mirror of
https://github.com/MODSetter/SurfSense.git
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Merge remote-tracking branch 'upstream/dev' into feat/azure-ocr
This commit is contained in:
Anish Sarkar
commit
6038f6dfc0
84 changed files with 6041 additions and 1065 deletions
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@ -0,0 +1,190 @@
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"""Add vision LLM configs table and rename preference column
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Revision ID: 120
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Revises: 119
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Changes:
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1. Create visionprovider enum type
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2. Create vision_llm_configs table
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3. Rename vision_llm_id -> vision_llm_config_id on searchspaces
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4. Add vision config permissions to existing system roles
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"""
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from __future__ import annotations
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from collections.abc import Sequence
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import sqlalchemy as sa
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from sqlalchemy.dialects.postgresql import ENUM as PG_ENUM, UUID
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from alembic import op
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revision: str = "120"
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down_revision: str | None = "119"
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branch_labels: str | Sequence[str] | None = None
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depends_on: str | Sequence[str] | None = None
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VISION_PROVIDER_VALUES = (
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"OPENAI",
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"ANTHROPIC",
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"GOOGLE",
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"AZURE_OPENAI",
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"VERTEX_AI",
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"BEDROCK",
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"XAI",
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"OPENROUTER",
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"OLLAMA",
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"GROQ",
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"TOGETHER_AI",
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"FIREWORKS_AI",
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"DEEPSEEK",
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"MISTRAL",
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"CUSTOM",
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)
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def upgrade() -> None:
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connection = op.get_bind()
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# 1. Create visionprovider enum
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connection.execute(
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sa.text(
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"""
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DO $$
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BEGIN
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IF NOT EXISTS (SELECT 1 FROM pg_type WHERE typname = 'visionprovider') THEN
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CREATE TYPE visionprovider AS ENUM (
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'OPENAI', 'ANTHROPIC', 'GOOGLE', 'AZURE_OPENAI', 'VERTEX_AI',
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'BEDROCK', 'XAI', 'OPENROUTER', 'OLLAMA', 'GROQ',
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'TOGETHER_AI', 'FIREWORKS_AI', 'DEEPSEEK', 'MISTRAL', 'CUSTOM'
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);
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END IF;
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END
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$$;
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"""
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)
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)
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# 2. Create vision_llm_configs table
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result = connection.execute(
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sa.text(
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"SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = 'vision_llm_configs')"
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)
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)
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if not result.scalar():
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op.create_table(
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"vision_llm_configs",
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sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
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sa.Column("name", sa.String(100), nullable=False),
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sa.Column("description", sa.String(500), nullable=True),
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sa.Column(
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"provider",
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PG_ENUM(*VISION_PROVIDER_VALUES, name="visionprovider", create_type=False),
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nullable=False,
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),
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sa.Column("custom_provider", sa.String(100), nullable=True),
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sa.Column("model_name", sa.String(100), nullable=False),
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sa.Column("api_key", sa.String(), nullable=False),
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sa.Column("api_base", sa.String(500), nullable=True),
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sa.Column("api_version", sa.String(50), nullable=True),
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sa.Column("litellm_params", sa.JSON(), nullable=True),
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sa.Column("search_space_id", sa.Integer(), nullable=False),
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sa.Column("user_id", UUID(as_uuid=True), nullable=False),
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sa.Column(
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"created_at",
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sa.TIMESTAMP(timezone=True),
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server_default=sa.text("now()"),
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nullable=False,
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),
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sa.PrimaryKeyConstraint("id"),
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sa.ForeignKeyConstraint(
|
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["search_space_id"], ["searchspaces.id"], ondelete="CASCADE"
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),
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sa.ForeignKeyConstraint(
|
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["user_id"], ["user.id"], ondelete="CASCADE"
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),
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)
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op.execute(
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"CREATE INDEX IF NOT EXISTS ix_vision_llm_configs_name "
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"ON vision_llm_configs (name)"
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)
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op.execute(
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"CREATE INDEX IF NOT EXISTS ix_vision_llm_configs_search_space_id "
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"ON vision_llm_configs (search_space_id)"
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)
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# 3. Rename vision_llm_id -> vision_llm_config_id on searchspaces
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existing_columns = [
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col["name"] for col in sa.inspect(connection).get_columns("searchspaces")
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]
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if "vision_llm_id" in existing_columns and "vision_llm_config_id" not in existing_columns:
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op.alter_column("searchspaces", "vision_llm_id", new_column_name="vision_llm_config_id")
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elif "vision_llm_config_id" not in existing_columns:
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op.add_column(
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"searchspaces",
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sa.Column("vision_llm_config_id", sa.Integer(), nullable=True, server_default="0"),
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)
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# 4. Add vision config permissions to existing system roles
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connection.execute(
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sa.text(
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"""
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UPDATE search_space_roles
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SET permissions = array_cat(
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permissions,
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ARRAY['vision_configs:create', 'vision_configs:read']
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)
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WHERE is_system_role = true
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AND name = 'Editor'
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AND NOT ('vision_configs:create' = ANY(permissions))
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"""
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)
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)
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connection.execute(
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sa.text(
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"""
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UPDATE search_space_roles
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SET permissions = array_cat(
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permissions,
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ARRAY['vision_configs:read']
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)
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WHERE is_system_role = true
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AND name = 'Viewer'
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AND NOT ('vision_configs:read' = ANY(permissions))
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"""
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)
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)
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def downgrade() -> None:
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connection = op.get_bind()
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# Remove permissions
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connection.execute(
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sa.text(
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"""
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UPDATE search_space_roles
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SET permissions = array_remove(
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array_remove(
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array_remove(permissions, 'vision_configs:create'),
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'vision_configs:read'
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),
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'vision_configs:delete'
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)
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WHERE is_system_role = true
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"""
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)
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)
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|
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# Rename column back
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existing_columns = [
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col["name"] for col in sa.inspect(connection).get_columns("searchspaces")
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]
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if "vision_llm_config_id" in existing_columns:
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op.alter_column("searchspaces", "vision_llm_config_id", new_column_name="vision_llm_id")
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|
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# Drop table and enum
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op.execute("DROP INDEX IF EXISTS ix_vision_llm_configs_search_space_id")
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op.execute("DROP INDEX IF EXISTS ix_vision_llm_configs_name")
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op.execute("DROP TABLE IF EXISTS vision_llm_configs")
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op.execute("DROP TYPE IF EXISTS visionprovider")
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11
surfsense_backend/app/agents/autocomplete/__init__.py
Normal file
11
surfsense_backend/app/agents/autocomplete/__init__.py
Normal file
|
|
@ -0,0 +1,11 @@
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"""Agent-based vision autocomplete with scoped filesystem exploration."""
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|
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from app.agents.autocomplete.autocomplete_agent import (
|
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create_autocomplete_agent,
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stream_autocomplete_agent,
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)
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|
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__all__ = [
|
||||
"create_autocomplete_agent",
|
||||
"stream_autocomplete_agent",
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||||
]
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497
surfsense_backend/app/agents/autocomplete/autocomplete_agent.py
Normal file
497
surfsense_backend/app/agents/autocomplete/autocomplete_agent.py
Normal file
|
|
@ -0,0 +1,497 @@
|
|||
"""Vision autocomplete agent with scoped filesystem exploration.
|
||||
|
||||
Converts the stateless single-shot vision autocomplete into an agent that
|
||||
seeds a virtual filesystem from KB search results and lets the vision LLM
|
||||
explore documents via ``ls``, ``read_file``, ``glob``, ``grep``, etc.
|
||||
before generating the final completion.
|
||||
|
||||
Performance: KB search and agent graph compilation run in parallel so
|
||||
the only sequential latency is KB-search (or agent compile, whichever is
|
||||
slower) + the agent's LLM turns. There is no separate "query extraction"
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||||
LLM call — the window title is used directly as the KB search query.
|
||||
"""
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||||
|
||||
from __future__ import annotations
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|
||||
import asyncio
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import json
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import logging
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||||
import re
|
||||
import uuid
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from collections.abc import AsyncGenerator
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||||
from typing import Any
|
||||
|
||||
from deepagents.graph import BASE_AGENT_PROMPT
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from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
|
||||
from langchain.agents import create_agent
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||||
from langchain_anthropic.middleware import AnthropicPromptCachingMiddleware
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from langchain_core.language_models import BaseChatModel
|
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from langchain_core.messages import AIMessage, ToolMessage
|
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|
||||
from app.agents.new_chat.middleware.filesystem import SurfSenseFilesystemMiddleware
|
||||
from app.agents.new_chat.middleware.knowledge_search import (
|
||||
build_scoped_filesystem,
|
||||
search_knowledge_base,
|
||||
)
|
||||
from app.services.new_streaming_service import VercelStreamingService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
KB_TOP_K = 10
|
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|
||||
# ---------------------------------------------------------------------------
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||||
# System prompt
|
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# ---------------------------------------------------------------------------
|
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|
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AUTOCOMPLETE_SYSTEM_PROMPT = """You are a smart writing assistant that analyzes the user's screen to draft or complete text.
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||||
|
||||
You will receive a screenshot of the user's screen. Your PRIMARY source of truth is the screenshot itself — the visual context determines what to write.
|
||||
|
||||
Your job:
|
||||
1. Analyze the ENTIRE screenshot to understand what the user is working on (email thread, chat conversation, document, code editor, form, etc.).
|
||||
2. Identify the text area where the user will type.
|
||||
3. Generate the text the user most likely wants to write based on the visual context.
|
||||
|
||||
You also have access to the user's knowledge base documents via filesystem tools. However:
|
||||
- ONLY consult the knowledge base if the screenshot clearly involves a topic where your KB documents are DIRECTLY relevant (e.g., the user is writing about a specific project/topic that matches a document title).
|
||||
- Do NOT explore documents just because they exist. Most autocomplete requests can be answered purely from the screenshot.
|
||||
- If you do read a document, only incorporate information that is 100% relevant to what the user is typing RIGHT NOW. Do not add extra details, background, or tangential information from the KB.
|
||||
- Keep your output SHORT — autocomplete should feel like a natural continuation, not an essay.
|
||||
|
||||
Key behavior:
|
||||
- If the text area is EMPTY, draft a concise response or message based on what you see on screen (e.g., reply to an email, respond to a chat message, continue a document).
|
||||
- If the text area already has text, continue it naturally — typically just a sentence or two.
|
||||
|
||||
Rules:
|
||||
- Be CONCISE. Prefer a single paragraph or a few sentences. Autocomplete is a quick assist, not a full draft.
|
||||
- Match the tone and formality of the surrounding context.
|
||||
- If the screen shows code, write code. If it shows a casual chat, be casual. If it shows a formal email, be formal.
|
||||
- Do NOT describe the screenshot or explain your reasoning.
|
||||
- Do NOT cite or reference documents explicitly — just let the knowledge inform your writing naturally.
|
||||
- If you cannot determine what to write, output an empty JSON array: []
|
||||
|
||||
## Output Format
|
||||
|
||||
You MUST provide exactly 3 different suggestion options. Each should be a distinct, plausible completion — vary the tone, detail level, or angle.
|
||||
|
||||
Return your suggestions as a JSON array of exactly 3 strings. Output ONLY the JSON array, nothing else — no markdown fences, no explanation, no commentary.
|
||||
|
||||
Example format:
|
||||
["First suggestion text here.", "Second suggestion — a different take.", "Third option with another approach."]
|
||||
|
||||
## Filesystem Tools `ls`, `read_file`, `write_file`, `edit_file`, `glob`, `grep`
|
||||
|
||||
All file paths must start with a `/`.
|
||||
- ls: list files and directories at a given path.
|
||||
- read_file: read a file from the filesystem.
|
||||
- write_file: create a temporary file in the session (not persisted).
|
||||
- edit_file: edit a file in the session (not persisted for /documents/ files).
|
||||
- glob: find files matching a pattern (e.g., "**/*.xml").
|
||||
- grep: search for text within files.
|
||||
|
||||
## When to Use Filesystem Tools
|
||||
|
||||
BEFORE reaching for any tool, ask yourself: "Can I write a good completion purely from the screenshot?" If yes, just write it — do NOT explore the KB.
|
||||
|
||||
Only use tools when:
|
||||
- The user is clearly writing about a specific topic that likely has detailed information in their KB.
|
||||
- You need a specific fact, name, number, or reference that the screenshot doesn't provide.
|
||||
|
||||
When you do use tools, be surgical:
|
||||
- Check the `ls` output first. If no document title looks relevant, stop — do not read files just to see what's there.
|
||||
- If a title looks relevant, read only the `<chunk_index>` (first ~20 lines) and jump to matched chunks. Do not read entire documents.
|
||||
- Extract only the specific information you need and move on to generating the completion.
