semvec/pypi-packages huggingface dataset:
ChatSearch 2023.4.25.9.51.41
Create a Python package.
License: MIT license
Requires Python: >=3.6
Keywords: chatllm

![image](https://img.shields.io/pypi/v/llm4gpt.svg) ![image](https://img.shields.io/travis/yuanjie-ai/llm4gpt.svg) ![image](https://readthedocs.org/projects/llm4gpt/badge/?version=latest)



<h1 align = "center">🔥ChatLLM🔥</h1>

---

# Install

```python
pip install -U llm4gpt
```

# [Docs](https://jie-yuan.github.io/llm4gpt/)

# Usages

```python
from chatllm.qa import QA
from chatllm.kb import FaissANN
from chatllm.chatllm import ChatLLM
from chatllm.utils import llm_load

from meutils.pipe import *

# 解析知识库
texts = []
metadatas = []
for p in Path('data').glob('*.txt'):
    texts.append(p.read_text())
    metadatas.append({'source': p})

# 文档向量化
faissann = FaissANN(model_name_or_path="shibing624/text2vec-base-chinese")
faissann.add_texts(texts, metadatas)

# 构建pipeline
model, tokenizer = llm_load(model_name_or_path="THUDM/chatglm-6b", device='cpu')
glm = ChatLLM()
glm.chat_func = partial(model.chat, tokenizer=tokenizer)

qa = QA(glm, faiss_ann=faissann.faiss_ann)

qa.get_knowledge_based_answer('周杰伦在干吗')
qa.get_knowledge_based_answer('姚明住哪里')
```

---

# TODO

- [ ] 增加UI

- [x] 增加本地知识库组件

- [ ] 增加互联网搜索组件

- [ ] 增加知识图谱组件

- [ ] 增加微调模块

- [ ] 增加流式输出





=======
History
=======

0.0.0 (2023-04-11)
------------------

* First release on PyPI.
ChatAudio 2023.4.25.9.51.6
Create a Python package.
License: MIT license
Requires Python: >=3.6
Keywords: chatllm

![image](https://img.shields.io/pypi/v/llm4gpt.svg) ![image](https://img.shields.io/travis/yuanjie-ai/llm4gpt.svg) ![image](https://readthedocs.org/projects/llm4gpt/badge/?version=latest)



<h1 align = "center">🔥ChatLLM🔥</h1>

---

# Install

```python
pip install -U llm4gpt
```

# [Docs](https://jie-yuan.github.io/llm4gpt/)

# Usages

```python
from chatllm.qa import QA
from chatllm.kb import FaissANN
from chatllm.chatllm import ChatLLM
from chatllm.utils import llm_load

from meutils.pipe import *

# 解析知识库
texts = []
metadatas = []
for p in Path('data').glob('*.txt'):
    texts.append(p.read_text())
    metadatas.append({'source': p})

# 文档向量化
faissann = FaissANN(model_name_or_path="shibing624/text2vec-base-chinese")
faissann.add_texts(texts, metadatas)

# 构建pipeline
model, tokenizer = llm_load(model_name_or_path="THUDM/chatglm-6b", device='cpu')
glm = ChatLLM()
glm.chat_func = partial(model.chat, tokenizer=tokenizer)

qa = QA(glm, faiss_ann=faissann.faiss_ann)

qa.get_knowledge_based_answer('周杰伦在干吗')
qa.get_knowledge_based_answer('姚明住哪里')
```

---

# TODO

- [ ] 增加UI

- [x] 增加本地知识库组件

- [ ] 增加互联网搜索组件

- [ ] 增加知识图谱组件

- [ ] 增加微调模块

- [ ] 增加流式输出





=======
History
=======

0.0.0 (2023-04-11)
------------------

* First release on PyPI.
ChatDocument 2023.4.25.9.50.0
Create a Python package.
License: MIT license
Requires Python: >=3.6
Keywords: chatllm

![image](https://img.shields.io/pypi/v/llm4gpt.svg) ![image](https://img.shields.io/travis/yuanjie-ai/llm4gpt.svg) ![image](https://readthedocs.org/projects/llm4gpt/badge/?version=latest)



