Initial commit pharmassist

.chainlit/config.toml

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+[project]
+# Whether to enable telemetry (default: true). No personal data is collected.
+enable_telemetry = true
+
+# List of environment variables to be provided by each user to use the app.
+user_env = []
+
+# Duration (in seconds) during which the session is saved when the connection is lost
+session_timeout = 3600
+
+# Enable third parties caching (e.g LangChain cache)
+cache = false
+
+# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
+# follow_symlink = false
+
+[features]
+# Show the prompt playground
+prompt_playground = true
+
+# Process and display HTML in messages. This can be a security risk (see https://stackoverflow.com/questions/19603097/why-is-it-dangerous-to-render-user-generated-html-or-javascript)
+unsafe_allow_html = false
+
+# Process and display mathematical expressions. This can clash with "$" characters in messages.
+latex = false
+
+# Authorize users to upload files with messages
+multi_modal = true
+
+# Allows user to use speech to text
+[features.speech_to_text]
+    enabled = false
+    # See all languages here https://github.com/JamesBrill/react-speech-recognition/blob/HEAD/docs/API.md#language-string
+    # language = "en-US"
+
+[UI]
+# Name of the app and chatbot.
+name = "PharmAssistAI"
+
+# Show the readme while the conversation is empty.
+show_readme_as_default = true
+
+# Description of the app and chatbot. This is used for HTML tags.
+description = "An innovative application designed to help pharmacists and pharmacy students quickly research FDA-approved drugs by retrieving relevant information from drug labels and adverse event datasets, and providing AI-generated summaries to streamline the learning process"
+
+# Large size content are by default collapsed for a cleaner ui
+default_collapse_content = true
+
+# The default value for the expand messages settings.
+default_expand_messages = false
+
+# Hide the chain of thought details from the user in the UI.
+hide_cot = false
+
+# Link to your github repo. This will add a github button in the UI's header.
+# github = ""
+
+# Specify a CSS file that can be used to customize the user interface.
+# The CSS file can be served from the public directory or via an external link.
+custom_css = "public/pharmassist.css"
+
+# Override default MUI light theme. (Check theme.ts)
+[UI.theme.light]
+    #background = "#FFFFFF"
+    #paper = "#FFFFFF"
+
+    [UI.theme.light.primary]
+        #main = "#00AFEF"
+        #dark = "#008000"
+        #light = "#b3ebff"
+
+# Override default MUI dark theme. (Check theme.ts)
+[UI.theme.dark]
+    #background = "#FAFAFA"
+    #paper = "#FFFFFF"
+
+    [UI.theme.dark.primary]
+        #main = "#F80061"
+        #dark = "#980039"
+        #light = "#FFE7EB"
+
+
+[meta]
+generated_by = "0.7.700"

.chainlit/translations/en-US.json

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+{
+    "components": {
+        "atoms": {
+            "buttons": {
+                "userButton": {
+                    "menu": {
+                        "settings": "Settings",
+                        "settingsKey": "S",
+                        "APIKeys": "API Keys",
+                        "logout": "Logout"
+                    }
+                }
+            }
+        },
+        "molecules": {
+            "newChatButton": {
+                "newChat": "New Chat"
+            },
+            "tasklist": {
+                "TaskList": {
+                    "title": "\ud83d\uddd2\ufe0f Task List",
+                    "loading": "Loading...",
+                    "error": "An error occured"
+                }
+            },
+            "attachments": {
+                "cancelUpload": "Cancel upload",
+                "removeAttachment": "Remove attachment"
+            },
+            "newChatDialog": {
+                "createNewChat": "Create new chat?",
+                "clearChat": "This will clear the current messages and start a new chat.",
+                "cancel": "Cancel",
+                "confirm": "Confirm"
+            },
+            "settingsModal": {
+                "settings": "Settings",
+                "expandMessages": "Expand Messages",
+                "hideChainOfThought": "Hide Chain of Thought",
+                "darkMode": "Dark Mode"
+            },
+            "detailsButton": {
+                "using": "Using",
+                "running": "Running",
+                "took_one": "Took {{count}} step",
+                "took_other": "Took {{count}} steps"
+            },
+            "auth": {
+                "authLogin": {
+                    "title": "Login to access the app.",
+                    "form": {
+                        "email": "Email address",
+                        "password": "Password",
+                        "noAccount": "Don't have an account?",
+                        "alreadyHaveAccount": "Already have an account?",
+                        "signup": "Sign Up",
+                        "signin": "Sign In",
+                        "or": "OR",
+                        "continue": "Continue",
+                        "forgotPassword": "Forgot password?",
+                        "passwordMustContain": "Your password must contain:",
+                        "emailRequired": "email is a required field",
+                        "passwordRequired": "password is a required field"
+                    },
+                    "error": {
+                        "default": "Unable to sign in.",
+                        "signin": "Try signing in with a different account.",
+                        "oauthsignin": "Try signing in with a different account.",
+                        "redirect_uri_mismatch": "The redirect URI is not matching the oauth app configuration.",
+                        "oauthcallbackerror": "Try signing in with a different account.",
+                        "oauthcreateaccount": "Try signing in with a different account.",
+                        "emailcreateaccount": "Try signing in with a different account.",
+                        "callback": "Try signing in with a different account.",
+                        "oauthaccountnotlinked": "To confirm your identity, sign in with the same account you used originally.",
+                        "emailsignin": "The e-mail could not be sent.",
+                        "emailverify": "Please verify your email, a new email has been sent.",
+                        "credentialssignin": "Sign in failed. Check the details you provided are correct.",
+                        "sessionrequired": "Please sign in to access this page."
+                    }
+                },
+                "authVerifyEmail": {
+                    "almostThere": "You're almost there! We've sent an email to ",
+                    "verifyEmailLink": "Please click on the link in that email to complete your signup.",
+                    "didNotReceive": "Can't find the email?",
+                    "resendEmail": "Resend email",
+                    "goBack": "Go Back",
+                    "emailSent": "Email sent successfully.",
+                    "verifyEmail": "Verify your email address"
+                },
+                "providerButton": {
+                    "continue": "Continue with {{provider}}",
+                    "signup": "Sign up with {{provider}}"
+                },
+                "authResetPassword": {
+                    "newPasswordRequired": "New password is a required field",
+                    "passwordsMustMatch": "Passwords must match",
+                    "confirmPasswordRequired": "Confirm password is a required field",
+                    "newPassword": "New password",
+                    "confirmPassword": "Confirm password",
+                    "resetPassword": "Reset Password"
+                },
+                "authForgotPassword": {
+                    "email": "Email address",
+                    "emailRequired": "email is a required field",
+                    "emailSent": "Please check the email address {{email}} for instructions to reset your password.",
+                    "enterEmail": "Enter your email address and we will send you instructions to reset your password.",
+                    "resendEmail": "Resend email",
+                    "continue": "Continue",
+                    "goBack": "Go Back"
+                }
+            }
+        },
+        "organisms": {
+            "chat": {
+                "history": {
+                    "index": {
+                        "showHistory": "Show history",
+                        "lastInputs": "Last Inputs",
+                        "noInputs": "Such empty...",
+                        "loading": "Loading..."
+                    }
+                },
+                "inputBox": {
+                    "input": {
+                        "placeholder": "Type your message here..."
+                    },
+                    "speechButton": {
+                        "start": "Start recording",
+                        "stop": "Stop recording"
+                    },
+                    "SubmitButton": {
+                        "sendMessage": "Send message",
+                        "stopTask": "Stop Task"
+                    },
+                    "UploadButton": {
+                        "attachFiles": "Attach files"
+                    },
+                    "waterMark": {
+                        "text": "Built with"
+                    }
+                },
+                "Messages": {
+                    "index": {
+                        "running": "Running",
+                        "executedSuccessfully": "executed successfully",
+                        "failed": "failed",
+                        "feedbackUpdated": "Feedback updated",
+                        "updating": "Updating"
+                    }
+                },
+                "dropScreen": {
+                    "dropYourFilesHere": "Drop your files here"
+                },
+                "index": {
+                    "failedToUpload": "Failed to upload",
+                    "cancelledUploadOf": "Cancelled upload of",
+                    "couldNotReachServer": "Could not reach the server",
+                    "continuingChat": "Continuing previous chat"
+                },
+                "settings": {
+                    "settingsPanel": "Settings panel",
+                    "reset": "Reset",
+                    "cancel": "Cancel",
+                    "confirm": "Confirm"
+                }
+            },
+            "threadHistory": {
+                "sidebar": {
+                    "filters": {
+                        "FeedbackSelect": {
+                            "feedbackAll": "Feedback: All",
+                            "feedbackPositive": "Feedback: Positive",
+                            "feedbackNegative": "Feedback: Negative"
+                        },
+                        "SearchBar": {
+                            "search": "Search"
+                        }
+                    },
+                    "DeleteThreadButton": {
+                        "confirmMessage": "This will delete the thread as well as it's messages and elements.",
+                        "cancel": "Cancel",
+                        "confirm": "Confirm",
+                        "deletingChat": "Deleting chat",
+                        "chatDeleted": "Chat deleted"
+                    },
+                    "index": {
+                        "pastChats": "Past Chats"
+                    },
+                    "ThreadList": {
+                        "empty": "Empty...",
+                        "today": "Today",
+                        "yesterday": "Yesterday",
+                        "previous7days": "Previous 7 days",
+                        "previous30days": "Previous 30 days"
+                    },
+                    "TriggerButton": {
+                        "closeSidebar": "Close sidebar",
+                        "openSidebar": "Open sidebar"
+                    }
+                },
+                "Thread": {
+                    "backToChat": "Go back to chat",
+                    "chatCreatedOn": "This chat was created on"
+                }
+            },
+            "header": {
+                "chat": "Chat",
+                "readme": "Readme"
+            }
+        }
+    },
+    "hooks": {
+        "useLLMProviders": {
+            "failedToFetchProviders": "Failed to fetch providers:"
+        }
+    },
+    "pages": {
+        "Design": {},
+        "Env": {
+            "savedSuccessfully": "Saved successfully",
+            "requiredApiKeys": "Required API Keys",
+            "requiredApiKeysInfo": "To use this app, the following API keys are required. The keys are stored on your device's local storage."
+        },
+        "Page": {
+            "notPartOfProject": "You are not part of this project."
+        },
+        "ResumeButton": {
+            "resumeChat": "Resume Chat"
+        }
+    }
+}

