DAML-RAG Framework
Copyright 2025 薛小川 (Xue Xiaochuan)

This product includes software developed by 薛小川 as part of the BUILD_BODY project.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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This project incorporates components from the following open source projects:

1. FastAPI (https://fastapi.tiangolo.com/)
   License: MIT License
   Copyright (c) 2018 Sebastián Ramírez

2. Neo4j Python Driver (https://neo4j.com/)
   License: Apache License 2.0
   Copyright (c) Neo4j, Inc.

3. Qdrant Client (https://qdrant.tech/)
   License: Apache License 2.0
   Copyright (c) Qdrant Solutions

4. Pydantic (https://pydantic-docs.helpmanual.io/)
   License: MIT License
   Copyright (c) 2017 Samuel Colvin

5. httpx (https://www.python-httpx.org/)
   License: BSD 3-Clause License
   Copyright (c) 2019 Encode OSS Ltd.

For a complete list of dependencies, see requirements.txt

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Third-Party Academic References:

This framework builds upon theoretical foundations from:
- RAG: Lewis et al. (2020) "Retrieval-Augmented Generation"
- GraphRAG: Microsoft Research (2024)
- In-Context Learning: Brown et al. (2020) GPT-3 paper
- Knowledge Graphs: Hogan et al. (2021)

See REFERENCES.md for complete bibliography.

