diff --git a/biochatter/prompts.py b/biochatter/prompts.py index a544013e..ec755024 100644 --- a/biochatter/prompts.py +++ b/biochatter/prompts.py @@ -1,26 +1,177 @@ -from typing import Optional -import yaml +import abc import json import os -from ._misc import sentencecase_to_pascalcase, ensure_iterable -from .llm_connect import GptConversation +from typing import Optional + +import yaml +from simple_ddl_parser import DDLParser + +from biochatter._misc import ensure_iterable, sentencecase_to_pascalcase +from biochatter.llm_connect import GptConversation + +class PromptEngine: + """ + Using the schema definition of a data structure, allow + the generation of prompts for a large language model, informing it of + the schema constituents and their properties, to enable the + parameterisation of function calls to the data structure. + + Currently implemented for knowledge graphs (from BioCypher) and relational databases. + """ -class BioCypherPromptEngine: def __init__( self, schema_config_or_info_path: Optional[str] = None, schema_config_or_info_dict: Optional[dict] = None, model_name: str = "gpt-3.5-turbo", + ): + self.model_name = model_name + self.question = "" + self.entities = {} + self.relationships = {} + self.properties = {} + self.selected_entities = [] + self.selected_relationships = [] # used in property selection + self.selected_relationship_labels = {} # copy to deal with labels that + # are not the same as the relationship name, used in query generation + # dictionary to also include source and target types + self.rel_directions = {} + + if not schema_config_or_info_path and not schema_config_or_info_dict: + raise ValueError( + "Please provide the schema configuration or schema info as a " + "path to a file or as a dictionary." + ) + + if schema_config_or_info_path and schema_config_or_info_dict: + raise ValueError( + "Please provide the schema configuration or schema info as a " + "path to a file or as a dictionary, not both." + ) + + self.load_schema(schema_config_or_info_path, schema_config_or_info_dict) + + @abc.abstractmethod + def load_schema( + self, schema_config_or_info_path, schema_config_or_info_dict + ): + pass + + def generate_query(self, question: str, query_language: str) -> str: + """ + Wrap entity and property selection and query generation; return the + generated query. + + Args: + question: A user's question. + + query_language: The language of the query to generate. + + Returns: + A database query that could answer the user's question. + """ + + success1 = self._select_entities(question) + if not success1: + raise ValueError( + "Entity selection failed. Please try again with a different " + "question." + ) + success2 = self._select_relationships() + if not success2: + raise ValueError( + "Relationship selection failed. Please try again with a " + "different question." + ) + success3 = self._select_properties() + if not success3: + raise ValueError( + "Property selection failed. Please try again with a different " + "question." + ) + + return self._generate_query( + question=question, + entities=self.selected_entities, + relationships=self.selected_relationship_labels, + properties=self.selected_properties, + query_language=query_language, + ) + + def _generate_query( + self, + question: str, + entities: list, + relationships: dict, + properties: dict, + query_language: str, + ): + """ + Generate a query in the specified query language that answers the user's + question. + + Args: + question: A user's question. + + entities: A list of entities that are relevant to the question. + + relationships: A list of relationships that are relevant to the + question. + + properties: A dictionary of properties that are relevant to the + question. + + query_language: The language of the query to generate. + """ + msg = ( + f"Generate a database query in {query_language} that answers " + f"the user's question. " + f"You can use the following entities: {entities}, " + f"relationships: {list(relationships.keys())}, and " + f"properties: {properties}. " + ) + + for relationship, values in relationships.items(): + self._expand_pairs(relationship, values) + + if self.rel_directions: + msg += "Given the following valid combinations of source, relationship, and target: " + for key, value in self.rel_directions.items(): + for pair in value: + msg += f"'(:{pair[0]})-(:{key})->(:{pair[1]})', " + msg += f"generate a {query_language} query using one of these combinations. " + + msg += "Only return the query, without any additional text." + + conversation = GptConversation( + model_name=self.model_name, + prompts={}, + correct=False, + ) + + conversation.set_api_key( + api_key=os.getenv("OPENAI_API_KEY"), user="query_generator" + ) + + conversation.append_system_message(msg) + + out_msg, token_usage, correction = conversation.query(question) + + return out_msg + + +class BioCypherPromptEngine(PromptEngine): + def load_schema( + self, + schema_config_or_info_path: Optional[str] = None, + schema_config_or_info_dict: Optional[dict] = None, ): """ Given a biocypher schema configuration, extract the entities and relationships, and for each extract their mode of representation (node - or edge), properties, and identifier namespace. Using these data, allow - the generation of prompts for a large language model, informing it of - the schema constituents and their properties, to enable the - parameterisation of function calls to a knowledge graph. + or edge), properties, and identifier namespace. Args: schema_config_or_info_path: Path to a biocypher schema configuration @@ -35,19 +186,6 @@ def __init__( Todo: inject conversation directly instead of specifying model name? """ - - if not