NHS Medicines Chatbot

74 |

Ask questions about medicines and I will answer using NHS guidance for patients

I will also provide direct links to sources

75 |

76 |

77 |

78 |

79 | {chatHistory.messages.map((message, index) => ( 80 |

84 |

{message.content}

85 |

86 | ))} 87 |

88 |

89 |

setChatCurrent(e.target.value)}
 94 |             />
 95 |             <button className="btn bg-blue-300 text-gray w-full" onClick={handleSendMessage}>
 96 |               Send
 97 |             </button>
 98 |           </div>
 99 |         </div>
100 |         <div className="flex-col content-end w-1/3 bg-slate-400 border-x-2 border-slate-500">
101 |           <div className="info-urls content-end overflow-y-auto snap-y h-5/6" ref={urlsRef}>
102 |             {urlHistory.map((urls, index) => (
103 |               <ul key={index} className="menu menu-dropdown bg-blue-100 rounded-lg m-1">
104 |                 {urls.map((url, index)=>(
105 |                   <li key={index} className= {`bg-blue-300 rounded-md border border-blue-800 ${index != 0 && 'mt-2'}`}>
106 |                     <a href={url} target='_blank' className="text-blue-900 font-bold hover:text-blue-600">{parseTitle(url)}</a>
107 |                   </li>
108 |                 ))}
109 |               </ul>
110 |             ))}
111 |           </div>
112 |           <div  className='h-1/6 content-center bg-white border-2 rounded-md border-slate-500 hover:bg-blue-100'>
113 |             <a href="https://www.nhs.uk/medicines/" target="_blank"><img src={nhslogo} alt="Content supplied by the NHS website: nhs.uk" className='p-4'></img></a>
114 |           </div>
115 |         </div>
116 |       </div>
117 |     </div>
118 |   );
119 | }
120 | 
121 | 
122 | export default App
123 |

