fix some error

.gitignore

@@ -131,4 +131,8 @@ dmypy.json
 .pyre/
 .DS_Store
 logs
+<<<<<<< HEAD
+=======
+
+>>>>>>> 2eeb22ccae5b2a5d695ac267044129f3b672c3dc
 .vectordb

README.md

@@ -1,8 +1,10 @@
 # DB-GPT
-A Open Database-GPT Experiment
+A Open Database-GPT Experiment, A fully localized project.
 
 ![GitHub Repo stars](https://img.shields.io/github/stars/csunny/db-gpt?style=social)
 
+一个数据库相关的GPT实验项目, 模型与数据全部本地化部署, 绝对保障数据的隐私安全。 同时此GPT项目可以直接本地部署连接到私有数据库, 进行私有数据处理。 
+
 [DB-GPT](https://github.com/csunny/DB-GPT) 是一个实验性的开源应用程序，它基于[FastChat](https://github.com/lm-sys/FastChat)，并使用[vicuna-13b](https://huggingface.co/Tribbiani/vicuna-13b)作为基础模型。此外，此程序结合了[langchain](https://github.com/hwchase17/langchain)和[llama-index](https://github.com/jerryjliu/llama_index)基于现有知识库进行[In-Context Learning](https://arxiv.org/abs/2301.00234)来对其进行数据库相关知识的增强。它可以进行SQL生成、SQL诊断、数据库知识问答等一系列的工作。 
 
 
@@ -30,6 +32,26 @@ Run on an RTX 4090 GPU (The origin mov not sped up!, [YouTube地址](https://www
 
 <img src="https://github.com/csunny/DB-GPT/blob/main/asserts/DB_QA.png" margin-left="auto" margin-right="auto" width="600">
 
+# Dependencies
+1. First you need to install python requirements.
+```
+python>=3.9
+pip install -r requirements
+```
+or if you use conda envirenment, you can use this command
+```
+cd DB-GPT
+conda env create -f environment.yml
+```
+
+2. MySQL Install
+
+In this project examples, we connect mysql and run SQL-Generate. so you need install mysql local for test. recommand docker
+```
+docker run --name=mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=aa123456 -dit mysql:latest
+```
+The password just for test, you can change this if necessary
+
 # Install
 1. 基础模型下载
 关于基础模型, 可以根据[vicuna](https://github.com/lm-sys/FastChat/blob/main/README.md#model-weights)合成教程进行合成。 
@@ -52,3 +74,7 @@ python webserver.py
 - SQL-diagnosis
 
 总的来说，它是一个用于数据库的复杂且创新的AI工具。如果您对如何在工作中使用或实施DB-GPT有任何具体问题，请联系我, 我会尽力提供帮助, 同时也欢迎大家参与到项目建设中, 做一些有趣的事情。
+
+
+# Licence
+[MIT](https://github.com/csunny/DB-GPT/blob/main/LICENSE)

environment.yml

@@ -61,3 +61,7 @@ dependencies:
     - gradio-client==0.0.8
     - wandb
     - fschat=0.1.10
+    - llama-index=0.5.27
+    - pymysql
+    - unstructured==0.6.3
+    - pytesseract==0.3.10

pilot/chain/audio.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-

pilot/chain/visual.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-

pilot/client/chart.py

@@ -0,0 +1,2 @@
+#/usr/bin/env python3
+# -*- coding: utf-8 -*-

pilot/configs/model_config.py

@@ -11,6 +11,7 @@ PILOT_PATH = os.path.join(ROOT_PATH, "pilot")
 VECTORE_PATH = os.path.join(PILOT_PATH, "vector_store")
 LOGDIR = os.path.join(ROOT_PATH, "logs")
 DATASETS_DIR = os.path.join(PILOT_PATH, "datasets")
+DATA_DIR = os.path.join(PILOT_PATH, "data")
 
 nltk.data.path = [os.path.join(PILOT_PATH, "nltk_data")] + nltk.data.path
 
@@ -22,6 +23,7 @@ LLM_MODEL_CONFIG = {
 }
 
 
+VECTOR_SEARCH_TOP_K = 3
 LLM_MODEL = "vicuna-13b"
 LIMIT_MODEL_CONCURRENCY = 5
 MAX_POSITION_EMBEDDINGS = 2048

pilot/conversation.py

@@ -146,8 +146,24 @@ conv_vicuna_v1 = Conversation(
     sep2="</s>",
 )
 
+
+conv_qa_prompt_template = """ 基于以下已知的信息, 专业、详细的回答用户的问题。
+            如果无法从提供的恶内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题", 但是你可以给出一些与问题相关答案的建议:   
+            
+            已知内容: 
+            {context}
+            问题:
+            {question}
+"""
+
 default_conversation = conv_one_shot
 
+conversation_types = {
+    "native": "LLM原生对话",
+    "default_knownledge": "默认知识库对话",
+    "custome":  "新增知识库对话",
+}
+
 conv_templates = {
     "conv_one_shot": conv_one_shot,
     "vicuna_v1": conv_vicuna_v1,

pilot/model/vicuna_llm.py

@@ -10,33 +10,29 @@ from typing import Any, Mapping, Optional, List
 from langchain.llms.base import LLM
 from pilot.configs.model_config import *
 
-class VicunaRequestLLM(LLM):
+class VicunaLLM(LLM):
 
-    vicuna_generate_path = "generate"
+    vicuna_generate_path = "generate_stream"
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+    def _call(self, prompt: str, temperature: float, max_new_tokens: int, stop: Optional[List[str]] = None) -> str:
-        if isinstance(stop, list):
-            stop = stop + ["Observation:"]
 
-        skip_echo_len = len(prompt.replace("</s>", " ")) + 1
         params = {
             "prompt": prompt,
-            "temperature": 0.7,
+            "temperature": temperature,
-            "max_new_tokens": 1024,
+            "max_new_tokens": max_new_tokens,
             "stop": stop
         }
         response = requests.post(
             url=urljoin(VICUNA_MODEL_SERVER, self.vicuna_generate_path),
             data=json.dumps(params),
         )
-        response.raise_for_status()
+
-        # for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
+        skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
-        #     if chunk:
+        for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
-        #         data = json.loads(chunk.decode())
+            if chunk:
-        #         if data["error_code"] == 0:
+                data = json.loads(chunk.decode())
-        #             output = data["text"][skip_echo_len:].strip()
+                if data["error_code"] == 0:
-        #             output = self.post_process_code(output)
+                    output = data["text"][skip_echo_len:].strip()
-        #             yield output
+                    yield output
-        return response.json()["response"]
 
     @property
     def _llm_type(self) -> str:

pilot/pturning/lora/finetune.py

@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os
+import json
+import transformers
+from transformers import LlamaTokenizer, LlamaForCausalLM
+
+from typing import List
+from peft import (
+    LoraConfig,
+    get_peft_model,
+    get_peft_model_state_dict,
+    prepare_model_for_int8_training,
+)
+
+import torch
+from datasets import load_dataset
+import pandas as pd
+
+
+from pilot.configs.model_config import DATA_DIR, LLM_MODEL, LLM_MODEL_CONFIG
+device = "cuda" if torch.cuda.is_available() else "cpu"
+CUTOFF_LEN = 50
+
+df = pd.read_csv(os.path.join(DATA_DIR, "BTC_Tweets_Updated.csv"))
+
+def sentiment_score_to_name(score: float):
+    if score > 0:
+        return "Positive"
+    elif score < 0:
+        return "Negative"
+    return "Neutral"
+
+
+dataset_data = [
+    {
+        "instruction": "Detect the sentiment of the tweet.",
+        "input": row_dict["Tweet"],
+        "output": sentiment_score_to_name(row_dict["New_Sentiment_State"])
+    } 
+    for row_dict in df.to_dict(orient="records")
+]
+
+with open(os.path.join(DATA_DIR, "alpaca-bitcoin-sentiment-dataset.json"), "w") as f:
+    json.dump(dataset_data, f) 
+
+
+data = load_dataset("json", data_files=os.path.join(DATA_DIR, "alpaca-bitcoin-sentiment-dataset.json"))
+print(data["train"])
+
+BASE_MODEL = LLM_MODEL_CONFIG[LLM_MODEL]
+model = LlamaForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    offload_folder=os.path.join(DATA_DIR, "vicuna-lora")
+) 
+
+tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
+tokenizer.pad_token_id = (0)
+tokenizer.padding_side = "left"
+
+def generate_prompt(data_point):
+    return f"""Blow is an instruction that describes a task, paired with an input that provide future context.
+    Write a response that appropriately completes the request. #noqa:
+
+    ### Instruct:
+    {data_point["instruction"]}
+    ### Input
+    {data_point["input"]}
+    ### Response
+    {data_point["output"]}
+    """
+
+def tokenize(prompt, add_eos_token=True):
+    result = tokenizer(
+        prompt,
+        truncation=True,
+        max_length=CUTOFF_LEN,
+        padding=False,
+        return_tensors=None,
+    )
+
+    if (result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < CUTOFF_LEN and add_eos_token):
+        result["input_ids"].append(tokenizer.eos_token_id)
+        result["attention_mask"].append(1)
+
+    result["labels"] = result["input_ids"].copy()
+    return result
+
+def generate_and_tokenize_prompt(data_point):
+    full_prompt = generate_prompt(data_point)
+    tokenized_full_prompt = tokenize(full_prompt)
+    return tokenized_full_prompt
+
+
+train_val = data["train"].train_test_split(
+    test_size=200, shuffle=True, seed=42
+)
+
+train_data = (
+    train_val["train"].map(generate_and_tokenize_prompt)
+)
+
+val_data = (
+    train_val["test"].map(generate_and_tokenize_prompt)
+)
+
+# Training
+LORA_R = 8
+LORA_ALPHA = 16
+LORA_DROPOUT = 0.05
+LORA_TARGET_MODULES = [
+    "q_proj",
+    "v_proj",
+]
+
+BATCH_SIZE = 128
+MICRO_BATCH_SIZE = 4
+GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
+LEARNING_RATE = 3e-4
+TRAIN_STEPS = 300
+OUTPUT_DIR = "experiments"
+
+# We can now prepare model for training
+model = prepare_model_for_int8_training(model)
+config = LoraConfig(
+    r = LORA_R,
+    lora_alpha=LORA_ALPHA,
+    target_modules=LORA_TARGET_MODULES,
+    lora_dropout=LORA_DROPOUT,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+model = get_peft_model(model, config)
+model.print_trainable_parameters()
+
+training_arguments = transformers.TrainingArguments(
+    per_device_train_batch_size=MICRO_BATCH_SIZE,
+    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
+    warmup_steps=100,
+    max_steps=TRAIN_STEPS,
+    no_cuda=True,
+    learning_rate=LEARNING_RATE,
+    logging_steps=10,
+    optim="adamw_torch",
+    evaluation_strategy="steps",
+    save_strategy="steps",
+    eval_steps=50,
+    save_steps=50,
+    output_dir=OUTPUT_DIR,
+    save_total_limit=3,
+    load_best_model_at_end=True,
+    report_to="tensorboard"
+)
+
+data_collector = transformers.DataCollatorForSeq2Seq(
+    tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+)
+
+trainer = transformers.Trainer(
+    model=model,
+    train_dataset=train_data,
+    eval_dataset=val_data,
+    args=training_arguments,
+    data_collector=data_collector
+)
+
+model.config.use_cache = False
+old_state_dict = model.state_dict
+model.state_dict = (
+    lambda self, *_, **__: get_peft_model_state_dict(
+        self, old_state_dict()
+    )
+).__get__(model, type(model))
+
+trainer.train()
+model.save_pretrained(OUTPUT_DIR)

pilot/server/embdserver.py

@@ -4,29 +4,44 @@
 import requests
 import json
 import time
+import uuid
 from urllib.parse import urljoin
 import gradio as gr
 from pilot.configs.model_config import *
-vicuna_base_uri = "http://192.168.31.114:21002/"
+from pilot.conversation import conv_qa_prompt_template, conv_templates
-vicuna_stream_path = "worker_generate_stream"
+from langchain.prompts import PromptTemplate
-vicuna_status_path = "worker_get_status"
 
-def generate(prompt):
+vicuna_stream_path = "generate_stream"
+
+def generate(query):
+
+    template_name = "conv_one_shot"
+    state = conv_templates[template_name].copy()
+
+    pt = PromptTemplate(
+        template=conv_qa_prompt_template,
+        input_variables=["context", "question"]
+    )
+
+    result = pt.format(context="This page covers how to use the Chroma ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.",
+              question=query)
+
+    print(result)
+
+    state.append_message(state.roles[0], result)
+    state.append_message(state.roles[1], None)
+
+    prompt = state.get_prompt()
     params = {
         "model": "vicuna-13b",
         "prompt": prompt,
         "temperature": 0.7,
-        "max_new_tokens": 512,
+        "max_new_tokens": 1024,
         "stop": "###"
     }
 
-    sts_response = requests.post(
-        url=urljoin(vicuna_base_uri, vicuna_status_path)
-    )
-    print(sts_response.text)
-
     response = requests.post(
-        url=urljoin(vicuna_base_uri, vicuna_stream_path), data=json.dumps(params)
+        url=urljoin(VICUNA_MODEL_SERVER, vicuna_stream_path), data=json.dumps(params)
     )
 
     skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
@@ -34,11 +49,10 @@ def generate(prompt):
         if chunk:
             data = json.loads(chunk.decode())
             if data["error_code"] == 0:
-                output = data["text"]
+                output = data["text"][skip_echo_len:].strip()
+                state.messages[-1][-1] = output + "▌"
                 yield(output) 
  
-            time.sleep(0.02)
-
 if __name__ == "__main__":
     print(LLM_MODEL)
     with gr.Blocks() as demo:

pilot/server/vectordb_qa.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from pilot.vector_store.file_loader import KnownLedge2Vector
+from langchain.prompts import PromptTemplate
+from pilot.conversation import conv_qa_prompt_template
+from pilot.configs.model_config import VECTOR_SEARCH_TOP_K
+from pilot.model.vicuna_llm import VicunaLLM
+
+class KnownLedgeBaseQA:
+
+    def __init__(self) -> None:
+        k2v = KnownLedge2Vector()
+        self.vector_store = k2v.init_vector_store()
+        self.llm = VicunaLLM()
+    
+    def get_similar_answer(self, query):
+        
+        prompt = PromptTemplate(
+            template=conv_qa_prompt_template,
+            input_variables=["context", "question"]
+        )
+
+        retriever = self.vector_store.as_retriever(search_kwargs={"k": VECTOR_SEARCH_TOP_K})
+        docs = retriever.get_relevant_documents(query=query)
+
+        context = [d.page_content for d in docs] 
+        result = prompt.format(context="\n".join(context), question=query)
+        return result

pilot/server/webserver.py

@@ -11,6 +11,7 @@ import datetime
 import requests
 from urllib.parse import urljoin
 from pilot.configs.model_config import DB_SETTINGS
+from pilot.server.vectordb_qa import KnownLedgeBaseQA
 from pilot.connections.mysql_conn import MySQLOperator
 from pilot.vector_store.extract_tovec import get_vector_storelist, load_knownledge_from_doc, knownledge_tovec_st
 
@@ -19,6 +20,7 @@ from pilot.configs.model_config import LOGDIR, VICUNA_MODEL_SERVER, LLM_MODEL, D
 from pilot.conversation import (
     default_conversation,
     conv_templates,
+    conversation_types,
     SeparatorStyle
 )
 
@@ -149,7 +151,7 @@ def post_process_code(code):
         code = sep.join(blocks)
     return code
 
-def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Request):
+def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.Request):
     start_tstamp = time.time()
     model_name = LLM_MODEL
 
@@ -170,7 +172,8 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
         
         query = state.messages[-2][1]
 
-        # prompt 中添加上下文提示
+        # prompt 中添加上下文提示, 根据已有知识对话, 上下文提示是否也应该放在第一轮, 还是每一轮都添加上下文? 
+        # 如果用户侧的问题跨度很大, 应该每一轮都加提示。
         if db_selector:
             new_state.append_message(new_state.roles[0], gen_sqlgen_conversation(dbname) + query)
             new_state.append_message(new_state.roles[1], None)
@@ -180,13 +183,11 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
             new_state.append_message(new_state.roles[1], None)
             state = new_state
 
-    # try: 
+    if mode == conversation_types["default_knownledge"] and not db_selector:
-    #     if not db_selector: 
+        query = state.messages[-2][1]
-    #         sim_q = get_simlar(query)
+        knqa = KnownLedgeBaseQA()
-    #         print("********vector similar info*************: ", sim_q)
+        state.messages[-2][1] = knqa.get_similar_answer(query)
-    #         state.append_message(new_state.roles[0], sim_q + query)
+        
-    # except Exception as e:
-    #     print(e)
 
     prompt = state.get_prompt()
     
@@ -222,7 +223,7 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
                     state.messages[-1][-1] = output
                     yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
                     return
-                time.sleep(0.02)
+
     except requests.exceptions.RequestException as e:
         state.messages[-1][-1] = server_error_msg + f" (error_code: 4)"
         yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
@@ -231,6 +232,7 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
     state.messages[-1][-1] = state.messages[-1][-1][:-1]
     yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
 
+    # 记录运行日志
     finish_tstamp = time.time()
     logger.info(f"{output}")
 
@@ -266,7 +268,7 @@ def change_tab(tab):
     pass
 
 def change_mode(mode):
-    if mode == "默认知识库对话":
+    if mode in ["默认知识库对话", "LLM原生对话"]:
         return gr.update(visible=False)
     else:
         return gr.update(visible=True)
@@ -281,7 +283,7 @@ def build_single_model_ui():
     """
     learn_more_markdown = """ 
         ### Licence
-        The service is a research preview intended for non-commercial use only. subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA 
+        The service is a research preview intended for non-commercial use only. subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of Vicuna-13B 
     """
 
     state = gr.State()
@@ -318,7 +320,8 @@ def build_single_model_ui():
                     show_label=True).style(container=False) 
 
         with gr.TabItem("知识问答", elem_id="QA"):
-            mode = gr.Radio(["默认知识库对话", "新增知识库"], show_label=False, value="默认知识库对话")
+            
+            mode = gr.Radio(["LLM原生对话", "默认知识库对话", "新增知识库对话"], show_label=False, value="LLM原生对话")
             vs_setting = gr.Accordion("配置知识库", open=False)
             mode.change(fn=change_mode, inputs=mode, outputs=vs_setting)
             with vs_setting:
@@ -363,7 +366,7 @@ def build_single_model_ui():
     btn_list = [regenerate_btn, clear_btn]
     regenerate_btn.click(regenerate, state, [state, chatbot, textbox] + btn_list).then(
         http_bot,
-        [state, db_selector, temperature, max_output_tokens],
+        [state, mode, db_selector, temperature, max_output_tokens],
         [state, chatbot] + btn_list,
     )
     clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list)
@@ -372,7 +375,7 @@ def build_single_model_ui():
         add_text, [state, textbox], [state, chatbot, textbox] + btn_list
     ).then(
         http_bot,
-        [state, db_selector, temperature, max_output_tokens],
+        [state, mode, db_selector, temperature, max_output_tokens],
         [state, chatbot] + btn_list,
     )
 
@@ -380,7 +383,7 @@ def build_single_model_ui():
         add_text, [state, textbox], [state, chatbot, textbox] + btn_list
     ).then(
         http_bot,
-        [state, db_selector, temperature, max_output_tokens],
+        [state, mode, db_selector, temperature, max_output_tokens],
         [state, chatbot] + btn_list
     )
 

pilot/vector_store/file_loader.py

@@ -10,9 +10,8 @@ from langchain.text_splitter import CharacterTextSplitter
 from langchain.document_loaders import UnstructuredFileLoader, UnstructuredPDFLoader, TextLoader
 from langchain.chains import VectorDBQA
 from langchain.embeddings import HuggingFaceEmbeddings
-from pilot.configs.model_config import VECTORE_PATH, DATASETS_DIR, LLM_MODEL_CONFIG
+from pilot.configs.model_config import VECTORE_PATH, DATASETS_DIR, LLM_MODEL_CONFIG, VECTOR_SEARCH_TOP_K
 
-VECTOR_SEARCH_TOP_K = 5
 
 class KnownLedge2Vector:
 
@@ -26,21 +25,21 @@ class KnownLedge2Vector:
             self.embeddings = HuggingFaceEmbeddings(model_name=self.model_name) 
         
     def init_vector_store(self):
-        documents = self.load_knownlege()
         persist_dir = os.path.join(VECTORE_PATH, ".vectordb")
         print("向量数据库持久化地址: ", persist_dir)
         if os.path.exists(persist_dir):
             # 从本地持久化文件中Load
+            print("从本地向量加载数据...")
             vector_store = Chroma(persist_directory=persist_dir, embedding_function=self.embeddings)
-            vector_store.add_documents(documents=documents)
+            # vector_store.add_documents(documents=documents)
         else:
+            documents = self.load_knownlege()
             # 重新初始化
             vector_store = Chroma.from_documents(documents=documents, 
                                                  embedding=self.embeddings,
                                                  persist_directory=persist_dir)
             vector_store.persist()
-            vector_store = None 
+        return vector_store 
-        return persist_dir
 
     def load_knownlege(self):
         docments = []
@@ -70,10 +69,23 @@ class KnownLedge2Vector:
         return docs
 
     def _load_from_url(self, url):
+        """Load data from url address"""
         pass
 
     
+    def query(self, q):
+        """Query similar doc from Vector """
+        vector_store = self.init_vector_store()
+        docs = vector_store.similarity_search_with_score(q, k=self.top_k)
+        for doc in docs:
+            dc, s = doc
+            yield s, dc
+
 if __name__ == "__main__":
     k2v = KnownLedge2Vector()
-    k2v.init_vector_store()
+
+    persist_dir = os.path.join(VECTORE_PATH, ".vectordb") 
+    print(persist_dir)
+    for s, dc in k2v.query("什么是OceanBase"):
+        print(s, dc.page_content, dc.metadata)
             

requirements.txt

@@ -53,3 +53,5 @@ wandb
 fschat=0.1.10
 llama-index=0.5.27
 pymysql
+unstructured==0.6.3
+pytesseract==0.3.10