docs, templates: update schema imports to core (#17885)

- chat models, messages
- documents
- agentaction/finish
- baseretriever,document
- stroutputparser
- more messages
- basemessage
- format_document
- baseoutputparser

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

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

@@ -3,8 +3,8 @@ import os
 import cassio
 import langchain
 from langchain.cache import CassandraCache
-from langchain.schema import BaseMessage
 from langchain_community.chat_models import ChatOpenAI
+from langchain_core.messages import BaseMessage
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import RunnableLambda
 

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

@@ -1,7 +1,7 @@
 from langchain import hub
-from langchain.schema import StrOutputParser
 from langchain_community.chat_models import ChatAnthropic
 from langchain_community.utilities import WikipediaAPIWrapper
+from langchain_core.output_parsers import StrOutputParser
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnableLambda, RunnablePassthrough
 

templates/chat-bot-feedback/README.md

@@ -69,7 +69,7 @@ from functools import partial
 from typing import Dict, Optional, Callable, List
 from langserve import RemoteRunnable
 from langchain.callbacks.manager import tracing_v2_enabled
-from langchain.schema import BaseMessage, AIMessage, HumanMessage
+from langchain_core.messages import BaseMessage, AIMessage, HumanMessage
 
 # Update with the URL provided by your LangServe server
 chain = RemoteRunnable("http://127.0.0.1:8031/chat-bot-feedback")

templates/neo4j-generation/neo4j_generation/chain.py

@@ -3,10 +3,10 @@ from typing import List, Optional
 from langchain.chains.openai_functions import (
     create_structured_output_chain,
 )
-from langchain.schema import Document
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.graphs import Neo4jGraph
 from langchain_community.graphs.graph_document import GraphDocument
+from langchain_core.documents import Document
 from langchain_core.prompts import ChatPromptTemplate
 
 from neo4j_generation.utils import (

templates/neo4j-semantic-layer/neo4j_semantic_layer/agent.py

@@ -5,9 +5,9 @@ from langchain.agents.format_scratchpad import format_to_openai_function_message
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.pydantic_v1 import BaseModel, Field
-from langchain.schema import AIMessage, HumanMessage
 from langchain.tools.render import format_tool_to_openai_function
 from langchain_community.chat_models import ChatOpenAI
+from langchain_core.messages import AIMessage, HumanMessage
 
 from neo4j_semantic_layer.information_tool import InformationTool
 from neo4j_semantic_layer.memory_tool import MemoryTool
@@ -45,9 +45,9 @@ def _format_chat_history(chat_history: List[Tuple[str, str]]):
 agent = (
     {
         "input": lambda x: x["input"],
-        "chat_history": lambda x: _format_chat_history(x["chat_history"])
-        if x.get("chat_history")
-        else [],
+        "chat_history": lambda x: (
+            _format_chat_history(x["chat_history"]) if x.get("chat_history") else []
+        ),
         "agent_scratchpad": lambda x: format_to_openai_function_messages(
             x["intermediate_steps"]
         ),

templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py

@@ -8,9 +8,9 @@ from langchain.agents.output_parsers import (
 )
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.pydantic_v1 import BaseModel, Field
-from langchain.schema import AIMessage, HumanMessage
 from langchain.tools.render import render_text_description_and_args
 from langchain_community.chat_models import ChatOllama
+from langchain_core.messages import AIMessage, HumanMessage
 
 from neo4j_semantic_ollama.information_tool import InformationTool
 from neo4j_semantic_ollama.memory_tool import MemoryTool
@@ -87,9 +87,9 @@ agent = (
     {
         "input": lambda x: x["input"],
         "agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]),
-        "chat_history": lambda x: _format_chat_history(x["chat_history"])
-        if x.get("chat_history")
-        else [],
+        "chat_history": lambda x: (
+            _format_chat_history(x["chat_history"]) if x.get("chat_history") else []
+        ),
     }
     | prompt
     | chat_model_with_stop

templates/neo4j-vector-memory/neo4j_vector_memory/history.py

@@ -1,8 +1,8 @@
 from typing import Any, Dict, List, Union
 
 from langchain.memory import ChatMessageHistory
-from langchain.schema import AIMessage, HumanMessage
 from langchain_community.graphs import Neo4jGraph
+from langchain_core.messages import AIMessage, HumanMessage
 
 graph = Neo4jGraph()
 

templates/openai-functions-tool-retrieval-agent/openai_functions_tool_retrieval_agent/agent.py

