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Code Issues 6 Packages Projects Releases Wiki Activity

Merge pull request #18 from aimingmed/feature/evaluation-rag-cot

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Working docker and LangSmith for chat result tracing
This commit is contained in:
Hong Kai LEE 2025-03-12 14:47:26 +08:00 committed by GitHub
commit e8a8fbf826
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12 changed files with 1001 additions and 309 deletions
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2
app/llmops/config.yaml
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@ -12,5 +12,5 @@ etl:
prompt_engineering:
run_id_chromadb: None
chat_model_provider: gemini
query: "怎么治疗有kras的肺癌?"
query: "如何治疗乳腺癌?"

30
app/llmops/main.py
View File

@ -9,6 +9,7 @@ _steps = [
"etl_chromadb_pdf",
"etl_chromadb_scanned_pdf", # the performance for scanned pdf may not be good
"rag_cot_evaluation",
"adaptive_rag_evaluation",
"test_rag_cot"
]
@ -130,7 +131,36 @@ def go(config: DictConfig):
"chat_model_provider": config["prompt_engineering"]["chat_model_provider"]
},
)
if "adaptive_rag_evaluation" in active_steps:
if config["prompt_engineering"]["run_id_chromadb"] == "None":
# Look for run_id that has artifact logged as documents
run_id = None
client = mlflow.tracking.MlflowClient()
for run in client.search_runs(experiment_ids=[client.get_experiment_by_name(config["main"]["experiment_name"]).experiment_id]):
for artifact in client.list_artifacts(run.info.run_id):
if artifact.path == "chromadb":
run_id = run.info.run_id
break
if run_id:
break
if run_id is None:
raise ValueError("No run_id found with artifact logged as documents")
else:
run_id = config["prompt_engineering"]["run_id_chromadb"]
_ = mlflow.run(
os.path.join(hydra.utils.get_original_cwd(), "src", "adaptive_rag_evaluation"),
"main",
parameters={
"query": config["prompt_engineering"]["query"],
"input_chromadb_artifact": f'runs:/{run_id}/chromadb/chroma_db.zip',
"embedding_model": config["etl"]["embedding_model"],
"chat_model_provider": config["prompt_engineering"]["chat_model_provider"]
},
)
if "test_rag_cot" in active_steps:

29
app/llmops/src/adaptive_rag_evaluation/MLproject Normal file
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@ -0,0 +1,29 @@
name: adaptive_rag_evaluation
python_env: python_env.yml
entry_points:
main:
parameters:
query:
description: Query to run
type: string
input_chromadb_artifact:
description: Fully-qualified name for the input artifact
type: string
embedding_model:
description: Fully-qualified name for the embedding model
type: string
chat_model_provider:
description: Fully-qualified name for the chat model provider
type: string
command: >-
python run.py --query {query} \
--input_chromadb_artifact {input_chromadb_artifact} \
--embedding_model {embedding_model} \
--chat_model_provider {chat_model_provider}

29
app/llmops/src/adaptive_rag_evaluation/python_env.yml Normal file
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@ -0,0 +1,29 @@
# Python version required to run the project.
python: "3.11.11"
# Dependencies required to build packages. This field is optional.
build_dependencies:
- pip==23.3.1
- setuptools
- wheel==0.37.1
- chromadb
- langchain
- sentence_transformers
- python-decouple
- langchain_google_genai
- langchain-deepseek
- langchain-openai
- langchain-community
- mlflow[genai]
- langsmith
- openai
- tiktoken
- langchainhub
- langgraph
- langchain-text-splitters
- langchain-cohere
- tavily-python
- langchain_huggingface
- pydantic
# Dependencies required to run the project.
dependencies:
- mlflow==2.8.1

