update multi index templates (#13569)

templates/rag-multi-index-fusion/rag_multi_index_fusion/chain.py

@@ -1,3 +1,5 @@
+from operator import itemgetter
+
 import numpy as np
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
@@ -11,7 +13,6 @@ from langchain.retrievers import (
 )
 from langchain.schema import StrOutputParser
 from langchain.schema.runnable import (
-    RunnableLambda,
     RunnableParallel,
     RunnablePassthrough,
 )
@@ -51,14 +52,6 @@ def fuse_retrieved_docs(input):
     ]
 
 
-retriever_map = {
-    "medical paper": pubmed,
-    "scientific paper": arxiv,
-    "public company finances report": sec,
-    "general": wiki,
-}
-
-
 def format_named_docs(named_docs):
     return "\n\n".join(
         f"Source: {source}\n\n{doc.page_content}" for source, doc in named_docs
@@ -83,19 +76,26 @@ class Question(BaseModel):
     __root__: str
 
 
+answer_chain = (
+    {
+        "question": itemgetter("question"),
+        "sources": lambda x: format_named_docs(x["sources"]),
+    }
+    | prompt
+    | ChatOpenAI(model="gpt-3.5-turbo-1106")
+    | StrOutputParser()
+).with_config(run_name="answer")
 chain = (
     (
         RunnableParallel(
             {"question": RunnablePassthrough(), "sources": retrieve_all}
         ).with_config(run_name="add_sources")
-        | RunnablePassthrough.assign(
+        | RunnablePassthrough.assign(sources=fuse_retrieved_docs).with_config(
-            sources=(
+            run_name="fuse"
-                RunnableLambda(fuse_retrieved_docs) | format_named_docs
+        )
-            ).with_config(run_name="fuse_and_format")
+        | RunnablePassthrough.assign(answer=answer_chain).with_config(
-        ).with_config(run_name="update_sources")
+            run_name="add_answer"
-        | prompt
+        )
-        | ChatOpenAI(model="gpt-3.5-turbo-1106")
-        | StrOutputParser()
     )
     .with_config(run_name="QA with fused results")
     .with_types(input_type=Question)

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

@@ -28,7 +28,7 @@ wiki = WikipediaRetriever(top_k_results=5, doc_content_chars_max=2000).with_conf
     run_name="wiki"
 )
 
-llm = ChatOpenAI(model="gpt-3.5-turbo-1106")
+llm = ChatOpenAI(model="gpt-3.5-turbo")
 
 
 class Search(BaseModel):
@@ -45,18 +45,29 @@ class Search(BaseModel):
     )
 
 
-classifier = llm.bind(
+retriever_name = {
-    functions=[convert_pydantic_to_openai_function(Search)],
+    "medical paper": "PubMed",
-    function_call={"name": "Search"},
+    "scientific paper": "ArXiv",
-) | PydanticAttrOutputFunctionsParser(
+    "public company finances report": "SEC filings (Kay AI)",
-    pydantic_schema=Search, attr_name="question_resource"
+    "general": "Wikipedia",
+}
+
+classifier = (
+    llm.bind(
+        functions=[convert_pydantic_to_openai_function(Search)],
+        function_call={"name": "Search"},
+    )
+    | PydanticAttrOutputFunctionsParser(
+        pydantic_schema=Search, attr_name="question_resource"
+    )
+    | retriever_name.get
 )
 
 retriever_map = {
-    "medical paper": pubmed,
+    "PubMed": pubmed,
-    "scientific paper": arxiv,
+    "ArXiv": arxiv,
-    "public company finances report": sec,
+    "SEC filings (Kay AI)": sec,
-    "general": wiki,
+    "Wikipedia": wiki,
 }
 router_retriever = RouterRunnable(runnables=retriever_map)
 
@@ -79,17 +90,23 @@ class Question(BaseModel):
     __root__: str
 
 
+retriever_chain = (
+    {"input": itemgetter("question"), "key": itemgetter("retriever_choice")}
+    | router_retriever
+    | format_docs
+).with_config(run_name="retrieve")
+answer_chain = (
+    {"sources": retriever_chain, "question": itemgetter("question")}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
 chain = (
     (
         RunnableParallel(
-            {"input": RunnablePassthrough(), "key": classifier}
+            question=RunnablePassthrough(), retriever_choice=classifier
         ).with_config(run_name="classify")
-        | RunnableParallel(
+        | RunnablePassthrough.assign(answer=answer_chain).with_config(run_name="answer")
-            {"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)