templates: Lanceb RAG template (#17809)

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---------

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

templates/rag-lancedb/rag_lancedb/chain.py

@@ -0,0 +1,60 @@
+from langchain_community.vectorstores import LanceDB
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+# Example for document loading (from url), splitting, and creating vectostore
+
+""" 
+# Load
+from langchain_community.document_loaders import WebBaseLoader
+loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+data = loader.load()
+
+# Split
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+all_splits = text_splitter.split_documents(data)
+
+# Add to vectorDB
+vectorstore = LanceDB.from_documents(documents=all_splits, 
+                                    collection_name="rag-chroma",
+                                    embedding=OpenAIEmbeddings(),
+                                    )
+retriever = vectorstore.as_retriever()
+"""
+
+# Embed a single document for test
+vectorstore = LanceDB.from_texts(
+    ["harrison worked at kensho"], embedding=OpenAIEmbeddings()
+)
+
+retriever = vectorstore.as_retriever()
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# LLM
+model = ChatOpenAI()
+
+# RAG chain
+chain = (
+    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)