557
app/llmops/src/adaptive_rag_evaluation/run.py Normal file
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@ -0,0 +1,557 @@
import os
import logging
import argparse
import mlflow
import shutil
from decouple import config
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_deepseek import ChatDeepSeek
from langchain_community.llms.moonshot import Moonshot
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores.chroma import Chroma
from typing import Literal, List
from typing_extensions import TypedDict
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain.schema import Document
from pprint import pprint
from langgraph.graph import END, StateGraph, START
logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
logger = logging.getLogger()
GEMINI_API_KEY = config("GOOGLE_API_KEY", cast=str)
DEEKSEEK_API_KEY = config("DEEKSEEK_API_KEY", cast=str)
MOONSHOT_API_KEY = config("MOONSHOT_API_KEY", cast=str)
TAVILY_API_KEY = config("TAVILY_API_KEY", cast=str)
LANGSMITH_API_KEY = config("LANGSMITH_API_KEY", cast=str)
LANGSMITH_TRACING = config("LANGSMITH_TRACING", cast=str)
LANGSMITH_PROJECT = config("LANGSMITH_PROJECT", cast=str)
os.environ["TAVILY_API_KEY"] = TAVILY_API_KEY
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["LANGSMITH_API_KEY"] = LANGSMITH_API_KEY
os.environ["LANGSMITH_TRACING"] = LANGSMITH_TRACING
os.environ["LANGSMITH_ENDPOINT"] = "https://api.smith.langchain.com"
os.environ["LANGSMITH_PROJECT"] = LANGSMITH_PROJECT
def go(args):
# start a new MLflow run
with mlflow.start_run(experiment_id=mlflow.get_experiment_by_name("development").experiment_id, run_name="etl_chromdb_pdf"):
existing_params = mlflow.get_run(mlflow.active_run().info.run_id).data.params
if 'query' not in existing_params:
mlflow.log_param('query', args.query)
# Log parameters to MLflow
mlflow.log_params({
"input_chromadb_artifact": args.input_chromadb_artifact,
"embedding_model": args.embedding_model,
"chat_model_provider": args.chat_model_provider
})
logger.info("Downloading chromadb artifact")
artifact_chromadb_local_path = mlflow.artifacts.download_artifacts(artifact_uri=args.input_chromadb_artifact)
# unzip the artifact
logger.info("Unzipping the artifact")
shutil.unpack_archive(artifact_chromadb_local_path, "chroma_db")
# Initialize embedding model (do this ONCE)
embedding_model = HuggingFaceEmbeddings(model_name=args.embedding_model)
if args.chat_model_provider == 'deepseek':
llm = ChatDeepSeek(
model="deepseek-chat",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=DEEKSEEK_API_KEY
)
elif args.chat_model_provider == 'gemini':
llm = ChatGoogleGenerativeAI(
model="gemini-1.5-flash",
google_api_key=GEMINI_API_KEY,
temperature=0,
max_retries=3,
streaming=True
)
elif args.chat_model_provider == 'moonshot':
llm = Moonshot(
model="moonshot-v1-128k",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=MOONSHOT_API_KEY
)
# Load data from ChromaDB
db_folder = "chroma_db"
db_path = os.path.join(os.getcwd(), db_folder)
collection_name = "rag-chroma"
vectorstore = Chroma(persist_directory=db_path, collection_name=collection_name, embedding_function=embedding_model)
retriever = vectorstore.as_retriever()
# Data model
class RouteQuery(BaseModel):
"""Route a user query to the most relevant datasource."""
datasource: Literal["vectorstore", "web_search"] = Field(
...,
description="Given a user question choose to route it to web search or a vectorstore.",
)
structured_llm_router = llm.with_structured_output(RouteQuery)
# Prompt
system = """You are an expert at routing a user question to a vectorstore or web search.
The vectorstore contains documents related to any cancer/tumor disease. The question may be
asked in a variety of languages, and may be phrased in a variety of ways.
Use the vectorstore for questions on these topics. Otherwise, use web-search.
"""
route_prompt = ChatPromptTemplate.from_messages(
[
("system", system),
("human", "{question}"),
]
)
question_router = route_prompt | structured_llm_router
### Retrieval Grader
# Data model
class GradeDocuments(BaseModel):
"""Binary score for relevance check on retrieved documents."""
binary_score: str = Field(
description="Documents are relevant to the question, 'yes' or 'no'"
)
structured_llm_grader = llm.with_structured_output(GradeDocuments)
# Prompt
system = """You are a grader assessing relevance of a retrieved document to a user question. \n
If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n
Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
grade_prompt = ChatPromptTemplate.from_messages(
