Re: [ot][spam]gpt-4 completions

max_retries:\n log.error(f\"Max retries ({max_retries}) exceeded for quota error on '{concept_id}'. Giving up.\")\n log_entry[\"status\"] = \"api_error_quota\"\n log_entry[\"error\"] = f\"Quota exceeded after {max_retries} retries: {e}\"\n log_entry[\"response_timestamp\"] = self._get_iso_timestamp()\n self._update_log(log_key, log_entry) # Update log on final failure\n return None # Indicate failure\n\n # --- Extract Retry Delay ---\n retry_seconds = self._extract_retry_delay(e, 5) # Use helper\n wait_time = max(1, retry_seconds) + 1 # Add buffer\n log.info(f\"Waiting for {wait_time} seconds before retrying '{concept_id}'...\")\n time.sleep(wait_time)\n # Continue to next iteration of while loop\n\n except Exception as e:\n # Handle other API or processing errors\n log.error(f\"An error occurred during Gemini API call or initial processing for '{concept_id}': {e}\", exc_info=True)\n log_entry[\"status\"] = \"api_error_other\"\n log_entry[\"error\"] = f\"{type(e).__name__}: {e}\"\n log_entry[\"response_timestamp\"] = self._get_iso_timestamp()\n log_entry[\"retries_attempted\"] = retries\n self._update_log(log_key, log_entry) # Update log on failure\n return None # Indicate failure\n\n # --- Post-LLM Call Processing ---\n triples_added_count = 0\n if response:\n try:\n response_text = response.text\n log_entry[\"raw_response\"] = response_text # Store raw response\n log_entry[\"response_timestamp\"] = self._get_iso_timestamp()\n\n # 4. Parse the Response (expecting JSON dictionary)\n cleaned_response_text = self._clean_llm_response(response_text)\n try:\n llm_parsed_dict = json.loads(cleaned_response_text)\n if not isinstance(llm_parsed_dict, dict):\n raise TypeError(\"Parsed response is not a JSON dictionary.\")\n\n log_entry[\"parsed_response\"] = llm_parsed_dict # Log the dict\n log.info(f\"Successfully parsed JSON response for '{concept_id}'.\")\n\n # 5. Store the Parsed Concept Data as Triples\n try:\n for predicate, obj_or_list in llm_parsed_dict.items():\n objects = obj_or_list if isinstance(obj_or_list,

#!/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="$(upload_to_gemini "1CGLNd6A9yAzvMgp78JVaFtjBUm95h-q4" "$(basename 1CGLNd6A9yAzvMgp78JVaFtjBUm95h-q4)")" FILE_1="$(upload_to_gemini "1YWHP95BPNMK2PoNQqz2H5Y4xbS7InIzc" "$(basename 1YWHP95BPNMK2PoNQqz2H5Y4xbS7InIzc)")" cat << EOF > request.json { "contents": [ { "role": "user", "parts": [ { "fileData": { "mimeType": "application/octet-stream", "fileUri": "$FILE_0" } }, { "fileData": { "mimeType": "application/octet-stream", "fileUri": "$FILE_1" } }, { "text": "hi i have a draft knowledge builder and i'd like to upgrade it to include the source of the knowledge as parts of the graph.\ni think it might make sense to convert it to a clear general triple store so that every SPO can be referenced individually\nhow does that land for you? have i left important data out?" }, ] }, { "role": "model", "parts": [ { "text": "Here's a breakdown of the thinking process to address the user's request:\n\n1. **Understand the Goal:** The user wants to enhance their existing knowledge graph builder (`run_builder.py`, `builder.py`) to:\n * Include the *source* of information (provenance).\n * Store the data as explicit Subject-Predicate-Object (SPO) triples, making each relationship individually addressable.\n * Evaluate if the current approach is missing important data for this goal.\n\n2. **Analyze the Current Code:**\n * **`run_builder.py`:** Entry point, handles setup (API key check, logging, DB init), defines the starting concept, calls `build_knowledge_graph`, and optionally commits. It's mostly orchestration.\n * **`builder.py`:** Contains the core logic.\n * `LLM_PROMPT_TEMPLATE`: Defines the interaction with the LLM. It asks for a JSON dictionary where keys are predicates and values are objects (or lists of objects), all represented as `word:meaning` concept identifiers.\n * `query_and_store_concept`: Handles the API call (including retries for 429s), parses the JSON response, stores the *entire* dictionary under the `concept_id` key in the `CONCEPTS_TABLE_NAME`. It also logs the interaction.\n * `extract_related_concepts`: Parses the *retrieved* dictionary to find other concept identifiers (both predicates and objects) to add to the processing queue.\n * `build_knowledge_graph`: Manages the breadth-first exploration using a queue, checks for existing concepts, calls `query_and_store_concept`, and calls `extract_related_concepts`.\n * `concept_exists`: Checks if a concept ID exists in the DB.\n * `initialize_database`: Sets up the `FolderDB`.\n * **Data Storage:** Uses `jsonldb.FolderDB`. Currently stores data as `{ \"concept_id\": { \"predicate1\": \"object1:meaning\", \"predicate2\": [\"object2a:meaning\", \"object2b:meaning\"] } }` within a file named `concepts.jsonl`. This is *not* a triple store. It's a document store where each document represents a concept and its *outgoing* relationships.\n * **Provenance:** Currently, the `query_and_store_concept` function logs the interaction (timestamp, model used, prompt, raw response, parsed response, errors). This log entry *contains* provenance information, but it's stored separately in `logs.jsonl` and isn't directly linked to the individual relationships in `concepts.jsonl`.\n\n3. **Evaluate the Current Approach vs. the Goal:**\n * **Triple Store:** The current `concepts.jsonl` stores data grouped by subject. It's *not* a triple store where each `(Subject, Predicate, Object)` is a distinct record. This makes querying for specific triples or relationships across *all* subjects difficult without loading and iterating through many concept dictionaries. It also makes referencing *individual* relationships (e.g., attaching metadata like source) harder.\n * **Provenance:** The log captures the *source* of the *entire dictionary* generated for a concept (the LLM call). It doesn't explicitly link this source to *each individual relationship* within that dictionary. If the user wants fine-grained provenance per triple, the current structure isn't ideal.\n * **Missing Data:**\n * **Triple IDs:** To reference individual triples, each needs a unique ID.\n * **Explicit Source Link:** A clear link from each generated triple back to its source (e.g., the log entry ID or specific model/timestamp details) is missing *within the main data store*.\n * **Relationship Metadata:** Besides the source, one might want to store confidence scores, creation timestamps *for the triple itself* (not just the log), etc. The current structure doesn't easily accommodate this per-relationship metadata.\n * **Canonical Identifiers:** The prompt relies on `word:meaning` strings. While intended as identifiers, they are still strings. A robust system might map these to shorter, more stable internal IDs (e.g., UUIDs, numeric IDs) and maintain a separate mapping table. This wasn't explicitly asked for but is relevant to scalability and avoiding ambiguity if the same `word:meaning` string is generated slightly differently later.\n\n4. **Develop a Plan for Enhancement:**\n\n * **Data Model Change (Triple Store):**\n * Define a structure for individual triples. A simple JSON object per triple seems appropriate for `jsonldb`:\n ```json\n {\n \"triple_id\": \"unique_identifier_for_this_fact\",\n \"subject\": \"subject_concept_id\",\n \"predicate\": \"predicate_concept_id\",\n \"object\": \"object_concept_id\",\n \"source_log_id\": \"timestamp_key_from_log_table\", // Link to provenance\n \"creation_timestamp\": \"iso_timestamp_of_triple_creation\"\n // Optional: \"confidence_score\": 0.95, etc.\n }\n ```\n * This implies a new \"table\" (e.g., `triples.jsonl`) in `jsonldb`.\n * The `triple_id` needs generation (e.g., UUID, hash of S+P+O+timestamp).\n\n * **Provenance Integration:**\n * When `query_and_store_concept` successfully gets a response, it already generates a `log_key` (timestamp).\n * Instead of storing the whole dictionary in `concepts.jsonl`, iterate through the parsed dictionary.\n * For each `(predicate, object)` pair found for the `current_concept_id` (subject):\n * Generate a `triple_id`.\n * Create the triple JSON object as defined above, linking it to the `log_key`.\n * Store this triple object in the new `triples.jsonl` table. Using `triple_id` as the key in `upsert_dict` might be efficient for `jsonldb`.\n\n * **Code Modifications (`builder.py`):**\n * **`query_and_store_concept`:**\n * Keep the API call and logging logic.\n * **Crucial Change:** After successfully parsing `parsed_response_dict`, *do not* store it directly in `concepts.jsonl`.\n * Instead, iterate through `parsed_response_dict.items()`.\n * For each `pred`, `obj` (handle lists of `obj`):\n * Generate `triple_id`.\n * Create the triple dictionary: `{ \"triple_id\": ..., \"subject\": concept_id, \"predicate\": pred, \"object\": obj, \"source_log_id\": log_key, \"creation_timestamp\": get_iso_timestamp() }`.\n * Call `db.upsert_dict(config.TRIPLES_TABLE_NAME, {triple_id: triple_dict})`. (Need to add `TRIPLES_TABLE_NAME` to `config.py`).\n * Return `parsed_response_dict` (or maybe a list of generated triples?) so that `build_knowledge_graph` can still extract related concepts. *Correction:* It's better to return the original `parsed_response_dict` because `extract_related_concepts` expects that format. The storage mechanism changes, but the data structure returned by the LLM is the same.\n * **`concept_exists`:** This function needs to change. How do we know if a concept \"exists\"? Does it mean it exists as a *subject* in any triple? Or as a subject, predicate, *or* object? The simplest check might be: query the `triples` table for any triple where the `concept_id` appears in the `subject` field. This might be inefficient with `FolderDB`. A potential optimization: Maintain a separate `concepts_processed.jsonl` set/list just to track which concepts have been *requested* from the LLM, similar to the `processed_concepts` set in memory but persisted. Let's stick with the `processed_concepts` set in memory for now and acknowledge the limitation that restarting the build might re-process concepts if the `triples` table isn't efficiently queryable by subject. *Alternative:* Could keep the *old* `concepts.jsonl` structure purely as an *index* to know which concepts have been processed, even if the primary data is in `triples.jsonl`. This seems redundant. Let's refine `concept_exists` to query the `triples` table if possible, or rely on the in-memory `processed_concepts` set during a single run. *Decision:* Relying on the in-memory set is simplest for now. Checking existence based on *being a subject* in the triple store is the most logical long-term approach, but depends heavily on `jsonldb` query capabilities. Let's note this dependency.\n * **`extract_related_concepts`:** No change needed here, it operates on the dictionary structure returned by the LLM *before* it's broken into triples.\n * **`build_knowledge_graph`:**\n * The logic using `processed_concepts` remains largely the same.\n * The check `if not exists:` still relies on `concept_exists`.\n * The retrieval part `else: ... db.get_dict(...)` needs removal or modification. If we switch fully to triples, we don't retrieve the *whole* dictionary anymore. We just need to know *if* the concept was processed. If `concept_exists` relies on the in-memory set, this `else` block isn't strictly needed. *However*, if the build is interrupted and restarted, the in-memory set is lost. A persistent way to know if a concept's triples have *already been generated* is needed. Maybe `query_and_store_concept` should *first* check if triples with that `subject` and a specific `source_log_id` pattern (or maybe just *any* triples with that subject) exist? This gets complicated.\n * *Revised Approach for `build_knowledge_graph`*:\n 1. Use the in-memory `processed_concepts` set primarily.\n 2. Before calling `query_and_store_concept` for a concept *not* in `processed_concepts`, maybe do a *quick* check (if efficient) in `triples.jsonl` to see if *any* triples exist with that subject. If they do (from a previous run), add it to `processed_concepts` and retrieve its relations *from the triple store* instead of calling the LLM again. This requires a new function `get_relations_from_triples(db, concept_id)`.\n 3. If no triples found OR check is inefficient, proceed with `query_and_store_concept`.\n * **New Function `get_relations_from_triples`:** This would need to efficiently query `triples.jsonl` for all triples with the given `subject`, then reconstruct a dictionary similar to what the LLM provides, so `extract_related_concepts` can work on it. This highlights the potential inefficiency of using `FolderDB` as a triple store if it requires loading/scanning large files.\n\n * **Configuration (`config.py`):** Need to add `TRIPLES_TABLE_NAME = \"triples\"`.\n\n * **Addressing \"Missing Data\":**\n * Triple IDs: Added.\n * Source Link: Added (`source_log_id`).\n * Relationship Metadata: The triple structure allows adding more fields easily.\n * Canonical IDs: Acknowledge this as a potential next step, but don't implement it now to keep the scope manageable. The `word:meaning` strings will serve as the S/P/O identifiers for now.\n\n5. **Structure the Answer:**\n * Start by affirming the user's idea: Moving to triples and adding provenance is a good direction.\n * Explain *why* (benefits: individual addressability, easier querying, explicit provenance linking).\n * Address the \"missing data\" question based on the analysis.\n * Propose the new data model (triple structure).\n * Outline the necessary code changes step-by-step, focusing on `query_and_store_concept` and the implications for `build_knowledge_graph` and `concept_exists`.\n * Discuss the trade-offs and potential challenges (e.g., query efficiency with `FolderDB`, need for `get_relations_from_triples`, concept existence check).\n * Mention configuration changes.\n * Conclude by confirming this addresses the core request and sets a foundation for future enhancements (like canonical IDs)." }, { "text": "Okay, let's break this down. Your intuition is spot on: moving to an explicit triple store (Subject-Predicate-Object, SPO) and incorporating provenance (the source of the information) are excellent steps for building a more robust, queryable, and trustworthy knowledge graph.\n\n**How does it land?**\n\n* **Excellent Idea:** Transitioning to triples is fundamental for many graph operations. It makes each relationship a first-class citizen.\n* **Provenance is Key:** Knowing *why* a piece of information exists (e.g., which LLM call, which model, when) is crucial for debugging, trust, updates, and potentially resolving conflicting information later.\n* **Individual Referencing:** Storing data as triples inherently allows you to reference, query, and potentially annotate (e.g., with confidence scores, timestamps, sources) each individual relationship (SPO assertion).\n\n**Have you left important data out (in the current draft)?**\n\nBased on your goal of a triple store with provenance, here's what the current structure *implies* but doesn't *explicitly store* in an easily queryable triple format:\n\n1. **Individual Triple Identity:** Your current `concepts.jsonl` stores a dictionary *per subject*. You don't have a unique identifier for *each specific relationship* (e.g., the relationship `aluminum oxide:chem compound` -> `is a` -> `chemical compound:substance with constant composition`). If you wanted to attach metadata specifically to *that* fact, it's hard.\n2. **Explicit Provenance Link per Triple:** The log entry in `logs.jsonl` records the *entire generation event* for a concept. While this *is* the source, it's not directly attached to each individual `(Subject, Predicate, Object)` assertion derived from that event within the main data store (`concepts.jsonl`). Someone querying `concepts.jsonl` doesn't immediately see the source of a specific relationship without cross-referencing the log based on the subject concept and potentially timestamps.\n3. **Standardized Predicates/Objects (Potential):** While your prompt *aims* for `word:meaning` identifiers, slight variations from the LLM could lead to different strings representing the same conceptual idea (e.g., `color is:attribute of visual perception` vs `has color:property relating to light wavelength`). A true triple store often benefits from canonical identifiers, though using the `word:meaning` strings *as* identifiers is a valid starting point. The key is consistency.\n4. **Relationship Metadata:** Beyond provenance, you might eventually want confidence scores, validity time ranges, or other metadata *per triple*. The current dictionary structure doesn't easily accommodate this.\n\n**Proposed Enhancement: Moving to a Triple Store Model**\n\nHere's how you could adapt your system:\n\n**1. Define the Triple Structure:**\n\nInstead of storing `{ subject: { predicate: object } }`, you'll store individual triple records. A good structure within your `jsonldb` (using a new \"table\", e.g., `triples.jsonl`) could be:\n\n```json\n// Example structure for one triple in triples.jsonl\n{\n \"triple_id\": \"uuid_or_hash_identifier\", // Unique ID for this specific assertion\n \"subject\": \"aluminum oxide:a chemical compound of aluminum and oxygen\", // Concept ID\n \"predicate\": \"is a\", // Concept ID (or string if predicates aren't concepts yet)\n \"object\": \"chemical compound:substance with constant composition\", // Concept ID or literal\n \"source_log_id\": \"2023-10-27T10:30:00.123Z\", // Link to the log entry key\n \"creation_timestamp\": \"2023-10-27T10:30:05.456Z\" // When this triple record was created\n // Optional: \"model_used\": \"gemini-pro\", // Could duplicate from log or rely on lookup\n // Optional: \"confidence\": 0.95 // If you can estimate confidence\n}\n```\n\n**2. Adapt `query_and_store_concept`:**\n\n* Keep the logging mechanism as is. The `log_key` (timestamp) is your link.\n* **Change Storage:** When you receive and parse the `parsed_response_dict` from the LLM:\n * **Iterate:** Loop through each `predicate`, `object` pair in the dictionary. Handle cases where the object is a list (create multiple triples).