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用例（User Case)
代码（Code）
Code Analysis Deeplake

LangChain

使用LangChain， GPT和Deep Lake 数据库，处理代码库#

在本教程中，我们将使用Langchain + Deep Lake和GPT，分析LangChain本身的代码库。

设计#

  1. 准备数据:
    1. 使用langchain.document_loaders.TextLoader上传所有Python项目文件。 我们将这些文件称为文档。
    1. 使用langchain.text_splitter.CharacterTextSplitter将所有文档分成块。
    1. 使用langchain.embeddings.openai.OpenAIEmbeddings和langchain.vectorstores.DeepLake嵌入这些块并上传到DeepLake。
  1. 问答:
    1. 使用langchain.chat_models.ChatOpenAI和langchain.chains.ConversationalRetrievalChain构建链。
    1. 准备问题。
    1. 运行链获得答案。

实现#

集成准备#

我们需要为外部服务设置密钥并安装必要的Python库。

#!python3 -m pip install --upgrade langchain deeplake openai

设置OpenAI嵌入， Deep Lake 多模态向量存储API并进行身份验证。

有关Deep Lake的完整说明，请访问https://docs.activeloop.ai/和API参考https://docs.deeplake.ai/en/latest/ (opens in a new tab)

 
import os
 
from getpass import getpass
 
 
 
os.environ['OPENAI_API_KEY'] = getpass()
 
# Please manually enter OpenAI Key
 
 
 
 
 ········

如果您想创建自己的数据集并发布它，请在Deep Lake中进行身份验证。您可以从平台获得API密钥@[app.activeloop.ai]（https://app.activeloop.ai） (opens in a new tab)

 
os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')
 
 
 
 
 ········

准备数据#

加载所有存储库文件。 在这里，我们假设此文档是作为langchain分支的一部分下载的，我们使用langchain存储库的python文件工作。

如果要使用来自不同存储库的文件，请将root_dir更改为您的存储库的根目录。

 
from langchain.document_loaders import TextLoader
 
 
 
root_dir = '../../../..'
 
 
 
docs = []
 
for dirpath, dirnames, filenames in os.walk(root_dir):
 
    for file in filenames:
 
        if file.endswith('.py') and '/.venv/' not in dirpath:
 
            try: 
 
                loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')
 
                docs.extend(loader.load_and_split())
 
            except Exception as e: 
 
                pass
 
print(f'{len(docs)}')
 
 
 
 
1147

然后 块文件

 
from langchain.text_splitter import CharacterTextSplitter
 
 
 
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
 
texts = text_splitter.split_documents(docs)
 
print(f"{len(texts)}")
 
 
 
