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Langchain-Chatchat
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Merge remote-tracking branch 'origin/dev' into dev 

# Conflicts:
#	configs/model_config.py
This commit is contained in:
glide-the 2023-05-02 12:43:27 +08:00
parent bf2466d91c 35aa8119f8
commit a0b312d749
10 changed files with 170 additions and 111 deletions
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8
README.md
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@ -14,8 +14,14 @@
![实现原理图](img/langchain+chatglm.png)
从文档处理角度来看，实现流程如下：
![实现原理图2](img/langchain+chatglm2.png)
🚩 本项目未涉及微调、训练过程，但可利用微调或训练对本项目效果进行优化。
🌐 [AutoDL 镜像](https://www.codewithgpu.com/i/imClumsyPanda/langchain-ChatGLM/langchain-ChatGLM)
📓 [ModelWhale 在线运行项目](https://www.heywhale.com/mw/project/643977aa446c45f4592a1e59)
## 变更日志
@ -166,6 +172,6 @@ Web UI 可以实现如下功能：
- [ ] 实现调用 API 的 Web UI Demo
## 项目交流群
![二维码](img/qr_code_9.jpg)
![二维码](img/qr_code_10.jpg)
🎉 langchain-ChatGLM 项目交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。

146
chains/local_doc_qa.py
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@ -8,6 +8,7 @@ from textsplitter import ChineseTextSplitter
from typing import List, Tuple
from langchain.docstore.document import Document
import numpy as np
from utils import torch_gc
# return top-k text chunk from vector store
VECTOR_SEARCH_TOP_K = 6
@ -15,6 +16,10 @@ VECTOR_SEARCH_TOP_K = 6
# LLM input history length
LLM_HISTORY_LEN = 3
DEVICE_ = EMBEDDING_DEVICE
DEVICE_ID = "0" if torch.cuda.is_available() else None
DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_
def load_file(filepath):
if filepath.lower().endswith(".md"):
@ -30,6 +35,7 @@ def load_file(filepath):
docs = loader.load_and_split(text_splitter=textsplitter)
return docs
def generate_prompt(related_docs: List[str],
query: str,
prompt_template=PROMPT_TEMPLATE) -> str:
@ -39,7 +45,7 @@ def generate_prompt(related_docs: List[str],
def get_docs_with_score(docs_with_score):
docs=[]
docs = []
for doc, score in docs_with_score:
doc.metadata["score"] = score
docs.append(doc)
@ -50,7 +56,7 @@ def seperate_list(ls: List[int]) -> List[List[int]]:
lists = []
ls1 = [ls[0]]
for i in range(1, len(ls)):
if ls[i-1] + 1 == ls[i]:
if ls[i - 1] + 1 == ls[i]:
ls1.append(ls[i])
else:
lists.append(ls1)
@ -59,49 +65,52 @@ def seperate_list(ls: List[int]) -> List[List[int]]:
return lists
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
) -> List[Tuple[Document, float]]:
scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
docs = []
id_set = set()
for j, i in enumerate(indices[0]):
if i == -1:
# This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
id_set.add(i)
docs_len = len(doc.page_content)
for k in range(1, max(i, len(docs)-i)):
for l in [i+k, i-k]:
if 0 <= l < len(self.index_to_docstore_id):
_id0 = self.index_to_docstore_id[l]
doc0 = self.docstore.search(_id0)
if docs_len + len(doc0.page_content) > self.chunk_size:
break
elif doc0.metadata["source"] == doc.metadata["source"]:
docs_len += len(doc0.page_content)
id_set.add(l)
id_list = sorted(list(id_set))
id_lists = seperate_list(id_list)
for id_seq in id_lists:
for id in id_seq:
if id == id_seq[0]:
_id = self.index_to_docstore_id[id]
doc = self.docstore.search(_id)
else:
_id0 = self.index_to_docstore_id[id]
) -> List[Tuple[Document, float]]:
scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
docs = []
id_set = set()
for j, i in enumerate(indices[0]):
if i == -1:
# This happens when not enough docs are returned.
continue
_id = self.index_to_docstore_id[i]
doc = self.docstore.search(_id)
id_set.add(i)
docs_len = len(doc.page_content)
for k in range(1, max(i, len(docs) - i)):
break_flag = False
for l in [i + k, i - k]:
if 0 <= l < len(self.index_to_docstore_id):
_id0 = self.index_to_docstore_id[l]
doc0 = self.docstore.search(_id0)
doc.page_content += doc0.page_content
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, scores[0][j]))
return docs
if docs_len + len(doc0.page_content) > self.chunk_size:
break_flag=True
break
elif doc0.metadata["source"] == doc.metadata["source"]:
docs_len += len(doc0.page_content)
id_set.add(l)
if break_flag:
break
id_list = sorted(list(id_set))
id_lists = seperate_list(id_list)
for id_seq in id_lists:
for id in id_seq:
if id == id_seq[0]:
_id = self.index_to_docstore_id[id]
doc = self.docstore.search(_id)
else:
_id0 = self.index_to_docstore_id[id]
doc0 = self.docstore.search(_id0)
doc.page_content += doc0.page_content
if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append((doc, scores[0][j]))
torch_gc(DEVICE)
return docs
class LocalDocQA:
@ -172,10 +181,12 @@ class LocalDocQA:
if vs_path and os.path.isdir(vs_path):
vector_store = FAISS.load_local(vs_path, self.embeddings)
vector_store.add_documents(docs)
torch_gc(DEVICE)
else:
if not vs_path:
vs_path = f"""{VS_ROOT_PATH}{os.path.splitext(file)[0]}_FAISS_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}"""
vector_store = FAISS.from_documents(docs, self.embeddings)
torch_gc(DEVICE)
vector_store.save_local(vs_path)
return vs_path, loaded_files
@ -187,29 +198,54 @@ class LocalDocQA:
query,
vs_path,
chat_history=[],
streaming=True):
self.llm.streaming = streaming
streaming: bool = STREAMING):
vector_store = FAISS.load_local(vs_path, self.embeddings)
FAISS.similarity_search_with_score_by_vector = similarity_search_with_score_by_vector
vector_store.chunk_size=self.chunk_size
vector_store.chunk_size = self.chunk_size
related_docs_with_score = vector_store.similarity_search_with_score(query,
k=self.top_k)
related_docs = get_docs_with_score(related_docs_with_score)
prompt = generate_prompt(related_docs, query)
if streaming:
for result, history in self.llm._call(prompt=prompt,
history=chat_history):
history[-1][0] = query
response = {"query": query,
"result": result,
"source_documents": related_docs}
yield response, history
else:
result, history = self.llm._call(prompt=prompt,
history=chat_history)
# if streaming:
# for result, history in self.llm._stream_call(prompt=prompt,
# history=chat_history):
# history[-1][0] = query
# response = {"query": query,
# "result": result,
# "source_documents": related_docs}
# yield response, history
# else:
for result, history in self.llm._call(prompt=prompt,
history=chat_history,
streaming=streaming):
history[-1][0] = query
response = {"query": query,
"result": result,
"source_documents": related_docs}
return response, history
yield response, history
if __name__ == "__main__":
local_doc_qa = LocalDocQA()
local_doc_qa.init_cfg()
query = "本项目使用的embedding模型是什么，消耗多少显存"
vs_path = "/Users/liuqian/Downloads/glm-dev/vector_store/aaa"
last_print_len = 0
for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
vs_path=vs_path,
chat_history=[],
streaming=True):
print(resp["result"][last_print_len:], end="", flush=True)
last_print_len = len(resp["result"])
source_text = [f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
# f"""相关度：{doc.metadata['score']}\n\n"""
for inum, doc in
enumerate(resp["source_documents"])]
print("\n\n" + "\n\n".join(source_text))
# for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
# vs_path=vs_path,
# chat_history=[],
# streaming=False):
# print(resp["result"])
pass

