diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index 13af457676d..bed01beef35 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -5,6 +5,7 @@ from typing import Dict LANGCHAIN_DIRS = [ "libs/core", + "libs/text-splitters", "libs/community", "libs/langchain", "libs/experimental", diff --git a/.github/scripts/get_min_versions.py b/.github/scripts/get_min_versions.py index 16a47254b2e..2f8db84197f 100644 --- a/.github/scripts/get_min_versions.py +++ b/.github/scripts/get_min_versions.py @@ -4,7 +4,7 @@ import tomllib from packaging.version import parse as parse_version import re -MIN_VERSION_LIBS = ["langchain-core", "langchain-community", "langchain"] +MIN_VERSION_LIBS = ["langchain-core", "langchain-community", "langchain", "langchain-text-splitters"] def get_min_version(version: str) -> str: diff --git a/cookbook/Multi_modal_RAG.ipynb b/cookbook/Multi_modal_RAG.ipynb index 79f311328a1..72a2d1f727c 100644 --- a/cookbook/Multi_modal_RAG.ipynb +++ b/cookbook/Multi_modal_RAG.ipynb @@ -116,7 +116,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "from unstructured.partition.pdf import partition_pdf\n", "\n", "\n", diff --git a/cookbook/advanced_rag_eval.ipynb b/cookbook/advanced_rag_eval.ipynb index 02e86817b29..a1c80ee6a74 100644 --- a/cookbook/advanced_rag_eval.ipynb +++ b/cookbook/advanced_rag_eval.ipynb @@ -68,7 +68,7 @@ "pdf_pages = loader.load()\n", "\n", "# Split\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n", "all_splits_pypdf = text_splitter.split_documents(pdf_pages)\n", diff --git a/cookbook/agent_vectorstore.ipynb b/cookbook/agent_vectorstore.ipynb index 388e4702a3a..a997bd0aee2 100644 --- a/cookbook/agent_vectorstore.ipynb +++ b/cookbook/agent_vectorstore.ipynb @@ -28,9 +28,9 @@ "outputs": [], "source": [ "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "llm = OpenAI(temperature=0)" ] diff --git a/cookbook/autogpt/marathon_times.ipynb b/cookbook/autogpt/marathon_times.ipynb index 44f2445e640..23c26668a9b 100644 --- a/cookbook/autogpt/marathon_times.ipynb +++ b/cookbook/autogpt/marathon_times.ipynb @@ -227,8 +227,8 @@ " BaseCombineDocumentsChain,\n", " load_qa_with_sources_chain,\n", ")\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain.tools import BaseTool, DuckDuckGoSearchRun\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "from pydantic import Field\n", "\n", "\n", diff --git a/cookbook/code-analysis-deeplake.ipynb b/cookbook/code-analysis-deeplake.ipynb index 67c1ecbe39f..b6edfd98bef 100644 --- a/cookbook/code-analysis-deeplake.ipynb +++ b/cookbook/code-analysis-deeplake.ipynb @@ -24,7 +24,7 @@ "source": [ "1. Prepare data:\n", " 1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n", - " 2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n", + " 2. Split all documents to chunks using the `langchain_text_splitters.CharacterTextSplitter`.\n", " 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n", "2. Question-Answering:\n", " 1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n", @@ -621,7 +621,7 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", "texts = text_splitter.split_documents(docs)\n", diff --git a/cookbook/deeplake_semantic_search_over_chat.ipynb b/cookbook/deeplake_semantic_search_over_chat.ipynb index 3dd2c920049..ba8108c9b60 100644 --- a/cookbook/deeplake_semantic_search_over_chat.ipynb +++ b/cookbook/deeplake_semantic_search_over_chat.ipynb @@ -52,12 +52,12 @@ "import os\n", "\n", "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import (\n", + "from langchain_community.vectorstores import DeepLake\n", + "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import (\n", " CharacterTextSplitter,\n", " RecursiveCharacterTextSplitter,\n", ")\n", - "from langchain_community.vectorstores import DeepLake\n", - "from langchain_openai import OpenAI, OpenAIEmbeddings\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n", diff --git a/cookbook/fireworks_rag.ipynb b/cookbook/fireworks_rag.ipynb index e6dbd61a855..563532c99d5 100644 --- a/cookbook/fireworks_rag.ipynb +++ b/cookbook/fireworks_rag.ipynb @@ -132,7 +132,7 @@ "data = loader.load()\n", "\n", "# Split\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)\n", "all_splits = text_splitter.split_documents(data)\n", diff --git a/cookbook/hypothetical_document_embeddings.ipynb b/cookbook/hypothetical_document_embeddings.ipynb index 58cde25fe9c..d421b6eaf59 100644 --- a/cookbook/hypothetical_document_embeddings.ipynb +++ b/cookbook/hypothetical_document_embeddings.ipynb @@ -170,8 +170,8 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import Chroma\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "with open(\"../../state_of_the_union.txt\") as f:\n", " state_of_the_union = f.read()\n", diff --git a/cookbook/nomic_embedding_rag.ipynb b/cookbook/nomic_embedding_rag.ipynb index dd1e06e8157..8a01fec8db8 100644 --- a/cookbook/nomic_embedding_rag.ipynb +++ b/cookbook/nomic_embedding_rag.ipynb @@ -124,7 +124,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_splitter = CharacterTextSplitter.from_tiktoken_encoder(\n", " chunk_size=7500, chunk_overlap=100\n", diff --git a/cookbook/openai_functions_retrieval_qa.ipynb b/cookbook/openai_functions_retrieval_qa.ipynb index 621e997088e..3d3b4d5b018 100644 --- a/cookbook/openai_functions_retrieval_qa.ipynb +++ b/cookbook/openai_functions_retrieval_qa.ipynb @@ -20,10 +20,10 @@ "outputs": [], "source": [ "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Chroma\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/cookbook/qianfan_baidu_elasticesearch_RAG.ipynb b/cookbook/qianfan_baidu_elasticesearch_RAG.ipynb index 082c12eacfb..a62ee148ff1 100644 --- a/cookbook/qianfan_baidu_elasticesearch_RAG.ipynb +++ b/cookbook/qianfan_baidu_elasticesearch_RAG.ipynb @@ -59,13 +59,13 @@ "from baidubce.auth.bce_credentials import BceCredentials\n", "from baidubce.bce_client_configuration import BceClientConfiguration\n", "from langchain.chains.retrieval_qa import RetrievalQA\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders.baiducloud_bos_directory import (\n", " BaiduBOSDirectoryLoader,\n", ")\n", "from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\n", "from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n", - "from langchain_community.vectorstores import BESVectorStore" + "from langchain_community.vectorstores import BESVectorStore\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/cookbook/rag_with_quantized_embeddings.ipynb b/cookbook/rag_with_quantized_embeddings.ipynb index a0113ff6ce8..79a85d5cc50 100644 --- a/cookbook/rag_with_quantized_embeddings.ipynb +++ b/cookbook/rag_with_quantized_embeddings.ipynb @@ -36,9 +36,6 @@ "from bs4 import BeautifulSoup as Soup\n", "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", "from langchain.storage import InMemoryByteStore, LocalFileStore\n", - "\n", - "# For our example, we'll load docs from the web\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter # noqa\n", "from langchain_community.document_loaders.recursive_url_loader import (\n", " RecursiveUrlLoader,\n", ")\n", @@ -46,6 +43,9 @@ "# noqa\n", "from langchain_community.vectorstores import Chroma\n", "\n", + "# For our example, we'll load docs from the web\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter # noqa\n", + "\n", "DOCSTORE_DIR = \".\"\n", "DOCSTORE_ID_KEY = \"doc_id\"" ] diff --git a/cookbook/sales_agent_with_context.ipynb b/cookbook/sales_agent_with_context.ipynb index e125046af92..490b75502ff 100644 --- a/cookbook/sales_agent_with_context.ipynb +++ b/cookbook/sales_agent_with_context.ipynb @@ -51,11 +51,11 @@ "from langchain.chains.base import Chain\n", "from langchain.prompts import PromptTemplate\n", "from langchain.prompts.base import StringPromptTemplate\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.llms import BaseLLM\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_core.agents import AgentAction, AgentFinish\n", "from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "from pydantic import BaseModel, Field" ] }, diff --git a/cookbook/together_ai.ipynb b/cookbook/together_ai.ipynb index ed6dd906a2f..a6c41bdbb62 100644 --- a/cookbook/together_ai.ipynb +++ b/cookbook/together_ai.ipynb @@ -39,7 +39,7 @@ "data = loader.load()\n", "\n", "# Split\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)\n", "all_splits = text_splitter.split_documents(data)\n", diff --git a/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb b/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb index 4f540fa5abd..04f42f449c2 100644 --- a/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb +++ b/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb @@ -2610,7 +2610,7 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", "texts = text_splitter.split_documents(docs)" diff --git a/docs/docs/get_started/quickstart.mdx b/docs/docs/get_started/quickstart.mdx index 37da0128aec..993b9f93224 100644 --- a/docs/docs/get_started/quickstart.mdx +++ b/docs/docs/get_started/quickstart.mdx @@ -281,7 +281,7 @@ Then we can build our index: ```python from langchain_community.vectorstores import FAISS -from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_text_splitters import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter() @@ -531,7 +531,7 @@ from langchain_openai import ChatOpenAI from langchain_community.document_loaders import WebBaseLoader from langchain_openai import OpenAIEmbeddings from langchain_community.vectorstores import FAISS -from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain.tools.retriever import create_retriever_tool from langchain_community.tools.tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI diff --git a/docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb b/docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb index 1bf0b77ab48..3b97e41a1aa 100644 --- a/docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb +++ b/docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb @@ -643,9 +643,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "# 2. Load the data: In our case data's already loaded\n", "# 3. Anonymize the data before indexing\n", diff --git a/docs/docs/integrations/callbacks/confident.ipynb b/docs/docs/integrations/callbacks/confident.ipynb index f0033825690..18f3ae2dbe0 100644 --- a/docs/docs/integrations/callbacks/confident.ipynb +++ b/docs/docs/integrations/callbacks/confident.ipynb @@ -215,10 +215,10 @@ "source": [ "import requests\n", "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_file_url = \"https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt\"\n", "\n", diff --git a/docs/docs/integrations/document_loaders/psychic.ipynb b/docs/docs/integrations/document_loaders/psychic.ipynb index edd94d57c04..30a91493454 100644 --- a/docs/docs/integrations/document_loaders/psychic.ipynb +++ b/docs/docs/integrations/document_loaders/psychic.ipynb @@ -78,9 +78,9 @@ "outputs": [], "source": [ "from langchain.chains import RetrievalQAWithSourcesChain\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import Chroma\n", - "from langchain_openai import OpenAI, OpenAIEmbeddings" + "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/document_loaders/source_code.ipynb b/docs/docs/integrations/document_loaders/source_code.ipynb index c6a75afde81..043feaf75da 100644 --- a/docs/docs/integrations/document_loaders/source_code.ipynb +++ b/docs/docs/integrations/document_loaders/source_code.ipynb @@ -62,9 +62,9 @@ "warnings.filterwarnings(\"ignore\")\n", "from pprint import pprint\n", "\n", - "from langchain.text_splitter import Language\n", "from langchain_community.document_loaders.generic import GenericLoader\n", - "from langchain_community.document_loaders.parsers import LanguageParser" + "from langchain_community.document_loaders.parsers import LanguageParser\n", + "from langchain_text_splitters import Language" ] }, { @@ -323,7 +323,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import (\n", + "from langchain_text_splitters import (\n", " Language,\n", " RecursiveCharacterTextSplitter,\n", ")" @@ -426,6 +426,7 @@ }, { "cell_type": "markdown", + "id": "7fb27b941602401d91542211134fc71a", "metadata": {}, "source": [ "## Adding Languages using Tree-sitter Template\n", diff --git a/docs/docs/integrations/document_loaders/youtube_audio.ipynb b/docs/docs/integrations/document_loaders/youtube_audio.ipynb index 7676c239939..bcdd7191b6f 100644 --- a/docs/docs/integrations/document_loaders/youtube_audio.ipynb +++ b/docs/docs/integrations/document_loaders/youtube_audio.ipynb @@ -168,9 +168,9 @@ "outputs": [], "source": [ "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.vectorstores import FAISS\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/llms/llm_caching.ipynb b/docs/docs/integrations/llms/llm_caching.ipynb index f428939a2a8..ba6331b8228 100644 --- a/docs/docs/integrations/llms/llm_caching.ipynb +++ b/docs/docs/integrations/llms/llm_caching.ipynb @@ -1463,7 +1463,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_splitter = CharacterTextSplitter()" ] diff --git a/docs/docs/integrations/llms/manifest.ipynb b/docs/docs/integrations/llms/manifest.ipynb index 005141cf9ad..8ac42dc524e 100644 --- a/docs/docs/integrations/llms/manifest.ipynb +++ b/docs/docs/integrations/llms/manifest.ipynb @@ -82,7 +82,7 @@ "# Map reduce example\n", "from langchain.chains.mapreduce import MapReduceChain\n", "from langchain.prompts import PromptTemplate\n", - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "_prompt = \"\"\"Write a concise summary of the following:\n", "\n", diff --git a/docs/docs/integrations/platforms/openai.mdx b/docs/docs/integrations/platforms/openai.mdx index 9e74120231d..415aed521b5 100644 --- a/docs/docs/integrations/platforms/openai.mdx +++ b/docs/docs/integrations/platforms/openai.mdx @@ -68,7 +68,7 @@ for OpenAI LLMs. You can also use it to count tokens when splitting documents with ```python -from langchain.text_splitter import CharacterTextSplitter +from langchain_text_splitters import CharacterTextSplitter CharacterTextSplitter.from_tiktoken_encoder(...) ``` For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/document_transformers/split_by_token#tiktoken) diff --git a/docs/docs/integrations/providers/elasticsearch.mdx b/docs/docs/integrations/providers/elasticsearch.mdx index 280066f467a..123e8bbab7c 100644 --- a/docs/docs/integrations/providers/elasticsearch.mdx +++ b/docs/docs/integrations/providers/elasticsearch.mdx @@ -34,7 +34,7 @@ The vector store is a simple wrapper around Elasticsearch. It provides a simple from langchain_elasticsearch import ElasticsearchStore from langchain_community.document_loaders import TextLoader -from langchain.text_splitter import CharacterTextSplitter +from langchain_text_splitters import CharacterTextSplitter loader = TextLoader("./state_of_the_union.txt") documents = loader.load() diff --git a/docs/docs/integrations/providers/ragatouille.ipynb b/docs/docs/integrations/providers/ragatouille.ipynb index 46f77ed5a5d..6f7da3b6dd7 100644 --- a/docs/docs/integrations/providers/ragatouille.ipynb +++ b/docs/docs/integrations/providers/ragatouille.ipynb @@ -87,9 +87,9 @@ "outputs": [], "source": [ "import requests\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "\n", "def get_wikipedia_page(title: str):\n", diff --git a/docs/docs/integrations/providers/spacy.mdx b/docs/docs/integrations/providers/spacy.mdx index 572bb582d75..bd388837e65 100644 --- a/docs/docs/integrations/providers/spacy.mdx +++ b/docs/docs/integrations/providers/spacy.mdx @@ -16,7 +16,7 @@ pip install spacy See a [usage example](/docs/modules/data_connection/document_transformers/split_by_token#spacy). ```python -from langchain.text_splitter import SpacyTextSplitter +from langchain_text_splitters import SpacyTextSplitter ``` ## Text Embedding Models diff --git a/docs/docs/integrations/retrievers/activeloop.ipynb b/docs/docs/integrations/retrievers/activeloop.ipynb index e703ecabf06..0fe9ae6ed9d 100644 --- a/docs/docs/integrations/retrievers/activeloop.ipynb +++ b/docs/docs/integrations/retrievers/activeloop.ipynb @@ -192,7 +192,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "chunk_size = 4096\n", "docs_new = []\n", diff --git a/docs/docs/integrations/retrievers/cohere-reranker.ipynb b/docs/docs/integrations/retrievers/cohere-reranker.ipynb index 24c58ff5d48..ae63322370d 100644 --- a/docs/docs/integrations/retrievers/cohere-reranker.ipynb +++ b/docs/docs/integrations/retrievers/cohere-reranker.ipynb @@ -301,10 +301,10 @@ } ], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import CohereEmbeddings\n", "from langchain_community.vectorstores import FAISS\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n", diff --git a/docs/docs/integrations/retrievers/flashrank-reranker.ipynb b/docs/docs/integrations/retrievers/flashrank-reranker.ipynb index 7f53ed00a68..27a19dbc29f 100644 --- a/docs/docs/integrations/retrievers/flashrank-reranker.ipynb +++ b/docs/docs/integrations/retrievers/flashrank-reranker.ipynb @@ -288,10 +288,10 @@ } ], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "documents = TextLoader(\n", " \"../../modules/state_of_the_union.txt\",\n", diff --git a/docs/docs/integrations/retrievers/jaguar.ipynb b/docs/docs/integrations/retrievers/jaguar.ipynb index 35b89bdb331..3d3287a69ee 100644 --- a/docs/docs/integrations/retrievers/jaguar.ipynb +++ b/docs/docs/integrations/retrievers/jaguar.ipynb @@ -52,10 +52,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores.jaguar import Jaguar\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "\"\"\" \n", "Load a text file into a set of documents \n", diff --git a/docs/docs/integrations/retrievers/llmlingua.ipynb b/docs/docs/integrations/retrievers/llmlingua.ipynb index 29543e0eebe..81a56f139e7 100644 --- a/docs/docs/integrations/retrievers/llmlingua.ipynb +++ b/docs/docs/integrations/retrievers/llmlingua.ipynb @@ -282,10 +282,10 @@ } ], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "documents = TextLoader(\n", " \"../../modules/state_of_the_union.txt\",\n", diff --git a/docs/docs/integrations/retrievers/re_phrase.ipynb b/docs/docs/integrations/retrievers/re_phrase.ipynb index 630ac943b8b..c99be7db796 100644 --- a/docs/docs/integrations/retrievers/re_phrase.ipynb +++ b/docs/docs/integrations/retrievers/re_phrase.ipynb @@ -28,10 +28,10 @@ "import logging\n", "\n", "from langchain.retrievers import RePhraseQueryRetriever\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import Chroma\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb b/docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb index cedc3eb5103..c95fe311df2 100644 --- a/docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb +++ b/docs/docs/integrations/retrievers/self_query/vectara_self_query.ipynb @@ -89,12 +89,12 @@ "from langchain.chains import ConversationalRetrievalChain\n", "from langchain.chains.query_constructor.base import AttributeInfo\n", "from langchain.retrievers.self_query.base import SelfQueryRetriever\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import FakeEmbeddings\n", "from langchain_community.vectorstores import Vectara\n", "from langchain_core.documents import Document\n", - "from langchain_openai import OpenAI" + "from langchain_openai import OpenAI\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/retrievers/singlestoredb.ipynb b/docs/docs/integrations/retrievers/singlestoredb.ipynb index 7dc42c34842..fca6deda430 100644 --- a/docs/docs/integrations/retrievers/singlestoredb.ipynb +++ b/docs/docs/integrations/retrievers/singlestoredb.ipynb @@ -50,10 +50,10 @@ "# We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.\n", "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", "\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import SingleStoreDB\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/toolkits/document_comparison_toolkit.ipynb b/docs/docs/integrations/toolkits/document_comparison_toolkit.ipynb index ad8634a07d2..6a7ea058b2e 100644 --- a/docs/docs/integrations/toolkits/document_comparison_toolkit.ipynb +++ b/docs/docs/integrations/toolkits/document_comparison_toolkit.ipynb @@ -21,10 +21,10 @@ "source": [ "from langchain.agents import Tool\n", "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import PyPDFLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "from pydantic import BaseModel, Field" ] }, diff --git a/docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb b/docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb index aa836ce5231..8aa95f1559e 100644 --- a/docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb +++ b/docs/docs/integrations/vectorstores/activeloop_deeplake.ipynb @@ -51,9 +51,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import DeepLake\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb b/docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb index 813f5a7966d..6c2f03c223e 100644 --- a/docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb +++ b/docs/docs/integrations/vectorstores/alibabacloud_opensearch.ipynb @@ -129,12 +129,12 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import (\n", " AlibabaCloudOpenSearch,\n", " AlibabaCloudOpenSearchSettings,\n", ")\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/analyticdb.ipynb b/docs/docs/integrations/vectorstores/analyticdb.ipynb index 84a2480ddfe..dc335d89f25 100644 --- a/docs/docs/integrations/vectorstores/analyticdb.ipynb +++ b/docs/docs/integrations/vectorstores/analyticdb.ipynb @@ -23,9 +23,9 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import AnalyticDB\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/annoy.ipynb b/docs/docs/integrations/vectorstores/annoy.ipynb index 45e6f541cea..fb953031ea9 100644 --- a/docs/docs/integrations/vectorstores/annoy.ipynb +++ b/docs/docs/integrations/vectorstores/annoy.ipynb @@ -148,8 +148,8 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txtn.txtn.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/apache_doris.ipynb b/docs/docs/integrations/vectorstores/apache_doris.ipynb index 1cee7b8f537..a4970ed912f 100644 --- a/docs/docs/integrations/vectorstores/apache_doris.ipynb +++ b/docs/docs/integrations/vectorstores/apache_doris.ipynb @@ -70,7 +70,6 @@ "outputs": [], "source": [ "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import TokenTextSplitter\n", "from langchain_community.document_loaders import (\n", " DirectoryLoader,\n", " UnstructuredMarkdownLoader,\n", @@ -80,6 +79,7 @@ " ApacheDorisSettings,\n", ")\n", "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import TokenTextSplitter\n", "\n", "update_vectordb = False" ] diff --git a/docs/docs/integrations/vectorstores/astradb.ipynb b/docs/docs/integrations/vectorstores/astradb.ipynb index 92f2b3d0f36..d7fa83ef08f 100644 --- a/docs/docs/integrations/vectorstores/astradb.ipynb +++ b/docs/docs/integrations/vectorstores/astradb.ipynb @@ -91,13 +91,13 @@ "from datasets import (\n", " load_dataset,\n", ")\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import PyPDFLoader\n", "from langchain_core.documents import Document\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/atlas.ipynb b/docs/docs/integrations/vectorstores/atlas.ipynb index 7b3942c057a..6f1950a1328 100644 --- a/docs/docs/integrations/vectorstores/atlas.ipynb +++ b/docs/docs/integrations/vectorstores/atlas.ipynb @@ -71,9 +71,9 @@ "source": [ "import time\n", "\n", - "from langchain.text_splitter import SpacyTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", - "from langchain_community.vectorstores import AtlasDB" + "from langchain_community.vectorstores import AtlasDB\n", + "from langchain_text_splitters import SpacyTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/awadb.ipynb b/docs/docs/integrations/vectorstores/awadb.ipynb index 804f35c4d32..9df3cf8bab9 100644 --- a/docs/docs/integrations/vectorstores/awadb.ipynb +++ b/docs/docs/integrations/vectorstores/awadb.ipynb @@ -28,9 +28,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", - "from langchain_community.vectorstores import AwaDB" + "from langchain_community.vectorstores import AwaDB\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb b/docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb index 9c00fe0d7e1..f082b868aae 100644 --- a/docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb +++ b/docs/docs/integrations/vectorstores/azure_cosmos_db.ipynb @@ -130,13 +130,13 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores.azure_cosmos_db import (\n", " AzureCosmosDBVectorSearch,\n", " CosmosDBSimilarityType,\n", ")\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "SOURCE_FILE_NAME = \"../../modules/state_of_the_union.txt\"\n", "\n", diff --git a/docs/docs/integrations/vectorstores/azuresearch.ipynb b/docs/docs/integrations/vectorstores/azuresearch.ipynb index 600ac3465d1..65ddb5d4089 100644 --- a/docs/docs/integrations/vectorstores/azuresearch.ipynb +++ b/docs/docs/integrations/vectorstores/azuresearch.ipynb @@ -234,8 +234,8 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\", encoding=\"utf-8\")\n", "\n", diff --git a/docs/docs/integrations/vectorstores/bageldb.ipynb b/docs/docs/integrations/vectorstores/bageldb.ipynb index 5e19ad33a8a..795f1c85f8a 100644 --- a/docs/docs/integrations/vectorstores/bageldb.ipynb +++ b/docs/docs/integrations/vectorstores/bageldb.ipynb @@ -108,8 +108,8 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb b/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb index cc978bfe5ad..6ccbcbde0fa 100644 --- a/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb +++ b/docs/docs/integrations/vectorstores/baiducloud_vector_search.ipynb @@ -77,8 +77,8 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../../state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/cassandra.ipynb b/docs/docs/integrations/vectorstores/cassandra.ipynb index b18376c73e8..ab43444f623 100644 --- a/docs/docs/integrations/vectorstores/cassandra.ipynb +++ b/docs/docs/integrations/vectorstores/cassandra.ipynb @@ -74,13 +74,13 @@ "from datasets import (\n", " load_dataset,\n", ")\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import PyPDFLoader\n", "from langchain_core.documents import Document\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/chroma.ipynb b/docs/docs/integrations/vectorstores/chroma.ipynb index c202d3d93af..1b43327c15e 100644 --- a/docs/docs/integrations/vectorstores/chroma.ipynb +++ b/docs/docs/integrations/vectorstores/chroma.ipynb @@ -65,12 +65,12 @@ ], "source": [ "# import\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings.sentence_transformer import (\n", " SentenceTransformerEmbeddings,\n", ")\n", "from langchain_community.vectorstores import Chroma\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "# load the document and split it into chunks\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", diff --git a/docs/docs/integrations/vectorstores/clarifai.ipynb b/docs/docs/integrations/vectorstores/clarifai.ipynb index 00dc7270fb3..c91ff706408 100644 --- a/docs/docs/integrations/vectorstores/clarifai.ipynb +++ b/docs/docs/integrations/vectorstores/clarifai.ipynb @@ -79,9 +79,9 @@ "outputs": [], "source": [ "# Import the required modules\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", - "from langchain_community.vectorstores import Clarifai" + "from langchain_community.vectorstores import Clarifai\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/clickhouse.ipynb b/docs/docs/integrations/vectorstores/clickhouse.ipynb index 3df3e3bbe63..9a2d3930f27 100644 --- a/docs/docs/integrations/vectorstores/clickhouse.ipynb +++ b/docs/docs/integrations/vectorstores/clickhouse.ipynb @@ -101,9 +101,9 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import Clickhouse, ClickhouseSettings\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/dashvector.ipynb b/docs/docs/integrations/vectorstores/dashvector.ipynb index 84437540383..7ad14a0f78c 100644 --- a/docs/docs/integrations/vectorstores/dashvector.ipynb +++ b/docs/docs/integrations/vectorstores/dashvector.ipynb @@ -101,9 +101,9 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.embeddings.dashscope import DashScopeEmbeddings\n", - "from langchain_community.vectorstores import DashVector" + "from langchain_community.vectorstores import DashVector\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb index 09e00be5f55..fa01ba052f0 100644 --- a/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb +++ b/docs/docs/integrations/vectorstores/databricks_vector_search.ipynb @@ -59,9 +59,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/dingo.ipynb b/docs/docs/integrations/vectorstores/dingo.ipynb index 73c74645569..0f9ece6f72c 100644 --- a/docs/docs/integrations/vectorstores/dingo.ipynb +++ b/docs/docs/integrations/vectorstores/dingo.ipynb @@ -68,10 +68,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Dingo\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { @@ -130,10 +130,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Dingo\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/docarray_hnsw.ipynb b/docs/docs/integrations/vectorstores/docarray_hnsw.ipynb index a89c6cf113e..53e7211c444 100644 --- a/docs/docs/integrations/vectorstores/docarray_hnsw.ipynb +++ b/docs/docs/integrations/vectorstores/docarray_hnsw.ipynb @@ -73,10 +73,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import DocArrayHnswSearch\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/docarray_in_memory.ipynb b/docs/docs/integrations/vectorstores/docarray_in_memory.ipynb index a29120a5ff8..ebb9ef9162e 100644 --- a/docs/docs/integrations/vectorstores/docarray_in_memory.ipynb +++ b/docs/docs/integrations/vectorstores/docarray_in_memory.ipynb @@ -70,10 +70,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import DocArrayInMemorySearch\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/elasticsearch.ipynb b/docs/docs/integrations/vectorstores/elasticsearch.ipynb index 3579ea8e6a2..777b2ad1f77 100644 --- a/docs/docs/integrations/vectorstores/elasticsearch.ipynb +++ b/docs/docs/integrations/vectorstores/elasticsearch.ipynb @@ -216,8 +216,8 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/epsilla.ipynb b/docs/docs/integrations/vectorstores/epsilla.ipynb index 8cc4795ec11..b81d5b474eb 100644 --- a/docs/docs/integrations/vectorstores/epsilla.ipynb +++ b/docs/docs/integrations/vectorstores/epsilla.ipynb @@ -67,8 +67,8 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/faiss.ipynb b/docs/docs/integrations/vectorstores/faiss.ipynb index 12f3f1e45d8..cdb600a4397 100644 --- a/docs/docs/integrations/vectorstores/faiss.ipynb +++ b/docs/docs/integrations/vectorstores/faiss.ipynb @@ -88,10 +88,10 @@ "# Uncomment the following line if you need to initialize FAISS with no AVX2 optimization\n", "# os.environ['FAISS_NO_AVX2'] = '1'\n", "\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/faiss_async.ipynb b/docs/docs/integrations/vectorstores/faiss_async.ipynb index 38f94c4f53c..adc9f7f2d0e 100644 --- a/docs/docs/integrations/vectorstores/faiss_async.ipynb +++ b/docs/docs/integrations/vectorstores/faiss_async.ipynb @@ -56,10 +56,10 @@ "# Uncomment the following line if you need to initialize FAISS with no AVX2 optimization\n", "# os.environ['FAISS_NO_AVX2'] = '1'\n", "\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../../extras/modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/google_memorystore_redis.ipynb b/docs/docs/integrations/vectorstores/google_memorystore_redis.ipynb index bd1419e2998..8e44701a3c5 100644 --- a/docs/docs/integrations/vectorstores/google_memorystore_redis.ipynb +++ b/docs/docs/integrations/vectorstores/google_memorystore_redis.ipynb @@ -184,8 +184,8 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"./state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/hippo.ipynb b/docs/docs/integrations/vectorstores/hippo.ipynb index 5f5ee66d51a..a6c30f100ba 100644 --- a/docs/docs/integrations/vectorstores/hippo.ipynb +++ b/docs/docs/integrations/vectorstores/hippo.ipynb @@ -97,10 +97,10 @@ "source": [ "import os\n", "\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores.hippo import Hippo\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/hologres.ipynb b/docs/docs/integrations/vectorstores/hologres.ipynb index 8cadb282b39..43a5cf91a56 100644 --- a/docs/docs/integrations/vectorstores/hologres.ipynb +++ b/docs/docs/integrations/vectorstores/hologres.ipynb @@ -33,9 +33,9 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import Hologres\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/jaguar.ipynb b/docs/docs/integrations/vectorstores/jaguar.ipynb index 0b318948795..4a3b67782f9 100644 --- a/docs/docs/integrations/vectorstores/jaguar.ipynb +++ b/docs/docs/integrations/vectorstores/jaguar.ipynb @@ -56,13 +56,13 @@ "outputs": [], "source": [ "from langchain.chains import RetrievalQAWithSourcesChain\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores.jaguar import Jaguar\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.runnables import RunnablePassthrough\n", "from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "\"\"\" \n", "Load a text file into a set of documents \n", diff --git a/docs/docs/integrations/vectorstores/kinetica.ipynb b/docs/docs/integrations/vectorstores/kinetica.ipynb index bd74dc47c99..5ff269ee445 100644 --- a/docs/docs/integrations/vectorstores/kinetica.ipynb +++ b/docs/docs/integrations/vectorstores/kinetica.ipynb @@ -114,14 +114,14 @@ "outputs": [], "source": [ "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import (\n", " DistanceStrategy,\n", " Kinetica,\n", " KineticaSettings,\n", ")\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/lancedb.ipynb b/docs/docs/integrations/vectorstores/lancedb.ipynb index 18eb519eecd..7d619539535 100644 --- a/docs/docs/integrations/vectorstores/lancedb.ipynb +++ b/docs/docs/integrations/vectorstores/lancedb.ipynb @@ -104,7 +104,7 @@ "outputs": [], "source": [ "from langchain.document_loaders import TextLoader\n", - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/lantern.ipynb b/docs/docs/integrations/vectorstores/lantern.ipynb index a9121bb7319..2a8a54ef997 100644 --- a/docs/docs/integrations/vectorstores/lantern.ipynb +++ b/docs/docs/integrations/vectorstores/lantern.ipynb @@ -107,11 +107,11 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import OpenAIEmbeddings\n", "from langchain_community.vectorstores import Lantern\n", - "from langchain_core.documents import Document" + "from langchain_core.documents import Document\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/marqo.ipynb b/docs/docs/integrations/vectorstores/marqo.ipynb index ea70b91e506..bb1abe10d14 100644 --- a/docs/docs/integrations/vectorstores/marqo.ipynb +++ b/docs/docs/integrations/vectorstores/marqo.ipynb @@ -38,9 +38,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", - "from langchain_community.vectorstores import Marqo" + "from langchain_community.vectorstores import Marqo\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/meilisearch.ipynb b/docs/docs/integrations/vectorstores/meilisearch.ipynb index 0257f6d046b..11777cceda8 100644 --- a/docs/docs/integrations/vectorstores/meilisearch.ipynb +++ b/docs/docs/integrations/vectorstores/meilisearch.ipynb @@ -126,9 +126,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import Meilisearch\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "embeddings = OpenAIEmbeddings()" ] diff --git a/docs/docs/integrations/vectorstores/milvus.ipynb b/docs/docs/integrations/vectorstores/milvus.ipynb index d9f78e033ca..c4cf8d6693c 100644 --- a/docs/docs/integrations/vectorstores/milvus.ipynb +++ b/docs/docs/integrations/vectorstores/milvus.ipynb @@ -66,10 +66,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Milvus\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { @@ -204,6 +204,7 @@ }, { "cell_type": "markdown", + "id": "7fb27b941602401d91542211134fc71a", "metadata": { "collapsed": false, "pycharm": { @@ -221,6 +222,7 @@ { "cell_type": "code", "execution_count": 2, + "id": "acae54e37e7d407bbb7b55eff062a284", "metadata": { "collapsed": false, "pycharm": { @@ -246,6 +248,7 @@ }, { "cell_type": "markdown", + "id": "9a63283cbaf04dbcab1f6479b197f3a8", "metadata": { "collapsed": false, "pycharm": { @@ -267,6 +270,7 @@ { "cell_type": "code", "execution_count": 3, + "id": "8dd0d8092fe74a7c96281538738b07e2", "metadata": { "collapsed": false, "pycharm": { @@ -295,6 +299,7 @@ { "cell_type": "code", "execution_count": 4, + "id": "72eea5119410473aa328ad9291626812", "metadata": { "collapsed": false, "pycharm": { diff --git a/docs/docs/integrations/vectorstores/momento_vector_index.ipynb b/docs/docs/integrations/vectorstores/momento_vector_index.ipynb index f5d72e5ef3d..9c7588980bb 100644 --- a/docs/docs/integrations/vectorstores/momento_vector_index.ipynb +++ b/docs/docs/integrations/vectorstores/momento_vector_index.ipynb @@ -143,10 +143,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import MomentoVectorIndex\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb index 28149ca3917..2bf3a137c48 100644 --- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb +++ b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb @@ -171,7 +171,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)\n", "docs = text_splitter.split_documents(data)" diff --git a/docs/docs/integrations/vectorstores/myscale.ipynb b/docs/docs/integrations/vectorstores/myscale.ipynb index d7fc916382c..77ed9aa4686 100644 --- a/docs/docs/integrations/vectorstores/myscale.ipynb +++ b/docs/docs/integrations/vectorstores/myscale.ipynb @@ -98,10 +98,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import MyScale\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/neo4jvector.ipynb b/docs/docs/integrations/vectorstores/neo4jvector.ipynb index 12ec603aa0c..1b5c75455ab 100644 --- a/docs/docs/integrations/vectorstores/neo4jvector.ipynb +++ b/docs/docs/integrations/vectorstores/neo4jvector.ipynb @@ -73,10 +73,10 @@ "outputs": [], "source": [ "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Neo4jVector\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/opensearch.ipynb b/docs/docs/integrations/vectorstores/opensearch.ipynb index 20fe8a8235f..a63920bd75c 100644 --- a/docs/docs/integrations/vectorstores/opensearch.ipynb +++ b/docs/docs/integrations/vectorstores/opensearch.ipynb @@ -68,10 +68,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import OpenSearchVectorSearch\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/pgembedding.ipynb b/docs/docs/integrations/vectorstores/pgembedding.ipynb index ec65b690350..89a4279bc36 100644 --- a/docs/docs/integrations/vectorstores/pgembedding.ipynb +++ b/docs/docs/integrations/vectorstores/pgembedding.ipynb @@ -82,10 +82,10 @@ "outputs": [], "source": [ "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import PGEmbedding\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/pgvecto_rs.ipynb b/docs/docs/integrations/vectorstores/pgvecto_rs.ipynb index e72aefe9ec8..1f4c2647084 100644 --- a/docs/docs/integrations/vectorstores/pgvecto_rs.ipynb +++ b/docs/docs/integrations/vectorstores/pgvecto_rs.ipynb @@ -27,10 +27,10 @@ "from typing import List\n", "\n", "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings.fake import FakeEmbeddings\n", - "from langchain_community.vectorstores.pgvecto_rs import PGVecto_rs" + "from langchain_community.vectorstores.pgvecto_rs import PGVecto_rs\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/pgvector.ipynb b/docs/docs/integrations/vectorstores/pgvector.ipynb index 2dbb77cde85..484f0d6d7c3 100644 --- a/docs/docs/integrations/vectorstores/pgvector.ipynb +++ b/docs/docs/integrations/vectorstores/pgvector.ipynb @@ -101,10 +101,10 @@ "outputs": [], "source": [ "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores.pgvector import PGVector\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/docs/integrations/vectorstores/pinecone.ipynb index f8c1643e0aa..072b05beb1a 100644 --- a/docs/docs/integrations/vectorstores/pinecone.ipynb +++ b/docs/docs/integrations/vectorstores/pinecone.ipynb @@ -52,9 +52,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/qdrant.ipynb b/docs/docs/integrations/vectorstores/qdrant.ipynb index 1942993002c..3abca8952f5 100644 --- a/docs/docs/integrations/vectorstores/qdrant.ipynb +++ b/docs/docs/integrations/vectorstores/qdrant.ipynb @@ -78,10 +78,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Qdrant\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/rockset.ipynb b/docs/docs/integrations/vectorstores/rockset.ipynb index 96620c0ef78..1c494079298 100644 --- a/docs/docs/integrations/vectorstores/rockset.ipynb +++ b/docs/docs/integrations/vectorstores/rockset.ipynb @@ -108,10 +108,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Rockset\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/sap_hanavector.ipynb b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb index 63fe3add655..0db433dbbc0 100644 --- a/docs/docs/integrations/vectorstores/sap_hanavector.ipynb +++ b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb @@ -110,10 +110,10 @@ "outputs": [], "source": [ "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores.hanavector import HanaDB\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n", "text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n", diff --git a/docs/docs/integrations/vectorstores/scann.ipynb b/docs/docs/integrations/vectorstores/scann.ipynb index f580a9b6721..c033238cffe 100644 --- a/docs/docs/integrations/vectorstores/scann.ipynb +++ b/docs/docs/integrations/vectorstores/scann.ipynb @@ -59,10 +59,10 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import HuggingFaceEmbeddings\n", "from langchain_community.vectorstores import ScaNN\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/semadb.ipynb b/docs/docs/integrations/vectorstores/semadb.ipynb index 65591e93a35..597b7fa1029 100644 --- a/docs/docs/integrations/vectorstores/semadb.ipynb +++ b/docs/docs/integrations/vectorstores/semadb.ipynb @@ -61,8 +61,8 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/singlestoredb.ipynb b/docs/docs/integrations/vectorstores/singlestoredb.ipynb index 2278b867645..ca84bc916dc 100644 --- a/docs/docs/integrations/vectorstores/singlestoredb.ipynb +++ b/docs/docs/integrations/vectorstores/singlestoredb.ipynb @@ -46,10 +46,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import SingleStoreDB\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/sklearn.ipynb b/docs/docs/integrations/vectorstores/sklearn.ipynb index b1422aa0052..740ebfa9724 100644 --- a/docs/docs/integrations/vectorstores/sklearn.ipynb +++ b/docs/docs/integrations/vectorstores/sklearn.ipynb @@ -60,10 +60,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import SKLearnVectorStore\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/sqlitevss.ipynb b/docs/docs/integrations/vectorstores/sqlitevss.ipynb index fe0d5891f28..c53d6f8730a 100644 --- a/docs/docs/integrations/vectorstores/sqlitevss.ipynb +++ b/docs/docs/integrations/vectorstores/sqlitevss.ipynb @@ -69,12 +69,12 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings.sentence_transformer import (\n", " SentenceTransformerEmbeddings,\n", ")\n", "from langchain_community.vectorstores import SQLiteVSS\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "# load the document and split it into chunks\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", @@ -146,12 +146,12 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings.sentence_transformer import (\n", " SentenceTransformerEmbeddings,\n", ")\n", "from langchain_community.vectorstores import SQLiteVSS\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "# load the document and split it into chunks\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", diff --git a/docs/docs/integrations/vectorstores/starrocks.ipynb b/docs/docs/integrations/vectorstores/starrocks.ipynb index b1d3e51c1fb..f00cec80492 100644 --- a/docs/docs/integrations/vectorstores/starrocks.ipynb +++ b/docs/docs/integrations/vectorstores/starrocks.ipynb @@ -58,7 +58,6 @@ ], "source": [ "from langchain.chains import RetrievalQA\n", - "from langchain.text_splitter import TokenTextSplitter\n", "from langchain_community.document_loaders import (\n", " DirectoryLoader,\n", " UnstructuredMarkdownLoader,\n", @@ -66,6 +65,7 @@ "from langchain_community.vectorstores import StarRocks\n", "from langchain_community.vectorstores.starrocks import StarRocksSettings\n", "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import TokenTextSplitter\n", "\n", "update_vectordb = False" ] diff --git a/docs/docs/integrations/vectorstores/supabase.ipynb b/docs/docs/integrations/vectorstores/supabase.ipynb index e20f389bf3c..5db5a33947c 100644 --- a/docs/docs/integrations/vectorstores/supabase.ipynb +++ b/docs/docs/integrations/vectorstores/supabase.ipynb @@ -183,8 +183,8 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/surrealdb.ipynb b/docs/docs/integrations/vectorstores/surrealdb.ipynb index 1542ae75032..9da27930cc5 100644 --- a/docs/docs/integrations/vectorstores/surrealdb.ipynb +++ b/docs/docs/integrations/vectorstores/surrealdb.ipynb @@ -73,10 +73,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import HuggingFaceEmbeddings\n", - "from langchain_community.vectorstores import SurrealDBStore" + "from langchain_community.vectorstores import SurrealDBStore\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/tair.ipynb b/docs/docs/integrations/vectorstores/tair.ipynb index a7ec2a53d03..090bce30e64 100644 --- a/docs/docs/integrations/vectorstores/tair.ipynb +++ b/docs/docs/integrations/vectorstores/tair.ipynb @@ -20,9 +20,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.embeddings.fake import FakeEmbeddings\n", - "from langchain_community.vectorstores import Tair" + "from langchain_community.vectorstores import Tair\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/tencentvectordb.ipynb b/docs/docs/integrations/vectorstores/tencentvectordb.ipynb index 98047438eca..7eba7216026 100644 --- a/docs/docs/integrations/vectorstores/tencentvectordb.ipynb +++ b/docs/docs/integrations/vectorstores/tencentvectordb.ipynb @@ -33,11 +33,11 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings.fake import FakeEmbeddings\n", "from langchain_community.vectorstores import TencentVectorDB\n", - "from langchain_community.vectorstores.tencentvectordb import ConnectionParams" + "from langchain_community.vectorstores.tencentvectordb import ConnectionParams\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/tigris.ipynb b/docs/docs/integrations/vectorstores/tigris.ipynb index 34ea6acd70e..3d5ea550f25 100644 --- a/docs/docs/integrations/vectorstores/tigris.ipynb +++ b/docs/docs/integrations/vectorstores/tigris.ipynb @@ -85,10 +85,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Tigris\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/tiledb.ipynb b/docs/docs/integrations/vectorstores/tiledb.ipynb index 9eb38ff5a6f..7d74205ce21 100644 --- a/docs/docs/integrations/vectorstores/tiledb.ipynb +++ b/docs/docs/integrations/vectorstores/tiledb.ipynb @@ -43,10 +43,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import HuggingFaceEmbeddings\n", "from langchain_community.vectorstores import TileDB\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "raw_documents = TextLoader(\"../../modules/state_of_the_union.txt\").load()\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", diff --git a/docs/docs/integrations/vectorstores/timescalevector.ipynb b/docs/docs/integrations/vectorstores/timescalevector.ipynb index 927eee45050..d7733da8b9c 100644 --- a/docs/docs/integrations/vectorstores/timescalevector.ipynb +++ b/docs/docs/integrations/vectorstores/timescalevector.ipynb @@ -123,11 +123,11 @@ "from datetime import datetime, timedelta\n", "\n", "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.document_loaders.json_loader import JSONLoader\n", "from langchain_community.vectorstores.timescalevector import TimescaleVector\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/typesense.ipynb b/docs/docs/integrations/vectorstores/typesense.ipynb index a201bb2924b..d6a5b7d8785 100644 --- a/docs/docs/integrations/vectorstores/typesense.ipynb +++ b/docs/docs/integrations/vectorstores/typesense.ipynb @@ -84,10 +84,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Typesense\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/usearch.ipynb b/docs/docs/integrations/vectorstores/usearch.ipynb index b1fbdf978f1..c691799ce8d 100644 --- a/docs/docs/integrations/vectorstores/usearch.ipynb +++ b/docs/docs/integrations/vectorstores/usearch.ipynb @@ -55,10 +55,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import USearch\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/vald.ipynb b/docs/docs/integrations/vectorstores/vald.ipynb index fdcb0c31baa..eba72f91b12 100644 --- a/docs/docs/integrations/vectorstores/vald.ipynb +++ b/docs/docs/integrations/vectorstores/vald.ipynb @@ -42,10 +42,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import HuggingFaceEmbeddings\n", "from langchain_community.vectorstores import Vald\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "raw_documents = TextLoader(\"state_of_the_union.txt\").load()\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", @@ -189,10 +189,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings import HuggingFaceEmbeddings\n", "from langchain_community.vectorstores import Vald\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "raw_documents = TextLoader(\"state_of_the_union.txt\").load()\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", diff --git a/docs/docs/integrations/vectorstores/vearch.ipynb b/docs/docs/integrations/vectorstores/vearch.ipynb index f6a87e788c0..079819653b9 100644 --- a/docs/docs/integrations/vectorstores/vearch.ipynb +++ b/docs/docs/integrations/vectorstores/vearch.ipynb @@ -52,10 +52,10 @@ } ], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\n", "from langchain_community.vectorstores.vearch import Vearch\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "from transformers import AutoModel, AutoTokenizer\n", "\n", "# repalce to your local model path\n", diff --git a/docs/docs/integrations/vectorstores/vectara.ipynb b/docs/docs/integrations/vectorstores/vectara.ipynb index dbf8ea9bdfd..03c1252097e 100644 --- a/docs/docs/integrations/vectorstores/vectara.ipynb +++ b/docs/docs/integrations/vectorstores/vectara.ipynb @@ -84,10 +84,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.embeddings.fake import FakeEmbeddings\n", - "from langchain_community.vectorstores import Vectara" + "from langchain_community.vectorstores import Vectara\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/vespa.ipynb b/docs/docs/integrations/vectorstores/vespa.ipynb index 06422f7660e..c984a208751 100644 --- a/docs/docs/integrations/vectorstores/vespa.ipynb +++ b/docs/docs/integrations/vectorstores/vespa.ipynb @@ -150,8 +150,8 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", diff --git a/docs/docs/integrations/vectorstores/vikingdb.ipynb b/docs/docs/integrations/vectorstores/vikingdb.ipynb index aa8726966ab..af44c2456be 100644 --- a/docs/docs/integrations/vectorstores/vikingdb.ipynb +++ b/docs/docs/integrations/vectorstores/vikingdb.ipynb @@ -69,9 +69,9 @@ "outputs": [], "source": [ "from langchain.document_loaders import TextLoader\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain.vectorstores.vikingdb import VikingDB, VikingDBConfig\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/weaviate.ipynb b/docs/docs/integrations/vectorstores/weaviate.ipynb index ba82e6ec993..ba8fa2791ea 100644 --- a/docs/docs/integrations/vectorstores/weaviate.ipynb +++ b/docs/docs/integrations/vectorstores/weaviate.ipynb @@ -119,10 +119,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Weaviate\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/xata.ipynb b/docs/docs/integrations/vectorstores/xata.ipynb index 07c829862fb..f9e62e9a1e3 100644 --- a/docs/docs/integrations/vectorstores/xata.ipynb +++ b/docs/docs/integrations/vectorstores/xata.ipynb @@ -107,10 +107,10 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores.xata import XataVectorStore\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/integrations/vectorstores/yellowbrick.ipynb b/docs/docs/integrations/vectorstores/yellowbrick.ipynb index c047bd2b885..efd9e9bf9fb 100644 --- a/docs/docs/integrations/vectorstores/yellowbrick.ipynb +++ b/docs/docs/integrations/vectorstores/yellowbrick.ipynb @@ -99,9 +99,9 @@ "from IPython.display import Markdown, display\n", "from langchain.chains import LLMChain, RetrievalQAWithSourcesChain\n", "from langchain.docstore.document import Document\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.vectorstores import Yellowbrick\n", "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "# Establish connection parameters to Yellowbrick. If you've signed up for Sandbox, fill in the information from your welcome mail here:\n", "yellowbrick_connection_string = (\n", diff --git a/docs/docs/integrations/vectorstores/zep.ipynb b/docs/docs/integrations/vectorstores/zep.ipynb index 36d52c8abf4..588bd352995 100644 --- a/docs/docs/integrations/vectorstores/zep.ipynb +++ b/docs/docs/integrations/vectorstores/zep.ipynb @@ -77,10 +77,10 @@ "source": [ "from uuid import uuid4\n", "\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import ZepVectorStore\n", "from langchain_community.vectorstores.zep import CollectionConfig\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "ZEP_API_URL = \"http://localhost:8000\" # this is the API url of your Zep instance\n", "ZEP_API_KEY = \"\" # optional API Key for your Zep instance\n", diff --git a/docs/docs/integrations/vectorstores/zilliz.ipynb b/docs/docs/integrations/vectorstores/zilliz.ipynb index 17c8f159760..eacee33e8c3 100644 --- a/docs/docs/integrations/vectorstores/zilliz.ipynb +++ b/docs/docs/integrations/vectorstores/zilliz.ipynb @@ -76,10 +76,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Milvus\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/modules/agents/how_to/agent_structured.ipynb b/docs/docs/modules/agents/how_to/agent_structured.ipynb index a271f64aee9..0c1d550bbbf 100644 --- a/docs/docs/modules/agents/how_to/agent_structured.ipynb +++ b/docs/docs/modules/agents/how_to/agent_structured.ipynb @@ -53,10 +53,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Chroma\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/modules/agents/quick_start.ipynb b/docs/docs/modules/agents/quick_start.ipynb index 887a857b60c..5b7e4ee517b 100644 --- a/docs/docs/modules/agents/quick_start.ipynb +++ b/docs/docs/modules/agents/quick_start.ipynb @@ -117,10 +117,10 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "loader = WebBaseLoader(\"https://docs.smith.langchain.com/overview\")\n", "docs = loader.load()\n", diff --git a/docs/docs/modules/data_connection/document_transformers/HTML_header_metadata.ipynb b/docs/docs/modules/data_connection/document_transformers/HTML_header_metadata.ipynb index 87db1253510..85bf37b9cac 100644 --- a/docs/docs/modules/data_connection/document_transformers/HTML_header_metadata.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/HTML_header_metadata.ipynb @@ -50,7 +50,7 @@ } ], "source": [ - "from langchain.text_splitter import HTMLHeaderTextSplitter\n", + "from langchain_text_splitters import HTMLHeaderTextSplitter\n", "\n", "html_string = \"\"\"\n", "\n", @@ -131,7 +131,7 @@ } ], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "url = \"https://plato.stanford.edu/entries/goedel/\"\n", "\n", diff --git a/docs/docs/modules/data_connection/document_transformers/character_text_splitter.ipynb b/docs/docs/modules/data_connection/document_transformers/character_text_splitter.ipynb index 01ba4bfb062..b4bebd2e01a 100644 --- a/docs/docs/modules/data_connection/document_transformers/character_text_splitter.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/character_text_splitter.ipynb @@ -32,7 +32,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_splitter = CharacterTextSplitter(\n", " separator=\"\\n\\n\",\n", diff --git a/docs/docs/modules/data_connection/document_transformers/code_splitter.ipynb b/docs/docs/modules/data_connection/document_transformers/code_splitter.ipynb index 1f8e2831256..04729a409c8 100644 --- a/docs/docs/modules/data_connection/document_transformers/code_splitter.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/code_splitter.ipynb @@ -17,7 +17,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import (\n", + "from langchain_text_splitters import (\n", " Language,\n", " RecursiveCharacterTextSplitter,\n", ")" diff --git a/docs/docs/modules/data_connection/document_transformers/markdown_header_metadata.ipynb b/docs/docs/modules/data_connection/document_transformers/markdown_header_metadata.ipynb index f2cea000efa..6f4880379d3 100644 --- a/docs/docs/modules/data_connection/document_transformers/markdown_header_metadata.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/markdown_header_metadata.ipynb @@ -50,7 +50,7 @@ }, "outputs": [], "source": [ - "from langchain.text_splitter import MarkdownHeaderTextSplitter" + "from langchain_text_splitters import MarkdownHeaderTextSplitter" ] }, { @@ -201,7 +201,7 @@ "md_header_splits = markdown_splitter.split_text(markdown_document)\n", "\n", "# Char-level splits\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "chunk_size = 250\n", "chunk_overlap = 30\n", diff --git a/docs/docs/modules/data_connection/document_transformers/recursive_json_splitter.ipynb b/docs/docs/modules/data_connection/document_transformers/recursive_json_splitter.ipynb index 14d69ceb6b3..6148ddf7386 100644 --- a/docs/docs/modules/data_connection/document_transformers/recursive_json_splitter.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/recursive_json_splitter.ipynb @@ -43,7 +43,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveJsonSplitter" + "from langchain_text_splitters import RecursiveJsonSplitter" ] }, { diff --git a/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb b/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb index 63d1614fa6b..f57b6bd3aae 100644 --- a/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb @@ -32,7 +32,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter" + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/modules/data_connection/document_transformers/split_by_token.ipynb b/docs/docs/modules/data_connection/document_transformers/split_by_token.ipynb index 4236d0e38ed..50a5d59ed6c 100644 --- a/docs/docs/modules/data_connection/document_transformers/split_by_token.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/split_by_token.ipynb @@ -46,7 +46,7 @@ "# This is a long document we can split up.\n", "with open(\"../../state_of_the_union.txt\") as f:\n", " state_of_the_union = f.read()\n", - "from langchain.text_splitter import CharacterTextSplitter" + "from langchain_text_splitters import CharacterTextSplitter" ] }, { @@ -103,7 +103,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import TokenTextSplitter\n", + "from langchain_text_splitters import TokenTextSplitter\n", "\n", "text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0)\n", "\n", @@ -155,7 +155,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import SpacyTextSplitter\n", + "from langchain_text_splitters import SpacyTextSplitter\n", "\n", "text_splitter = SpacyTextSplitter(chunk_size=1000)" ] @@ -242,7 +242,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import SentenceTransformersTokenTextSplitter" + "from langchain_text_splitters import SentenceTransformersTokenTextSplitter" ] }, { @@ -363,7 +363,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import NLTKTextSplitter\n", + "from langchain_text_splitters import NLTKTextSplitter\n", "\n", "text_splitter = NLTKTextSplitter(chunk_size=1000)" ] @@ -465,7 +465,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import KonlpyTextSplitter\n", + "from langchain_text_splitters import KonlpyTextSplitter\n", "\n", "text_splitter = KonlpyTextSplitter()" ] @@ -555,7 +555,7 @@ "# This is a long document we can split up.\n", "with open(\"../../../state_of_the_union.txt\") as f:\n", " state_of_the_union = f.read()\n", - "from langchain.text_splitter import CharacterTextSplitter" + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/modules/data_connection/indexing.ipynb b/docs/docs/modules/data_connection/indexing.ipynb index ec252c99a1d..aafc86b37c1 100644 --- a/docs/docs/modules/data_connection/indexing.ipynb +++ b/docs/docs/modules/data_connection/indexing.ipynb @@ -613,7 +613,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter" + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb b/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb index 76c8b0cbd49..814aba352a0 100644 --- a/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb +++ b/docs/docs/modules/data_connection/retrievers/MultiQueryRetriever.ipynb @@ -20,10 +20,10 @@ "outputs": [], "source": [ "# Build a sample vectorDB\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "# Load blog post\n", "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", diff --git a/docs/docs/modules/data_connection/retrievers/contextual_compression.ipynb b/docs/docs/modules/data_connection/retrievers/contextual_compression.ipynb index 3cf335d931e..28276f4edb4 100644 --- a/docs/docs/modules/data_connection/retrievers/contextual_compression.ipynb +++ b/docs/docs/modules/data_connection/retrievers/contextual_compression.ipynb @@ -118,10 +118,10 @@ } ], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "documents = TextLoader(\"../../state_of_the_union.txt\").load()\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", @@ -346,8 +346,8 @@ "outputs": [], "source": [ "from langchain.retrievers.document_compressors import DocumentCompressorPipeline\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_transformers import EmbeddingsRedundantFilter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=0, separator=\". \")\n", "redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)\n", diff --git a/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb b/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb index 77ad852d88e..5952202d054 100644 --- a/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb +++ b/docs/docs/modules/data_connection/retrievers/multi_vector.ipynb @@ -37,10 +37,10 @@ "outputs": [], "source": [ "from langchain.storage import InMemoryByteStore\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Chroma\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/modules/data_connection/retrievers/parent_document_retriever.ipynb b/docs/docs/modules/data_connection/retrievers/parent_document_retriever.ipynb index 42963d39c1a..7fde529167e 100644 --- a/docs/docs/modules/data_connection/retrievers/parent_document_retriever.ipynb +++ b/docs/docs/modules/data_connection/retrievers/parent_document_retriever.ipynb @@ -43,10 +43,10 @@ "outputs": [], "source": [ "from langchain.storage import InMemoryStore\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import Chroma\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb b/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb index 751f935c8b6..367dd9462ac 100644 --- a/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb +++ b/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb @@ -42,9 +42,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "documents = loader.load()\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", diff --git a/docs/docs/modules/data_connection/text_embedding/caching_embeddings.ipynb b/docs/docs/modules/data_connection/text_embedding/caching_embeddings.ipynb index 78704562137..de2947e3124 100644 --- a/docs/docs/modules/data_connection/text_embedding/caching_embeddings.ipynb +++ b/docs/docs/modules/data_connection/text_embedding/caching_embeddings.ipynb @@ -71,10 +71,10 @@ "outputs": [], "source": [ "from langchain.storage import LocalFileStore\n", - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "underlying_embeddings = OpenAIEmbeddings()\n", "\n", diff --git a/docs/docs/modules/data_connection/vectorstores/index.mdx b/docs/docs/modules/data_connection/vectorstores/index.mdx index 3b6d12699b8..4f750338c73 100644 --- a/docs/docs/modules/data_connection/vectorstores/index.mdx +++ b/docs/docs/modules/data_connection/vectorstores/index.mdx @@ -45,7 +45,7 @@ os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:') ```python from langchain_community.document_loaders import TextLoader from langchain_openai import OpenAIEmbeddings -from langchain.text_splitter import CharacterTextSplitter +from langchain_text_splitters import CharacterTextSplitter from langchain_community.vectorstores import Chroma # Load the document, split it into chunks, embed each chunk and load it into the vector store. @@ -77,7 +77,7 @@ os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:') ```python from langchain_community.document_loaders import TextLoader from langchain_openai import OpenAIEmbeddings -from langchain.text_splitter import CharacterTextSplitter +from langchain_text_splitters import CharacterTextSplitter from langchain_community.vectorstores import FAISS # Load the document, split it into chunks, embed each chunk and load it into the vector store. @@ -109,7 +109,7 @@ os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:') ```python from langchain_community.document_loaders import TextLoader from langchain_openai import OpenAIEmbeddings -from langchain.text_splitter import CharacterTextSplitter +from langchain_text_splitters import CharacterTextSplitter from langchain_community.vectorstores import LanceDB import lancedb diff --git a/docs/docs/modules/memory/adding_memory_chain_multiple_inputs.ipynb b/docs/docs/modules/memory/adding_memory_chain_multiple_inputs.ipynb index f8a6d894760..2806cf2f800 100644 --- a/docs/docs/modules/memory/adding_memory_chain_multiple_inputs.ipynb +++ b/docs/docs/modules/memory/adding_memory_chain_multiple_inputs.ipynb @@ -17,9 +17,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import Chroma\n", - "from langchain_openai import OpenAIEmbeddings" + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter" ] }, { diff --git a/docs/docs/use_cases/chatbots/quickstart.ipynb b/docs/docs/use_cases/chatbots/quickstart.ipynb index ea2748b75e4..7ddc5a84b15 100644 --- a/docs/docs/use_cases/chatbots/quickstart.ipynb +++ b/docs/docs/use_cases/chatbots/quickstart.ipynb @@ -426,7 +426,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n", "all_splits = text_splitter.split_documents(data)" diff --git a/docs/docs/use_cases/chatbots/retrieval.ipynb b/docs/docs/use_cases/chatbots/retrieval.ipynb index 5f69fb0a5da..83e660cad2b 100644 --- a/docs/docs/use_cases/chatbots/retrieval.ipynb +++ b/docs/docs/use_cases/chatbots/retrieval.ipynb @@ -110,7 +110,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n", "all_splits = text_splitter.split_documents(data)" diff --git a/docs/docs/use_cases/code_understanding.ipynb b/docs/docs/use_cases/code_understanding.ipynb index d3da700c936..bb8e24cc854 100644 --- a/docs/docs/use_cases/code_understanding.ipynb +++ b/docs/docs/use_cases/code_understanding.ipynb @@ -78,9 +78,9 @@ "outputs": [], "source": [ "# from git import Repo\n", - "from langchain.text_splitter import Language\n", "from langchain_community.document_loaders.generic import GenericLoader\n", - "from langchain_community.document_loaders.parsers import LanguageParser" + "from langchain_community.document_loaders.parsers import LanguageParser\n", + "from langchain_text_splitters import Language" ] }, { @@ -162,7 +162,7 @@ } ], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "python_splitter = RecursiveCharacterTextSplitter.from_language(\n", " language=Language.PYTHON, chunk_size=2000, chunk_overlap=200\n", diff --git a/docs/docs/use_cases/query_analysis/quickstart.ipynb b/docs/docs/use_cases/query_analysis/quickstart.ipynb index 10f79dd7e2f..0cd76c474be 100644 --- a/docs/docs/use_cases/query_analysis/quickstart.ipynb +++ b/docs/docs/use_cases/query_analysis/quickstart.ipynb @@ -249,9 +249,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)\n", "chunked_docs = text_splitter.split_documents(docs)\n", diff --git a/docs/docs/use_cases/question_answering/chat_history.ipynb b/docs/docs/use_cases/question_answering/chat_history.ipynb index 0478fb846cb..8ab004844dc 100644 --- a/docs/docs/use_cases/question_answering/chat_history.ipynb +++ b/docs/docs/use_cases/question_answering/chat_history.ipynb @@ -118,12 +118,12 @@ "source": [ "import bs4\n", "from langchain import hub\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/use_cases/question_answering/citations.ipynb b/docs/docs/use_cases/question_answering/citations.ipynb index 9725fd5cb43..428f1656c54 100644 --- a/docs/docs/use_cases/question_answering/citations.ipynb +++ b/docs/docs/use_cases/question_answering/citations.ipynb @@ -573,7 +573,7 @@ "source": [ "## Retrieval post-processing\n", "\n", - "Another approach is to post-process our retrieved documents to compress the content, so that the source content is already minimal enough that we don't need the model to cite specific sources or spans. For example, we could break up each document into a sentence or two, embed those and keep only the most relevant ones. LangChain has some built-in components for this. Here we'll use a [RecursiveCharacterTextSplitter](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.RecursiveCharacterTextSplitter.html#langchain.text_splitter.RecursiveCharacterTextSplitter), which creates chunks of a sepacified size by splitting on separator substrings, and an [EmbeddingsFilter](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html#langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter), which keeps only the texts with the most relevant embeddings." + "Another approach is to post-process our retrieved documents to compress the content, so that the source content is already minimal enough that we don't need the model to cite specific sources or spans. For example, we could break up each document into a sentence or two, embed those and keep only the most relevant ones. LangChain has some built-in components for this. Here we'll use a [RecursiveCharacterTextSplitter](https://api.python.langchain.com/en/latest/text_splitter/langchain_text_splitters.RecursiveCharacterTextSplitter.html#langchain_text_splitters.RecursiveCharacterTextSplitter), which creates chunks of a sepacified size by splitting on separator substrings, and an [EmbeddingsFilter](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter.html#langchain.retrievers.document_compressors.embeddings_filter.EmbeddingsFilter), which keeps only the texts with the most relevant embeddings." ] }, { @@ -631,8 +631,8 @@ ], "source": [ "from langchain.retrievers.document_compressors import EmbeddingsFilter\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=400,\n", diff --git a/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb b/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb index 4c73f06ae95..4712a031481 100644 --- a/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb +++ b/docs/docs/use_cases/question_answering/conversational_retrieval_agents.ipynb @@ -52,9 +52,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.vectorstores import FAISS\n", "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", "texts = text_splitter.split_documents(documents)\n", diff --git a/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb b/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb index 17eb937b733..953bec2dfd2 100644 --- a/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb +++ b/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb @@ -47,8 +47,8 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", "data = loader.load()\n", diff --git a/docs/docs/use_cases/question_answering/quickstart.ipynb b/docs/docs/use_cases/question_answering/quickstart.ipynb index 11b2475aec8..14934de06f3 100644 --- a/docs/docs/use_cases/question_answering/quickstart.ipynb +++ b/docs/docs/use_cases/question_answering/quickstart.ipynb @@ -151,12 +151,12 @@ "source": [ "import bs4\n", "from langchain import hub\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { @@ -367,7 +367,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "text_splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=1000, chunk_overlap=200, add_start_index=True\n", @@ -451,7 +451,7 @@ " - [Markdown files](/docs/modules/data_connection/document_transformers/markdown_header_metadata)\n", " - [Code (py or js)](/docs/integrations/document_loaders/source_code)\n", " - [Scientific papers](/docs/integrations/document_loaders/grobid)\n", - "- [Interface](https://api.python.langchain.com/en/latest/text_splitter/langchain.text_splitter.TextSplitter.html): API reference for the base interface.\n", + "- [Interface](https://api.python.langchain.com/en/latest/text_splitter/langchain_text_splitters.TextSplitter.html): API reference for the base interface.\n", "\n", "`DocumentTransformer`: Object that performs a transformation on a list of `Document`s.\n", "- [Docs](/docs/modules/data_connection/document_transformers/): Detailed documentation on how to use `DocumentTransformers`\n", diff --git a/docs/docs/use_cases/question_answering/sources.ipynb b/docs/docs/use_cases/question_answering/sources.ipynb index 333b6995e51..d721ec15b1a 100644 --- a/docs/docs/use_cases/question_answering/sources.ipynb +++ b/docs/docs/use_cases/question_answering/sources.ipynb @@ -113,12 +113,12 @@ "source": [ "import bs4\n", "from langchain import hub\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/use_cases/question_answering/streaming.ipynb b/docs/docs/use_cases/question_answering/streaming.ipynb index dea951804c5..9eabb2327c5 100644 --- a/docs/docs/use_cases/question_answering/streaming.ipynb +++ b/docs/docs/use_cases/question_answering/streaming.ipynb @@ -113,12 +113,12 @@ "source": [ "import bs4\n", "from langchain import hub\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.document_loaders import WebBaseLoader\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnableParallel, RunnablePassthrough\n", - "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" ] }, { diff --git a/docs/docs/use_cases/summarization.ipynb b/docs/docs/use_cases/summarization.ipynb index 35917b74721..f22e56cfb7e 100644 --- a/docs/docs/use_cases/summarization.ipynb +++ b/docs/docs/use_cases/summarization.ipynb @@ -298,7 +298,7 @@ "outputs": [], "source": [ "from langchain.chains import MapReduceDocumentsChain, ReduceDocumentsChain\n", - "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain_text_splitters import CharacterTextSplitter\n", "\n", "llm = ChatOpenAI(temperature=0)\n", "\n", diff --git a/docs/docs/use_cases/web_scraping.ipynb b/docs/docs/use_cases/web_scraping.ipynb index 40a28caf564..d85aa96d498 100644 --- a/docs/docs/use_cases/web_scraping.ipynb +++ b/docs/docs/use_cases/web_scraping.ipynb @@ -407,7 +407,7 @@ "source": [ "import pprint\n", "\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "\n", "def scrape_with_playwright(urls, schema):\n", diff --git a/libs/community/langchain_community/document_loaders/base.py b/libs/community/langchain_community/document_loaders/base.py index fc266ff51d6..91ad2a17306 100644 --- a/libs/community/langchain_community/document_loaders/base.py +++ b/libs/community/langchain_community/document_loaders/base.py @@ -7,10 +7,10 @@ from typing import TYPE_CHECKING, AsyncIterator, Iterator, List, Optional from langchain_core.documents import Document from langchain_core.runnables import run_in_executor -from langchain_community.document_loaders.blob_loaders import Blob - if TYPE_CHECKING: - from langchain.text_splitter import TextSplitter + from langchain_text_splitters import TextSplitter + +from langchain_community.document_loaders.blob_loaders import Blob class BaseLoader(ABC): @@ -42,9 +42,17 @@ class BaseLoader(ABC): Returns: List of Documents. """ - from langchain.text_splitter import RecursiveCharacterTextSplitter if text_splitter is None: + try: + from langchain_text_splitters import RecursiveCharacterTextSplitter + except ImportError as e: + raise ImportError( + "Unable to import from langchain_text_splitters. Please specify " + "text_splitter or install langchain_text_splitters with " + "`pip install -U langchain-text-splitters`." + ) from e + _text_splitter: TextSplitter = RecursiveCharacterTextSplitter() else: _text_splitter = text_splitter diff --git a/libs/community/langchain_community/document_loaders/generic.py b/libs/community/langchain_community/document_loaders/generic.py index 0ec6ca60bdf..1573ec5c478 100644 --- a/libs/community/langchain_community/document_loaders/generic.py +++ b/libs/community/langchain_community/document_loaders/generic.py @@ -22,7 +22,7 @@ from langchain_community.document_loaders.blob_loaders import ( from langchain_community.document_loaders.parsers.registry import get_parser if TYPE_CHECKING: - from langchain.text_splitter import TextSplitter + from langchain_text_splitters import TextSplitter _PathLike = Union[str, Path] diff --git a/libs/community/langchain_community/document_loaders/mediawikidump.py b/libs/community/langchain_community/document_loaders/mediawikidump.py index 4868e5f89cd..6d0f1d0e42f 100644 --- a/libs/community/langchain_community/document_loaders/mediawikidump.py +++ b/libs/community/langchain_community/document_loaders/mediawikidump.py @@ -15,6 +15,7 @@ class MWDumpLoader(BaseLoader): Example: .. code-block:: python + from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain_community.document_loaders import MWDumpLoader loader = MWDumpLoader( @@ -22,7 +23,6 @@ class MWDumpLoader(BaseLoader): encoding="utf8" ) docs = loader.load() - from langchain.text_splitter import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=0 ) diff --git a/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py b/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py index 8709b58becf..611244eaa0f 100644 --- a/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py +++ b/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Any, Dict, Iterator, Optional +from typing import Any, Dict, Iterator, Literal, Optional from langchain_core.documents import Document @@ -26,50 +26,67 @@ from langchain_community.document_loaders.parsers.language.typescript import ( TypeScriptSegmenter, ) -if TYPE_CHECKING: - from langchain.text_splitter import Language +LANGUAGE_EXTENSIONS: Dict[str, str] = { + "py": "python", + "js": "js", + "cobol": "cobol", + "c": "c", + "cpp": "cpp", + "cs": "csharp", + "rb": "ruby", + "scala": "scala", + "rs": "rust", + "go": "go", + "kt": "kotlin", + "lua": "lua", + "pl": "perl", + "ts": "ts", + "java": "java", +} -try: - from langchain.text_splitter import Language +LANGUAGE_SEGMENTERS: Dict[str, Any] = { + "python": PythonSegmenter, + "js": JavaScriptSegmenter, + "cobol": CobolSegmenter, + "c": CSegmenter, + "cpp": CPPSegmenter, + "csharp": CSharpSegmenter, + "ruby": RubySegmenter, + "rust": RustSegmenter, + "scala": ScalaSegmenter, + "go": GoSegmenter, + "kotlin": KotlinSegmenter, + "lua": LuaSegmenter, + "perl": PerlSegmenter, + "ts": TypeScriptSegmenter, + "java": JavaSegmenter, +} - LANGUAGE_EXTENSIONS: Dict[str, str] = { - "py": Language.PYTHON, - "js": Language.JS, - "cobol": Language.COBOL, - "c": Language.C, - "cpp": Language.CPP, - "cs": Language.CSHARP, - "rb": Language.RUBY, - "scala": Language.SCALA, - "rs": Language.RUST, - "go": Language.GO, - "kt": Language.KOTLIN, - "lua": Language.LUA, - "pl": Language.PERL, - "ts": Language.TS, - "java": Language.JAVA, - } - - LANGUAGE_SEGMENTERS: Dict[str, Any] = { - Language.PYTHON: PythonSegmenter, - Language.JS: JavaScriptSegmenter, - Language.COBOL: CobolSegmenter, - Language.C: CSegmenter, - Language.CPP: CPPSegmenter, - Language.CSHARP: CSharpSegmenter, - Language.RUBY: RubySegmenter, - Language.RUST: RustSegmenter, - Language.SCALA: ScalaSegmenter, - Language.GO: GoSegmenter, - Language.KOTLIN: KotlinSegmenter, - Language.LUA: LuaSegmenter, - Language.PERL: PerlSegmenter, - Language.TS: TypeScriptSegmenter, - Language.JAVA: JavaSegmenter, - } -except ImportError: - LANGUAGE_EXTENSIONS = {} - LANGUAGE_SEGMENTERS = {} +Language = Literal[ + "cpp", + "go", + "java", + "kotlin", + "js", + "ts", + "php", + "proto", + "python", + "rst", + "ruby", + "rust", + "scala", + "swift", + "markdown", + "latex", + "html", + "sol", + "csharp", + "cobol", + "c", + "lua", + "perl", +] class LanguageParser(BaseBlobParser): @@ -83,21 +100,21 @@ class LanguageParser(BaseBlobParser): The supported languages for code parsing are: - - C (*) - - C++ (*) - - C# (*) - - COBOL - - Go (*) - - Java (*) - - JavaScript (requires package `esprima`) - - Kotlin (*) - - Lua (*) - - Perl (*) - - Python - - Ruby (*) - - Rust (*) - - Scala (*) - - TypeScript (*) + - C: "c" (*) + - C++: "cpp" (*) + - C#: "csharp" (*) + - COBOL: "cobol" + - Go: "go" (*) + - Java: "java" (*) + - JavaScript: "js" (requires package `esprima`) + - Kotlin: "kotlin" (*) + - Lua: "lua" (*) + - Perl: "perl" (*) + - Python: "python" + - Ruby: "ruby" (*) + - Rust: "rust" (*) + - Scala: "scala" (*) + - TypeScript: "ts" (*) Items marked with (*) require the packages `tree_sitter` and `tree_sitter_languages`. It is straightforward to add support for additional @@ -113,7 +130,6 @@ class LanguageParser(BaseBlobParser): .. code-block:: python - from langchain.text_splitter.Language from langchain_community.document_loaders.generic import GenericLoader from langchain_community.document_loaders.parsers import LanguageParser @@ -129,13 +145,12 @@ class LanguageParser(BaseBlobParser): .. code-block:: python - from langchain.text_splitter import Language loader = GenericLoader.from_filesystem( "./code", glob="**/*", suffixes=[".py"], - parser=LanguageParser(language=Language.PYTHON) + parser=LanguageParser(language="python") ) Example instantiations to set number of lines threshold: diff --git a/libs/community/langchain_community/document_loaders/telegram.py b/libs/community/langchain_community/document_loaders/telegram.py index 5a9e3b06fd0..b4c94b29746 100644 --- a/libs/community/langchain_community/document_loaders/telegram.py +++ b/libs/community/langchain_community/document_loaders/telegram.py @@ -48,7 +48,13 @@ class TelegramChatFileLoader(BaseLoader): def text_to_docs(text: Union[str, List[str]]) -> List[Document]: """Convert a string or list of strings to a list of Documents with metadata.""" - from langchain.text_splitter import RecursiveCharacterTextSplitter + from langchain_text_splitters import RecursiveCharacterTextSplitter + + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=800, + separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""], + chunk_overlap=20, + ) if isinstance(text, str): # Take a single string as one page @@ -63,11 +69,6 @@ def text_to_docs(text: Union[str, List[str]]) -> List[Document]: doc_chunks = [] for doc in page_docs: - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=800, - separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""], - chunk_overlap=20, - ) chunks = text_splitter.split_text(doc.page_content) for i, chunk in enumerate(chunks): doc = Document( diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index 278caa2e6bb..65f71afa7b5 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -3674,6 +3674,25 @@ extended-testing = ["jinja2 (>=3,<4)"] type = "directory" url = "../core" +[[package]] +name = "langchain-text-splitters" +version = "0.0.1" +description = "LangChain text splitting utilities" +optional = false +python-versions = ">=3.8.1,<4.0" +files = [] +develop = true + +[package.dependencies] +langchain-core = "^0.1.28" + +[package.extras] +extended-testing = [] + +[package.source] +type = "directory" +url = "../text-splitters" + [[package]] name = "langsmith" version = "0.1.1" @@ -9162,4 +9181,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "200df001a4e2099d4c41040984391acbdbc5719a072f80e3ee929221a7e8dfeb" +content-hash = "7af07f4d9c43d4bc23fe11776bc1afd9874f6c3696bffb063b6453e9862dc4df" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index a85ba859d3c..09a26f95113 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -169,6 +169,7 @@ types-chardet = "^5.0.4.6" types-redis = "^4.3.21.6" mypy-protobuf = "^3.0.0" langchain-core = {path = "../core", develop = true} +langchain-text-splitters = {path = "../text-splitters", develop = true} [tool.poetry.group.dev] optional = true diff --git a/libs/experimental/poetry.lock b/libs/experimental/poetry.lock index 35d66a29870..69079af38d3 100644 --- a/libs/experimental/poetry.lock +++ b/libs/experimental/poetry.lock @@ -1656,6 +1656,7 @@ dataclasses-json = ">= 0.5.7, < 0.7" jsonpatch = "^1.33" langchain-community = ">=0.0.21,<0.1" langchain-core = ">=0.1.26,<0.2" +langchain-text-splitters = {path = "../text-splitters", develop = true} langsmith = "^0.1.0" numpy = "^1" pydantic = ">=1,<3" @@ -1696,6 +1697,7 @@ develop = true aiohttp = "^3.8.3" dataclasses-json = ">= 0.5.7, < 0.7" langchain-core = ">=0.1.26,<0.2" +langchain-text-splitters = {path = "../text-splitters", develop = true} langsmith = "^0.1.0" numpy = "^1" PyYAML = ">=5.3" @@ -1705,7 +1707,7 @@ tenacity = "^8.1.0" [package.extras] cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "llmlingua (>=0.1.6,<0.2.0)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"] +extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)", "zhipuai (>=1.0.7,<2.0.0)"] [package.source] type = "directory" @@ -1713,7 +1715,7 @@ url = "../community" [[package]] name = "langchain-core" -version = "0.1.27" +version = "0.1.28" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1737,6 +1739,25 @@ extended-testing = ["jinja2 (>=3,<4)"] type = "directory" url = "../core" +[[package]] +name = "langchain-text-splitters" +version = "0.0.1" +description = "LangChain text splitting utilities" +optional = false +python-versions = ">=3.8.1,<4.0" +files = [] +develop = true + +[package.dependencies] +langchain-core = "^0.1.28" + +[package.extras] +extended-testing = [] + +[package.source] +type = "directory" +url = "../text-splitters" + [[package]] name = "langcodes" version = "3.3.0" diff --git a/libs/langchain/langchain/chains/combine_documents/base.py b/libs/langchain/langchain/chains/combine_documents/base.py index 89ad181dace..6746c9df8f5 100644 --- a/libs/langchain/langchain/chains/combine_documents/base.py +++ b/libs/langchain/langchain/chains/combine_documents/base.py @@ -12,9 +12,9 @@ from langchain_core.prompts import BasePromptTemplate, PromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables.config import RunnableConfig from langchain_core.runnables.utils import create_model +from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter from langchain.chains.base import Chain -from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter DEFAULT_DOCUMENT_SEPARATOR = "\n\n" DOCUMENTS_KEY = "context" diff --git a/libs/langchain/langchain/chains/mapreduce.py b/libs/langchain/langchain/chains/mapreduce.py index 0bcf9907ba6..591def175d1 100644 --- a/libs/langchain/langchain/chains/mapreduce.py +++ b/libs/langchain/langchain/chains/mapreduce.py @@ -12,6 +12,7 @@ from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Extra +from langchain_text_splitters import TextSplitter from langchain.chains import ReduceDocumentsChain from langchain.chains.base import Chain @@ -19,7 +20,6 @@ from langchain.chains.combine_documents.base import BaseCombineDocumentsChain from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.llm import LLMChain -from langchain.text_splitter import TextSplitter class MapReduceChain(Chain): diff --git a/libs/langchain/langchain/chains/qa_generation/base.py b/libs/langchain/langchain/chains/qa_generation/base.py index d2ce08c80ec..bdc3444cf70 100644 --- a/libs/langchain/langchain/chains/qa_generation/base.py +++ b/libs/langchain/langchain/chains/qa_generation/base.py @@ -7,11 +7,11 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import Field +from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter from langchain.chains.base import Chain from langchain.chains.llm import LLMChain from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR -from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter class QAGenerationChain(Chain): diff --git a/libs/langchain/langchain/document_loaders/generic.py b/libs/langchain/langchain/document_loaders/generic.py index 6c1e0ef64e9..026b114889c 100644 --- a/libs/langchain/langchain/document_loaders/generic.py +++ b/libs/langchain/langchain/document_loaders/generic.py @@ -1,5 +1,3 @@ -from langchain_community.document_loaders.generic import ( - GenericLoader, -) +from langchain_community.document_loaders.generic import GenericLoader __all__ = ["GenericLoader"] diff --git a/libs/langchain/langchain/indexes/vectorstore.py b/libs/langchain/langchain/indexes/vectorstore.py index 25a70a65d25..4db2775ef72 100644 --- a/libs/langchain/langchain/indexes/vectorstore.py +++ b/libs/langchain/langchain/indexes/vectorstore.py @@ -9,10 +9,10 @@ from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.pydantic_v1 import BaseModel, Extra, Field from langchain_core.vectorstores import VectorStore +from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain from langchain.chains.retrieval_qa.base import RetrievalQA -from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter def _get_default_text_splitter() -> TextSplitter: diff --git a/libs/langchain/langchain/retrievers/parent_document_retriever.py b/libs/langchain/langchain/retrievers/parent_document_retriever.py index 5095c13540e..3c8ac83e7a1 100644 --- a/libs/langchain/langchain/retrievers/parent_document_retriever.py +++ b/libs/langchain/langchain/retrievers/parent_document_retriever.py @@ -2,9 +2,9 @@ import uuid from typing import List, Optional, Sequence from langchain_core.documents import Document +from langchain_text_splitters import TextSplitter from langchain.retrievers import MultiVectorRetriever -from langchain.text_splitter import TextSplitter class ParentDocumentRetriever(MultiVectorRetriever): @@ -33,7 +33,7 @@ class ParentDocumentRetriever(MultiVectorRetriever): from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import Chroma - from langchain.text_splitter import RecursiveCharacterTextSplitter + from langchain_text_splitters import RecursiveCharacterTextSplitter from langchain.storage import InMemoryStore # This text splitter is used to create the parent documents diff --git a/libs/langchain/langchain/retrievers/web_research.py b/libs/langchain/langchain/retrievers/web_research.py index b992490f617..378c77a267a 100644 --- a/libs/langchain/langchain/retrievers/web_research.py +++ b/libs/langchain/langchain/retrievers/web_research.py @@ -17,10 +17,10 @@ from langchain_core.prompts import BasePromptTemplate, PromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain_core.vectorstores import VectorStore +from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter from langchain.chains import LLMChain from langchain.chains.prompt_selector import ConditionalPromptSelector -from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter logger = logging.getLogger(__name__) diff --git a/libs/langchain/langchain/text_splitter.py b/libs/langchain/langchain/text_splitter.py index 1f382a53d0e..36afa7ec609 100644 --- a/libs/langchain/langchain/text_splitter.py +++ b/libs/langchain/langchain/text_splitter.py @@ -1,1608 +1,49 @@ -"""**Text Splitters** are classes for splitting text. - - -**Class hierarchy:** - -.. code-block:: - - BaseDocumentTransformer --> TextSplitter --> TextSplitter # Example: CharacterTextSplitter - RecursiveCharacterTextSplitter --> TextSplitter - -Note: **MarkdownHeaderTextSplitter** and **HTMLHeaderTextSplitter do not derive from TextSplitter. - - -**Main helpers:** - -.. code-block:: - - Document, Tokenizer, Language, LineType, HeaderType - -""" # noqa: E501 - -from __future__ import annotations - -import copy -import json -import logging -import pathlib -import re -from abc import ABC, abstractmethod -from dataclasses import dataclass -from enum import Enum -from io import BytesIO, StringIO -from typing import ( - AbstractSet, - Any, - Callable, - Collection, - Dict, - Iterable, - List, - Literal, - Optional, - Sequence, - Tuple, - Type, - TypedDict, - TypeVar, - Union, - cast, +"""Kept for backwards compatibility.""" +from langchain_text_splitters import ( + Language, + RecursiveCharacterTextSplitter, + TextSplitter, + Tokenizer, + TokenTextSplitter, ) - -import requests -from langchain_core.documents import BaseDocumentTransformer, Document - -logger = logging.getLogger(__name__) - -TS = TypeVar("TS", bound="TextSplitter") - - -def _make_spacy_pipeline_for_splitting( - pipeline: str, *, max_length: int = 1_000_000 -) -> Any: # avoid importing spacy - try: - import spacy - except ImportError: - raise ImportError( - "Spacy is not installed, please install it with `pip install spacy`." - ) - if pipeline == "sentencizer": - from spacy.lang.en import English - - sentencizer = English() - sentencizer.add_pipe("sentencizer") - else: - sentencizer = spacy.load(pipeline, exclude=["ner", "tagger"]) - sentencizer.max_length = max_length - return sentencizer - - -def _split_text_with_regex( - text: str, separator: str, keep_separator: bool -) -> List[str]: - # Now that we have the separator, split the text - if separator: - if keep_separator: - # The parentheses in the pattern keep the delimiters in the result. - _splits = re.split(f"({separator})", text) - splits = [_splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)] - if len(_splits) % 2 == 0: - splits += _splits[-1:] - splits = [_splits[0]] + splits - else: - splits = re.split(separator, text) - else: - splits = list(text) - return [s for s in splits if s != ""] - - -class TextSplitter(BaseDocumentTransformer, ABC): - """Interface for splitting text into chunks.""" - - def __init__( - self, - chunk_size: int = 4000, - chunk_overlap: int = 200, - length_function: Callable[[str], int] = len, - keep_separator: bool = False, - add_start_index: bool = False, - strip_whitespace: bool = True, - ) -> None: - """Create a new TextSplitter. - - Args: - chunk_size: Maximum size of chunks to return - chunk_overlap: Overlap in characters between chunks - length_function: Function that measures the length of given chunks - keep_separator: Whether to keep the separator in the chunks - add_start_index: If `True`, includes chunk's start index in metadata - strip_whitespace: If `True`, strips whitespace from the start and end of - every document - """ - if chunk_overlap > chunk_size: - raise ValueError( - f"Got a larger chunk overlap ({chunk_overlap}) than chunk size " - f"({chunk_size}), should be smaller." - ) - self._chunk_size = chunk_size - self._chunk_overlap = chunk_overlap - self._length_function = length_function - self._keep_separator = keep_separator - self._add_start_index = add_start_index - self._strip_whitespace = strip_whitespace - - @abstractmethod - def split_text(self, text: str) -> List[str]: - """Split text into multiple components.""" - - def create_documents( - self, texts: List[str], metadatas: Optional[List[dict]] = None - ) -> List[Document]: - """Create documents from a list of texts.""" - _metadatas = metadatas or [{}] * len(texts) - documents = [] - for i, text in enumerate(texts): - index = 0 - previous_chunk_len = 0 - for chunk in self.split_text(text): - metadata = copy.deepcopy(_metadatas[i]) - if self._add_start_index: - offset = index + previous_chunk_len - self._chunk_overlap - index = text.find(chunk, max(0, offset)) - metadata["start_index"] = index - previous_chunk_len = len(chunk) - new_doc = Document(page_content=chunk, metadata=metadata) - documents.append(new_doc) - return documents - - def split_documents(self, documents: Iterable[Document]) -> List[Document]: - """Split documents.""" - texts, metadatas = [], [] - for doc in documents: - texts.append(doc.page_content) - metadatas.append(doc.metadata) - return self.create_documents(texts, metadatas=metadatas) - - def _join_docs(self, docs: List[str], separator: str) -> Optional[str]: - text = separator.join(docs) - if self._strip_whitespace: - text = text.strip() - if text == "": - return None - else: - return text - - def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]: - # We now want to combine these smaller pieces into medium size - # chunks to send to the LLM. - separator_len = self._length_function(separator) - - docs = [] - current_doc: List[str] = [] - total = 0 - for d in splits: - _len = self._length_function(d) - if ( - total + _len + (separator_len if len(current_doc) > 0 else 0) - > self._chunk_size - ): - if total > self._chunk_size: - logger.warning( - f"Created a chunk of size {total}, " - f"which is longer than the specified {self._chunk_size}" - ) - if len(current_doc) > 0: - doc = self._join_docs(current_doc, separator) - if doc is not None: - docs.append(doc) - # Keep on popping if: - # - we have a larger chunk than in the chunk overlap - # - or if we still have any chunks and the length is long - while total > self._chunk_overlap or ( - total + _len + (separator_len if len(current_doc) > 0 else 0) - > self._chunk_size - and total > 0 - ): - total -= self._length_function(current_doc[0]) + ( - separator_len if len(current_doc) > 1 else 0 - ) - current_doc = current_doc[1:] - current_doc.append(d) - total += _len + (separator_len if len(current_doc) > 1 else 0) - doc = self._join_docs(current_doc, separator) - if doc is not None: - docs.append(doc) - return docs - - @classmethod - def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter: - """Text splitter that uses HuggingFace tokenizer to count length.""" - try: - from transformers import PreTrainedTokenizerBase - - if not isinstance(tokenizer, PreTrainedTokenizerBase): - raise ValueError( - "Tokenizer received was not an instance of PreTrainedTokenizerBase" - ) - - def _huggingface_tokenizer_length(text: str) -> int: - return len(tokenizer.encode(text)) - - except ImportError: - raise ValueError( - "Could not import transformers python package. " - "Please install it with `pip install transformers`." - ) - return cls(length_function=_huggingface_tokenizer_length, **kwargs) - - @classmethod - def from_tiktoken_encoder( - cls: Type[TS], - encoding_name: str = "gpt2", - model_name: Optional[str] = None, - allowed_special: Union[Literal["all"], AbstractSet[str]] = set(), - disallowed_special: Union[Literal["all"], Collection[str]] = "all", - **kwargs: Any, - ) -> TS: - """Text splitter that uses tiktoken encoder to count length.""" - try: - import tiktoken - except ImportError: - raise ImportError( - "Could not import tiktoken python package. " - "This is needed in order to calculate max_tokens_for_prompt. " - "Please install it with `pip install tiktoken`." - ) - - if model_name is not None: - enc = tiktoken.encoding_for_model(model_name) - else: - enc = tiktoken.get_encoding(encoding_name) - - def _tiktoken_encoder(text: str) -> int: - return len( - enc.encode( - text, - allowed_special=allowed_special, - disallowed_special=disallowed_special, - ) - ) - - if issubclass(cls, TokenTextSplitter): - extra_kwargs = { - "encoding_name": encoding_name, - "model_name": model_name, - "allowed_special": allowed_special, - "disallowed_special": disallowed_special, - } - kwargs = {**kwargs, **extra_kwargs} - - return cls(length_function=_tiktoken_encoder, **kwargs) - - def transform_documents( - self, documents: Sequence[Document], **kwargs: Any - ) -> Sequence[Document]: - """Transform sequence of documents by splitting them.""" - return self.split_documents(list(documents)) - - -class CharacterTextSplitter(TextSplitter): - """Splitting text that looks at characters.""" - - def __init__( - self, separator: str = "\n\n", is_separator_regex: bool = False, **kwargs: Any - ) -> None: - """Create a new TextSplitter.""" - super().__init__(**kwargs) - self._separator = separator - self._is_separator_regex = is_separator_regex - - def split_text(self, text: str) -> List[str]: - """Split incoming text and return chunks.""" - # First we naively split the large input into a bunch of smaller ones. - separator = ( - self._separator if self._is_separator_regex else re.escape(self._separator) - ) - splits = _split_text_with_regex(text, separator, self._keep_separator) - _separator = "" if self._keep_separator else self._separator - return self._merge_splits(splits, _separator) - - -class LineType(TypedDict): - """Line type as typed dict.""" - - metadata: Dict[str, str] - content: str - - -class HeaderType(TypedDict): - """Header type as typed dict.""" - - level: int - name: str - data: str - - -class MarkdownHeaderTextSplitter: - """Splitting markdown files based on specified headers.""" - - def __init__( - self, - headers_to_split_on: List[Tuple[str, str]], - return_each_line: bool = False, - strip_headers: bool = True, - ): - """Create a new MarkdownHeaderTextSplitter. - - Args: - headers_to_split_on: Headers we want to track - return_each_line: Return each line w/ associated headers - strip_headers: Strip split headers from the content of the chunk - """ - # Output line-by-line or aggregated into chunks w/ common headers - self.return_each_line = return_each_line - # Given the headers we want to split on, - # (e.g., "#, ##, etc") order by length - self.headers_to_split_on = sorted( - headers_to_split_on, key=lambda split: len(split[0]), reverse=True - ) - # Strip headers split headers from the content of the chunk - self.strip_headers = strip_headers - - def aggregate_lines_to_chunks(self, lines: List[LineType]) -> List[Document]: - """Combine lines with common metadata into chunks - Args: - lines: Line of text / associated header metadata - """ - aggregated_chunks: List[LineType] = [] - - for line in lines: - if ( - aggregated_chunks - and aggregated_chunks[-1]["metadata"] == line["metadata"] - ): - # If the last line in the aggregated list - # has the same metadata as the current line, - # append the current content to the last lines's content - aggregated_chunks[-1]["content"] += " \n" + line["content"] - elif ( - aggregated_chunks - and aggregated_chunks[-1]["metadata"] != line["metadata"] - # may be issues if other metadata is present - and len(aggregated_chunks[-1]["metadata"]) < len(line["metadata"]) - and aggregated_chunks[-1]["content"].split("\n")[-1][0] == "#" - and not self.strip_headers - ): - # If the last line in the aggregated list - # has different metadata as the current line, - # and has shallower header level than the current line, - # and the last line is a header, - # and we are not stripping headers, - # append the current content to the last line's content - aggregated_chunks[-1]["content"] += " \n" + line["content"] - # and update the last line's metadata - aggregated_chunks[-1]["metadata"] = line["metadata"] - else: - # Otherwise, append the current line to the aggregated list - aggregated_chunks.append(line) - - return [ - Document(page_content=chunk["content"], metadata=chunk["metadata"]) - for chunk in aggregated_chunks - ] - - def split_text(self, text: str) -> List[Document]: - """Split markdown file - Args: - text: Markdown file""" - - # Split the input text by newline character ("\n"). - lines = text.split("\n") - # Final output - lines_with_metadata: List[LineType] = [] - # Content and metadata of the chunk currently being processed - current_content: List[str] = [] - current_metadata: Dict[str, str] = {} - # Keep track of the nested header structure - # header_stack: List[Dict[str, Union[int, str]]] = [] - header_stack: List[HeaderType] = [] - initial_metadata: Dict[str, str] = {} - - in_code_block = False - opening_fence = "" - - for line in lines: - stripped_line = line.strip() - - if not in_code_block: - # Exclude inline code spans - if stripped_line.startswith("```") and stripped_line.count("```") == 1: - in_code_block = True - opening_fence = "```" - elif stripped_line.startswith("~~~"): - in_code_block = True - opening_fence = "~~~" - else: - if stripped_line.startswith(opening_fence): - in_code_block = False - opening_fence = "" - - if in_code_block: - current_content.append(stripped_line) - continue - - # Check each line against each of the header types (e.g., #, ##) - for sep, name in self.headers_to_split_on: - # Check if line starts with a header that we intend to split on - if stripped_line.startswith(sep) and ( - # Header with no text OR header is followed by space - # Both are valid conditions that sep is being used a header - len(stripped_line) == len(sep) or stripped_line[len(sep)] == " " - ): - # Ensure we are tracking the header as metadata - if name is not None: - # Get the current header level - current_header_level = sep.count("#") - - # Pop out headers of lower or same level from the stack - while ( - header_stack - and header_stack[-1]["level"] >= current_header_level - ): - # We have encountered a new header - # at the same or higher level - popped_header = header_stack.pop() - # Clear the metadata for the - # popped header in initial_metadata - if popped_header["name"] in initial_metadata: - initial_metadata.pop(popped_header["name"]) - - # Push the current header to the stack - header: HeaderType = { - "level": current_header_level, - "name": name, - "data": stripped_line[len(sep) :].strip(), - } - header_stack.append(header) - # Update initial_metadata with the current header - initial_metadata[name] = header["data"] - - # Add the previous line to the lines_with_metadata - # only if current_content is not empty - if current_content: - lines_with_metadata.append( - { - "content": "\n".join(current_content), - "metadata": current_metadata.copy(), - } - ) - current_content.clear() - - if not self.strip_headers: - current_content.append(stripped_line) - - break - else: - if stripped_line: - current_content.append(stripped_line) - elif current_content: - lines_with_metadata.append( - { - "content": "\n".join(current_content), - "metadata": current_metadata.copy(), - } - ) - current_content.clear() - - current_metadata = initial_metadata.copy() - - if current_content: - lines_with_metadata.append( - {"content": "\n".join(current_content), "metadata": current_metadata} - ) - - # lines_with_metadata has each line with associated header metadata - # aggregate these into chunks based on common metadata - if not self.return_each_line: - return self.aggregate_lines_to_chunks(lines_with_metadata) - else: - return [ - Document(page_content=chunk["content"], metadata=chunk["metadata"]) - for chunk in lines_with_metadata - ] - - -class ElementType(TypedDict): - """Element type as typed dict.""" - - url: str - xpath: str - content: str - metadata: Dict[str, str] - - -class HTMLHeaderTextSplitter: - """ - Splitting HTML files based on specified headers. - Requires lxml package. - """ - - def __init__( - self, - headers_to_split_on: List[Tuple[str, str]], - return_each_element: bool = False, - ): - """Create a new HTMLHeaderTextSplitter. - - Args: - headers_to_split_on: list of tuples of headers we want to track mapped to - (arbitrary) keys for metadata. Allowed header values: h1, h2, h3, h4, - h5, h6 e.g. [("h1", "Header 1"), ("h2", "Header 2)]. - return_each_element: Return each element w/ associated headers. - """ - # Output element-by-element or aggregated into chunks w/ common headers - self.return_each_element = return_each_element - self.headers_to_split_on = sorted(headers_to_split_on) - - def aggregate_elements_to_chunks( - self, elements: List[ElementType] - ) -> List[Document]: - """Combine elements with common metadata into chunks - - Args: - elements: HTML element content with associated identifying info and metadata - """ - aggregated_chunks: List[ElementType] = [] - - for element in elements: - if ( - aggregated_chunks - and aggregated_chunks[-1]["metadata"] == element["metadata"] - ): - # If the last element in the aggregated list - # has the same metadata as the current element, - # append the current content to the last element's content - aggregated_chunks[-1]["content"] += " \n" + element["content"] - else: - # Otherwise, append the current element to the aggregated list - aggregated_chunks.append(element) - - return [ - Document(page_content=chunk["content"], metadata=chunk["metadata"]) - for chunk in aggregated_chunks - ] - - def split_text_from_url(self, url: str) -> List[Document]: - """Split HTML from web URL - - Args: - url: web URL - """ - r = requests.get(url) - return self.split_text_from_file(BytesIO(r.content)) - - def split_text(self, text: str) -> List[Document]: - """Split HTML text string - - Args: - text: HTML text - """ - return self.split_text_from_file(StringIO(text)) - - def split_text_from_file(self, file: Any) -> List[Document]: - """Split HTML file - - Args: - file: HTML file - """ - try: - from lxml import etree - except ImportError as e: - raise ImportError( - "Unable to import lxml, please install with `pip install lxml`." - ) from e - # use lxml library to parse html document and return xml ElementTree - # Explicitly encoding in utf-8 allows non-English - # html files to be processed without garbled characters - parser = etree.HTMLParser(encoding="utf-8") - tree = etree.parse(file, parser) - - # document transformation for "structure-aware" chunking is handled with xsl. - # see comments in html_chunks_with_headers.xslt for more detailed information. - xslt_path = ( - pathlib.Path(__file__).parent - / "document_transformers/xsl/html_chunks_with_headers.xslt" - ) - xslt_tree = etree.parse(xslt_path) - transform = etree.XSLT(xslt_tree) - result = transform(tree) - result_dom = etree.fromstring(str(result)) - - # create filter and mapping for header metadata - header_filter = [header[0] for header in self.headers_to_split_on] - header_mapping = dict(self.headers_to_split_on) - - # map xhtml namespace prefix - ns_map = {"h": "http://www.w3.org/1999/xhtml"} - - # build list of elements from DOM - elements = [] - for element in result_dom.findall("*//*", ns_map): - if element.findall("*[@class='headers']") or element.findall( - "*[@class='chunk']" - ): - elements.append( - ElementType( - url=file, - xpath="".join( - [ - node.text - for node in element.findall("*[@class='xpath']", ns_map) - ] - ), - content="".join( - [ - node.text - for node in element.findall("*[@class='chunk']", ns_map) - ] - ), - metadata={ - # Add text of specified headers to metadata using header - # mapping. - header_mapping[node.tag]: node.text - for node in filter( - lambda x: x.tag in header_filter, - element.findall("*[@class='headers']/*", ns_map), - ) - }, - ) - ) - - if not self.return_each_element: - return self.aggregate_elements_to_chunks(elements) - else: - return [ - Document(page_content=chunk["content"], metadata=chunk["metadata"]) - for chunk in elements - ] - - -# should be in newer Python versions (3.10+) -# @dataclass(frozen=True, kw_only=True, slots=True) -@dataclass(frozen=True) -class Tokenizer: - """Tokenizer data class.""" - - chunk_overlap: int - """Overlap in tokens between chunks""" - tokens_per_chunk: int - """Maximum number of tokens per chunk""" - decode: Callable[[List[int]], str] - """ Function to decode a list of token ids to a string""" - encode: Callable[[str], List[int]] - """ Function to encode a string to a list of token ids""" - - -def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]: - """Split incoming text and return chunks using tokenizer.""" - splits: List[str] = [] - input_ids = tokenizer.encode(text) - start_idx = 0 - cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) - chunk_ids = input_ids[start_idx:cur_idx] - while start_idx < len(input_ids): - splits.append(tokenizer.decode(chunk_ids)) - if cur_idx == len(input_ids): - break - start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap - cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) - chunk_ids = input_ids[start_idx:cur_idx] - return splits - - -class TokenTextSplitter(TextSplitter): - """Splitting text to tokens using model tokenizer.""" - - def __init__( - self, - encoding_name: str = "gpt2", - model_name: Optional[str] = None, - allowed_special: Union[Literal["all"], AbstractSet[str]] = set(), - disallowed_special: Union[Literal["all"], Collection[str]] = "all", - **kwargs: Any, - ) -> None: - """Create a new TextSplitter.""" - super().