--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # RAG Chatbot for NHS Medicines
  2 | 
  3 | This chatbot allows to you to talk to the [NHS Medicines A-Z](https://www.nhs.uk/medicines/), a site containing information for patients about common medicines. It will answer only based on this information and provide direct links to the pages or even the paragraphs used.
  4 | 
  5 | This is a personal exploration of Agentic Retrieval Augmented Generation (RAG) models and their potential for interacting reliably with verified information sources. It utilizes the OpenAI API, LangChain, Chroma and LangGraph to query a vector database built from the freely accessible [NHS Medicines API](https://developer.api.nhs.uk/nhs-api/documentation/5b8e85b396097ba52552d63b). While conventional RAG is effective for simple queries, this solution uses multiple agents working in concert to drastically improve the retrieval search and filter out irrelevant context.
  6 | 
  7 | ![chatbot-narrow](https://github.com/shyamdhokia1/Medicines-RAG-ChatBot/assets/92919658/24b35203-6461-4328-944c-a253f9e7a7b8)
  8 | 
  9 | **Important Disclaimer:**
 10 | 
 11 | * This is a personal project for educational and testing purposes only. 
 12 | * It is **not** developed in collaboration with any NHS organization.
 13 | * Regulation as a medical device has not been explored yet and therefore the chatbot should not be hosted and made accessible to the public.
 14 | 
 15 | ## Technologies
 16 | 
 17 | * **Backend**
 18 |     * Python
 19 |     * Flask
 20 |     * OpenAI API
 21 |     * LangChain
 22 |     * LangGraph
 23 |     * ChromaDB
 24 | 
 25 | * **Frontend**
 26 |     * React.js
 27 |     * TailwindCSS
 28 |     * DaisyUI
 29 |     * Vite
 30 | 
 31 | ## Optimising the RAG
 32 | ![Task management](https://github.com/shyamdhokia1/Medicines-RAG-ChatBot/assets/92919658/2f5d60bb-6717-4e81-b9d8-f15dee001dcc)
 33 | 
 34 | ### Pre-Retrieval
 35 | The text from the NHS Medicines API is converted into **Markdown** and stored alongside the JSON metadata in **LangChain Documents**. LLMs are fine-tuned on Markdown text so it is the most effective format for 'consumption' by LLMs. The metadata will allow us to augment our search using keywords later, as well as display URLs to users.
 36 | 
 37 | `chunk_size=512, chunk_overlap=64` ensures that the majority of paragraphs are preserved as entire units of information to preserve context and improve semantic retrieval. Larger paragraphs retain leading information due to the overlap.
 38 | 
 39 | ### Retrieval
 40 | #### Guardrails (verify)
 41 | The `verify` edge act as a straightforward guardrail, rejecting questions that are inappropriate given the chatbot's purpose and preventing unnecessary retrieval. The `reject` node will then give a polite response and redirect to appropriate resources.
 42 | 
 43 | #### Query Translation (rewrite)
 44 | The `rewrite` node translates the query into a clearer version, correcting spelling errors and adding
 45 | generic names for medications, increasing efficiency of retrieval.
 46 | 
 47 | #### Query Expansion (retrieve)
 48 | Query expansion creates N versions of the query from alternate perspectives, then performs a seperate search for each to maximise the embedding area. This results in more relevant chunks being retrieved.
 49 | 
 50 | #### Self Query/Hybrid Retrieval (retrieve)
 51 | Self query extracts metadata search queries from the original query. A search is then done using a hybrid of semantic similarity and keyword similarity (using the metadata query). With organised data like this, we can effectively find relevant chunks that would otherwise be missed.
 52 | 
 53 | ### Post Retrieval
 54 | The `rerank` node uses `FlashRank` to rerank our retrieved chunks, returning only the top N most relevant ones. This reduces noises from irrelevant context, reduces prompt size, and helps prevent the "lost in the middle" problem.
 55 | 
 56 | ## API Scraping
 57 | 
 58 | NHS-medicines-scraper.py calls the NHS Medicines API to create Markdown files and Documents on all available medicines. A new version of the API has been released so the current scraper is outdated.
 59 | 
 60 | Here's an overview of the API structure:
 61 | 
 62 | **Example Responses:**
 63 | 
 64 | * **Base URL:** [https://api.nhs.uk/medicines](https://api.nhs.uk/medicines)
 65 | 
 66 | ```
 67 | {
 68 |   "significantLink": [
 69 |     {
 70 |       "name": "drugname",
 71 |       "url": "API url",
 72 |       "mainEntityOfPage": {
 73 |         "dateModified": "DATE"
 74 |       }
 75 |     }
 76 |   ]
 77 | }
 78 | ```
 79 | 
 80 | * **Individual Medication URL:** https://api.nhs.uk/medicines/medicationname
 81 | 
 82 | ```
 83 | {
 84 |   "name": "drugname",
 85 |   "about": {
 86 |     "alernateName": "alternatename"
 87 |   },
 88 |   "description": "page description",
 89 |   "url": "page url",
 90 |   "hasPart": [
 91 |     {
 92 |       "@type": "HealthTopicContent",
 93 |       "url": "segment url",
 94 |       "description": "segment description",
 95 |       "hasPart": [
 96 |         {
 97 |           "text": "text in html",
 98 |           "headline": "headline for paragraph (often empty)"
 99 |         },
100 |         {
101 |           "text": "text in html",
102 |           "headline": "headline for paragraph (often empty)"
103 |         }
104 |       ],
105 |       "headline": "headline for whole segment"
106 |     }
107 |   ],
108 |   "mainEntityOfPage": [
109 |     {
110 |       "name": "keylinks",
111 |       "mainEntityOfPage": [
112 |         {
113 |           "headline": "headline for segment",
114 |           "url": "url"
115 |         }
116 |       ]
117 |     },
118 |     {
119 |       "name": "RelatedLinks",
120 |       "headline": "Related conditions/resources",
121 |       "mainEntityOfPage": [
122 |         {
123 |           "headline": "name of condition",
124 |           "url": "NHS overview url"
125 |         }
126 |       ]
127 |     }
128 |   ]
129 | }
130 | ```
131 | ## Running a Test Environment:
132 | 
133 | Clone this repo.
134 | 
135 | To run this project, you'll need the following Python libraries:
136 | 
137 | ```pip install -U flask langchain-nomic langchain_community tiktoken langchainhub chromadb langchain langgraph markdownify flashrank lark```
138 | 
139 | In `.env` set the `LANGCHAIN_API_KEY` for LangTrace tracing and `NHS_API_KEY` for the scraper.
140 | 
141 | Using CUDA with local GPU is recommended for running the local embedding model. Fully local LLM is planned for future development.
142 | 
143 | ### Backend
144 | ```
145 | cd backend
146 | 
147 | # Scrape the pages for all medications
148 | NHS-medicines-scraper.py
149 | 
150 | # Run Flask app
151 | app.py
152 | ```
153 | 
154 | ### Frontend
155 | ```
156 | cd frontend
157 | npm run dev
158 | ```
159 | ## Getting Involved
160 | If this project interests you and you'd like to get involved please email me at sergiou1923@gmail.com
161 |