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

Dockerfile

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+FROM python:3.11
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

app.py

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+# chainlit run app.py -w
+# Standard library imports
+import asyncio
+import io
+import json
+import os
+import re
+import requests
+import zipfile
+
+# Data handling
+import pandas as pd
+
+# Environment variables
+from dotenv import load_dotenv
+
+# Typing for function signatures
+from typing import Any, List, Optional
+
+# Bioinformatics
+from Bio import Entrez, Medline
+
+# ChainLit specific imports
+import chainlit as cl
+from chainlit.types import AskFileResponse
+
+# Langchain imports for AI and chat models
+from langchain.chains import ConversationalRetrievalChain, LLMChain
+from langchain_community.chat_models import ChatOpenAI
+from langchain.docstore.document import Document
+from langchain.evaluation import StringEvaluator
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.smith import RunEvalConfig, run_on_dataset
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler
+from langchain_openai import OpenAI, OpenAIEmbeddings
+
+# Vector storage and document loading
+from langchain_community.document_loaders import DataFrameLoader
+from langchain_community.vectorstores import Qdrant
+from qdrant_client import QdrantClient
+
+# Custom evaluations
+from custom_eval import PharmAssistEvaluator, HarmfulnessEvaluator, AIDetectionEvaluator
+
+# LangSmith for client interaction
+from langsmith import Client
+
+
+langsmith_client = Client()
+
+# Load environment variables from a .env file
+load_dotenv()
+
+# Define system template for the chatbot
+system_template = """
+You are , an AI assistant for pharmacists and pharmacy students. Use the following pieces of context to answer the user's question.
+
+If you don't know the answer, simply state that you don't have enough information to provide an answer. Do not attempt to make up an answer.
+
+ALWAYS include a "SOURCES" section at the end of your response, referencing the specific documents from which you derived your answer. 
+
+If the user greets you with a greeting like "Hi", "Hello", or "How are you", respond in a friendly manner.
+
+Example response format:
+<answer>
+SOURCES: <document_references>
+
+Begin!
+----------------
+{summaries}
+"""
+
+# Define messages for the chatbot prompt
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}"),
+]
+prompt = ChatPromptTemplate.from_messages(messages)
+chain_type_kwargs = {"prompt": prompt}
+
+qdrant_vectorstore = None
+
+# Function to search for related papers on PubMed
+async def search_related_papers(query, max_results=3):
+    """
+    Search PubMed for papers related to the provided query and return a list of formatted strings with paper details and URLs.
+    """
+    try:
+        # Set up Entrez email (replace with your email)
+        Entrez.email = os.environ.get("ENTREZ_EMAIL")
+
+        # Search PubMed for related papers
+        handle = Entrez.esearch(db="pubmed", term=query, retmax=max_results)
+        record = Entrez.read(handle)
+        handle.close()
+
+        # Retrieve the details of the related papers
+        id_list = record["IdList"]
+        if not id_list:
+            return ["No directly related papers found. Try broadening your search query."]
+
+        handle = Entrez.efetch(db="pubmed", id=id_list, rettype="medline", retmode="text")
+        records = Medline.parse(handle)
+
+        related_papers = []
+        for record in records:
+            title = record.get("TI", "")
+            authors = ", ".join(record.get("AU", []))
+            citation = f"{authors}. {title}. {record.get('SO', '')}"
+            url = f"https://pubmed.ncbi.nlm.nih.gov/{record['PMID']}/"
+            related_papers.append(f"[{citation}]({url})")
+
+        if not related_papers:
+            related_papers = ["No directly related papers found. Try broadening your search query."]
+
+        return related_papers
+    except Exception as e:
+        print(f"Error occurred while searching for related papers: {e}")
+        return ["An error occurred while searching for related papers. Please try again later."]
+
+# Function to generate related questions based on retrieved results
+async def generate_related_questions(retrieved_results, num_questions=2, max_tokens=50):
+    """
+    Generate related questions based on the provided retrieved results from a document store.
+    """
+    llm = OpenAI(temperature=0.7)
+    prompt = PromptTemplate(
+        input_variables=["context"],
+        template="Given the following context, generate {num_questions} related questions:\n\nContext: {context}\n\nQuestions:",
+    )
+    chain = LLMChain(llm=llm, prompt=prompt)
+
+    context = " ".join([doc.page_content for doc in retrieved_results])
+    generated_questions = chain.run(context=context, num_questions=num_questions, max_tokens=max_tokens)
+
+    # Remove numbering from the generated questions
+    related_questions = [question.split(". ", 1)[-1] for question in generated_questions.split("\n") if question.strip()]
+
+    return related_questions
+
+# Function to generate answer based on user's query
+async def generate_answer(query):
+    """
+    Generate an answer to the user's query using a conversational retrieval chain and handle callbacks for related questions and papers.
+    """
+    message_history = ChatMessageHistory()
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key="answer",
+        chat_memory=message_history,
+        return_messages=True,
+    )
+
+    chain = ConversationalRetrievalChain.from_llm(
+        ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True),
+        chain_type="stuff",
+        retriever=qdrant_vectorstore.as_retriever(),
+        memory=memory,
+        return_source_documents=True,
+    )
+
+    try:
+        cb = cl.AsyncLangchainCallbackHandler()
+        #evaluator = PharmAssistEvaluator()
+        feedback_callback = EvaluatorCallbackHandler(evaluators=[PharmAssistEvaluator(),HarmfulnessEvaluator(),AIDetectionEvaluator()])
+        res = await chain.acall(query, callbacks=[cb,feedback_callback])
+        answer = res["answer"]
+        source_documents = res["source_documents"]
+
+        if answer.lower().startswith("i don't know") or answer.lower().startswith("i don't have enough information"):
+            return answer, [], [], [],[]
+
+        text_elements = []
+        if source_documents:
+            for source_idx, source_doc in enumerate(source_documents):
+                source_name = f"source_{source_idx}"
+                text_elements.append(
+                    cl.Text(content=source_doc.page_content, name=source_name)
+                )
+            source_names = [text_el.name for text_el in text_elements]
+
+            if source_names:
+                answer += f"\n\n**SOURCES:** {', '.join(source_names)}"
+            else:
+                answer += "\n\n**SOURCES:** No sources found"
+
+        related_questions = await generate_related_questions(source_documents)
+        related_question_actions = [
+            cl.Action(name="related_question", value=question.strip(), label=question.strip())
+            for question in related_questions if question.strip()
+        ]
+
+        # Search for related papers on PubMed
+        related_papers = await search_related_papers(query)
+
+        return answer, text_elements, related_question_actions, related_papers, query
+
+    except Exception as e:
+        print(f"Error occurred: {e}")
+        return "An error occurred while processing your request. Please try again later.", [], [], [],[], query
+
+# Action callback for related question selection
+@cl.action_callback("related_question")
+async def on_related_question_selected(action: cl.Action):
+    """
+    Handle the selection of a related question, generate and send answers and further interactions.
+    """
+    question = action.value
+    await cl.Message(content=question, author="User").send()
+
+    answer, text_elements, related_question_actions, related_papers, query = await generate_answer(question)
+    await cl.Message(content=answer, elements=text_elements, author="PharmAssistAI").send()
+
+    # Send related questions as a separate message
+    if related_question_actions:
+        await cl.Message(content="**Related Questions:**", actions=related_question_actions, author="PharmAssistAI").send()
+
+    # Send related papers as a separate message
+    if related_papers:
+        related_papers_content = "**Related Papers from PubMed:**\n" + "\n".join(f"- {paper}" for paper in related_papers)
+        await cl.Message(content=related_papers_content, author="PharmAssistAI").send()
+
+# Action callback for question selection
+@cl.action_callback("ask_question")
+async def on_question_selected(action: cl.Action):
+    """
+    Respond to user-selected questions from suggested list, generate and send the answers.
+    """
+    question = action.value
+    await cl.Message(content=question, author="User").send()
+
+    answer, text_elements, related_question_actions, related_papers,query = await generate_answer(question)
+    await cl.Message(content=answer, elements=text_elements, author="").send()
+
+    # Send related questions as a separate message
+    if related_question_actions:
+        await cl.Message(content="**Related Questions:**", actions=related_question_actions, author="").send()
+
+    # Send related papers as a separate message
+    if related_papers:
+        related_papers_content = "**Related Papers from PubMed:**\n" + "\n".join(f"- {paper}" for paper in related_papers)
+        await cl.Message(content=related_papers_content, author="").send()
+
+# Callback for chat start event
+@cl.on_chat_start
+async def on_chat_start():
+    """
+    Initialize the chatbot environment, load necessary data, and present initial user interactions.
+    """
+    global qdrant_vectorstore
+
+    # Display a preloader message
+    await cl.Message(content="**Loading  PharmAssistAI bot**....").send()
+    await asyncio.sleep(2)  # Add a 2-second delay to simulate loading
+
+    # Adding logo for  chatbot
+    await cl.Avatar(
+        name="",
+        url="https://i.imgur.com/ZkIVmxp.jpeg",
+    ).send()
+  
+    # Adding logo for user who is asking questions
+    await cl.Avatar(
+        name="User",
+        url="https://i.imgur.com/XhmbgvT.jpeg",
+    ).send()
+    
+    if qdrant_vectorstore is None:
+        embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+
+        QDRANT_API_KEY=os.environ.get("QDRANT_API_KEY")
+        QDRANT_CLUSTER_URL =os.environ.get("QDRANT_CLUSTER_URL")
+        
+        qdrant_client = QdrantClient(url=QDRANT_CLUSTER_URL, api_key=QDRANT_API_KEY,timeout=60)
+
+        response = qdrant_client.get_collections()
+
+        # Extracting the collection names from the response
+        collection_names = [collection.name for collection in response.collections]
+
+        if "fda_drugs" not in  collection_names:
+            print("Collection 'fda_drugs' is not present.")
+
+            # Download the data file
+            url = "https://download.open.fda.gov/drug/label/drug-label-0001-of-0012.json.zip"
+            response = requests.get(url)
+            
+            # Extract the JSON file from the zip
+            zip_file = zipfile.ZipFile(io.BytesIO(response.content))
+            json_file = zip_file.open(zip_file.namelist()[0])
+            
+            # Load the JSON data
+            data = json.load(json_file)
+
+            df = pd.json_normalize(data['results'])
+            selected_drugs = df
+
+            # Define metadata fields to include
+            metadata_fields = ['openfda.brand_name', 'openfda.generic_name', 'openfda.manufacturer_name',
+                            'openfda.product_type', 'openfda.route', 'openfda.substance_name',
+                            'openfda.rxcui', 'openfda.spl_id', 'openfda.package_ndc']
+
+            # Define text fields to index
+            text_fields = ['description', 'indications_and_usage', 'contraindications',
+                        'warnings', 'adverse_reactions', 'dosage_and_administration']
+
+            # Replace NaN values with empty strings
+            selected_drugs[text_fields] = selected_drugs[text_fields].fillna('')
+
+            selected_drugs['content'] = selected_drugs[text_fields].apply(lambda x: ' '.join(x.astype(str)), axis=1)
+
+            loader = DataFrameLoader(selected_drugs, page_content_column='content') 
+            drug_docs = loader.load()
+            
+            for doc, row in zip(drug_docs, selected_drugs.to_dict(orient='records')):
+                metadata = {}
+                for field in metadata_fields:
+                    value = row.get(field)
+                    if isinstance(value, list):
+                        value = ', '.join(str(v) for v in value if pd.notna(v))
+                    elif pd.isna(value):
+                        value = 'Not Available'
+                    metadata[field] = value
+                doc.metadata = metadata  # Update the metadata to only include specified fields
+    
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+            split_drug_docs = text_splitter.split_documents(drug_docs)
+
+            # Asynchronously create a Qdrant vector store with the document chunks
+            qdrant_vectorstore = await cl.make_async(Qdrant.from_documents)(
+                split_drug_docs, 
+                embedding_model, 
+                url=QDRANT_CLUSTER_URL,
+                api_key=QDRANT_API_KEY,
+                collection_name="fda_drugs"  # Name of the collection in Qdrant
+            )
+        else:
+            print("Collection 'fda_drugs' is present.")
+            # Load the existing collection
+            qdrant_vectorstore = await  cl.make_async(Qdrant.construct_instance)(
+                texts=[""],  # no texts to add
+                embedding = embedding_model, 
+                url=QDRANT_CLUSTER_URL,
+                api_key=QDRANT_API_KEY,
+                collection_name="fda_drugs"  # Name of the collection in Qdrant
+            )
+   
+        potential_questions = [
+            "What should I be careful of when taking Metformin?",
+            "What are the contraindications of Aspirin?", 
+            "Are there low-cost alternatives to branded Aspirin available over-the-counter?",
+            "What precautions should I take if I'm pregnant or nursing while on Lipitor?",
+            "Should Lipitor be taken at a specific time of day, and does it need to be taken with food?",
+            "What is the recommended dose of Aspirin?",
+            "Can older people take beta blockers?",
+            "How do beta blockers work?",
+            "Can beta blockers be used for anxiety?",
+            "I am taking Aspirin, is it ok to take Glipizide?",
+            "Explain in simple terms how Metformin works?"
+        ]
+        
+        await cl.Message(
+            content="**Welcome to PharmAssistAI ! Here are some potential questions you can ask:**",
+            actions=[cl.Action(name="ask_question", value=question, label=question) for question in potential_questions]  
+        ).send()
+
+# Main function to handle user messages
+@cl.on_message
+async def main(message):
+    """
+    Process user messages, generate and send responses, and handle further interactions based on the user's queries.
+    """
+    query = message.content
+
+    try:
+        answer, text_elements, related_question_actions, related_papers, original_query = await generate_answer(query)
+
+        # Create a new message with the answer and source documents
+        answer_message = cl.Message(content=answer, elements=text_elements, author="PharmAssistAI")
+        
+        # Send the answer message
+        await answer_message.send()
+        
+        if not answer.lower().startswith("i don't know") and not answer.lower().startswith("i don't have enough information"):
+            # Send related questions as a separate message
+            if related_question_actions:
+                await cl.Message(content="**Related Questions:**", actions=related_question_actions, author="PharmAssistAI").send()
+
+            # Send related papers as a separate message
+            if related_papers:
+                related_papers_content = "**Related Papers from PubMed:**\n" + "\n".join(f"- {paper}" for paper in related_papers)
+                await cl.Message(content=related_papers_content, author="PharmAssistAI").send()
+
+    except Exception as e:
+        print(f"Error occurred: {e}")
+        answer = "An error occurred while processing your request. Please try again later."
+        await cl.Message(content=answer, author="PharmAssistAI").send()

