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Author SHA1 Message Date
github-merge-queue[bot]
f2b24f700f Update frontend build based on commit 9c7c598c7d 2026-02-08 23:31:49 +00:00
245 changed files with 9721 additions and 14606 deletions

View File

@@ -42,7 +42,7 @@ jobs:
- name: Get CI failure details
id: failure_details
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
const run = await github.rest.actions.getWorkflowRun({

View File

@@ -41,7 +41,7 @@ jobs:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -78,7 +78,7 @@ jobs:
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22"
@@ -91,7 +91,7 @@ jobs:
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -124,7 +124,7 @@ jobs:
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes

View File

@@ -57,7 +57,7 @@ jobs:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -94,7 +94,7 @@ jobs:
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22"
@@ -107,7 +107,7 @@ jobs:
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -140,7 +140,7 @@ jobs:
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes

View File

@@ -39,7 +39,7 @@ jobs:
python-version: "3.11" # Use standard version matching CI
- name: Set up Python dependency cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -76,7 +76,7 @@ jobs:
# Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22"
@@ -89,7 +89,7 @@ jobs:
echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
- name: Cache frontend dependencies
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -132,7 +132,7 @@ jobs:
# Phase 1: Cache and load Docker images for faster setup
- name: Set up Docker image cache
id: docker-cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/docker-cache
# Use a versioned key for cache invalidation when image list changes

View File

@@ -33,7 +33,7 @@ jobs:
python-version: "3.11"
- name: Set up Python dependency cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

View File

@@ -33,7 +33,7 @@ jobs:
python-version: "3.11"
- name: Set up Python dependency cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

View File

@@ -38,7 +38,7 @@ jobs:
python-version: "3.11"
- name: Set up Python dependency cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

View File

@@ -88,7 +88,7 @@ jobs:
run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
- name: Set up Python dependency cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.cache/pypoetry
key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

View File

@@ -17,7 +17,7 @@ jobs:
- name: Check comment permissions and deployment status
id: check_status
if: github.event_name == 'issue_comment' && github.event.issue.pull_request
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
const commentBody = context.payload.comment.body.trim();
@@ -55,7 +55,7 @@ jobs:
- name: Post permission denied comment
if: steps.check_status.outputs.permission_denied == 'true'
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({
@@ -68,7 +68,7 @@ jobs:
- name: Get PR details for deployment
id: pr_details
if: steps.check_status.outputs.should_deploy == 'true' || steps.check_status.outputs.should_undeploy == 'true'
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
const pr = await github.rest.pulls.get({
@@ -98,7 +98,7 @@ jobs:
- name: Post deploy success comment
if: steps.check_status.outputs.should_deploy == 'true'
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({
@@ -126,7 +126,7 @@ jobs:
- name: Post undeploy success comment
if: steps.check_status.outputs.should_undeploy == 'true'
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({
@@ -139,7 +139,7 @@ jobs:
- name: Check deployment status on PR close
id: check_pr_close
if: github.event_name == 'pull_request' && github.event.action == 'closed'
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
const comments = await github.rest.issues.listComments({
@@ -187,7 +187,7 @@ jobs:
github.event_name == 'pull_request' &&
github.event.action == 'closed' &&
steps.check_pr_close.outputs.should_undeploy == 'true'
uses: actions/github-script@v8
uses: actions/github-script@v7
with:
script: |
await github.rest.issues.createComment({

View File

@@ -42,7 +42,7 @@ jobs:
- 'autogpt_platform/frontend/src/components/**'
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
@@ -54,7 +54,7 @@ jobs:
run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
- name: Cache dependencies
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ steps.cache-key.outputs.key }}
@@ -74,7 +74,7 @@ jobs:
uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
@@ -82,7 +82,7 @@ jobs:
run: corepack enable
- name: Restore dependencies cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}
@@ -112,7 +112,7 @@ jobs:
fetch-depth: 0
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
@@ -120,7 +120,7 @@ jobs:
run: corepack enable
- name: Restore dependencies cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}
@@ -153,7 +153,7 @@ jobs:
submodules: recursive
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
@@ -176,7 +176,7 @@ jobs:
uses: docker/setup-buildx-action@v3
- name: Cache Docker layers
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: /tmp/.buildx-cache
key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
@@ -231,7 +231,7 @@ jobs:
fi
- name: Restore dependencies cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}
@@ -282,7 +282,7 @@ jobs:
submodules: recursive
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
@@ -290,7 +290,7 @@ jobs:
run: corepack enable
- name: Restore dependencies cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}

View File

@@ -32,7 +32,7 @@ jobs:
uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
@@ -44,7 +44,7 @@ jobs:
run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
- name: Cache dependencies
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ steps.cache-key.outputs.key }}
@@ -56,7 +56,7 @@ jobs:
run: pnpm install --frozen-lockfile
types:
runs-on: big-boi
runs-on: ubuntu-latest
needs: setup
strategy:
fail-fast: false
@@ -68,7 +68,7 @@ jobs:
submodules: recursive
- name: Set up Node.js
uses: actions/setup-node@v6
uses: actions/setup-node@v4
with:
node-version: "22.18.0"
@@ -85,10 +85,10 @@ jobs:
- name: Run docker compose
run: |
docker compose -f ../docker-compose.yml --profile local up -d deps_backend
docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
- name: Restore dependencies cache
uses: actions/cache@v5
uses: actions/cache@v4
with:
path: ~/.pnpm-store
key: ${{ needs.setup.outputs.cache-key }}

View File

@@ -99,101 +99,84 @@ files = [
[[package]]
name = "cffi"
version = "2.0.0"
version = "1.17.1"
description = "Foreign Function Interface for Python calling C code."
optional = false
python-versions = ">=3.9"
python-versions = ">=3.8"
groups = ["main"]
markers = "platform_python_implementation != \"PyPy\""
files = [
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typing-extensions = {version = ">=4.13.2", markers = "python_full_version < \"3.11.0\""}
cffi = {version = ">=1.14", markers = "platform_python_implementation != \"PyPy\""}
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nox = ["nox (>=2024.4.15)", "nox[uv] (>=2024.3.2) ; python_full_version >= \"3.8.0\""]
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sdist = ["build (>=1.0.0)"]
ssh = ["bcrypt (>=3.1.5)"]
test = ["certifi (>=2024)", "cryptography-vectors (==46.0.4)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
test = ["certifi (>=2024)", "cryptography-vectors (==45.0.6)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
test-randomorder = ["pytest-randomly"]
[[package]]
@@ -1698,7 +1650,7 @@ description = "C parser in Python"
optional = false
python-versions = ">=3.8"
groups = ["main"]
markers = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\""
markers = "platform_python_implementation != \"PyPy\""
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@@ -2020,14 +1972,14 @@ files = [
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version = "1.1.404"
description = "Command line wrapper for pyright"
optional = false
python-versions = ">=3.7"
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@@ -2159,20 +2111,19 @@ dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "requests
[[package]]
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version = "1.3.0"
version = "1.1.0"
description = "Pytest support for asyncio"
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python-versions = ">=3.9"
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typing-extensions = {version = ">=4.12", markers = "python_version < \"3.13\""}
pytest = ">=8.2,<9"
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@@ -2180,34 +2131,34 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
[[package]]
name = "pytest-cov"
version = "7.0.0"
version = "6.2.1"
description = "Pytest plugin for measuring coverage."
optional = false
python-versions = ">=3.9"
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coverage = {version = ">=7.5", extras = ["toml"]}
pluggy = ">=1.2"
pytest = ">=7"
pytest = ">=6.2.5"
[package.extras]
testing = ["process-tests", "pytest-xdist", "virtualenv"]
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
[[package]]
name = "pytest-mock"
version = "3.15.1"
version = "3.14.1"
description = "Thin-wrapper around the mock package for easier use with pytest"
optional = false
python-versions = ">=3.9"
python-versions = ">=3.8"
groups = ["dev"]
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@@ -2341,30 +2292,31 @@ pyasn1 = ">=0.1.3"
[[package]]
name = "ruff"
version = "0.15.0"
version = "0.12.11"
description = "An extremely fast Python linter and code formatter, written in Rust."
optional = false
python-versions = ">=3.7"
groups = ["dev"]
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@@ -2911,4 +2863,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<4.0"
content-hash = "40eae94995dc0a388fa832ed4af9b6137f28d5b5ced3aaea70d5f91d4d9a179d"
content-hash = "5f15a9c9381c9a374f3d18e087c23b1f1ba8cce192d6f67463a3e3a7a18fee44"

View File

@@ -9,7 +9,7 @@ packages = [{ include = "autogpt_libs" }]
[tool.poetry.dependencies]
python = ">=3.10,<4.0"
colorama = "^0.4.6"
cryptography = "^46.0"
cryptography = "^45.0"
expiringdict = "^1.2.2"
fastapi = "^0.128.0"
google-cloud-logging = "^3.13.0"
@@ -22,12 +22,12 @@ supabase = "^2.27.2"
uvicorn = "^0.40.0"
[tool.poetry.group.dev.dependencies]
pyright = "^1.1.408"
pyright = "^1.1.404"
pytest = "^8.4.1"
pytest-asyncio = "^1.3.0"
pytest-mock = "^3.15.1"
pytest-cov = "^7.0.0"
ruff = "^0.15.0"
pytest-asyncio = "^1.1.0"
pytest-mock = "^3.14.1"
pytest-cov = "^6.2.1"
ruff = "^0.12.11"
[build-system]
requires = ["poetry-core"]

View File

@@ -15,9 +15,9 @@ from prisma.enums import APIKeyPermission
from pydantic import BaseModel, Field
from backend.api.external.middleware import require_permission
from backend.copilot.model import ChatSession
from backend.copilot.tools import find_agent_tool, run_agent_tool
from backend.copilot.tools.models import ToolResponseBase
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.tools import find_agent_tool, run_agent_tool
from backend.api.features.chat.tools.models import ToolResponseBase
from backend.data.auth.base import APIAuthorizationInfo
logger = logging.getLogger(__name__)

View File

@@ -119,9 +119,8 @@ class ChatCompletionConsumer:
"""Lazily initialize Prisma client on first use."""
if self._prisma is None:
database_url = os.getenv("DATABASE_URL", "postgresql://localhost:5432")
prisma = Prisma(datasource={"url": database_url})
await prisma.connect()
self._prisma = prisma
self._prisma = Prisma(datasource={"url": database_url})
await self._prisma.connect()
logger.info("[COMPLETION] Consumer Prisma client connected (lazy init)")
return self._prisma

View File

@@ -93,12 +93,6 @@ class ChatConfig(BaseSettings):
description="Name of the prompt in Langfuse to fetch",
)
# Extended thinking configuration for Claude models
thinking_enabled: bool = Field(
default=True,
description="Enable adaptive thinking for Claude models via OpenRouter",
)
@field_validator("api_key", mode="before")
@classmethod
def get_api_key(cls, v):

View File

@@ -45,7 +45,10 @@ async def create_chat_session(
successfulAgentRuns=SafeJson({}),
successfulAgentSchedules=SafeJson({}),
)
return await PrismaChatSession.prisma().create(data=data)
return await PrismaChatSession.prisma().create(
data=data,
include={"Messages": True},
)
async def update_chat_session(

View File

@@ -18,10 +18,6 @@ class ResponseType(str, Enum):
START = "start"
FINISH = "finish"
# Step lifecycle (one LLM API call within a message)
START_STEP = "start-step"
FINISH_STEP = "finish-step"
# Text streaming
TEXT_START = "text-start"
TEXT_DELTA = "text-delta"
@@ -61,16 +57,6 @@ class StreamStart(StreamBaseResponse):
description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
)
def to_sse(self) -> str:
"""Convert to SSE format, excluding non-protocol fields like taskId."""
import json
data: dict[str, Any] = {
"type": self.type.value,
"messageId": self.messageId,
}
return f"data: {json.dumps(data)}\n\n"
class StreamFinish(StreamBaseResponse):
"""End of message/stream."""
@@ -78,26 +64,6 @@ class StreamFinish(StreamBaseResponse):
type: ResponseType = ResponseType.FINISH
class StreamStartStep(StreamBaseResponse):
"""Start of a step (one LLM API call within a message).
The AI SDK uses this to add a step-start boundary to message.parts,
enabling visual separation between multiple LLM calls in a single message.
"""
type: ResponseType = ResponseType.START_STEP
class StreamFinishStep(StreamBaseResponse):
"""End of a step (one LLM API call within a message).
The AI SDK uses this to reset activeTextParts and activeReasoningParts,
so the next LLM call in a tool-call continuation starts with clean state.
"""
type: ResponseType = ResponseType.FINISH_STEP
# ========== Text Streaming ==========
@@ -151,7 +117,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
toolCallId: str = Field(..., description="Tool call ID this responds to")
output: str | dict[str, Any] = Field(..., description="Tool execution output")
# Keep these for internal backend use
# Additional fields for internal use (not part of AI SDK spec but useful)
toolName: str | None = Field(
default=None, description="Name of the tool that was executed"
)
@@ -159,17 +125,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
default=True, description="Whether the tool execution succeeded"
)
def to_sse(self) -> str:
"""Convert to SSE format, excluding non-spec fields."""
import json
data = {
"type": self.type.value,
"toolCallId": self.toolCallId,
"output": self.output,
}
return f"data: {json.dumps(data)}\n\n"
# ========== Other ==========

View File

@@ -6,49 +6,19 @@ from collections.abc import AsyncGenerator
from typing import Annotated
from autogpt_libs import auth
from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from backend.copilot import service as chat_service
from backend.copilot import stream_registry
from backend.copilot.completion_handler import (
process_operation_failure,
process_operation_success,
)
from backend.copilot.config import ChatConfig
from backend.copilot.executor.utils import enqueue_copilot_task
from backend.copilot.model import (
ChatSession,
create_chat_session,
get_chat_session,
get_user_sessions,
)
from backend.copilot.response_model import StreamFinish, StreamHeartbeat
from backend.copilot.tools.models import (
AgentDetailsResponse,
AgentOutputResponse,
AgentPreviewResponse,
AgentSavedResponse,
AgentsFoundResponse,
BlockListResponse,
BlockOutputResponse,
ClarificationNeededResponse,
DocPageResponse,
DocSearchResultsResponse,
ErrorResponse,
ExecutionStartedResponse,
InputValidationErrorResponse,
NeedLoginResponse,
NoResultsResponse,
OperationInProgressResponse,
OperationPendingResponse,
OperationStartedResponse,
SetupRequirementsResponse,
UnderstandingUpdatedResponse,
)
from backend.util.exceptions import NotFoundError
from . import service as chat_service
from . import stream_registry
from .completion_handler import process_operation_failure, process_operation_success
from .config import ChatConfig
from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
from .response_model import StreamFinish, StreamHeartbeat, StreamStart
config = ChatConfig()
@@ -296,36 +266,12 @@ async def stream_chat_post(
"""
import asyncio
import time
stream_start_time = time.perf_counter()
log_meta = {"component": "ChatStream", "session_id": session_id}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"[TIMING] stream_chat_post STARTED, session={session_id}, "
f"user={user_id}, message_len={len(request.message)}",
extra={"json_fields": log_meta},
)
_session = await _validate_and_get_session(session_id, user_id) # noqa: F841
logger.info(
f"[TIMING] session validated in {(time.perf_counter() - stream_start_time)*1000:.1f}ms",
extra={
"json_fields": {
**log_meta,
"duration_ms": (time.perf_counter() - stream_start_time) * 1000,
}
},
)
session = await _validate_and_get_session(session_id, user_id)
# Create a task in the stream registry for reconnection support
task_id = str(uuid_module.uuid4())
operation_id = str(uuid_module.uuid4())
log_meta["task_id"] = task_id
task_create_start = time.perf_counter()
await stream_registry.create_task(
task_id=task_id,
session_id=session_id,
@@ -334,46 +280,40 @@ async def stream_chat_post(
tool_name="chat",
operation_id=operation_id,
)
logger.info(
f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start)*1000:.1f}ms",
extra={
"json_fields": {
**log_meta,
"duration_ms": (time.perf_counter() - task_create_start) * 1000,
}
},
)
# Enqueue the task to RabbitMQ for processing by the CoPilot executor
await enqueue_copilot_task(
task_id=task_id,
session_id=session_id,
user_id=user_id,
operation_id=operation_id,
message=request.message,
is_user_message=request.is_user_message,
context=request.context,
)
# Background task that runs the AI generation independently of SSE connection
async def run_ai_generation():
try:
# Emit a start event with task_id for reconnection
start_chunk = StreamStart(messageId=task_id, taskId=task_id)
await stream_registry.publish_chunk(task_id, start_chunk)
setup_time = (time.perf_counter() - stream_start_time) * 1000
logger.info(
f"[TIMING] Task enqueued to RabbitMQ, setup={setup_time:.1f}ms",
extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
)
async for chunk in chat_service.stream_chat_completion(
session_id,
request.message,
is_user_message=request.is_user_message,
user_id=user_id,
session=session, # Pass pre-fetched session to avoid double-fetch
context=request.context,
):
# Write to Redis (subscribers will receive via XREAD)
await stream_registry.publish_chunk(task_id, chunk)
# Mark task as completed
await stream_registry.mark_task_completed(task_id, "completed")
except Exception as e:
logger.error(
f"Error in background AI generation for session {session_id}: {e}"
)
await stream_registry.mark_task_completed(task_id, "failed")
# Start the AI generation in a background task
bg_task = asyncio.create_task(run_ai_generation())
await stream_registry.set_task_asyncio_task(task_id, bg_task)
# SSE endpoint that subscribes to the task's stream
async def event_generator() -> AsyncGenerator[str, None]:
import time as time_module
event_gen_start = time_module.perf_counter()
logger.info(
f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
f"user={user_id}",
extra={"json_fields": log_meta},
)
subscriber_queue = None
first_chunk_yielded = False
chunks_yielded = 0
try:
# Subscribe to the task stream (this replays existing messages + live updates)
subscriber_queue = await stream_registry.subscribe_to_task(
@@ -388,70 +328,22 @@ async def stream_chat_post(
return
# Read from the subscriber queue and yield to SSE
logger.info(
"[TIMING] Starting to read from subscriber_queue",
extra={"json_fields": log_meta},
)
while True:
try:
chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
chunks_yielded += 1
if not first_chunk_yielded:
first_chunk_yielded = True
elapsed = time_module.perf_counter() - event_gen_start
logger.info(
f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
f"type={type(chunk).__name__}",
extra={
"json_fields": {
**log_meta,
"chunk_type": type(chunk).__name__,
"elapsed_ms": elapsed * 1000,
}
},
)
yield chunk.to_sse()
# Check for finish signal
if isinstance(chunk, StreamFinish):
total_time = time_module.perf_counter() - event_gen_start
logger.info(
f"[TIMING] StreamFinish received in {total_time:.2f}s; "
f"n_chunks={chunks_yielded}",
extra={
"json_fields": {
**log_meta,
"chunks_yielded": chunks_yielded,
"total_time_ms": total_time * 1000,
}
},
)
break
except asyncio.TimeoutError:
# Send heartbeat to keep connection alive
yield StreamHeartbeat().to_sse()
except GeneratorExit:
logger.info(
f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
extra={
"json_fields": {
**log_meta,
"chunks_yielded": chunks_yielded,
"reason": "client_disconnect",
}
},
)
pass # Client disconnected - background task continues
except Exception as e:
elapsed = (time_module.perf_counter() - event_gen_start) * 1000
logger.error(
f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
extra={
"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
},
)
logger.error(f"Error in SSE stream for task {task_id}: {e}")
finally:
# Unsubscribe when client disconnects or stream ends to prevent resource leak
if subscriber_queue is not None:
@@ -465,18 +357,6 @@ async def stream_chat_post(
exc_info=True,
)
# AI SDK protocol termination - always yield even if unsubscribe fails
total_time = time_module.perf_counter() - event_gen_start
logger.info(
f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time * 1000,
"chunks_yielded": chunks_yielded,
}
},
)
yield "data: [DONE]\n\n"
return StreamingResponse(
@@ -494,90 +374,63 @@ async def stream_chat_post(
@router.get(
"/sessions/{session_id}/stream",
)
async def resume_session_stream(
async def stream_chat_get(
session_id: str,
message: Annotated[str, Query(min_length=1, max_length=10000)],
user_id: str | None = Depends(auth.get_user_id),
is_user_message: bool = Query(default=True),
):
"""
Resume an active stream for a session.
Stream chat responses for a session (GET - legacy endpoint).
Called by the AI SDK's ``useChat(resume: true)`` on page load.
Checks for an active (in-progress) task on the session and either replays
the full SSE stream or returns 204 No Content if nothing is running.
Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
- Text fragments as they are generated
- Tool call UI elements (if invoked)
- Tool execution results
Args:
session_id: The chat session identifier.
session_id: The chat session identifier to associate with the streamed messages.
message: The user's new message to process.
user_id: Optional authenticated user ID.
is_user_message: Whether the message is a user message.
Returns:
StreamingResponse (SSE) when an active stream exists,
or 204 No Content when there is nothing to resume.
StreamingResponse: SSE-formatted response chunks.
"""
import asyncio
active_task, _last_id = await stream_registry.get_active_task_for_session(
session_id, user_id
)
if not active_task:
return Response(status_code=204)
subscriber_queue = await stream_registry.subscribe_to_task(
task_id=active_task.task_id,
user_id=user_id,
last_message_id="0-0", # Full replay so useChat rebuilds the message
)
if subscriber_queue is None:
return Response(status_code=204)
session = await _validate_and_get_session(session_id, user_id)
async def event_generator() -> AsyncGenerator[str, None]:
chunk_count = 0
first_chunk_type: str | None = None
try:
while True:
try:
chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
if chunk_count < 3:
logger.info(
"Resume stream chunk",
extra={
"session_id": session_id,
"chunk_type": str(chunk.type),
},
)
if not first_chunk_type:
first_chunk_type = str(chunk.type)
chunk_count += 1
yield chunk.to_sse()
if isinstance(chunk, StreamFinish):
break
except asyncio.TimeoutError:
yield StreamHeartbeat().to_sse()
except GeneratorExit:
pass
except Exception as e:
logger.error(f"Error in resume stream for session {session_id}: {e}")
finally:
try:
await stream_registry.unsubscribe_from_task(
active_task.task_id, subscriber_queue
async for chunk in chat_service.stream_chat_completion(
session_id,
message,
is_user_message=is_user_message,
user_id=user_id,
session=session, # Pass pre-fetched session to avoid double-fetch
):
if chunk_count < 3:
logger.info(
"Chat stream chunk",
extra={
"session_id": session_id,
"chunk_type": str(chunk.type),
},
)
except Exception as unsub_err:
logger.error(
f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
exc_info=True,
)
logger.info(
"Resume stream completed",
extra={
"session_id": session_id,
"n_chunks": chunk_count,
"first_chunk_type": first_chunk_type,
},
)
yield "data: [DONE]\n\n"
if not first_chunk_type:
first_chunk_type = str(chunk.type)
chunk_count += 1
yield chunk.to_sse()
logger.info(
"Chat stream completed",
extra={
"session_id": session_id,
"chunk_count": chunk_count,
"first_chunk_type": first_chunk_type,
},
)
# AI SDK protocol termination
yield "data: [DONE]\n\n"
return StreamingResponse(
event_generator(),
@@ -585,8 +438,8 @@ async def resume_session_stream(
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
"x-vercel-ai-ui-message-stream": "v1",
"X-Accel-Buffering": "no", # Disable nginx buffering
"x-vercel-ai-ui-message-stream": "v1", # AI SDK protocol header
},
)
@@ -898,42 +751,3 @@ async def health_check() -> dict:
"service": "chat",
"version": "0.1.0",
}
# ========== Schema Export (for OpenAPI / Orval codegen) ==========
ToolResponseUnion = (
AgentsFoundResponse
| NoResultsResponse
| AgentDetailsResponse
| SetupRequirementsResponse
| ExecutionStartedResponse
| NeedLoginResponse
| ErrorResponse
| InputValidationErrorResponse
| AgentOutputResponse
| UnderstandingUpdatedResponse
| AgentPreviewResponse
| AgentSavedResponse
| ClarificationNeededResponse
| BlockListResponse
| BlockOutputResponse
| DocSearchResultsResponse
| DocPageResponse
| OperationStartedResponse
| OperationPendingResponse
| OperationInProgressResponse
)
@router.get(
"/schema/tool-responses",
response_model=ToolResponseUnion,
include_in_schema=True,
summary="[Dummy] Tool response type export for codegen",
description="This endpoint is not meant to be called. It exists solely to "
"expose tool response models in the OpenAPI schema for frontend codegen.",
)
async def _tool_response_schema() -> ToolResponseUnion: # type: ignore[return]
"""Never called at runtime. Exists only so Orval generates TS types."""
raise HTTPException(status_code=501, detail="Schema-only endpoint")