|
||||
|
||||
## Reading Documents Efficiently
|
||||
|
||||
Documents are formatted as XML. Each document contains:
|
||||
- `<document_metadata>` — title, type, URL, etc.
|
||||
- `<chunk_index>` — a table of every chunk with its **line range** and a
|
||||
`matched="true"` flag for chunks that matched the search query.
|
||||
- `<document_content>` — the actual chunks in original document order.
|
||||
|
||||
**Workflow**: read the first ~20 lines to see the `<chunk_index>`, identify
|
||||
chunks marked `matched="true"`, then use `read_file(path, offset=<start_line>,
|
||||
limit=<lines>)` to jump directly to those sections."""
|
||||
|
||||
APP_CONTEXT_BLOCK = """
|
||||
|
||||
The user is currently working in "{app_name}" (window: "{window_title}"). Use this to understand the type of application and adapt your tone and format accordingly."""
|
||||
|
||||
|
||||
def _build_autocomplete_system_prompt(app_name: str, window_title: str) -> str:
|
||||
prompt = AUTOCOMPLETE_SYSTEM_PROMPT
|
||||
if app_name:
|
||||
prompt += APP_CONTEXT_BLOCK.format(app_name=app_name, window_title=window_title)
|
||||
return prompt
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Pre-compute KB filesystem (runs in parallel with agent compilation)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class _KBResult:
|
||||
"""Container for pre-computed KB filesystem results."""
|
||||
|
||||
__slots__ = ("files", "ls_ai_msg", "ls_tool_msg")
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
files: dict[str, Any] | None = None,
|
||||
ls_ai_msg: AIMessage | None = None,
|
||||
ls_tool_msg: ToolMessage | None = None,
|
||||
) -> None:
|
||||
self.files = files
|
||||
self.ls_ai_msg = ls_ai_msg
|
||||
self.ls_tool_msg = ls_tool_msg
|
||||
|
||||
@property
|
||||
def has_documents(self) -> bool:
|
||||
return bool(self.files)
|
||||
|
||||
|
||||
async def precompute_kb_filesystem(
|
||||
search_space_id: int,
|
||||
query: str,
|
||||
top_k: int = KB_TOP_K,
|
||||
) -> _KBResult:
|
||||
"""Search the KB and build the scoped filesystem outside the agent.
|
||||
|
||||
This is designed to be called via ``asyncio.gather`` alongside agent
|
||||
graph compilation so the two run concurrently.
|
||||
"""
|
||||
if not query:
|
||||
return _KBResult()
|
||||
|
||||
try:
|
||||
search_results = await search_knowledge_base(
|
||||
query=query,
|
||||
search_space_id=search_space_id,
|
||||
top_k=top_k,
|
||||
)
|
||||
|
||||
if not search_results:
|
||||
return _KBResult()
|
||||
|
||||
new_files, _ = await build_scoped_filesystem(
|
||||
documents=search_results,
|
||||
search_space_id=search_space_id,
|
||||
)
|
||||
|
||||
if not new_files:
|
||||
return _KBResult()
|
||||
|
||||
doc_paths = [
|
||||
p
|
||||
for p, v in new_files.items()
|
||||
if p.startswith("/documents/") and v is not None
|
||||
]
|
||||
tool_call_id = f"auto_ls_{uuid.uuid4().hex[:12]}"
|
||||
ai_msg = AIMessage(
|
||||
content="",
|
||||
tool_calls=[
|
||||
{"name": "ls", "args": {"path": "/documents"}, "id": tool_call_id}
|
||||
],
|
||||
)
|
||||
tool_msg = ToolMessage(
|
||||
content=str(doc_paths) if doc_paths else "No documents found.",
|
||||
tool_call_id=tool_call_id,
|
||||
)
|
||||
return _KBResult(files=new_files, ls_ai_msg=ai_msg, ls_tool_msg=tool_msg)
|
||||
|
||||
except Exception:
|
||||
logger.warning(
|
||||
"KB pre-computation failed, proceeding without KB", exc_info=True
|
||||
)
|
||||
return _KBResult()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Filesystem middleware — no save_document, no persistence
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class AutocompleteFilesystemMiddleware(SurfSenseFilesystemMiddleware):
|
||||
"""Filesystem middleware for autocomplete — read-only exploration only.
|
||||
|
||||
Strips ``save_document`` (permanent KB persistence) and passes
|
||||
``search_space_id=None`` so ``write_file`` / ``edit_file`` stay ephemeral.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
super().__init__(search_space_id=None, created_by_id=None)
|
||||
self.tools = [t for t in self.tools if t.name != "save_document"]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Agent factory
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def _compile_agent(
|
||||
llm: BaseChatModel,
|
||||
app_name: str,
|
||||
window_title: str,
|
||||
) -> Any:
|
||||
"""Compile the agent graph (CPU-bound, runs in a thread)."""
|
||||
system_prompt = _build_autocomplete_system_prompt(app_name, window_title)
|
||||
final_system_prompt = system_prompt + "\n\n" + BASE_AGENT_PROMPT
|
||||
|
||||
middleware = [
|
||||
AutocompleteFilesystemMiddleware(),
|
||||
PatchToolCallsMiddleware(),
|
||||
AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
|
||||
]
|
||||
|
||||
agent = await asyncio.to_thread(
|
||||
create_agent,
|
||||
llm,
|
||||
system_prompt=final_system_prompt,
|
||||
tools=[],
|
||||
middleware=middleware,
|
||||
)
|
||||
return agent.with_config({"recursion_limit": 200})
|
||||
|
||||
|
||||
async def create_autocomplete_agent(
|
||||
llm: BaseChatModel,
|
||||
*,
|
||||
search_space_id: int,
|
||||
kb_query: str,
|
||||
app_name: str = "",
|
||||
window_title: str = "",
|
||||
) -> tuple[Any, _KBResult]:
|
||||
"""Create the autocomplete agent and pre-compute KB in parallel.
|
||||
|
||||
Returns ``(agent, kb_result)`` so the caller can inject the pre-computed
|
||||
filesystem into the agent's initial state without any middleware delay.
|
||||
"""
|
||||
agent, kb = await asyncio.gather(
|
||||
_compile_agent(llm, app_name, window_title),
|
||||
precompute_kb_filesystem(search_space_id, kb_query),
|
||||
)
|
||||
return agent, kb
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# JSON suggestion parsing (with fallback)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def _parse_suggestions(raw: str) -> list[str]:
|
||||
"""Extract a list of suggestion strings from the agent's output.
|
||||
|
||||
Tries, in order:
|
||||
1. Direct ``json.loads``
|
||||
2. Extract content between ```json ... ``` fences
|
||||
3. Find the first ``[`` … ``]`` span
|
||||
Falls back to wrapping the raw text as a single suggestion.
|
||||
"""
|
||||
text = raw.strip()
|
||||
if not text:
|
||||
return []
|
||||
|
||||
for candidate in _json_candidates(text):
|
||||
try:
|
||||
parsed = json.loads(candidate)
|
||||
if isinstance(parsed, list) and all(isinstance(s, str) for s in parsed):
|
||||
return [s for s in parsed if s.strip()]
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
continue
|
||||
|
||||
return [text]
|
||||
|
||||
|
||||
def _json_candidates(text: str) -> list[str]:
|
||||
"""Yield candidate JSON strings from raw text."""
|
||||
candidates = [text]
|
||||
|
||||
fence = re.search(r"```(?:json)?\s*\n?(.*?)```", text, re.DOTALL)
|
||||
if fence:
|
||||
candidates.append(fence.group(1).strip())
|
||||
|
||||
bracket = re.search(r"\[.*]", text, re.DOTALL)
|
||||
if bracket:
|
||||
candidates.append(bracket.group(0))
|
||||
|
||||
return candidates
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Streaming helper
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def stream_autocomplete_agent(
|
||||
agent: Any,
|
||||
input_data: dict[str, Any],
|
||||
streaming_service: VercelStreamingService,
|
||||
*,
|
||||
emit_message_start: bool = True,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Stream agent events as Vercel SSE, with thinking steps for tool calls.
|
||||
|
||||
When ``emit_message_start`` is False the caller has already sent the
|
||||
``message_start`` event (e.g. to show preparation steps before the agent
|
||||
runs).
|
||||
"""
|
||||
thread_id = uuid.uuid4().hex
|
||||
config = {"configurable": {"thread_id": thread_id}}
|
||||
|
||||
text_buffer: list[str] = []
|
||||
active_tool_depth = 0
|
||||
thinking_step_counter = 0
|
||||
tool_step_ids: dict[str, str] = {}
|
||||
step_titles: dict[str, str] = {}
|
||||
completed_step_ids: set[str] = set()
|
||||
last_active_step_id: str | None = None
|
||||
|
||||
def next_thinking_step_id() -> str:
|
||||
nonlocal thinking_step_counter
|
||||
thinking_step_counter += 1
|
||||
return f"autocomplete-step-{thinking_step_counter}"
|
||||
|
||||
def complete_current_step() -> str | None:
|
||||
nonlocal last_active_step_id
|
||||
if last_active_step_id and last_active_step_id not in completed_step_ids:
|
||||
completed_step_ids.add(last_active_step_id)
|
||||
title = step_titles.get(last_active_step_id, "Done")
|
||||
event = streaming_service.format_thinking_step(
|
||||
step_id=last_active_step_id,
|
||||
title=title,
|
||||
status="complete",
|
||||
)
|
||||
last_active_step_id = None
|
||||
return event
|
||||
return None
|
||||
|
||||
if emit_message_start:
|
||||
yield streaming_service.format_message_start()
|
||||
|
||||
gen_step_id = next_thinking_step_id()
|
||||
last_active_step_id = gen_step_id
|
||||
step_titles[gen_step_id] = "Generating suggestions"
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=gen_step_id,
|
||||
title="Generating suggestions",
|
||||
status="in_progress",
|
||||
)
|
||||
|
||||
try:
|
||||
async for event in agent.astream_events(
|
||||
input_data, config=config, version="v2"
|
||||
):
|
||||
event_type = event.get("event", "")
|
||||
if event_type == "on_chat_model_stream":
|
||||
if active_tool_depth > 0:
|
||||
continue
|
||||
if "surfsense:internal" in event.get("tags", []):
|
||||
continue
|
||||
chunk = event.get("data", {}).get("chunk")
|
||||
if chunk and hasattr(chunk, "content"):
|
||||
content = chunk.content
|
||||
if content and isinstance(content, str):
|
||||
text_buffer.append(content)
|
||||
|
||||
elif event_type == "on_chat_model_end":
|
||||
if active_tool_depth > 0:
|
||||
continue
|
||||
if "surfsense:internal" in event.get("tags", []):
|
||||
continue
|
||||
output = event.get("data", {}).get("output")
|
||||
if output and hasattr(output, "content"):
|
||||
if getattr(output, "tool_calls", None):
|
||||
continue
|
||||
content = output.content
|
||||
if content and isinstance(content, str) and not text_buffer:
|
||||
text_buffer.append(content)
|
||||
|
||||
elif event_type == "on_tool_start":
|
||||
active_tool_depth += 1
|
||||
tool_name = event.get("name", "unknown_tool")
|
||||
run_id = event.get("run_id", "")
|
||||
tool_input = event.get("data", {}).get("input", {})
|
||||
|
||||
step_event = complete_current_step()
|
||||
if step_event:
|
||||
yield step_event
|
||||
|
||||
tool_step_id = next_thinking_step_id()
|
||||
tool_step_ids[run_id] = tool_step_id
|
||||
last_active_step_id = tool_step_id
|
||||
|
||||
title, items = _describe_tool_call(tool_name, tool_input)
|
||||
step_titles[tool_step_id] = title
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title=title,
|
||||
status="in_progress",
|
||||
items=items,
|
||||
)
|
||||
|
||||
elif event_type == "on_tool_end":
|
||||
active_tool_depth = max(0, active_tool_depth - 1)
|
||||
run_id = event.get("run_id", "")
|
||||
step_id = tool_step_ids.pop(run_id, None)
|
||||
if step_id and step_id not in completed_step_ids:
|
||||
completed_step_ids.add(step_id)
|
||||
title = step_titles.get(step_id, "Done")
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=step_id,
|
||||
title=title,
|
||||
status="complete",
|
||||
)
|
||||
if last_active_step_id == step_id:
|
||||
last_active_step_id = None
|
||||
|
||||
step_event = complete_current_step()
|
||||
if step_event:
|
||||
yield step_event
|
||||
|
||||
raw_text = "".join(text_buffer)
|
||||
suggestions = _parse_suggestions(raw_text)
|
||||
|
||||
yield streaming_service.format_data(
|
||||
"suggestions", {"options": suggestions}
|
||||
)
|
||||
|
||||
yield streaming_service.format_finish()
|
||||
yield streaming_service.format_done()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Autocomplete agent streaming error: {e}", exc_info=True)
|
||||
yield streaming_service.format_error("Autocomplete failed. Please try again.")
