Retriever that can re-phase user inputs (#8026)

Simple retriever that applies an LLM between the user input and the
query pass the to retriever.

It can be used to pre-process the user input in any way.

The default prompt:

```
DEFAULT_QUERY_PROMPT = PromptTemplate(
    input_variables=["question"],
    template="""You are an assistant tasked with taking a natural languge query from a user
    and converting it into a query for a vectorstore. In this process, you strip out
    information that is not relevant for the retrieval task. Here is the user query: {question} """
)
```

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

libs/langchain/langchain/retrievers/__init__.py

@@ -42,6 +42,7 @@ from langchain.retrievers.milvus import MilvusRetriever
 from langchain.retrievers.multi_query import MultiQueryRetriever
 from langchain.retrievers.pinecone_hybrid_search import PineconeHybridSearchRetriever
 from langchain.retrievers.pubmed import PubMedRetriever
+from langchain.retrievers.re_phraser import RePhraseQueryRetriever
 from langchain.retrievers.remote_retriever import RemoteLangChainRetriever
 from langchain.retrievers.self_query.base import SelfQueryRetriever
 from langchain.retrievers.svm import SVMRetriever
@@ -86,6 +87,7 @@ __all__ = [
     "ZepRetriever",
     "ZillizRetriever",
     "DocArrayRetriever",
+    "RePhraseQueryRetriever",
     "WebResearchRetriever",
     "EnsembleRetriever",
 ]

libs/langchain/langchain/retrievers/re_phraser.py

@@ -0,0 +1,87 @@
+import logging
+from typing import List
+
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
+from langchain.chains.llm import LLMChain
+from langchain.llms.base import BaseLLM
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema import BaseRetriever, Document
+
+logger = logging.getLogger(__name__)
+
+# Default template
+DEFAULT_TEMPLATE = """You are an assistant tasked with taking a natural language \
+query from a user and converting it into a query for a vectorstore. \
+In this process, you strip out information that is not relevant for \
+the retrieval task. Here is the user query: {question}"""
+
+# Default prompt
+DEFAULT_QUERY_PROMPT = PromptTemplate.from_template(DEFAULT_TEMPLATE)
+
+
+class RePhraseQueryRetriever(BaseRetriever):
+
+    """Given a user query, use an LLM to re-phrase it.
+    Then, retrieve docs for re-phrased query."""
+
+    retriever: BaseRetriever
+    llm_chain: LLMChain
+
+    @classmethod
+    def from_llm(
+        cls,
+        retriever: BaseRetriever,
+        llm: BaseLLM,
+        prompt: PromptTemplate = DEFAULT_QUERY_PROMPT,
+    ) -> "RePhraseQueryRetriever":
+        """Initialize from llm using default template.
+
+        The prompt used here expects a single input: `question`
+
+        Args:
+            retriever: retriever to query documents from
+            llm: llm for query generation using DEFAULT_QUERY_PROMPT
+            prompt: prompt template for query generation
+
+        Returns:
+            RePhraseQueryRetriever
+        """
+
+        llm_chain = LLMChain(llm=llm, prompt=prompt)
+        return cls(
+            retriever=retriever,
+            llm_chain=llm_chain,
+        )
+
+    def _get_relevant_documents(
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+    ) -> List[Document]:
+        """Get relevated documents given a user question.
+
+        Args:
+            query: user question
+
+        Returns:
+            Relevant documents for re-phrased question
+        """
+        response = self.llm_chain(query, callbacks=run_manager.get_child())
+        re_phrased_question = response["text"]
+        logger.info(f"Re-phrased question: {re_phrased_question}")
+        docs = self.retriever.get_relevant_documents(
+            re_phrased_question, callbacks=run_manager.get_child()
+        )
+        return docs
+
+    async def _aget_relevant_documents(
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+    ) -> List[Document]:
+        raise NotImplementedError