Add template for self-query-qdrant (#12795)

This PR adds a self-querying template using Qdrant as a vector store.
The template uses an artificial dataset and was implemented in a way
that simplifies passing different components and choosing LLM and
embedding providers.

---------

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

templates/self-query-qdrant/self_query_qdrant/prompts.py

@@ -0,0 +1,16 @@
+from langchain.prompts import PromptTemplate
+
+llm_context_prompt_template = """
+Answer the user query using provided passages. Each passage has metadata given as 
+a nested JSON object you can also use. 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}
+----
+Query: {query}
+"""  # noqa: E501
+
+LLM_CONTEXT_PROMPT = PromptTemplate.from_template(llm_context_prompt_template)