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88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
# flake8: noqa
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from langchain.prompts.prompt import PromptTemplate
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_DEFAULT_ENTITY_EXTRACTION_TEMPLATE = """Extract all entities from the following text. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.
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Return the output as a single comma-separated list, or NONE if there is nothing of note to return.
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EXAMPLE
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i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.
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Output: Langchain
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END OF EXAMPLE
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EXAMPLE
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i'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Sam.
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Output: Langchain, Sam
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END OF EXAMPLE
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Begin!
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{input}
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Output:"""
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ENTITY_EXTRACTION_PROMPT = PromptTemplate(
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input_variables=["input"], template=_DEFAULT_ENTITY_EXTRACTION_TEMPLATE
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)
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prompt_template = """Use the following knowledge triplets to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
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{context}
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Question: {question}
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Helpful Answer:"""
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PROMPT = PromptTemplate(
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template=prompt_template, input_variables=["context", "question"]
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)
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CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
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Instructions:
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Use only the provided relationship types and properties in the schema.
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Do not use any other relationship types or properties that are not provided.
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Schema:
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{schema}
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Note: Do not include any explanations or apologies in your responses.
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Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
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Do not include any text except the generated Cypher statement.
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The question is:
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{question}"""
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CYPHER_GENERATION_PROMPT = PromptTemplate(
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input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
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)
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NEBULAGRAPH_EXTRA_INSTRUCTIONS = """
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Instructions:
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First, generate cypher then convert it to NebulaGraph Cypher dialect(rather than standard):
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1. it requires explicit label specification when referring to node properties: v.`Foo`.name
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2. it uses double equals sign for comparison: `==` rather than `=`
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For instance:
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```diff
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< MATCH (p:person)-[:directed]->(m:movie) WHERE m.name = 'The Godfather II'
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< RETURN p.name;
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---
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> MATCH (p:`person`)-[:directed]->(m:`movie`) WHERE m.`movie`.`name` == 'The Godfather II'
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> RETURN p.`person`.`name`;
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```\n"""
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NGQL_GENERATION_TEMPLATE = CYPHER_GENERATION_TEMPLATE.replace(
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"Generate Cypher", "Generate NebulaGraph Cypher"
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).replace("Instructions:", NEBULAGRAPH_EXTRA_INSTRUCTIONS)
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NGQL_GENERATION_PROMPT = PromptTemplate(
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input_variables=["schema", "question"], template=NGQL_GENERATION_TEMPLATE
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)
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CYPHER_QA_TEMPLATE = """You are an assistant that helps to form nice and human understandable answers.
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The information part contains the provided information that you must use to construct an answer.
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The provided information is authorative, you must never doubt it or try to use your internal knowledge to correct it.
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Make the answer sound as a response to the question. Do not mention that you based the result on the given information.
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If the provided information is empty, say that you don't know the answer.
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Information:
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{context}
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Question: {question}
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Helpful Answer:"""
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CYPHER_QA_PROMPT = PromptTemplate(
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input_variables=["context", "question"], template=CYPHER_QA_TEMPLATE
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)
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