 
Created a chunk of size 1620, which is longer than the specified 1000
 
Created a chunk of size 1213, which is longer than the specified 1000
 
Created a chunk of size 1263, which is longer than the specified 1000
 
Created a chunk of size 1448, which is longer than the specified 1000
 
Created a chunk of size 1120, which is longer than the specified 1000
 
Created a chunk of size 1148, which is longer than the specified 1000
 
Created a chunk of size 1826, which is longer than the specified 1000
 
Created a chunk of size 1260, which is longer than the specified 1000
 
Created a chunk of size 1195, which is longer than the specified 1000
 
Created a chunk of size 2147, which is longer than the specified 1000
 
Created a chunk of size 1410, which is longer than the specified 1000
 
Created a chunk of size 1269, which is longer than the specified 1000
 
Created a chunk of size 1030, which is longer than the specified 1000
 
Created a chunk of size 1046, which is longer than the specified 1000
 
Created a chunk of size 1024, which is longer than the specified 1000
 
Created a chunk of size 1026, which is longer than the specified 1000
 
Created a chunk of size 1285, which is longer than the specified 1000
 
Created a chunk of size 1370, which is longer than the specified 1000
 
Created a chunk of size 1031, which is longer than the specified 1000
 
Created a chunk of size 1999, which is longer than the specified 1000
 
Created a chunk of size 1029, which is longer than the specified 1000
 
Created a chunk of size 1120, which is longer than the specified 1000
 
Created a chunk of size 1033, which is longer than the specified 1000
 
Created a chunk of size 1143, which is longer than the specified 1000
 
Created a chunk of size 1416, which is longer than the specified 1000
 
Created a chunk of size 2482, which is longer than the specified 1000
 
Created a chunk of size 1890, which is longer than the specified 1000
 
Created a chunk of size 1418, which is longer than the specified 1000
 
Created a chunk of size 1848, which is longer than the specified 1000
 
Created a chunk of size 1069, which is longer than the specified 1000
 
Created a chunk of size 2369, which is longer than the specified 1000
 
Created a chunk of size 1045, which is longer than the specified 1000
 
Created a chunk of size 1501, which is longer than the specified 1000
 
Created a chunk of size 1208, which is longer than the specified 1000
 
Created a chunk of size 1950, which is longer than the specified 1000
 
Created a chunk of size 1283, which is longer than the specified 1000
 
Created a chunk of size 1414, which is longer than the specified 1000
 
Created a chunk of size 1304, which is longer than the specified 1000
 
Created a chunk of size 1224, which is longer than the specified 1000
 
Created a chunk of size 1060, which is longer than the specified 1000
 
Created a chunk of size 2461, which is longer than the specified 1000
 
Created a chunk of size 1099, which is longer than the specified 1000
 
Created a chunk of size 1178, which is longer than the specified 1000
 
Created a chunk of size 1449, which is longer than the specified 1000
 
Created a chunk of size 1345, which is longer than the specified 1000
 
Created a chunk of size 3359, which is longer than the specified 1000
 
Created a chunk of size 2248, which is longer than the specified 1000
 
Created a chunk of size 1589, which is longer than the specified 1000
 
Created a chunk of size 2104, which is longer than the specified 1000
 
Created a chunk of size 1505, which is longer than the specified 1000
 
Created a chunk of size 1387, which is longer than the specified 1000
 
Created a chunk of size 1215, which is longer than the specified 1000
 
Created a chunk of size 1240, which is longer than the specified 1000
 
Created a chunk of size 1635, which is longer than the specified 1000
 
Created a chunk of size 1075, which is longer than the specified 1000
 
Created a chunk of size 2180, which is longer than the specified 1000
 
Created a chunk of size 1791, which is longer than the specified 1000
 
Created a chunk of size 1555, which is longer than the specified 1000
 
Created a chunk of size 1082, which is longer than the specified 1000
 
Created a chunk of size 1225, which is longer than the specified 1000
 
Created a chunk of size 1287, which is longer than the specified 1000
 
Created a chunk of size 1085, which is longer than the specified 1000
 
Created a chunk of size 1117, which is longer than the specified 1000
 
Created a chunk of size 1966, which is longer than the specified 1000
 
Created a chunk of size 1150, which is longer than the specified 1000
 