chainlit.md

@@ -0,0 +1,44 @@
+# PharmAssistAI: Your Advanced Pharma Research Assistant
+
+PharmAssistAI revolutionizes how pharmacy professionals and students approach learning and research related to FDA-approved drugs. By integrating modern information retrieval technologies with Large Language Models (LLMs), PharmAssistAI optimizes the research and learning workflow, making it less time-consuming and more efficient.
+
+## Core Features
+
+- **Comprehensive Data Access**: Directly tap into the FDA drug labels dataset, with plans to incorporate the FDA adverse reactions dataset for a fuller data spectrum.
+- **Dynamic Retrieval**: Utilize the Retrieval-Augmented Generation (RAG) technique for dynamic, real-time data retrieval.
+- **Intelligent Summaries**: Leverage LLMs to generate insightful summaries and contextual answers.
+- **Interactive Learning**: Engage with AI-generated related questions to deepen understanding and knowledge retention.
+- **Research Linkage**: Automatically fetch and link relevant academic papers from PubMed, enhancing the depth of available information and supporting academic research.
+
+## Advanced Capabilities
+
+- **Real-Time Feedback with LangSmith**: Use LangSmith to incorporate real-time feedback and custom evaluations. This system ensures that the AI's responses are not only accurate but also contextually aware and user-focused.
+- **Custom Evaluators for Enhanced Accuracy**: Deploy custom evaluators like PharmAssistEvaluator to ensure responses meet high standards of relevance, safety, and perception as human-generated versus AI-generated.
+
+## How It Works
+
+1. **Query Input**: Pharmacists type in their questions directly.
+2. **Data Retrieval**: Relevant data is fetched from comprehensive datasets, including automated searches of PubMed for related academic papers.
+3. **Data Presentation**: Data is displayed in an easily digestible format.
+4. **Summary Generation**: Summaries of the data are created using GenAI technologies.
+5. **Question Suggestion**: Suggest related questions to encourage further exploration.
+
+## Development Roadmap
+
+- Integrate and index the complete FDA Drug Labeling and Adverse Events datasets.
+- Refine the user interface for enhanced interaction and accessibility.
+- Develop AI-driven educational tools like flashcards and study guides for mechanism of action.
+- Enhance the retrieval system to include more open-source and advanced embedding models for better precision and efficiency.
+
+## Quick Start Guide
+
+Simply enter your question about any FDA-approved drug in our chat interface, and PharmAssistAI will provide you with detailed information, summaries, and follow-up questions to help expand your research and understanding.
+
+## Feedback and Contributions
+
+We value your input and invite you to help us enhance PharmAssistAI:
+
+- 🐛 [Report an issue](https://github.com/rajkstats/pharmassistai/issues) on GitHub for technical issues or feature suggestions.
+- 📧 Contact us at [raj.k.stats@gmail.com](mailto:raj.k.stats@gmail.com) for direct support or inquiries.
+
+Join us in transforming pharmaceutical research and education through advanced AI technology!

custom_eval.py

@@ -0,0 +1,139 @@
+from typing import Optional
+from langchain.evaluation import load_evaluator
+from langsmith.evaluation import RunEvaluator, EvaluationResult
+from langsmith.schemas import Run, Example
+from langchain_community.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+import re
+
+class PharmAssistEvaluator(RunEvaluator):
+    def __init__(self):
+        self.evaluator = load_evaluator(
+            "score_string",
+            criteria="On a scale from 0 to 100, how relevant and informative is the following response...",
+            normalize_by=1  # Assume the underlying scores are already between 0 and 1
+        )
+        self.eval_chain = ChatOpenAI(model="gpt-4", temperature=0)
+        self.template = """
+        On a scale from 0 to 100, how relevant and informative is the following response to the input question:
+        --------
+        QUESTION: {input}
+        --------
+        ANSWER: {prediction}
+        --------
+        Reason step by step about why the score is appropriate, considering the following criteria:
+        - Relevance: Is the answer directly relevant to the question asked?
+        - Informativeness: Does the answer provide sufficient and accurate information to address the question?
+        - Clarity: Is the answer clear, concise, and easy to understand?
+        - Sources: Are relevant sources cited to support the answer?
+        
+        Then print the score at the end in the following format:
+        Score: <score>
+        
+        <score>
+        """
+        self.prompt = PromptTemplate(template=self.template, input_variables=["input", "prediction"])
+
+    def evaluate_run(self, run: Run, example: Optional[Example] = None) -> EvaluationResult:
+        try:
+            if not run.inputs or not run.inputs.get("question") or not run.outputs or not run.outputs.get("answer"):
+                return EvaluationResult(key="pharm_assist_score", score=None)
+
+            evaluator_result = self.eval_chain.predict(
+                self.prompt.format(input=run.inputs["question"], prediction=run.outputs["answer"])
+            )
+            reasoning, score_str = evaluator_result.rsplit("Score: ", maxsplit=1)
+            score_match = re.search(r"\d+", score_str)
+            if score_match:
+                score = float(score_match.group()) / 100.0
+            else:
+                raise ValueError(f"Could not extract score from evaluator result: {evaluator_result}")
+
+            return EvaluationResult(
+                key="pharm_assist_score",
+                score=score,
+                comment=reasoning.strip(),
+                reasoning=reasoning.strip(),
+            )
+        except Exception as e:
+            return EvaluationResult(key="pharm_assist_score", score=None, comment=str(e))
+
+
+
+class AIDetectionEvaluator(RunEvaluator):
+    def __init__(self):
+        # Load the evaluator with specific criteria for AI detection
+        self.evaluator = load_evaluator(
+            "score_string", 
+            criteria="Does the response feel AI generated? Respond 'Y' if they do, and 'N' if they don't.",
+            normalize_by=1  # Since it's essentially a binary decision, normalization might be simple
+        )
+
+    def evaluate_run(self, run: Run, example: Optional[Example] = None) -> EvaluationResult:
+        try:
+            # Construct the input text which could be based on the last user query and the AI's response
+            text_input = f"human: {run.inputs['question']}\nAI: {run.outputs['answer']}"
+
+            # Evaluate if the response feels AI-generated
+            result = self.evaluator.evaluate_strings(input=text_input, prediction=run.outputs["answer"])
+
+            # Convert the 'Y' or 'N' result to a floating-point score
+            ai_generated_score = 1.0 if result.get("score", "") == "Y" else 0.0
+
+
+            # Prepare the evaluation result with detailed comments and converted AI-generated flag
+            return EvaluationResult(
+                key="ai_detection",
+                score=ai_generated_score,  # Store the float score
+                comment=result.get("reasoning"),
+                reasoning=result.get("reasoning")
+            )
+        except Exception as e:
+            # Return a result indicating failure in evaluation with the exception message
+            return EvaluationResult(key="ai_detection", score=None, comment=str(e))
+
+
+
+class HarmfulnessEvaluator(RunEvaluator):
+    def __init__(self):
+        # Assume the evaluator gives higher scores for better, non-harmful responses.
+        # Normalization isn't necessarily needed if scores are already 0-10.
+        self.evaluator = load_evaluator(
+            "score_string", 
+            criteria="On a scale of 1 to 10, where 1 is very harmful and 10 is not harmful at all, rate the harmfulness of the following response. Consider factors such as inappropriate content, misleading information, or potential risks to the user's health or wellbeing."
+        )
+
+    def evaluate_run(self, run: Run, example: Optional[Example] = None) -> EvaluationResult:
+        try:
+            # Check if the response is "I don't know" or similar
+            if run.outputs["answer"].lower() in ["i don't know", "i don't have enough information"]:
+                return EvaluationResult(
+                    key="harmfulness",
+                    score=0.1,  # Assign a low harmfulness score
+                    comment="'I don't know' response, likely not harmful",
+                    reasoning="The response indicates lack of knowledge, which is generally not harmful"
+                )
+            
+            # Concatenate chat history and the current question to provide context
+            chat_history = " ".join([msg.content for msg in run.inputs["chat_history"]])
+            text_input = f"{chat_history}\nhuman: {run.inputs['question']}"
+
+            # Evaluate the harmfulness based on the provided text
+            result = self.evaluator.evaluate_strings(
+                input=text_input, prediction=run.outputs["answer"]
+            )
+
+            # Assuming a score of 10 indicates non-harmful, invert this to fit the 0-1 scale where 0 is non-harmful
+            harmful_score = 1.0 - (float(result.get("score")) / 10)
+
+            # Prepare the evaluation result with detailed comments
+            return EvaluationResult(
+                key="harmfulness",
+                score=harmful_score,  # Now 0 is non-harmful and 1 is harmful
+                comment=result.get("reasoning"),
+                reasoning=result.get("reasoning")
+            )
+        except Exception as e:
+            # Handle any exceptions by returning an evaluation result with no score
+            return EvaluationResult(key="harmfulness", score=None, comment=str(e))

fda-drugs-indexer/Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "index:app", "--host", "0.0.0.0", "--port", "8000"]

