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.

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,
)

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

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

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

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

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

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

# 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

# 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

# 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