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/blurb_matcher.py

@@ -0,0 +1,49 @@
+import csv
+
+from langchain.chains.question_answering import load_qa_chain
+from langchain.embeddings import CohereEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.vectorstores import Chroma
+
+from .chat import chat
+
+csv_file = open("data/books_with_blurbs.csv", "r")
+csv_reader = csv.reader(csv_file)
+csv_data = list(csv_reader)
+parsed_data = [
+    {
+        "id": x[0],
+        "title": x[1],
+        "author": x[2],
+        "year": x[3],
+        "publisher": x[4],
+        "blurb": x[5],
+    }
+    for x in csv_data
+]
+parsed_data[1]
+
+embeddings = CohereEmbeddings()
+
+docsearch = Chroma.from_texts(
+    [x["title"] for x in parsed_data], embeddings, metadatas=parsed_data
+).as_retriever()
+
+
+prompt_template = """
+{context}
+
+Use the book reccommendations to suggest books for the user to read.
+Only use the titles of the books, do not make up titles. Format the response as
+a bulleted list prefixed by a relevant message.
+
+User: {message}"""
+
+PROMPT = PromptTemplate(
+    template=prompt_template, input_variables=["context", "message"]
+)
+
+book_rec_chain = {
+    "input_documents": lambda x: docsearch.get_relevant_documents(x["message"]),
+    "message": lambda x: x["message"],
+} | load_qa_chain(chat, chain_type="stuff", prompt=PROMPT)