docs[patch], templates[patch]: Import from core (#14575)

Update imports to use core for the low-hanging fruit changes. Ran
following

```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook}  | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'


```

cookbook/LLaMA2_sql_chat.ipynb

@@ -164,8 +164,8 @@
     ")\n",
     "\n",
     "# Chain to query\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "sql_response = (\n",
     "    RunnablePassthrough.assign(schema=get_schema)\n",
@@ -293,7 +293,7 @@
     "memory = ConversationBufferMemory(return_messages=True)\n",
     "\n",
     "# Chain to query with memory\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "sql_chain = (\n",
     "    RunnablePassthrough.assign(\n",

cookbook/Multi_modal_RAG.ipynb

@@ -200,7 +200,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "\n",
     "# Generate summaries of text elements\n",
@@ -270,7 +270,7 @@
     "import base64\n",
     "import os\n",
     "\n",
-    "from langchain.schema.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "\n",
     "def encode_image(image_path):\n",
@@ -355,9 +355,9 @@
     "\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
     "from langchain.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "\n",
     "def create_multi_vector_retriever(\n",
@@ -442,7 +442,7 @@
     "import re\n",
     "\n",
     "from IPython.display import HTML, display\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
     "from PIL import Image\n",
     "\n",
     "\n",

cookbook/Semi_Structured_RAG.ipynb

@@ -237,7 +237,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {
@@ -320,9 +320,9 @@
     "\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
     "from langchain.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
@@ -374,7 +374,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -213,7 +213,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {
@@ -375,9 +375,9 @@
     "\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
     "from langchain.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
@@ -646,7 +646,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -211,7 +211,7 @@
    "source": [
     "from langchain.chat_models import ChatOllama\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {
@@ -378,9 +378,9 @@
     "\n",
     "from langchain.embeddings import GPT4AllEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
     "from langchain.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(\n",
@@ -532,7 +532,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",

cookbook/advanced_rag_eval.ipynb

@@ -162,7 +162,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "# Prompt\n",
     "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text for retrieval. \\\n",
@@ -202,7 +202,7 @@
     "import os\n",
     "from io import BytesIO\n",
     "\n",
-    "from langchain.schema.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "from PIL import Image\n",
     "\n",
     "\n",
@@ -273,8 +273,8 @@
     "from base64 import b64decode\n",
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "\n",
     "def create_multi_vector_retriever(\n",
@@ -475,7 +475,7 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",
@@ -521,7 +521,7 @@
     "import re\n",
     "\n",
     "from langchain.schema import Document\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "\n",
     "def looks_like_base64(sb):\n",

cookbook/docugami_xml_kg_rag.ipynb

@@ -476,7 +476,7 @@
     "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
     ")\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {
@@ -547,9 +547,9 @@
     "\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
     "from langchain.vectorstores.chroma import Chroma\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "\n",
     "def build_retriever(text_elements, tables, table_summaries):\n",
@@ -605,7 +605,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "system_prompt = SystemMessagePromptTemplate.from_template(\n",
     "    \"You are a helpful assistant that answers questions based on provided context. Your provided context can include text or tables, \"\n",

cookbook/extraction_openai_tools.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "from langchain.chains.openai_tools import create_extraction_chain_pydantic\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.pydantic_v1 import BaseModel"
+    "from langchain_core.pydantic_v1 import BaseModel"
    ]
   },
   {
@@ -151,11 +151,11 @@
     "\n",
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
-    "from langchain.schema.runnable import Runnable\n",
+    "from langchain_core.runnables import Runnable\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.messages import SystemMessage\n",
+    "from langchain_core.messages import SystemMessage\n",
-    "from langchain.schema.language_model import BaseLanguageModel\n",
+    "from langchain_core.language_models import BaseLanguageModel\n",
     "\n",
     "_EXTRACTION_TEMPLATE = \"\"\"Extract and save the relevant entities mentioned \\\n",
     "in the following passage together with their properties.\n",

cookbook/multi_modal_QA.ipynb

@@ -92,7 +92,7 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.messages import HumanMessage, SystemMessage"
+    "from langchain_core.messages import HumanMessage, SystemMessage"
    ]
   },
   {

cookbook/multi_modal_RAG_chroma.ipynb

@@ -316,9 +316,9 @@
     "from operator import itemgetter\n",
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.messages import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
     "\n",
     "\n",
     "def prompt_func(data_dict):\n",

cookbook/openai_v1_cookbook.ipynb

@@ -29,7 +29,7 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.messages import HumanMessage, SystemMessage"
+    "from langchain_core.messages import HumanMessage, SystemMessage"
    ]
   },
   {
@@ -252,7 +252,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.agent import AgentFinish\n",
+    "from langchain_core.agents import AgentFinish\n",
     "\n",
     "\n",
     "def execute_agent(agent, tools, input):\n",
@@ -457,8 +457,8 @@
     "\n",
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetCurrentWeather(BaseModel):\n",