@@ -6,13 +6,13 @@ from langchain.agents import (
 )
 from langchain.agents.format_scratchpad import format_to_openai_functions
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.schema import Document
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.embeddings import OpenAIEmbeddings
 from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
 from langchain_community.tools.tavily_search import TavilySearchResults
 from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
 from langchain_community.vectorstores import FAISS
+from langchain_core.documents import Document
 from langchain_core.messages import AIMessage, HumanMessage
 from langchain_core.prompts import (
     ChatPromptTemplate,

templates/rag-conversation-zep/rag_conversation_zep/chain.py

@@ -2,13 +2,16 @@ import os
 from operator import itemgetter
 from typing import List, Tuple
 
-from langchain.schema import AIMessage, HumanMessage, format_document
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.vectorstores.zep import CollectionConfig, ZepVectorStore
 from langchain_core.documents import Document
-from langchain_core.messages import BaseMessage
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    MessagesPlaceholder,
+    format_document,
+)
 from langchain_core.prompts.prompt import PromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import (

templates/rag-conversation/rag_conversation/chain.py

@@ -2,11 +2,15 @@ import os
 from operator import itemgetter
 from typing import List, Tuple
 
-from langchain.schema import AIMessage, HumanMessage, format_document
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_core.messages import AIMessage, HumanMessage
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    MessagesPlaceholder,
+    format_document,
+)
 from langchain_core.prompts.prompt import PromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import (

templates/rag-elasticsearch/rag_elasticsearch/chain.py

@@ -1,11 +1,12 @@
 from operator import itemgetter
 from typing import List, Optional, Tuple
 
-from langchain.schema import BaseMessage, format_document
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores.elasticsearch import ElasticsearchStore
+from langchain_core.messages import BaseMessage
 from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import format_document
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 

templates/rag-gpt-crawler/rag_gpt_crawler/chain.py

@@ -1,11 +1,11 @@
 import json
 from pathlib import Path
 
-from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.embeddings import OpenAIEmbeddings
 from langchain_community.vectorstores import Chroma
+from langchain_core.documents import Document
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.pydantic_v1 import BaseModel

templates/rag-multi-index-fusion/rag_multi_index_fusion/chain.py

@@ -7,10 +7,10 @@ from langchain.retrievers import (
     PubMedRetriever,
     WikipediaRetriever,
 )
-from langchain.schema import StrOutputParser
 from langchain.utils.math import cosine_similarity
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import (

templates/rag-multi-index-router/rag_multi_index_router/chain.py

@@ -8,9 +8,9 @@ from langchain.retrievers import (
     PubMedRetriever,
     WikipediaRetriever,
 )
-from langchain.schema import StrOutputParser
 from langchain.utils.openai_functions import convert_pydantic_to_openai_function
 from langchain_community.chat_models import ChatOpenAI
+from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import (

templates/rag-self-query/rag_self_query/chain.py

@@ -3,11 +3,11 @@ from operator import itemgetter
 from typing import List, Tuple
 
 from langchain.retrievers import SelfQueryRetriever
-from langchain.schema import format_document
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.embeddings import OpenAIEmbeddings
 from langchain_community.vectorstores.elasticsearch import ElasticsearchStore
 from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import format_document
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 

templates/rag-timescale-conversation/rag_timescale_conversation/chain.py

@@ -4,12 +4,16 @@ from operator import itemgetter
 from typing import List, Optional, Tuple
 
 from dotenv import find_dotenv, load_dotenv
-from langchain.schema import AIMessage, HumanMessage, format_document
 from langchain_community.chat_models import ChatOpenAI
 from langchain_community.embeddings import OpenAIEmbeddings
 from langchain_community.vectorstores.timescalevector import TimescaleVector
+from langchain_core.messages import AIMessage, HumanMessage
 from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.prompts import (
+    ChatPromptTemplate,
+    MessagesPlaceholder,
+    format_document,
+)
 from langchain_core.prompts.prompt import PromptTemplate
 from langchain_core.pydantic_v1 import BaseModel, Field
 from langchain_core.runnables import (
@@ -147,12 +151,16 @@ _inputs = RunnableParallel(
 )
 
