict[str, Any]]:\n \"\"\"\n Retrieves the data dictionary for a specific concept name.\n Returns None if the concept is not found or an error occurs.\n \"\"\"\n db = _get_db()\n table_name = config.CONCEPTS_TABLE_NAME\n try:\n # get_dict returns {table_name: {key: value}} if key is found\n result = db.get_dict(names=[table_name], lower_key=concept_name, upper_key=concept_name)\n\n # Extract the concept data if found\n if table_name in result and concept_name in result[table_name]:\n return result[table_name][concept_name]\n return None # Concept not found in the result dict\n except FileNotFoundError:\n # Can happen if .idx is missing/corrupt or .jsonl file was deleted externally\n print(f\"WARN: File/Index issue for table '{table_name}' accessing key '{concept_name}'.\")\n # Consider attempting index rebuild here if it becomes common,\n # but for now, just report not found.\n return None\n except Exception as e:\n print(f\"ERROR: Failed to get concept '{concept_name}': {e}\")\n return None # Return None on other unexpected errors\n\ndef upsert_concept(concept_name: str, concept_data: Dict[str, Any]) -> bool:\n \"\"\"\n Adds or updates a single concept's data in the concepts table.\n Returns True on success, False on failure.\n \"\"\"\n db = _get_db()\n try:\n # upsert_dict takes the table name and a dict {key: value}\n data_to_upsert = {concept_name: concept_data}\n db.upsert_dict(name=config.CONCEPTS_TABLE_NAME, data_dict=data_to_upsert)\n # print(f\"DEBUG: Upserted concept '{concept_name}'\") # Optional debug log\n return True\n except Exception as e:\n print(f\"ERROR: Failed to upsert concept '{concept_name}': {e}\")\n return False\n\ndef upsert_concepts_batch(concepts: Dict[str, Dict[str, Any]]) -> bool:\n \"\"\"\n Adds or updates multiple concepts efficiently in the concepts table.\n Returns True on success, False on failure.\n\n Args:\n concepts: A dictionary where keys are concept names and values are\n their corresponding data dictionaries.\n \"\"\"\n if not concepts:\n print(\"INFO: upsert_concepts_batch called with empty dictionary. Nothing to do.\")\n return True # No failure if nothing to do\n\n db = _get_db()\n try:\n db.upsert_dict(name=config.CONCEPTS_TABLE_NAME, data_dict=concepts)\n print(f\"INFO: Successfully upserted batch of {len(concepts)} concepts.\")\n return True\n except Exception as e:\n # Consider logging the keys that failed if possible, though jsonldb might not provide that detail\n print(f\"ERROR: Failed to upsert concepts batch ({len(concepts)} concepts): {e}\")\n return False\n\ndef ensure_concept_placeholder(concept_name: str) -> bool:\n \"\"\"\n Checks if a concept exists. If not, creates a minimal placeholder entry.\n Returns True if the concept exists or was successfully created, False otherwise.\n \"\"\"\n if concept_exists(concept_name):\n # print(f\"DEBUG: Concept '{concept_name}' already exists.\") # Optional debug log\n return True # Already exists, nothing to do\n else:\n print(f\"INFO: Creating placeholder for concept: '{concept_name}'\")\n placeholder_data = _create_placeholder_data()\n # Pass the concept name explicitly to upsert_concept\n return upsert_concept(concept_name, placeholder_data)\n\ndef rebuild_metadata() -> None:\n \"\"\"\n Triggers the rebuild of the db.meta file for the entire database folder.\n Useful for ensuring metadata reflects the current state of all .jsonl files.\n \"\"\"\n db = _get_db()\n try:\n print(\"INFO: Rebuilding database metadata (db.meta)...\")\n db.build_dbmeta()\n print(\"INFO: Database metadata rebuild complete.\")\n except Exception as e:\n print(f\"ERROR: Failed to rebuild database metadata: {e}\")\n # Decide if this should raise an error or just log\n\ndef lint_database() -> None:\n \"\"\"\n Triggers linting (sorting, cleaning) of all JSONL files in the database.\n Can be useful periodically to optimize files.\n \"\"\"\n db = _get_db()\n try:\n print(\"INFO: Linting all database files...\")\n db.lint_db()\n print(\"INFO: Database linting complete.