update for final working main

.gitignore

@@ -204,4 +204,5 @@ data/*
 **/*.pdf
 **/.env
 **/llm-template2/*
-**/llmops/outputs/*
+**/llmops/outputs/*
+**/*.zip

app/llmops/components/combine_chromadb/run.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python
+import argparse
+import logging
+import os
+import wandb
+import shutil
+import chromadb
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
+logger = logging.getLogger()
+
+def combine_chromadb(chromadb_pdf_path, chromadb_scanned_pdf_path, output_path):
+    """
+    Combines two ChromaDB instances into a single ChromaDB.
+    """
+
+    # Load the ChromaDB instances
+    chromadb_pdf_client = chromadb.PersistentClient(path=chromadb_pdf_path)
+    chromadb_scanned_pdf_client = chromadb.PersistentClient(path=chromadb_scanned_pdf_path)
+
+    # Get the collections
+    collection_name = "rag_experiment"
+    try:
+        chromadb_pdf_collection = chromadb_pdf_client.get_collection(name=collection_name)
+    except ValueError as e:
+        raise ValueError(f"Collection '{collection_name}' not found in ChromaDB at '{chromadb_pdf_path}'. Ensure the etl_chromdb_pdf step was run successfully.") from e
+    try:
+        chromadb_scanned_pdf_collection = chromadb_scanned_pdf_client.get_collection(name=collection_name)
+    except ValueError as e:
+        raise ValueError(f"Collection '{collection_name}' not found in ChromaDB at '{chromadb_scanned_pdf_path}'. Ensure the etl_chromdb_scanned_pdf step was run successfully.") from e
+
+    # Get all data from the collections
+    chromadb_pdf_data = chromadb_pdf_collection.get(include=["documents", "metadatas", "embeddings"])
+    chromadb_scanned_pdf_data = chromadb_scanned_pdf_collection.get(include=["documents", "metadatas", "embeddings"])
+
+    # Create a new ChromaDB instance
+    combined_chromadb_client = chromadb.PersistentClient(path=output_path)
+    combined_chromadb_collection = combined_chromadb_client.create_collection(name=collection_name)
+
+    # Add the data to the combined ChromaDB
+    combined_chromadb_collection.add(
+        documents=chromadb_pdf_data["documents"] + chromadb_scanned_pdf_data["documents"],
+        metadatas=chromadb_pdf_data["metadatas"] + chromadb_scanned_pdf_data["metadatas"],
+        ids=chromadb_pdf_data["ids"] + chromadb_scanned_pdf_data["ids"],
+        embeddings=chromadb_pdf_data["embeddings"] + chromadb_scanned_pdf_data["embeddings"],
+    )
+
+    logger.info(f"Combined ChromaDB created at {output_path}")
+
+
+def go(args):
+    """
+    Run the combine chromadb component.
+    """
+
+    run = wandb.init(job_type="combine_chromadb", entity='aimingmed')
+    run.config.update(args)
+
+    # Download the ChromaDB artifacts
+    logger.info("Downloading chromadb_pdf artifact")
+    chromadb_pdf_artifact = run.use_artifact(args.chromadb_pdf_artifact).file()
+    chromadb_pdf_path = os.path.join(chromadb_pdf_artifact, "chroma_db")
+
+    logger.info("Downloading chromadb_scanned_pdf artifact")
+    chromadb_scanned_pdf_artifact = run.use_artifact(args.chromadb_scanned_pdf_artifact).file()
+    chromadb_scanned_pdf_path = os.path.join(chromadb_scanned_pdf_artifact, "chroma_db")
+
+    # Create the output directory
+    output_folder = "combined_chromadb"
+    output_path = os.path.join(os.getcwd(), output_folder)
+    if os.path.exists(output_path):
+        shutil.rmtree(output_path)
+    os.makedirs(output_path)
+
+    # Combine the ChromaDB instances
+    combine_chromadb(chromadb_pdf_path, chromadb_scanned_pdf_path, output_path)
+
+    # Create a new artifact
+    artifact = wandb.Artifact(
+        args.output_artifact,
+        type=args.output_type,
+        description=args.output_description
+    )
+
+    # Zip the database folder first
+    shutil.make_archive(output_path, 'zip', output_path)
+
+    # Add the database to the artifact
+    artifact.add_file(output_path + '.zip')
+
+    # Log the artifact
+    run.log_artifact(artifact)
+
+    # Finish the run
+    run.finish()
+
+    # clean up - remove zip
+    os.remove(output_path + '.zip')
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Combine two ChromaDB instances into one.")
+
+    parser.add_argument(
+        "--chromadb_pdf_artifact",
+        type=str,
+        required=True,
+        help="Fully-qualified name for the ChromaDB PDF artifact",
+    )
+    parser.add_argument(
+        "--chromadb_scanned_pdf_artifact",
+        type=str,
+        required=True,
+        help="Fully-qualified name for the ChromaDB Scanned PDF artifact",
+    )
+    parser.add_argument(
+        "--output_artifact",
+        type=str,
+        required=True,
+        help="Name for the output artifact",
+    )
+    parser.add_argument(
+        "--output_type",
+        type=str,
+        required=True,
+        help="Type for the output artifact",
+    )
+    parser.add_argument(
+        "--output_description",
+        type=str,
+        required=True,
+        help="Description for the output artifact",
+    )
+
+    args = parser.parse_args()
+    go(args)

