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from typing import TYPE_CHECKING, Literal
from injector import inject, singleton
from llama_index.core.indices import VectorStoreIndex
from llama_index.core.schema import NodeWithScore
from llama_index.core.storage import StorageContext
from pydantic import BaseModel, Field
from private_gpt.components.embedding.embedding_component import EmbeddingComponent
from private_gpt.components.llm.llm_component import LLMComponent
from private_gpt.components.node_store.node_store_component import NodeStoreComponent
from private_gpt.components.vector_store.vector_store_component import (
VectorStoreComponent,
)
from private_gpt.open_ai.extensions.context_filter import ContextFilter
from private_gpt.server.ingest.model import IngestedDoc
if TYPE_CHECKING:
from llama_index.core.schema import RelatedNodeInfo
class Chunk(BaseModel):
object: Literal["context.chunk"]
score: float = Field(examples=[0.023])
document: IngestedDoc
text: str = Field(examples=["Outbound sales increased 20%, driven by new leads."])
previous_texts: list[str] | None = Field(
default=None,
examples=[["SALES REPORT 2023", "Inbound didn't show major changes."]],
)
next_texts: list[str] | None = Field(
default=None,
examples=[
[
"New leads came from Google Ads campaign.",
"The campaign was run by the Marketing Department",
]
],
)
@classmethod
def from_node(cls: type["Chunk"], node: NodeWithScore) -> "Chunk":
doc_id = node.node.ref_doc_id if node.node.ref_doc_id is not None else "-"
return cls(
object="context.chunk",
score=node.score or 0.0,
document=IngestedDoc(
object="ingest.document",
doc_id=doc_id,
doc_metadata=node.metadata,
),
text=node.get_content(),
)
@singleton
class ChunksService:
@inject
def __init__(
self,
llm_component: LLMComponent,
vector_store_component: VectorStoreComponent,
embedding_component: EmbeddingComponent,
node_store_component: NodeStoreComponent,
) -> None:
self.vector_store_component = vector_store_component
self.llm_component = llm_component
self.embedding_component = embedding_component
self.storage_context = StorageContext.from_defaults(
vector_store=vector_store_component.vector_store,
docstore=node_store_component.doc_store,
index_store=node_store_component.index_store,
)
def _get_sibling_nodes_text(
self, node_with_score: NodeWithScore, related_number: int, forward: bool = True
) -> list[str]:
explored_nodes_texts = []
current_node = node_with_score.node
for _ in range(related_number):
explored_node_info: RelatedNodeInfo | None = (
current_node.next_node if forward else current_node.prev_node
)
if explored_node_info is None:
break
explored_node = self.storage_context.docstore.get_node(
explored_node_info.node_id
)
explored_nodes_texts.append(explored_node.get_content())
current_node = explored_node
return explored_nodes_texts
def retrieve_relevant(
self,
text: str,
context_filter: ContextFilter | None = None,
limit: int = 10,
prev_next_chunks: int = 0,
) -> list[Chunk]:
index = VectorStoreIndex.from_vector_store(
self.vector_store_component.vector_store,
storage_context=self.storage_context,
llm=self.llm_component.llm,
embed_model=self.embedding_component.embedding_model,
show_progress=True,
)
vector_index_retriever = self.vector_store_component.get_retriever(
index=index, context_filter=context_filter, similarity_top_k=limit
)
nodes = vector_index_retriever.retrieve(text)
nodes.sort(key=lambda n: n.score or 0.0, reverse=True)
retrieved_nodes = []
for node in nodes:
chunk = Chunk.from_node(node)
chunk.previous_texts = self._get_sibling_nodes_text(
node, prev_next_chunks, False
)
chunk.next_texts = self._get_sibling_nodes_text(node, prev_next_chunks)
retrieved_nodes.append(chunk)
return retrieved_nodes
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