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Working refactor. Dependency clean-up pending.

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imartinez 2024-02-28 18:45:54 +01:00
parent 12f3a39e8a
commit d0a7d991a2
20 changed files with 877 additions and 907 deletions
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1424
poetry.lock generated
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7
private_gpt/components/embedding/embedding_component.py
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@ -1,8 +1,7 @@
import logging
from injector import inject, singleton
from llama_index import MockEmbedding
from llama_index.embeddings.base import BaseEmbedding
from llama_index.core.embeddings import BaseEmbedding, MockEmbedding
from private_gpt.paths import models_cache_path
from private_gpt.settings.settings import Settings
@ -20,7 +19,7 @@ class EmbeddingComponent:
logger.info("Initializing the embedding model in mode=%s", embedding_mode)
match embedding_mode:
case "local":
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
self.embedding_model = HuggingFaceEmbedding(
model_name=settings.local.embedding_hf_model_name,
@ -36,7 +35,7 @@ class EmbeddingComponent:
endpoint_name=settings.sagemaker.embedding_endpoint_name,
)
case "openai":
from llama_index import OpenAIEmbedding
from llama_index.embeddings.openai import OpenAIEmbedding
openai_settings = settings.openai.api_key
self.embedding_model = OpenAIEmbedding(api_key=openai_settings)

