From d5af6001fc4a409ed3b63d2806c3cbbeed552b87 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 6 Jul 2026 22:25:38 +0000 Subject: [PATCH 1/2] [pre-commit.ci] pre-commit autoupdate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit updates: - [github.com/pre-commit/pre-commit-hooks: v5.0.0 → v6.0.0](https://github.com/pre-commit/pre-commit-hooks/compare/v5.0.0...v6.0.0) - [github.com/Lucas-C/pre-commit-hooks: v1.5.5 → v1.5.6](https://github.com/Lucas-C/pre-commit-hooks/compare/v1.5.5...v1.5.6) - [github.com/pycqa/isort: 6.0.1 → 9.0.0a3](https://github.com/pycqa/isort/compare/6.0.1...9.0.0a3) - [github.com/PyCQA/docformatter: v1.7.7 → v1.7.8](https://github.com/PyCQA/docformatter/compare/v1.7.7...v1.7.8) - https://github.com/psf/black.git → https://github.com/psf/black-pre-commit-mirror - [github.com/psf/black-pre-commit-mirror: 25.1.0 → 26.5.1](https://github.com/psf/black-pre-commit-mirror/compare/25.1.0...26.5.1) - [github.com/asottile/blacken-docs: 1.19.1 → 1.20.0](https://github.com/asottile/blacken-docs/compare/1.19.1...1.20.0) - [github.com/codespell-project/codespell: v2.4.1 → v2.4.2](https://github.com/codespell-project/codespell/compare/v2.4.1...v2.4.2) - [github.com/astral-sh/ruff-pre-commit: v0.12.2 → v0.15.20](https://github.com/astral-sh/ruff-pre-commit/compare/v0.12.2...v0.15.20) --- .pre-commit-config.yaml | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 972776fbd4..66a2a2fb7a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,7 +7,7 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 + rev: v6.0.0 hooks: - id: end-of-file-fixer files: (.*\.(py|md|rst|yaml|yml|json|ts|js|html|svelte|sh))$ @@ -26,7 +26,7 @@ repos: args: [--fix=lf] - repo: https://github.com/Lucas-C/pre-commit-hooks - rev: v1.5.5 + rev: v1.5.6 hooks: - id: insert-license files: (Dockerfile)$ @@ -74,12 +74,12 @@ repos: name: Unused noqa - repo: https://github.com/pycqa/isort - rev: 6.0.1 + rev: 9.0.0a3 hooks: - id: isort - repo: https://github.com/PyCQA/docformatter - rev: v1.7.7 + rev: v1.7.8 hooks: - id: docformatter args: [ @@ -99,14 +99,14 @@ repos: additional_dependencies: - prettier@3.2.5 - - repo: https://github.com/psf/black.git - rev: 25.1.0 + - repo: https://github.com/psf/black-pre-commit-mirror + rev: 26.5.1 hooks: - id: black files: (.*\.py)$ - repo: https://github.com/asottile/blacken-docs - rev: 1.19.1 + rev: 1.20.0 hooks: - id: blacken-docs args: [--line-length=120, --skip-errors] @@ -114,7 +114,7 @@ repos: - black==24.10.0 - repo: https://github.com/codespell-project/codespell - rev: v2.4.1 + rev: v2.4.2 hooks: - id: codespell args: [-w] @@ -122,7 +122,7 @@ repos: - tomli - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.12.2 + rev: v0.15.20 hooks: - id: ruff args: [--fix, --exit-non-zero-on-fix, --no-cache] From 26ee4f5508da0354421a87ec98e043799a6a266b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 6 Jul 2026 22:27:03 +0000 Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../gradio/app_gradio_demo_avatarchatbot.py | 36 ++-- .../docker_compose/amd/cpu/epyc/milvus.yaml | 2 +- .../docker_compose/intel/cpu/xeon/milvus.yaml | 2 +- CodeGen/codegen.py | 2 - CogniwareIms/backend/app/main.py | 1 - .../backend/app/services/dbqna_service.py | 18 +- .../backend/app/services/interactive_agent.py | 1 - CogniwareIms/cogniwareims.py | 1 - DeepResearchAgent/benchmark/accuracy/eval.py | 2 - .../intel/cpu/xeon/config/milvus.yaml | 2 +- .../intel/hpu/gaudi/config/milvus.yaml | 2 +- DocSum/ui/gradio/docsum_ui_gradio.py | 3 +- EdgeCraftRAG/cli/README.md | 2 + EdgeCraftRAG/cli/client.py | 19 +- EdgeCraftRAG/cli/main.py | 13 +- EdgeCraftRAG/cli/quickstart.py | 9 +- EdgeCraftRAG/cli/setup.py | 3 +- .../docker_compose/intel/gpu/arc/README.md | 13 +- .../docker_compose/intel/gpu/arc/README_zh.md | 25 +-- .../intel/gpu/arc/milvus-config.yaml | 2 +- EdgeCraftRAG/docs/API_Guide.md | 1 + EdgeCraftRAG/docs/Advanced_Setup.md | 4 +- EdgeCraftRAG/docs/Advanced_Setup_zh.md | 3 +- EdgeCraftRAG/docs/Explore_Edge_Craft_RAG.md | 15 +- .../docs/Explore_Edge_Craft_RAG_zh.md | 8 +- EdgeCraftRAG/edgecraftrag/api/v1/chatqna.py | 6 +- EdgeCraftRAG/edgecraftrag/api/v1/data.py | 3 +- .../edgecraftrag/api/v1/knowledge_base.py | 56 +++--- EdgeCraftRAG/edgecraftrag/api/v1/model.py | 4 +- EdgeCraftRAG/edgecraftrag/api/v1/pipeline.py | 8 +- .../components/agents/deep_search/config.py | 3 +- .../agents/deep_search/deep_search.py | 5 +- .../edgecraftrag/components/agents/simple.py | 4 +- .../edgecraftrag/components/agents/utils.py | 33 ++-- .../edgecraftrag/components/benchmark.py | 6 +- .../edgecraftrag/components/generator.py | 28 ++- .../edgecraftrag/components/indexer.py | 3 +- .../edgecraftrag/components/knowledge_base.py | 7 +- EdgeCraftRAG/edgecraftrag/components/model.py | 113 ++++++------ .../components/ov_llamaindex_helper.py | 35 ++-- .../edgecraftrag/components/pipeline.py | 98 ++++++---- .../components/query_preprocess.py | 1 - .../edgecraftrag/components/retriever.py | 4 +- .../edgecraftrag/config_repository.py | 2 +- .../edgecraftrag/controllers/agentmgr.py | 14 +- .../edgecraftrag/controllers/filemgr.py | 2 +- .../controllers/knowledge_basemgr.py | 4 +- .../edgecraftrag/controllers/modelmgr.py | 11 +- .../edgecraftrag/controllers/pipelinemgr.py | 15 +- EdgeCraftRAG/edgecraftrag/requirements.txt | 26 +-- EdgeCraftRAG/tests/common.sh | 2 +- .../configs/test_pipeline_local_llm.json | 2 +- .../tests/test_pipeline_local_llm.json | 2 +- EdgeCraftRAG/tools/README.md | 6 +- EdgeCraftRAG/tools/README_zh.md | 9 +- EdgeCraftRAG/tools/build_images.sh | 3 + EdgeCraftRAG/ui/vue/components.d.ts | 5 +- EdgeCraftRAG/ui/vue/src/auto-imports.d.ts | 168 ++++++++++-------- EdgeCraftRAG/ui/vue/src/i18n/en.ts | 60 +++---- EdgeCraftRAG/ui/vue/src/i18n/zh.ts | 15 +- .../ui/vue/src/utils/customRenderer.ts | 32 +--- FinanceAgent/tools/research_tools.py | 32 ++-- .../amd/cpu/epyc/config/milvus.yaml | 2 +- .../intel/cpu/xeon/config/milvus.yaml | 2 +- .../intel/hpu/gaudi/config/milvus.yaml | 2 +- MultimodalQnA/ui/gradio/utils.py | 3 +- .../tools/components/component.py | 1 - .../tools/components/workflow.py | 3 - WorkflowExecAgent/tools/sdk.py | 1 - WorkflowExecAgent/tools/tools.py | 10 +- benchmark.py | 2 - deploy.py | 3 - one_click_deploy/core/deployer.py | 2 +- one_click_deploy/core/tester.py | 1 - 74 files changed, 539 insertions(+), 509 deletions(-) diff --git a/AvatarChatbot/ui/gradio/app_gradio_demo_avatarchatbot.py b/AvatarChatbot/ui/gradio/app_gradio_demo_avatarchatbot.py index 1aad8c56cc..067108bb06 100644 --- a/AvatarChatbot/ui/gradio/app_gradio_demo_avatarchatbot.py +++ b/AvatarChatbot/ui/gradio/app_gradio_demo_avatarchatbot.py @@ -51,7 +51,7 @@ def base64_to_int16(base64_string): async def transcribe(audio_input, face_input, model_choice): - """Input: mic audio; Output: ai audio, text, text""" + """Input: mic audio; Output: ai audio, text, text.""" global ai_chatbot_url, chat_history, count chat_history = "" # Preprocess the audio @@ -195,12 +195,11 @@ def initial_process(audio_input, face_input, model_choice): gr.Markdown("

A PyTorch and OPEA based AI Avatar Audio Chatbot

") with gr.Row(): with gr.Column(scale=8): - gr.Markdown( - """ -

Welcome to our AI Avatar Audio Chatbot! This application leverages PyTorch and OPEA (Open Platform for Enterprise AI) v0.8 to provide you with a human-like conversational experience. It's run on Intel® Gaudi® AI Accelerator and Intel® Xeon® Processor, with hardware and software optimizations.
- Please feel free to interact with the AI avatar by choosing your own avatar and talking into the mic.

- """ - ) + gr.Markdown("""

Welcome to our AI Avatar Audio Chatbot! + + This application leverages PyTorch and OPEA (Open Platform for Enterprise AI) v0.8 to provide you with a human-like conversational experience. It's run on Intel® Gaudi® AI Accelerator and Intel® Xeon® Processor, with hardware and software optimizations.
+ Please feel free to interact with the AI avatar by choosing your own avatar and talking into the mic.

+ """) with gr.Column(scale=1): # with gr.Row(): # gr.Markdown(f""" @@ -285,45 +284,40 @@ def initial_process(audio_input, face_input, model_choice): # Technical details gr.Markdown("
") # Divider with gr.Row(): - gr.Markdown( - """ -

OPEA megaservice deployed:
+ gr.Markdown("""

OPEA megaservice deployed:

OPEA microservices deployed: +

- """ - ) + """) with gr.Row(): gr.Image("assets/img/flowchart.png", label="Megaservice Flowchart") with gr.Row(): gr.Markdown( - """ -

The AI Avatar Audio Chatbot is powered by the following Intel® AI software:
+ """

The AI Avatar Audio Chatbot is powered by the following Intel® AI software:

- """ +

""" ) - # Disclaimer gr.Markdown("
") # Divider gr.Markdown("

Notices & Disclaimers

") gr.Markdown( + """

Intel is committed to respecting human rights and avoiding complicity in human rights abuses. + + See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.

© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

+

You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.

""" -

Intel is committed to respecting human rights and avoiding complicity in human rights abuses. See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.

-

© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

-

You may not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein.

