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

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+
+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)

templates/rag-redis/rag_redis/config.py

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+import os
+
+
+def get_boolean_env_var(var_name, default_value=False):
+    """Retrieve the boolean value of an environment variable.
+
+    Args:
+    var_name (str): The name of the environment variable to retrieve.
+    default_value (bool): The default value to return if the variable
+    is not found.
+
+    Returns:
+    bool: The value of the environment variable, interpreted as a boolean.
+    """
+    true_values = {'true', '1', 't', 'y', 'yes'}
+    false_values = {'false', '0', 'f', 'n', 'no'}
+
+    # Retrieve the environment variable's value
+    value = os.getenv(var_name, '').lower()
+
+    # Decide the boolean value based on the content of the string
+    if value in true_values:
+        return True
+    elif value in false_values:
+        return False
+    else:
+        return default_value
+
+
+# Check for openai API key
+if "OPENAI_API_KEY" not in os.environ:
+    raise Exception("Must provide an OPENAI_API_KEY as an env var.")
+
+
+# Whether or not to enable langchain debugging
+DEBUG = get_boolean_env_var("DEBUG", False)
+# Set DEBUG env var to "true" if you wish to enable LC debugging module
+if DEBUG:
+    import langchain
+    langchain.debug=True
+
+
+# Embedding model
+EMBED_MODEL = os.getenv("EMBED_MODEL",
+                        "sentence-transformers/all-MiniLM-L6-v2")
+
+# Redis Connection Information
+REDIS_HOST = os.getenv("REDIS_HOST", "localhost")
+REDIS_PORT = int(os.getenv("REDIS_PORT", 6379))
+
+def format_redis_conn_from_env():
+    redis_url = os.getenv("REDIS_URL", None)
+    if redis_url:
+        return redis_url
+    else:
+        using_ssl = get_boolean_env_var("REDIS_SSL", False)
+        start = "rediss://" if using_ssl else "redis://"
+
+        # if using RBAC
+        password = os.getenv("REDIS_PASSWORD", None)
+        username = os.getenv("REDIS_USERNAME", "default")
+        if password is not None:
+            start += f"{username}:{password}@"
+
+        return start + f"{REDIS_HOST}:{REDIS_PORT}"
+
+REDIS_URL = format_redis_conn_from_env()
+
+# Vector Index Configuration
+INDEX_NAME = os.getenv("INDEX_NAME", "rag-redis")
+
+
+current_file_path = os.path.abspath(__file__)
+parent_dir = os.path.dirname(current_file_path)
+schema_path = os.path.join(parent_dir, 'schema.yml')
+INDEX_SCHEMA = schema_path

templates/rag-redis/rag_redis/schema.yml

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+text:
+- name: content
+- name: source
+numeric:
+- name: start_index
+vector:
+- name: content_vector
+  algorithm: HNSW
+  datatype: FLOAT32
+  dims: 384
+  distance_metric: COSINE