76
private_gpt/components/ingest/ingest_component.py
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@ -8,16 +8,13 @@ import threading
from pathlib import Path
from typing import Any
from llama_index import (
Document,
ServiceContext,
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
from llama_index.data_structs import IndexDict
from llama_index.indices.base import BaseIndex
from llama_index.ingestion import run_transformations
from llama_index.core.data_structs import IndexDict
from llama_index.core.embeddings.utils import EmbedType
from llama_index.core.indices import VectorStoreIndex, load_index_from_storage
from llama_index.core.indices.base import BaseIndex
from llama_index.core.ingestion import run_transformations
from llama_index.core.schema import Document, TransformComponent
from llama_index.core.storage import StorageContext
from private_gpt.components.ingest.ingest_helper import IngestionHelper
from private_gpt.paths import local_data_path
@ -30,13 +27,15 @@ class BaseIngestComponent(abc.ABC):
def __init__(
self,
storage_context: StorageContext,
service_context: ServiceContext,
embed_model: EmbedType,
transformations: list[TransformComponent],
*args: Any,
**kwargs: Any,
) -> None:
logger.debug("Initializing base ingest component type=%s", type(self).__name__)
self.storage_context = storage_context
self.service_context = service_context
self.embed_model = embed_model
self.transformations = transformations
@abc.abstractmethod
def ingest(self, file_name: str, file_data: Path) -> list[Document]:
@ -55,11 +54,12 @@ class BaseIngestComponentWithIndex(BaseIngestComponent, abc.ABC):
def __init__(
self,
storage_context: StorageContext,
service_context: ServiceContext,
embed_model: EmbedType,
transformations: list[TransformComponent],
*args: Any,
**kwargs: Any,
) -> None:
super().__init__(storage_context, service_context, *args, **kwargs)
super().__init__(storage_context, embed_model, transformations, *args, **kwargs)
self.show_progress = True
self._index_thread_lock = (
@ -73,9 +73,10 @@ class BaseIngestComponentWithIndex(BaseIngestComponent, abc.ABC):
# Load the index with store_nodes_override=True to be able to delete them
index = load_index_from_storage(
storage_context=self.storage_context,
service_context=self.service_context,
store_nodes_override=True, # Force store nodes in index and document stores
show_progress=self.show_progress,
embed_model=self.embed_model,
transformations=self.transformations,
)
except ValueError:
# There are no index in the storage context, creating a new one
@ -83,9 +84,10 @@ class BaseIngestComponentWithIndex(BaseIngestComponent, abc.ABC):
index = VectorStoreIndex.from_documents(
[],
storage_context=self.storage_context,
service_context=self.service_context,
store_nodes_override=True, # Force store nodes in index and document stores
show_progress=self.show_progress,
embed_model=self.embed_model,
transformations=self.transformations,
)
index.storage_context.persist(persist_dir=local_data_path)
return index
@ -106,11 +108,12 @@ class SimpleIngestComponent(BaseIngestComponentWithIndex):
def __init__(
self,
storage_context: StorageContext,
service_context: ServiceContext,
embed_model: EmbedType,
transformations: list[TransformComponent],
*args: Any,
**kwargs: Any,
) -> None:
super().__init__(storage_context, service_context, *args, **kwargs)
super().__init__(storage_context, embed_model, transformations, *args, **kwargs)
def ingest(self, file_name: str, file_data: Path) -> list[Document]:
logger.info("Ingesting file_name=%s", file_name)
@ -151,16 +154,17 @@ class BatchIngestComponent(BaseIngestComponentWithIndex):
def __init__(
self,
storage_context: StorageContext,
service_context: ServiceContext,
embed_model: EmbedType,
transformations: list[TransformComponent],
count_workers: int,
*args: Any,
**kwargs: Any,
) -> None:
super().__init__(storage_context, service_context, *args, **kwargs)
super().__init__(storage_context, embed_model, transformations, *args, **kwargs)
# Make an efficient use of the CPU and GPU, the embedding
# must be in the transformations
assert (
len(self.service_context.transformations) >= 2
len(self.transformations) >= 2
), "Embeddings must be in the transformations"
assert count_workers > 0, "count_workers must be > 0"
self.count_workers = count_workers
@ -197,7 +201,7 @@ class BatchIngestComponent(BaseIngestComponentWithIndex):
logger.debug("Transforming count=%s documents into nodes", len(documents))
nodes = run_transformations(
documents, # type: ignore[arg-type]
self.service_context.transformations,
self.transformations,
show_progress=self.show_progress,
)
# Locking the index to avoid concurrent writes
@ -225,16 +229,17 @@ class ParallelizedIngestComponent(BaseIngestComponentWithIndex):
def __init__(
self,
storage_context: StorageContext,
service_context: ServiceContext,
embed_model: EmbedType,
transformations: list[TransformComponent],
count_workers: int,
*args: Any,
**kwargs: Any,
) -> None:
super().__init__(storage_context, service_context, *args, **kwargs)
super().__init__(storage_context, embed_model, transformations, *args, **kwargs)
# To make an efficient use of the CPU and GPU, the embeddings
# must be in the transformations (to be computed in batches)
assert (
len(self.service_context.transformations) >= 2
len(self.transformations) >= 2
), "Embeddings must be in the transformations"
assert count_workers > 0, "count_workers must be > 0"
self.count_workers = count_workers
@ -278,7 +283,7 @@ class ParallelizedIngestComponent(BaseIngestComponentWithIndex):
logger.debug("Transforming count=%s documents into nodes", len(documents))
nodes = run_transformations(
documents, # type: ignore[arg-type]
self.service_context.transformations,
self.transformations,
show_progress=self.show_progress,
)
# Locking the index to avoid concurrent writes
@ -311,18 +316,29 @@ class ParallelizedIngestComponent(BaseIngestComponentWithIndex):
def get_ingestion_component(
storage_context: StorageContext,
service_context: ServiceContext,
embed_model: EmbedType,
transformations: list[TransformComponent],
settings: Settings,
) -> BaseIngestComponent:
"""Get the ingestion component for the given configuration."""
ingest_mode = settings.embedding.ingest_mode
if ingest_mode == "batch":
return BatchIngestComponent(
storage_context, service_context, settings.embedding.count_workers
storage_context=storage_context,
embed_model=embed_model,
transformations=transformations,
count_workers=settings.embedding.count_workers,
)
elif ingest_mode == "parallel":
return ParallelizedIngestComponent(
storage_context, service_context, settings.embedding.count_workers
storage_context=storage_context,
embed_model=embed_model,
transformations=transformations,
count_workers=settings.embedding.count_workers,
)
else:
return SimpleIngestComponent(storage_context, service_context)
return SimpleIngestComponent(
storage_context=storage_context,
embed_model=embed_model,
transformations=transformations,
)