app/llmops/config.yaml

@@ -8,4 +8,6 @@ etl:
   document_folder: documents
   path_document_folder: "../../../../data"
   embedding_model: paraphrase-multilingual-mpnet-base-v2
-  
+prompt_engineering:
+  query: "怎么治疗肺癌?"
+  

app/llmops/main.py

@@ -12,16 +12,7 @@ _steps = [
     "get_documents",
     "etl_chromdb_pdf",
     "etl_chromdb_scanned_pdf",
-    "data_check",
-    "data_split",
-    "train_random_forest_propensity",
-    "train_random_forest_revenue",
-    "train_lasso_revenue",
-    # NOTE: We do not include this in the steps so it is not run by mistake.
-    # You first need to promote a model export to "prod" before you can run this,
-    # then you need to run this step explicitly
-   "test_model",
-   "test_production"
+    "chain_of_thought"
 ]
 
 GEMINI_API_KEY = config("GOOGLE_API_KEY", cast=str)
@@ -80,136 +71,15 @@ def go(config: DictConfig):
                     "embedding_model": config["etl"]["embedding_model"]
                 },
             )
-
-        if "data_check" in active_steps:
-            _ = mlflow.run(
-                os.path.join(hydra.utils.get_original_cwd(), "src", "data_check"),
-                "main",
-                parameters={
-                    "csv": f"{config['data_check']['csv_to_check']}:latest",
-                    "ref": "clean_sample.csv:reference",
-                    "kl_threshold": config['data_check']['kl_threshold'],
-                    "min_age": config['etl']['min_age'],
-                    "max_age": config['etl']['max_age'],
-                    "min_tenure": config['etl']['min_tenure'],
-                    "max_tenure": config['etl']['max_tenure']
-                },
-            )
-
-        if "data_split" in active_steps:
-            _ = mlflow.run(
-                os.path.join(hydra.utils.get_original_cwd(), "components", "train_val_test_split"),
-                "main",
-                parameters={
-                    "input": "clean_sample.csv:latest",
-                    "test_size": config['modeling']['test_size'],
-                    "random_seed": config['modeling']['random_seed'],
-                    "stratify_by": config['modeling']['stratify_by'],
-                },
-            )
-
-        if "train_random_forest_propensity" in active_steps:
-
-            # NOTE: we need to serialize the random forest configuration into JSON
-            rf_config = os.path.abspath("rf_config.json")
-            with open(rf_config, "w+") as fp:
-                json.dump(dict(config["modeling"]["random_forest_classifier_propensity"].items()), fp)  # DO NOT TOUCH
-
-            # NOTE: use the rf_config we created as the rf_config parameter for the train_random_forest
-            # step
-            _ = mlflow.run(
-                os.path.join(hydra.utils.get_original_cwd(), "src", "train_random_forest_propensity"),
-                "main",
-                parameters={
-                    "trainval_artifact": "trainval_data.csv:latest",
-                    "val_size": config['modeling']['val_size'],
-                    "random_seed": config['modeling']['random_seed'],
-                    "ls_output_columns": ','.join(config['modeling']['ls_output_columns']),
-                    "product": config['modeling']['product_to_train'],
-                    "stratify_by": config['modeling']['stratify_by'],
-                    "n_folds": config['modeling']['n_folds'],
-                    "rf_config": rf_config,
-                    "output_artifact": "random_forest_export",
-                },
-            )
         