View File

@@ -52,10 +52,8 @@ from .response_model import (
StreamBaseResponse,
StreamError,
StreamFinish,
StreamFinishStep,
StreamHeartbeat,
StreamStart,
StreamStartStep,
StreamTextDelta,
StreamTextEnd,
StreamTextStart,
@@ -353,10 +351,6 @@ async def stream_chat_completion(
retry_count: int = 0,
session: ChatSession | None = None,
context: dict[str, str] | None = None, # {url: str, content: str}
_continuation_message_id: (
str | None
) = None, # Internal: reuse message ID for tool call continuations
_task_id: str | None = None, # Internal: task ID for SSE reconnection support
) -> AsyncGenerator[StreamBaseResponse, None]:
"""Main entry point for streaming chat completions with database handling.
@@ -377,45 +371,21 @@ async def stream_chat_completion(
ValueError: If max_context_messages is exceeded
"""
completion_start = time.monotonic()
# Build log metadata for structured logging
log_meta = {"component": "ChatService", "session_id": session_id}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"[TIMING] stream_chat_completion STARTED, session={session_id}, user={user_id}, "
f"message_len={len(message) if message else 0}, is_user={is_user_message}",
extra={
"json_fields": {
**log_meta,
"message_len": len(message) if message else 0,
"is_user_message": is_user_message,
}
},
f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
)
# Only fetch from Redis if session not provided (initial call)
if session is None:
fetch_start = time.monotonic()
session = await get_chat_session(session_id, user_id)
fetch_time = (time.monotonic() - fetch_start) * 1000
logger.info(
f"[TIMING] get_chat_session took {fetch_time:.1f}ms, "
f"n_messages={len(session.messages) if session else 0}",
extra={
"json_fields": {
**log_meta,
"duration_ms": fetch_time,
"n_messages": len(session.messages) if session else 0,
}
},
f"Fetched session from Redis: {session.session_id if session else 'None'}, "
f"message_count={len(session.messages) if session else 0}"
)
else:
logger.info(
f"[TIMING] Using provided session, messages={len(session.messages)}",
extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
f"Using provided session object: {session.session_id}, "
f"message_count={len(session.messages)}"
)
if not session:
@@ -436,25 +406,17 @@ async def stream_chat_completion(
# Track user message in PostHog
if is_user_message:
posthog_start = time.monotonic()
track_user_message(
user_id=user_id,
session_id=session_id,
message_length=len(message),
)
posthog_time = (time.monotonic() - posthog_start) * 1000
logger.info(
f"[TIMING] track_user_message took {posthog_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": posthog_time}},
)
upsert_start = time.monotonic()
session = await upsert_chat_session(session)
upsert_time = (time.monotonic() - upsert_start) * 1000
logger.info(
f"[TIMING] upsert_chat_session took {upsert_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
f"Upserting session: {session.session_id} with user id {session.user_id}, "
f"message_count={len(session.messages)}"
)
session = await upsert_chat_session(session)
assert session, "Session not found"
# Generate title for new sessions on first user message (non-blocking)
@@ -492,13 +454,7 @@ async def stream_chat_completion(
asyncio.create_task(_update_title())
# Build system prompt with business understanding
prompt_start = time.monotonic()
system_prompt, understanding = await _build_system_prompt(user_id)
prompt_time = (time.monotonic() - prompt_start) * 1000
logger.info(
f"[TIMING] _build_system_prompt took {prompt_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": prompt_time}},
)
# Initialize variables for streaming
assistant_response = ChatMessage(
@@ -523,27 +479,13 @@ async def stream_chat_completion(
# Generate unique IDs for AI SDK protocol
import uuid as uuid_module
is_continuation = _continuation_message_id is not None
message_id = _continuation_message_id or str(uuid_module.uuid4())
message_id = str(uuid_module.uuid4())
text_block_id = str(uuid_module.uuid4())
# Only yield message start for the initial call, not for continuations.
setup_time = (time.monotonic() - completion_start) * 1000
logger.info(
f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
)
if not is_continuation:
yield StreamStart(messageId=message_id, taskId=_task_id)
# Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
yield StreamStartStep()
# Yield message start
yield StreamStart(messageId=message_id)
try:
logger.info(
"[TIMING] Calling _stream_chat_chunks",
extra={"json_fields": log_meta},
)
async for chunk in _stream_chat_chunks(
session=session,
tools=tools,
@@ -643,10 +585,6 @@ async def stream_chat_completion(
)
yield chunk
elif isinstance(chunk, StreamFinish):
if has_done_tool_call:
# Tool calls happened — close the step but don't send message-level finish.
# The continuation will open a new step, and finish will come at the end.
yield StreamFinishStep()
if not has_done_tool_call:
# Emit text-end before finish if we received text but haven't closed it
if has_received_text and not text_streaming_ended:
@@ -678,8 +616,6 @@ async def stream_chat_completion(
has_saved_assistant_message = True
has_yielded_end = True
# Emit finish-step before finish (resets AI SDK text/reasoning state)
yield StreamFinishStep()
yield chunk
elif isinstance(chunk, StreamError):
has_yielded_error = True
@@ -729,10 +665,6 @@ async def stream_chat_completion(
logger.info(
f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
)
# Close the current step before retrying so the recursive call's
# StreamStartStep doesn't produce unbalanced step events.
if not has_yielded_end:
yield StreamFinishStep()
should_retry = True
else:
# Non-retryable error or max retries exceeded
@@ -768,7 +700,6 @@ async def stream_chat_completion(
error_response = StreamError(errorText=error_message)
yield error_response
if not has_yielded_end:
yield StreamFinishStep()
yield StreamFinish()
return
@@ -783,8 +714,6 @@ async def stream_chat_completion(
retry_count=retry_count + 1,
session=session,
context=context,
_continuation_message_id=message_id, # Reuse message ID since start was already sent
_task_id=_task_id,
):
yield chunk
return # Exit after retry to avoid double-saving in finally block
@@ -854,8 +783,6 @@ async def stream_chat_completion(
session=session, # Pass session object to avoid Redis refetch
context=context,
tool_call_response=str(tool_response_messages),
_continuation_message_id=message_id, # Reuse message ID to avoid duplicates
_task_id=_task_id,
):
yield chunk
@@ -966,21 +893,9 @@ async def _stream_chat_chunks(
SSE formatted JSON response objects
"""
import time as time_module
stream_chunks_start = time_module.perf_counter()
model = config.model
# Build log metadata for structured logging
log_meta = {"component": "ChatService", "session_id": session.session_id}
if session.user_id:
log_meta["user_id"] = session.user_id
logger.info(
f"[TIMING] _stream_chat_chunks STARTED, session={session.session_id}, "
f"user={session.user_id}, n_messages={len(session.messages)}",
extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
)
logger.info("Starting pure chat stream")
messages = session.to_openai_messages()
if system_prompt:
@@ -991,18 +906,12 @@ async def _stream_chat_chunks(
messages = [system_message] + messages
# Apply context window management
context_start = time_module.perf_counter()
context_result = await _manage_context_window(
messages=messages,
model=model,
api_key=config.api_key,
base_url=config.base_url,
)
context_time = (time_module.perf_counter() - context_start) * 1000
logger.info(
f"[TIMING] _manage_context_window took {context_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": context_time}},
)
if context_result.error:
if "System prompt dropped" in context_result.error:
@@ -1037,19 +946,9 @@ async def _stream_chat_chunks(
while retry_count <= MAX_RETRIES:
try:
elapsed = (time_module.perf_counter() - stream_chunks_start) * 1000
retry_info = (
f" (retry {retry_count}/{MAX_RETRIES})" if retry_count > 0 else ""
)
logger.info(
f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"retry_count": retry_count,
}
},
f"Creating OpenAI chat completion stream..."
f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
)
# Build extra_body for OpenRouter tracing and PostHog analytics
@@ -1066,11 +965,6 @@ async def _stream_chat_chunks(
:128
] # OpenRouter limit
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in model.lower():
extra_body["reasoning"] = {"enabled": True}
api_call_start = time_module.perf_counter()
stream = await client.chat.completions.create(
model=model,
messages=cast(list[ChatCompletionMessageParam], messages),
@@ -1080,11 +974,6 @@ async def _stream_chat_chunks(
stream_options=ChatCompletionStreamOptionsParam(include_usage=True),
extra_body=extra_body,
)
api_init_time = (time_module.perf_counter() - api_call_start) * 1000
logger.info(
f"[TIMING] OpenAI stream object returned in {api_init_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": api_init_time}},
)
# Variables to accumulate tool calls
tool_calls: list[dict[str, Any]] = []
@@ -1095,13 +984,10 @@ async def _stream_chat_chunks(
# Track if we've started the text block
text_started = False
first_content_chunk = True
chunk_count = 0
# Process the stream
chunk: ChatCompletionChunk
async for chunk in stream:
chunk_count += 1
if chunk.usage:
yield StreamUsage(
promptTokens=chunk.usage.prompt_tokens,
@@ -1124,23 +1010,6 @@ async def _stream_chat_chunks(
if not text_started and text_block_id:
yield StreamTextStart(id=text_block_id)
text_started = True
# Log timing for first content chunk
if first_content_chunk:
first_content_chunk = False
ttfc = (
time_module.perf_counter() - api_call_start
) * 1000
logger.info(
f"[TIMING] FIRST CONTENT CHUNK at {ttfc:.1f}ms "
f"(since API call), n_chunks={chunk_count}",
extra={
"json_fields": {
**log_meta,
"time_to_first_chunk_ms": ttfc,
"n_chunks": chunk_count,
}
},
)
# Stream the text delta
text_response = StreamTextDelta(
id=text_block_id or "",
@@ -1197,21 +1066,7 @@ async def _stream_chat_chunks(
toolName=tool_calls[idx]["function"]["name"],
)
emitted_start_for_idx.add(idx)
stream_duration = time_module.perf_counter() - api_call_start
logger.info(
f"[TIMING] OpenAI stream COMPLETE, finish_reason={finish_reason}, "
f"duration={stream_duration:.2f}s, "
f"n_chunks={chunk_count}, n_tool_calls={len(tool_calls)}",
extra={
"json_fields": {
**log_meta,
"stream_duration_ms": stream_duration * 1000,
"finish_reason": finish_reason,
"n_chunks": chunk_count,
"n_tool_calls": len(tool_calls),
}
},
)
logger.info(f"Stream complete. Finish reason: {finish_reason}")
# Yield all accumulated tool calls after the stream is complete
# This ensures all tool call arguments have been fully received
@@ -1231,12 +1086,6 @@ async def _stream_chat_chunks(
# Re-raise to trigger retry logic in the parent function
raise
total_time = (time_module.perf_counter() - stream_chunks_start) * 1000
logger.info(
f"[TIMING] _stream_chat_chunks COMPLETED in {total_time/1000:.1f}s; "
f"session={session.session_id}, user={session.user_id}",
extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
)
yield StreamFinish()
return
except Exception as e:
@@ -1716,7 +1565,6 @@ async def _execute_long_running_tool_with_streaming(
task_id,
StreamError(errorText=str(e)),
)
await stream_registry.publish_chunk(task_id, StreamFinishStep())
await stream_registry.publish_chunk(task_id, StreamFinish())
await _update_pending_operation(
@@ -1833,10 +1681,6 @@ async def _generate_llm_continuation(
if session_id:
extra_body["session_id"] = session_id[:128]
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in config.model.lower():
extra_body["reasoning"] = {"enabled": True}
retry_count = 0
last_error: Exception | None = None
response = None
@@ -1967,10 +1811,6 @@ async def _generate_llm_continuation_with_streaming(
if session_id:
extra_body["session_id"] = session_id[:128]
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in config.model.lower():
extra_body["reasoning"] = {"enabled": True}
# Make streaming LLM call (no tools - just text response)
from typing import cast
@@ -1982,7 +1822,6 @@ async def _generate_llm_continuation_with_streaming(
# Publish start event
await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
await stream_registry.publish_chunk(task_id, StreamStartStep())
await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
# Stream the response
@@ -2006,7 +1845,6 @@ async def _generate_llm_continuation_with_streaming(
# Publish end events
await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
await stream_registry.publish_chunk(task_id, StreamFinishStep())
if assistant_content:
# Reload session from DB to avoid race condition with user messages
@@ -2048,5 +1886,4 @@ async def _generate_llm_continuation_with_streaming(
task_id,
StreamError(errorText=f"Failed to generate response: {e}"),
)
await stream_registry.publish_chunk(task_id, StreamFinishStep())
await stream_registry.publish_chunk(task_id, StreamFinish())

View File

@@ -104,24 +104,6 @@ async def create_task(
Returns:
The created ActiveTask instance (metadata only)
"""
import time
start_time = time.perf_counter()
# Build log metadata for structured logging
log_meta = {
"component": "StreamRegistry",
"task_id": task_id,
"session_id": session_id,
}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"[TIMING] create_task STARTED, task={task_id}, session={session_id}, user={user_id}",
extra={"json_fields": log_meta},
)
task = ActiveTask(
task_id=task_id,
session_id=session_id,
@@ -132,18 +114,10 @@ async def create_task(
)
# Store metadata in Redis
redis_start = time.perf_counter()
redis = await get_redis_async()
redis_time = (time.perf_counter() - redis_start) * 1000
logger.info(
f"[TIMING] get_redis_async took {redis_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": redis_time}},
)
meta_key = _get_task_meta_key(task_id)
op_key = _get_operation_mapping_key(operation_id)
hset_start = time.perf_counter()
await redis.hset( # type: ignore[misc]
meta_key,
mapping={
@@ -157,22 +131,12 @@ async def create_task(
"created_at": task.created_at.isoformat(),
},
)
hset_time = (time.perf_counter() - hset_start) * 1000
logger.info(
f"[TIMING] redis.hset took {hset_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": hset_time}},
)
await redis.expire(meta_key, config.stream_ttl)
# Create operation_id -> task_id mapping for webhook lookups
await redis.set(op_key, task_id, ex=config.stream_ttl)
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] create_task COMPLETED in {total_time:.1f}ms; task={task_id}, session={session_id}",
extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
)
logger.debug(f"Created task {task_id} for session {session_id}")
return task
@@ -192,60 +156,26 @@ async def publish_chunk(
Returns:
The Redis Stream message ID
"""
import time
start_time = time.perf_counter()
chunk_type = type(chunk).__name__
chunk_json = chunk.model_dump_json()
message_id = "0-0"
# Build log metadata
log_meta = {
"component": "StreamRegistry",
"task_id": task_id,
"chunk_type": chunk_type,
}
try:
redis = await get_redis_async()
stream_key = _get_task_stream_key(task_id)
# Write to Redis Stream for persistence and real-time delivery
xadd_start = time.perf_counter()
raw_id = await redis.xadd(
stream_key,
{"data": chunk_json},
maxlen=config.stream_max_length,
)
xadd_time = (time.perf_counter() - xadd_start) * 1000
message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
# Set TTL on stream to match task metadata TTL
await redis.expire(stream_key, config.stream_ttl)
total_time = (time.perf_counter() - start_time) * 1000
# Only log timing for significant chunks or slow operations
if (
chunk_type
in ("StreamStart", "StreamFinish", "StreamTextStart", "StreamTextEnd")
or total_time > 50
):
logger.info(
f"[TIMING] publish_chunk {chunk_type} in {total_time:.1f}ms (xadd={xadd_time:.1f}ms)",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"xadd_time_ms": xadd_time,
"message_id": message_id,
}
},
)
except Exception as e:
elapsed = (time.perf_counter() - start_time) * 1000
logger.error(
f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
f"Failed to publish chunk for task {task_id}: {e}",
exc_info=True,
)
@@ -270,61 +200,24 @@ async def subscribe_to_task(
An asyncio Queue that will receive stream chunks, or None if task not found
or user doesn't have access
"""
import time
start_time = time.perf_counter()
# Build log metadata
log_meta = {"component": "StreamRegistry", "task_id": task_id}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"[TIMING] subscribe_to_task STARTED, task={task_id}, user={user_id}, last_msg={last_message_id}",
extra={"json_fields": {**log_meta, "last_message_id": last_message_id}},
)
redis_start = time.perf_counter()
redis = await get_redis_async()
meta_key = _get_task_meta_key(task_id)
meta: dict[Any, Any] = await redis.hgetall(meta_key) # type: ignore[misc]
hgetall_time = (time.perf_counter() - redis_start) * 1000
logger.info(
f"[TIMING] Redis hgetall took {hgetall_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": hgetall_time}},
)
if not meta:
elapsed = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"reason": "task_not_found",
}
},
)
logger.debug(f"Task {task_id} not found in Redis")
return None
# Note: Redis client uses decode_responses=True, so keys are strings
task_status = meta.get("status", "")
task_user_id = meta.get("user_id", "") or None
log_meta["session_id"] = meta.get("session_id", "")
# Validate ownership - if task has an owner, requester must match
if task_user_id:
if user_id != task_user_id:
logger.warning(
f"[TIMING] Access denied: user {user_id} tried to access task owned by {task_user_id}",
extra={
"json_fields": {
**log_meta,
"task_owner": task_user_id,
"reason": "access_denied",
}
},
f"User {user_id} denied access to task {task_id} "
f"owned by {task_user_id}"
)
return None
@@ -332,19 +225,7 @@ async def subscribe_to_task(
stream_key = _get_task_stream_key(task_id)
# Step 1: Replay messages from Redis Stream
xread_start = time.perf_counter()
messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
xread_time = (time.perf_counter() - xread_start) * 1000
logger.info(
f"[TIMING] Redis xread (replay) took {xread_time:.1f}ms, status={task_status}",
extra={
"json_fields": {
**log_meta,
"duration_ms": xread_time,
"task_status": task_status,
}
},
)
replayed_count = 0
replay_last_id = last_message_id
@@ -363,48 +244,19 @@ async def subscribe_to_task(
except Exception as e:
logger.warning(f"Failed to replay message: {e}")
logger.info(
f"[TIMING] Replayed {replayed_count} messages, last_id={replay_last_id}",
extra={
"json_fields": {
**log_meta,
"n_messages_replayed": replayed_count,
"replay_last_id": replay_last_id,
}
},
)
logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
# Step 2: If task is still running, start stream listener for live updates
if task_status == "running":
logger.info(
"[TIMING] Task still running, starting _stream_listener",
extra={"json_fields": {**log_meta, "task_status": task_status}},
)
listener_task = asyncio.create_task(
_stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
_stream_listener(task_id, subscriber_queue, replay_last_id)
)
# Track listener task for cleanup on unsubscribe
_listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
else:
# Task is completed/failed - add finish marker
logger.info(
f"[TIMING] Task already {task_status}, adding StreamFinish",
extra={"json_fields": {**log_meta, "task_status": task_status}},
)
await subscriber_queue.put(StreamFinish())
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] subscribe_to_task COMPLETED in {total_time:.1f}ms; task={task_id}, "
f"n_messages_replayed={replayed_count}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"n_messages_replayed": replayed_count,
}
},
)
return subscriber_queue
@@ -412,7 +264,6 @@ async def _stream_listener(
task_id: str,
subscriber_queue: asyncio.Queue[StreamBaseResponse],
last_replayed_id: str,
log_meta: dict | None = None,
) -> None:
"""Listen to Redis Stream for new messages using blocking XREAD.
@@ -423,27 +274,10 @@ async def _stream_listener(
task_id: Task ID to listen for
subscriber_queue: Queue to deliver messages to
last_replayed_id: Last message ID from replay (continue from here)
log_meta: Structured logging metadata
"""
import time
start_time = time.perf_counter()
# Use provided log_meta or build minimal one
if log_meta is None:
log_meta = {"component": "StreamRegistry", "task_id": task_id}
logger.info(
f"[TIMING] _stream_listener STARTED, task={task_id}, last_id={last_replayed_id}",
extra={"json_fields": {**log_meta, "last_replayed_id": last_replayed_id}},
)
queue_id = id(subscriber_queue)
# Track the last successfully delivered message ID for recovery hints
last_delivered_id = last_replayed_id
messages_delivered = 0
first_message_time = None
xread_count = 0
try:
redis = await get_redis_async()
@@ -453,39 +287,9 @@ async def _stream_listener(
while True:
# Block for up to 30 seconds waiting for new messages
# This allows periodic checking if task is still running
xread_start = time.perf_counter()
xread_count += 1
messages = await redis.xread(
{stream_key: current_id}, block=30000, count=100
)
xread_time = (time.perf_counter() - xread_start) * 1000
if messages:
msg_count = sum(len(msgs) for _, msgs in messages)
logger.info(
f"[TIMING] xread #{xread_count} returned {msg_count} messages in {xread_time:.1f}ms",
extra={
"json_fields": {
**log_meta,
"xread_count": xread_count,
"n_messages": msg_count,
"duration_ms": xread_time,
}
},
)
elif xread_time > 1000:
# Only log timeouts (30s blocking)
logger.info(
f"[TIMING] xread #{xread_count} timeout after {xread_time:.1f}ms",
extra={
"json_fields": {
**log_meta,
"xread_count": xread_count,
"duration_ms": xread_time,
"reason": "timeout",
}
},
)
if not messages:
# Timeout - check if task is still running
@@ -522,30 +326,10 @@ async def _stream_listener(
)
# Update last delivered ID on successful delivery
last_delivered_id = current_id
messages_delivered += 1
if first_message_time is None:
first_message_time = time.perf_counter()
elapsed = (first_message_time - start_time) * 1000
logger.info(
f"[TIMING] FIRST live message at {elapsed:.1f}ms, type={type(chunk).__name__}",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"chunk_type": type(chunk).__name__,
}
},
)
except asyncio.TimeoutError:
logger.warning(
f"[TIMING] Subscriber queue full, delivery timed out after {QUEUE_PUT_TIMEOUT}s",
extra={
"json_fields": {
**log_meta,
"timeout_s": QUEUE_PUT_TIMEOUT,
"reason": "queue_full",
}
},
f"Subscriber queue full for task {task_id}, "
f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
)
# Send overflow error with recovery info
try:
@@ -567,44 +351,15 @@ async def _stream_listener(
# Stop listening on finish
if isinstance(chunk, StreamFinish):
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] StreamFinish received in {total_time/1000:.1f}s; delivered={messages_delivered}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"messages_delivered": messages_delivered,
}
},
)
return
except Exception as e:
logger.warning(
f"Error processing stream message: {e}",
extra={"json_fields": {**log_meta, "error": str(e)}},
)
logger.warning(f"Error processing stream message: {e}")
except asyncio.CancelledError:
elapsed = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] _stream_listener CANCELLED after {elapsed:.1f}ms, delivered={messages_delivered}",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"messages_delivered": messages_delivered,
"reason": "cancelled",
}
},
)
logger.debug(f"Stream listener cancelled for task {task_id}")
raise # Re-raise to propagate cancellation
except Exception as e:
elapsed = (time.perf_counter() - start_time) * 1000
logger.error(
f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
)
logger.error(f"Stream listener error for task {task_id}: {e}")
# On error, send finish to unblock subscriber
try:
await asyncio.wait_for(
@@ -613,24 +368,10 @@ async def _stream_listener(
)
except (asyncio.TimeoutError, asyncio.QueueFull):
logger.warning(
"Could not deliver finish event after error",
extra={"json_fields": log_meta},
f"Could not deliver finish event for task {task_id} after error"
)
finally:
# Clean up listener task mapping on exit
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] _stream_listener FINISHED in {total_time/1000:.1f}s; task={task_id}, "
f"delivered={messages_delivered}, xread_count={xread_count}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"messages_delivered": messages_delivered,
"xread_count": xread_count,
}
},
)
_listener_tasks.pop(queue_id, None)
@@ -857,10 +598,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
ResponseType,
StreamError,
StreamFinish,
StreamFinishStep,
StreamHeartbeat,
StreamStart,
StreamStartStep,
StreamTextDelta,
StreamTextEnd,
StreamTextStart,
@@ -874,8 +613,6 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
type_to_class: dict[str, type[StreamBaseResponse]] = {
ResponseType.START.value: StreamStart,
ResponseType.FINISH.value: StreamFinish,
ResponseType.START_STEP.value: StreamStartStep,
ResponseType.FINISH_STEP.value: StreamFinishStep,
ResponseType.TEXT_START.value: StreamTextStart,
ResponseType.TEXT_DELTA.value: StreamTextDelta,
ResponseType.TEXT_END.value: StreamTextEnd,