- """ ) # State transitions diff --git a/ChatQnA/docker_compose/amd/cpu/epyc/milvus.yaml b/ChatQnA/docker_compose/amd/cpu/epyc/milvus.yaml index 0526415e12..295cde04cc 100644 --- a/ChatQnA/docker_compose/amd/cpu/epyc/milvus.yaml +++ b/ChatQnA/docker_compose/amd/cpu/epyc/milvus.yaml @@ -424,7 +424,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/ChatQnA/docker_compose/intel/cpu/xeon/milvus.yaml b/ChatQnA/docker_compose/intel/cpu/xeon/milvus.yaml index b9f22cb3d1..8118a41ece 100644 --- a/ChatQnA/docker_compose/intel/cpu/xeon/milvus.yaml +++ b/ChatQnA/docker_compose/intel/cpu/xeon/milvus.yaml @@ -423,7 +423,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/CodeGen/codegen.py b/CodeGen/codegen.py index 76f1760aee..f77ef66f4f 100644 --- a/CodeGen/codegen.py +++ b/CodeGen/codegen.py @@ -59,7 +59,6 @@ def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **k Returns: - inputs: The aligned inputs for the current node. """ - # Check if the current service type is EMBEDDING if self.services[cur_node].service_type == ServiceType.EMBEDDING: # Store the input query for later use @@ -100,7 +99,6 @@ def __init__(self, host="0.0.0.0", port=8000): def add_remote_service(self): """Adds remote microservices to the service orchestrators and defines the flow between them.""" - # Define the embedding microservice embedding = MicroService( name="embedding", diff --git a/CogniwareIms/backend/app/main.py b/CogniwareIms/backend/app/main.py index 48e0984fce..d7f7215442 100644 --- a/CogniwareIms/backend/app/main.py +++ b/CogniwareIms/backend/app/main.py @@ -152,7 +152,6 @@ async def root(): @app.get("/api/health") async def health_check(): """Comprehensive health check including all OPEA services.""" - embedding_health = await embedding_service.health_check() retrieval_health = await retrieval_service.health_check() llm_health = await llm_service.health_check() diff --git a/CogniwareIms/backend/app/services/dbqna_service.py b/CogniwareIms/backend/app/services/dbqna_service.py index 8446f4861c..35ae7bd6b4 100644 --- a/CogniwareIms/backend/app/services/dbqna_service.py +++ b/CogniwareIms/backend/app/services/dbqna_service.py @@ -45,25 +45,21 @@ async def get_schema(self) -> Dict[str, Any]: # Get table information with engine.connect() as conn: # Get all tables - tables_query = text( - """ + tables_query = text(""" SELECT table_name FROM information_schema.tables WHERE table_schema = 'public' - """ - ) + """) tables = conn.execute(tables_query).fetchall() for (table_name,) in tables: # Get columns for each table - columns_query = text( - """ + columns_query = text(""" SELECT column_name, data_type FROM information_schema.columns WHERE table_name = :table_name ORDER BY ordinal_position - """ - ) + """) columns = conn.execute(columns_query, {"table_name": table_name}).fetchall() schema["tables"][table_name] = {"columns": [{"name": col, "type": dtype} for col, dtype in columns]} @@ -146,8 +142,7 @@ async def _get_product_inventory(self, sku: str, warehouse: str) -> Dict[str, An try: engine = self.get_engine() with engine.connect() as conn: - query = text( - """ + query = text(""" SELECT p.name as product, p.sku, @@ -159,8 +154,7 @@ async def _get_product_inventory(self, sku: str, warehouse: str) -> Dict[str, An JOIN products p ON i.product_id = p.id JOIN warehouses w ON i.warehouse_id = w.id WHERE p.sku = :sku AND w.name = :warehouse - """ - ) + """) result = conn.execute(query, {"sku": sku, "warehouse": warehouse}) row = result.fetchone() diff --git a/CogniwareIms/backend/app/services/interactive_agent.py b/CogniwareIms/backend/app/services/interactive_agent.py index a741ec7cde..5def0bea87 100644 --- a/CogniwareIms/backend/app/services/interactive_agent.py +++ b/CogniwareIms/backend/app/services/interactive_agent.py @@ -183,7 +183,6 @@ def _build_chat_messages( user_role: str, ) -> List[Dict[str, str]]: """Build message list for LLM including context and history.""" - # System prompt based on user role role_prompts = { "Consumer": "You are a helpful AI assistant for product research and PC building. Help users find products and make informed decisions.", diff --git a/CogniwareIms/cogniwareims.py b/CogniwareIms/cogniwareims.py index 46954be657..0b4bb49f1c 100644 --- a/CogniwareIms/cogniwareims.py +++ b/CogniwareIms/cogniwareims.py @@ -32,7 +32,6 @@ def __init__(self, host: str = "0.0.0.0", port: int = 8888): def add_remote_service(self): """Configure and add microservices to the megaservice.""" - # LLM Microservice - Text Generation (Intel neural-chat) llm_service = MicroService( name="llm", diff --git a/DeepResearchAgent/benchmark/accuracy/eval.py b/DeepResearchAgent/benchmark/accuracy/eval.py index 8c5ee71f90..8397f22c1c 100644 --- a/DeepResearchAgent/benchmark/accuracy/eval.py +++ b/DeepResearchAgent/benchmark/accuracy/eval.py @@ -253,7 +253,6 @@ def run_benchmark( Returns: Tuple of (accuracy score, detailed results) """ - results = [] total_questions = len(questions) details = [] @@ -275,7 +274,6 @@ def run_benchmark( def main(): """Main function to run the benchmark.""" - # Set up argument parser parser = argparse.ArgumentParser(description="Run scoring with benchmarking options") parser.add_argument( diff --git a/DocIndexRetriever/docker_compose/intel/cpu/xeon/config/milvus.yaml b/DocIndexRetriever/docker_compose/intel/cpu/xeon/config/milvus.yaml index b9f22cb3d1..8118a41ece 100644 --- a/DocIndexRetriever/docker_compose/intel/cpu/xeon/config/milvus.yaml +++ b/DocIndexRetriever/docker_compose/intel/cpu/xeon/config/milvus.yaml @@ -423,7 +423,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/DocIndexRetriever/docker_compose/intel/hpu/gaudi/config/milvus.yaml b/DocIndexRetriever/docker_compose/intel/hpu/gaudi/config/milvus.yaml index b9f22cb3d1..8118a41ece 100644 --- a/DocIndexRetriever/docker_compose/intel/hpu/gaudi/config/milvus.yaml +++ b/DocIndexRetriever/docker_compose/intel/hpu/gaudi/config/milvus.yaml @@ -423,7 +423,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/DocSum/ui/gradio/docsum_ui_gradio.py b/DocSum/ui/gradio/docsum_ui_gradio.py index 8d8a440ce3..119bbc5d9f 100644 --- a/DocSum/ui/gradio/docsum_ui_gradio.py +++ b/DocSum/ui/gradio/docsum_ui_gradio.py @@ -44,7 +44,6 @@ def read_url(self, url): Returns: str: The content of the website or an error message if the url is unsupported. """ - self.page_content = "" logger.info(">>> Reading url: %s", url) @@ -91,7 +90,7 @@ def process_response(self, response): ("\n\ndata: b", ""), ("'\n\n", ""), ("'\n", ""), - ('''\'"''', ""), + ("""\'".""", ""), ] for old, new in replacements: cleaned_text = cleaned_text.replace(old, new) diff --git a/EdgeCraftRAG/cli/README.md b/EdgeCraftRAG/cli/README.md index 98dfaf5eb6..66711967d1 100644 --- a/EdgeCraftRAG/cli/README.md +++ b/EdgeCraftRAG/cli/README.md @@ -329,12 +329,14 @@ The CLI will display error messages from the API in JSON format. Network errors ## Tips - Use `--help` with any command to see detailed help: + ```bash ecrag pipeline --help ecrag pipeline create --help ``` - Pipe JSON output to other tools: + ```bash ecrag kb list | jq '.[]' | head -n 20 ``` diff --git a/EdgeCraftRAG/cli/client.py b/EdgeCraftRAG/cli/client.py index b96c2d7ffb..8c4730464b 100644 --- a/EdgeCraftRAG/cli/client.py +++ b/EdgeCraftRAG/cli/client.py @@ -2,17 +2,18 @@ # SPDX-License-Identifier: Apache-2.0 import json -import requests -from typing import Optional, Dict, Any +from typing import Any, Dict, Optional from urllib.parse import urljoin +import requests + class EcragApiClient: """API client for Edge Craft RAG.""" def __init__(self, host: str = "http://localhost", server_port: int = 16010, mega_port: int = 16011): """Initialize the API client. - + Args: host: The host URL (default: http://localhost) server_port: The server port (default: 16010) @@ -21,10 +22,10 @@ def __init__(self, host: str = "http://localhost", server_port: int = 16010, meg # Normalize host URL if not host.startswith(("http://", "https://")): host = f"http://{host}" - + # Remove trailing slash if present host = host.rstrip("/") - + self.server_url = f"{host}:{server_port}" self.mega_url = f"{host}:{mega_port}" @@ -170,12 +171,16 @@ def delete_knowledge_base(self, kb_name: str) -> Dict[str, Any]: def add_files_to_kb(self, kb_name: str, local_paths: list) -> Dict[str, Any]: """Add files to a knowledge base.""" url = urljoin(self.server_url, f"/v1/knowledge/{kb_name}/files") - return self._request("POST", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"}) + return self._request( + "POST", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"} + ) def delete_files_from_kb(self, kb_name: str, local_paths: list) -> Dict[str, Any]: """Delete files from a knowledge base.""" url = urljoin(self.server_url, f"/v1/knowledge/{kb_name}/files") - return self._request("DELETE", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"}) + return self._request( + "DELETE", url, json={"local_paths": local_paths}, headers={"Content-Type": "application/json"} + ) # Experience Management def get_experiences(self) -> Dict[str, Any]: diff --git a/EdgeCraftRAG/cli/main.py b/EdgeCraftRAG/cli/main.py index 33d7df33f3..63cab34ab6 100644 --- a/EdgeCraftRAG/cli/main.py +++ b/EdgeCraftRAG/cli/main.py @@ -2,10 +2,11 @@ # SPDX-License-Identifier: Apache-2.0 import json -import click import os from pathlib import Path from typing import Optional + +import click from cli.client import EcragApiClient from cli.config import get_config @@ -34,26 +35,26 @@ def run_chatqna_query(client: EcragApiClient, query: str, top_n: int, max_tokens @click.pass_context def cli(ctx, host: Optional[str], port: Optional[int], mega_port: Optional[int]): """EdgeCraft RAG CLI Tool. - + Configure server connection via command-line options or environment variables: - ECRAG_HOST: Server host (default: http://localhost) - ECRAG_PORT: Server port (default: 16010) - ECRAG_MEGA_PORT: Mega service port (default: 16011) """ ctx.ensure_object(dict) - + # Get defaults from config config = get_config() - + # Use provided options or environment/defaults final_host = host or config.host final_port = port or config.port final_mega_port = mega_port or config.mega_port - + # Normalize host URL if not final_host.startswith(("http://", "https://")): final_host = f"http://{final_host}" - + ctx.obj["client"] = EcragApiClient(host=final_host, server_port=final_port, mega_port=final_mega_port) diff --git a/EdgeCraftRAG/cli/quickstart.py b/EdgeCraftRAG/cli/quickstart.py index d37188763f..e522ade562 100644 --- a/EdgeCraftRAG/cli/quickstart.py +++ b/EdgeCraftRAG/cli/quickstart.py @@ -5,21 +5,22 @@ import json import sys + from cli.client import EcragApiClient def test_connection(host: str = "http://localhost", port: int = 16010): """Test connection to EdgeCraft RAG server.""" client = EcragApiClient(host=host, server_port=port) - + try: print(f"Testing connection to {client.server_url}...") result = client.get_system_info() - + if "error" in result: print(f"❌ Connection failed: {result['error']}") return False - + print("✓ Connection successful!") print(f" System Info: {json.dumps(result, indent=2)}") return True @@ -62,7 +63,7 @@ def quick_start_guide(): export ECRAG_MEGA_PORT=16011 COMMON COMMANDS: - + Pipeline Management: ecrag pipeline list ecrag pipeline get --name diff --git a/EdgeCraftRAG/cli/setup.py b/EdgeCraftRAG/cli/setup.py index ca5255ba0a..25dcb303ea 100644 --- a/EdgeCraftRAG/cli/setup.py +++ b/EdgeCraftRAG/cli/setup.py @@ -6,7 +6,6 @@ from setuptools import setup - setup( name="ecrag-cli", version="0.1.0", @@ -35,4 +34,4 @@ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", ], -) \ No newline at end of file +) diff --git a/EdgeCraftRAG/docker_compose/intel/gpu/arc/README.md b/EdgeCraftRAG/docker_compose/intel/gpu/arc/README.md index 62baca9037..f69f8fe3bf 100755 --- a/EdgeCraftRAG/docker_compose/intel/gpu/arc/README.md +++ b/EdgeCraftRAG/docker_compose/intel/gpu/arc/README.md @@ -20,19 +20,22 @@ This section describes how to quickly deploy and test the EdgeCraftRAG service m ### 1. Prerequisites -EC-RAG supports vLLM deployment(default method) and local OpenVINO deployment for Intel Arc GPU and Core Ultra Platform. Prerequisites are shown as below: +EC-RAG supports vLLM deployment(default method) and local OpenVINO deployment for Intel Arc GPU and Core Ultra Platform. Prerequisites are shown as below: #### Core Ultra + **OS**: Ubuntu 24.04 or newer **Driver & libraries**: Please refer to [Installing Client GPUs on Ubuntu Desktop](https://dgpu-docs.intel.com/driver/client/overview.html#installing-client-gpus-on-ubuntu-desktop) **Available Inferencing Framework**: openVINO #### Intel Arc B60 -**OS**: Ubuntu 25.04 Desktop (for Core Ultra and Xeon-W), Ubuntu 25.04 Server (for Xeon-SP). + +**OS**: Ubuntu 25.04 Desktop (for Core Ultra and Xeon-W), Ubuntu 25.04 Server (for Xeon-SP). **Driver & libraries**: Please refer to [Install Bare Metal Environment](https://github.com/intel/llm-scaler/tree/main/vllm#11-install-bare-metal-environment) for detailed setup **Available Inferencing Framework**: openVINO, vLLM #### Intel Arc A770 + **OS**: Ubuntu Server 22.04.1 or newer (at least 6.2 LTS kernel) **Driver & libraries**: Please refer to [Installing GPUs Drivers](https://dgpu-docs.intel.com/driver/client/overview.html#ubuntu-22.04) for detailed driver & libraries setup **Available Inferencing Framework**: openVINO, vLLM @@ -48,9 +51,9 @@ cd GenAIExamples/EdgeCraftRAG > **NOTE**: If you want to checkout a released version, such as v1.5: > ->``` ->git checkout v1.5 ->``` +> ``` +> git checkout v1.5 +> ``` ### 3. Run quick_start.sh diff --git a/EdgeCraftRAG/docker_compose/intel/gpu/arc/README_zh.md b/EdgeCraftRAG/docker_compose/intel/gpu/arc/README_zh.md index c1320d2ead..31a7bd8dac 100644 --- a/EdgeCraftRAG/docker_compose/intel/gpu/arc/README_zh.md +++ b/EdgeCraftRAG/docker_compose/intel/gpu/arc/README_zh.md @@ -23,16 +23,19 @@ EC-RAG 支持 vLLM 部署（默认方式）以及面向 Intel Arc GPU 和 Core Ultra 平台的本地 OpenVINO 部署。前置条件如下： #### Core Ultra + **操作系统**：Ubuntu 24.04 或更高版本 **驱动与库**：请参考 [Installing Client GPUs on Ubuntu Desktop](https://dgpu-docs.intel.com/driver/client/overview.html#installing-client-gpus-on-ubuntu-desktop) **可用推理框架**：openVINO #### Intel Arc B60 + **操作系统**：Ubuntu 25.04 Desktop（适用于 Core Ultra 和 Xeon-W），Ubuntu 25.04 Server（适用于 Xeon-SP）。 **驱动与库**：详细安装请参考 [Install Bare Metal Environment](https://github.com/intel/llm-scaler/tree/main/vllm#11-install-bare-metal-environment) **可用推理框架**：openVINO、vLLM #### Intel Arc A770 + **操作系统**：Ubuntu Server 22.04.1 或更高版本（至少 6.2 LTS 内核） **驱动与库**：详细驱动与库安装请参考 [Installing GPUs Drivers](https://dgpu-docs.intel.com/driver/client/overview.html#ubuntu-22.04) **可用推理框架**：openVINO、vLLM @@ -48,9 +51,9 @@ cd GenAIExamples/EdgeCraftRAG > **注意**：如果你想切换到某个发布版本，例如 v1.5： > ->``` ->git checkout v1.5 ->``` +> ``` +> git checkout v1.5 +> ``` ### 3. 运行 quick_start.sh @@ -112,11 +115,11 @@ If you are accessing from another machine, replace ${HOST_IP} with your server's 下表全面概述了示例 Docker Compose 文件中各类部署所使用的 EdgeCraftRAG 服务。表中每一行代表一个独立服务，详细说明了可用镜像及其在部署架构中的功能描述。 -| 服务名称 | 可选镜像名称 | 可选 | 描述 | -| ------------------- | ---------------------------------------- | ---- | ------------------------------------------------------------------------------------------------ | -| etcd | quay.io/coreos/etcd:v3.5.5 | 否 | 提供分布式键值存储，用于服务发现和配置管理。 | -| minio | minio/minio:RELEASE.2023-03-20T20-16-18Z | 否 | 提供对象存储服务，用于存储文档和模型文件。 | -| milvus-standalone | milvusdb/milvus:v2.4.6 | 否 | 提供向量数据库能力，用于管理 embedding 和相似度检索。 | -| edgecraftrag-server | opea/edgecraftrag-server:latest | 否 | 作为 EdgeCraftRAG 服务后端，具体形态随部署方式不同而变化。 | -| edgecraftrag-ui | opea/edgecraftrag-ui:latest | 否 | 提供 EdgeCraftRAG 服务的用户界面。 | -| ecrag | opea/edgecraftrag:latest | 否 | 作为反向代理，管理 UI 与后端服务之间的流量。 | +| 服务名称 | 可选镜像名称 | 可选 | 描述 | +| ------------------- | ---------------------------------------- | ---- | ---------------------------------------------------------- | +| etcd | quay.io/coreos/etcd:v3.5.5 | 否 | 提供分布式键值存储，用于服务发现和配置管理。 | +| minio | minio/minio:RELEASE.2023-03-20T20-16-18Z | 否 | 提供对象存储服务，用于存储文档和模型文件。 | +| milvus-standalone | milvusdb/milvus:v2.4.6 | 否 | 提供向量数据库能力，用于管理 embedding 和相似度检索。 | +| edgecraftrag-server | opea/edgecraftrag-server:latest | 否 | 作为 EdgeCraftRAG 服务后端，具体形态随部署方式不同而变化。 | +| edgecraftrag-ui | opea/edgecraftrag-ui:latest | 否 | 提供 EdgeCraftRAG 服务的用户界面。 | +| ecrag | opea/edgecraftrag:latest | 否 | 作为反向代理，管理 UI 与后端服务之间的流量。 | diff --git a/EdgeCraftRAG/docker_compose/intel/gpu/arc/milvus-config.yaml b/EdgeCraftRAG/docker_compose/intel/gpu/arc/milvus-config.yaml index 7c847efd36..65fe64dece 100755 --- a/EdgeCraftRAG/docker_compose/intel/gpu/arc/milvus-config.yaml +++ b/EdgeCraftRAG/docker_compose/intel/gpu/arc/milvus-config.yaml @@ -423,7 +423,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/EdgeCraftRAG/docs/API_Guide.md b/EdgeCraftRAG/docs/API_Guide.md index 2b0c318bc6..0fbb9fd50f 100644 --- a/EdgeCraftRAG/docs/API_Guide.md +++ b/EdgeCraftRAG/docs/API_Guide.md @@ -1,6 +1,7 @@ # Edge Craft Retrieval-Augmented Generation API Guide > **Base URLs** +> > - EC-RAG Server: `http://${HOST_IP}:16010` > - EC-RAG Mega Service: `http://${HOST_IP}:16011` diff --git a/EdgeCraftRAG/docs/Advanced_Setup.md b/EdgeCraftRAG/docs/Advanced_Setup.md index 3c57e6cc1f..167ee3acd7 100644 --- a/EdgeCraftRAG/docs/Advanced_Setup.md +++ b/EdgeCraftRAG/docs/Advanced_Setup.md @@ -42,6 +42,7 @@ optimum-cli export openvino -m BAAI/bge-reranker-large ${MODEL_PATH}/BAAI/bge-re #### LLM ##### openVINO + If you have Core Ultra platform only, please prepare openVINO models: You can also run openVINO models on discrete GPU. @@ -51,6 +52,7 @@ optimum-cli export openvino --model Qwen/Qwen3-8B ${MODEL_PATH}/OpenVINO/Qwen3-8 ``` ##### vLLM + Alternatively, if you have discrete GPU and want to use vLLM, please prepare models for vLLM: ```bash @@ -178,5 +180,3 @@ export TP=4 # for multi GPU, you can change TP value export ZE_AFFINITY_MASK=0,1,2,3 # for multi GPU, you can export ZE_AFFINITY_MASK=0,1,2... docker compose --profile b60 -f docker_compose/intel/gpu/arc/compose.yaml up -d ``` - - diff --git a/EdgeCraftRAG/docs/Advanced_Setup_zh.md b/EdgeCraftRAG/docs/Advanced_Setup_zh.md index f8b16dc0d7..537c57eba4 100644 --- a/EdgeCraftRAG/docs/Advanced_Setup_zh.md +++ b/EdgeCraftRAG/docs/Advanced_Setup_zh.md @@ -42,6 +42,7 @@ optimum-cli export openvino -m BAAI/bge-reranker-large ${MODEL_PATH}/BAAI/bge-re #### LLM ##### openVINO + 如果仅使用 Core Ultra 平台，请准备 openVINO 模型： 你也可以在独立 GPU 上运行 openVINO 模型。 @@ -51,6 +52,7 @@ optimum-cli export openvino --model Qwen/Qwen3-8B ${MODEL_PATH}/OpenVINO/Qwen3-8 ``` ##### vLLM + 如果你有独立 GPU 并希望使用 vLLM，可按如下方式准备模型： ```bash @@ -105,7 +107,6 @@ export MILVUS_ENABLED=0 # export CHAT_HISTORY_ROUND= # 按需修改 ``` - ### 使用 Docker Compose 在 Intel GPU 上部署服务 #### 选项 a：为 Core Ultra / Arc B60 / Arc A770 部署基于 openVINO LLM 的 EC-RAG diff --git a/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG.md b/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG.md index ee091a5fc8..9cebfcb5b2 100644 --- a/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG.md +++ b/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG.md @@ -37,18 +37,17 @@ Then, you can submit messages in the chat box in `Chat` page. ![alt text](../assets/img/Explore_Edge_Craft_RAG_08.jpg) ## ChatQnA with Kbadmin in UI - + ### Kbadmin Knowledge Base - + Go to `Knowledge Base` page and click `Create Knowledge Base` button to create your knowledge base. Please select 'kbadmin' in `Type`and select kb name from the kbs you created in kbadmin UI page. Loading kb name might be slow ,please wait with patient - + ![alt text](../assets/img/Explore_Edge_Craft_RAG_09.png) - -Ten you can select embedding infomation in 'Indexer' page - + +Ten you can select embedding information in 'Indexer' page + ![alt text](../assets/img/Explore_Edge_Craft_RAG_10.png) - + After creation, you can see kbadmin tag in knowledge base then you can submit messages in the chat box in `Chat` page. ![alt text](../assets/img/Explore_Edge_Craft_RAG_11.png) - diff --git a/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG_zh.md b/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG_zh.md index 10534cd127..f01bb4ef47 100644 --- a/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG_zh.md +++ b/EdgeCraftRAG/docs/Explore_Edge_Craft_RAG_zh.md @@ -42,12 +42,12 @@ 流水线创建完成后，前往 `Knowledge Base` 页面，点击 `Create Knowledge Base` 按钮创建知识库。 请在 `Type` 中选择 `kbadmin`，并从 kbadmin UI 页面中已创建的知识库列表中选择 kb 名称。加载kb名称可能比较耗时，请耐心等待。 - + ![alt text](../assets/img/Explore_Edge_Craft_RAG_09.png) - + 在 `Indexer` 页面，填写 Embedding 服务和向量数据库信息，注意 Embedding 服务端口为 13020，向量数据库端口为 29530。 - + ![alt text](../assets/img/Explore_Edge_Craft_RAG_10.png) - + 然后，在 `Chat` 页面的聊天框中提交您的问题。 ![alt text](../assets/img/Explore_Edge_Craft_RAG_11.png) diff --git a/EdgeCraftRAG/edgecraftrag/api/v1/chatqna.py b/EdgeCraftRAG/edgecraftrag/api/v1/chatqna.py index 01ddaabc65..332bcf1055 100644 --- a/EdgeCraftRAG/edgecraftrag/api/v1/chatqna.py +++ b/EdgeCraftRAG/edgecraftrag/api/v1/chatqna.py @@ -124,7 +124,9 @@ async def res_gen_json(): async def context_suffix_gen(): yield '","contexts":' + json.dumps(serialize_retrievals(retrievals)) + "}" - query_gen = stream_generator('{"query":' + json.dumps(original_query, ensure_ascii=False) + ',"response":"') + query_gen = stream_generator( + '{"query":' + json.dumps(original_query, ensure_ascii=False) + ',"response":"' + ) output_gen = chain_async_generators([query_gen, res_gen_json(), context_suffix_gen()]) return StreamingResponse(output_gen, media_type="text/plain") @@ -149,7 +151,7 @@ async def res_gen_json(): yield json.dumps(token, ensure_ascii=False)[1:-1] # Reconstruct RagOut in stream response - query_gen = stream_generator('{"query":' + json.dumps(request.messages, ensure_ascii=False) + ',') + query_gen = stream_generator('{"query":' + json.dumps(request.messages, ensure_ascii=False) + ",") s_contexts = json.dumps(serialize_contexts(contexts)) context_gen = stream_generator('"contexts":' + s_contexts + ',"response":"') diff --git a/EdgeCraftRAG/edgecraftrag/api/v1/data.py b/EdgeCraftRAG/edgecraftrag/api/v1/data.py index fff065f86e..aad19e0ed6 100644 --- a/EdgeCraftRAG/edgecraftrag/api/v1/data.py +++ b/EdgeCraftRAG/edgecraftrag/api/v1/data.py @@ -5,12 +5,13 @@ import os from typing import List +from edgecraftrag.api.v1.knowledge_base import add_file_to_knowledge_base from edgecraftrag.api_schema import DataIn, FilesIn from edgecraftrag.config_repository import MilvusConfigRepository from edgecraftrag.context import ctx from edgecraftrag.env import UI_DIRECTORY from fastapi import FastAPI, File, HTTPException, UploadFile, status -from edgecraftrag.api.v1.knowledge_base import add_file_to_knowledge_base + data_app = FastAPI() diff --git a/EdgeCraftRAG/edgecraftrag/api/v1/knowledge_base.py b/EdgeCraftRAG/edgecraftrag/api/v1/knowledge_base.py index c17298c138..5a7b14df2f 100644 --- a/EdgeCraftRAG/edgecraftrag/api/v1/knowledge_base.py +++ b/EdgeCraftRAG/edgecraftrag/api/v1/knowledge_base.py @@ -8,26 +8,13 @@ from typing import Dict, List, Union from edgecraftrag.api_schema import DataIn, ExperienceIn, KnowledgeBaseCreateIn -from edgecraftrag.components.query_preprocess import query_search -from edgecraftrag.components.indexer import get_kbs_info -from edgecraftrag.config_repository import ( - MilvusConfigRepository, - save_knowledge_configurations, -) -from edgecraftrag.context import ctx -from edgecraftrag.env import ( - KNOWLEDGEBASE_FILE, - SEARCH_CONFIG_PATH, - SEARCH_DIR, - UI_DIRECTORY, -) from edgecraftrag.base import ( IndexerType, ModelType, NodeParserType, ) from edgecraftrag.components.benchmark import Benchmark -from edgecraftrag.components.indexer import KBADMINIndexer, VectorIndexer +from edgecraftrag.components.indexer import KBADMINIndexer, VectorIndexer, get_kbs_info from edgecraftrag.components.node_parser import ( HierarchyNodeParser, KBADMINParser, @@ -35,7 +22,19 @@ SWindowNodeParser, UnstructedNodeParser, ) -from fastapi import FastAPI, HTTPException, status, Query +from edgecraftrag.components.query_preprocess import query_search +from edgecraftrag.config_repository import ( + MilvusConfigRepository, + save_knowledge_configurations, +) +from edgecraftrag.context import ctx +from edgecraftrag.env import ( + KNOWLEDGEBASE_FILE, + SEARCH_CONFIG_PATH, + SEARCH_DIR, + UI_DIRECTORY, +) +from fastapi import FastAPI, HTTPException, Query, status kb_app = FastAPI() @@ -51,7 +50,9 @@ async def get_all_knowledge_bases(): # Get knowledge base files in a certain range. @kb_app.get("/v1/knowledge/{knowledge_name}/filemap") -async def get_knowledge_base_filemap(knowledge_name: str, page_num: int = Query(1, ge=1), page_size: int = Query(20, ge=1)): +async def get_knowledge_base_filemap( + knowledge_name: str, page_num: int = Query(1, ge=1), page_size: int = Query(20, ge=1) +): kb = ctx.knowledgemgr.get_knowledge_base_by_name_or_id(knowledge_name) if kb and kb.file_map: file_map = kb.file_map @@ -61,7 +62,7 @@ async def get_knowledge_base_filemap(knowledge_name: str, page_num: int = Query( if start >= filemap_len: return None file_map_subset = itertools.islice(file_map.items(), start, end) - return {"file_map": dict(file_map_subset),"total": kb.calculate_totals()} + return {"file_map": dict(file_map_subset), "total": kb.calculate_totals()} else: return None @@ -101,7 +102,7 @@ async def create_knowledge_base(knowledge: KnowledgeBaseCreateIn): active_pl.update_retriever_list(ctx.knowledgemgr.get_active_knowledge_base()) except Exception as e: ctx.knowledgemgr.delete_knowledge_base(knowledge.name) - raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)) + raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)) await save_knowledge_configurations("add", kb) return "Create knowledge base successfully" except Exception as e: @@ -156,7 +157,7 @@ async def update_knowledge_base(knowledge: KnowledgeBaseCreateIn): raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e)) # reload data for knowledge base - node_parser_changed = (kb_node_parser != kb.node_parser) + node_parser_changed = kb_node_parser != kb.node_parser if node_parser_changed or kb_indexer != kb.indexer: await handle_reload_data(kb, node_parser_changed) elif kb.comp_subtype == "kbadmin_kb": @@ -181,10 +182,10 @@ async def update_knowledge_base(knowledge: KnowledgeBaseCreateIn): @kb_app.post(path="/v1/knowledge/{knowledge_name}/files") async def add_file_to_knowledge_base(knowledge_name, file_path: DataIn): """ - 1. Parse file into Llamaindex Document and add file to filemgr - 2. Add file path to knowledge base - 3. Update nodes and vector store for knowledge base - 4. Update pipeline retriever if active knowledge base's indexer changed + 1. Parse file into Llamaindex Document and add file to filemgr + 2. Add file path to knowledge base + 3. Update nodes and vector store for knowledge base + 4. Update pipeline retriever if active knowledge base's indexer changed """ try: kb = ctx.knowledgemgr.get_knowledge_base_by_name_or_id(knowledge_name) @@ -227,7 +228,7 @@ async def add_file_to_knowledge_base(knowledge_name, file_path: DataIn): raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Error uploading file.") # update retriever with indexer since indexer updated - if kb.active: + if kb.active: active_pl = ctx.get_pipeline_mgr().get_active_pipeline() if active_pl: active_pl.update_retriever(kb, prev_indexer) @@ -263,7 +264,7 @@ async def remove_file_from_knowledge_base(knowledge_name, file_path: DataIn): ) await remove_document_handler(document_list, kb) # update retriever with indexer since indexer updated - if kb.active: + if kb.active: active_pl = ctx.get_pipeline_mgr().get_active_pipeline() if active_pl: active_pl.update_retriever(kb, prev_indexer) @@ -465,6 +466,7 @@ async def handle_reload_data(kb, node_parser_changed: bool = False): # update indexer await kb.update_nodes_to_indexer() + async def update_kb_handler(kb, knowledge): if kb.enable_benchmark: kb.benchmark = Benchmark(True, "") @@ -499,7 +501,9 @@ async def update_kb_handler(kb, knowledge): ctx.get_node_parser_mgr().add(kb.node_parser) if knowledge.indexer is not None: ind = knowledge.indexer - found_indexer = ctx.get_indexer_mgr().search_indexer(ind) if ind.indexer_type != IndexerType.MILVUS_VECTOR else None + found_indexer = ( + ctx.get_indexer_mgr().search_indexer(ind) if ind.indexer_type != IndexerType.MILVUS_VECTOR else None + ) if found_indexer is not None: kb.indexer = found_indexer else: diff --git a/EdgeCraftRAG/edgecraftrag/api/v1/model.py b/EdgeCraftRAG/edgecraftrag/api/v1/model.py index 7dbccaf284..dadd8a5ea7 100644 --- a/EdgeCraftRAG/edgecraftrag/api/v1/model.py +++ b/EdgeCraftRAG/edgecraftrag/api/v1/model.py @@ -198,9 +198,7 @@ def get_available_models(model_type): normalized_model_type = (model_type or "").strip().lower() def _is_llm_model_dir(file_names: set) -> bool: - if "openvino_model.xml" in file_names and any( - name.endswith(".bin") for name in file_names - ): + if "openvino_model.xml" in file_names and any(name.endswith(".bin") for name in file_names): return True if "config.json" in file_names and ( diff --git a/EdgeCraftRAG/edgecraftrag/api/v1/pipeline.py b/EdgeCraftRAG/edgecraftrag/api/v1/pipeline.py index 35cf91a875..48e3e13463 100644 --- a/EdgeCraftRAG/edgecraftrag/api/v1/pipeline.py +++ b/EdgeCraftRAG/edgecraftrag/api/v1/pipeline.py @@ -6,7 +6,7 @@ import re import time import weakref -from openvino import Core, Type + from edgecraftrag.api_schema import MilvusConnectRequest, PipelineCreateIn from edgecraftrag.base import ( GeneratorType, @@ -17,11 +17,11 @@ from edgecraftrag.components.benchmark import Benchmark from edgecraftrag.components.generator import FreeChatGenerator, QnAGenerator from edgecraftrag.components.postprocessor import MetadataReplaceProcessor, RerankProcessor - from edgecraftrag.config_repository import MilvusConfigRepository, save_pipeline_configurations from edgecraftrag.context import ctx from edgecraftrag.env import PIPELINE_FILE from fastapi import FastAPI, File, HTTPException, UploadFile, status +from openvino import Core, Type from pymilvus import connections pipeline_app = FastAPI() @@ -236,7 +236,9 @@ async def update_pipeline_handler(pl, req): if flag == True: await save_pipeline_configurations("update", pl) if pl.status.active != req.active: - ctx.get_pipeline_mgr().activate_pipeline(pl.name, req.active, ctx.get_knowledge_mgr().get_active_knowledge_base()) + ctx.get_pipeline_mgr().activate_pipeline( + pl.name, req.active, ctx.get_knowledge_mgr().get_active_knowledge_base() + ) return pl diff --git a/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/config.py b/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/config.py index e7066c767d..9d458e5c9a 100644 --- a/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/config.py +++ b/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/config.py @@ -4,8 +4,8 @@ from __future__ import annotations -from copy import deepcopy import json +from copy import deepcopy from pathlib import Path from typing import Any, Dict @@ -95,7 +95,6 @@ def load_config(config_path: str) -> Config: Returns: A fully-populated :class:`Config` instance. """ - config_file = Path(config_path).expanduser().resolve() with config_file.open("r", encoding="utf-8") as handle: config_dict: Dict[str, Any] = json.load(handle) diff --git a/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/deep_search.py b/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/deep_search.py index 34fba173af..b1a2f185f0 100644 --- a/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/deep_search.py +++ b/EdgeCraftRAG/edgecraftrag/components/agents/deep_search/deep_search.py @@ -22,6 +22,7 @@ from .postprocessing import postproc_query as default_postproc_query from .utils import Role, import_module_from_path + class DeepSearchState(BaseModel): question: str query: str @@ -259,7 +260,9 @@ async def check_retrieved(self, state: DeepSearchState) -> str: bold=True, ), ) - await stream_writer(f"\n\n⚠️ **Reached maximum retrievals: {self.cfg.max_retrievals}, stopping search**\n\n") + await stream_writer( + f"\n\n⚠️ **Reached maximum retrievals: {self.cfg.max_retrievals}, stopping search**\n\n" + ) return "stop" response = await self.llm_generate_astream_writer(state.request) diff --git a/EdgeCraftRAG/edgecraftrag/components/agents/simple.py b/EdgeCraftRAG/edgecraftrag/components/agents/simple.py index 131afe63e8..69c260d0b2 100644 --- a/EdgeCraftRAG/edgecraftrag/components/agents/simple.py +++ b/EdgeCraftRAG/edgecraftrag/components/agents/simple.py @@ -37,10 +37,10 @@ class Config(BaseModel): "domain_knowledge": "", "max_retrievals": 3, "prompt_templates": { - "system": """{system_instruction}\n\n{query_instruction}\n\n{domain_knowledge}\n\n""", + "system": """{system_instruction}\n\n{query_instruction}\n\n{domain_knowledge}\n\n.""", "generate_query": "Now generate a query for the next retrieval.", "context": """\n{context}\n\n""", - "contexts": """The following are the retrieved contexts for current query.