langchain[patch], templates[patch]: fix multi query retriever, web re… (#17434)

…search retriever

Fixes #17352

libs/langchain/langchain/retrievers/multi_query.py

@@ -1,39 +1,28 @@
 import asyncio
 import logging
-from typing import List, Sequence
+from typing import List, Optional, Sequence
 
 from langchain_core.callbacks import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
 )
 from langchain_core.documents import Document
-from langchain_core.language_models import BaseLLM
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.prompts.prompt import PromptTemplate
-from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.retrievers import BaseRetriever
 
 from langchain.chains.llm import LLMChain
-from langchain.output_parsers.pydantic import PydanticOutputParser
 
 logger = logging.getLogger(__name__)
 
 
-class LineList(BaseModel):
+class LineListOutputParser(BaseOutputParser[List[str]]):
-    """List of lines."""
-
-    lines: List[str] = Field(description="Lines of text")
-    """List of lines."""
-
-
-class LineListOutputParser(PydanticOutputParser):
     """Output parser for a list of lines."""
 
-    def __init__(self) -> None:
+    def parse(self, text: str) -> List[str]:
-        super().__init__(pydantic_object=LineList)
-
-    def parse(self, text: str) -> LineList:
         lines = text.strip().split("\n")
-        return LineList(lines=lines)
+        return lines
 
 
 # Default prompt
@@ -63,6 +52,7 @@ class MultiQueryRetriever(BaseRetriever):
     llm_chain: LLMChain
     verbose: bool = True
     parser_key: str = "lines"
+    """DEPRECATED. parser_key is no longer used and should not be specified."""
     include_original: bool = False
     """Whether to include the original query in the list of generated queries."""
 
@@ -70,9 +60,9 @@ class MultiQueryRetriever(BaseRetriever):
     def from_llm(
         cls,
         retriever: BaseRetriever,
-        llm: BaseLLM,
+        llm: BaseLanguageModel,
         prompt: PromptTemplate = DEFAULT_QUERY_PROMPT,
-        parser_key: str = "lines",
+        parser_key: Optional[str] = None,
         include_original: bool = False,
     ) -> "MultiQueryRetriever":
         """Initialize from llm using default template.
@@ -91,7 +81,6 @@ class MultiQueryRetriever(BaseRetriever):
         return cls(
             retriever=retriever,
             llm_chain=llm_chain,
-            parser_key=parser_key,
             include_original=include_original,
         )
 
@@ -129,7 +118,7 @@ class MultiQueryRetriever(BaseRetriever):
         response = await self.llm_chain.acall(
             inputs={"question": question}, callbacks=run_manager.get_child()
         )
-        lines = getattr(response["text"], self.parser_key, [])
+        lines = response["text"]
         if self.verbose:
             logger.info(f"Generated queries: {lines}")
         return lines
@@ -189,7 +178,7 @@ class MultiQueryRetriever(BaseRetriever):
         response = self.llm_chain(
             {"question": question}, callbacks=run_manager.get_child()
         )
-        lines = getattr(response["text"], self.parser_key, [])
+        lines = response["text"]
         if self.verbose:
             logger.info(f"Generated queries: {lines}")
         return lines

libs/langchain/langchain/retrievers/web_research.py

@@ -12,6 +12,7 @@ from langchain_core.callbacks import (
 )
 from langchain_core.documents import Document
 from langchain_core.language_models import BaseLLM
+from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.prompts import BasePromptTemplate, PromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.retrievers import BaseRetriever
@@ -19,7 +20,6 @@ from langchain_core.vectorstores import VectorStore
 
 from langchain.chains import LLMChain
 from langchain.chains.prompt_selector import ConditionalPromptSelector
-from langchain.output_parsers.pydantic import PydanticOutputParser
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
 
 logger = logging.getLogger(__name__)
@@ -50,21 +50,12 @@ should have a question mark at the end: {question}""",
 )
 
 
-class LineList(BaseModel):
+class QuestionListOutputParser(BaseOutputParser[List[str]]):
-    """List of questions."""
-
-    lines: List[str] = Field(description="Questions")
-
-
-class QuestionListOutputParser(PydanticOutputParser):
     """Output parser for a list of numbered questions."""
 
-    def __init__(self) -> None:
+    def parse(self, text: str) -> List[str]:
-        super().__init__(pydantic_object=LineList)
-
-    def parse(self, text: str) -> LineList:
         lines = re.findall(r"\d+\..*?(?:\n|$)", text)
-        return LineList(lines=lines)
+        return lines
 
 
 class WebResearchRetriever(BaseRetriever):
@@ -176,7 +167,7 @@ class WebResearchRetriever(BaseRetriever):
         logger.info("Generating questions for Google Search ...")
         result = self.llm_chain({"question": query})
         logger.info(f"Questions for Google Search (raw): {result}")
-        questions = getattr(result["text"], "lines", [])
+        questions = result["text"]
         logger.info(f"Questions for Google Search: {questions}")
 
         # Get urls

libs/langchain/tests/unit_tests/retrievers/test_web_research.py

@@ -33,4 +33,4 @@ from langchain.retrievers.web_research import QuestionListOutputParser
 def test_list_output_parser(text: str, expected: List[str]) -> None:
     parser = QuestionListOutputParser()
     result = parser.parse(text)
-    assert result.lines == expected
+    assert result == expected

templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py

@@ -1,7 +1,3 @@
-from typing import List
-
-from langchain.chains import LLMChain
-from langchain.output_parsers import PydanticOutputParser
 from langchain.retrievers.multi_query import MultiQueryRetriever
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.chat_models import ChatOllama, ChatOpenAI
@@ -10,7 +6,7 @@ from langchain_community.embeddings import OpenAIEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
-from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 
 # Load
@@ -29,23 +25,6 @@ vectorstore = Chroma.from_documents(
 )
 
 
-# Output parser will split the LLM result into a list of queries
-class LineList(BaseModel):
-    # "lines" is the key (attribute name) of the parsed output
-    lines: List[str] = Field(description="Lines of text")
-
-
-class LineListOutputParser(PydanticOutputParser):
-    def __init__(self) -> None:
-        super().__init__(pydantic_object=LineList)
-
-    def parse(self, text: str) -> LineList:
-        lines = text.strip().split("\n")
-        return LineList(lines=lines)
-
-
-output_parser = LineListOutputParser()
-
 QUERY_PROMPT = PromptTemplate(
     input_variables=["question"],
     template="""You are an AI language model assistant. Your task is to generate five
@@ -60,12 +39,9 @@ QUERY_PROMPT = PromptTemplate(
 ollama_llm = "zephyr"
 llm = ChatOllama(model=ollama_llm)
 
-# Chain
-llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)
-
 # Run
-retriever = MultiQueryRetriever(
+retriever = MultiQueryRetriever.from_llm(
-    retriever=vectorstore.as_retriever(), llm_chain=llm_chain, parser_key="lines"
+    vectorstore.as_retriever(), llm, prompt=QUERY_PROMPT
 )  # "lines" is the key (attribute name) of the parsed output
 
 # RAG prompt