[
("system", system),
("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),
]
)
retrieval_grader = grade_prompt | structured_llm_grader
### Generate
from langchain import hub
from langchain_core.output_parsers import StrOutputParser
# Prompt
prompt = hub.pull("rlm/rag-prompt")
# Post-processing
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# Chain
rag_chain = prompt | llm | StrOutputParser()
### Hallucination Grader
# Data model
class GradeHallucinations(BaseModel):
"""Binary score for hallucination present in generation answer."""
binary_score: str = Field(
description="Answer is grounded in the facts, 'yes' or 'no'"
)
# LLM with function call
structured_llm_grader = llm.with_structured_output(GradeHallucinations)
# Prompt
system = """You are a grader assessing whether an LLM generation is grounded in / supported by a set of retrieved facts. \n
Give a binary score 'yes' or 'no'. 'Yes' means that the answer is grounded in / supported by the set of facts."""
hallucination_prompt = ChatPromptTemplate.from_messages(
[
("system", system),
("human", "Set of facts: \n\n {documents} \n\n LLM generation: {generation}"),
]
)
hallucination_grader = hallucination_prompt | structured_llm_grader
### Answer Grader
# Data model
class GradeAnswer(BaseModel):
"""Binary score to assess answer addresses question."""
binary_score: str = Field(
description="Answer addresses the question, 'yes' or 'no'"
)
# LLM with function call
structured_llm_grader = llm.with_structured_output(GradeAnswer)
# Prompt
system = """You are a grader assessing whether an answer addresses / resolves a question \n
Give a binary score 'yes' or 'no'. Yes' means that the answer resolves the question."""
answer_prompt = ChatPromptTemplate.from_messages(
[
("system", system),
("human", "User question: \n\n {question} \n\n LLM generation: {generation}"),
]
)
answer_grader = answer_prompt | structured_llm_grader
### Question Re-writer
# LLM
# Prompt
system = """You a question re-writer that converts an input question to a better version that is optimized \n
for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning."""
re_write_prompt = ChatPromptTemplate.from_messages(
[
("system", system),
(
"human",
"Here is the initial question: \n\n {question} \n Formulate an improved question.",
),
]
)
question_rewriter = re_write_prompt | llm | StrOutputParser()
### Search
web_search_tool = TavilySearchResults(k=3)
class GraphState(TypedDict):
"""
Represents the state of our graph.
Attributes:
question: question
generation: LLM generation
documents: list of documents
"""
question: str
generation: str
documents: List[str]
def retrieve(state):
"""
Retrieve documents
Args:
state (dict): The current graph state
Returns:
state (dict): New key added to state, documents, that contains retrieved documents
"""
print("---RETRIEVE---")
question = state["question"]
# Retrieval
documents = retriever.invoke(question)
print(documents)
return {"documents": documents, "question": question}
def generate(state):
"""
Generate answer
Args:
state (dict): The current graph state
Returns:
state (dict): New key added to state, generation, that contains LLM generation
"""
print("---GENERATE---")
question = state["question"]
documents = state["documents"]
# RAG generation
generation = rag_chain.invoke({"context": documents, "question": question})
return {"documents": documents, "question": question, "generation": generation}
def grade_documents(state):
"""
Determines whether the retrieved documents are relevant to the question.
Args:
state (dict): The current graph state
Returns:
state (dict): Updates documents key with only filtered relevant documents
"""
print("---CHECK DOCUMENT RELEVANCE TO QUESTION---")
question = state["question"]
documents = state["documents"]
# Score each doc
filtered_docs = []
for d in documents:
score = retrieval_grader.invoke(
{"question": question, "document": d.page_content}
)
grade = score.binary_score
if grade == "yes":
print("---GRADE: DOCUMENT RELEVANT---")
filtered_docs.append(d)
else:
print("---GRADE: DOCUMENT NOT RELEVANT---")
continue
return {"documents": filtered_docs, "question": question}
def transform_query(state):
"""
Transform the query to produce a better question.
Args:
state (dict): The current graph state
Returns:
state (dict): Updates question key with a re-phrased question
"""
print("---TRANSFORM QUERY---")
question = state["question"]
documents = state["documents"]
# Re-write question
better_question = question_rewriter.invoke({"question": question})