\n{contexts}\n""", + "contexts": """The following are the retrieved contexts for current query.\n{contexts}\n.""", "continue_decision": "Is more information needed? Answer Yes or No. Then explain why or why not.", }, } diff --git a/EdgeCraftRAG/edgecraftrag/components/agents/utils.py b/EdgeCraftRAG/edgecraftrag/components/agents/utils.py index b11df280ca..7c71c7194e 100644 --- a/EdgeCraftRAG/edgecraftrag/components/agents/utils.py +++ b/EdgeCraftRAG/edgecraftrag/components/agents/utils.py @@ -323,18 +323,19 @@ def format_terminal_str(text: str, color: str = "", bold: bool = False, italic: _LLM_EVAL_DEFAULT_TEMPLATE_MESSAGES = [ { "role": "system", - "content": """You are an impartial quality rater for troubleshooting answers. Your task is to rate if the answer by user well covers the steps in the reference answer. - -Task instructions: -- Parse the reference answer into its essential checkpoints (split on punctuation such as "?", ";", or line breaks) and understand what each step expects the technician to do or verify. The order of the checkpoints has low importance. -- Examine the user's answer and decide if each checkpoint is substantively addressed with accurate, actionable guidance. -- Treat synonymous language or additional helpful context as a match when it fulfills the intent of the checkpoint. -- Mark a checkpoint as uncovered if the user's answer omits it, contradicts it, or gives incorrect or unsafe guidance. -- Ignore extra steps that do not conflict with the reference; they should not reduce the score. -- The mismatch of the step number between user's answer and reference answer does not matter, as long as all the content is well covered. -- Keep all reasoning internal; do not expose the intermediate analysis in the final reply. -- Focus solely on the provided texts. Do not rely on your knowledge. -""", + "content": """You are an impartial quality rater for troubleshooting answers. + + Your task is to rate if the answer by user well covers the steps in the reference answer. + Task instructions: + - Parse the reference answer into its essential checkpoints (split on punctuation such as "?", ";", or line breaks) and understand what each step expects the technician to do or verify. The order of the checkpoints has low importance. + - Examine the user's answer and decide if each checkpoint is substantively addressed with accurate, actionable guidance. + - Treat synonymous language or additional helpful context as a match when it fulfills the intent of the checkpoint. + - Mark a checkpoint as uncovered if the user's answer omits it, contradicts it, or gives incorrect or unsafe guidance. + - Ignore extra steps that do not conflict with the reference; they should not reduce the score. + - The mismatch of the step number between user's answer and reference answer does not matter, as long as all the content is well covered. + - Keep all reasoning internal; do not expose the intermediate analysis in the final reply. + - Focus solely on the provided texts. Do not rely on your knowledge. + """, }, { "role": "user", @@ -350,10 +351,10 @@ def format_terminal_str(text: str, color: str = "", bold: bool = False, italic: "role": "system", "content": """Does the user's answer well cover the steps in the reference answer? Yes or No. -Scoring rubric: -- Answer "Yes" only when every checkpoint from the reference is fully covered and nothing in the user's answer conflicts with the reference guidance. -- Answer "No" if any checkpoint is missing, incorrectly addressed, or contradicted by the user's answer. -""", + Scoring rubric: + - Answer "Yes" only when every checkpoint from the reference is fully covered and nothing in the user's answer conflicts with the reference guidance. + - Answer "No" if any checkpoint is missing, incorrectly addressed, or contradicted by the user's answer. + """, }, {"role": "assistant", "content": '{"label": "'}, ] diff --git a/EdgeCraftRAG/edgecraftrag/components/benchmark.py b/EdgeCraftRAG/edgecraftrag/components/benchmark.py index 20872e59cc..bc9ffd39bb 100644 --- a/EdgeCraftRAG/edgecraftrag/components/benchmark.py +++ b/EdgeCraftRAG/edgecraftrag/components/benchmark.py @@ -118,9 +118,9 @@ def insert_llm_data_genai(self, idx, input_token_size=-1, model=None): metrics = {} metrics["input_token_size"] = input_token_size metrics["output_token_size"] = model().perf_metrics.get_num_generated_tokens() - metrics["generation_time"] = model().perf_metrics.get_inference_duration().mean/1000 - metrics["first_token_latency"] = model().perf_metrics.get_ttft().mean/1000 - metrics["other_tokens_avg_latency"] = model().perf_metrics.get_tpot().mean/1000 + metrics["generation_time"] = model().perf_metrics.get_inference_duration().mean / 1000 + metrics["first_token_latency"] = model().perf_metrics.get_ttft().mean / 1000 + metrics["other_tokens_avg_latency"] = model().perf_metrics.get_tpot().mean / 1000 self.llm_data_list[idx] = metrics diff --git a/EdgeCraftRAG/edgecraftrag/components/generator.py b/EdgeCraftRAG/edgecraftrag/components/generator.py index fcad2433d8..d9fcf5dfe9 100644 --- a/EdgeCraftRAG/edgecraftrag/components/generator.py +++ b/EdgeCraftRAG/edgecraftrag/components/generator.py @@ -5,15 +5,15 @@ import json import os import time -import weakref import urllib.request +import weakref from concurrent.futures import ThreadPoolExecutor from urllib.parse import urlparse from comps.cores.proto.api_protocol import ChatCompletionRequest from edgecraftrag.base import BaseComponent, CompType, GeneratorType, InferenceType, NodeParserType -from edgecraftrag.utils import get_prompt_template, resolve_prompt_template_path from edgecraftrag.components.agents.utils import build_document_node_block +from edgecraftrag.utils import get_prompt_template, resolve_prompt_template_path from fastapi.responses import StreamingResponse from llama_index.llms.openai_like import OpenAILike from pydantic import model_serializer @@ -103,13 +103,18 @@ async def local_stream_generator(lock, llm, prompt_str, unstructured_str, benchm if unstructured_str: yield unstructured_str if enable_benchmark: - benchmark.update_benchmark_data_genai(benchmark_index, CompType.GENERATOR, time.perf_counter() - start_time, weakref.ref(llm)) - benchmark.insert_llm_data_genai(benchmark_index, benchmark.cal_input_token_size(prompt_str), weakref.ref(llm)) + benchmark.update_benchmark_data_genai( + benchmark_index, CompType.GENERATOR, time.perf_counter() - start_time, weakref.ref(llm) + ) + benchmark.insert_llm_data_genai( + benchmark_index, benchmark.cal_input_token_size(prompt_str), weakref.ref(llm) + ) except Exception as e: start_idx = str(e).find("message") + len("message") result_error = str(e)[start_idx:] yield f"code:0000{result_error}" + async def stream_generator(llm, prompt_str, unstructured_str, benchmark=None, benchmark_index=None): enable_benchmark = benchmark.is_enabled() if benchmark else False start_time = time.perf_counter() if enable_benchmark else None @@ -219,7 +224,7 @@ def __init__( ) self.llm = llm_model - self.vllm_name = llm_model().model_id if not isinstance(llm_model, str) else llm_model + self.vllm_name = llm_model().model_id if not isinstance(llm_model, str) else llm_model if self.inference_type == InferenceType.LOCAL: self.lock = asyncio.Lock() if self.inference_type == InferenceType.VLLM: @@ -324,7 +329,14 @@ async def run(self, chat_request, retrieved_nodes, node_parser_type, **kwargs): sub_questions = kwargs.get("sub_questions", None) text_gen_context, prompt_str = self.query_transform(chat_request, retrieved_nodes, sub_questions=sub_questions) # self.llm().config.update_generation_config(config) - self.llm().config.update_generation_config(temperature=chat_request.temperature,top_p=chat_request.top_p, top_k=chat_request.top_k, typical_p=chat_request.typical_p, repetition_penalty=chat_request.repetition_penalty, do_sample=chat_request.temperature > 0.0) + self.llm().config.update_generation_config( + temperature=chat_request.temperature, + top_p=chat_request.top_p, + top_k=chat_request.top_k, + typical_p=chat_request.typical_p, + repetition_penalty=chat_request.repetition_penalty, + do_sample=chat_request.temperature > 0.0, + ) self.llm().config.max_new_tokens = chat_request.max_tokens unstructured_str = "" if node_parser_type == NodeParserType.UNSTRUCTURED: @@ -332,7 +344,9 @@ async def run(self, chat_request, retrieved_nodes, node_parser_type, **kwargs): if chat_request.stream: # Asynchronous generator async def generator(): - async for chunk in local_stream_generator(self.lock, self.llm(), prompt_str, unstructured_str, benchmark, benchmark_index): + async for chunk in local_stream_generator( + self.lock, self.llm(), prompt_str, unstructured_str, benchmark, benchmark_index + ): yield chunk or "" await asyncio.sleep(0) diff --git a/EdgeCraftRAG/edgecraftrag/components/indexer.py b/EdgeCraftRAG/edgecraftrag/components/indexer.py index 1e2349aa12..6248c87db3 100644 --- a/EdgeCraftRAG/edgecraftrag/components/indexer.py +++ b/EdgeCraftRAG/edgecraftrag/components/indexer.py @@ -6,14 +6,15 @@ import faiss from edgecraftrag.base import BaseComponent, CompType, IndexerType from edgecraftrag.context import ctx +from langchain_milvus import Milvus from langchain_openai import OpenAIEmbeddings from llama_index.core import StorageContext, VectorStoreIndex from llama_index.vector_stores.faiss import FaissVectorStore from llama_index.vector_stores.milvus import MilvusVectorStore from pydantic import model_serializer -from langchain_milvus import Milvus from pymilvus import Collection, MilvusException, connections, utility + class VectorIndexer(BaseComponent, VectorStoreIndex): def __init__(self, embed_model, vector_type, vector_url="http://localhost:19530", kb_name="default_kb"): BaseComponent.__init__( diff --git a/EdgeCraftRAG/edgecraftrag/components/knowledge_base.py b/EdgeCraftRAG/edgecraftrag/components/knowledge_base.py index 0a1071a344..543af8139d 100644 --- a/EdgeCraftRAG/edgecraftrag/components/knowledge_base.py +++ b/EdgeCraftRAG/edgecraftrag/components/knowledge_base.py @@ -3,8 +3,8 @@ import json import os -import uuid import time +import uuid from typing import Any, Dict, List, Optional, Union from edgecraftrag.base import BaseComponent, BenchType, CompType @@ -16,6 +16,7 @@ from llama_index.core.schema import Document from pydantic import Field, model_serializer + class Knowledge(BaseComponent): node_parser: Optional[BaseComponent] = Field(default=None) @@ -346,7 +347,7 @@ def calculate_totals(self): else: total = None return total - + def update_nodes(self, nodes: List[Document]): self.nodes = nodes @@ -369,7 +370,7 @@ async def run_node_parser(self, docs: List[Document]) -> Any: self.benchmark.update_benchmark_data(benchmark_index, BenchType.CHUNK_NUM, benchmark_data) self.add_nodes(nodes) return nodes - + async def update_nodes_to_indexer(self) -> Any: if self.indexer is not None: self.indexer.insert_nodes(self.nodes) diff --git a/EdgeCraftRAG/edgecraftrag/components/model.py b/EdgeCraftRAG/edgecraftrag/components/model.py index 3fec80ac82..ec1575a3d6 100644 --- a/EdgeCraftRAG/edgecraftrag/components/model.py +++ b/EdgeCraftRAG/edgecraftrag/components/model.py @@ -1,24 +1,26 @@ # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 +import asyncio import io import os from pathlib import Path -import asyncio +from threading import Event, Thread from typing import Any, Optional -import openvino_genai -import openvino as ov + import numpy as np +import openvino as ov +import openvino_genai from edgecraftrag.base import BaseComponent, CompType, ModelType +from edgecraftrag.components.ov_llamaindex_helper import OpenVINOGenAIEmbedding, OpenVINOGenAIReranking +from llama_index.core.base.llms.types import CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen from llama_index.embeddings.huggingface_openvino import OpenVINOEmbedding from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.llms.openvino import OpenVINOLLM -from llama_index.postprocessor.openvino_rerank import OpenVINORerank -from edgecraftrag.components.ov_llamaindex_helper import OpenVINOGenAIEmbedding, OpenVINOGenAIReranking from llama_index.llms.openvino_genai import OpenVINOGenAILLM +from llama_index.postprocessor.openvino_rerank import OpenVINORerank from pydantic import Field, model_serializer -from llama_index.core.base.llms.types import CompletionResponse, CompletionResponseAsyncGen, CompletionResponseGen -from threading import Event, Thread + def resolve_model_path(model_path: str) -> str: if not model_path: @@ -127,18 +129,13 @@ def __init__(self, model_id, model_path, device, weight): model_path = resolve_model_path(model_path) if not model_exist(model_path): OpenVINOEmbedding.create_and_save_openvino_model(model_id, model_path) - model_kwargs={ - "ov_config": { - "NUM_STREAMS": "1", - "PERFORMANCE_HINT": "LATENCY" - } - } + model_kwargs = {"ov_config": {"NUM_STREAMS": "1", "PERFORMANCE_HINT": "LATENCY"}} OpenVINOEmbedding.__init__(self, model_id_or_path=model_path, device=device, model_kwargs=model_kwargs) if device == "AUTO": - real_device=self._model.request.get_property("EXECUTION_DEVICES")[0] + real_device = self._model.request.get_property("EXECUTION_DEVICES")[0] self._model.to(real_device) self._model.compile() - device=real_device + device = real_device buf = io.BytesIO() self._model.request.export_model(buf) self.size_mb = len(buf.getvalue()) / 1024 / 1024 @@ -150,18 +147,38 @@ def __init__(self, model_id, model_path, device, weight): self.device = device self.weight = "" + class OpenVINOGenAIEmbeddingModel(BaseModelComponent, OpenVINOGenAIEmbedding): def __init__(self, model_id, model_path, device, weight): - max_length=512 + max_length = 512 model_path = resolve_model_path(model_path) if not model_exist(model_path): OpenVINOGenAIEmbedding.create_and_save_openvino_model(model_id, model_path) if device == "NPU": - OpenVINOGenAIEmbedding.__init__(self, model_path=model_path, device=device, embed_batch_size=1, pad_to_max_length=True, max_length=512, normalize=True, pooling="mean", padding_side="right") + OpenVINOGenAIEmbedding.__init__( + self, + model_path=model_path, + device=device, + embed_batch_size=1, + pad_to_max_length=True, + max_length=512, + normalize=True, + pooling="mean", + padding_side="right", + ) else: - OpenVINOGenAIEmbedding.__init__(self, model_path=model_path, device=device, pad_to_max_length=True, max_length=max_length, normalize=True, pooling="mean", padding_side="right") - self.size_mb = round(os.path.getsize(model_path+"/openvino_model.bin")/(1024*1024),3) + OpenVINOGenAIEmbedding.__init__( + self, + model_path=model_path, + device=device, + pad_to_max_length=True, + max_length=max_length, + normalize=True, + pooling="mean", + padding_side="right", + ) + self.size_mb = round(os.path.getsize(model_path + "/openvino_model.bin") / (1024 * 1024), 3) self.comp_type = CompType.MODEL self.comp_subtype = ModelType.EMBEDDING self.model_id = model_id @@ -170,30 +187,21 @@ def __init__(self, model_id, model_path, device, weight): self.weight = "" self.model_id_or_path = model_path + class OpenVINORerankModel(BaseModelComponent, OpenVINORerank): def __init__(self, model_id, model_path, device, weight): model_path = resolve_model_path(model_path) if not model_exist(model_path): OpenVINORerank.create_and_save_openvino_model(model_id, model_path) - model_kwargs={ - "ov_config": { - "NUM_STREAMS": "1", - "PERFORMANCE_HINT": "LATENCY" - } - } + model_kwargs = {"ov_config": {"NUM_STREAMS": "1", "PERFORMANCE_HINT": "LATENCY"}} - OpenVINORerank.