<h1 align = "center">🔥ChatLLM🔥</h1>

---

# Install

```python
pip install -U llm4gpt
```

# [Docs](https://jie-yuan.github.io/llm4gpt/)

# Usages

```python
from chatllm.qa import QA
from chatllm.kb import FaissANN
from chatllm.chatllm import ChatLLM
from chatllm.utils import llm_load

from meutils.pipe import *

# 解析知识库
texts = []
metadatas = []
for p in Path('data').glob('*.txt'):
    texts.append(p.read_text())
    metadatas.append({'source': p})

# 文档向量化
faissann = FaissANN(model_name_or_path="shibing624/text2vec-base-chinese")
faissann.add_texts(texts, metadatas)

# 构建pipeline
model, tokenizer = llm_load(model_name_or_path="THUDM/chatglm-6b", device='cpu')
glm = ChatLLM()
glm.chat_func = partial(model.chat, tokenizer=tokenizer)

qa = QA(glm, faiss_ann=faissann.faiss_ann)

qa.get_knowledge_based_answer('周杰伦在干吗')
qa.get_knowledge_based_answer('姚明住哪里')
```

---

# TODO

- [ ] 增加UI

- [x] 增加本地知识库组件

- [ ] 增加互联网搜索组件

- [ ] 增加知识图谱组件

- [ ] 增加微调模块

- [ ] 增加流式输出





=======
History
=======

0.0.0 (2023-04-11)
------------------

* First release on PyPI.
ChatMind 2023.4.25.9.49.38
Create a Python package.
License: MIT license
Requires Python: >=3.6
Keywords: chatllm

![image](https://img.shields.io/pypi/v/llm4gpt.svg) ![image](https://img.shields.io/travis/yuanjie-ai/llm4gpt.svg) ![image](https://readthedocs.org/projects/llm4gpt/badge/?version=latest)



<h1 align = "center">🔥ChatLLM🔥</h1>

---

# Install

```python
pip install -U llm4gpt
```

# [Docs](https://jie-yuan.github.io/llm4gpt/)

# Usages

```python
from chatllm.qa import QA
from chatllm.kb import FaissANN
from chatllm.chatllm import ChatLLM
from chatllm.utils import llm_load

from meutils.pipe import *

# 解析知识库
texts = []
metadatas = []
for p in Path('data').glob('*.txt'):
    texts.append(p.read_text())
    metadatas.append({'source': p})

# 文档向量化
faissann = FaissANN(model_name_or_path="shibing624/text2vec-base-chinese")
faissann.add_texts(texts, metadatas)

# 构建pipeline
model, tokenizer = llm_load(model_name_or_path="THUDM/chatglm-6b", device='cpu')
glm = ChatLLM()
glm.chat_func = partial(model.chat, tokenizer=tokenizer)

qa = QA(glm, faiss_ann=faissann.faiss_ann)

qa.get_knowledge_based_answer('周杰伦在干吗')
qa.get_knowledge_based_answer('姚明住哪里')
```

---

# TODO

- [ ] 增加UI

- [x] 增加本地知识库组件

- [ ] 增加互联网搜索组件

- [ ] 增加知识图谱组件

- [ ] 增加微调模块

- [ ] 增加流式输出





=======
History
=======

0.0.0 (2023-04-11)
------------------

* First release on PyPI.
ChatOCR 2023.4.25.9.49.0
Create a Python package.
License: MIT license
Requires Python: >=3.6
Keywords: chatllm

![image](https://img.shields.io/pypi/v/llm4gpt.svg) ![image](https://img.shields.io/travis/yuanjie-ai/llm4gpt.svg) ![image](https://readthedocs.org/projects/llm4gpt/badge/?version=latest)



<h1 align = "center">🔥ChatLLM🔥</h1>

---

# Install

```python
pip install -U llm4gpt
```

# [Docs](https://jie-yuan.github.io/llm4gpt/)

# Usages

```python
from chatllm.qa import QA
from chatllm.kb import FaissANN
from chatllm.chatllm import ChatLLM
from chatllm.utils import llm_load

from meutils.pipe import *

# 解析知识库
texts = []
metadatas = []
for p in Path('data').glob('*.txt'):
    texts.append(p.read_text())
    metadatas.append({'source': p})

# 文档向量化
faissann = FaissANN(model_name_or_path="shibing624/text2vec-base-chinese")
faissann.add_texts(texts, metadatas)

# 构建pipeline
model, tokenizer = llm_load(model_name_or_path="THUDM/chatglm-6b", device='cpu')
glm = ChatLLM()
glm.chat_func = partial(model.chat, tokenizer=tokenizer)

qa = QA(glm, faiss_ann=faissann.faiss_ann)

qa.get_knowledge_based_answer('周杰伦在干吗')
qa.get_knowledge_based_answer('姚明住哪里')
```

---

# TODO

- [ ] 增加UI

- [x] 增加本地知识库组件

- [ ] 增加互联网搜索组件

- [ ] 增加知识图谱组件

- [ ] 增加微调模块

- [ ] 增加流式输出





=======
History
=======

0.0.0 (2023-04-11)
------------------

* First release on PyPI.