text stringlengths 14 100k | source stringclasses 1
value | repo stringclasses 810
values | language stringclasses 13
values |
|---|---|---|---|
<|fim_prefix|>from injector import inject, singleton
from private_gpt.components.sandbox.base import SandboxSession
from private_gpt.components.sandbox.registry import SandboxProviderRegistry
from private_gpt.settings.settings import Settings
@singleton
class SandboxComponent:
@inject
def __init__(self, sett... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>t schema",
up=_create_skills_tables,
down=_drop_skills_tables,
),
]
<|fim_prefix|>from sqlalchemy import text
from sqlalchemy.engine import Connection
from private_gpt.components.migrations.models import Migration
def _create_skills_tables(connection: Connection) -> None:
statem... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>Resolved skill version.")
class SkillReference(BaseModel):
"""Compact pointer to a skill stored in the zylon-gpt skills store.
Stored by any entity (org, project, future backend artifact) that owns skills.
"""
skill_id: str = Field(description="Skill identifier in the zylon-gpt skills ... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> (
UniqueConstraint("skill_id", "version", name="uq_skill_version"),
{"schema": "app"},
)
id: Mapped[str] = mapped_column(String(255), primary_key=True)
skill_id: Mapped[str] = mapped_column(
String(255),
ForeignKey("app.skills.id", ondelete="CASCAD... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>return value
class ParsedSkillDocument(BaseModel):
frontmatter: SkillFrontmatter
body: str = Field(default="")
def parse_skill_markdown(skill_markdown: str) -> ParsedSkillDocument:
match = _FRONTMATTER_RE.match(skill_markdown)
if not match:
raise ValueError("SKILL.md must start... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>ls/{collection}/{skill_id}/{version_id}"
<|fim_prefix|>def skill_path(collection: str, skill_id: str, version_id: str) -> st<|fim_middle|>r:
return f"skil<|endoftext|> | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import time
import uuid
from abc import ABC, abstractmethod
from datetime import UTC, datetime
from typing import Literal, cast
from injector import inject, singleton
from pydantic import BaseModel, Field
from sqlalchemy import and_, desc, func, select
from sqlalchemy.ext.asyncio import AsyncSession
from... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>repository.delete_version(
skill_id=skill_id,
version=version,
collection=collection,
)
if deleted and item is not None:
await self._storage_component.delete_prefix(item.storage_prefix)
return deleted
async def recover_versions(
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>_path).parent.mkdir(parents=True, exist_ok=True)
self._sync_engine = create_engine(f"sqlite:///{self._local_path}")
self._sync_engine = self._sync_engine.execution_options(
schema_translate_map={"app": None}
)
self._async_engine = create_async_engine(
... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from pydantic import BaseModel, <|fim_suffix|>,
)
content: bytes = Field(description="Raw file content bytes.")
mime_type: str | None = Field(
default=None,
description="Optional MIME type stored with the object.",
)
<|fim_middle|>Field
class StoredFile(BaseModel):
pa... | fim | zylon-ai/private-gpt | python |
import shutil
from abc import ABC, abstractmethod
from pathlib import Path
from anyio import to_thread
from private_gpt.components.storage.models import StoredFile
from private_gpt.components.storage.s3_helper import S3Helper
class ObjectStorage(ABC):
@abstractmethod
async def write_bundle(self, prefix: str... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from __future__ import annotations
import io
import logging
from typing import TYPE_CHECKING, Any, BinaryIO
from injector import inject, singleton
from private_gpt.settings.settings import settings
if TYPE_CHECKING:
from mypy_boto3_s3.client import S3Client
from private_gpt.settings.settings ... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import logging
import threading
from injector import Injector, inject, singleton
from private_gpt.components.storage.object_storage import (
LocalObjectStorage,
ObjectStorage,
S3ObjectStorage,
)
from private_gpt.components.storage.s3_helper import S3Helper
logger = logging.getLogger(__name_... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> StreamStatus.COMPLETED,
StreamStatus.CANCELLED,
StreamStatus.ERROR,
]:
stream_meta.completed_at = datetime.now(UTC)
if metadata:
stream_meta.metadata.update(metadata)
async def get_stream_metadata(self, corr... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>:
data = self.model_dump()
return {
k: v.model_dump_json()
if isinstance(v, BaseModel)
else json.dumps(v)
if isinstance(v, dict | list)
else v.isoformat()
if isinstance(v, datetime)
else str(v)
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> cls._instance = None