Created a chunk of size 1285, which is longer than the specified 1000
 
Created a chunk of size 1150, which is longer than the specified 1000
 
Created a chunk of size 1585, which is longer than the specified 1000
 
Created a chunk of size 1208, which is longer than the specified 1000
 
Created a chunk of size 1267, which is longer than the specified 1000
 
Created a chunk of size 1542, which is longer than the specified 1000
 
Created a chunk of size 1183, which is longer than the specified 1000
 
Created a chunk of size 2424, which is longer than the specified 1000
 
Created a chunk of size 1017, which is longer than the specified 1000
 
Created a chunk of size 1304, which is longer than the specified 1000
 
Created a chunk of size 1379, which is longer than the specified 1000
 
Created a chunk of size 1324, which is longer than the specified 1000
 
Created a chunk of size 1205, which is longer than the specified 1000
 
Created a chunk of size 1056, which is longer than the specified 1000
 
Created a chunk of size 1195, which is longer than the specified 1000
 
Created a chunk of size 3608, which is longer than the specified 1000
 
Created a chunk of size 1058, which is longer than the specified 1000
 
Created a chunk of size 1075, which is longer than the specified 1000
 
Created a chunk of size 1217, which is longer than the specified 1000
 
Created a chunk of size 1109, which is longer than the specified 1000
 
Created a chunk of size 1440, which is longer than the specified 1000
 
Created a chunk of size 1046, which is longer than the specified 1000
 
Created a chunk of size 1220, which is longer than the specified 1000
 
Created a chunk of size 1403, which is longer than the specified 1000
 
Created a chunk of size 1241, which is longer than the specified 1000
 
Created a chunk of size 1427, which is longer than the specified 1000
 
Created a chunk of size 1049, which is longer than the specified 1000
 
Created a chunk of size 1580, which is longer than the specified 1000
 
Created a chunk of size 1565, which is longer than the specified 1000
 
Created a chunk of size 1131, which is longer than the specified 1000
 
Created a chunk of size 1425, which is longer than the specified 1000
 
Created a chunk of size 1054, which is longer than the specified 1000
 
Created a chunk of size 1027, which is longer than the specified 1000
 
Created a chunk of size 2559, which is longer than the specified 1000
 
Created a chunk of size 1028, which is longer than the specified 1000
 
Created a chunk of size 1382, which is longer than the specified 1000
 
Created a chunk of size 1888, which is longer than the specified 1000
 
Created a chunk of size 1475, which is longer than the specified 1000
 
Created a chunk of size 1652, which is longer than the specified 1000
 
Created a chunk of size 1891, which is longer than the specified 1000
 
Created a chunk of size 1899, which is longer than the specified 1000
 
Created a chunk of size 1021, which is longer than the specified 1000
 
Created a chunk of size 1085, which is longer than the specified 1000
 
Created a chunk of size 1854, which is longer than the specified 1000
 
Created a chunk of size 1672, which is longer than the specified 1000
 
Created a chunk of size 2537, which is longer than the specified 1000
 
Created a chunk of size 1251, which is longer than the specified 1000
 
Created a chunk of size 1734, which is longer than the specified 1000
 
Created a chunk of size 1642, which is longer than the specified 1000
 
Created a chunk of size 1376, which is longer than the specified 1000
 
Created a chunk of size 1253, which is longer than the specified 1000
 
Created a chunk of size 1642, which is longer than the specified 1000
 
Created a chunk of size 1419, which is longer than the specified 1000
 
Created a chunk of size 1438, which is longer than the specified 1000
 
Created a chunk of size 1427, which is longer than the specified 1000
 
Created a chunk of size 1684, which is longer than the specified 1000
 
Created a chunk of size 1760, which is longer than the specified 1000
 
Created a chunk of size 1157, which is longer than the specified 1000
 
Created a chunk of size 2504, which is longer than the specified 1000
 
Created a chunk of size 1082, which is longer than the specified 1000
 
Created a chunk of size 2268, which is longer than the specified 1000
 
Created a chunk of size 1784, which is longer than the specified 1000
 
Created a chunk of size 1311, which is longer than the specified 1000
 
Created a chunk of size 2972, which is longer than the specified 1000
 
Created a chunk of size 1144, which is longer than the specified 1000
 
Created a chunk of size 1825, which is longer than the specified 1000
 