-        if "train_random_forest_revenue" in active_steps:
-
-            # NOTE: we need to serialize the random forest configuration into JSON
-            rf_config = os.path.abspath("rf_config_revenue.json")
-            with open(rf_config, "w+") as fp:
-                json.dump(dict(config["modeling"]["random_forest_regression_revenue"].items()), fp)
-            
-            # NOTE: use the rf_config we created as the rf_config parameter for the train_random_forest
-            # step
+        if "chain_of_thought" in active_steps:
             _ = mlflow.run(
-                os.path.join(hydra.utils.get_original_cwd(), "src", "train_random_forest_revenue"),
+                os.path.join(hydra.utils.get_original_cwd(), "src", "chain_of_thought"),
                 "main",
                 parameters={
-                    "trainval_artifact": "trainval_data.csv:latest",
-                    "val_size": config['modeling']['val_size'],
-                    "random_seed": config['modeling']['random_seed'],
-                    "ls_output_columns": ','.join(config['modeling']['ls_output_columns']),
-                    "product": config['modeling']['product_to_train'],
-                    "stratify_by": config['modeling']['stratify_by'],
-                    "n_folds": config['modeling']['n_folds'],
-                    "rf_config": rf_config,
-                    "output_artifact": "random_forest_export",
-                },
-            )
-
-        if "train_lasso_revenue" in active_steps:
-
-            # NOTE: use the lasso_config we created as the lasso_config parameter for the train_lasso
-            lasso_config = os.path.abspath("lasso_config.json")
-            with open(lasso_config, "w+") as fp:
-                json.dump(dict(config["modeling"]["lasso_regression_revenue"].items()), fp)
-
-            _ = mlflow.run(
-                os.path.join(hydra.utils.get_original_cwd(), "src", "train_lasso_revenue"),
-                "main",
-                parameters={
-                    "trainval_artifact": "trainval_data.csv:latest",
-                    "val_size": config['modeling']['val_size'],
-                    "random_seed": config['modeling']['random_seed'],
-                    "ls_output_columns": ','.join(config['modeling']['ls_output_columns']),
-                    "product": config['modeling']['product_to_train'],
-                    "stratify_by": config['modeling']['stratify_by'],
-                    "n_folds": config['modeling']['n_folds'],
-                    "lasso_config": lasso_config,
-                    "output_artifact": "lasso_export",
-                },
-            )
-
-        if "test_model" in active_steps:
-
-            _ = mlflow.run(
-                os.path.join(hydra.utils.get_original_cwd(), "components", "test_model"),
-                "main",
-                parameters={
-                    "model_propensity_cc": config['best_model_propensity']['propensity_cc'],
-                    "model_propensity_cl": config['best_model_propensity']['propensity_cl'],
-                    "model_propensity_mf": config['best_model_propensity']['propensity_mf'],
-                    "model_revenue_cc": config['best_model_revenue']['revenue_cc'],
-                    "model_revenue_cl": config['best_model_revenue']['revenue_cl'],
-                    "model_revenue_mf": config['best_model_revenue']['revenue_mf'],
-                    "test_dataset": "test_data.csv:latest",
-                },
-            )
-        
-        if "test_production" in active_steps:
-            
-            _ = mlflow.run(
-                os.path.join(hydra.utils.get_original_cwd(), "components", "test_production"),
-                "main",
-                parameters={
-                    "model_propensity_cc": config['best_model_propensity']['propensity_cc'],
-                    "model_propensity_cl": config['best_model_propensity']['propensity_cl'],
-                    "model_propensity_mf": config['best_model_propensity']['propensity_mf'],
-                    "model_revenue_cc": config['best_model_revenue']['revenue_cc'],
-                    "model_revenue_cl": config['best_model_revenue']['revenue_cl'],
-                    "model_revenue_mf": config['best_model_revenue']['revenue_mf'],
-                    "test_dataset": f"{config['production']['test_csv']}:latest",
+                    "query": config["prompt_engineering"]["query"],
+                    "input_chromadb_artifact": "chromdb.zip:latest",
+                    "embedding_model": config["etl"]["embedding_model"],
                 },
             )
 

