UpTrain [github || 网站 || 文档] 是一个用于评估和改进 LLM 应用程序的开源平台。它提供 20 多个预配置检查的评分（涵盖语言、代码、嵌入用例），对故障案例实例进行根本原因分析，并提供解决这些问题的指导。

UpTrain 回调处理程序

本笔记本展示了 UpTrain 回调处理程序如何无缝集成到您的管道中，从而促进多样化的评估。我们选择了一些我们认为适合评估链的评估。这些评估自动运行，结果显示在输出中。有关 UpTrain 评估的更多详细信息，请参见此处。突出显示 LangChain 中的选定检索器以供演示：

1. 香草 RAG

RAG 在检索上下文和生成响应方面发挥着关键作用。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：确定从查询中提取的上下文是否与响应相关。
事实准确性：评估 LLM 是否产生幻觉或提供不正确的信息。
响应完整性：检查响应是否包含查询请求的所有信息。

2. 多查询生成

MultiQueryRetriever 创建与原始问题具有相似含义的多个问题变体。考虑到复杂性，我们包括以前的评估并添加

多查询准确性：确保生成的多个查询与原始查询的含义相同。

3. 上下文压缩和重新排序

重新排序涉及根据与查询的相关性重新排序节点并选择前 n 个节点。由于重新排序完成后节点数量可能会减少，我们执行以下评估

上下文重新排序：检查重新排序的节点顺序是否比原始顺序与查询更相关。
上下文简洁性：检查减少的节点数量是否仍提供所有所需信息。

这些评估共同确保了链中 RAG、MultiQueryRetriever 和重新排序过程的稳健性和有效性。

安装依赖项

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)

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_classic.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_classic.retrievers.document_compressors import FlashrankRerank
from langchain_classic.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()

将文档分成块

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 LLM

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

设置

UpTrain 为您提供

具有高级钻取和过滤选项的仪表盘
失败案例的洞察和常见主题
生产数据的可观测性和实时监控
通过与您的 CI/CD 管道无缝集成进行回归测试

您可以使用以下选项通过 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 LLM 生成的响应。您可以在此处获取您的 API 密钥。为了在 UpTrain 仪表盘中查看您的评估，您需要在终端中运行以下命令进行设置：

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 仪表盘。您可以通过 `https://:3000/dashboard` 访问它。参数：

key_type=“openai”
api_key=“OPENAI_API_KEY”
project_name=“PROJECT_NAME”

2. UpTrain 托管服务和仪表盘

或者，您可以使用 UpTrain 的托管服务来评估您的模型。您可以在此处创建免费的 UpTrain 帐户并获得免费试用积分。如果您想要更多试用积分，请在此处与 UpTrain 的维护者预约通话。使用托管服务的好处是：

无需在本地机器上设置 UpTrain 仪表盘。
无需 API 密钥即可访问许多 LLM。

执行评估后，您可以在 `https://dashboard.uptrain.ai/dashboard` 的 UpTrain 仪表盘中查看它们。参数：

key_type=“uptrain”
api_key=“UPTRAIN_API_KEY”
project_name=“PROJECT_NAME”

注意： `project_name` 将是在 UpTrain 仪表盘中显示所执行评估的项目名称。

设置 API 密钥

笔记本将提示您输入 API 密钥。您可以通过更改以下单元格中的 `key_type` 参数来选择 OpenAI API 密钥或 UpTrain API 密钥。

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

1. 香草 RAG

UpTrain 回调处理程序将自动捕获生成的查询、上下文和响应，并对响应运行以下三项评估*（评分从 0 到 1）*

上下文相关性：检查从查询中提取的上下文是否与响应相关。
事实准确性：检查响应的事实准确性。
响应完整性：检查响应是否包含查询请求的所有信息。

# 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!

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。它生成与原始查询含义相同的多个查询，然后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：

多查询准确性：检查生成的多个查询是否与原始查询含义相同。

# 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!

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

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!

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. 上下文压缩和重新排序

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

上下文重新排序：检查重新排序的节点顺序是否比原始顺序与查询更相关。
上下文简洁性：检查减少的节点数量是否仍提供所有所需信息。

# 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!

Question: What did the president say about Ketanji Brown Jackson

Context Conciseness Score: 0.0
Context Reranking Score: 1.0

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!

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 的仪表盘和洞察

这是一个展示仪表盘和洞察的短视频：

在 GitHub 上编辑此页面源文件。

以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。

热门提供商

按组件划分的集成

UpTrain

UpTrain 回调处理程序

1. 香草 RAG

2. 多查询生成

3. 上下文压缩和重新排序

安装依赖项

导入库

加载文档

将文档分成块

创建检索器

定义 LLM

设置

1. UpTrain 的开源软件 (OSS)

2. UpTrain 托管服务和仪表盘

设置 API 密钥

1. 香草 RAG

2. 多查询生成

3. 上下文压缩和重新排序

UpTrain 的仪表盘和洞察

热门提供商

按组件划分的集成

​UpTrain 回调处理程序

​1. 香草 RAG

​2. 多查询生成

​3. 上下文压缩和重新排序

​安装依赖项

​导入库

​加载文档

​将文档分成块

​创建检索器

​定义 LLM

​设置

​1. UpTrain 的开源软件 (OSS)

​2. UpTrain 托管服务和仪表盘

​设置 API 密钥

​1. 香草 RAG

​2. 多查询生成

​3. 上下文压缩和重新排序

​UpTrain 的仪表盘和洞察

UpTrain 回调处理程序

1. 香草 RAG

2. 多查询生成

3. 上下文压缩和重新排序

安装依赖项

导入库

加载文档

将文档分成块

创建检索器

定义 LLM

设置

1. UpTrain 的开源软件 (OSS)

2. UpTrain 托管服务和仪表盘

设置 API 密钥

1. 香草 RAG

2. 多查询生成

3. 上下文压缩和重新排序

UpTrain 的仪表盘和洞察