47
private_gpt/components/ingest/ingest_helper.py
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@ -1,14 +1,53 @@
import logging
from pathlib import Path
from typing import Dict, Type
from llama_index import Document
from llama_index.readers import JSONReader, StringIterableReader
from llama_index.readers.file.base import DEFAULT_FILE_READER_CLS
from llama_index.core.readers import StringIterableReader
from llama_index.core.readers.base import BaseReader
from llama_index.core.readers.json import JSONReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
# Inspired by the `llama_index.core.readers.file.base` module
def _try_loading_included_file_formats() -> Dict[str, Type[BaseReader]]:
try:
from llama_index.readers.file.docs import DocxReader, HWPReader, PDFReader
from llama_index.readers.file.epub import EpubReader
from llama_index.readers.file.image import ImageReader
from llama_index.readers.file.ipynb import IPYNBReader
from llama_index.readers.file.markdown import MarkdownReader
from llama_index.readers.file.mbox import MboxReader
from llama_index.readers.file.tabular import PandasCSVReader
from llama_index.readers.file.slides import PptxReader
from llama_index.readers.file.video_audio import VideoAudioReader
except ImportError:
raise ImportError("`llama-index-readers-file` package not found")
default_file_reader_cls: Dict[str, Type[BaseReader]] = {
".hwp": HWPReader,
".pdf": PDFReader,
".docx": DocxReader,
".pptx": PptxReader,
".ppt": PptxReader,
".pptm": PptxReader,
".jpg": ImageReader,
".png": ImageReader,
".jpeg": ImageReader,
".mp3": VideoAudioReader,
".mp4": VideoAudioReader,
".csv": PandasCSVReader,
".epub": EpubReader,
".md": MarkdownReader,
".mbox": MboxReader,
".ipynb": IPYNBReader,
}
return default_file_reader_cls
# Patching the default file reader to support other file types
FILE_READER_CLS = DEFAULT_FILE_READER_CLS.copy()
FILE_READER_CLS = _try_loading_included_file_formats()
FILE_READER_CLS.update(
{
".json": JSONReader,

16
private_gpt/components/llm/llm_component.py
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@ -1,15 +1,16 @@
import logging
from injector import inject, singleton
from llama_index import set_global_tokenizer
from llama_index.llms import MockLLM
from llama_index.llms.base import LLM
from llama_index.core.llms import LLM, MockLLM
from llama_index.core.utils import set_global_tokenizer
from llama_index.core.settings import Settings as LlamaIndexSettings
from transformers import AutoTokenizer # type: ignore
from private_gpt.components.llm.prompt_helper import get_prompt_style
from private_gpt.paths import models_cache_path, models_path
from private_gpt.settings.settings import Settings
logger = logging.getLogger(__name__)
@ -31,7 +32,7 @@ class LLMComponent:
logger.info("Initializing the LLM in mode=%s", llm_mode)
match settings.llm.mode:
case "local":
from llama_index.llms import LlamaCPP
from llama_index.llms.llama_cpp import LlamaCPP
prompt_style = get_prompt_style(settings.local.prompt_style)
@ -41,6 +42,7 @@ class LLMComponent:
max_new_tokens=settings.llm.max_new_tokens,
context_window=settings.llm.context_window,
generate_kwargs={},
callback_manager=LlamaIndexSettings.callback_manager,
# All to GPU
model_kwargs={"n_gpu_layers": -1, "offload_kqv": True},
# transform inputs into Llama2 format
@ -58,7 +60,7 @@ class LLMComponent:
context_window=settings.llm.context_window,
)
case "openai":
from llama_index.llms import OpenAI
from llama_index.llms.openai import OpenAI
openai_settings = settings.openai
self.llm = OpenAI(
@ -67,7 +69,7 @@ class LLMComponent:
model=openai_settings.model,
)
case "openailike":
from llama_index.llms import OpenAILike
from llama_index.llms.openai_like import OpenAILike
openai_settings = settings.openai
self.llm = OpenAILike(
@ -81,7 +83,7 @@ class LLMComponent:
case "mock":
self.llm = MockLLM()
case "ollama":
from llama_index.llms import Ollama
from llama_index.llms.ollama import Ollama
ollama_settings = settings.ollama
self.llm = Ollama(

64
private_gpt/components/llm/prompt_helper.py
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@ -3,11 +3,7 @@ import logging
from collections.abc import Sequence
from typing import Any, Literal
from llama_index.llms import ChatMessage, MessageRole
from llama_index.llms.llama_utils import (
completion_to_prompt,
messages_to_prompt,
)
from llama_index.core.llms import ChatMessage, MessageRole
logger = logging.getLogger(__name__)
@ -73,7 +69,9 @@ class DefaultPromptStyle(AbstractPromptStyle):
class Llama2PromptStyle(AbstractPromptStyle):
"""Simple prompt style that just uses the default llama_utils functions.
"""Simple prompt style that uses llama 2 prompt style.
Inspired by llama_index/legacy/llms/llama_utils.py
It transforms the sequence of messages into a prompt that should look like:
```text
@ -83,11 +81,61 @@ class Llama2PromptStyle(AbstractPromptStyle):
```
"""
BOS, EOS = "<s>", "</s>"
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. \
Always answer as helpfully as possible and follow ALL given instructions. \
Do not speculate or make up information. \
Do not reference any given instructions or context. \
"""
def _messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
return messages_to_prompt(messages)
string_messages: list[str] = []
if messages[0].role == MessageRole.SYSTEM:
# pull out the system message (if it exists in messages)
system_message_str = messages[0].content or ""
messages = messages[1:]
else:
system_message_str = self.DEFAULT_SYSTEM_PROMPT
system_message_str = f"{self.B_SYS} {system_message_str.strip()} {self.E_SYS}"
for i in range(0, len(messages), 2):
# first message should always be a user
user_message = messages[i]
assert user_message.role == MessageRole.USER
if i == 0:
# make sure system prompt is included at the start
str_message = f"{self.BOS} {self.B_INST} {system_message_str} "
else:
# end previous user-assistant interaction
string_messages[-1] += f" {self.EOS}"
# no need to include system prompt
str_message = f"{self.BOS} {self.B_INST} "
# include user message content
str_message += f"{user_message.content} {self.E_INST}"
if len(messages) > (i + 1):
# if assistant message exists, add to str_message
assistant_message = messages[i + 1]
assert assistant_message.role == MessageRole.ASSISTANT
str_message += f" {assistant_message.content}"
string_messages.append(str_message)
return "".join(string_messages)
def _completion_to_prompt(self, completion: str) -> str:
return completion_to_prompt(completion)
system_prompt_str = self.DEFAULT_SYSTEM_PROMPT
return (
f"{self.BOS} {self.B_INST} {self.B_SYS} {system_prompt_str.strip()} {self.E_SYS} "
f"{completion.strip()} {self.E_INST}"
)
class TagPromptStyle(AbstractPromptStyle):

6
private_gpt/components/node_store/node_store_component.py
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@ -1,9 +1,9 @@
import logging
from injector import inject, singleton
from llama_index.storage.docstore import BaseDocumentStore, SimpleDocumentStore
from llama_index.storage.index_store import SimpleIndexStore
from llama_index.storage.index_store.types import BaseIndexStore
from llama_index.core.storage.docstore import BaseDocumentStore, SimpleDocumentStore
from llama_index.core.storage.index_store import SimpleIndexStore
from llama_index.core.storage.index_store.types import BaseIndexStore
from private_gpt.paths import local_data_path

24
private_gpt/components/vector_store/batched_chroma.py
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@ -1,9 +1,25 @@
from collections.abc import Generator
from typing import Any
from llama_index.schema import BaseNode, MetadataMode
from llama_index.vector_stores import ChromaVectorStore
from llama_index.vector_stores.chroma import chunk_list
from llama_index.vector_stores.utils import node_to_metadata_dict
from llama_index.core.schema import BaseNode, MetadataMode
from llama_index.core.vector_stores.utils import node_to_metadata_dict
from llama_index.vector_stores.chroma import ChromaVectorStore
def chunk_list(
lst: list[BaseNode], max_chunk_size: int
) -> Generator[list[BaseNode], None, None]:
"""Yield successive max_chunk_size-sized chunks from lst.
Args:
lst (List[BaseNode]): list of nodes with embeddings
max_chunk_size (int): max chunk size
Yields:
Generator[List[BaseNode], None, None]: list of nodes with embeddings
"""
for i in range(0, len(lst), max_chunk_size):
yield lst[i : i + max_chunk_size]
class BatchedChromaVectorStore(ChromaVectorStore):

7
private_gpt/components/vector_store/vector_store_component.py
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@ -2,9 +2,8 @@ import logging
import typing
from injector import inject, singleton
from llama_index import VectorStoreIndex
from llama_index.indices.vector_store import VectorIndexRetriever
from llama_index.vector_stores.types import VectorStore
from llama_index.core.indices.vector_store import VectorIndexRetriever, VectorStoreIndex
from llama_index.core.vector_stores.types import VectorStore
from private_gpt.components.vector_store.batched_chroma import BatchedChromaVectorStore
from private_gpt.open_ai.extensions.context_filter import ContextFilter
@ -41,7 +40,7 @@ class VectorStoreComponent:
def __init__(self, settings: Settings) -> None:
match settings.vectorstore.database:
case "pgvector":
from llama_index.vector_stores import PGVectorStore
from llama_index.vector_stores.postgres import PGVectorStore
if settings.pgvector is None:
raise ValueError(

7
private_gpt/launcher.py
View File

@ -4,6 +4,8 @@ import logging
from fastapi import Depends, FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from injector import Injector
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks.global_handlers import create_global_handler
from private_gpt.server.chat.chat_router import chat_router
from private_gpt.server.chunks.chunks_router import chunks_router
@ -12,6 +14,7 @@ from private_gpt.server.embeddings.embeddings_router import embeddings_router
from private_gpt.server.health.health_router import health_router
from private_gpt.server.ingest.ingest_router import ingest_router
from private_gpt.settings.settings import Settings
from llama_index.core.settings import Settings as LlamaIndexSettings
logger = logging.getLogger(__name__)
@ -31,6 +34,10 @@ def create_app(root_injector: Injector) -> FastAPI:
app.include_router(embeddings_router)
app.include_router(health_router)
# Add LlamaIndex simple observability
global_handler = create_global_handler("simple")
LlamaIndexSettings.callback_manager = CallbackManager([global_handler])
settings = root_injector.get(Settings)
if settings.server.cors.enabled:
logger.debug("Setting up CORS middleware")

5
private_gpt/main.py
View File

@ -1,11 +1,6 @@
"""FastAPI app creation, logger configuration and main API routes."""
import llama_index
from private_gpt.di import global_injector
from private_gpt.launcher import create_app
# Add LlamaIndex simple observability
llama_index.set_global_handler("simple")
app = create_app(global_injector)

2
private_gpt/open_ai/openai_models.py
View File

@ -3,7 +3,7 @@ import uuid
from collections.abc import Iterator
from typing import Literal
from llama_index.llms import ChatResponse, CompletionResponse
from llama_index.core.llms import ChatResponse, CompletionResponse
from pydantic import BaseModel, Field
from private_gpt.server.chunks.chunks_service import Chunk