cookbook/plan_and_execute_agent.ipynb

@@ -29,11 +29,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents.tools import Tool\n",
     "from langchain.chains import LLMMathChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.llms import OpenAI\n",
     "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
+    "from langchain_core.tools import Tool\n",
     "from langchain_experimental.plan_and_execute import (\n",
     "    PlanAndExecute,\n",
     "    load_agent_executor,\n",

cookbook/rag_fusion.ipynb

@@ -87,7 +87,7 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {

cookbook/retrieval_in_sql.ipynb

@@ -268,8 +268,8 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "db = SQLDatabase.from_uri(\n",
     "    CONNECTION_STRING\n",
@@ -324,7 +324,7 @@
    "source": [
     "import re\n",
     "\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "\n",
     "def replace_brackets(match):\n",

cookbook/rewrite.ipynb

@@ -33,9 +33,9 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.utilities import DuckDuckGoSearchAPIWrapper"
+    "from langchain_core.runnables import RunnablePassthrough"
    ]
   },
   {

cookbook/selecting_llms_based_on_context_length.ipynb

@@ -19,8 +19,8 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.prompt import PromptValue"
+    "from langchain_core.prompt_values import PromptValue"
    ]
   },
   {

cookbook/stepback-qa.ipynb

@@ -25,8 +25,8 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda"
+    "from langchain_core.runnables import RunnableLambda"
    ]
   },
   {

docs/docs/expression_language/cookbook/code_writing.ipynb

@@ -21,7 +21,7 @@
     "from langchain.prompts import (\n",
     "    ChatPromptTemplate,\n",
     ")\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_experimental.utilities import PythonREPL"
    ]
   },

docs/docs/expression_language/cookbook/embedding_router.ipynb

@@ -22,9 +22,9 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
     "from langchain.utils.math import cosine_similarity\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
     "\n",
     "physics_template = \"\"\"You are a very smart physics professor. \\\n",
     "You are great at answering questions about physics in a concise and easy to understand manner. \\\n",

docs/docs/expression_language/cookbook/memory.ipynb

@@ -22,7 +22,7 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
     "\n",
     "model = ChatOpenAI()\n",
     "prompt = ChatPromptTemplate.from_messages(\n",

docs/docs/expression_language/cookbook/multiple_chains.ipynb

@@ -69,7 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "prompt1 = ChatPromptTemplate.from_template(\n",
     "    \"generate a {attribute} color. Return the name of the color and nothing else:\"\n",

docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb

@@ -191,7 +191,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "chain = prompt | model | StrOutputParser()"
    ]
@@ -327,7 +327,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
     "\n",
     "map_ = RunnableParallel(foo=RunnablePassthrough())\n",
     "chain = (\n",

docs/docs/expression_language/cookbook/retrieval.ipynb

@@ -41,9 +41,9 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.vectorstores import FAISS\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough"
    ]
   },
   {
@@ -171,9 +171,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import format_document\n",
-    "from langchain.schema.messages import get_buffer_string\n",
+    "from langchain_core.messages import AIMessage, HumanMessage, get_buffer_string\n",
-    "from langchain.schema.runnable import RunnableParallel\n",
+    "from langchain_core.runnables import RunnableParallel"
-    "from langchain_core.messages import AIMessage, HumanMessage"
    ]
   },
   {

docs/docs/expression_language/cookbook/sql_db.ipynb

@@ -94,8 +94,8 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "model = ChatOpenAI()\n",
     "\n",

docs/docs/expression_language/cookbook/tools.ipynb

@@ -29,8 +29,8 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.tools import DuckDuckGoSearchRun\n",
-    "from langchain.tools import DuckDuckGoSearchRun"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {

docs/docs/expression_language/get_started.ipynb

@@ -49,7 +49,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n",
     "model = ChatOpenAI()\n",
@@ -326,9 +326,9 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough\n",
     "from langchain.vectorstores import DocArrayInMemorySearch\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
     "\n",
     "vectorstore = DocArrayInMemorySearch.from_texts(\n",
     "    [\"harrison worked at kensho\", \"bears like to eat honey\"],\n",

docs/docs/expression_language/how_to/binding.ipynb

@@ -22,7 +22,7 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough"
+    "from langchain_core.runnables import RunnablePassthrough"
    ]
   },
   {

docs/docs/expression_language/how_to/configure.ipynb

@@ -43,7 +43,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.runnable import ConfigurableField\n",
+    "from langchain_core.runnables import ConfigurableField\n",
     "\n",
     "model = ChatOpenAI(temperature=0).configurable_fields(\n",
     "    temperature=ConfigurableField(\n",
@@ -265,7 +265,7 @@
    "source": [
     "from langchain.chat_models import ChatAnthropic, ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.runnable import ConfigurableField"
+    "from langchain_core.runnables import ConfigurableField"
    ]
   },
   {