 _datetime_to_string = RunnablePassthrough.assign(
-    start_date=lambda x: x.get("start_date", None).isoformat()
-    if x.get("start_date", None) is not None
-    else None,
-    end_date=lambda x: x.get("end_date", None).isoformat()
-    if x.get("end_date", None) is not None
-    else None,
+    start_date=lambda x: (
+        x.get("start_date", None).isoformat()
+        if x.get("start_date", None) is not None
+        else None
+    ),
+    end_date=lambda x: (
+        x.get("end_date", None).isoformat()
+        if x.get("end_date", None) is not None
+        else None
+    ),
 ).with_types(input_type=ChatHistory)
 
 chain = (

templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py

@@ -5,12 +5,13 @@ from langchain.agents import AgentExecutor
 from langchain.agents.format_scratchpad import format_log_to_str
 from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
-from langchain.schema import BaseRetriever, Document
 from langchain.tools.render import render_text_description
 from langchain.tools.retriever import create_retriever_tool
 from langchain_community.chat_models.fireworks import ChatFireworks
 from langchain_community.utilities.arxiv import ArxivAPIWrapper
+from langchain_core.documents import Document
 from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.retrievers import BaseRetriever
 
 MODEL_ID = "accounts/fireworks/models/mixtral-8x7b-instruct"
 

templates/retrieval-agent/retrieval_agent/chain.py

@@ -5,13 +5,14 @@ from langchain.agents import AgentExecutor
 from langchain.agents.format_scratchpad import format_to_openai_function_messages
 from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
-from langchain.schema import BaseRetriever, Document
 from langchain.tools.retriever import create_retriever_tool
 from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
 from langchain_community.utilities.arxiv import ArxivAPIWrapper
+from langchain_core.documents import Document
 from langchain_core.messages import AIMessage, HumanMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.retrievers import BaseRetriever
 from langchain_openai import AzureChatOpenAI
 
 

templates/self-query-qdrant/README.md

@@ -63,7 +63,7 @@ You can find the documents in the `packages/self-query-qdrant/self_query_qdrant/
 Here is one of the documents:
 
 ```python
-from langchain.schema import Document
+from langchain_core.documents import Document
 
 Document(
     page_content="Spaghetti with meatballs and tomato sauce",
@@ -108,7 +108,7 @@ chain = create_chain(
 The same goes for the `initialize` function that creates a Qdrant collection and indexes the documents:
 
 ```python
-from langchain.schema import Document
+from langchain_core.documents import Document
 from langchain_community.embeddings import HuggingFaceEmbeddings
 
 from self_query_qdrant.chain import initialize

templates/self-query-qdrant/self_query_qdrant/chain.py

@@ -3,11 +3,11 @@ from typing import List, Optional
 
 from langchain.chains.query_constructor.schema import AttributeInfo
 from langchain.retrievers import SelfQueryRetriever
-from langchain.schema import Document, StrOutputParser
 from langchain_community.embeddings import OpenAIEmbeddings
 from langchain_community.llms import BaseLLM
 from langchain_community.llms.openai import OpenAI
 from langchain_community.vectorstores.qdrant import Qdrant
+from langchain_core.documents import Document, StrOutputParser
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnableParallel, RunnablePassthrough

templates/self-query-qdrant/self_query_qdrant/defaults.py

@@ -1,5 +1,5 @@
 from langchain.chains.query_constructor.schema import AttributeInfo
-from langchain.schema import Document
+from langchain_core.documents import Document
 
 # Qdrant collection name
 DEFAULT_COLLECTION_NAME = "restaurants"

templates/self-query-qdrant/self_query_qdrant/helper.py

@@ -1,7 +1,7 @@
 from string import Formatter
 from typing import List
 
-from langchain.schema import Document
+from langchain_core.documents import Document
 
 document_template = """
 PASSAGE: {page_content}

templates/solo-performance-prompting-agent/solo_performance_prompting_agent/parser.py

@@ -1,4 +1,4 @@
-from langchain.schema import AgentAction, AgentFinish
+from langchain_core.agents import AgentAction, AgentFinish
 
 
 def parse_output(message: str):

templates/xml-agent/xml_agent/agent.py

@@ -2,10 +2,10 @@ from typing import List, Tuple
 
 from langchain.agents import AgentExecutor
 from langchain.agents.format_scratchpad import format_xml
-from langchain.schema import AIMessage, HumanMessage
 from langchain.tools import DuckDuckGoSearchRun
 from langchain.tools.render import render_text_description
 from langchain_community.chat_models import ChatAnthropic
+from langchain_core.messages import AIMessage, HumanMessage
 from langchain_core.pydantic_v1 import BaseModel, Field
 
 from xml_agent.prompts import conversational_prompt, parse_output

templates/xml-agent/xml_agent/prompts.py

@@ -1,4 +1,4 @@
-from langchain.schema import AgentAction, AgentFinish
+from langchain_core.agents import AgentAction, AgentFinish
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
 template = """You are a helpful assistant. Help the user answer any questions.