Feature: Added a chat RAG example with sample medical provide note data supplied from mtsamples.com.

week5/community-contributions/rag_chat_example/README.md

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+# Overview
+
+This uses de-identified medical dictation data supplied by [mtsamples](https://mtsamples.com). The data from the mtsamples
+website was download from [kaggle](https://www.kaggle.com/datasets/tboyle10/medicaltranscriptions). There are four 
+sample notes in different directories (see knowledge_base/mtsamples_dictations) that will added to a chromaDb 
+vector database and will be available during chat using RAG (Retrieval Augmented Generation).
+
+# How to run
+
+- Run example
+
+```shell
+conda activate <your_environment>
+cd <your_directory_where_script_lives>
+python run_rag_chat.py
+```
+
+# Chat example
+
+![Chat Example](img.png)
+
+# Questions to ask?
+
+1) How old is Ms. Connor?
+2) What are Ms. Connor's vital signs?
+3) How old is Ms. Mouse?
+4) What is Ms. Mouse concerned about?
+5) What are Ms. Mouse's vital signs?
+6) How old is Mr. Duck?
+7) Why did Mr. Duck go to the doctor?
+8) How old is Ms. Barbara? 
+9) Why did Ms. Barbara go to the doctor?
+10) Is Ms. Barbara allergic to anything?
+
+
+
+

week5/community-contributions/rag_chat_example/knowledge_base/mtsample_dictations/test_patient_1_f/progress_note.txt

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+HISTORY OF PRESENT ILLNESS:
+
+Ms. Connor is a 50-year-old female who returns to clinic for a wound check.
+The patient underwent an APR secondary to refractory ulcerative colitis.
+Subsequently, she developed a wound infection, which has since healed.
+On our most recent visit to our clinic, she has her perineal stitches removed and presents today for followup of
+her perineal wound.  She describes no drainage or erythema from her bottom.  She is having good ostomy output.
+She does not describe any fevers, chills, nausea, or vomiting.  The patient does describe some intermittent
+pain beneath the upper portion of the incision as well as in the right lower quadrant below her ostomy.
+She has been taking Percocet for this pain and it does work.  She has since run out has been trying
+extra strength Tylenol, which will occasionally help this intermittent pain.  She is requesting additional
+pain medications for this occasional abdominal pain, which she still experiences.
+
+PHYSICAL EXAMINATION:
+
+Temperature 95.8, pulse 68, blood pressure 132/73, and weight 159 pounds.
+
+This is a pleasant female in no acute distress.
+The patient's abdomen is soft, nontender, nondistended with a well-healed midline scar.
+There is an ileostomy in the right hemiabdomen, which is pink, patent, productive, and protuberant.
+There are no signs of masses or hernias over the patient's abdomen.
+
+ASSESSMENT AND PLAN:
+
+This is a pleasant 50-year-old female who has undergone an APR secondary to refractory ulcerative colitis.
+Overall, her quality of life has significantly improved since she had her APR.  She is functioning well with her ileostomy.
+She did have concerns or questions about her diet and we discussed the BRAT diet, which consisted of foods that would
+slow down the digestive tract such as bananas, rice, toast, cheese, and peanut butter.
+I discussed the need to monitor her ileostomy output and preferential amount of daily output is 2 liters or less.
+I have counseled her on refraining from soft drinks and fruit drinks.  I have also discussed with her that this diet
+is moreover a trial and error and that she may try certain foods that did not agree with her ileostomy,
+however others may and that this is something she will just have to perform trials with over the next several
+months until she finds what foods that she can and cannot eat with her ileostomy.  She also had questions about
+her occasional abdominal pain.  I told her that this was probably continue to improve as months went by and I
+gave her a refill of her Percocet for the continued occasional pain.  I told her that this would the last time
+I would refill the Percocet and if she has continued pain after she finishes this bottle then she would need to
+start ibuprofen or Tylenol if she had continued pain.  The patient then brought up some right hand and arm numbness,
+which has been there postsurgically and was thought to be from positioning during surgery.
+This is all primarily gone away except for a little bit of numbness at the tip of the third digit as well as
+some occasional forearm muscle cramping.  I told her that I felt that this would continue to improve as it
+ has done over the past two months since her surgery.  I told her to continue doing hand exercises as she has
+ been doing and this seems to be working for her.  Overall, I think she has healed from her surgery and is doing
+ very well.  Again, her quality of life is significantly improved.  She is happy with her performance.  We will see
+ her back in six months just for a general routine checkup and see how she is doing at that time.

