From 4081622ecb5456c715051d5870446fd9e301ea81 Mon Sep 17 00:00:00 2001 From: Mingyi Li Date: Sun, 30 Aug 2026 16:30:07 +0800 Subject: [PATCH] docs: present EMKG and organize project root --- .github/workflows/repository-checks.yml | 28 +++ .../LICENSE => LICENSE | 0 .../LLM => LLM}/MobileVLM/.gitignore | 0 .../LLM => LLM}/MobileVLM/LICENSE | 0 .../LLM => LLM}/MobileVLM/README.md | 0 .../LLM => LLM}/MobileVLM/assets/READMEv1.md | 0 .../MobileVLM/assets/mobilevlm_arch.png | Bin .../MobileVLM/assets/mobilevlm_v2_arch.png | Bin .../MobileVLM/assets/samples/demo.jpg | Bin .../LLM => LLM}/MobileVLM/example.py | 0 .../mobilellama/sft/MobileLLaMA_SFT.md | 0 .../sft/sft_MobileLLaMA-1.4B-Base.sh | 0 .../sft/sft_MobileLLaMA-2.7B-Base.sh | 0 .../MobileVLM/mobilevlm/constants.py | 0 .../MobileVLM/mobilevlm/conversation.py | 0 .../MobileVLM/mobilevlm/eval/m4c_evaluator.py | 0 .../mobilevlm/eval/model_vqa_loader.py | 0 .../mobilevlm/eval/model_vqa_mmbench.py | 0 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new file mode 100644 index 0000000..e7ffe96 --- /dev/null +++ b/.github/workflows/repository-checks.yml @@ -0,0 +1,28 @@ +name: Repository checks + +on: + push: + pull_request: + +permissions: + contents: read + +jobs: + static-checks: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.10" + - name: Validate repository structure and public artifacts + run: python scripts/check_repository.py + - name: Compile project entry points + run: >- + python -m py_compile + project_config.py + build_rag.py + model_LLM.py + server.py + update.py + diff --git a/multimodal_rag-main_v1-master/LICENSE b/LICENSE similarity index 100% rename from multimodal_rag-main_v1-master/LICENSE rename to LICENSE diff --git a/multimodal_rag-main_v1-master/LLM/MobileVLM/.gitignore b/LLM/MobileVLM/.gitignore similarity index 100% rename from multimodal_rag-main_v1-master/LLM/MobileVLM/.gitignore rename to LLM/MobileVLM/.gitignore diff --git 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LLM/mipha/model/language_model/phi1_5/modeling_phi.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/model/language_model/phi3/configuration_phi3.py b/LLM/mipha/model/language_model/phi3/configuration_phi3.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/model/language_model/phi3/configuration_phi3.py rename to LLM/mipha/model/language_model/phi3/configuration_phi3.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/model/language_model/phi3/modeling_phi3.py b/LLM/mipha/model/language_model/phi3/modeling_phi3.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/model/language_model/phi3/modeling_phi3.py rename to LLM/mipha/model/language_model/phi3/modeling_phi3.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/model/mipha_arch.py b/LLM/mipha/model/mipha_arch.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/model/mipha_arch.py rename to LLM/mipha/model/mipha_arch.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_encoder/clip_encoder.py b/LLM/mipha/model/multimodal_encoder/clip_encoder.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_encoder/clip_encoder.py rename to LLM/mipha/model/multimodal_encoder/clip_encoder.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_encoder/dinov2_encoder.py b/LLM/mipha/model/multimodal_encoder/dinov2_encoder.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_encoder/dinov2_encoder.py rename to LLM/mipha/model/multimodal_encoder/dinov2_encoder.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_encoder/siglip_encoder.py b/LLM/mipha/model/multimodal_encoder/siglip_encoder.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_encoder/siglip_encoder.py rename to LLM/mipha/model/multimodal_encoder/siglip_encoder.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_projector/builder.py b/LLM/mipha/model/multimodal_projector/builder.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/model/multimodal_projector/builder.py rename to LLM/mipha/model/multimodal_projector/builder.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/serve/__init__.py b/LLM/mipha/serve/__init__.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/serve/__init__.py rename to LLM/mipha/serve/__init__.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/serve/cli.py b/LLM/mipha/serve/cli.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/serve/cli.py rename to LLM/mipha/serve/cli.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/serve/examples/extreme_ironing.jpg b/LLM/mipha/serve/examples/extreme_ironing.jpg similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/serve/examples/extreme_ironing.jpg rename to LLM/mipha/serve/examples/extreme_ironing.jpg diff --git a/multimodal_rag-main_v1-master/LLM/mipha/serve/examples/waterview.jpg b/LLM/mipha/serve/examples/waterview.jpg similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/serve/examples/waterview.jpg rename to LLM/mipha/serve/examples/waterview.jpg diff --git a/multimodal_rag-main_v1-master/LLM/mipha/train/convert_model2base_mipha.py b/LLM/mipha/train/convert_model2base_mipha.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/train/convert_model2base_mipha.py rename to LLM/mipha/train/convert_model2base_mipha.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/train/mipha_trainer.py b/LLM/mipha/train/mipha_trainer.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/train/mipha_trainer.py rename to LLM/mipha/train/mipha_trainer.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/train/train.py b/LLM/mipha/train/train.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/train/train.py rename to LLM/mipha/train/train.py diff --git a/multimodal_rag-main_v1-master/LLM/mipha/utils.py b/LLM/mipha/utils.py similarity index 100% rename from multimodal_rag-main_v1-master/LLM/mipha/utils.py rename to LLM/mipha/utils.py diff --git a/README.md b/README.md index 7e41a5e..4c77a5b 100644 --- a/README.md +++ b/README.md @@ -1,41 +1,201 @@ -# Multimodal--RAG +
-Embodied Multimodal Knowledge Graph +# EMKG -download https://huggingface.co/google/siglip-so400m-patch14-384 +### Embodied Memory Knowledge Graphs for Object-Goal Navigation in Dynamic Open Worlds -change config/config.json for vis_model_path and model_LLM_path +**Multimodal memory, retrieval, 3D grounding, and adaptive control for open-world ObjectNav** -conda create rag python==3.10 +![Python 3.10](https://img.shields.io/badge/Python-3.10-3776AB?logo=python&logoColor=white) +![Research prototype](https://img.shields.io/badge/status-research%20prototype-F59E0B) -conda activate rag (for latest anaconda/miniconda use source activate ) +
-pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121 +EMKG is an embodied multimodal RAG research prototype that connects RGB-D scene observations, 3D coordinates, natural-language goals, and region-structured memory. It retrieves goal-relevant visual and semantic evidence, grounds the target in space, and passes that evidence to a vision-language model for navigation and locomotion decisions. +> [!IMPORTANT] +> This repository is a research snapshot, not yet a one-command reproduction of the complete simulation-to-real system. It currently exposes the multimodal retrieval service, spatial-region data, update API, Gradio interface, and several vendored VLM backends. See [Reproduction status](#reproduction-status) for the current boundaries. + +## System overview + +

+ EMKG system overview +

+ +

EMKG grounds RGB-D observations and language goals in multimodal memory, retrieves a 3D target, and converts the result into adaptive navigation actions.

