TEMPLATES Add rag-opensearch template (#13501)

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Adding rag-opensearch template.

---------

Signed-off-by: kalyanr <kalyan.ben10@live.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

templates/rag-opensearch/rag_opensearch/chain.py

@@ -0,0 +1,60 @@
+import os
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+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.opensearch_vector_search import OpenSearchVectorSearch
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+OPENSEARCH_URL = os.getenv("OPENSEARCH_URL", "https://localhost:9200")
+OPENSEARCH_USERNAME = os.getenv("OPENSEARCH_USERNAME", "admin")
+OPENSEARCH_PASSWORD = os.getenv("OPENSEARCH_PASSWORD", "admin")
+OPENSEARCH_INDEX_NAME = os.getenv("OPENSEARCH_INDEX_NAME", "langchain-test")
+
+
+embedding_function = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+
+vector_store = OpenSearchVectorSearch(
+    opensearch_url=OPENSEARCH_URL,
+    http_auth=(OPENSEARCH_USERNAME, OPENSEARCH_PASSWORD),
+    index_name=OPENSEARCH_INDEX_NAME,
+    embedding_function=embedding_function,
+    verify_certs=False,
+)
+
+
+retriever = vector_store.as_retriever()
+
+
+def format_docs(docs):
+    return "\n\n".join([d.page_content for d in docs])
+
+
+# RAG prompt
+template = """Answer the question based only on the following context:
+{context}
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# RAG
+model = ChatOpenAI(openai_api_key=OPENAI_API_KEY)
+chain = (
+    RunnableParallel(
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    )
+    | prompt
+    | model
+    | StrOutputParser()
+)
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)