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UpTrain

UpTrain [github || 网站 || 文档] 是一个开源平台，用于评估和改进大语言模型（LLM）应用。它提供20多项预配置检查的评分（涵盖语言、代码、嵌入式使用场景），对失败案例进行根本原因分析，并提供解决这些问题的指导建议。

UpTrain 回调处理器​

该笔记本展示了 UpTrain 回调处理器如何无缝集成到您的管道中，以支持多种评估。我们选择了一些我们认为适合用于评估链的评估方法。这些评估会自动运行，结果将显示在输出中。有关 UpTrain 评估的更多详细信息，请参见 此处。

从Langchain中选择的检索器被突出显示以进行演示：

1. 基础RAG:​

RAG 在检索上下文和生成响应方面起着至关重要的作用。为了确保其性能和响应质量，我们进行以下评估：

  • 上下文相关性：判断从查询中提取的上下文是否与响应相关。
  • 事实准确性：评估大语言模型是否存在幻觉或提供错误信息。
  • 响应完整性：检查响应是否包含查询所请求的全部信息。

2. 多查询生成:​

MultiQueryRetriever 会创建多个与原始问题含义相似的问题变体。鉴于其复杂性，我们包含了之前的评估，并添加了：

3. 上下文压缩与重排序:​

重排序涉及根据节点与查询的相关性对节点进行重新排序，并选择前 n 个节点。由于在重排序完成后节点数量可能会减少，因此我们执行以下评估：

  • 上下文重排序：检查重新排序后的节点顺序是否比原始顺序更符合查询的相关性。
  • 上下文简洁性：检查减少的节点数量是否仍然提供了所有必要的信息。

这些评估共同确保了链中RAG、MultiQueryRetriever以及重排序过程的鲁棒性和有效性。

安装依赖​

%pip install -qU langchain langchain_openai langchain-community uptrain faiss-cpu flashrank
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
``````output
WARNING: There was an error checking the latest version of pip.
Note: you may need to restart the kernel to use updated packages.

注意：如果您想使用支持 GPU 的库版本，也可以安装 faiss-gpu 而不是 faiss-cpu。

导入库​

from getpass import getpass

from langchain.chains import RetrievalQA
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import FlashrankRerank
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_community.callbacks.uptrain_callback import UpTrainCallbackHandler
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers.string import StrOutputParser
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables.passthrough import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import (
    RecursiveCharacterTextSplitter,
)

加载文档​

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()

将文档拆分为块​

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
chunks = text_splitter.split_documents(documents)

创建检索器​

embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(chunks, embeddings)
retriever = db.as_retriever()

定义大语言模型​

llm = ChatOpenAI(temperature=0, model="gpt-4")

设置​

UpTrain 为您提供：

  1. 具有高级下钻和筛选选项的仪表板
  2. 失败案例中的洞察与常见主题
  3. 生产数据的可观测性和实时监控
  4. 通过与您的 CI/CD 管道无缝集成进行回归测试

您可以选择以下选项，使用UpTrain进行评估：

1. UpTrain 的开源软件 (OSS):​

您可以使用开源评估服务来评估您的模型。在这种情况下，您需要提供一个OpenAI API密钥。UpTrain 使用 GPT 模型来评估由大语言模型（LLM）生成的响应。您可以在这里获取您的密钥。

为了在 UpTrain 仪表板中查看您的评估结果，您需要通过在终端中运行以下命令来设置它：

git clone https://github.com/uptrain-ai/uptrain
cd uptrain
bash run_uptrain.sh

这将在您的本地机器上启动 UpTrain 仪表板。您可以通过 http://localhost:3000/dashboard 访问它。

参数：

  • key_type="openai"
  • api_key="OPENAI_API_KEY"
  • project_name="PROJECT_NAME"

2. UpTrain 托管服务和仪表板:​

您也可以使用 UpTrain 的托管服务来评估您的模型。您可以在此处创建一个免费的 UpTrain 账户并获得免费试用积分。如果您需要更多试用积分，请在此处预约与 UpTrain 维护人员的通话。

使用托管服务的好处包括：

  1. 无需在本地机器上设置 UpTrain 仪表板。
  2. 无需其API密钥即可访问多种大语言模型。

执行评估后，您可以在 UpTrain 仪表板中查看它们，地址为 https://dashboard.uptrain.ai/dashboard

参数：

  • key_type="uptrain"
  • api_key="UPTRAIN_API_KEY"
  • project_name="PROJECT_NAME"

注意： project_name 将作为项目名称，评估结果将在 UpTrain 仪表板中以此项目名称显示。

设置API密钥​

笔记本会提示您输入API密钥。您可以通过更改下方单元格中的key_type参数，选择使用OpenAI API密钥或UpTrain API密钥。

KEY_TYPE = "openai"  # or "uptrain"
API_KEY = getpass()

1. 原始RAG

UpTrain 回调处理器将自动捕获生成的查询、上下文和响应，并对响应执行以下三项评估 （评分范围为 0 到 1）：

# Create the RAG prompt
template = """Answer the question based only on the following context, which can include text and tables:
{context}
Question: {question}
"""
rag_prompt_text = ChatPromptTemplate.from_template(template)

# Create the chain
chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | rag_prompt_text
    | llm
    | StrOutputParser()
)

# Create the uptrain callback handler
uptrain_callback = UpTrainCallbackHandler(key_type=KEY_TYPE, api_key=API_KEY)
config = {"callbacks": [uptrain_callback]}

# Invoke the chain with a query
query = "What did the president say about Ketanji Brown Jackson"
docs = chain.invoke(query, config=config)
2024-04-17 17:03:44.969 | INFO     | uptrain.framework.evalllm:evaluate_on_server:378 - Sending evaluation request for rows 0 to <50 to the Uptrain
2024-04-17 17:04:05.809 | INFO     | uptrain.framework.evalllm:evaluate:367 - Local server not running, start the server to log data and visualize in the dashboard!
``````output