View File

@@ -3,8 +3,8 @@ from typing import TYPE_CHECKING, Any
from openai.types.chat import ChatCompletionToolParam
from backend.copilot.model import ChatSession
from backend.copilot.tracking import track_tool_called
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.tracking import track_tool_called
from .add_understanding import AddUnderstandingTool
from .agent_output import AgentOutputTool
@@ -27,7 +27,7 @@ from .workspace_files import (
)
if TYPE_CHECKING:
from backend.copilot.response_model import StreamToolOutputAvailable
from backend.api.features.chat.response_model import StreamToolOutputAvailable
logger = logging.getLogger(__name__)

View File

@@ -6,11 +6,11 @@ import pytest
from prisma.types import ProfileCreateInput
from pydantic import SecretStr
from backend.api.features.chat.model import ChatSession
from backend.api.features.store import db as store_db
from backend.blocks.firecrawl.scrape import FirecrawlScrapeBlock
from backend.blocks.io import AgentInputBlock, AgentOutputBlock
from backend.blocks.llm import AITextGeneratorBlock
from backend.copilot.model import ChatSession
from backend.data.db import prisma
from backend.data.graph import Graph, Link, Node, create_graph
from backend.data.model import APIKeyCredentials

View File

@@ -3,7 +3,7 @@
import logging
from typing import Any
from backend.copilot.model import ChatSession
from backend.api.features.chat.model import ChatSession
from backend.data.understanding import (
BusinessUnderstandingInput,
upsert_business_understanding,

View File

@@ -7,9 +7,9 @@ from typing import Any
from pydantic import BaseModel, field_validator
from backend.api.features.chat.model import ChatSession
from backend.api.features.library import db as library_db
from backend.api.features.library.model import LibraryAgent
from backend.copilot.model import ChatSession
from backend.data import execution as execution_db
from backend.data.execution import ExecutionStatus, GraphExecution, GraphExecutionMeta

View File

@@ -5,8 +5,8 @@ from typing import Any
from openai.types.chat import ChatCompletionToolParam
from backend.copilot.model import ChatSession
from backend.copilot.response_model import StreamToolOutputAvailable
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.response_model import StreamToolOutputAvailable
from .models import ErrorResponse, NeedLoginResponse, ToolResponseBase

View File

@@ -3,7 +3,7 @@
import logging
from typing import Any
from backend.copilot.model import ChatSession
from backend.api.features.chat.model import ChatSession
from .agent_generator import (
AgentGeneratorNotConfiguredError,

View File

@@ -3,9 +3,9 @@
import logging
from typing import Any
from backend.api.features.chat.model import ChatSession
from backend.api.features.store import db as store_db
from backend.api.features.store.exceptions import AgentNotFoundError
from backend.copilot.model import ChatSession
from .agent_generator import (
AgentGeneratorNotConfiguredError,

View File

@@ -3,7 +3,7 @@
import logging
from typing import Any
from backend.copilot.model import ChatSession
from backend.api.features.chat.model import ChatSession
from .agent_generator import (
AgentGeneratorNotConfiguredError,

View File

@@ -2,7 +2,7 @@
from typing import Any
from backend.copilot.model import ChatSession
from backend.api.features.chat.model import ChatSession
from .agent_search import search_agents
from .base import BaseTool

View File

@@ -0,0 +1,193 @@
import logging
from typing import Any
from prisma.enums import ContentType
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
from backend.api.features.chat.tools.models import (
BlockInfoSummary,
BlockInputFieldInfo,
BlockListResponse,
ErrorResponse,
NoResultsResponse,
)
from backend.api.features.store.hybrid_search import unified_hybrid_search
from backend.data.block import get_block
logger = logging.getLogger(__name__)
class FindBlockTool(BaseTool):
"""Tool for searching available blocks."""
@property
def name(self) -> str:
return "find_block"
@property
def description(self) -> str:
return (
"Search for available blocks by name or description. "
"Blocks are reusable components that perform specific tasks like "
"sending emails, making API calls, processing text, etc. "
"IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
"The response includes each block's id, required_inputs, and input_schema."
)
@property
def parameters(self) -> dict[str, Any]:
return {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": (
"Search query to find blocks by name or description. "
"Use keywords like 'email', 'http', 'text', 'ai', etc."
),
},
},
"required": ["query"],
}
@property
def requires_auth(self) -> bool:
return True
async def _execute(
self,
user_id: str | None,
session: ChatSession,
**kwargs,
) -> ToolResponseBase:
"""Search for blocks matching the query.
Args:
user_id: User ID (required)
session: Chat session
query: Search query
Returns:
BlockListResponse: List of matching blocks
NoResultsResponse: No blocks found
ErrorResponse: Error message
"""
query = kwargs.get("query", "").strip()
session_id = session.session_id
if not query:
return ErrorResponse(
message="Please provide a search query",
session_id=session_id,
)
try:
# Search for blocks using hybrid search
results, total = await unified_hybrid_search(
query=query,
content_types=[ContentType.BLOCK],
page=1,
page_size=10,
)
if not results:
return NoResultsResponse(
message=f"No blocks found for '{query}'",
suggestions=[
"Try broader keywords like 'email', 'http', 'text', 'ai'",
"Check spelling of technical terms",
],
session_id=session_id,
)
# Enrich results with full block information
blocks: list[BlockInfoSummary] = []
for result in results:
block_id = result["content_id"]
block = get_block(block_id)
# Skip disabled blocks
if block and not block.disabled:
# Get input/output schemas
input_schema = {}
output_schema = {}
try:
input_schema = block.input_schema.jsonschema()
except Exception:
pass
try:
output_schema = block.output_schema.jsonschema()
except Exception:
pass
# Get categories from block instance
categories = []
if hasattr(block, "categories") and block.categories:
categories = [cat.value for cat in block.categories]
# Extract required inputs for easier use
required_inputs: list[BlockInputFieldInfo] = []
if input_schema:
properties = input_schema.get("properties", {})
required_fields = set(input_schema.get("required", []))
# Get credential field names to exclude from required inputs
credentials_fields = set(
block.input_schema.get_credentials_fields().keys()
)
for field_name, field_schema in properties.items():
# Skip credential fields - they're handled separately
if field_name in credentials_fields:
continue
required_inputs.append(
BlockInputFieldInfo(
name=field_name,
type=field_schema.get("type", "string"),
description=field_schema.get("description", ""),
required=field_name in required_fields,
default=field_schema.get("default"),
)
)
blocks.append(
BlockInfoSummary(
id=block_id,
name=block.name,
description=block.description or "",
categories=categories,
input_schema=input_schema,
output_schema=output_schema,
required_inputs=required_inputs,
)
)
if not blocks:
return NoResultsResponse(
message=f"No blocks found for '{query}'",
suggestions=[
"Try broader keywords like 'email', 'http', 'text', 'ai'",
],
session_id=session_id,
)
return BlockListResponse(
message=(
f"Found {len(blocks)} block(s) matching '{query}'. "
"To execute a block, use run_block with the block's 'id' field "
"and provide 'input_data' matching the block's input_schema."
),
blocks=blocks,
count=len(blocks),
query=query,
session_id=session_id,
)
except Exception as e:
logger.error(f"Error searching blocks: {e}", exc_info=True)
return ErrorResponse(
message="Failed to search blocks",
error=str(e),
session_id=session_id,
)

View File

@@ -2,7 +2,7 @@
from typing import Any
from backend.copilot.model import ChatSession
from backend.api.features.chat.model import ChatSession
from .agent_search import search_agents
from .base import BaseTool

View File

@@ -4,9 +4,9 @@ import logging
from pathlib import Path
from typing import Any
from backend.copilot.model import ChatSession
from backend.copilot.tools.base import BaseTool
from backend.copilot.tools.models import (
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.tools.base import BaseTool
from backend.api.features.chat.tools.models import (
DocPageResponse,
ErrorResponse,
ToolResponseBase,

View File

@@ -5,10 +5,13 @@ from typing import Any
from pydantic import BaseModel, Field, field_validator
from backend.api.features.chat.config import ChatConfig
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.tracking import (
track_agent_run_success,
track_agent_scheduled,
)
from backend.api.features.library import db as library_db
from backend.copilot.config import ChatConfig
from backend.copilot.model import ChatSession
from backend.copilot.tracking import track_agent_run_success, track_agent_scheduled
from backend.data.graph import GraphModel
from backend.data.model import CredentialsMetaInput
from backend.data.user import get_user_by_id
@@ -21,7 +24,6 @@ from backend.util.timezone_utils import (
)
from .base import BaseTool
from .helpers import get_inputs_from_schema
from .models import (
AgentDetails,
AgentDetailsResponse,
@@ -259,7 +261,7 @@ class RunAgentTool(BaseTool):
),
requirements={
"credentials": requirements_creds_list,
"inputs": get_inputs_from_schema(graph.input_schema),
"inputs": self._get_inputs_list(graph.input_schema),
"execution_modes": self._get_execution_modes(graph),
},
),
@@ -367,6 +369,22 @@ class RunAgentTool(BaseTool):
session_id=session_id,
)
def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
"""Extract inputs list from schema."""
inputs_list = []
if isinstance(input_schema, dict) and "properties" in input_schema:
for field_name, field_schema in input_schema["properties"].items():
inputs_list.append(
{
"name": field_name,
"title": field_schema.get("title", field_name),
"type": field_schema.get("type", "string"),
"description": field_schema.get("description", ""),
"required": field_name in input_schema.get("required", []),
}
)
return inputs_list
def _get_execution_modes(self, graph: GraphModel) -> list[str]:
"""Get available execution modes for the graph."""
trigger_info = graph.trigger_setup_info
@@ -380,7 +398,7 @@ class RunAgentTool(BaseTool):
suffix: str,
) -> str:
"""Build a message describing available inputs for an agent."""
inputs_list = get_inputs_from_schema(graph.input_schema)
inputs_list = self._get_inputs_list(graph.input_schema)
required_names = [i["name"] for i in inputs_list if i["required"]]
optional_names = [i["name"] for i in inputs_list if not i["required"]]

View File

@@ -7,20 +7,15 @@ from typing import Any
from pydantic_core import PydanticUndefined
from backend.copilot.model import ChatSession
from backend.copilot.tools.find_block import (
COPILOT_EXCLUDED_BLOCK_IDS,
COPILOT_EXCLUDED_BLOCK_TYPES,
)
from backend.data.block import AnyBlockSchema, get_block
from backend.api.features.chat.model import ChatSession
from backend.data.block import get_block
from backend.data.execution import ExecutionContext
from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
from backend.data.model import CredentialsMetaInput
from backend.data.workspace import get_or_create_workspace
from backend.integrations.creds_manager import IntegrationCredentialsManager
from backend.util.exceptions import BlockError
from .base import BaseTool
from .helpers import get_inputs_from_schema
from .models import (
BlockOutputResponse,
ErrorResponse,
@@ -29,10 +24,7 @@ from .models import (
ToolResponseBase,
UserReadiness,
)
from .utils import (
build_missing_credentials_from_field_info,
match_credentials_to_requirements,
)
from .utils import build_missing_credentials_from_field_info
logger = logging.getLogger(__name__)
@@ -81,6 +73,91 @@ class RunBlockTool(BaseTool):
def requires_auth(self) -> bool:
return True
async def _check_block_credentials(
self,
user_id: str,
block: Any,
input_data: dict[str, Any] | None = None,
) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
"""
Check if user has required credentials for a block.
Args:
user_id: User ID
block: Block to check credentials for
input_data: Input data for the block (used to determine provider via discriminator)
Returns:
tuple[matched_credentials, missing_credentials]
"""
matched_credentials: dict[str, CredentialsMetaInput] = {}
missing_credentials: list[CredentialsMetaInput] = []
input_data = input_data or {}
# Get credential field info from block's input schema
credentials_fields_info = block.input_schema.get_credentials_fields_info()
if not credentials_fields_info:
return matched_credentials, missing_credentials
# Get user's available credentials
creds_manager = IntegrationCredentialsManager()
available_creds = await creds_manager.store.get_all_creds(user_id)
for field_name, field_info in credentials_fields_info.items():
effective_field_info = field_info
if field_info.discriminator and field_info.discriminator_mapping:
# Get discriminator from input, falling back to schema default
discriminator_value = input_data.get(field_info.discriminator)
if discriminator_value is None:
field = block.input_schema.model_fields.get(
field_info.discriminator
)
if field and field.default is not PydanticUndefined:
discriminator_value = field.default
if (
discriminator_value
and discriminator_value in field_info.discriminator_mapping
):
effective_field_info = field_info.discriminate(discriminator_value)
logger.debug(
f"Discriminated provider for {field_name}: "
f"{discriminator_value} -> {effective_field_info.provider}"
)
matching_cred = next(
(
cred
for cred in available_creds
if cred.provider in effective_field_info.provider
and cred.type in effective_field_info.supported_types
),
None,
)
if matching_cred:
matched_credentials[field_name] = CredentialsMetaInput(
id=matching_cred.id,
provider=matching_cred.provider, # type: ignore
type=matching_cred.type,
title=matching_cred.title,
)
else:
# Create a placeholder for the missing credential
provider = next(iter(effective_field_info.provider), "unknown")
cred_type = next(iter(effective_field_info.supported_types), "api_key")
missing_credentials.append(
CredentialsMetaInput(
id=field_name,
provider=provider, # type: ignore
type=cred_type, # type: ignore
title=field_name.replace("_", " ").title(),
)
)
return matched_credentials, missing_credentials
async def _execute(
self,
user_id: str | None,
@@ -135,24 +212,11 @@ class RunBlockTool(BaseTool):
session_id=session_id,
)
# Check if block is excluded from CoPilot (graph-only blocks)
if (
block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
or block.id in COPILOT_EXCLUDED_BLOCK_IDS
):
return ErrorResponse(
message=(
f"Block '{block.name}' cannot be run directly in CoPilot. "
"This block is designed for use within graphs only."
),
session_id=session_id,
)
logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
creds_manager = IntegrationCredentialsManager()
matched_credentials, missing_credentials = (
await self._resolve_block_credentials(user_id, block, input_data)
matched_credentials, missing_credentials = await self._check_block_credentials(
user_id, block, input_data
)
if missing_credentials:
@@ -281,75 +345,29 @@ class RunBlockTool(BaseTool):
session_id=session_id,
)
async def _resolve_block_credentials(
self,
user_id: str,
block: AnyBlockSchema,
input_data: dict[str, Any] | None = None,
) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
"""
Resolve credentials for a block by matching user's available credentials.
Args:
user_id: User ID
block: Block to resolve credentials for
input_data: Input data for the block (used to determine provider via discriminator)
Returns:
tuple of (matched_credentials, missing_credentials) - matched credentials
are used for block execution, missing ones indicate setup requirements.
"""
input_data = input_data or {}
requirements = self._resolve_discriminated_credentials(block, input_data)
if not requirements:
return {}, []
return await match_credentials_to_requirements(user_id, requirements)
def _get_inputs_list(self, block: AnyBlockSchema) -> list[dict[str, Any]]:
def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
"""Extract non-credential inputs from block schema."""
inputs_list = []
schema = block.input_schema.jsonschema()
properties = schema.get("properties", {})
required_fields = set(schema.get("required", []))
# Get credential field names to exclude
credentials_fields = set(block.input_schema.get_credentials_fields().keys())
return get_inputs_from_schema(schema, exclude_fields=credentials_fields)
def _resolve_discriminated_credentials(
self,
block: AnyBlockSchema,
input_data: dict[str, Any],
) -> dict[str, CredentialsFieldInfo]:
"""Resolve credential requirements, applying discriminator logic where needed."""
credentials_fields_info = block.input_schema.get_credentials_fields_info()
if not credentials_fields_info:
return {}
for field_name, field_schema in properties.items():
# Skip credential fields
if field_name in credentials_fields:
continue
resolved: dict[str, CredentialsFieldInfo] = {}
inputs_list.append(
{
"name": field_name,
"title": field_schema.get("title", field_name),
"type": field_schema.get("type", "string"),
"description": field_schema.get("description", ""),
"required": field_name in required_fields,
}
)
for field_name, field_info in credentials_fields_info.items():
effective_field_info = field_info
if field_info.discriminator and field_info.discriminator_mapping:
discriminator_value = input_data.get(field_info.discriminator)
if discriminator_value is None:
field = block.input_schema.model_fields.get(
field_info.discriminator
)
if field and field.default is not PydanticUndefined:
discriminator_value = field.default
if (
discriminator_value
and discriminator_value in field_info.discriminator_mapping
):
effective_field_info = field_info.discriminate(discriminator_value)
# For host-scoped credentials, add the discriminator value
# (e.g., URL) so _credential_is_for_host can match it
effective_field_info.discriminator_values.add(discriminator_value)
logger.debug(
f"Discriminated provider for {field_name}: "
f"{discriminator_value} -> {effective_field_info.provider}"
)
resolved[field_name] = effective_field_info
return resolved
return inputs_list

View File

@@ -5,16 +5,16 @@ from typing import Any
from prisma.enums import ContentType
from backend.api.features.store.hybrid_search import unified_hybrid_search
from backend.copilot.model import ChatSession
from backend.copilot.tools.base import BaseTool
from backend.copilot.tools.models import (
from backend.api.features.chat.model import ChatSession
from backend.api.features.chat.tools.base import BaseTool
from backend.api.features.chat.tools.models import (
DocSearchResult,
DocSearchResultsResponse,
ErrorResponse,
NoResultsResponse,
ToolResponseBase,
)
from backend.api.features.store.hybrid_search import unified_hybrid_search
logger = logging.getLogger(__name__)

View File

@@ -8,7 +8,6 @@ from backend.api.features.library import model as library_model
from backend.api.features.store import db as store_db
from backend.data.graph import GraphModel
from backend.data.model import (
Credentials,
CredentialsFieldInfo,
CredentialsMetaInput,
HostScopedCredentials,
@@ -224,99 +223,6 @@ async def get_or_create_library_agent(
return library_agents[0]
async def match_credentials_to_requirements(
user_id: str,
requirements: dict[str, CredentialsFieldInfo],
) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
"""
Match user's credentials against a dictionary of credential requirements.
This is the core matching logic shared by both graph and block credential matching.
"""
matched: dict[str, CredentialsMetaInput] = {}
missing: list[CredentialsMetaInput] = []
if not requirements:
return matched, missing
available_creds = await get_user_credentials(user_id)
for field_name, field_info in requirements.items():
matching_cred = find_matching_credential(available_creds, field_info)
if matching_cred:
try:
matched[field_name] = create_credential_meta_from_match(matching_cred)
except Exception as e:
logger.error(
f"Failed to create CredentialsMetaInput for field '{field_name}': "
f"provider={matching_cred.provider}, type={matching_cred.type}, "
f"credential_id={matching_cred.id}",
exc_info=True,
)
provider = next(iter(field_info.provider), "unknown")
cred_type = next(iter(field_info.supported_types), "api_key")
missing.append(
CredentialsMetaInput(
id=field_name,
provider=provider, # type: ignore
type=cred_type, # type: ignore
title=f"{field_name} (validation failed: {e})",
)
)
else:
provider = next(iter(field_info.provider), "unknown")
cred_type = next(iter(field_info.supported_types), "api_key")
missing.append(
CredentialsMetaInput(
id=field_name,
provider=provider, # type: ignore
type=cred_type, # type: ignore
title=field_name.replace("_", " ").title(),
)
)
return matched, missing
async def get_user_credentials(user_id: str) -> list[Credentials]:
"""Get all available credentials for a user."""
creds_manager = IntegrationCredentialsManager()
return await creds_manager.store.get_all_creds(user_id)
def find_matching_credential(
available_creds: list[Credentials],
field_info: CredentialsFieldInfo,
) -> Credentials | None:
"""Find a credential that matches the required provider, type, scopes, and host."""
for cred in available_creds:
if cred.provider not in field_info.provider:
continue
if cred.type not in field_info.supported_types:
continue
if cred.type == "oauth2" and not _credential_has_required_scopes(
cred, field_info
):
continue
if cred.type == "host_scoped" and not _credential_is_for_host(cred, field_info):
continue
return cred
return None
def create_credential_meta_from_match(
matching_cred: Credentials,
) -> CredentialsMetaInput:
"""Create a CredentialsMetaInput from a matched credential."""
return CredentialsMetaInput(
id=matching_cred.id,
provider=matching_cred.provider, # type: ignore
type=matching_cred.type,
title=matching_cred.title,
)
async def match_user_credentials_to_graph(
user_id: str,
graph: GraphModel,
@@ -425,6 +331,8 @@ def _credential_has_required_scopes(
# If no scopes are required, any credential matches
if not requirements.required_scopes:
return True
# Check that credential scopes are a superset of required scopes
return set(credential.scopes).issuperset(requirements.required_scopes)

View File

@@ -6,7 +6,7 @@ from typing import Any, Optional
from pydantic import BaseModel
from backend.copilot.model import ChatSession
from backend.api.features.chat.model import ChatSession
from backend.data.workspace import get_or_create_workspace
from backend.util.settings import Config
from backend.util.virus_scanner import scan_content_safe

View File

@@ -8,7 +8,6 @@ Includes BM25 reranking for improved lexical relevance.
import logging
import re
import time
from dataclasses import dataclass
from typing import Any, Literal
@@ -363,11 +362,7 @@ async def unified_hybrid_search(
LIMIT {limit_param} OFFSET {offset_param}
"""
try:
results = await query_raw_with_schema(sql_query, *params)
except Exception as e:
await _log_vector_error_diagnostics(e)
raise
results = await query_raw_with_schema(sql_query, *params)
total = results[0]["total_count"] if results else 0
# Apply BM25 reranking
@@ -691,11 +686,7 @@ async def hybrid_search(
LIMIT {limit_param} OFFSET {offset_param}
"""
try:
results = await query_raw_with_schema(sql_query, *params)
except Exception as e:
await _log_vector_error_diagnostics(e)
raise
results = await query_raw_with_schema(sql_query, *params)
total = results[0]["total_count"] if results else 0
@@ -727,87 +718,6 @@ async def hybrid_search_simple(
return await hybrid_search(query=query, page=page, page_size=page_size)
# ============================================================================
# Diagnostics
# ============================================================================
# Rate limit: only log vector error diagnostics once per this interval
_VECTOR_DIAG_INTERVAL_SECONDS = 60
_last_vector_diag_time: float = 0
async def _log_vector_error_diagnostics(error: Exception) -> None:
"""Log diagnostic info when 'type vector does not exist' error occurs.
Note: Diagnostic queries use query_raw_with_schema which may run on a different
pooled connection than the one that failed. Session-level search_path can differ,
so these diagnostics show cluster-wide state, not necessarily the failed session.
Includes rate limiting to avoid log spam - only logs once per minute.
Caller should re-raise the error after calling this function.
"""
global _last_vector_diag_time
# Check if this is the vector type error
error_str = str(error).lower()
if not (
"type" in error_str and "vector" in error_str and "does not exist" in error_str
):
return
# Rate limit: only log once per interval
now = time.time()
if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
return
_last_vector_diag_time = now
try:
diagnostics: dict[str, object] = {}
try:
search_path_result = await query_raw_with_schema("SHOW search_path")
diagnostics["search_path"] = search_path_result
except Exception as e:
diagnostics["search_path"] = f"Error: {e}"
try:
schema_result = await query_raw_with_schema("SELECT current_schema()")
diagnostics["current_schema"] = schema_result
except Exception as e:
diagnostics["current_schema"] = f"Error: {e}"
try:
user_result = await query_raw_with_schema(
"SELECT current_user, session_user, current_database()"
)
diagnostics["user_info"] = user_result
except Exception as e:
diagnostics["user_info"] = f"Error: {e}"
try:
# Check pgvector extension installation (cluster-wide, stable info)
ext_result = await query_raw_with_schema(
"SELECT extname, extversion, nspname as schema "
"FROM pg_extension e "
"JOIN pg_namespace n ON e.extnamespace = n.oid "
"WHERE extname = 'vector'"
)
diagnostics["pgvector_extension"] = ext_result
except Exception as e:
diagnostics["pgvector_extension"] = f"Error: {e}"
logger.error(
f"Vector type error diagnostics:\n"
f" Error: {error}\n"
f" search_path: {diagnostics.get('search_path')}\n"
f" current_schema: {diagnostics.get('current_schema')}\n"
f" user_info: {diagnostics.get('user_info')}\n"
f" pgvector_extension: {diagnostics.get('pgvector_extension')}"
)
except Exception as diag_error:
logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
# Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
# for existing code that expects the popularity parameter
HybridSearchWeights = StoreAgentSearchWeights

View File

@@ -40,11 +40,11 @@ import backend.data.user
import backend.integrations.webhooks.utils
import backend.util.service
import backend.util.settings
from backend.blocks.llm import DEFAULT_LLM_MODEL
from backend.copilot.completion_consumer import (
from backend.api.features.chat.completion_consumer import (
start_completion_consumer,
stop_completion_consumer,
)
from backend.blocks.llm import DEFAULT_LLM_MODEL
from backend.data.model import Credentials
from backend.integrations.providers import ProviderName
from backend.monitoring.instrumentation import instrument_fastapi

View File

@@ -38,7 +38,6 @@ def main(**kwargs):
from backend.api.rest_api import AgentServer
from backend.api.ws_api import WebsocketServer
from backend.copilot.executor.manager import CoPilotExecutor
from backend.executor import DatabaseManager, ExecutionManager, Scheduler
from backend.notifications import NotificationManager
@@ -49,7 +48,6 @@ def main(**kwargs):
WebsocketServer(),
AgentServer(),
ExecutionManager(),
CoPilotExecutor(),
**kwargs,
)

View File

@@ -531,12 +531,12 @@ class LLMResponse(BaseModel):
def convert_openai_tool_fmt_to_anthropic(
openai_tools: list[dict] | None = None,
) -> Iterable[ToolParam] | anthropic.Omit:
) -> Iterable[ToolParam] | anthropic.NotGiven:
"""
Convert OpenAI tool format to Anthropic tool format.
"""
if not openai_tools or len(openai_tools) == 0:
return anthropic.omit
return anthropic.NOT_GIVEN
anthropic_tools = []
for tool in openai_tools:

View File

@@ -1,8 +0,0 @@
"""CoPilot module - AI assistant for AutoGPT platform.
This module contains the core CoPilot functionality including:
- AI generation service (LLM calls)
- Tool execution
- Session management
- Stream registry for SSE reconnection
"""

View File

@@ -1,5 +0,0 @@
"""CoPilot Executor - Dedicated service for AI generation and tool execution.
This module contains the executor service that processes CoPilot tasks
from RabbitMQ, following the graph executor pattern.
"""

View File

@@ -1,18 +0,0 @@
"""Entry point for running the CoPilot Executor service.
Usage:
python -m backend.copilot.executor
"""
from backend.app import run_processes
from .manager import CoPilotExecutor
def main():
"""Run the CoPilot Executor service."""
run_processes(CoPilotExecutor())
if __name__ == "__main__":
main()

View File

@@ -1,462 +0,0 @@
"""CoPilot Executor Manager - main service for CoPilot task execution.
This module contains the CoPilotExecutor class that consumes chat tasks from
RabbitMQ and processes them using a thread pool, following the graph executor pattern.
"""
import logging
import os
import threading
import time
import uuid
from concurrent.futures import Future, ThreadPoolExecutor
from pika.adapters.blocking_connection import BlockingChannel
from pika.spec import Basic, BasicProperties
from prometheus_client import Gauge, start_http_server
from backend.data import redis_client as redis
from backend.data.rabbitmq import SyncRabbitMQ
from backend.executor.cluster_lock import ClusterLock
from backend.util.decorator import error_logged
from backend.util.logging import TruncatedLogger
from backend.util.process import AppProcess
from backend.util.retry import continuous_retry, func_retry
from backend.util.settings import Settings
from .processor import execute_copilot_task, init_worker
from .utils import (
COPILOT_CANCEL_QUEUE_NAME,
COPILOT_EXECUTION_QUEUE_NAME,
GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS,
CancelCoPilotEvent,
CoPilotExecutionEntry,
create_copilot_queue_config,
)
logger = TruncatedLogger(logging.getLogger(__name__), prefix="[CoPilotExecutor]")
settings = Settings()
# Prometheus metrics
active_tasks_gauge = Gauge(
"copilot_executor_active_tasks",
"Number of active CoPilot tasks",
)
pool_size_gauge = Gauge(
"copilot_executor_pool_size",
"Maximum number of CoPilot executor workers",
)
utilization_gauge = Gauge(
"copilot_executor_utilization_ratio",
"Ratio of active tasks to pool size",
)
class CoPilotExecutor(AppProcess):
"""CoPilot Executor service for processing chat generation tasks.
This service consumes tasks from RabbitMQ, processes them using a thread pool,
and publishes results to Redis Streams. It follows the graph executor pattern
for reliable message handling and graceful shutdown.
Key features:
- RabbitMQ-based task distribution with manual acknowledgment
- Thread pool executor for concurrent task processing
- Cluster lock for duplicate prevention across pods
- Graceful shutdown with timeout for in-flight tasks
- FANOUT exchange for cancellation broadcast
"""
def __init__(self):
super().__init__()
self.pool_size = settings.config.num_copilot_workers
self.active_tasks: dict[str, tuple[Future, threading.Event]] = {}
self.executor_id = str(uuid.uuid4())
self._executor = None
self._stop_consuming = None
self._cancel_thread = None
self._cancel_client = None
self._run_thread = None
self._run_client = None
self._task_locks: dict[str, ClusterLock] = {}
# ============ Main Entry Points (AppProcess interface) ============ #
def run(self):
"""Main service loop - consume from RabbitMQ."""
logger.info(f"Pod assigned executor_id: {self.executor_id}")
logger.info(f"Spawn max-{self.pool_size} workers...")
pool_size_gauge.set(self.pool_size)
self._update_metrics()
start_http_server(settings.config.copilot_executor_port)
self.cancel_thread.start()
self.run_thread.start()
while True:
time.sleep(1e5)
def cleanup(self):
"""Graceful shutdown with active execution waiting."""
pid = os.getpid()
logger.info(f"[cleanup {pid}] Starting graceful shutdown...")
# Signal the consumer thread to stop
try:
self.stop_consuming.set()
run_channel = self.run_client.get_channel()
run_channel.connection.add_callback_threadsafe(
lambda: run_channel.stop_consuming()
)
logger.info(f"[cleanup {pid}] Consumer has been signaled to stop")
except Exception as e:
logger.error(f"[cleanup {pid}] Error stopping consumer: {e}")
# Wait for active executions to complete
if self.active_tasks:
logger.info(
f"[cleanup {pid}] Waiting for {len(self.active_tasks)} active tasks to complete (timeout: {GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS}s)..."
)
start_time = time.monotonic()
last_refresh = start_time
lock_refresh_interval = settings.config.cluster_lock_timeout / 10
while (
self.active_tasks
and (time.monotonic() - start_time) < GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS
):
self._cleanup_completed_tasks()
if not self.active_tasks:
break
# Refresh cluster locks periodically
current_time = time.monotonic()
if current_time - last_refresh >= lock_refresh_interval:
for lock in self._task_locks.values():
try:
lock.refresh()
except Exception as e:
logger.warning(
f"[cleanup {pid}] Failed to refresh lock: {e}"
)
last_refresh = current_time
logger.info(
f"[cleanup {pid}] {len(self.active_tasks)} tasks still active, waiting..."
)
time.sleep(10.0)
# Stop message consumers
if self._run_thread:
self._stop_message_consumers(
self._run_thread, self.run_client, "[cleanup][run]"
)
if self._cancel_thread:
self._stop_message_consumers(
self._cancel_thread, self.cancel_client, "[cleanup][cancel]"
)
# Shutdown executor
if self._executor:
logger.info(f"[cleanup {pid}] Shutting down executor...")
self._executor.shutdown(wait=False)
# Release any remaining locks
for task_id, lock in list(self._task_locks.items()):
try:
lock.release()
logger.info(f"[cleanup {pid}] Released lock for {task_id}")
except Exception as e:
logger.error(
f"[cleanup {pid}] Failed to release lock for {task_id}: {e}"
)
logger.info(f"[cleanup {pid}] Graceful shutdown completed")
# ============ RabbitMQ Consumer Methods ============ #
@continuous_retry()
def _consume_cancel(self):
"""Consume cancellation messages from FANOUT exchange."""
if self.stop_consuming.is_set() and not self.active_tasks:
logger.info("Stop reconnecting cancel consumer - service cleaned up")
return
if not self.cancel_client.is_ready:
self.cancel_client.disconnect()
self.cancel_client.connect()
cancel_channel = self.cancel_client.get_channel()
cancel_channel.basic_consume(
queue=COPILOT_CANCEL_QUEUE_NAME,
on_message_callback=self._handle_cancel_message,
auto_ack=True,
)
logger.info("Starting cancel message consumer...")
cancel_channel.start_consuming()
if not self.stop_consuming.is_set() or self.active_tasks:
raise RuntimeError("Cancel message consumer stopped unexpectedly")
logger.info("Cancel message consumer stopped gracefully")
@continuous_retry()
def _consume_run(self):
"""Consume run messages from DIRECT exchange."""
if self.stop_consuming.is_set():
logger.info("Stop reconnecting run consumer - service cleaned up")
return
if not self.run_client.is_ready:
self.run_client.disconnect()
self.run_client.connect()
run_channel = self.run_client.get_channel()
run_channel.basic_qos(prefetch_count=self.pool_size)
run_channel.basic_consume(
queue=COPILOT_EXECUTION_QUEUE_NAME,
on_message_callback=self._handle_run_message,
auto_ack=False,
consumer_tag="copilot_execution_consumer",
)
run_channel.confirm_delivery()
logger.info("Starting to consume run messages...")
run_channel.start_consuming()
if not self.stop_consuming.is_set():
raise RuntimeError("Run message consumer stopped unexpectedly")
logger.info("Run message consumer stopped gracefully")
# ============ Message Handlers ============ #
@error_logged(swallow=True)
def _handle_cancel_message(
self,
_channel: BlockingChannel,
_method: Basic.Deliver,
_properties: BasicProperties,
body: bytes,
):
"""Handle cancel message from FANOUT exchange."""
request = CancelCoPilotEvent.model_validate_json(body)
task_id = request.task_id
if not task_id:
logger.warning("Cancel message missing 'task_id'")
return
if task_id not in self.active_tasks:
logger.debug(f"Cancel received for {task_id} but not active")
return
_, cancel_event = self.active_tasks[task_id]
logger.info(f"Received cancel for {task_id}")
if not cancel_event.is_set():
cancel_event.set()
else:
logger.debug(f"Cancel already set for {task_id}")
def _handle_run_message(
self,
_channel: BlockingChannel,
method: Basic.Deliver,
_properties: BasicProperties,
body: bytes,
):
"""Handle run message from DIRECT exchange."""
delivery_tag = method.delivery_tag
@func_retry
def ack_message(reject: bool, requeue: bool):
"""Acknowledge or reject the message."""
channel = self.run_client.get_channel()
if reject:
channel.connection.add_callback_threadsafe(
lambda: channel.basic_nack(delivery_tag, requeue=requeue)
)
else:
channel.connection.add_callback_threadsafe(
lambda: channel.basic_ack(delivery_tag)
)
# Check if we're shutting down
if self.stop_consuming.is_set():
logger.info("Rejecting new task during shutdown")
ack_message(reject=True, requeue=True)
return
# Check if we can accept more tasks
self._cleanup_completed_tasks()
if len(self.active_tasks) >= self.pool_size:
ack_message(reject=True, requeue=True)
return
try:
entry = CoPilotExecutionEntry.model_validate_json(body)
except Exception as e:
logger.error(f"Could not parse run message: {e}, body={body}")
ack_message(reject=True, requeue=False)
return
task_id = entry.task_id
# Check for local duplicate
if task_id in self.active_tasks:
logger.warning(f"Task {task_id} already running locally")
ack_message(reject=True, requeue=True)
return
# Try to acquire cluster-wide lock
cluster_lock = ClusterLock(
redis=redis.get_redis(),
key=f"copilot_lock:{task_id}",
owner_id=self.executor_id,
timeout=settings.config.cluster_lock_timeout,
)
current_owner = cluster_lock.try_acquire()
if current_owner != self.executor_id:
if current_owner is not None:
logger.warning(f"Task {task_id} already running on pod {current_owner}")
ack_message(reject=True, requeue=False)
else:
logger.warning(
f"Could not acquire lock for {task_id} - Redis unavailable"
)
ack_message(reject=True, requeue=True)
return
# Execute the task
try:
self._task_locks[task_id] = cluster_lock
logger.info(
f"Acquired cluster lock for {task_id}, executor_id={self.executor_id}"
)
cancel_event = threading.Event()
future = self.executor.submit(
execute_copilot_task, entry, cancel_event, cluster_lock
)
self.active_tasks[task_id] = (future, cancel_event)
except Exception as e:
logger.warning(f"Failed to setup execution for {task_id}: {e}")
cluster_lock.release()
if task_id in self._task_locks:
del self._task_locks[task_id]
ack_message(reject=True, requeue=True)
return
self._update_metrics()
def on_run_done(f: Future):
logger.info(f"Run completed for {task_id}")
try:
if exec_error := f.exception():
logger.error(f"Execution for {task_id} failed: {exec_error}")
ack_message(reject=True, requeue=True)
else:
ack_message(reject=False, requeue=False)
except BaseException as e:
logger.exception(f"Error in run completion callback: {e}")
finally:
# Release the cluster lock
if task_id in self._task_locks:
logger.info(f"Releasing cluster lock for {task_id}")
self._task_locks[task_id].release()
del self._task_locks[task_id]
self._cleanup_completed_tasks()
future.add_done_callback(on_run_done)
# ============ Helper Methods ============ #
def _cleanup_completed_tasks(self) -> list[str]:
"""Remove completed futures from active_tasks and update metrics."""
completed_tasks = []
for task_id, (future, _) in self.active_tasks.items():
if future.done():
completed_tasks.append(task_id)
for task_id in completed_tasks:
logger.info(f"Cleaned up completed task {task_id}")
self.active_tasks.pop(task_id, None)
self._update_metrics()
return completed_tasks
def _update_metrics(self):
"""Update Prometheus metrics."""
active_count = len(self.active_tasks)
active_tasks_gauge.set(active_count)
if self.stop_consuming.is_set():
utilization_gauge.set(1.0)
else:
utilization_gauge.set(
active_count / self.pool_size if self.pool_size > 0 else 0
)
def _stop_message_consumers(
self, thread: threading.Thread, client: SyncRabbitMQ, prefix: str
):
"""Stop a message consumer thread."""
try:
channel = client.get_channel()
channel.connection.add_callback_threadsafe(lambda: channel.stop_consuming())
try:
thread.join(timeout=300)
except TimeoutError:
logger.error(
f"{prefix} Thread did not finish in time, forcing disconnect"
)
client.disconnect()
logger.info(f"{prefix} Client disconnected")
except Exception as e:
logger.error(f"{prefix} Error disconnecting client: {e}")
# ============ Lazy-initialized Properties ============ #
@property
def cancel_thread(self) -> threading.Thread:
if self._cancel_thread is None:
self._cancel_thread = threading.Thread(
target=lambda: self._consume_cancel(),
daemon=True,
)
return self._cancel_thread
@property
def run_thread(self) -> threading.Thread:
if self._run_thread is None:
self._run_thread = threading.Thread(
target=lambda: self._consume_run(),
daemon=True,
)
return self._run_thread
@property
def stop_consuming(self) -> threading.Event:
if self._stop_consuming is None:
self._stop_consuming = threading.Event()
return self._stop_consuming
@property
def executor(self) -> ThreadPoolExecutor:
if self._executor is None:
self._executor = ThreadPoolExecutor(
max_workers=self.pool_size,
initializer=init_worker,
)
return self._executor
@property
def cancel_client(self) -> SyncRabbitMQ:
if self._cancel_client is None:
self._cancel_client = SyncRabbitMQ(create_copilot_queue_config())
return self._cancel_client
@property
def run_client(self) -> SyncRabbitMQ:
if self._run_client is None:
self._run_client = SyncRabbitMQ(create_copilot_queue_config())
return self._run_client

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"""CoPilot execution processor - per-worker execution logic.
This module contains the processor class that handles CoPilot task execution
in a thread-local context, following the graph executor pattern.
"""
import asyncio
import logging
import threading
import time
from backend.copilot import service as copilot_service
from backend.copilot import stream_registry
from backend.copilot.response_model import StreamError, StreamFinish, StreamFinishStep
from backend.executor.cluster_lock import ClusterLock
from backend.util.decorator import error_logged
from backend.util.logging import TruncatedLogger, configure_logging
from backend.util.process import set_service_name
from backend.util.retry import func_retry
from .utils import CoPilotExecutionEntry, CoPilotLogMetadata
logger = TruncatedLogger(logging.getLogger(__name__), prefix="[CoPilotExecutor]")
# ============ Module Entry Points ============ #
# Thread-local storage for processor instances
_tls = threading.local()
def execute_copilot_task(
entry: CoPilotExecutionEntry,
cancel: threading.Event,
cluster_lock: ClusterLock,
):
"""Execute a CoPilot task using the thread-local processor.
This function is the entry point called by the thread pool executor.
Args:
entry: The task payload
cancel: Threading event to signal cancellation
cluster_lock: Distributed lock for this execution
"""
processor: CoPilotProcessor = _tls.processor
return processor.execute(entry, cancel, cluster_lock)
def init_worker():
"""Initialize the processor for the current worker thread.
This function is called by the thread pool executor when a new worker
thread is created. It ensures each worker has its own processor instance.
"""
_tls.processor = CoPilotProcessor()
_tls.processor.on_executor_start()
# ============ Processor Class ============ #
class CoPilotProcessor:
"""Per-worker execution logic for CoPilot tasks.
This class is instantiated once per worker thread and handles the execution
of CoPilot chat generation tasks. It maintains an async event loop for
running the async service code.
The execution flow:
1. CoPilot task is picked from RabbitMQ queue
2. Manager submits task to thread pool
3. Processor executes the task in its event loop
4. Results are published to Redis Streams
"""
@func_retry
def on_executor_start(self):
"""Initialize the processor when the worker thread starts.
This method is called once per worker thread to set up the async event
loop, connect to Prisma, and initialize any required resources.
"""
configure_logging()
set_service_name("CoPilotExecutor")
self.tid = threading.get_ident()
self.execution_loop = asyncio.new_event_loop()
self.execution_thread = threading.Thread(
target=self.execution_loop.run_forever, daemon=True
)
self.execution_thread.start()
# Connect to Prisma in the worker's event loop
# This is required because the CoPilot service uses Prisma directly
# TODO: Use DatabaseManager, avoid direct Prisma connection(?)
asyncio.run_coroutine_threadsafe(
self._connect_prisma(), self.execution_loop
).result(timeout=30.0)
logger.info(f"[CoPilotExecutor] Worker {self.tid} started")
async def _connect_prisma(self):
"""Connect to Prisma database in the worker's event loop."""
from backend.data import db
if not db.is_connected():
await db.connect()
logger.info(f"[CoPilotExecutor] Worker {self.tid} connected to Prisma")
@error_logged(swallow=False)
def execute(
self,
entry: CoPilotExecutionEntry,
cancel: threading.Event,
cluster_lock: ClusterLock,
):
"""Execute a CoPilot task.
This is the main entry point for task execution. It runs the async
execution logic in the worker's event loop and handles errors.
Args:
entry: The task payload containing session and message info
cancel: Threading event to signal cancellation
cluster_lock: Distributed lock to prevent duplicate execution
"""
log = CoPilotLogMetadata(
logging.getLogger(__name__),
task_id=entry.task_id,
session_id=entry.session_id,
user_id=entry.user_id,
)
log.info("Starting execution")
start_time = time.monotonic()
try:
# Run the async execution in our event loop
future = asyncio.run_coroutine_threadsafe(
self._execute_async(entry, cancel, cluster_lock, log),
self.execution_loop,
)
# Wait for completion, checking cancel periodically
while not future.done():
try:
future.result(timeout=1.0)
except asyncio.TimeoutError:
if cancel.is_set():
log.info("Cancellation requested")
future.cancel()
break
# Refresh cluster lock to maintain ownership
cluster_lock.refresh()
if not future.cancelled():
# Get result to propagate any exceptions
future.result()
elapsed = time.monotonic() - start_time
log.info(f"Execution completed in {elapsed:.2f}s")
except Exception as e:
elapsed = time.monotonic() - start_time
log.error(f"Execution failed after {elapsed:.2f}s: {e}")
# Ensure task is marked as failed in stream registry
asyncio.run_coroutine_threadsafe(
self._mark_task_failed(entry.task_id, str(e)),
self.execution_loop,
).result(timeout=10.0)
raise
async def _execute_async(
self,
entry: CoPilotExecutionEntry,
cancel: threading.Event,
cluster_lock: ClusterLock,
log: CoPilotLogMetadata,
):
"""Async execution logic for CoPilot task.
This method calls the existing stream_chat_completion service function
and publishes results to the stream registry.
Args:
entry: The task payload
cancel: Threading event to signal cancellation
cluster_lock: Distributed lock for refresh
log: Structured logger for this task
"""
last_refresh = time.monotonic()
refresh_interval = 30.0 # Refresh lock every 30 seconds
try:
# Stream chat completion and publish chunks to Redis
async for chunk in copilot_service.stream_chat_completion(
session_id=entry.session_id,
message=entry.message if entry.message else None,
is_user_message=entry.is_user_message,
user_id=entry.user_id,
context=entry.context,
_task_id=entry.task_id,
):
# Check for cancellation
if cancel.is_set():
log.info("Cancelled during streaming")
await stream_registry.publish_chunk(
entry.task_id, StreamError(errorText="Operation cancelled")
)
await stream_registry.publish_chunk(
entry.task_id, StreamFinishStep()
)
await stream_registry.publish_chunk(entry.task_id, StreamFinish())
await stream_registry.mark_task_completed(
entry.task_id, status="failed"
)
return
# Refresh cluster lock periodically
current_time = time.monotonic()
if current_time - last_refresh >= refresh_interval:
cluster_lock.refresh()
last_refresh = current_time
# Publish chunk to stream registry
await stream_registry.publish_chunk(entry.task_id, chunk)
# Mark task as completed
await stream_registry.mark_task_completed(entry.task_id, status="completed")
log.info("Task completed successfully")
except asyncio.CancelledError:
log.info("Task cancelled")
await stream_registry.mark_task_completed(entry.task_id, status="failed")
raise
except Exception as e:
log.error(f"Task failed: {e}")
await self._mark_task_failed(entry.task_id, str(e))
raise
async def _mark_task_failed(self, task_id: str, error_message: str):
"""Mark a task as failed and publish error to stream registry."""
try:
await stream_registry.publish_chunk(
task_id, StreamError(errorText=error_message)
)
await stream_registry.publish_chunk(task_id, StreamFinishStep())
await stream_registry.publish_chunk(task_id, StreamFinish())
await stream_registry.mark_task_completed(task_id, status="failed")
except Exception as e:
logger.error(f"Failed to mark task {task_id} as failed: {e}")

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"""RabbitMQ queue configuration for CoPilot executor.
Defines two exchanges and queues following the graph executor pattern:
- 'copilot_execution' (DIRECT) for chat generation tasks
- 'copilot_cancel' (FANOUT) for cancellation requests
"""
import logging
from pydantic import BaseModel
from backend.data.rabbitmq import Exchange, ExchangeType, Queue, RabbitMQConfig
from backend.util.logging import TruncatedLogger, is_structured_logging_enabled
logger = logging.getLogger(__name__)
# ============ Logging Helper ============ #
class CoPilotLogMetadata(TruncatedLogger):
"""Structured logging helper for CoPilot executor.
In cloud environments (structured logging enabled), uses a simple prefix
and passes metadata via json_fields. In local environments, uses a detailed
prefix with all metadata key-value pairs for easier debugging.
Args:
logger: The underlying logger instance
max_length: Maximum log message length before truncation
**kwargs: Metadata key-value pairs (e.g., task_id="abc", session_id="xyz")
These are added to json_fields in cloud mode, or to the prefix in local mode.
"""
def __init__(
self,
logger: logging.Logger,
max_length: int = 1000,
**kwargs: str | None,
):
# Filter out None values
metadata = {k: v for k, v in kwargs.items() if v is not None}
metadata["component"] = "CoPilotExecutor"
if is_structured_logging_enabled():
prefix = "[CoPilotExecutor]"
else:
# Build prefix from metadata key-value pairs
meta_parts = "|".join(
f"{k}:{v}" for k, v in metadata.items() if k != "component"
)
prefix = (
f"[CoPilotExecutor|{meta_parts}]" if meta_parts else "[CoPilotExecutor]"
)
super().__init__(
logger,
max_length=max_length,
prefix=prefix,
metadata=metadata,
)
# ============ Exchange and Queue Configuration ============ #
COPILOT_EXECUTION_EXCHANGE = Exchange(
name="copilot_execution",
type=ExchangeType.DIRECT,
durable=True,
auto_delete=False,
)
COPILOT_EXECUTION_QUEUE_NAME = "copilot_execution_queue"
COPILOT_EXECUTION_ROUTING_KEY = "copilot.run"
COPILOT_CANCEL_EXCHANGE = Exchange(
name="copilot_cancel",
type=ExchangeType.FANOUT,
durable=True,
auto_delete=True,
)
COPILOT_CANCEL_QUEUE_NAME = "copilot_cancel_queue"
# CoPilot operations can include extended thinking and agent generation
# which may take 30+ minutes to complete
COPILOT_CONSUMER_TIMEOUT_SECONDS = 60 * 60 # 1 hour
# Graceful shutdown timeout - allow in-flight operations to complete
GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS = 30 * 60 # 30 minutes
def create_copilot_queue_config() -> RabbitMQConfig:
"""Create RabbitMQ configuration for CoPilot executor.
Defines two exchanges and queues:
- 'copilot_execution' (DIRECT) for chat generation tasks
- 'copilot_cancel' (FANOUT) for cancellation requests
Returns:
RabbitMQConfig with exchanges and queues defined
"""
run_queue = Queue(
name=COPILOT_EXECUTION_QUEUE_NAME,
exchange=COPILOT_EXECUTION_EXCHANGE,
routing_key=COPILOT_EXECUTION_ROUTING_KEY,
durable=True,
auto_delete=False,
arguments={
# Extended consumer timeout for long-running LLM operations
# Default 30-minute timeout is insufficient for extended thinking
# and agent generation which can take 30+ minutes
"x-consumer-timeout": COPILOT_CONSUMER_TIMEOUT_SECONDS
* 1000,
},
)
cancel_queue = Queue(
name=COPILOT_CANCEL_QUEUE_NAME,
exchange=COPILOT_CANCEL_EXCHANGE,
routing_key="", # not used for FANOUT
durable=True,
auto_delete=False,
)
return RabbitMQConfig(
vhost="/",
exchanges=[COPILOT_EXECUTION_EXCHANGE, COPILOT_CANCEL_EXCHANGE],
queues=[run_queue, cancel_queue],
)
# ============ Message Models ============ #
class CoPilotExecutionEntry(BaseModel):
"""Task payload for CoPilot AI generation.
This model represents a chat generation task to be processed by the executor.
"""
task_id: str
"""Unique identifier for this task (used for stream registry)"""
session_id: str
"""Chat session ID"""
user_id: str | None
"""User ID (may be None for anonymous users)"""
operation_id: str
"""Operation ID for webhook callbacks and completion tracking"""
message: str
"""User's message to process"""
is_user_message: bool = True
"""Whether the message is from the user (vs system/assistant)"""
context: dict[str, str] | None = None
"""Optional context for the message (e.g., {url: str, content: str})"""
class CancelCoPilotEvent(BaseModel):
"""Event to cancel a CoPilot operation."""
task_id: str
"""Task ID to cancel"""
# ============ Queue Publishing Helpers ============ #
async def enqueue_copilot_task(
task_id: str,
session_id: str,
user_id: str | None,
operation_id: str,
message: str,
is_user_message: bool = True,
context: dict[str, str] | None = None,
) -> None:
"""Enqueue a CoPilot task for processing by the executor service.
Args:
task_id: Unique identifier for this task (used for stream registry)
session_id: Chat session ID
user_id: User ID (may be None for anonymous users)
operation_id: Operation ID for webhook callbacks and completion tracking
message: User's message to process
is_user_message: Whether the message is from the user (vs system/assistant)
context: Optional context for the message (e.g., {url: str, content: str})
"""
from backend.util.clients import get_async_copilot_queue
entry = CoPilotExecutionEntry(
task_id=task_id,
session_id=session_id,
user_id=user_id,
operation_id=operation_id,
message=message,
is_user_message=is_user_message,
context=context,
)
queue_client = await get_async_copilot_queue()
await queue_client.publish_message(
routing_key=COPILOT_EXECUTION_ROUTING_KEY,
message=entry.model_dump_json(),
exchange=COPILOT_EXECUTION_EXCHANGE,
)

View File

@@ -1,245 +0,0 @@
import logging
from typing import Any
from prisma.enums import ContentType
from backend.api.features.store.hybrid_search import unified_hybrid_search
from backend.copilot.model import ChatSession
from backend.copilot.tools.base import BaseTool, ToolResponseBase
from backend.copilot.tools.models import (
BlockInfoSummary,
BlockInputFieldInfo,
BlockListResponse,
ErrorResponse,
NoResultsResponse,
)
from backend.data.block import BlockType, get_block
logger = logging.getLogger(__name__)
_TARGET_RESULTS = 10
# Over-fetch to compensate for post-hoc filtering of graph-only blocks.
# 40 is 2x current removed; speed of query 10 vs 40 is minimial
_OVERFETCH_PAGE_SIZE = 40
# Block types that only work within graphs and cannot run standalone in CoPilot.
COPILOT_EXCLUDED_BLOCK_TYPES = {
BlockType.INPUT, # Graph interface definition - data enters via chat, not graph inputs
BlockType.OUTPUT, # Graph interface definition - data exits via chat, not graph outputs
BlockType.WEBHOOK, # Wait for external events - would hang forever in CoPilot
BlockType.WEBHOOK_MANUAL, # Same as WEBHOOK
BlockType.NOTE, # Visual annotation only - no runtime behavior
BlockType.HUMAN_IN_THE_LOOP, # Pauses for human approval - CoPilot IS human-in-the-loop
BlockType.AGENT, # AgentExecutorBlock requires execution_context - use run_agent tool
}
# Specific block IDs excluded from CoPilot (STANDARD type but still require graph context)
COPILOT_EXCLUDED_BLOCK_IDS = {
# SmartDecisionMakerBlock - dynamically discovers downstream blocks via graph topology
"3b191d9f-356f-482d-8238-ba04b6d18381",
}
class FindBlockTool(BaseTool):
"""Tool for searching available blocks."""
@property
def name(self) -> str:
return "find_block"
@property
def description(self) -> str:
return (
"Search for available blocks by name or description. "
"Blocks are reusable components that perform specific tasks like "
"sending emails, making API calls, processing text, etc. "
"IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
"The response includes each block's id, required_inputs, and input_schema."
)
@property
def parameters(self) -> dict[str, Any]:
return {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": (
"Search query to find blocks by name or description. "
"Use keywords like 'email', 'http', 'text', 'ai', etc."
),
},
},
"required": ["query"],
}
@property
def requires_auth(self) -> bool:
return True
async def _execute(
self,
user_id: str | None,
session: ChatSession,
**kwargs,
) -> ToolResponseBase:
"""Search for blocks matching the query.
Args:
user_id: User ID (required)
session: Chat session
query: Search query
Returns:
BlockListResponse: List of matching blocks
NoResultsResponse: No blocks found
ErrorResponse: Error message
"""
query = kwargs.get("query", "").strip()
session_id = session.session_id
if not query:
return ErrorResponse(
message="Please provide a search query",
session_id=session_id,
)
try:
# Search for blocks using hybrid search
results, total = await unified_hybrid_search(
query=query,
content_types=[ContentType.BLOCK],
page=1,
page_size=_OVERFETCH_PAGE_SIZE,
)
if not results:
return NoResultsResponse(
message=f"No blocks found for '{query}'",
suggestions=[
"Try broader keywords like 'email', 'http', 'text', 'ai'",
"Check spelling of technical terms",
],
session_id=session_id,
)
# Enrich results with full block information
blocks: list[BlockInfoSummary] = []
for result in results:
block_id = result["content_id"]
block = get_block(block_id)
# Skip disabled blocks
if not block or block.disabled:
continue
# Skip blocks excluded from CoPilot (graph-only blocks)
if (
block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
or block.id in COPILOT_EXCLUDED_BLOCK_IDS
):
continue
# Get input/output schemas
input_schema = {}
output_schema = {}
try:
input_schema = block.input_schema.jsonschema()
except Exception as e:
logger.debug(
"Failed to generate input schema for block %s: %s",
block_id,
e,
)
try:
output_schema = block.output_schema.jsonschema()
except Exception as e:
logger.debug(
"Failed to generate output schema for block %s: %s",
block_id,
e,
)
# Get categories from block instance
categories = []
if hasattr(block, "categories") and block.categories:
categories = [cat.value for cat in block.categories]
# Extract required inputs for easier use
required_inputs: list[BlockInputFieldInfo] = []
if input_schema:
properties = input_schema.get("properties", {})
required_fields = set(input_schema.get("required", []))
# Get credential field names to exclude from required inputs
credentials_fields = set(
block.input_schema.get_credentials_fields().keys()
)
for field_name, field_schema in properties.items():
# Skip credential fields - they're handled separately
if field_name in credentials_fields:
continue
required_inputs.append(
BlockInputFieldInfo(
name=field_name,
type=field_schema.get("type", "string"),
description=field_schema.get("description", ""),
required=field_name in required_fields,
default=field_schema.get("default"),
)
)
blocks.append(
BlockInfoSummary(
id=block_id,
name=block.name,
description=block.description or "",
categories=categories,
input_schema=input_schema,
output_schema=output_schema,
required_inputs=required_inputs,
)
)
if len(blocks) >= _TARGET_RESULTS:
break
if blocks and len(blocks) < _TARGET_RESULTS:
logger.debug(
"find_block returned %d/%d results for query '%s' "
"(filtered %d excluded/disabled blocks)",
len(blocks),
_TARGET_RESULTS,
query,
len(results) - len(blocks),
)
if not blocks:
return NoResultsResponse(
message=f"No blocks found for '{query}'",
suggestions=[
"Try broader keywords like 'email', 'http', 'text', 'ai'",
],
session_id=session_id,
)
return BlockListResponse(
message=(
f"Found {len(blocks)} block(s) matching '{query}'. "
"To execute a block, use run_block with the block's 'id' field "
"and provide 'input_data' matching the block's input_schema."
),
blocks=blocks,
count=len(blocks),
query=query,
session_id=session_id,
)
except Exception as e:
logger.error(f"Error searching blocks: {e}", exc_info=True)
return ErrorResponse(
message="Failed to search blocks",
error=str(e),
session_id=session_id,
)

View File

@@ -1,139 +0,0 @@
"""Tests for block filtering in FindBlockTool."""
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from backend.copilot.tools.find_block import (
COPILOT_EXCLUDED_BLOCK_IDS,
COPILOT_EXCLUDED_BLOCK_TYPES,
FindBlockTool,
)
from backend.copilot.tools.models import BlockListResponse
from backend.data.block import BlockType
from ._test_data import make_session
_TEST_USER_ID = "test-user-find-block"
def make_mock_block(
block_id: str, name: str, block_type: BlockType, disabled: bool = False
):
"""Create a mock block for testing."""
mock = MagicMock()
mock.id = block_id
mock.name = name
mock.description = f"{name} description"
mock.block_type = block_type
mock.disabled = disabled
mock.input_schema = MagicMock()
mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
mock.input_schema.get_credentials_fields.return_value = {}
mock.output_schema = MagicMock()
mock.output_schema.jsonschema.return_value = {}
mock.categories = []
return mock
class TestFindBlockFiltering:
"""Tests for block filtering in FindBlockTool."""
def test_excluded_block_types_contains_expected_types(self):
"""Verify COPILOT_EXCLUDED_BLOCK_TYPES contains all graph-only types."""
assert BlockType.INPUT in COPILOT_EXCLUDED_BLOCK_TYPES
assert BlockType.OUTPUT in COPILOT_EXCLUDED_BLOCK_TYPES
assert BlockType.WEBHOOK in COPILOT_EXCLUDED_BLOCK_TYPES
assert BlockType.WEBHOOK_MANUAL in COPILOT_EXCLUDED_BLOCK_TYPES
assert BlockType.NOTE in COPILOT_EXCLUDED_BLOCK_TYPES
assert BlockType.HUMAN_IN_THE_LOOP in COPILOT_EXCLUDED_BLOCK_TYPES
assert BlockType.AGENT in COPILOT_EXCLUDED_BLOCK_TYPES
def test_excluded_block_ids_contains_smart_decision_maker(self):
"""Verify SmartDecisionMakerBlock is in COPILOT_EXCLUDED_BLOCK_IDS."""
assert "3b191d9f-356f-482d-8238-ba04b6d18381" in COPILOT_EXCLUDED_BLOCK_IDS
@pytest.mark.asyncio(loop_scope="session")
async def test_excluded_block_type_filtered_from_results(self):
"""Verify blocks with excluded BlockTypes are filtered from search results."""
session = make_session(user_id=_TEST_USER_ID)
# Mock search returns an INPUT block (excluded) and a STANDARD block (included)
search_results = [
{"content_id": "input-block-id", "score": 0.9},
{"content_id": "standard-block-id", "score": 0.8},
]
input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
standard_block = make_mock_block(
"standard-block-id", "HTTP Request", BlockType.STANDARD
)
def mock_get_block(block_id):
return {
"input-block-id": input_block,
"standard-block-id": standard_block,
}.get(block_id)
with patch(
"backend.api.features.chat.tools.find_block.unified_hybrid_search",
new_callable=AsyncMock,
return_value=(search_results, 2),
):
with patch(
"backend.api.features.chat.tools.find_block.get_block",
side_effect=mock_get_block,
):
tool = FindBlockTool()
response = await tool._execute(
user_id=_TEST_USER_ID, session=session, query="test"
)
# Should only return the standard block, not the INPUT block
assert isinstance(response, BlockListResponse)
assert len(response.blocks) == 1
assert response.blocks[0].id == "standard-block-id"
@pytest.mark.asyncio(loop_scope="session")
async def test_excluded_block_id_filtered_from_results(self):
"""Verify SmartDecisionMakerBlock is filtered from search results."""
session = make_session(user_id=_TEST_USER_ID)
smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
search_results = [
{"content_id": smart_decision_id, "score": 0.9},
{"content_id": "normal-block-id", "score": 0.8},
]
# SmartDecisionMakerBlock has STANDARD type but is excluded by ID
smart_block = make_mock_block(
smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
)
normal_block = make_mock_block(
"normal-block-id", "Normal Block", BlockType.STANDARD
)
def mock_get_block(block_id):
return {
smart_decision_id: smart_block,
"normal-block-id": normal_block,
}.get(block_id)
with patch(
"backend.api.features.chat.tools.find_block.unified_hybrid_search",
new_callable=AsyncMock,
return_value=(search_results, 2),
):
with patch(
"backend.api.features.chat.tools.find_block.get_block",
side_effect=mock_get_block,
):
tool = FindBlockTool()
response = await tool._execute(
user_id=_TEST_USER_ID, session=session, query="decision"
)
# Should only return normal block, not SmartDecisionMakerBlock
assert isinstance(response, BlockListResponse)
assert len(response.blocks) == 1
assert response.blocks[0].id == "normal-block-id"

View File

@@ -1,29 +0,0 @@
"""Shared helpers for chat tools."""
from typing import Any
def get_inputs_from_schema(
input_schema: dict[str, Any],
exclude_fields: set[str] | None = None,
) -> list[dict[str, Any]]:
"""Extract input field info from JSON schema."""
if not isinstance(input_schema, dict):
return []
exclude = exclude_fields or set()
properties = input_schema.get("properties", {})
required = set(input_schema.get("required", []))
return [
{
"name": name,
"title": schema.get("title", name),
"type": schema.get("type", "string"),
"description": schema.get("description", ""),
"required": name in required,
"default": schema.get("default"),
}
for name, schema in properties.items()
if name not in exclude
]

View File

@@ -1,106 +0,0 @@
"""Tests for block execution guards in RunBlockTool."""
from unittest.mock import MagicMock, patch
import pytest
from backend.copilot.tools.models import ErrorResponse
from backend.copilot.tools.run_block import RunBlockTool
from backend.data.block import BlockType
from ._test_data import make_session
_TEST_USER_ID = "test-user-run-block"
def make_mock_block(
block_id: str, name: str, block_type: BlockType, disabled: bool = False
):
"""Create a mock block for testing."""
mock = MagicMock()
mock.id = block_id
mock.name = name
mock.block_type = block_type
mock.disabled = disabled
mock.input_schema = MagicMock()
mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
mock.input_schema.get_credentials_fields_info.return_value = []
return mock
class TestRunBlockFiltering:
"""Tests for block execution guards in RunBlockTool."""
@pytest.mark.asyncio(loop_scope="session")
async def test_excluded_block_type_returns_error(self):
"""Attempting to execute a block with excluded BlockType returns error."""
session = make_session(user_id=_TEST_USER_ID)
input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
with patch(
"backend.api.features.chat.tools.run_block.get_block",
return_value=input_block,
):
tool = RunBlockTool()
response = await tool._execute(
user_id=_TEST_USER_ID,
session=session,
block_id="input-block-id",
input_data={},
)
assert isinstance(response, ErrorResponse)
assert "cannot be run directly in CoPilot" in response.message
assert "designed for use within graphs only" in response.message
@pytest.mark.asyncio(loop_scope="session")
async def test_excluded_block_id_returns_error(self):
"""Attempting to execute SmartDecisionMakerBlock returns error."""
session = make_session(user_id=_TEST_USER_ID)
smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
smart_block = make_mock_block(
smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
)
with patch(
"backend.api.features.chat.tools.run_block.get_block",
return_value=smart_block,
):
tool = RunBlockTool()
response = await tool._execute(
user_id=_TEST_USER_ID,
session=session,
block_id=smart_decision_id,
input_data={},
)
assert isinstance(response, ErrorResponse)
assert "cannot be run directly in CoPilot" in response.message
@pytest.mark.asyncio(loop_scope="session")
async def test_non_excluded_block_passes_guard(self):
"""Non-excluded blocks pass the filtering guard (may fail later for other reasons)."""
session = make_session(user_id=_TEST_USER_ID)
standard_block = make_mock_block(
"standard-id", "HTTP Request", BlockType.STANDARD
)
with patch(
"backend.api.features.chat.tools.run_block.get_block",
return_value=standard_block,
):
tool = RunBlockTool()
response = await tool._execute(
user_id=_TEST_USER_ID,
session=session,
block_id="standard-id",
input_data={},
)
# Should NOT be an ErrorResponse about CoPilot exclusion
# (may be other errors like missing credentials, but not the exclusion guard)
if isinstance(response, ErrorResponse):
assert "cannot be run directly in CoPilot" not in response.message

View File

@@ -3,6 +3,8 @@
import queue
import threading
import pytest
from backend.data.execution import ExecutionQueue

View File

@@ -1,4 +1,3 @@
import asyncio
import logging
from abc import ABC, abstractmethod
from enum import Enum
@@ -226,10 +225,6 @@ class SyncRabbitMQ(RabbitMQBase):
class AsyncRabbitMQ(RabbitMQBase):
"""Asynchronous RabbitMQ client"""
def __init__(self, config: RabbitMQConfig):
super().__init__(config)
self._reconnect_lock: asyncio.Lock | None = None
@property
def is_connected(self) -> bool:
return bool(self._connection and not self._connection.is_closed)
@@ -240,17 +235,7 @@ class AsyncRabbitMQ(RabbitMQBase):
@conn_retry("AsyncRabbitMQ", "Acquiring async connection")
async def connect(self):
if self.is_connected and self._channel and not self._channel.is_closed:
return
if (
self.is_connected
and self._connection
and (self._channel is None or self._channel.is_closed)
):
self._channel = await self._connection.channel()
await self._channel.set_qos(prefetch_count=1)
await self.declare_infrastructure()
if self.is_connected:
return
self._connection = await aio_pika.connect_robust(
@@ -306,46 +291,24 @@ class AsyncRabbitMQ(RabbitMQBase):
exchange, routing_key=queue.routing_key or queue.name
)
@property
def _lock(self) -> asyncio.Lock:
if self._reconnect_lock is None:
self._reconnect_lock = asyncio.Lock()
return self._reconnect_lock
async def _ensure_channel(self) -> aio_pika.abc.AbstractChannel:
"""Get a valid channel, reconnecting if the current one is stale.
Uses a lock to prevent concurrent reconnection attempts from racing.
"""
if self.is_ready:
return self._channel # type: ignore # is_ready guarantees non-None
async with self._lock:
# Double-check after acquiring lock
if self.is_ready:
return self._channel # type: ignore
self._channel = None
await self.connect()
if self._channel is None:
raise RuntimeError("Channel should be established after connect")
return self._channel
async def _publish_once(
@func_retry
async def publish_message(
self,
routing_key: str,
message: str,
exchange: Optional[Exchange] = None,
persistent: bool = True,
) -> None:
channel = await self._ensure_channel()
if not self.is_ready:
await self.connect()
if self._channel is None:
raise RuntimeError("Channel should be established after connect")
if exchange:
exchange_obj = await channel.get_exchange(exchange.name)
exchange_obj = await self._channel.get_exchange(exchange.name)
else:
exchange_obj = channel.default_exchange
exchange_obj = self._channel.default_exchange
await exchange_obj.publish(
aio_pika.Message(
@@ -359,23 +322,9 @@ class AsyncRabbitMQ(RabbitMQBase):
routing_key=routing_key,
)
@func_retry
async def publish_message(
self,
routing_key: str,
message: str,
exchange: Optional[Exchange] = None,
persistent: bool = True,
) -> None:
try:
await self._publish_once(routing_key, message, exchange, persistent)
except aio_pika.exceptions.ChannelInvalidStateError:
logger.warning(
"RabbitMQ channel invalid, forcing reconnect and retrying publish"
)
async with self._lock:
self._channel = None
await self._publish_once(routing_key, message, exchange, persistent)
async def get_channel(self) -> aio_pika.abc.AbstractChannel:
return await self._ensure_channel()
if not self.is_ready:
await self.connect()
if self._channel is None:
raise RuntimeError("Channel should be established after connect")
return self._channel

View File

@@ -11,6 +11,7 @@ settings = Settings()
if TYPE_CHECKING:
from openai import AsyncOpenAI
from supabase import AClient, Client
from backend.data.execution import (
AsyncRedisExecutionEventBus,
@@ -21,7 +22,6 @@ if TYPE_CHECKING:
from backend.executor.scheduler import SchedulerClient
from backend.integrations.credentials_store import IntegrationCredentialsStore
from backend.notifications.notifications import NotificationManagerClient
from supabase import AClient, Client
@thread_cached
@@ -106,20 +106,6 @@ async def get_async_execution_queue() -> "AsyncRabbitMQ":
return client
# ============ CoPilot Queue Helpers ============ #
@thread_cached
async def get_async_copilot_queue() -> "AsyncRabbitMQ":
"""Get a thread-cached AsyncRabbitMQ CoPilot queue client."""
from backend.copilot.executor.utils import create_copilot_queue_config
from backend.data.rabbitmq import AsyncRabbitMQ
client = AsyncRabbitMQ(create_copilot_queue_config())
await client.connect()
return client
# ============ Integration Credentials Store ============ #

View File

@@ -342,14 +342,6 @@ async def store_media_file(
if not target_path.is_file():
raise ValueError(f"Local file does not exist: {target_path}")
# Virus scan the local file before any further processing
local_content = target_path.read_bytes()
if len(local_content) > MAX_FILE_SIZE_BYTES:
raise ValueError(
f"File too large: {len(local_content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
)
await scan_content_safe(local_content, filename=sanitized_file)
# Return based on requested format
if return_format == "for_local_processing":
# Use when processing files locally with tools like ffmpeg, MoviePy, PIL

View File

@@ -247,100 +247,3 @@ class TestFileCloudIntegration:
execution_context=make_test_context(graph_exec_id=graph_exec_id),
return_format="for_local_processing",
)
@pytest.mark.asyncio
async def test_store_media_file_local_path_scanned(self):
"""Test that local file paths are scanned for viruses."""
graph_exec_id = "test-exec-123"
local_file = "test_video.mp4"
file_content = b"fake video content"
with patch(
"backend.util.file.get_cloud_storage_handler"
) as mock_handler_getter, patch(
"backend.util.file.scan_content_safe"
) as mock_scan, patch(
"backend.util.file.Path"
) as mock_path_class:
# Mock cloud storage handler - not a cloud path
mock_handler = MagicMock()
mock_handler.is_cloud_path.return_value = False
mock_handler_getter.return_value = mock_handler
# Mock virus scanner
mock_scan.return_value = None
# Mock file system operations
mock_base_path = MagicMock()
mock_target_path = MagicMock()
mock_resolved_path = MagicMock()
mock_path_class.return_value = mock_base_path
mock_base_path.mkdir = MagicMock()
mock_base_path.__truediv__ = MagicMock(return_value=mock_target_path)
mock_target_path.resolve.return_value = mock_resolved_path
mock_resolved_path.is_relative_to.return_value = True
mock_resolved_path.is_file.return_value = True
mock_resolved_path.read_bytes.return_value = file_content
mock_resolved_path.relative_to.return_value = Path(local_file)
mock_resolved_path.name = local_file
result = await store_media_file(
file=MediaFileType(local_file),
execution_context=make_test_context(graph_exec_id=graph_exec_id),
return_format="for_local_processing",
)
# Verify virus scan was called for local file
mock_scan.assert_called_once_with(file_content, filename=local_file)
# Result should be the relative path
assert str(result) == local_file
@pytest.mark.asyncio
async def test_store_media_file_local_path_virus_detected(self):
"""Test that infected local files raise VirusDetectedError."""
from backend.api.features.store.exceptions import VirusDetectedError
graph_exec_id = "test-exec-123"
local_file = "infected.exe"
file_content = b"malicious content"
with patch(
"backend.util.file.get_cloud_storage_handler"
) as mock_handler_getter, patch(
"backend.util.file.scan_content_safe"
) as mock_scan, patch(
"backend.util.file.Path"
) as mock_path_class:
# Mock cloud storage handler - not a cloud path
mock_handler = MagicMock()
mock_handler.is_cloud_path.return_value = False
mock_handler_getter.return_value = mock_handler
# Mock virus scanner to detect virus
mock_scan.side_effect = VirusDetectedError(
"EICAR-Test-File", "File rejected due to virus detection"
)
# Mock file system operations
mock_base_path = MagicMock()
mock_target_path = MagicMock()
mock_resolved_path = MagicMock()
mock_path_class.return_value = mock_base_path
mock_base_path.mkdir = MagicMock()
mock_base_path.__truediv__ = MagicMock(return_value=mock_target_path)
mock_target_path.resolve.return_value = mock_resolved_path
mock_resolved_path.is_relative_to.return_value = True
mock_resolved_path.is_file.return_value = True
mock_resolved_path.read_bytes.return_value = file_content
with pytest.raises(VirusDetectedError):
await store_media_file(
file=MediaFileType(local_file),
execution_context=make_test_context(graph_exec_id=graph_exec_id),
return_format="for_local_processing",
)

View File

@@ -211,23 +211,16 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
description="The port for execution manager daemon to run on",
)
num_copilot_workers: int = Field(
default=10,
ge=1,
le=100,
description="Number of concurrent CoPilot executor workers",
)
copilot_executor_port: int = Field(
default=8008,
description="The port for CoPilot executor daemon to run on",
)
execution_scheduler_port: int = Field(
default=8003,
description="The port for execution scheduler daemon to run on",
)
agent_server_port: int = Field(
default=8004,
description="The port for agent server daemon to run on",
)
database_api_port: int = Field(
default=8005,
description="The port for database server API to run on",

View File

@@ -269,20 +269,19 @@ files = [
[[package]]
name = "anthropic"
version = "0.79.0"
version = "0.59.0"
description = "The official Python library for the anthropic API"
optional = false
python-versions = ">=3.9"
python-versions = ">=3.8"
groups = ["main"]
files = [
{file = "anthropic-0.79.0-py3-none-any.whl", hash = "sha256:04cbd473b6bbda4ca2e41dd670fe2f829a911530f01697d0a1e37321eb75f3cf"},
{file = "anthropic-0.79.0.tar.gz", hash = "sha256:8707aafb3b1176ed6c13e2b1c9fb3efddce90d17aee5d8b83a86c70dcdcca871"},
{file = "anthropic-0.59.0-py3-none-any.whl", hash = "sha256:cbc8b3dccef66ad6435c4fa1d317e5ebb092399a4b88b33a09dc4bf3944c3183"},
{file = "anthropic-0.59.0.tar.gz", hash = "sha256:d710d1ef0547ebbb64b03f219e44ba078e83fc83752b96a9b22e9726b523fd8f"},
]
[package.dependencies]
anyio = ">=3.5.0,<5"
distro = ">=1.7.0,<2"
docstring-parser = ">=0.15,<1"
httpx = ">=0.25.0,<1"
jiter = ">=0.4.0,<1"
pydantic = ">=1.9.0,<3"
@@ -290,7 +289,7 @@ sniffio = "*"
typing-extensions = ">=4.10,<5"
[package.extras]
aiohttp = ["aiohttp", "httpx-aiohttp (>=0.1.9)"]
aiohttp = ["aiohttp", "httpx-aiohttp (>=0.1.8)"]
bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
vertex = ["google-auth[requests] (>=2,<3)"]
@@ -439,7 +438,7 @@ develop = true
[package.dependencies]
colorama = "^0.4.6"
cryptography = "^46.0"
cryptography = "^45.0"
expiringdict = "^1.2.2"
fastapi = "^0.128.0"
google-cloud-logging = "^3.13.0"
@@ -971,75 +970,62 @@ pytz = ">2021.1"
[[package]]
name = "cryptography"
version = "46.0.4"
version = "45.0.7"
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
optional = false
python-versions = "!=3.9.0,!=3.9.1,>=3.8"
python-versions = "!=3.9.0,!=3.9.1,>=3.7"
groups = ["main"]
files = [
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{file = "tiktoken-0.9.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:e88f121c1c22b726649ce67c089b90ddda8b9662545a8aeb03cfef15967ddd03"},
{file = "tiktoken-0.9.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a6600660f2f72369acb13a57fb3e212434ed38b045fd8cc6cdd74947b4b5d210"},
{file = "tiktoken-0.9.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:95e811743b5dfa74f4b227927ed86cbc57cad4df859cb3b643be797914e41794"},
{file = "tiktoken-0.9.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:99376e1370d59bcf6935c933cb9ba64adc29033b7e73f5f7569f3aad86552b22"},
{file = "tiktoken-0.9.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:badb947c32739fb6ddde173e14885fb3de4d32ab9d8c591cbd013c22b4c31dd2"},
{file = "tiktoken-0.9.0-cp312-cp312-win_amd64.whl", hash = "sha256:5a62d7a25225bafed786a524c1b9f0910a1128f4232615bf3f8257a73aaa3b16"},
{file = "tiktoken-0.9.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2b0e8e05a26eda1249e824156d537015480af7ae222ccb798e5234ae0285dbdb"},
{file = "tiktoken-0.9.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:27d457f096f87685195eea0165a1807fae87b97b2161fe8c9b1df5bd74ca6f63"},
{file = "tiktoken-0.9.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2cf8ded49cddf825390e36dd1ad35cd49589e8161fdcb52aa25f0583e90a3e01"},
{file = "tiktoken-0.9.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc156cb314119a8bb9748257a2eaebd5cc0753b6cb491d26694ed42fc7cb3139"},
{file = "tiktoken-0.9.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:cd69372e8c9dd761f0ab873112aba55a0e3e506332dd9f7522ca466e817b1b7a"},
{file = "tiktoken-0.9.0-cp313-cp313-win_amd64.whl", hash = "sha256:5ea0edb6f83dc56d794723286215918c1cde03712cbbafa0348b33448faf5b95"},
{file = "tiktoken-0.9.0-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:c6386ca815e7d96ef5b4ac61e0048cd32ca5a92d5781255e13b31381d28667dc"},
{file = "tiktoken-0.9.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:75f6d5db5bc2c6274b674ceab1615c1778e6416b14705827d19b40e6355f03e0"},
{file = "tiktoken-0.9.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e15b16f61e6f4625a57a36496d28dd182a8a60ec20a534c5343ba3cafa156ac7"},
{file = "tiktoken-0.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ebcec91babf21297022882344c3f7d9eed855931466c3311b1ad6b64befb3df"},
{file = "tiktoken-0.9.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:e5fd49e7799579240f03913447c0cdfa1129625ebd5ac440787afc4345990427"},
{file = "tiktoken-0.9.0-cp39-cp39-win_amd64.whl", hash = "sha256:26242ca9dc8b58e875ff4ca078b9a94d2f0813e6a535dcd2205df5d49d927cc7"},
{file = "tiktoken-0.9.0.tar.gz", hash = "sha256:d02a5ca6a938e0490e1ff957bc48c8b078c88cb83977be1625b1fd8aac792c5d"},
]
[package.dependencies]
@@ -8440,4 +8382,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<3.14"
content-hash = "fc135114e01de39c8adf70f6132045e7d44a19473c1279aee0978de65aad1655"
content-hash = "40b2c87c3c86bd10214bd30ad291cead75da5060ab894105025ee4c0a3b3828e"

View File

@@ -12,16 +12,16 @@ python = ">=3.10,<3.14"
aio-pika = "^9.5.5"
aiohttp = "^3.10.0"
aiodns = "^3.5.0"
anthropic = "^0.79.0"
anthropic = "^0.59.0"
apscheduler = "^3.11.1"
autogpt-libs = { path = "../autogpt_libs", develop = true }
bleach = { extras = ["css"], version = "^6.2.0" }
click = "^8.2.0"
cryptography = "^46.0"
cryptography = "^45.0"
discord-py = "^2.5.2"
e2b-code-interpreter = "^1.5.2"
elevenlabs = "^1.50.0"
fastapi = "^0.128.6"
fastapi = "^0.128.0"
feedparser = "^6.0.11"
flake8 = "^7.3.0"
google-api-python-client = "^2.177.0"
@@ -34,11 +34,11 @@ html2text = "^2024.2.26"
jinja2 = "^3.1.6"
jsonref = "^1.1.0"
jsonschema = "^4.25.0"
langfuse = "^3.14.1"
langfuse = "^3.11.0"
launchdarkly-server-sdk = "^9.14.1"
mem0ai = "^0.1.115"
moviepy = "^2.1.2"
ollama = "^0.6.1"
ollama = "^0.5.1"
openai = "^1.97.1"
orjson = "^3.10.0"
pika = "^1.3.2"
@@ -48,7 +48,7 @@ postmarker = "^1.0"
praw = "~7.8.1"
prisma = "^0.15.0"
rank-bm25 = "^0.2.2"
prometheus-client = "^0.24.1"
prometheus-client = "^0.22.1"
prometheus-fastapi-instrumentator = "^7.0.0"
psutil = "^7.0.0"
psycopg2-binary = "^2.9.10"
@@ -57,7 +57,7 @@ pydantic-settings = "^2.12.0"
pytest = "^8.4.1"
pytest-asyncio = "^1.1.0"
python-dotenv = "^1.1.1"
python-multipart = "^0.0.22"
python-multipart = "^0.0.20"
redis = "^6.2.0"
regex = "^2025.9.18"
replicate = "^1.0.6"
@@ -65,8 +65,8 @@ sentry-sdk = {extras = ["anthropic", "fastapi", "launchdarkly", "openai", "sqlal
sqlalchemy = "^2.0.40"
strenum = "^0.4.9"
stripe = "^11.5.0"
supabase = "2.27.3"
tenacity = "^9.1.4"
supabase = "2.27.2"
tenacity = "^9.1.2"
todoist-api-python = "^2.1.7"
tweepy = "^4.16.0"
uvicorn = { extras = ["standard"], version = "^0.40.0" }
@@ -77,7 +77,7 @@ zerobouncesdk = "^1.1.2"
# NOTE: please insert new dependencies in their alphabetical location
pytest-snapshot = "^0.9.0"
aiofiles = "^24.1.0"
tiktoken = "^0.12.0"
tiktoken = "^0.9.0"
aioclamd = "^1.0.0"
setuptools = "^80.9.0"
gcloud-aio-storage = "^9.5.0"
@@ -95,13 +95,13 @@ black = "^24.10.0"
faker = "^38.2.0"
httpx = "^0.28.1"
isort = "^5.13.2"
poethepoet = "^0.41.0"
poethepoet = "^0.37.0"
pre-commit = "^4.4.0"
pyright = "^1.1.407"
pytest-mock = "^3.15.1"
pytest-watcher = "^0.6.3"
pytest-watcher = "^0.4.2"
requests = "^2.32.5"
ruff = "^0.15.0"
ruff = "^0.14.5"
# NOTE: please insert new dependencies in their alphabetical location
[build-system]
@@ -116,7 +116,6 @@ ws = "backend.ws:main"
scheduler = "backend.scheduler:main"
notification = "backend.notification:main"
executor = "backend.exec:main"
copilot-executor = "backend.copilot.executor.__main__:main"
cli = "backend.cli:main"
format = "linter:format"
lint = "linter:lint"

View File

@@ -9,8 +9,10 @@ from unittest.mock import AsyncMock, patch
import pytest
from backend.copilot.tools.agent_generator import core
from backend.copilot.tools.agent_generator.core import AgentGeneratorNotConfiguredError
from backend.api.features.chat.tools.agent_generator import core
from backend.api.features.chat.tools.agent_generator.core import (
AgentGeneratorNotConfiguredError,
)
class TestServiceNotConfigured:

View File

@@ -9,7 +9,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from backend.copilot.tools.agent_generator import core
from backend.api.features.chat.tools.agent_generator import core
class TestGetLibraryAgentsForGeneration:

View File

@@ -10,7 +10,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
import httpx
import pytest
from backend.copilot.tools.agent_generator import service
from backend.api.features.chat.tools.agent_generator import service
class TestServiceConfiguration:

View File

@@ -158,41 +158,6 @@ services:
max-size: "10m"
max-file: "3"
copilot_executor:
build:
context: ../
dockerfile: autogpt_platform/backend/Dockerfile
target: server
command: ["python", "-m", "backend.copilot.executor"]
develop:
watch:
- path: ./
target: autogpt_platform/backend/
action: rebuild
depends_on:
redis:
condition: service_healthy
rabbitmq:
condition: service_healthy
db:
condition: service_healthy
migrate:
condition: service_completed_successfully
database_manager:
condition: service_started
<<: *backend-env-files
environment:
<<: *backend-env
ports:
- "8008:8008"
networks:
- app-network
logging:
driver: json-file
options:
max-size: "10m"
max-file: "3"
websocket_server:
build:
context: ../

View File

@@ -53,12 +53,6 @@ services:
file: ./docker-compose.platform.yml
service: executor
copilot_executor:
<<: *agpt-services
extends:
file: ./docker-compose.platform.yml
service: copilot_executor
websocket_server:
<<: *agpt-services
extends:
@@ -180,6 +174,5 @@ services:
- deps
- rest_server
- executor
- copilot_executor
- websocket_server
- database_manager

View File

@@ -25,12 +25,8 @@ RUN if [ -f .env.production ]; then \
cp .env.default .env; \
fi
RUN pnpm run generate:api
# Disable source-map generation in Docker builds to halve webpack memory usage.
# Source maps are only useful when SENTRY_AUTH_TOKEN is set (Vercel deploys);
# the Docker image never uploads them, so generating them just wastes RAM.
ENV NEXT_PUBLIC_SOURCEMAPS="false"
# In CI, we want NEXT_PUBLIC_PW_TEST=true during build so Next.js inlines it
RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=8192" pnpm build; else NODE_OPTIONS="--max-old-space-size=8192" pnpm build; fi
RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=4096" pnpm build; else NODE_OPTIONS="--max-old-space-size=4096" pnpm build; fi
# Prod stage - based on NextJS reference Dockerfile https://github.com/vercel/next.js/blob/64271354533ed16da51be5dce85f0dbd15f17517/examples/with-docker/Dockerfile
FROM node:21-alpine AS prod

View File

@@ -1,12 +1,8 @@
import { withSentryConfig } from "@sentry/nextjs";
// Allow Docker builds to skip source-map generation (halves memory usage).
// Defaults to true so Vercel/local builds are unaffected.
const enableSourceMaps = process.env.NEXT_PUBLIC_SOURCEMAPS !== "false";
/** @type {import('next').NextConfig} */
const nextConfig = {
productionBrowserSourceMaps: enableSourceMaps,
productionBrowserSourceMaps: true,
// Externalize OpenTelemetry packages to fix Turbopack HMR issues
serverExternalPackages: [
"@opentelemetry/instrumentation",
@@ -18,37 +14,9 @@ const nextConfig = {
serverActions: {
bodySizeLimit: "256mb",
},
// Increase body size limit for API routes (file uploads) - 256MB to match backend limit
proxyClientMaxBodySize: "256mb",
middlewareClientMaxBodySize: "256mb",
// Limit parallel webpack workers to reduce peak memory during builds.
cpus: 2,
},
// Work around cssnano "Invalid array length" bug in Next.js's bundled
// cssnano-simple comment parser when processing very large CSS chunks.
// CSS is still bundled correctly; gzip handles most of the size savings anyway.
webpack: (config, { dev }) => {
if (!dev) {
// Next.js adds CssMinimizerPlugin internally (after user config), so we
// can't filter it from config.plugins. Instead, intercept the webpack
// compilation hooks and replace the buggy plugin's tap with a no-op.
config.plugins.push({
apply(compiler) {
compiler.hooks.compilation.tap(
"DisableCssMinimizer",
(compilation) => {
compilation.hooks.processAssets.intercept({
register: (tap) => {
if (tap.name === "CssMinimizerPlugin") {
return { ...tap, fn: async () => {} };
}
return tap;
},
});
},
);
},
});
}
return config;
},
images: {
domains: [
@@ -86,16 +54,9 @@ const nextConfig = {
transpilePackages: ["geist"],
};
// Only run the Sentry webpack plugin when we can actually upload source maps
// (i.e. on Vercel with SENTRY_AUTH_TOKEN set). The Sentry *runtime* SDK
// (imported in app code) still captures errors without the plugin.
// Skipping the plugin saves ~1 GB of peak memory during `next build`.
const skipSentryPlugin =
process.env.NODE_ENV !== "production" ||
!enableSourceMaps ||
!process.env.SENTRY_AUTH_TOKEN;
const isDevelopmentBuild = process.env.NODE_ENV !== "production";
export default skipSentryPlugin
export default isDevelopmentBuild
? nextConfig
: withSentryConfig(nextConfig, {
// For all available options, see:
@@ -135,7 +96,7 @@ export default skipSentryPlugin
// This helps Sentry with sourcemaps... https://docs.sentry.io/platforms/javascript/guides/nextjs/sourcemaps/
sourcemaps: {
disable: !enableSourceMaps,
disable: false,
assets: [".next/**/*.js", ".next/**/*.js.map"],
ignore: ["**/node_modules/**"],
deleteSourcemapsAfterUpload: false, // Source is public anyway :)

View File

@@ -7,7 +7,7 @@
},
"scripts": {
"dev": "pnpm run generate:api:force && next dev --turbo",
"build": "cross-env NODE_OPTIONS=--max-old-space-size=16384 next build",
"build": "next build",
"start": "next start",
"start:standalone": "cd .next/standalone && node server.js",
"lint": "next lint && prettier --check .",
@@ -30,7 +30,6 @@
"defaults"
],
"dependencies": {
"@ai-sdk/react": "3.0.61",
"@faker-js/faker": "10.0.0",
"@hookform/resolvers": "5.2.2",
"@next/third-parties": "15.4.6",
@@ -61,10 +60,6 @@
"@rjsf/utils": "6.1.2",
"@rjsf/validator-ajv8": "6.1.2",
"@sentry/nextjs": "10.27.0",
"@streamdown/cjk": "1.0.1",
"@streamdown/code": "1.0.1",
"@streamdown/math": "1.0.1",
"@streamdown/mermaid": "1.0.1",
"@supabase/ssr": "0.7.0",
"@supabase/supabase-js": "2.78.0",
"@tanstack/react-query": "5.90.6",
@@ -73,7 +68,6 @@
"@vercel/analytics": "1.5.0",
"@vercel/speed-insights": "1.2.0",
"@xyflow/react": "12.9.2",
"ai": "6.0.59",
"boring-avatars": "1.11.2",
"class-variance-authority": "0.7.1",
"clsx": "2.1.1",
@@ -93,6 +87,7 @@
"launchdarkly-react-client-sdk": "3.9.0",
"lodash": "4.17.21",
"lucide-react": "0.552.0",
"moment": "2.30.1",
"next": "15.4.10",
"next-themes": "0.4.6",
"nuqs": "2.7.2",
@@ -107,7 +102,7 @@
"react-markdown": "9.0.3",
"react-modal": "3.16.3",
"react-shepherd": "6.1.9",
"react-window": "2.2.0",
"react-window": "1.8.11",
"recharts": "3.3.0",
"rehype-autolink-headings": "7.1.0",
"rehype-highlight": "7.0.2",
@@ -117,11 +112,9 @@
"remark-math": "6.0.0",
"shepherd.js": "14.5.1",
"sonner": "2.0.7",
"streamdown": "2.1.0",
"tailwind-merge": "2.6.0",
"tailwind-scrollbar": "3.1.0",
"tailwindcss-animate": "1.0.7",
"use-stick-to-bottom": "1.1.2",
"uuid": "11.1.0",
"vaul": "1.1.2",
"zod": "3.25.76",
@@ -147,7 +140,7 @@
"@types/react": "18.3.17",
"@types/react-dom": "18.3.5",
"@types/react-modal": "3.16.3",
"@types/react-window": "2.0.0",
"@types/react-window": "1.8.8",
"@vitejs/plugin-react": "5.1.2",
"axe-playwright": "2.2.2",
"chromatic": "13.3.3",
@@ -179,8 +172,7 @@
},
"pnpm": {
"overrides": {
"@opentelemetry/instrumentation": "0.209.0",
"lodash-es": "4.17.23"
"@opentelemetry/instrumentation": "0.209.0"
}
},
"packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"

File diff suppressed because it is too large Load Diff

View File

@@ -1,4 +1,4 @@
import debounce from "lodash/debounce";
import { debounce } from "lodash";
import { useCallback, useEffect, useRef, useState } from "react";
import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
import { getQueryClient } from "@/lib/react-query/queryClient";

View File

@@ -70,10 +70,10 @@ export const HorizontalScroll: React.FC<HorizontalScrollAreaProps> = ({
{children}
</div>
{canScrollLeft && (
<div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-background via-background/80 to-background/0" />
<div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-white via-white/80 to-white/0" />
)}
{canScrollRight && (
<div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-background via-background/80 to-background/0" />
<div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-white via-white/80 to-white/0" />
)}
{canScrollLeft && (
<button

View File

@@ -1,76 +0,0 @@
"use client";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { SidebarProvider } from "@/components/ui/sidebar";
import { ChatContainer } from "./components/ChatContainer/ChatContainer";
import { ChatSidebar } from "./components/ChatSidebar/ChatSidebar";
import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
import { MobileHeader } from "./components/MobileHeader/MobileHeader";
import { useCopilotPage } from "./useCopilotPage";
export function CopilotPage() {
const {
sessionId,
messages,
status,
error,
stop,
createSession,
onSend,
isLoadingSession,
isCreatingSession,
isUserLoading,
isLoggedIn,
// Mobile drawer
isMobile,
isDrawerOpen,
sessions,
isLoadingSessions,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
handleSelectSession,
handleNewChat,
} = useCopilotPage();
if (isUserLoading || !isLoggedIn) {
return <LoadingSpinner size="large" cover />;
}
return (
<SidebarProvider
defaultOpen={true}
className="h-[calc(100vh-72px)] min-h-0"
>
{!isMobile && <ChatSidebar />}
<div className="relative flex h-full w-full flex-col overflow-hidden bg-[#f8f8f9] px-0">
{isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
<div className="flex-1 overflow-hidden">
<ChatContainer
messages={messages}
status={status}
error={error}
sessionId={sessionId}
isLoadingSession={isLoadingSession}
isCreatingSession={isCreatingSession}
onCreateSession={createSession}
onSend={onSend}
onStop={stop}
/>
</div>
</div>
{isMobile && (
<MobileDrawer
isOpen={isDrawerOpen}
sessions={sessions}
currentSessionId={sessionId}
isLoading={isLoadingSessions}
onSelectSession={handleSelectSession}
onNewChat={handleNewChat}
onClose={handleCloseDrawer}
onOpenChange={handleDrawerOpenChange}
/>
)}
</SidebarProvider>
);
}

View File

@@ -1,74 +0,0 @@
"use client";
import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
import { UIDataTypes, UIMessage, UITools } from "ai";
import { LayoutGroup, motion } from "framer-motion";
import { ChatMessagesContainer } from "../ChatMessagesContainer/ChatMessagesContainer";
import { CopilotChatActionsProvider } from "../CopilotChatActionsProvider/CopilotChatActionsProvider";
import { EmptySession } from "../EmptySession/EmptySession";
export interface ChatContainerProps {
messages: UIMessage<unknown, UIDataTypes, UITools>[];
status: string;
error: Error | undefined;
sessionId: string | null;
isLoadingSession: boolean;
isCreatingSession: boolean;
onCreateSession: () => void | Promise<string>;
onSend: (message: string) => void | Promise<void>;
onStop: () => void;
}
export const ChatContainer = ({
messages,
status,
error,
sessionId,
isLoadingSession,
isCreatingSession,
onCreateSession,
onSend,
onStop,
}: ChatContainerProps) => {
const inputLayoutId = "copilot-2-chat-input";
return (
<CopilotChatActionsProvider onSend={onSend}>
<LayoutGroup id="copilot-2-chat-layout">
<div className="flex h-full min-h-0 w-full flex-col bg-[#f8f8f9] px-2 lg:px-0">
{sessionId ? (
<div className="mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col">
<ChatMessagesContainer
messages={messages}
status={status}
error={error}
isLoading={isLoadingSession}
/>
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.3 }}
className="relative px-3 pb-2 pt-2"
>
<div className="pointer-events-none absolute left-0 right-0 top-[-18px] z-10 h-6 bg-gradient-to-b from-transparent to-[#f8f8f9]" />
<ChatInput
inputId="chat-input-session"
onSend={onSend}
disabled={status === "streaming"}
isStreaming={status === "streaming"}
onStop={onStop}
placeholder="What else can I help with?"
/>
</motion.div>
</div>
) : (
<EmptySession
inputLayoutId={inputLayoutId}
isCreatingSession={isCreatingSession}
onCreateSession={onCreateSession}
onSend={onSend}
/>
)}
</div>
</LayoutGroup>
</CopilotChatActionsProvider>
);
};

View File

@@ -1,274 +0,0 @@
import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
import {
Conversation,
ConversationContent,
ConversationScrollButton,
} from "@/components/ai-elements/conversation";
import {
Message,
MessageContent,
MessageResponse,
} from "@/components/ai-elements/message";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
import { useEffect, useState } from "react";
import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
import { FindAgentsTool } from "../../tools/FindAgents/FindAgents";
import { FindBlocksTool } from "../../tools/FindBlocks/FindBlocks";
import { RunAgentTool } from "../../tools/RunAgent/RunAgent";
import { RunBlockTool } from "../../tools/RunBlock/RunBlock";
import { SearchDocsTool } from "../../tools/SearchDocs/SearchDocs";
import { ViewAgentOutputTool } from "../../tools/ViewAgentOutput/ViewAgentOutput";
// ---------------------------------------------------------------------------
// Workspace media support
// ---------------------------------------------------------------------------
/**
* Resolve workspace:// URLs in markdown text to proxy download URLs.
* Detects MIME type from the hash fragment (e.g. workspace://id#video/mp4)
* and prefixes the alt text with "video:" so the custom img component can
* render a <video> element instead.
*/
function resolveWorkspaceUrls(text: string): string {
return text.replace(
/!\[([^\]]*)\]\(workspace:\/\/([^)#\s]+)(?:#([^)\s]*))?\)/g,
(_match, alt: string, fileId: string, mimeHint?: string) => {
const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
const url = `/api/proxy${apiPath}`;
if (mimeHint?.startsWith("video/")) {
return `![video:${alt || "Video"}](${url})`;
}
return `![${alt || "Image"}](${url})`;
},
);
}
/**
* Custom img component for Streamdown that renders <video> elements
* for workspace video files (detected via "video:" alt-text prefix).
* Falls back to <video> when an <img> fails to load for workspace files.
*/
function WorkspaceMediaImage(props: React.JSX.IntrinsicElements["img"]) {
const { src, alt, ...rest } = props;
const [imgFailed, setImgFailed] = useState(false);
const isWorkspace = src?.includes("/workspace/files/") ?? false;
if (!src) return null;
if (alt?.startsWith("video:") || (imgFailed && isWorkspace)) {
return (
<span className="my-2 inline-block">
<video
controls
className="h-auto max-w-full rounded-md border border-zinc-200"
preload="metadata"
>
<source src={src} />
Your browser does not support the video tag.
</video>
</span>
);
}
return (
// eslint-disable-next-line @next/next/no-img-element
<img
src={src}
alt={alt || "Image"}
className="h-auto max-w-full rounded-md border border-zinc-200"
loading="lazy"
onError={() => {
if (isWorkspace) setImgFailed(true);
}}
{...rest}
/>
);
}
/** Stable components override for Streamdown (avoids re-creating on every render). */
const STREAMDOWN_COMPONENTS = { img: WorkspaceMediaImage };
const THINKING_PHRASES = [
"Thinking...",
"Considering this...",
"Working through this...",
"Analyzing your request...",
"Reasoning...",
"Looking into it...",
"Processing your request...",
"Mulling this over...",
"Piecing it together...",
"On it...",
];
function getRandomPhrase() {
return THINKING_PHRASES[Math.floor(Math.random() * THINKING_PHRASES.length)];
}
interface ChatMessagesContainerProps {
messages: UIMessage<unknown, UIDataTypes, UITools>[];
status: string;
error: Error | undefined;
isLoading: boolean;
}
export const ChatMessagesContainer = ({
messages,
status,
error,
isLoading,
}: ChatMessagesContainerProps) => {
const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
useEffect(() => {
if (status === "submitted") {
setThinkingPhrase(getRandomPhrase());
}
}, [status]);
const lastMessage = messages[messages.length - 1];
const lastAssistantHasVisibleContent =
lastMessage?.role === "assistant" &&
lastMessage.parts.some(
(p) =>
(p.type === "text" && p.text.trim().length > 0) ||
p.type.startsWith("tool-"),
);
const showThinking =
status === "submitted" ||
(status === "streaming" && !lastAssistantHasVisibleContent);
return (
<Conversation className="min-h-0 flex-1">
<ConversationContent className="gap-6 px-3 py-6">
{isLoading && messages.length === 0 && (
<div className="flex flex-1 items-center justify-center">
<LoadingSpinner size="large" className="text-neutral-400" />
</div>
)}
{messages.map((message, messageIndex) => {
const isLastAssistant =
messageIndex === messages.length - 1 &&
message.role === "assistant";
const messageHasVisibleContent = message.parts.some(
(p) =>
(p.type === "text" && p.text.trim().length > 0) ||
p.type.startsWith("tool-"),
);
return (
<Message from={message.role} key={message.id}>
<MessageContent
className={
"text-[1rem] leading-relaxed " +
"group-[.is-user]:rounded-xl group-[.is-user]:bg-purple-100 group-[.is-user]:px-3 group-[.is-user]:py-2.5 group-[.is-user]:text-slate-900 group-[.is-user]:[border-bottom-right-radius:0] " +
"group-[.is-assistant]:bg-transparent group-[.is-assistant]:text-slate-900"
}
>
{message.parts.map((part, i) => {
switch (part.type) {
case "text":
return (
<MessageResponse
key={`${message.id}-${i}`}
components={STREAMDOWN_COMPONENTS}
>
{resolveWorkspaceUrls(part.text)}
</MessageResponse>
);
case "tool-find_block":
return (
<FindBlocksTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-find_agent":
case "tool-find_library_agent":
return (
<FindAgentsTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-search_docs":
case "tool-get_doc_page":
return (
<SearchDocsTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-run_block":
return (
<RunBlockTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-run_agent":
case "tool-schedule_agent":
return (
<RunAgentTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-create_agent":
return (
<CreateAgentTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-edit_agent":
return (
<EditAgentTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-view_agent_output":
return (
<ViewAgentOutputTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
default:
return null;
}
})}
{isLastAssistant &&
!messageHasVisibleContent &&
showThinking && (
<span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
{thinkingPhrase}
</span>
)}
</MessageContent>
</Message>
);
})}
{showThinking && lastMessage?.role !== "assistant" && (
<Message from="assistant">
<MessageContent className="text-[1rem] leading-relaxed">
<span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
{thinkingPhrase}
</span>
</MessageContent>
</Message>
)}
{error && (
<div className="rounded-lg bg-red-50 p-3 text-red-600">
Error: {error.message}
</div>
)}
</ConversationContent>
<ConversationScrollButton />
</Conversation>
);
};

View File

@@ -1,188 +0,0 @@
"use client";
import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
import { Button } from "@/components/atoms/Button/Button";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { Text } from "@/components/atoms/Text/Text";
import {
Sidebar,
SidebarContent,
SidebarFooter,
SidebarHeader,
SidebarTrigger,
useSidebar,
} from "@/components/ui/sidebar";
import { cn } from "@/lib/utils";
import { PlusCircleIcon, PlusIcon } from "@phosphor-icons/react";
import { motion } from "framer-motion";
import { parseAsString, useQueryState } from "nuqs";
export function ChatSidebar() {
const { state } = useSidebar();
const isCollapsed = state === "collapsed";
const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
const { data: sessionsResponse, isLoading: isLoadingSessions } =
useGetV2ListSessions({ limit: 50 });
const sessions =
sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
function handleNewChat() {
setSessionId(null);
}
function handleSelectSession(id: string) {
setSessionId(id);
}
function formatDate(dateString: string) {
const date = new Date(dateString);
const now = new Date();
const diffMs = now.getTime() - date.getTime();
const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
if (diffDays === 0) return "Today";
if (diffDays === 1) return "Yesterday";
if (diffDays < 7) return `${diffDays} days ago`;
const day = date.getDate();
const ordinal =
day % 10 === 1 && day !== 11
? "st"
: day % 10 === 2 && day !== 12
? "nd"
: day % 10 === 3 && day !== 13
? "rd"
: "th";
const month = date.toLocaleDateString("en-US", { month: "short" });
const year = date.getFullYear();
return `${day}${ordinal} ${month} ${year}`;
}
return (
<Sidebar
variant="inset"
collapsible="icon"
className="!top-[50px] !h-[calc(100vh-50px)] border-r border-zinc-100 px-0"
>
{isCollapsed && (
<SidebarHeader
className={cn(
"flex",
isCollapsed
? "flex-row items-center justify-between gap-y-4 md:flex-col md:items-start md:justify-start"
: "flex-row items-center justify-between",
)}
>
<motion.div
key={isCollapsed ? "header-collapsed" : "header-expanded"}
className="flex flex-col items-center gap-3 pt-4"
initial={{ opacity: 0, filter: "blur(3px)" }}
animate={{ opacity: 1, filter: "blur(0px)" }}
transition={{ type: "spring", bounce: 0.2 }}
>
<div className="flex flex-col items-center gap-2">
<SidebarTrigger />
<Button
variant="ghost"
onClick={handleNewChat}
style={{ minWidth: "auto", width: "auto" }}
>
<PlusCircleIcon className="!size-5" />
<span className="sr-only">New Chat</span>
</Button>
</div>
</motion.div>
</SidebarHeader>
)}
<SidebarContent className="gap-4 overflow-y-auto px-4 py-4 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
{!isCollapsed && (
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.2, delay: 0.1 }}
className="flex items-center justify-between px-3"
>
<Text variant="h3" size="body-medium">
Your chats
</Text>
<div className="relative left-6">
<SidebarTrigger />
</div>
</motion.div>
)}
{!isCollapsed && (
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.2, delay: 0.15 }}
className="mt-4 flex flex-col gap-1"
>
{isLoadingSessions ? (
<div className="flex items-center justify-center py-4">
<LoadingSpinner size="small" className="text-neutral-400" />
</div>
) : sessions.length === 0 ? (
<p className="py-4 text-center text-sm text-neutral-500">
No conversations yet
</p>
) : (
sessions.map((session) => (
<button
key={session.id}
onClick={() => handleSelectSession(session.id)}
className={cn(
"w-full rounded-lg px-3 py-2.5 text-left transition-colors",
session.id === sessionId
? "bg-zinc-100"
: "hover:bg-zinc-50",
)}
>
<div className="flex min-w-0 max-w-full flex-col overflow-hidden">
<div className="min-w-0 max-w-full">
<Text
variant="body"
className={cn(
"truncate font-normal",
session.id === sessionId
? "text-zinc-600"
: "text-zinc-800",
)}
>
{session.title || `Untitled chat`}
</Text>
</div>
<Text variant="small" className="text-neutral-400">
{formatDate(session.updated_at)}
</Text>
</div>
</button>
))
)}
</motion.div>
)}
</SidebarContent>
{!isCollapsed && sessionId && (
<SidebarFooter className="shrink-0 bg-zinc-50 p-3 pb-1 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.2, delay: 0.2 }}
>
<Button
variant="primary"
size="small"
onClick={handleNewChat}
className="w-full"
leftIcon={<PlusIcon className="h-4 w-4" weight="bold" />}
>
New Chat
</Button>
</motion.div>
</SidebarFooter>
)}
</Sidebar>
);
}

View File

@@ -1,16 +0,0 @@
"use client";
import { CopilotChatActionsContext } from "./useCopilotChatActions";
interface Props {
onSend: (message: string) => void | Promise<void>;
children: React.ReactNode;
}
export function CopilotChatActionsProvider({ onSend, children }: Props) {
return (
<CopilotChatActionsContext.Provider value={{ onSend }}>
{children}
</CopilotChatActionsContext.Provider>
);
}

View File

@@ -1,23 +0,0 @@
"use client";
import { createContext, useContext } from "react";
interface CopilotChatActions {
onSend: (message: string) => void | Promise<void>;
}
const CopilotChatActionsContext = createContext<CopilotChatActions | null>(
null,
);
export function useCopilotChatActions(): CopilotChatActions {
const ctx = useContext(CopilotChatActionsContext);
if (!ctx) {
throw new Error(
"useCopilotChatActions must be used within CopilotChatActionsProvider",
);
}
return ctx;
}
export { CopilotChatActionsContext };

View File

@@ -0,0 +1,99 @@
"use client";
import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
import { Text } from "@/components/atoms/Text/Text";
import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
import type { ReactNode } from "react";
import { DesktopSidebar } from "./components/DesktopSidebar/DesktopSidebar";
import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
import { MobileHeader } from "./components/MobileHeader/MobileHeader";
import { useCopilotShell } from "./useCopilotShell";
interface Props {
children: ReactNode;
}
export function CopilotShell({ children }: Props) {
const {
isMobile,
isDrawerOpen,
isLoading,
isCreatingSession,
isLoggedIn,
hasActiveSession,
sessions,
currentSessionId,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
handleNewChatClick,
handleSessionClick,
hasNextPage,
isFetchingNextPage,
fetchNextPage,
} = useCopilotShell();
if (!isLoggedIn) {
return (
<div className="flex h-full items-center justify-center">
<ChatLoader />
</div>
);
}
return (
<div
className="flex overflow-hidden bg-[#EFEFF0]"
style={{ height: `calc(100vh - ${NAVBAR_HEIGHT_PX}px)` }}
>
{!isMobile && (
<DesktopSidebar
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={handleSessionClick}
onFetchNextPage={fetchNextPage}
onNewChat={handleNewChatClick}
hasActiveSession={Boolean(hasActiveSession)}
/>
)}
<div className="relative flex min-h-0 flex-1 flex-col">
{isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
<div className="flex min-h-0 flex-1 flex-col">
{isCreatingSession ? (
<div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
<div className="flex flex-col items-center gap-4">
<ChatLoader />
<Text variant="body" className="text-zinc-500">
Creating your chat...
</Text>
</div>
</div>
) : (
children
)}
</div>
</div>
{isMobile && (
<MobileDrawer
isOpen={isDrawerOpen}
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={handleSessionClick}
onFetchNextPage={fetchNextPage}
onNewChat={handleNewChatClick}
onClose={handleCloseDrawer}
onOpenChange={handleDrawerOpenChange}
hasActiveSession={Boolean(hasActiveSession)}
/>
)}
</div>
);
}

View File

@@ -0,0 +1,70 @@
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { Button } from "@/components/atoms/Button/Button";
import { Text } from "@/components/atoms/Text/Text";
import { scrollbarStyles } from "@/components/styles/scrollbars";
import { cn } from "@/lib/utils";
import { Plus } from "@phosphor-icons/react";
import { SessionsList } from "../SessionsList/SessionsList";
interface Props {
sessions: SessionSummaryResponse[];
currentSessionId: string | null;
isLoading: boolean;
hasNextPage: boolean;
isFetchingNextPage: boolean;
onSelectSession: (sessionId: string) => void;
onFetchNextPage: () => void;
onNewChat: () => void;
hasActiveSession: boolean;
}
export function DesktopSidebar({
sessions,
currentSessionId,
isLoading,
hasNextPage,
isFetchingNextPage,
onSelectSession,
onFetchNextPage,
onNewChat,
hasActiveSession,
}: Props) {
return (
<aside className="flex h-full w-80 flex-col border-r border-zinc-100 bg-zinc-50">
<div className="shrink-0 px-6 py-4">
<Text variant="h3" size="body-medium">
Your chats
</Text>
</div>
<div
className={cn(
"flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
scrollbarStyles,
)}
>
<SessionsList
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={onSelectSession}
onFetchNextPage={onFetchNextPage}
/>
</div>
{hasActiveSession && (
<div className="shrink-0 bg-zinc-50 p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
<Button
variant="primary"
size="small"
onClick={onNewChat}
className="w-full"
leftIcon={<Plus width="1rem" height="1rem" />}
>
New Chat
</Button>
</div>
)}
</aside>
);
}

View File

@@ -0,0 +1,91 @@
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { Button } from "@/components/atoms/Button/Button";
import { scrollbarStyles } from "@/components/styles/scrollbars";
import { cn } from "@/lib/utils";
import { PlusIcon, X } from "@phosphor-icons/react";
import { Drawer } from "vaul";
import { SessionsList } from "../SessionsList/SessionsList";
interface Props {
isOpen: boolean;
sessions: SessionSummaryResponse[];
currentSessionId: string | null;
isLoading: boolean;
hasNextPage: boolean;
isFetchingNextPage: boolean;
onSelectSession: (sessionId: string) => void;
onFetchNextPage: () => void;
onNewChat: () => void;
onClose: () => void;
onOpenChange: (open: boolean) => void;
hasActiveSession: boolean;
}
export function MobileDrawer({
isOpen,
sessions,
currentSessionId,
isLoading,
hasNextPage,
isFetchingNextPage,
onSelectSession,
onFetchNextPage,
onNewChat,
onClose,
onOpenChange,
hasActiveSession,
}: Props) {
return (
<Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
<Drawer.Portal>
<Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
<Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
<div className="shrink-0 border-b border-zinc-200 p-4">
<div className="flex items-center justify-between">
<Drawer.Title className="text-lg font-semibold text-zinc-800">
Your chats
</Drawer.Title>
<Button
variant="icon"
size="icon"
aria-label="Close sessions"
onClick={onClose}
>
<X width="1.25rem" height="1.25rem" />
</Button>
</div>
</div>
<div
className={cn(
"flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
scrollbarStyles,
)}
>
<SessionsList
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={onSelectSession}
onFetchNextPage={onFetchNextPage}
/>
</div>
{hasActiveSession && (
<div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
<Button
variant="primary"
size="small"
onClick={onNewChat}
className="w-full"
leftIcon={<PlusIcon width="1rem" height="1rem" />}
>
New Chat
</Button>
</div>
)}
</Drawer.Content>
</Drawer.Portal>
</Drawer.Root>
);
}

View File

@@ -0,0 +1,24 @@
import { useState } from "react";
export function useMobileDrawer() {
const [isDrawerOpen, setIsDrawerOpen] = useState(false);
const handleOpenDrawer = () => {
setIsDrawerOpen(true);
};
const handleCloseDrawer = () => {
setIsDrawerOpen(false);
};
const handleDrawerOpenChange = (open: boolean) => {
setIsDrawerOpen(open);
};
return {
isDrawerOpen,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
};
}

View File

@@ -0,0 +1,80 @@
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { Skeleton } from "@/components/__legacy__/ui/skeleton";
import { Text } from "@/components/atoms/Text/Text";
import { InfiniteList } from "@/components/molecules/InfiniteList/InfiniteList";
import { cn } from "@/lib/utils";
import { getSessionTitle } from "../../helpers";
interface Props {
sessions: SessionSummaryResponse[];
currentSessionId: string | null;
isLoading: boolean;
hasNextPage: boolean;
isFetchingNextPage: boolean;
onSelectSession: (sessionId: string) => void;
onFetchNextPage: () => void;
}
export function SessionsList({
sessions,
currentSessionId,
isLoading,
hasNextPage,
isFetchingNextPage,
onSelectSession,
onFetchNextPage,
}: Props) {
if (isLoading) {
return (
<div className="space-y-1">
{Array.from({ length: 5 }).map((_, i) => (
<div key={i} className="rounded-lg px-3 py-2.5">
<Skeleton className="h-5 w-full" />
</div>
))}
</div>
);
}
if (sessions.length === 0) {
return (
<div className="flex h-full items-center justify-center">
<Text variant="body" className="text-zinc-500">
You don&apos;t have previous chats
</Text>
</div>
);
}
return (
<InfiniteList
items={sessions}
hasMore={hasNextPage}
isFetchingMore={isFetchingNextPage}
onEndReached={onFetchNextPage}
className="space-y-1"
renderItem={(session) => {
const isActive = session.id === currentSessionId;
return (
<button
onClick={() => onSelectSession(session.id)}
className={cn(
"w-full rounded-lg px-3 py-2.5 text-left transition-colors",
isActive ? "bg-zinc-100" : "hover:bg-zinc-50",
)}
>
<Text
variant="body"
className={cn(
"font-normal",
isActive ? "text-zinc-600" : "text-zinc-800",
)}
>
{getSessionTitle(session)}
</Text>
</button>
);
}}
/>
);
}

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import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { okData } from "@/app/api/helpers";
import { useEffect, useState } from "react";
const PAGE_SIZE = 50;
export interface UseSessionsPaginationArgs {
enabled: boolean;
}
export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
const [offset, setOffset] = useState(0);
const [accumulatedSessions, setAccumulatedSessions] = useState<
SessionSummaryResponse[]
>([]);
const [totalCount, setTotalCount] = useState<number | null>(null);
const { data, isLoading, isFetching, isError } = useGetV2ListSessions(
{ limit: PAGE_SIZE, offset },
{
query: {
enabled: enabled && offset >= 0,
},
},
);
useEffect(() => {
const responseData = okData(data);
if (responseData) {
const newSessions = responseData.sessions;
const total = responseData.total;
setTotalCount(total);
if (offset === 0) {
setAccumulatedSessions(newSessions);
} else {
setAccumulatedSessions((prev) => [...prev, ...newSessions]);
}
} else if (!enabled) {
setAccumulatedSessions([]);
setTotalCount(null);
}
}, [data, offset, enabled]);
const hasNextPage =
totalCount !== null && accumulatedSessions.length < totalCount;
const areAllSessionsLoaded =
totalCount !== null &&
accumulatedSessions.length >= totalCount &&
!isFetching &&
!isLoading;
useEffect(() => {
if (
hasNextPage &&
!isFetching &&
!isLoading &&
!isError &&
totalCount !== null
) {
setOffset((prev) => prev + PAGE_SIZE);
}
}, [hasNextPage, isFetching, isLoading, isError, totalCount]);
const fetchNextPage = () => {
if (hasNextPage && !isFetching) {
setOffset((prev) => prev + PAGE_SIZE);
}
};
const reset = () => {
// Only reset the offset - keep existing sessions visible during refetch
// The effect will replace sessions when new data arrives at offset 0
setOffset(0);
};
return {
sessions: accumulatedSessions,
isLoading,
isFetching,
hasNextPage,
areAllSessionsLoaded,
totalCount,
fetchNextPage,
reset,
};
}

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import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { format, formatDistanceToNow, isToday } from "date-fns";
export function convertSessionDetailToSummary(session: SessionDetailResponse) {
return {
id: session.id,
created_at: session.created_at,
updated_at: session.updated_at,
title: undefined,
};
}
export function filterVisibleSessions(sessions: SessionSummaryResponse[]) {
const fiveMinutesAgo = Date.now() - 5 * 60 * 1000;
return sessions.filter((session) => {
const hasBeenUpdated = session.updated_at !== session.created_at;
if (hasBeenUpdated) return true;
const isRecentlyCreated =
new Date(session.created_at).getTime() > fiveMinutesAgo;
return isRecentlyCreated;
});
}
export function getSessionTitle(session: SessionSummaryResponse) {
if (session.title) return session.title;
const isNewSession = session.updated_at === session.created_at;
if (isNewSession) {
const createdDate = new Date(session.created_at);
if (isToday(createdDate)) {
return "Today";
}
return format(createdDate, "MMM d, yyyy");
}
return "Untitled Chat";
}
export function getSessionUpdatedLabel(session: SessionSummaryResponse) {
if (!session.updated_at) return "";
return formatDistanceToNow(new Date(session.updated_at), { addSuffix: true });
}
export function mergeCurrentSessionIntoList(
accumulatedSessions: SessionSummaryResponse[],
currentSessionId: string | null,
currentSessionData: SessionDetailResponse | null | undefined,
recentlyCreatedSessions?: Map<string, SessionSummaryResponse>,
) {
const filteredSessions: SessionSummaryResponse[] = [];
const addedIds = new Set<string>();
if (accumulatedSessions.length > 0) {
const visibleSessions = filterVisibleSessions(accumulatedSessions);
if (currentSessionId) {
const currentInAll = accumulatedSessions.find(
(s) => s.id === currentSessionId,
);
if (currentInAll) {
const isInVisible = visibleSessions.some(
(s) => s.id === currentSessionId,
);
if (!isInVisible) {
filteredSessions.push(currentInAll);
addedIds.add(currentInAll.id);
}
}
}
for (const session of visibleSessions) {
if (!addedIds.has(session.id)) {
filteredSessions.push(session);
addedIds.add(session.id);
}
}
}
if (currentSessionId && currentSessionData) {
if (!addedIds.has(currentSessionId)) {
const summarySession = convertSessionDetailToSummary(currentSessionData);
filteredSessions.unshift(summarySession);
addedIds.add(currentSessionId);
}
}
if (recentlyCreatedSessions) {
for (const [sessionId, sessionData] of recentlyCreatedSessions) {
if (!addedIds.has(sessionId)) {
filteredSessions.unshift(sessionData);
addedIds.add(sessionId);
}
}
}
return filteredSessions;
}
export function getCurrentSessionId(searchParams: URLSearchParams) {
return searchParams.get("sessionId");
}

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"use client";
import {
getGetV2GetSessionQueryKey,
getGetV2ListSessionsQueryKey,
useGetV2GetSession,
} from "@/app/api/__generated__/endpoints/chat/chat";
import { okData } from "@/app/api/helpers";
import { useChatStore } from "@/components/contextual/Chat/chat-store";
import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
import { useQueryClient } from "@tanstack/react-query";
import { usePathname, useSearchParams } from "next/navigation";
import { useCopilotStore } from "../../copilot-page-store";
import { useCopilotSessionId } from "../../useCopilotSessionId";
import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
import { getCurrentSessionId } from "./helpers";
import { useShellSessionList } from "./useShellSessionList";
export function useCopilotShell() {
const pathname = usePathname();
const searchParams = useSearchParams();
const queryClient = useQueryClient();
const breakpoint = useBreakpoint();
const { isLoggedIn } = useSupabase();
const isMobile =
breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
const isOnHomepage = pathname === "/copilot";
const paramSessionId = searchParams.get("sessionId");
const {
isDrawerOpen,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
} = useMobileDrawer();
const paginationEnabled = !isMobile || isDrawerOpen || !!paramSessionId;
const currentSessionId = getCurrentSessionId(searchParams);
const { data: currentSessionData } = useGetV2GetSession(
currentSessionId || "",
{
query: {
enabled: !!currentSessionId,
select: okData,
},
},
);
const {
sessions,
isLoading,
isSessionsFetching,
hasNextPage,
fetchNextPage,
resetPagination,
recentlyCreatedSessionsRef,
} = useShellSessionList({
paginationEnabled,
currentSessionId,
currentSessionData,
isOnHomepage,
paramSessionId,
});
const stopStream = useChatStore((s) => s.stopStream);
const isCreatingSession = useCopilotStore((s) => s.isCreatingSession);
function handleSessionClick(sessionId: string) {
if (sessionId === currentSessionId) return;
// Stop current stream - SSE reconnection allows resuming later
if (currentSessionId) {
stopStream(currentSessionId);
}
if (recentlyCreatedSessionsRef.current.has(sessionId)) {
queryClient.invalidateQueries({
queryKey: getGetV2GetSessionQueryKey(sessionId),
});
}
setUrlSessionId(sessionId, { shallow: false });
if (isMobile) handleCloseDrawer();
}
function handleNewChatClick() {
// Stop current stream - SSE reconnection allows resuming later
if (currentSessionId) {
stopStream(currentSessionId);
}
resetPagination();
queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
setUrlSessionId(null, { shallow: false });
if (isMobile) handleCloseDrawer();
}
return {
isMobile,
isDrawerOpen,
isLoggedIn,
hasActiveSession:
Boolean(currentSessionId) && (!isOnHomepage || Boolean(paramSessionId)),
isLoading: isLoading || isCreatingSession,
isCreatingSession,
sessions,
currentSessionId: urlSessionId,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
handleNewChatClick,
handleSessionClick,
hasNextPage,
isFetchingNextPage: isSessionsFetching,
fetchNextPage,
};
}

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import { getGetV2ListSessionsQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { useChatStore } from "@/components/contextual/Chat/chat-store";
import { useQueryClient } from "@tanstack/react-query";
import { useEffect, useMemo, useRef } from "react";
import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
import {
convertSessionDetailToSummary,
filterVisibleSessions,
mergeCurrentSessionIntoList,
} from "./helpers";
interface UseShellSessionListArgs {
paginationEnabled: boolean;
currentSessionId: string | null;
currentSessionData: SessionDetailResponse | null | undefined;
isOnHomepage: boolean;
paramSessionId: string | null;
}
export function useShellSessionList({
paginationEnabled,
currentSessionId,
currentSessionData,
isOnHomepage,
paramSessionId,
}: UseShellSessionListArgs) {
const queryClient = useQueryClient();
const onStreamComplete = useChatStore((s) => s.onStreamComplete);
const {
sessions: accumulatedSessions,
isLoading: isSessionsLoading,
isFetching: isSessionsFetching,
hasNextPage,
fetchNextPage,
reset: resetPagination,
} = useSessionsPagination({
enabled: paginationEnabled,
});
const recentlyCreatedSessionsRef = useRef<
Map<string, SessionSummaryResponse>
>(new Map());
useEffect(() => {
if (isOnHomepage && !paramSessionId) {
queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
}
}, [isOnHomepage, paramSessionId, queryClient]);
useEffect(() => {
if (currentSessionId && currentSessionData) {
const isNewSession =
currentSessionData.updated_at === currentSessionData.created_at;
const isNotInAccumulated = !accumulatedSessions.some(
(s) => s.id === currentSessionId,
);
if (isNewSession || isNotInAccumulated) {
const summary = convertSessionDetailToSummary(currentSessionData);
recentlyCreatedSessionsRef.current.set(currentSessionId, summary);
}
}
}, [currentSessionId, currentSessionData, accumulatedSessions]);
useEffect(() => {
for (const sessionId of recentlyCreatedSessionsRef.current.keys()) {
if (accumulatedSessions.some((s) => s.id === sessionId)) {
recentlyCreatedSessionsRef.current.delete(sessionId);
}
}
}, [accumulatedSessions]);
useEffect(() => {
const unsubscribe = onStreamComplete(() => {
queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
});
return unsubscribe;
}, [onStreamComplete, queryClient]);
const sessions = useMemo(
() =>
mergeCurrentSessionIntoList(
accumulatedSessions,
currentSessionId,
currentSessionData,
recentlyCreatedSessionsRef.current,
),
[accumulatedSessions, currentSessionId, currentSessionData],
);
const visibleSessions = useMemo(
() => filterVisibleSessions(sessions),
[sessions],
);
const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
return {
sessions: visibleSessions,
isLoading,
isSessionsFetching,
hasNextPage,
fetchNextPage,
resetPagination,
recentlyCreatedSessionsRef,
};
}

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