9
cli_demo.py
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@ -32,9 +32,12 @@ if __name__ == "__main__":
for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
vs_path=vs_path,
chat_history=history,
streaming=True):
print(resp["result"][last_print_len:], end="", flush=True)
last_print_len = len(resp["result"])
streaming=STREAMING):
if STREAMING:
print(resp["result"][last_print_len:], end="", flush=True)
last_print_len = len(resp["result"])
else:
print(resp["result"])
if REPLY_WITH_SOURCE:
source_text = [f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
# f"""相关度：{doc.metadata['score']}\n\n"""

11
configs/model_config.py
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@ -27,6 +27,9 @@ llm_model_dict = {
# LLM model name
LLM_MODEL = "chatglm-6b"
# LLM streaming reponse
STREAMING = True
# Use p-tuning-v2 PrefixEncoder
USE_PTUNING_V2 = False
@ -38,14 +41,10 @@ VS_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "vector_
UPLOAD_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "content", "")
# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
PROMPT_TEMPLATE = """已知信息在下方"="包裹的段落，基于以下已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
====================================已知信息=====================================================
PROMPT_TEMPLATE = """已知信息：
{context}
================================================================================================
问题:"{question}"
答案:"""
根据上述已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""
# 匹配后单段上下文长度
CHUNK_SIZE = 500