<|fim_prefix|>import asyncio
import json
import threading
import uuid
from collections import defaultdict
from datetime import UTC, datetime
from typing import Any, cast
import redis.asyncio as redis # type: ignore[import-untyped]
from pydantic import BaseModel
from private_gpt.co... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from abc import ABC, abstractmethod
from typing import Any, NamedTuple
from private_gpt.components.streaming.providers.models import (
StreamMetadata,
StreamStatus,
)
class Event(NamedTuple):
correlation_id: str
event_data: str
class StreamService(ABC):
"""Abstract base class for ... | fim | zylon-ai/private-gpt | python |
from collections.abc import Callable
from private_gpt.components.streaming.providers.stream_service import StreamService
from private_gpt.settings.settings import Settings
from private_gpt.utils.dependencies import format_missing_dependency_message
StreamProvider = Callable[[Settings], StreamService]
def _redis_str... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>e stream is currently being processed."""
...
def error_event(self, correlation_id: str, error: Exception) -> BaseModel:
"""Convert an Exception to a serializable error event."""
...
<|fim_prefix|>from typing import Protocol
from pydantic import BaseModel
from private_gpt.co... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> stream_type: str,
event_generator: AsyncGenerator[Any, None],
event_handler: EventHandler,
correlation_id: str | None = None,
metadata: dict[str, Any] | None = None,
) -> str:
"""Create a stream and start processing it."""
correlation_id = await self... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> correlation_id,
StreamStatus.COMPLETED,
)
except asyncio.CancelledError:
await self.stream_service.update_stream_status(
correlation_id,
StreamStatus.CANCELLED,
)
await self._handle_cancellat... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>s:
send_tasks.append(consumer.send(event))
await asyncio.gather(*send_tasks)
elif await self.stream_reader.check_terminal_status(
correlation_id,
consumer_list[0].event_handler,
state.cached_events,
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>ovider(settings)
<|fim_prefix|>from injector import inject, singleton
from private_gpt.components.streaming.providers.stream_service import StreamService
from private_gpt.components.streaming.registry import _PROVIDERS, register_stre<|fim_middle|>am
from private_gpt.settings.settings import Settings
__a... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>get_task(self, correlation_id: str) -> asyncio.Task[Any | None] | None:
"""Get task by correlation ID."""
return self._active_tasks.get(correlation_id)
def get_cancellation_token(self, correlation_id: str) -> asyncio.Event | None:
"""Get cancellation token by correlation ID.""... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import contextlib
import enum
import functools
import logging
import pickle
import re
import time
from collections.abc import Generator, Sequence
from typing import TYPE_CHECKING, Any, ClassVar, cast
from urllib.parse import parse_qs, urlencode, urlparse, urlunparse
from cachetools import ... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import re
from datetime import datetime
from llama_index.core.base.llms.types import ChatMessage, MessageRole
from llama_index.core.llms import LLM
from pandasai.agent.state import AgentState # type: ignore
from pandasai.core.prompts import BasePrompt # type: ignore
from pandasai.llm import LLM as Pand... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import json
import logging
import os
import re
import shutil
import tempfile
import textwrap
import uuid
from pathlib import Path
from typing import Any, ClassVar
import pandas as pd
from pandasai import Sandbox # type: ignore
from pandasai.exceptions import CodeExecutionError # type: ignore
from priv... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>y:
result = self._execute_chat(query, smart_dataframes, sandbox)
except Exception as e:
logger.error(f"Error during PandasAI analysis: {e}")
result = ErrorResponse(error=str(e))
return PandasAIOutput(
response=result,
error=resul... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> },
# create
"file_text": {
"type": "string",
"description": "Full file content (create).",
},
# insert
"insert_line": {
"type": "integer",
... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import functools
import logging
import uuid
from collections.abc import Awaitable, Callable
from typing import Any, ParamSpec
from private_gpt.chat.input_models import BlobVisibilityMode
from private_gpt.components.storage.s3_helper import S3Helper
from private_gpt.di import get_global_inj... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> session = self._component.get_or_create_session(session_id)
if session is None:
raise ValueError("code_execution provider is not configured.")
async def run_bash(
command: str,
timeout: int | None = None,
restart: bool = False,
)... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>",
)
blocks.append(csv_block)
if len(csv) > sample_size:
blocks.append(
TextBlock(
text="Representative data from the query result. Infor... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>mpt, _ = self.prompt_builder_service.create_context_prompt(
documents=documents,
generate_citations=generate_citations,
token_limit=token_limit,
tokenizer_fn=tokenizer,
)
return [
*content_blocks,
... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import json
from typing import Any
from private_gpt.components.chat.models.chat_config_models import ToolSpec
from private_gpt.components.skills.models.skill_entities import (
SkillFilter,
SkillVersionEntity,
)
from private_gpt.components.skills.services.skill_service import SkillService
from pri... | fim | zylon-ai/private-gpt | python |
import asyncio
from collections.abc import Awaitable, Callable
from typing import TYPE_CHECKING, Any, Literal
from injector import inject, singleton
from llama_index.core.llms import LLM
from pydantic import BaseModel
from private_gpt.artifact_index.vector_artifact_index import VectorArtifactIndex
from private_gpt.ch... | fim | zylon-ai/private-gpt | python |
import asyncio
from collections.abc import Generator
from typing import TYPE_CHECKING, Any, Literal
from injector import inject, singleton
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.llms import LLM
from llama_index.... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> self,
session_id: str,
name: str = TEXT_EDITOR_VIEW_TOOL_NAME,
type: str = TEXT_EDITOR_VIEW_TOOL_NAME + "_v1",
description: str = TEXT_EDITOR_VIEW_TOOL_FN.metadata.description,
) -> ToolSpec:
session = self._session(session_id)
async def view(
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>ontent:
return [
TextBlock(
text="No content could be fetched from the provided URL.",
)
]
page_content = result.markdown_content
return [
TextBlock(
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> if not content:
return [
TextBlock(
text="No results found for the given query.",
)
]
websites = [Website.from_website_result(res) for res in content]
return [
*fr... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> logger.warning(
"Discarding tool '%s' (type '%s'): no internal implementation available.",
tool.name,
tool.type,
)
_replace_tool(request, tool, [])
return True
return False
<|fim_prefix|>import logging
from i... | fim | zylon-ai/private-gpt | python |
import uuid
from abc import ABC, abstractmethod
from private_gpt.components.chat.models.chat_config_models import (
ResolvedChatRequest,
ToolSpec,
_dummy_tool_async_fn,
)
from private_gpt.components.tools.tool_names import resolve_internal_tool_name
from private_gpt.server.utils.artifact_input import Artif... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from injector import inject, singleton
from private_gpt.components.chat.models.chat_config_models import ResolvedChatRequest
from private_gpt.components.tools.builders.bash_tool_builder import BashToolBuilder
from private_gpt.components.tools.processors.base import (
ToolProcessor,
_is_unresolved... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from injector import inject, singleton
from private_gpt.components.chat.models.chat_config_models import ResolvedChatRequest
from private_gpt.components.tools.processors.base import (
ToolProcessor,
_is_unresolved_tool,
_replace_tool,
_tool_matches,
_wrapper_tool,
)
from private_gpt.c... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>turn _replace_tool(request, tool, [resolved])
return False
<|fim_prefix|>from injec<|fim_middle|>tor import inject, singleton
from llama_index.core.base.llms.types import ChatMessage, MessageRole
from private_gpt.components.chat.models.chat_config_models import ResolvedChatRequest
from private_gp... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>abled,
validate=request.tool_config.validation_mode,