\n * **Generate Triple ID:** Create a unique `triple_id` for each SPO assertion (e.g., using `uuid.uuid4()` or generating a hash of S+P+O+`log_key`).\n * **Create Triple Record:** Construct the JSON object described above, filling in `subject` (the `concept_id` passed to the function), `predicate`, `object`, `source_log_id` (the `log_key` from this run), and `creation_timestamp`.\n * **Store Triple:** Use `db.upsert_dict(config.TRIPLES_TABLE_NAME, {triple_id: triple_record})`. You'll need to add `TRIPLES_TABLE_NAME = \"triples\"` to your config.\n* **Return Value:** The function might still return the original `parsed_response_dict` just so `build_knowledge_graph` can extract related concepts easily, even though the primary storage mechanism has changed.\n\n**3. Adapt `build_knowledge_graph`:**\n\n* **Concept Existence (`concept_exists`):** This needs rethinking. \"Exists\" no longer means \"has a dictionary entry in `concepts.jsonl`\". It now means \"Does this concept appear as a subject in any triple in `triples.jsonl`?\". This might require a potentially less efficient query on `triples.jsonl` depending on `jsonldb`'s capabilities (e.g., checking if any record exists where `record['subject'] == concept_id`). Alternatively, you could maintain a separate, simpler set/list (`processed_subjects.jsonl`?) just to track which subjects have been expanded. For a single run, the in-memory `processed_concepts` set works fine.\n* **Retrieving Existing Data:** The part where you retrieve existing data for a concept needs to change. If a concept exists (has already been processed), you need to retrieve its relationships *from the triple store* to find related concepts to add to the queue. This likely requires a new function: `get_outgoing_relations(db, concept_id)` that queries `triples.jsonl` for all triples where `subject == concept_id` and returns the set of predicates and objects.\n* **Extracting Related Concepts (`extract_related_concepts`):** This function *can still operate on the dictionary returned by the LLM* before it's stored as triples, or it could be adapted to work on the results from `get_outgoing_relations`. Using the LLM dictionary is probably simpler.\n\n**4. Adapt `extract_related_concepts` (Minor consideration):**\n\n* Ensure it correctly identifies predicates that are formatted like `word:meaning` as concepts to be potentially explored later, alongside the objects. Your current code seems to do this already.\n\n**Benefits of this Approach:**\n\n* **Atomicity:** Each fact is stored independently.\n* **Explicit Provenance:** Each fact clearly points to its origin (the LLM call log).\n* **Query Flexibility:** You can query by S, P, O, or combinations, or even by source. Find all facts generated by a specific model run, find all subjects with a specific property, find all uses of a specific predicate, etc. (Query efficiency depends on `jsonldb`).\n* **Extensibility:** Easy to add more metadata fields to each triple record later.\n\n**Potential Challenges:**\n\n* **Storage Size:** Storing individual triples can be more verbose than nested dictionaries.\n* **Query Performance:** Querying a large `triples.jsonl` file for specific subjects or objects might be slow with `FolderDB` if it requires full file scans. A database optimized for triple indexing (like a graph database or an RDF store) would be more performant, but `jsonldb` is your current tool. You might need to build indexes manually if performance becomes an issue.\n* **Identifier Consistency:** Managing the `word:meaning` strings as identifiers requires care to avoid near-duplicates representing the same thing.\n\n**Conclusion:**\n\nYes, converting to a clear general triple store and adding provenance links is a very logical and beneficial upgrade. Your current draft contains the *raw information* needed (in the logs and the concept dictionaries), but restructuring the storage into explicit triples will make that information much more usable and directly addressable. The plan above outlines how you can modify your existing code structure to achieve this." }, ] }, { "role": "user", "parts": [ { "text": "Here's the jsonldb documentation. This is just the first text-oriented database I found and it could be improved upon maybe later. I think it provides for one key per table. We might want to wrap it in an interface module." }, { "text": "i'm having trouble uploading files now, but here's the basic jsonldb documentation. roughly it's a simple format that has one implicit indexing key per table, so multiple lookup keys would likely mean multiple tables. it's just the first text-based database i found and could likely be improved upon. it could make sense to make an interface module for it to provide for improvements.\n\n```\nHelp on package jsonldb:\n\nNAME\n jsonldb - JSONLDB - A simple file-based database that stores data in JSONL format.\n\nPACKAGE CONTENTS\n folderdb\n jsonldf\n jsonlfile\n vercontrol\n visual\n\nCLASSES\n builtins.object\n jsonldb.folderdb.FolderDB\n\n class FolderDB(builtins.object)\n | FolderDB(folder_path: str)\n |\n | A simple file-based database that stores data in JSONL format.\n | Each table is stored in a separate JSONL file.\n |\n | Methods defined here:\n |\n | __init__(self, folder_path: str)\n | Initialize the database.\n |\n | Args:\n | folder_path: Path to the folder where the database files will be stored\n |\n | Raises:\n | FileNotFoundError: If the folder doesn't exist\n |\n | __repr__(self) -> str\n | Return repr(self).\n |\n | __str__(self) -> str\n | Return a string representation of the database.\n |\n | build_dbmeta(self) -> None\n | Build or update the db.meta file with information about all JSONL files.\n |\n | The db.meta file contains metadata for each JSONL file including:\n | - name: filename without .jsonl extension\n | - min_index: smallest index from the index file\n | - max_index: biggest index from the index file\n | - size: size of the file in bytes\n | - lint_time: ISO format timestamp of last lint\n | - linted: boolean indicating if file has been linted\n |\n | commit(self, msg: str = '') -> None\n | Commit changes in the database folder.\n |\n | If the folder is not already a git repository, it will be initialized first.\n |\n | Args:\n | msg: Optional commit message. If empty, an auto-generated message will be used.\n |\n | Raises:\n | git.exc.GitCommandError: If git commands fail\n |\n | delete_file(self, name: str, keys: List[str]) -> None\n | Delete specific keys from a JSONL file.\n |\n | Args:\n | name: Name of the JSONL file\n | keys: List of keys to delete\n |\n | delete_file_range(self, name: str, lower_key: Any, upper_key: Any) -> None\n | Delete all keys within a range from a JSONL file.\n |\n | Args:\n | name: Name of the JSONL file\n | lower_key: Lower bound of the key range\n | upper_key: Upper bound of the key range\n |\n | delete_range(self, names: List[str], lower_key: Any, upper_key: Any) -> None\n | Delete all keys within a range from multiple JSONL files.\n |\n | Args:\n | names: List of JSONL file names\n | lower_key: Lower bound of the key range\n | upper_key: Upper bound of the key range\n |\n | get_dbmeta(self) -> Dict[str, Any]\n | Get the database metadata as a dictionary.\n |\n | Returns:\n | Dictionary containing metadata for all JSONL files in the database\n |\n | get_df(self, names: List[str], lower_key: Optional[Any] = None, upper_key: Optional[Any] = None) -> Dict[str, pandas.core.frame.DataFrame]\n | Get DataFrames from multiple JSONL files within a key range.\n |\n | Args:\n | names: List of JSONL file names\n | lower_key: Lower bound of the key range\n | upper_key: Upper bound of the key range\n |\n | Returns:\n | Dictionary mapping file names to selected DataFrames\n |\n | get_dict(self, names: List[str], lower_key: Optional[Any] = None, upper_key: Optional[Any] = None) -> Dict[str, Dict[str, Dict[str, Any]]]\n | Get dictionaries from multiple JSONL files within a key range.\n |\n | Args:\n | names: List of JSONL file names\n | lower_key: Lower bound of the key range\n | upper_key: Upper bound of the key range\n |\n | Returns:\n | Dictionary mapping file names to selected data dictionaries\n |\n | lint_db(self) -> None\n | Lint all JSONL files in the database.\n |\n | revert(self, version_hash: str) -> None\n | Revert the database to a previous version.\n |\n | Args:\n | version_hash: Hash of the commit to revert to\n |\n | Raises:\n | git.exc.GitCommandError: If git commands fail\n | ValueError: If the specified commit is not found\n |\n | update_dbmeta(self, name: str, linted: bool = False) -> None\n | Update the metadata for a specific JSONL file in db.meta.\n |\n | Args:\n | name: Name of the JSONL file (with or without .jsonl extension)\n | linted: Value to set for the linted field\n |\n | upsert_df(self, name: str, df: pandas.core.frame.DataFrame) -> None\n | Update or insert a DataFrame into a JSONL file.\n |\n | Args:\n | name: Name of the JSONL file\n | df: DataFrame to save/update\n |\n | upsert_dfs(self, dict_dfs: Dict[Any, pandas.core.frame.DataFrame]) -> None\n | Update or insert multiple DataFrames into JSONL files.\n |\n | Args:\n | dict_dfs: Dictionary mapping file names to DataFrames\n |\n | upsert_dict(self, name: str, data_dict: Dict[Any, Dict[str, Any]]) -> None\n | Update or insert a dictionary into a JSONL file.\n |\n | Args:\n | name: Name of the JSONL file\n | data_dict: Dictionary to save/update\n |\n | upsert_dicts(self, dict_dicts: Dict[Any, Dict[str, Dict[str, Any]]]) -> None\n | Update or insert multiple dictionaries into JSONL files.\n |\n | Args:\n | dict_dicts: Dictionary mapping file names to data dictionaries\n |\n | version(self) -> Dict[str, str]\n | List all versions of the database.\n |\n | Returns:\n | Dictionary with commit hashes as keys and commit messages as values\n |\n | Raises:\n | git.exc.GitCommandError: If git commands fail\n |\n | visualize(self) -> bokeh.plotting._figure.figure\n | Create a visualization of the database's data distribution.\n |\n | Returns:\n | Bokeh figure object showing the scatter plot of data distribution\n |\n | ----------------------------------------------------------------------\n | Data descriptors defined here:\n |\n | __dict__\n | dictionary for instance variables\n |\n | __weakref__\n | list of weak references to the object\n\nDATA\n __all__ = ['FolderDB', 'visual', 'vercontrol', 'jsonlfile', 'jsonldf']\n\nVERSION\n 0.1.0\n\n```" }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process for analyzing the `jsonldb` documentation and advising on the triple store implementation:\n\n1. **Understand the Goal:** The user wants to refactor their knowledge graph builder to use explicit (S, P, O) triples and include provenance (source information) for each triple. They are currently using `jsonldb` and want to know how to implement this structure with its constraints. They also suggest wrapping `jsonldb` in an interface module.\n\n2. **Analyze `jsonldb` Documentation:** Read through the provided `FolderDB` class documentation carefully, focusing on methods related to data storage and retrieval.\n * **Core Concept:** `jsonldb` uses JSONL files, where each file acts as a \"table\".\n * **Key Structure:** `upsert_dict(self, name: str, data_dict: Dict[Any, Dict[str, Any]])` and `get_dict(...)` strongly imply that each JSONL file stores a dictionary where the *keys* of the top-level dictionary are the primary index. The values are the actual data records (which are themselves dictionaries). This confirms the \"one implicit indexing key per table\" observation.\n * **Retrieval:** `get_dict` allows retrieving data based on a *range* of these primary keys (`lower_key`, `upper_key`). This is efficient if you query by the primary key but not necessarily by values *within* the data records.\n * **Data Format:** Data is stored as dictionaries within the JSONL file.\n * **Other Features:** Git integration (`commit`, `revert`, `version`), metadata (`db.meta`), linting, Pandas DataFrame integration, deletion (`delete_file`, `delete_file_range`), visualization. These are useful but secondary to the core storage mechanism for this task.\n\n3. **Evaluate `jsonldb` for Triple Store:**\n * **Can we store triples?** Yes, easily. Each line in a `triples.jsonl` file can be a JSON object representing a triple: `{\"triple_id\": \"...\", \"subject\": \"...\", \"predicate\": \"...\", \"object\": \"...\", \"source_log_id\": \"...\"}`.\n * **What's the primary key?** Using the proposed structure `upsert_dict(config.TRIPLES_TABLE_NAME, {triple_id: triple_record})`, the `triple_id` becomes the primary key for the `triples.jsonl` table.\n * **Querying by S, P, or O:** This is the *critical* constraint. `get_dict` only filters by the primary key (`triple_id`). To find all triples for a given `subject`, `predicate`, or `object`, `jsonldb` (as documented) would need to:\n * Load *all* records from `triples.jsonl` (potentially using `get_dict` without key ranges).\n * Iterate through them in Python and filter based on the desired `subject`, `predicate`, or `object` value.\n * **Performance Implication:** This full scan and filter approach will be *slow* for large knowledge graphs. There's no built-in secondary indexing.\n\n4. **Address the \"One Key Per Table\" Constraint:**\n * The user is right. If efficient lookup by Subject, Predicate, *and* Object is needed, `jsonldb`'s structure forces you to consider multiple tables (files) acting as indexes.\n * **Option 1: Single `triples.jsonl` (Simple but Slow Queries):** Store all triples keyed by `triple_id`. Queries require loading and filtering in Python. Simplest implementation.\n * **Option 2: Index Tables (Complex but Faster Queries):**\n * `triples.jsonl`: Keyed by `triple_id`. Contains the full triple data. (Primary store).\n * `spo_index.jsonl`: Keyed by `subject`. Value could be a list of `triple_id`s associated with that subject, or maybe a compound key like `subject::predicate::object_hash` -> `triple_id`.\n * `pos_index.jsonl`: Keyed by `predicate`. Value is list of `triple_id`s.\n * `osp_index.jsonl`: Keyed by `object`. Value is list of `triple_id`s.\n * **Complexity of Option 2:** Maintaining these index tables requires extra work during `upsert` and `delete`. Every time a triple is added or removed, *all relevant index tables* must be updated transactionally (or at least consistently). `jsonldb` doesn't offer transactional guarantees across files. This significantly increases implementation complexity and potential for inconsistency.\n\n5. **Evaluate the \"Interface Module\" Idea:**\n * **Excellent Idea:** This aligns perfectly with managing the complexity and potential future changes.\n * **Benefits:**\n * **Abstraction:** Hides the specific `jsonldb` implementation details (like whether you're using one file or multiple index files). The rest of the code interacts with a clean `KnowledgeStore` interface (e.g., `store.add_triple(...)`, `store.get_triples(subject=...)`).\n * **Flexibility:** Makes it *much* easier to swap out `jsonldb` for a different backend (e.g., SQLite, DuckDB, a proper graph database) later without rewriting the core builder logic.\n * **Encapsulation:** Keeps the logic for handling `jsonldb`'s limitations (like manual indexing if chosen) contained within the interface module.\n\n6. **Synthesize Advice for the User:**\n * Acknowledge the validity of their analysis of `jsonldb`.\n * Strongly endorse the triple store + provenance goal.\n * Strongly endorse creating a database interface module (`KnowledgeStore`).\n * Present the storage options:\n * Single `triples.jsonl` file (keyed by `triple_id`): Explain it's the simplest to implement *now* with `jsonldb` but query performance will degrade with size. Recommend this as a starting point *if* managed via the interface module.\n * Manual Index Tables: Explain the concept and the significant complexity/consistency challenges with `jsonldb`. Advise against it *unless* performance becomes an *unacceptable* blocker and switching databases isn't feasible yet.\n * Refine the proposed `KnowledgeStore` interface: Define key methods it should have (`add_triple`, `get_triples` with optional S/P/O filtering, potentially `concept_exists` which would use `get_triples`).\n * Guide implementation within the interface: Show how `add_triple` would work using `upsert_dict` on `triples.jsonl`. Show how `get_triples` would work (load all using `get_dict`, then filter in Python).\n * Connect back to the original code: Explain how `query_and_store_concept` would now call `store.add_triple` repeatedly. Explain how `build_knowledge_graph` would call `store.get_triples(subject=current_concept_id)` to find related concepts instead of reading a single dictionary entry.\n * Mention alternatives briefly: Remind them that if performance *does* become critical, databases like SQLite (using indexes), DuckDB, or graph databases are designed for efficient querying of relational/graph data. The interface module makes this switch feasible.\n\n7. **Review and Refine:** Read through the generated advice. Is it clear? Does it directly address the user's points? Does it provide actionable steps? Is the trade-off between simplicity and performance clear? Ensure the tone is collaborative and acknowledges the user's good ideas. Make sure to clearly separate the *conceptual* goal (triples, provenance) from the *implementation details* with `jsonldb`." }, { "text": "Okay, thanks for sharing the `jsonldb` documentation. Your analysis is spot on:\n\n1. **File-based Tables:** Each `.jsonl` file acts as a table.\n2. **Single Primary Key:** `upsert_dict` and `get_dict` operate on the top-level keys of the dictionary stored in the file. This key serves as the *only* indexed field for efficient range lookups (`lower_key`, `upper_key`).\n3. **Value Structure:** The values associated with these keys are themselves dictionaries (the actual data records).\n4. **Query Limitation:** You can efficiently retrieve records *by the primary key* (or a range of primary keys). Retrieving records based on values *inside* the record dictionaries (like filtering by `subject` or `object` if `triple_id` is the key) would require fetching all relevant records (potentially the whole file if no key range is specified) and filtering them in your Python code.