Source code for neo4j_graphrag.generation.prompts

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#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
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#      https://www.apache.org/licenses/LICENSE-2.0
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from __future__ import annotations

import warnings
from typing import Any, Optional

from neo4j_graphrag.exceptions import (
    PromptMissingInputError,
    PromptMissingPlaceholderError,
)


[docs] class PromptTemplate: """This class is used to generate a parameterized prompt. It is defined from a string (the template) using the Python format syntax (parameters between curly braces `{}`) and a list of required inputs. Before sending the instructions to an LLM, call the `format` method that will replace parameters with the provided values. If any of the expected inputs is missing, a `PromptMissingInputError` is raised. """ DEFAULT_TEMPLATE: str = "" EXPECTED_INPUTS: list[str] = list() def __init__( self, template: Optional[str] = None, expected_inputs: Optional[list[str]] = None, ) -> None: self.template = template or self.DEFAULT_TEMPLATE self.expected_inputs = expected_inputs or self.EXPECTED_INPUTS for e in self.expected_inputs: if f"{{{e}}}" not in self.template: raise PromptMissingPlaceholderError( f"`template` is missing placeholder {e}" ) def _format(self, **kwargs: Any) -> str: for e in self.EXPECTED_INPUTS: if e not in kwargs: raise PromptMissingInputError(f"Missing input '{e}'") return self.template.format(**kwargs)
[docs] def format(self, *args: Any, **kwargs: Any) -> str: """This method is used to replace parameters with the provided values. Parameters must be provided: - as kwargs - as args if using the same order as in the expected inputs Example: .. code-block:: python prompt_template = PromptTemplate( template='''Explain the following concept to {target_audience}: Concept: {concept} Answer: ''', expected_inputs=['target_audience', 'concept'] ) prompt = prompt_template.format('12 yo children', concept='graph database') print(prompt) # Result: # '''Explain the following concept to 12 yo children: # Concept: graph database # Answer: # ''' """ data = kwargs data.update({k: v for k, v in zip(self.expected_inputs, args)}) return self._format(**data)
[docs] class RagTemplate(PromptTemplate): DEFAULT_TEMPLATE = """Answer the user question using the following context Context: {context} Examples: {examples} Question: {query_text} Answer: """ EXPECTED_INPUTS = ["context", "query_text", "examples"]
[docs] def format(self, query_text: str, context: str, examples: str) -> str: return super().format(query_text=query_text, context=context, examples=examples)
class Text2CypherTemplate(PromptTemplate): DEFAULT_TEMPLATE = """ Task: Generate a Cypher statement for querying a Neo4j graph database from a user input. Schema: {schema} Examples (optional): {examples} Input: {query_text} Do not use any properties or relationships not included in the schema. Do not include triple backticks ``` or any additional text except the generated Cypher statement in your response. Cypher query: """ EXPECTED_INPUTS = ["query_text"] def format( self, schema: Optional[str] = None, examples: Optional[str] = None, query_text: str = "", query: Optional[str] = None, **kwargs: Any, ) -> str: if query is not None: if query_text: warnings.warn( "Both 'query' and 'query_text' are provided, 'query_text' will be used.", DeprecationWarning, stacklevel=2, ) elif isinstance(query, str): warnings.warn( "'query' is deprecated and will be removed in a future version, please use 'query_text' instead.", DeprecationWarning, stacklevel=2, ) query_text = query return super().format( query_text=query_text, schema=schema, examples=examples, **kwargs )
[docs] class ERExtractionTemplate(PromptTemplate): DEFAULT_TEMPLATE = """ You are a top-tier algorithm designed for extracting information in structured formats to build a knowledge graph. Extract the entities (nodes) and specify their type from the following text. Also extract the relationships between these nodes. Return result as JSON using the following format: {{"nodes": [ {{"id": "0", "label": "Person", "properties": {{"name": "John"}} }}], "relationships": [{{"type": "KNOWS", "start_node_id": "0", "end_node_id": "1", "properties": {{"since": "2024-08-01"}} }}] }} Use only the following nodes and relationships (if provided): {schema} Assign a unique ID (string) to each node, and reuse it to define relationships. Do respect the source and target node types for relationship and the relationship direction. Do not return any additional information other than the JSON in it. Examples: {examples} Input text: {text} """ EXPECTED_INPUTS = ["text"]
[docs] def format( self, schema: dict[str, Any], examples: str, text: str = "", ) -> str: return super().format(text=text, schema=schema, examples=examples)