__init__(**kwargs) - try: - import tiktoken - except ImportError: - raise ImportError( - "Could not import tiktoken python package. " - "This is needed in order to for TokenTextSplitter. " - "Please install it with `pip install tiktoken`." - ) - - if model_name is not None: - enc = tiktoken.encoding_for_model(model_name) - else: - enc = tiktoken.get_encoding(encoding_name) - self._tokenizer = enc - self._allowed_special = allowed_special - self._disallowed_special = disallowed_special - - def split_text(self, text: str) -> List[str]: - def _encode(_text: str) -> List[int]: - return self._tokenizer.encode( - _text, - allowed_special=self._allowed_special, - disallowed_special=self._disallowed_special, - ) - - tokenizer = Tokenizer( - chunk_overlap=self._chunk_overlap, - tokens_per_chunk=self._chunk_size, - decode=self._tokenizer.decode, - encode=_encode, - ) - - return split_text_on_tokens(text=text, tokenizer=tokenizer) - - -class SentenceTransformersTokenTextSplitter(TextSplitter): - """Splitting text to tokens using sentence model tokenizer.""" - - def __init__( - self, - chunk_overlap: int = 50, - model_name: str = "sentence-transformers/all-mpnet-base-v2", - tokens_per_chunk: Optional[int] = None, - **kwargs: Any, - ) -> None: - """Create a new TextSplitter.""" - super().__init__(**kwargs, chunk_overlap=chunk_overlap) - - try: - from sentence_transformers import SentenceTransformer - except ImportError: - raise ImportError( - "Could not import sentence_transformer python package. " - "This is needed in order to for SentenceTransformersTokenTextSplitter. " - "Please install it with `pip install sentence-transformers`." - ) - - self.model_name = model_name - self._model = SentenceTransformer(self.model_name) - self.tokenizer = self._model.tokenizer - self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk) - - def _initialize_chunk_configuration( - self, *, tokens_per_chunk: Optional[int] - ) -> None: - self.maximum_tokens_per_chunk = cast(int, self._model.max_seq_length) - - if tokens_per_chunk is None: - self.tokens_per_chunk = self.maximum_tokens_per_chunk - else: - self.tokens_per_chunk = tokens_per_chunk - - if self.tokens_per_chunk > self.maximum_tokens_per_chunk: - raise ValueError( - f"The token limit of the models '{self.model_name}'" - f" is: {self.maximum_tokens_per_chunk}." - f" Argument tokens_per_chunk={self.tokens_per_chunk}" - f" > maximum token limit." - ) - - def split_text(self, text: str) -> List[str]: - def encode_strip_start_and_stop_token_ids(text: str) -> List[int]: - return self._encode(text)[1:-1] - - tokenizer = Tokenizer( - chunk_overlap=self._chunk_overlap, - tokens_per_chunk=self.tokens_per_chunk, - decode=self.tokenizer.decode, - encode=encode_strip_start_and_stop_token_ids, - ) - - return split_text_on_tokens(text=text, tokenizer=tokenizer) - - def count_tokens(self, *, text: str) -> int: - return len(self._encode(text)) - - _max_length_equal_32_bit_integer: int = 2**32 - - def _encode(self, text: str) -> List[int]: - token_ids_with_start_and_end_token_ids = self.tokenizer.encode( - text, - max_length=self._max_length_equal_32_bit_integer, - truncation="do_not_truncate", - ) - return token_ids_with_start_and_end_token_ids - - -class Language(str, Enum): - """Enum of the programming languages.""" - - CPP = "cpp" - GO = "go" - JAVA = "java" - KOTLIN = "kotlin" - JS = "js" - TS = "ts" - PHP = "php" - PROTO = "proto" - PYTHON = "python" - RST = "rst" - RUBY = "ruby" - RUST = "rust" - SCALA = "scala" - SWIFT = "swift" - MARKDOWN = "markdown" - LATEX = "latex" - HTML = "html" - SOL = "sol" - CSHARP = "csharp" - COBOL = "cobol" - C = "c" - LUA = "lua" - PERL = "perl" - - -class RecursiveCharacterTextSplitter(TextSplitter): - """Splitting text by recursively look at characters. - - Recursively tries to split by different characters to find one - that works. - """ - - def __init__( - self, - separators: Optional[List[str]] = None, - keep_separator: bool = True, - is_separator_regex: bool = False, - **kwargs: Any, - ) -> None: - """Create a new TextSplitter.""" - super().__init__(keep_separator=keep_separator, **kwargs) - self._separators = separators or ["\n\n", "\n", " ", ""] - self._is_separator_regex = is_separator_regex - - def _split_text(self, text: str, separators: List[str]) -> List[str]: - """Split incoming text and return chunks.""" - final_chunks = [] - # Get appropriate separator to use - separator = separators[-1] - new_separators = [] - for i, _s in enumerate(separators): - _separator = _s if self._is_separator_regex else re.escape(_s) - if _s == "": - separator = _s - break - if re.search(_separator, text): - separator = _s - new_separators = separators[i + 1 :] - break - - _separator = separator if self._is_separator_regex else re.escape(separator) - splits = _split_text_with_regex(text, _separator, self._keep_separator) - - # Now go merging things, recursively splitting longer texts. - _good_splits = [] - _separator = "" if self._keep_separator else separator - for s in splits: - if self._length_function(s) < self._chunk_size: - _good_splits.append(s) - else: - if _good_splits: - merged_text = self._merge_splits(_good_splits, _separator) - final_chunks.extend(merged_text) - _good_splits = [] - if not new_separators: - final_chunks.append(s) - else: - other_info = self._split_text(s, new_separators) - final_chunks.extend(other_info) - if _good_splits: - merged_text = self._merge_splits(_good_splits, _separator) - final_chunks.extend(merged_text) - return final_chunks - - def split_text(self, text: str) -> List[str]: - return self._split_text(text, self._separators) - - @classmethod - def from_language( - cls, language: Language, **kwargs: Any - ) -> RecursiveCharacterTextSplitter: - separators = cls.get_separators_for_language(language) - return cls(separators=separators, is_separator_regex=True, **kwargs) - - @staticmethod - def get_separators_for_language(language: Language) -> List[str]: - if language == Language.CPP: - return [ - # Split along class definitions - "\nclass ", - # Split along function definitions - "\nvoid ", - "\nint ", - "\nfloat ", - "\ndouble ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nwhile ", - "\nswitch ", - "\ncase ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.GO: - return [ - # Split along function definitions - "\nfunc ", - "\nvar ", - "\nconst ", - "\ntype ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nswitch ", - "\ncase ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.JAVA: - return [ - # Split along class definitions - "\nclass ", - # Split along method definitions - "\npublic ", - "\nprotected ", - "\nprivate ", - "\nstatic ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nwhile ", - "\nswitch ", - "\ncase ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.KOTLIN: - return [ - # Split along class definitions - "\nclass ", - # Split along method definitions - "\npublic ", - "\nprotected ", - "\nprivate ", - "\ninternal ", - "\ncompanion ", - "\nfun ", - "\nval ", - "\nvar ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nwhile ", - "\nwhen ", - "\ncase ", - "\nelse ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.JS: - return [ - # Split along function definitions - "\nfunction ", - "\nconst ", - "\nlet ", - "\nvar ", - "\nclass ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nwhile ", - "\nswitch ", - "\ncase ", - "\ndefault ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.TS: - return [ - "\nenum ", - "\ninterface ", - "\nnamespace ", - "\ntype ", - # Split along class definitions - "\nclass ", - # Split along function definitions - "\nfunction ", - "\nconst ", - "\nlet ", - "\nvar ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nwhile ", - "\nswitch ", - "\ncase ", - "\ndefault ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.PHP: - return [ - # Split along function definitions - "\nfunction ", - # Split along class definitions - "\nclass ", - # Split along control flow statements - "\nif ", - "\nforeach ", - "\nwhile ", - "\ndo ", - "\nswitch ", - "\ncase ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.PROTO: - return [ - # Split along message definitions - "\nmessage ", - # Split along service definitions - "\nservice ", - # Split along enum definitions - "\nenum ", - # Split along option definitions - "\noption ", - # Split along import statements - "\nimport ", - # Split along syntax declarations - "\nsyntax ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.PYTHON: - return [ - # First, try to split along class definitions - "\nclass ", - "\ndef ", - "\n\tdef ", - # Now split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.RST: - return [ - # Split along section titles - "\n=+\n", - "\n-+\n", - "\n\\*+\n", - # Split along directive markers - "\n\n.. *\n\n", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.RUBY: - return [ - # Split along method definitions - "\ndef ", - "\nclass ", - # Split along control flow statements - "\nif ", - "\nunless ", - "\nwhile ", - "\nfor ", - "\ndo ", - "\nbegin ", - "\nrescue ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.RUST: - return [ - # Split along function definitions - "\nfn ", - "\nconst ", - "\nlet ", - # Split along control flow statements - "\nif ", - "\nwhile ", - "\nfor ", - "\nloop ", - "\nmatch ", - "\nconst ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.SCALA: - return [ - # Split along class definitions - "\nclass ", - "\nobject ", - # Split along method definitions - "\ndef ", - "\nval ", - "\nvar ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nwhile ", - "\nmatch ", - "\ncase ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.SWIFT: - return [ - # Split along function definitions - "\nfunc ", - # Split along class definitions - "\nclass ", - "\nstruct ", - "\nenum ", - # Split along control flow statements - "\nif ", - "\nfor ", - "\nwhile ", - "\ndo ", - "\nswitch ", - "\ncase ", - # Split by the normal type of lines - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.MARKDOWN: - return [ - # First, try to split along Markdown headings (starting with level 2) - "\n#{1,6} ", - # Note the alternative syntax for headings (below) is not handled here - # Heading level 2 - # --------------- - # End of code block - "```\n", - # Horizontal lines - "\n\\*\\*\\*+\n", - "\n---+\n", - "\n___+\n", - # Note that this splitter doesn't handle horizontal lines defined - # by *three or more* of ***, ---, or ___, but this is not handled - "\n\n", - "\n", - " ", - "", - ] - elif language == Language.LATEX: - return [ - # First, try to split along Latex sections - "\n\\\\chapter{", - "\n\\\\section{", - "\n\\\\subsection{", - "\n\\\\subsubsection{", - # Now split by environments - "\n\\\\begin{enumerate}", - "\n\\\\begin{itemize}", - "\n\\\\begin{description}", - "\n\\\\begin{list}", - "\n\\\\begin{quote}", - "\n\\\\begin{quotation}", - "\n\\\\begin{verse}", - "\n\\\\begin{verbatim}", - # Now split by math environments - "\n\\\begin{align}", - "$$", - "$", - # Now split by the normal type of lines - " ", - "", - ] - elif language == Language.HTML: - return [ - # First, try to split along HTML tags - " None: - """Initialize the NLTK splitter.""" - super().__init__(**kwargs) - try: - from nltk.tokenize import sent_tokenize - - self._tokenizer = sent_tokenize - except ImportError: - raise ImportError( - "NLTK is not installed, please install it with `pip install nltk`." - ) - self._separator = separator - self._language = language - - def split_text(self, text: str) -> List[str]: - """Split incoming text and return chunks.""" - # First we naively split the large input into a bunch of smaller ones. - splits = self._tokenizer(text, language=self._language) - return self._merge_splits(splits, self._separator) - - -class SpacyTextSplitter(TextSplitter): - """Splitting text using Spacy package. - - - Per default, Spacy's `en_core_web_sm` model is used and - its default max_length is 1000000 (it is the length of maximum character - this model takes which can be increased for large files). For a faster, but - potentially less accurate splitting, you can use `pipeline='sentencizer'`. - """ - - def __init__( - self, - separator: str = "\n\n", - pipeline: str = "en_core_web_sm", - max_length: int = 1_000_000, - **kwargs: Any, - ) -> None: - """Initialize the spacy text splitter.""" - super().__init__(**kwargs) - self._tokenizer = _make_spacy_pipeline_for_splitting( - pipeline, max_length=max_length - ) - self._separator = separator - - def split_text(self, text: str) -> List[str]: - """Split incoming text and return chunks.""" - splits = (s.text for s in self._tokenizer(text).sents) - return self._merge_splits(splits, self._separator) - - -class KonlpyTextSplitter(TextSplitter): - """Splitting text using Konlpy package. - - It is good for splitting Korean text. - """ - - def __init__( - self, - separator: str = "\n\n", - **kwargs: Any, - ) -> None: - """Initialize the Konlpy text splitter.""" - super().__init__(**kwargs) - self._separator = separator - try: - from konlpy.tag import Kkma - except ImportError: - raise ImportError( - """ - Konlpy is not installed, please install it with - `pip install konlpy` - """ - ) - self.kkma = Kkma() - - def split_text(self, text: str) -> List[str]: - """Split incoming text and return chunks.""" - splits = self.kkma.sentences(text) - return self._merge_splits(splits, self._separator) - - -# For backwards compatibility -class PythonCodeTextSplitter(RecursiveCharacterTextSplitter): - """Attempts to split the text along Python syntax.""" - - def __init__(self, **kwargs: Any) -> None: - """Initialize a PythonCodeTextSplitter.""" - separators = self.get_separators_for_language(Language.PYTHON) - super().__init__(separators=separators, **kwargs) - - -class MarkdownTextSplitter(RecursiveCharacterTextSplitter): - """Attempts to split the text along Markdown-formatted headings.""" - - def __init__(self, **kwargs: Any) -> None: - """Initialize a MarkdownTextSplitter.""" - separators = self.get_separators_for_language(Language.MARKDOWN) - super().__init__(separators=separators, **kwargs) - - -class LatexTextSplitter(RecursiveCharacterTextSplitter): - """Attempts to split the text along Latex-formatted layout elements.""" - - def __init__(self, **kwargs: Any) -> None: - """Initialize a LatexTextSplitter.""" - separators = self.get_separators_for_language(Language.LATEX) - super().__init__(separators=separators, **kwargs) - - -class RecursiveJsonSplitter: - def __init__( - self, max_chunk_size: int = 2000, min_chunk_size: Optional[int] = None - ): - super().__init__() - self.max_chunk_size = max_chunk_size - self.min_chunk_size = ( - min_chunk_size - if min_chunk_size is not None - else max(max_chunk_size - 200, 50) - ) - - @staticmethod - def _json_size(data: Dict) -> int: - """Calculate the size of the serialized JSON object.""" - return len(json.dumps(data)) - - @staticmethod - def _set_nested_dict(d: Dict, path: List[str], value: Any) -> None: - """Set a value in a nested dictionary based on the given path.""" - for key in path[:-1]: - d = d.setdefault(key, {}) - d[path[-1]] = value - - def _list_to_dict_preprocessing(self, data: Any) -> Any: - if isinstance(data, dict): - # Process each key-value pair in the dictionary - return {k: self._list_to_dict_preprocessing(v) for k, v in data.items()} - elif isinstance(data, list): - # Convert the list to a dictionary with index-based keys - return { - str(i): self._list_to_dict_preprocessing(item) - for i, item in enumerate(data) - } - else: - # Base case: the item is neither a dict nor a list, so return it unchanged - return data - - def _json_split( - self, - data: Dict[str, Any], - current_path: List[str] = [], - chunks: List[Dict] = [{}], - ) -> List[Dict]: - """ - Split json into maximum size dictionaries while preserving structure. - """ - if isinstance(data, dict): - for key, value in data.items(): - new_path = current_path + [key] - chunk_size = self._json_size(chunks[-1]) - size = self._json_size({key: value}) - remaining = self.max_chunk_size - chunk_size - - if size < remaining: - # Add item to current chunk - self._set_nested_dict(chunks[-1], new_path, value) - else: - if chunk_size >= self.min_chunk_size: - # Chunk is big enough, start a new chunk - chunks.append({}) - - # Iterate - self._json_split(value, new_path, chunks) - else: - # handle single item - self._set_nested_dict(chunks[-1], current_path, data) - return chunks - - def split_json( - self, - json_data: Dict[str, Any], - convert_lists: bool = False, - ) -> List[Dict]: - """Splits JSON into a list of JSON chunks""" - - if convert_lists: - chunks = self._json_split(self._list_to_dict_preprocessing(json_data)) - else: - chunks = self._json_split(json_data) - - # Remove the last chunk if it's empty - if not chunks[-1]: - chunks.pop() - return chunks - - def split_text( - self, json_data: Dict[str, Any], convert_lists: bool = False - ) -> List[str]: - """Splits JSON into a list of JSON formatted strings""" - - chunks = self.split_json(json_data=json_data, convert_lists=convert_lists) - - # Convert to string - return [json.dumps(chunk) for chunk in chunks] - - def create_documents( - self, - texts: List[Dict], - convert_lists: bool = False, - metadatas: Optional[List[dict]] = None, - ) -> List[Document]: - """Create documents from a list of json objects (Dict).""" - _metadatas = metadatas or [{}] * len(texts) - documents = [] - for i, text in enumerate(texts): - for chunk in self.split_text(json_data=text, convert_lists=convert_lists): - metadata = copy.deepcopy(_metadatas[i]) - new_doc = Document(page_content=chunk, metadata=metadata) - documents.append(new_doc) - return documents +from langchain_text_splitters.base import split_text_on_tokens +from langchain_text_splitters.character import CharacterTextSplitter +from langchain_text_splitters.html import ElementType, HTMLHeaderTextSplitter +from langchain_text_splitters.json import RecursiveJsonSplitter +from langchain_text_splitters.konlpy import KonlpyTextSplitter +from langchain_text_splitters.latex import LatexTextSplitter +from langchain_text_splitters.markdown import ( + HeaderType, + LineType, + MarkdownHeaderTextSplitter, + MarkdownTextSplitter, +) +from langchain_text_splitters.nltk import NLTKTextSplitter +from langchain_text_splitters.python import PythonCodeTextSplitter +from langchain_text_splitters.sentence_transformers import ( + SentenceTransformersTokenTextSplitter, +) +from langchain_text_splitters.spacy import SpacyTextSplitter + +__all__ = [ + "TokenTextSplitter", + "TextSplitter", + "Tokenizer", + "Language", + "RecursiveCharacterTextSplitter", + "RecursiveJsonSplitter", + "LatexTextSplitter", + "PythonCodeTextSplitter", + "KonlpyTextSplitter", + "SpacyTextSplitter", + "NLTKTextSplitter", + "split_text_on_tokens", + "SentenceTransformersTokenTextSplitter", + "ElementType", + "HeaderType", + "LineType", + "HTMLHeaderTextSplitter", + "MarkdownHeaderTextSplitter", + "MarkdownTextSplitter", + "CharacterTextSplitter", +] diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index 2ca427c1068..5ded4ba303b 100644 --- a/libs/langchain/poetry.lock +++ b/libs/langchain/poetry.lock @@ -3446,7 +3446,7 @@ files = [ [[package]] name = "langchain-community" -version = "0.0.21" +version = "0.0.24" description = "Community contributed LangChain integrations." optional = false python-versions = ">=3.8.1,<4.0" @@ -3456,7 +3456,8 @@ develop = true [package.dependencies] aiohttp = "^3.8.3" dataclasses-json = ">= 0.5.7, < 0.7" -langchain-core = ">=0.1.24,<0.2" +langchain-core = ">=0.1.26,<0.2" +langchain-text-splitters = {path = "../text-splitters", develop = true} langsmith = "^0.1.0" numpy = "^1" PyYAML = ">=5.3" @@ -3474,7 +3475,7 @@ url = "../community" [[package]] name = "langchain-core" -version = "0.1.26" +version = "0.1.28" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -3515,6 +3516,25 @@ numpy = ">=1,<2" openai = ">=1.6.1,<2.0.0" tiktoken = ">=0.5.2,<0.6.0" +[[package]] +name = "langchain-text-splitters" +version = "0.0.1" +description = "LangChain text splitting utilities" +optional = false +python-versions = ">=3.8.1,<4.0" +files = [] +develop = true + +[package.dependencies] +langchain-core = "^0.1.28" + +[package.extras] +extended-testing = [] + +[package.source] +type = "directory" +url = "../text-splitters" + [[package]] name = "langsmith" version = "0.1.1" @@ -9150,4 +9170,4 @@ text-helpers = ["chardet"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "30df9669a4ed14c320cf8aac20f43d96234bad6d12ec6b6ac49984320832e029" +content-hash = "14fd23dceb33bb70afb364587b46e53806c5ac38a61a7e435684bf3f63f6b6f9" diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml index f008d8a122a..053f7b0cce4 100644 --- a/libs/langchain/pyproject.toml +++ b/libs/langchain/pyproject.toml @@ -13,6 +13,7 @@ langchain-server = "langchain.server:main" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" langchain-core = ">=0.1.26,<0.2" +langchain-text-splitters = {path = "../text-splitters", develop = true} langchain-community = ">=0.0.21,<0.1" langsmith = "^0.1.0" pydantic = ">=1,<3" @@ -135,6 +136,7 @@ pytest-socket = "^0.6.0" syrupy = "^4.0.2" requests-mock = "^1.11.0" langchain-core = {path = "../core", develop = true} +langchain-text-splitters = {path = "../text-splitters", develop = true} [tool.poetry.group.codespell] optional = true @@ -170,6 +172,7 @@ tiktoken = ">=0.3.2,<0.6.0" anthropic = "^0.3.11" langchain-core = {path = "../core", develop = true} langchain-community = {path = "../community", develop = true} +langchain-text-splitters = {path = "../text-splitters", develop = true} [tool.poetry.group.lint] optional = true @@ -191,6 +194,7 @@ types-chardet = "^5.0.4.6" mypy-protobuf = "^3.0.0" langchain-core = {path = "../core", develop = true} langchain-community = {path = "../community", develop = true} +langchain-text-splitters = {path = "../text-splitters", develop = true} [tool.poetry.group.dev] optional = true @@ -201,6 +205,7 @@ playwright = "^1.28.0" setuptools = "^67.6.1" langchain-core = {path = "../core", develop = true} langchain-community = {path = "../community", develop = true} +langchain-text-splitters = {path = "../text-splitters", develop = true} [tool.poetry.extras] llms = ["clarifai", "cohere", "openai", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"] diff --git a/libs/langchain/tests/integration_tests/chains/test_retrieval_qa.py b/libs/langchain/tests/integration_tests/chains/test_retrieval_qa.py index dec373ceb1c..eb7e2811368 100644 --- a/libs/langchain/tests/integration_tests/chains/test_retrieval_qa.py +++ b/libs/langchain/tests/integration_tests/chains/test_retrieval_qa.py @@ -5,10 +5,10 @@ from langchain_community.document_loaders import TextLoader from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.llms import OpenAI from langchain_community.vectorstores import FAISS +from langchain_text_splitters.character import CharacterTextSplitter from langchain.chains import RetrievalQA from langchain.chains.loading import load_chain -from langchain.text_splitter import CharacterTextSplitter def test_retrieval_qa_saving_loading(tmp_path: Path) -> None: diff --git a/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py b/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py index 9c3b65ca1bf..c8752cd7e7a 100644 --- a/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py +++ b/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py @@ -3,10 +3,10 @@ from langchain_community.document_loaders import DirectoryLoader from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.llms import OpenAI from langchain_community.vectorstores import FAISS +from langchain_text_splitters.character import CharacterTextSplitter from langchain.chains import RetrievalQAWithSourcesChain from langchain.chains.loading import load_chain -from langchain.text_splitter import CharacterTextSplitter def test_retrieval_qa_with_sources_chain_saving_loading(tmp_path: str) -> None: diff --git a/libs/langchain/tests/integration_tests/retrievers/document_compressors/test_base.py b/libs/langchain/tests/integration_tests/retrievers/document_compressors/test_base.py index 80ccd54f674..9414592511a 100644 --- a/libs/langchain/tests/integration_tests/retrievers/document_compressors/test_base.py +++ b/libs/langchain/tests/integration_tests/retrievers/document_compressors/test_base.py @@ -2,12 +2,12 @@ from langchain_community.document_transformers import EmbeddingsRedundantFilter from langchain_community.embeddings import OpenAIEmbeddings from langchain_core.documents import Document +from langchain_text_splitters.character import CharacterTextSplitter from langchain.retrievers.document_compressors import ( DocumentCompressorPipeline, EmbeddingsFilter, ) -from langchain.text_splitter import CharacterTextSplitter def test_document_compressor_pipeline() -> None: diff --git a/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py b/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py index 0b24795b780..0f248300de6 100644 --- a/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py +++ b/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py @@ -1,10 +1,10 @@ from typing import Any, List, Sequence from langchain_core.documents import Document +from langchain_text_splitters.character import CharacterTextSplitter from langchain.retrievers import