week5/community-contributions/rag_chat_example/knowledge_base/mtsample_dictations/test_patient_2_f/progress_note.txt

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+HISTORY OF PRESENT ILLNESS:,
+
+Ms. Mouse is a 67-year-old white female with a history of uterine papillary serous carcinoma who is
+status post 6 cycles of carboplatin and Taxol, is here today for followup.  Her last cycle of chemotherapy
+was finished on 01/18/08, and she complains about some numbness in her right upper extremity.
+This has not gotten worse recently and there is no numbness in her toes.  She denies any tingling or burning.,
+
+REVIEW OF SYSTEMS:
+
+Negative for any fever, chills, nausea, vomiting, headache, chest pain, shortness of breath, abdominal pain,
+constipation, diarrhea, melena, hematochezia or dysuria.
+
+The patient is concerned about her blood pressure being up a little bit and also a mole that she had noticed for the
+past few months in her head.
+
+PHYSICAL EXAMINATION:
+
+VITAL SIGNS:  Temperature 35.6, blood pressure 143/83, pulse 65, respirations 18, and weight 66.5 kg.
+GENERAL:  She is a middle-aged white female, not in any distress.
+HEENT:  No lymphadenopathy or mucositis.
+CARDIOVASCULAR:  Regular rate and rhythm.
+LUNGS:  Clear to auscultation bilaterally.
+EXTREMITIES:  No cyanosis, clubbing or edema.
+NEUROLOGICAL:  No focal deficits noted.
+PELVIC:  Normal-appearing external genitalia.  Vaginal vault with no masses or bleeding.,
+
+LABORATORY DATA:
+
+None today.
+
+RADIOLOGIC DATA:
+
+CT of the chest, abdomen, and pelvis from 01/28/08 revealed status post total abdominal hysterectomy/bilateral
+salpingo-oophorectomy with an unremarkable vaginal cuff.  No local or distant metastasis.
+Right probably chronic gonadal vein thrombosis.
+
+ASSESSMENT:
+
+This is a 67-year-old white female with history of uterine papillary serous carcinoma, status post total
+abdominal hysterectomy and bilateral salpingo-oophorectomy and 6 cycles of carboplatin and Taxol chemotherapy.
+She is doing well with no evidence of disease clinically or radiologically.
+
+PLAN:
+
+1.  Plan to follow her every 3 months and CT scans every 6 months for the first 2 years.
+2.  The patient was advised to contact the primary physician for repeat blood pressure check and get started on
+antihypertensives if it is persistently elevated.
+3.  The patient was told that the mole that she is mentioning in her head is no longer palpable and just to observe it for now.
+4.  The patient was advised about doing Kegel exercises for urinary incontinence, and we will address this issue again
+during next clinic visit if it is persistent.

week5/community-contributions/rag_chat_example/knowledge_base/mtsample_dictations/test_patient_3_m/progress_note.txt

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+SUBJECTIVE:
+
+Mr. Duck is a 29-year-old white male who is a patient of Dr. XYZ and he comes in today
+complaining that he was stung by a Yellow Jacket Wasp yesterday and now has a lot of
+swelling in his right hand and right arm.  He says that he has been stung by wasps before and had similar
+reactions.  He just said that he wanted to catch it early before he has too bad of a severe reaction like he has had in the past.
+He has had a lot of swelling, but no anaphylaxis-type reactions in the past; no shortness of breath or difficultly with his
+throat feeling like it is going to close up or anything like that in the past; no racing heart beat or anxiety feeling,
+just a lot of localized swelling where the sting occurs.
+
+OBJECTIVE:
+
+Vitals:  His temperature is 98.4.  Respiratory rate is 18.  Weight is 250 pounds.
+Extremities:  Examination of his right hand and forearm reveals that he has an apparent sting just around his
+wrist region on his right hand on the medial side as well as significant swelling in his hand and his right forearm;
+extending up to the elbow.  He says that it is really not painful or anything like that.  It is really not all that
+red and no signs of infection at this time.
+
+ASSESSMENT:,  Wasp sting to the right wrist area.
+
+PLAN:
+
+1.  Solu-Medrol 125 mg IM X 1.
+2.  Over-the-counter Benadryl, ice and elevation of that extremity.
+3.  Follow up with Dr. XYZ if any further evaluation is needed.