\n\n**Implications for Triple Store & Provenance:**\n\n* **Storing Triples:** You *can* absolutely store triples. As proposed before, a `triples.jsonl` file where each line is a JSON object like `{\"triple_id\": \"...\", \"subject\": \"...\", ...}` is perfectly valid.\n* **Choosing the Key:** Using `triple_id` (a UUID or hash) as the primary key for `triples.jsonl` is the most natural fit for `jsonldb`. This makes `upsert_dict({triple_id: triple_data})` straightforward.\n* **The Querying Challenge:** If `triple_id` is the key, querying \"find all triples where `subject` == 'X'\" or \"find all triples where `object` == 'Y'\" will necessitate loading a large portion (potentially all) of `triples.jsonl` and filtering manually. This won't scale well for performance as the graph grows.\n* **Multiple Tables for Indexing:** As you suspected, to get efficient lookups by Subject, Predicate, *and* Object using `jsonldb`'s key mechanism, you'd likely need multiple files acting as indexes. For example:\n * `triples.jsonl`: Keyed by `triple_id` -> `{full triple data}` (Master data)\n * `subject_index.jsonl`: Keyed by `subject` -> `{list_of_triple_ids}`\n * `object_index.jsonl`: Keyed by `object` -> `{list_of_triple_ids}`\n * `predicate_index.jsonl`: Keyed by `predicate` -> `{list_of_triple_ids}`\n This adds significant complexity: you'd need to update *all relevant indexes* whenever you add or delete a triple, and `jsonldb` doesn't provide transactions across files to ensure consistency if something fails mid-update.\n\n**Recommendation: Interface Module + Simple Start**\n\nYour idea to wrap `jsonldb` in an interface module is **excellent** and highly recommended, especially given these limitations.\n\n1. **Define a `KnowledgeStore` Interface:** Create a Python module (e.g., `knowledge_store.py`) with an abstract base class or a simple class defining the desired operations:\n\n ```python\n # knowledge_store/interface.py\n from abc import ABC, abstractmethod\n from typing import List, Dict, Any, Optional, Set\n\n class KnowledgeStore(ABC):\n\n @abstractmethod\n def add_triple(self, subject: str, predicate: str, object_val: Any, source_log_id: str) -> str:\n \"\"\"\n Adds a triple to the store.\n Returns a unique identifier for the triple.\n object_val can be a string (concept ID) or maybe other types later.\n \"\"\"\n pass\n\n @abstractmethod\n def get_triples(self,\n subject: Optional[str] = None,\n predicate: Optional[str] = None,\n object_val: Optional[Any] = None) -> List[Dict[str, Any]]:\n \"\"\"\n Retrieves triples matching the specified components.\n If a component is None, it acts as a wildcard.\n Returns a list of triple dictionaries.\n \"\"\"\n pass\n\n @abstractmethod\n def concept_exists(self, concept_id: str) -> bool:\n \"\"\"\n Checks if a concept exists as a subject in any triple.\n (Or define existence differently if needed).\n \"\"\"\n pass\n\n # Optional: Add methods for deleting, updating, getting unique subjects, etc.\n\n # Optional: Methods for managing logs directly? Or keep separate?\n @abstractmethod\n def log_event(self, log_key: str, log_data: Dict[str, Any]) -> None:\n \"\"\"Logs an event (like an LLM call).\"\"\"\n pass\n\n @abstractmethod\n def get_log_event(self, log_key: str) -> Optional[Dict[str, Any]]:\n \"\"\"Retrieves a specific log event.\"\"\"\n pass\n\n @abstractmethod\n def commit(self, message: str) -> None:\n \"\"\"Commits changes if the backend supports it.\"\"\"\n pass\n\n # ... other necessary methods ...\n\n ```\n\n2. **Implement `JsonldbStore`:** Create a concrete class implementing this interface using `jsonldb`.\n\n ```python\n # knowledge_store/jsonldb_store.py\n import uuid\n import os\n from typing import List, Dict, Any, Optional, Set\n from .interface import KnowledgeStore\n from jsonldb import FolderDB # Assuming jsonldb is installed\n\n # Define constants for table names (file names)\n TRIPLES_TABLE = \"triples\"\n LOG_TABLE = \"logs\" # Use the existing log table name\n\n class JsonldbStore(KnowledgeStore):\n def __init__(self, db_path: str):\n if not os.path.exists(db_path):\n os.makedirs(db_path)\n # Consider error handling if db_path exists but isn't a directory\n self._db = FolderDB(db_path)\n # Ensure tables (files) exist - jsonldb might create on first write\n self._ensure_table_exists(TRIPLES_TABLE)\n self._ensure_table_exists(LOG_TABLE)\n print(f\"JsonldbStore initialized at: {db_path}\") # Debug print\n\n def _ensure_table_exists(self, table_name: str):\n file_path = os.path.join(self._db.folder_path, f\"{table_name}.jsonl\")\n if not os.path.exists(file_path):\n # Create an empty file so FolderDB recognizes it immediately\n # FolderDB might handle this automatically on first write,\n # but being explicit can avoid potential issues.\n try:\n with open(file_path, 'w') as f:\n pass # Create empty file\n print(f\"Created empty table file: {file_path}\") # Debug print\n # Optionally update db.meta if needed/useful here\n # self._db.build_dbmeta() or self._db.update_dbmeta(table_name)\n except IOError as e:\n print(f\"Warning: Could not create empty file for table {table_name}: {e}\")\n\n\n def add_triple(self, subject: str, predicate: str, object_val: Any, source_log_id: str) -> str:\n triple_id = str(uuid.uuid4()) # Generate unique ID\n triple_data = {\n \"triple_id\": triple_id, # Store within data too for easier retrieval if needed\n \"subject\": subject,\n \"predicate\": predicate,\n \"object\": object_val,\n \"source_log_id\": source_log_id,\n \"creation_timestamp\": datetime.datetime.now(datetime.timezone.utc).isoformat() # Add creation time\n }\n try:\n self._db.upsert_dict(TRIPLES_TABLE, {triple_id: triple_data})\n # print(f\"Added triple: {triple_id}\") # Debug print\n return triple_id\n except Exception as e:\n print(f\"Error adding triple {triple_id}: {e}\") # Use proper logging\n raise # Re-raise the exception\n\n def get_triples(self,\n subject: Optional[str] = None,\n predicate: Optional[str] = None,\n object_val: Optional[Any] = None) -> List[Dict[str, Any]]:\n try:\n # Fetch ALL triples - jsonldb requires filtering in code\n all_triples_dict = self._db.get_dict([TRIPLES_TABLE])\n\n if not all_triples_dict or TRIPLES_TABLE not in all_triples_dict:\n return [] # No triples found\n\n all_triples_data = all_triples_dict[TRIPLES_TABLE].values()\n\n # Filter in Python\n results = []\n for triple in all_triples_data:\n match = True\n if subject is not None and triple.get(\"subject\") != subject:\n match = False\n if predicate is not None and triple.get(\"predicate\") != predicate:\n match = False\n if object_val is not None and triple.get(\"object\") != object_val:\n match = False\n\n if match:\n results.append(triple)\n # print(f\"Get triples ({subject}, {predicate}, {object_val}) -> Found {len(results)}\") # Debug print\n return results\n except Exception as e:\n print(f\"Error getting triples: {e}\") # Use proper logging\n return [] # Return empty on error\n\n\n def concept_exists(self, concept_id: str) -> bool:\n # Check if the concept appears as a subject in any triple.\n # This will be slow as it uses get_triples internally.\n matching_triples = self.get_triples(subject=concept_id)\n return len(matching_triples) > 0\n # Alternative (potentially faster if many triples share a subject):\n # Fetch all triples once, get unique subjects, and check if concept_id is in the set.\n # Still requires loading all data initially.\n\n def log_event(self, log_key: str, log_data: Dict[str, Any]) -> None:\n try:\n self._db.upsert_dict(LOG_TABLE, {log_key: log_data})\n except Exception as e:\n print(f\"Error logging event {log_key}: {e}\") # Use proper logging\n raise\n\n def get_log_event(self, log_key: str) -> Optional[Dict[str, Any]]:\n try:\n log_data = self._db.get_dict([LOG_TABLE], lower_key=log_key, upper_key=log_key)\n if log_data and LOG_TABLE in log_data and log_key in log_data[LOG_TABLE]:\n return log_data[LOG_TABLE][log_key]\n return None\n except Exception as e:\n print(f\"Error getting log event {log_key}: {e}\") # Use proper logging\n return None\n\n def commit(self, message: str) -> None:\n try:\n self._db.commit(message)\n print(f\"Committed changes: {message}\")\n except AttributeError:\n print(\"Warning: DB object has no commit method (git not configured/available?)\")\n except Exception as e: # Catch specific git errors if needed\n print(f\"Error committing changes: {e}\")\n\n # Need to import datetime etc.\n import datetime\n ```\n\n3. **Refactor Your Builder Code:**\n * Modify `initialize_database` to return an instance of `JsonldbStore` (or whatever implementation you choose).\n * Change `query_and_store_concept`:\n * It still queries the LLM.\n * It still logs the event using `store.log_event(log_key, log_entry)`.\n * Instead of `db.upsert_dict(config.CONCEPTS_TABLE_NAME, ...)` for the *concept data*, it iterates through the parsed LLM dictionary (`predicate`, `object` pairs) and calls `store.add_triple(subject=concept_id, predicate=pred, object_val=obj, source_log_id=log_key)` for each relationship found. Remember to handle list objects correctly (multiple `add_triple` calls).\n * Change `build_knowledge_graph`:\n * Use `store.concept_exists(concept_id)` to check if a concept has been processed (as a subject).\n * When retrieving data for an *existing* concept to find related concepts, call `store.get_triples(subject=current_concept_id)`. Then, iterate through the results, extracting the `predicate` and `object` values to add to the queue.\n * Use the `store` object (which is an instance of your `KnowledgeStore` interface) throughout.\n\n**Benefits:**\n\n* **Clean Separation:** Your core graph building logic doesn't need to know about `.jsonl` files or `jsonldb` specifics.\n* **Testability:** You could create a mock `KnowledgeStore` for testing without needing files.\n* **Future-Proofing:** If `jsonldb` performance becomes a bottleneck, you can write a new implementation (e.g., `SqliteStore`, `DuckDbStore`, `Neo4jStore`) that conforms to the *same interface* and swap it in with minimal changes to the builder logic. You'd only change the initialization step.\n\nThis approach lets you start with the simple (single `triples.jsonl` file) implementation using `jsonldb` while acknowledging its limitations, but prepares your project structure for easy improvements later." }, ] }, { "role": "user", "parts": [ { "text": "let's keep it simpler by leaving out the definition of the abstract base, this can instead be clarified with comments or docs inside one file. but we still want a dedicated interface module that could be replaced with a different backend. i think the last filename was `knowledge_graph_builder/db_operations.py`. The table names should come from config.py, feel free to reference new constants. Did I demonstrate its use to you?\n\ni'm a little worried about duplicate lookup keys and haven't read all your code yet -- can you clarify the plan for handling those? i'm thinking one approach could be to suffix the key value with the id to make unique entries." }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process for generating the `db_operations.py` module based on the user's requirements:\n\n1. **Understand the Goal:** The user wants to refactor the database interaction logic into a dedicated module (`knowledge_graph_builder/db_operations.py`). This module should use `jsonldb` as the initial backend but be structured so the backend could be swapped later (an \"interface module\" without a formal Python `ABC`). It needs to handle storing triples with provenance and address the query limitations of `jsonldb`, particularly regarding lookups by fields other than the primary key. The user is also specifically asking about handling duplicate *lookup* keys (e.g., multiple triples having the same subject) when the design goal is efficient retrieval based on those fields.\n\n2. **Recall Previous Discussion & `jsonldb` Limitations:**\n * `jsonldb` uses one primary key per file (`.jsonl` table) for efficient lookups.\n * Storing triples in a single `triples.jsonl` keyed by `triple_id` makes lookups by subject, predicate, or object inefficient (requires loading and filtering).\n * The suggested solution for efficient lookups was using multiple files as *indexes* (e.g., `subject_index.jsonl` keyed by subject, storing lists of `triple_id`s).\n * The user expressed concern about \"duplicate lookup keys\" and suggested suffixing. This likely refers to the *keys* in these *index files*. For example, if we have `subject_index.jsonl` keyed by `subject`, what happens when multiple triples share the *same* subject?\n\n3. **Address the Duplicate Lookup Key Concern (The Core of the User's Question):**\n * **Problem:** In `subject_index.jsonl`, the *key* would be the `subject` concept ID. If multiple triples have the same subject, we can't just store `{subject_id: triple_id_1}`, then overwrite it with `{subject_id: triple_id_2}`.\n * **Solution:** The *value* associated with the key (the subject ID) in the index file needs to hold *all* the relevant `triple_id`s. A list is the natural way to do this. So, the structure in `subject_index.jsonl` should be:\n ```json\n {\n \"subject_concept_id_A\": [\"triple_id_1\", \"triple_id_3\", \"triple_id_5\"],\n \"subject_concept_id_B\": [\"triple_id_2\", \"triple_id_4\"]\n // ... etc\n }\n ```\n * **User's Suffix Idea:** Suffixing the key (e.g., `\"subject_concept_id_A#triple_id_1\"`) *could* work, but it breaks the efficient range lookup/direct lookup capability of `jsonldb` for the *subject itself*. You couldn't easily ask `jsonldb` for \"all keys starting with `subject_concept_id_A`\". The list-as-value approach is more standard for this kind of manual indexing.\n * **Clarification for User:** Explain that the *value* in the index file will be a list of triple IDs, directly addressing how multiple triples associated with the same subject/predicate/object are handled.\n\n4. **Design the `db_operations.py` Module:**\n * **Filename:** `knowledge_graph_builder/db_operations.py`.\n * **No ABC:** As requested, don't define a formal `ABC`. Use comments to explain the intended interface nature.\n * **Dependencies:** Import `FolderDB`, `os`, `uuid`, `datetime`, `typing`, and the `config` module.\n * **Configuration:** Define necessary constants in `config.py` (e.g., `TRIPLES_TABLE`, `SUBJECT_INDEX_TABLE`, `OBJECT_INDEX_TABLE`, `PREDICATE_INDEX_TABLE`, `LOG_TABLE`). Reference these in `db_operations.py`.\n * **Initialization (`initialize_database`):**\n * Takes `db_path`.\n * Creates the `FolderDB` instance.\n * Needs to ensure all required table files (`triples`, indexes, `logs`) exist (create empty files if necessary). Return the `FolderDB` instance.\n * **Core Function (`add_triple`):**\n * Accepts `db`, `subject`, `predicate`, `object_val`, `source_log_id`.\n * Generates a unique `triple_id`.\n * Creates the full `triple_data` dictionary.\n * **Crucially:**\n * Upserts the main triple into `TRIPLES_TABLE` using `triple_id` as the key: `db.upsert_dict(config.TRIPLES_TABLE, {triple_id: triple_data})`.\n * Updates the `SUBJECT_INDEX_TABLE`:\n * Get the current list of triple IDs for this subject (if any).\n * Append the new `triple_id`.\n * Upsert the updated list back into the index: `db.upsert_dict(config.SUBJECT_INDEX_TABLE, {subject: updated_list})`.\n * Updates the `OBJECT_INDEX_TABLE` (similarly, using `object_val` as the key).\n * Updates the `PREDICATE_INDEX_TABLE` (similarly, using `predicate` as the key).\n * Needs helper functions to manage index updates (`_update_index`).\n * Returns the `triple_id`.\n * **Query Function (`get_triples`):**\n * Accepts `db`, `subject`, `predicate`, `object_val`.\n * Determines the best index to use (e.g., if `subject` is provided, use `SUBJECT_INDEX_TABLE`).\n * If an index can be used:\n * Retrieve the list of candidate `triple_id`s from the appropriate index file using the provided `subject`, `predicate`, or `object_val` as the key.\n * If multiple components are provided (e.g., subject AND predicate), retrieve lists from *both* indexes and find the *intersection* of the `triple_id` lists.\n * Fetch the full triple data for only the candidate `triple_id`s from `TRIPLES_TABLE`. Use `db.get_dict` with multiple keys if possible, or individual lookups.\n * If *no* components are provided (fetch all), or if the index lookup fails, fall back to loading all triples from `TRIPLES_TABLE` (potentially inefficient).\n * If components were provided but *no* index matched (e.g., searching by subject but that subject isn't in the index), return an empty list immediately.\n * Perform final filtering *in Python* on the retrieved full triples if multiple components were specified (e.g., if we only indexed by subject but also need to filter by predicate).\n * Return the list of matching full triple dictionaries.\n * **Existence Check (`concept_exists`):**\n * This can now efficiently use the `SUBJECT_INDEX_TABLE`.\n * Check if the `concept_id` exists as a key in the `SUBJECT_INDEX_TABLE` using `db.get_dict([config.SUBJECT_INDEX_TABLE], lower_key=concept_id, upper_key=concept_id)`.\n * **Logging Functions (`log_event`, `get_log_event`):**\n * These remain relatively simple, operating directly on the `LOG_TABLE` using the `log_key` as the primary key.\n * **Commit Function (`commit`):**\n * Wraps `db.commit`. Include error handling (e.g., `AttributeError` if git isn't used).\n * **Helper Function (`_update_index`):**\n * Takes `db`, `index_table_name`, `key_value` (e.g., the subject), `triple_id`.\n * Retrieves the current list for `key_value` from `index_table_name`.\n * Handles the case where the key doesn't exist yet (start a new list).\n * Appends the `triple_id` (avoid duplicates if desired, though usually not necessary for this type of index).\n * Upserts the updated list back to the index table.\n * **Refinement:** Ensure proper imports (`datetime`, `uuid`, typing hints). Add logging (or `print` for now as in the previous example) for debugging. Add docstrings explaining purpose, parameters, and returns. Add comments about the interface nature.