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from dataclasses import dataclass
from injector import inject, singleton
from llama_index.core.chat_engine import ContextChatEngine, SimpleChatEngine
from llama_index.core.chat_engine.types import (
BaseChatEngine,
)
from llama_index.core.indices import VectorStoreIndex
from llama_index.core.indices.postprocessor import MetadataReplacementPostProcessor
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core.postprocessor import (
SentenceTransformerRerank,
SimilarityPostprocessor,
)
from llama_index.core.storage import StorageContext
from llama_index.core.types import TokenGen
from pydantic import BaseModel
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.chunks.chunks_service import Chunk
from private_gpt.settings.settings import Settings
class Completion(BaseModel):
response: str
sources: list[Chunk] | None = None
class CompletionGen(BaseModel):
response: TokenGen
sources: list[Chunk] | None = None
@dataclass
class ChatEngineInput:
system_message: ChatMessage | None = None
last_message: ChatMessage | None = None
chat_history: list[ChatMessage] | None = None
@classmethod
def from_messages(cls, messages: list[ChatMessage]) -> "ChatEngineInput":
# Detect if there is a system message, extract the last message and chat history
system_message = (
messages[0]
if len(messages) > 0 and messages[0].role == MessageRole.SYSTEM
else None
)
last_message = (
messages[-1]
if len(messages) > 0 and messages[-1].role == MessageRole.USER
else None
)
# Remove from messages list the system message and last message,
# if they exist. The rest is the chat history.
if system_message:
messages.pop(0)
if last_message:
messages.pop(-1)
chat_history = messages if len(messages) > 0 else None
return cls(
system_message=system_message,
last_message=last_message,
chat_history=chat_history,
)
@singleton
class ChatService:
settings: Settings
@inject
def __init__(
self,
settings: Settings,
llm_component: LLMComponent,
vector_store_component: VectorStoreComponent,
embedding_component: EmbeddingComponent,
node_store_component: NodeStoreComponent,
) -> None:
self.settings = settings
self.llm_component = llm_component
self.embedding_component = embedding_component
self.vector_store_component = vector_store_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,
)
self.index = VectorStoreIndex.from_vector_store(
vector_store_component.vector_store,
storage_context=self.storage_context,
llm=llm_component.llm,
embed_model=embedding_component.embedding_model,
show_progress=True,
)
def _chat_engine(
self,
system_prompt: str | None = None,
use_context: bool = False,
context_filter: ContextFilter | None = None,
) -> BaseChatEngine:
settings = self.settings
if use_context:
vector_index_retriever = self.vector_store_component.get_retriever(
index=self.index,
context_filter=context_filter,
similarity_top_k=self.settings.rag.similarity_top_k,
)
node_postprocessors = [
MetadataReplacementPostProcessor(target_metadata_key="window"),
SimilarityPostprocessor(
similarity_cutoff=settings.rag.similarity_value
),
]
if settings.rag.rerank.enabled:
rerank_postprocessor = SentenceTransformerRerank(
model=settings.rag.rerank.model, top_n=settings.rag.rerank.top_n
)
node_postprocessors.append(rerank_postprocessor)
return ContextChatEngine.from_defaults(
system_prompt=system_prompt,
retriever=vector_index_retriever,
llm=self.llm_component.llm, # Takes no effect at the moment
node_postprocessors=node_postprocessors,
)
else:
return SimpleChatEngine.from_defaults(
system_prompt=system_prompt,
llm=self.llm_component.llm,
)
def stream_chat(
self,
messages: list[ChatMessage],
use_context: bool = False,
context_filter: ContextFilter | None = None,
) -> CompletionGen:
chat_engine_input = ChatEngineInput.from_messages(messages)
last_message = (
chat_engine_input.last_message.content
if chat_engine_input.last_message
else None
)
system_prompt = (
chat_engine_input.system_message.content
if chat_engine_input.system_message
else None
)
chat_history = (
chat_engine_input.chat_history if chat_engine_input.chat_history else None
)
chat_engine = self._chat_engine(
system_prompt=system_prompt,
use_context=use_context,
context_filter=context_filter,
)
streaming_response = chat_engine.stream_chat(
message=last_message if last_message is not None else "",
chat_history=chat_history,
)
sources = [Chunk.from_node(node) for node in streaming_response.source_nodes]
completion_gen = CompletionGen(
response=streaming_response.response_gen, sources=sources
)
return completion_gen
def chat(
self,
messages: list[ChatMessage],
use_context: bool = False,
context_filter: ContextFilter | None = None,
) -> Completion:
chat_engine_input = ChatEngineInput.from_messages(messages)
last_message = (
chat_engine_input.last_message.content
if chat_engine_input.last_message
else None
)
system_prompt = (
chat_engine_input.system_message.content
if chat_engine_input.system_message
else None
)
chat_history = (
chat_engine_input.chat_history if chat_engine_input.chat_history else None
)
chat_engine = self._chat_engine(
system_prompt=system_prompt,
use_context=use_context,
context_filter=context_filter,
)
wrapped_response = chat_engine.chat(
message=last_message if last_message is not None else "",
chat_history=chat_history,
)
sources = [Chunk.from_node(node) for node in wrapped_response.source_nodes]
completion = Completion(response=wrapped_response.response, sources=sources)
return completion
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