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https://github.com/hwchase17/langchain.git
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docs: document citations in ChatAnthropic (#29401)
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ccurme
GitHub
@@ -315,6 +315,163 @@
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"ai_msg.tool_calls"
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]
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},
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{
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"cell_type": "markdown",
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"id": "301d372f-4dec-43e6-b58c-eee25633e1a6",
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"metadata": {},
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"source": [
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"## Citations\n",
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"\n",
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"Anthropic supports a [citations](https://docs.anthropic.com/en/docs/build-with-claude/citations) feature that lets Claude attach context to its answers based on source documents supplied by the user. When [document content blocks](https://docs.anthropic.com/en/docs/build-with-claude/citations#document-types) with `\"citations\": {\"enabled\": True}` are included in a query, Claude may generate citations in its response.\n",
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"\n",
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"### Simple example\n",
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"\n",
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"In this example we pass a [plain text document](https://docs.anthropic.com/en/docs/build-with-claude/citations#plain-text-documents). In the background, Claude [automatically chunks](https://docs.anthropic.com/en/docs/build-with-claude/citations#plain-text-documents) the input text into sentences, which are used when generating citations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "e5370e6e-5a9a-4546-848b-5f5bf313c3e7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'text': 'Based on the document, ', 'type': 'text'},\n",
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" {'text': 'the grass is green',\n",
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" 'type': 'text',\n",
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" 'citations': [{'type': 'char_location',\n",
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" 'cited_text': 'The grass is green. ',\n",
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" 'document_index': 0,\n",
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" 'document_title': 'My Document',\n",
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" 'start_char_index': 0,\n",
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" 'end_char_index': 20}]},\n",
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" {'text': ', and ', 'type': 'text'},\n",
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" {'text': 'the sky is blue',\n",
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" 'type': 'text',\n",
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" 'citations': [{'type': 'char_location',\n",
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" 'cited_text': 'The sky is blue.',\n",
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" 'document_index': 0,\n",
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" 'document_title': 'My Document',\n",
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" 'start_char_index': 20,\n",
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" 'end_char_index': 36}]},\n",
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" {'text': '.', 'type': 'text'}]"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_anthropic import ChatAnthropic\n",
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"\n",
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"llm = ChatAnthropic(model=\"claude-3-5-haiku-latest\")\n",
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"\n",
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"messages = [\n",
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" {\n",
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" \"role\": \"user\",\n",
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" \"content\": [\n",
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" {\n",
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" \"type\": \"document\",\n",
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" \"source\": {\n",
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" \"type\": \"text\",\n",
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" \"media_type\": \"text/plain\",\n",
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" \"data\": \"The grass is green. The sky is blue.\",\n",
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" },\n",
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" \"title\": \"My Document\",\n",
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" \"context\": \"This is a trustworthy document.\",\n",
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" \"citations\": {\"enabled\": True},\n",
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" },\n",
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" {\"type\": \"text\", \"text\": \"What color is the grass and sky?\"},\n",
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" ],\n",
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" }\n",
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"]\n",
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"response = llm.invoke(messages)\n",
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"response.content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "69956596-0e6c-492b-934d-c08ed3c9de9a",
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"metadata": {},
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"source": [
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"### Using with text splitters\n",
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"\n",
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"Anthropic also lets you specify your own splits using [custom document](https://docs.anthropic.com/en/docs/build-with-claude/citations#custom-content-documents) types. LangChain [text splitters](/docs/concepts/text_splitters/) can be used to generate meaningful splits for this purpose. See the below example, where we split the LangChain README (a markdown document) and pass it to Claude as context:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "04cc2841-7987-47a5-906c-09ea7fa28323",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'text': \"You can find LangChain's tutorials at https://python.langchain.com/docs/tutorials/\\n\\nThe tutorials section is recommended for those looking to build something specific or who prefer a hands-on learning approach. It's considered the best place to get started with LangChain.\",\n",
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" 'type': 'text',\n",
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" 'citations': [{'type': 'content_block_location',\n",
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" 'cited_text': \"[Tutorials](https://python.langchain.com/docs/tutorials/):If you're looking to build something specific orare more of a hands-on learner, check out ourtutorials. This is the best place to get started.\",\n",
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" 'document_index': 0,\n",
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" 'document_title': None,\n",
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" 'start_block_index': 243,\n",
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" 'end_block_index': 248}]}]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import requests\n",
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"from langchain_anthropic import ChatAnthropic\n",
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"from langchain_text_splitters import MarkdownTextSplitter\n",
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"\n",
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"\n",
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"def format_to_anthropic_documents(documents: list[str]):\n",
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" return {\n",
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" \"type\": \"document\",\n",
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" \"source\": {\n",
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" \"type\": \"content\",\n",
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" \"content\": [{\"type\": \"text\", \"text\": document} for document in documents],\n",
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" },\n",
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" \"citations\": {\"enabled\": True},\n",
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" }\n",
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"\n",
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"\n",
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"# Pull readme\n",
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"get_response = requests.get(\n",
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" \"https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md\"\n",
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")\n",
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"readme = get_response.text\n",
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"\n",
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"# Split into chunks\n",
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"splitter = MarkdownTextSplitter(\n",
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" chunk_overlap=0,\n",
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" chunk_size=50,\n",
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")\n",
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"documents = splitter.split_text(readme)\n",
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"\n",
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"# Construct message\n",
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"message = {\n",
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" \"role\": \"user\",\n",
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" \"content\": [\n",
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" format_to_anthropic_documents(documents),\n",
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" {\"type\": \"text\", \"text\": \"Give me a link to LangChain's tutorials.\"},\n",
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" ],\n",
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"}\n",
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"\n",
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"# Query LLM\n",
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"llm = ChatAnthropic(model=\"claude-3-5-haiku-latest\")\n",
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"response = llm.invoke([message])\n",
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"\n",
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"response.content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
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@@ -342,7 +499,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.5"
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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