--------------------------------------------------------------------------------
/backend/NHS-medicines-scraper.py:
--------------------------------------------------------------------------------
  1 | import sys, os, time, requests, markdownify, re, json
  2 | from langchain.docstore.document import Document
  3 | from dotenv import load_dotenv
  4 | load_dotenv()
  5 | 
  6 | class NHSMedicationAPI:
  7 |     def __init__(self):
  8 |         load_dotenv()
  9 |         self.api_key = self._load_api_key()
 10 |         self.base_url = "https://api.nhs.uk/medicines"
 11 |         self.base_params = {
 12 |             "subscription-key": self.api_key,
 13 |         }
 14 |         self.date_last_run = os.getenv("DATE_LAST_RUN")
 15 | 
 16 |     def _load_api_key(self):
 17 |         if "NHS_API_KEY" in os.environ:
 18 |             print("NHS API Key Loaded")
 19 |             return os.environ["NHS_API_KEY"]
 20 |         else:
 21 |             print("NHS API Key Missing from .env")
 22 |             sys.exit()
 23 | 
 24 |     def get_medication_list(self):
 25 |         medication_table = {"data": []}
 26 |         prev_page_medication = ""
 27 |         
 28 |         for page in range(1, 100):
 29 |             try:
 30 |                 self.base_params["page"] = page
 31 |                 response = requests.get(self.base_url, params=self.base_params)
 32 |                 response.raise_for_status()
 33 |                 results = response.json()
 34 |                 
 35 |                 if prev_page_medication == results["significantLink"][-1]["name"]:
 36 |                     break
 37 |                 
 38 |                 for object in results["significantLink"]:
 39 |                     name, url, dateModified = object["name"], object["url"], object["mainEntityOfPage"]["dateModified"]
 40 |                     medication_table["data"].append({"name": name, "url": url, "dateModified": dateModified})
 41 |                     print(f"Drug: {name}, URL: {url}, Date Modified: {dateModified}")
 42 |                     prev_page_medication = name
 43 |             except requests.exceptions.RequestException as e:
 44 |                 print(f"Error occurred while fetching medication list: {e}")
 45 |             except ValueError as e:
 46 |                 print(f"Error occurred while parsing JSON response for medication list: {e}")
 47 |             except Exception as e:
 48 |                 print(f"An unexpected error occurred for API request to medication list: {e}")
 49 |             
 50 |             time.sleep(7)
 51 |         
 52 |         self._save_medication_table(medication_table)
 53 | 
 54 |     def _save_medication_table(self, medication_table):
 55 |         with open('testdata/NHSmed/medication_table.json', 'w', encoding='utf-8') as json_file:
 56 |             json.dump(medication_table, json_file, ensure_ascii=False, indent=4)
 57 | 
 58 |     def load_med_list(self):
 59 |         with open('testdata/NHSmed/medication_table.json', 'r', encoding='utf-8') as json_file:
 60 |             return json.load(json_file)
 61 | 
 62 |     def get_all_medications(self, medication_table):
 63 |         for med in medication_table["data"]:
 64 |             self._process_medication(med)
 65 | 
 66 |     def _process_medication(self, med):
 67 |         url = med["url"]
 68 |         try:
 69 |             response = requests.get(url, params=self.base_params)
 70 |             response.raise_for_status()
 71 |             results = response.json()
 72 |             
 73 |             name, description, url = results['name'], results['description'], results['url']
 74 |             alternateName = " ".join(results['about']['alternateName'])
 75 |             
 76 |             
 77 |             whole_page = self._create_page_header(name, description, alternateName)
 78 |             documentjson = {}
 79 |             
 80 |             for section in results['hasPart']:
 81 |                 whole_page, documentjson = self._process_section(section, name, description, alternateName, whole_page, documentjson)
 82 |             
 83 |             self._save_markdown(name, whole_page)
 84 |             self._save_json(name, documentjson)
 85 | 
 86 |         except requests.exceptions.RequestException as e:
 87 |             print(f"Error occurred while fetching data for {med}: {e}")
 88 |         except ValueError as e:
 89 |             print(f"Error occurred while parsing JSON response for {med}: {e}")
 90 |         except Exception as e:
 91 |             print(f"An unexpected error occurred for {med}: {e}")
 92 |         
 93 |         time.sleep(7)
 94 | 
 95 |     def _create_page_header(self, name, description, alternateName):
 96 |         if alternateName == "":
 97 |             return f"# {name}\n\n## {description}\n\n"
 98 |         else:
 99 |             return f"# {name} ({alternateName})\n\n## {description}\n\n"
100 | 
101 |     def _process_section(self, section, name, description, alternateName, whole_page, documentjson):
102 |         subdescription = section.get("description", "")
103 |         headline = section.get("headline", "")
104 |         apiurl = section.get("url", "")
105 |         suburl = re.sub(r'/api.', r'/', apiurl)
106 |         
107 |         paragraph_content = self._create_paragraph_header(headline, subdescription)
108 |         
109 |         for paragraph in section["hasPart"]:
110 |             paragraph_content += self._process_paragraph(paragraph)
111 |         
112 |         whole_page += paragraph_content
113 | 
114 |         titlefromurl = self._get_title_from_url(suburl, headline)
115 |         
116 |         doc = self._create_document(paragraph_content, name, suburl, alternateName, description, titlefromurl)
117 |         documentjson[titlefromurl] = doc.json()
118 |         print(f"Document created for {name} - {titlefromurl}")
119 |         
120 |         return whole_page, documentjson
121 | 
122 |     def _create_paragraph_header(self, headline, subdescription):
123 |         if headline == "":
124 |             return f"**{subdescription}**\n\n"
125 |         else:
126 |             return f"## {headline}\n\n**{subdescription}**\n\n"
127 | 
128 |     def _process_paragraph(self, paragraph):
129 |         if paragraph.get("@type") == "Question":
130 |             text = paragraph["acceptedAnswer"].get("text", "")
131 |             subhead = paragraph.get("name", "")
132 |         else:
133 |             subhead = paragraph.get("headline","")
134 |             text = paragraph.get("text", "")
135 |         
136 |         mdtext = markdownify.markdownify(text)
137 |         cleaned_md = re.sub(r'\[(.*?)\](.*?\))', r'\1', mdtext)
138 |         
139 |         if subhead == "":
140 |             return f"{cleaned_md}\n\n"
141 |         else:
142 |             return f"### {subhead}\n\n{cleaned_md}\n\n"
143 | 
144 |     def _get_title_from_url(self, suburl, headline):
145 |         nameroot = suburl.split("/")[4]
146 |         pattern = rf'{nameroot}/([^"]+?)/#'
147 |         match = re.findall(pattern, suburl)
148 |         if match == []:
149 |             return headline
150 |         else:
151 |             return match[0].replace("-"," ").replace("/"," - ")
152 | 
153 |     def _create_document(self, paragraph_content, name, suburl, alternateName, description, titlefromurl):
154 |         return Document(
155 |             page_content=paragraph_content,
156 |             metadata={
157 |                 "med_name": name,
158 |                 "url": suburl,
159 |                 "alternate_names": alternateName,
160 |                 "page_description": description,
161 |                 "document_description": titlefromurl,
162 |             }
163 |         )
164 | 
165 |     def _save_markdown(self, name, whole_page):
166 |         mdname = f"testdata/NHSmed/{name}.md"
167 |         with open(mdname, 'w', encoding='utf-8') as md_file:
168 |             md_file.write(whole_page)
169 |             print(f"Markdown created for {name}")
170 | 
171 |     def _save_json(self, name, documentjson):
172 |         file_name = f"testdata/NHSmed/{name}.json"
173 |         with open(file_name, 'w') as json_file:
174 |             json.dump(documentjson, json_file)
175 |             print(f"Document JSON created for {name}")
176 | 
177 | def main():
178 |     api = NHSMedicationAPI()
179 |     api.get_medication_list()
180 |     medication_table = api.load_med_list()
181 |     api.get_all_medications(medication_table)
182 | 
183 | if __name__ == "__main__":
184 |     main()