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -216,7 +216,7 @@
    "source": [
     "# First let's create a chain with a ChatModel\n",
     "# We add in a string output parser here so the outputs between the two are the same type\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "chat_prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",

docs/docs/expression_language/how_to/functions.ipynb

@@ -34,7 +34,7 @@
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "\n",
     "def length_function(text):\n",
@@ -103,8 +103,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableConfig"
+    "from langchain_core.runnables import RunnableConfig"
    ]
   },
   {

docs/docs/expression_language/how_to/generators.ipynb

@@ -34,7 +34,7 @@
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts.chat import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\n",
     "    \"Write a comma-separated list of 5 animals similar to: {animal}\"\n",

docs/docs/expression_language/how_to/map.ipynb

@@ -47,9 +47,9 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
     "from langchain.vectorstores import FAISS\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "vectorstore = FAISS.from_texts(\n",
     "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
@@ -131,9 +131,9 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
     "from langchain.vectorstores import FAISS\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "vectorstore = FAISS.from_texts(\n",
     "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
@@ -194,7 +194,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.runnable import RunnableParallel\n",
+    "from langchain_core.runnables import RunnableParallel\n",
     "\n",
     "model = ChatOpenAI()\n",
     "joke_chain = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",

docs/docs/expression_language/how_to/message_history.ipynb

@@ -132,8 +132,8 @@
     "from langchain.chat_models import ChatAnthropic\n",
     "from langchain.memory.chat_message_histories import RedisChatMessageHistory\n",
     "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain.schema.chat_history import BaseChatMessageHistory\n",
+    "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain.schema.runnable.history import RunnableWithMessageHistory"
+    "from langchain_core.runnables.history import RunnableWithMessageHistory"
    ]
   },
   {
@@ -292,8 +292,8 @@
     }
    ],
    "source": [
-    "from langchain.schema.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
-    "from langchain.schema.runnable import RunnableParallel\n",
+    "from langchain_core.runnables import RunnableParallel\n",
     "\n",
     "chain = RunnableParallel({\"output_message\": ChatAnthropic(model=\"claude-2\")})\n",
     "chain_with_history = RunnableWithMessageHistory(\n",

docs/docs/expression_language/how_to/passthrough.ipynb

@@ -46,7 +46,7 @@
     }
    ],
    "source": [
-    "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n",
     "\n",
     "runnable = RunnableParallel(\n",
     "    passed=RunnablePassthrough(),\n",
@@ -100,9 +100,9 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
     "from langchain.vectorstores import FAISS\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "vectorstore = FAISS.from_texts(\n",
     "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",

docs/docs/expression_language/how_to/routing.ipynb

@@ -53,7 +53,7 @@
    "source": [
     "from langchain.chat_models import ChatAnthropic\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {
@@ -164,7 +164,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableBranch\n",
+    "from langchain_core.runnables import RunnableBranch\n",
     "\n",
     "branch = RunnableBranch(\n",
     "    (lambda x: \"anthropic\" in x[\"topic\"].lower(), anthropic_chain),\n",
@@ -279,7 +279,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "full_chain = {\"topic\": chain, \"question\": lambda x: x[\"question\"]} | RunnableLambda(\n",
     "    route\n",

docs/docs/expression_language/interface.ipynb

@@ -660,9 +660,9 @@
    ],
    "source": [
     "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
     "from langchain.vectorstores import FAISS\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "template = \"\"\"Answer the question based only on the following context:\n",
     "{context}\n",
@@ -920,7 +920,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableParallel\n",
+    "from langchain_core.runnables import RunnableParallel\n",
     "\n",
     "chain1 = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
     "chain2 = (\n",

docs/docs/expression_language/why.ipynb

@@ -44,7 +44,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "\n",
     "prompt = ChatPromptTemplate.from_template(\"Tell me a short joke about {topic}\")\n",

docs/docs/guides/fallbacks.ipynb

@@ -181,7 +181,7 @@
    "source": [
     "# First let's create a chain with a ChatModel\n",
     "# We add in a string output parser here so the outputs between the two are the same type\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "chat_prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",

docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb

@@ -666,8 +666,8 @@
     "\n",
     "from langchain.chat_models.openai import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import (\n",
+    "from langchain_core.runnables import (\n",
     "    RunnableLambda,\n",
     "    RunnableParallel,\n",
     "    RunnablePassthrough,\n",

docs/docs/guides/pydantic_compatibility.md

@@ -73,7 +73,7 @@ CustomTool(
 **YES**
 
 ```python
-from langchain.tools.base import Tool
+from langchain_core.tools import Tool
 from pydantic.v1 import BaseModel, Field # <-- Uses v1 namespace
 
 class CalculatorInput(BaseModel):
@@ -90,7 +90,7 @@ Tool.from_function( # <-- tool uses v1 namespace
 **NO**
 