|
||||
yield streaming_service.format_done()
|
||||
|
||||
|
||||
def _describe_tool_call(tool_name: str, tool_input: Any) -> tuple[str, list[str]]:
|
||||
"""Return a human-readable (title, items) for a tool call thinking step."""
|
||||
inp = tool_input if isinstance(tool_input, dict) else {}
|
||||
if tool_name == "ls":
|
||||
path = inp.get("path", "/")
|
||||
return "Listing files", [path]
|
||||
if tool_name == "read_file":
|
||||
fp = inp.get("file_path", "")
|
||||
display = fp if len(fp) <= 80 else "…" + fp[-77:]
|
||||
return "Reading file", [display]
|
||||
if tool_name == "write_file":
|
||||
fp = inp.get("file_path", "")
|
||||
display = fp if len(fp) <= 80 else "…" + fp[-77:]
|
||||
return "Writing file", [display]
|
||||
if tool_name == "edit_file":
|
||||
fp = inp.get("file_path", "")
|
||||
display = fp if len(fp) <= 80 else "…" + fp[-77:]
|
||||
return "Editing file", [display]
|
||||
if tool_name == "glob":
|
||||
pat = inp.get("pattern", "")
|
||||
base = inp.get("path", "/")
|
||||
return "Searching files", [f"{pat} in {base}"]
|
||||
if tool_name == "grep":
|
||||
pat = inp.get("pattern", "")
|
||||
path = inp.get("path", "")
|
||||
display_pat = pat[:60] + ("…" if len(pat) > 60 else "")
|
||||
return "Searching content", [
|
||||
f'"{display_pat}"' + (f" in {path}" if path else "")
|
||||
]
|
||||
return f"Using {tool_name}", []
|
||||
|
|
@ -25,7 +25,12 @@ from app.agents.new_chat.checkpointer import (
|
|||
close_checkpointer,
|
||||
setup_checkpointer_tables,
|
||||
)
|
||||
from app.config import config, initialize_image_gen_router, initialize_llm_router
|
||||
from app.config import (
|
||||
config,
|
||||
initialize_image_gen_router,
|
||||
initialize_llm_router,
|
||||
initialize_vision_llm_router,
|
||||
)
|
||||
from app.db import User, create_db_and_tables, get_async_session
|
||||
from app.routes import router as crud_router
|
||||
from app.routes.auth_routes import router as auth_router
|
||||
|
|
@ -223,6 +228,7 @@ async def lifespan(app: FastAPI):
|
|||
await setup_checkpointer_tables()
|
||||
initialize_llm_router()
|
||||
initialize_image_gen_router()
|
||||
initialize_vision_llm_router()
|
||||
try:
|
||||
await asyncio.wait_for(seed_surfsense_docs(), timeout=120)
|
||||
except TimeoutError:
|
||||
|
|
|
|||
|
|
@ -18,10 +18,15 @@ def init_worker(**kwargs):
|
|||
This ensures the Auto mode (LiteLLM Router) is available for background tasks
|
||||
like document summarization and image generation.
|
||||
"""
|
||||
from app.config import initialize_image_gen_router, initialize_llm_router
|
||||
from app.config import (
|
||||
initialize_image_gen_router,
|
||||
initialize_llm_router,
|
||||
initialize_vision_llm_router,
|
||||
)
|
||||
|
||||
initialize_llm_router()
|
||||
initialize_image_gen_router()
|
||||
initialize_vision_llm_router()
|
||||
|
||||
|
||||
# Get Celery configuration from environment
|
||||
|
|
|
|||
|
|
@ -102,6 +102,44 @@ def load_global_image_gen_configs():
|
|||
return []
|
||||
|
||||
|
||||
def load_global_vision_llm_configs():
|
||||
global_config_file = BASE_DIR / "app" / "config" / "global_llm_config.yaml"
|
||||
|
||||
if not global_config_file.exists():
|
||||
return []
|
||||
|
||||
try:
|
||||
with open(global_config_file, encoding="utf-8") as f:
|
||||
data = yaml.safe_load(f)
|
||||
return data.get("global_vision_llm_configs", [])
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to load global vision LLM configs: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def load_vision_llm_router_settings():
|
||||
default_settings = {
|
||||
"routing_strategy": "usage-based-routing",
|
||||
"num_retries": 3,
|
||||
"allowed_fails": 3,
|
||||
"cooldown_time": 60,
|
||||
}
|
||||
|
||||
global_config_file = BASE_DIR / "app" / "config" / "global_llm_config.yaml"
|
||||
|
||||
if not global_config_file.exists():
|
||||
return default_settings
|
||||
|
||||
try:
|
||||
with open(global_config_file, encoding="utf-8") as f:
|
||||
data = yaml.safe_load(f)
|
||||
settings = data.get("vision_llm_router_settings", {})
|
||||
return {**default_settings, **settings}
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to load vision LLM router settings: {e}")
|
||||
return default_settings
|
||||
|
||||
|
||||
def load_image_gen_router_settings():
|
||||
"""
|
||||
Load router settings for image generation Auto mode from YAML file.
|
||||
|
|
@ -182,6 +220,29 @@ def initialize_image_gen_router():
|
|||
print(f"Warning: Failed to initialize Image Generation Router: {e}")
|
||||
|
||||
|
||||
def initialize_vision_llm_router():
|
||||
vision_configs = load_global_vision_llm_configs()
|
||||
router_settings = load_vision_llm_router_settings()
|
||||
|
||||
if not vision_configs:
|
||||
print(
|
||||
"Info: No global vision LLM configs found, "
|
||||
"Vision LLM Auto mode will not be available"
|
||||
)
|
||||
return
|
||||
|
||||
try:
|
||||
from app.services.vision_llm_router_service import VisionLLMRouterService
|
||||
|
||||
VisionLLMRouterService.initialize(vision_configs, router_settings)
|
||||
print(
|
||||
f"Info: Vision LLM Router initialized with {len(vision_configs)} models "
|
||||
f"(strategy: {router_settings.get('routing_strategy', 'usage-based-routing')})"
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to initialize Vision LLM Router: {e}")
|
||||
|
||||
|
||||
class Config:
|
||||
# Check if ffmpeg is installed
|
||||
if not is_ffmpeg_installed():
|
||||
|
|
@ -335,6 +396,12 @@ class Config:
|
|||
# Router settings for Image Generation Auto mode
|
||||
IMAGE_GEN_ROUTER_SETTINGS = load_image_gen_router_settings()
|
||||
|
||||
# Global Vision LLM Configurations (optional)
|
||||
GLOBAL_VISION_LLM_CONFIGS = load_global_vision_llm_configs()
|
||||
|
||||
# Router settings for Vision LLM Auto mode
|
||||
VISION_LLM_ROUTER_SETTINGS = load_vision_llm_router_settings()
|
||||
|
||||
# Chonkie Configuration | Edit this to your needs
|
||||
EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL")
|
||||
# Azure OpenAI credentials from environment variables
|
||||
|
|
|
|||
|
|
@ -263,6 +263,82 @@ global_image_generation_configs:
|
|||
# rpm: 30
|
||||
# litellm_params: {}
|
||||
|
||||
# =============================================================================
|
||||
# Vision LLM Configuration
|
||||
# =============================================================================
|
||||
# These configurations power the vision autocomplete feature (screenshot analysis).
|
||||
# Only vision-capable models should be used here (e.g. GPT-4o, Gemini Pro, Claude 3).
|
||||
# Supported providers: OpenAI, Anthropic, Google, Azure OpenAI, Vertex AI, Bedrock,
|
||||
# xAI, OpenRouter, Ollama, Groq, Together AI, Fireworks AI, DeepSeek, Mistral, Custom
|
||||
#
|
||||
# Auto mode (ID 0) uses LiteLLM Router for load balancing across all vision configs.
|
||||
|
||||
# Router Settings for Vision LLM Auto Mode
|
||||
vision_llm_router_settings:
|
||||
routing_strategy: "usage-based-routing"
|
||||
num_retries: 3
|
||||
allowed_fails: 3
|
||||
cooldown_time: 60
|
||||
|
||||
global_vision_llm_configs:
|
||||
# Example: OpenAI GPT-4o (recommended for vision)
|
||||
- id: -1
|
||||
name: "Global GPT-4o Vision"
|
||||
description: "OpenAI's GPT-4o with strong vision capabilities"
|
||||
provider: "OPENAI"
|
||||
model_name: "gpt-4o"
|
||||
api_key: "sk-your-openai-api-key-here"
|
||||
api_base: ""
|
||||
rpm: 500
|
||||
tpm: 100000
|
||||
litellm_params:
|
||||
temperature: 0.3
|
||||
max_tokens: 1000
|
||||
|
||||
# Example: Google Gemini 2.0 Flash
|
||||
- id: -2
|
||||
name: "Global Gemini 2.0 Flash"
|
||||
description: "Google's fast vision model with large context"
|
||||
provider: "GOOGLE"
|
||||
model_name: "gemini-2.0-flash"
|
||||
api_key: "your-google-ai-api-key-here"
|
||||
api_base: ""
|
||||
rpm: 1000
|
||||
tpm: 200000
|
||||
litellm_params:
|
||||
temperature: 0.3
|
||||
max_tokens: 1000
|
||||
|
||||
# Example: Anthropic Claude 3.5 Sonnet
|
||||
- id: -3
|
||||
name: "Global Claude 3.5 Sonnet Vision"
|
||||
description: "Anthropic's Claude 3.5 Sonnet with vision support"
|
||||
provider: "ANTHROPIC"
|
||||
model_name: "claude-3-5-sonnet-20241022"
|
||||
api_key: "sk-ant-your-anthropic-api-key-here"
|
||||
api_base: ""
|
||||
rpm: 1000
|
||||
tpm: 100000
|
||||
litellm_params:
|
||||
temperature: 0.3
|
||||
max_tokens: 1000
|
||||
|
||||
# Example: Azure OpenAI GPT-4o
|
||||
# - id: -4
|
||||
# name: "Global Azure GPT-4o Vision"
|
||||
# description: "Azure-hosted GPT-4o for vision analysis"
|
||||
# provider: "AZURE_OPENAI"
|
||||
# model_name: "azure/gpt-4o-deployment"
|
||||
# api_key: "your-azure-api-key-here"
|
||||
# api_base: "https://your-resource.openai.azure.com"
|
||||
# api_version: "2024-02-15-preview"
|
||||
# rpm: 500
|
||||
# tpm: 100000
|
||||
# litellm_params:
|
||||
# temperature: 0.3
|
||||
# max_tokens: 1000
|
||||
# base_model: "gpt-4o"
|
||||
|
||||
# Notes:
|
||||
# - ID 0 is reserved for "Auto" mode - uses LiteLLM Router for load balancing
|
||||
# - Use negative IDs to distinguish global configs from user configs (NewLLMConfig in DB)
|
||||
|
|
@ -283,3 +359,9 @@ global_image_generation_configs:
|
|||
# - The router uses litellm.aimage_generation() for async image generation
|
||||
# - Only RPM (requests per minute) is relevant for image generation rate limiting.
|
||||
# TPM (tokens per minute) does not apply since image APIs are billed/rate-limited per request, not per token.
|
||||
#
|
||||
# VISION LLM NOTES:
|
||||
# - Vision configs use the same ID scheme (negative for global, positive for user DB)
|
||||
# - Only use vision-capable models (GPT-4o, Gemini, Claude 3, etc.)
|
||||
# - Lower temperature (0.3) is recommended for accurate screenshot analysis
|
||||
# - Lower max_tokens (1000) is sufficient since autocomplete produces short suggestions
|
||||
|
|
|
|||
23
surfsense_backend/app/config/vision_model_list_fallback.json
Normal file
23
surfsense_backend/app/config/vision_model_list_fallback.json
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
[
|
||||
{"value": "gpt-4o", "label": "GPT-4o", "provider": "OPENAI", "context_window": "128K"},
|
||||
{"value": "gpt-4o-mini", "label": "GPT-4o Mini", "provider": "OPENAI", "context_window": "128K"},
|
||||
{"value": "gpt-4-turbo", "label": "GPT-4 Turbo", "provider": "OPENAI", "context_window": "128K"},
|
||||
{"value": "claude-sonnet-4-20250514", "label": "Claude Sonnet 4", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-7-sonnet-20250219", "label": "Claude 3.7 Sonnet", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-5-sonnet-20241022", "label": "Claude 3.5 Sonnet", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-opus-20240229", "label": "Claude 3 Opus", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "claude-3-haiku-20240307", "label": "Claude 3 Haiku", "provider": "ANTHROPIC", "context_window": "200K"},
|
||||
{"value": "gemini-2.5-flash", "label": "Gemini 2.5 Flash", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-2.5-pro", "label": "Gemini 2.5 Pro", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-2.0-flash", "label": "Gemini 2.0 Flash", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-1.5-pro", "label": "Gemini 1.5 Pro", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "gemini-1.5-flash", "label": "Gemini 1.5 Flash", "provider": "GOOGLE", "context_window": "1M"},
|
||||
{"value": "pixtral-large-latest", "label": "Pixtral Large", "provider": "MISTRAL", "context_window": "128K"},
|
||||
{"value": "pixtral-12b-2409", "label": "Pixtral 12B", "provider": "MISTRAL", "context_window": "128K"},
|
||||
{"value": "grok-2-vision-1212", "label": "Grok 2 Vision", "provider": "XAI", "context_window": "32K"},
|
||||
{"value": "llava", "label": "LLaVA", "provider": "OLLAMA"},
|
||||
{"value": "bakllava", "label": "BakLLaVA", "provider": "OLLAMA"},
|
||||
{"value": "llava-llama3", "label": "LLaVA Llama 3", "provider": "OLLAMA"},
|
||||
{"value": "llama-4-scout-17b-16e-instruct", "label": "Llama 4 Scout 17B", "provider": "GROQ", "context_window": "128K"},
|
||||
{"value": "meta-llama/Llama-4-Scout-17B-16E-Instruct", "label": "Llama 4 Scout 17B", "provider": "TOGETHER_AI", "context_window": "128K"}
|
||||
]
|
||||
|
|
@ -260,6 +260,24 @@ class ImageGenProvider(StrEnum):
|
|||
NSCALE = "NSCALE"
|
||||
|
||||
|
||||
class VisionProvider(StrEnum):
|
||||
OPENAI = "OPENAI"
|
||||
ANTHROPIC = "ANTHROPIC"
|
||||
GOOGLE = "GOOGLE"
|
||||
AZURE_OPENAI = "AZURE_OPENAI"
|
||||
VERTEX_AI = "VERTEX_AI"
|
||||
BEDROCK = "BEDROCK"
|
||||
XAI = "XAI"
|
||||
OPENROUTER = "OPENROUTER"
|
||||
OLLAMA = "OLLAMA"
|
||||
GROQ = "GROQ"
|
||||
TOGETHER_AI = "TOGETHER_AI"
|
||||
FIREWORKS_AI = "FIREWORKS_AI"
|
||||
DEEPSEEK = "DEEPSEEK"
|
||||
MISTRAL = "MISTRAL"
|
||||
CUSTOM = "CUSTOM"
|
||||
|
||||
|
||||
class LogLevel(StrEnum):
|
||||
DEBUG = "DEBUG"
|
||||
INFO = "INFO"
|
||||
|
|
@ -377,6 +395,11 @@ class Permission(StrEnum):
|
|||
IMAGE_GENERATIONS_READ = "image_generations:read"
|
||||
IMAGE_GENERATIONS_DELETE = "image_generations:delete"
|
||||
|
||||
# Vision LLM Configs
|
||||
VISION_CONFIGS_CREATE = "vision_configs:create"
|
||||
VISION_CONFIGS_READ = "vision_configs:read"
|
||||
VISION_CONFIGS_DELETE = "vision_configs:delete"
|
||||
|
||||
# Connectors
|
||||
CONNECTORS_CREATE = "connectors:create"
|
||||
CONNECTORS_READ = "connectors:read"
|
||||
|
|
@ -445,6 +468,9 @@ DEFAULT_ROLE_PERMISSIONS = {
|
|||
# Image Generations (create and read, no delete)
|
||||
Permission.IMAGE_GENERATIONS_CREATE.value,
|
||||
Permission.IMAGE_GENERATIONS_READ.value,
|
||||
# Vision Configs (create and read, no delete)
|
||||
Permission.VISION_CONFIGS_CREATE.value,
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
# Connectors (no delete)
|
||||
Permission.CONNECTORS_CREATE.value,
|
||||
Permission.CONNECTORS_READ.value,
|
||||
|
|
@ -478,6 +504,8 @@ DEFAULT_ROLE_PERMISSIONS = {
|
|||
Permission.VIDEO_PRESENTATIONS_READ.value,
|
||||
# Image Generations (read only)
|
||||
Permission.IMAGE_GENERATIONS_READ.value,
|
||||
# Vision Configs (read only)
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
# Connectors (read only)
|
||||
Permission.CONNECTORS_READ.value,
|
||||
# Logs (read only)
|
||||
|
|
@ -1263,6 +1291,35 @@ class ImageGenerationConfig(BaseModel, TimestampMixin):
|
|||
user = relationship("User", back_populates="image_generation_configs")
|
||||
|
||||
|
||||
class VisionLLMConfig(BaseModel, TimestampMixin):
|
||||
__tablename__ = "vision_llm_configs"
|
||||
|
||||
name = Column(String(100), nullable=False, index=True)
|
||||
description = Column(String(500), nullable=True)
|
||||
|
||||
provider = Column(SQLAlchemyEnum(VisionProvider), nullable=False)
|
||||
custom_provider = Column(String(100), nullable=True)
|
||||
model_name = Column(String(100), nullable=False)
|
||||
|
||||
api_key = Column(String, nullable=False)
|
||||
api_base = Column(String(500), nullable=True)
|
||||
api_version = Column(String(50), nullable=True)
|
||||
|
||||
litellm_params = Column(JSON, nullable=True, default={})
|
||||
|
||||
search_space_id = Column(
|
||||
Integer, ForeignKey("searchspaces.id", ondelete="CASCADE"), nullable=False
|
||||
)
|
||||
search_space = relationship(
|
||||
"SearchSpace", back_populates="vision_llm_configs"
|
||||
)
|
||||
|
||||
user_id = Column(
|
||||
UUID(as_uuid=True), ForeignKey("user.id", ondelete="CASCADE"), nullable=False
|
||||
)
|
||||
user = relationship("User", back_populates="vision_llm_configs")
|
||||
|
||||
|
||||
class ImageGeneration(BaseModel, TimestampMixin):
|
||||
"""
|
||||
Stores image generation requests and results using litellm.aimage_generation().
|
||||
|
|
@ -1351,7 +1408,7 @@ class SearchSpace(BaseModel, TimestampMixin):
|
|||
image_generation_config_id = Column(
|
||||
Integer, nullable=True, default=0
|
||||
) # For image generation, defaults to Auto mode
|
||||
vision_llm_id = Column(
|
||||
vision_llm_config_id = Column(
|
||||
Integer, nullable=True, default=0
|
||||
) # For vision/screenshot analysis, defaults to Auto mode
|
||||
|
||||
|
|
@ -1432,6 +1489,12 @@ class SearchSpace(BaseModel, TimestampMixin):
|
|||
order_by="ImageGenerationConfig.id",
|
||||
cascade="all, delete-orphan",
|
||||
)
|
||||
vision_llm_configs = relationship(
|
||||
"VisionLLMConfig",
|
||||
back_populates="search_space",
|
||||
order_by="VisionLLMConfig.id",
|
||||
cascade="all, delete-orphan",
|
||||
)
|
||||
|
||||
# RBAC relationships
|
||||
roles = relationship(
|
||||
|
|
@ -1961,6 +2024,12 @@ if config.AUTH_TYPE == "GOOGLE":
|
|||
passive_deletes=True,
|
||||
)
|
||||
|
||||
vision_llm_configs = relationship(
|
||||
"VisionLLMConfig",
|
||||
back_populates="user",
|
||||
passive_deletes=True,
|
||||
)
|
||||
|
||||
# User memories for personalized AI responses
|
||||
memories = relationship(
|
||||
"UserMemory",
|
||||
|
|
@ -2075,6 +2144,12 @@ else:
|
|||
passive_deletes=True,
|
||||
)
|
||||
|
||||
vision_llm_configs = relationship(
|
||||
"VisionLLMConfig",
|
||||
back_populates="user",
|
||||
passive_deletes=True,
|
||||
)
|
||||
|
||||
# User memories for personalized AI responses
|
||||
memories = relationship(
|
||||
"UserMemory",
|
||||
|
|
|
|||
|
|
@ -49,6 +49,7 @@ from .stripe_routes import router as stripe_router
|
|||
from .surfsense_docs_routes import router as surfsense_docs_router
|
||||
from .teams_add_connector_route import router as teams_add_connector_router
|
||||
from .video_presentations_routes import router as video_presentations_router
|
||||
from .vision_llm_routes import router as vision_llm_router
|
||||
from .youtube_routes import router as youtube_router
|
||||
|
||||
router = APIRouter()
|
||||
|
|
@ -68,6 +69,7 @@ router.include_router(
|
|||
) # Video presentation status and streaming
|
||||
router.include_router(reports_router) # Report CRUD and multi-format export
|
||||
router.include_router(image_generation_router) # Image generation via litellm
|
||||
router.include_router(vision_llm_router) # Vision LLM configs for screenshot analysis
|
||||
router.include_router(search_source_connectors_router)
|
||||
router.include_router(google_calendar_add_connector_router)
|
||||
router.include_router(google_gmail_add_connector_router)
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ from app.db import (
|
|||
SearchSpaceMembership,
|
||||
SearchSpaceRole,
|
||||
User,
|
||||
VisionLLMConfig,
|
||||
get_async_session,
|
||||
get_default_roles_config,
|
||||
)
|
||||
|
|
@ -483,6 +484,63 @@ async def _get_image_gen_config_by_id(
|
|||
return None
|
||||
|
||||
|
||||
async def _get_vision_llm_config_by_id(
|
||||
session: AsyncSession, config_id: int | None
|
||||
) -> dict | None:
|
||||
if config_id is None:
|
||||
return None
|
||||
|
||||
if config_id == 0:
|
||||
return {
|
||||
"id": 0,
|
||||
"name": "Auto (Fastest)",
|
||||
"description": "Automatically routes requests across available vision LLM providers",
|
||||
"provider": "AUTO",
|
||||
"model_name": "auto",
|
||||
"is_global": True,
|
||||
"is_auto_mode": True,
|
||||
}
|
||||
|
||||
if config_id < 0:
|
||||
for cfg in config.GLOBAL_VISION_LLM_CONFIGS:
|
||||
if cfg.get("id") == config_id:
|
||||
return {
|
||||
"id": cfg.get("id"),
|
||||
"name": cfg.get("name"),
|
||||
"description": cfg.get("description"),
|
||||
"provider": cfg.get("provider"),
|
||||
"custom_provider": cfg.get("custom_provider"),
|
||||
"model_name": cfg.get("model_name"),
|
||||
"api_base": cfg.get("api_base") or None,
|
||||
"api_version": cfg.get("api_version") or None,
|
||||
"litellm_params": cfg.get("litellm_params", {}),
|
||||
"is_global": True,
|
||||
}
|
||||
return None
|
||||
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if db_config:
|
||||
return {
|
||||
"id": db_config.id,
|
||||
"name": db_config.name,
|
||||
"description": db_config.description,
|
||||
"provider": db_config.provider.value if db_config.provider else None,
|
||||
"custom_provider": db_config.custom_provider,
|
||||
"model_name": db_config.model_name,
|
||||
"api_base": db_config.api_base,
|
||||
"api_version": db_config.api_version,
|
||||
"litellm_params": db_config.litellm_params or {},
|
||||
"created_at": db_config.created_at.isoformat()
|
||||
if db_config.created_at
|
||||
else None,
|
||||
"search_space_id": db_config.search_space_id,
|
||||
}
|
||||
return None
|
||||
|
||||
|
||||
@router.get(
|
||||
"/search-spaces/{search_space_id}/llm-preferences",
|
||||
response_model=LLMPreferencesRead,
|
||||
|
|
@ -522,17 +580,19 @@ async def get_llm_preferences(
|
|||
image_generation_config = await _get_image_gen_config_by_id(
|
||||
session, search_space.image_generation_config_id
|
||||
)
|
||||
vision_llm = await _get_llm_config_by_id(session, search_space.vision_llm_id)
|
||||
vision_llm_config = await _get_vision_llm_config_by_id(
|
||||
session, search_space.vision_llm_config_id
|
||||
)
|
||||
|
||||
return LLMPreferencesRead(
|
||||
agent_llm_id=search_space.agent_llm_id,
|
||||
document_summary_llm_id=search_space.document_summary_llm_id,
|
||||
image_generation_config_id=search_space.image_generation_config_id,
|
||||
vision_llm_id=search_space.vision_llm_id,
|
||||
vision_llm_config_id=search_space.vision_llm_config_id,
|
||||
agent_llm=agent_llm,
|
||||
document_summary_llm=document_summary_llm,
|
||||
image_generation_config=image_generation_config,
|
||||
vision_llm=vision_llm,
|
||||
vision_llm_config=vision_llm_config,
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
|
|
@ -592,17 +652,19 @@ async def update_llm_preferences(
|
|||
image_generation_config = await _get_image_gen_config_by_id(
|
||||
session, search_space.image_generation_config_id
|
||||
)
|
||||
vision_llm = await _get_llm_config_by_id(session, search_space.vision_llm_id)
|
||||
vision_llm_config = await _get_vision_llm_config_by_id(
|
||||
session, search_space.vision_llm_config_id
|
||||
)
|
||||
|
||||
return LLMPreferencesRead(
|
||||
agent_llm_id=search_space.agent_llm_id,
|
||||
document_summary_llm_id=search_space.document_summary_llm_id,
|
||||
image_generation_config_id=search_space.image_generation_config_id,
|
||||
vision_llm_id=search_space.vision_llm_id,
|
||||
vision_llm_config_id=search_space.vision_llm_config_id,
|
||||
agent_llm=agent_llm,
|
||||
document_summary_llm=document_summary_llm,
|
||||
image_generation_config=image_generation_config,
|
||||
vision_llm=vision_llm,
|
||||
vision_llm_config=vision_llm_config,
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
|
|
|
|||
295
surfsense_backend/app/routes/vision_llm_routes.py
Normal file
295
surfsense_backend/app/routes/vision_llm_routes.py
Normal file
|
|
@ -0,0 +1,295 @@
|
|||
import logging
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from pydantic import BaseModel
|
||||
from sqlalchemy import select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.config import config
|
||||
from app.db import (
|
||||
Permission,
|
||||
User,
|
||||
VisionLLMConfig,
|
||||
get_async_session,
|
||||
)
|
||||
from app.schemas import (
|
||||
GlobalVisionLLMConfigRead,
|
||||
VisionLLMConfigCreate,
|
||||
VisionLLMConfigRead,
|
||||
VisionLLMConfigUpdate,
|
||||
)
|
||||
from app.services.vision_model_list_service import get_vision_model_list
|
||||
from app.users import current_active_user
|
||||
from app.utils.rbac import check_permission
|
||||
|
||||
router = APIRouter()
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Vision Model Catalogue (from OpenRouter, filtered for image-input models)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class VisionModelListItem(BaseModel):
|
||||
value: str
|
||||
label: str
|
||||
provider: str
|
||||
context_window: str | None = None
|
||||
|
||||
|
||||
@router.get("/vision-models", response_model=list[VisionModelListItem])
|
||||
async def list_vision_models(
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
"""Return vision-capable models sourced from OpenRouter (filtered by image input)."""
