Merge pull request #12 from BoardWare-Genius/main

merge

sample/chroma_client1.py

@@ -0,0 +1,199 @@
+import os
+import time
+import chromadb
+from chromadb.config import Settings
+
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader, TextLoader, UnstructuredHTMLLoader, JSONLoader, Docx2txtLoader, UnstructuredExcelLoader
+from langchain_community.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings, HuggingFaceEmbeddings
+
+
+
+def get_all_files(folder_path):
+    # 获取文件夹下所有文件和文件夹的名称列表
+    files = os.listdir(folder_path)
+
+    # 初始化空列表，用于存储所有文件的绝对路径
+    absolute_paths = []
+
+    # 遍历文件和文件夹名称列表
+    for file in files:
+        # 拼接文件的绝对路径
+        absolute_path = os.path.join(folder_path, file)
+        # 如果是文件，将其绝对路径添加到列表中
+        if os.path.isfile(absolute_path):
+            absolute_paths.append(absolute_path)
+
+    return absolute_paths
+
+# start_time = time.time()
+# # 加载文档
+# folder_path = "./text"
+# txt_files = get_all_files(folder_path)
+# docs = []
+# ids = []
+# for txt_file in txt_files:
+#     loader = PyPDFLoader(txt_file)
+
+#     documents = loader.load()
+
+#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=0)
+
+#     docs_txt = text_splitter.split_documents(documents)
+
+#     docs.extend(docs_txt)
+
+#     ids.extend([os.path.basename(txt_file) + str(i) for i in range(len(docs_txt))])
+# start_time1 = time.time()
+# print(start_time1 - start_time)
+
+
+# loader = PyPDFLoader("/code/memory/text/大语言模型应用.pdf")
+# loader = TextLoader("/code/memory/text/test.txt")
+# loader = CSVLoader("/code/memory/text/test1.csv")
+# loader = UnstructuredHTMLLoader("/"example_data/fake-content.html"")
+# pip install docx2txt
+# loader = Docx2txtLoader("/code/memory/text/tesou.docx")
+# pip install openpyxl
+# loader = UnstructuredExcelLoader("/code/memory/text/AI Team Planning 2023.xlsx")
+# pip install jq
+# loader = JSONLoader("/code/memory/text/config.json", jq_schema='.', text_content=False)
+
+# documents = loader.load()
+
+# text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=0)
+# docs = text_splitter.split_documents(documents)
+# print(len(docs))
+# ids = ["大语言模型应用"+str(i) for i in range(len(docs))]
+
+
+# 加载文档和拆分文档
+loader = TextLoader("/home/administrator/Workspace/jarvis-models/sample/20240529_store.txt")
+
+documents = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=50)
+
+docs = text_splitter.split_documents(documents)
+
+print("len(docs)", len(docs))
+
+ids = ["20240521_store"+str(i) for i in range(len(docs))]
+
+
+# 加载embedding模型和chroma server
+embedding_model = SentenceTransformerEmbeddings(model_name='/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5', model_kwargs={"device": "cuda"})
+client = chromadb.HttpClient(host='172.16.5.8', port=7000)
+
+id = "g2e"
+client.delete_collection(id)
+collection_number = client.get_or_create_collection(id).count()
+print("collection_number",collection_number)
+start_time2 = time.time()
+# 插入向量(如果ids已存在，则会更新向量)
+db = Chroma.from_documents(documents=docs, embedding=embedding_model, ids=ids, collection_name=id, client=client)
+
+# db = Chroma.from_texts(texts=['test by tom'], embedding=embedding_model, ids=["大语言模型应用0"], persist_directory="./data/test1", collection_name="123", metadatas=[{"source": "string"}])
+
+start_time3 = time.time()
+print("insert time ", start_time3 - start_time2)
+
+
+
+
+# chroma 召回
+from chromadb.utils import embedding_functions
+embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda")
+client = chromadb.HttpClient(host='172.16.5.8', port=7000)
+collection = client.get_collection("g2e", embedding_function=embedding_model)
+
+print(collection.count())
+import time
+start_time = time.time()
+query = "如何前往威尼斯人"
+# query it
+results = collection.query(
+    query_texts=[query],
+    n_results=3,    
+)
+
+response = results["documents"]
+print("response: ", response)
+print("time: ", time.time() - start_time)
+
+
+# 结合大模型进行总结
+import requests
+
+model_name = "Qwen1.5-14B-Chat"
+chat_inputs={
+    "model": model_name,
+    "messages": [
+        {
+            "role": "user",
+            "content": f"问题: {query}。- 根据知识库内的检索结果，以清晰简洁的表达方式回答问题。- 只从检索内容中选取与问题密切相关的信息。- 不要编造答案，如果答案不在经核实的资料中或无法从经核实的资料中得出，请回答“我无法回答您的问题。”检索内容：{response}"
+        }
+    ],
+    # "temperature": 0,
+    # "top_p": user_top_p,
+    # "n": user_n,
+    # "max_tokens": user_max_tokens,
+    # "frequency_penalty": user_frequency_penalty,
+    # "presence_penalty": user_presence_penalty,
+    # "stop": 100
+}
+
+key ="YOUR_API_KEY"
+
+header = {
+    'Content-Type': 'application/json',
+    'Authorization': "Bearer " + key
+}
+url = "http://172.16.5.8:23333/v1/chat/completions"
+
+fastchat_response = requests.post(url, json=chat_inputs, headers=header)
+# print(fastchat_response.json())
+
+print("\n question: ", query)
+print("\n ",model_name, fastchat_response.json()["choices"][0]["message"]["content"])
+
+
+
+
+# start_time4 = time.time()
+# db = Chroma(
+#     client=client,
+#     collection_name=id,
+#     embedding_function=embedding_model,
+# )
+
+# 更新文档
+# db = db.update_documents(ids, documents)
+# 删除文档
+# db.delete([ids])
+# 删除集合
+# db.delete_collection()
+
+# query = "智能体核心思想"
+# docs = db.similarity_search(query, k=2)
+
+# print("result: ",docs)
+# for doc in docs:
+#     print(doc, "\n")
+
+# start_time5 = time.time()
+# print("search time ", start_time5 - start_time4)
+
+# docs = db._collection.get(ids=['大语言模型应用0'])
+
+# print(docs)
+
+# docs = db.get(where={"source": "text/大语言模型应用.pdf"})
+# docs = db.get()
+# print(docs)
+
+
+
+

