Use when you need structured source attribution in AI responses — verifiable citations that link claims to specific passages in source documents. Common scenarios - RAG with citation extraction, grounded answers with document references, legal or compliance use cases requiring source proof, building AI that cites its sources, or verifying which document an answer came from. Related - ai-stopping-hallucinations, ai-searching-docs, dspy-retrieval. Also used for dspy.experimental.Citations, dspy.experimental.Document, cite sources in DSPy, structured citations, source attribution, verify which document answer came from, RAG with citations, grounded answers with references, citation extraction, Anthropic Citations API DSPy, cited_text, document_index, adapt_to_native_lm_feature, parse_lm_response citations, streaming citations.
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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
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Use when you need structured source attribution in AI responses — verifiable citations that link claims to specific passages in source documents. Common scenarios - RAG with citation extraction, grounded answers with document references, legal or compliance use cases requiring source proof, building AI that cites its sources, or verifying which document an answer came from. Related - ai-stopping-hallucinations, ai-searching-docs, dspy-retrieval. Also used for dspy.experimental.Citations, dspy.experimental.Document, cite sources in DSPy, structured citations, source attribution, verify which document answer came from, RAG with citations, grounded answers with references, citation extraction, Anthropic Citations API DSPy, cited_text, document_index, adapt_to_native_lm_feature, parse_lm_response citations, streaming citations.
Add Structured Citations to DSPy Outputs
Guide the user through adding structured citations to DSPy outputs so AI answers can be traced back to specific source passages.
What are DSPy Citations
dspy.experimental.Citations provides structured source attribution for LM outputs. Instead of inline quotes, you get machine-readable citation objects that identify exactly which passage from which document supports each claim. Works natively with Anthropic's Citations API and falls back to prompt-based extraction for other providers.
When to use Citations
Use Citations when...
Use something else when...
You need to verify which document supports a claim
Simple RAG where inline quotes are enough
Legal/compliance requires source traceability
Output does not reference source material
Users need clickable references back to source docs
You only have one source document
You want machine-readable citation metadata
Human-readable quotes in text are sufficient
Building fact-checking or grounding verification
The task is creative generation (no sources)
Step 1: Set up Documents
Create Document objects from your source material:
import dspy
from dspy.experimental import Citations, Document
lm = dspy.LM("openai/gpt-4o-mini") # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)
# Create documents from your sources
documents = [
Document(
data="DSPy is a framework for programming language models. It replaces prompting with composable modules that can be optimized.",
title="DSPy Overview",
),
Document(
data="MIPROv2 is the most powerful DSPy optimizer. It jointly optimizes instructions and few-shot demonstrations.",
title="Optimizers Guide",
),
]
Step 2: Build a signature with Citations output
classCitedQA(dspy.Signature):
"""Answer the question using the provided documents. Cite your sources."""
documents: list[Document] = dspy.InputField(desc="source documents")
question: str = dspy.InputField()
answer: str = dspy.OutputField()
citations: Citations = dspy.OutputField(desc="structured citations for claims in the answer")
Step 3: Use with a standard module
qa = dspy.ChainOfThought(CitedQA)
result = qa(
documents=documents,
question="What is the most powerful DSPy optimizer?",
)
print(result.answer)
# "MIPROv2 is the most powerful DSPy optimizer..."for citation in result.citations.citations:
print(f" Cited: '{citation.cited_text}' from document {citation.document_index}")
Step 4: Anthropic native Citations API
Native citations are automatic for Anthropic models. There is no special adapter
configuration -- you just configure an Anthropic LM, declare a Citations output field,
and pass documents: list[Document] as input. DSPy handles the rest.
lm = dspy.LM("anthropic/claude-sonnet-4-5-20250929")
dspy.configure(lm=lm)
# The same CitedQA signature (Citations output + documents input) now uses# Anthropic's built-in citation extraction -- no adapter kwarg needed.
result = qa(
documents=documents,
question="How does DSPy replace prompting?",
)
Native mode advantages:
Citations are extracted by the model during generation (not post-hoc)
cited_text exactly matches source text (character-level accuracy)
document_index reliably maps to the input document list
Step 5: Parse and validate citations
# Each citation has these fieldsfor citation in result.citations.citations:
print(f"Cited text: {citation.cited_text}")
print(f"Document index: {citation.document_index}")
print(f"Start char: {citation.start_char_index}")
print(f"End char: {citation.end_char_index}")
# Validate that cited text exists in the source documentfor citation in result.citations.citations:
source_doc = documents[citation.document_index]
if citation.cited_text in source_doc.data:
print(f"VALID: Citation found in {source_doc.title}")
else:
print(f"INVALID: Citation not found in source")
Step 6: Citations with streaming
Stream answers while accumulating citations:
from dspy.streaming import streamify, StreamListener
qa = dspy.ChainOfThought(CitedQA)
answer_listener = StreamListener(signature_field_name="answer")
streaming_qa = streamify(qa, stream_listeners=[answer_listener])
asyncfor chunk in streaming_qa(documents=documents, question="..."):
ifhasattr(chunk, "answer"):
print(chunk.answer, end="", flush=True)
elifisinstance(chunk, dspy.Prediction):
# Citations are available in the final predictionfor citation in chunk.citations.citations:
print(f"\n[{citation.document_index}] {citation.cited_text}")
Step 7: Non-Anthropic fallback patterns
For providers without native citation support (OpenAI, local models, etc.), the
Citations output field falls back to prompt-based parsing. For maximum control you
can skip the Citations type entirely and use plain-string context with inline markers
-- this is the prompt-based fallback path, not the native Anthropic path:
classCitedAnswer(dspy.Signature):
"""Answer using ONLY information from the provided documents.
For each claim, include [doc_N] inline where N is the document number."""
context: list[str] = dspy.InputField(desc="numbered source documents")
question: str = dspy.InputField()
answer: str = dspy.OutputField(desc="answer with [doc_N] inline citations")
# Number your documents explicitly (plain strings, not Document objects)
numbered_context = [
f"[doc_{i}] {doc.title}: {doc.data}"for i, doc inenumerate(documents)
]
qa = dspy.ChainOfThought(CitedAnswer)
result = qa(context=numbered_context, question="...")
# Parse [doc_N] references from the answer text
Gotchas
Claude omits the Citations type import. You must import from dspy.experimental -- it is not in the main dspy namespace. Use from dspy.experimental import Citations, Document.
Native citations activate automatically for Anthropic models. When you configure an Anthropic LM and declare a Citations output field, DSPy uses Anthropic's native Citations API -- there is no adapt_to_native_lm_feature kwarg or special adapter to set. On non-Anthropic models (OpenAI, local), the same Citations field silently falls back to prompt-based parsing, which may be less accurate.
Claude hardcodes document indices. The document_index in citations maps to the position in the context list. If you reorder documents, indices change. Always use the index to look up the source, do not hardcode.
Citations are experimental. The API is in dspy.experimental and may change between versions. Pin your DSPy version in production.
Claude generates citations without source material. Citations only make sense when you provide context documents. Without context, the model fabricates citation metadata. Always pair Citations with a retrieval step.
Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>
Stopping hallucinations with grounding -- see /ai-stopping-hallucinations
Searching docs for RAG pipelines -- see /ai-searching-docs
Retrieval modules for getting context -- see /dspy-retrieval
Streaming citations progressively -- see /dspy-streaming
Install /ai-do if you do not have it -- it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do