|
||||
try:
|
||||
return await get_vision_model_list()
|
||||
except Exception as e:
|
||||
logger.exception("Failed to fetch vision model list")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch vision model list: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Global Vision LLM Configs (from YAML)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@router.get(
|
||||
"/global-vision-llm-configs",
|
||||
response_model=list[GlobalVisionLLMConfigRead],
|
||||
)
|
||||
async def get_global_vision_llm_configs(
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
global_configs = config.GLOBAL_VISION_LLM_CONFIGS
|
||||
safe_configs = []
|
||||
|
||||
if global_configs and len(global_configs) > 0:
|
||||
safe_configs.append(
|
||||
{
|
||||
"id": 0,
|
||||
"name": "Auto (Fastest)",
|
||||
"description": "Automatically routes across available vision LLM providers.",
|
||||
"provider": "AUTO",
|
||||
"custom_provider": None,
|
||||
"model_name": "auto",
|
||||
"api_base": None,
|
||||
"api_version": None,
|
||||
"litellm_params": {},
|
||||
"is_global": True,
|
||||
"is_auto_mode": True,
|
||||
}
|
||||
)
|
||||
|
||||
for cfg in global_configs:
|
||||
safe_configs.append(
|
||||
{
|
||||
"id": cfg.get("id"),
|
||||
"name": cfg.get("name"),
|
||||
"description": cfg.get("description"),
|
||||
"provider": cfg.get("provider"),
|
||||
"custom_provider": cfg.get("custom_provider"),
|
||||
"model_name": cfg.get("model_name"),
|
||||
"api_base": cfg.get("api_base") or None,
|
||||
"api_version": cfg.get("api_version") or None,
|
||||
"litellm_params": cfg.get("litellm_params", {}),
|
||||
"is_global": True,
|
||||
}
|
||||
)
|
||||
|
||||
return safe_configs
|
||||
except Exception as e:
|
||||
logger.exception("Failed to fetch global vision LLM configs")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch configs: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# VisionLLMConfig CRUD
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@router.post("/vision-llm-configs", response_model=VisionLLMConfigRead)
|
||||
async def create_vision_llm_config(
|
||||
config_data: VisionLLMConfigCreate,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
config_data.search_space_id,
|
||||
Permission.VISION_CONFIGS_CREATE.value,
|
||||
"You don't have permission to create vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
db_config = VisionLLMConfig(**config_data.model_dump(), user_id=user.id)
|
||||
session.add(db_config)
|
||||
await session.commit()
|
||||
await session.refresh(db_config)
|
||||
return db_config
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
logger.exception("Failed to create VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to create config: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.get("/vision-llm-configs", response_model=list[VisionLLMConfigRead])
|
||||
async def list_vision_llm_configs(
|
||||
search_space_id: int,
|
||||
skip: int = 0,
|
||||
limit: int = 100,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
search_space_id,
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
"You don't have permission to view vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig)
|
||||
.filter(VisionLLMConfig.search_space_id == search_space_id)
|
||||
.order_by(VisionLLMConfig.created_at.desc())
|
||||
.offset(skip)
|
||||
.limit(limit)
|
||||
)
|
||||
return result.scalars().all()
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.exception("Failed to list VisionLLMConfigs")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch configs: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.get(
|
||||
"/vision-llm-configs/{config_id}", response_model=VisionLLMConfigRead
|
||||
)
|
||||
async def get_vision_llm_config(
|
||||
config_id: int,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if not db_config:
|
||||
raise HTTPException(status_code=404, detail="Config not found")
|
||||
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
db_config.search_space_id,
|
||||
Permission.VISION_CONFIGS_READ.value,
|
||||
"You don't have permission to view vision LLM configs in this search space",
|
||||
)
|
||||
return db_config
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.exception("Failed to get VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to fetch config: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.put(
|
||||
"/vision-llm-configs/{config_id}", response_model=VisionLLMConfigRead
|
||||
)
|
||||
async def update_vision_llm_config(
|
||||
config_id: int,
|
||||
update_data: VisionLLMConfigUpdate,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if not db_config:
|
||||
raise HTTPException(status_code=404, detail="Config not found")
|
||||
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
db_config.search_space_id,
|
||||
Permission.VISION_CONFIGS_CREATE.value,
|
||||
"You don't have permission to update vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
for key, value in update_data.model_dump(exclude_unset=True).items():
|
||||
setattr(db_config, key, value)
|
||||
|
||||
await session.commit()
|
||||
await session.refresh(db_config)
|
||||
return db_config
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
logger.exception("Failed to update VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to update config: {e!s}"
|
||||
) from e
|
||||
|
||||
|
||||
@router.delete("/vision-llm-configs/{config_id}", response_model=dict)
|
||||
async def delete_vision_llm_config(
|
||||
config_id: int,
|
||||
session: AsyncSession = Depends(get_async_session),
|
||||
user: User = Depends(current_active_user),
|
||||
):
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).filter(VisionLLMConfig.id == config_id)
|
||||
)
|
||||
db_config = result.scalars().first()
|
||||
if not db_config:
|
||||
raise HTTPException(status_code=404, detail="Config not found")
|
||||
|
||||
await check_permission(
|
||||
session,
|
||||
user,
|
||||
db_config.search_space_id,
|
||||
Permission.VISION_CONFIGS_DELETE.value,
|
||||
"You don't have permission to delete vision LLM configs in this search space",
|
||||
)
|
||||
|
||||
await session.delete(db_config)
|
||||
await session.commit()
|
||||
return {
|
||||
"message": "Vision LLM config deleted successfully",
|
||||
"id": config_id,
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
logger.exception("Failed to delete VisionLLMConfig")
|
||||
raise HTTPException(
|
||||
status_code=500, detail=f"Failed to delete config: {e!s}"
|
||||
) from e
|
||||
|
|
@ -125,6 +125,13 @@ from .video_presentations import (
|
|||
VideoPresentationRead,
|
||||
VideoPresentationUpdate,
|
||||
)
|
||||
from .vision_llm import (
|
||||
GlobalVisionLLMConfigRead,
|
||||
VisionLLMConfigCreate,
|
||||
VisionLLMConfigPublic,
|
||||
VisionLLMConfigRead,
|
||||
VisionLLMConfigUpdate,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# Folder schemas
|
||||
|
|
@ -163,6 +170,8 @@ __all__ = [
|
|||
"FolderUpdate",
|
||||
"GlobalImageGenConfigRead",
|
||||
"GlobalNewLLMConfigRead",
|
||||
# Vision LLM Config schemas
|
||||
"GlobalVisionLLMConfigRead",
|
||||
"GoogleDriveIndexRequest",
|
||||
"GoogleDriveIndexingOptions",
|
||||
# Base schemas
|
||||
|
|
@ -264,4 +273,8 @@ __all__ = [
|
|||
"VideoPresentationCreate",
|
||||
"VideoPresentationRead",
|
||||
"VideoPresentationUpdate",
|
||||
"VisionLLMConfigCreate",
|
||||
"VisionLLMConfigPublic",
|
||||
"VisionLLMConfigRead",
|
||||
"VisionLLMConfigUpdate",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -182,8 +182,8 @@ class LLMPreferencesRead(BaseModel):
|
|||
image_generation_config_id: int | None = Field(
|
||||
None, description="ID of the image generation config to use"
|
||||
)
|
||||
vision_llm_id: int | None = Field(
|
||||
None, description="ID of the LLM config to use for vision/screenshot analysis"
|
||||
vision_llm_config_id: int | None = Field(
|
||||
None, description="ID of the vision LLM config to use for vision/screenshot analysis"
|
||||
)
|
||||
agent_llm: dict[str, Any] | None = Field(
|
||||
None, description="Full config for agent LLM"
|
||||
|
|
@ -194,7 +194,7 @@ class LLMPreferencesRead(BaseModel):
|
|||
image_generation_config: dict[str, Any] | None = Field(
|
||||
None, description="Full config for image generation"
|
||||
)
|
||||
vision_llm: dict[str, Any] | None = Field(
|
||||
vision_llm_config: dict[str, Any] | None = Field(
|
||||
None, description="Full config for vision LLM"
|
||||
)
|
||||
|
||||
|
|
@ -213,6 +213,6 @@ class LLMPreferencesUpdate(BaseModel):
|
|||
image_generation_config_id: int | None = Field(
|
||||
None, description="ID of the image generation config to use"
|
||||
)
|
||||
vision_llm_id: int | None = Field(
|
||||
None, description="ID of the LLM config to use for vision/screenshot analysis"
|
||||
vision_llm_config_id: int | None = Field(
|
||||
None, description="ID of the vision LLM config to use for vision/screenshot analysis"
|
||||
)
|
||||
|
|
|
|||
75
surfsense_backend/app/schemas/vision_llm.py
Normal file
75
surfsense_backend/app/schemas/vision_llm.py
Normal file
|
|
@ -0,0 +1,75 @@
|
|||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
from app.db import VisionProvider
|
||||
|
||||
|
||||
class VisionLLMConfigBase(BaseModel):
|
||||
name: str = Field(..., max_length=100)
|
||||
description: str | None = Field(None, max_length=500)
|
||||
provider: VisionProvider = Field(...)
|
||||
custom_provider: str | None = Field(None, max_length=100)
|
||||
model_name: str = Field(..., max_length=100)
|
||||
api_key: str = Field(...)
|
||||
api_base: str | None = Field(None, max_length=500)
|
||||
api_version: str | None = Field(None, max_length=50)
|
||||
litellm_params: dict[str, Any] | None = Field(default=None)
|
||||
|
||||
|
||||
class VisionLLMConfigCreate(VisionLLMConfigBase):
|
||||
search_space_id: int = Field(...)