ParentDocumentRetriever from langchain.storage import InMemoryStore -from langchain.text_splitter import CharacterTextSplitter from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore diff --git a/libs/langchain/tests/unit_tests/test_dependencies.py b/libs/langchain/tests/unit_tests/test_dependencies.py index 3b0858c02b6..5e8c9d01dda 100644 --- a/libs/langchain/tests/unit_tests/test_dependencies.py +++ b/libs/langchain/tests/unit_tests/test_dependencies.py @@ -44,6 +44,7 @@ def test_required_dependencies(poetry_conf: Mapping[str, Any]) -> None: "dataclasses-json", "jsonpatch", "langchain-core", + "langchain-text-splitters", "langsmith", "numpy", "pydantic", @@ -77,6 +78,7 @@ def test_test_group_dependencies(poetry_conf: Mapping[str, Any]) -> None: "duckdb-engine", "freezegun", "langchain-core", + "langchain-text-splitters", "lark", "pandas", "pytest", diff --git a/libs/partners/airbyte/langchain_airbyte/document_loaders.py b/libs/partners/airbyte/langchain_airbyte/document_loaders.py index 73f47b6f1c5..08b1513d54c 100644 --- a/libs/partners/airbyte/langchain_airbyte/document_loaders.py +++ b/libs/partners/airbyte/langchain_airbyte/document_loaders.py @@ -21,8 +21,7 @@ from langchain_core.runnables import run_in_executor from langchain_core.vectorstores import VectorStore if TYPE_CHECKING: - from langchain.text_splitter import TextSplitter - from langchain_core.documents import Document + from langchain_text_splitters import TextSplitter VST = TypeVar("VST", bound=VectorStore) @@ -72,9 +71,16 @@ class AirbyteLoader: Returns: List of Documents. """ - from langchain.text_splitter import RecursiveCharacterTextSplitter if text_splitter is None: + try: + from langchain_text_splitters import RecursiveCharacterTextSplitter + except ImportError as e: + raise ImportError( + "Unable to import from langchain_text_splitters. Please specify " + "text_splitter or install langchain_text_splitters with " + "`pip install -U langchain-text-splitters`." + ) from e _text_splitter: TextSplitter = RecursiveCharacterTextSplitter() else: _text_splitter = text_splitter diff --git a/libs/partners/airbyte/poetry.lock b/libs/partners/airbyte/poetry.lock index 1ca216bd1c2..c742f89136e 100644 --- a/libs/partners/airbyte/poetry.lock +++ b/libs/partners/airbyte/poetry.lock @@ -1254,7 +1254,7 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15. [[package]] name = "langchain-core" -version = "0.1.27" +version = "0.1.28" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1278,6 +1278,25 @@ extended-testing = ["jinja2 (>=3,<4)"] type = "directory" url = "../../core" +[[package]] +name = "langchain-text-splitters" +version = "0.0.1" +description = "LangChain text splitting utilities" +optional = false +python-versions = ">=3.8.1,<4.0" +files = [] +develop = true + +[package.dependencies] +langchain-core = "^0.1.28" + +[package.extras] +extended-testing = ["lxml (>=5.1.0,<6.0.0)"] + +[package.source] +type = "directory" +url = "../../text-splitters" + [[package]] name = "langsmith" version = "0.1.10" @@ -2994,4 +3013,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "a0298bf85ec39e325d4bbf3c4a5d48a1ca2c93e4d20bd9ee2dc74365df34e3dd" +content-hash = "9b260aa9b4965cf414e9c68d2a32d8f8b084e7073e298b4b71ab9c213bb536d6" diff --git a/libs/partners/airbyte/pyproject.toml b/libs/partners/airbyte/pyproject.toml index 7dde73c7953..7d9f2c962fd 100644 --- a/libs/partners/airbyte/pyproject.toml +++ b/libs/partners/airbyte/pyproject.toml @@ -43,6 +43,7 @@ ruff = "^0.1.8" [tool.poetry.group.typing.dependencies] mypy = "^1.7.1" langchain-core = { path = "../../core", develop = true } +langchain-text-splitters = { path = "../../text-splitters", develop = true } langchain = "^0.1.9" [tool.poetry.group.dev] diff --git a/libs/text-splitters/Makefile b/libs/text-splitters/Makefile new file mode 100644 index 00000000000..9bf3794ee7f --- /dev/null +++ b/libs/text-splitters/Makefile @@ -0,0 +1,71 @@ +.PHONY: all format lint test tests test_watch integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +test: + poetry run pytest $(TEST_FILE) + +tests: + poetry run pytest $(TEST_FILE) + +test_watch: + poetry run ptw --snapshot-update --now . -- -vv -x tests/unit_tests + +test_profile: + poetry run pytest -vv tests/unit_tests/ --profile-svg + +check_imports: $(shell find langchain_text_splitters -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +extended_tests: + poetry run pytest --only-extended $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/core --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_text_splitters +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + ./scripts/check_pydantic.sh . + ./scripts/lint_imports.sh + poetry run ruff . + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' + @echo 'test_watch - run unit tests in watch mode' diff --git a/libs/text-splitters/README.md b/libs/text-splitters/README.md new file mode 100644 index 00000000000..193cb401498 --- /dev/null +++ b/libs/text-splitters/README.md @@ -0,0 +1,37 @@ +# 🦜✂️ LangChain Text Splitters + +[![Downloads](https://static.pepy.tech/badge/langchain_core/month)](https://pepy.tech/project/langchain_text_splitters) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) + +## Quick Install + +```bash +pip install langchain-text-splitters +``` + +## What is it? + +LangChain Text Splitters contains + +For full documentation see the [API reference](https://api.python.langchain.com/en/stable/text_splitters_api_reference.html). + +## 📕 Releases & Versioning + +`langchain-text-splitters` is currently on version `0.0.x`. + +Minor version increases will occur for: + +- Breaking changes for any public interfaces NOT marked `beta` + +Patch version increases will occur for: + +- Bug fixes +- New features +- Any changes to private interfaces +- Any changes to `beta` features + +## 💁 Contributing + +As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation. + +For detailed information on how to contribute, see the [Contributing Guide](https://python.langchain.com/docs/contributing/). diff --git a/libs/text-splitters/langchain_text_splitters/__init__.py b/libs/text-splitters/langchain_text_splitters/__init__.py new file mode 100644 index 00000000000..2f74b35251b --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/__init__.py @@ -0,0 +1,71 @@ +"""**Text Splitters** are classes for splitting text. + + +**Class hierarchy:** + +.. code-block:: + + BaseDocumentTransformer --> TextSplitter --> TextSplitter # Example: CharacterTextSplitter + RecursiveCharacterTextSplitter --> TextSplitter + +Note: **MarkdownHeaderTextSplitter** and **HTMLHeaderTextSplitter do not derive from TextSplitter. + + +**Main helpers:** + +.. code-block:: + + Document, Tokenizer, Language, LineType, HeaderType + +""" # noqa: E501 + +from langchain_text_splitters.base import ( + Language, + TextSplitter, + Tokenizer, + TokenTextSplitter, + split_text_on_tokens, +) +from langchain_text_splitters.character import ( + CharacterTextSplitter, + RecursiveCharacterTextSplitter, +) +from langchain_text_splitters.html import ElementType, HTMLHeaderTextSplitter +from langchain_text_splitters.json import RecursiveJsonSplitter +from langchain_text_splitters.konlpy import KonlpyTextSplitter +from langchain_text_splitters.latex import LatexTextSplitter +from langchain_text_splitters.markdown import ( + HeaderType, + LineType, + MarkdownHeaderTextSplitter, + MarkdownTextSplitter, +) +from langchain_text_splitters.nltk import NLTKTextSplitter +from langchain_text_splitters.python import PythonCodeTextSplitter +from langchain_text_splitters.sentence_transformers import ( + SentenceTransformersTokenTextSplitter, +) +from langchain_text_splitters.spacy import SpacyTextSplitter + +__all__ = [ + "TokenTextSplitter", + "TextSplitter", + "Tokenizer", + "Language", + "RecursiveCharacterTextSplitter", + "RecursiveJsonSplitter", + "LatexTextSplitter", + "PythonCodeTextSplitter", + "KonlpyTextSplitter", + "SpacyTextSplitter", + "NLTKTextSplitter", + "split_text_on_tokens", + "SentenceTransformersTokenTextSplitter", + "ElementType", + "HeaderType", + "LineType", + "HTMLHeaderTextSplitter", + "MarkdownHeaderTextSplitter", + "MarkdownTextSplitter", + "CharacterTextSplitter", +] diff --git a/libs/text-splitters/langchain_text_splitters/base.py b/libs/text-splitters/langchain_text_splitters/base.py new file mode 100644 index 00000000000..16480a16ed0 --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/base.py @@ -0,0 +1,324 @@ +from __future__ import annotations + +import copy +import logging +from abc import ABC, abstractmethod +from dataclasses import dataclass +from enum import Enum +from typing import ( + AbstractSet, + Any, + Callable, + Collection, + Iterable, + List, + Literal, + Optional, + Sequence, + Type, + TypeVar, + Union, +) + +from langchain_core.documents import BaseDocumentTransformer, Document + +logger = logging.getLogger(__name__) + +TS = TypeVar("TS", bound="TextSplitter") + + +class TextSplitter(BaseDocumentTransformer, ABC): + """Interface for splitting text into chunks.""" + + def __init__( + self, + chunk_size: int = 4000, + chunk_overlap: int = 200, + length_function: Callable[[str], int] = len, + keep_separator: bool = False, + add_start_index: bool = False, + strip_whitespace: bool = True, + ) -> None: + """Create a new TextSplitter. + + Args: + chunk_size: Maximum size of chunks to return + chunk_overlap: Overlap in characters between chunks + length_function: Function that measures the length of given chunks + keep_separator: Whether to keep the separator in the chunks + add_start_index: If `True`, includes chunk's start index in metadata + strip_whitespace: If `True`, strips whitespace from the start and end of + every document + """ + if chunk_overlap > chunk_size: + raise ValueError( + f"Got a larger chunk overlap ({chunk_overlap}) than chunk size " + f"({chunk_size}), should be smaller." + ) + self._chunk_size = chunk_size + self._chunk_overlap = chunk_overlap + self._length_function = length_function + self._keep_separator = keep_separator + self._add_start_index = add_start_index + self._strip_whitespace = strip_whitespace + + @abstractmethod + def split_text(self, text: str) -> List[str]: + """Split text into multiple components.""" + + def create_documents( + self, texts: List[str], metadatas: Optional[List[dict]] = None + ) -> List[Document]: + """Create documents from a list of texts.""" + _metadatas = metadatas or [{}] * len(texts) + documents = [] + for i, text in enumerate(texts): + index = 0 + previous_chunk_len = 0 + for chunk in self.split_text(text): + metadata = copy.deepcopy(_metadatas[i]) + if self._add_start_index: + offset = index + previous_chunk_len - self._chunk_overlap + index = text.find(chunk, max(0, offset)) + metadata["start_index"] = index + previous_chunk_len = len(chunk) + new_doc = Document(page_content=chunk, metadata=metadata) + documents.append(new_doc) + return documents + + def split_documents(self, documents: Iterable[Document]) -> List[Document]: + """Split documents.""" + texts, metadatas = [], [] + for doc in documents: + texts.append(doc.page_content) + metadatas.append(doc.metadata) + return self.create_documents(texts, metadatas=metadatas) + + def _join_docs(self, docs: List[str], separator: str) -> Optional[str]: + text = separator.join(docs) + if self._strip_whitespace: + text = text.strip() + if text == "": + return None + else: + return text + + def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]: + # We now want to combine these smaller pieces into medium size + # chunks to send to the LLM. + separator_len = self._length_function(separator) + + docs = [] + current_doc: List[str] = [] + total = 0 + for d in splits: + _len = self._length_function(d) + if ( + total + _len + (separator_len if len(current_doc) > 0 else 0) + > self._chunk_size + ): + if total > self._chunk_size: + logger.warning( + f"Created a chunk of size {total}, " + f"which is longer than the specified {self._chunk_size}" + ) + if len(current_doc) > 0: + doc = self._join_docs(current_doc, separator) + if doc is not None: + docs.append(doc) + # Keep on popping if: + # - we have a larger chunk than in the chunk overlap + # - or if we still have any chunks and the length is long + while total > self._chunk_overlap or ( + total + _len + (separator_len if len(current_doc) > 0 else 0) + > self._chunk_size + and total > 0 + ): + total -= self._length_function(current_doc[0]) + ( + separator_len if len(current_doc) > 1 else 0 + ) + current_doc = current_doc[1:] + current_doc.append(d) + total += _len + (separator_len if len(current_doc) > 1 else 0) + doc = self._join_docs(current_doc, separator) + if doc is not None: + docs.append(doc) + return docs + + @classmethod + def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter: + """Text splitter that uses HuggingFace tokenizer to count length.""" + try: + from transformers import PreTrainedTokenizerBase + + if not isinstance(tokenizer, PreTrainedTokenizerBase): + raise ValueError( + "Tokenizer received was not an instance of PreTrainedTokenizerBase" + ) + + def _huggingface_tokenizer_length(text: str) -> int: + return len(tokenizer.encode(text)) + + except ImportError: + raise ValueError( + "Could not import transformers python package. " + "Please install it with `pip install transformers`." + ) + return cls(length_function=_huggingface_tokenizer_length, **kwargs) + + @classmethod + def from_tiktoken_encoder( + cls: Type[TS], + encoding_name: str = "gpt2", + model_name: Optional[str] = None, + allowed_special: Union[Literal["all"], AbstractSet[str]] = set(), + disallowed_special: Union[Literal["all"], Collection[str]] = "all", + **kwargs: Any, + ) -> TS: + """Text splitter that uses tiktoken encoder to count length.""" + try: + import tiktoken + except ImportError: + raise ImportError( + "Could not import tiktoken python package. " + "This is needed in order to calculate max_tokens_for_prompt. " + "Please install it with `pip install tiktoken`." + ) + + if model_name is not None: + enc = tiktoken.encoding_for_model(model_name) + else: + enc = tiktoken.get_encoding(encoding_name) + + def _tiktoken_encoder(text: str) -> int: + return len( + enc.encode( + text, + allowed_special=allowed_special, + disallowed_special=disallowed_special, + ) + ) + + if issubclass(cls, TokenTextSplitter): + extra_kwargs = { + "encoding_name": encoding_name, + "model_name": model_name, + "allowed_special": allowed_special, + "disallowed_special": disallowed_special, + } + kwargs = {**kwargs, **extra_kwargs} + + return cls(length_function=_tiktoken_encoder, **kwargs) + + def transform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: + """Transform sequence of documents by splitting them.""" + return self.split_documents(list(documents)) + + +class TokenTextSplitter(TextSplitter): + """Splitting text to tokens using model tokenizer.""" + + def __init__( + self, + encoding_name: str = "gpt2", + model_name: Optional[str] = None, + allowed_special: Union[Literal["all"], AbstractSet[str]] = set(), + disallowed_special: Union[Literal["all"], Collection[str]] = "all", + **kwargs: Any, + ) -> None: + """Create a new TextSplitter.""" + super().__init__(**kwargs) + try: + import tiktoken + except ImportError: + raise ImportError( + "Could not import tiktoken python package. " + "This is needed in order to for TokenTextSplitter. " + "Please install it with `pip install tiktoken`." + ) + + if model_name is not None: + enc = tiktoken.encoding_for_model(model_name) + else: + enc = tiktoken.get_encoding(encoding_name) + self._tokenizer = enc + self._allowed_special = allowed_special + self._disallowed_special = disallowed_special + + def split_text(self, text: str) -> List[str]: + def _encode(_text: str) -> List[int]: + return self._tokenizer.encode( + _text, + allowed_special=self._allowed_special, + disallowed_special=self._disallowed_special, + ) + + tokenizer = Tokenizer( + chunk_overlap=self._chunk_overlap, + tokens_per_chunk=self._chunk_size, + decode=self._tokenizer.decode, + encode=_encode, + ) + + return split_text_on_tokens(text=text, tokenizer=tokenizer) + + +class Language(str, Enum): + """Enum of the programming languages.""" + + CPP = "cpp" + GO = "go" + JAVA = "java" + KOTLIN = "kotlin" + JS = "js" + TS = "ts" + PHP = "php" + PROTO = "proto" + PYTHON = "python" + RST = "rst" + RUBY = "ruby" + RUST = "rust" + SCALA = "scala" + SWIFT = "swift" + MARKDOWN = "markdown" + LATEX = "latex" + HTML = "html" + SOL = "sol" + CSHARP = "csharp" + COBOL = "cobol" + C = "c" + LUA = "lua" + PERL = "perl" + + +@dataclass(frozen=True) +class Tokenizer: + """Tokenizer data class.""" + + chunk_overlap: int + """Overlap in tokens between chunks""" + tokens_per_chunk: int + """Maximum number of tokens per chunk""" + decode: Callable[[List[int]], str] + """ Function to decode a list of token ids to a string""" + encode: Callable[[str], List[int]] + """ Function to encode a string to a list of token ids""" + + +def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]: + """Split incoming text and return chunks using tokenizer.""" + splits: List[str] = [] + input_ids = tokenizer.encode(text) + start_idx = 0 + cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) + chunk_ids = input_ids[start_idx:cur_idx] + while start_idx < len(input_ids): + splits.append(tokenizer.decode(chunk_ids)) + if cur_idx == len(input_ids): + break + start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap + cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids)) + chunk_ids = input_ids[start_idx:cur_idx] + return splits diff --git a/libs/text-splitters/langchain_text_splitters/character.py b/libs/text-splitters/langchain_text_splitters/character.py new file mode 100644 index 00000000000..090f6cc7f67 --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/character.py @@ -0,0 +1,579 @@ +from __future__ import annotations + +import re +from typing import Any, List, Optional + +from langchain_text_splitters.base import Language, TextSplitter + + +class CharacterTextSplitter(TextSplitter): + """Splitting text that looks at characters.""" + + def __init__( + self, separator: str = "\n\n", is_separator_regex: bool = False, **kwargs: Any + ) -> None: + """Create a new TextSplitter.""" + super().__init__(**kwargs) + self._separator = separator + self._is_separator_regex = is_separator_regex + + def split_text(self, text: str) -> List[str]: + """Split incoming text and return chunks.""" + # First we naively split the large input into a bunch of smaller ones. + separator = ( + self._separator if self._is_separator_regex else re.escape(self._separator) + ) + splits = _split_text_with_regex(text, separator, self._keep_separator) + _separator = "" if self._keep_separator else self._separator + return self._merge_splits(splits, _separator) + + +def _split_text_with_regex( + text: str, separator: str, keep_separator: bool +) -> List[str]: + # Now that we have the separator, split the text + if separator: + if keep_separator: + # The parentheses in the pattern keep the delimiters in the result. + _splits = re.split(f"({separator})", text) + splits = [_splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)] + if len(_splits) % 2 == 0: + splits += _splits[-1:] + splits = [_splits[0]] + splits + else: + splits = re.split(separator, text) + else: + splits = list(text) + return [s for s in splits if s != ""] + + +class RecursiveCharacterTextSplitter(TextSplitter): + """Splitting text by recursively look at characters. + + Recursively tries to split by different characters to find one + that works. + """ + + def __init__( + self, + separators: Optional[List[str]] = None, + keep_separator: bool = True, + is_separator_regex: bool = False, + **kwargs: Any, + ) -> None: + """Create a new TextSplitter.""" + super().__init__(keep_separator=keep_separator, **kwargs) + self._separators = separators or ["\n\n", "\n", " ", ""] + self._is_separator_regex = is_separator_regex + + def _split_text(self, text: str, separators: List[str]) -> List[str]: + """Split incoming text and return chunks.""" + final_chunks = [] + # Get appropriate separator to use + separator = separators[-1] + new_separators = [] + for i, _s in enumerate(separators): + _separator = _s if self._is_separator_regex else re.escape(_s) + if _s == "": + separator = _s + break + if re.search(_separator, text): + separator = _s + new_separators = separators[i + 1 :] + break + + _separator = separator if self._is_separator_regex else re.escape(separator) + splits = _split_text_with_regex(text, _separator, self._keep_separator) + + # Now go merging things, recursively splitting longer texts. + _good_splits = [] + _separator = "" if self._keep_separator else separator + for s in splits: + if self._length_function(s) < self._chunk_size: + _good_splits.append(s) + else: + if _good_splits: + merged_text = self._merge_splits(_good_splits, _separator) + final_chunks.extend(merged_text) + _good_splits = [] + if not new_separators: + final_chunks.append(s) + else: + other_info = self._split_text(s, new_separators) + final_chunks.extend(other_info) + if _good_splits: + merged_text = self._merge_splits(_good_splits, _separator) + final_chunks.extend(merged_text) + return final_chunks + + def split_text(self, text: str) -> List[str]: + return self._split_text(text, self._separators) + + @classmethod + def from_language( + cls, language: Language, **kwargs: Any + ) -> RecursiveCharacterTextSplitter: + separators = cls.get_separators_for_language(language) + return cls(separators=separators, is_separator_regex=True, **kwargs) + + @staticmethod + def get_separators_for_language(language: Language) -> List[str]: + if language == Language.CPP: + return [ + # Split along class definitions + "\nclass ", + # Split along function definitions + "\nvoid ", + "\nint ", + "\nfloat ", + "\ndouble ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nwhile ", + "\nswitch ", + "\ncase ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.GO: + return [ + # Split along function definitions + "\nfunc ", + "\nvar ", + "\nconst ", + "\ntype ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nswitch ", + "\ncase ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.JAVA: + return [ + # Split along class definitions + "\nclass ", + # Split along method definitions + "\npublic ", + "\nprotected ", + "\nprivate ", + "\nstatic ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nwhile ", + "\nswitch ", + "\ncase ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.KOTLIN: + return [ + # Split along class definitions + "\nclass ", + # Split along method definitions + "\npublic ", + "\nprotected ", + "\nprivate ", + "\ninternal ", + "\ncompanion ", + "\nfun ", + "\nval ", + "\nvar ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nwhile ", + "\nwhen ", + "\ncase ", + "\nelse ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.JS: + return [ + # Split along function definitions + "\nfunction ", + "\nconst ", + "\nlet ", + "\nvar ", + "\nclass ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nwhile ", + "\nswitch ", + "\ncase ", + "\ndefault ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.TS: + return [ + "\nenum ", + "\ninterface ", + "\nnamespace ", + "\ntype ", + # Split along class definitions + "\nclass ", + # Split along function definitions + "\nfunction ", + "\nconst ", + "\nlet ", + "\nvar ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nwhile ", + "\nswitch ", + "\ncase ", + "\ndefault ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.PHP: + return [ + # Split along function definitions + "\nfunction ", + # Split along class definitions + "\nclass ", + # Split along control flow statements + "\nif ", + "\nforeach ", + "\nwhile ", + "\ndo ", + "\nswitch ", + "\ncase ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.PROTO: + return [ + # Split along message definitions + "\nmessage ", + # Split along service definitions + "\nservice ", + # Split along enum definitions + "\nenum ", + # Split along option definitions + "\noption ", + # Split along import statements + "\nimport ", + # Split along syntax declarations + "\nsyntax ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.PYTHON: + return [ + # First, try to split along class definitions + "\nclass ", + "\ndef ", + "\n\tdef ", + # Now split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.RST: + return [ + # Split along section titles + "\n=+\n", + "\n-+\n", + "\n\\*+\n", + # Split along directive markers + "\n\n.. *\n\n", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.RUBY: + return [ + # Split along method definitions + "\ndef ", + "\nclass ", + # Split along control flow statements + "\nif ", + "\nunless ", + "\nwhile ", + "\nfor ", + "\ndo ", + "\nbegin ", + "\nrescue ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.RUST: + return [ + # Split along function definitions + "\nfn ", + "\nconst ", + "\nlet ", + # Split along control flow statements + "\nif ", + "\nwhile ", + "\nfor ", + "\nloop ", + "\nmatch ", + "\nconst ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.SCALA: + return [ + # Split along class definitions + "\nclass ", + "\nobject ", + # Split along method definitions + "\ndef ", + "\nval ", + "\nvar ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nwhile ", + "\nmatch ", + "\ncase ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.SWIFT: + return [ + # Split along function definitions + "\nfunc ", + # Split along class definitions + "\nclass ", + "\nstruct ", + "\nenum ", + # Split along control flow statements + "\nif ", + "\nfor ", + "\nwhile ", + "\ndo ", + "\nswitch ", + "\ncase ", + # Split by the normal type of lines + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.MARKDOWN: + return [ + # First, try to split along Markdown headings (starting with level 2) + "\n#{1,6} ", + # Note the alternative syntax for headings (below) is not handled here + # Heading level 2 + # --------------- + # End of code block + "```\n", + # Horizontal lines + "\n\\*\\*\\*+\n", + "\n---+\n", + "\n___+\n", + # Note that this splitter doesn't handle horizontal lines defined + # by *three or more* of ***, ---, or ___, but this is not handled + "\n\n", + "\n", + " ", + "", + ] + elif language == Language.LATEX: + return [ + # First, try to split along Latex sections + "\n\\\\chapter{", + "\n\\\\section{", + "\n\\\\subsection{", + "\n\\\\subsubsection{", + # Now split by environments + "\n\\\\begin{enumerate}", + "\n\\\\begin{itemize}", + "\n\\\\begin{description}", + "\n\\\\begin{list}", + "\n\\\\begin{quote}", + "\n\\\\begin{quotation}", + "\n\\\\begin{verse}", + "\n\\\\begin{verbatim}", + # Now split by math environments + "\n\\\begin{align}", + "$$", + "$", + # Now split by the normal type of lines + " ", + "", + ] + elif language == Language.HTML: + return [ + # First, try to split along HTML tags + " List[Document]: + """Combine elements with common metadata into chunks + + Args: + elements: HTML element content with associated identifying info and metadata + """ + aggregated_chunks: List[ElementType] = [] + + for element in elements: + if ( + aggregated_chunks + and aggregated_chunks[-1]["metadata"] == element["metadata"] + ): + # If the last element in the aggregated list + # has the same metadata as the current element, + # append the current content to the last element's content + aggregated_chunks[-1]["content"] += " \n" + element["content"] + else: + # Otherwise, append the current element to the aggregated list + aggregated_chunks.append(element) + + return [ + Document(page_content=chunk["content"], metadata=chunk["metadata"]) + for chunk in aggregated_chunks + ] + + def split_text_from_url(self, url: str) -> List[Document]: + """Split HTML from web URL + + Args: + url: web URL + """ + r = requests.get(url) + return self.split_text_from_file(BytesIO(r.content)) + + def split_text(self, text: str) -> List[Document]: + """Split HTML text string + + Args: + text: HTML text + """ + return self.split_text_from_file(StringIO(text)) + + def split_text_from_file(self, file: Any) -> List[Document]: + """Split HTML file + + Args: + file: HTML file + """ + try: + from lxml import etree + except ImportError as e: + raise ImportError( + "Unable to import lxml, please install with `pip install lxml`." + ) from e + # use lxml library to parse html document and return xml ElementTree + # Explicitly encoding in utf-8 allows non-English + # html files to be processed without garbled characters + parser = etree.HTMLParser(encoding="utf-8") + tree = etree.parse(file, parser) + + # document transformation for "structure-aware" chunking is handled with xsl. + # see comments in html_chunks_with_headers.xslt for more detailed information. + xslt_path = pathlib.Path(__file__).parent / "xsl/html_chunks_with_headers.xslt" + xslt_tree = etree.parse(xslt_path) + transform = etree.XSLT(xslt_tree) + result = transform(tree) + result_dom = etree.fromstring(str(result)) + + # create filter and mapping for header metadata + header_filter = [header[0] for header in self.headers_to_split_on] + header_mapping = dict(self.headers_to_split_on) + + # map xhtml namespace prefix + ns_map = {"h": "http://www.w3.org/1999/xhtml"} + + # build list of elements from DOM + elements = [] + for element in result_dom.findall("*//*", ns_map): + if element.findall("*[@class='headers']") or element.findall( + "*[@class='chunk']" + ): + elements.append( + ElementType( + url=file, + xpath="".join( + [ + node.text or "" + for node in element.findall("*[@class='xpath']", ns_map) + ] + ), + content="".join( + [ + node.text or "" + for node in element.findall("*[@class='chunk']", ns_map) + ] + ), + metadata={ + # Add text of specified headers to metadata using header + # mapping. + header_mapping[node.tag]: node.text or "" + for node in filter( + lambda x: x.tag in header_filter, + element.findall("*[@class='headers']/*", ns_map), + ) + }, + ) + ) + + if not self.return_each_element: + return self.aggregate_elements_to_chunks(elements) + else: + return [ + Document(page_content=chunk["content"], metadata=chunk["metadata"]) + for chunk in elements + ] diff --git a/libs/text-splitters/langchain_text_splitters/json.py b/libs/text-splitters/langchain_text_splitters/json.py new file mode 100644 index 00000000000..8e5f128161c --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/json.py @@ -0,0 +1,120 @@ +from __future__ import annotations + +import copy +import json +from typing import Any, Dict, List, Optional + +from langchain_core.documents import Document + + +class RecursiveJsonSplitter: + def __init__( + self, max_chunk_size: int = 2000, min_chunk_size: Optional[int] = None + ): + super().