__init__( - self, - model_id_or_path=model_path, - device=device, - model_kwargs=model_kwargs - ) + OpenVINORerank.__init__(self, model_id_or_path=model_path, device=device, model_kwargs=model_kwargs) if device == "AUTO": - real_device=self._model.request.get_property("EXECUTION_DEVICES")[0] + real_device = self._model.request.get_property("EXECUTION_DEVICES")[0] self._model.to(real_device) self._model.compile() - device=real_device + device = real_device buf = io.BytesIO() self._model.request.export_model(buf) self.size_mb = len(buf.getvalue()) / 1024 / 1024 @@ -205,10 +213,11 @@ def __init__(self, model_id, model_path, device, weight): self.device = device self.weight = "" + class OpenVINOGenAIRerankModel(BaseModelComponent, OpenVINOGenAIReranking): def __init__(self, model_id, model_path, device, weight): - max_length=512 + max_length = 512 model_path = resolve_model_path(model_path) if not model_exist(model_path): OpenVINOGenAIReranking.create_and_save_openvino_model(model_id, model_path) @@ -217,10 +226,10 @@ def __init__(self, model_id, model_path, device, weight): model_id_or_path=model_path, device=device, max_length=max_length, - pad_to_max_length=True, - padding_side="right" + pad_to_max_length=True, + padding_side="right", ) - self.size_mb = round(os.path.getsize(model_path+"/openvino_model.bin")/(1024*1024),3) + self.size_mb = round(os.path.getsize(model_path + "/openvino_model.bin") / (1024 * 1024), 3) self.comp_type = CompType.MODEL self.comp_subtype = ModelType.RERANKER self.model_id = model_id @@ -246,6 +255,7 @@ def __init__(self, model_id, model_path, device, weight, model=None): self.device = device self.weight = weight + class OpenVINOGenAILLMModel(BaseModelComponent, OpenVINOGenAILLM): def __init__(self, model_id, model_path, device, weight, model=None): @@ -266,10 +276,6 @@ def __init__(self, model_id, model_path, device, weight, model=None): self.device_map = device self._model = self._pipe - - - - async def astream_complete_with_bench( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseAsyncGen: @@ -298,12 +304,10 @@ def worker() -> None: if "error" in error_holder: raise error_holder["error"] - + return gen() - def stream_complete_with_bench( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponseGen: + def stream_complete_with_bench(self, prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponseGen: """Streaming completion endpoint.""" full_prompt = prompt if not formatted: @@ -313,7 +317,7 @@ def stream_complete_with_bench( full_prompt = f"{self.system_prompt} {full_prompt}" input_data = self._tokenizer.encode(full_prompt) - input_ids = input_data.input_ids.data + input_ids = input_data.input_ids.data attention_mask = input_data.attention_mask full_prompt = openvino_genai.TokenizedInputs(ov.Tensor(input_ids), attention_mask) generation_holder = {} @@ -322,10 +326,7 @@ def stream_complete_with_bench( def run_generation() -> None: try: generation_holder["result"] = self._pipe.generate( - full_prompt, - self.config, - streamer=self._streamer, - **kwargs + full_prompt, self.config, streamer=self._streamer, **kwargs ) except Exception as exc: error_holder["error"] = exc @@ -350,10 +351,7 @@ def gen() -> CompletionResponseGen: return gen() - - def complete_with_bench( - self, prompt: str, formatted: bool = False, **kwargs: Any - ) -> CompletionResponse: + def complete_with_bench(self, prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponse: """Completion endpoint.""" full_prompt = prompt if not formatted: @@ -364,14 +362,13 @@ def complete_with_bench( elif self.system_prompt: full_prompt = f"{self.system_prompt} {full_prompt}" - input_data = self._tokenizer.encode(full_prompt) - input_ids = input_data.input_ids.data + input_ids = input_data.input_ids.data attention_mask = input_data.attention_mask full_prompt = openvino_genai.TokenizedInputs(ov.Tensor(input_ids), attention_mask) generation_result = self._pipe.generate(full_prompt, self.config, **kwargs) self.perf_metrics = generation_result.perf_metrics generated_tokens = np.array(generation_result.tokens) completion = self._tokenizer.decode(generated_tokens) - token = completion[0] - return CompletionResponse(text=token, raw={"model_output": token}) \ No newline at end of file + token = completion[0] + return CompletionResponse(text=token, raw={"model_output": token}) diff --git a/EdgeCraftRAG/edgecraftrag/components/ov_llamaindex_helper.py b/EdgeCraftRAG/edgecraftrag/components/ov_llamaindex_helper.py index 5abcd1a06e..55d009467d 100644 --- a/EdgeCraftRAG/edgecraftrag/components/ov_llamaindex_helper.py +++ b/EdgeCraftRAG/edgecraftrag/components/ov_llamaindex_helper.py @@ -1,20 +1,21 @@ +# Copyright (C) 2026 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any, Dict, List, Optional + from llama_index.core.base.embeddings.base import ( DEFAULT_EMBED_BATCH_SIZE, BaseEmbedding, ) -from llama_index.core.postprocessor.types import BaseNodePostprocessor -from typing import Any, List, Optional, Dict from llama_index.core.bridge.pydantic import Field, PrivateAttr -from llama_index.core.callbacks import CallbackManager -from llama_index.core.callbacks import CBEventType, EventPayload +from llama_index.core.callbacks import CallbackManager, CBEventType, EventPayload from llama_index.core.instrumentation import get_dispatcher from llama_index.core.instrumentation.events.rerank import ( ReRankEndEvent, ReRankStartEvent, ) +from llama_index.core.postprocessor.types import BaseNodePostprocessor from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle -from llama_index.core.instrumentation import get_dispatcher - dispatcher = get_dispatcher(__name__) @@ -48,7 +49,9 @@ def __init__( import openvino_genai except ImportError: - raise ImportError("Could not import OpenVINO GenAI package. " "Please install it with `pip install openvino-genai`.") + raise ImportError( + "Could not import OpenVINO GenAI package. " "Please install it with `pip install openvino-genai`." + ) if pooling not in ["cls", "mean"]: raise ValueError(f"Pooling {pooling} not supported.") @@ -62,7 +65,9 @@ def __init__( if padding_side: config.padding_side = padding_side config.pooling_type = ( - openvino_genai.TextEmbeddingPipeline.PoolingType.MEAN if pooling == "mean" else openvino_genai.TextEmbeddingPipeline.PoolingType.CLS + openvino_genai.TextEmbeddingPipeline.PoolingType.MEAN + if pooling == "mean" + else openvino_genai.TextEmbeddingPipeline.PoolingType.CLS ) config.query_instruction = query_instruction try: @@ -78,7 +83,7 @@ def __init__( normalize=normalize, query_instruction=query_instruction, text_instruction=text_instruction, - pad_to_max_length=pad_to_max_length + pad_to_max_length=pad_to_max_length, ) self._ov_pipe = openvino_genai.TextEmbeddingPipeline(model_path, device, config, **model_kwargs) self._device = device @@ -133,9 +138,17 @@ def __init__( try: import openvino_genai except ImportError: - raise ImportError("Could not import OpenVINO GenAI package. " "Please install it with `pip install openvino-genai`.") + raise ImportError( + "Could not import OpenVINO GenAI package. " "Please install it with `pip install openvino-genai`." + ) - super().__init__(top_n=top_n, max_length=max_length, model_id_or_path=model_id_or_path, device=device, keep_retrieval_score=keep_retrieval_score) + super().__init__( + top_n=top_n, + max_length=max_length, + model_id_or_path=model_id_or_path, + device=device, + keep_retrieval_score=keep_retrieval_score, + ) config = openvino_genai.TextRerankPipeline.Config() config.top_n = top_n diff --git a/EdgeCraftRAG/edgecraftrag/components/pipeline.py b/EdgeCraftRAG/edgecraftrag/components/pipeline.py index 42fe354bc0..832cc0924b 100644 --- a/EdgeCraftRAG/edgecraftrag/components/pipeline.py +++ b/EdgeCraftRAG/edgecraftrag/components/pipeline.py @@ -2,13 +2,13 @@ # SPDX-License-Identifier: Apache-2.0 import asyncio +import gc import json import os import time -import gc from concurrent.futures import ThreadPoolExecutor from typing import Any, Callable, List, Optional -from openvino import Core + from comps.cores.proto.api_protocol import ChatCompletionRequest from edgecraftrag.base import ( BaseComponent, @@ -16,17 +16,23 @@ CompType, GeneratorType, InferenceType, + NodeParserType, RetrieverType, ) -from edgecraftrag.base import NodeParserType from edgecraftrag.components.generator import clone_generator +from edgecraftrag.components.knowledge_base import Knowledge from edgecraftrag.components.postprocessor import RerankProcessor from edgecraftrag.components.query_preprocess import query_search -from edgecraftrag.components.knowledge_base import Knowledge -from edgecraftrag.components.retriever import AutoMergeRetriever, SimpleBM25Retriever, VectorSimRetriever, KBadminRetriever +from edgecraftrag.components.retriever import ( + AutoMergeRetriever, + KBadminRetriever, + SimpleBM25Retriever, + VectorSimRetriever, +) from edgecraftrag.env import SEARCH_CONFIG_PATH, SEARCH_DIR from fastapi.responses import StreamingResponse from llama_index.core.schema import QueryBundle +from openvino import Core from pydantic import BaseModel, Field, model_serializer @@ -60,11 +66,9 @@ def __init__( self.idx = str(idx) self.enable_benchmark = os.getenv("ENABLE_BENCHMARK", "False").lower() == "true" - self.max_util = round(( - 0.95 - float(os.environ.get("GPU_MEMORY_UTIL", 0)) - if "LLM_MODEL" in os.environ - else 0.95 - ),3) + self.max_util = round( + (0.95 - float(os.environ.get("GPU_MEMORY_UTIL", 0)) if "LLM_MODEL" in os.environ else 0.95), 3 + ) self.run_pipeline_cb = run_pipeline self.run_retriever_postprocessor_cb = run_retrieve_postprocess self.run_retriever_cb = run_retrieve @@ -74,7 +78,7 @@ def __init__( self._origin_json = origin_json if origin_json is not None else "{}" self.retriever_type = "" self.retrieve_topk = 0 - self.max_retrieve_topk=0 + self.max_retrieve_topk = 0 self.retrievers = [] # TODO: consider race condition @@ -163,6 +167,7 @@ def get_generator(self, generator_type: str) -> Optional[BaseComponent]: if gen.comp_subtype == generator_type: return gen return None + def update_retriever_config(self, retriever_type: str, retrieve_topk: int): self.retriever_type = retriever_type self.retrieve_topk = retrieve_topk @@ -210,7 +215,6 @@ def update_retriever(self, kb, prev_indexer): raise ValueError(f"Retriever type {self.retriever_type} not supported") break - def clear_retrievers(self): self.retrievers = [] @@ -238,29 +242,28 @@ def _update_config_and_retrievers(self, changed: bool) -> None: origin_json = json.loads(self._origin_json) origin_json["retriever"]["retrieve_topk"] = self.retrieve_topk origin_json["retriever"]["max_retrieve_topk"] = self.max_retrieve_topk - + for retriever in self.retrievers: retriever.topk = self.retrieve_topk - + if self.postprocessor: for i, processor in enumerate(self.postprocessor): processor.top_n = min(processor.top_n, self.max_retrieve_topk) origin_json["postprocessor"][i]["top_n"] = processor.top_n - + self._origin_json = json.dumps(origin_json) def _resolve_max_util(self, reranker_device: str, core: Core) -> float: """Resolve memory utilization rate based on device and inference type.""" - if self.generator[0].inference_type == InferenceType.LOCAL: if self.generator[0].llm().device == reranker_device: return 0.5 else: return 0.95 - + if reranker_device == "CPU" or reranker_device == "NPU": return 0.95 - + device_type_obj = self._safe_get_property(reranker_device, "DEVICE_TYPE", core) reranker_card = 0 if reranker_device == "CPU": @@ -275,19 +278,22 @@ def _resolve_max_util(self, reranker_device: str, core: Core) -> float: dgpu_number = 0 for d in core.available_devices: - if d.startswith("GPU") and getattr(self._safe_get_property(d, "DEVICE_TYPE", core), "name", "") == "DISCRETE": + if ( + d.startswith("GPU") + and getattr(self._safe_get_property(d, "DEVICE_TYPE", core), "name", "") == "DISCRETE" + ): dgpu_number += 1 mask = os.getenv("VLLM_AFFINITY_MASK", "") allowed = set(int(x) for x in mask.split(",") if x.strip().isdigit()) max_gpu = max(allowed) if allowed else None - + if max_gpu >= dgpu_number and int(os.getenv("TP", 1)) > 1: vllm_device_type = "iGPU" else: vllm_device_type = "dGPU" if vllm_device_type == "iGPU" and reranker_device_type == "iGPU": return self.max_util - + if vllm_device_type == "dGPU" and reranker_device_type == "dGPU": if reranker_card in allowed: return self.max_util @@ -313,15 +319,19 @@ def _safe_get_property(device_name: str, property_name: str, core: Core): return None def _calculate_max_retrieve_topk( - self, available_memory_mb: float, hidden_size: Optional[int], num_hidden_layers: Optional[int], embedding_length: int + self, + available_memory_mb: float, + hidden_size: Optional[int], + num_hidden_layers: Optional[int], + embedding_length: int, ) -> int: """Calculate maximum top-k based on available memory and model config.""" # Constants for calculation MEMORY_CALC_DIVISORS = 2 * 2 * 0.2 # From original formula - + if not hidden_size or not num_hidden_layers or embedding_length <= 0: return self.retrieve_topk - + denominator = hidden_size * num_hidden_layers * MEMORY_CALC_DIVISORS * embedding_length max_topk = int(available_memory_mb * 1024 * 1024 / denominator) return max(1, max_topk) # Ensure at least 1 @@ -330,7 +340,7 @@ def _get_reranker_config(self) -> dict: """Safely retrieve reranker model configuration.""" if not self.postprocessor: return {} - + try: model = self.postprocessor[0].model if hasattr(model, "_model") and hasattr(model._model, "config"): @@ -363,7 +373,7 @@ def check_top_k(self, active_kbs: list[Knowledge]): reranker_size = reranker_model.size_mb if reranker_model else 0 embedding_size = sum(getattr(kb.indexer.model, "size_mb", 0) for kb in valid_kbs) embedding_length = max((getattr(kb.indexer, "d", 0) for kb in valid_kbs), default=0) - + # Apply default minimums embedding_size = embedding_size or 512 embedding_length = embedding_length or 256 @@ -380,14 +390,20 @@ def check_top_k(self, active_kbs: list[Knowledge]): available_memory_mb = gpu_max_alloc_mem_size / 1024 / 1024 * max_util - reranker_size - embedding_size # Get model configuration and calculate max top-k config = self._get_reranker_config() - if not isinstance(config, dict) : + if not isinstance(config, dict): if not hasattr(config, "to_dict"): config = {} else: config = config.to_dict() - - num_hidden_layers = config.get("num_hidden_layers") if isinstance(config, dict) else getattr(config, "num_hidden_layers", None) - hidden_size = (config.get("hidden_size") or config.get("hidden_dim")) if isinstance(config, dict) else (getattr(config, "hidden_size", None) or getattr(config, "hidden_dim", None)) + + num_hidden_layers = ( + config.get("num_hidden_layers") if isinstance(config, dict) else getattr(config, "num_hidden_layers", None) + ) + hidden_size = ( + (config.get("hidden_size") or config.get("hidden_dim")) + if isinstance(config, dict) + else (getattr(config, "hidden_size", None) or getattr(config, "hidden_dim", None)) + ) self.max_retrieve_topk = self._calculate_max_retrieve_topk( available_memory_mb, hidden_size, num_hidden_layers, embedding_length ) @@ -402,6 +418,7 @@ def check_top_k(self, active_kbs: list[Knowledge]): self._update_config_and_retrievers(changed) return changed + async def run_retrieve(pl: Pipeline, chat_request: ChatCompletionRequest) -> Any: query = chat_request.messages top_k = None if chat_request.k == ChatCompletionRequest.model_fields["k"].default else chat_request.k @@ -581,7 +598,14 @@ async def run_pipeline( if pl.enable_benchmark: start = time.perf_counter() if target_generator.inference_type == InferenceType.LOCAL: - ret = await target_generator.run(chat_request, retri_res, np_type, enable_benchmark=pl.enable_benchmark, benchmark=pl.benchmark, benchmark_index=benchmark_index) + ret = await target_generator.run( + chat_request, + retri_res, + np_type, + enable_benchmark=pl.enable_benchmark, + benchmark=pl.benchmark, + benchmark_index=benchmark_index, + ) elif target_generator.inference_type in (InferenceType.VLLM, InferenceType.OVMS): ret = await target_generator.run_remote( chat_request, @@ -594,15 +618,17 @@ async def run_pipeline( else: raise ValueError("LLM inference_type not supported") if not isinstance(ret, StreamingResponse) and pl.enable_benchmark: - if ( target_generator.inference_type == InferenceType.LOCAL ): - if ( not chat_request.stream ): - pl.benchmark.update_benchmark_data_genai(benchmark_index, CompType.GENERATOR, time.perf_counter() - start, pl.generator[0].llm) + if target_generator.inference_type == InferenceType.LOCAL: + if not chat_request.stream: + pl.benchmark.update_benchmark_data_genai( + benchmark_index, CompType.GENERATOR, time.perf_counter() - start, pl.generator[0].llm + ) pl.benchmark.insert_llm_data_genai(benchmark_index, input_token_size, pl.generator[0].llm) cleanup_pipeline_resources(retri_res, post_res, np_types, sub_questionss_result) return ret, contexts pl.benchmark.update_benchmark_data(benchmark_index, CompType.GENERATOR, time.perf_counter() - start) pl.benchmark.insert_llm_data(benchmark_index, input_token_size) - + cleanup_pipeline_resources(retri_res, post_res, np_types, sub_questionss_result) return ret, contexts @@ -611,7 +637,7 @@ async def run_generator( pl: Pipeline, chat_request: ChatCompletionRequest, generator_type: str = GeneratorType.CHATQNA ) -> Any: active_kbs = chat_request.user if chat_request.user else [] - # If using multiple knowledge bases, unstructured node parser cannot work with other types of node parser + # If using multiple knowledge bases, unstructured node parser cannot work with other types of node parser np_types = {kb.node_parser.comp_subtype for kb in active_kbs} if len(np_types) > 1 and NodeParserType.UNSTRUCTURED in np_types: raise ValueError("unstructured node parser cannot work with other types of node parser") diff --git a/EdgeCraftRAG/edgecraftrag/components/query_preprocess.py b/EdgeCraftRAG/edgecraftrag/components/query_preprocess.py index 1f0d21c2e8..d5ead3bbd9 100644 --- a/EdgeCraftRAG/edgecraftrag/components/query_preprocess.py +++ b/EdgeCraftRAG/edgecraftrag/components/query_preprocess.py @@ -93,7 +93,6 @@ def __init__( **kwargs, ): """Initialize the LLM-based relevance estimator.""" - super().