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340 | class SlideNodeParser(NodeParser):
"""Node parser using the SLIDE based approach using LLMs to improve chunk context."""
chunk_size: int = Field(
default=1200,
description="tokens per base chunk",
)
window_size: int = Field(
default=11,
description="Window size for the sliding window approach. This is the total number chunks to include in the context window, ideall an odd number.",
)
llm_workers: int = Field(
default=1,
description="Number of workers to use for LLM calls. This is only used when using the async version of the parser.",
)
llm: LLM = Field(
description="The LLM model to use for generating local context"
)
token_counter: TokenCounter = Field(
description="Token counter for sentences"
)
sentence_splitter: SentenceSplitterCallable = Field(
default_factory=split_by_sentence_tokenizer,
description="Sentence splitter to use for splitting text into sentences.",
exclude=True,
)
@classmethod
def class_name(cls) -> str:
return "SlideNodeParser"
@classmethod
def from_defaults(
cls,
chunk_size: int = 1200,
window_size: int = 11,
llm_workers: int = 1,
llm: Optional[LLM] = None,
token_counter: Optional[TokenCounter] = None,
sentence_splitter: Optional[Callable[[str], List[str]]] = None,
callback_manager: Optional[CallbackManager] = None,
id_func: Optional[Callable[[int, Document], str]] = None,
) -> "SlideNodeParser":
"""Create instance of the class with default values."""
from llama_index.core import Settings
callback_manager = callback_manager or CallbackManager([])
id_func = id_func or default_id_func
llm = llm or Settings.llm
token_counter = token_counter or TokenCounter()
sentence_splitter = sentence_splitter or split_by_sentence_tokenizer()
return cls(
callback_manager=callback_manager,
id_func=id_func,
chunk_size=chunk_size,
window_size=window_size,
llm_workers=llm_workers,
llm=llm,
token_counter=token_counter,
sentence_splitter=sentence_splitter,
)
@model_validator(mode="after")
def validate_slide_config(self):
# 1) chunk_size ≥ 1
if self.chunk_size < 1:
raise ValueError("`chunk_size` must be greater than or equal to 1.")
# 2) Warn if chunk_size is impractically small
if self.chunk_size < 50:
warnings.warn(
f"chunk_size={self.chunk_size} may be too small for meaningful chunking. "
"This could lead to poor context quality and high LLM call overhead.",
stacklevel=2
)
# 3) window_size ≥ 1
if self.window_size < 1:
raise ValueError("`window_size` must be greater than or equal to 1.")
# 4) Validate LLM context budget: chunk_size × window_size
context_window = getattr(getattr(self.llm, "metadata", None), "context_window", None)
if context_window is not None:
estimated_tokens = self.chunk_size * self.window_size
if estimated_tokens > context_window:
raise ValueError(
f"SLIDE configuration exceeds LLM context window: "
f"{self.chunk_size} × {self.window_size} = {estimated_tokens} tokens, "
f"but the LLM supports only {context_window} tokens."
)
else:
# 5) Warn if context_window not provided
warnings.warn(
"The LLM does not expose `metadata.context_window`. "
"SLIDE cannot validate token usage, which may lead to truncation or generation failures.",
stacklevel=2
)
return self
def _parse_nodes(
self,
nodes: Sequence[BaseNode],
show_progress: bool = False,
) -> List[BaseNode]:
"""Parse document into nodes."""
# Warn if someone set llm_workers > 1 but is using sync parsing
if self.llm_workers != 1:
warnings.warn("llm_workers has no effect when using synchronous parsing. "
"If you want parallel LLM calls, use `aget_nodes_from_documents(...)` "
"with llm_workers > 1.",
stacklevel=2,
)
all_nodes: List[BaseNode] = []
nodes_with_progress = get_tqdm_iterable(nodes, show_progress, "Parsing nodes")
for node in nodes_with_progress:
nodes = self.build_slide_nodes_from_documents([node])
all_nodes.extend(nodes)
return all_nodes
async def _aparse_nodes(
self,
nodes: Sequence[BaseNode],
show_progress: bool = False,
) -> List[BaseNode]:
"""Asynchronous parse document into nodes."""