+ +The research pipeline has four stages: + +1. **Multimodal encoding** - visual observations, depth or point-cloud geometry, and text goals are encoded into aligned representations. +2. **Dynamic memory retrieval** - a region-structured semantic memory stores object observations, captions, and coordinates; retrieval ranks the most relevant evidence for the current goal. +3. **Embodied planning** - retrieved evidence is verified and grounded to a 3D target before a compact VLM selects the next high-level behavior. +4. **Adaptive control** - visual feedback supports navigation and locomotion-mode switching, including wheeled motion and stair traversal. + +```mermaid +flowchart LR + A[RGB-D observation] --> D[Multimodal encoders] + B[Natural-language goal] --> D + C[3D geometry] --> D + D --> E[Region-structured multimodal memory] + E --> F[Retrieve, rerank, and verify] + F --> G[3D target grounding] + G --> H[Embodied VLM] + H --> I[Navigation and locomotion action] + I -->|new observation| E +``` + +## What is implemented here + +| Capability | Main files | Current role | +| --- | --- | --- | +| Multimodal indexing and retrieval | `build_rag.py`, `multi_modal_lndex/` | Builds text, image, and spatial-region collections in Qdrant and retrieves goal-relevant evidence. | +| VLM backends | `model_LLM.py`, `LLM/` | Adapters for MiniCPM-V, MobileVLM, and LLaVA-HD/SliME checkpoints. | +| Interactive demo | `server.py` | Gradio interface for uploading a knowledge base and issuing text or image queries. | +| Online memory update | `update.py`, `interface.py` | API and robot-facing update path for adding observations to the active memory. | +| Spatial region schema | `config.json`, `json_schema.json`, `storage/regions_output/` | Example room, region, coordinate, access-point, and object metadata. | + +The public code represents its structured knowledge primarily through spatial-region JSON plus multimodal Qdrant collections. The complete graph-update and robot-control stack described in the manuscript is not yet fully packaged in this repository. + +## Results reported in the manuscript + +The accompanying manuscript evaluates EMKG in Habitat on Gibson and HM3D-Semantics v0.1. These values are reported results, not numbers reproduced by the current public release. + +| Benchmark | Success Rate (SR) | SPL | Distance to Goal (DTG) | +| --- | ---: | ---: | ---: | +| Gibson | 83.6 | 51.3 | 2.1 | +| HM3D | 78.6 | 47.3 | 2.6 | + +EMKG is competitive with the listed Gibson baselines and records the strongest HM3D result among the baselines reported in the paper. It should not be described as an across-the-board state-of-the-art result: VLFM is slightly higher on Gibson SR and SPL in the same table. + +For the onboard 1.7B-parameter configuration on Jetson AGX Orin, the paper reports **81.2% recognition accuracy**, **84.3% navigation success**, and **1.2 s average response time**. + +## Qualitative tasks + +

+ Qualitative Habitat ObjectNav comparison +

+ +

Qualitative Habitat ObjectNav example. The baseline detours toward a yellow pillow, while EMKG retrieves and localizes the white-pillow target.

+ +
+Simulation-to-real task gallery + +
+ +

+ Simulation-to-real EMKG task gallery +

+ +The manuscript demonstrates Habitat ObjectNav, room-level search, and object-level navigation on a wheeled-legged robot. The gallery is included as qualitative evidence; the corresponding evaluation scripts and raw episodes are not yet part of this release. + +
+ +## Quick start + +### 1. Create the environment + +The original prototype targets Python 3.10 and a CUDA 12.1 PyTorch build. + +```bash +conda create -n emkg python=3.10 -y +conda activate emkg + +pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 \ + --index-url https://download.pytorch.org/whl/cu121 pip install -r requirements.txt +``` + +The three VLM backends carry different upstream dependency constraints. Use one backend per environment and consult its README under `LLM/` before installing backend-specific packages. + +### 2. Configure models + +Edit `config/config.json`, or point `EMKG_CONFIG` to another JSON file. Hugging Face model IDs and local paths are both accepted by the underlying loaders. + +```json +{ + "vis_model_path": "google/siglip-so400m-patch14-384", + "embedding_model_path": "BAAI/bge-small-zh-v1.5", + "model_LLM_path": "openbmb/MiniCPM-V" +} +``` + +When switching the VLM backend, update `model_LLM_path` to the matching checkpoint: + +| Backend | Example checkpoint | Launch command | +| --- | --- | --- | +| MiniCPM-V | `openbmb/MiniCPM-V` | `python server.py MiniCPM` | +| MobileVLM | `mtgv/MobileVLM_V2-1.7B` | `python server.py MobileVLM` | +| LLaVA-HD | `yifanzhang114/SliME-vicuna-7B` | `python server.py LLaVA-HD` | + +### 3. Start Qdrant + +The main retrieval path expects a Qdrant service at `localhost:6333`. Start a local Qdrant instance before launching the application. Runtime databases are intentionally excluded from Git because stored paths and vector dimensions are environment-specific. + +### 4. Prepare a knowledge base + +The Gradio demo accepts a ZIP containing paired image and text folders. Matching files should share the same stem. + +```text +knowledge-base.zip +├── image/ +│ ├── observation-001.jpg +│ └── observation-002.jpg +└── text/ + ├── observation-001.txt + └── observation-002.txt +``` + +Launch one configured backend, upload the archive, and select **Vectorize** in the interface. +## Repository layout +```text +. +├── README.md +├── config/ # model configuration +├── docs/assets/ # manuscript figures used in this page +├── examples/ # small Gradio and PreFLMR examples +├── LLM/ # vendored VLM backends +├── multi_modal_lndex/ # multimodal indexing and retrieval code +├── scripts/ # dependency-free repository checks +├── storage/regions_output/ # example spatial-region documents +├── tools/ # supporting robot and image utilities +├── build_rag.py # index construction and retrieval +├── model_LLM.py # VLM adapters +├── server.py # Gradio application +├── update.py # online update API +└── THIRD_PARTY_NOTICES.md # provenance and license boundaries +``` -## run MobileVLM +The previous release