token_limit=request.context.maximum_context_length,
)
return _replace_tool(request, tool, [resolved])
return False
<|fim_prefix|>from injector import inject, singleton
from private_gpt.com... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from injector import inject, singleton
from private_gpt.components.chat.models.chat_config_models import ResolvedChatRequest
from private_gpt.components.skills.services.skill_service import SkillService
from private_gpt.components.tools.builders.skill_management_builder import (
SkillManagementToolBu... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import logging
from injector import inject, singleton
from private_gpt.components.chat.models.chat_config_models import ResolvedChatRequest
from private_gpt.components.tools.builders.tabular_data_builder import (
TabularDataToolBuilder,
)
from private_gpt.components.tools.processors.base import (
... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from injector import inject, singleton
from private_gpt.components.chat.models.chat_config_models import ResolvedChatRequest
from private_gpt.components.tools.builders.text_editor_tool_builder import (
TextEditorToolBuilder,
)
from private_gpt.components.tools.processors.base import (
ToolProcess... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> _replace_tool,
_tool_matches,
)
from private_gpt.components.tools.tool_names import WEB_FETCH_TOOL_NAME
@singleton
class WebFetchProcessor(ToolProcessor):
@inject
def __init__(self, web_fetch_tool_builder: WebFetchToolBuilder) -> None:
self._builder = web_fetch_tool_builder
... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from injector import inject, singleton
from private_gpt.components.chat.models.chat_config_mode<|fim_suffix|>@singleton
class WebSearchProcessor(ToolProcessor):
@inject
def __init__(self, web_search_tool_builder: WebSearchToolBuilder) -> None:
self._builder = web_search_tool_builder
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>nt
self.ingest_component = ingest_component
self.sandbox_component = sandbox_component
def create(self) -> "TabularDataToolBuilder":
from private_gpt.components.tools.builders.tabular_data_builder import (
TabularDataToolBuilder,
)
return TabularDa... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import re
# General tools
SEMANTIC_SEARCH_TOOL_NAME = "semantic_search"
TABULAR_DATA_ANALYSIS = "tabular_analysis"
SUMMARIZE_TOOL_NAME = "summarize"
DATABASE_QUERY_TOOL_NAME = "database_query"
WEB_FETCH_TOOL_NAME = "web_fetch"
WEB_FETCH_LEGACY_TOOL_NAMES = ["web_extract"]
WEB_SE<|fim_suffix|>ecution tool... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>ocessor,
semantic_search_processor,
tabular_data_processor,
database_query_processor,
web_fetch_processor,
web_search_processor,
skill_management_processor,
code_execution_processor,
bash_processor,
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>aceholder_tool(
TEXT_EDITOR_VIEW_TOOL_NAME,
"View a file or directory in the session workspace.",
)
TEXT_EDITOR_STR_REPLACE_TOOL_FN = _placeholder_tool(
TEXT_EDITOR_STR_REPLACE_TOOL_NAME,
"Replace a single exact string in a file.",
)
TEXT_EDITOR_CREATE_TOOL_FN = _placeholder_tool(
TE... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>
elif mode_str == "lazy":
return cls.LAZY
else:
raise ValueError(f"Invalid ToolValidationMode: {mode_str}")
<|fim_prefix|>import enum
class ToolValidationMode(enum.StrEnum):
EAGER = "eager"
LAZY = "lazy"
def __str__(self) -> str:
return self.v... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from typing import TypeVar
T = TypeVar("T")
def is_list_of(
value: object,
typ: type[T<|fim_suffix|>== s2[i]:
prefix += s1[i]
else:
break
return prefix
<|fim_middle|>],
) -> bool:
"""Check if the value is a list of the given type."""
return isinstance... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>(self, message: str) -> None:
"""Initialize an invalid-toolset error."""
super().__init__(ToolsetsErrorCode.INVALID_TOOLSET, message)
<|fim_prefix|>"""Define typed errors for the toolsets module."""