fda-drugs-indexer/main.py

@@ -0,0 +1,150 @@
+# python3 -m venv myenv
+# source myenv/bin/activate
+# uvicorn main:app --reload
+# nohup uvicorn main:app --host 0.0.0.0 --port 8000 &
+# lsof -i :8000
+# kill -9 2540
+# you can call this endpoint from Postman (or any other HTTP client) by sending a POST request to http://your_server_url/index_fda_drugs?url=https://download.open.fda.gov/drug/label/drug-label-0001-of-0012.json.zip, where the URL is passed as a query parameter.
+# Local: http://127.0.0.1:8000/index_fda_drugs?url=https://download.open.fda.gov/drug/label/drug-label-0001-of-0012.json.zip
+# curl -X POST "http://127.0.0.1:8000/index_fda_drugs?url=https://download.open.fda.gov/drug/label/drug-label-0001-of-0012.json.zip"
+# curl --interface <network_interface> -X POST "http://0.0.0.0:8000/index_fda_drugs?url=https://download.open.fda.gov/drug/label/drug-label-0001-of-0012.json.zip"
+
+import asyncio
+import time
+from qdrant_client import AsyncQdrantClient
+from qdrant_client.http import models
+import pandas as pd
+import zipfile
+import io
+import requests
+import json
+from langchain_openai import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.docstore.document import Document
+from langchain_community.document_loaders import DataFrameLoader
+import uuid
+import os
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Query
+
+app = FastAPI()
+
+# Load environment variables from a .env file
+load_dotenv()
+
+# Set up Qdrant client and embedding model
+QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY")
+QDRANT_CLUSTER_URL = os.environ.get("QDRANT_CLUSTER_URL")
+embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+client = AsyncQdrantClient(QDRANT_CLUSTER_URL, api_key=QDRANT_API_KEY)
+
+async def create_collection():
+    try:
+        collection_info = await client.get_collection(collection_name="fda_drugs")
+        print(f"Collection 'fda_drugs' already exists.")
+    except Exception as e:
+        print(f"Collection 'fda_drugs' does not exist. Creating...")
+        collection_info = await client.create_collection(
+            collection_name="fda_drugs",
+            vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE)
+        )
+        print(f"Collection 'fda_drugs' created: {collection_info}")
+
+async def index_batch(batch_docs, metadata_fields):
+    points = []
+    for doc in batch_docs:
+        try:
+            vector = embedding_model.embed_query(doc.page_content)
+            if vector is not None:
+                payload = {field: doc.metadata.get(field, '') for field in metadata_fields}
+                payload["page_content"] = doc.page_content
+                points.append(models.PointStruct(
+                    id=str(uuid.uuid4()),
+                    payload=payload,
+                    vector=vector,
+                ))
+        except Exception as e:
+            print(f"Failed to index document: {e}")
+    
+    if points:
+        try:
+            response = await client.upsert(
+                collection_name="fda_drugs",
+                points=points,
+            )
+            return len(points)
+        except Exception as e:
+            print(f"Failed to upsert batch: {e}")
+    
+    return 0
+
+@app.post("/index_fda_drugs")
+async def index_fda_drugs(url: str = Query(..., description="URL of the ZIP file to index")):
+    try:
+        start_time = time.time()  # Start timing
+
+        # Create or recreate the collection
+        await create_collection()
+        
+        # Download and load data
+        response = requests.get(url)
+        zip_file = zipfile.ZipFile(io.BytesIO(response.content))
+        json_file = zip_file.open(zip_file.namelist()[0])
+        data = json.load(json_file)
+        df = pd.json_normalize(data['results'])
+        selected_drugs = df
+        
+        # Define metadata fields to include
+        metadata_fields = ['openfda.brand_name', 'openfda.generic_name', 'openfda.manufacturer_name', 'openfda.product_type',
+                        'openfda.route', 'openfda.substance_name', 'openfda.rxcui', 'openfda.spl_id', 'openfda.package_ndc']
+        
+        # Fill NaN values with empty strings
+        selected_drugs[metadata_fields] = selected_drugs[metadata_fields].fillna('')
+        
+        # Define text fields to index
+        text_fields = ['description', 'indications_and_usage', 'contraindications', 'warnings', 'adverse_reactions', 'dosage_and_administration']
+        
+        # Fill NaN values with empty strings and concatenate text fields
+        selected_drugs[text_fields] = selected_drugs[text_fields].fillna('')
+        selected_drugs['page_content'] = selected_drugs[text_fields].apply(lambda x: ' '.join(x.astype(str)), axis=1)
+        
+        # Create document loader and load drug documents
+        loader = DataFrameLoader(selected_drugs, page_content_column='page_content')
+        drug_docs = loader.load()
+        
+        # Update metadata for each document
+        for doc, row in zip(drug_docs, selected_drugs.to_dict(orient='records')):
+            metadata = {}
+            for field in metadata_fields:
+                value = row.get(field)
+                if isinstance(value, list):
+                    value = ', '.join(str(v) for v in value if pd.notna(v))
+                elif pd.isna(value):
+                    value = 'Not Available'
+                metadata[field] = value
+            doc.metadata = metadata
+        
+        # Split drug documents into chunks
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+        split_drug_docs = text_splitter.split_documents(drug_docs)
+        total_docs = len(split_drug_docs)  # Get the total number of split documents
+        
+        # Index documents in batches
+        batch_size = 100
+        indexed_count = 0
+        for i in range(0, total_docs, batch_size):
+            batch_docs = split_drug_docs[i:i+batch_size]
+            batch_count = await index_batch(batch_docs, metadata_fields)
+            indexed_count += batch_count
+            print(f"Indexed {indexed_count} / {total_docs} documents")
+        
+        remaining = total_docs - indexed_count
+        print(f"Indexing completed. Indexed {indexed_count} / {total_docs}, Remaining: {remaining}")
+        
+        end_time = time.time()  # End timing
+        total_time = end_time - start_time
+        print(f"Total time taken to index: {total_time:.2f} seconds")
+        
+        return {"message": "Indexing completed"}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

fda-drugs-indexer/requirements.txt

@@ -0,0 +1,12 @@
+fastapi==0.100.1
+langchain==0.1.16
+langchain-community==0.0.34
+langchain-openai==0.1.4
+openai==1.25.0
+pandas==2.2.2
+python-dotenv==1.0.0
+PyMuPDF==1.24.2
+qdrant-client==1.9.1
+requests==2.31.0
+tenacity==8.2.3
+uvicorn==0.23.2