placed the entire project inside a duplicate `multimodal_rag-main_v1-master/` directory and also committed a ZIP copy. The project now runs from the repository root, and generated archives, model weights, caches, and Qdrant databases are ignored. -download https://huggingface.co/mtgv/MobileVLM_V2-1.7B +## Reproduction status -change config/config.json for model_LLM_path +| Component | Status | +| --- | --- | +| Root-level project layout and English documentation | Available | +| Multimodal retrieval and UI source | Available | +| Model checkpoints | External downloads required | +| Portable sample Qdrant index | Not included; rebuild locally | +| Minimal public knowledge-base example | Planned | +| Habitat benchmark configuration and evaluation scripts | Not yet packaged | +| Full robot deployment and locomotion stack | Not yet packaged | +| Automated tests and CI | Planned | -python server.py MobileVLM +Please treat the repository as inspectable research code rather than a fully reproducible benchmark release. Contributions that improve packaging, tests, and backend isolation are welcome. -## run MiniCPM +## Provenance, data, and licensing -download https://huggingface.co/openbmb/MiniCPM-V +This snapshot contains project-specific code together with vendored research components. See [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) before redistributing or relicensing any part of the tree. The root `LICENSE` does not override the licenses, model terms, dataset terms, or missing permissions of third-party and upstream material. -change config/config.json for model_LLM_path +The qualitative Habitat examples rely on Habitat-compatible Gibson/HM3D assets; obtain datasets through their official channels and follow their terms. Model weights are not distributed in this repository and remain subject to their publishers' licenses. -python server.py MiniCPM +For security reports, see [SECURITY.md](SECURITY.md). Do not commit API keys, access tokens, private datasets, or machine-specific model paths. -## run LLaVA-HD +## Citation -download https://huggingface.co/yifanzhang114/SliME-vicuna-7B +The supplied manuscript is an anonymous draft without public author, venue, DOI, or archival metadata. A verified citation record will be added when one is publicly available. Until then, cite this repository by URL and commit hash, and do not infer bibliographic details from the draft. -change config/config.json for model_LLM_path +## Acknowledgements -python server.py LLaVA-HD +EMKG builds on open research ecosystems including Habitat, Qdrant, LlamaIndex, SigLIP, BGE, MiniCPM-V, MobileVLM, SliME/LLaVA, FLMR, and ColBERT. We thank their authors and maintainers. Exact repository and license references are documented in [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md). diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..e6ec041 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,19 @@ +# Security policy + +This repository is a research prototype and is not currently maintained as a production service. Do not expose the Gradio or update APIs to untrusted networks without adding authentication, request limits, input validation, and deployment isolation. + +## Reporting a vulnerability + +Please report security issues privately to `limingyi@bit.edu.cn`. Include the affected file or endpoint, impact, reproduction steps, and a minimal proof of concept. Do not open a public issue for an unpatched credential or remotely exploitable vulnerability. + +## Credentials and private data + +- Load credentials from the environment or a secret manager. +- Never commit API keys, access tokens, private model URLs, unpublished datasets, or personally identifying robot logs. +- Treat any credential that has entered Git history as compromised. Removing it from the latest commit is not sufficient; revoke or rotate it first, then coordinate a history rewrite. +- Review images, point clouds, room maps, and robot recordings for people, location identifiers, and redistribution rights before publishing them. + +## Untrusted inputs + +The demo accepts uploaded ZIP archives and images. The current extractor rejects absolute paths and path traversal, but uploaded content should still be processed in a disposable, least-privileged environment with strict size and resource limits. + diff --git a/THIRD_PARTY_NOTICES.md b/THIRD_PARTY_NOTICES.md new file mode 100644 index 0000000..9e82d12 --- /dev/null +++ b/THIRD_PARTY_NOTICES.md @@ -0,0 +1,81 @@ +# Third-Party Notices + +This repository includes, adapts, or refers to software and other materials from third parties. + +This document is a provenance and attribution record only. It is not a license and does not grant any rights. The repository-level `LICENSE` applies only to material for which the stated licensor owns or controls the necessary rights. It does not supersede, replace, or relicense any third-party material. + +All applicable copyright notices, license texts, attribution requirements, and usage restrictions must be preserved. + +## Components with an identified source-code license + +### MobileVLM + +- Local path: `LLM/MobileVLM/` +- Upstream project: +- Source-code license: Apache License 2.0 +- Upstream license: +- Local license copy: `LLM/MobileVLM/LICENSE` + +This entry concerns source code only. Model weights, checkpoints, datasets, and other downloaded materials remain subject to their respective terms. + +### SliME + +- Local path: `LLM/SliME/` +- Upstream project: +- Source-code license: Apache License 2.0 +- Upstream license: +- Local license copy: `LLM/SliME/LICENSE` + +The SliME upstream documentation separately describes restrictions for its dataset, checkpoints, and models, including research-use and non-commercial conditions. Those materials are not relicensed under Apache-2.0 by this repository. Users must review and comply with the current upstream terms and the terms of any underlying models or datasets. + +### ColBERT + +- Local path: `multi_modal_lndex/FLMR/third_party/ColBERT/` +- Upstream project: +- License: MIT License +- Upstream license: +- Local license copy: `multi_modal_lndex/FLMR/third_party/ColBERT/LICENSE` + +Copyright (c) 2019, 2020 Stanford Future Data Systems. + +## Provenance entries with unresolved redistribution terms + +No license is asserted for the entries in this section. Their inclusion here records provenance and must not be interpreted as permission to copy, modify, or redistribute the corresponding material. + +### Aggregate multimodal RAG source + +A substantial portion of this repository corresponds to . + +No repository-level license file or GitHub-detected license was found in that upstream project at the time of review. Permission and attribution terms should be confirmed with the upstream copyright holder before redistribution. + +### FLMR + +- Local path: `multi_modal_lndex/FLMR/` +- Implementation upstream: + +No root license file or GitHub-detected license was found for this implementation at the time of review. + +The implementation README refers to an [earlier official research implementation](https://github.com/LinWeizheDragon/Retrieval-Augmented-Visual-Question-Answering) published under GPL-3.0. That license does not, by itself, establish the license applicable to the separate FLMR implementation included here. Clarification from the upstream maintainer is required before redistributing the FLMR code. + +The nested ColBERT component is covered separately above. + +### Mipha and LLaVA-Phi + +Local paths: + +- `LLM/llava-phi-main/` +- `LLM/mipha/` +- `LLM/llava_phi/` + +The included documentation refers to the historical source . GitHub currently redirects the historical LLaVA-Phi URL to . + +No license file or GitHub-detected license was found in the currently available repository at the time of review. Its package metadata contains an Apache Software License classifier, but a classifier alone is not treated here as a complete or unambiguous redistribution grant. No license is asserted for these directories pending confirmation from the upstream copyright holders. + +## Models, datasets, checkpoints, and hosted services + +Unless expressly stated otherwise, this repository does not grant rights to model weights, checkpoints, datasets, generated annotations, benchmark data, API outputs, trademarks, or hosted services referenced by its documentation or source code. Such materials remain governed by their respective providers' current terms. + +## Modifications + +Some third-party files may have been modified. Version-control history should be consulted for the changes. Where an applicable license requires modified files to carry a notice of modification, those notices must be added and preserved. + diff --git a/multimodal_rag-main_v1-master/ap_list.json b/ap_list.json similarity index 100% rename from multimodal_rag-main_v1-master/ap_list.json rename to ap_list.json diff --git a/multimodal_rag-main_v1-master/build_rag.py b/build_rag.py similarity index 95% rename from multimodal_rag-main_v1-master/build_rag.py rename to build_rag.py index 293b7d2..ddc857c 100644 --- a/multimodal_rag-main_v1-master/build_rag.py +++ b/build_rag.py @@ -17,7 +17,10 @@ from llama_index.vector_stores.qdrant import QdrantVectorStore # Create a local Qdrant vector store import qdrant_client +from project_config import load_project_config +PROJECT_CONFIG = load_project_config() +EMBEDDING_MODEL_PATH = PROJECT_CONFIG["embedding_model_path"] def retrieve(retriever_engine, query_str): @@ -134,7 +137,7 @@ def get_retriever_engine_from_local(): storage_context_json = StorageContext.from_defaults(vector_store=json_store ) service_context = ServiceContext.from_defaults( - embed_model=HuggingFaceEmbedding(r"E:\model\bge-small-zh-v1.5"), llm=None + embed_model=HuggingFaceEmbedding(EMBEDDING_MODEL_PATH), llm=None ) index_json = VectorStoreIndex.from_vector_store( vector_store=json_store, storage_context=storage_context_json, service_context=service_context @@ -163,16 +166,18 @@ def get_retriever_engine_from_local(): def update_nodes(file_path, _answer, cli_json): + file_path = os.fspath(file_path) file_list = [file_path] client = cli_json["client"] index = cli_json["index"] try: documents = SimpleDirectoryReader(input_files=file_list).load_data() for item in range(len(documents)): - documents[item].metadata['file_name_img'] = file_path[0] - with open(file_path[0].replace(".jpg",".txt"), "r", encoding="utf-8") as f: + text_path = os.path.splitext(file_path)[0] + ".txt" + documents[item].metadata['file_name_img'] = file_path + with open(text_path, "w", encoding="utf-8") as f: f.write(_answer) - documents[item].metadata['file_name_text'] = file_path[0].replace(".jpg",".txt") + documents[item].metadata['file_name_text'] = text_path parser = SentenceSplitter() nodes = parser.get_nodes_from_documents(documents) index.insert_nodes(nodes=nodes) @@ -253,14 +258,14 @@ def get_retriever_engine(path, image_folder, text_folder): # create the pipeline with transformations pipeline = IngestionPipeline( transformations=[ - HuggingFaceEmbedding(r"E:\model\bge-small-zh-v1.5"), + HuggingFaceEmbedding(EMBEDDING_MODEL_PATH), ] ) # run the pipeline nodes = pipeline.run(documents=documents_) service_context = ServiceContext.from_defaults( - embed_model=HuggingFaceEmbedding(r"E:\model\bge-small-zh-v1.5"),llm=None + embed_model=HuggingFaceEmbedding(EMBEDDING_MODEL_PATH),llm=None ) index_json = VectorStoreIndex( nodes=nodes, storage_context=storage_context_json, service_context=service_context diff --git a/multimodal_rag-main_v1-master/config.json b/config.json similarity index 100% rename from multimodal_rag-main_v1-master/config.json rename to config.json diff --git a/config/config.json b/config/config.json new file mode 100644 index 0000000..c6c0e41 --- /dev/null +++ b/config/config.json @@ -0,0 +1,5 @@ +{ + "vis_model_path": "google/siglip-so400m-patch14-384", + "embedding_model_path": "BAAI/bge-small-zh-v1.5", + "model_LLM_path": "openbmb/MiniCPM-V" +} diff --git a/docs/assets/README.md b/docs/assets/README.md new file mode 100644 index 0000000..574d1a8 --- /dev/null +++ b/docs/assets/README.md @@ -0,0 +1,12 @@ +# Figure provenance + +These images were extracted from the author-supplied anonymous manuscript **“EMKG: Embodied Memory Knowledge Graphs for Object-Goal Navigation in Dynamic Open Worlds.”