#!/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'