__init__() + self.max_chunk_size = max_chunk_size + self.min_chunk_size = ( + min_chunk_size + if min_chunk_size is not None + else max(max_chunk_size - 200, 50) + ) + + @staticmethod + def _json_size(data: Dict) -> int: + """Calculate the size of the serialized JSON object.""" + return len(json.dumps(data)) + + @staticmethod + def _set_nested_dict(d: Dict, path: List[str], value: Any) -> None: + """Set a value in a nested dictionary based on the given path.""" + for key in path[:-1]: + d = d.setdefault(key, {}) + d[path[-1]] = value + + def _list_to_dict_preprocessing(self, data: Any) -> Any: + if isinstance(data, dict): + # Process each key-value pair in the dictionary + return {k: self._list_to_dict_preprocessing(v) for k, v in data.items()} + elif isinstance(data, list): + # Convert the list to a dictionary with index-based keys + return { + str(i): self._list_to_dict_preprocessing(item) + for i, item in enumerate(data) + } + else: + # Base case: the item is neither a dict nor a list, so return it unchanged + return data + + def _json_split( + self, + data: Dict[str, Any], + current_path: List[str] = [], + chunks: List[Dict] = [{}], + ) -> List[Dict]: + """ + Split json into maximum size dictionaries while preserving structure. + """ + if isinstance(data, dict): + for key, value in data.items(): + new_path = current_path + [key] + chunk_size = self._json_size(chunks[-1]) + size = self._json_size({key: value}) + remaining = self.max_chunk_size - chunk_size + + if size < remaining: + # Add item to current chunk + self._set_nested_dict(chunks[-1], new_path, value) + else: + if chunk_size >= self.min_chunk_size: + # Chunk is big enough, start a new chunk + chunks.append({}) + + # Iterate + self._json_split(value, new_path, chunks) + else: + # handle single item + self._set_nested_dict(chunks[-1], current_path, data) + return chunks + + def split_json( + self, + json_data: Dict[str, Any], + convert_lists: bool = False, + ) -> List[Dict]: + """Splits JSON into a list of JSON chunks""" + + if convert_lists: + chunks = self._json_split(self._list_to_dict_preprocessing(json_data)) + else: + chunks = self._json_split(json_data) + + # Remove the last chunk if it's empty + if not chunks[-1]: + chunks.pop() + return chunks + + def split_text( + self, json_data: Dict[str, Any], convert_lists: bool = False + ) -> List[str]: + """Splits JSON into a list of JSON formatted strings""" + + chunks = self.split_json(json_data=json_data, convert_lists=convert_lists) + + # Convert to string + return [json.dumps(chunk) for chunk in chunks] + + def create_documents( + self, + texts: List[Dict], + convert_lists: bool = False, + metadatas: Optional[List[dict]] = None, + ) -> List[Document]: + """Create documents from a list of json objects (Dict).""" + _metadatas = metadatas or [{}] * len(texts) + documents = [] + for i, text in enumerate(texts): + for chunk in self.split_text(json_data=text, convert_lists=convert_lists): + metadata = copy.deepcopy(_metadatas[i]) + new_doc = Document(page_content=chunk, metadata=metadata) + documents.append(new_doc) + return documents diff --git a/libs/text-splitters/langchain_text_splitters/konlpy.py b/libs/text-splitters/langchain_text_splitters/konlpy.py new file mode 100644 index 00000000000..692f9d48687 --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/konlpy.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +from typing import Any, List + +from langchain_text_splitters.base import TextSplitter + + +class KonlpyTextSplitter(TextSplitter): + """Splitting text using Konlpy package. + + It is good for splitting Korean text. + """ + + def __init__( + self, + separator: str = "\n\n", + **kwargs: Any, + ) -> None: + """Initialize the Konlpy text splitter.""" + super().__init__(**kwargs) + self._separator = separator + try: + from konlpy.tag import Kkma + except ImportError: + raise ImportError( + """ + Konlpy is not installed, please install it with + `pip install konlpy` + """ + ) + self.kkma = Kkma() + + def split_text(self, text: str) -> List[str]: + """Split incoming text and return chunks.""" + splits = self.kkma.sentences(text) + return self._merge_splits(splits, self._separator) diff --git a/libs/text-splitters/langchain_text_splitters/latex.py b/libs/text-splitters/langchain_text_splitters/latex.py new file mode 100644 index 00000000000..55d47f7851e --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/latex.py @@ -0,0 +1,15 @@ +from __future__ import annotations + +from typing import Any + +from langchain_text_splitters.base import Language +from langchain_text_splitters.character import RecursiveCharacterTextSplitter + + +class LatexTextSplitter(RecursiveCharacterTextSplitter): + """Attempts to split the text along Latex-formatted layout elements.""" + + def __init__(self, **kwargs: Any) -> None: + """Initialize a LatexTextSplitter.""" + separators = self.get_separators_for_language(Language.LATEX) + super().__init__(separators=separators, **kwargs) diff --git a/libs/text-splitters/langchain_text_splitters/markdown.py b/libs/text-splitters/langchain_text_splitters/markdown.py new file mode 100644 index 00000000000..fb9d16dcf25 --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/markdown.py @@ -0,0 +1,221 @@ +from __future__ import annotations + +from typing import Any, Dict, List, Tuple, TypedDict + +from langchain_core.documents import Document + +from langchain_text_splitters.base import Language +from langchain_text_splitters.character import RecursiveCharacterTextSplitter + + +class MarkdownTextSplitter(RecursiveCharacterTextSplitter): + """Attempts to split the text along Markdown-formatted headings.""" + + def __init__(self, **kwargs: Any) -> None: + """Initialize a MarkdownTextSplitter.""" + separators = self.get_separators_for_language(Language.MARKDOWN) + super().__init__(separators=separators, **kwargs) + + +class MarkdownHeaderTextSplitter: + """Splitting markdown files based on specified headers.""" + + def __init__( + self, + headers_to_split_on: List[Tuple[str, str]], + return_each_line: bool = False, + strip_headers: bool = True, + ): + """Create a new MarkdownHeaderTextSplitter. + + Args: + headers_to_split_on: Headers we want to track + return_each_line: Return each line w/ associated headers + strip_headers: Strip split headers from the content of the chunk + """ + # Output line-by-line or aggregated into chunks w/ common headers + self.return_each_line = return_each_line + # Given the headers we want to split on, + # (e.g., "#, ##, etc") order by length + self.headers_to_split_on = sorted( + headers_to_split_on, key=lambda split: len(split[0]), reverse=True + ) + # Strip headers split headers from the content of the chunk + self.strip_headers = strip_headers + + def aggregate_lines_to_chunks(self, lines: List[LineType]) -> List[Document]: + """Combine lines with common metadata into chunks + Args: + lines: Line of text / associated header metadata + """ + aggregated_chunks: List[LineType] = [] + + for line in lines: + if ( + aggregated_chunks + and aggregated_chunks[-1]["metadata"] == line["metadata"] + ): + # If the last line in the aggregated list + # has the same metadata as the current line, + # append the current content to the last lines's content + aggregated_chunks[-1]["content"] += " \n" + line["content"] + elif ( + aggregated_chunks + and aggregated_chunks[-1]["metadata"] != line["metadata"] + # may be issues if other metadata is present + and len(aggregated_chunks[-1]["metadata"]) < len(line["metadata"]) + and aggregated_chunks[-1]["content"].split("\n")[-1][0] == "#" + and not self.strip_headers + ): + # If the last line in the aggregated list + # has different metadata as the current line, + # and has shallower header level than the current line, + # and the last line is a header, + # and we are not stripping headers, + # append the current content to the last line's content + aggregated_chunks[-1]["content"] += " \n" + line["content"] + # and update the last line's metadata + aggregated_chunks[-1]["metadata"] = line["metadata"] + else: + # Otherwise, append the current line to the aggregated list + aggregated_chunks.append(line) + + return [ + Document(page_content=chunk["content"], metadata=chunk["metadata"]) + for chunk in aggregated_chunks + ] + + def split_text(self, text: str) -> List[Document]: + """Split markdown file + Args: + text: Markdown file""" + + # Split the input text by newline character ("\n"). + lines = text.split("\n") + # Final output + lines_with_metadata: List[LineType] = [] + # Content and metadata of the chunk currently being processed + current_content: List[str] = [] + current_metadata: Dict[str, str] = {} + # Keep track of the nested header structure + # header_stack: List[Dict[str, Union[int, str]]] = [] + header_stack: List[HeaderType] = [] + initial_metadata: Dict[str, str] = {} + + in_code_block = False + opening_fence = "" + + for line in lines: + stripped_line = line.strip() + + if not in_code_block: + # Exclude inline code spans + if stripped_line.startswith("```") and stripped_line.count("```") == 1: + in_code_block = True + opening_fence = "```" + elif stripped_line.startswith("~~~"): + in_code_block = True + opening_fence = "~~~" + else: + if stripped_line.startswith(opening_fence): + in_code_block = False + opening_fence = "" + + if in_code_block: + current_content.append(stripped_line) + continue + + # Check each line against each of the header types (e.g., #, ##) + for sep, name in self.headers_to_split_on: + # Check if line starts with a header that we intend to split on + if stripped_line.startswith(sep) and ( + # Header with no text OR header is followed by space + # Both are valid conditions that sep is being used a header + len(stripped_line) == len(sep) or stripped_line[len(sep)] == " " + ): + # Ensure we are tracking the header as metadata + if name is not None: + # Get the current header level + current_header_level = sep.count("#") + + # Pop out headers of lower or same level from the stack + while ( + header_stack + and header_stack[-1]["level"] >= current_header_level + ): + # We have encountered a new header + # at the same or higher level + popped_header = header_stack.pop() + # Clear the metadata for the + # popped header in initial_metadata + if popped_header["name"] in initial_metadata: + initial_metadata.pop(popped_header["name"]) + + # Push the current header to the stack + header: HeaderType = { + "level": current_header_level, + "name": name, + "data": stripped_line[len(sep) :].strip(), + } + header_stack.append(header) + # Update initial_metadata with the current header + initial_metadata[name] = header["data"] + + # Add the previous line to the lines_with_metadata + # only if current_content is not empty + if current_content: + lines_with_metadata.append( + { + "content": "\n".join(current_content), + "metadata": current_metadata.copy(), + } + ) + current_content.clear() + + if not self.strip_headers: + current_content.append(stripped_line) + + break + else: + if stripped_line: + current_content.append(stripped_line) + elif current_content: + lines_with_metadata.append( + { + "content": "\n".join(current_content), + "metadata": current_metadata.copy(), + } + ) + current_content.clear() + + current_metadata = initial_metadata.copy() + + if current_content: + lines_with_metadata.append( + {"content": "\n".join(current_content), "metadata": current_metadata} + ) + + # lines_with_metadata has each line with associated header metadata + # aggregate these into chunks based on common metadata + if not self.return_each_line: + return self.aggregate_lines_to_chunks(lines_with_metadata) + else: + return [ + Document(page_content=chunk["content"], metadata=chunk["metadata"]) + for chunk in lines_with_metadata + ] + + +class LineType(TypedDict): + """Line type as typed dict.""" + + metadata: Dict[str, str] + content: str + + +class HeaderType(TypedDict): + """Header type as typed dict.""" + + level: int + name: str + data: str diff --git a/libs/text-splitters/langchain_text_splitters/nltk.py b/libs/text-splitters/langchain_text_splitters/nltk.py new file mode 100644 index 00000000000..4a38b1d419d --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/nltk.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +from typing import Any, List + +from langchain_text_splitters.base import TextSplitter + + +class NLTKTextSplitter(TextSplitter): + """Splitting text using NLTK package.""" + + def __init__( + self, separator: str = "\n\n", language: str = "english", **kwargs: Any + ) -> None: + """Initialize the NLTK splitter.""" + super().__init__(**kwargs) + try: + from nltk.tokenize import sent_tokenize + + self._tokenizer = sent_tokenize + except ImportError: + raise ImportError( + "NLTK is not installed, please install it with `pip install nltk`." + ) + self._separator = separator + self._language = language + + def split_text(self, text: str) -> List[str]: + """Split incoming text and return chunks.""" + # First we naively split the large input into a bunch of smaller ones. + splits = self._tokenizer(text, language=self._language) + return self._merge_splits(splits, self._separator) diff --git a/libs/text-splitters/langchain_text_splitters/py.typed b/libs/text-splitters/langchain_text_splitters/py.typed new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/text-splitters/langchain_text_splitters/python.py b/libs/text-splitters/langchain_text_splitters/python.py new file mode 100644 index 00000000000..ffd4a10f62e --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/python.py @@ -0,0 +1,15 @@ +from __future__ import annotations + +from typing import Any + +from langchain_text_splitters.base import Language +from langchain_text_splitters.character import RecursiveCharacterTextSplitter + + +class PythonCodeTextSplitter(RecursiveCharacterTextSplitter): + """Attempts to split the text along Python syntax.""" + + def __init__(self, **kwargs: Any) -> None: + """Initialize a PythonCodeTextSplitter.""" + separators = self.get_separators_for_language(Language.PYTHON) + super().__init__(separators=separators, **kwargs) diff --git a/libs/text-splitters/langchain_text_splitters/sentence_transformers.py b/libs/text-splitters/langchain_text_splitters/sentence_transformers.py new file mode 100644 index 00000000000..beb314d810d --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/sentence_transformers.py @@ -0,0 +1,77 @@ +from __future__ import annotations + +from typing import Any, List, Optional, cast + +from langchain_text_splitters.base import TextSplitter, Tokenizer, split_text_on_tokens + + +class SentenceTransformersTokenTextSplitter(TextSplitter): + """Splitting text to tokens using sentence model tokenizer.""" + + def __init__( + self, + chunk_overlap: int = 50, + model_name: str = "sentence-transformers/all-mpnet-base-v2", + tokens_per_chunk: Optional[int] = None, + **kwargs: Any, + ) -> None: + """Create a new TextSplitter.""" + super().__init__(**kwargs, chunk_overlap=chunk_overlap) + + try: + from sentence_transformers import SentenceTransformer + except ImportError: + raise ImportError( + "Could not import sentence_transformer python package. " + "This is needed in order to for SentenceTransformersTokenTextSplitter. " + "Please install it with `pip install sentence-transformers`." + ) + + self.model_name = model_name + self._model = SentenceTransformer(self.model_name) + self.tokenizer = self._model.tokenizer + self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk) + + def _initialize_chunk_configuration( + self, *, tokens_per_chunk: Optional[int] + ) -> None: + self.maximum_tokens_per_chunk = cast(int, self._model.max_seq_length) + + if tokens_per_chunk is None: + self.tokens_per_chunk = self.maximum_tokens_per_chunk + else: + self.tokens_per_chunk = tokens_per_chunk + + if self.tokens_per_chunk > self.maximum_tokens_per_chunk: + raise ValueError( + f"The token limit of the models '{self.model_name}'" + f" is: {self.maximum_tokens_per_chunk}." + f" Argument tokens_per_chunk={self.tokens_per_chunk}" + f" > maximum token limit." + ) + + def split_text(self, text: str) -> List[str]: + def encode_strip_start_and_stop_token_ids(text: str) -> List[int]: + return self._encode(text)[1:-1] + + tokenizer = Tokenizer( + chunk_overlap=self._chunk_overlap, + tokens_per_chunk=self.tokens_per_chunk, + decode=self.tokenizer.decode, + encode=encode_strip_start_and_stop_token_ids, + ) + + return split_text_on_tokens(text=text, tokenizer=tokenizer) + + def count_tokens(self, *, text: str) -> int: + return len(self._encode(text)) + + _max_length_equal_32_bit_integer: int = 2**32 + + def _encode(self, text: str) -> List[int]: + token_ids_with_start_and_end_token_ids = self.tokenizer.encode( + text, + max_length=self._max_length_equal_32_bit_integer, + truncation="do_not_truncate", + ) + return token_ids_with_start_and_end_token_ids diff --git a/libs/text-splitters/langchain_text_splitters/spacy.py b/libs/text-splitters/langchain_text_splitters/spacy.py new file mode 100644 index 00000000000..d83aea9a001 --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/spacy.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +from typing import Any, List + +from langchain_text_splitters.base import TextSplitter + + +class SpacyTextSplitter(TextSplitter): + """Splitting text using Spacy package. + + + Per default, Spacy's `en_core_web_sm` model is used and + its default max_length is 1000000 (it is the length of maximum character + this model takes which can be increased for large files). For a faster, but + potentially less accurate splitting, you can use `pipeline='sentencizer'`. + """ + + def __init__( + self, + separator: str = "\n\n", + pipeline: str = "en_core_web_sm", + max_length: int = 1_000_000, + **kwargs: Any, + ) -> None: + """Initialize the spacy text splitter.""" + super().__init__(**kwargs) + self._tokenizer = _make_spacy_pipeline_for_splitting( + pipeline, max_length=max_length + ) + self._separator = separator + + def split_text(self, text: str) -> List[str]: + """Split incoming text and return chunks.""" + splits = (s.text for s in self._tokenizer(text).sents) + return self._merge_splits(splits, self._separator) + + +def _make_spacy_pipeline_for_splitting( + pipeline: str, *, max_length: int = 1_000_000 +) -> Any: # avoid importing spacy + try: + import spacy + except ImportError: + raise ImportError( + "Spacy is not installed, please install it with `pip install spacy`." + ) + if pipeline == "sentencizer": + from spacy.lang.en import English + + sentencizer: Any = English() + sentencizer.add_pipe("sentencizer") + else: + sentencizer = spacy.load(pipeline, exclude=["ner", "tagger"]) + sentencizer.max_length = max_length + return sentencizer diff --git a/libs/text-splitters/langchain_text_splitters/xsl/html_chunks_with_headers.xslt b/libs/text-splitters/langchain_text_splitters/xsl/html_chunks_with_headers.xslt new file mode 100644 index 00000000000..285edfe892d --- /dev/null +++ b/libs/text-splitters/langchain_text_splitters/xsl/html_chunks_with_headers.xslt @@ -0,0 +1,199 @@ + + + + + div|p|blockquote|ol|ul + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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(>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[extras] +extended-testing = ["lxml"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<4.0" +content-hash = "2091dd8bede19182cda2c2d8a7b21977da03e5636e8228b9dc652f9f45ff46ee" diff --git a/libs/text-splitters/pyproject.toml b/libs/text-splitters/pyproject.toml new file mode 100644 index 00000000000..5544d692f21 --- /dev/null +++ b/libs/text-splitters/pyproject.toml @@ -0,0 +1,104 @@ +[tool.poetry] +name = "langchain-text-splitters" +version = "0.0.1" +description = "LangChain text splitting utilities" +authors = [] +license = "MIT" +readme = "README.md" +repository = "https://github.com/langchain-ai/langchain" + + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain-core = "^0.1.28" +lxml = {version = "^5.1.0", optional = true} + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" +langchain-core = {path = "../core", develop = true} + +[tool.poetry.group.typing] +optional = true + +[tool.poetry.group.typing.dependencies] +mypy = "^1" +lxml-stubs = "^0.5.1" +types-requests = "^2.31.0.20240218" +tiktoken = "^0.6.0" +spacy = "^3.7.4" + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +jupyter = "^1.0.0" +langchain-core = {path = "../core", develop = true} + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +# The only dependencies that should be added are +# dependencies used for running tests (e.g., pytest, freezegun, response). +# Any dependencies that do not meet that criteria will be removed. +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +pytest-profiling = "^1.7.0" +langchain-core = {path = "../core", develop = true} + + +[tool.poetry.group.test_integration] +optional = true +dependencies = {} + +[tool.poetry.extras] +extended_testing = [ + "lxml", +] + +[tool.ruff.lint] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[[tool.mypy.overrides]] +module = ["transformers", "sentence_transformers", "nltk.tokenize", "konlpy.tag"] +ignore_missing_imports = "True" + +[tool.coverage.run] +omit = ["tests/*", ] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +addopts = "--strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" + diff --git a/libs/text-splitters/scripts/check_imports.py b/libs/text-splitters/scripts/check_imports.py new file mode 100644 index 00000000000..825bea5b487 --- /dev/null +++ b/libs/text-splitters/scripts/check_imports.py @@ -0,0 +1,22 @@ +import random +import string +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + module_name = "".join( + random.choice(string.ascii_letters) for _ in range(20) + ) + SourceFileLoader(module_name, file).load_module() + except Exception: + has_failure = True + print(file) # noqa: T201 + traceback.print_exc() + print() # noqa: T201 + + sys.exit(1 if has_failure else 0) diff --git a/libs/text-splitters/scripts/check_pydantic.sh b/libs/text-splitters/scripts/check_pydantic.sh new file mode 100755 index 00000000000..06b5bb81ae2 --- /dev/null +++ b/libs/text-splitters/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/text-splitters/scripts/lint_imports.sh b/libs/text-splitters/scripts/lint_imports.sh new file mode 100755 index 00000000000..90d21f7295a --- /dev/null +++ b/libs/text-splitters/scripts/lint_imports.