** + +| File | Manuscript source | Purpose | +| --- | --- | --- | +| `emkg-system-overview.png` | Figure 1, PDF page 2 | End-to-end system overview | +| `habitat-objectnav-comparison.png` | Figure 3, PDF page 5 | Qualitative Habitat comparison | +| `sim-to-real-tasks.png` | Figure 4, PDF page 7 | Simulation-to-real task gallery | + +The manuscript does not contain an explicit image license. Inclusion in this repository records project provenance and does not grant third parties additional reuse rights. 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examples/gradio_multimodal_demo.py diff --git a/examples/preflmr_scoring.py b/examples/preflmr_scoring.py new file mode 100644 index 0000000..fa50237 --- /dev/null +++ b/examples/preflmr_scoring.py @@ -0,0 +1,73 @@ +"""Minimal PreFLMR text scoring example. + +Example: + python examples/preflmr_scoring.py \ + --checkpoint LinWeizheDragon/PreFLMR_ViT-B +""" + +from __future__ import annotations + +import argparse +import os + +import torch + +os.environ["GIT_PYTHON_REFRESH"] = "quiet" + +from multi_modal_lndex.FLMR.flmr import ( + FLMRContextEncoderTokenizer, + FLMRModelForRetrieval, + FLMRQueryEncoderTokenizer, +) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Score sample text with PreFLMR.") + parser.add_argument( + "--checkpoint", + default="LinWeizheDragon/PreFLMR_ViT-B", + help="Hugging Face model ID or local PreFLMR checkpoint path", + ) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + query_tokenizer = FLMRQueryEncoderTokenizer.from_pretrained( + args.checkpoint, subfolder="query_tokenizer" + ) + context_tokenizer = FLMRContextEncoderTokenizer.from_pretrained( + args.checkpoint, subfolder="context_tokenizer" + ) + model = FLMRModelForRetrieval.from_pretrained( + args.checkpoint, + query_tokenizer=query_tokenizer, + context_tokenizer=context_tokenizer, + ) + + documents = context_tokenizer( + ["an apple on a table", "a chair in a room", "a refrigerator in a kitchen"] + ) + query = query_tokenizer(["find the apple"]) + + text_embeddings = model.doc( + input_ids=documents["input_ids"], + attention_mask=documents["attention_mask"], + ) + document_vectors = text_embeddings.late_interaction_output + document_mask = text_embeddings.context_mask + query_vectors = model.query( + input_ids=query["input_ids"], + attention_mask=query["attention_mask"], + concat_output_from_vision_encoder=False, + ).late_interaction_output + query_vectors = query_vectors.repeat_interleave(3, dim=0).contiguous() + + with torch.inference_mode(): + scores = model.score(query_vectors, document_vectors, document_mask) + print(scores) + + +if __name__ == "__main__": + main() + diff --git a/multimodal_rag-main_v1-master/interface.py b/interface.py similarity index 100% rename from multimodal_rag-main_v1-master/interface.py rename to interface.py diff --git a/multimodal_rag-main_v1-master/json_schema.json b/json_schema.json similarity index 100% rename from multimodal_rag-main_v1-master/json_schema.json rename to json_schema.json diff --git a/multimodal_rag-main_v1-master/mllm_rag.py b/mllm_rag.py similarity index 100% rename from multimodal_rag-main_v1-master/mllm_rag.py rename to mllm_rag.py diff --git a/multimodal_rag-main_v1-master/model_LLM.py b/model_LLM.py similarity index 98% rename from multimodal_rag-main_v1-master/model_LLM.py rename to model_LLM.py index 087cd0d..c3a905d 100644 --- a/multimodal_rag-main_v1-master/model_LLM.py +++ b/model_LLM.py @@ -1,4 +1,3 @@ -import json import re from typing import Optional, Any @@ -24,17 +23,9 @@ from LLM.mipha.serve.cli import load_image from LLM.SliME.llava.model.builder import load_pretrained_model as load_pretrained_model_LLaVAHD from LLM.SliME.llava.conversation import conv_templates as conv_templates_SLIME +from project_config import load_project_config -with open('config/config.json') as user_file: - config = user_file.read() -config = json.loads(config) -# { -# "name": "John", -# "age": 50, -# "is_married": false, -# "profession": null, -# "hobbies": ["travelling", "photography"] -# } +config = load_project_config() DEFAULT_LLM_MODEL = config["model_LLM_path"] class MobileVLM(): diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/FLMR/.gitignore b/multi_modal_lndex/FLMR/.gitignore similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/FLMR/.gitignore rename to multi_modal_lndex/FLMR/.gitignore diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/FLMR/README.md 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multi_modal_lndex/FLMR/tools/save_model.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/FLMR/tools/test.py b/multi_modal_lndex/FLMR/tools/test.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/FLMR/tools/test.py rename to multi_modal_lndex/FLMR/tools/test.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/FLMR/tools/upload_model_to_hub.py b/multi_modal_lndex/FLMR/tools/upload_model_to_hub.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/FLMR/tools/upload_model_to_hub.py rename to multi_modal_lndex/FLMR/tools/upload_model_to_hub.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/base.py b/multi_modal_lndex/base.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/base.py rename to multi_modal_lndex/base.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/clip_base.py b/multi_modal_lndex/clip_base.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/clip_base.py rename to multi_modal_lndex/clip_base.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/flmr_base.py b/multi_modal_lndex/flmr_base.py similarity index 95% rename from multimodal_rag-main_v1-master/multi_modal_lndex/flmr_base.py rename to multi_modal_lndex/flmr_base.py index 566e38b..9afb458 100644 --- a/multimodal_rag-main_v1-master/multi_modal_lndex/flmr_base.py +++ b/multi_modal_lndex/flmr_base.py @@ -1,5 +1,4 @@ -import json import logging from typing import Any, List @@ -14,6 +13,7 @@ from transformers import AutoModel, AutoProcessor, AutoImageProcessor from multi_modal_lndex.FLMR.flmr import FLMRQueryEncoderTokenizer, FLMRContextEncoderTokenizer, FLMRModelForRetrieval +from project_config import load_project_config logger = logging.getLogger(__name__) @@ -29,16 +29,7 @@ "ViT-L/14", "ViT-L/14@336px", ) -with open('config/config.json') as user_file: - config = user_file.read() -config = json.loads(config) -# { -# "name": "John", -# "age": 50, -# "is_married": false, -# "profession": null, -# "hobbies": ["travelling", "photography"] -# } +config = load_project_config() DEFAULT_CLIP_MODEL = config["vis_model_path"] DEFAULT_EMBEDDING_MODEL = config["embedding_model_path"] class FLMREmbedding(MultiModalEmbedding): diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/flmr_index_base.py b/multi_modal_lndex/flmr_index_base.