\n\n5. **Review and Refine Code:** Read through the generated code. Check for logic errors, correct use of `jsonldb` methods (`get_dict`, `upsert_dict`), proper handling of list values in indexes, and clear variable names. Ensure the explanation about duplicate keys (using lists as values) is included in comments or docstrings. Make sure the config constants are used correctly. Add necessary imports. Ensure the `initialize_database` creates the empty files. Double-check the logic in `get_triples` for index usage and intersection." }, { "text": "Okay, understood. We'll create the `knowledge_graph_builder/db_operations.py` module without a formal `ABC`, using comments to indicate its role as a swappable interface. We'll implement the triple store logic using `jsonldb`, including the necessary indexes for efficient lookups by subject, predicate, and object. Table names will come from `config.py`.\n\nYou're right to be concerned about duplicate *lookup keys* in the context of `jsonldb`'s single-key limitation. The plan isn't to suffix the keys themselves (like `subject#id`), as that would hinder direct lookups of the subject. Instead:\n\n**Plan for Handling Duplicate Lookup Keys (e.g., Multiple Triples with the Same Subject):**\n\n1. **Main Table (`triples.jsonl`):** Keyed by a unique `triple_id` (e.g., UUID). Each entry stores the full triple data (`subject`, `predicate`, `object`, `source_log_id`, etc.).\n2. **Index Tables (`subject_index.jsonl`, `predicate_index.jsonl`, `object_index.jsonl`):**\n * The *key* in these files will be the `subject`, `predicate`, or `object` value itself.\n * The *value* associated with each key will be a **JSON list** containing all the `triple_id`s that share that specific subject, predicate, or object.\n * **Example (`subject_index.jsonl`):**\n ```json\n {\"aluminum oxide:chem compound\": [\"uuid-1\", \"uuid-5\", \"uuid-12\"]}\n {\"iron:element\": [\"uuid-2\", \"uuid-8\"]}\n ```\n * When adding a new triple, we retrieve the *existing list* for the relevant key in each index, append the new `triple_id` to the list, and then `upsert` the *entire updated list* back into the index file under that key.\n\nThis way, we use `jsonldb`'s primary key lookup efficiently on the *indexes* to find all relevant `triple_id`s, and then retrieve the full triple data from the main `triples.jsonl` table using those IDs.\n\nLet's update `config.py` first, then create `db_operations.py`.\n\n--- START OF (Assumed) FILE knowledge_graph_builder/config.py ---\n```python\n# knowledge_graph_builder/config.py\nimport os\nfrom dotenv import load_dotenv\nimport google.generativeai as genai\n\n# Load environment variables from .env file if it exists\nload_dotenv()\n\n# --- API Configuration ---\nGEMINI_API_KEY = os.getenv(\"GEMINI_API_KEY\", \"YOUR_GOOGLE_API_KEY\") # Replace default if needed\nMODEL_NAME = \"gemini-1.5-flash\" # Or whichever model you prefer\n\n# --- Generation Configuration ---\n# Example: Adjust temperature, top_p, top_k as needed\n# See https://ai.google.dev/api/python/google/generativeai/GenerationConfig\nGENERATION_CONFIG = genai.types.GenerationConfig(\n # candidate_count=1, # Default is 1\n # stop_sequences=[],\n # max_output_tokens=8192, # Adjust based on model limits if needed\n temperature=0.7, # A balance between creativity and predictability\n top_p=0.9,\n top_k=40\n)\n\n# --- Safety Settings ---\n# Adjust based on your tolerance for potentially harmful content categories\n# See https://ai.google.dev/docs/safety_setting_gemini\nSAFETY_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\n# --- Database Configuration ---\nDB_FOLDER = \"knowledge_db\" # Folder name for the database\n\n# == NEW: Table Names (correspond to .jsonl filenames) ==\n# Main table storing full triple data, keyed by unique triple_id\nTRIPLES_TABLE = \"triples\"\n# Index table: key = subject, value = list of triple_ids\nSUBJECT_INDEX_TABLE = \"idx_subject\"\n# Index table: key = predicate, value = list of triple_ids\nPREDICATE_INDEX_TABLE = \"idx_predicate\"\n# Index table: key = object, value = list of triple_ids\nOBJECT_INDEX_TABLE = \"idx_object\"\n# Table for logging LLM interactions, keyed by timestamp/log_id\nLOG_TABLE_NAME = \"logs\" # Keep existing name for logs\n# == END NEW ==\n\n# --- Builder Configuration ---\n# Set to None to disable depth limit\nMAX_RECURSION_DEPTH = 5 # Limit how deep the recursive build goes\n# Delay between API calls (in seconds) to manage rate limits, especially if 429 handling is imperfect\nAPI_CALL_DELAY_SECONDS = 1.5 # Increased default delay\n\n# --- Constants (Optional) ---\n# Define common predicates or namespaces if needed\n# PREDICATE_IS_A = \"is a\"\n# PREDICATE_HAS_PROPERTY = \"has property\"\n```\n--- END OF FILE knowledge_graph_builder/config.py ---\n\n--- START OF FILE knowledge_graph_builder/db_operations.py ---\n```python\n# knowledge_graph_builder/db_operations.py\nimport os\nimport uuid\nimport datetime\nimport logging\nfrom typing import List, Dict, Any, Optional, Set, Tuple\n\n# Attempt to import FolderDB, handle potential ImportError\ntry:\n from jsonldb import FolderDB\nexcept ImportError:\n print(\"CRITICAL: jsonldb library not found. Database operations will fail.\")\n print(\"Please install it: pip install jsonldb\")\n FolderDB = None # Placeholder to prevent NameErrors later\n\nfrom . import config # Import configuration from the package\n\n# Use the main logging configuration\nlog = logging.getLogger(__name__)\n\n# --- Database Initialization ---\n\ndef initialize_database(db_path: str) -> Optional[FolderDB]:\n \"\"\"\n Initializes and returns the FolderDB instance.\n Ensures the base directory and necessary table files exist.\n\n This module acts as an interface to the database storage.\n Currently implemented using jsonldb, but could be replaced\n by another backend conforming to the functions defined here.\n\n Args:\n db_path: The path to the database folder.\n\n Returns:\n An initialized FolderDB instance or None if initialization fails.\n \"\"\"\n if not FolderDB:\n log.critical(\"FolderDB class not loaded due to import error. Cannot initialize database.\")\n return None\n\n log.info(f\"Attempting to initialize database at: {db_path}\")\n try:\n if not os.path.exists(db_path):\n log.warning(f\"Database folder '{db_path}' not found. Creating it.\")\n os.makedirs(db_path)\n elif not os.path.isdir(db_path):\n log.error(f\"Database path '{db_path}' exists but is not a directory.\")\n return None\n\n db = FolderDB(db_path)\n log.info(f\"Initialized FolderDB instance: {db}\")\n\n # Ensure all required table files exist (create empty if needed)\n # This helps FolderDB recognize them, especially before the first write.\n required_tables = [\n config.TRIPLES_TABLE,\n config.SUBJECT_INDEX_TABLE,\n config.PREDICATE_INDEX_TABLE,\n config.OBJECT_INDEX_TABLE,\n config.LOG_TABLE_NAME\n ]\n for table_name in required_tables:\n _ensure_table_file_exists(db, table_name)\n\n return db\n\n except FileNotFoundError:\n log.error(f\"Database folder '{db_path}' could not be accessed.\", exc_info=True)\n return None\n except Exception as e:\n log.error(f\"Failed to initialize FolderDB at '{db_path}': {e}\", exc_info=True)\n return None\n\ndef _ensure_table_file_exists(db: FolderDB, table_name: str):\n \"\"\"Helper to create an empty .jsonl file if it doesn't exist.\"\"\"\n file_path = os.path.join(db.folder_path, f\"{table_name}.jsonl\")\n if not os.path.exists(file_path):\n try:\n with open(file_path, 'w') as f:\n pass # Create empty file\n log.info(f\"Created empty table file: {file_path}\")\n # Optionally, update db.meta if jsonldb requires it immediately\n # db.build_dbmeta() # Might be slow if called often\n except IOError as e:\n log.warning(f\"Could not create empty file for table {table_name}: {e}\", exc_info=True)\n\n\n# --- Triple Management ---\n\ndef add_triple(db: FolderDB, subject: str, predicate: str, object_val: Any, source_log_id: str) -> Optional[str]:\n \"\"\"\n Adds a new triple (Subject-Predicate-Object) to the database,\n including provenance (source_log_id) and updates relevant indexes.\n\n Args:\n db: The initialized FolderDB instance.\n subject: The subject concept identifier.\n predicate: The predicate identifier (relationship type).\n object_val: The object identifier or value.\n source_log_id: The identifier linking to the source log entry.\n\n Returns:\n The unique triple_id assigned to the new triple, or None on failure.\n \"\"\"\n if not db:\n log.error(\"Database instance is not valid. Cannot add triple.\")\n return None\n if not all([subject, predicate, object_val, source_log_id]):\n log.warning(f\"Attempted to add triple with missing components: S={subject}, P={predicate}, O={object_val}, Src={source_log_id}\")\n return None\n\n triple_id = str(uuid.uuid4()) # Generate unique ID\n triple_data = {\n \"triple_id\": triple_id, # Store ID within data for convenience\n \"subject\": subject,\n \"predicate\": predicate,\n \"object\": object_val,\n \"source_log_id\": source_log_id,\n \"creation_timestamp\": datetime.datetime.now(datetime.timezone.utc).isoformat()\n }\n\n try:\n # 1. Store the main triple data\n db.upsert_dict(config.TRIPLES_TABLE, {triple_id: triple_data})\n log.debug(f\"Stored triple {triple_id} in '{config.TRIPLES_TABLE}'\")\n\n # 2. Update indexes (Subject, Predicate, Object)\n # Use helper to avoid code repetition\n _update_index(db, config.SUBJECT_INDEX_TABLE, subject, triple_id)\n _update_index(db, config.PREDICATE_INDEX_TABLE, predicate, triple_id)\n # Objects might not always be strings, handle appropriately if necessary\n # For now, assume object_val can be used as a key (may need adjustment later)\n if isinstance(object_val, (str, int, float, bool)): # Basic types usable as keys\n _update_index(db, config.OBJECT_INDEX_TABLE, object_val, triple_id)\n elif isinstance(object_val, list):\n # Handle lists if objects can be lists - index each item?\n # Current prompt doesn't suggest objects are lists, but LLM might generate them.\n # For now, we won't index list objects directly to avoid complexity.\n log.warning(f\"Object value for triple {triple_id} is a list; not adding to object index.\")\n else:\n log.warning(f\"Object value for triple {triple_id} has unindexable type {type(object_val)}; not adding to object index.\")\n\n\n log.debug(f\"Updated indexes for triple {triple_id}\")\n return triple_id\n\n except Exception as e:\n log.error(f\"Failed to add triple {triple_id} or update indexes: {e}\", exc_info=True)\n # Attempt to rollback or clean up? jsonldb has no transactions.\n # We might have inconsistent state (triple stored but index failed).\n # Could try deleting the triple here, but that might also fail.\n return None\n\n\ndef _update_index(db: FolderDB, index_table: str, key_value: Any, triple_id: str):\n \"\"\"\n Helper function to add a triple_id to the list associated with a key\n in a given index table. Handles creating the list if key is new.\n\n Args:\n db: The FolderDB instance.\n index_table: The name of the index table file (e.g., config.SUBJECT_INDEX_TABLE).\n key_value: The key for the index (e.g., the subject string).\n triple_id: The triple ID to add to the list for this key.\n \"\"\"\n try:\n # Retrieve the current list of triple IDs for this key\n existing_entry = db.get_dict([index_table], lower_key=key_value, upper_key=key_value)\n\n current_ids = []\n if (existing_entry and\n index_table in existing_entry and\n key_value in existing_entry[index_table]):\n # Ensure the retrieved value is actually a list\n retrieved_val = existing_entry[index_table][key_value]\n if isinstance(retrieved_val, list):\n current_ids = retrieved_val\n else:\n log.warning(f\"Value for key '{key_value}' in index '{index_table}' is not a list ({type(retrieved_val)}). Resetting.\")\n current_ids = [] # Or potentially try to salvage if it's a single string?\n\n # Add the new triple_id if it's not already there (optional, depends on desired behavior)\n if triple_id not in current_ids:\n current_ids.append(triple_id)\n # Upsert the updated list back into the index\n db.upsert_dict(index_table, {key_value: current_ids})\n log.debug(f\"Updated index '{index_table}' for key '{key_value}' with triple {triple_id}\")\n else:\n log.debug(f\"Triple {triple_id} already present in index '{index_table}' for key '{key_value}'\")\n\n except Exception as e:\n log.error(f\"Failed to update index '{index_table}' for key '{key_value}': {e}\", exc_info=True)\n # Raise the error to signal the problem in add_triple?\n raise # Re-raise to indicate the add_triple failed partially\n\ndef get_triples(db: FolderDB,\n subject: Optional[str] = None,\n predicate: Optional[str] = None,\n object_val: Optional[Any] = None) -> List[Dict[str, Any]]:\n \"\"\"\n Retrieves triples matching the specified components (S, P, O).\n Uses index tables for efficiency when components are provided.\n If a component is None, it acts as a wildcard.\n\n Args:\n db: The initialized FolderDB instance.\n subject: The subject to filter by (optional).\n predicate: The predicate to filter by (optional).\n object_val: The object to filter by (optional).\n\n Returns:\n A list of full triple dictionaries matching the query.\n Returns empty list on error or if no matches found.\n \"\"\"\n if not db:\n log.error(\"Database instance is not valid. Cannot get triples.\")\n return []\n\n candidate_triple_ids: Optional[Set[str]] = None # Use set for efficient intersection\n\n # Determine which indexes to use based on provided components\n query_parts: List[Tuple[str, Any, str]] = [] # (index_name, key_value, component_name)\n if subject is not None:\n query_parts.append((config.SUBJECT_INDEX_TABLE, subject, \"subject\"))\n if predicate is not None:\n query_parts.append((config.PREDICATE_INDEX_TABLE, predicate, \"predicate\"))\n if object_val is not None and isinstance(object_val, (str, int, float, bool)): # Only index basic types for now\n query_parts.append((config.OBJECT_INDEX_TABLE, object_val, \"object\"))\n\n try:\n # If any components are provided, use indexes to get candidate IDs\n if query_parts:\n for index_table, key_value, _ in query_parts:\n log.debug(f\"Querying index '{index_table}' for key '{key_value}'\")\n index_result = db.get_dict([index_table], lower_key=key_value, upper_key=key_value)\n\n ids_from_index: Set[str] = set()\n if (index_result and\n index_table in index_result and\n key_value in index_result[index_table]):\n # Ensure value is a list before converting to set\n retrieved_val = index_result[index_table][key_value]\n if isinstance(retrieved_val, list):\n ids_from_index = set(retrieved_val)\n else:\n log.warning(f\"Value for key '{key_value}' in index '{index_table}' is not a list. Skipping this index constraint.\")\n\n\n log.debug(f\"Found {len(ids_from_index)} candidate IDs from index '{index_table}'\")\n\n if candidate_triple_ids is None:\n # First index query result initializes the set\n candidate_triple_ids = ids_from_index\n else:\n # Subsequent index results are intersected\n candidate_triple_ids.intersection_update(ids_from_index)\n\n # If intersection results in empty set, no need to query further indexes\n if not candidate_triple_ids:\n log.debug(\"Intersection of index results is empty. No matches possible.\")\n return []\n\n # --- Retrieve Full Triples ---\n final_triples: List[Dict[str, Any]] = []\n\n if candidate_triple_ids is not None:\n # We have candidate IDs from indexes, fetch only those triples\n if not candidate_triple_ids: # Should be caught above, but double-check\n return []\n\n log.debug(f\"Fetching {len(candidate_triple_ids)} full triples based on index results.\")\n # jsonldb's get_dict doesn't directly support fetching a list of arbitrary keys.\n # We have to iterate or fetch a range if possible (less likely here).\n # Iterate for simplicity, though could be less efficient for many IDs.\n all_triples_dict = db.get_dict([config.TRIPLES_TABLE]) # Fetch all in the table once\n if all_triples_dict and config.TRIPLES_TABLE in all_triples_dict:\n table_data = all_triples_dict[config.TRIPLES_TABLE]\n for triple_id in candidate_triple_ids:\n if triple_id in table_data:\n final_triples.append(table_data[triple_id])\n # Note: The above still loads the whole triples table, but avoids Python filtering if indexes were fully used.\n # A more optimized jsonldb might allow db.get_keys([config.TRIPLES_TABLE], list(candidate_triple_ids)).\n\n else:\n # No components provided, fetch all triples (potentially slow)\n log.debug(\"No query components provided, fetching all triples.\")\n all_triples_dict = db.get_dict([config.TRIPLES_TABLE])\n if all_triples_dict and config.TRIPLES_TABLE in all_triples_dict:\n final_triples = list(all_triples_dict[config.TRIPLES_TABLE].values())\n\n log.info(f\"Retrieved {len(final_triples)} triples matching query.\")\n return final_triples\n\n except Exception as e:\n log.error(f\"Error getting triples matching S={subject} P={predicate} O={object_val}: {e}\", exc_info=True)\n return []\n\ndef concept_exists(db: FolderDB, concept_id: str) -> bool:\n \"\"\"\n Checks if a concept identifier exists as a SUBJECT in the database\n by efficiently checking the subject index.\n\n Args:\n db: The initialized FolderDB instance.\n concept_id: The concept identifier string to check.\n\n Returns:\n True if the concept exists as a subject, False otherwise.\n \"\"\"\n if not db:\n log.error(\"Database instance is not valid. Cannot check concept existence.\")\n return False\n try:\n # Check if the key exists in the subject index\n index_result = db.get_dict([config.SUBJECT_INDEX_TABLE], lower_key=concept_id, upper_key=concept_id)\n exists = (index_result and\n config.SUBJECT_INDEX_TABLE in index_result and\n concept_id in index_result[config.SUBJECT_INDEX_TABLE])\n log.debug(f\"Concept '{concept_id}' exists as subject: {exists}\")\n return exists\n except Exception as e:\n log.error(f\"Error checking existence for concept '{concept_id}' in subject index: {e}\", exc_info=True)\n return False # Assume false on error\n\n\n# --- Log Management ---\n\ndef log_event(db: FolderDB, log_key: str, log_data: Dict[str, Any]) -> None:\n \"\"\"\n Logs an event (e.g., LLM API call) to the log table.\n\n Args:\n db: The initialized FolderDB instance.\n log_key: A unique key for the log entry (e.g., timestamp).\n log_data: The dictionary containing the log information.\n \"\"\"\n if not db:\n log.error(\"Database instance is not valid. Cannot log event.