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/text-splitters/tests/__init__.py b/libs/text-splitters/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/text-splitters/tests/integration_tests/__init__.py b/libs/text-splitters/tests/integration_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/text-splitters/tests/integration_tests/test_compile.py b/libs/text-splitters/tests/integration_tests/test_compile.py new file mode 100644 index 00000000000..33ecccdfa0f --- /dev/null +++ b/libs/text-splitters/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/langchain/tests/integration_tests/test_nlp_text_splitters.py b/libs/text-splitters/tests/integration_tests/test_nlp_text_splitters.py similarity index 91% rename from libs/langchain/tests/integration_tests/test_nlp_text_splitters.py rename to libs/text-splitters/tests/integration_tests/test_nlp_text_splitters.py index 0f2809ede47..9886cbe80e0 100644 --- a/libs/langchain/tests/integration_tests/test_nlp_text_splitters.py +++ b/libs/text-splitters/tests/integration_tests/test_nlp_text_splitters.py @@ -1,7 +1,8 @@ """Test text splitting functionality using NLTK and Spacy based sentence splitters.""" import pytest -from langchain.text_splitter import NLTKTextSplitter, SpacyTextSplitter +from langchain_text_splitters.nltk import NLTKTextSplitter +from langchain_text_splitters.spacy import SpacyTextSplitter def test_nltk_text_splitting_args() -> None: diff --git a/libs/langchain/tests/integration_tests/test_text_splitter.py b/libs/text-splitters/tests/integration_tests/test_text_splitter.py similarity index 95% rename from libs/langchain/tests/integration_tests/test_text_splitter.py rename to libs/text-splitters/tests/integration_tests/test_text_splitter.py index e27108f9887..c88c5df6df1 100644 --- a/libs/langchain/tests/integration_tests/test_text_splitter.py +++ b/libs/text-splitters/tests/integration_tests/test_text_splitter.py @@ -2,11 +2,13 @@ import pytest -from langchain.text_splitter import ( - CharacterTextSplitter, - SentenceTransformersTokenTextSplitter, +from langchain_text_splitters import ( TokenTextSplitter, ) +from langchain_text_splitters.character import CharacterTextSplitter +from langchain_text_splitters.sentence_transformers import ( + SentenceTransformersTokenTextSplitter, +) def test_huggingface_type_check() -> None: diff --git a/libs/text-splitters/tests/unit_tests/__init__.py b/libs/text-splitters/tests/unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/text-splitters/tests/unit_tests/conftest.py b/libs/text-splitters/tests/unit_tests/conftest.py new file mode 100644 index 00000000000..f1746902fce --- /dev/null +++ b/libs/text-splitters/tests/unit_tests/conftest.py @@ -0,0 +1,87 @@ +"""Configuration for unit tests.""" +from importlib import util +from typing import Dict, Sequence + +import pytest +from pytest import Config, Function, Parser + + +def pytest_addoption(parser: Parser) -> None: + """Add custom command line options to pytest.""" + parser.addoption( + "--only-extended", + action="store_true", + help="Only run extended tests. Does not allow skipping any extended tests.", + ) + parser.addoption( + "--only-core", + action="store_true", + help="Only run core tests. Never runs any extended tests.", + ) + + +def pytest_collection_modifyitems(config: Config, items: Sequence[Function]) -> None: + """Add implementations for handling custom markers. + + At the moment, this adds support for a custom `requires` marker. + + The `requires` marker is used to denote tests that require one or more packages + to be installed to run. If the package is not installed, the test is skipped. + + The `requires` marker syntax is: + + .. code-block:: python + + @pytest.mark.requires("package1", "package2") + def test_something(): + ... + """ + # Mapping from the name of a package to whether it is installed or not. + # Used to avoid repeated calls to `util.find_spec` + required_pkgs_info: Dict[str, bool] = {} + + only_extended = config.getoption("--only-extended") or False + only_core = config.getoption("--only-core") or False + + if only_extended and only_core: + raise ValueError("Cannot specify both `--only-extended` and `--only-core`.") + + for item in items: + requires_marker = item.get_closest_marker("requires") + if requires_marker is not None: + if only_core: + item.add_marker(pytest.mark.skip(reason="Skipping not a core test.")) + continue + + # Iterate through the list of required packages + required_pkgs = requires_marker.args + for pkg in required_pkgs: + # If we haven't yet checked whether the pkg is installed + # let's check it and store the result. + if pkg not in required_pkgs_info: + try: + installed = util.find_spec(pkg) is not None + except Exception: + installed = False + required_pkgs_info[pkg] = installed + + if not required_pkgs_info[pkg]: + if only_extended: + pytest.fail( + f"Package `{pkg}` is not installed but is required for " + f"extended tests. Please install the given package and " + f"try again.", + ) + + else: + # If the package is not installed, we immediately break + # and mark the test as skipped. + item.add_marker( + pytest.mark.skip(reason=f"Requires pkg: `{pkg}`") + ) + break + else: + if only_extended: + item.add_marker( + pytest.mark.skip(reason="Skipping not an extended test.") + ) diff --git a/libs/langchain/tests/unit_tests/test_text_splitter.py b/libs/text-splitters/tests/unit_tests/test_text_splitters.py similarity index 98% rename from libs/langchain/tests/unit_tests/test_text_splitter.py rename to libs/text-splitters/tests/unit_tests/test_text_splitters.py index 4e8a01d4188..057e5d8aa4d 100644 --- a/libs/langchain/tests/unit_tests/test_text_splitter.py +++ b/libs/text-splitters/tests/unit_tests/test_text_splitters.py @@ -8,18 +8,18 @@ from typing import Any, List import pytest from langchain_core.documents import Document -from langchain.text_splitter import ( - CharacterTextSplitter, - HTMLHeaderTextSplitter, +from langchain_text_splitters import ( Language, - MarkdownHeaderTextSplitter, - PythonCodeTextSplitter, RecursiveCharacterTextSplitter, - RecursiveJsonSplitter, TextSplitter, Tokenizer, - split_text_on_tokens, ) +from langchain_text_splitters.base import split_text_on_tokens +from langchain_text_splitters.character import CharacterTextSplitter +from langchain_text_splitters.html import HTMLHeaderTextSplitter +from langchain_text_splitters.json import RecursiveJsonSplitter +from langchain_text_splitters.markdown import MarkdownHeaderTextSplitter +from langchain_text_splitters.python import PythonCodeTextSplitter FAKE_PYTHON_TEXT = """ class Foo: diff --git a/poetry.lock b/poetry.lock index 5255a2fb7fc..014e89c8d3e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1697,7 +1697,7 @@ files = [ [[package]] name = "langchain" -version = "0.1.7" +version = "0.1.9" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1709,8 +1709,9 @@ aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} dataclasses-json = ">= 0.5.7, < 0.7" jsonpatch = "^1.33" -langchain-community = ">=0.0.20,<0.1" -langchain-core = ">=0.1.22,<0.2" +langchain-community = ">=0.0.21,<0.1" +langchain-core = ">=0.1.26,<0.2" +langchain-text-splitters = {path = "../text-splitters", develop = true} langsmith = "^0.1.0" numpy = "^1" pydantic = ">=1,<3" @@ -1740,7 +1741,7 @@ url = "libs/langchain" [[package]] name = "langchain-community" -version = "0.0.20" +version = "0.0.24" description = "Community contributed LangChain integrations." optional = false python-versions = ">=3.8.1,<4.0" @@ -1750,7 +1751,8 @@ develop = true [package.dependencies] aiohttp = "^3.8.3" dataclasses-json = ">= 0.5.7, < 0.7" -langchain-core = ">=0.1.21,<0.2" +langchain-core = ">=0.1.26,<0.2" +langchain-text-splitters = {path = "../text-splitters", develop = true} langsmith = "^0.1.0" numpy = "^1" PyYAML = ">=5.3" @@ -1768,7 +1770,7 @@ url = "libs/community" [[package]] name = "langchain-core" -version = "0.1.23" +version = "0.1.28" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1794,7 +1796,7 @@ url = "libs/core" [[package]] name = "langchain-experimental" -version = "0.0.51" +version = "0.0.53" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1802,8 +1804,8 @@ files = [] develop = true [package.dependencies] -langchain = "^0.1.5" -langchain-core = "^0.1.16" +langchain = "^0.1.8" +langchain-core = "^0.1.27" [package.extras] extended-testing = ["faker (>=19.3.1,<20.0.0)", "jinja2 (>=3,<4)", "pandas (>=2.0.1,<3.0.0)", "presidio-analyzer (>=2.2.352,<3.0.0)", "presidio-anonymizer (>=2.2.352,<3.0.0)", "sentence-transformers (>=2,<3)", "tabulate (>=0.9.0,<0.10.0)", "vowpal-wabbit-next (==0.6.0)"] @@ -1814,7 +1816,7 @@ url = "libs/experimental" [[package]] name = "langchain-openai" -version = "0.0.6" +version = "0.0.8" description = "An integration package connecting OpenAI and LangChain" optional = false python-versions = ">=3.8.1,<4.0" @@ -1822,8 +1824,7 @@ files = [] develop = true [package.dependencies] -langchain-core = "^0.1.16" -numpy = "^1" +langchain-core = "^0.1.27" openai = "^1.10.0" tiktoken = ">=0.5.2,<1" @@ -1831,6 +1832,25 @@ tiktoken = ">=0.5.2,<1" type = "directory" url = "libs/partners/openai" +[[package]] +name = "langchain-text-splitters" +version = "0.0.1" +description = "LangChain text splitting utilities" +optional = false +python-versions = ">=3.8.1,<4.0" +files = [] +develop = true + +[package.dependencies] +langchain-core = "^0.1.28" + +[package.extras] +extended-testing = [] + +[package.source] +type = "directory" +url = "libs/text-splitters" + [[package]] name = "langsmith" version = "0.1.5" @@ -4297,4 +4317,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "eb6dcaa7c3a5204dea3a9783acd5e39af28562ba4a783e89a082bb7c0cda8541" +content-hash = "801d7e68178472d1086f428b5944899fb61db3ba6e415b5231413237754d7879" diff --git a/pyproject.toml b/pyproject.toml index 3ccb4610db6..5dcdcd50f61 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,7 @@ nbdoc = "^0.0.82" [tool.poetry.group.lint.dependencies] ruff = "^0.1.5" langchain-core = { path = "libs/core/", develop = true } +langchain-text-splitters = { path = "libs/text-splitters", develop = true } langchain-community = { path = "libs/community/", develop = true } langchain = { path = "libs/langchain/", develop = true } langchain-experimental = { path = "libs/experimental/", develop = true } @@ -42,6 +43,7 @@ codespell = "^2.2.0" [tool.poetry.group.dev.dependencies] langchain-core = { path = "libs/core/", develop = true } +langchain-text-splitters = { path = "libs/text-splitters", develop = true } langchain-community = { path = "libs/community/", develop = true } langchain = { path = "libs/langchain/", develop = true } langchain-experimental = { path = "libs/experimental/", develop = true } diff --git a/templates/hyde/hyde/chain.py b/templates/hyde/hyde/chain.py index 27f8089725a..537a4d4501f 100644 --- a/templates/hyde/hyde/chain.py +++ b/templates/hyde/hyde/chain.py @@ -17,7 +17,7 @@ loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") data = loader.load() # Split -from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_text_splitters import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) all_splits = text_splitter.split_documents(data) diff --git a/templates/mongo-parent-document-retrieval/ingest.py b/templates/mongo-parent-document-retrieval/ingest.py index 7dd34646477..0c8e1bb294c 100644 --- a/templates/mongo-parent-document-retrieval/ingest.py +++ b/templates/mongo-parent-document-retrieval/ingest.py @@ -1,10 +1,10 @@ import os import uuid -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import PyPDFLoader from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import MongoDBAtlasVectorSearch +from langchain_text_splitters import RecursiveCharacterTextSplitter from pymongo import MongoClient PARENT_DOC_ID_KEY = "parent_doc_id" diff --git a/templates/neo4j-advanced-rag/ingest.py b/templates/neo4j-advanced-rag/ingest.py index 142b266e813..6a33c27237b 100644 --- a/templates/neo4j-advanced-rag/ingest.py +++ b/templates/neo4j-advanced-rag/ingest.py @@ -2,13 +2,13 @@ from pathlib import Path from typing import List from langchain.chains.openai_functions import create_structured_output_chain -from langchain.text_splitter import TokenTextSplitter from langchain_community.chat_models import ChatOpenAI from langchain_community.document_loaders import TextLoader from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.graphs import Neo4jGraph from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field +from langchain_text_splitters import TokenTextSplitter from neo4j.exceptions import ClientError txt_path = Path(__file__).parent / "dune.txt" diff --git a/templates/neo4j-parent/ingest.py b/templates/neo4j-parent/ingest.py index 1ac3d6bb42a..4ec90888979 100644 --- a/templates/neo4j-parent/ingest.py +++ b/templates/neo4j-parent/ingest.py @@ -1,10 +1,10 @@ from pathlib import Path -from langchain.text_splitter import TokenTextSplitter from langchain_community.document_loaders import TextLoader from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.graphs import Neo4jGraph from langchain_community.vectorstores import Neo4jVector +from langchain_text_splitters import TokenTextSplitter txt_path = Path(__file__).parent / "dune.txt" diff --git a/templates/neo4j-vector-memory/ingest.py b/templates/neo4j-vector-memory/ingest.py index 5a8be54a565..df4a5f2ae4f 100644 --- a/templates/neo4j-vector-memory/ingest.py +++ b/templates/neo4j-vector-memory/ingest.py @@ -1,9 +1,9 @@ from pathlib import Path -from langchain.text_splitter import TokenTextSplitter from langchain_community.document_loaders import TextLoader from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.vectorstores import Neo4jVector +from langchain_text_splitters import TokenTextSplitter txt_path = Path(__file__).parent / "dune.txt" diff --git a/templates/nvidia-rag-canonical/ingest.py b/templates/nvidia-rag-canonical/ingest.py index 1abdc9ffa60..e4ebd0fc2bc 100644 --- a/templates/nvidia-rag-canonical/ingest.py +++ b/templates/nvidia-rag-canonical/ingest.py @@ -2,9 +2,9 @@ import getpass import os from langchain.document_loaders import PyPDFLoader -from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores.milvus import Milvus from langchain_nvidia_aiplay import NVIDIAEmbeddings +from langchain_text_splitters.character import CharacterTextSplitter if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"): print("Valid NVIDIA_API_KEY already in environment. Delete to reset") # noqa: T201 diff --git a/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py b/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py index 5e7eb24c548..98f1b8fc7b3 100644 --- a/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py +++ b/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py @@ -1,7 +1,6 @@ import getpass import os -from langchain.text_splitter import CharacterTextSplitter from langchain_community.document_loaders import PyPDFLoader from langchain_community.vectorstores import Milvus from langchain_core.output_parsers import StrOutputParser @@ -13,6 +12,7 @@ from langchain_core.runnables import ( RunnablePassthrough, ) from langchain_nvidia_aiplay import ChatNVIDIA, NVIDIAEmbeddings +from langchain_text_splitters.character import CharacterTextSplitter EMBEDDING_MODEL = "nvolveqa_40k" CHAT_MODEL = "llama2_13b" diff --git a/templates/propositional-retrieval/propositional_retrieval/ingest.py b/templates/propositional-retrieval/propositional_retrieval/ingest.py index 83b233acb40..336f8410b7d 100644 --- a/templates/propositional-retrieval/propositional_retrieval/ingest.py +++ b/templates/propositional-retrieval/propositional_retrieval/ingest.py @@ -65,7 +65,7 @@ def create_index( if __name__ == "__main__": # For our example, we'll load docs from the web - from langchain.text_splitter import RecursiveCharacterTextSplitter # noqa + from langchain_text_splitters import RecursiveCharacterTextSplitter # noqa from langchain_community.document_loaders.recursive_url_loader import ( RecursiveUrlLoader, ) # noqa diff --git a/templates/rag-chroma-private/rag_chroma_private/chain.py b/templates/rag-chroma-private/rag_chroma_private/chain.py index 0d8ae155979..378c312686c 100644 --- a/templates/rag-chroma-private/rag_chroma_private/chain.py +++ b/templates/rag-chroma-private/rag_chroma_private/chain.py @@ -1,5 +1,4 @@ # Load -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.chat_models import ChatOllama from langchain_community.document_loaders import WebBaseLoader from langchain_community.embeddings import GPT4AllEmbeddings @@ -8,6 +7,7 @@ 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 RunnableParallel, RunnablePassthrough +from langchain_text_splitters import RecursiveCharacterTextSplitter loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") data = loader.load() diff --git a/templates/rag-chroma/rag_chroma/chain.py b/templates/rag-chroma/rag_chroma/chain.py index 5f714b54614..c920363b5a5 100644 --- a/templates/rag-chroma/rag_chroma/chain.py +++ b/templates/rag-chroma/rag_chroma/chain.py @@ -15,7 +15,7 @@ loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") data = loader.load() # Split -from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_text_splitters import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) all_splits = text_splitter.split_documents(data) diff --git a/templates/rag-codellama-fireworks/rag_codellama_fireworks/chain.py b/templates/rag-codellama-fireworks/rag_codellama_fireworks/chain.py index 8375e486471..4e0d7d5af59 100644 --- a/templates/rag-codellama-fireworks/rag_codellama_fireworks/chain.py +++ b/templates/rag-codellama-fireworks/rag_codellama_fireworks/chain.py @@ -1,7 +1,6 @@ import os from git import Repo -from langchain.text_splitter import Language, RecursiveCharacterTextSplitter from langchain_community.document_loaders.generic import GenericLoader from langchain_community.document_loaders.parsers import LanguageParser from langchain_community.embeddings import GPT4AllEmbeddings @@ -11,6 +10,7 @@ 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 RunnableParallel, RunnablePassthrough +from langchain_text_splitters import Language, RecursiveCharacterTextSplitter # Check API key if os.environ.get("FIREWORKS_API_KEY", None) is None: diff --git a/templates/rag-conversation-zep/ingest.py b/templates/rag-conversation-zep/ingest.py index 86809a7a089..dfbf239f66d 100644 --- a/templates/rag-conversation-zep/ingest.py +++ b/templates/rag-conversation-zep/ingest.py @@ -1,10 +1,10 @@ # Ingest Documents into a Zep Collection import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import WebBaseLoader from langchain_community.embeddings import FakeEmbeddings from langchain_community.vectorstores.zep import CollectionConfig, ZepVectorStore +from langchain_text_splitters import RecursiveCharacterTextSplitter ZEP_API_URL = os.environ.get("ZEP_API_URL", "http://localhost:8000") ZEP_API_KEY = os.environ.get("ZEP_API_KEY", None) diff --git a/templates/rag-conversation/rag_conversation/chain.py b/templates/rag-conversation/rag_conversation/chain.py index 6607e028ec1..d7e43396591 100644 --- a/templates/rag-conversation/rag_conversation/chain.py +++ b/templates/rag-conversation/rag_conversation/chain.py @@ -36,7 +36,7 @@ PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") # data = loader.load() # # Split -# from langchain.text_splitter import RecursiveCharacterTextSplitter +# from langchain_text_splitters import RecursiveCharacterTextSplitter # text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) # all_splits = text_splitter.split_documents(data) diff --git a/templates/rag-elasticsearch/ingest.py b/templates/rag-elasticsearch/ingest.py index e0393c78a4a..ad5b2ae54d6 100644 --- a/templates/rag-elasticsearch/ingest.py +++ b/templates/rag-elasticsearch/ingest.py @@ -1,9 +1,9 @@ import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import JSONLoader from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores.elasticsearch import ElasticsearchStore +from langchain_text_splitters import RecursiveCharacterTextSplitter ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID") ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic") diff --git a/templates/rag-gpt-crawler/rag_gpt_crawler/chain.py b/templates/rag-gpt-crawler/rag_gpt_crawler/chain.py index 3e322446b67..0c312c31780 100644 --- a/templates/rag-gpt-crawler/rag_gpt_crawler/chain.py +++ b/templates/rag-gpt-crawler/rag_gpt_crawler/chain.py @@ -1,7 +1,6 @@ import json from pathlib import Path -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 @@ -10,6 +9,7 @@ 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 RunnableParallel, RunnablePassthrough +from langchain_text_splitters import RecursiveCharacterTextSplitter # Load output from gpt crawler path_to_gptcrawler = Path(__file__).parent.parent / "output.json" diff --git a/templates/rag-momento-vector-index/rag_momento_vector_index/ingest.py b/templates/rag-momento-vector-index/rag_momento_vector_index/ingest.py index ca5eb0dc415..91d044dba23 100644 --- a/templates/rag-momento-vector-index/rag_momento_vector_index/ingest.py +++ b/templates/rag-momento-vector-index/rag_momento_vector_index/ingest.py @@ -1,10 +1,10 @@ ### Ingest code - you may need to run this the first time import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import WebBaseLoader from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import MomentoVectorIndex +from langchain_text_splitters import RecursiveCharacterTextSplitter from momento import ( CredentialProvider, PreviewVectorIndexClient, diff --git a/templates/rag-mongo/ingest.py b/templates/rag-mongo/ingest.py index e396d0d1872..5c018a93836 100644 --- a/templates/rag-mongo/ingest.py +++ b/templates/rag-mongo/ingest.py @@ -1,9 +1,9 @@ import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import PyPDFLoader from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import MongoDBAtlasVectorSearch +from langchain_text_splitters import RecursiveCharacterTextSplitter from pymongo import MongoClient MONGO_URI = os.environ["MONGO_URI"] diff --git a/templates/rag-mongo/rag_mongo/chain.py b/templates/rag-mongo/rag_mongo/chain.py index f125637ba34..39ca44160bb 100644 --- a/templates/rag-mongo/rag_mongo/chain.py +++ b/templates/rag-mongo/rag_mongo/chain.py @@ -1,6 +1,5 @@ import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.chat_models import ChatOpenAI from langchain_community.document_loaders import PyPDFLoader from langchain_community.embeddings import OpenAIEmbeddings @@ -13,6 +12,7 @@ from langchain_core.runnables import ( RunnableParallel, RunnablePassthrough, ) +from langchain_text_splitters import RecursiveCharacterTextSplitter from pymongo import MongoClient # Set DB diff --git a/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py b/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py index 1bc81584532..840a8080426 100644 --- a/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py +++ b/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py @@ -1,5 +1,4 @@ from langchain.retrievers.multi_query import MultiQueryRetriever -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.chat_models import ChatOllama, ChatOpenAI from langchain_community.document_loaders import WebBaseLoader from langchain_community.embeddings import OpenAIEmbeddings @@ -8,6 +7,7 @@ from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate, PromptTemplate from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableParallel, RunnablePassthrough +from langchain_text_splitters import RecursiveCharacterTextSplitter # Load loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") diff --git a/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py index 50e88f2070a..818fc3ec681 100644 --- a/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py +++ b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py @@ -24,7 +24,7 @@ PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") # data = loader.load() # # Split -# from langchain.text_splitter import RecursiveCharacterTextSplitter +# from langchain_text_splitters import RecursiveCharacterTextSplitter # text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) # all_splits = text_splitter.split_documents(data) diff --git a/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py b/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py index 690e538b78b..e073fa71174 100644 --- a/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py +++ b/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py @@ -25,7 +25,7 @@ PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") # data = loader.load() # # Split -# from langchain.text_splitter import RecursiveCharacterTextSplitter +# from langchain_text_splitters import RecursiveCharacterTextSplitter # text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) # all_splits = text_splitter.split_documents(data) diff --git a/templates/rag-pinecone/rag_pinecone/chain.py b/templates/rag-pinecone/rag_pinecone/chain.py index cf30ba29a0e..437228ed42a 100644 --- a/templates/rag-pinecone/rag_pinecone/chain.py +++ b/templates/rag-pinecone/rag_pinecone/chain.py @@ -23,7 +23,7 @@ PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") # data = loader.load() # # Split -# from langchain.text_splitter import RecursiveCharacterTextSplitter +# from langchain_text_splitters import RecursiveCharacterTextSplitter # text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) # all_splits = text_splitter.split_documents(data) diff --git a/templates/rag-redis/ingest.py b/templates/rag-redis/ingest.py index fe7992a3cce..b4963f6dc5d 100644 --- a/templates/rag-redis/ingest.py +++ b/templates/rag-redis/ingest.py @@ -1,9 +1,9 @@ import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import UnstructuredFileLoader from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores import Redis +from langchain_text_splitters import RecursiveCharacterTextSplitter from rag_redis.config import EMBED_MODEL, INDEX_NAME, INDEX_SCHEMA, REDIS_URL diff --git a/templates/rag-self-query/ingest.py b/templates/rag-self-query/ingest.py index a4be4e970cd..7edfed66df9 100644 --- a/templates/rag-self-query/ingest.py +++ b/templates/rag-self-query/ingest.py @@ -1,9 +1,9 @@ import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.document_loaders import JSONLoader from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import ElasticsearchStore +from langchain_text_splitters import RecursiveCharacterTextSplitter ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID") ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic") diff --git a/templates/rag-singlestoredb/rag_singlestoredb/chain.py b/templates/rag-singlestoredb/rag_singlestoredb/chain.py index 0db4635edea..4aca83be1ab 100644 --- a/templates/rag-singlestoredb/rag_singlestoredb/chain.py +++ b/templates/rag-singlestoredb/rag_singlestoredb/chain.py @@ -21,7 +21,7 @@ if os.environ.get("SINGLESTOREDB_URL", None) is None: # data = loader.load() # # Split -# from langchain.text_splitter import RecursiveCharacterTextSplitter +# from langchain_text_splitters import RecursiveCharacterTextSplitter # text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) # all_splits = text_splitter.split_documents(data) diff --git a/templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py b/templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py index 65946fef905..223e76194b3 100644 --- a/templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py +++ b/templates/rag-timescale-conversation/rag_timescale_conversation/load_sample_dataset.py @@ -3,10 +3,10 @@ import tempfile from datetime import datetime, timedelta import requests -from langchain.text_splitter import CharacterTextSplitter from langchain_community.document_loaders import JSONLoader from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.vectorstores.timescalevector import TimescaleVector +from langchain_text_splitters.character import CharacterTextSplitter from timescale_vector import client diff --git a/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py b/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py index 65946fef905..223e76194b3 100644 --- a/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py +++ b/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py @@ -3,10 +3,10 @@ import tempfile from datetime import datetime, timedelta import requests -from langchain.text_splitter import CharacterTextSplitter from langchain_community.document_loaders import JSONLoader from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.vectorstores.timescalevector import TimescaleVector +from langchain_text_splitters.character import CharacterTextSplitter from timescale_vector import client diff --git a/templates/rag-weaviate/rag_weaviate/chain.py b/templates/rag-weaviate/rag_weaviate/chain.py index 93cb412468c..e2e56230b9a 100644 --- a/templates/rag-weaviate/rag_weaviate/chain.py +++ b/templates/rag-weaviate/rag_weaviate/chain.py @@ -1,6 +1,5 @@ import os -from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.chat_models import ChatOpenAI from langchain_community.document_loaders import WebBaseLoader from langchain_community.embeddings import OpenAIEmbeddings @@ -9,6 +8,7 @@ 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 RunnableParallel, RunnablePassthrough +from langchain_text_splitters import RecursiveCharacterTextSplitter if os.environ.get("WEAVIATE_API_KEY", None) is None: raise Exception("Missing `WEAVIATE_API_KEY` environment variable.")