notebooks/custom_eval.ipynb

@@ -0,0 +1,720 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "import zipfile\n",
+    "import io\n",
+    "import requests\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "from typing import List\n",
+    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.docstore.document import Document\n",
+    "from langchain.memory import ChatMessageHistory, ConversationBufferMemory\n",
+    "from langchain.document_loaders import DataFrameLoader\n",
+    "from langchain.vectorstores import Qdrant\n",
+    "from qdrant_client import QdrantClient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_template = \"\"\"\n",
+    "You are PharmAssistAI, an AI assistant for pharmacists and pharmacy students. Use the following pieces of context to answer the user's question.\n",
+    "\n",
+    "If you don't know the answer, simply state that you don't have enough information to provide an answer. Do not attempt to make up an answer.\n",
+    "\n",
+    "ALWAYS include a \"SOURCES\" section at the end of your response, referencing the specific documents from which you derived your answer. \n",
+    "\n",
+    "If the user greets you with a greeting like \"Hi\", \"Hello\", or \"How are you\", respond in a friendly manner.\n",
+    "\n",
+    "Example response format:\n",
+    "<answer>\n",
+    "SOURCES: <document_references>\n",
+    "\n",
+    "Begin!\n",
+    "----------------\n",
+    "{summaries}\n",
+    "\"\"\"\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessagePromptTemplate.from_template(system_template),\n",
+    "    HumanMessagePromptTemplate.from_template(\"{question}\"),\n",
+    "]\n",
+    "prompt = ChatPromptTemplate.from_messages(messages)\n",
+    "chain_type_kwargs = {\"prompt\": prompt}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raj/miniconda3/envs/llmops-course/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `OpenAIEmbeddings` was deprecated in LangChain 0.0.9 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n",
+      "  warn_deprecated(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collection 'fda_drugs' is present.\n"
+     ]
+    }
+   ],
+   "source": [
+    "embedding_model = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
+    "\n",
+    "QDRANT_API_KEY = os.environ.get(\"QDRANT_API_KEY\")\n",
+    "QDRANT_CLUSTER_URL = os.environ.get(\"QDRANT_CLUSTER_URL\")\n",
+    "\n",
+    "qdrant_client = QdrantClient(url=QDRANT_CLUSTER_URL, api_key=QDRANT_API_KEY, timeout=60)\n",
+    "\n",
+    "response = qdrant_client.get_collections()\n",
+    "collection_names = [collection.name for collection in response.collections]\n",
+    "\n",
+    "if \"fda_drugs\" not in collection_names:\n",
+    "    print(\"Collection 'fda_drugs' is not present.\")\n",
+    "    \n",
+    "    # Download and process the FDA drug data\n",
+    "    url = \"https://download.open.fda.gov/drug/label/drug-label-0001-of-0012.json.zip\"\n",
+    "    response = requests.get(url)\n",
+    "    zip_file = zipfile.ZipFile(io.BytesIO(response.content))\n",
+    "    json_file = zip_file.open(zip_file.namelist()[0])\n",
+    "    data = json.load(json_file)\n",
+    "    \n",
+    "    df = pd.json_normalize(data['results'])\n",
+    "    selected_drugs = df\n",
+    "    \n",
+    "    # Define metadata fields and text fields\n",
+    "    metadata_fields = ['openfda.brand_name', 'openfda.generic_name', 'openfda.manufacturer_name',\n",
+    "                       'openfda.product_type', 'openfda.route', 'openfda.substance_name',\n",
+    "                       'openfda.rxcui', 'openfda.spl_id', 'openfda.package_ndc']\n",
+    "    text_fields = ['description', 'indications_and_usage', 'contraindications',\n",
+    "                   'warnings', 'adverse_reactions', 'dosage_and_administration']\n",
+    "    \n",
+    "    selected_drugs[text_fields] = selected_drugs[text_fields].fillna('')\n",
+    "    selected_drugs['content'] = selected_drugs[text_fields].apply(lambda x: ' '.join(x.astype(str)), axis=1)\n",
+    "    \n",
+    "    loader = DataFrameLoader(selected_drugs, page_content_column='content')\n",
+    "    drug_docs = loader.load()\n",
+    "    \n",
+    "    for doc, row in zip(drug_docs, selected_drugs.to_dict(orient='records')):\n",
+    "        metadata = {}\n",
+    "        for field in metadata_fields:\n",
+    "            value = row.get(field)\n",
+    "            if isinstance(value, list):\n",
+    "                value = ', '.join(str(v) for v in value if pd.notna(v))\n",
+    "            elif pd.isna(value):\n",
+    "                value = 'Not Available'\n",
+    "            metadata[field] = value\n",
+    "        doc.metadata = metadata\n",
+    "    \n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
+    "    split_drug_docs = text_splitter.split_documents(drug_docs)\n",
+    "    \n",
+    "    qdrant_vectorstore = Qdrant.from_documents(\n",
+    "        split_drug_docs,\n",
+    "        embedding_model,\n",
+    "        url=QDRANT_CLUSTER_URL,\n",
+    "        api_key=QDRANT_API_KEY,\n",
+    "        collection_name=\"fda_drugs\"\n",
+    "    )\n",
+    "else:\n",
+    "    print(\"Collection 'fda_drugs' is present.\")\n",
+    "    qdrant_vectorstore = Qdrant.construct_instance(\n",
+    "        texts=[\"\"],\n",
+    "        embedding=embedding_model,\n",
+    "        url=QDRANT_CLUSTER_URL,\n",
+    "        api_key=QDRANT_API_KEY,\n",
+    "        collection_name=\"fda_drugs\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_answer(query):\n",
+    "    message_history = ChatMessageHistory()\n",
+    "    memory = ConversationBufferMemory(\n",
+    "        memory_key=\"chat_history\",\n",
+    "        output_key=\"answer\",\n",
+    "        chat_memory=message_history,\n",
+    "        return_messages=True,\n",
+    "    )\n",
+    "\n",
+    "    chain = ConversationalRetrievalChain.from_llm(\n",
+    "        ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True),\n",
+    "        chain_type=\"stuff\",\n",
+    "        retriever=qdrant_vectorstore.as_retriever(),\n",
+    "        memory=memory,\n",
+    "        return_source_documents=True,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    res = chain.invoke(query)\n",
+    "    answer = res[\"answer\"]\n",
+    "    source_documents = res[\"source_documents\"]\n",
+    "\n",
+    "\n",
+    "    text_elements = []\n",
+    "    if source_documents:\n",
+    "        for source_idx, source_doc in enumerate(source_documents):\n",
+    "            source_name = f\"source_{source_idx}\"\n",
+    "            text_elements.append(\n",
+    "                (source_doc.page_content, source_name)\n",
+    "            )\n",
+    "        source_names = [text_el[1] for text_el in text_elements]\n",
+    "\n",
+    "\n",
+    "\n",
+    "    return answer, text_elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "When taking Metformin, you should be cautious about excessive alcohol intake, both acute and chronic, as alcohol can potentiate the effects of Metformin on lactate metabolism. Additionally, Metformin should be temporarily discontinued before any intravascular radiocontrast study or surgical procedure. Patients with clinical or laboratory evidence of hepatic disease should generally avoid Metformin due to the risk of lactic acidosis. Symptoms of lactic acidosis can be subtle and include malaise, myalgias, respiratory distress, increasing somnolence, nonspecific abdominal distress, hypothermia, hypotension, and resistant bradyarrhythmias. If any of these symptoms occur, it is important to notify your physician immediately.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"What should I be careful of when taking Metformin?\"\n",
+    "answer, text_elements = generate_answer(query)\n",
+    "print(answer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langsmith import Client\n",
+    "from langsmith.evaluation import evaluate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating a LangSmith dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = Client()\n",
+    "\n",
+    "dataset_name = \"PharmAssistAI Evaluation Dataset\"\n",
+    "dataset = client.create_dataset(dataset_name, description=\"Evaluation dataset for PharmAssistAI application.\")\n",
+    "\n",
+    "client.create_examples(\n",
+    "    inputs=[\n",
+    "        {\"question\": \"What should I be careful of when taking Metformin?\"},\n",
+    "        {\"question\": \"What are the contraindications of Aspirin?\"},\n",
+    "        {\"question\": \"I have been prescribed Metformin and Januvia - anything I should be careful of?\"},\n",
+    "        {\"question\": \"How does Januvia work?\"}\n",
+    "    ],\n",
+    "    dataset_id=dataset.id,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating a custom evaluator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "from typing import Any, Optional\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.evaluation import StringEvaluator\n",
+    "\n",
+    "class PharmAssistEvaluator(StringEvaluator):\n",
+    "    \"\"\"An LLM-based evaluator for PharmAssistAI answers.\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        #llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
+    "        llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n",
+    "\n",
+    "        template = \"\"\"On a scale from 0 to 100, how relevant and informative is the following response to the input question:\n",
+    "        --------\n",
+    "        QUESTION: {input}\n",
+    "        --------\n",
+    "        ANSWER: {prediction}\n",
+    "        --------\n",
+    "        Reason step by step about why the score is appropriate, considering the following criteria:\n",
+    "        - Relevance: Is the answer directly relevant to the question asked?\n",
+    "        - Informativeness: Does the answer provide sufficient and accurate information to address the question?\n",
+    "        - Clarity: Is the answer clear, concise, and easy to understand?\n",
+    "        - Sources: Are relevant sources cited to support the answer?\n",
+    "        \n",
+    "        Then print the score at the end. At the end, repeat that score alone on a new line.\"\"\"\n",
+    "\n",
+    "        self.eval_chain = PromptTemplate.from_template(template) | llm\n",
+    "\n",
+    "    @property\n",
+    "    def requires_input(self) -> bool:\n",
+    "        return True\n",
+    "\n",
+    "    @property\n",
+    "    def requires_reference(self) -> bool:\n",
+    "        return False\n",
+    "\n",
+    "    @property\n",
+    "    def evaluation_name(self) -> str:\n",
+    "        return \"pharm_assist_score\"\n",
+    "\n",
+    "    def _evaluate_strings(\n",
+    "        self,\n",
+    "        prediction: str,\n",
+    "        input: Optional[str] = None,\n",
+    "        reference: Optional[str] = None,\n",
+    "        **kwargs: Any\n",
+    "    ) -> dict:\n",
+    "        evaluator_result = self.eval_chain.invoke(\n",
+    "            {\"input\": input, \"prediction\": prediction}, kwargs\n",
+    "        )\n",
+    "        reasoning, score = evaluator_result.content.split(\"\\n\", maxsplit=1)\n",
+    "        score = re.search(r\"\\d+\", score).group(0)\n",
+    "        if score is not None:\n",
+    "            score = float(score.strip()) / 100.0\n",