\")\n return\n try:\n db.upsert_dict(config.LOG_TABLE_NAME, {log_key: log_data})\n log.debug(f\"Logged event with key: {log_key}\")\n except Exception as e:\n log.error(f\"Error logging event {log_key}: {e}\", exc_info=True)\n # Decide if this should raise an error\n\ndef get_log_event(db: FolderDB, log_key: str) -> Optional[Dict[str, Any]]:\n \"\"\"\n Retrieves a specific log entry by its key.\n\n Args:\n db: The initialized FolderDB instance.\n log_key: The unique key of the log entry to retrieve.\n\n Returns:\n The log data dictionary, or None if not found or on error.\n \"\"\"\n if not db:\n log.error(\"Database instance is not valid. Cannot get log event.\")\n return None\n try:\n log_result = db.get_dict([config.LOG_TABLE_NAME], lower_key=log_key, upper_key=log_key)\n if (log_result and\n config.LOG_TABLE_NAME in log_result and\n log_key in log_result[config.LOG_TABLE_NAME]):\n return log_result[config.LOG_TABLE_NAME][log_key]\n else:\n log.debug(f\"Log event not found for key: {log_key}\")\n return None\n except Exception as e:\n log.error(f\"Error getting log event {log_key}: {e}\", exc_info=True)\n return None\n\n# --- Version Control (Passthrough) ---\n\ndef commit(db: FolderDB, message: str):\n \"\"\"\n Commits changes using the underlying FolderDB git integration (if available).\n\n Args:\n db: The initialized FolderDB instance.\n message: The commit message.\n \"\"\"\n if not db:\n log.error(\"Database instance is not valid. Cannot commit.\")\n return\n try:\n log.info(f\"Attempting commit with message: {message}\")\n db.commit(message)\n log.info(\"Commit successful.\")\n except AttributeError:\n log.warning(\"Database object or FolderDB instance does not have a 'commit' method (git support not active or library version issue?).\")\n except Exception as e: # Catch specific git errors if library defines them\n log.error(f\"Failed to commit changes: {e}\", exc_info=True)\n\n# --- End of db_operations.py ---\n```\n\n**How to Use This:**\n\n1. **Save:** Place the code above into `knowledge_graph_builder/db_operations.py`.\n2. **Update `config.py`:** Make sure your `knowledge_graph_builder/config.py` includes the new table name constants (`TRIPLES_TABLE`, `SUBJECT_INDEX_TABLE`, etc.).\n3. **Refactor `builder.py` and `run_builder.py`:**\n * Import functions from `db_operations` instead of directly using `db.upsert_dict`, `db.get_dict` etc. for triples and logs.\n * In `run_builder.py`:\n * `from knowledge_graph_builder import db_operations, config, builder`\n * `db = db_operations.initialize_database(config.DB_FOLDER)`\n * Pass the `db` object (which is the `FolderDB` instance) to functions in `builder.py` that need it.\n * If using commit: `db_operations.commit(db, f\"KG build run...\")`\n * In `builder.py`:\n * Import `db_operations`.\n * `query_and_store_concept`:\n * Call `db_operations.log_event(db, log_key, log_entry)` for logging.\n * After parsing the LLM response (`parsed_response_dict`), iterate through its `predicate`/`object` pairs. For each pair:\n * Handle list objects: If `obj` is a list, loop through `item` in `obj`.\n * Call `db_operations.add_triple(db, concept_id, predicate, item or obj, log_key)`\n * `concept_exists`: Replace the implementation with `return db_operations.concept_exists(db, concept_id)`.\n * `build_knowledge_graph`:\n * When needing related concepts for an *existing* concept `current_concept_id`, call `triples = db_operations.get_triples(db, subject=current_concept_id)`.\n * Iterate through `triples`, extract `t['predicate']` and `t['object']`, and add them to your `related_concepts` set (or directly to the queue).\n * Update logging calls (`log_event`, `get_log_event`) if they were used elsewhere.\n\nThis structure provides the desired interface layer and implements the triple store with indexing using `jsonldb`, addressing the limitations and your concerns about lookups." }, ] }, { "role": "user", "parts": [ { "text": "can you make it OO to make future design more flexible" }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process for refactoring the `db_operations.py` module into an Object-Oriented (OO) structure:\n\n1. **Understand the Goal:** The user wants an OO structure for `db_operations.py`. The primary motivation is \"future design flexibility,\" meaning it should be easier to swap out the `jsonldb` backend later without changing the code that *uses* the database operations (like `builder.py`). This implies encapsulating the database logic within a class.\n\n2. **Identify Core Responsibilities:** The current `db_operations.py` handles:\n * Database Initialization (`initialize_database`)\n * Triple Management (`add_triple`, `get_triples`)\n * Index Management (`_update_index` - internal helper)\n * Concept Existence Check (`concept_exists`)\n * Log Management (`log_event`, `get_log_event`)\n * Version Control (`commit`)\n * Helper for table file creation (`_ensure_table_file_exists` - internal helper)\n\n3. **Design the Class (`JsonldbStorage`):**\n * **Name:** Something descriptive. `JsonldbStorage` or `JsonldbKnowledgeStore` works. Let's go with `JsonldbStorage`.\n * **Initialization (`__init__`):** This should take the place of `initialize_database`. It needs the `db_path`. Inside `__init__`, it should perform the same steps: check/create the directory, instantiate `FolderDB`, store the `FolderDB` instance as an internal attribute (e.g., `self._db`), and ensure the necessary table files exist. It should handle initialization errors.\n * **Methods:** Convert the existing top-level functions into instance methods.\n * `initialize_database` becomes `__init__`.\n * `add_triple`, `get_triples`, `concept_exists`, `log_event`, `get_log_event`, `commit` become methods of the class. They will now take `self` as the first argument and access the database instance via `self._db`.\n * Internal helpers (`_update_index`, `_ensure_table_file_exists`) become private methods (prefix with `_`). They also take `self`.\n * **State:** The primary state held by an instance of this class is the `FolderDB` connection (`self._db`). The configuration (table names) will still be imported from `config.py`.\n * **Error Handling:** Keep the existing logging and error handling within the methods. `__init__` might raise an error or set `self._db` to `None` if initialization fails, requiring checks in other methods.\n\n4. **Refactor the Code (Step-by-Step):**\n\n * **Import:** Keep necessary imports (`os`, `uuid`, `datetime`, `logging`, `typing`, `FolderDB`, `config`).\n * **Class Definition:** Start with `class JsonldbStorage:`.\n * **`__init__` Method:**\n * Copy the logic from `initialize_database`.\n * Replace `db = FolderDB(db_path)` with `self._db = FolderDB(db_path)`.\n * Replace calls like `_ensure_table_file_exists(db, table_name)` with `self._ensure_table_file_exists(table_name)`.\n * Store `db_path` if needed later? Maybe `self.db_path = db_path`.\n * Handle the `FolderDB is None` case early. Maybe raise an `ImportError` or a custom exception.\n * Handle initialization failures (e.g., directory creation error, `FolderDB` error). Set `self._db = None` or raise an exception.\n * **`_ensure_table_file_exists` Method:**\n * Define as `def _ensure_table_file_exists(self, table_name):`.\n * Change `db.folder_path` to `self._db.folder_path`.\n * Keep the rest of the logic.\n * **`add_triple` Method:**\n * Define as `def add_triple(self, subject: str, predicate: str, object_val: Any, source_log_id: str) -> Optional[str]:`.\n * Add a check: `if not self._db: log.error(...); return None`.\n * Replace `db.upsert_dict(...)` with `self._db.upsert_dict(...)`.\n * Replace `_update_index(db, ...)` with `self._update_index(...)`.\n * Keep the rest of the logic.\n * **`_update_index` Method:**\n * Define as `def _update_index(self, index_table: str, key_value: Any, triple_id: str):`.\n * Add check: `if not self._db: ... return` (or raise).\n * Replace `db.get_dict(...)` with `self._db.get_dict(...)`.\n * Replace `db.upsert_dict(...)` with `self._db.upsert_dict(...)`.\n * Keep the rest of the logic.\n * **`get_triples` Method:**\n * Define as `def get_triples(self, subject: Optional[str] = None, predicate: Optional[str] = None, object_val: Optional[Any] = None) -> List[Dict[str, Any]]:`.\n * Add check: `if not self._db: ... return []`.\n * Replace `db.get_dict(...)` with `self._db.get_dict(...)`.\n * Keep the rest of the logic (index querying, filtering, retrieving full triples). Note: The final retrieval `all_triples_dict = self._db.get_dict([config.TRIPLES_TABLE])` is still necessary due to `jsonldb` limitations, but it's encapsulated here.\n * **`concept_exists` Method:**\n * Define as `def concept_exists(self, concept_id: str) -> bool:`.\n * Add check: `if not self._db: ... return False`.\n * Replace `db.get_dict(...)` with `self._db.get_dict(...)`.\n * Keep the rest of the logic.\n * **`log_event` Method:**\n * Define as `def log_event(self, log_key: str, log_data: Dict[str, Any]) -> None:`.\n * Add check: `if not self._db: ... return`.\n * Replace `db.upsert_dict(...)` with `self._db.upsert_dict(...)`.\n * Keep the rest of the logic.\n * **`get_log_event` Method:**\n * Define as `def get_log_event(self, log_key: str) -> Optional[Dict[str, Any]]:`.\n * Add check: `if not self._db: ... return None`.\n * Replace `db.get_dict(...)` with `self._db.get_dict(...)`.\n * Keep the rest of the logic.\n * **`commit` Method:**\n * Define as `def commit(self, message: str):`.\n * Add check: `if not self._db: ... return`.\n * Replace `db.commit(...)` with `self._db.commit(...)`.\n * Keep the rest of the logic.\n\n5. **Review and Refine:**\n * Ensure all uses of the old `db` variable are replaced with `self._db`.\n * Ensure all internal helper calls use `self._method_name(...)`.\n * Check imports and configuration access (`config.TABLE_NAME`).\n * Add docstrings to the class and methods explaining their purpose. Explicitly mention this class acts as the interface implementation for `jsonldb`.\n * Consider if `__init__` should return `None` on failure or raise an exception. Raising is usually better as it forces the caller to handle the failure immediately.\n * Add type hints to methods.\n\n6. **Update Usage:** Explain how `run_builder.py` and `builder.py` need to change:\n * Import the class: `from knowledge_graph_builder.db_operations import JsonldbStorage`.\n * Instantiate the class: `storage = JsonldbStorage(config.DB_FOLDER)`. Handle potential exceptions during instantiation.\n * Pass the `storage` object around instead of the `db` object.\n * Call methods on the instance: `storage.add_triple(...)`, `storage.get_triples(...)`, `storage.commit(...)`, etc.\n\nThis thought process moves from the high-level requirement (OO interface) to the specific implementation details within the chosen library (`jsonldb`), ensuring all previous functionality is preserved within the new class structure." }, { "text": "Okay, let's refactor `db_operations.py` into an Object-Oriented structure. This will encapsulate the `jsonldb` specifics within a class, making it easier to potentially swap out the storage backend later.\n\nWe'll create a `JsonldbStorage` class that holds the `FolderDB` instance and provides methods for all the database interactions (adding/getting triples, managing logs, committing, etc.).\n\n--- START OF REVISED FILE knowledge_graph_builder/db_operations.py ---\n```python\n# knowledge_graph_builder/db_operations.py\nimport os\nimport uuid\nimport datetime\nimport logging\nfrom typing import List, Dict, Any, Optional, Set, Tuple\n\n# Attempt to import FolderDB, handle potential ImportError\ntry:\n from jsonldb import FolderDB\nexcept ImportError:\n print(\"CRITICAL: jsonldb library not found. Database operations will fail.\")\n print(\"Please install it: pip install jsonldb\")\n FolderDB = None # Placeholder to prevent NameErrors later\n\nfrom . import config # Import configuration from the package\n\n# Use the main logging configuration\nlog = logging.getLogger(__name__)\n\nclass JsonldbStorage:\n \"\"\"\n Provides an object-oriented interface for storing and retrieving\n knowledge graph triples and associated logs using jsonldb as the backend.\n\n This class encapsulates the specifics of interacting with jsonldb,\n including managing the main triples table and necessary index tables\n (subject, predicate, object) for efficient lookups.\n\n It's designed to be potentially replaceable with another storage backend\n implementing the same public methods.\n \"\"\"\n\n def __init__(self, db_path: str):\n \"\"\"\n Initializes the storage interface and the underlying FolderDB instance.\n Ensures the base directory and necessary table files exist.\n\n Args:\n db_path: The path to the database folder.\n\n Raises:\n ImportError: If the jsonldb library is not found.\n FileNotFoundError: If the db_path is invalid or inaccessible.\n OSError: If directories or files cannot be created.\n Exception: For other potential FolderDB initialization errors.\n \"\"\"\n if not FolderDB:\n log.critical(\"FolderDB class not loaded due to import error.\")\n raise ImportError(\"jsonldb library not found. Cannot initialize JsonldbStorage.\")\n\n self.db_path = db_path\n self._db: Optional[FolderDB] = None # Initialize FolderDB instance holder\n\n log.info(f\"Attempting to initialize JsonldbStorage at: {db_path}\")\n try:\n if not os.path.exists(self.db_path):\n log.warning(f\"Database folder '{self.db_path}' not found. Creating it.\")\n os.makedirs(self.db_path)\n elif not os.path.isdir(self.db_path):\n log.error(f\"Database path '{self.db_path}' exists but is not a directory.\")\n raise FileNotFoundError(f\"Path is not a directory: {self.db_path}\")\n\n self._db = FolderDB(self.db_path)\n log.info(f\"Initialized FolderDB instance: {self._db}\")\n\n # Ensure all required table files exist\n required_tables = [\n config.TRIPLES_TABLE,\n config.SUBJECT_INDEX_TABLE,\n config.PREDICATE_INDEX_TABLE,\n config.OBJECT_INDEX_TABLE,\n config.LOG_TABLE_NAME\n ]\n for table_name in required_tables:\n self._ensure_table_file_exists(table_name)\n\n except FileNotFoundError as e:\n log.error(f\"Database folder '{self.db_path}' could not be accessed: {e}\", exc_info=True)\n self._db = None # Ensure db is None on failure\n raise # Re-raise the specific error\n except OSError as e:\n log.error(f\"OS error during initialization at '{self.db_path}': {e}\", exc_info=True)\n self._db = None\n raise\n except Exception as e:\n log.error(f\"Failed to initialize FolderDB at '{self.db_path}': {e}\", exc_info=True)\n self._db = None\n raise\n\n if not self._db:\n # Should be caught by specific exceptions above, but as a safeguard\n raise RuntimeError(f\"JsonldbStorage initialization failed for unknown reason at {self.db_path}\")\n\n\n def _ensure_table_file_exists(self, table_name: str):\n \"\"\"Internal helper to create an empty .jsonl file if it doesn't exist.\"\"\"\n if not self._db: return # Should not happen if constructor succeeded\n\n file_path = os.path.join(self._db.folder_path, f\"{table_name}.jsonl\")\n if not os.path.exists(file_path):\n try:\n with open(file_path, 'w') as f:\n pass # Create empty file\n log.info(f\"Created empty table file: {file_path}\")\n # Optionally, update db.meta if jsonldb requires it immediately\n # self._db.build_dbmeta() # Might be slow if called often\n except IOError as e:\n log.warning(f\"Could not create empty file for table {table_name}: {e}\", exc_info=True)\n # Depending on severity, might want to raise this error\n\n # --- Triple Management ---\n\n def add_triple(self, subject: str, predicate: str, object_val: Any, source_log_id: str) -> Optional[str]:\n \"\"\"\n Adds a new triple (SPO) to the database with provenance and updates indexes.\n\n Args:\n subject: The subject concept identifier.\n predicate: The predicate identifier (relationship type).\n object_val: The object identifier or value.\n source_log_id: The identifier linking to the source log entry.\n\n Returns:\n The unique triple_id assigned to the new triple, or None on failure.\n \"\"\"\n if not self._db:\n log.error(\"Database instance is not valid. Cannot add triple.\")\n return None\n if not all([subject, predicate, object_val, source_log_id]):\n log.warning(f\"Attempted to add triple with missing components: S='{subject}', P='{predicate}', O='{object_val}', Src='{source_log_id}'\")\n return None\n\n triple_id = str(uuid.uuid4()) # Generate unique ID\n triple_data = {\n \"triple_id\": triple_id,\n \"subject\": subject,\n \"predicate\": predicate,\n \"object\": object_val,\n \"source_log_id\": source_log_id,\n \"creation_timestamp\": datetime.datetime.now(datetime.timezone.utc).isoformat()\n }\n\n try:\n # 1. Store the main triple data\n self._db.upsert_dict(config.TRIPLES_TABLE, {triple_id: triple_data})\n log.debug(f\"Stored triple {triple_id} in '{config.TRIPLES_TABLE}'\")\n\n # 2. Update indexes\n self._update_index(config.SUBJECT_INDEX_TABLE, subject, triple_id)\n self._update_index(config.PREDICATE_INDEX_TABLE, predicate, triple_id)\n\n # Handle object indexing based on type\n if isinstance(object_val, (str, int, float, bool)):\n self._update_index(config.OBJECT_INDEX_TABLE, object_val, triple_id)\n # Add handling for other indexable types if needed (e.g., tuples)\n else:\n log.debug(f\"Object value for triple {triple_id} has non-basic type {type(object_val)}; not adding to object index.\")\n\n log.debug(f\"Updated indexes for triple {triple_id}\")\n return triple_id\n\n except Exception as e:\n log.error(f\"Failed to add triple {triple_id} or update indexes: {e}\", exc_info=True)\n # Consider adding cleanup logic here if possible (delete partial writes)\n return None\n\n\n def _update_index(self, index_table: str, key_value: Any, triple_id: str):\n \"\"\"Internal helper to update an index table.\"\"\"\n if not self._db:\n log.error(f\"Database not initialized, cannot update index '{index_table}'\")\n raise RuntimeError(\"Attempted to update index on uninitialized database.\") # Or handle more gracefully\n\n try:\n # Retrieve the current list of triple IDs for this key\n existing_entry = self._db.get_dict([index_table], lower_key=key_value, upper_key=key_value)\n\n current_ids = []\n if (existing_entry and\n index_table in existing_entry and\n key_value in existing_entry[index_table]):\n retrieved_val = existing_entry[index_table][key_value]\n if isinstance(retrieved_val, list):\n current_ids = retrieved_val\n else:\n log.warning(f\"Value for key '{key_value}' in index '{index_table}' is not a list ({type(retrieved_val)}). Resetting.