2
private_gpt/server/chat/chat_router.py
View File

@ -1,5 +1,5 @@
from fastapi import APIRouter, Depends, Request
from llama_index.llms import ChatMessage, MessageRole
from llama_index.core.llms import ChatMessage, MessageRole
from pydantic import BaseModel
from starlette.responses import StreamingResponse

33
private_gpt/server/chat/chat_service.py
View File

@ -1,14 +1,15 @@
from dataclasses import dataclass
from injector import inject, singleton
from llama_index import ServiceContext, StorageContext, VectorStoreIndex
from llama_index.chat_engine import ContextChatEngine, SimpleChatEngine
from llama_index.chat_engine.types import (
from llama_index.core.chat_engine import ContextChatEngine, SimpleChatEngine
from llama_index.core.chat_engine.types import (
BaseChatEngine,
)
from llama_index.indices.postprocessor import MetadataReplacementPostProcessor
from llama_index.llms import ChatMessage, MessageRole
from llama_index.types import TokenGen
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.storage import StorageContext
from llama_index.core.types import TokenGen
from pydantic import BaseModel
from private_gpt.components.embedding.embedding_component import EmbeddingComponent
@ -75,20 +76,19 @@ class ChatService:
embedding_component: EmbeddingComponent,
node_store_component: NodeStoreComponent,
) -> None:
self.llm_service = llm_component
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.service_context = ServiceContext.from_defaults(
llm=llm_component.llm, embed_model=embedding_component.embedding_model
)
self.index = VectorStoreIndex.from_vector_store(
vector_store_component.vector_store,
storage_context=self.storage_context,
service_context=self.service_context,
llm=llm_component.llm,
embed_model=embedding_component.embedding_model,
show_progress=True,
)
@ -102,10 +102,17 @@ class ChatService:
vector_index_retriever = self.vector_store_component.get_retriever(
index=self.index, context_filter=context_filter
)
# TODO ContextChatEngine is still not migrated by LlamaIndex to accept
# llm directly, so we are passing legacy ServiceContext until it is fixed.
from llama_index.core import ServiceContext
return ContextChatEngine.from_defaults(
system_prompt=system_prompt,
retriever=vector_index_retriever,
service_context=self.service_context,
llm=self.llm_component.llm, # Takes no effect at the moment
service_context=ServiceContext.from_defaults(
llm=self.llm_component.llm,
embed_model=self.embedding_component.embedding_model,
),
node_postprocessors=[
MetadataReplacementPostProcessor(target_metadata_key="window"),
],
@ -113,7 +120,7 @@ class ChatService:
else:
return SimpleChatEngine.from_defaults(
system_prompt=system_prompt,
service_context=self.service_context,
llm=self.llm_component.llm,
)
def stream_chat(

15
private_gpt/server/chunks/chunks_service.py
View File

@ -1,8 +1,9 @@
from typing import TYPE_CHECKING, Literal
from injector import inject, singleton
from llama_index import ServiceContext, StorageContext, VectorStoreIndex
from llama_index.schema import NodeWithScore
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
@ -15,7 +16,7 @@ from private_gpt.open_ai.extensions.context_filter import ContextFilter
from private_gpt.server.ingest.model import IngestedDoc
if TYPE_CHECKING:
from llama_index.schema import RelatedNodeInfo
from llama_index.core.schema import RelatedNodeInfo
class Chunk(BaseModel):
@ -63,14 +64,13 @@ class ChunksService:
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,
)
self.query_service_context = ServiceContext.from_defaults(
llm=llm_component.llm, embed_model=embedding_component.embedding_model
)
def _get_sibling_nodes_text(
self, node_with_score: NodeWithScore, related_number: int, forward: bool = True
@ -103,7 +103,8 @@ class ChunksService:
index = VectorStoreIndex.from_vector_store(
self.vector_store_component.vector_store,
storage_context=self.storage_context,
service_context=self.query_service_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(

