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"""Node postprocessor."""
import re
from abc import abstractmethod
from typing import Dict, List, Optional, cast
from pydantic import BaseModel, Field, validator
import logging
from gpt_index.indices.query.schema import QueryBundle
from gpt_index.indices.service_context import ServiceContext
from gpt_index.prompts.prompts import QuestionAnswerPrompt, RefinePrompt
from gpt_index.docstore import DocumentStore
from gpt_index.data_structs.node_v2 import Node, DocumentRelationship
from gpt_index.indices.postprocessor.base import BasePostprocessor
from gpt_index.indices.query.embedding_utils import SimilarityTracker
from gpt_index.indices.response.builder import ResponseBuilder, TextChunk
logger = logging.getLogger(__name__)
class BaseNodePostprocessor(BasePostprocessor, BaseModel):
"""Node postprocessor."""
@abstractmethod
def postprocess_nodes(
self, nodes: List[Node], extra_info: Optional[Dict] = None
) -> List[Node]:
"""Postprocess nodes."""
class KeywordNodePostprocessor(BaseNodePostprocessor):
"""Keyword-based Node processor."""
required_keywords: List[str] = Field(default_factory=list)
exclude_keywords: List[str] = Field(default_factory=list)
def postprocess_nodes(
self, nodes: List[Node], extra_info: Optional[Dict] = None
) -> List[Node]:
"""Postprocess nodes."""
new_nodes = []
for node in nodes:
words = re.findall(r"\w+", node.get_text())
should_use_node = True
if self.required_keywords is not None:
for w in self.required_keywords:
if w not in words:
should_use_node = False
if self.exclude_keywords is not None:
for w in self.exclude_keywords:
if w in words:
should_use_node = False
if should_use_node:
new_nodes.append(node)
return new_nodes
class SimilarityPostprocessor(BaseNodePostprocessor):
"""Similarity-based Node processor."""
similarity_cutoff: float = Field(default=None)
def postprocess_nodes(
self, nodes: List[Node], extra_info: Optional[Dict] = None
) -> List[Node]:
"""Postprocess nodes."""
extra_info = extra_info or {}
similarity_tracker = extra_info.get("similarity_tracker", None)
if similarity_tracker is None:
return nodes
sim_cutoff_exists = (
similarity_tracker is not None and self.similarity_cutoff is not None
)
new_nodes = []
for node in nodes:
should_use_node = True
if sim_cutoff_exists:
similarity = cast(SimilarityTracker, similarity_tracker).find(node)
if similarity is None:
should_use_node = False
if cast(float, similarity) < cast(float, self.similarity_cutoff):
should_use_node = False
if should_use_node:
new_nodes.append(node)
return new_nodes
def get_forward_nodes(
node: Node, num_nodes: int, docstore: DocumentStore
) -> Dict[str, Node]:
"""Get forward nodes."""
nodes: Dict[str, Node] = {node.get_doc_id(): node}
cur_count = 0
# get forward nodes in an iterative manner
while cur_count < num_nodes:
if DocumentRelationship.NEXT not in node.relationships:
break
next_node_id = node.relationships[DocumentRelationship.NEXT]
next_node = docstore.get_node(next_node_id)
if next_node is None:
break
nodes[next_node.get_doc_id()] = next_node
node = next_node
cur_count += 1
return nodes
def get_backward_nodes(
node: Node, num_nodes: int, docstore: DocumentStore
) -> Dict[str, Node]:
"""Get backward nodes."""
# get backward nodes in an iterative manner
nodes: Dict[str, Node] = {node.get_doc_id(): node}
cur_count = 0
while cur_count < num_nodes:
if DocumentRelationship.PREVIOUS not in node.relationships:
break
prev_node_id = node.relationships[DocumentRelationship.PREVIOUS]
prev_node = docstore.get_node(prev_node_id)
if prev_node is None:
break
nodes[prev_node.get_doc_id()] = prev_node
node = prev_node
cur_count += 1
return nodes
class PrevNextNodePostprocessor(BaseNodePostprocessor):
"""Previous/Next Node post-processor.
Allows users to fetch additional nodes from the document store,
based on the relationships of the nodes.
NOTE: this is a beta feature.
Args:
docstore (DocumentStore): The document store.
num_nodes (int): The number of nodes to return (default: 1)
mode (str): The mode of the post-processor.
Can be "previous", "next", or "both.
"""
docstore: DocumentStore
num_nodes: int = Field(default=1)
mode: str = Field(default="next")
def _get_backward_nodes(self, node: Node) -> Dict[str, Node]:
"""Get backward nodes."""