\")\n except Exception as e:\n print(f\"ERROR: Failed during database linting: {e}\")\n\n# --- Optional: Add functions for deleting if needed ---\n# def delete_concept(concept_name: str) -> bool:\n# \"\"\" Deletes a single concept. \"\"\"\n# db = _get_db()\n# try:\n# db.delete_file(name=config.CONCEPTS_TABLE_NAME, keys=[concept_name])\n# print(f\"INFO: Deleted concept '{concept_name}'.\")\n# return True\n# except Exception as e:\n# print(f\"ERROR: Failed to delete concept '{concept_name}': {e}\")\n# return False\n```\n\n**Changes in `db_operations.py`:**\n\n1. **`initialize_db`:** Added a check for `db.meta` and calls `build_dbmeta` if it's missing on the first run. Added a small `time.sleep` just in case the OS needs a moment after `touch`ing a file before `jsonldb` accesses it (often unnecessary, but harmless). Improved error message for `FileNotFoundError`.\n2. **`_create_placeholder_data`:** Added a comment emphasizing alignment with the LLM output structure and added an initial `validation_notes` entry.\n3. **`concept_exists`:** Added more robust error handling for `FileNotFoundError` (distinguishing it from a simple non-existent key) and general exceptions during lookup.\n4. **`upsert_concepts_batch`:** Implemented the batch upsert function using `db.upsert_dict`. This will be more efficient if you process multiple concepts before saving.\n5. **`lint_database`:** Added a convenience function to call `db.lint_db()`.\n\n---\n\n**3. Next Steps (Orchestration in `main.py`)**\n\nYou'll need a `main.py` (or similar) script to tie these components together. Here's a conceptual outline:\n\n```python\n# knowledge_graph_builder/main.py\n\nimport time\nfrom . import config\nfrom . import state_manager\nfrom . import db_operations\nfrom . import llm_interface\n\ndef process_single_concept(concept_name: str) -> bool:\n \"\"\"Processes one concept: generate data, upsert, mark processed.\"\"\"\n print(f\"\\n--- Processing Concept: {concept_name} ---\")\n\n # 1. Ensure placeholder exists (optional but good practice)\n if not db_operations.ensure_concept_placeholder(concept_name):\n print(f\"ERROR: Failed to ensure placeholder for '{concept_name}'. Skipping.\")\n # Decide how to handle this - maybe move to a failed queue?\n return False\n\n # 2. Generate data using LLM\n generated_data = llm_interface.generate_concept_data(concept_name)\n\n if not generated_data:\n print(f\"ERROR: Failed to generate data for '{concept_name}' after retries. Skipping.\")\n # Optionally update status in DB to 'generation_failed'\n # db_operations.update_concept_status(concept_name, \"generation_failed\")\n return False # Failed this concept\n\n # 3. Upsert the generated data into the database\n # The concept_name is the key, generated_data is the value\n if not db_operations.upsert_concept(concept_name, generated_data):\n print(f\"ERROR: Failed to save generated data for '{concept_name}' to DB. Skipping.\")\n # This is more serious - data was generated but not saved.\n return False # Failed this concept\n\n # 4. Add newly discovered related concepts to the queue\n # (Check if they exist/are queued before adding)\n related_concepts = set()\n if \"relationships\" in generated_data:\n for rel in generated_data.get(\"relationships\", []):\n target = rel.get(\"target_concept\")\n if target and isinstance(target, str): # Basic validation\n related_concepts.add(target)\n\n print(f\"INFO: Found {len(related_concepts)} related concepts for '{concept_name}'.\")\n for related_concept in related_concepts:\n if not db_operations.concept_exists(related_concept) and \\\n not state_manager.has_been_processed(related_concept):\n # Add to low priority unless specific rules dictate otherwise\n print(f\"INFO: Adding related concept '{related_concept}' to low-priority queue.\")\n state_manager.add_low_priority(related_concept)\n # else:\n # print(f\"DEBUG: Related concept '{related_concept}' already exists or processed.\")\n\n\n # 5. Mark the original concept as processed in the state manager\n state_manager.mark_processed(concept_name)\n print(f\"--- Finished Processing Concept: {concept_name} ---\")\n return True # Success for this concept\n\ndef main_loop():\n \"\"\"Main processing loop.