📃 **LangChain-Chatchat** (formerly Langchain-ChatGLM): A LLM application aims to implement knowledge and search engine based QA based on Langchain and open-source or remote LLM API.
🤖️ A Q&A application based on local knowledge base implemented using the idea of [langchain](https://github.com/hwchase17/langchain). The goal is to build a KBQA(Knowledge based Q&A) solution that is friendly to Chinese scenarios and open source models and can run both offline and online.
💡 Inspried by [document.ai](https://github.com/GanymedeNil/document.ai) and [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) , we build a local knowledge base question answering application that can be implemented using an open source model or remote LLM api throughout the process. In the latest version of this project, [FastChat](https://github.com/lm-sys/FastChat) is used to access Vicuna, Alpaca, LLaMA, Koala, RWKV and many other models. Relying on [langchain](https:// github.com/langchain-ai/langchain) , this project supports calling services through the API provided based on [FastAPI](https://github.com/tiangolo/fastapi), or using the WebUI based on [Streamlit](https://github.com /streamlit/streamlit) .
✅ Relying on the open source LLM and Embedding models, this project can realize full-process **offline private deployment**. At the same time, this project also supports the call of OpenAI GPT API- and Zhipu API, and will continue to expand the access to various models and remote APIs in the future.
⛓️ The implementation principle of this project is shown in the graph below. The main process includes: loading files -> reading text -> text segmentation -> text vectorization -> question vectorization -> matching the `top-k` most similar to the question vector in the text vector -> The matched text is added to `prompt `as context and question -> submitted to `LLM` to generate an answer.
plese refer to [version change log](https://github.com/imClumsyPanda/langchain-ChatGLM/releases)
### Current Features
* **Consistent LLM Service based on FastChat**. The project use [FastChat](https://github.com/lm-sys/FastChat) to provide the API service of the open source LLM models and access it in the form of OpenAI API interface to improve the loading effect of the LLM model;
* **Chain and Agent based on Langchian**. Use the existing Chain implementation in [langchain](https://github.com/langchain-ai/langchain) to facilitate subsequent access to different types of Chain, and will test Agent access;
* **Full fuction API service based on FastAPI**. All interfaces can be tested in the docs automatically generated by [FastAPI](https://github.com/tiangolo/fastapi), and all dialogue interfaces support streaming or non-streaming output through parameters. ;
* **WebUI service based on Streamlit**. With [Streamlit](https://github.com/streamlit/streamlit), you can choose whether to start WebUI based on API services, add session management, customize session themes and switch, and will support different display of content forms of output in the future;
* **Abundant open source LLM and Embedding models**. The default LLM model in the project is changed to [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b), and the default Embedding model is changed to [moka-ai/m3e-base](https:// huggingface.co/moka-ai/m3e-base), the file loading method and the paragraph division method have also been adjusted. In the future, context expansion will be re-implemented and optional settings will be added;
* **Multiply vector libraries**. The project has expanded support for different types of vector libraries. Including [FAISS](https://github.com/facebookresearch/faiss), [Milvus](https://github.com/milvus -io/milvus), and [PGVector](https://github.com/pgvector/pgvector);
* **Varied Search engines**. We provide two search engines now: Bing and DuckDuckGo. DuckDuckGo search does not require configuring an API Key and can be used directly in environments with access to foreign services.
The default LLM model in the project is changed to [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b), and the default Embedding model is changed to [moka-ai/m3e-base](https:// huggingface.co/moka-ai/m3e-base).
* Any [EleutherAI](https://huggingface.co/EleutherAI) pythia model such as [pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b)(任何 [EleutherAI](https://huggingface.co/EleutherAI) 的 pythia 模型，如 [pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b))
* Any [Peft](https://github.com/huggingface/peft) adapter trained on top of a model above. To activate, must have `peft` in the model path. Note: If loading multiple peft models, you can have them share the base model weights by setting the environment variable `PEFT_SHARE_BASE_WEIGHTS=true` in any model worker.
The above model support list may be updated continuously as [FastChat](https://github.com/lm-sys/FastChat) is updated, see [FastChat Supported Models List](https://github.com/lm-sys/FastChat/blob/main /docs/model_support.md).
In addition to local models, this project also supports direct access to online models such as OpenAI API, Wisdom Spectrum AI, etc. For specific settings, please refer to the configuration information of `llm_model_dict` in `configs/model_configs.py.example`.
The default LLM type used in the project is `THUDM/chatglm2-6b`, if you need to use other LLM types, please modify `llm_model_dict` and `LLM_MODEL` in [configs/model_config.py].