20
private_gpt/server/ingest/ingest_service.py
View File

@ -4,11 +4,8 @@ from pathlib import Path
from typing import AnyStr, BinaryIO
from injector import inject, singleton
from llama_index import (
ServiceContext,
StorageContext,
)
from llama_index.node_parser import SentenceWindowNodeParser
from llama_index.core.node_parser import SentenceWindowNodeParser
from llama_index.core.storage import StorageContext
from private_gpt.components.embedding.embedding_component import EmbeddingComponent
from private_gpt.components.ingest.ingest_component import get_ingestion_component
@ -40,17 +37,12 @@ class IngestService:
index_store=node_store_component.index_store,
)
node_parser = SentenceWindowNodeParser.from_defaults()
self.ingest_service_context = ServiceContext.from_defaults(
llm=self.llm_service.llm,
embed_model=embedding_component.embedding_model,
node_parser=node_parser,
# Embeddings done early in the pipeline of node transformations, right
# after the node parsing
transformations=[node_parser, embedding_component.embedding_model],
)
self.ingest_component = get_ingestion_component(
self.storage_context, self.ingest_service_context, settings=settings()
self.storage_context,
embed_model=embedding_component.embedding_model,
transformations=[node_parser, embedding_component.embedding_model],
settings=settings(),
)
def _ingest_data(self, file_name: str, file_data: AnyStr) -> list[IngestedDoc]:

2
private_gpt/server/ingest/model.py
View File

@ -1,6 +1,6 @@
from typing import Any, Literal
from llama_index import Document
from llama_index.core.schema import Document
from pydantic import BaseModel, Field

2
private_gpt/ui/ui.py
View File

@ -10,7 +10,7 @@ import gradio as gr # type: ignore
from fastapi import FastAPI
from gradio.themes.utils.colors import slate # type: ignore
from injector import inject, singleton
from llama_index.llms import ChatMessage, ChatResponse, MessageRole
from llama_index.core.llms import ChatMessage, ChatResponse, MessageRole
from pydantic import BaseModel
from private_gpt.constants import PROJECT_ROOT_PATH

23
pyproject.toml
View File

@ -6,15 +6,24 @@ authors = ["Zylon <hi@zylon.ai>"]
[tool.poetry.dependencies]
python = ">=3.11,<3.12"
fastapi = { extras = ["all"], version = "^0.103.1" }
boto3 = "^1.28.56"
fastapi = { extras = ["all"], version = "^0.110.0" }
boto3 = "^1.34.51"
injector = "^0.21.0"
pyyaml = "^6.0.1"
python-multipart = "^0.0.6"
pypdf = "^3.16.2"
llama-index = { extras = ["local_models"], version = "0.9.3" }
watchdog = "^3.0.0"
qdrant-client = "^1.6.9"
python-multipart = "^0.0.9"
llama-index-core = "^0.10.13"
llama-index-readers-file = "^0.1.6"
llama-index-embeddings-huggingface = "^0.1.4"
llama-index-embeddings-openai = "^0.1.6"
llama-index-vector-stores-qdrant = "^0.1.3"
llama-index-vector-stores-chroma = "^0.1.4"
llama-index-llms-llama-cpp = "^0.1.3"
llama-index-llms-openai = "^0.1.6"
llama-index-llms-openai-like = "^0.1.3"
llama-index-llms-ollama = "^0.1.2"
llama-index-vector-stores-postgres = "^0.1.2"
watchdog = "^4.0.0"
qdrant-client = "^1.7.3"
chromadb = {version = "^0.4.13", optional = true}
asyncpg = {version = "^0.29.0", optional = true}
pgvector = {version = "^0.2.5", optional = true}

2
tests/test_prompt_helper.py
View File

@ -1,5 +1,5 @@
import pytest
from llama_index.llms import ChatMessage, MessageRole
from llama_index.core.llms import ChatMessage, MessageRole
from private_gpt.components.llm.prompt_helper import (
ChatMLPromptStyle,