week5/community-contributions/rag_chat_example/knowledge_base/mtsample_dictations/test_patient_4_f/progress_note.txt

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+CHIEF COMPLAINT:
+
+Ms. Barbara is a thirty one year old female patient comes for three-week postpartum checkup, complaining of allergies.
+
+HISTORY OF PRESENT ILLNESS:
+
+She is doing well postpartum.  She has had no headache.  She is breastfeeding and feels like her milk is adequate.
+She has not had much bleeding.  She is using about a mini pad twice a day, not any cramping or clotting and the
+discharge is turned from red to brown to now slightly yellowish.  She has not yet had sexual intercourse.
+She does complain that she has had a little pain with the bowel movement, and every now and then she
+notices a little bright red bleeding.  She has not been particularly constipated but her husband says
+she is not eating her vegetables like she should.  Her seasonal allergies have back developed and she is
+complaining of extremely itchy watery eyes, runny nose, sneezing, and kind of a pressure sensation in her ears.
+
+MEDICATIONS:
+
+Prenatal vitamins.
+
+ALLERGIES:
+
+She thinks to Benadryl.
+
+FAMILY HISTORY:
+
+Mother is 50 and healthy.  Dad is 40 and healthy.  Half-sister, age 34, is healthy.
+She has a sister who is age 10 who has some yeast infections.
+
+ PHYSICAL EXAMINATION:
+
+ VITALS:  Weight:  124 pounds.  Blood pressure 96/54.  Pulse:  72. Respirations:  16.  LMP:  10/18/03.  Age:  39.
+ HEENT:  Head is normocephalic.
+ Eyes:  EOMs intact.
+ PERRLA.  Conjunctiva clear.
+ Fundi:  Discs flat, cups normal.
+ No AV nicking, hemorrhage or exudate.
+ Ears:  TMs intact.
+ Mouth:  No lesion.
+ Throat:  No inflammation.
+ She has allergic rhinitis with clear nasal drainage, clear watery discharge from the eyes.
+ Abdomen:  Soft.  No masses.
+ Pelvic:  Uterus is involuting.
+ Rectal:  She has one external hemorrhoid which has inflamed.  Stool is guaiac negative and using anoscope,
+ no other lesions are identified.
+
+ ASSESSMENT/PLAN:
+
+ Satisfactory three-week postpartum course, seasonal allergies.  We will try Patanol eyedrops and Allegra 60
+ mg twice a day.  She was cautioned about the possibility that this may alter her milk supply.  She is to
+ drink extra fluids and call if she has problems with that.  We will try ProctoFoam HC.  For the hemorrhoids,
+ also increase the fiber in her diet.  That prescription was written, as well as one for Allegra and Patanol.
+ She additionally will be begin on Micronor because she would like to protect herself from pregnancy until
+ her husband get scheduled in and has a vasectomy, which is their ultimate plan for birth control, and she
+ anticipates that happening fairly soon.  She will call and return if she continues to have problems with allergies.
+ Meantime, rechecking in three weeks for her final six-week postpartum checkup.

week5/community-contributions/rag_chat_example/run_rag_chat.py

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+import gradio as gr
+from langchain_chroma import Chroma
+from pathlib import Path
+from utils import create_vector_db, Rag, get_chunks, get_conversation_chain, get_local_vector_db
+
+
+def chat(question, history) -> str:
+
+    """
+    Get the chat data need for the gradio app
+
+    :param question:
+        The question being asked in the chat app.
+    :type question: str
+    :param history:
+        A list of the conversation questions and answers.
+    :type history: list
+    :return:
+        The answer from the current question.
+    """
+
+    result = conversation_chain.invoke({"question": question})
+    answer = result['answer']
+
+    # include source documents if they exist
+    # grab the first one as that should be related to the answer
+    source_doc = ""
+    if result.get('source_documents'):
+        source_doc = result['source_documents'][0]
+
+    response = f"{answer}\n\n**Source:**\n{source_doc.metadata.get('source', 'Source')}" \
+               if source_doc \
+               else answer
+    return response
+
+
+def main():
+
+    gr.ChatInterface(chat, type="messages").launch(inbrowser=True)
+
+
+if __name__ == '__main__':
+
+    create_new_db = False if Path('vector_db').exists() else True
+
+    if create_new_db:
+        folders = Path('knowledge_base').glob('*')
+        chunks = get_chunks(folders=folders)
+        vector_store = create_vector_db(chunks=chunks, db_name=Rag.DB_NAME.value, embeddings=Rag.EMBED_MODEL.value)
+    else:
+        client = get_local_vector_db(path='../rag_chat_example/vector_db')
+        vector_store = Chroma(client=client, embedding_function=Rag.EMBED_MODEL.value)
+
+    conversation_chain = get_conversation_chain(vectorstore=vector_store)
+
+    main()
+
+
+