The default Embedding type used in the project is `sensenova/piccolo-base-zh`, if you want to use other Embedding types, please modify `embedding_model_dict` and `embedding_model_dict` and `embedding_model_dict` in [configs/model_config.py]. MODEL` in [configs/model_config.py].
### Build your own Agent tool!
See [Custom Agent Instructions](docs/自定义Agent.md) for details.
- The image size of this version is `33.9GB`, using `v0.2.0`, with `nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04` as the base image
- This version has a built-in `embedding` model: `m3e-large`, built-in `chatglm2-6b-32k`
- This version is designed to facilitate one-click deployment. Please make sure you have installed the NVIDIA driver on your Linux distribution.
- Please note that you do not need to install the CUDA toolkit on the host system, but you need to install the `NVIDIA Driver` and the `NVIDIA Container Toolkit`, please refer to the [Installation Guide](https://docs.nvidia.com/datacenter/cloud -native/container-toolkit/latest/install-guide.html)
- It takes a certain amount of time to pull and start for the first time. When starting for the first time, please refer to the figure below to use `docker logs -f <container id>` to view the log.
- If the startup process is stuck in the `Waiting..` step, it is recommended to use `docker exec -it <container id> bash` to enter the `/logs/` directory to view the corresponding stage logs
If you want to run this project in a local or offline environment, you need to first download the models required for the project to your local computer. Usually the open source LLM and Embedding models can be downloaded from [HuggingFace](https://huggingface.co/models).
Take the LLM model [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b) and Embedding model [moka-ai/m3e-base](https://huggingface. co/moka-ai/m3e-base) for example:
To download the model, you need to [install Git LFS](https://docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage), and then run:
Copy the model-related parameter configuration template file [configs/model_config.py.example](configs/model_config.py.example) and save it in the `./configs` path under the project path, and rename it to `model_config.py`.
Copy the service-related parameter configuration template file [configs/server_config.py.example](configs/server_config.py.example) to save in the `./configs` path under the project path, and rename it to `server_config.py`.
Before starting to execute Web UI or command line interaction, please check whether each model parameter in `configs/model_config.py` and `configs/server_config.py` meets the requirements.
* Please confirm that the path to local LLM model and embedding model have been written in `llm_dict` of `configs/model_config.py`, here is an example:
* If you choose to use OpenAI's Embedding model, please write the model's ``key`` into `embedding_model_dict`. To use this model, you need to be able to access the OpenAI official API, or set up a proxy.
The knowledge base information is stored in the database, please initialize the database before running the project (we strongly recommend one back up the knowledge files before performing operations).
- If you migrate from `0.1.x`, for the established knowledge base, please confirm that the vector library type and Embedding model of the knowledge base are consistent with the default settings in `configs/model_config.py`, if there is no change, simply add the existing repository information to the database with the following command:
```shell
$ python init_database.py
```
- If you are a beginner of the project whose knowledge base has not been established, or the knowledge base type and embedding model in the configuration file have changed, or the previous vector library did not enable `normalize_L2`, you need the following command to initialize or rebuild the knowledge base:
* These parameters now can be specified by `server_config.FSCHST_MODEL_WORKERD`.
* In some extreme senses, `gpus` doesn't work, then one should specify the used gpus with environment variable `CUDA_VISIBLE_DEVICES`, here is an example:
Including lora,p-tuning,prefix tuning, prompt tuning,ia3
This project loads the LLM service based on FastChat, so one must load the PEFT in a FastChat way, that is, ensure that the word `peft` must be in the path name, the name of the configuration file must be `adapter_config.json`, and the path contains PEFT weights in `.bin` format. The peft path is specified in `args.model_names` of the `create_model_worker_app` function in `startup.py`, and enable the environment variable `PEFT_SHARE_BASE_WEIGHTS=true` parameter.
If the above method fails, you need to start standard fastchat service step by step. Step-by-step procedure could be found Section 6. For further steps, please refer to [Model invalid after loading lora fine-tuning](https://github. com/chatchat-space/Langchain-Chatchat/issues/1130#issuecomment-1685291822).
1.**The `startup.py` uses multi-process mode to start the services of each module, which may cause printing order problems. Please wait for all services to be initiated before calling, and call the service according to the default or specified port (default LLM API service port: `127.0.0.1:8888 `, default API service port:`127.0.0.1:7861 `, default WebUI service port: `127.0.0.1: 8501`)**
2.**The startup time of the service differs across devices, usually it takes 3-10 minutes. If it does not start for a long time, please go to the `./logs` directory to monitor the logs and locate the problem.**
3.**Using ctrl+C to exit on Linux may cause orphan processes due to the multi-process mechanism of Linux. You can exit through `shutdown_all.sh`**