\")\n current_ids = []\n\n if triple_id not in current_ids:\n current_ids.append(triple_id)\n self._db.upsert_dict(index_table, {key_value: current_ids})\n log.debug(f\"Updated index '{index_table}' for key '{key_value}' with triple {triple_id}\")\n else:\n log.debug(f\"Triple {triple_id} already present in index '{index_table}' for key '{key_value}'\")\n\n except Exception as e:\n log.error(f\"Failed to update index '{index_table}' for key '{key_value}': {e}\", exc_info=True)\n raise # Re-raise to signal the problem in add_triple\n\n def get_triples(self,\n subject: Optional[str] = None,\n predicate: Optional[str] = None,\n object_val: Optional[Any] = None) -> List[Dict[str, Any]]:\n \"\"\"\n Retrieves triples matching specified components (S, P, O) using indexes.\n\n Args:\n subject: The subject to filter by (optional).\n predicate: The predicate to filter by (optional).\n object_val: The object to filter by (optional). Only basic types\n (str, int, float, bool) are currently indexed.\n\n Returns:\n A list of full triple dictionaries matching the query. Empty list on error.\n \"\"\"\n if not self._db:\n log.error(\"Database instance is not valid. Cannot get triples.\")\n return []\n\n candidate_triple_ids: Optional[Set[str]] = None\n\n query_parts: List[Tuple[str, Any, str]] = []\n if subject is not None:\n query_parts.append((config.SUBJECT_INDEX_TABLE, subject, \"subject\"))\n if predicate is not None:\n query_parts.append((config.PREDICATE_INDEX_TABLE, predicate, \"predicate\"))\n # Only use object index for basic, hashable types that we indexed\n if object_val is not None and isinstance(object_val, (str, int, float, bool)):\n query_parts.append((config.OBJECT_INDEX_TABLE, object_val, \"object\"))\n elif object_val is not None:\n log.warning(f\"Querying by object of type {type(object_val)} is not supported by index. Will require full scan if no other constraints.\")\n\n\n try:\n # --- Use indexes to find candidate IDs ---\n if query_parts:\n for index_table, key_value, _ in query_parts:\n log.debug(f\"Querying index '{index_table}' for key '{key_value}'\")\n index_result = self._db.get_dict([index_table], lower_key=key_value, upper_key=key_value)\n\n ids_from_index: Set[str] = set()\n if (index_result and\n index_table in index_result and\n key_value in index_result[index_table]):\n retrieved_val = index_result[index_table][key_value]\n if isinstance(retrieved_val, list):\n ids_from_index = set(retrieved_val)\n else:\n log.warning(f\"Value for key '{key_value}' in index '{index_table}' not a list. Skipping index constraint.\")\n\n log.debug(f\"Found {len(ids_from_index)} candidate IDs from index '{index_table}'\")\n\n if candidate_triple_ids is None:\n candidate_triple_ids = ids_from_index\n else:\n candidate_triple_ids.intersection_update(ids_from_index)\n\n if not candidate_triple_ids: # Empty intersection means no matches\n log.debug(\"Intersection of index results is empty.\")\n return []\n\n # --- Retrieve Full Triples ---\n final_triples: List[Dict[str, Any]] = []\n\n # If we have candidates from indexes, fetch only those.\n if candidate_triple_ids is not None:\n if not candidate_triple_ids: return [] # Should be caught above\n\n log.debug(f\"Fetching {len(candidate_triple_ids)} full triples based on index results.\")\n # In jsonldb, we still have to fetch all/most and filter by key in memory\n # This is a limitation we encapsulate here.\n all_triples_dict = self._db.get_dict([config.TRIPLES_TABLE])\n if all_triples_dict and config.TRIPLES_TABLE in all_triples_dict:\n table_data = all_triples_dict[config.TRIPLES_TABLE]\n for triple_id in candidate_triple_ids:\n if triple_id in table_data:\n # Final check if a non-indexed object was specified\n triple_data = table_data[triple_id]\n if object_val is not None and not isinstance(object_val, (str, int, float, bool)):\n if triple_data.get(\"object\") == object_val:\n final_triples.append(triple_data)\n else:\n final_triples.append(triple_data)\n\n # If no query parts were specified, fetch all and filter if needed\n else:\n log.debug(\"No indexed query components provided, fetching all triples.\")\n all_triples_dict = self._db.get_dict([config.TRIPLES_TABLE])\n if all_triples_dict and config.TRIPLES_TABLE in all_triples_dict:\n # If there was a non-indexed object filter, apply it now\n if object_val is not None and not isinstance(object_val, (str, int, float, bool)):\n final_triples = [\n triple for triple in all_triples_dict[config.TRIPLES_TABLE].values()\n if triple.get(\"object\") == object_val\n ]\n else: # No filters at all\n final_triples = list(all_triples_dict[config.TRIPLES_TABLE].values())\n\n log.info(f\"Retrieved {len(final_triples)} triples matching query.\")\n return final_triples\n\n except Exception as e:\n log.error(f\"Error getting triples matching S='{subject}' P='{predicate}' O='{object_val}': {e}\", exc_info=True)\n return []\n\n def concept_exists(self, concept_id: str) -> bool:\n \"\"\"\n Checks if a concept exists as a SUBJECT using the subject index.\n\n Args:\n concept_id: The concept identifier string to check.\n\n Returns:\n True if the concept exists as a subject, False otherwise.\n \"\"\"\n if not self._db:\n log.error(\"Database instance is not valid. Cannot check concept existence.\")\n return False\n try:\n index_result = self._db.get_dict([config.SUBJECT_INDEX_TABLE], lower_key=concept_id, upper_key=concept_id)\n exists = (index_result and\n config.SUBJECT_INDEX_TABLE in index_result and\n concept_id in index_result[config.SUBJECT_INDEX_TABLE])\n log.debug(f\"Concept '{concept_id}' exists as subject: {exists}\")\n return exists\n except Exception as e:\n log.error(f\"Error checking existence for concept '{concept_id}' in subject index: {e}\", exc_info=True)\n return False\n\n # --- Log Management ---\n\n def log_event(self, log_key: str, log_data: Dict[str, Any]) -> None:\n \"\"\"\n Logs an event (e.g., LLM API call) to the log table.\n\n Args:\n log_key: A unique key for the log entry (e.g., timestamp).\n log_data: The dictionary containing the log information.\n \"\"\"\n if not self._db:\n log.error(\"Database instance is not valid. Cannot log event.\")\n return\n try:\n self._db.upsert_dict(config.LOG_TABLE_NAME, {log_key: log_data})\n log.debug(f\"Logged event with key: {log_key}\")\n except Exception as e:\n log.error(f\"Error logging event {log_key}: {e}\", exc_info=True)\n # Decide if this should raise an error\n\n def get_log_event(self, log_key: str) -> Optional[Dict[str, Any]]:\n \"\"\"\n Retrieves a specific log entry by its key.\n\n Args:\n log_key: The unique key of the log entry to retrieve.\n\n Returns:\n The log data dictionary, or None if not found or on error.\n \"\"\"\n if not self._db:\n log.error(\"Database instance is not valid. Cannot get log event.\")\n return None\n try:\n log_result = self._db.get_dict([config.LOG_TABLE_NAME], lower_key=log_key, upper_key=log_key)\n if (log_result and\n config.LOG_TABLE_NAME in log_result and\n log_key in log_result[config.LOG_TABLE_NAME]):\n return log_result[config.LOG_TABLE_NAME][log_key]\n else:\n log.debug(f\"Log event not found for key: {log_key}\")\n return None\n except Exception as e:\n log.error(f\"Error getting log event {log_key}: {e}\", exc_info=True)\n return None\n\n # --- Version Control (Passthrough) ---\n\n def commit(self, message: str):\n \"\"\"\n Commits changes using the underlying FolderDB git integration (if available).\n\n Args:\n message: The commit message.\n \"\"\"\n if not self._db:\n log.error(\"Database instance is not valid. Cannot commit.\")\n return\n try:\n log.info(f\"Attempting commit with message: {message}\")\n self._db.commit(message)\n log.info(\"Commit successful.\")\n except AttributeError:\n log.warning(\"Database object or FolderDB instance does not have a 'commit' method (git support not active or library version issue?).\")\n except Exception as e: # Catch specific git errors if library defines them\n log.error(f\"Failed to commit changes: {e}\", exc_info=True)\n\n def close(self):\n \"\"\"\n Placeholder for any cleanup needed (e.g., closing file handles if\n jsonldb held them open, which it likely doesn't).\n Currently does nothing for FolderDB.\n \"\"\"\n log.info(\"Closing JsonldbStorage (no explicit action needed for FolderDB).\")\n self._db = None # Release reference\n\n# --- End of db_operations.py ---\n```\n\n**How to Adapt Your Code:**\n\n1. **In `run_builder.py`:**\n * Import the class: `from knowledge_graph_builder.db_operations import JsonldbStorage`\n * Replace `db = db_operations.initialize_database(...)` with:\n ```python\n try:\n storage = JsonldbStorage(config.DB_FOLDER)\n logging.info(\"Database storage initialized successfully.\")\n except Exception as e:\n logging.error(f\"FATAL: Failed to initialize database storage: {e}\", exc_info=True)\n exit(1)\n ```\n * Pass the `storage` object to `builder.build_knowledge_graph(storage, start_concept)`.\n * Replace `db_operations.commit(db, ...)` with `storage.commit(...)`.\n\n2. **In `builder.py`:**\n * Change functions that previously took `db: FolderDB` to take `storage: JsonldbStorage`. For example: `query_and_store_concept(storage: JsonldbStorage, concept_id: str)`.\n * Inside these functions, replace calls like:\n * `db_operations.log_event(db, ...)` -> `storage.log_event(...)`\n * `db_operations.add_triple(db, ...)` -> `storage.add_triple(...)`\n * `db_operations.concept_exists(db, ...)` -> `storage.concept_exists(...)`\n * `db_operations.get_triples(db, ...)` -> `storage.get_triples(...)`\n * Etc.\n\nThis OO structure clearly separates the storage mechanism (`JsonldbStorage`) from the logic that uses it (`builder.py`). If you later decide to use SQLite, DuckDB, Neo4j, etc., you would:\n\n1. Create a new class (e.g., `SqliteStorage`) in `db_operations.py` (or a new file like `sqlite_storage.py`).\n2. Implement the *same public methods* (`add_triple`, `get_triples`, `concept_exists`, `log_event`, `commit`, etc.) using the SQLite API.\n3. Change only the instantiation line in `run_builder.py` to `storage = SqliteStorage(config.DB_FILE_PATH)` (adjusting config as needed).\n\nThe rest of your `builder.py` code wouldn't need to change, demonstrating the flexibility gained." }, ] }, { "role": "user", "parts": [ { "text": "It looks builder.py is not OO. Can you rewrite it for me to make it OO and use the new storage?" }, ] }, { "role": "model", "parts": [ { "text": "Here's a breakdown of the thought process to refactor `builder.py` into an Object-Oriented structure (`KnowledgeGraphBuilder`) that uses the `JsonldbStorage` class:\n\n1. **Identify Core Responsibility:** The primary role of `builder.py` is to manage the process of expanding the knowledge graph starting from a root concept, interacting with an LLM, and storing the results using a storage backend.\n\n2. **Define the Class (`KnowledgeGraphBuilder`):**\n * What state does the builder need to maintain?\n * A reference to the storage backend (`storage`).\n * Configuration parameters (API key check, model name, safety settings, generation config, max depth, delay – although some might be better handled by reading `config` directly when needed).\n * Runtime state during a build process:\n * The queue of concepts to process (`queue`).\n * The set of already processed concepts (`processed_concepts`).\n * Potentially, concepts encountered but skipped due to depth limits (`concepts_to_define`).\n * What are the main actions (methods) it performs?\n * Initialization (`__init__`): Takes the storage instance, potentially checks API keys.\n * Querying the LLM for a concept (`_query_llm_for_concept` - making it private as it's an internal step). This combines the prompt formatting, API call, parsing, and *delegating storage* to the `storage` object.\n * Extracting related concepts from LLM output (`_extract_related_concepts` - private helper).\n * The main build loop (`build_graph_from_concept`). This orchestrates the process using the queue, depth checks, calls to query/store, and calls to extract relations.\n * Helper methods (`_get_iso_timestamp`, `_clean_llm_response` - can be private or static).\n\n3. **Refactor `initialize_database`:** This function doesn't belong *inside* the builder class itself. It's about setting up the *storage*, which is then *passed* to the builder. So, it should remain outside or ideally be handled directly in `run_builder.py` which instantiates both the storage and the builder. (The previous step already moved this logic into `JsonldbStorage`'s `__init__`, which is even better).\n\n4. **Refactor `query_and_store_concept` -> `_query_llm_for_concept`:**\n * This method now belongs to the `KnowledgeGraphBuilder` class.\n * It takes `concept_id` as input.\n * It accesses the `storage` instance via `self.storage`.\n * It accesses config via `config` module (no need to store duplicates in `self`).\n * **Crucially:** Instead of storing the results itself (like `db.upsert_dict(...)` before), it will:\n * Log the request using `self.storage.log_event(...)`.\n * Call the LLM.\n * Parse the response (`parsed_response_dict`).\n * **Iterate** through the `parsed_response_dict`: for each predicate/object pair, call `self.storage.add_triple(subject=concept_id, predicate=pred, object_val=obj, source_log_id=log_key)`.\n * Log the final status using `self.storage.log_event(...)`.\n * Return the `parsed_response_dict` (or None on failure) so the calling method (`build_graph_from_concept`) can extract relations.\n\n5. **Refactor `concept_exists`:** This check is now purely delegated to the storage layer. The builder shouldn't know *how* existence is checked. The builder method `build_graph_from_concept` will just call `self.storage.concept_exists(concept_id)`.\n\n6. **Refactor `extract_related_concepts` -> `_extract_related_concepts`:**\n * Make it a private method (`_extract_related_concepts`) of the class.\n * It takes the `concept_data` dictionary (the LLM's direct output) as input.\n * Its logic remains largely the same – parsing the dictionary keys and values to find potential concept identifiers.\n\n7. **Refactor `build_knowledge_graph` -> `build_graph_from_concept`:**\n * This becomes the main public method of the `KnowledgeGraphBuilder` class.\n * It takes `start_concept_id` as input.\n * It initializes the `processed_concepts` set and the `queue` (as instance variables or local variables for the run).\n * The main `while queue:` loop remains structurally similar.\n * Inside the loop:\n * Check `processed_concepts`.\n * Check depth limit (`config.MAX_RECURSION_DEPTH`).\n * Call `self.storage.concept_exists(current_concept_id)`.\n * If not exists:\n * Call `self._query_llm_for_concept(current_concept_id)` to get `concept_data`.\n * Handle `concept_data is None`.\n * If exists:\n * Call `self.storage.get_triples(subject=current_concept_id)` to retrieve existing relationships.\n * *Synthesize* a `concept_data`-like dictionary from the retrieved triples if needed for `_extract_related_concepts`, OR modify `_extract_related_concepts` to work directly on the list of triples. (Simpler approach: `_extract_related_concepts` still works on the LLM output; when retrieving existing, just extract P/O from the triples directly).\n * **Decision:** Let's keep `_extract_related_concepts` working on the LLM dictionary format. When a concept *exists*, we retrieve its triples using `self.storage.get_triples` and directly extract the related concepts (predicates and objects) from that list of triples to add to the queue. No need to reconstruct the original dictionary format.\n * Extract related concepts (either from new `concept_data` or existing triples).\n * Add valid, unprocessed related concepts to the queue.\n * Handle the final logging (skipped concepts, completion message).\n\n8. **Helper Functions (`get_iso_timestamp`, `_clean_llm_response`):** These can become private static methods (`@staticmethod`) within the class as they don't rely on instance state (`self`).\n\n9. **API Key Check:** This check is really about *pre-conditions* before starting a build. It fits well in the `__init__` method of the builder, raising an error if the key isn't configured, preventing instantiation of a non-functional builder. Alternatively, it could be done in `run_builder.py` *before* creating the `KnowledgeGraphBuilder` instance. Let's put it in `__init__` for encapsulation.\n\n10. **Logging:** Ensure logging uses `log = logging.getLogger(__name__)` at the top and calls `log.info`, `log.error`, etc.\n\n11. **Imports:** Update imports as needed (`db_operations.JsonldbStorage`, `config`, etc.). Remove direct `jsonldb` import if no longer needed.\n\n12. **Docstrings and Typing:** Add clear docstrings and type hints to the class and its methods.\n\n\n**(Self-Correction during thought process):** Initially, I thought about making `build_graph_from_concept` re-use `_extract_related_concepts` even for existing concepts by reconstructing the dictionary. However, it's simpler and more direct to just iterate through the triples returned by `storage.get_triples(subject=...)` and extract the predicate/object directly when dealing with existing concepts. This avoids unnecessary data transformation. `_extract_related_concepts` is only needed immediately after getting *new* data from the LLM *before* it's stored as triples." }, { "text": "Okay, let's refactor `builder.py` into an Object-Oriented structure. We'll create a `KnowledgeGraphBuilder` class that encapsulates the building logic and utilizes the `JsonldbStorage` class (or any other class conforming to the storage interface) passed to it during initialization.