 ```python
-from langchain.tools.base import Tool
+from langchain_core.tools import Tool
 from pydantic import BaseModel, Field # <-- Uses v2 namespace
 
 class CalculatorInput(BaseModel):

docs/docs/integrations/chat/baidu_qianfan_endpoint.ipynb

@@ -71,7 +71,7 @@
     "import os\n",
     "\n",
     "from langchain.chat_models import QianfanChatEndpoint\n",
-    "from langchain.chat_models.base import HumanMessage\n",
+    "from langchain_core.language_models.chat_models import HumanMessage\n",
     "\n",
     "os.environ[\"QIANFAN_AK\"] = \"your_ak\"\n",
     "os.environ[\"QIANFAN_SK\"] = \"your_sk\"\n",

docs/docs/integrations/chat/fireworks.ipynb

@@ -159,7 +159,7 @@
     "from langchain.chat_models import ChatFireworks\n",
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "llm = ChatFireworks(\n",
     "    model=\"accounts/fireworks/models/llama-v2-13b-chat\",\n",

docs/docs/integrations/chat/pai_eas_chat_endpoint.ipynb

@@ -41,7 +41,7 @@
     "import os\n",
     "\n",
     "from langchain.chat_models import PaiEasChatEndpoint\n",
-    "from langchain.chat_models.base import HumanMessage\n",
+    "from langchain_core.language_models.chat_models import HumanMessage\n",
     "\n",
     "os.environ[\"EAS_SERVICE_URL\"] = \"Your_EAS_Service_URL\"\n",
     "os.environ[\"EAS_SERVICE_TOKEN\"] = \"Your_EAS_Service_Token\"\n",

docs/docs/integrations/chat_loaders/facebook.ipynb

@@ -516,7 +516,7 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",

docs/docs/integrations/chat_loaders/imessage.ipynb

@@ -360,7 +360,7 @@
    "outputs": [],
    "source": [
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
     "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
     "    [\n",

docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb

@@ -72,7 +72,7 @@
    "source": [
     "from enum import Enum\n",
     "\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Operation(Enum):\n",
@@ -135,8 +135,8 @@
    "source": [
     "from pprint import pprint\n",
     "\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_function\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "\n",
     "openai_function_def = convert_pydantic_to_openai_function(Calculator)\n",
     "pprint(openai_function_def)"

docs/docs/integrations/document_loaders/docugami.ipynb

@@ -472,7 +472,7 @@
     "from typing import Dict, List\n",
     "\n",
     "from langchain.document_loaders import DocugamiLoader\n",
-    "from langchain.schema.document import Document\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "loader = DocugamiLoader(docset_id=\"zo954yqy53wp\")\n",
     "loader.include_xml_tags = (\n",

docs/docs/integrations/document_transformers/nuclia_transformer.ipynb

@@ -74,7 +74,7 @@
     "import asyncio\n",
     "\n",
     "from langchain.document_transformers.nuclia_text_transform import NucliaTextTransformer\n",
-    "from langchain.schema.document import Document\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "\n",
     "async def process():\n",

docs/docs/integrations/llms/databricks.ipynb

@@ -80,7 +80,7 @@
    ],
    "source": [
     "from langchain.chat_models import ChatDatabricks\n",
-    "from langchain.schema.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "from mlflow.deployments import get_deploy_client\n",
     "\n",
     "client = get_deploy_client(\"databricks\")\n",

docs/docs/integrations/llms/opaqueprompts.ipynb

@@ -174,8 +174,8 @@
    "outputs": [],
    "source": [
     "import langchain.utilities.opaqueprompts as op\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "prompt = (PromptTemplate.from_template(prompt_template),)\n",
     "llm = OpenAI()\n",

docs/docs/integrations/llms/volcengine_maas.ipynb

@@ -40,7 +40,7 @@
    "source": [
     "from langchain.llms import VolcEngineMaasLLM\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {

docs/docs/integrations/providers/databricks.md

@@ -31,7 +31,7 @@ Databricks External Models
 
 ```python
 from langchain.chat_models import ChatDatabricks
-from langchain.schema.messages import HumanMessage
+from langchain_core.messages import HumanMessage
 from mlflow.deployments import get_deploy_client
 
 

docs/docs/integrations/retrievers/cohere.ipynb

@@ -21,7 +21,7 @@
    "source": [
     "from langchain.chat_models import ChatCohere\n",
     "from langchain.retrievers import CohereRagRetriever\n",
-    "from langchain.schema.document import Document"
+    "from langchain_core.documents import Document"
    ]
   },
   {

docs/docs/integrations/vectorstores/astradb.ipynb

@@ -65,9 +65,9 @@
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema import Document\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter"
+    "from langchain_core.runnables import RunnablePassthrough"
    ]
   },
   {

docs/docs/integrations/vectorstores/weaviate.ipynb

@@ -563,8 +563,8 @@
     }
    ],
    "source": [
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "rag_chain = (\n",
     "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",

docs/docs/modules/agents/agent_types/openai_assistants.ipynb

@@ -193,7 +193,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.agent import AgentFinish\n",
+    "from langchain_core.agents import AgentFinish\n",
     "\n",
     "\n",
     "def execute_agent(agent, tools, input):\n",

docs/docs/modules/agents/how_to/agent_iter.ipynb

@@ -23,9 +23,9 @@
    "outputs": [],
    "source": [
     "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain.agents.tools import Tool\n",
     "from langchain.chains import LLMMathChain\n",
     "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_core.tools import Tool\n",
     "from pydantic.v1 import BaseModel, Field"
    ]
   },

docs/docs/modules/agents/how_to/agent_structured.ipynb

@@ -166,7 +166,7 @@
    "source": [
     "import json\n",
     "\n",
-    "from langchain.schema.agent import AgentActionMessageLog, AgentFinish"
+    "from langchain_core.agents import AgentActionMessageLog, AgentFinish"
    ]
   },
   {

docs/docs/modules/agents/index.ipynb

@@ -357,7 +357,7 @@
     }
    ],
    "source": [
-    "from langchain.schema.agent import AgentFinish\n",
+    "from langchain_core.agents import AgentFinish\n",
     "\n",
     "user_input = \"how many letters in the word educa?\"\n",
     "intermediate_steps = []\n",
@@ -519,7 +519,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.messages import AIMessage, HumanMessage\n",
+    "from langchain_core.messages import AIMessage, HumanMessage\n",
     "\n",
     "chat_history = []"
    ]

docs/docs/modules/agents/tools/custom_tools.ipynb

@@ -938,8 +938,8 @@
     "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.tools import Tool\n",
-    "from langchain.tools.base import ToolException\n",
     "from langchain.utilities import SerpAPIWrapper\n",
+    "from langchain_core.tools import ToolException\n",
     "\n",
     "\n",
     "def _handle_error(error: ToolException) -> str:\n",

docs/docs/modules/chains/document/map_reduce.ipynb

@@ -35,8 +35,8 @@
     "from langchain.chat_models import ChatAnthropic\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.prompt_template import format_document\n",
+    "from langchain_core.prompts import format_document\n",
-    "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough"
+    "from langchain_core.runnables import RunnableParallel, RunnablePassthrough"
    ]
   },
   {

docs/docs/modules/chains/document/map_rerank.ipynb

@@ -32,9 +32,9 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.output_parsers.openai_functions import PydanticOutputFunctionsParser\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from langchain.utils.openai_functions import convert_pydantic_to_openai_function\n",
-    "from langchain.schema.prompt_template import format_document\n",
+    "from langchain_core.prompts import format_document\n",
-    "from langchain.utils.openai_functions import convert_pydantic_to_openai_function"
+    "from langchain_core.pydantic_v1 import BaseModel, Field"
    ]
   },
   {

docs/docs/modules/chains/document/refine.ipynb

@@ -51,7 +51,7 @@
     "from langchain.chat_models import ChatAnthropic\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.prompt_template import format_document"
+    "from langchain_core.prompts import format_document"
    ]
   },
   {

docs/docs/modules/chains/document/stuff.ipynb

@@ -43,7 +43,7 @@
     "from langchain.chat_models import ChatAnthropic\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.prompt_template import format_document"
+    "from langchain_core.prompts import format_document"
    ]
   },
   {

docs/docs/modules/chains/foundational/router.ipynb

@@ -58,8 +58,8 @@
    "outputs": [],
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableBranch"
+    "from langchain_core.runnables import RunnableBranch"
    ]
   },
   {
@@ -89,8 +89,8 @@
     "from typing import Literal\n",
     "\n",
     "from langchain.output_parsers.openai_functions import PydanticAttrOutputFunctionsParser\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_function\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "\n",
     "\n",
     "class TopicClassifier(BaseModel):\n",
@@ -119,8 +119,8 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "final_chain = (\n",
     "    RunnablePassthrough.assign(topic=itemgetter(\"input\") | classifier_chain)\n",

docs/docs/modules/chains/foundational/sequential_chains.ipynb

@@ -107,7 +107,7 @@
     }
    ],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "synopsis_chain = synopsis_prompt | llm | StrOutputParser()\n",
     "review_chain = review_prompt | llm | StrOutputParser()\n",

docs/docs/modules/chains/how_to/custom_chain.ipynb

@@ -29,7 +29,7 @@
     ")\n",
     "from langchain.chains.base import Chain\n",
     "from langchain.prompts.base import BasePromptTemplate\n",
-    "from langchain.schema.language_model import BaseLanguageModel\n",
+    "from langchain_core.language_models import BaseLanguageModel\n",
     "from pydantic import Extra\n",
     "\n",
     "\n",

docs/docs/modules/chains/how_to/openai_functions.ipynb

@@ -56,7 +56,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class Person(BaseModel):\n",

docs/docs/modules/data_connection/retrievers/index.ipynb

@@ -118,7 +118,7 @@
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "template = \"\"\"Answer the question based only on the following context:\n",
     "\n",

docs/docs/modules/data_connection/retrievers/multi_vector.ipynb

@@ -232,8 +232,8 @@
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.document import Document\n",
+    "from langchain_core.documents import Document\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {

docs/docs/modules/model_io/chat/index.ipynb

@@ -104,7 +104,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.messages import HumanMessage, SystemMessage\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
     "\n",
     "messages = [\n",
     "    SystemMessage(content=\"You're a helpful assistant\"),\n",

docs/docs/modules/model_io/llms/custom_llm.ipynb

@@ -30,7 +30,7 @@
     "from typing import Any, List, Mapping, Optional\n",
     "\n",
     "from langchain.callbacks.manager import CallbackManagerForLLMRun\n",
-    "from langchain.llms.base import LLM"
+    "from langchain_core.language_models.llms import LLM"
    ]
   },
   {

docs/docs/modules/model_io/output_parsers/index.ipynb

@@ -53,7 +53,7 @@
     "from langchain.llms import OpenAI\n",
     "from langchain.output_parsers import PydanticOutputParser\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.pydantic_v1 import BaseModel, Field, validator\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
     "\n",
     "model = OpenAI(model_name=\"text-davinci-003\", temperature=0.0)\n",
     "\n",

docs/docs/modules/model_io/output_parsers/output_fixing_parser.mdx

@@ -9,7 +9,7 @@ For this example, we'll use the above Pydantic output parser. Here's what happen
 ```python
 from langchain.chat_models import ChatOpenAI
 from langchain.output_parsers import PydanticOutputParser
-from langchain.pydantic_v1 import BaseModel, Field
+from langchain_core.pydantic_v1 import BaseModel, Field
 from typing import List
 ```
 

docs/docs/modules/model_io/output_parsers/pydantic.ipynb

@@ -25,7 +25,7 @@
     "from langchain.llms import OpenAI\n",
     "from langchain.output_parsers import PydanticOutputParser\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.pydantic_v1 import BaseModel, Field, validator"
+    "from langchain_core.pydantic_v1 import BaseModel, Field, validator"
    ]
   },
   {

docs/docs/modules/model_io/prompts/prompt_templates/index.ipynb

@@ -203,7 +203,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import HumanMessagePromptTemplate\n",
-    "from langchain.schema.messages import SystemMessage\n",
+    "from langchain_core.messages import SystemMessage\n",
     "\n",
     "chat_template = ChatPromptTemplate.from_messages(\n",
     "    [\n",

docs/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates.mdx

@@ -38,7 +38,7 @@ chat_prompt = ChatPromptTemplate.from_messages([MessagesPlaceholder(variable_nam
 
 
 ```python
-from langchain.schema.messages import HumanMessage, AIMessage
+from langchain_core.messages import HumanMessage, AIMessage
 
