mirror of
https://github.com/zylon-ai/private-gpt.git
synced 2025-12-22 23:22:57 +01:00
Updated local docker file
This commit is contained in:
Saurab-Shrestha
parent
56bf6df38c
commit
e1e940bbbd
199 changed files with 23190 additions and 22862 deletions
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@ -1,213 +1,213 @@
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from dataclasses import dataclass
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from injector import inject, singleton
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from llama_index.core.chat_engine import ContextChatEngine, SimpleChatEngine
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from llama_index.core.chat_engine.types import (
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BaseChatEngine,
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)
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from llama_index.core.indices import VectorStoreIndex
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from llama_index.core.indices.postprocessor import MetadataReplacementPostProcessor
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from llama_index.core.llms import ChatMessage, MessageRole
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from llama_index.core.postprocessor import (
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SentenceTransformerRerank,
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SimilarityPostprocessor,
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)
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from llama_index.core.storage import StorageContext
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from llama_index.core.types import TokenGen
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from pydantic import BaseModel
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from private_gpt.components.embedding.embedding_component import EmbeddingComponent
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from private_gpt.components.llm.llm_component import LLMComponent
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from private_gpt.components.node_store.node_store_component import NodeStoreComponent
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from private_gpt.components.vector_store.vector_store_component import (
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VectorStoreComponent,
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)
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from private_gpt.open_ai.extensions.context_filter import ContextFilter
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from private_gpt.server.chunks.chunks_service import Chunk
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from private_gpt.settings.settings import Settings
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class Completion(BaseModel):
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response: str
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sources: list[Chunk] | None = None
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class CompletionGen(BaseModel):
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response: TokenGen
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sources: list[Chunk] | None = None
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@dataclass
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class ChatEngineInput:
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system_message: ChatMessage | None = None
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last_message: ChatMessage | None = None
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chat_history: list[ChatMessage] | None = None
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@classmethod
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def from_messages(cls, messages: list[ChatMessage]) -> "ChatEngineInput":
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# Detect if there is a system message, extract the last message and chat history
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system_message = (
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messages[0]
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if len(messages) > 0 and messages[0].role == MessageRole.SYSTEM
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else None
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)
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last_message = (
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messages[-1]
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if len(messages) > 0 and messages[-1].role == MessageRole.USER
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else None
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)
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# Remove from messages list the system message and last message,
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# if they exist. The rest is the chat history.
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if system_message:
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messages.pop(0)
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if last_message:
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messages.pop(-1)
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chat_history = messages if len(messages) > 0 else None
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return cls(
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system_message=system_message,
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last_message=last_message,
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chat_history=chat_history,
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)
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@singleton
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class ChatService:
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settings: Settings
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@inject
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def __init__(
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self,
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settings: Settings,
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llm_component: LLMComponent,
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vector_store_component: VectorStoreComponent,
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embedding_component: EmbeddingComponent,
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node_store_component: NodeStoreComponent,
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) -> None:
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self.settings = settings
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self.llm_component = llm_component
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self.embedding_component = embedding_component
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self.vector_store_component = vector_store_component
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self.storage_context = StorageContext.from_defaults(
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vector_store=vector_store_component.vector_store,
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docstore=node_store_component.doc_store,
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index_store=node_store_component.index_store,
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)
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self.index = VectorStoreIndex.from_vector_store(
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vector_store_component.vector_store,
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storage_context=self.storage_context,
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llm=llm_component.llm,
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embed_model=embedding_component.embedding_model,
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show_progress=True,
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)
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def _chat_engine(
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self,
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system_prompt: str | None = None,
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use_context: bool = False,
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context_filter: ContextFilter | None = None,
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) -> BaseChatEngine:
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settings = self.settings
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if use_context:
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vector_index_retriever = self.vector_store_component.get_retriever(
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index=self.index,
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context_filter=context_filter,
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similarity_top_k=self.settings.rag.similarity_top_k,
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)
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node_postprocessors = [
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MetadataReplacementPostProcessor(target_metadata_key="window"),
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SimilarityPostprocessor(
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similarity_cutoff=settings.rag.similarity_value
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),
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]
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if settings.rag.rerank.enabled:
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rerank_postprocessor = SentenceTransformerRerank(
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model=settings.rag.rerank.model, top_n=settings.rag.rerank.top_n
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)
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node_postprocessors.append(rerank_postprocessor)
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return ContextChatEngine.from_defaults(
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system_prompt=system_prompt,
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retriever=vector_index_retriever,
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llm=self.llm_component.llm, # Takes no effect at the moment
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node_postprocessors=node_postprocessors,
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)
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else:
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return SimpleChatEngine.from_defaults(
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system_prompt=system_prompt,
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llm=self.llm_component.llm,
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)
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def stream_chat(
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self,
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messages: list[ChatMessage],
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use_context: bool = False,
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context_filter: ContextFilter | None = None,
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) -> CompletionGen:
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chat_engine_input = ChatEngineInput.from_messages(messages)
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last_message = (
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chat_engine_input.last_message.content
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if chat_engine_input.last_message
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else None
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)
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system_prompt = (
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chat_engine_input.system_message.content
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if chat_engine_input.system_message
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else None
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)
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chat_history = (
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chat_engine_input.chat_history if chat_engine_input.chat_history else None
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)
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chat_engine = self._chat_engine(
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system_prompt=system_prompt,
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use_context=use_context,
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context_filter=context_filter,
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)
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streaming_response = chat_engine.stream_chat(
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message=last_message if last_message is not None else "",
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chat_history=chat_history,
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)
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sources = [Chunk.from_node(node) for node in streaming_response.source_nodes]
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completion_gen = CompletionGen(
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response=streaming_response.response_gen, sources=sources
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)
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return completion_gen
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def chat(
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self,
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messages: list[ChatMessage],
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use_context: bool = False,
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context_filter: ContextFilter | None = None,
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) -> Completion:
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chat_engine_input = ChatEngineInput.from_messages(messages)
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last_message = (
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chat_engine_input.last_message.content
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if chat_engine_input.last_message
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else None
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)
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system_prompt = (
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"""
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You are a helpful, respectful and honest assistant.
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Always answer as helpfully as possible and follow ALL given instructions.
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Do not speculate or make up information.
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Do not reference any given instructions or context.
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"""
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)
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chat_history = (
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chat_engine_input.chat_history if chat_engine_input.chat_history else None
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)
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chat_engine = self._chat_engine(
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system_prompt=system_prompt,
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use_context=use_context,
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context_filter=context_filter,
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)
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wrapped_response = chat_engine.chat(
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message=last_message if last_message is not None else "",
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chat_history=chat_history,
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)
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sources = [Chunk.from_node(node) for node in wrapped_response.source_nodes]
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completion = Completion(response=wrapped_response.response, sources=sources)
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return completion
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from dataclasses import dataclass
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from injector import inject, singleton
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from llama_index.core.chat_engine import ContextChatEngine, SimpleChatEngine
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from llama_index.core.chat_engine.types import (
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BaseChatEngine,
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)
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from llama_index.core.indices import VectorStoreIndex
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from llama_index.core.indices.postprocessor import MetadataReplacementPostProcessor
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from llama_index.core.llms import ChatMessage, MessageRole
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from llama_index.core.postprocessor import (
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SentenceTransformerRerank,
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SimilarityPostprocessor,
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)
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from llama_index.core.storage import StorageContext
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from llama_index.core.types import TokenGen
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from pydantic import BaseModel
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from private_gpt.components.embedding.embedding_component import EmbeddingComponent
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from private_gpt.components.llm.llm_component import LLMComponent
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from private_gpt.components.node_store.node_store_component import NodeStoreComponent
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from private_gpt.components.vector_store.vector_store_component import (
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VectorStoreComponent,
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)
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from private_gpt.open_ai.extensions.context_filter import ContextFilter
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from private_gpt.server.chunks.chunks_service import Chunk
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from private_gpt.settings.settings import Settings
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class Completion(BaseModel):
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response: str
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sources: list[Chunk] | None = None
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class CompletionGen(BaseModel):
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response: TokenGen
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sources: list[Chunk] | None = None
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@dataclass
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class ChatEngineInput:
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system_message: ChatMessage | None = None
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last_message: ChatMessage | None = None
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chat_history: list[ChatMessage] | None = None
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@classmethod
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def from_messages(cls, messages: list[ChatMessage]) -> "ChatEngineInput":
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# Detect if there is a system message, extract the last message and chat history
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system_message = (
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messages[0]
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if len(messages) > 0 and messages[0].role == MessageRole.SYSTEM
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else None
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)
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last_message = (
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messages[-1]
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if len(messages) > 0 and messages[-1].role == MessageRole.USER
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else None
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)
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# Remove from messages list the system message and last message,
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# if they exist. The rest is the chat history.