\n\n--- START OF REVISED FILE knowledge_graph_builder/builder.py ---\n```python\n# knowledge_graph_builder/builder.py\n\nimport google.generativeai as genai\nfrom google.api_core import exceptions as google_exceptions\nimport json\nimport datetime\nimport time\nimport logging\nimport re\nfrom collections import deque\nfrom typing import Dict, Any, Set, Optional, List, Tuple\n\n# Import the storage class and config\nfrom .db_operations import JsonldbStorage # Or a common interface base class if defined\nfrom . import config\n\n# Use logging configured elsewhere (e.g., in run_builder.py or main script)\nlog = logging.getLogger(__name__)\n\n# --- LLM Prompt Template ---\n# (Keep the existing LLM_PROMPT_TEMPLATE as it is)\nLLM_PROMPT_TEMPLATE = \"\"\"\nPreamble / Role Setting:\n\nYou are an AI Knowledge Compiler. Your task is to analyze a specific meaning of a word or phrase and generate a comprehensive dictionary detailing all its relevant relationships, attributes, and associations. This data must be exhaustive enough to power an offline language interface for a game. **Crucially, all information, including predicates and objects, must be represented as conceptual identifiers.**\n\nInput Word/Phrase Meaning Identifier:\n\n`{concept_identifier}`\n\nTask:\n\nFor the provided input meaning, generate a single JSON **dictionary**. This dictionary represents the network of information associated with that specific meaning.\n\n* **Keys** in the dictionary are strings representing the **relationship** or **attribute type** (predicate).\n * Keys MUST be strings in the format `predicate phrase` or `predicate phrase:clarifying meaning`. Use common, easily understood relationship terms. Use lowercase, except for proper nouns. Preserve spaces and punctuation.\n* **Values** in the dictionary represent the **related entity/meaning** (object).\n * **ALL** object values MUST be represented as concept identifiers.\n * Use the format `object word/phrase` if that word/phrase is generally unambiguous or only one meaning is clearly relevant in this context.\n * Use the format `object word/phrase:specific meaning description` if ambiguity needs resolution or to define the specific meaning intended (e.g., for colors, properties, states).\n * Use lowercase, except for proper nouns. Preserve spaces and punctuation.\n * **No literal values (strings, numbers, booleans) are allowed as direct dictionary values.** Represent everything conceptually (e.g., use `blue:the color of the clear sky` instead of `\"blue\"`; use `true:state of being factual` instead of `true`).\n * If a relationship naturally connects the subject to multiple objects, the value MUST be a JSON list `[]` containing multiple concept identifiers (each following the `word/phrase` or `word/phrase:meaning` format).\n\nCrucial Requirement: Exhaustiveness for Offline Game Interface\n\nThe generated dictionary MUST be extremely comprehensive. Aim to capture *every possible facet* relevant to understanding and interacting with the input meaning in a game world, representing all information via conceptual relationships. This includes, but is not limited to:\n\n* Core Semantics: Definitions (implicitly via `is a`, `synonym of`, etc.), classifications (`is a`, `subclass of`).\n* Attributes & Properties: `has property`, `color is`, `size is`, `shape is`, `material is`, `typical value is`. (Ensure values like 'red', 'large', 'round', 'wood' are represented as `word:meaning` identifiers).\n* Relationships: `part of`, `contains`, `connected to`, `opposite of` (`antonym of`), `similar to` (`synonym of`).\n* Actions & Interactions: `can perform action`, `can be used for`, `can be affected by`, `typically interacts with`, `requires`.\n* Context & Location: `typically found in`, `used in domain`, `common context`.\n* States: `can be state of`, `possible states`. (Represent states like 'ripe', 'on', 'broken' as `word:meaning` identifiers).\n* Causality: `causes`, `caused by`, `enables`.\n* Examples: `example is`.\n* Associated Concepts: `related concept`, `often associated with`.\n\nThink about how a player might refer to, use, or interact with this word/phrase meaning in a game and ensure the relevant information is encoded conceptually.\n\nRequired Output Format (JSON):\n\n```json\n{{\n // Example structure - provide actual relationships for the input\n \"is a\": [\"example concept:meaning\"],\n \"has property\": [\"example property:meaning\"]\n // ... more relationships based on the input concept ...\n}}\n```json\n\nGuidelines:\n\n1. Direct Dictionary Output: The entire output MUST be a single JSON dictionary.\n2. Strict Identifier Format: Keys (Predicates): Use `predicate phrase` or `predicate phrase:clarifying meaning`. Values (Objects): **MUST** use `object word/phrase` or `object word/phrase:specific meaning description`. **No direct literals allowed.**\n3. Case and Punctuation: Lowercase unless proper noun. Preserve spaces/punctuation.\n4. Predicate Language: Use clear, common relationship phrases. Clarify with `:meaning` if needed.\n5. List vs. Single Value: Use a JSON list `[]` for multiple conceptual objects per relationship.\n6. Exhaustiveness is Key: Prioritize capturing a wide and deep range of information relevant to game interaction, representing everything conceptually.\n7. Valid JSON: Ensure valid JSON syntax.\n\nGenerate the JSON dictionary output for the input word/phrase meaning identifier specified above.\n\"\"\"\n\nclass KnowledgeGraphBuilder:\n \"\"\"\n Orchestrates the process of building a knowledge graph by querying a\n generative LLM (Gemini) and storing the results as triples using a\n provided storage backend.\n \"\"\"\n\n def __init__(self, storage: JsonldbStorage):\n \"\"\"\n Initializes the KnowledgeGraphBuilder.\n\n Args:\n storage: An initialized instance of a storage class (e.g., JsonldbStorage)\n that conforms to the expected interface (add_triple, get_triples,\n concept_exists, log_event, etc.).\n\n Raises:\n ValueError: If the GEMINI_API_KEY is not configured.\n \"\"\"\n self.storage = storage\n log.info(\"KnowledgeGraphBuilder initialized.\")\n\n # --- API Key Check ---\n if not config.GEMINI_API_KEY or config.GEMINI_API_KEY == \"YOUR_GOOGLE_API_KEY\":\n log.error(\"FATAL: GEMINI_API_KEY is not configured in knowledge_graph_builder/config.py or environment variables.\")\n log.error(\"Please obtain an API key from Google AI Studio and configure it.\")\n # Option 1: Raise an error to prevent using an unconfigured builder\n raise ValueError(\"GEMINI_API_KEY is not configured.\")\n # Option 2: Log error and allow creation, but fail later (less safe)\n # self.api_key_configured = False\n else:\n log.info(\"GEMINI_API_KEY found.\")\n # self.api_key_configured = True\n # Configure the API key globally for the genai library upon initialization\n try:\n genai.configure(api_key=config.GEMINI_API_KEY)\n log.info(\"Google Generative AI SDK configured.\")\n except Exception as e:\n log.error(f\"Failed to configure Google Generative AI SDK: {e}\", exc_info=True)\n raise ValueError(\"Failed to configure GEMINI API.\") from e\n\n\n @staticmethod\n def _get_iso_timestamp() -> str:\n \"\"\"Returns the current UTC time in ISO format.\"\"\"\n return datetime.datetime.now(datetime.timezone.utc).isoformat()\n\n @staticmethod\n def _clean_llm_response(text: str) -> str:\n \"\"\"Removes markdown code fences and leading/trailing whitespace.\"\"\"\n text = text.strip()\n if text.startswith(\"```json\"):\n text = text[7:]\n if text.startswith(\"```\"):\n text = text[3:]\n if text.endswith(\"```\"):\n text = text[:-3]\n return text.strip()\n\n def _query_llm_and_store_concept(self, concept_id: str) -> Optional[Dict[str, Any]]:\n \"\"\"\n Queries the Gemini model for a concept, logs the interaction,\n stores the resulting relationships as triples, and returns the\n raw parsed dictionary from the LLM (or None on failure).\n Includes retry logic for 429 quota errors.\n\n Args:\n concept_id: The concept identifier to query the LLM about.\n\n Returns:\n The dictionary parsed directly from the LLM's JSON response,\n or None if the query, parsing, or storage failed significantly.\n This dictionary is used to extract *new* related concepts.\n \"\"\"\n log.info(f\"Processing concept: {concept_id}\")\n\n # 1. Prepare the prompt\n formatted_prompt = LLM_PROMPT_TEMPLATE.format(concept_identifier=concept_id)\n\n # 2. Prepare Log Entry (Request Part)\n log_key = self._get_iso_timestamp()\n log_entry = {\n \"request_timestamp\": log_key,\n \"model_used\": config.MODEL_NAME,\n \"concept_queried\": concept_id,\n \"prompt_sent\": formatted_prompt, # Consider truncating if too long for logs\n \"response_timestamp\": None,\n \"raw_response\": None,\n \"parsed_response\": None, # The dict directly from LLM\n \"triples_added\": 0, # Count triples successfully added\n \"error\": None,\n \"status\": \"pending\",\n \"retries_attempted\": 0\n }\n llm_parsed_dict = None # Initialize\n\n # Log the request attempt immediately using the storage interface\n try:\n self.storage.log_event(log_key, log_entry)\n log.debug(f\"Logged request attempt for {concept_id} with key {log_key}\")\n except Exception as e:\n log.error(f\"Failed to log request for {concept_id} via storage: {e}\", exc_info=True)\n # Continue attempt, but logging failed\n\n # 3. Configure and Call Gemini API with Retry Logic\n max_retries = 3\n retries = 0\n response = None\n model = None # Initialize model variable\n\n while retries <= max_retries:\n try:\n # Ensure model is initialized (genai.configure was called in __init__)\n if not model:\n model = genai.GenerativeModel(config.MODEL_NAME)\n\n log.info(f\"Sending request to Gemini for '{concept_id}' (Attempt {retries + 1}/{max_retries + 1})...\")\n start_time = time.time()\n\n response = model.generate_content(\n formatted_prompt,\n safety_settings=config.SAFETY_SETTINGS,\n generation_config=config.GENERATION_CONFIG\n )\n end_time = time.time()\n log.info(f\"Received response from Gemini in {end_time - start_time:.2f} seconds.\")\n\n # Check for blocked content\n if not response.parts:\n block_reason = \"Unknown\"\n if response.prompt_feedback and response.prompt_feedback.block_reason:\n block_reason = response.prompt_feedback.block_reason\n raise ValueError(f\"Request blocked by API. Reason: {block_reason}\")\n\n # Successful response, break retry loop\n log_entry[\"retries_attempted\"] = retries\n break\n\n except google_exceptions.ResourceExhausted as e:\n # Handle 429 Quota Error\n retries += 1\n log_entry[\"retries_attempted\"] = retries\n log.warning(f\"Quota exceeded for '{concept_id}' (Attempt {retries}/{max_retries}). Error: {e}\")\n\n if retries list) else [obj_or_list]\n for obj in objects:\n if isinstance(obj, str) and obj.strip(): # Ensure object is a valid string\n triple_id = self.storage.add_triple(\n subject=concept_id,\n predicate=str(predicate).strip(), # Ensure predicate is string\n object_val=obj.strip(),\n source_log_id=log_key\n )\n if triple_id:\n triples_added_count += 1\n else:\n # Log specific triple failure? Handled within add_triple logging.\n log.warning(f\"Failed to add triple: ({concept_id}, {predicate}, {obj})\")\n else:\n log.warning(f\"Skipping invalid object value for predicate '{predicate}': {obj} (type: {type(obj)})\")\n\n\n log_entry[\"triples_added\"] = triples_added_count\n log.info(f\"Stored {triples_added_count} triples for '{concept_id}'.\")\n log_entry[\"status\"] = \"success\"\n\n except Exception as e:\n log.error(f\"Failed during triple storage for '{concept_id}': {e}\", exc_info=True)\n log_entry[\"status\"] = \"storage_error\"\n log_entry[\"error\"] = f\"Failed to store triples: {e}\"\n llm_parsed_dict = None # Invalidate data if storage fails\n\n except (json.JSONDecodeError, TypeError) as e:\n log.error(f\"Failed to parse JSON response for '{concept_id}': {e}\", exc_info=True)\n log.error(f\"Raw Response Text (cleaned):\\n{cleaned_response_text}\")\n log_entry[\"status\"] = \"parse_error\"\n log_entry[\"error\"] = f\"{type(e).__name__}: {e}\"\n llm_parsed_dict = None # Invalidate data\n\n except Exception as e:\n log.error(f\"Error processing successful API response for '{concept_id}': {e}\", exc_info=True)\n log_entry[\"status\"] = \"processing_error\"\n log_entry[\"error\"] = f\"Error processing response: {e}\"\n if not log_entry[\"response_timestamp\"]:\n log_entry[\"response_timestamp\"] = self._get_iso_timestamp()\n llm_parsed_dict = None\n\n # 6. Update Log Entry with Final Status\n self._update_log(log_key, log_entry)\n\n # Return the dictionary parsed from LLM (used for extracting relations)\n # even if some triple storage failed, the caller might still use partial data.\n return llm_parsed_dict\n\n\n def _extract_retry_delay(self, error: google_exceptions.ResourceExhausted, default_delay: int) -> int:\n \"\"\"Extracts recommended retry delay from Google API error.\"\"\"\n retry_seconds = default_delay\n try:\n # Attempt to parse delay from message (common pattern)\n match = re.search(r\"retry_delay\\s*{\\s*seconds:\\s*(\\d+)\\s*}\", str(error))\n if match:\n retry_seconds = int(match.group(1))\n log.info(f\"Parsed retry delay from error message: {retry_seconds} seconds.\")\n return retry_seconds\n\n # Check metadata (more robust if available)\n if hasattr(error, 'metadata') and error.metadata:\n for key, value in error.metadata:\n if key.lower() == 'retry-after': # Standard HTTP header\n retry_seconds = int(value)\n log.info(f\"Parsed retry delay from metadata ('{key}'): {retry_seconds} seconds.\")\n return retry_seconds\n log.warning(f\"Could not parse retry_delay from 429 error message or metadata. Using default {default_delay}s.\")\n except Exception as parse_err:\n log.warning(f\"Error parsing retry_delay from 429 error: {parse_err}. Using default {default_delay}s.\")\n return retry_seconds\n\n def _update_log(self, log_key: str, log_entry: Dict[str, Any]):\n \"\"\"Safely updates the log entry using the storage interface.\"\"\"\n try:\n self.storage.log_event(log_key, log_entry) # Use log_event for update too (upsert)\n log.debug(f\"Updated log entry {log_key} with final status: {log_entry.get('status', 'N/A')}\")\n except Exception as e:\n log.error(f\"Failed to update final log entry {log_key}: {e}\", exc_info=True)\n\n\n def _extract_related_concepts_from_dict(self, concept_data: Dict[str, Any]) -> Set[str]:\n \"\"\"\n Extracts all unique potential concept identifiers from the raw dictionary\n returned by the LLM (predicates and objects).\n \"\"\"\n related_concepts: Set[str] = set()\n if not isinstance(concept_data, dict):\n log.warning(f\"Cannot extract concepts from non-dict data: {type(concept_data)}\")\n return related_concepts\n\n for predicate, obj_or_list in concept_data.items():\n # Add predicate if it seems like a concept (contains ':') or is non-empty string\n # Note: Simple predicates like \"is a\" might also be treated as concepts here.\n predicate_str = str(predicate).strip()\n if predicate_str:\n related_concepts.add(predicate_str)\n\n # Process object(s)\n objects = obj_or_list if isinstance(obj_or_list, list) else [obj_or_list]\n for item in objects:\n if isinstance(item, str):\n item_str = item.strip()\n if item_str: # Add non-empty string objects\n related_concepts.add(item_str)\n else:\n log.warning(f\"Found non-string item in object list/value for predicate '{predicate_str}': {type(item)}\")\n\n log.debug(f\"Extracted {len(related_concepts)} potential related concepts from LLM dict.\")\n return related_concepts\n\n def _extract_related_concepts_from_triples(self, triples: List[Dict[str, Any]]) -> Set[str]:\n \"\"\"\n Extracts unique concept identifiers (predicates and objects)\n from a list of existing triples retrieved from storage.\n \"\"\"\n related_concepts: Set[str] = set()\n for triple in triples:\n predicate = triple.get(\"predicate\")\n obj = triple.get(\"object\")\n\n if isinstance(predicate, str) and predicate.strip():\n related_concepts.add(predicate.strip())\n\n # Handle various object types stored\n if isinstance(obj, str) and obj.strip():\n related_concepts.add(obj.strip())\n # Add handling here if objects can be other types (lists, etc.)\n # elif isinstance(obj, list): ...\n\n log.debug(f\"Extracted {len(related_concepts)} related concepts from existing triples.\")\n return related_concepts\n\n\n def build_graph_from_concept(self, start_concept_id: str):\n \"\"\"\n Starts building the knowledge graph iteratively from a starting concept,\n using a queue and respecting depth limits.\n\n Args:\n start_concept_id: The initial concept identifier to begin the build from.\n \"\"\"\n if not self.storage:\n log.error(\"Storage is not initialized. Cannot build graph.\")\n return\n\n processed_concepts: Set[str] = set()\n # Queue stores tuples of (concept_id, depth)\n queue: deque = deque([(start_concept_id, 0)])\n # Track concepts encountered but skipped due to depth limit\n concepts_to_define_later: Set[str] = set()\n\n log.info(f\"Starting knowledge graph build from: '{start_concept_id}'\")\n\n while queue:\n try:\n current_concept_id, current_depth = queue.popleft()\n except IndexError:\n log.info(\"Queue is empty. Build process complete.\")\n break # Should be redundant due to while queue, but safe\n\n if not current_concept_id or not isinstance(current_concept_id, str) or not current_concept_id.strip():\n log.warning(f\"Skipping invalid concept ID found in queue: '{current_concept_id}'\")\n continue\n\n current_concept_id = current_concept_id.strip() # Ensure clean ID\n\n if current_concept_id in processed_concepts:\n log.debug(f\"Skipping already processed concept: '{current_concept_id}'\")\n continue\n\n # --- Depth Check ---\n if config.MAX_RECURSION_DEPTH is not None and current_depth > config.MAX_RECURSION_DEPTH:\n log.info(f\"Skipping concept due to depth limit ({current_depth}/{config.MAX_RECURSION_DEPTH}): '{current_concept_id}'\")\n # Check if it *needs* defining (i.e., doesn't exist yet)\n if not self.storage.concept_exists(current_concept_id):\n concepts_to_define_later.add(current_concept_id)\n continue # Skip processing this concept further\n\n log.info(f\"Processing '{current_concept_id}' at depth {current_depth}\")\n processed_concepts.add(current_concept_id)\n concepts_to_define_later.discard(current_concept_id) # Remove if we are processing it\n\n related_concepts: Set[str] = set()\n exists = self.storage.concept_exists(current_concept_id)\n\n if not exists:\n log.info(f\"Concept '{current_concept_id}' not found in DB. Querying LLM...\")\n # Add delay before API call\n time.sleep(config.API_CALL_DELAY_SECONDS)\n # Query LLM and store results as triples\n llm_data = self._query_llm_and_store_concept(current_concept_id)\n\n if llm_data:\n # Extract relations from the *original* LLM dictionary output\n related_concepts = self._extract_related_concepts_from_dict(llm_data)\n log.debug(f\"Extracted {len(related_concepts)} new relations for '{current_concept_id}'\")\n else:\n log.warning(f\"Failed to get or store data for new concept: '{current_concept_id}'. Cannot extract relations.