# get backward nodes in an iterative manner
nodes: Dict[str, Node] = {node.get_doc_id(): node}
cur_count = 0
while cur_count < self.num_nodes:
if DocumentRelationship.PREVIOUS not in node.relationships:
break
prev_node_id = node.relationships[DocumentRelationship.PREVIOUS]
prev_node = self.docstore.get_node(prev_node_id)
if prev_node is None:
break
nodes[prev_node.get_doc_id()] = prev_node
node = prev_node
cur_count += 1
return nodes
@validator("mode")
def _validate_mode(cls, v: str) -> str:
"""Validate mode."""
if v not in ["next", "previous", "both"]:
raise ValueError(f"Invalid mode: {v}")
return v
def postprocess_nodes(
self, nodes: List[Node], extra_info: Optional[Dict] = None
) -> List[Node]:
"""Postprocess nodes."""
all_nodes: Dict[str, Node] = {}
for node in nodes:
all_nodes[node.get_doc_id()] = node
if self.mode == "next":
all_nodes.update(get_forward_nodes(node, self.num_nodes, self.docstore))
elif self.mode == "previous":
all_nodes.update(
get_backward_nodes(node, self.num_nodes, self.docstore)
)
elif self.mode == "both":
all_nodes.update(get_forward_nodes(node, self.num_nodes, self.docstore))
all_nodes.update(
get_backward_nodes(node, self.num_nodes, self.docstore)
)
else:
raise ValueError(f"Invalid mode: {self.mode}")
sorted_nodes = sorted(all_nodes.values(), key=lambda x: x.get_doc_id())
return list(sorted_nodes)
DEFAULT_INFER_PREV_NEXT_TMPL = (
"The current context information is provided. \n"
"A question is also provided. \n"
"You are a retrieval agent deciding whether to search the "
"document store for additional prior context or future context. \n"
"Given the context and question, return PREVIOUS or NEXT or NONE. \n"
"Examples: \n\n"
"Context: Describes the author's experience at Y Combinator."
"Question: What did the author do after his time at Y Combinator? \n"
"Answer: NEXT \n\n"
"Context: Describes the author's experience at Y Combinator."
"Question: What did the author do before his time at Y Combinator? \n"
"Answer: PREVIOUS \n\n"
"Context: Describe the author's experience at Y Combinator."
"Question: What did the author do at Y Combinator? \n"
"Answer: NONE \n\n"
"Context: {context_str}\n"
"Question: {query_str}\n"
"Answer: "
)
DEFAULT_REFINE_INFER_PREV_NEXT_TMPL = (
"The current context information is provided. \n"
"A question is also provided. \n"
"An existing answer is also provided.\n"
"You are a retrieval agent deciding whether to search the "
"document store for additional prior context or future context. \n"
"Given the context, question, and previous answer, "
"return PREVIOUS or NEXT or NONE.\n"
"Examples: \n\n"
"Context: {context_msg}\n"
"Question: {query_str}\n"
"Existing Answer: {existing_answer}\n"
"Answer: "
)
class AutoPrevNextNodePostprocessor(BaseNodePostprocessor):
"""Previous/Next Node post-processor.
Allows users to fetch additional nodes from the document store,
based on the prev/next relationships of the nodes.
NOTE: difference with PrevNextPostprocessor is that
this infers forward/backwards direction.
NOTE: this is a beta feature.
Args:
docstore (DocumentStore): The document store.
llm_predictor (LLMPredictor): The LLM predictor.
num_nodes (int): The number of nodes to return (default: 1)
infer_prev_next_tmpl (str): The template to use for inference.
Required fields are {context_str} and {query_str}.
"""
docstore: DocumentStore
service_context: ServiceContext
num_nodes: int = Field(default=1)
infer_prev_next_tmpl: str = Field(default=DEFAULT_INFER_PREV_NEXT_TMPL)
refine_prev_next_tmpl: str = Field(default=DEFAULT_REFINE_INFER_PREV_NEXT_TMPL)
verbose: bool = Field(default=False)
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _parse_prediction(self, raw_pred: str) -> str:
"""Parse prediction."""
pred = raw_pred.strip().lower()
if "previous" in pred:
return "previous"
elif "next" in pred:
return "next"
elif "none" in pred:
return "none"
raise ValueError(f"Invalid prediction: {raw_pred}")
def postprocess_nodes(
self, nodes: List[Node], extra_info: Optional[Dict] = None
) -> List[Node]:
"""Postprocess nodes."""
if extra_info is None or "query_bundle" not in extra_info:
raise ValueError("Missing query bundle in extra info.")
query_bundle = cast(QueryBundle, extra_info["query_bundle"])
infer_prev_next_prompt = QuestionAnswerPrompt(
self.infer_prev_next_tmpl,
)
refine_infer_prev_next_prompt = RefinePrompt(self.refine_prev_next_tmpl)
all_nodes: Dict[str, Node] = {}
for node in nodes:
all_nodes[node.get_doc_id()] = node
# use response builder instead of llm_predictor directly
# to be more robust to handling long context
response_builder = ResponseBuilder(
self.service_context,
infer_prev_next_prompt,
refine_infer_prev_next_prompt,
)
response_builder.add_text_chunks([TextChunk(node.get_text())])
raw_pred = response_builder.get_response(
query_str=query_bundle.query_str,
response_mode="tree_summarize",
)
raw_pred = cast(str, raw_pred)
mode = self._parse_prediction(raw_pred)
logger.debug(f"> Postprocessor Predicted mode: {mode}")
if self.verbose:
print(f"> Postprocessor Predicted mode: {mode}")
if mode == "next":
all_nodes.update(get_forward_nodes(node, self.num_nodes, self.docstore))
elif mode == "previous":
all_nodes.update(
get_backward_nodes(node, self.num_nodes, self.docstore)
)
elif mode == "none":
pass
else:
raise ValueError(f"Invalid mode: {mode}")
sorted_nodes = sorted(all_nodes.values(), key=lambda x: x.get_doc_id())
return list(sorted_nodes)
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