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52
models/chatglm_llm.py
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@ -4,21 +4,15 @@ from typing import Optional, List
from langchain.llms.utils import enforce_stop_tokens
from transformers import AutoTokenizer, AutoModel, AutoConfig
import torch
from configs.model_config import LLM_DEVICE
from configs.model_config import *
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from typing import Dict, Tuple, Union, Optional
from utils import torch_gc
DEVICE = LLM_DEVICE
DEVICE_ = LLM_DEVICE
DEVICE_ID = "0" if torch.cuda.is_available() else None
CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
def torch_gc():
if torch.cuda.is_available():
with torch.cuda.device(CUDA_DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_
def auto_configure_device_map(num_gpus: int) -> Dict[str, int]:
@ -59,7 +53,6 @@ class ChatGLM(LLM):
tokenizer: object = None
model: object = None
history_len: int = 10
streaming: bool = True
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
def __init__(self):
@ -72,8 +65,8 @@ class ChatGLM(LLM):
def _call(self,
prompt: str,
history: List[List[str]] = [],
stop: Optional[List[str]] = None) -> str:
if self.streaming:
streaming: bool = STREAMING): # -> Tuple[str, List[List[str]]]:
if streaming:
for inum, (stream_resp, _) in enumerate(self.model.stream_chat(
self.tokenizer,
prompt,
@ -81,25 +74,23 @@ class ChatGLM(LLM):
max_length=self.max_token,
temperature=self.temperature,
)):
torch_gc(DEVICE)
if inum == 0:
history += [[prompt, stream_resp]]
else:
history[-1] = [prompt, stream_resp]
yield stream_resp, history
else:
response, _ = self.model.chat(
self.tokenizer,
prompt,
history=history[-self.history_len:] if self.history_len > 0 else [],
max_length=self.max_token,
temperature=self.temperature,
self.tokenizer,
prompt,
history=history[-self.history_len:] if self.history_len > 0 else [],
max_length=self.max_token,
temperature=self.temperature,
)
torch_gc()
if stop is not None:
response = enforce_stop_tokens(response, stop)
history = history + [[None, response]]
return response, history
torch_gc(DEVICE)
history += [[prompt, response]]
yield response, history
# def chat(self,
# prompt: str) -> str:
@ -191,3 +182,16 @@ class ChatGLM(LLM):
print("加载PrefixEncoder模型参数失败")
self.model = self.model.eval()
if __name__ == "__main__":
llm = ChatGLM()
llm.load_model(model_name_or_path=llm_model_dict[LLM_MODEL],
llm_device=LLM_DEVICE, )
last_print_len=0
for resp, history in llm._call("你好", streaming=True):
print(resp[last_print_len:], end="", flush=True)
last_print_len = len(resp)
for resp, history in llm._call("你好", streaming=False):
print(resp)
pass

11
utils/__init__.py Normal file
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@ -0,0 +1,11 @@
import torch.cuda
import torch.mps
import torch.backends
def torch_gc(DEVICE):
if torch.cuda.is_available():
with torch.cuda.device(DEVICE):
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
elif torch.backends.mps.is_available():
torch.mps.empty_cache()

44
webui.py
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@ -29,28 +29,28 @@ llm_model_dict_list = list(llm_model_dict.keys())
local_doc_qa = LocalDocQA()
def get_answer(query, vs_path, history, mode):
if mode == "知识库问答":
if vs_path:
for resp, history in local_doc_qa.get_knowledge_based_answer(
query=query, vs_path=vs_path, chat_history=history):
source = "\n\n"
source += "".join(
[f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history[-1][-1] += source
yield history, ""
else:
for resp, history in local_doc_qa.llm._call(query, history):
history[-1][-1] = resp + (
"\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "")
yield history, ""
def get_answer(query, vs_path, history, mode,
streaming: bool = STREAMING):
if mode == "知识库问答" and vs_path:
for resp, history in local_doc_qa.get_knowledge_based_answer(
query=query,
vs_path=vs_path,
chat_history=history,
streaming=streaming):
source = "\n\n"
source += "".join(
[f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history[-1][-1] += source
yield history, ""
else:
for resp, history in local_doc_qa.llm._call(query, history):
history[-1][-1] = resp
for resp, history in local_doc_qa.llm._call(query, history,
streaming=streaming):
history[-1][-1] = resp + (
"\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "")
yield history, ""
@ -84,7 +84,7 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, to
embedding_model=embedding_model,
llm_history_len=llm_history_len,
use_ptuning_v2=use_ptuning_v2,
top_k=top_k)
top_k=top_k,)
model_status = """模型已成功重新加载，可以开始对话，或从右侧选择模式后开始对话"""
print(model_status)
except Exception as e: