templates[patch]: Add cohere librarian template (#14601)

Adding the example I build for the Cohere hackathon.

It can:

use a vector database to reccommend books

<img width="840" alt="image"
src="https://github.com/langchain-ai/langchain/assets/144115527/96543a18-217b-4445-ab4b-950c7cced915">

Use a prompt template to provide information about the library

<img width="834" alt="image"
src="https://github.com/langchain-ai/langchain/assets/144115527/996c8e0f-cab0-4213-bcc9-9baf84f1494b">

Use Cohere RAG to provide grounded results

<img width="822" alt="image"
src="https://github.com/langchain-ai/langchain/assets/144115527/7bb4a883-5316-41a9-9d2e-19fd49a43dcb">

---------

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

templates/cohere-librarian/cohere_librarian/router.py

@@ -0,0 +1,43 @@
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableBranch
+
+from .blurb_matcher import book_rec_chain
+from .chat import chat
+from .library_info import library_info
+from .rag import librarian_rag
+
+chain = (
+    ChatPromptTemplate.from_template(
+        """Given the user message below,
+classify it as either being about `recommendation`, `library` or `other`.
+
+'{message}'
+
+Respond with just one word.
+For example, if the message is about a book recommendation,respond with 
+`recommendation`.
+"""
+    )
+    | chat
+    | StrOutputParser()
+)
+
+
+def extract_op_field(x):
+    return x["output_text"]
+
+
+branch = RunnableBranch(
+    (
+        lambda x: "recommendation" in x["topic"].lower(),
+        book_rec_chain | extract_op_field,
+    ),
+    (
+        lambda x: "library" in x["topic"].lower(),
+        {"message": lambda x: x["message"]} | library_info,
+    ),
+    librarian_rag,
+)
+
+branched_chain = {"topic": chain, "message": lambda x: x["message"]} | branch