src/blackbox/chat.py

@@ -5,17 +5,22 @@ from fastapi.responses import JSONResponse
 
 from ..log.logging_time import logging_time
 from .blackbox import Blackbox
+from .chroma_query import ChromaQuery
 
 import requests
 import json
 from openai import OpenAI
 import re
 
-from injector import singleton
+from injector import singleton,inject
 
 @singleton
 class Chat(Blackbox):
     
+    @inject
+    def __init__(self, chroma_query: ChromaQuery):
+        self.chroma_query = chroma_query
+
     def __call__(self, *args, **kwargs):
         return self.processing(*args, **kwargs)
 
@@ -24,7 +29,7 @@ class Chat(Blackbox):
         return isinstance(data, list)
 
     # model_name有 Qwen1.5-14B-Chat , internlm2-chat-20b
-    @logging_time()
+    # @logging_time()
     def processing(self, prompt: str, context: list, settings: dict)  -> str:
 
         if settings is None:
@@ -42,6 +47,9 @@ class Chat(Blackbox):
         user_presence_penalty = settings.get("presence_penalty")
         user_model_url = settings.get("model_url")
         user_model_key = settings.get("model_key")
+        
+        chroma_embedding_model = settings.get("chroma_embedding_model")
+        chroma_response = ''
 
         if user_context == None:
             user_context = []
@@ -82,6 +90,14 @@ class Chat(Blackbox):
         if user_model_key is None or user_model_key.isspace() or user_model_key == "":
             user_model_key = "YOUR_API_KEY"
 
+        if chroma_embedding_model != None:
+            chroma_response = self.chroma_query(user_question, settings)
+            print(chroma_response)
+
+        if chroma_response != None or chroma_response != '':
+            user_question = f"问题: {user_question}。- 根据知识库内的检索结果，以清晰简洁的表达方式回答问题。只从检索的内容中选取与问题相关信息。- 不要编造答案，如果答案不在经核实的资料中或无法从经核实的资料中得出，请回答“我无法回答您的问题。”检索内容：{chroma_response}"
+
+
         # 文心格式和openai的不一样，需要单独处理
         if re.search(r"ernie", user_model_name):
             # key = "24.22873ef3acf61fb343812681e4df251a.2592000.1719453781.282335-46723715" 没充钱，只有ernie-speed-128k能用
@@ -121,12 +137,17 @@ class Chat(Blackbox):
             url = 'https://api.openai.com/v1/completions' 
             # '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'
             key = user_model_key
-
+            header = {
+                'Content-Type': 'application/json',
+                'Authorization': "Bearer " + key
+            }
         # 自定义model
         else:
             url = user_model_url
             key = user_model_key
-              
+            header = {
+                'Content-Type': 'application/json',
+            }
 
         prompt_template = [
            {"role": "system", "content": user_template},
@@ -149,11 +170,6 @@ class Chat(Blackbox):
             "stop": str(user_stop)
         }
 
-        header = {
-            'Content-Type': 'application/json',
-            'Authorization': "Bearer " + key
-        }
-
         fastchat_response = requests.post(url, json=chat_inputs, headers=header)
 
         return fastchat_response.json()["choices"][0]["message"]["content"]

src/blackbox/chroma_query.py

@@ -20,9 +20,9 @@ class ChromaQuery(Blackbox):
     def __init__(self, *args, **kwargs) -> None:
         # config = read_yaml(args[0])
         # load chromadb and embedding model
-        self.embedding_model_1 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/model/Weight/BAAI/bge-large-zh-v1.5", device = "cuda")
+        self.embedding_model_1 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda")
         # self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/model/Weight/BAAI/bge-small-en-v1.5", device = "cuda")
-        self.client_1 = chromadb.HttpClient(host='10.6.82.192', port=8000)
+        self.client_1 = chromadb.HttpClient(host='172.16.5.8', port=7000)
         # self.client_2 = chromadb.HttpClient(host='10.6.82.192', port=8000)
 
     def __call__(self, *args, **kwargs):
@@ -32,7 +32,7 @@ class ChromaQuery(Blackbox):
         data = args[0]
         return isinstance(data, list)
 
-    @logging_time(logger=logger)
+    # @logging_time(logger=logger)
     def processing(self, question: str, settings: dict) -> str:
 
         if settings is None:
@@ -51,13 +51,13 @@ class ChromaQuery(Blackbox):
             return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
 
         if chroma_embedding_model is None or chroma_embedding_model.isspace() or chroma_embedding_model == "":
-            chroma_embedding_model = "/model/Weight/BAAI/bge-large-zh-v1.5"
+            chroma_embedding_model = "/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5"
 
         if chroma_host is None or chroma_host.isspace() or chroma_host == "":
-            chroma_host = "10.6.82.192"
+            chroma_host = "172.16.5.8"
         
         if chroma_port is None or chroma_port.isspace() or chroma_port == "":
-            chroma_port = "8000"
+            chroma_port = "7000"
         
         if chroma_collection_id is None or chroma_collection_id.isspace() or chroma_collection_id == "":
             chroma_collection_id = "g2e"
@@ -66,12 +66,12 @@ class ChromaQuery(Blackbox):
             chroma_n_results = 3
 
         # load client and embedding model from init
-        if re.search(r"10.6.82.192", chroma_host) and re.search(r"8000", chroma_port):
+        if re.search(r"172.16.5.8", chroma_host) and re.search(r"7000", chroma_port):
             client = self.client_1
         else:
             client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
 
-        if re.search(r"/model/Weight/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
+        if re.search(r"/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
             embedding_model = self.embedding_model_1
         else:
             embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=chroma_embedding_model, device = "cuda")