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/flmr_index_base.py rename to multi_modal_lndex/flmr_index_base.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/flmr_retriever_base.py b/multi_modal_lndex/flmr_retriever_base.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/flmr_retriever_base.py rename to multi_modal_lndex/flmr_retriever_base.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/siglip_base.py b/multi_modal_lndex/siglip_base.py similarity index 97% rename from multimodal_rag-main_v1-master/multi_modal_lndex/siglip_base.py rename to multi_modal_lndex/siglip_base.py index 2338662..879f4e5 100644 --- a/multimodal_rag-main_v1-master/multi_modal_lndex/siglip_base.py +++ b/multi_modal_lndex/siglip_base.py @@ -1,4 +1,3 @@ -import json import logging from typing import Any, List @@ -11,6 +10,7 @@ import os from transformers import AutoModel, AutoProcessor +from project_config import load_project_config logger = logging.getLogger(__name__) AVAILABLE_CLIP_MODELS = ( "RN50", @@ -23,9 +23,7 @@ "ViT-L/14", "ViT-L/14@336px", ) -with open('config/config.json') as user_file: - config = user_file.read() -config = json.loads(config) +config = load_project_config() DEFAULT_CLIP_MODEL = config["vis_model_path"] class SiglipEmbedding(MultiModalEmbedding): """CLIP embedding models for encoding text and image for Multi-Modal purpose. diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/single_client.py b/multi_modal_lndex/single_client.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/single_client.py rename to multi_modal_lndex/single_client.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/utils.py b/multi_modal_lndex/utils.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/utils.py rename to multi_modal_lndex/utils.py diff --git a/multimodal_rag-main_v1-master/multi_modal_lndex/vector_stores/vecterstore_types.py b/multi_modal_lndex/vector_stores/vecterstore_types.py similarity index 100% rename from multimodal_rag-main_v1-master/multi_modal_lndex/vector_stores/vecterstore_types.py rename to multi_modal_lndex/vector_stores/vecterstore_types.py diff --git a/multimodal_rag-main_v1-master/README.md b/multimodal_rag-main_v1-master/README.md deleted file mode 100644 index 7e41a5e..0000000 --- a/multimodal_rag-main_v1-master/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# Multimodal--RAG - -Embodied Multimodal Knowledge Graph - -download https://huggingface.co/google/siglip-so400m-patch14-384 - -change config/config.json for vis_model_path and model_LLM_path - -conda create rag python==3.10 - -conda activate rag (for latest anaconda/miniconda use source activate ) - -pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121 - -pip install -r requirements.txt - - - -## run MobileVLM - -download https://huggingface.co/mtgv/MobileVLM_V2-1.7B - -change config/config.json for model_LLM_path - -python server.py MobileVLM - -## run MiniCPM - -download https://huggingface.co/openbmb/MiniCPM-V - -change config/config.json for model_LLM_path - -python server.py MiniCPM - -## run LLaVA-HD - -download https://huggingface.co/yifanzhang114/SliME-vicuna-7B - -change config/config.json for model_LLM_path - -python server.py LLaVA-HD diff --git a/multimodal_rag-main_v1-master/config/config.json b/multimodal_rag-main_v1-master/config/config.json deleted file mode 100644 index a05fc3c..0000000 --- a/multimodal_rag-main_v1-master/config/config.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "vis_model_path": "E:\\model\\siglip-base-patch16-224", - "embedding_model_path": "E:\\model\\bge-small-zh-v1.5", - "model_LLM_path": "E:\\model\\miniCPM-V" -} \ No newline at end of file diff --git a/multimodal_rag-main_v1-master/json_tool/split_json.py b/multimodal_rag-main_v1-master/json_tool/split_json.py deleted file mode 100644 index e462209..0000000 --- a/multimodal_rag-main_v1-master/json_tool/split_json.py +++ /dev/null @@ -1,18 +0,0 @@ -# 定义输出目录 -import os -import json -data = json.loads(open("../config.json").read()) -output_dir = "../storage/regions_output" -os.makedirs(output_dir, exist_ok=True) - -# 遍历每个区域，将每个区域保存为单独的 JSON 文件 -for region in data["regions"]: - # 生成区域文件名，使用区域名称命名文件 - file_name = f"{region['name']}.json" - file_path = os.path.join(output_dir, file_name) - - # 将区域信息保存到 JSON 文件 - with open(file_path, "w") as f: - json.dump(region, f, indent=4) - - 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"replication_factor": null, "write_consistency_factor": null, "on_disk_payload": null, "hnsw_config": null, "wal_config": null, "optimizers_config": null, "init_from": null, "quantization_config": null, "sparse_vectors": null}, "json_collection": {"vectors": {"size": 512, "distance": "Cosine", "hnsw_config": null, "quantization_config": null, "on_disk": null, "datatype": null}, "shard_number": null, "sharding_method": null, "replication_factor": null, "write_consistency_factor": null, "on_disk_payload": null, "hnsw_config": null, "wal_config": null, "optimizers_config": null, "init_from": null, "quantization_config": null, "sparse_vectors": null}}, "aliases": {}} \ No newline at end of file diff --git a/multimodal_rag-main_v1-master/test_preflmr.py b/multimodal_rag-main_v1-master/test_preflmr.py deleted file mode 100644 index c2a99a3..0000000 --- a/multimodal_rag-main_v1-master/test_preflmr.py +++ /dev/null @@ -1,41 +0,0 @@ -import os -import torch -import pandas as pd -import numpy as np -from torchvision.transforms import ToPILImage -from transformers import AutoImageProcessor -os.environ["GIT_PYTHON_REFRESH"] = "quiet" -from multi_modal_lndex.FLMR.flmr import index_custom_collection, FLMRModelForIndexing -from multi_modal_lndex.FLMR.flmr import FLMRQueryEncoderTokenizer, FLMRContextEncoderTokenizer, FLMRModelForRetrieval - -# load models -checkpoint_path = "D:\project\PreFLMR_ViT-B" -image_processor_name = "D:\project\clip-vit-base-patch16" - -query_tokenizer = FLMRQueryEncoderTokenizer.from_pretrained(checkpoint_path, subfolder="query_tokenizer") -context_tokenizer = FLMRContextEncoderTokenizer.from_pretrained( - checkpoint_path, subfolder="context_tokenizer" -) - -model = FLMRModelForRetrieval.from_pretrained( - checkpoint_path, - query_tokenizer=query_tokenizer, - context_tokenizer=context_tokenizer, -) -image_processor = AutoImageProcessor.from_pretrained(image_processor_name) -Q_pixel_values = torch.zeros(1, 3, 224, 224) - -D_encoding = context_tokenizer(["a apple in","aaaaa","sssss"]) -context_input_ids = D_encoding['input_ids'] -context_attention_mask = D_encoding['attention_mask'] - -Q_encoding = query_tokenizer(["a apple in here"]) -qcontext_input_ids = Q_encoding['input_ids'] -qcontext_attention_mask = Q_encoding['attention_mask'] - -text_embeddings = model.doc(input_ids=context_input_ids, attention_mask=context_attention_mask) -D, D_mask = text_embeddings.late_interaction_output, text_embeddings.context_mask -Q_duplicated = model.query(input_ids=qcontext_input_ids, attention_mask=qcontext_attention_mask, concat_output_from_vision_encoder=False).late_interaction_output -Q_duplicated = Q_duplicated.repeat_interleave(3, dim=0).contiguous() -scores = model.score(Q_duplicated, D, D_mask) -print(scores) diff --git a/multimodal_rag-main_v1-master/mygradio/chatbot.py b/mygradio/chatbot.py similarity index 100% rename from multimodal_rag-main_v1-master/mygradio/chatbot.py rename to mygradio/chatbot.py diff --git a/multimodal_rag-main_v1-master/mygradio/chatbot.pyi b/mygradio/chatbot.pyi similarity index 100% rename from multimodal_rag-main_v1-master/mygradio/chatbot.pyi rename to mygradio/chatbot.pyi diff --git a/project_config.py b/project_config.py new file mode 100644 index 0000000..b0134b1 --- /dev/null +++ b/project_config.py @@ -0,0 +1,43 @@ +"""Project configuration helpers. + +The configuration file defaults to ``config/config.json`` relative to this +repository. Set ``EMKG_CONFIG`` to use a different file without changing +tracked source files. +""" + +from __future__ import annotations + +import json +import os +from functools import lru_cache +from pathlib import Path +from typing import Any + + +PROJECT_ROOT = Path(__file__).resolve().parent +DEFAULT_CONFIG_PATH = PROJECT_ROOT / "config" / "config.json" +REQUIRED_KEYS = { + "vis_model_path", + "embedding_model_path", + "model_LLM_path", +} + + +@lru_cache(maxsize=1) +def load_project_config() -> dict[str, Any]: + """Load and validate the model configuration.""" + + config_path = Path(os.environ.get("EMKG_CONFIG", DEFAULT_CONFIG_PATH)).expanduser() + if not config_path.is_absolute(): + config_path = PROJECT_ROOT / config_path + + with config_path.open(encoding="utf-8") as config_file: + config = json.load(config_file) + + missing = sorted(REQUIRED_KEYS.difference(config)) + if missing: + raise KeyError( + f"Missing required keys in {config_path}: {', '.join(missing)}" + ) + return config + diff --git a/multimodal_rag-main_v1-master/requirements.txt b/requirements.txt similarity index 81% rename from multimodal_rag-main_v1-master/requirements.txt rename to requirements.txt index f7ffc17..59e098c 100644 --- a/multimodal_rag-main_v1-master/requirements.txt +++ b/requirements.txt @@ -1,7 +1,5 @@ -einops fastapi markdown2[all] -sentencepiece tokenizers==0.15.0 uvicorn wandb @@ -15,10 +13,13 @@ sentencepiece==0.1.99 einops==0.6.1 einops-exts==0.0.4 timm==1.0.7 +numpy<2 +Pillow>=9 +qdrant-client>=1.7,<2 gradio_client==0.2.9 llama-index==0.10.43 llama-index-embeddings-clip==0.1.1 llama-index-embeddings-huggingface==0.2.1 gradio==4.26.0 IPython -llama-index-vector-stores-qdrant==0.2.8 \ No newline at end of file +llama-index-vector-stores-qdrant==0.2.8 diff --git a/scripts/check_repository.py b/scripts/check_repository.py new file mode 100644 index 0000000..088f464 --- /dev/null +++ b/scripts/check_repository.py @@ -0,0 +1,81 @@ +"""Dependency-free repository checks used by CI.""" + +from __future__ import annotations + +import json +import re +import subprocess +import sys +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +JSON_FILES = ( + ROOT / "config.json", + ROOT / "config" / "config.json", + ROOT / "json_schema.json", +) +REQUIRED_ASSETS = ( + ROOT / "docs" / "assets" / "emkg-system-overview.png", + ROOT / "docs" / "assets" / "habitat-objectnav-comparison.png", + ROOT / "docs" / "assets" / "sim-to-real-tasks.png", +) +FORBIDDEN_TRACKED_SUFFIXES = (".zip", ".ckpt", ".pth", ".safetensors") +SECRET_PATTERNS = ( + re.compile(r"WANDB_API_KEY[^\n=]{0,20}=\s*['\"][0-9a-f]{40}['\"]"), + re.compile(r"(? list[Path]: + result = subprocess.run( + ["git", "ls-files", "-z"], + cwd=ROOT, + check=True, + capture_output=True, + ) + return [ROOT / item.decode() for item in result.stdout.split(b"\0") if item] + + +def main() -> int: + failures: list[str] = [] + + for path in JSON_FILES: + try: + with path.open(encoding="utf-8") as stream: + json.load(stream) + except (OSError, json.JSONDecodeError) as exc: + failures.append(f"invalid JSON: {path.relative_to(ROOT)} ({exc})") + + for path in REQUIRED_ASSETS: + if not path.is_file() or path.stat().st_size == 0: + failures.append(f"missing README asset: {path.relative_to(ROOT)}") + + for path in tracked_files(): + relative = path.relative_to(ROOT).as_posix() + if relative.startswith("qdrant_mm_db/"): + failures.append(f"runtime database is tracked: {relative}") + if path.suffix.lower() in FORBIDDEN_TRACKED_SUFFIXES: + failures.append(f"generated/model artifact is tracked: {relative}") + if not path.is_file() or path.stat().st_size > 2_000_000: + continue + try: + text = path.read_text(encoding="utf-8") + except (UnicodeDecodeError, OSError): + continue + for line_number, line in enumerate(text.splitlines(), start=1): + if any(pattern.search(line) for pattern in SECRET_PATTERNS): + failures.append(f"possible embedded credential: {relative}:{line_number}") + + if failures: + print("Repository checks failed:", file=sys.stderr) + for failure in failures: + print(f"- {failure}", file=sys.stderr) + return 1 + + print("Repository checks passed.