__init__( model_id, device, diff --git a/EdgeCraftRAG/edgecraftrag/components/retriever.py b/EdgeCraftRAG/edgecraftrag/components/retriever.py index 5469c0f8a1..62ee1b5630 100644 --- a/EdgeCraftRAG/edgecraftrag/components/retriever.py +++ b/EdgeCraftRAG/edgecraftrag/components/retriever.py @@ -5,7 +5,6 @@ from typing import Any, List, Optional, cast import requests - from edgecraftrag.base import BaseComponent, CompType, RetrieverType from llama_index.core.indices.vector_store.retrievers import VectorIndexRetriever from llama_index.core.retrievers import AutoMergingRetriever @@ -14,7 +13,6 @@ from pydantic import model_serializer - class VectorSimRetriever(BaseComponent, VectorIndexRetriever): def __init__(self, indexer, **kwargs): @@ -116,7 +114,7 @@ def __init__(self, indexer, **kwargs): def run(self, **kwargs) -> Any: for k, v in kwargs.items(): if k == "query": - if self._index.comp_subtype == 'milvus_vector': + if self._index.comp_subtype == "milvus_vector": raise NotImplementedError("not support BM25 retriever for Milvus vector store") top_k = kwargs["top_k"] if kwargs["top_k"] else self.topk nodes = cast(List[BaseNode], list(self._docstore.docs.values())) diff --git a/EdgeCraftRAG/edgecraftrag/config_repository.py b/EdgeCraftRAG/edgecraftrag/config_repository.py index c2f42f4900..6e7cb52a5d 100644 --- a/EdgeCraftRAG/edgecraftrag/config_repository.py +++ b/EdgeCraftRAG/edgecraftrag/config_repository.py @@ -291,7 +291,7 @@ async def save_pipeline_configurations(operation: str = None, pipeline=None): chatqna_gen = pipeline.get_generator(GeneratorType.CHATQNA) if chatqna_gen: if GeneratorType.CHATQNA in gens_data: - gens_data[GeneratorType.CHATQNA]["prompt_content"] = chatqna_gen.prompt_content + gens_data[GeneratorType.CHATQNA]["prompt_content"] = chatqna_gen.prompt_content target_data["active"] = pipeline.status.active if pipeline_milvus_repo: diff --git a/EdgeCraftRAG/edgecraftrag/controllers/agentmgr.py b/EdgeCraftRAG/edgecraftrag/controllers/agentmgr.py index 1db38340c9..ba1ac2a4b2 100644 --- a/EdgeCraftRAG/edgecraftrag/controllers/agentmgr.py +++ b/EdgeCraftRAG/edgecraftrag/controllers/agentmgr.py @@ -51,11 +51,19 @@ def create_agent(self, cfgs: AgentCreateIn): return "Create Agent failed. Pipeline id not found." if cfgs.type == AgentType.SIMPLE: new_agent = SimpleRAGAgent(cfgs.idx, cfgs.name, cfgs.pipeline_idx, cfgs.configs) - new_agent.configs["max_retrievals"]=min(new_agent.configs["max_retrievals"], self.get_pipeline_by_name_or_id(cfgs.pipeline_idx).max_retrieve_topk) + new_agent.configs["max_retrievals"] = min( + new_agent.configs["max_retrievals"], + self.get_pipeline_by_name_or_id(cfgs.pipeline_idx).max_retrieve_topk, + ) elif cfgs.type == AgentType.DEEPSEARCH: new_agent = DeepSearchAgent(cfgs.idx, cfgs.name, cfgs.pipeline_idx, cfgs.configs) - new_agent.configs["retrieve_top_k"]=min(new_agent.configs["retrieve_top_k"], self.get_pipeline_by_name_or_id(cfgs.pipeline_idx).max_retrieve_topk) - new_agent.configs["rerank_top_k"]=min(new_agent.configs["rerank_top_k"], self.get_pipeline_by_name_or_id(cfgs.pipeline_idx).max_retrieve_topk) + new_agent.configs["retrieve_top_k"] = min( + new_agent.configs["retrieve_top_k"], + self.get_pipeline_by_name_or_id(cfgs.pipeline_idx).max_retrieve_topk, + ) + new_agent.configs["rerank_top_k"] = min( + new_agent.configs["rerank_top_k"], self.get_pipeline_by_name_or_id(cfgs.pipeline_idx).max_retrieve_topk + ) if new_agent is not None: self.set_manager(new_agent) diff --git a/EdgeCraftRAG/edgecraftrag/controllers/filemgr.py b/EdgeCraftRAG/edgecraftrag/controllers/filemgr.py index 7777d148de..839ac23ef7 100644 --- a/EdgeCraftRAG/edgecraftrag/controllers/filemgr.py +++ b/EdgeCraftRAG/edgecraftrag/controllers/filemgr.py @@ -3,9 +3,9 @@ import asyncio import os +from pathlib import Path from typing import Any, Callable, List, Optional -from pathlib import Path from edgecraftrag.base import BaseMgr from edgecraftrag.components.data import File from llama_index.core.schema import Document diff --git a/EdgeCraftRAG/edgecraftrag/controllers/knowledge_basemgr.py b/EdgeCraftRAG/edgecraftrag/controllers/knowledge_basemgr.py index e59d5d5beb..d25daab64b 100644 --- a/EdgeCraftRAG/edgecraftrag/controllers/knowledge_basemgr.py +++ b/EdgeCraftRAG/edgecraftrag/controllers/knowledge_basemgr.py @@ -1,9 +1,9 @@ # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 +import gc from typing import Any, Dict, List, Optional -import gc from edgecraftrag.api_schema import KnowledgeBaseCreateIn from edgecraftrag.base import BaseMgr from edgecraftrag.components.knowledge_base import Knowledge @@ -108,7 +108,7 @@ def delete_knowledge_base(self, name: str): except Exception as e: pass try: - del kb.indexer.model._ov_pipe + del kb.indexer.model._ov_pipe except Exception as e: pass kb.indexer.model = None diff --git a/EdgeCraftRAG/edgecraftrag/controllers/modelmgr.py b/EdgeCraftRAG/edgecraftrag/controllers/modelmgr.py index 307108ff7b..a55f017234 100644 --- a/EdgeCraftRAG/edgecraftrag/controllers/modelmgr.py +++ b/EdgeCraftRAG/edgecraftrag/controllers/modelmgr.py @@ -3,6 +3,7 @@ import asyncio import os + from edgecraftrag.api_schema import ModelIn from edgecraftrag.base import BaseComponent, BaseMgr, CompType, ModelType from edgecraftrag.components.model import ( @@ -10,10 +11,10 @@ OpenAIEmbeddingModel, OpenVINOEmbeddingModel, OpenVINOGenAIEmbeddingModel, - OpenVINOLLMModel, OpenVINOGenAILLMModel, - OpenVINORerankModel, OpenVINOGenAIRerankModel, + OpenVINOLLMModel, + OpenVINORerankModel, resolve_model_path, ) @@ -85,7 +86,7 @@ def load_model(model_para: ModelIn): enable_genai = os.getenv("ENABLE_GENAI", "").lower() == "true" match model_para.model_type: case ModelType.EMBEDDING: - if model_para.device == "NPU" or enable_genai== True: + if model_para.device == "NPU" or enable_genai == True: model = OpenVINOGenAIEmbeddingModel( model_id=model_para.model_id, model_path=model_para.model_path, @@ -105,7 +106,7 @@ def load_model(model_para: ModelIn): api_base=model_para.api_base, ) case ModelType.RERANKER: - if enable_genai== True: + if enable_genai == True: model = OpenVINOGenAIRerankModel( model_id=model_para.model_id, model_path=model_para.model_path, @@ -168,7 +169,7 @@ def load_model_ben(model_para: ModelIn): model_id=model_para.model_id, model_path=resolved_model_path, device=model_para.device, - weight=model_para.weight + weight=model_para.weight, ) from transformers import AutoTokenizer diff --git a/EdgeCraftRAG/edgecraftrag/controllers/pipelinemgr.py b/EdgeCraftRAG/edgecraftrag/controllers/pipelinemgr.py index 2f34f6c7fc..88adfc589a 100644 --- a/EdgeCraftRAG/edgecraftrag/controllers/pipelinemgr.py +++ b/EdgeCraftRAG/edgecraftrag/controllers/pipelinemgr.py @@ -2,14 +2,15 @@ # SPDX-License-Identifier: Apache-2.0 import asyncio -import json import gc +import json from typing import Any -from openvino import Core, Type + from comps.cores.proto.api_protocol import ChatCompletionRequest from edgecraftrag.base import BaseMgr, CallbackType, InferenceType -from edgecraftrag.components.pipeline import Pipeline from edgecraftrag.components.knowledge_base import Knowledge +from edgecraftrag.components.pipeline import Pipeline +from openvino import Core, Type class PipelineMgr(BaseMgr): @@ -54,15 +55,15 @@ def remove_pipeline_by_name_or_id(self, name: str): pass try: del post.model._model - post.model._model=None + post.model._model = None except Exception as e: pass try: del post.model._ov_pipe except Exception as e: pass - post.model=None - post=None + post.model = None + post = None pl.postprocessor = None for gen in pl.generator: if gen.inference_type: @@ -80,7 +81,7 @@ def remove_pipeline_by_name_or_id(self, name: str): del llm_model._pipe except Exception as e: pass - llm_model._model=None + llm_model._model = None del llm_model del gen pl.generator = None diff --git a/EdgeCraftRAG/edgecraftrag/requirements.txt b/EdgeCraftRAG/edgecraftrag/requirements.txt index 74f2084355..56e9ec19bb 100644 --- a/EdgeCraftRAG/edgecraftrag/requirements.txt +++ b/EdgeCraftRAG/edgecraftrag/requirements.txt @@ -7,19 +7,7 @@ langchain-core==0.3.81 langchain-milvus==0.2.1 langchain-openai==0.3.35 langgraph==0.6.10 -opea-comps==1.3 -openai==2.15.0 -pillow>=10.4.0 -py-cpuinfo>=9.0.0 -pymilvus==2.6.6 -python-docx==1.1.2 -torch==2.8.0+cpu -torchvision==0.23.0+cpu -transformers==4.53.3 -unstructured[all-docs]==0.18.27 -werkzeug==3.1.3 llama-index==0.14.13 -pyarrow==22.0.0 llama-index-embeddings-openvino==0.6.1 llama-index-embeddings-openvino-genai==0.6.1 llama-index-llms-openai==0.6.13 @@ -30,4 +18,16 @@ llama-index-postprocessor-openvino-rerank==0.5.1 llama-index-readers-file==0.5.4 llama-index-retrievers-bm25==0.6.5 llama-index-vector-stores-faiss==0.5.2 -llama-index-vector-stores-milvus==0.9.6 \ No newline at end of file +llama-index-vector-stores-milvus==0.9.6 +opea-comps==1.3 +openai==2.15.0 +pillow>=10.4.0 +py-cpuinfo>=9.0.0 +pyarrow==22.0.0 +pymilvus==2.6.6 +python-docx==1.1.2 +torch==2.8.0+cpu +torchvision==0.23.0+cpu +transformers==4.53.3 +unstructured[all-docs]==0.18.27 +werkzeug==3.1.3 diff --git a/EdgeCraftRAG/tests/common.sh b/EdgeCraftRAG/tests/common.sh index 8a43fb30ed..67388822c5 100644 --- a/EdgeCraftRAG/tests/common.sh +++ b/EdgeCraftRAG/tests/common.sh @@ -64,4 +64,4 @@ function validate_knowledge() { "data" \ "edgecraftrag-server" \ '{"local_path":"/home/user/ui_cache"}' -} \ No newline at end of file +} diff --git a/EdgeCraftRAG/tests/configs/test_pipeline_local_llm.json b/EdgeCraftRAG/tests/configs/test_pipeline_local_llm.json index cb379ca2c5..ee9822844d 100644 --- a/EdgeCraftRAG/tests/configs/test_pipeline_local_llm.json +++ b/EdgeCraftRAG/tests/configs/test_pipeline_local_llm.json @@ -30,4 +30,4 @@ } ], "active": "True" -} \ No newline at end of file +} diff --git a/EdgeCraftRAG/tests/test_pipeline_local_llm.json b/EdgeCraftRAG/tests/test_pipeline_local_llm.json index cb379ca2c5..ee9822844d 100644 --- a/EdgeCraftRAG/tests/test_pipeline_local_llm.json +++ b/EdgeCraftRAG/tests/test_pipeline_local_llm.json @@ -30,4 +30,4 @@ } ], "active": "True" -} \ No newline at end of file +} diff --git a/EdgeCraftRAG/tools/README.md b/EdgeCraftRAG/tools/README.md index e5fde632bd..cc8c56723c 100644 --- a/EdgeCraftRAG/tools/README.md +++ b/EdgeCraftRAG/tools/README.md @@ -19,9 +19,9 @@ The main scripts in this directory are: Deployment methods: -| Method | Description | Requirements | Milvus Support | -|------|------|----------|-------------| -| baremetal | Start services as Python processes | Python 3.10+ | No (in-memory only) | +| Method | Description | Requirements | Milvus Support | +| --------- | ----------------------------------- | ----------------------- | ------------------------ | +| baremetal | Start services as Python processes | Python 3.10+ | No (in-memory only) | | container | Start services in Docker containers | Docker / Docker Compose | Yes (enabled by default) | Note: If you need Milvus, use the container deployment method. diff --git a/EdgeCraftRAG/tools/README_zh.md b/EdgeCraftRAG/tools/README_zh.md index da4c2d34c4..a68d87b8f1 100644 --- a/EdgeCraftRAG/tools/README_zh.md +++ b/EdgeCraftRAG/tools/README_zh.md @@ -19,10 +19,10 @@ 部署方式说明： -| 方式 | 描述 | 环境要求 | Milvus 支持 | -|------|------|----------|-------------| -| baremetal（裸金属） | 以 Python 进程方式启动服务 | Python 3.10+ | 否（仅内存） | -| container（容器） | 以 Docker 容器方式启动服务 | Docker / Docker Compose | 是（默认启用） | +| 方式 | 描述 | 环境要求 | Milvus 支持 | +| ------------------- | -------------------------- | ----------------------- | -------------- | +| baremetal（裸金属） | 以 Python 进程方式启动服务 | Python 3.10+ | 否（仅内存） | +| container（容器） | 以 Docker 容器方式启动服务 | Docker / Docker Compose | 是（默认启用） | 提示：如需使用 Milvus，请选择容器部署。 @@ -138,7 +138,6 @@ OVMS 相关行为说明： - `vllm_b60` - `ovms` - ## 2.2 交互模式 ```bash diff --git a/EdgeCraftRAG/tools/build_images.sh b/EdgeCraftRAG/tools/build_images.sh index 6a2bd0ed2f..abfb0a42dc 100755 --- a/EdgeCraftRAG/tools/build_images.sh +++ b/EdgeCraftRAG/tools/build_images.sh @@ -1,4 +1,7 @@ #!