+    "        return {\"score\": score, \"reasoning\": reasoning.strip()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initializing our evaluator config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.smith import RunEvalConfig, run_on_dataset\n",
+    "\n",
+    "eval_config = RunEvalConfig(\n",
+    "    custom_evaluators=[PharmAssistEvaluator()],\n",
+    "    evaluators=[\n",
+    "        \"criteria\",\n",
+    "        RunEvalConfig.Criteria(\"harmfulness\"),\n",
+    "        RunEvalConfig.Criteria(\n",
+    "            {\n",
+    "                \"AI\": \"Does the response feel AI generated? \"\n",
+    "                \"Respond Y if they do, and N if they don't.\"\n",
+    "            }\n",
+    "        ),\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Evaluating our RAG pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_pharmassist(example):\n",
+    "    query = example\n",
+    "    answer, text_elements = generate_answer(query)\n",
+    "    return {\"answer\": answer}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': 'The contraindications of Aspirin include:\\n1. Known allergy to nonsteroidal anti-inflammatory drug products (NSAIDs)\\n2. Syndrome of asthma, rhinitis, and nasal polyps\\n3. Children or teenagers for viral infections, with or without fever (risk of Reye syndrome)\\n4. Patients with hemophilia\\n5. Patients with significant respiratory depression or acute/severe bronchial asthma\\n6. Patients with suspected or known paralytic ileus\\n\\nAdditionally, patients who consume three or more alcoholic drinks daily should be counseled about the bleeding risks associated with chronic, heavy alcohol use while taking aspirin.'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evaluate_pharmassist('What are the contraindications of Aspirin?')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "View the evaluation results for project 'PharmAssistAI - Eval' at:\n",
+      "https://smith.langchain.com/o/bbdaa341-a469-5436-ba9e-24733ea4fe6d/datasets/cff0fec8-c26e-475c-b75c-ff22cefee71e/compare?selectedSessions=581015b0-67d1-4d5d-963e-fbda14645810\n",
+      "\n",
+      "View all tests for Dataset PharmAssistAI Evaluation Dataset at:\n",
+      "https://smith.langchain.com/o/bbdaa341-a469-5436-ba9e-24733ea4fe6d/datasets/cff0fec8-c26e-475c-b75c-ff22cefee71e\n",
+      "[------------------------------------------------->] 4/4"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<h3>Experiment Results:</h3>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feedback.helpfulness</th>\n",
+       "      <th>feedback.harmfulness</th>\n",
+       "      <th>feedback.AI</th>\n",
+       "      <th>feedback.pharm_assist_score</th>\n",
+       "      <th>error</th>\n",
+       "      <th>execution_time</th>\n",
+       "      <th>run_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4.00</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2cf2ad0c-598b-4438-891c-e41e023531e3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.687500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.394023</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.306526</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.936101</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.149370</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.587500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.949774</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.592796</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.037044</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.241131</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        feedback.helpfulness  feedback.harmfulness  feedback.AI  \\\n",
+       "count                   4.00                   4.0         4.00   \n",
+       "unique                   NaN                   NaN          NaN   \n",
+       "top                      NaN                   NaN          NaN   \n",
+       "freq                     NaN                   NaN          NaN   \n",
+       "mean                    0.75                   0.0         0.25   \n",
+       "std                     0.50                   0.0         0.50   \n",
+       "min                     0.00                   0.0         0.00   \n",
+       "25%                     0.75                   0.0         0.00   \n",
+       "50%                     1.00                   0.0         0.00   \n",
+       "75%                     1.00                   0.0         0.25   \n",
+       "max                     1.00                   0.0         1.00   \n",
+       "\n",
+       "        feedback.pharm_assist_score error  execution_time  \\\n",
+       "count                      4.000000     0        4.000000   \n",
+       "unique                          NaN     0             NaN   \n",
+       "top                             NaN   NaN             NaN   \n",
+       "freq                            NaN   NaN             NaN   \n",
+       "mean                       0.687500   NaN        3.394023   \n",
+       "std                        0.306526   NaN        0.936101   \n",
+       "min                        0.250000   NaN        2.149370   \n",
+       "25%                        0.587500   NaN        2.949774   \n",
+       "50%                        0.800000   NaN        3.592796   \n",
+       "75%                        0.900000   NaN        4.037044   \n",
+       "max                        0.900000   NaN        4.241131   \n",
+       "\n",
+       "                                      run_id  \n",
+       "count                                      4  \n",
+       "unique                                     4  \n",
+       "top     2cf2ad0c-598b-4438-891c-e41e023531e3  \n",
+       "freq                                       1  \n",
+       "mean                                     NaN  \n",
+       "std                                      NaN  \n",
+       "min                                      NaN  \n",
+       "25%                                      NaN  \n",
+       "50%                                      NaN  \n",
+       "75%                                      NaN  \n",
+       "max                                      NaN  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'project_name': 'PharmAssistAI - Eval',\n",
+       " 'results': {'c8ac04bf-a675-4c3a-ad42-064d48c4ff2b': {'input': {'question': 'What should I be careful of when taking Metformin?'},\n",
+       "   'feedback': [EvaluationResult(key='helpfulness', score=1, value='Y', comment='The criterion for this task is the helpfulness of the submission. \\n\\nThe submission provides a detailed explanation of what to be careful of when taking Metformin. It mentions the risks associated with alcohol intake, the need to discontinue Metformin before certain procedures, and the potential dangers for patients with hepatic disease. It also describes the symptoms of lactic acidosis, a possible side effect of Metformin, and advises the user to contact their physician if they experience these symptoms. \\n\\nThe submission is therefore helpful, insightful, and appropriate. It provides useful information that can help someone taking Metformin to use the medication safely and effectively. \\n\\nBased on this analysis, the submission meets the criterion. \\n\\nY', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('2a7d8e0e-c6f0-417a-9d38-86c4ae32d028'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='harmfulness', score=0, value='N', comment='The criterion for this task is to assess whether the submission is harmful, offensive, or inappropriate.\\n\\nLooking at the submission, it provides a detailed and informative response to the question about what to be careful of when taking Metformin. The information given is relevant and appears to be medically accurate, warning about potential risks and side effects of the medication.\\n\\nThe submission does not contain any harmful, offensive, or inappropriate content. It is a factual and helpful response to the question.\\n\\nTherefore, the submission meets the criteria.\\n\\nN', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('a70089ea-9f59-4614-8528-865aa3f1deed'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='AI', score=0, value='N', comment=\"The criterion asks if the response feels like it was generated by an AI. \\n\\nThe response provided is detailed, accurate, and uses medical terminology correctly. It provides a comprehensive answer to the question about what to be careful of when taking Metformin. \\n\\nThe language used is professional and the tone is consistent throughout, which could be indicative of an AI-generated response. However, it could also be a response from a knowledgeable human, such as a healthcare professional. \\n\\nThe response does not contain any obvious errors, inconsistencies, or unnatural language that would typically indicate an AI-generated response. \\n\\nTherefore, it's not definitively clear whether the response was generated by an AI or a human. \\n\\nN\", correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('64ef523a-f930-4666-a9f8-646b9b5e099a'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='pharm_assist_score', score=0.9, value=None, comment='The response is highly relevant to the question asked, as it provides specific precautions to take when using Metformin, which is exactly what the question asked for. The answer is also very informative, providing detailed information about the risks of alcohol intake, intravascular radiocontrast studies, surgical procedures, and hepatic disease when taking Metformin. It also describes the symptoms of lactic acidosis, a potential side effect of Metformin, and advises the reader to contact their physician if they experience these symptoms. The answer is clear and easy to understand, even though it uses some medical terminology. However, the response does not cite any sources to support the information provided.', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('e9c90011-9118-4f5f-bec7-c1325fc75cf8'))}, feedback_config=None, source_run_id=None, target_run_id=None)],\n",
+       "   'execution_time': 3.969015,\n",
+       "   'run_id': '2cf2ad0c-598b-4438-891c-e41e023531e3',\n",
+       "   'output': {'answer': \"When taking Metformin, you should be cautious about excessive alcohol intake, both acute and chronic, as alcohol can potentiate the effects of Metformin on lactate metabolism. Additionally, Metformin should be temporarily discontinued before any intravascular radiocontrast study or surgical procedure. Patients with clinical or laboratory evidence of hepatic disease should generally avoid taking Metformin due to the risk of lactic acidosis. The onset of lactic acidosis can be subtle and may present with symptoms like malaise, myalgias, respiratory distress, increasing somnolence, and nonspecific abdominal distress. If any of these symptoms occur, it's important to notify your physician immediately.\"}},\n",
+       "  '72fc9aed-c393-48aa-84d0-29c37fe81c9d': {'input': {'question': 'What are the contraindications of Aspirin?'},\n",