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if system_message:
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messages.pop(0)
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if last_message:
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messages.pop(-1)
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chat_history = messages if len(messages) > 0 else None
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return cls(
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system_message=system_message,
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last_message=last_message,
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chat_history=chat_history,
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)
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@singleton
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class ChatService:
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settings: Settings
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@inject
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def __init__(
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self,
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settings: Settings,
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llm_component: LLMComponent,
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vector_store_component: VectorStoreComponent,
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embedding_component: EmbeddingComponent,
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node_store_component: NodeStoreComponent,
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) -> None:
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self.settings = settings
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self.llm_component = llm_component
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self.embedding_component = embedding_component
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self.vector_store_component = vector_store_component
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self.storage_context = StorageContext.from_defaults(
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vector_store=vector_store_component.vector_store,
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docstore=node_store_component.doc_store,
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index_store=node_store_component.index_store,
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)
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self.index = VectorStoreIndex.from_vector_store(
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vector_store_component.vector_store,
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storage_context=self.storage_context,
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llm=llm_component.llm,
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embed_model=embedding_component.embedding_model,
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show_progress=True,
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)
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def _chat_engine(
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self,
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system_prompt: str | None = None,
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use_context: bool = False,
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context_filter: ContextFilter | None = None,
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) -> BaseChatEngine:
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settings = self.settings
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if use_context:
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vector_index_retriever = self.vector_store_component.get_retriever(
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index=self.index,
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context_filter=context_filter,
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similarity_top_k=self.settings.rag.similarity_top_k,
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)
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node_postprocessors = [
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MetadataReplacementPostProcessor(target_metadata_key="window"),
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SimilarityPostprocessor(
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similarity_cutoff=settings.rag.similarity_value
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),
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]
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if settings.rag.rerank.enabled:
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rerank_postprocessor = SentenceTransformerRerank(
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model=settings.rag.rerank.model, top_n=settings.rag.rerank.top_n
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)
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node_postprocessors.append(rerank_postprocessor)
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return ContextChatEngine.from_defaults(
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system_prompt=system_prompt,
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retriever=vector_index_retriever,
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llm=self.llm_component.llm, # Takes no effect at the moment
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node_postprocessors=node_postprocessors,
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)
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else:
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return SimpleChatEngine.from_defaults(
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system_prompt=system_prompt,
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llm=self.llm_component.llm,
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)
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def stream_chat(
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self,
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messages: list[ChatMessage],
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use_context: bool = False,
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context_filter: ContextFilter | None = None,
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) -> CompletionGen:
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chat_engine_input = ChatEngineInput.from_messages(messages)
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last_message = (
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chat_engine_input.last_message.content
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if chat_engine_input.last_message
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else None
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)
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system_prompt = (
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chat_engine_input.system_message.content
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if chat_engine_input.system_message
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else None
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)
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chat_history = (
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chat_engine_input.chat_history if chat_engine_input.chat_history else None
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)
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chat_engine = self._chat_engine(
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system_prompt=system_prompt,
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use_context=use_context,
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context_filter=context_filter,
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)
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streaming_response = chat_engine.stream_chat(
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message=last_message if last_message is not None else "",
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chat_history=chat_history,
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)
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sources = [Chunk.from_node(node) for node in streaming_response.source_nodes]
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completion_gen = CompletionGen(
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response=streaming_response.response_gen, sources=sources
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)
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return completion_gen
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def chat(
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self,
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messages: list[ChatMessage],
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use_context: bool = False,
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context_filter: ContextFilter | None = None,
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) -> Completion:
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chat_engine_input = ChatEngineInput.from_messages(messages)
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last_message = (
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chat_engine_input.last_message.content
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if chat_engine_input.last_message
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else None
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)
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system_prompt = (
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"""
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You are a helpful, respectful and honest assistant.
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Always answer as helpfully as possible and follow ALL given instructions.
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Do not speculate or make up information.
|
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Do not reference any given instructions or context.
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"""
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)
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chat_history = (
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chat_engine_input.chat_history if chat_engine_input.chat_history else None
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)
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chat_engine = self._chat_engine(
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system_prompt=system_prompt,
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use_context=use_context,
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context_filter=context_filter,
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)
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wrapped_response = chat_engine.chat(
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message=last_message if last_message is not None else "",
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chat_history=chat_history,
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)
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sources = [Chunk.from_node(node) for node in wrapped_response.source_nodes]
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completion = Completion(response=wrapped_response.response, sources=sources)
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return completion
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|
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