|
||||
|
||||
|
||||
class VisionLLMConfigUpdate(BaseModel):
|
||||
name: str | None = Field(None, max_length=100)
|
||||
description: str | None = Field(None, max_length=500)
|
||||
provider: VisionProvider | None = None
|
||||
custom_provider: str | None = Field(None, max_length=100)
|
||||
model_name: str | None = Field(None, max_length=100)
|
||||
api_key: str | None = None
|
||||
api_base: str | None = Field(None, max_length=500)
|
||||
api_version: str | None = Field(None, max_length=50)
|
||||
litellm_params: dict[str, Any] | None = None
|
||||
|
||||
|
||||
class VisionLLMConfigRead(VisionLLMConfigBase):
|
||||
id: int
|
||||
created_at: datetime
|
||||
search_space_id: int
|
||||
user_id: uuid.UUID
|
||||
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
|
||||
class VisionLLMConfigPublic(BaseModel):
|
||||
id: int
|
||||
name: str
|
||||
description: str | None = None
|
||||
provider: VisionProvider
|
||||
custom_provider: str | None = None
|
||||
model_name: str
|
||||
api_base: str | None = None
|
||||
api_version: str | None = None
|
||||
litellm_params: dict[str, Any] | None = None
|
||||
created_at: datetime
|
||||
search_space_id: int
|
||||
user_id: uuid.UUID
|
||||
|
||||
model_config = ConfigDict(from_attributes=True)
|
||||
|
||||
|
||||
class GlobalVisionLLMConfigRead(BaseModel):
|
||||
id: int = Field(...)
|
||||
name: str
|
||||
description: str | None = None
|
||||
provider: str
|
||||
custom_provider: str | None = None
|
||||
model_name: str
|
||||
api_base: str | None = None
|
||||
api_version: str | None = None
|
||||
litellm_params: dict[str, Any] | None = None
|
||||
is_global: bool = True
|
||||
is_auto_mode: bool = False
|
||||
|
|
@ -32,7 +32,6 @@ logger = logging.getLogger(__name__)
|
|||
class LLMRole:
|
||||
AGENT = "agent" # For agent/chat operations
|
||||
DOCUMENT_SUMMARY = "document_summary" # For document summarization
|
||||
VISION = "vision" # For vision/screenshot analysis
|
||||
|
||||
|
||||
def get_global_llm_config(llm_config_id: int) -> dict | None:
|
||||
|
|
@ -188,7 +187,7 @@ async def get_search_space_llm_instance(
|
|||
Args:
|
||||
session: Database session
|
||||
search_space_id: Search Space ID
|
||||
role: LLM role ('agent', 'document_summary', or 'vision')
|
||||
role: LLM role ('agent' or 'document_summary')
|
||||
|
||||
Returns:
|
||||
ChatLiteLLM or ChatLiteLLMRouter instance, or None if not found
|
||||
|
|
@ -210,8 +209,6 @@ async def get_search_space_llm_instance(
|
|||
llm_config_id = search_space.agent_llm_id
|
||||
elif role == LLMRole.DOCUMENT_SUMMARY:
|
||||
llm_config_id = search_space.document_summary_llm_id
|
||||
elif role == LLMRole.VISION:
|
||||
llm_config_id = search_space.vision_llm_id
|
||||
else:
|
||||
logger.error(f"Invalid LLM role: {role}")
|
||||
return None
|
||||
|
|
@ -411,8 +408,118 @@ async def get_document_summary_llm(
|
|||
async def get_vision_llm(
|
||||
session: AsyncSession, search_space_id: int
|
||||
) -> ChatLiteLLM | ChatLiteLLMRouter | None:
|
||||
"""Get the search space's vision LLM instance for screenshot analysis."""
|
||||
return await get_search_space_llm_instance(session, search_space_id, LLMRole.VISION)
|
||||
"""Get the search space's vision LLM instance for screenshot analysis.
|
||||
|
||||
Resolves from the dedicated VisionLLMConfig system:
|
||||
- Auto mode (ID 0): VisionLLMRouterService
|
||||
- Global (negative ID): YAML configs
|
||||
- DB (positive ID): VisionLLMConfig table
|
||||
"""
|
||||
from app.db import VisionLLMConfig
|
||||
from app.services.vision_llm_router_service import (
|
||||
VISION_PROVIDER_MAP,
|
||||
VisionLLMRouterService,
|
||||
get_global_vision_llm_config,
|
||||
is_vision_auto_mode,
|
||||
)
|
||||
|
||||
try:
|
||||
result = await session.execute(
|
||||
select(SearchSpace).where(SearchSpace.id == search_space_id)
|
||||
)
|
||||
search_space = result.scalars().first()
|
||||
if not search_space:
|
||||
logger.error(f"Search space {search_space_id} not found")
|
||||
return None
|
||||
|
||||
config_id = search_space.vision_llm_config_id
|
||||
if config_id is None:
|
||||
logger.error(
|
||||
f"No vision LLM configured for search space {search_space_id}"
|
||||
)
|
||||
return None
|
||||
|
||||
if is_vision_auto_mode(config_id):
|
||||
if not VisionLLMRouterService.is_initialized():
|
||||
logger.error(
|
||||
"Vision Auto mode requested but Vision LLM Router not initialized"
|
||||
)
|
||||
return None
|
||||
try:
|
||||
return ChatLiteLLMRouter(
|
||||
router=VisionLLMRouterService.get_router(),
|
||||
streaming=True,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create vision ChatLiteLLMRouter: {e}")
|
||||
return None
|
||||
|
||||
if config_id < 0:
|
||||
global_cfg = get_global_vision_llm_config(config_id)
|
||||
if not global_cfg:
|
||||
logger.error(f"Global vision LLM config {config_id} not found")
|
||||
return None
|
||||
|
||||
if global_cfg.get("custom_provider"):
|
||||
model_string = (
|
||||
f"{global_cfg['custom_provider']}/{global_cfg['model_name']}"
|
||||
)
|
||||
else:
|
||||
prefix = VISION_PROVIDER_MAP.get(
|
||||
global_cfg["provider"].upper(),
|
||||
global_cfg["provider"].lower(),
|
||||
)
|
||||
model_string = f"{prefix}/{global_cfg['model_name']}"
|
||||
|
||||
litellm_kwargs = {
|
||||
"model": model_string,
|
||||
"api_key": global_cfg["api_key"],
|
||||
}
|
||||
if global_cfg.get("api_base"):
|
||||
litellm_kwargs["api_base"] = global_cfg["api_base"]
|
||||
if global_cfg.get("litellm_params"):
|
||||
litellm_kwargs.update(global_cfg["litellm_params"])
|
||||
|
||||
return ChatLiteLLM(**litellm_kwargs)
|
||||
|
||||
result = await session.execute(
|
||||
select(VisionLLMConfig).where(
|
||||
VisionLLMConfig.id == config_id,
|
||||
VisionLLMConfig.search_space_id == search_space_id,
|
||||
)
|
||||
)
|
||||
vision_cfg = result.scalars().first()
|
||||
if not vision_cfg:
|
||||
logger.error(
|
||||
f"Vision LLM config {config_id} not found in search space {search_space_id}"
|
||||
)
|
||||
return None
|
||||
|
||||
if vision_cfg.custom_provider:
|
||||
model_string = f"{vision_cfg.custom_provider}/{vision_cfg.model_name}"
|
||||
else:
|
||||
prefix = VISION_PROVIDER_MAP.get(
|
||||
vision_cfg.provider.value.upper(),
|
||||
vision_cfg.provider.value.lower(),
|
||||
)
|
||||
model_string = f"{prefix}/{vision_cfg.model_name}"
|
||||
|
||||
litellm_kwargs = {
|
||||
"model": model_string,
|
||||
"api_key": vision_cfg.api_key,
|
||||
}
|
||||
if vision_cfg.api_base:
|
||||
litellm_kwargs["api_base"] = vision_cfg.api_base
|
||||
if vision_cfg.litellm_params:
|
||||
litellm_kwargs.update(vision_cfg.litellm_params)
|
||||
|
||||
return ChatLiteLLM(**litellm_kwargs)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
f"Error getting vision LLM for search space {search_space_id}: {e!s}"
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
# Backward-compatible alias (LLM preferences are now per-search-space, not per-user)
|
||||
|
|
|
|||
|
|
@ -1,149 +1,40 @@
|
|||
"""Vision autocomplete service — agent-based with scoped filesystem.
|
||||
|
||||
Optimized pipeline:
|
||||
1. Start the SSE stream immediately so the UI shows progress.
|
||||
2. Derive a KB search query from window_title (no separate LLM call).
|
||||
3. Run KB filesystem pre-computation and agent graph compilation in PARALLEL.
|
||||
4. Inject pre-computed KB files as initial state and stream the agent.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from collections.abc import AsyncGenerator
|
||||
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
||||
from langchain_core.messages import HumanMessage
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.retriever.chunks_hybrid_search import ChucksHybridSearchRetriever
|
||||
from app.agents.autocomplete import create_autocomplete_agent, stream_autocomplete_agent
|
||||
from app.services.llm_service import get_vision_llm
|
||||
from app.services.new_streaming_service import VercelStreamingService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
KB_TOP_K = 5
|
||||
KB_MAX_CHARS = 4000
|
||||
|
||||
EXTRACT_QUERY_PROMPT = """Look at this screenshot and describe in 1-2 short sentences what the user is working on and what topic they need to write about. Be specific about the subject matter. Output ONLY the description, nothing else."""
|
||||
|
||||
EXTRACT_QUERY_PROMPT_WITH_APP = """The user is currently in the application "{app_name}" with the window titled "{window_title}".
|
||||
|
||||
Look at this screenshot and describe in 1-2 short sentences what the user is working on and what topic they need to write about. Be specific about the subject matter. Output ONLY the description, nothing else."""
|
||||
|
||||
VISION_SYSTEM_PROMPT = """You are a smart writing assistant that analyzes the user's screen to draft or complete text.
|
||||
|
||||
You will receive a screenshot of the user's screen. Your job:
|
||||
1. Analyze the ENTIRE screenshot to understand what the user is working on (email thread, chat conversation, document, code editor, form, etc.).
|
||||
2. Identify the text area where the user will type.
|
||||
3. Based on the full visual context, generate the text the user most likely wants to write.
|
||||
|
||||
Key behavior:
|
||||
- If the text area is EMPTY, draft a full response or message based on what you see on screen (e.g., reply to an email, respond to a chat message, continue a document).
|
||||
- If the text area already has text, continue it naturally.
|
||||
|
||||
Rules:
|
||||
- Output ONLY the text to be inserted. No quotes, no explanations, no meta-commentary.
|
||||
- Be concise but complete — a full thought, not a fragment.
|
||||
- Match the tone and formality of the surrounding context.
|
||||
- If the screen shows code, write code. If it shows a casual chat, be casual. If it shows a formal email, be formal.
|
||||
- Do NOT describe the screenshot or explain your reasoning.
|
||||
- If you cannot determine what to write, output nothing."""
|
||||
|
||||
APP_CONTEXT_BLOCK = """
|
||||
|
||||
The user is currently working in "{app_name}" (window: "{window_title}"). Use this to understand the type of application and adapt your tone and format accordingly."""
|
||||
|
||||
KB_CONTEXT_BLOCK = """
|
||||
|
||||
You also have access to the user's knowledge base documents below. Use them to write more accurate, informed, and contextually relevant text. Do NOT cite or reference the documents explicitly — just let the knowledge inform your writing naturally.
|
||||
|
||||
<knowledge_base>
|
||||
{kb_context}
|
||||
</knowledge_base>"""
|
||||
PREP_STEP_ID = "autocomplete-prep"
|
||||
|
||||
|
||||
def _build_system_prompt(app_name: str, window_title: str, kb_context: str) -> str:
|
||||
"""Assemble the system prompt from optional context blocks."""
|
||||
prompt = VISION_SYSTEM_PROMPT
|
||||
if app_name:
|
||||
prompt += APP_CONTEXT_BLOCK.format(app_name=app_name, window_title=window_title)
|
||||
if kb_context:
|
||||
prompt += KB_CONTEXT_BLOCK.format(kb_context=kb_context)
|
||||
return prompt
|
||||
def _derive_kb_query(app_name: str, window_title: str) -> str:
|
||||
parts = [p for p in (window_title, app_name) if p]
|
||||
return " ".join(parts)
|
||||
|
||||
|
||||
def _is_vision_unsupported_error(e: Exception) -> bool:
|
||||
"""Check if an exception indicates the model doesn't support vision/images."""
|
||||
msg = str(e).lower()
|
||||
return "content must be a string" in msg or "does not support image" in msg
|
||||
|
||||
|
||||
async def _extract_query_from_screenshot(
|
||||
llm,
|
||||
screenshot_data_url: str,
|
||||
app_name: str = "",
|
||||
window_title: str = "",
|
||||
) -> str | None:
|
||||
"""Ask the Vision LLM to describe what the user is working on.
|
||||
|
||||
Raises vision-unsupported errors so the caller can return a
|
||||
friendly message immediately instead of retrying with astream.
