langchain[minor]: Add PebbloRetrievalQA chain with Identity & Semanti…

…c Enforcement support (#20641)

- **Description:** PebbloRetrievalQA chain introduces identity
enforcement using vector-db metadata filtering
- **Dependencies:** None
- **Issue:** None
- **Documentation:** Adding documentation for PebbloRetrievalQA chain in
a separate PR(#20746)
- **Unit tests:** New unit-tests added

---------

Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>

libs/community/langchain_community/chains/__init__.py

@@ -0,0 +1,24 @@
+"""
+Chains module for langchain_community
+
+This module contains the community chains.
+"""
+
+import importlib
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from langchain_community.chains.pebblo_retrieval.base import PebbloRetrievalQA
+
+__all__ = ["PebbloRetrievalQA"]
+
+_module_lookup = {
+    "PebbloRetrievalQA": "langchain_community.chains.pebblo_retrieval.base"
+}
+
+
+def __getattr__(name: str) -> Any:
+    if name in _module_lookup:
+        module = importlib.import_module(_module_lookup[name])
+        return getattr(module, name)
+    raise AttributeError(f"module {__name__} has no attribute {name}")

libs/community/langchain_community/chains/pebblo_retrieval/base.py

@@ -0,0 +1,218 @@
+"""
+Pebblo Retrieval Chain with Identity & Semantic Enforcement for question-answering
+against a vector database.
+"""
+
+import inspect
+from typing import Any, Dict, List, Optional
+
+from langchain.chains.base import Chain
+from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForChainRun,
+    CallbackManagerForChainRun,
+)
+from langchain_core.documents import Document
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.pydantic_v1 import Extra, Field, validator
+from langchain_core.vectorstores import VectorStoreRetriever
+
+from langchain_community.chains.pebblo_retrieval.enforcement_filters import (
+    SUPPORTED_VECTORSTORES,
+    set_enforcement_filters,
+)
+from langchain_community.chains.pebblo_retrieval.models import (
+    AuthContext,
+    SemanticContext,
+)
+
+
+class PebbloRetrievalQA(Chain):
+    """
+    Retrieval Chain with Identity & Semantic Enforcement for question-answering
+    against a vector database.
+    """
+
+    combine_documents_chain: BaseCombineDocumentsChain
+    """Chain to use to combine the documents."""
+    input_key: str = "query"  #: :meta private:
+    output_key: str = "result"  #: :meta private:
+    return_source_documents: bool = False
+    """Return the source documents or not."""
+
+    retriever: VectorStoreRetriever = Field(exclude=True)
+    """VectorStore to use for retrieval."""
+    auth_context_key: str = "auth_context"  #: :meta private:
+    """Authentication context for identity enforcement."""
+    semantic_context_key: str = "semantic_context"  #: :meta private:
+    """Semantic context for semantic enforcement."""
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """Run get_relevant_text and llm on input query.
+
+        If chain has 'return_source_documents' as 'True', returns
+        the retrieved documents as well under the key 'source_documents'.
+
+        Example:
+        .. code-block:: python
+
+        res = indexqa({'query': 'This is my query'})
+        answer, docs = res['result'], res['source_documents']
+        """
+        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
+        question = inputs[self.input_key]
+        auth_context = inputs.get(self.auth_context_key)
+        semantic_context = inputs.get(self.semantic_context_key)
+        accepts_run_manager = (
+            "run_manager" in inspect.signature(self._get_docs).parameters
+        )
+        if accepts_run_manager:
+            docs = self._get_docs(
+                question, auth_context, semantic_context, run_manager=_run_manager
+            )
+        else:
+            docs = self._get_docs(question, auth_context, semantic_context)  # type: ignore[call-arg]
+        answer = self.combine_documents_chain.run(
+            input_documents=docs, question=question, callbacks=_run_manager.get_child()
+        )
+
+        if self.return_source_documents:
+            return {self.output_key: answer, "source_documents": docs}
+        else:
+            return {self.output_key: answer}
+
+    async def _acall(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """Run get_relevant_text and llm on input query.
