Add template for gpt-crawler (#13625)

Template for RAG using
[gpt-crawler](https://github.com/BuilderIO/gpt-crawler).

---------

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

templates/rag-gpt-crawler/rag_gpt_crawler/chain.py

@@ -0,0 +1,62 @@
+import json
+from pathlib import Path
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema import Document
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+
+# Load output from gpt crawler
+path_to_gptcrawler = Path(__file__).parent.parent / "output.json"
+data = json.loads(Path(path_to_gptcrawler).read_text())
+docs = [
+    Document(
+        page_content=dict_["html"],
+        metadata={"title": dict_["title"], "url": dict_["url"]},
+    )
+    for dict_ in data
+]
+
+# Split
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+all_splits = text_splitter.split_documents(docs)
+
+# Add to vectorDB
+vectorstore = Chroma.from_documents(
+    documents=all_splits,
+    collection_name="rag-gpt-builder",
+    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()
+)
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)