INTPYTHON-809 - Update to langchain 1.0 and langgraph 1.0

libs/langchain-mongodb/langchain_mongodb/agent_toolkit/toolkit.py

@@ -2,8 +2,6 @@
 
 from typing import List
 
-from langchain_core.caches import BaseCache as BaseCache
-from langchain_core.callbacks import Callbacks as Callbacks
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit

libs/langchain-mongodb/langchain_mongodb/retrievers/__init__.py

@@ -12,6 +12,7 @@
 from langchain_mongodb.retrievers.parent_document import (
     MongoDBAtlasParentDocumentRetriever,
 )
+
 from langchain_mongodb.retrievers.self_querying import MongoDBAtlasSelfQueryRetriever
 
 __all__ = [

libs/langchain-mongodb/langchain_mongodb/retrievers/parent_document.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
+import uuid
 from typing import Any, List, Optional
 
 import pymongo
-from langchain.retrievers.parent_document_retriever import ParentDocumentRetriever
+from langchain_core.retrievers import BaseRetriever
 from langchain_core.callbacks import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -20,10 +21,10 @@
 from langchain_mongodb.utils import DRIVER_METADATA, make_serializable
 
 
-class MongoDBAtlasParentDocumentRetriever(ParentDocumentRetriever):
+class MongoDBAtlasParentDocumentRetriever(BaseRetriever):
     """MongoDB Atlas's ParentDocumentRetriever
 
-    “Parent Document Retrieval” is a common approach to enhance the performance of
+    "Parent Document Retrieval" is a common approach to enhance the performance of
     retrieval methods in RAG by providing the LLM with a broader context to consider.
     In essence, we divide the original documents into relatively small chunks,
     embed each one, and store them in a vector database.
@@ -38,25 +39,25 @@ class MongoDBAtlasParentDocumentRetriever(ParentDocumentRetriever):
     and the docstore  :class:`~langchain_mongodb.docstores.MongoDBDocStore`
     by the same MongoDB Collection.
 
-    For more details, see superclasses
-        :class:`~langchain.retrievers.parent_document_retriever.ParentDocumentRetriever`
-        and :class:`~langchain.retrievers.MultiVectorRetriever`.
+    This retriever extends :class:`~langchain_core.retrievers.BaseRetriever` and
+    implements the parent document retrieval pattern without requiring the legacy
+    langchain-classic package, making it compatible with LangChain 1.0+.
 
     Examples:
         >>> from langchain_mongodb.retrievers.parent_document import (
-        >>>     ParentDocumentRetriever
+        >>>     MongoDBAtlasParentDocumentRetriever
         >>> )
         >>> from langchain_text_splitters import RecursiveCharacterTextSplitter
         >>> from langchain_openai import OpenAIEmbeddings
         >>>
-        >>> retriever = ParentDocumentRetriever.from_connection_string(
+        >>> retriever = MongoDBAtlasParentDocumentRetriever.from_connection_string(
         >>>     "mongodb+srv://<user>:<clustername>.mongodb.net",
         >>>     OpenAIEmbeddings(model="text-embedding-3-large"),
         >>>     RecursiveCharacterTextSplitter(chunk_size=400),
         >>>     "example_database"
         >>> )
-        retriever.add_documents([Document(..., technical_report_pages)
-        >>> resp = retriever.invoke("Langchain MongDB Partnership Ecosystem")
+        >>> retriever.add_documents([Document(...), ...])  # Parent documents
+        >>> resp = retriever.invoke("Langchain MongoDB Partnership Ecosystem")
         >>> print(resp)
         [Document(...), ...]
 
@@ -65,17 +66,22 @@ class MongoDBAtlasParentDocumentRetriever(ParentDocumentRetriever):
     vectorstore: MongoDBAtlasVectorSearch
     """Vectorstore API to add, embed, and search through child documents"""
 
+    child_splitter: TextSplitter
+
     docstore: MongoDBDocStore
     """Provides an API around the Collection to add the parent documents"""
 
     id_key: str = "doc_id"
     """Key stored in metadata pointing to parent document"""
 
+    search_kwargs: dict = {}
+    """Additional search parameters for vector search"""
+
     def _get_relevant_documents(
-        self,
-        query: str,
-        *,
-        run_manager: Optional[CallbackManagerForRetrieverRun] = None,
+            self,
+            query: str,
+            *,
+            run_manager: Optional[CallbackManagerForRetrieverRun] = None,
     ) -> List[Document]:
         query_vector = self.vectorstore._embedding.embed_query(query)
 
@@ -120,10 +126,10 @@ def _get_relevant_documents(
         return docs
 
     async def _aget_relevant_documents(
-        self,
-        query: str,
-        *,
-        run_manager: AsyncCallbackManagerForRetrieverRun,
+            self,
+            query: str,
+            *,
+            run_manager: AsyncCallbackManagerForRetrieverRun,
     ) -> List[Document]:
         """Asynchronous version of get_relevant_documents"""
 
@@ -136,14 +142,14 @@ async def _aget_relevant_documents(
 
     @classmethod
     def from_connection_string(
-        cls,
-        connection_string: str,
-        embedding_model: Embeddings,
-        child_splitter: TextSplitter,
-        database_name: str,
-        collection_name: str = "document_with_chunks",
-        id_key: str = "doc_id",
-        **kwargs: Any,
+            cls,
+            connection_string: str,
+            embedding_model: Embeddings,
+            child_splitter: TextSplitter,
+            database_name: str,
+            collection_name: str = "document_with_chunks",
+            id_key: str = "doc_id",
+            **kwargs: Any,
     ) -> MongoDBAtlasParentDocumentRetriever:
         """Construct Retriever using one Collection for VectorStore and one for DocStore
 
@@ -158,7 +164,7 @@ def from_connection_string(
                 If parent_splitter is given, the documents will have already been split.
             database_name: Name of database to connect to. Created if it does not exist.
             collection_name: Name of collection to use.
-                It includes parent documents, sub-documents and their  embeddings.
+                It includes parent documents, sub-documents, and their  embeddings.
             id_key: Key used to identify parent documents.
             **kwargs: Additional keyword arguments. See parent classes for more.
 
@@ -184,6 +190,44 @@ def from_connection_string(
             **kwargs,
         )
 
+    def add_documents(
+        self,
+        documents: List[Document],
+        ids: Optional[List[str]] = None,
+        add_to_docstore: bool = True,
+    ) -> None:
+        """Add documents to vectorstore and docstore.
+
+        Args:
+            documents: List of parent documents to add
+            ids: Optional list of ids for documents. If not provided, will be generated.
+            add_to_docstore: Whether to add parent documents to docstore
+        """
+        if ids is None:
+            doc_ids = [str(uuid.uuid4()) for _ in documents]
+        else:
+            doc_ids = ids
+
+        # Split documents into chunks
+        sub_docs = []
+        for i, doc in enumerate(documents):
+            _id = doc_ids[i]
+            # Use child_splitter to split the document
+            _sub_docs = self.child_splitter.split_documents([doc])
+            # Add parent doc id to metadata
+            for _doc in _sub_docs:
+                _doc.metadata[self.id_key] = _id
+            sub_docs.extend(_sub_docs)
+
+        # Add chunks to vectorstore (OUTSIDE the loop)
+        self.vectorstore.add_documents(sub_docs)
+
+        # Add parent documents to docstore using mset (OUTSIDE the loop)
+        if add_to_docstore:
+            # mset expects a sequence of tuples (key, Document)
+            key_value_pairs = [(doc_id, doc) for doc_id, doc in zip(doc_ids, documents)]
+            self.docstore.mset(key_value_pairs)
+
     def close(self) -> None:
         """Close the resources used by the MongoDBAtlasParentDocumentRetriever."""
         self.vectorstore.close()

libs/langchain-mongodb/langchain_mongodb/retrievers/self_querying.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Sequence, Tuple, Union
 
-from langchain.chains.query_constructor.schema import AttributeInfo
-from langchain.retrievers.self_query.base import SelfQueryRetriever
+
+from langchain_classic.retrievers.self_query.base import SelfQueryRetriever
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.runnables import Runnable
 from langchain_core.structured_query import (
@@ -16,6 +16,7 @@
 from pydantic import Field
 
 from langchain_mongodb import MongoDBAtlasVectorSearch
+from langchain_classic.chains.query_constructor.schema import AttributeInfo
 
 
 class MongoDBStructuredQueryTranslator(Visitor):

libs/langchain-mongodb/pyproject.toml

@@ -5,25 +5,25 @@ build-backend = "hatchling.build"
 
 [project]
 name = "langchain-mongodb"
-version = "0.7.2"
+version = "0.8.0"
 description = "An integration package connecting MongoDB and LangChain"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-    "langchain-core>=0.3,<1.0",
-    "langchain>=0.3,<1.0",
+    "langchain-core>=1.0.0,<1.1.0",
+    "langchain>=1.0.0,<1.1.0",
     "pymongo>=4.6.1",
-    "langchain-text-splitters>=0.3,<1.0",
+    "langchain-text-splitters>=1.0.0,<1.1.0",
     "numpy>=1.26",
     "lark<2.0.0,>=1.1.9",
 ]
 
 [dependency-groups]
 dev = [
     "freezegun>=1.2.2",
-    "langchain>=0.3.14,<1.0",
-    "langchain-core>=0.3.29,<1.0",
-    "langchain-text-splitters>=0.3.5,<1.0",
+    "langchain>=1.0.0,<1.1.0",
+    "langchain-core>=1.0.0,<1.1.0",
+    "langchain-text-splitters>=1.0.0,<1.1.0",
     "pytest-mock>=3.10.0",
     "pytest>=7.3.0",
     "syrupy>=4.0.2",
@@ -33,13 +33,13 @@ dev = [
     "pre-commit>=4.0",
     "mypy>=1.10",
     "simsimd>=6.5.0",
-    "langchain-ollama>=0.2.2,<1.0",
-    "langchain-openai>=0.2.14,<1.0",
-    "langchain-community>=0.3.27,<1.0",
+    "langchain-ollama>=1.0.0,<1.1.0",
+    "langchain-openai>=1.0.0,<1.1.0",
+    "langchain-community>=1.0.0a1,<1.1.0",
     "pypdf>=5.0.1",
-    "langgraph>=0.2.72",
+    "langgraph>=1.0.0,<1.1.0",
     "flaky>=3.8.1",
-    "langchain-tests==0.3.22,<1.0",
+    "langchain-tests>=1.0.0,<1.1.0",
     "pip>=25.0.1",
     "typing-extensions>=4.12.2",
 ]
@@ -60,6 +60,8 @@ filterwarnings = [
     "error",
     # Ignore ResourceWarning raised by langchain standardized base classes.
     "ignore:unclosed <socket.socket:ResourceWarning",
+    # Ignore LangGraph deprecation warnings from internal imports
+    "ignore::langgraph.warnings.LangGraphDeprecatedSinceV10",
 ]
 
 [tool.mypy]
@@ -81,3 +83,4 @@ lint.ignore = ["E501", "B008", "UP007", "UP006", "UP035", "UP045"]
 
 [tool.coverage.run]
 omit = ["tests/*"]
+

libs/langchain-mongodb/tests/integration_tests/test_agent_toolkit.py

@@ -5,7 +5,7 @@
 import requests
 from flaky import flaky  # type:ignore[import-untyped]
 from langchain_openai import AzureChatOpenAI, ChatOpenAI
-from langgraph.prebuilt import create_react_agent
+from langchain.agents import create_agent
 from pymongo import MongoClient
 
 from langchain_mongodb.agent_toolkit import (
@@ -64,7 +64,9 @@ def test_toolkit_response(db):
     prompt = MONGODB_AGENT_SYSTEM_PROMPT.format(top_k=5)
 
     test_query = "Which country's customers spent the most?"
-    agent = create_react_agent(llm, toolkit.get_tools(), prompt=prompt)
+    agent = create_agent(llm,
+                               tools=toolkit.get_tools(),
+                               system_prompt=prompt)
     agent.step_timeout = 60
     events = agent.stream(
         {"messages": [("user", test_query)]},

libs/langchain-mongodb/tests/integration_tests/test_chat_message_histories.py

@@ -1,7 +1,7 @@
 import json
 import warnings
 
-from langchain.memory import ConversationBufferMemory  # type: ignore[import-not-found]
+from langchain_classic.memory import ConversationBufferMemory  # type: ignore[import-not-found]
 from langchain_core.messages import message_to_dict
 
 from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory

libs/langchain-mongodb/tests/integration_tests/test_retriever_selfquerying.py

@@ -4,8 +4,8 @@
 from typing import Generator, Sequence, Union
 
 import pytest
-from langchain.chains.query_constructor.schema import AttributeInfo
-from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain_classic.chains.query_constructor.schema import AttributeInfo
+from langchain_classic.retrievers.self_query.base import SelfQueryRetriever
 from langchain_core.documents import Document
 from langchain_openai import AzureChatOpenAI, ChatOpenAI
 from langchain_openai.chat_models.base import BaseChatOpenAI

libs/langchain-mongodb/tests/unit_tests/test_chat_message_histories.py

@@ -3,7 +3,7 @@
 
 import mongomock
 import pytest
-from langchain.memory import ConversationBufferMemory  # type: ignore[import-not-found]
+from langchain_classic.memory import ConversationBufferMemory  # type: ignore[import-not-found]
 from langchain_core.messages import message_to_dict
 from pytest_mock import MockerFixture