langchain: default to Runnable in MultiQueryRetriever (#21770)

- `llm_chain` becomes `Union[LLMChain, Runnable]`
- `.from_llm` creates a runnable

tested by verifying that docs/how_to/MultiQueryRetriever.ipynb runs
unchanged with sync/async invoke (and that it runs if we specifically
instantiate with LLMChain).

docs/docs/how_to/MultiQueryRetriever.ipynb

@@ -138,20 +138,10 @@
    "execution_count": 5,
    "id": "d9afb0ca",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/chestercurme/.pyenv/versions/3.10.4/envs/sandbox310/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 0.3.0. Use RunnableSequence, e.g., `prompt | llm` instead.\n",
-      "  warn_deprecated(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from typing import List\n",
     "\n",
-    "from langchain.chains import LLMChain\n",
     "from langchain_core.output_parsers import BaseOutputParser\n",
     "from langchain_core.prompts import PromptTemplate\n",
     "from langchain_core.pydantic_v1 import BaseModel, Field\n",
@@ -180,7 +170,7 @@
     "llm = ChatOpenAI(temperature=0)\n",
     "\n",
     "# Chain\n",
-    "llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)\n",
+    "llm_chain = QUERY_PROMPT | llm | output_parser\n",
     "\n",
     "# Other inputs\n",
     "question = \"What are the approaches to Task Decomposition?\""
@@ -189,14 +179,14 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2eca2d96-8057-4ed9-873d-fa1064c09acf",
+   "id": "59c75c56-dbd7-4887-b9ba-0b5b21069f51",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer about regression analysis?', '4. What are the teachings of the course regarding regression?', '5. In what manner is regression covered in the course curriculum?']\n"
+      "INFO:langchain.retrievers.multi_query:Generated queries: ['1. Can you provide insights on regression from the course material?', '2. How is regression discussed in the course content?', '3. What information does the course offer about regression?', '4. In what way is regression covered in the course?', '5. What are the teachings of the course regarding regression?']\n"
      ]
     },
     {

libs/langchain/langchain/retrievers/multi_query.py

@@ -11,6 +11,7 @@ from langchain_core.language_models import BaseLanguageModel
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.prompts.prompt import PromptTemplate
 from langchain_core.retrievers import BaseRetriever
+from langchain_core.runnables import Runnable
 
 from langchain.chains.llm import LLMChain
 
@@ -49,7 +50,7 @@ class MultiQueryRetriever(BaseRetriever):
     """
 
     retriever: BaseRetriever
-    llm_chain: LLMChain
+    llm_chain: Runnable
     verbose: bool = True
     parser_key: str = "lines"
     """DEPRECATED. parser_key is no longer used and should not be specified."""
@@ -77,7 +78,7 @@ class MultiQueryRetriever(BaseRetriever):
             MultiQueryRetriever
         """
         output_parser = LineListOutputParser()
-        llm_chain = LLMChain(llm=llm, prompt=prompt, output_parser=output_parser)
+        llm_chain = prompt | llm | output_parser
         return cls(
             retriever=retriever,
             llm_chain=llm_chain,
@@ -115,10 +116,13 @@ class MultiQueryRetriever(BaseRetriever):
         Returns:
             List of LLM generated queries that are similar to the user input
         """
-        response = await self.llm_chain.acall(
-            inputs={"question": question}, callbacks=run_manager.get_child()
+        response = await self.llm_chain.ainvoke(
+            {"question": question}, config={"callbacks": run_manager.get_child()}
         )
-        lines = response["text"]
+        if isinstance(self.llm_chain, LLMChain):
+            lines = response["text"]
+        else:
+            lines = response
         if self.verbose:
             logger.info(f"Generated queries: {lines}")
         return lines
@@ -175,10 +179,13 @@ class MultiQueryRetriever(BaseRetriever):
         Returns:
             List of LLM generated queries that are similar to the user input
         """
-        response = self.llm_chain(
-            {"question": question}, callbacks=run_manager.get_child()
+        response = self.llm_chain.invoke(
+            {"question": question}, config={"callbacks": run_manager.get_child()}
         )
-        lines = response["text"]
+        if isinstance(self.llm_chain, LLMChain):
+            lines = response["text"]
+        else:
+            lines = response
         if self.verbose:
             logger.info(f"Generated queries: {lines}")
         return lines