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/ingest.py

@@ -0,0 +1,49 @@
+import os
+
+from langchain.document_loaders import UnstructuredFileLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Redis
+from rag_redis.config import EMBED_MODEL, INDEX_NAME, INDEX_SCHEMA, REDIS_URL
+
+
+def ingest_documents():
+    """
+    Ingest PDF to Redis from the data/ directory that
+    contains Edgar 10k filings data for Nike.
+    """
+    # Load list of pdfs
+    company_name = "Nike"
+    data_path = "data/"
+    doc = [
+        os.path.join(data_path, file) for file in os.listdir(data_path)
+    ][0]
+
+    print("Parsing 10k filing doc for NIKE", doc)
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1500, chunk_overlap=100, add_start_index=True
+    )
+    loader = UnstructuredFileLoader(doc, mode="single", strategy="fast")
+    chunks = loader.load_and_split(text_splitter)
+
+    print("Done preprocessing. Created", len(chunks), "chunks of the original pdf")
+    # Create vectorstore
+    embedder = HuggingFaceEmbeddings(
+        model_name=EMBED_MODEL
+    )
+
+    _ = Redis.from_texts(
+        # appending this little bit can sometimes help with semantic retrieval
+        # especially with multiple companies
+        texts=[f"Company: {company_name}. " + chunk.page_content for chunk in chunks],
+        metadatas=[chunk.metadata for chunk in chunks],
+        embedding=embedder,
+        index_name=INDEX_NAME,
+        index_schema=INDEX_SCHEMA,
+        redis_url=REDIS_URL
+    )
+
+
+if __name__ == "__main__":
+    ingest_documents()