Self-query template (#12694)

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/rag-self-query/rag_self_query/prompts.py

@@ -0,0 +1,39 @@
+from langchain.prompts import ChatPromptTemplate, PromptTemplate
+
+# Used to condense a question and chat history into a single question
+condense_question_prompt_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. If there is no chat history, just rephrase the question to be a standalone question.
+
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+"""  # noqa: E501
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(
+    condense_question_prompt_template
+)
+
+# RAG Prompt to provide the context and question for LLM to answer
+# We also ask the LLM to cite the source of the passage it is answering from
+llm_context_prompt_template = """
+Use the following passages to answer the user's question.
+Each passage has a SOURCE which is the title of the document. When answering, cite source name of the passages you are answering from below the answer in a unique bullet point list.
+
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+----
+{context}
+----
+Question: {question}
+"""  # noqa: E501
+
+LLM_CONTEXT_PROMPT = ChatPromptTemplate.from_template(llm_context_prompt_template)
+
+# Used to build a context window from passages retrieved
+document_prompt_template = """
+---
+NAME: {name}
+PASSAGE:
+{page_content}
+---
+"""
+
+DOCUMENT_PROMPT = PromptTemplate.from_template(document_prompt_template)