return {"documents": documents, "question": better_question}
def web_search(state):
"""
Web search based on the re-phrased question.
Args:
state (dict): The current graph state
Returns:
state (dict): Updates documents key with appended web results
"""
print("---WEB SEARCH---")
question = state["question"]
# Web search
docs = web_search_tool.invoke({"query": question})
web_results = "\n".join([d["content"] for d in docs])
web_results = Document(page_content=web_results)
return {"documents": web_results, "question": question}
### Edges ###
def route_question(state):
"""
Route question to web search or RAG.
Args:
state (dict): The current graph state
Returns:
str: Next node to call
"""
print("---ROUTE QUESTION---")
question = state["question"]
source = question_router.invoke({"question": question})
if source.datasource == "web_search":
print("---ROUTE QUESTION TO WEB SEARCH---")
return "web_search"
elif source.datasource == "vectorstore":
print("---ROUTE QUESTION TO RAG---")
return "vectorstore"
def decide_to_generate(state):
"""
Determines whether to generate an answer, or re-generate a question.
Args:
state (dict): The current graph state
Returns:
str: Binary decision for next node to call
"""
print("---ASSESS GRADED DOCUMENTS---")
state["question"]
filtered_documents = state["documents"]
if not filtered_documents:
# All documents have been filtered check_relevance
# We will re-generate a new query
print(
"---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---"
)
return "transform_query"
else:
# We have relevant documents, so generate answer
print("---DECISION: GENERATE---")
return "generate"
def grade_generation_v_documents_and_question(state):
"""
Determines whether the generation is grounded in the document and answers question.
Args:
state (dict): The current graph state
Returns:
str: Decision for next node to call
"""
print("---CHECK HALLUCINATIONS---")
question = state["question"]
documents = state["documents"]
generation = state["generation"]
score = hallucination_grader.invoke(
{"documents": documents, "generation": generation}
)
grade = score.binary_score
# Check hallucination
if grade == "yes":
print("---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---")
# Check question-answering
print("---GRADE GENERATION vs QUESTION---")
score = answer_grader.invoke({"question": question, "generation": generation})
grade = score.binary_score
if grade == "yes":
print("---DECISION: GENERATION ADDRESSES QUESTION---")
return "useful"
else:
print("---DECISION: GENERATION DOES NOT ADDRESS QUESTION---")
return "not useful"
else:
pprint("---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---")
return "not supported"
workflow = StateGraph(GraphState)
# Define the nodes
workflow.add_node("web_search", web_search) # web search
workflow.add_node("retrieve", retrieve) # retrieve
workflow.add_node("grade_documents", grade_documents) # grade documents
workflow.add_node("generate", generate) # generatae
workflow.add_node("transform_query", transform_query) # transform_query
# Build graph
workflow.add_conditional_edges(
START,
route_question,
{
"web_search": "web_search",
"vectorstore": "retrieve",
},
)
workflow.add_edge("web_search", "generate")
workflow.add_edge("retrieve", "grade_documents")
workflow.add_conditional_edges(
"grade_documents",
decide_to_generate,
{
"transform_query": "transform_query",
"generate": "generate",
},
)
workflow.add_edge("transform_query", "retrieve")
workflow.add_conditional_edges(
"generate",
grade_generation_v_documents_and_question,
{
"not supported": "generate",
"useful": END,
"not useful": "transform_query",
},
)
# Compile
app = workflow.compile()
# Run
inputs = {
"question": args.query
}
for output in app.stream(inputs):
for key, value in output.items():
# Node
pprint(f"Node '{key}':")
# Optional: print full state at each node
# pprint.pprint(value["keys"], indent=2, width=80, depth=None)
pprint("\n---\n")
# Final generation
pprint(value["generation"])
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Chain of Thought RAG")
parser.add_argument(
"--query",
type=str,
help="Question to ask the model",
required=True
)
parser.add_argument(
"--input_chromadb_artifact",
type=str,
help="Fully-qualified name for the chromadb artifact",
required=True
)
parser.add_argument(
"--embedding_model",
type=str,
default="paraphrase-multilingual-mpnet-base-v2",
help="Sentence Transformer model name"
)
parser.add_argument(
"--chat_model_provider",
type=str,
default="gemini",
help="Chat model provider"
)
args = parser.parse_args()
go(args)