week5/community-contributions/rag_chat_example/utils.py

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+from chromadb import PersistentClient
+from dotenv import load_dotenv
+from enum import Enum
+
+import plotly.graph_objects as go
+from langchain.document_loaders import DirectoryLoader, TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.schema import Document
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_chroma import Chroma
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+import numpy as np
+import os
+from pathlib import Path
+from sklearn.manifold import TSNE
+from typing import Any, List, Tuple, Generator
+
+cur_path = Path(__file__)
+env_path = cur_path.parent.parent.parent.parent / '.env'
+assert env_path.exists(), f"Please add an .env to the root project path"
+
+load_dotenv(dotenv_path=env_path)
+
+
+class Rag(Enum):
+
+    GPT_MODEL = "gpt-4o-mini"
+    HUG_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+    EMBED_MODEL = OpenAIEmbeddings()
+    DB_NAME = "vector_db"
+
+
+def add_metadata(doc: Document, doc_type: str) -> Document:
+    """
+    Add metadata to a Document object.
+
+    :param doc: The Document object to add metadata to.
+    :type doc: Document
+    :param doc_type: The type of document to be added as metadata.
+    :type doc_type: str
+    :return: The Document object with added metadata.
+    :rtype: Document
+    """
+    doc.metadata["doc_type"] = doc_type
+    return doc
+
+
+def get_chunks(folders: Generator[Path, None, None], file_ext='.txt') -> List[Document]:
+    """
+    Load documents from specified folders, add metadata, and split them into chunks.
+
+    :param folders: List of folder paths containing documents.
+    :type folders: List[str]
+    :param file_ext:
+        The file extension to get from a local knowledge base (e.g. '.txt')
+    :type file_ext: str
+    :return: List of document chunks.
+    :rtype: List[Document]
+    """
+    text_loader_kwargs = {'encoding': 'utf-8'}
+    documents = []
+    for folder in folders:
+        doc_type = os.path.basename(folder)
+        loader = DirectoryLoader(
+            folder, glob=f"**/*{file_ext}", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs
+        )
+        folder_docs = loader.load()
+        documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])
+
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = text_splitter.split_documents(documents)
+
+    return chunks
+
+
+def create_vector_db(db_name: str, chunks: List[Document], embeddings: Any) -> Any:
+    """
+    Create a vector database from document chunks.
+
+    :param db_name: Name of the database to create.
+    :type db_name: str
+    :param chunks: List of document chunks.
+    :type chunks: List[Document]
+    :param embeddings: Embedding function to use.
+    :type embeddings: Any
+    :return: Created vector store.
+    :rtype: Any
+    """
+    # Delete if already exists
+    if os.path.exists(db_name):
+        Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()
+
+    # Create vectorstore
+    vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)
+
+    return vectorstore
+
+
+def get_local_vector_db(path: str) -> Any:
+    """
+    Get a local vector database.
+
+    :param path: Path to the local vector database.
+    :type path: str
+    :return: Persistent client for the vector database.
+    :rtype: Any
+    """
+    return PersistentClient(path=path)
+
+
+def get_vector_db_info(vector_store: Any) -> None:
+    """
+    Print information about the vector database.
+
+    :param vector_store: Vector store to get information from.
+    :type vector_store: Any
+    """
+    collection = vector_store._collection
+    count = collection.count()
+
+    sample_embedding = collection.get(limit=1, include=["embeddings"])["embeddings"][0]
+    dimensions = len(sample_embedding)
+
+    print(f"There are {count:,} vectors with {dimensions:,} dimensions in the vector store")
+
+
+def get_plot_data(collection: Any) -> Tuple[np.ndarray, List[str], List[str], List[str]]:
+    """
+    Get plot data from a collection.
+
+    :param collection: Collection to get data from.
+    :type collection: Any