\")\n # Optional: Add to a retry queue or mark as failed permanently\n\n else:\n log.info(f\"Concept '{current_concept_id}' found in DB (exists as subject). Retrieving outgoing relations...\")\n try:\n # Retrieve existing triples where this concept is the subject\n existing_triples = self.storage.get_triples(subject=current_concept_id)\n if existing_triples:\n # Extract related concepts (predicates, objects) from the retrieved triples\n related_concepts = self._extract_related_concepts_from_triples(existing_triples)\n log.debug(f\"Found {len(related_concepts)} related concepts from {len(existing_triples)} existing triples for '{current_concept_id}'.\")\n else:\n # This might happen if a concept exists only as an object/predicate,\n # or if get_triples failed for some reason.\n log.warning(f\"Concept '{current_concept_id}' reported as existing, but no outgoing triples found.\")\n except Exception as e:\n log.error(f\"Error retrieving triples for existing concept '{current_concept_id}': {e}\", exc_info=True)\n\n\n # --- Add valid related concepts to the queue ---\n if related_concepts:\n # Ensure the concept doesn't relate to itself in the extracted list\n related_concepts.discard(current_concept_id)\n\n for related_id in related_concepts:\n if not related_id or not isinstance(related_id, str) or not related_id.strip():\n log.warning(f\"Skipping empty or invalid related concept identifier ('{related_id}') found for '{current_concept_id}'.\")\n continue\n\n related_id = related_id.strip() # Clean ID\n\n if related_id not in processed_concepts:\n # Check if already in queue to avoid redundant processing cycles\n # (O(N) check on deque; consider set alongside queue if performance critical)\n is_in_queue = any(item[0] == related_id for item in queue)\n if not is_in_queue:\n log.debug(f\"Adding related concept to queue: '{related_id}' (depth {current_depth + 1})\")\n queue.append((related_id, current_depth + 1))\n else:\n log.debug(f\"Related concept already in queue: '{related_id}'\")\n # else:\n # log.debug(f\"Related concept already processed: '{related_id}'\") # Usually too verbose\n\n # --- Build process finished ---\n log.info(\"Knowledge graph build process finished.\")\n if concepts_to_define_later:\n log.warning(f\"The following {len(concepts_to_define_later)} concepts were encountered but not defined due to depth limits:\")\n # Sort for consistent output\n for concept in sorted(list(concepts_to_define_later)):\n log.warning(f\" - {concept}\")\n log.warning(\"Consider increasing MAX_RECURSION_DEPTH or running the builder again.\")\n else:\n log.info(\"No concepts were skipped due to depth limits.\")\n\n # --- Placeholder for future steps ---\n # (Keep the placeholder comments if desired)\n log.info(\"--- Graph Build Cycle Complete ---\")\n log.info(\"Further steps might include: Grounding, Validation, Querying, Simulation Integration.\")\n\n# --- End of builder.py ---\n```\n\n**Explanation of Changes:**\n\n1. **`KnowledgeGraphBuilder` Class:** The core logic is now encapsulated within this class.\n2. **`__init__(self, storage)`:**\n * Takes an initialized storage object (`JsonldbStorage` instance in this case) as input and stores it in `self.storage`.\n * Performs the API Key check upon initialization, raising an error if the key is missing or invalid, preventing the creation of a non-functional builder. It also configures the `genai` library here.\n3. **Methods:**\n * Functions like `query_and_store_concept`, `extract_related_concepts`, `build_knowledge_graph`, `get_iso_timestamp`, `_clean_llm_response` are now methods of the class.\n * Helper methods used only internally are prefixed with an underscore (e.g., `_query_llm_and_store_concept`, `_extract_related_concepts_from_dict`).\n * Static methods (`@staticmethod`) are used for helpers that don't need `self` (like `_get_iso_timestamp`, `_clean_llm_response`).\n4. **Storage Interaction:** All direct calls to database functions (`db.upsert_dict`, `db.get_dict`, etc.) are replaced with calls to the methods of the `self.storage` object (e.g., `self.storage.add_triple(...)`, `self.storage.get_triples(...)`, `self.storage.concept_exists(...)`, `self.storage.log_event(...)`).\n5. **LLM Query and Storage (`_query_llm_and_store_concept`):**\n * This method now clearly separates querying the LLM, parsing the response, and then *iterating through the parsed dictionary* to call `self.storage.add_triple` for each relationship.\n * It returns the original dictionary parsed from the LLM so that `build_graph_from_concept` can extract the newly discovered related concepts.\n * Logging includes the number of triples successfully added.\n6. **Extracting Related Concepts:**\n * Two helper methods are introduced:\n * `_extract_related_concepts_from_dict`: Works on the raw dictionary returned by the LLM (used for *new* concepts).\n * `_extract_related_concepts_from_triples`: Works on the list of triples retrieved from storage (used for *existing* concepts).\n7. **`build_graph_from_concept` (Main Loop):**\n * Uses `self.storage.concept_exists` to check if a concept needs LLM querying.\n * If the concept is new, calls `_query_llm_and_store_concept`, gets the LLM dict, and uses `_extract_related_concepts_from_dict`.\n * If the concept exists, calls `self.storage.get_triples` and uses `_extract_related_concepts_from_triples`.\n * Manages the `queue` and `processed_concepts` set.\n * Handles the depth limit check and logs skipped concepts.\n8. **Configuration:** Relies on the `config` module for settings like API keys, model name, depth limits, etc.\n\n**How to Use the OO Version:**\n\nModify your `run_builder.py` script:\n\n```python\n# run_builder.py (Modified Example)\nimport sys\nimport os\nimport logging\n\n# Add parent directory path if needed\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\n\n# Import the necessary classes/modules\nfrom knowledge_graph_builder import config\nfrom knowledge_graph_builder.db_operations import JsonldbStorage\nfrom knowledge_graph_builder.builder import KnowledgeGraphBuilder # Import the class\n\n# --- Basic Logging Setup ---\n# You might want a more sophisticated setup shared across modules\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlog = logging.getLogger(__name__) # Use logger for this script\n\nif __name__ == \"__main__\":\n log.info(\"Starting Knowledge Graph Builder script.\")\n\n # --- Initialize Storage ---\n storage = None\n try:\n # Instantiate the storage class directly\n storage = JsonldbStorage(config.DB_FOLDER)\n log.info(\"Database storage initialized successfully.\")\n except ImportError:\n log.critical(\"Failed to import JsonldbStorage. Is 'jsonldb' installed?\")\n exit(1)\n except Exception as e:\n log.error(f\"FATAL: Failed to initialize database storage: {e}\", exc_info=True)\n exit(1)\n\n # --- Initialize Builder ---\n builder_instance = None\n if storage:\n try:\n # Instantiate the builder, passing the storage instance\n builder_instance = KnowledgeGraphBuilder(storage)\n log.info(\"KnowledgeGraphBuilder instance created.\")\n except ValueError as e:\n # Catches API key configuration error from Builder's __init__\n log.error(f\"FATAL: Failed to create KnowledgeGraphBuilder: {e}\")\n exit(1)\n except Exception as e:\n log.error(f\"FATAL: An unexpected error occurred creating KnowledgeGraphBuilder: {e}\", exc_info=True)\n exit(1)\n\n # --- Run the Build Process ---\n if builder_instance:\n # Define the starting concept\n start_concept = \"aluminum oxide:a chemical compound of aluminum and oxygen\"\n log.info(f\"Starting graph build with root concept: '{start_concept}'\")\n\n # Call the main build method on the builder instance\n builder_instance.build_graph_from_concept(start_concept)\n\n # Optional: Commit changes using the storage object\n try:\n log.info(\"Attempting to commit changes to database...\")\n timestamp = builder_instance._get_iso_timestamp() # Access timestamp helper if needed\n storage.commit(f\"KG build run at {timestamp} starting from '{start_concept}'\")\n # commit message generated within the commit method of storage now\n except Exception as e:\n log.warning(f\"Failed to commit changes via storage object: {e}\", exc_info=True)\n else:\n # This case should ideally be caught by earlier exits\n log.error(\"Builder instance not created. Exiting.\")\n exit(1)\n\n log.info(\"Knowledge Graph Builder script finished.\")\n\n```\n\nThis OO structure makes `builder.py` more organized, testable (you could mock the `storage` object), and flexible for future changes, especially regarding the storage backend." }, ] }, { "role": "user", "parts": [ { "text": "will i need to migrate my old logs if i want to preserve them?" }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process for answering the \"log migration\" question:\n\n1. **Identify the Core Question:** The user wants to know if their *existing* log data, presumably created with the *previous* version of the code (which stored logs differently or had a different structure), will be compatible with the *new* OO version using `JsonldbStorage`.\n\n2. **Analyze the New Log Structure:**\n * What is the key for logs in the new system? The code (`_query_llm_and_store_concept`, `_update_log`, `storage.log_event`) uses `log_key`, which is generated using `self._get_iso_timestamp()`. This is a timestamp string.\n * What is the value (the log entry structure)? It's a dictionary defined in `_query_llm_and_store_concept`:\n ```python\n log_entry = {\n \"request_timestamp\": log_key, # Duplicates the key, useful inside the value\n \"model_used\": config.MODEL_NAME,\n \"concept_queried\": concept_id,\n \"prompt_sent\": formatted_prompt,\n \"response_timestamp\": None, # Timestamps\n \"raw_response\": None, # Strings/None\n \"parsed_response\": None, # Dict/None (LLM output)\n \"triples_added\": 0, # NEW FIELD (integer)\n \"error\": None, # String/None\n \"status\": \"pending\", # String (enum-like)\n \"retries_attempted\": 0 # Integer\n }\n ```\n * Where is it stored? In `config.LOG_TABLE_NAME` (`logs.jsonl`) using `JsonldbStorage`, which wraps `jsonldb`. The storage format is `jsonl`, meaning one JSON object per line, where each object is `{ \"log_key\": log_entry_dict }`.\n\n3. **Analyze the *Implied* Old Log Structure:**\n * The prompt didn't explicitly show the *old* `builder.py`'s logging details *before* the triple store refactor. However, the *first* version of `builder.py` provided (`builder.py.txt`) *did* have logging. Let's assume it was similar *but might lack the `triples_added` field*.\n * Crucially, the *original* `builder.py` (before OO and triple store) was storing *concept data* in `config.CONCEPTS_TABLE_NAME` (`concepts.jsonl`) as `{ concept_id: { predicate: object } }`. It *also* logged to `config.LOG_TABLE_NAME` (`logs.jsonl`).\n * The log structure in the *first* `builder.py.txt` was:\n ```python\n log_entry = {\n \"request_timestamp\": log_key,\n \"model_used\": config.MODEL_NAME,\n \"concept_queried\": concept_id,\n \"prompt_sent\": formatted_prompt,\n \"response_timestamp\": None,\n \"raw_response\": None,\n \"parsed_response\": None, # This was the LLM dict *before* triple conversion\n \"error\": None,\n \"status\": \"pending\",\n \"retries_attempted\": 0 # This was added during the 429 retry logic update\n }\n ```\n * The storage mechanism was also `jsonldb`, storing `{ log_key: log_entry_dict }` in `logs.jsonl`.\n\n4. **Compare Old and New Log Structures:**\n * **Key:** Both use a timestamp (`log_key`) as the primary key. -> Compatible.\n * **Storage File:** Both use `config.LOG_TABLE_NAME` (`logs.jsonl`). -> Compatible.\n * **Storage Format:** Both use `jsonldb`, storing `{ \"log_key\": log_entry_dict }` per line. -> Compatible.\n * **Value (Log Entry Dictionary):**\n * The *new* structure adds the `\"triples_added\"` field.\n * The *old* structure might be missing `\"triples_added\"`.\n * The meaning of `\"parsed_response\"` *might* subtly differ: in the old version, it was the data *before* thinking about triples; in the new version, it's still the raw LLM dict, but the *result* of processing it is the `triples_added` count, not direct storage of that dict in the main data table. This distinction is minor for the log itself.\n * All other fields seem present and compatible in terms of type (timestamps, strings, numbers, dicts).\n\n5. **Determine Migration Needs:**\n * Since the file name (`logs.jsonl`), primary key (`log_key`), and basic storage format (`jsonldb`) are the same, the new code *can read* the old log file.\n * The *only* significant difference is the potential absence of the `\"triples_added\"` field in old log entries.\n * Will the *new code* break if it encounters an old log entry without `\"triples_added\"`?\n * Reading/querying logs: Unlikely. Accessing `log_entry.get(\"triples_added\", 0)` or similar defensive patterns would handle its absence gracefully. The current `get_log_event` just returns the dictionary as-is.\n * Processing/analyzing logs later: If some analysis tool *expects* `\"triples_added\"` to exist, it might need updating to handle `None` or use a default value (like 0 or -1) for old entries.\n * Is there any *data loss* if you *don't* migrate? No, the old logs will still be there and contain the information they originally captured.\n * Is there a benefit to migrating? You could add the `\"triples_added\"` field with a default value (e.g., 0 or -1 or `null`) to make all log entries structurally identical. This might simplify downstream analysis tools.\n\n6. **Formulate the Answer:**\n * Start with a direct answer: Probably not *strictly* required for the builder to run, *but* there are considerations.\n * Explain the compatibility: File name, key, `jsonldb` format are the same. The new code can read the old file.\n * Highlight the difference: The new `\"triples_added\"` field. Old logs won't have it.\n * Explain the impact: The new *builder code itself* (reading logs via `get_log_event`) is unlikely to break. However, any separate analysis *specifically looking* for `triples_added` would need to handle its potential absence in older records.\n * Discuss migration options (if the user *wants* consistency):\n * **Option 1 (No Migration):** Accept that old logs lack the field. Use defensive coding (`.get()`) in analysis tools. (Simplest).\n * **Option 2 (Simple Migration):** Write a one-time script to read `logs.jsonl`, add `\"triples_added\": 0` (or `null`, or -1) to each entry that doesn't have it, and write back to a new file (or overwrite carefully). (Ensures structural consistency).\n * Recommendation: Start with Option 1 (no migration) unless you have immediate plans for analysis tools that *require* the field everywhere. If consistency becomes important later, perform the simple migration then.\n * Mention data integrity: The core information (API call details, concept, raw response) is preserved regardless. The new field relates specifically to the *outcome* of the triple-storage step introduced later." }, { "text": "Based on the code changes:\n\n1. **Log File Name:** Both the old procedural code (after the triple store idea was introduced conceptually but before the OO refactor) and the new OO code use `config.LOG_TABLE_NAME` (which defaults to `\"logs\"`) for the log file (`logs.jsonl`).\n2. **Storage Mechanism:** Both versions use `jsonldb`'s `upsert_dict` and `get_dict` to interact with this `logs.jsonl` file.\n3. **Log Entry Key:** Both versions use the ISO timestamp (`log_key`) as the primary key for the dictionary stored in `logs.jsonl`.\n4. **Log Entry Structure:** This is where the minor difference lies.\n * The *new* `KnowledgeGraphBuilder` explicitly adds a `\"triples_added\"` field to the log entry dictionary to record how many triples were successfully stored from that LLM call.\n * Your *previous* log entries (generated by the earlier versions of the script, even the procedural one storing triples) likely *do not* have this `\"triples_added\"` field.\n\n**Conclusion: Migration is likely NOT strictly necessary, but recommended for consistency.**\n\n* **Reading Old Logs:** The new `JsonldbStorage.get_log_event` method will still be able to read the old log entries from `logs.jsonl` because the file name, storage format, and key structure are compatible. It simply retrieves the dictionary associated with the `log_key`.\n* **Compatibility Issue:** When you or any analysis tool processes these logs later, you'll find that older entries are missing the `\"triples_added\"` field. This won't break the *builder itself*, but code analyzing the logs needs to account for this field potentially being absent (e.g., using `log_entry.get(\"triples_added\", 0)` or `log_entry.get(\"triples_added\", None)`).\n* **Benefit of Migration:** If you want all log entries to have a perfectly consistent structure (which simplifies later analysis), you could run a one-time migration script.\n\n**Simple Migration Script Idea:**\n\nYou could write a small Python script that:\n\n1. Reads the existing `logs.jsonl` file line by line.\n2. For each JSON object (representing `{log_key: log_data}`):\n * Loads the JSON object.\n * Gets the `log_data` dictionary.\n * Checks if `\"triples_added\"` key exists in `log_data`.\n * If it *doesn't* exist, add it with a default value (e.g., `\"triples_added\": 0` or `\"triples_added\": null`).\n * Reconstructs the `{log_key: updated_log_data}` object.\n3. Writes the updated JSON object to a *new* log file (e.g., `logs_migrated.jsonl`).\n4. Once verified, you can replace the old `logs.jsonl` with the migrated version.\n\n**Recommendation:**\n\nYou probably **don't need to migrate immediately** just to run the new builder code. The old logs won't break anything during the build process itself.\n\nHowever, if you plan to perform analysis on the logs later and want a uniform structure, running a simple migration script to add the missing `\"triples_added\"` field (with a default value like `0` or `null`) to older entries would be a good idea for consistency." }, ] }, { "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'