TEMPLATES: Add multi-index templates (#13490)

One that routes and one that fuses

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/rag-multi-index-router/rag_multi_index_router/__init__.py

@@ -0,0 +1,3 @@
+from rag_multi_index_router.chain import chain
+
+__all__ = ["chain"]

templates/rag-multi-index-router/rag_multi_index_router/chain.py

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+from operator import itemgetter
+from typing import Literal
+
+from langchain.chat_models import ChatOpenAI
+from langchain.output_parsers.openai_functions import PydanticAttrOutputFunctionsParser
+from langchain.prompts import ChatPromptTemplate
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.retrievers import (
+    ArxivRetriever,
+    KayAiRetriever,
+    PubMedRetriever,
+    WikipediaRetriever,
+)
+from langchain.schema import StrOutputParser
+from langchain.schema.runnable import (
+    RouterRunnable,
+    RunnableParallel,
+    RunnablePassthrough,
+)
+from langchain.utils.openai_functions import convert_pydantic_to_openai_function
+
+pubmed = PubMedRetriever(top_k_results=5).with_config(run_name="pubmed")
+arxiv = ArxivRetriever(top_k_results=5).with_config(run_name="arxiv")
+sec = KayAiRetriever.create(
+    dataset_id="company", data_types=["10-K"], num_contexts=5
+).with_config(run_name="sec_filings")
+wiki = WikipediaRetriever(top_k_results=5, doc_content_chars_max=2000).with_config(
+    run_name="wiki"
+)
+
+llm = ChatOpenAI(model="gpt-3.5-turbo-1106")
+
+
+class Search(BaseModel):
+    """Search for relevant documents by question topic."""
+
+    question_resource: Literal[
+        "medical paper", "scientific paper", "public company finances report", "general"
+    ] = Field(
+        ...,
+        description=(
+            "The type of resource that would best help answer the user's question. "
+            "If none of the types are relevant return 'general'."
+        ),
+    )
+
+
+classifier = llm.bind(
+    functions=[convert_pydantic_to_openai_function(Search)],
+    function_call={"name": "Search"},
+) | PydanticAttrOutputFunctionsParser(
+    pydantic_schema=Search, attr_name="question_resource"
+)
+
+retriever_map = {
+    "medical paper": pubmed,
+    "scientific paper": arxiv,
+    "public company finances report": sec,
+    "general": wiki,
+}
+router_retriever = RouterRunnable(runnables=retriever_map)
+
+
+def format_docs(docs):
+    return "\n\n".join(f"Source {i}:\n{doc.page_content}" for i, doc in enumerate(docs))
+
+
+system = """Answer the user question. Use the following sources to help \
+answer the question. If you don't know the answer say "I'm not sure, I couldn't \
+find information on {{topic}}."
+
+Sources:
+
+{sources}"""
+prompt = ChatPromptTemplate.from_messages([("system", system), ("human", "{question}")])
+
+
+class Question(BaseModel):
+    __root__: str
+
+
+chain = (
+    (
+        RunnableParallel(
+            {"input": RunnablePassthrough(), "key": classifier}
+        ).with_config(run_name="classify")
+        | RunnableParallel(
+            {"question": itemgetter("input"), "sources": router_retriever | format_docs}
+        ).with_config(run_name="retrieve")
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    .with_config(run_name="QA with router")
+    .with_types(input_type=Question)
+)