+    :return: Tuple containing vectors, colors, document types, and documents.
+    :rtype: Tuple[np.ndarray, List[str], List[str], List[str]]
+    """
+    result = collection.get(include=['embeddings', 'documents', 'metadatas'])
+    vectors = np.array(result['embeddings'])
+    documents = result['documents']
+    metadatas = result['metadatas']
+    doc_types = [metadata['doc_type'] for metadata in metadatas]
+    colors = [['blue', 'green', 'red', 'orange'][['products', 'employees', 'contracts', 'company'].index(t)] for t in
+              doc_types]
+
+    return vectors, colors, doc_types, documents
+
+
+def get_2d_plot(collection: Any) -> go.Figure:
+    """
+    Generate a 2D plot of the vector store.
+
+    :param collection: Collection to generate plot from.
+    :type collection: Any
+    :return: 2D scatter plot figure.
+    :rtype: go.Figure
+    """
+    vectors, colors, doc_types, documents = get_plot_data(collection)
+    tsne = TSNE(n_components=2, random_state=42)
+    reduced_vectors = tsne.fit_transform(vectors)
+
+    fig = go.Figure(data=[go.Scatter(
+        x=reduced_vectors[:, 0],
+        y=reduced_vectors[:, 1],
+        mode='markers',
+        marker=dict(size=5, color=colors, opacity=0.8),
+        text=[f"Type: {t}<br>Text: {d[:100]}..." for t, d in zip(doc_types, documents)],
+        hoverinfo='text'
+    )])
+
+    fig.update_layout(
+        title='2D Chroma Vector Store Visualization',
+        scene=dict(xaxis_title='x', yaxis_title='y'),
+        width=800,
+        height=600,
+        margin=dict(r=20, b=10, l=10, t=40)
+    )
+
+    return fig
+
+
+def get_3d_plot(collection: Any) -> go.Figure:
+    """
+    Generate a 3D plot of the vector store.
+
+    :param collection: Collection to generate plot from.
+    :type collection: Any
+    :return: 3D scatter plot figure.
+    :rtype: go.Figure
+    """
+    vectors, colors, doc_types, documents = get_plot_data(collection)
+    tsne = TSNE(n_components=3, random_state=42)
+    reduced_vectors = tsne.fit_transform(vectors)
+
+    fig = go.Figure(data=[go.Scatter3d(
+        x=reduced_vectors[:, 0],
+        y=reduced_vectors[:, 1],
+        z=reduced_vectors[:, 2],
+        mode='markers',
+        marker=dict(size=5, color=colors, opacity=0.8),
+        text=[f"Type: {t}<br>Text: {d[:100]}..." for t, d in zip(doc_types, documents)],
+        hoverinfo='text'
+    )])
+
+    fig.update_layout(
+        title='3D Chroma Vector Store Visualization',
+        scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),
+        width=900,
+        height=700,
+        margin=dict(r=20, b=10, l=10, t=40)
+    )
+
+    return fig
+
+
+def get_conversation_chain(vectorstore: Any) -> ConversationalRetrievalChain:
+    """
+    Create a conversation chain using the vector store.
+
+    :param vectorstore: Vector store to use in the conversation chain.
+    :type vectorstore: Any
+    :return: Conversational retrieval chain.
+    :rtype: ConversationalRetrievalChain
+    """
+    llm = ChatOpenAI(temperature=0.7, model_name=Rag.GPT_MODEL.value)
+
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key='answer')
+
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 25})
+
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=retriever,
+        memory=memory,
+        return_source_documents=True,
+    )
+
+    return conversation_chain
+
+
+def get_lang_doc(document_text, doc_id, metadata=None, encoding='utf-8'):
+
+    """
+    Build a langchain Document that can be used to create a chroma database
+
+    :type document_text: str
+    :param document_text:
+        The text to add to a document object
+    :type doc_id: str
+    :param doc_id:
+        The document id to include.
+    :type metadata: dict
+    :param metadata:
+        A dictionary of metadata to associate to the document object. This will help filter an item from a
+        vector database.
+    :type encoding: string
+    :param encoding:
+        The type of encoding to use for loading the text.
+
+    """
+    return Document(
+        page_content=document_text,
+        id=doc_id,
+        metadata=metadata,
+        encoding=encoding,
+    )
+
+