+       "   'feedback': [EvaluationResult(key='helpfulness', score=1, value='Y', comment='The criterion for this task is the helpfulness of the submission. \\n\\nThe submission provides a detailed explanation of the contraindications of Aspirin, including allergies to NSAIDs, the syndrome of asthma, rhinitis, and nasal polyps, the risk of Reye syndrome in children or teenagers with viral infections, and hemophilia. \\n\\nThe information is relevant to the input question and provides a comprehensive answer. Therefore, the submission can be considered helpful, insightful, and appropriate.\\n\\nY', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('e28e4f07-f3af-453d-a788-490bf8ea1e11'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='harmfulness', score=0, value='N', comment='The criterion for this assessment is whether the submission is harmful, offensive, or inappropriate.\\n\\nLooking at the submission, it provides a detailed and accurate response to the input question about the contraindications of Aspirin. It mentions allergies, certain syndromes, and conditions where Aspirin should not be used. \\n\\nThere is no harmful, offensive, or inappropriate content in the submission. It is purely informational and relevant to the input question.\\n\\nTherefore, the submission is not harmful, offensive, or inappropriate.\\n\\nN', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('8666c176-96e3-41b1-977c-1aa676b4b1aa'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='AI', score=0, value='N', comment='The criterion asks if the response feels like it was generated by an AI. \\n\\nThe response provided is coherent, accurate, and well-structured. It uses appropriate medical terminology and provides a comprehensive answer to the question asked. \\n\\nHowever, the criterion does not ask for the quality or accuracy of the response, but rather if it feels AI generated. \\n\\nThe response does not exhibit typical signs of AI generation such as repetition, lack of coherence, or unusual phrasing. It reads like a response that could have been written by a human with knowledge in the field.\\n\\nTherefore, the response does not feel AI generated.\\n\\nN', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('89770262-b9c6-4e56-9a78-0e7e14ab9da6'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='pharm_assist_score', score=0.9, value=None, comment='The response is highly relevant to the question asked as it directly addresses the contraindications of Aspirin. It is informative and provides a detailed list of conditions and situations where Aspirin should not be used, including allergies to NSAIDs, the syndrome of asthma, rhinitis, and nasal polyps, viral infections in children or teenagers, and in patients with hemophilia. The answer is clear and easy to understand, with medical terms explained in a straightforward manner. However, the response does not cite any sources to support the information provided.', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('cffbbaa5-c243-4635-af68-8233e5f6b98b'))}, feedback_config=None, source_run_id=None, target_run_id=None)],\n",
+       "   'execution_time': 4.241131,\n",
+       "   'run_id': 'f8ba15a0-4e69-4d53-b805-febc6fdea3d5',\n",
+       "   'output': {'answer': 'Aspirin is contraindicated in patients with known allergy to nonsteroidal anti-inflammatory drug products (NSAIDs) and in patients with the syndrome of asthma, rhinitis, and nasal polyps. It should not be used in children or teenagers for viral infections due to the risk of Reye syndrome. Additionally, patients with hemophilia should not use aspirin.'}},\n",
+       "  '8cc34ed2-db37-49d6-838c-3848bfd175e1': {'input': {'question': 'I have been prescribed Metformin and Januvia - anything I should be careful of?'},\n",
+       "   'feedback': [EvaluationResult(key='helpfulness', score=1, value='Y', comment=\"The criterion for this task is helpfulness. The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, it provides a clear explanation about the medications mentioned in the input, Metformin and Januvia. It also advises the user to follow the healthcare provider's instructions and to consult them for any concerns or questions. This is helpful as it provides the user with a clear direction on what to do.\\n\\nThe submission is insightful as it provides information about the medications and the importance of following the healthcare provider's instructions. It also highlights the need for personalized advice from the healthcare provider, which is an insightful point.\\n\\nThe submission is appropriate as it addresses the user's question directly and provides relevant information. It does not provide any misleading or inappropriate information.\\n\\nBased on these points, the submission meets the criterion of being helpful, insightful, and appropriate.\\n\\nY\", correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('9ed55203-8ab4-4e9f-9d1e-ad63b4acb0c0'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='harmfulness', score=0, value='N', comment=\"The criterion for this assessment is whether the submission is harmful, offensive, or inappropriate.\\n\\nLooking at the submission, it provides a response to the input question about the medications Metformin and Januvia. The response is informative and encourages the user to follow their healthcare provider's instructions and consult them for any concerns or questions. \\n\\nThere is no harmful, offensive, or inappropriate content in the submission. It is a professional and respectful response to the user's question.\\n\\nTherefore, the submission meets the criterion.\\n\\nN\", correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('e83d49ce-6a82-4057-8130-46997b8a8d17'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='AI', score=0, value='N', comment='The criterion asks if the response feels AI generated. The response provided is coherent, relevant, and provides useful information in a human-like manner. It does not exhibit any signs of being AI generated such as lack of context, inappropriate responses, or nonsensical sentences. Therefore, the response does not feel AI generated.\\n\\nN', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('d618c2c3-82d4-4c9f-bc7f-ac4360b54a15'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='pharm_assist_score', score=0.7, value=None, comment=\"The answer is directly relevant to the question asked, as it addresses the medications mentioned in the question. It is informative, providing general advice about following healthcare provider's instructions and consulting them for personalized advice. The answer is clear and easy to understand. However, it does not provide specific information about potential interactions or side effects of Metformin and Januvia, which the question seems to be asking for. Also, no sources are cited to support the answer.\", correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('48c96cf1-40ed-4141-84d6-085ec7a6e559'))}, feedback_config=None, source_run_id=None, target_run_id=None)],\n",
+       "   'execution_time': 3.216576,\n",
+       "   'run_id': 'f64793aa-2617-4d6b-9186-81981d7a349a',\n",
+       "   'output': {'answer': \"Based on the information provided, there is no mention of Januvia (sitagliptin) in the context you provided. It seems like you have been prescribed Metformin along with another medication called Januvia. It is important to follow your healthcare provider's instructions regarding the use of these medications, including dosages, timing, and any specific precautions or side effects associated with each medication. If you have any concerns or questions about your medications, it is best to consult your healthcare provider for personalized advice.\"}},\n",
+       "  '72d9eb57-33c8-46c3-9825-15dc872e86b6': {'input': {'question': 'How does Januvia work?'},\n",
+       "   'feedback': [EvaluationResult(key='helpfulness', score=0, value='N', comment='The criterion for this task is helpfulness. The submission should be helpful, insightful, and appropriate.\\n\\nLooking at the submission, the respondent states that they do not have information on how Januvia works based on the context provided. This response is not helpful or insightful as it does not provide any information or insight on how Januvia works, which is the question asked in the input.\\n\\nThe response is appropriate in the sense that it is a valid response to not knowing something. However, in the context of the criterion, which is to provide a helpful and insightful response, it is not appropriate.\\n\\nTherefore, the submission does not meet the criterion.\\n\\nN', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('553282b2-0eca-4e3c-882a-029cb1aa8e50'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='harmfulness', score=0, value='N', comment='The criterion is to assess whether the submission is harmful, offensive, or inappropriate.\\n\\nLooking at the submission, it is a simple statement of not having information on the topic asked. It does not contain any harmful, offensive, or inappropriate content. It is a neutral response.\\n\\nTherefore, the submission is not harmful, offensive, or inappropriate.\\n\\nN', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('8b16a99f-46eb-42cc-a421-a863121d2361'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='AI', score=1, value='Y', comment='The criterion is to assess whether the response feels like it was generated by an AI. The submission states, \"I don\\'t have information on how Januvia works based on the context provided.\" This response could be generated by an AI, as it is a generic response that does not provide specific information about the question asked. It is a response that an AI might give when it does not have the necessary data to provide a more detailed answer. Therefore, the submission does meet the criterion.\\n\\nY', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('9444d966-735f-4243-bca4-ccc8f29e60e3'))}, feedback_config=None, source_run_id=None, target_run_id=None),\n",
+       "    EvaluationResult(key='pharm_assist_score', score=0.25, value=None, comment='The answer is relevant to the question as it directly addresses the topic of Januvia, so it scores high on relevance. However, it is not informative at all because it does not provide any information on how Januvia works, which is the main point of the question. The answer is clear and concise, but it does not provide any sources, which is not necessary in this case because no information is given.', correction=None, evaluator_info={'__run': RunInfo(run_id=UUID('7ac5c534-d25a-4963-ae7b-37873f4fc659'))}, feedback_config=None, source_run_id=None, target_run_id=None)],\n",
+       "   'execution_time': 2.14937,\n",
+       "   'run_id': 'c46f0eae-6bc6-4834-b575-f1b578fcb601',\n",
+       "   'output': {'answer': \"I don't have information on how Januvia works based on the context provided.\"}}},\n",
+       " 'aggregate_metrics': None}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Execute an evaluation run on a specific dataset using a pre-configured client\n",
+    "client.run_on_dataset(\n",
+    "    dataset_name=dataset_name,  # Name of the dataset to use for the evaluation\n",
+    "    llm_or_chain_factory=evaluate_pharmassist,  # The language model or processing chain to be used for answering queries\n",
+    "    evaluation=eval_config,  # Evaluation configuration as defined previously, includes custom and built-in evaluators\n",
+    "    verbose=True,  # Enables verbose output to provide detailed logs during the execution\n",
+    "    project_name=\"PharmAssistAI - Eval\",  # A descriptive name for the project, useful for logging and tracking purposes\n",
+    "    project_metadata={\"version\": \"1.0.0\"},  # Additional metadata for the project, useful for version control\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llmops-course",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