from enum import StrEnum
class ToolsetsErrorCode(StrEnum):
"""Define stable error co... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> directory (recursively) from the virtual filesystem.",
input_schema={
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Absolute path to dele... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>l_set import ToolSet
__all__ = ["ToolDefinition", "ToolSet"]
<|fim_prefix|>"""Export toolset models."""
from private_gpt.components.toolsets.models.tool_definition<|fim_middle|> import ToolDefinition
from private_gpt.components.toolsets.models.too<|endoftext|> | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>"""Define tool definition model and ToolSpec conversion."""
import re
from typing import Any
from pydantic import BaseModel, ConfigDict, Field, field_validator
from private_gpt.components.chat.models.chat_config_models import ToolSpec
_TOOL_NAME_PATTERN = re.compile(r"^[a-zA-Z0-9_-]+$")
class ToolDe... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> ValueError("Tool names must be unique within a toolset")
return self
<|fim_prefix|>"""Define toolset group model."""
from uuid import UUID
from pydantic import BaseModel, ConfigDict, model_validator
from private_gpt.components.toolsets.models.tool_definition import ToolDefinition
class Tool<... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>"""Export toolset repositories."""
from private_gpt.components.toolsets.repositories.in_memory_toolset_repository import (
InMemoryToolSetRepository,
)
from private_gpt.components.toolsets.repositories.toolset_repository import (
<|fim_suffix|> = ["InMemoryToolSetRepository", "ToolSetRepository"]
<|f... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>self) -> list[ToolSet]:
"""List all toolsets from memory."""
return list(self.by_name.values())
def delete(self, name: str) -> bool:
"""Delete one toolset by name from memory."""
removed = self.by_name.pop(name, None)
return removed is not None
<|fim_prefix|>""... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>lset by name."""
@abstractmethod
def list(self) -> list[ToolSet]:
"""Return all registered toolsets."""
@abstractmethod
def delete(self, name: str) -> bool:
"""Delete a toolset by name."""
<|fim_prefix|>"""Define toolset repository abstraction."""
from abc import ABC, ab... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>lSetService
__all__ = ["ToolSetService"]
<|fim_prefix|>"""Export toolset servic<|fim_middle|>es."""
from private_gpt.components.toolsets.services.toolset_service import Too<|endoftext|> | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>ave(toolset)
def get(self, name: str) -> ToolSet | None:
"""Return one toolset by name when it exists."""
return self.repository.get_by_name(name)
def list(self) -> list[ToolSet]:
"""Return all registered toolsets."""
return self.repository.list()
def delete(... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> str) -> BasePydanticVectorStore:
...
<|fim_prefix|>from abc import ABC, abstractmethod
from collections.abc import Callable
from llama_index.core.vector_stores.types import BasePydanticVectorStore
from private_gpt.settings.settings import Settings
VectorStoreProvider = (
type["VectorStoreF... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>h no filters or with only one filter
def reduce_tree(
f: Union[MetadataFilter, ExactMatchFilter, "MetadataFilters"]
) -> Union[MetadataFilter, ExactMatchFilter, "MetadataFilters"] | None:
if isinstance(f, MetadataFilter):
return f
if len(... | fim | zylon-ai/private-gpt | python |
import contextlib
import logging
import os
from typing import Any
from qdrant_client import ( # type: ignore[import-not-found]
AsyncQdrantClient,
QdrantClient,
)
from private_gpt.settings.settings import Settings
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
class QdrantClients:
... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import typing
from typing import Any
from llama_index.core.vector_stores.types import BasePydanticVectorStore
from private_gpt.components.ingest.metadata_helper import MetadataKeys
from private_gpt.components.vector_store.factory import VectorStoreFactory
from private_gpt.settings.settings import Settin... | fim | zylon-ai/private-gpt | python |
import logging
import typing
from injector import inject, singleton
from llama_index.core.callbacks import CallbackManager
from llama_index.core.indices.vector_store import VectorIndexRetriever, VectorStoreIndex
from llama_index.core.vector_stores.types import (
BasePydanticVectorStore,
FilterCondition,
Me... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import importlib
import logging
import os
import re
from typing import Any
import html2text
from injector import inject, singleton
from pydantic import BaseModel
from private_gpt.settings.settings import Settings
from private_gpt.utils.dependencies import format_missing_dependency_message... | fim | zylon-ai/private-gpt | python |
from typing import Any
from pydantic import BaseModel
class WebSearchResult(BaseModel):
idx: int | None = None
title: str
url: str
favicon_url: str | None = None
description: str
age: str | None = None
content: str | None = None
content_type: str | None = None
tokens: int = 0
... | fim | zylon-ai/private-gpt | python |
from abc import ABC, abstractmethod
from private_gpt.components.web.web_search.models import WebSearchResult
class BaseWebSearchResultProcessor(ABC):
"""Base class for web search result processors.
This abstract class defines the interface that all web search result
processors must implement. Processors... | fim | zylon-ai/private-gpt | python |
import logging
from typing import TYPE_CHECKING
from private_gpt.components.web.web_scraper_service import (
WebScraperService,
)
from private_gpt.components.web.web_search.models import WebSearchResult
from private_gpt.components.web.web_search.processors.base import (
BaseWebSearchResultProcessor,
)
from pri... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import logging
from private_gpt.components.web.web_scraper_service import WebScraperService
from private<|fim_suffix|>True
else:
logger.debug(
f"ScrapedContentProcessor: Successfully scraped {result.url}"
)
res... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import logging
import time
from asyncio import CancelledError
from typing import TYPE_CHECKING, Any
from llama_index.core import PromptTemplate
from pydantic import BaseModel
from private_gpt.components.llm.llm_component import LLMComponent
from private_gpt.components.tools.builders.summa... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>],
model_id: str | None = None,
) -> list[WebSearchResult]:
return results
<|fim_prefix|>import logging
from inje<|fim_middle|>ctor import inject
from private_gpt.components.web.web_search.models import WebSearchResult
from private_gpt.components.web.web_search.processors.base import... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>nt, offset,
language, filters, etc.).
Returns:
List of standardized search results from the provider.
"""
pass
@abstractmethod
async def close(self) -> None:
"""Close and cleanup provider resources.
This method should be c... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import logging
from typing import Any
import aiohttp
from aiohttp import ClientResponse
from injector import inject, singleton
from private_gpt.components.web.web_search.models import WebSearchResult
from private_gpt.components.web.web_search.providers.base import BaseWebSearchProvider
fr... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> offset=offset,
result_filter=result_filter,
safesearch=safesearch,
freshness=freshness,
spellcheck=spellcheck,
language=language,
**kwargs,
)
await asyncio.to_thread(self._save_to_cache, cache_key, results)
... | fim | zylon-ai/private-gpt | python |
import logging
from typing import Any
from injector import singleton
from private_gpt.components.web.web_search.models import WebSearchResult
from private_gpt.components.web.web_search.providers.base import BaseWebSearchProvider
logger = logging.getLogger(__name__)
@singleton
class MockSearchProvider(BaseWebSearch... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import logging
from typing import Any
from injector import inject, singleton
from private_gpt.components.llm.llm_component import LLMComponent
from private_gpt.components.tools.builders.summary_builder import (
SummarizeWorkflowBuilder,
)
from private_gpt.components.web.web_scraper_service import We... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> condensed_query=final_condensed_query, original_query=query
)
except Exception as e:
logger.error(f"Error in condense_question: {e}")
return CondenseResultEvent(condensed_query=query, original_query=query)
<|fim_prefix|>import logging
from typing import Any... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import logging
from collections.abc import Awaitable, Callable, Iterator
from typing import TYPE_CHECKING, Any
from llama_index.core import BasePromptTemplate, ChatPromptTemplate
from llama_index.core.base.llms.types import ChatMessage, MessageRole
from llama_index.core.base.response.schem... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
from collections.abc import Awaitable, Callable, Sequence
from typing import Any
from llama_index.core import QueryBundle
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import (
RESPONSE_TYPE,
AsyncStreamingResponse,
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>f from_nodes(cls, nodes: list[BaseNode]) -> "InMemoryRetriever":
node_with_scores = [NodeWithScore(node=node) for node in nodes]
