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Updated prompt-template
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JackColquitt
parent
977a8019ac
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81a368737d
4 changed files with 152 additions and 42 deletions
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@ -3,64 +3,90 @@ def to_relationships(text):
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prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.
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Read the provided text. You will model the text as an information network for a RDF knowledge graph.
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Read the provided text. You will model the text as an information network for a RDF knowledge graph in JSON.
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Information network rules:
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Information Network Rules:
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- An information network has subjects connected by predicates to objects.
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- A subject can have many predicates and objects.
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- A subject is a named-entity or a conceptual topic.
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- One subject can have many predicates and objects.
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- An object is a property or attribute of a subject.
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- A subject can be connected by a predicate to another subject.
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- Objects shall be either nouns or adjectives.
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Here is the provided text:
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Reading Instructions:
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- Ignore document formatting in the provided text.
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- Study the provided text carefully.
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Here is the text:
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{text}
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Instructions:
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- Obey the information network rules.
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- Ignore document formatting.
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- Do not provide explanations or any additional text.
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- Do not use special characters.
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- The key "object-entity" is true if it is a Named-Entity.
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- Respond only with a well-formed JSON using the following example:
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Response Instructions:
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- Obey the information network rules.
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- Do not return special characters.
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- Respond only with well-formed JSON.
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- The JSON response shall be an array of JSON objects with keys "subject", "predicate", "object", and "object-entity".
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- The JSON response shall use the following structure:
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JSON example: [{{"subject": string, "predicate": string, "object": string, "object-entity": boolean}}]
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```json
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[{{"subject": string, "predicate": string, "object": string, "object-entity": boolean}}]
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```
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- The key "object-entity" is TRUE only if the "object" is a subject.
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- Do not write any additional text or explanations.
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"""
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return prompt
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def to_topics(text):
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prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.\nRead the provided text. You will identify topics and their definitions.
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prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.\nRead the provided text. You will identify topics and their definitions in JSON.
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Here is the provided text:
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Reading Instructions:
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- Ignore document formatting in the provided text.
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- Study the provided text carefully.
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Here is the text:
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{text}
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Instructions:
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- Ignore document formatting.
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- Do not provide explanations or any additional text.
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- Do not use special characters.
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- Identify only topics that are unique to the provided text.
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- Respond only with a well-formed JSON using the following example:
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Response Instructions:
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- Do not respond with special characters.
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- Return only topics that are concepts and unique to the provided text.
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- Respond only with well-formed JSON.
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- The JSON response shall be an array of objects with keys "topic" and "definition".
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- The JSON response shall use the following structure:
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JSON example: [{{"topic": string, "definition": string}}]
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```json
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[{{"topic": string, "definition": string}}]
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```
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- Do not write any additional text or explanations.
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"""
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return prompt
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def to_definitions(text):
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prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.\nRead the provided text. You will identify named-entities and their definitions.
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prompt = f"""You are a helpful assistant that performs information extraction tasks for a provided text.\nRead the provided text. You will identify entities and their definitions in JSON.
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Here is the provided text:
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Reading Instructions:
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- Ignore document formatting in the provided text.
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- Study the provided text carefully.
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Here is the text:
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{text}
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Instructions:
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- Ignore document formatting.
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- Do not provide explanations or any additional text.
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- Do not use special characters.
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- Identity only entities that are named-entities.
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- Respond only with a well-formed JSON using the following example:
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Response Instructions:
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- Do not respond with special characters.
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- Return only entities that are named-entities such as: people, organizations, physical objects, locations, animals, products, commodotities, or substances.
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- Respond only with well-formed JSON.
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- The JSON response shall be an array of objects with keys "entity" and "definition".
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- The JSON response shall use the following structure:
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JSON example: [{{"entity": string, "definition": string}}]"""
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```json
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[{{"entity": string, "definition": string}}]
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```
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- Do not write any additional text or explanations.
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"""
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return prompt
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@ -3,6 +3,7 @@ Language service abstracts prompt engineering from LLM.