 human_message = HumanMessage(content="What is the best way to learn programming?")
 ai_message = AIMessage(content="""\

docs/docs/use_cases/data_generation.ipynb

@@ -66,7 +66,7 @@
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import FewShotPromptTemplate, PromptTemplate\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "from langchain_experimental.tabular_synthetic_data.openai import (\n",
     "    OPENAI_TEMPLATE,\n",
     "    create_openai_data_generator,\n",

docs/docs/use_cases/extraction.ipynb

@@ -382,7 +382,7 @@
     "from typing import Optional\n",
     "\n",
     "from langchain.chains import create_extraction_chain_pydantic\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
     "\n",
     "\n",
     "# Pydantic data class\n",

docs/docs/use_cases/question_answering/code_understanding.ipynb

@@ -250,7 +250,7 @@
     {
      "data": {
       "text/plain": [
-       "'To initialize a ReAct agent, you need to follow these steps:\\n\\n1. Initialize a language model `llm` of type `BaseLanguageModel`.\\n\\n2. Initialize a document store `docstore` of type `Docstore`.\\n\\n3. Create a `DocstoreExplorer` with the initialized `docstore`. The `DocstoreExplorer` is used to search for and look up terms in the document store.\\n\\n4. Create an array of `Tool` objects. The `Tool` objects represent the actions that the agent can perform. In the case of `ReActDocstoreAgent`, the tools must be \"Search\" and \"Lookup\" with their corresponding functions from the `DocstoreExplorer`.\\n\\n5. Initialize the `ReActDocstoreAgent` using the `from_llm_and_tools` method with the `llm` (language model) and `tools` as parameters.\\n\\n6. Initialize the `ReActChain` (which is the `AgentExecutor`) using the `ReActDocstoreAgent` and `tools` as parameters.\\n\\nHere is an example of how to do this:\\n\\n```python\\nfrom langchain.chains import ReActChain, OpenAI\\nfrom langchain.docstore.base import Docstore\\nfrom langchain.docstore.document import Document\\nfrom langchain.tools.base import BaseTool\\n\\n# Initialize the LLM and a docstore\\nllm = OpenAI()\\ndocstore = Docstore()\\n\\ndocstore_explorer = DocstoreExplorer(docstore)\\ntools = [\\n    Tool(\\n        name=\"Search\",\\n        func=docstore_explorer.search,\\n        description=\"Search for a term in the docstore.\",\\n    ),\\n    Tool(\\n        name=\"Lookup\",\\n        func=docstore_explorer.lookup,\\n        description=\"Lookup a term in the docstore.\",\\n    ),\\n]\\nagent = ReActDocstoreAgent.from_llm_and_tools(llm, tools)\\nreact = ReActChain(agent=agent, tools=tools)\\n```\\n\\nKeep in mind that this is a simplified example and you might need to adapt it to your specific needs.'"
+       "'To initialize a ReAct agent, you need to follow these steps:\\n\\n1. Initialize a language model `llm` of type `BaseLanguageModel`.\\n\\n2. Initialize a document store `docstore` of type `Docstore`.\\n\\n3. Create a `DocstoreExplorer` with the initialized `docstore`. The `DocstoreExplorer` is used to search for and look up terms in the document store.\\n\\n4. Create an array of `Tool` objects. The `Tool` objects represent the actions that the agent can perform. In the case of `ReActDocstoreAgent`, the tools must be \"Search\" and \"Lookup\" with their corresponding functions from the `DocstoreExplorer`.\\n\\n5. Initialize the `ReActDocstoreAgent` using the `from_llm_and_tools` method with the `llm` (language model) and `tools` as parameters.\\n\\n6. Initialize the `ReActChain` (which is the `AgentExecutor`) using the `ReActDocstoreAgent` and `tools` as parameters.\\n\\nHere is an example of how to do this:\\n\\n```python\\nfrom langchain.chains import ReActChain, OpenAI\\nfrom langchain.docstore.base import Docstore\\nfrom langchain.docstore.document import Document\\nfrom langchain_core.tools import BaseTool\\n\\n# Initialize the LLM and a docstore\\nllm = OpenAI()\\ndocstore = Docstore()\\n\\ndocstore_explorer = DocstoreExplorer(docstore)\\ntools = [\\n    Tool(\\n        name=\"Search\",\\n        func=docstore_explorer.search,\\n        description=\"Search for a term in the docstore.\",\\n    ),\\n    Tool(\\n        name=\"Lookup\",\\n        func=docstore_explorer.lookup,\\n        description=\"Lookup a term in the docstore.\",\\n    ),\\n]\\nagent = ReActDocstoreAgent.from_llm_and_tools(llm, tools)\\nreact = ReActChain(agent=agent, tools=tools)\\n```\\n\\nKeep in mind that this is a simplified example and you might need to adapt it to your specific needs.'"
       ]
      },
      "execution_count": 43,

docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb

@@ -429,7 +429,7 @@
    "source": [
     "from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\n",
     "from langchain.prompts import MessagesPlaceholder\n",
-    "from langchain.schema.messages import SystemMessage"
+    "from langchain_core.messages import SystemMessage"
    ]
   },
   {

docs/docs/use_cases/question_answering/index.ipynb

@@ -162,9 +162,9 @@
     "from langchain.document_loaders import WebBaseLoader\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
     "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain.vectorstores import Chroma"
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain_core.runnables import RunnablePassthrough"
    ]
   },
   {
@@ -687,7 +687,7 @@
    "outputs": [],
    "source": [
     "from langchain.schema import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "\n",
     "def format_docs(docs):\n",
@@ -855,7 +855,7 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.schema.runnable import RunnableParallel\n",
+    "from langchain_core.runnables import RunnableParallel\n",
     "\n",
     "rag_chain_from_docs = (\n",
     "    {\n",
@@ -943,7 +943,7 @@
     }
    ],
    "source": [
-    "from langchain.schema.messages import AIMessage, HumanMessage\n",
+    "from langchain_core.messages import AIMessage, HumanMessage\n",
     "\n",
     "condense_q_chain.invoke(\n",
     "    {\n",

docs/docs/use_cases/tagging.ipynb

@@ -323,7 +323,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.pydantic_v1 import BaseModel, Field"
+    "from langchain_core.pydantic_v1 import BaseModel, Field"
    ]
   },
   {

docs/scripts/model_feat_table.py

@@ -2,8 +2,8 @@ import os
 from pathlib import Path
 
 from langchain import chat_models, llms
-from langchain.chat_models.base import BaseChatModel, SimpleChatModel
+from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel
-from langchain.llms.base import LLM, BaseLLM
+from langchain_core.language_models.llms import LLM, BaseLLM
 
 INTEGRATIONS_DIR = Path(os.path.abspath(__file__)).parents[1] / "docs" / "integrations"
 LLM_IGNORE = ("FakeListLLM", "OpenAIChat", "PromptLayerOpenAIChat")

templates/anthropic-iterative-search/anthropic_iterative_search/chain.py

@@ -1,8 +1,8 @@
 from langchain.chat_models import ChatAnthropic
 from langchain.prompts import ChatPromptTemplate
-from langchain.pydantic_v1 import BaseModel
+from langchain_core.output_parsers import StrOutputParser
-from langchain.schema.output_parser import StrOutputParser
+from langchain_core.pydantic_v1 import BaseModel
-from langchain.schema.runnable import ConfigurableField
+from langchain_core.runnables import ConfigurableField
 
 from .prompts import answer_prompt
 from .retriever_agent import executor

templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py

@@ -1,6 +1,6 @@
 import re
 
-from langchain.schema.agent import AgentAction, AgentFinish
+from langchain_core.agents import AgentAction, AgentFinish
 
 from .agent_scratchpad import _format_docs
 

templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py

@@ -1,8 +1,8 @@
 from langchain.agents import AgentExecutor
 from langchain.chat_models import ChatAnthropic
 from langchain.prompts import ChatPromptTemplate
-from langchain.schema.output_parser import StrOutputParser
+from langchain_core.output_parsers import StrOutputParser
-from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 
 from .agent_scratchpad import format_agent_scratchpad
 from .output_parser import parse_output