\"\"\"\n print(\"Starting Knowledge Graph Builder...\")\n db_operations.initialize_db()\n state_manager.load_state()\n\n # --- Example: Seed initial concepts if queues are empty ---\n status = state_manager.get_queue_status()\n if status[\"high_priority\"] == 0 and status[\"low_priority\"] == 0 and status[\"processed_total\"] == 0:\n print(\"INFO: Queues are empty. Seeding initial concepts.\")\n initial_concepts = [\"apple\", \"gravity\", \"cat\", \"computer\", \"house\"] # Add your starting points\n for concept in initial_concepts:\n state_manager.add_high_priority(concept)\n # Save state immediately after seeding\n state_manager.save_state()\n # --- End Example Seed ---\n\n try:\n processed_in_cycle = 0\n while True:\n status = state_manager.get_queue_status()\n print(f\"\\nQueue Status: High={status['high_priority']}, Low={status['low_priority']}, Processed={status['processed_total']}\")\n\n concept_to_process = state_manager.get_next_high_priority()\n priority = \"High\"\n\n if concept_to_process is None:\n # If high priority is empty, process a batch from low priority\n low_priority_processed = 0\n while low_priority_processed < config.MAX_LOW_PRIORITY_PER_CYCLE:\n concept_to_process = state_manager.get_next_low_priority()\n if concept_to_process is None:\n break # Low priority queue is also empty\n priority = \"Low\"\n if state_manager.has_been_processed(concept_to_process):\n print(f\"INFO: Skipping already processed low-priority concept: '{concept_to_process}'\")\n continue # Skip if somehow already processed\n\n if process_single_concept(concept_to_process):\n processed_in_cycle += 1\n low_priority_processed += 1\n else:\n # Handle failure - maybe put back in queue or move to failed?\n print(f\"WARN: Failed to process low-priority concept '{concept_to_process}'.\")\n # For now, just continue to next\n\n if low_priority_processed == 0 and status['high_priority'] == 0:\n print(\"INFO: Both queues are empty. Exiting.\")\n break # Exit loop if both queues are empty\n\n else: # Process the high-priority item\n if state_manager.has_been_processed(concept_to_process):\n print(f\"INFO: Skipping already processed high-priority concept: '{concept_to_process}'\")\n continue # Skip if somehow already processed\n\n if process_single_concept(concept_to_process):\n processed_in_cycle += 1\n else:\n # Handle failure\n print(f\"WARN: Failed to process high-priority concept '{concept_to_process}'.\")\n # For now, just continue to next\n\n # Optional: Save state periodically\n if processed_in_cycle > 0 and processed_in_cycle % 10 == 0: # Save every 10 concepts processed\n print(\"INFO: Saving intermediate state...\")\n state_manager.save_state()\n # Optional: Rebuild metadata periodically if many files change\n # db_operations.rebuild_metadata()\n\n # Optional: Add a small delay to avoid hammering APIs or disk\n # time.sleep(1)\n\n except KeyboardInterrupt:\n print(\"\\nINFO: Keyboard interrupt received. Shutting down gracefully...\")\n finally:\n # State saving is handled by atexit in state_manager\n print(\"Knowledge Graph Builder finished.\")\n\nif __name__ == \"__main__\":\n # This structure assumes you run python -m knowledge_graph_builder.main\n # from the your_project_root directory.\n # If running `python knowledge_graph_builder/main.py` directly,\n # relative imports might need adjustment depending on PYTHONPATH.\n main_loop()\n\n```\n\nRemember to install the necessary libraries:\n\n```bash\npip install google-generativeai python-dotenv jsonldb pandas bokeh # jsonldb might pull in pandas/bokeh\n```\n\nThis setup provides a solid foundation for your data collection phase. You can run `main.py`, let it process concepts, and inspect the resulting `.jsonl` file in your `_app_data/knowledge_graph_db` directory. Don't forget to adjust the prompt in `llm_interface.py` to match the exact data structure you need!" }, ] }, { "role": "user", "parts": [ { "text": "INSERT_INPUT_HERE" }, ] }, ], "generationConfig": { "temperature": 0, "topP": 1, "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'