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"""
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import json
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import re
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from .... schema import Definition, Relationship, Triple
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from .... schema import PromptRequest, PromptResponse, Error
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@ -56,12 +57,15 @@ class Processor(ConsumerProducer):
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)
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def parse_json(self, text):
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# Hacky, workaround temperamental JSON markdown
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text = text.replace("```json", "")
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text = text.replace("```", "")
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json_match = re.search(r'```(?:json)?(.*?)```', text, re.DOTALL)
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if json_match:
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json_str = json_match.group(1).strip()
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else:
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# If no delimiters, assume the entire output is JSON
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json_str = text.strip()
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return json.loads(text)
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return json.loads(json_str)
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def handle(self, msg):
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@ -5,6 +5,9 @@ def to_relationships(template, text):
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def to_definitions(template, text):
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return template.format(text=text)
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def to_topics(template, text):
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return template.format(text=text)
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def to_rows(template, schema, text):
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field_schema = [
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@ -4,6 +4,7 @@ Language service abstracts prompt engineering from LLM.
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"""
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import json
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import re
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from .... schema import Definition, Relationship, Triple
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from .... schema import PromptRequest, PromptResponse, Error
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@ -15,7 +16,7 @@ from .... base import ConsumerProducer
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from .... clients.llm_client import LlmClient
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from . prompts import to_definitions, to_relationships, to_rows
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from . prompts import to_kg_query, to_document_query
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from . prompts import to_kg_query, to_document_query, to_topics
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module = ".".join(__name__.split(".")[1:-1])
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@ -38,6 +39,7 @@ class Processor(ConsumerProducer):
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)
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definition_template = params.get("definition_template")
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relationship_template = params.get("relationship_template")
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topic_template = params.get("topic_template")
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rows_template = params.get("rows_template")
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knowledge_query_template = params.get("knowledge_query_template")
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document_query_template = params.get("document_query_template")
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@ -62,18 +64,22 @@ class Processor(ConsumerProducer):
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)
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self.definition_template = definition_template
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self.topic_template = topic_template
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self.relationship_template = relationship_template
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self.rows_template = rows_template
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self.knowledge_query_template = knowledge_query_template
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self.document_query_template = document_query_template
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def parse_json(self, text):
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# Hacky, workaround temperamental JSON markdown
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text = text.replace("```json", "")
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text = text.replace("```", "")
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json_match = re.search(r'```(?:json)?(.*?)```', text, re.DOTALL)
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if json_match:
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json_str = json_match.group(1).strip()
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else:
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# If no delimiters, assume the entire output is JSON
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json_str = text.strip()
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return json.loads(text)
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return json.loads(json_str)
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def handle(self, msg):
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@ -92,6 +98,11 @@ class Processor(ConsumerProducer):
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self.handle_extract_definitions(id, v)
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return
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elif kind == "extract-topics":
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self.handle_extract_topics(id, v)
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return
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elif kind == "extract-relationships":
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self.handle_extract_relationships(id, v)
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@ -176,6 +187,66 @@ class Processor(ConsumerProducer):
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self.producer.send(r, properties={"id": id})
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def handle_extract_topics(self, id, v):
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try:
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prompt = to_topics(self.topic_template, v.chunk)
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ans = self.llm.request(prompt)
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# Silently ignore JSON parse error
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try:
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defs = self.parse_json(ans)
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except:
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print("JSON parse error, ignored", flush=True)
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defs = []
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output = []
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for defn in defs:
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try:
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e = defn["topic"]
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d = defn["definition"]
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if e == "": continue
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if e is None: continue
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if d == "": continue
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if d is None: continue
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output.append(
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Definition(
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name=e, definition=d
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)
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)
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except:
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print("definition fields missing, ignored", flush=True)
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print("Send response...", flush=True)
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r = PromptResponse(topics=output, error=None)
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self.producer.send(r, properties={"id": id})
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print("Done.", flush=True)
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except Exception as e:
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print(f"Exception: {e}")
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print("Send error response...", flush=True)
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r = PromptResponse(
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error=Error(
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type = "llm-error",
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message = str(e),
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),
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response=None,
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)
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self.producer.send(r, properties={"id": id})
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def handle_extract_relationships(self, id, v):
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try:
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@ -415,6 +486,12 @@ class Processor(ConsumerProducer):
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help=f'Definition extraction template',
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)
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parser.add_argument(
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'--topic-template',
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required=True,
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help=f'Topic extraction template',
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)
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parser.add_argument(
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'--rows-template',
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required=True,
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