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/split_regions.py b/scripts/split_regions.py new file mode 100644 index 0000000..f5afcbd --- /dev/null +++ b/scripts/split_regions.py @@ -0,0 +1,46 @@ +"""Split the region map into one JSON document per region.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--input", + type=Path, + default=ROOT / "config.json", + help="region map JSON", + ) + parser.add_argument( + "--output", + type=Path, + default=ROOT / "storage" / "regions_output", + help="directory for individual region documents", + ) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + with args.input.open(encoding="utf-8") as stream: + data = json.load(stream) + + args.output.mkdir(parents=True, exist_ok=True) + for region in data["regions"]: + destination = args.output / f"{region['name']}.json" + with destination.open("w", encoding="utf-8") as stream: + json.dump(region, stream, indent=2, ensure_ascii=False) + stream.write("\n") + print(f"Wrote {destination}") + + +if __name__ == "__main__": + main() + diff --git a/multimodal_rag-main_v1-master/server.py b/server.py similarity index 90% rename from multimodal_rag-main_v1-master/server.py rename to server.py index e2d987c..c48b100 100644 --- a/multimodal_rag-main_v1-master/server.py +++ b/server.py @@ -1,8 +1,10 @@ import os os.environ['KMP_DUPLICATE_LIB_OK']='True' +import argparse import shutil import traceback +from pathlib import Path import gradio as gr from gradio.components.chatbot import FileMessage @@ -13,9 +15,17 @@ from PIL import Image import numpy as np import sys -from model_LLM import OurLLM, MiniCPM, MobileVLM, LLaVAHD +parser = argparse.ArgumentParser(description="Run the EMKG multimodal RAG demo.") +parser.add_argument( + "backend", + choices=("MobileVLM", "MiniCPM", "LLaVA-HD"), + help="vision-language model backend", +) +args = parser.parse_args() -model_name = sys.argv[1] +from model_LLM import MiniCPM, MobileVLM, LLaVAHD + +model_name = args.backend if model_name == "MobileVLM": llm = MobileVLM() elif model_name == "MiniCPM": @@ -100,15 +110,26 @@ def upload_img(file, _chatbot, _app_session): return _chatbot, _app_session def unzip(zip_file, output=None): - """解压zip文件""" - if not os.path.exists(output): - os.mkdir(output) - output = output or os.path.dirname(zip_file) # 默认解压到当前目录同名文件夹中 + """Extract a knowledge-base archive while rejecting unsafe paths.""" + output = output or os.path.dirname(zip_file) + output_root = Path(output).resolve() + output_root.mkdir(parents=True, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zip_ref: for zip_info in zip_ref.infolist(): - # 解决中文文件名乱码问题 - zip_info.filename = zip_info.filename.encode('cp437').decode('gbk') - zip_ref.extract(zip_info, output) + try: + decoded_name = zip_info.filename.encode('cp437').decode('gbk') + except (UnicodeEncodeError, UnicodeDecodeError): + decoded_name = zip_info.filename + + relative_path = Path(decoded_name) + destination = (output_root / relative_path).resolve() + if relative_path.is_absolute() or ( + destination != output_root and output_root not in destination.parents + ): + raise ValueError(f"Unsafe path in ZIP archive: {decoded_name}") + + zip_info.filename = decoded_name + zip_ref.extract(zip_info, output_root) def load_knowledge(_app_cfg, _chat_bot): if _app_cfg['ret'] is not None: diff --git a/multimodal_rag-main_v1-master/storage/regions_output/corridor1.json b/storage/regions_output/corridor1.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/corridor1.json rename to storage/regions_output/corridor1.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/corridor2.json b/storage/regions_output/corridor2.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/corridor2.json rename to storage/regions_output/corridor2.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/r6-6.json b/storage/regions_output/r6-6.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/r6-6.json rename to storage/regions_output/r6-6.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/r6-7.json b/storage/regions_output/r6-7.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/r6-7.json rename to storage/regions_output/r6-7.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/r6-8.json b/storage/regions_output/r6-8.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/r6-8.json rename to storage/regions_output/r6-8.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/tea_room.json b/storage/regions_output/tea_room.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/tea_room.json rename to storage/regions_output/tea_room.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/work1.json b/storage/regions_output/work1.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/work1.json rename to storage/regions_output/work1.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/work2.json b/storage/regions_output/work2.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/work2.json rename to storage/regions_output/work2.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/work3.json b/storage/regions_output/work3.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/work3.json rename to storage/regions_output/work3.json diff --git a/multimodal_rag-main_v1-master/storage/regions_output/work4.json b/storage/regions_output/work4.json similarity index 100% rename from multimodal_rag-main_v1-master/storage/regions_output/work4.json rename to storage/regions_output/work4.json diff --git a/multimodal_rag-main_v1-master/quantify_image.py b/tools/image_dedup.py similarity index 100% rename from multimodal_rag-main_v1-master/quantify_image.py rename to tools/image_dedup.py diff --git a/multimodal_rag-main_v1-master/tools/message.py b/tools/message.py similarity index 100% rename from multimodal_rag-main_v1-master/tools/message.py rename to tools/message.py diff --git a/multimodal_rag-main_v1-master/update.py b/update.py similarity index 100% rename from multimodal_rag-main_v1-master/update.py rename to update.py

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