schema_config_or_info_path and not schema_config_or_info_dict: - raise ValueError( - "Please provide the schema configuration or schema info as a " - "path to a file or as a dictionary." - ) - - if schema_config_or_info_path and schema_config_or_info_dict: - raise ValueError( - "Please provide the schema configuration or schema info as a " - "path to a file or as a dictionary, not both." - ) - if schema_config_or_info_path: # read the schema configuration with open(schema_config_or_info_path, "r") as f: @@ -61,8 +199,6 @@ def __init__( # extract the entities and relationships: each top level key that has # a 'represented_as' key - self.entities = {} - self.relationships = {} if not is_schema_info: for key, value in schema_config.items(): # hacky, better with biocypher output @@ -98,15 +234,6 @@ def __init__( value = self._capitalise_source_and_target(value) self.relationships[sentencecase_to_pascalcase(key)] = value - self.question = "" - self.selected_entities = [] - self.selected_relationships = [] # used in property selection - self.selected_relationship_labels = {} # copy to deal with labels that - # are not the same as the relationship name, used in query generation - # dictionary to also include source and target types - self.rel_directions = {} - self.model_name = model_name - def _capitalise_source_and_target(self, relationship: dict) -> dict: """ Make sources and targets PascalCase to match the entities. Sources and @@ -134,47 +261,6 @@ def _capitalise_source_and_target(self, relationship: dict) -> dict: ] return relationship - def generate_query(self, question: str, query_language: str) -> str: - """ - Wrap entity and property selection and query generation; return the - generated query. - - Args: - question: A user's question. - - query_language: The language of the query to generate. - - Returns: - A database query that could answer the user's question. - """ - - success1 = self._select_entities(question) - if not success1: - raise ValueError( - "Entity selection failed. Please try again with a different " - "question." - ) - success2 = self._select_relationships() - if not success2: - raise ValueError( - "Relationship selection failed. Please try again with a " - "different question." - ) - success3 = self._select_properties() - if not success3: - raise ValueError( - "Property selection failed. Please try again with a different " - "question." - ) - - return self._generate_query( - question=question, - entities=self.selected_entities, - relationships=self.selected_relationship_labels, - properties=self.selected_properties, - query_language=query_language, - ) - def _select_entities(self, question: str) -> bool: """ @@ -457,50 +543,188 @@ def _select_properties( return bool(self.selected_properties) - def _generate_query( + def _expand_pairs(self, relationship, values): + if not self.rel_directions.get(relationship): + self.rel_directions[relationship] = [] + if isinstance(values["source"], list): + for source in values["source"]: + if isinstance(values["target"], list): + for target in values["target"]: + self.rel_directions[relationship].append( + (source, target) + ) + else: + self.rel_directions[relationship].append( + (source, values["target"]) + ) + elif isinstance(values["target"], list): + for target in values["target"]: + self.rel_directions[relationship].append( + (values["source"], target) + ) + else: + self.rel_directions[relationship].append( + (values["source"], values["target"]) + ) + + +class SQLPromptEngine(PromptEngine): + def load_schema( self, - question: str, - entities: list, - relationships: dict, - properties: dict, - query_language: str, + schema_config_or_info_path: Optional[str] = None, + schema_config_or_info_dict: Optional[dict] = None, ): """ - Generate a query in the specified query language that answers the user's - question. + Given a relational database schema (SQL), extract the entities, + relationships, and properties. Args: - question: A user's question. + schema_config_or_info_path: Path to a SQL file. - entities: A list of entities that are relevant to the question. + schema_config_or_info_dict: A json dictionary containing the schema + definition as generated by simple-ddl-parser. + """ + if schema_config_or_info_path: + # read the schema SQL definition + with open(schema_config_or_info_path, "r") as f: + ddl = f.read() + schema_config = DDLParser(ddl).run(group_by_type=True) + elif schema_config_or_info_dict: + schema_config = schema_config_or_info_dict - relationships: A list of relationships that are relevant to the - question. + # extract the entities from the tables + for table in schema_config["tables"]: + name_indicates_relationship = ( + "to" in table["table_name"].split("_") + or "interaction" in table["table_name"] + ) + if not name_indicates_relationship: + self.entities[table["table_name"].lower()] = { + "properties": [ + column["name"].lower() for column in table["columns"] + ] + # TODO can add other information here or take the complete dictionary from DDL parser + } + else: + self.relationships[table["table_name"].lower()] = { + "properties": [ + column["name"].lower() for column in table["columns"] + ] + # TODO can add other information here or take the complete dictionary from DDL parser + } - properties: A dictionary of properties that are relevant to the - question. + # TODO also get relationships from foreign keys? - query_language: The language of the query to generate. + self.properties = {} + for entity in self.entities: + if self.entities[entity].get("properties"): + self.properties[entity] = self.entities[entity]["properties"] + + def _select_entities(self, question: str) -> bool: """ - msg = ( - f"Generate a database query