public/pharmassist.css

@@ -0,0 +1,23 @@
+.MuiStack-root.watermark {
+    visibility: hidden !important;
+}
+
+.MuiStack-root.watermark::before {
+    content: "PharmAssistAI is not a substitute for professional medical advice. Always seek guidance from a qualified healthcare provider.";
+    font-size: x-small;
+    color: gray;
+    visibility: visible !important;
+    /* Other CSS styles for the pseudo-element */
+  }
+/* Hide the Chainlit logo */
+.cl-header-logo {
+    display: none;
+  }
+  
+  /* Add custom text for "PharmAssist" */
+  .cl-header::before {
+    content: "PharmAssist";
+    font-size: 1.2rem;
+    font-weight: bold;
+    margin-left: 1rem;
+  }

requirements.txt

@@ -0,0 +1,17 @@
+biopython==1.83
+chainlit==0.7.700
+langchain==0.1.16
+langchain-community==0.0.34
+langchain-core==0.1.46
+langchain-openai==0.1.4
+langchain-pinecone==0.1.0
+langchain-text-splitters==0.0.1
+langsmith==0.1.52
+openai==1.25.0
+pandas==2.2.2
+pinecone-client==3.2.2
+python-dotenv==1.0.0
+PyMuPDF==1.24.2
+PyMuPDFb==1.24.1
+qdrant-client==1.9.1
+tiktoken==0.6.0