templates/basic-critique-revise/basic_critique_revise/chain.py

@@ -7,8 +7,8 @@ from typing import Any, Dict, Sequence
 from langchain.chains.openai_functions import convert_to_openai_function
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
-from langchain.pydantic_v1 import BaseModel, Field, ValidationError, conint
+from langchain_core.pydantic_v1 import BaseModel, Field, ValidationError, conint
-from langchain.schema.runnable import (
+from langchain_core.runnables import (
     Runnable,
     RunnableBranch,
     RunnableLambda,

templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py

@@ -4,9 +4,9 @@ import cassio
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnablePassthrough
 from langchain.vectorstores import Cassandra
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
 
 from .populate_vector_store import populate
 

templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py

@@ -6,7 +6,7 @@ from langchain.cache import CassandraCache
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema import BaseMessage
-from langchain.schema.runnable import RunnableLambda
+from langchain_core.runnables import RunnableLambda
 
 use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0"))
 if use_cassandra:

templates/chain-of-note-wiki/chain_of_note_wiki/chain.py

@@ -1,9 +1,9 @@
 from langchain import hub
 from langchain.chat_models import ChatAnthropic
-from langchain.pydantic_v1 import BaseModel
 from langchain.schema import StrOutputParser
-from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
 from langchain.utilities import WikipediaAPIWrapper
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
 
 
 class Question(BaseModel):

templates/chat-bot-feedback/chat_bot_feedback/chain.py

@@ -15,7 +15,7 @@ from langchain.schema import (
     StrOutputParser,
     get_buffer_string,
 )
-from langchain.schema.runnable import Runnable
+from langchain_core.runnables import Runnable
 from langsmith.evaluation import EvaluationResult, RunEvaluator
 from langsmith.schemas import Example
 from pydantic import BaseModel, Field

templates/csv-agent/csv_agent/agent.py

@@ -5,9 +5,9 @@ from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.pydantic_v1 import BaseModel, Field
 from langchain.tools.retriever import create_retriever_tool
 from langchain.vectorstores import FAISS
+from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_experimental.tools import PythonAstREPLTool
 
 MAIN_DIR = Path(__file__).parents[1]

templates/elastic-query-generator/elastic_query_generator/chain.py

@@ -1,7 +1,7 @@
 from elasticsearch import Elasticsearch
 from langchain.chat_models import ChatOpenAI
 from langchain.output_parsers.json import SimpleJsonOutputParser
-from langchain.pydantic_v1 import BaseModel
+from langchain_core.pydantic_v1 import BaseModel
 
 from .elastic_index_info import get_indices_infos
 from .prompts import DSL_PROMPT

templates/extraction-anthropic-functions/extraction_anthropic_functions/chain.py

@@ -2,8 +2,8 @@ from typing import List, Optional
 
 from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
 from langchain.prompts import ChatPromptTemplate
-from langchain.pydantic_v1 import BaseModel
 from langchain.utils.openai_functions import convert_pydantic_to_openai_function
+from langchain_core.pydantic_v1 import BaseModel
 from langchain_experimental.llms.anthropic_functions import AnthropicFunctions
 
 template = """A article will be passed to you. Extract from it all papers that are mentioned by this article. 

templates/extraction-openai-functions/extraction_openai_functions/chain.py

@@ -3,8 +3,8 @@ from typing import List, Optional
 
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
-from langchain.pydantic_v1 import BaseModel
 from langchain.utils.openai_functions import convert_pydantic_to_openai_function
+from langchain_core.pydantic_v1 import BaseModel
 
 template = """A article will be passed to you. Extract from it all papers that are mentioned by this article. 
 

templates/hybrid-search-weaviate/hybrid_search_weaviate/chain.py

@@ -4,8 +4,8 @@ import weaviate
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
 from langchain.retrievers.weaviate_hybrid_search import WeaviateHybridSearchRetriever
-from langchain.schema.output_parser import StrOutputParser
+from langchain_core.output_parsers import StrOutputParser
-from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 
 # Check env vars
 if os.environ.get("WEAVIATE_API_KEY", None) is None:

templates/hyde/hyde/chain.py

@@ -1,10 +1,10 @@
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
-from langchain.pydantic_v1 import BaseModel
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnableParallel
 from langchain.vectorstores import Chroma
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import RunnableParallel
 
 from hyde.prompts import hyde_prompt
 

templates/mongo-parent-document-retrieval/mongo_parent_document_retrieval/chain.py

@@ -3,11 +3,11 @@ import os
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.prompts import ChatPromptTemplate
-from langchain.pydantic_v1 import BaseModel
-from langchain.schema.document import Document
-from langchain.schema.output_parser import StrOutputParser
-from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
 from langchain.vectorstores import MongoDBAtlasVectorSearch
+from langchain_core.documents import Document
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 from pymongo import MongoClient
 
 MONGO_URI = os.environ["MONGO_URI"]