in {query_language} that answers " - f"the user's question. " - f"You can use the following entities: {entities}, " - f"relationships: {list(relationships.keys())}, and " - f"properties: {properties}. " + + Given a question, select the entities that are relevant to the question + and store them in `selected_entities` and `selected_relationships`. Use + LLM conversation to do this. + + Args: + question: A user's question. + + Returns: + True if at least one entity was selected, False otherwise. + + """ + + self.question = question + + conversation = GptConversation( + model_name=self.model_name, + prompts={}, + correct=False, ) - for relationship, values in relationships.items(): - self._expand_pairs(relationship, values) + conversation.set_api_key( + api_key=os.getenv("OPENAI_API_KEY"), user="entity_selector" + ) - if self.rel_directions: - msg += "Given the following valid combinations of source, relationship, and target: " - for key, value in self.rel_directions.items(): - for pair in value: - msg += f"'(:{pair[0]})-(:{key})->(:{pair[1]})', " - msg += f"generate a {query_language} query using one of these combinations. " + conversation.append_system_message( + ( + "You have access to a relational database that contains " + f"these tables representing entities: {', '.join(self.entities)}. Your task is " + "to select the ones that are relevant to the user's question " + "for subsequent use in a query. Only return the entities, " + "comma-separated, without any additional text. " + ) + ) - msg += "Only return the query, without any additional text." + msg, token_usage, correction = conversation.query(question) + + result = msg.split(",") if msg else [] + # TODO: do we go back and retry if no entities were selected? or ask for + # a reason? offer visual selection of entities and relationships by the + # user? + + if result: + for entity in result: + entity = entity.strip() + if entity in self.entities: + self.selected_entities.append(entity) + + return bool(result) + + def _select_properties( + self, + ): + """ + + Given a question (optionally provided, but in the standard use case + reused from the entity selection step) and the selected entities, select + the properties that are relevant to the question and store them in + the dictionary `selected_properties`. + + Returns: + True if at least one property was selected, False otherwise. + + """ + + if not self.question: + raise ValueError( + "No question found. Please make sure to run entity and " + "relationship selection first." + ) + + if not self.selected_entities: + raise ValueError( + "No entities provided, and none available " + "from entity selection step. Please provide " + "entities or run the entity selection " + "(`select_entities()`) step first." + ) + + e_props = {} + for entity in self.selected_entities: + if self.entities[entity].get("properties"): + e_props[entity] = self.entities[entity]["properties"] + + r_props = {} + for relationship in self.selected_relationships: + if self.relationships[relationship].get("properties"): + r_props[relationship] = self.relationships[relationship][ + "properties" + ] + + msg = ( + "You have access to a knowledge graph that contains entities and " + "relationships. They have the following properties. Entities: " + f"{e_props}, Relationships: {r_props}. " + "Your task is to select the properties that are relevant to the " + "user's question for subsequent use in a query. Only return the " + "entities and relationships with their relevant properties in JSON " + "format, without any additional text. Return the " + "entities/relationships as top-level dictionary keys, and their " + "properties as dictionary values. " + "Do not return properties that are not relevant to the question." + ) conversation = GptConversation( model_name=self.model_name, @@ -509,35 +733,30 @@ def _generate_query( ) conversation.set_api_key( - api_key=os.getenv("OPENAI_API_KEY"), user="query_generator" + api_key=os.getenv("OPENAI_API_KEY"), user="property_selector" ) conversation.append_system_message(msg) - out_msg, token_usage, correction = conversation.query(question) + msg, token_usage, correction = conversation.query(self.question) - return out_msg + self.selected_properties = json.loads(msg) if msg else {} - def _expand_pairs(self, relationship, values): - if not self.rel_directions.get(relationship): - self.rel_directions[relationship] = [] - if isinstance(values["source"], list): - for source in values["source"]: - if isinstance(values["target"], list): - for target in values["target"]: - self.rel_directions[relationship].append( - (source, target) - ) - else: - self.rel_directions[relationship].append( - (source, values["target"]) - ) - elif isinstance(values["target"], list): - for target in values["target"]: - self.rel_directions[relationship].append( - (values["source"], target) - ) - else: - self.rel_directions[relationship].append( - (values["source"], values["target"]) - ) + return bool(self.selected_properties) + + def _select_relationships(self) -> bool: + """ + Given a question and the preselected entities, select relationships for + the query. + + Args: + question: A user's question. + + entities: A list of entities that are relevant to the question. + + Returns: + True + """ + # TODO implement, but not as relevant for relational db; + # it's also not essential so this should always return True + return True diff --git a/poetry.lock b/poetry.lock index 290e3850..485e5a0d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,26 +1,111 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + [[package]] name = "addict" version = "2.4.0" description = 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