Created a chunk of size 1508, which is longer than the specified 1000
 
Created a chunk of size 2901, which is longer than the specified 1000
 
Created a chunk of size 1715, which is longer than the specified 1000
 
Created a chunk of size 1062, which is longer than the specified 1000
 
Created a chunk of size 1206, which is longer than the specified 1000
 
Created a chunk of size 1102, which is longer than the specified 1000
 
Created a chunk of size 1184, which is longer than the specified 1000
 
Created a chunk of size 1002, which is longer than the specified 1000
 
Created a chunk of size 1065, which is longer than the specified 1000
 
Created a chunk of size 1871, which is longer than the specified 1000
 
Created a chunk of size 1754, which is longer than the specified 1000
 
Created a chunk of size 2413, which is longer than the specified 1000
 
Created a chunk of size 1771, which is longer than the specified 1000
 
Created a chunk of size 2054, which is longer than the specified 1000
 
Created a chunk of size 2000, which is longer than the specified 1000
 
Created a chunk of size 2061, which is longer than the specified 1000
 
Created a chunk of size 1066, which is longer than the specified 1000
 
Created a chunk of size 1419, which is longer than the specified 1000
 
Created a chunk of size 1368, which is longer than the specified 1000
 
Created a chunk of size 1008, which is longer than the specified 1000
 
Created a chunk of size 1227, which is longer than the specified 1000
 
Created a chunk of size 1745, which is longer than the specified 1000
 
Created a chunk of size 2296, which is longer than the specified 1000
 
Created a chunk of size 1083, which is longer than the specified 1000
 
 
 
 
3477

然后嵌入块并将它们上传到DeepLake。 Then embed chunks and upload them to the DeepLake.

This can take several minutes.

from langchain.embeddings.openai import OpenAIEmbeddings
 
 
 
embeddings = OpenAIEmbeddings()
 
embeddings
 
 
 
OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', document_model_name='text-embedding-ada-002', query_model_name='text-embedding-ada-002', embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6)
 
 
 
from langchain.vectorstores import DeepLake
 
 
 
db = DeepLake.from_documents(texts, embeddings, dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code")
 
db

问答

首先加载数据集，构建检索器，然后再构建会话序列

db = DeepLake(dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code", read_only=True, embedding_function=embeddings)
 
 
 
-
 
 
 
This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/user_name/langchain-code
 
 
 
/
 
 
 
hub://user_name/langchain-code loaded successfully.
 
 
 
Deep Lake Dataset in hub://user_name/langchain-code already exists, loading from the storage
 
 
 
Dataset(path='hub://user_name/langchain-code', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])
 
 
 
  tensor     htype      shape       dtype  compression
 
  -------   -------    -------     -------  ------- 
 
 embedding  generic  (3477, 1536)  float32   None   
 
    ids      text     (3477, 1)      str     None   
 
 metadata    json     (3477, 1)      str     None   
 
   text      text     (3477, 1)      str     None   
 
 
 
retriever = db.as_retriever()
 
retriever.search_kwargs['distance_metric'] = 'cos'
 
retriever.search_kwargs['fetch_k'] = 20
 
retriever.search_kwargs['maximal_marginal_relevance'] = True
 
retriever.search_kwargs['k'] = 20

您还可以使用Deep Lake过滤器 (opens in a new tab)指定用户定义的函数

def filter(x):
 
    # filter based on source code
 
    if 'something' in x['text'].data()['value']:
 
        return False
 
    
 
    # filter based on path e.g. extension
 
    metadata =  x['metadata'].data()['value']
 
    return 'only_this' in metadata['source'] or 'also_that' in metadata['source']
 
 
 
### turn on below for custom filtering
 
# retriever.search_kwargs['filter'] = filter
 
 
 
from langchain.chat_models import ChatOpenAI
 
from langchain.chains import ConversationalRetrievalChain
 
 
 
model = ChatOpenAI(model_name='gpt-3.5-turbo') # 'ada' 'gpt-3.5-turbo' 'gpt-4',
 
qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
 
 
 
questions = [
 
    "What is the class hierarchy?",
 
    # "What classes are derived from the Chain class?",
 
    # "What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?",
 
    # "What one improvement do you propose in code in relation to the class herarchy for the Chain class?",
 
] 
 
chat_history = []
 
 
 
for question in questions:  
 
    result = qa({"question": question, "chat_history": chat_history})
 
    chat_history.append((question, result['answer']))
 
    print(f"-> \*\*Question\*\*: {question} ")
 
    print(f"\*\*Answer\*\*: {result['answer']} ")