/usr/bin/env bash +# Copyright (C) 2026 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + set -euo pipefail PROJECT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" diff --git a/EdgeCraftRAG/ui/vue/components.d.ts b/EdgeCraftRAG/ui/vue/components.d.ts index 79fa8ff8f7..5c31b7cc21 100644 --- a/EdgeCraftRAG/ui/vue/components.d.ts +++ b/EdgeCraftRAG/ui/vue/components.d.ts @@ -1,8 +1,11 @@ +// Copyright (C) 2026 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 + /* eslint-disable */ // @ts-nocheck // Generated by unplugin-vue-components // Read more: https://github.com/vuejs/core/pull/3399 -export {} +export {}; /* prettier-ignore */ declare module 'vue' { diff --git a/EdgeCraftRAG/ui/vue/src/auto-imports.d.ts b/EdgeCraftRAG/ui/vue/src/auto-imports.d.ts index f6e2bab3ce..d07b1f9b7a 100644 --- a/EdgeCraftRAG/ui/vue/src/auto-imports.d.ts +++ b/EdgeCraftRAG/ui/vue/src/auto-imports.d.ts @@ -1,3 +1,6 @@ +// Copyright (C) 2026 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 + /* eslint-disable */ /* prettier-ignore */ // @ts-nocheck @@ -6,83 +9,98 @@ // biome-ignore lint: disable export {} declare global { - const EffectScope: typeof import('vue')['EffectScope'] - const acceptHMRUpdate: typeof import('pinia')['acceptHMRUpdate'] - const computed: typeof import('vue')['computed'] - const createApp: typeof import('vue')['createApp'] - const createPinia: typeof import('pinia')['createPinia'] - const customRef: typeof import('vue')['customRef'] - const defineAsyncComponent: typeof import('vue')['defineAsyncComponent'] - const defineComponent: typeof import('vue')['defineComponent'] - const defineStore: typeof import('pinia')['defineStore'] - const effectScope: typeof import('vue')['effectScope'] - const getActivePinia: typeof import('pinia')['getActivePinia'] - const getCurrentInstance: typeof import('vue')['getCurrentInstance'] - const getCurrentScope: typeof import('vue')['getCurrentScope'] - const h: typeof import('vue')['h'] - const inject: typeof import('vue')['inject'] - const isProxy: typeof import('vue')['isProxy'] - const isReactive: typeof import('vue')['isReactive'] - const isReadonly: typeof import('vue')['isReadonly'] - const isRef: typeof import('vue')['isRef'] - const mapActions: typeof import('pinia')['mapActions'] - const mapGetters: typeof import('pinia')['mapGetters'] - const mapState: typeof import('pinia')['mapState'] - const mapStores: typeof import('pinia')['mapStores'] - const mapWritableState: typeof import('pinia')['mapWritableState'] - const markRaw: typeof import('vue')['markRaw'] - const nextTick: typeof import('vue')['nextTick'] - const onActivated: typeof import('vue')['onActivated'] - const onBeforeMount: typeof import('vue')['onBeforeMount'] - const onBeforeRouteLeave: typeof import('vue-router')['onBeforeRouteLeave'] - const onBeforeRouteUpdate: typeof import('vue-router')['onBeforeRouteUpdate'] - const onBeforeUnmount: typeof import('vue')['onBeforeUnmount'] - const onBeforeUpdate: typeof import('vue')['onBeforeUpdate'] - const onDeactivated: typeof import('vue')['onDeactivated'] - const onErrorCaptured: typeof import('vue')['onErrorCaptured'] - const onMounted: typeof import('vue')['onMounted'] - const onRenderTracked: typeof import('vue')['onRenderTracked'] - const onRenderTriggered: typeof import('vue')['onRenderTriggered'] - const onScopeDispose: typeof import('vue')['onScopeDispose'] - const onServerPrefetch: typeof import('vue')['onServerPrefetch'] - const onUnmounted: typeof import('vue')['onUnmounted'] - const onUpdated: typeof import('vue')['onUpdated'] - const onWatcherCleanup: typeof import('vue')['onWatcherCleanup'] - const provide: typeof import('vue')['provide'] - const reactive: typeof import('vue')['reactive'] - const readonly: typeof import('vue')['readonly'] - const ref: typeof import('vue')['ref'] - const resolveComponent: typeof import('vue')['resolveComponent'] - const setActivePinia: typeof import('pinia')['setActivePinia'] - const setMapStoreSuffix: typeof import('pinia')['setMapStoreSuffix'] - const shallowReactive: typeof import('vue')['shallowReactive'] - const shallowReadonly: typeof import('vue')['shallowReadonly'] - const shallowRef: typeof import('vue')['shallowRef'] - const storeToRefs: typeof import('pinia')['storeToRefs'] - const toRaw: typeof import('vue')['toRaw'] - const toRef: typeof import('vue')['toRef'] - const toRefs: typeof import('vue')['toRefs'] - const toValue: typeof import('vue')['toValue'] - const triggerRef: typeof import('vue')['triggerRef'] - const unref: typeof import('vue')['unref'] - const useAttrs: typeof import('vue')['useAttrs'] - const useCssModule: typeof import('vue')['useCssModule'] - const useCssVars: typeof import('vue')['useCssVars'] - const useId: typeof import('vue')['useId'] - const useLink: typeof import('vue-router')['useLink'] - const useModel: typeof import('vue')['useModel'] - const useRoute: typeof import('vue-router')['useRoute'] - const useRouter: typeof import('vue-router')['useRouter'] - const useSlots: typeof import('vue')['useSlots'] - const useTemplateRef: typeof import('vue')['useTemplateRef'] - const watch: typeof import('vue')['watch'] - const watchEffect: typeof import('vue')['watchEffect'] - const watchPostEffect: typeof import('vue')['watchPostEffect'] - const watchSyncEffect: typeof import('vue')['watchSyncEffect'] + const EffectScope: (typeof import("vue"))["EffectScope"]; + const acceptHMRUpdate: (typeof import("pinia"))["acceptHMRUpdate"]; + const computed: (typeof import("vue"))["computed"]; + const createApp: (typeof import("vue"))["createApp"]; + const createPinia: (typeof import("pinia"))["createPinia"]; + const customRef: (typeof import("vue"))["customRef"]; + const defineAsyncComponent: (typeof import("vue"))["defineAsyncComponent"]; + const defineComponent: (typeof import("vue"))["defineComponent"]; + const defineStore: (typeof import("pinia"))["defineStore"]; + const effectScope: (typeof import("vue"))["effectScope"]; + const getActivePinia: (typeof import("pinia"))["getActivePinia"]; + const getCurrentInstance: (typeof import("vue"))["getCurrentInstance"]; + const getCurrentScope: (typeof import("vue"))["getCurrentScope"]; + const h: (typeof import("vue"))["h"]; + const inject: (typeof import("vue"))["inject"]; + const isProxy: (typeof import("vue"))["isProxy"]; + const isReactive: (typeof import("vue"))["isReactive"]; + const isReadonly: (typeof import("vue"))["isReadonly"]; + const isRef: (typeof import("vue"))["isRef"]; + const mapActions: (typeof import("pinia"))["mapActions"]; + const mapGetters: (typeof import("pinia"))["mapGetters"]; + const mapState: (typeof import("pinia"))["mapState"]; + const mapStores: (typeof import("pinia"))["mapStores"]; + const mapWritableState: (typeof import("pinia"))["mapWritableState"]; + const markRaw: (typeof import("vue"))["markRaw"]; + const nextTick: (typeof import("vue"))["nextTick"]; + const onActivated: (typeof import("vue"))["onActivated"]; + const onBeforeMount: (typeof import("vue"))["onBeforeMount"]; + const onBeforeRouteLeave: (typeof import("vue-router"))["onBeforeRouteLeave"]; + const onBeforeRouteUpdate: (typeof import("vue-router"))["onBeforeRouteUpdate"]; + const onBeforeUnmount: (typeof import("vue"))["onBeforeUnmount"]; + const onBeforeUpdate: (typeof import("vue"))["onBeforeUpdate"]; + const onDeactivated: (typeof import("vue"))["onDeactivated"]; + const onErrorCaptured: (typeof import("vue"))["onErrorCaptured"]; + const onMounted: (typeof import("vue"))["onMounted"]; + const onRenderTracked: (typeof import("vue"))["onRenderTracked"]; + const onRenderTriggered: (typeof import("vue"))["onRenderTriggered"]; + const onScopeDispose: (typeof import("vue"))["onScopeDispose"]; + const onServerPrefetch: (typeof import("vue"))["onServerPrefetch"]; + const onUnmounted: (typeof import("vue"))["onUnmounted"]; + const onUpdated: (typeof import("vue"))["onUpdated"]; + const onWatcherCleanup: (typeof import("vue"))["onWatcherCleanup"]; + const provide: (typeof import("vue"))["provide"]; + const reactive: (typeof import("vue"))["reactive"]; + const readonly: (typeof import("vue"))["readonly"]; + const ref: (typeof import("vue"))["ref"]; + const resolveComponent: (typeof import("vue"))["resolveComponent"]; + const setActivePinia: (typeof import("pinia"))["setActivePinia"]; + const setMapStoreSuffix: (typeof import("pinia"))["setMapStoreSuffix"]; + const shallowReactive: (typeof import("vue"))["shallowReactive"]; + const shallowReadonly: (typeof import("vue"))["shallowReadonly"]; + const shallowRef: (typeof import("vue"))["shallowRef"]; + const storeToRefs: (typeof import("pinia"))["storeToRefs"]; + const toRaw: (typeof import("vue"))["toRaw"]; + const toRef: (typeof import("vue"))["toRef"]; + const toRefs: (typeof import("vue"))["toRefs"]; + const toValue: (typeof import("vue"))["toValue"]; + const triggerRef: (typeof import("vue"))["triggerRef"]; + const unref: (typeof import("vue"))["unref"]; + const useAttrs: (typeof import("vue"))["useAttrs"]; + const useCssModule: (typeof import("vue"))["useCssModule"]; + const useCssVars: (typeof import("vue"))["useCssVars"]; + const useId: (typeof import("vue"))["useId"]; + const useLink: (typeof import("vue-router"))["useLink"]; + const useModel: (typeof import("vue"))["useModel"]; + const useRoute: (typeof import("vue-router"))["useRoute"]; + const useRouter: (typeof import("vue-router"))["useRouter"]; + const useSlots: (typeof import("vue"))["useSlots"]; + const useTemplateRef: (typeof import("vue"))["useTemplateRef"]; + const watch: (typeof import("vue"))["watch"]; + const watchEffect: (typeof import("vue"))["watchEffect"]; + const watchPostEffect: (typeof import("vue"))["watchPostEffect"]; + const watchSyncEffect: (typeof import("vue"))["watchSyncEffect"]; } // for type re-export declare global { // @ts-ignore - export type { Component, ComponentPublicInstance, ComputedRef, DirectiveBinding, ExtractDefaultPropTypes, ExtractPropTypes, ExtractPublicPropTypes, InjectionKey, PropType, Ref, MaybeRef, MaybeRefOrGetter, VNode, WritableComputedRef } from 'vue' - import('vue') + export type { + Component, + ComponentPublicInstance, + ComputedRef, + DirectiveBinding, + ExtractDefaultPropTypes, + ExtractPropTypes, + ExtractPublicPropTypes, + InjectionKey, + PropType, + Ref, + MaybeRef, + MaybeRefOrGetter, + VNode, + WritableComputedRef, + } from "vue"; + import("vue"); } diff --git a/EdgeCraftRAG/ui/vue/src/i18n/en.ts b/EdgeCraftRAG/ui/vue/src/i18n/en.ts index addcee16be..c0265b068c 100644 --- a/EdgeCraftRAG/ui/vue/src/i18n/en.ts +++ b/EdgeCraftRAG/ui/vue/src/i18n/en.ts @@ -30,8 +30,7 @@ export default { export: "Export", uploadTip: "Click or drag file to this area to upload", loading: "Loading", - waitTip: - "Please wait patiently and do not refresh the page during this period.", + waitTip: "Please wait patiently and do not refresh the page during this period.", copy: "Copy", send: "Send", regenerate: "Regenerate", @@ -89,8 +88,7 @@ export default { activated: "Activated", inactive: "Inactive", isActive: "Activated", - pipelineFormatTip: - "Supports JSON format, with file size not exceeding 10M.", + pipelineFormatTip: "Supports JSON format, with file size not exceeding 10M.", importSuccTip: "Files upload successful!", importErrTip: "Files upload failed!", name: "Name", @@ -100,8 +98,7 @@ export default { deactivateTip: "Are you sure deactivate this pipeline?", activeTip: "Are you sure activate this pipeline?", deleteTip: "Are you sure delete this pipeline?", - notActivatedTip: - "There is no available pipeline. Please create or activate it first.", + notActivatedTip: "There is no available pipeline. Please create or activate it first.", validErr: "Form validation failed !", urlValidTip: "Test URL or model to proceed.", config: { @@ -142,8 +139,7 @@ export default { ovms_url: "OVMS URL", kbadmin: "kbadmin", addAgent: "Agent Configuration", - deleteAgentTip: - "Are you sure you want to delete the agent generator configuration?", + deleteAgentTip: "Are you sure you want to delete the agent generator configuration?", }, valid: { nameValid1: "Please input name", @@ -151,11 +147,9 @@ export default { nameValid3: "The name only supports letters, numbers, and underscores.", nodeParserType: "Please select Node Parser Type", chunkSizeValid1: "Please select Chunk Size", - chunkSizeValid2: - "The value of Chunk Size cannot be less than Chunk Overlap", + chunkSizeValid2: "The value of Chunk Size cannot be less than Chunk Overlap", chunkOverlapValid1: "Please select Chunk Overlap", - chunkOverlapValid2: - "The value of Chunk Overlap cannot be greater than Chunk Size", + chunkOverlapValid2: "The value of Chunk Overlap cannot be greater than Chunk Size", windowSize: "Please select Chunk Window Size", indexerType: "Please select Indexer Type", embedding: "Please select embedding Model", @@ -195,18 +189,14 @@ export default { ovmsUrlValid4: "Test passed !", ovmsUrlValid5: "The OVMS model has not passed verification yet", remoteUrlValid5: "The remote model has not passed verification yet", - nodeParserTypeTip: - "Both Indexer Type and Retriever Type will be set to kbadmin at the same time", - indexerTypeTip: - "Both Node Parser Type and Retriever Type will be set to kbadmin at the same time", - retrieverTypeTip: - "Both Node Parser Type and Indexer Type will be set to kbadmin at the same time", + nodeParserTypeTip: "Both Indexer Type and Retriever Type will be set to kbadmin at the same time", + indexerTypeTip: "Both Node Parser Type and Retriever Type will be set to kbadmin at the same time", + retrieverTypeTip: "Both Node Parser Type and Indexer Type will be set to kbadmin at the same time", retrieverChangeTip: "Please go to the Indexer stage to complete the data", indexerTypeValid1: "Indexer type can only select kbadmin", modelRequired: "Please enter embedding model url", modelFormat: "Please enter the correct url", - retrieverValid: - "Please return to the Indexer stage to supplement information.", + retrieverValid: "Please return to the Indexer stage to supplement information.", modelTip: "Please connect to vLLM service", ovmsModelTip: "Please connect to OVMS service", }, @@ -215,18 +205,15 @@ export default { nodeParserType: "Node parsing type when you use RAG", chunkSize: "Size of each chunk for processing", chunkOverlap: "Overlap size between chunks", - windowSize: - "The number of sentences on each side of a sentence to capture", - indexerType: - "The type of index structure responsible for building based on the parsed nodes", + windowSize: "The number of sentences on each side of a sentence to capture", + indexerType: "The type of index structure responsible for building based on the parsed nodes", embedding: "Embed the text data to represent it and build a vector index", embeddingUrl: "Connecting embedding model url", embeddingDevice: "The device used by the embedding model", retrieverType: "The retrieval type used when retrieving relevant nodes from the index according to the user's experience", topk: "The number of top k results to return", - postProcessorType: - "Select postprocessors for post-processing of the context", + postProcessorType: "Select postprocessors for post-processing of the context", rerank: "Rerank Model", rerankDevice: "Rerank run device", generatorType: "Local inference generator or vLLM generator", @@ -234,21 +221,17 @@ export default { llmDevice: "The device used by the LLM", weights: "Model weight", reranker: "The model for reranking.", - metadataReplace: - "Used to replace the node content with a field from the node metadata.", + metadataReplace: "Used to replace the node content with a field from the node metadata.", vectorsimilarity: "retrieval according to vector similarity", - autoMerge: - "This retriever will try to merge context into parent context.", + autoMerge: "This retriever will try to merge context into parent context.", bm25: "A BM25 retriever that uses the BM25 algorithm to retrieve nodes.", faissVector: "Embeddings are stored within a Faiss index.", vector: "Vector Store Index.", simple: "Parse text with a preference for complete sentences.", - hierarchical: - "Splits a document into a recursive hierarchy Nodes using a NodeParser.", + hierarchical: "Splits a document into a recursive hierarchy Nodes using a NodeParser.", sentencewindow: "Sentence window node parser. Splits a document into Nodes, with each node being a sentence. Each node contains a window from the surrounding sentences in the metadata.", - unstructured: - "UnstructedNodeParser is a component that processes unstructured data.", + unstructured: "UnstructedNodeParser is a component that processes unstructured data.", milvusVector: "Embedding vectors stored in milvus", vector_url: "Connecting milvus vector url", test: "Test", @@ -307,8 +290,7 @@ export default { edit: "Edit Knowledge Base", deleteTip: "Are you sure delete this knowledge base?", activeTip: "Are you sure activate this knowledge base?", - uploadTip: - "Supports PDF, Word, TXT,Doc,Html,PPT,ZIP formats, with a single file size not exceeding 200M", + uploadTip: "Supports PDF, Word, TXT,Doc,Html,PPT,ZIP formats, with a single file size not exceeding 200M", notFileTip: "The knowledge base is empty. Go upload your files.", name: "Name", des: "Description", @@ -316,8 +298,7 @@ export default { activated: "Activated", nameValid1: "Please input knowledge base name", nameValid2: "Name should be between 2 and 30 characters", - nameValid3: - "Alphanumeric and underscore only, starting with a letter or underscore.", + nameValid3: "Alphanumeric and underscore only, starting with a letter or underscore.", desValid: "Please input knowledge base description", activeValid: "Please select whether to activate", uploadValid: "Single file size not exceeding 200M.", @@ -347,8 +328,7 @@ export default { desc: { name: "The name identifier of the knowledge base.", type: "The type identifier of the knowledge base.", - description: - "Briefly describe the purpose, content scope, or intended use of this knowledge base.", + description: "Briefly describe the purpose, content scope, or intended use of this knowledge base.", }, }, request: { diff --git a/EdgeCraftRAG/ui/vue/src/i18n/zh.ts b/EdgeCraftRAG/ui/vue/src/i18n/zh.ts index 0e8a914a6a..8d8e839209 100644 --- a/EdgeCraftRAG/ui/vue/src/i18n/zh.ts +++ b/EdgeCraftRAG/ui/vue/src/i18n/zh.ts @@ -74,8 +74,7 @@ export default { step1: "创建 Pipeline", step1Tip: "定制您的 RAG 流程，释放 AI 信息处理的最大能力。", step2: "前往对话", - step2Tip: - "开始与智能聊天机器人互动，它支持文件上传和信息检索，帮助您更高效地完成任务。", + step2Tip: "开始与智能聊天机器人互动，它支持文件上传和信息检索，帮助您更高效地完成任务。", create: "去创建", }, pipeline: { @@ -201,8 +200,7 @@ export default { }, desc: { name: "Pipeline的名称标识，用于区分不同工作流", - nodeParserType: - "RAG 处理时的文本拆分策略，支持简单句子、层次结构等解析方式", + nodeParserType: "RAG 处理时的文本拆分策略，支持简单句子、层次结构等解析方式", chunkSize: "文本处理时的单块数据大小", chunkOverlap: "相邻数据块的重叠部分大小，确保跨块语义连续性", windowSize: "每个节点捕获的上下文句子窗口大小，用于增强语义完整性", @@ -228,8 +226,7 @@ export default { vector: "矢量存储索引", simple: "解析文本，优先选择完整的句子。", hierarchical: "使用NodeParser将文档拆分为递归层次结构的节点。", - sentencewindow: - "将文档分割成节点，每个节点代表一个句子。每个节点包含一个来自元数据中周围句子的窗口", + sentencewindow: "将文档分割成节点，每个节点代表一个句子。每个节点包含一个来自元数据中周围句子的窗口", unstructured: "一个处理非结构化数据的组件", milvusVector: "矢量索引存储在Milvus中", vector_url: "测试Milvus地址是否可用", @@ -256,8 +253,7 @@ export default { desc: { top_n: "重排后结果的数量", temperature: "数值越高，输出越多样化", - top_p: - "从累积概率超过 top_p 的最小标记集中采样,设为1则禁用并从所有标记取样。", + top_p: "从累积概率超过 top_p 的最小标记集中采样,设为1则禁用并从所有标记取样。", top_k: "从概率前k的 Token 中采样", penalty: "抑制重复的系数,设为1.0表示禁用", maxToken: "生成回答的最大Token数量", @@ -289,8 +285,7 @@ export default { edit: "编辑知识库", deleteTip: "您确定要删除此知识库吗？此操作不可恢复。", activeTip: "您确定要激活此知识库吗？", - uploadTip: - "支持 PDF、Word、TXT、Doc、HTML、PPT、ZIP 格式，单个文件大小不超过 200M。", + uploadTip: "支持 PDF、Word、TXT、Doc、HTML、PPT、ZIP 格式，单个文件大小不超过 200M。", notFileTip: "您还没有上传任何文件，点击“上传”按钮开始添加内容吧～", name: "名称", des: "描述", diff --git a/EdgeCraftRAG/ui/vue/src/utils/customRenderer.ts b/EdgeCraftRAG/ui/vue/src/utils/customRenderer.ts index 59e6ce6442..d996e3873e 100644 --- a/EdgeCraftRAG/ui/vue/src/utils/customRenderer.ts +++ b/EdgeCraftRAG/ui/vue/src/utils/customRenderer.ts @@ -23,23 +23,15 @@ const getAnchorScope = (anchorLink: HTMLAnchorElement) => { return anchorLink.closest("[id='message-container']"); }; -const queryAnchorTargetInScope = ( - scope: Element | Document, - targetId: string, -) => { +const queryAnchorTargetInScope = (scope: Element | Document, targetId: string) => { const decodedTargetId = decodeURIComponent(targetId); if (scope instanceof Document) { - return ( - scope.getElementById(targetId) || scope.getElementById(decodedTargetId) - ); + return scope.getElementById(targetId) || scope.getElementById(decodedTargetId); } if (typeof CSS !== "undefined" && typeof CSS.escape === "function") { - return ( - scope.querySelector(`#${CSS.escape(targetId)}`) || - scope.querySelector(`#${CSS.escape(decodedTargetId)}`) - ); + return scope.querySelector(`#${CSS.escape(targetId)}`) || scope.querySelector(`#${CSS.escape(decodedTargetId)}`); } return null; @@ -51,18 +43,13 @@ const getAnchorScrollTarget = (targetElement: HTMLElement) => { !targetElement.textContent?.trim() && targetElement.childElementCount === 0 ) { - return ( - (targetElement.nextElementSibling as HTMLElement | null) || targetElement - ); + return (targetElement.nextElementSibling as HTMLElement | null) || targetElement; } return targetElement; }; -const resolveAnchorTarget = ( - anchorLink: HTMLAnchorElement, - targetId: string, -) => { +const resolveAnchorTarget = (anchorLink: HTMLAnchorElement, targetId: string) => { const decodedTargetId = decodeURIComponent(targetId); const anchorScope = getAnchorScope(anchorLink); @@ -73,10 +60,7 @@ const resolveAnchorTarget = ( } } - return ( - document.getElementById(targetId) || - document.getElementById(decodedTargetId) - ); + return document.getElementById(targetId) || document.getElementById(decodedTargetId); }; class ClipboardManager { @@ -91,9 +75,7 @@ class ClipboardManager { document.addEventListener("click", (e) => { const target = e.target as HTMLElement; const copyBtn = target.closest(".copy-btn"); - const anchorLink = target.closest( - "a[data-anchor-target]", - ) as HTMLAnchorElement | null; + const anchorLink = target.closest("a[data-anchor-target]") as HTMLAnchorElement | null; if (copyBtn) { e.preventDefault(); diff --git a/FinanceAgent/tools/research_tools.py b/FinanceAgent/tools/research_tools.py index f54527a3ee..e34c815a01 100644 --- a/FinanceAgent/tools/research_tools.py +++ b/FinanceAgent/tools/research_tools.py @@ -24,11 +24,10 @@ except: pass - - # https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/utilities/financial_datasets.py -""" -Util that calls several of financial datasets stock market REST APIs. +\ +"""Util that calls several of financial datasets stock market REST APIs. + Docs: https://docs.financialdatasets.ai/ """ @@ -133,7 +132,6 @@ def get_cash_flow_statements( :param limit: the number of results to return, default is 10 :return: a list of cash flow statements """ - url = ( f"{FINANCIAL_DATASETS_BASE_URL}financials/cash-flow-statements/" f"?ticker={ticker}" @@ -318,7 +316,6 @@ def analyze_balance_sheet( Then return with an instruction on how to analyze the balance sheet. """ - balance_sheet = financial_datasets_client.run( mode="get_balance_sheets", ticker=symbol, @@ -329,10 +326,11 @@ def analyze_balance_sheet( df_string = "Balance sheet:\n" + balance_sheet instruction = dedent( - """ - Delve into a detailed scrutiny of the company's balance sheet for the most recent fiscal year, pinpointing + """Delve into a detailed scrutiny of the company's balance sheet for the most recent fiscal year, pinpointing the structure of assets, liabilities, and shareholders' equity to decode the firm's financial stability and - operational efficiency. Focus on evaluating the liquidity through current assets versus current liabilities, + operational efficiency. + + Focus on evaluating the liquidity through current assets versus current liabilities, the solvency via long-term debt ratios, and the equity position to gauge long-term investment potential. Contrast these metrics with previous years' data to highlight financial trends, improvements, or deteriorations. Finalize with a strategic assessment of the company's financial leverage, asset management, and capital structure, @@ -366,8 +364,8 @@ def analyze_income_stmt( # Analysis instruction instruction = dedent( - """ - Conduct a comprehensive analysis of the company's income statement for the current fiscal year. + """Conduct a comprehensive analysis of the company's income statement for the current fiscal year. + Start with an overall revenue record, including Year-over-Year or Quarter-over-Quarter comparisons, and break down revenue sources to identify primary contributors and trends. Examine the Cost of Goods Sold for potential cost control issues. Review profit margins such as gross, operating, @@ -397,7 +395,6 @@ def analyze_cash_flow( Then return with an instruction on how to analyze the cash flow statement. """ - cash_flow = financial_datasets_client.run( mode="get_cash_flow_statements", ticker=symbol, @@ -408,9 +405,10 @@ def analyze_cash_flow( df_string = "Cash flow statement:\n" + cash_flow instruction = dedent( - """ - Dive into a comprehensive evaluation of the company's cash flow for the latest fiscal year, focusing on cash inflows - and outflows across operating, investing, and financing activities. Examine the operational cash flow to assess the + """Dive into a comprehensive evaluation of the company's cash flow for the latest fiscal year, focusing on cash inflows + and outflows across operating, investing, and financing activities. + + Examine the operational cash flow to assess the core business profitability, scrutinize investing activities for insights into capital expenditures and investments, and review financing activities to understand debt, equity movements, and dividend policies. Compare these cash movements to prior periods to discern trends, sustainability, and liquidity risks. Conclude with an informed analysis of the company's @@ -458,8 +456,8 @@ def get_share_performance( df_string = "Past 60 days Stock prices:\n" + json.dumps(prices) instruction = dedent( - """ - Dive into a comprehensive evaluation of the company's stock price for the latest 60 days. + """Dive into a comprehensive evaluation of the company's stock price for the latest 60 days. + Less than 130 words. """ ) diff --git a/MultimodalQnA/docker_compose/amd/cpu/epyc/config/milvus.yaml b/MultimodalQnA/docker_compose/amd/cpu/epyc/config/milvus.yaml index 8d94d7e949..c06f482c52 100644 --- a/MultimodalQnA/docker_compose/amd/cpu/epyc/config/milvus.yaml +++ b/MultimodalQnA/docker_compose/amd/cpu/epyc/config/milvus.yaml @@ -425,7 +425,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/MultimodalQnA/docker_compose/intel/cpu/xeon/config/milvus.yaml b/MultimodalQnA/docker_compose/intel/cpu/xeon/config/milvus.yaml index b9f22cb3d1..8118a41ece 100644 --- a/MultimodalQnA/docker_compose/intel/cpu/xeon/config/milvus.yaml +++ b/MultimodalQnA/docker_compose/intel/cpu/xeon/config/milvus.yaml @@ -423,7 +423,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/MultimodalQnA/docker_compose/intel/hpu/gaudi/config/milvus.yaml b/MultimodalQnA/docker_compose/intel/hpu/gaudi/config/milvus.yaml index b9f22cb3d1..8118a41ece 100644 --- a/MultimodalQnA/docker_compose/intel/hpu/gaudi/config/milvus.yaml +++ b/MultimodalQnA/docker_compose/intel/hpu/gaudi/config/milvus.yaml @@ -423,7 +423,7 @@ dataCoord: balanceSilentDuration: 300 # The duration after which the channel manager start background channel balancing balanceInterval: 360 # The interval with which the channel manager check dml channel balance status checkInterval: 1 # The interval in seconds with which the channel manager advances channel states - notifyChannelOperationTimeout: 5 # Timeout notifing channel operations (in seconds). + notifyChannelOperationTimeout: 5 # Timeout notifying channel operations (in seconds). segment: maxSize: 1024 # Maximum size of a segment in MB diskSegmentMaxSize: 2048 # Maximum size of a segment in MB for collection which has Disk index diff --git a/MultimodalQnA/ui/gradio/utils.py b/MultimodalQnA/ui/gradio/utils.py index 7d2e386bdb..33b4ac35c9 100644 --- a/MultimodalQnA/ui/gradio/utils.py +++ b/MultimodalQnA/ui/gradio/utils.py @@ -105,7 +105,7 @@ def flush(self): def maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA): # Grab the image size and initialize dimensions dim = None - (h, w) = image.shape[:2] + h, w = image.shape[:2] # Return original image if no need to resize if width is None and height is None: @@ -219,7 +219,6 @@ def convert_audio_to_base64(audio_path): def convert_base64_to_audio(b64_str): """Decodes the base64 encoded audio data and returns a saved filepath.""" - audio_data = base64.b64decode(b64_str) # Create a temporary file diff --git a/WorkflowExecAgent/tools/components/component.py b/WorkflowExecAgent/tools/components/component.py index 391109eaad..23cf27b38e 100644 --- a/WorkflowExecAgent/tools/components/component.py +++ b/WorkflowExecAgent/tools/components/component.py @@ -17,5 +17,4 @@ def _make_request(self, *args, **kwargs): :returns: API response """ - return self.request_handler._make_request(*args, **kwargs) diff --git a/WorkflowExecAgent/tools/components/workflow.py b/WorkflowExecAgent/tools/components/workflow.py index 9c48ffe996..e65e0589c2 100644 --- a/WorkflowExecAgent/tools/components/workflow.py +++ b/WorkflowExecAgent/tools/components/workflow.py @@ -33,7 +33,6 @@ def start(self, params: Dict[str, str]) -> Dict[str, str]: :rtype: string """ - data = json.dumps({"params": params}) endpoint = f"serving/servable_workflows/{self.workflow_id}/start" self.wf_key = self._make_request(endpoint, "POST", data).get("wf_key", None) @@ -52,7 +51,6 @@ def get_status(self) -> Dict[str, str]: :rtype: string """ - endpoint = f"serving/serving_workflows/{self.wf_key}/status" return self._make_request(endpoint, "GET") @@ -66,6 +64,5 @@ def result(self) -> list[Dict[str, str]]: :rtype: string """ - endpoint = f"serving/serving_workflows/{self.wf_key}/results" return self._make_request(endpoint, "GET") diff --git a/WorkflowExecAgent/tools/sdk.py b/WorkflowExecAgent/tools/sdk.py index 7b020edf1f..51021aefae 100644 --- a/WorkflowExecAgent/tools/sdk.py +++ b/WorkflowExecAgent/tools/sdk.py @@ -24,7 +24,6 @@ def create_workflow(self, workflow_id: int = None, workflow_key=None): :returns: Workflow """ - return Workflow( self.request_handler, workflow_id=workflow_id, diff --git a/WorkflowExecAgent/tools/tools.py b/WorkflowExecAgent/tools/tools.py index c51ae7bcc9..be81b69a16 100644 --- a/WorkflowExecAgent/tools/tools.py +++ b/WorkflowExecAgent/tools/tools.py @@ -7,15 +7,15 @@ def workflow_executor(params, workflow_id: int) -> dict: - """Workflow executor tool. Runs a workflow and returns the result. + """Workflow executor tool. - :param int workflow_id: Servable workflow id. + Runs a workflow and returns the result. + :param int workflow_id: Servable workflow id. - :returns: workflow output results + :returns: workflow output results - :rtype: dict + :rtype: dict """ - # Replace function logic with use-case sdk = DataInsightAutomationSDK() # Initialize SDK instance diff --git a/benchmark.py b/benchmark.py index a1efe78233..6774651e58 100644 --- a/benchmark.py +++ b/benchmark.py @@ -24,7 +24,6 @@ def load_yaml(file_path): def construct_benchmark_config(test_suite_config): """Extract relevant data from the YAML based on the specified test cases.""" - return { "user_queries": test_suite_config.get("user_queries", [1]), "concurrency": test_suite_config.get("concurrency", [1]), @@ -101,7 +100,6 @@ def _get_service_ip(service_name, deployment_type="k8s", service_ip=None, servic def _create_yaml_content(service, base_url, bench_target, test_phase, num_queries, test_params, concurrency=1): """Create content for the run.yaml file.""" - # calculate the number of concurrent users concurrent_level = int(num_queries // concurrency) diff --git a/deploy.py b/deploy.py index 9b0e350c46..6e4d237fff 100644 --- a/deploy.py +++ b/deploy.py @@ -285,7 +285,6 @@ def add_label_to_node(node_name, label): def add_labels_to_nodes(node_count=None, label=None, node_names=None): """Add a label to the specified number of nodes or to specified nodes.""" - if node_names: # Add label to the specified nodes for node_name in node_names: @@ -365,7 +364,6 @@ def install_helm_release(release_name, chart_name, namespace, hw_values_file, de - hw_values_file: The values file for hw specific - deploy_values_file: The values file for deployment. """ - # Check if the namespace exists; if not, create it try: command = ["kubectl", "get", "namespace", namespace] @@ -444,7 +442,6 @@ def update_service(release_name, chart_name, namespace, hw_values_file, deploy_v deploy_config: The deployment configuration chart_name: The chart name for the deployment """ - # Construct helm upgrade command command = [ "helm", diff --git a/one_click_deploy/core/deployer.py b/one_click_deploy/core/deployer.py index 4d3a6dc9fe..c385ffd1de 100644 --- a/one_click_deploy/core/deployer.py +++ b/one_click_deploy/core/deployer.py @@ -313,7 +313,7 @@ def _interactive_setup_for_deploy(self): # If this is an offline deployment, the mode is already set and should not be prompted. if getattr(self.args, "is_offline_deployment", False): - log_message("INFO", f"Offline Deployment Mode: {self.args.deploy_mode} (pre-selected)") + log_message("INFO", f"Offline Deployment Mode: {self.args.deploy_mode} (preselected)") else: self.args.deploy_mode = click.prompt( "Deployment Mode", type=click.Choice(["docker", "k8s"]), default="docker" diff --git a/one_click_deploy/core/tester.py b/one_click_deploy/core/tester.py index ba15524637..e1970234d7 100644 --- a/one_click_deploy/core/tester.py +++ b/one_click_deploy/core/tester.py @@ -258,7 +258,6 @@ def _gen_payload(self, test_case_config): Returns: Dictionary containing the payload for the request """ - # Ensure the required Python package is installed check_install_python_pkg("pybase64") check_install_python_pkg("urllib3")