|
||||
"""
|
||||
if app_name:
|
||||
prompt_text = EXTRACT_QUERY_PROMPT_WITH_APP.format(
|
||||
app_name=app_name,
|
||||
window_title=window_title,
|
||||
)
|
||||
else:
|
||||
prompt_text = EXTRACT_QUERY_PROMPT
|
||||
|
||||
try:
|
||||
response = await llm.ainvoke(
|
||||
[
|
||||
HumanMessage(
|
||||
content=[
|
||||
{"type": "text", "text": prompt_text},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {"url": screenshot_data_url},
|
||||
},
|
||||
]
|
||||
),
|
||||
]
|
||||
)
|
||||
query = response.content.strip() if hasattr(response, "content") else ""
|
||||
return query if query else None
|
||||
except Exception as e:
|
||||
if _is_vision_unsupported_error(e):
|
||||
raise
|
||||
logger.warning(f"Failed to extract query from screenshot: {e}")
|
||||
return None
|
||||
|
||||
|
||||
async def _search_knowledge_base(
|
||||
session: AsyncSession, search_space_id: int, query: str
|
||||
) -> str:
|
||||
"""Search the KB and return formatted context string."""
|
||||
try:
|
||||
retriever = ChucksHybridSearchRetriever(session)
|
||||
results = await retriever.hybrid_search(
|
||||
query_text=query,
|
||||
top_k=KB_TOP_K,
|
||||
search_space_id=search_space_id,
|
||||
)
|
||||
|
||||
if not results:
|
||||
return ""
|
||||
|
||||
parts: list[str] = []
|
||||
char_count = 0
|
||||
for doc in results:
|
||||
title = doc.get("document", {}).get("title", "Untitled")
|
||||
for chunk in doc.get("chunks", []):
|
||||
content = chunk.get("content", "").strip()
|
||||
if not content:
|
||||
continue
|
||||
entry = f"[{title}]\n{content}"
|
||||
if char_count + len(entry) > KB_MAX_CHARS:
|
||||
break
|
||||
parts.append(entry)
|
||||
char_count += len(entry)
|
||||
if char_count >= KB_MAX_CHARS:
|
||||
break
|
||||
|
||||
return "\n\n---\n\n".join(parts)
|
||||
except Exception as e:
|
||||
logger.warning(f"KB search failed, proceeding without context: {e}")
|
||||
return ""
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main entry point
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
async def stream_vision_autocomplete(
|
||||
|
|
@ -154,13 +45,7 @@ async def stream_vision_autocomplete(
|
|||
app_name: str = "",
|
||||
window_title: str = "",
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Analyze a screenshot with the vision LLM and stream a text completion.
|
||||
|
||||
Pipeline:
|
||||
1. Extract a search query from the screenshot (non-streaming)
|
||||
2. Search the knowledge base for relevant context
|
||||
3. Stream the final completion with screenshot + KB + app context
|
||||
"""
|
||||
"""Analyze a screenshot with a vision-LLM agent and stream a text completion."""
|
||||
streaming = VercelStreamingService()
|
||||
vision_error_msg = (
|
||||
"The selected model does not support vision. "
|
||||
|
|
@ -174,71 +59,100 @@ async def stream_vision_autocomplete(
|
|||
yield streaming.format_done()
|
||||
return
|
||||
|
||||
kb_context = ""
|
||||
# Start SSE stream immediately so the UI has something to show
|
||||
yield streaming.format_message_start()
|
||||
|
||||
kb_query = _derive_kb_query(app_name, window_title)
|
||||
|
||||
# Show a preparation step while KB search + agent compile run
|
||||
yield streaming.format_thinking_step(
|
||||
step_id=PREP_STEP_ID,
|
||||
title="Searching knowledge base",
|
||||
status="in_progress",
|
||||
items=[kb_query] if kb_query else [],
|
||||
)
|
||||
|
||||
try:
|
||||
query = await _extract_query_from_screenshot(
|
||||
agent, kb = await create_autocomplete_agent(
|
||||
llm,
|
||||
screenshot_data_url,
|
||||
search_space_id=search_space_id,
|
||||
kb_query=kb_query,
|
||||
app_name=app_name,
|
||||
window_title=window_title,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Vision autocomplete: selected model does not support vision: {e}"
|
||||
)
|
||||
yield streaming.format_message_start()
|
||||
yield streaming.format_error(vision_error_msg)
|
||||
if _is_vision_unsupported_error(e):
|
||||
logger.warning("Vision autocomplete: model does not support vision: %s", e)
|
||||
yield streaming.format_error(vision_error_msg)
|
||||
yield streaming.format_done()
|
||||
return
|
||||
logger.error("Failed to create autocomplete agent: %s", e, exc_info=True)
|
||||
yield streaming.format_error("Autocomplete failed. Please try again.")
|
||||
yield streaming.format_done()
|
||||
return
|
||||
|
||||
if query:
|
||||
kb_context = await _search_knowledge_base(session, search_space_id, query)
|
||||
has_kb = kb.has_documents
|
||||
doc_count = len(kb.files) if has_kb else 0 # type: ignore[arg-type]
|
||||
|
||||
system_prompt = _build_system_prompt(app_name, window_title, kb_context)
|
||||
yield streaming.format_thinking_step(
|
||||
step_id=PREP_STEP_ID,
|
||||
title="Searching knowledge base",
|
||||
status="complete",
|
||||
items=[f"Found {doc_count} document{'s' if doc_count != 1 else ''}"]
|
||||
if kb_query
|
||||
else ["Skipped"],
|
||||
)
|
||||
|
||||
messages = [
|
||||
SystemMessage(content=system_prompt),
|
||||
HumanMessage(
|
||||
content=[
|
||||
{
|
||||
"type": "text",
|
||||
"text": "Analyze this screenshot. Understand the full context of what the user is working on, then generate the text they most likely want to write in the active text area.",
|
||||
},
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {"url": screenshot_data_url},
|
||||
},
|
||||
]
|
||||
),
|
||||
]
|
||||
# Build agent input with pre-computed KB as initial state
|
||||
if has_kb:
|
||||
instruction = (
|
||||
"Analyze this screenshot, then explore the knowledge base documents "
|
||||
"listed above — read the chunk index of any document whose title "
|
||||
"looks relevant and check matched chunks for useful facts. "
|
||||
"Finally, generate a concise autocomplete for the active text area, "
|
||||
"enhanced with any relevant KB information you found."
|
||||
)
|
||||
else:
|
||||
instruction = (
|
||||
"Analyze this screenshot and generate a concise autocomplete "
|
||||
"for the active text area based on what you see."
|
||||
)
|
||||
|
||||
text_started = False
|
||||
text_id = ""
|
||||
user_message = HumanMessage(
|
||||
content=[
|
||||
{"type": "text", "text": instruction},
|
||||
{"type": "image_url", "image_url": {"url": screenshot_data_url}},
|
||||
]
|
||||
)
|
||||
|
||||
input_data: dict = {"messages": [user_message]}
|
||||
|
||||
if has_kb:
|
||||
input_data["files"] = kb.files
|
||||
input_data["messages"] = [kb.ls_ai_msg, kb.ls_tool_msg, user_message]
|
||||
logger.info(
|
||||
"Autocomplete: injected %d KB files into agent initial state", doc_count
|
||||
)
|
||||
else:
|
||||
logger.info(
|
||||
"Autocomplete: no KB documents found, proceeding with screenshot only"
|
||||
)
|
||||
|
||||
# Stream the agent (message_start already sent above)
|
||||
try:
|
||||
yield streaming.format_message_start()
|
||||
text_id = streaming.generate_text_id()
|
||||
yield streaming.format_text_start(text_id)
|
||||
text_started = True
|
||||
|
||||
async for chunk in llm.astream(messages):
|
||||
token = chunk.content if hasattr(chunk, "content") else str(chunk)
|
||||
if token:
|
||||
yield streaming.format_text_delta(text_id, token)
|
||||
|
||||
yield streaming.format_text_end(text_id)
|
||||
yield streaming.format_finish()
|
||||
yield streaming.format_done()
|
||||
|
||||
async for sse in stream_autocomplete_agent(
|
||||
agent,
|
||||
input_data,
|
||||
streaming,
|
||||
emit_message_start=False,
|
||||
):
|
||||
yield sse
|
||||
except Exception as e:
|
||||
if text_started:
|
||||
yield streaming.format_text_end(text_id)
|
||||
|
||||
if _is_vision_unsupported_error(e):
|
||||
logger.warning(
|
||||
f"Vision autocomplete: selected model does not support vision: {e}"
|
||||
)
|
||||
logger.warning("Vision autocomplete: model does not support vision: %s", e)
|
||||
yield streaming.format_error(vision_error_msg)
|
||||
yield streaming.format_done()
|
||||
else:
|
||||
logger.error(f"Vision autocomplete streaming error: {e}", exc_info=True)
|
||||
logger.error("Vision autocomplete streaming error: %s", e, exc_info=True)
|
||||
yield streaming.format_error("Autocomplete failed. Please try again.")
|
||||
yield streaming.format_done()
|
||||
yield streaming.format_done()
|
||||
|
|
|
|||
193
surfsense_backend/app/services/vision_llm_router_service.py
Normal file
193
surfsense_backend/app/services/vision_llm_router_service.py
Normal file
|
|
@ -0,0 +1,193 @@
|
|||
import logging
|
||||
from typing import Any
|
||||
|
||||
from litellm import Router
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
VISION_AUTO_MODE_ID = 0
|
||||
|
||||
VISION_PROVIDER_MAP = {
|
||||
"OPENAI": "openai",
|
||||
"ANTHROPIC": "anthropic",
|
||||
"GOOGLE": "gemini",
|
||||
"AZURE_OPENAI": "azure",
|
||||
"VERTEX_AI": "vertex_ai",
|
||||
"BEDROCK": "bedrock",
|
||||
"XAI": "xai",
|
||||
"OPENROUTER": "openrouter",
|
||||
"OLLAMA": "ollama_chat",
|
||||
"GROQ": "groq",
|
||||
"TOGETHER_AI": "together_ai",
|
||||
"FIREWORKS_AI": "fireworks_ai",
|
||||
"DEEPSEEK": "openai",
|
||||
"MISTRAL": "mistral",
|
||||
"CUSTOM": "custom",
|
||||
}
|
||||
|
||||
|
||||
class VisionLLMRouterService:
|
||||
_instance = None
|
||||
_router: Router | None = None
|
||||
_model_list: list[dict] = []
|
||||
_router_settings: dict = {}
|
||||
_initialized: bool = False
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
return cls._instance
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls) -> "VisionLLMRouterService":
|
||||
if cls._instance is None:
|
||||
cls._instance = cls()
|
||||
return cls._instance
|
||||
|
||||
@classmethod
|
||||
def initialize(
|
||||
cls,
|
||||
global_configs: list[dict],
|
||||
router_settings: dict | None = None,
|
||||
) -> None:
|
||||
instance = cls.get_instance()
|
||||
|
||||
if instance._initialized:
|
||||
logger.debug("Vision LLM Router already initialized, skipping")
|
||||
return
|
||||
|
||||
model_list = []
|
||||
for config in global_configs:
|
||||
deployment = cls._config_to_deployment(config)
|
||||
if deployment:
|
||||
model_list.append(deployment)
|
||||
|
||||
if not model_list:
|
||||
logger.warning(
|
||||
"No valid vision LLM configs found for router initialization"
|
||||
)
|
||||
return
|
||||
|
||||
instance._model_list = model_list
|
||||
instance._router_settings = router_settings or {}
|
||||
|
||||
default_settings = {
|
||||
"routing_strategy": "usage-based-routing",
|
||||
"num_retries": 3,
|
||||
"allowed_fails": 3,
|
||||
"cooldown_time": 60,
|
||||
"retry_after": 5,
|
||||
}
|
||||
|
||||
final_settings = {**default_settings, **instance._router_settings}
|
||||
|
||||
try:
|
||||
instance._router = Router(
|
||||
model_list=model_list,
|
||||
routing_strategy=final_settings.get(
|
||||
"routing_strategy", "usage-based-routing"
|
||||
),
|
||||
num_retries=final_settings.get("num_retries", 3),
|
||||
allowed_fails=final_settings.get("allowed_fails", 3),
|
||||
cooldown_time=final_settings.get("cooldown_time", 60),
|
||||
set_verbose=False,
|
||||
)
|
||||
instance._initialized = True
|
||||
logger.info(
|
||||
"Vision LLM Router initialized with %d deployments, strategy: %s",
|
||||
len(model_list),
|
||||
final_settings.get("routing_strategy"),
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to initialize Vision LLM Router: {e}")
|
||||
instance._router = None
|
||||
|
||||
@classmethod
|
||||
def _config_to_deployment(cls, config: dict) -> dict | None:
|
||||
try:
|
||||
if not config.get("model_name") or not config.get("api_key"):
|
||||
return None
|
||||
|
||||
if config.get("custom_provider"):
|
||||
model_string = f"{config['custom_provider']}/{config['model_name']}"
|
||||
else:
|
||||
provider = config.get("provider", "").upper()
|
||||
provider_prefix = VISION_PROVIDER_MAP.get(provider, provider.lower())
|
||||
model_string = f"{provider_prefix}/{config['model_name']}"
|
||||
|
||||
litellm_params: dict[str, Any] = {
|
||||
"model": model_string,
|
||||
"api_key": config.get("api_key"),
|
||||
}
|
||||
|
||||
if config.get("api_base"):
|
||||
litellm_params["api_base"] = config["api_base"]
|
||||
|
||||
if config.get("api_version"):
|
||||
litellm_params["api_version"] = config["api_version"]
|
||||
|
||||
if config.get("litellm_params"):
|
||||
litellm_params.update(config["litellm_params"])
|
||||
|
||||
deployment: dict[str, Any] = {
|
||||
"model_name": "auto",
|
||||
"litellm_params": litellm_params,
|
||||
}
|
||||
|
||||
if config.get("rpm"):
|
||||
deployment["rpm"] = config["rpm"]
|
||||
if config.get("tpm"):
|
||||
deployment["tpm"] = config["tpm"]
|
||||
|
||||
return deployment
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to convert vision config to deployment: {e}")
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def get_router(cls) -> Router | None:
|
||||
instance = cls.get_instance()
|
||||
return instance._router
|
||||
|
||||
@classmethod
|
||||
def is_initialized(cls) -> bool:
|
||||
instance = cls.get_instance()
|
||||
return instance._initialized and instance._router is not None
|
||||
|
||||
@classmethod
|
||||
def get_model_count(cls) -> int:
|
||||
instance = cls.get_instance()
|
||||
return len(instance._model_list)
|
||||
|
||||
|
||||
def is_vision_auto_mode(config_id: int | None) -> bool:
|
||||
return config_id == VISION_AUTO_MODE_ID
|
||||
|
||||
|
||||
def build_vision_model_string(
|
||||
provider: str, model_name: str, custom_provider: str | None
|
||||
) -> str:
|
||||
if custom_provider:
|
||||
return f"{custom_provider}/{model_name}"
|
||||
prefix = VISION_PROVIDER_MAP.get(provider.upper(), provider.lower())
|
||||
return f"{prefix}/{model_name}"
|
||||
|
||||
|
||||
def get_global_vision_llm_config(config_id: int) -> dict | None:
|
||||
from app.config import config
|
||||
|
||||
if config_id == VISION_AUTO_MODE_ID:
|
||||
return {
|
||||
"id": VISION_AUTO_MODE_ID,
|
||||
"name": "Auto (Fastest)",
|
||||
"provider": "AUTO",
|
||||
"model_name": "auto",
|
||||
"is_auto_mode": True,
|
||||
}
|
||||
if config_id > 0:
|
||||
return None
|
||||
for cfg in config.GLOBAL_VISION_LLM_CONFIGS:
|
||||
if cfg.get("id") == config_id:
|
||||
return cfg
|
||||
return None
|
||||
132
surfsense_backend/app/services/vision_model_list_service.py
Normal file
132
surfsense_backend/app/services/vision_model_list_service.py
Normal file
|
|
@ -0,0 +1,132 @@
|
|||
"""
|
||||
Service for fetching and caching the vision-capable model list.
|
||||
|
||||
Reuses the same OpenRouter public API and local fallback as the LLM model
|
||||
list service, but filters for models that accept image input.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import httpx
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
OPENROUTER_API_URL = "https://openrouter.ai/api/v1/models"
|
||||
FALLBACK_FILE = Path(__file__).parent.parent / "config" / "vision_model_list_fallback.json"
|
||||
CACHE_TTL_SECONDS = 86400 # 24 hours
|
||||
|
||||
_cache: list[dict] | None = None
|
||||
_cache_timestamp: float = 0
|
||||
|
||||
OPENROUTER_SLUG_TO_VISION_PROVIDER: dict[str, str] = {
|
||||
"openai": "OPENAI",
|
||||
"anthropic": "ANTHROPIC",
|
||||
"google": "GOOGLE",
|
||||
"mistralai": "MISTRAL",
|
||||
"x-ai": "XAI",
|
||||
}
|
||||
|
||||
|
||||
def _format_context_length(length: int | None) -> str | None:
|
||||
if not length:
|
||||
return None
|
||||
if length >= 1_000_000:
|
||||
return f"{length / 1_000_000:g}M"
|
||||
if length >= 1_000:
|
||||
return f"{length / 1_000:g}K"
|
||||
return str(length)
|
||||
|
||||
|
||||
async def _fetch_from_openrouter() -> list[dict] | None:
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=15) as client:
|
||||
response = await client.get(OPENROUTER_API_URL)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
return data.get("data", [])
|
||||
except Exception as e:
|
||||
logger.warning("Failed to fetch from OpenRouter API for vision models: %s", e)
|
||||
return None
|
||||
|
||||
|
||||
def _load_fallback() -> list[dict]:
|
||||
try:
|
||||
with open(FALLBACK_FILE, encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
logger.error("Failed to load vision model fallback list: %s", e)
|
||||
return []
|
||||
|
||||
|
||||
def _is_vision_model(model: dict) -> bool:
|
||||
"""Return True if the model accepts image input and outputs text."""
|
||||
arch = model.get("architecture", {})
|
||||
input_mods = arch.get("input_modalities", [])
|
||||
output_mods = arch.get("output_modalities", [])
|
||||
return "image" in input_mods and "text" in output_mods
|
||||
|
||||
|
||||
def _process_vision_models(raw_models: list[dict]) -> list[dict]:
|
||||
processed: list[dict] = []
|
||||
|
||||
for model in raw_models:
|
||||
model_id: str = model.get("id", "")
|
||||
name: str = model.get("name", "")
|
||||
context_length = model.get("context_length")
|
||||
|
||||
if "/" not in model_id:
|
||||
continue
|
||||
|
||||
if not _is_vision_model(model):
|
||||
continue
|
||||
|
||||
provider_slug, model_name = model_id.split("/", 1)
|
||||
context_window = _format_context_length(context_length)
|
||||
|
||||
processed.append(
|
||||
{
|
||||
"value": model_id,
|
||||
"label": name,
|
||||
"provider": "OPENROUTER",
|
||||
"context_window": context_window,
|
||||
}
|
||||
)
|
||||
|
||||
native_provider = OPENROUTER_SLUG_TO_VISION_PROVIDER.get(provider_slug)
|
||||
if native_provider:
|
||||
if native_provider == "GOOGLE" and not model_name.startswith("gemini-"):
|
||||
continue
|
||||
|
||||
processed.append(
|
||||
{
|
||||
"value": model_name,
|
||||
"label": name,
|
||||
"provider": native_provider,
|
||||
"context_window": context_window,
|
||||
}
|
||||
)
|
||||
|
||||
return processed
|
||||
|
||||
|
||||
async def get_vision_model_list() -> list[dict]:
|
||||
global _cache, _cache_timestamp
|
||||
|
||||
if _cache is not None and (time.time() - _cache_timestamp) < CACHE_TTL_SECONDS:
|
||||
return _cache
|
||||
|
||||
raw_models = await _fetch_from_openrouter()
|
||||
|
||||
if raw_models is None:
|
||||
logger.info("Using fallback vision model list")
|
||||
return _load_fallback()
|
||||
|
||||
processed = _process_vision_models(raw_models)
|
||||
|
||||
_cache = processed
|
||||
_cache_timestamp = time.time()
|
||||
|
||||
return processed
|
||||
|
|
@ -46,8 +46,6 @@ dependencies = [
|
|||
"redis>=5.2.1",
|
||||
"firecrawl-py>=4.9.0",
|
||||
"boto3>=1.35.0",
|
||||
"litellm>=1.80.10",
|
||||
"langchain-litellm>=0.3.5",
|
||||
"fake-useragent>=2.2.0",
|
||||
"trafilatura>=2.0.0",
|
||||
"fastapi-users[oauth,sqlalchemy]>=15.0.3",
|
||||
|
|
@ -76,6 +74,8 @@ dependencies = [
|
|||
"deepagents>=0.4.12",
|
||||
"stripe>=15.0.0",
|
||||
"azure-ai-documentintelligence>=1.0.2",
|
||||
"litellm>=1.83.0",
|
||||
"langchain-litellm>=0.6.4",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
|
|
|
|||
180
surfsense_backend/uv.lock
generated
180
surfsense_backend/uv.lock
generated
|
|
@ -62,7 +62,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
version = "3.13.3"
|
||||
version = "3.13.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "aiohappyeyeballs" },
|
||||
|
|
@ -73,76 +73,76 @@ dependencies = [
|
|||
{ name = "propcache" },
|
||||
{ name = "yarl" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556, upload-time = "2026-01-03T17:33:05.204Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/77/9a/152096d4808df8e4268befa55fba462f440f14beab85e8ad9bf990516918/aiohttp-3.13.5.tar.gz", hash = "sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1", size = 7858271, upload-time = "2026-03-31T22:01:03.343Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a0/be/4fc11f202955a69e0db803a12a062b8379c970c7c84f4882b6da17337cc1/aiohttp-3.13.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:b903a4dfee7d347e2d87697d0713be59e0b87925be030c9178c5faa58ea58d5c", size = 739732, upload-time = "2026-01-03T17:30:14.23Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/97/2c/621d5b851f94fa0bb7430d6089b3aa970a9d9b75196bc93bb624b0db237a/aiohttp-3.13.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a45530014d7a1e09f4a55f4f43097ba0fd155089372e105e4bff4ca76cb1b168", size = 494293, upload-time = "2026-01-03T17:30:15.96Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/5d/43/4be01406b78e1be8320bb8316dc9c42dbab553d281c40364e0f862d5661c/aiohttp-3.13.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:27234ef6d85c914f9efeb77ff616dbf4ad2380be0cda40b4db086ffc7ddd1b7d", size = 493533, upload-time = "2026-01-03T17:30:17.431Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/8d/a8/5a35dc56a06a2c90d4742cbf35294396907027f80eea696637945a106f25/aiohttp-3.13.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d32764c6c9aafb7fb55366a224756387cd50bfa720f32b88e0e6fa45b27dcf29", size = 1737839, upload-time = "2026-01-03T17:30:19.422Z" },
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/7c/f6/af16887b5d419e6a367095994c0b1332d154f647e7dc2bd50e61876e8e3d/aiohttp-3.13.3-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c014c7ea7fb775dd015b2d3137378b7be0249a448a1612268b5a90c2d81de04d", size = 1771906, upload-time = "2026-01-03T17:30:23.932Z" },
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||||
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@ -6797,11 +6797,11 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "python-dotenv"
|
||||
version = "1.2.2"
|
||||
version = "1.0.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135, upload-time = "2026-03-01T16:00:26.196Z" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/bc/57/e84d88dfe0aec03b7a2d4327012c1627ab5f03652216c63d49846d7a6c58/python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca", size = 39115, upload-time = "2024-01-23T06:33:00.505Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101, upload-time = "2026-03-01T16:00:25.09Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6a/3e/b68c118422ec867fa7ab88444e1274aa40681c606d59ac27de5a5588f082/python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a", size = 19863, upload-time = "2024-01-23T06:32:58.246Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -8082,12 +8082,12 @@ requires-dist = [
|
|||
{ name = "langchain", specifier = ">=1.2.13" },
|
||||
{ name = "langchain-community", specifier = ">=0.4.1" },
|
||||
{ name = "langchain-daytona", specifier = ">=0.0.2" },
|
||||
{ name = "langchain-litellm", specifier = ">=0.3.5" },
|
||||
{ name = "langchain-litellm", specifier = ">=0.6.4" },
|
||||
{ name = "langchain-unstructured", specifier = ">=1.0.1" },
|
||||
{ name = "langgraph", specifier = ">=1.1.3" },
|
||||
{ name = "langgraph-checkpoint-postgres", specifier = ">=3.0.2" },
|
||||
{ name = "linkup-sdk", specifier = ">=0.2.4" },
|
||||
{ name = "litellm", specifier = ">=1.80.10" },
|
||||
{ name = "litellm", specifier = ">=1.83.0" },
|
||||
{ name = "llama-cloud-services", specifier = ">=0.6.25" },
|
||||
{ name = "markdown", specifier = ">=3.7" },
|
||||
{ name = "markdownify", specifier = ">=0.14.1" },
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue