Update retriever imports to use langchain_classic for v1 compatibility

src/oss/python/integrations/callbacks/uptrain.mdx

@@ -62,9 +62,9 @@ NOTE: that you can also install `faiss-gpu` instead of `faiss-cpu` if you want t
 from getpass import getpass
 
 from langchain.chains import RetrievalQA
-from langchain.retrievers import ContextualCompressionRetriever
-from langchain.retrievers.document_compressors import FlashrankRerank
-from langchain.retrievers.multi_query import MultiQueryRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_classic.retrievers.document_compressors import FlashrankRerank
+from langchain_classic.retrievers.multi_query import MultiQueryRetriever
 from langchain_community.callbacks.uptrain_callback import UpTrainCallbackHandler
 from langchain_community.document_loaders import TextLoader
 from langchain_community.vectorstores import FAISS

src/oss/python/integrations/document_loaders/docugami.mdx

@@ -204,7 +204,7 @@ We can use a self-querying retriever to improve our query accuracy, using this a
 
 ```python
 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_chroma import Chroma
 
 EXCLUDE_KEYS = ["id", "xpath", "structure"]
@@ -322,7 +322,7 @@ CHUNK 21b4d9517f7ccdc0e3a028ce5043a2a0: page_content='1.1 Landlord.\n <Landlord>
 ```
 
 ```python
-from langchain.retrievers.multi_vector import MultiVectorRetriever, SearchType
+from langchain_classic.retrievers.multi_vector import MultiVectorRetriever, SearchType
 from langchain.storage import InMemoryStore
 from langchain_chroma import Chroma
 from langchain_openai import OpenAIEmbeddings

src/oss/python/integrations/document_transformers/cross_encoder_reranker.mdx

@@ -58,8 +58,8 @@ pretty_print_docs(docs)
 Now let's wrap our base retriever with a `ContextualCompressionRetriever`. `CrossEncoderReranker` uses `HuggingFaceCrossEncoder` to rerank the returned results.
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
-from langchain.retrievers.document_compressors import CrossEncoderReranker
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_classic.retrievers.document_compressors import CrossEncoderReranker
 from langchain_community.cross_encoders import HuggingFaceCrossEncoder
 
 model = HuggingFaceCrossEncoder(model_name="BAAI/bge-reranker-base")

src/oss/python/integrations/document_transformers/dashscope_rerank.mdx

@@ -263,7 +263,7 @@ And with an unwavering resolve that freedom will always triumph over tyranny.
 Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll use the `DashScopeRerank` to rerank the returned results.
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors.dashscope_rerank import DashScopeRerank
 
 compressor = DashScopeRerank()

src/oss/python/integrations/document_transformers/google_cloud_vertexai_rerank.mdx

@@ -64,7 +64,7 @@ Your 1 documents have been split into 266 chunks
 
 ```python
 import pandas as pd
-from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_google_community.vertex_rank import VertexAIRank
 
 # Instantiate the VertexAIReranker with the SDK manager

src/oss/python/integrations/document_transformers/infinity_rerank.mdx

@@ -280,7 +280,7 @@ Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll
 
 ```python
 from infinity_client import Client
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors.infinity_rerank import InfinityRerank
 
 client = Client(base_url="http://localhost:7997")

src/oss/python/integrations/document_transformers/jina_rerank.mdx

@@ -65,7 +65,7 @@ pretty_print_docs(docs)
 Now let's wrap our base retriever with a ContextualCompressionRetriever, using Jina Reranker as a compressor.
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors import JinaRerank
 
 compressor = JinaRerank()

src/oss/python/integrations/document_transformers/openvino_rerank.mdx

@@ -291,7 +291,7 @@ Metadata: {'source': '../../how_to/state_of_the_union.txt', 'id': 40}
 Now let's wrap our base retriever with a `ContextualCompressionRetriever`, using `OpenVINOReranker` as a compressor.
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors.openvino_rerank import OpenVINOReranker
 
 model_name = "BAAI/bge-reranker-large"

src/oss/python/integrations/document_transformers/rankllm-reranker.mdx

@@ -270,7 +270,7 @@ RankZephyr performs listwise reranking for improved retrieval quality but requir
 
 ```python
 import torch
-from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank
 
 torch.cuda.empty_cache()
@@ -567,7 +567,7 @@ One America.
 Retrieval + Reranking with RankGPT
 
 ```python
-from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank
 
 compressor = RankLLMRerank(top_n=3, model="gpt", gpt_model="gpt-4o-mini")

src/oss/python/integrations/document_transformers/volcengine_rerank.mdx

@@ -295,7 +295,7 @@ To disable this warning, you can either:
 Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll use the `VolcengineRerank` to rerank the returned results.
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors.volcengine_rerank import VolcengineRerank
 
 compressor = VolcengineRerank()

src/oss/python/integrations/document_transformers/voyageai-reranker.mdx

@@ -292,7 +292,7 @@ Now let's wrap our base retriever with a `ContextualCompressionRetriever`. We'll
 - `rerank-lite-1`
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_openai import OpenAI
 from langchain_voyageai import VoyageAIRerank
 

src/oss/python/integrations/providers/astradb.mdx

@@ -137,7 +137,7 @@ Learn more in the [example notebook](/oss/integrations/document_loaders/astradb)
 
 ```python
 from langchain_astradb import AstraDBVectorStore
-from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain_classic.retrievers.self_query.base import SelfQueryRetriever
 
 vector_store = AstraDBVectorStore(
     embedding=my_embedding,

src/oss/python/integrations/providers/breebs.mdx

@@ -11,7 +11,7 @@ title: Breebs (Open Knowledge)
 ## Retriever
 
 ```python
-from langchain.retrievers import BreebsRetriever
+from langchain_classic.retrievers import BreebsRetriever
 ```
 
 [See a usage example (Retrieval & ConversationalRetrievalChain)](/oss/integrations/retrievers/breebs)

src/oss/python/integrations/providers/chaindesk.mdx

@@ -15,5 +15,5 @@ We need the [API Key](https://docs.chaindesk.ai/api-reference/authentication).
 See a [usage example](/oss/integrations/retrievers/chaindesk).
 
 ```python
-from langchain.retrievers import ChaindeskRetriever
+from langchain_classic.retrievers import ChaindeskRetriever
 ```

src/oss/python/integrations/providers/chroma.mdx

@@ -31,5 +31,5 @@ For a more detailed walkthrough of the Chroma wrapper, see [this notebook](/oss/
 ## Retriever
 
 ```python
-from langchain.retrievers import SelfQueryRetriever
+from langchain_classic.retrievers import SelfQueryRetriever
 ```

src/oss/python/integrations/providers/cohere.mdx

@@ -25,9 +25,9 @@ Get a [Cohere api key](https://dashboard.cohere.ai/) and set it as an environmen
 |---|---|---|---|---|
 |Chat|Build chat bots|[chat](https://docs.cohere.com/reference/chat)|`from langchain_cohere import ChatCohere`|[cohere.ipynb](/oss/integrations/chat/cohere)|
 |LLM|Generate text|[generate](https://docs.cohere.com/reference/generate)|`from langchain_cohere.llms import Cohere`|[cohere.ipynb](/oss/integrations/llms/cohere)|
-|RAG Retriever|Connect to external data sources|[chat + rag](https://docs.cohere.com/reference/chat)|`from langchain.retrievers import CohereRagRetriever`|[cohere.ipynb](/oss/integrations/retrievers/cohere)|
+|RAG Retriever|Connect to external data sources|[chat + rag](https://docs.cohere.com/reference/chat)|`from langchain_classic.retrievers import CohereRagRetriever`|[cohere.ipynb](/oss/integrations/retrievers/cohere)|
 |Text Embedding|Embed strings to vectors|[embed](https://docs.cohere.com/reference/embed)|`from langchain_cohere import CohereEmbeddings`|[cohere.ipynb](/oss/integrations/text_embedding/cohere)|
-|Rerank Retriever|Rank strings based on relevance|[rerank](https://docs.cohere.com/reference/rerank)|`from langchain.retrievers.document_compressors import CohereRerank`|[cohere.ipynb](/oss/integrations/retrievers/cohere-reranker)|
+|Rerank Retriever|Rank strings based on relevance|[rerank](https://docs.cohere.com/reference/rerank)|`from langchain_classic.retrievers.document_compressors import CohereRerank`|[cohere.ipynb](/oss/integrations/retrievers/cohere-reranker)|
 
 ## Quick copy examples
 
@@ -140,7 +140,7 @@ The ReAct agent can be used to call multiple tools in sequence.
 
 ```python
 from langchain_cohere import ChatCohere
-from langchain.retrievers import CohereRagRetriever
+from langchain_classic.retrievers import CohereRagRetriever
 from langchain_core.documents import Document
 
 rag = CohereRagRetriever(llm=ChatCohere())

src/oss/python/integrations/providers/metal.mdx

@@ -15,7 +15,7 @@ Get started by [creating a Metal account](https://app.getmetal.io/signup).
 Then, you can easily take advantage of the `MetalRetriever` class to start retrieving your data for semantic search, prompting context, etc. This class takes a `Metal` instance and a dictionary of parameters to pass to the Metal API.
 
 ```python
-from langchain.retrievers import MetalRetriever
+from langchain_classic.retrievers import MetalRetriever
 from metal_sdk.metal import Metal
 
 

src/oss/python/integrations/providers/outline.mdx

@@ -20,5 +20,5 @@ os.environ["OUTLINE_INSTANCE_URL"] = "https://app.getoutline.com"
 See a [usage example](/oss/integrations/retrievers/outline).
 
 ```python
-from langchain.retrievers import OutlineRetriever
+from langchain_classic.retrievers import OutlineRetriever
 ```

src/oss/python/integrations/providers/pubmed.mdx

@@ -24,7 +24,7 @@ uv add xmltodict
 See a [usage example](/oss/integrations/retrievers/pubmed).
 
 ```python
-from langchain.retrievers import PubMedRetriever
+from langchain_classic.retrievers import PubMedRetriever
 ```
 
 ### Document Loader

src/oss/python/integrations/providers/ragatouille.mdx

@@ -104,7 +104,7 @@ We can see that the result isn't super relevant to the question asked
 ## Using ColBERT as a reranker
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 
 compression_retriever = ContextualCompressionRetriever(
         base_compressor=RAG.as_langchain_document_compressor(), base_retriever=retriever

src/oss/python/integrations/providers/vespa.mdx

@@ -25,5 +25,5 @@ uv add pyvespa
 See a [usage example](/oss/integrations/retrievers/vespa).
 
 ```python
-from langchain.retrievers import VespaRetriever
+from langchain_classic.retrievers import VespaRetriever
 ```

src/oss/python/integrations/providers/wikipedia.mdx

@@ -32,5 +32,5 @@ from langchain_community.document_loaders import WikipediaLoader
 See a [usage example](/oss/integrations/retrievers/wikipedia).
 
 ```python
-from langchain.retrievers import WikipediaRetriever
+from langchain_classic.retrievers import WikipediaRetriever
 ```

src/oss/python/integrations/retrievers/flashrank-reranker.mdx

@@ -271,7 +271,7 @@ May God bless you all. May God protect our troops.
 Now let's wrap our base retriever with a `ContextualCompressionRetriever`, using `FlashrankRerank` as a compressor.
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors import FlashrankRerank
 from langchain_openai import ChatOpenAI
 

src/oss/python/integrations/retrievers/fleet_context.mdx

@@ -17,7 +17,7 @@ from operator import itemgetter
 from typing import Any, Optional, Type
 
 import pandas as pd
-from langchain.retrievers import MultiVectorRetriever
+from langchain_classic.retrievers import MultiVectorRetriever
 from langchain_community.vectorstores import FAISS
 from langchain_core.documents import Document
 from langchain_core.stores import BaseStore

src/oss/python/integrations/retrievers/greennode_reranker.mdx

@@ -71,7 +71,7 @@ reranker = GreenNodeRerank(
 Reranking models enhance retrieval-augmented generation (RAG) workflows by refining and reordering initial search results based on semantic relevance. The example below demonstrates how to integrate GreenNodeRerank with a base retriever to improve the quality of retrieved documents.
 
 ```python
-from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.vectorstores import FAISS
 from langchain_core.documents import Document
 from langchain_greennode import GreenNodeEmbeddings

src/oss/python/integrations/retrievers/ibm_watsonx_ranker.mdx

@@ -180,7 +180,7 @@ wx_rerank = WatsonxRerank(
 ```
 
 ```python
-from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 
 compression_retriever = ContextualCompressionRetriever(
     base_compressor=wx_rerank, base_retriever=retriever

src/oss/python/integrations/retrievers/llmlingua.mdx

@@ -261,7 +261,7 @@ May God bless you all. May God protect our troops.
 Now let’s wrap our base retriever with a `ContextualCompressionRetriever`, using `LLMLinguaCompressor` as a compressor.
 
 ```python
-from langchain.retrievers import ContextualCompressionRetriever
+from langchain_classic.retrievers.contextual_compression import ContextualCompressionRetriever
 from langchain_community.document_compressors import LLMLinguaCompressor
 from langchain_openai import ChatOpenAI
 

src/oss/python/integrations/retrievers/merger_retriever.mdx

@@ -10,11 +10,11 @@ The `MergerRetriever` class can be used to improve the accuracy of document retr
 import os
 
 import chromadb
-from langchain.retrievers import (
+from langchain_classic.retrievers import (
     ContextualCompressionRetriever,
-    DocumentCompressorPipeline,
     MergerRetriever,
 )
+from langchain_classic.retrievers.document_compressors import DocumentCompressorPipeline
 from langchain_chroma import Chroma
 from langchain_community.document_transformers import (
     EmbeddingsClusteringFilter,

src/oss/python/integrations/retrievers/re_phrase.mdx

@@ -15,7 +15,7 @@ Create a vector store.
 ```python
 import logging
 
-from langchain.retrievers import RePhraseQueryRetriever
+from langchain_classic.retrievers import RePhraseQueryRetriever
 from langchain_chroma import Chroma
 from langchain_community.document_loaders import WebBaseLoader
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings

src/oss/python/integrations/vectorstores/timescalevector.mdx

@@ -789,7 +789,7 @@ Next we'll create our self-querying retriever. To do this we'll need to provide
 
 ```python
 from langchain.chains.query_constructor.base import AttributeInfo
-from langchain.retrievers.self_query.base import SelfQueryRetriever
+from langchain_classic.retrievers.self_query.base import SelfQueryRetriever
 from langchain_openai import OpenAI
 
 # Give LLM info about the metadata fields

src/oss/python/integrations/vectorstores/vectara.mdx

@@ -228,7 +228,7 @@ Vectara's "RAG as a service" does a lot of the heavy lifting in creating questio
 Since MQR uses an LLM we have to set that up - here we choose @[`ChatOpenAI`] :
 
 ```python
-from langchain.retrievers.multi_query import MultiQueryRetriever
+from langchain_classic.retrievers.multi_query import MultiQueryRetriever
 from langchain_openai.chat_models import ChatOpenAI
 
 llm = ChatOpenAI(temperature=0)