init

backend/prompts.py

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+from langchain_core.prompts.prompt import PromptTemplate
+from langchain_core.prompts import ChatPromptTemplate
+from datetime import datetime, timezone
+
+
+DATE_TODAY = "Today's date is " + datetime.now(timezone.utc).astimezone().isoformat() + '\n'
+
+CYPHER_QA_TEMPLATE = DATE_TODAY + """You are an assistant that helps to form nice and human understandable answers.
+The information part contains the provided information that you must use to construct an answer.
+The provided information is authoritative, you must never doubt it or try to use your internal knowledge to correct it.
+Make the answer sound as a response to the question. Do not mention that you based the result on the given information.
+Here are the examples:
+
+Question: Website on which the most time was spend on?
+Context:[{'d.VisitedWebPageURL': 'https://stackoverflow.com/questions/59873698/the-default-export-is-not-a-react-component-in-page-nextjs', 'totalDuration': 8889167}]
+Helpful Answer: You visited https://stackoverflow.com/questions/59873698/the-default-export-is-not-a-react-component-in-page-nextjs for 8889167 milliseconds or 8889.167 seconds.
+
+Follow this example when generating answers.
+If the provided information is empty, then and only then, return exactly 'don't know' as answer.
+
+Information:
+{context}
+
+Question: {question}
+Helpful Answer:"""
+
+CYPHER_QA_PROMPT = PromptTemplate(
+    input_variables=["context", "question"], template=CYPHER_QA_TEMPLATE
+)
+
+SIMILARITY_SEARCH_RAG = DATE_TODAY + """You are an assistant for question-answering tasks. 
+Use the following pieces of retrieved context to answer the question. 
+If you don't know the answer, return exactly 'don't know' as answer.
+Question: {question} 
+Context: {context} 
+Answer:"""
+
+
+SIMILARITY_SEARCH_PROMPT = PromptTemplate(
+    input_variables=["context", "question"], template=SIMILARITY_SEARCH_RAG
+)
+
+# doc_extract_chain = DOCUMENT_METADATA_EXTRACTION_PROMT | structured_llm
+
+
+CYPHER_GENERATION_TEMPLATE = DATE_TODAY + """Task:Generate Cypher statement to query a graph database.
+Instructions:
+Use only the provided relationship types and properties in the schema.
+Do not use any other relationship types or properties that are not provided.
+
+Schema:
+{schema}
+Note: Do not include any explanations or apologies in your responses.
+Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
+Do not include any text except the generated Cypher statement.
+
+
+The question is:
+{question}"""
+CYPHER_GENERATION_PROMPT = PromptTemplate(
+    input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
+)
+
+
+DOC_DESCRIPTION_TEMPLATE = """Task:Give Detailed Description of the page content of the given document.
+Instructions:
+Provide as much details about metadata & page content as if you need to give human readable report of this Browsing session event. 
+
+Document:
+{document}
+"""
+DOC_DESCRIPTION_PROMPT = PromptTemplate(
+    input_variables=["document"], template=DOC_DESCRIPTION_TEMPLATE
+)
+
+
+DOCUMENT_METADATA_EXTRACTION_SYSTEM_MESSAGE = DATE_TODAY + """You are a helpful assistant. You are given a Cypher statement result after quering the Neo4j graph database.
+Generate a very good Query that can be used to perform similarity search on the vector store of the Neo4j graph database"""
+
+DOCUMENT_METADATA_EXTRACTION_PROMT = ChatPromptTemplate.from_messages([("system", DOCUMENT_METADATA_EXTRACTION_SYSTEM_MESSAGE), ("human", "{input}")])
+
+
+
+