return cls(node_with_scores)
@classmethod
def from_texts(cls, texts: Sequence[str]) -> "InMemoryRetriever":
nodes = [TextNode(text=text) for tex... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|> summary_template,
context_str=text_chunk,
**response_kwargs,
)
for text_chunk in text_chunks
]
summaries = [summary.model_dump_json() for summa... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import logging
from collections.abc import Awaitable, Callable
from typing import Any
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks import CallbackManager
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_inde... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import logging
from collections.abc import Callable
from contextlib import suppress
from typing import TYPE_CHECKING, Any
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.callbacks import CallbackM... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import asyncio
import logging
import re
from collections.abc import Callable
from typing import TYPE_CHECKING, Any
import pandas as pd
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llms import LLM
from llama_ind... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from typing import TYPE_CHECKING, Any
if T<|fim_suffix|> Context
AnyContext = Context[Any]
else:
from workflows import Context
AnyContext = Context
<|fim_middle|>YPE_CHECKING:
from workflows import<|endoftext|> | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>import os
from pathlib import Path
PROJECT_ROOT_PATH: Path = (
Path(os.environ.get("PGPT_PROJECT_ROOT", str(Path(__file__).parents[1])))
.expanduser()
.resolve()
)
def _default_pgpt_home() -> str:
if os.name == "nt": # Windows
local_app_data = os.environ.get("LOCALAPPDATA")
... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>ly.
If running in an asyncio loop, stores the injector in the loop.
Otherwise, sets it as the global injector.
"""
try:
with _loop_injector_lock:
loop = asyncio.get_running_loop()
setattr(loop, _INJECTOR_KEY, injector)
except RuntimeError:
# Not... | fim | zylon-ai/private-gpt | python |
from typing import Any
from fastapi import FastAPI
TITLE = "Private-GPT API"
DESCRIPTION = """\
PrivateGPT -built by Zylon- is a production-ready AI project that allows you to ask questions about your documents using the power
of Large Language Models (LLMs), even in scenarios without an Internet connection. 100% pri... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>_ERROR
class PermissionDenied(Base):
error_type = _Types.PERMISSION_ERROR
status_code = 403
default_event_code = _Codes.PERMISSION_ERROR
class NotFound(Base):
error_type = _Types.NOT_FOUND_ERROR
status_code = 404
default_event_code = _Codes.NOT_FOU... | fim | zylon-ai/private-gpt | python |
<|fim_prefix|>from collections.abc import AsyncGenerator
from typing import TYPE_CHECKING
from pydantic import BaseModel, Field
from private_gpt.chat.extensions.citation import ZylonCitation
from private_gpt.components.chunk.models import SourceType
from private_gpt.events.models import (
ContentBlockType,
Ev... | fim | zylon-ai/private-gpt | python |
import json
from typing import cast
from pydantic import BaseModel
from private_gpt.components.streaming.providers.models import StreamStatus
from private_gpt.events.models import (
Event,
FatalError,
PingEvent,
RawContentBlockDeltaEvent,
RawContentBlockStartEvent,
RawContentBlockStopEvent,
... | fim | zylon-ai/private-gpt | python |
from abc import ABC, abstractmethod
from collections.abc import AsyncGenerator
from private_gpt.events.models import Event
class BaseEventInterceptor(ABC):
@abstractmethod
async def intercept(
self, gen: AsyncGenerator[Event, None]
) -> AsyncGenerator[Event, None]:
"""Intercepts events fr... | fim | zylon-ai/private-gpt | python |
<|fim_suffix|>ContentBlockStopEvent):
if event.block_id in active_blocks:
active_blocks.remove(event.block_id)
yield event
else:
continue # Edge case: If the content block never started, skip the end event
... | fim | zylon-ai/private-gpt | python |
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