app/llmops/src/chain_of_thought/MLproject

@@ -0,0 +1,24 @@
+name: chain_of_thought
+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
+
+    
+    command: >-
+        python run.py --query {query} \
+                      --input_chromadb_artifact {input_chromadb_artifact} \
+                      --embedding_model {embedding_model}

app/llmops/src/chain_of_thought/python_env.yml

@@ -0,0 +1,16 @@
+# 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
+# Dependencies required to run the project.
+dependencies:
+  - mlflow==2.8.1
+  - wandb==0.16.0

app/llmops/src/chain_of_thought/run.py

@@ -0,0 +1,104 @@
+import os
+import logging
+import argparse
+import wandb
+import chromadb
+import shutil
+from decouple import config
+from langchain.prompts import PromptTemplate
+from sentence_transformers import SentenceTransformer
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
+logger = logging.getLogger()
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+GEMINI_API_KEY = config("GOOGLE_API_KEY", cast=str)
+
+
+def go(args):
+    run = wandb.init(job_type="chain_of_thought", entity='aimingmed')
+    run.config.update(args)
+
+    logger.info("Downloading chromadb artifact")
+    artifact_chromadb_local_path = run.use_artifact(args.input_chromadb_artifact).file()
+
+    # unzip the artifact
+    logger.info("Unzipping the artifact")
+    shutil.unpack_archive(artifact_chromadb_local_path, "chroma_db")
+
+    # Load data from ChromaDB
+    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 = chroma_client.get_collection(name=collection_name)
+
+    # Formulate a question
+    question = args.query
+
+    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", google_api_key=GEMINI_API_KEY)
+    
+
+    # Chain of Thought Prompt
+    cot_template = """Let's think step by step. 
+    Given the following document in text: {documents_text}
+    Question: {question}
+    """
+    cot_prompt = PromptTemplate(template=cot_template, input_variables=["documents_text", "question"])
+    cot_chain = cot_prompt | llm
+
+    # Initialize embedding model (do this ONCE)
+    model = SentenceTransformer(args.embedding_model) 
+
+    # Query (prompt)
+    query_embedding = model.encode(question)  # Embed the query using the SAME model
+
+    # Search ChromaDB
+    documents_text = collection.query(query_embeddings=[query_embedding], n_results=5)
+
+    # Generate chain of thought
+    cot_output = cot_chain.invoke({"documents_text": documents_text, "question": question})
+    print("Chain of Thought: ", cot_output)
+
+    # Answer Prompt
+    answer_template = """Given the chain of thought: {cot}
+    Provide a concise answer to the question: {question}
+    Provide the answer with language that is similar to the question asked.
+    """
+    answer_prompt = PromptTemplate(template=answer_template, input_variables=["cot", "question"])
+    answer_chain = answer_prompt | llm
+
+    # Generate answer
+    answer_output = answer_chain.invoke({"cot": cot_output, "question": question})
+    print("Answer: ", answer_output)
+
+    run.finish()
+
+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"
+    )
+
+    args = parser.parse_args()
+    
+    go(args)

app/llmops/src/etl_chromdb_pdf/run.py

@@ -70,7 +70,7 @@ def go(args):
 
 
     # Initialize embedding model (do this ONCE)
-    model_embedding = SentenceTransformer('all-mpnet-base-v2')  # Or a multilingual model
+    model_embedding = SentenceTransformer(args.embedding_model)  # Or a multilingual model
 
 
     # Create database, delete the database directory if it exists
@@ -115,6 +115,30 @@ def go(args):
                         embeddings=[model_embedding.encode(split.page_content)]
                     )
 
+    # Create a new artifact
+    artifact = wandb.Artifact(
+        args.output_artifact,
+        type=args.output_type,
+        description=args.output_description
+    )
+
+    # zip the database folder first
+    shutil.make_archive(db_path, 'zip', db_path)
+
+    # Add the database to the artifact
+    artifact.add_file(db_path + '.zip')
+
+    # Log the artifact
+    run.log_artifact(artifact)
+
+    # Finish the run
+    run.finish()
+
+    # clean up
+    os.remove(db_path + '.zip')
+    os.remove(db_path)
+
+
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser(description="A very basic data cleaning")