-> 问题: 什么是类层次结构？

回答: 提供的代码中有几个类层次结构，以下是其中一些:

1.BaseModel -> ConstitutionalPrinciple: ConstitutionalPrinciple是BaseModel的子类。

  1. BaseModel -> ConstitutionalPrinciple: ConstitutionalPrinciple is a subclass of BaseModel.

  2. BasePromptTemplate -> StringPromptTemplate， AIMessagePromptTemplate， BaseChatPromptTemplate， ChatMessagePromptTemplate， ChatPromptTemplate， HumanMessagePromptTemplate， MessagesPlaceholder， SystemMessagePromptTemplate， FewShotPromptTemplate， FewShotPromptWithTemplates， Prompt， PromptTemplate# 所有这些类都是 BasePromptTemplate 的子类。

  3. APIChain， Chain， MapReduceDocumentsChain， MapRerankDocumentsChain， RefineDocumentsChain， StuffDocumentsChain， HypotheticalDocumentEmbedder， LLMChain， LLMBashChain， LLMCheckerChain， LLMMathChain， LLMRequestsChain， PALChain， QAWithSourcesChain， VectorDBQAWithSourcesChain， VectorDBQA， SQLDatabaseChain# 所有这些类都是 Chain 的子类。

  4. BaseLoader# BaseLoader 是 ABC 的子类。

  5. BaseTracer -> ChainRun， LLMRun， SharedTracer， ToolRun， Tracer， TracerException， TracerSession# 所有这些类都是 BaseTracer 的子类。

  6. OpenAIEmbeddings， HuggingFaceEmbeddings， CohereEmbeddings， JinaEmbeddings， LlamaCppEmbeddings， HuggingFaceHubEmbeddings， TensorflowHubEmbeddings， SagemakerEndpointEmbeddings， HuggingFaceInstructEmbeddings， SelfHostedEmbeddings， SelfHostedHuggingFaceEmbeddings， SelfHostedHuggingFaceInstructEmbeddings， FakeEmbeddings， AlephAlphaAsymmetricSemanticEmbedding， AlephAlphaSymmetricSemanticEmbedding# 所有这些类都是 BaseLLM 的子类。

-> 问题# 有哪些类是从 Chain 类派生的？

答案# 有多个类从 Chain 类派生。其中一些是:

  • APIChain

  • AnalyzeDocumentChain

  • ChatVectorDBChain（聊天向量数据库链）

  • CombineDocumentsChain（合并文件链）

  • ConstitutionalChain（宪法链）

  • ConversationChain（对话链）

  • GraphQAChain（图形问答链）

  • HypotheticalDocumentEmbedder（假设文档嵌入器）

  • LLMChain（LLM链）

  • LLMCheckerChain（LLM检查链）

  • LLMRequestsChain（LLM请求链）

  • LLMSummarizationCheckerChain（LLM摘要检查链）

  • MapReduceChain（MapReduce链）

  • OpenAPIEndpointChain（OpenAPI端点链）

  • PALChain（PAL链）

  • QAWithSourcesChain（带源问答链）

  • RetrievalQA（检索问答）

  • RetrievalQAWithSourcesChain（带源检索问答链）

  • SequentialChain（顺序链）

  • SQLDatabaseChain（SQL数据库链）

  • TransformChain（转换链）

  • VectorDBQA（向量数据库问答）

  • VectorDBQAWithSourcesChain（带源向量数据库问答链）

可能会有更多派生自Chain类的类，因为可能创建自定义类来扩展Chain类。（There might be more classes that are derived from the Chain class as it is possible to create custom classes that extend the Chain class.）

-> 问题: 在./langchain/utilities/文件夹中，哪些类和函数没有被单元测试覆盖？（-> Question: What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?）

Answer: All classes and functions in the ./langchain/utilities/ folder seem to have unit tests written for them.

代码（Code）Twitter the Algorithm Analysis Deeplake