Question: What did the president say about Ketanji Brown Jackson
Response: The president mentioned that he had nominated Ketanji Brown Jackson to serve on the United States Supreme Court 4 days ago. He described her as one of the nation's top legal minds who will continue Justice Breyer’s legacy of excellence. He also mentioned that she is a former top litigator in private practice, a former federal public defender, and comes from a family of public school educators and police officers. He described her as a consensus builder and noted that since her nomination, she has received a broad range of support from various groups, including the Fraternal Order of Police and former judges appointed by both Democrats and Republicans.

Context Relevance Score: 1.0
Factual Accuracy Score: 1.0
Response Completeness Score: 1.0

2. 多查询生成

MultiQueryRetriever 用于解决 RAG 管道可能无法根据查询返回最佳文档集的问题。它会生成多个与原始查询含义相同的查询，然后分别为每个查询获取文档。

为了评估此检索器，UpTrain 将运行以下评估：

# Create the retriever
multi_query_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=llm)

# Create the uptrain callback
uptrain_callback = UpTrainCallbackHandler(key_type=KEY_TYPE, api_key=API_KEY)
config = {"callbacks": [uptrain_callback]}

# Create the RAG prompt
template = """Answer the question based only on the following context, which can include text and tables:
{context}
Question: {question}
"""
rag_prompt_text = ChatPromptTemplate.from_template(template)

chain = (
    {"context": multi_query_retriever, "question": RunnablePassthrough()}
    | rag_prompt_text
    | llm
    | StrOutputParser()
)

# Invoke the chain with a query
question = "What did the president say about Ketanji Brown Jackson"
docs = chain.invoke(question, config=config)
2024-04-17 17:04:10.675 | INFO     | uptrain.framework.evalllm:evaluate_on_server:378 - Sending evaluation request for rows 0 to <50 to the Uptrain
2024-04-17 17:04:16.804 | INFO     | uptrain.framework.evalllm:evaluate:367 - Local server not running, start the server to log data and visualize in the dashboard!
``````output

Question: What did the president say about Ketanji Brown Jackson
Multi Queries:
  - How did the president comment on Ketanji Brown Jackson?
  - What were the president's remarks regarding Ketanji Brown Jackson?
  - What statements has the president made about Ketanji Brown Jackson?

Multi Query Accuracy Score: 0.5
``````output
2024-04-17 17:04:22.027 | INFO     | uptrain.framework.evalllm:evaluate_on_server:378 - Sending evaluation request for rows 0 to <50 to the Uptrain
2024-04-17 17:04:44.033 | INFO     | uptrain.framework.evalllm:evaluate:367 - Local server not running, start the server to log data and visualize in the dashboard!
``````output

Question: What did the president say about Ketanji Brown Jackson
Response: The president mentioned that he had nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court 4 days ago. He described her as one of the nation's top legal minds who will continue Justice Breyer’s legacy of excellence. He also mentioned that since her nomination, she has received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.

Context Relevance Score: 1.0
Factual Accuracy Score: 1.0
Response Completeness Score: 1.0

3. 上下文压缩与重排序

重排序过程涉及根据节点与查询的相关性重新排列节点，并选择前 n 个节点。由于重排序完成后节点数量可能会减少，因此我们执行以下评估：

  • 上下文重排序：检查重新排序后的节点顺序是否比原始顺序更符合查询的相关性。
  • 上下文简洁性：检查减少后的节点数量是否仍提供了所有必要的信息。
# Create the retriever
compressor = FlashrankRerank()
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever
)

# Create the chain
chain = RetrievalQA.from_chain_type(llm=llm, retriever=compression_retriever)

# Create the uptrain callback
uptrain_callback = UpTrainCallbackHandler(key_type=KEY_TYPE, api_key=API_KEY)
config = {"callbacks": [uptrain_callback]}

# Invoke the chain with a query
query = "What did the president say about Ketanji Brown Jackson"
result = chain.invoke(query, config=config)
2024-04-17 17:04:46.462 | INFO     | uptrain.framework.evalllm:evaluate_on_server:378 - Sending evaluation request for rows 0 to <50 to the Uptrain
2024-04-17 17:04:53.561 | INFO     | uptrain.framework.evalllm:evaluate:367 - Local server not running, start the server to log data and visualize in the dashboard!
``````output

Question: What did the president say about Ketanji Brown Jackson

Context Conciseness Score: 0.0
Context Reranking Score: 1.0
``````output
2024-04-17 17:04:56.947 | INFO     | uptrain.framework.evalllm:evaluate_on_server:378 - Sending evaluation request for rows 0 to <50 to the Uptrain
2024-04-17 17:05:16.551 | INFO     | uptrain.framework.evalllm:evaluate:367 - Local server not running, start the server to log data and visualize in the dashboard!
``````output

Question: What did the president say about Ketanji Brown Jackson
Response: The President mentioned that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court 4 days ago. He described her as one of the nation's top legal minds who will continue Justice Breyer’s legacy of excellence.

Context Relevance Score: 1.0
Factual Accuracy Score: 1.0
Response Completeness Score: 0.5

UpTrain 的仪表板和洞察

这是一个展示仪表板和洞察的简短视频：

langchain_uptrain.gif