5
app/llmops/src/etl_chromadb_pdf/python_env.yml
View File

@ -10,6 +10,11 @@ build_dependencies:
- pdfminer.six
- langchain
- sentence_transformers
- langchain-text-splitters
- langchain_huggingface
- langchain-community
- tiktoken
# Dependencies required to run the project.
dependencies:
- mlflow==2.8.1

37
app/llmops/src/etl_chromadb_pdf/run.py
View File

@ -8,16 +8,16 @@ import os
import mlflow
import shutil
import chromadb
import io
from pdfminer.converter import TextConverter
from pdfminer.pdfinterp import PDFPageInterpreter
from pdfminer.pdfinterp import PDFResourceManager
from pdfminer.pdfpage import PDFPage
from langchain.schema import Document
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.vectorstores.chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
logger = logging.getLogger()
@ -80,7 +80,7 @@ def go(args):
# Initialize embedding model (do this ONCE)
model_embedding = SentenceTransformer(args.embedding_model) # Or a multilingual model
model_embedding = HuggingFaceEmbeddings(model_name=args.embedding_model) # Or a multilingual model
# Create database, delete the database directory if it exists
@ -90,9 +90,6 @@ def go(args):
shutil.rmtree(db_path)
os.makedirs(db_path)
chroma_client = chromadb.PersistentClient(path=db_path)
collection_name = "rag_experiment"
db = chroma_client.create_collection(name=collection_name)
logger.info("Downloading artifact")
artifact_local_path = mlflow.artifacts.download_artifacts(artifact_uri=args.input_artifact)
@ -107,22 +104,28 @@ def go(args):
# show the unzipped folder
documents_folder = os.path.splitext(os.path.basename(artifact_local_path))[0]
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
chunk_size=1000, chunk_overlap=500
)
ls_docs = []
for root, _dir, files in os.walk(f"./{documents_folder}"):
for file in files:
if file.endswith(".pdf"):
read_text = extract_chinese_text_from_pdf(os.path.join(root, file))
document = Document(page_content=read_text)
all_splits = text_splitter.split_documents([document])
for i, split in enumerate(all_splits):
db.add(documents=[split.page_content],
metadatas=[{"filename": file}],
ids=[f'{file[:-4]}-{str(i)}'],
embeddings=[model_embedding.encode(split.page_content)]
)
document = Document(metadata={"file": file}, page_content=read_text)
ls_docs.append(document)
doc_splits = text_splitter.split_documents(ls_docs)
# Add to vectorDB
_vectorstore = Chroma.from_documents(
documents=doc_splits,
collection_name="rag-chroma",
embedding=model_embedding,
persist_directory=db_path
)
logger.info("Logging artifact with mlflow")
shutil.make_archive(db_path, 'zip', db_path)
mlflow.log_artifact(db_path + '.zip', args.output_artifact)

2
app/llmops/src/rag_cot_evaluation/run.py
View File

@ -46,7 +46,7 @@ def go(args):
db_folder = "chroma_db"
db_path = os.path.join(os.getcwd(), db_folder)
chroma_client = chromadb.PersistentClient(path=db_path)
collection_name = "rag_experiment"
collection_name = "rag-chroma"
collection = chroma_client.get_collection(name=collection_name)
# Formulate a question

2
app/streamlit/Chatbot.py
View File

@ -19,7 +19,7 @@ MOONSHOT_API_KEY = config("MOONSHOT_API_KEY", cast=str, default="123456")
CHAT_MODEL_PROVIDER = config("CHAT_MODEL_PROVIDER", cast=str, default="gemini")
INPUT_CHROMADB_LOCAL = config("INPUT_CHROMADB_LOCAL", cast=str, default="../llmops/src/rag_cot_evaluation/chroma_db")
EMBEDDING_MODEL = config("EMBEDDING_MODEL", cast=str, default="paraphrase-multilingual-mpnet-base-v2")
COLLECTION_NAME = config("COLLECTION_NAME", cast=str, default="rag_experiment")
COLLECTION_NAME = config("COLLECTION_NAME", cast=str, default="rag-chroma")
st.title("💬 RAG AI for Medical Guideline")
st.caption(f"🚀 A RAG AI for Medical Guideline powered by {CHAT_MODEL_PROVIDER}")

11
app/streamlit/Dockerfile
View File

@ -2,20 +2,23 @@ FROM python:3.11-slim
WORKDIR /app/streamlit
COPY requirements.txt ./
COPY Pipfile ./
# RUN pip install --no-cache-dir -r requirements.txt
RUN pip install -r requirements.txt
# RUN pip install -r requirements.txt
RUN pip install --upgrade pip setuptools wheel -i https://pypi.tuna.tsinghua.edu.cn/simple
RUN pip install pipenv -i https://pypi.tuna.tsinghua.edu.cn/simple
RUN pipenv install --deploy
COPY Chatbot.py .
COPY .env .
# Run python to initialize download of SentenceTransformer model
COPY initialize_sentence_transformer.py .
RUN python initialize_sentence_transformer.py
RUN pipenv run python initialize_sentence_transformer.py
COPY pages ./pages
EXPOSE 8501
ENTRYPOINT ["streamlit", "run", "Chatbot.py"]
ENTRYPOINT ["pipenv", "run", "streamlit", "run", "Chatbot.py"]

4
app/streamlit/Pipfile
View File

@ -4,7 +4,7 @@ verify_ssl = true
name = "pypi"
[packages]
streamlit = "==1.28"
streamlit = "*"
langchain = "*"
duckduckgo-search = "*"
anthropic = "*"
@ -12,7 +12,7 @@ trubrics = "*"
streamlit-feedback = "*"
langchain-community = "*"
watchdog = "*"
mlflow = "==2.8.1"
mlflow = "*"
python-decouple = "*"
langchain_google_genai = "*"
langchain-deepseek = "*"

602
app/streamlit/Pipfile.lock generated
View File

@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
"sha256": "68da9f2cf2dea795e4bb8d4f5b108a40e1fe4255c7d8dbe9233f9db6f993f876"
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