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| """ | |
| This module provides functions for generating optimized search messages, RAG prompt templates, | |
| and messages for queries with relevant source documents using the LangChain library. | |
| """ | |
| from langchain.schema import SystemMessage, HumanMessage | |
| from langchain.prompts.chat import ( | |
| HumanMessagePromptTemplate, | |
| SystemMessagePromptTemplate, | |
| ChatPromptTemplate | |
| ) | |
| from langchain.prompts.prompt import PromptTemplate | |
| def get_optimized_search_messages(query): | |
| """ | |
| Generate optimized search messages for a given query. | |
| Args: | |
| query (str): The user's query. | |
| Returns: | |
| list: A list containing the system message and human message for optimized search. | |
| """ | |
| system_message = SystemMessage( | |
| content=""" | |
| I want you to act as a prompt optimizer for web search. I will provide you with a chat prompt, and your goal is to optimize it into a search string that will yield the most relevant and useful information from a search engine like Google. | |
| To optimize the prompt: | |
| Identify the key information being requested | |
| Arrange the keywords into a concise search string | |
| Keep it short, around 1 to 5 words total | |
| Put the most important keywords first | |
| Some tips and things to be sure to remove: | |
| - Remove any conversational or instructional phrases | |
| - Removed style such as "in the style of", "engaging", "short", "long" | |
| - Remove lenght instruction (example: essay, article, letter, blog, post, blogpost, etc) | |
| - Remove style instructions (exmaple: "in the style of", engaging, short, long) | |
| - Remove lenght instruction (example: essay, article, letter, etc) | |
| Add "**" to the end of the search string to indicate the end of the query | |
| Example: | |
| Question: How do I bake chocolate chip cookies from scratch? | |
| Search query: chocolate chip cookies recipe from scratch** | |
| Example: | |
| Question: I would like you to show me a timeline of Marie Curie's life. Show results as a markdown table | |
| Search query: Marie Curie timeline** | |
| Example: | |
| Question: I would like you to write a long article on NATO vs Russia. Use known geopolitical frameworks. | |
| Search query: geopolitics nato russia** | |
| Example: | |
| Question: Write an engaging LinkedIn post about Andrew Ng | |
| Search query: Andrew Ng** | |
| Example: | |
| Question: Write a short article about the solar system in the style of Carl Sagan | |
| Search query: solar system** | |
| Example: | |
| Question: Should I use Kubernetes? Answer in the style of Gilfoyle from the TV show Silicon Valley | |
| Search query: Kubernetes decision** | |
| Example: | |
| Question: Biography of Napoleon. Include a table with the major events. | |
| Search query: napoleon biography events** | |
| Example: | |
| Question: Write a short article on the history of the United States. Include a table with the major events. | |
| Search query: united states history events** | |
| Example: | |
| Question: Write a short article about the solar system in the style of donald trump | |
| Search query: solar system** | |
| """ | |
| ) | |
| human_message = HumanMessage( | |
| content=f""" | |
| Question: {query} | |
| Search query: | |
| """ | |
| ) | |
| return [system_message, human_message] | |
| def get_rag_prompt_template(): | |
| """ | |
| Get the prompt template for Retrieval-Augmented Generation (RAG). | |
| Returns: | |
| ChatPromptTemplate: The prompt template for RAG. | |
| """ | |
| system_prompt = SystemMessagePromptTemplate( | |
| prompt=PromptTemplate( | |
| input_variables=[], | |
| template=""" | |
| You are an expert research assistant. | |
| You are provided with a Context in JSON format and a Question. | |
| Each JSON entry contains: content, title, link | |
| Use RAG to answer the Question, providing references and links to the Context material you retrieve and use in your answer: | |
| When generating your answer, follow these steps: | |
| - Retrieve the most relevant context material from your knowledge base to help answer the question | |
| - Cite the references you use by including the title, author, publication, and a link to each source | |
| - Synthesize the retrieved information into a clear, informative answer to the question | |
| - Format your answer in Markdown, using heading levels 2-3 as needed | |
| - Include a "References" section at the end with the full citations and link for each source you used | |
| If you cannot answer the question with confidence just say: "I'm not sure about the answer to be honest" | |
| If the provided context is not relevant to the question, just say: "The context provided is not relevant to the question" | |
| """ | |
| ) | |
| ) | |
| human_prompt = HumanMessagePromptTemplate( | |
| prompt=PromptTemplate( | |
| input_variables=["context", "query"], | |
| template=""" | |
| Context: | |
| --------------------- | |
| {context} | |
| --------------------- | |
| Question: {query} | |
| Answer: | |
| """ | |
| ) | |
| ) | |
| return ChatPromptTemplate( | |
| input_variables=["context", "query"], | |
| messages=[system_prompt, human_prompt], | |
| ) | |