# If llm_workers is left at 1, no parallelism will occur.
if self.llm_workers == 1:
warnings.warn(
"To parallelize LLM calls in async parsing, initialize with llm_workers > 1.",
stacklevel=2,
)
all_nodes: List[BaseNode] = []
nodes_with_progress = get_tqdm_iterable(nodes, show_progress, "Parsing nodes (async)")
for node in nodes_with_progress:
nodes = await self.abuild_slide_nodes_from_documents(
[node], show_progress
)
all_nodes.extend(nodes)
return all_nodes
def create_individual_chunks(
self,
sentences: List[str]
) -> List[str]:
"""Greedily add sentences to each chunk until we reach the chunk size limit."""
chunks = []
current_chunk = ""
for sentence in sentences:
potential_chunk = (current_chunk + " " + sentence).strip()
if not current_chunk or self.token_counter.get_string_tokens(potential_chunk) <= self.chunk_size:
current_chunk = potential_chunk
else:
chunks.append(current_chunk)
current_chunk = sentence
if current_chunk:
chunks.append(current_chunk)
return chunks
def build_localised_splits(
self,
chunks: List[str],
) -> List[Dict[str, str]]:
"""Generate localized context for each chunk using a sliding window approach."""
half_window = self.window_size // 2
localized_splits = []
for i in range(len(chunks)):
start = max(0, i - half_window)
end = min(len(chunks), i + half_window + 1)
window_chunk = " ".join(chunks[start:end])
# format prompt with current chunk and window chunk
llm_messages = [
ChatMessage(role="system", content=CONTEXT_GENERATION_SYSTEM_PROMPT),
ChatMessage(role="user", content=CONTEXT_GENERATION_USER_PROMPT.format(window_chunk=window_chunk, chunk=chunks[i])),
]
# generate localized context using LLM
localized_context = str(self.llm.chat(messages=llm_messages))
localized_splits.append({
"text": chunks[i],
"context": localized_context,
})
return localized_splits
async def abuild_localised_splits(
self,
chunks: List[str],
show_progress: bool = False,
) -> List[Dict[str, str]]:
"""Async version: batch all LLM calls for each chunk via run_jobs."""
half_window = self.window_size // 2
# prepare one achat() coroutine per chunk
jobs = []
for i, chunk in enumerate(chunks):
start = max(0, i - half_window)
end = min(len(chunks), i + half_window + 1)
window_chunk = " ".join(chunks[start:end])
llm_messages = [
ChatMessage(role="system", content=CONTEXT_GENERATION_SYSTEM_PROMPT),
ChatMessage(role="user", content=CONTEXT_GENERATION_USER_PROMPT.format(window_chunk=window_chunk, chunk=chunk)),
]
jobs.append(self.llm.achat(messages=llm_messages))
# run them up to a maximum of llm_workers at once, get ordered responses
responses = await run_jobs(
jobs=jobs,
workers=self.llm_workers,
show_progress=show_progress,
desc="Generating local contexts",
)
# reassemble into the split format
return [
{"text": chunks[i], "context": str(resp)}
for i, resp in enumerate(responses)
]
def post_process_nodes(
self,
nodes: List[BaseNode],
contexts: List[str],
) -> List[BaseNode]:
"""
Attach slide_context metadata to each node based on the provided contexts.
"""
for node, context in zip(nodes, contexts):
# Preserve any existing metadata, then add our slide context
node.metadata["local_context"] = context
return nodes
def build_slide_nodes_from_documents(
self,
documents: Sequence[Document],
) -> List[BaseNode]:
"""
Build nodes enriched with localized context using a sliding window approach.
This is the primary function of the class.
"""
all_nodes: List[BaseNode] = []
for document in documents:
# Split into sentences and base chunks
doctext = document.get_content()
sentences = self.sentence_splitter(doctext)
chunks = self.create_individual_chunks(sentences)
# build localized splits
splits = self.build_localised_splits(chunks)
texts = [split["text"] for split in splits]
contexts = [split["context"] for split in splits]
# build and annotate nodes
nodes = build_nodes_from_splits(
text_splits=texts,
document=document,
id_func=self.id_func
)
nodes = self.post_process_nodes(nodes, contexts)
all_nodes.extend(nodes)
return all_nodes
async def abuild_slide_nodes_from_documents(
self,
documents: Sequence[Document],
show_progress: bool = False,
) -> List[BaseNode]:
"""
Asynchronously build nodes enriched with localized context using a sliding window approach.
"""
all_nodes: List[BaseNode] = []
for document in documents:
# Split into sentences and base chunks
doctext = document.get_content()
sentences = self.sentence_splitter(doctext)
chunks = self.create_individual_chunks(sentences)
# get localized splits using an async function
splits = await self.abuild_localised_splits(chunks, show_progress)
texts = [s["text"] for s in splits]
contexts = [s["context"] for s in splits]
# build and annotate nodes
nodes = build_nodes_from_splits(
text_splits=texts,
document=document,
id_func=self.id_func
)
nodes = self.post_process_nodes(nodes, contexts)
all_nodes.extend(nodes)
return all_nodes
|