+
+        If chain has 'return_source_documents' as 'True', returns
+        the retrieved documents as well under the key 'source_documents'.
+
+        Example:
+        .. code-block:: python
+
+        res = indexqa({'query': 'This is my query'})
+        answer, docs = res['result'], res['source_documents']
+        """
+        _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
+        question = inputs[self.input_key]
+        auth_context = inputs.get(self.auth_context_key)
+        semantic_context = inputs.get(self.semantic_context_key)
+        accepts_run_manager = (
+            "run_manager" in inspect.signature(self._aget_docs).parameters
+        )
+        if accepts_run_manager:
+            docs = await self._aget_docs(
+                question, auth_context, semantic_context, run_manager=_run_manager
+            )
+        else:
+            docs = await self._aget_docs(question, auth_context, semantic_context)  # type: ignore[call-arg]
+        answer = await self.combine_documents_chain.arun(
+            input_documents=docs, question=question, callbacks=_run_manager.get_child()
+        )
+
+        if self.return_source_documents:
+            return {self.output_key: answer, "source_documents": docs}
+        else:
+            return {self.output_key: answer}
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+        allow_population_by_field_name = True
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Input keys.
+
+        :meta private:
+        """
+        return [self.input_key, self.auth_context_key, self.semantic_context_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Output keys.
+
+        :meta private:
+        """
+        _output_keys = [self.output_key]
+        if self.return_source_documents:
+            _output_keys += ["source_documents"]
+        return _output_keys
+
+    @property
+    def _chain_type(self) -> str:
+        """Return the chain type."""
+        return "pebblo_retrieval_qa"
+
+    @classmethod
+    def from_chain_type(
+        cls,
+        llm: BaseLanguageModel,
+        chain_type: str = "stuff",
+        chain_type_kwargs: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> "PebbloRetrievalQA":
+        """Load chain from chain type."""
+        from langchain.chains.question_answering import load_qa_chain
+
+        _chain_type_kwargs = chain_type_kwargs or {}
+        combine_documents_chain = load_qa_chain(
+            llm, chain_type=chain_type, **_chain_type_kwargs
+        )
+        return cls(combine_documents_chain=combine_documents_chain, **kwargs)
+
+    @validator("retriever", pre=True, always=True)
+    def validate_vectorstore(
+        cls, retriever: VectorStoreRetriever
+    ) -> VectorStoreRetriever:
+        """
+        Validate that the vectorstore of the retriever is supported vectorstores.
+        """
+        if not any(
+            isinstance(retriever.vectorstore, supported_class)
+            for supported_class in SUPPORTED_VECTORSTORES
+        ):
+            raise ValueError(
+                f"Vectorstore must be an instance of one of the supported "
+                f"vectorstores: {SUPPORTED_VECTORSTORES}. "
+                f"Got {type(retriever.vectorstore).__name__} instead."
+            )
+        return retriever
+
+    def _get_docs(
+        self,
+        question: str,
+        auth_context: Optional[AuthContext],
+        semantic_context: Optional[SemanticContext],
+        *,
+        run_manager: CallbackManagerForChainRun,
+    ) -> List[Document]:
+        """Get docs."""
+        set_enforcement_filters(self.retriever, auth_context, semantic_context)
+        return self.retriever.get_relevant_documents(
+            question, callbacks=run_manager.get_child()
+        )
+
+    async def _aget_docs(
+        self,
+        question: str,
+        auth_context: Optional[AuthContext],
+        semantic_context: Optional[SemanticContext],
+        *,
+        run_manager: AsyncCallbackManagerForChainRun,
+    ) -> List[Document]:
+        """Get docs."""
+        set_enforcement_filters(self.retriever, auth_context, semantic_context)
+        return await self.retriever.aget_relevant_documents(
+            question, callbacks=run_manager.get_child()
+        )