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闫旭隆
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AIEC-RAG/atlas_rag/llm_generator/format/__init__.py
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AIEC-RAG/atlas_rag/llm_generator/format/__init__.py
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AIEC-RAG/atlas_rag/llm_generator/format/validate_json_output.py
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AIEC-RAG/atlas_rag/llm_generator/format/validate_json_output.py
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import json
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from typing import List, Any
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import json_repair
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import jsonschema
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def normalize_key(key):
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return key.strip().lower()
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# recover function can be fix_triple_extraction_response, fix_filter_triplets
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def validate_output(output_str, **kwargs):
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schema = kwargs.get("schema")
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fix_function = kwargs.get("fix_function", None)
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allow_empty = kwargs.get("allow_empty", True)
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if fix_function:
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parsed_data = fix_function(output_str, **kwargs)
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jsonschema.validate(instance=parsed_data, schema=schema)
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if not allow_empty and (not parsed_data or len(parsed_data) == 0):
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raise ValueError("Parsed data is empty after validation.")
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return json.dumps(parsed_data, ensure_ascii=False)
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def fix_filter_triplets(data: str, **kwargs) -> dict:
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data = json_repair.loads(data)
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processed_facts = []
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def find_triplet(element: Any) -> List[str] | None:
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# Base case: a valid triplet
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if isinstance(element, list) and len(element) == 3 and all(isinstance(item, str) for item in element):
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return element
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# Recursive case: dig deeper into nested lists
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elif isinstance(element, list):
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for sub_element in element:
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result = find_triplet(sub_element)
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if result:
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return result
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return None
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for item in data.get("fact", []):
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triplet = find_triplet(item)
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if triplet:
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processed_facts.append(triplet)
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return {"fact": processed_facts}
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def fix_triple_extraction_response(response: str, **kwargs) -> str:
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"""Attempt to fix and validate JSON response based on the prompt type."""
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# Extract the JSON list from the response
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# raise error if prompt_type is not provided
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if "prompt_type" not in kwargs:
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raise ValueError("The 'prompt_type' argument is required.")
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prompt_type = kwargs.get("prompt_type")
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json_start_token = response.find("[")
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if json_start_token == -1:
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# add [ at the start
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response = "[" + response.strip() + "]"
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parsed_objects = json_repair.loads(response)
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if len(parsed_objects) == 0:
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return []
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# Define required keys for each prompt type
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required_keys = {
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"entity_relation": {"Head", "Relation", "Tail"},
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"event_entity": {"Event", "Entity"},
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"event_relation": {"Head", "Relation", "Tail"}
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}
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corrected_data = []
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seen_triples = set()
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for idx, item in enumerate(parsed_objects):
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if not isinstance(item, dict):
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print(f"Item {idx} must be a JSON object. Problematic item: {item}")
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continue
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# Correct the keys
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corrected_item = {}
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for key, value in item.items():
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norm_key = normalize_key(key)
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matching_expected_keys = [exp_key for exp_key in required_keys[prompt_type] if normalize_key(exp_key) in norm_key]
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if len(matching_expected_keys) == 1:
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corrected_key = matching_expected_keys[0]
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corrected_item[corrected_key] = value
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else:
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corrected_item[key] = value
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# Check for missing keys in corrected_item
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missing = required_keys[prompt_type] - corrected_item.keys()
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if missing:
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print(f"Item {idx} missing required keys: {missing}. Problematic item: {item}")
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continue
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# Validate and correct the values in corrected_item
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if prompt_type == "entity_relation":
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for key in ["Head", "Relation", "Tail"]:
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if not isinstance(corrected_item[key], str) or not corrected_item[key].strip():
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print(f"Item {idx} {key} must be a non-empty string. Problematic item: {corrected_item}")
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continue
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elif prompt_type == "event_entity":
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if not isinstance(corrected_item["Event"], str) or not corrected_item["Event"].strip():
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print(f"Item {idx} Event must be a non-empty string. Problematic item: {corrected_item}")
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continue
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if not isinstance(corrected_item["Entity"], list) or not corrected_item["Entity"]:
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print(f"Item {idx} Entity must be a non-empty array. Problematic item: {corrected_item}")
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continue
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else:
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corrected_item["Entity"] = [ent.strip() for ent in corrected_item["Entity"] if isinstance(ent, str)]
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elif prompt_type == "event_relation":
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for key in ["Head", "Tail", "Relation"]:
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if not isinstance(corrected_item[key], str) or not corrected_item[key].strip():
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print(f"Item {idx} {key} must be a non-empty sentence. Problematic item: {corrected_item}")
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continue
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triple_tuple = tuple((k, str(v)) for k, v in corrected_item.items())
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if triple_tuple in seen_triples:
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print(f"Item {idx} is a duplicate triple: {corrected_item}")
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continue
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else:
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seen_triples.add(triple_tuple)
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corrected_data.append(corrected_item)
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if not corrected_data:
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return []
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return corrected_data
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def fix_lkg_keywords(data: str, **kwargs) -> dict:
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"""
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Extract and flatten keywords into a list of strings, filtering invalid types.
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"""
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data = json_repair.loads(data)
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processed_keywords = []
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def collect_strings(element: Any) -> None:
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if isinstance(element, str):
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if len(element) <= 200: # Filter out keywords longer than 100 characters
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processed_keywords.append(element)
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elif isinstance(element, list):
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for item in element:
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collect_strings(item)
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# Start processing from the root "keywords" field
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collect_strings(data.get("keywords", []))
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return {"keywords": processed_keywords}
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@ -0,0 +1,93 @@
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filter_fact_json_schema = {
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"type": "object",
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"properties": {
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"fact": {
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"type": "array",
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"items": {
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"type": "array",
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"items": {
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"type": "string" # All items in the inner array must be strings
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},
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"minItems": 3,
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"maxItems": 3,
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"additionalItems": False # Block extra items
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},
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}
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},
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"required": ["fact"]
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}
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lkg_keyword_json_schema = {
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"type": "object",
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"properties": {
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"keywords": {
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"type": "array",
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"items": {
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"type": "string"
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},
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"minItems": 1,
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}
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},
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"required": ["keywords"]
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}
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triple_json_schema = {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"Head": {
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"type": "string"
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},
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"Relation": {
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"type": "string"
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},
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"Tail": {
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"type": "string"
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}
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},
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"required": ["Head", "Relation", "Tail"]
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},
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}
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event_relation_json_schema = {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"Head": {
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"type": "string"
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},
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"Relation": {
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"type": "string",
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},
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"Tail": {
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"type": "string"
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}
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},
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"required": ["Head", "Relation", "Tail"]
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},
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}
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event_entity_json_schema = {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"Event": {
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"type": "string"
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},
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"Entity": {
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"type": "array",
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"items": {
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"type": "string"
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},
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"minItems": 1
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}
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},
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"required": ["Event", "Entity"]
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},
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}
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stage_to_schema = {
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1: triple_json_schema,
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2: event_entity_json_schema,
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3: event_relation_json_schema
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}
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