Redis langserve template (#12443)

Add Redis langserve template! Eventually will add semantic caching to
this too. But I was struggling to get that to work for some reason with
the LCEL implementation here.

- **Description:** Introduces the Redis LangServe template. A simple RAG
based app built on top of Redis that allows you to chat with company's
public financial data (Edgar 10k filings)
  - **Issue:** None
- **Dependencies:** The template contains the poetry project
requirements to run this template
  - **Tag maintainer:** @baskaryan @Spartee 
  - **Twitter handle:** @tchutch94

**Note**: this requires the commit here that deletes the
`_aget_relevant_documents()` method from the Redis retriever class that
wasn't implemented. That was breaking the langserve app.

---------

Co-authored-by: Sam Partee <sam.partee@redis.com>

templates/rag-redis/rag_redis/chain.py

@@ -0,0 +1,68 @@
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain.vectorstores import Redis
+
+from rag_redis.config import (
+    EMBED_MODEL,
+    INDEX_NAME,
+    INDEX_SCHEMA,
+    REDIS_URL,
+)
+
+
+# Make this look better in the docs.
+class Question(BaseModel):
+    __root__: str
+
+
+# Init Embeddings
+embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
+
+# Connect to pre-loaded vectorstore
+# run the ingest.py script to populate this
+vectorstore = Redis.from_existing_index(
+    embedding=embedder,
+    index_name=INDEX_NAME,
+    schema=INDEX_SCHEMA,
+    redis_url=REDIS_URL
+)
+# TODO allow user to change parameters
+retriever = vectorstore.as_retriever(search_type="mmr")
+
+
+# Define our prompt
+template = """
+Use the following pieces of context from Nike's financial 10k filings
+dataset to answer the question. Do not make up an answer if there is no
+context provided to help answer it. Include the 'source' and 'start_index'
+from the metadata included in the context you used to answer the question
+
+Context:
+---------
+{context}
+
+---------
+Question: {question}
+---------
+
+Answer:
+"""
+
+
+prompt = ChatPromptTemplate.from_template(template)
+
+
+# RAG Chain
+model = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
+chain = (
+    RunnableParallel({"context": retriever,
+                      "question": RunnablePassthrough()})
+    | prompt
+    | model
+    | StrOutputParser()
+).with_types(input_type=Question)