--------------------------------------------------------------------------------
/backend/app.py:
--------------------------------------------------------------------------------
  1 | ############
  2 | ## FLASK ###
  3 | ############
  4 | 
  5 | from flask import Flask, request
  6 | app = Flask(__name__)
  7 | 
  8 | # POST method for chat messages
  9 | import pprint
 10 | 
 11 | @app.route('/messages', methods=['POST'])
 12 | def simple_message():
 13 |   """
 14 |     Receives JSON
 15 |     {
 16 |         "input": {
 17 |             "messages": [
 18 |                 {"role": "user","content": "question"}
 19 |             ]
 20 |         },
 21 |         "urls" : []
 22 |     }
 23 |   """
 24 |   data = request.get_json()  # Access the JSON data from the request body
 25 |   input = data["input"]
 26 |   urls = None
 27 |   for output in graph.stream(input):
 28 |     for key, value in output.items():
 29 |         # pprint.pprint(f"Output from node '{key}':")
 30 |         # pprint.pprint("---")
 31 |         # pprint.pprint(value, indent=2, width=80, depth=None)
 32 |         if key == "generate":
 33 |             urls = [doc.metadata["url"] for doc in value["documents"]]
 34 |         message = value["messages"][0].content
 35 |     pprint.pprint("\n---\n")
 36 |   data["input"]["messages"].append({"role": "assistant", "content": message})
 37 |   data["urls"].append(urls)
 38 |   return data
 39 | 
 40 | #############################
 41 | ### Setup LLM and VectorDB ##
 42 | #############################
 43 | 
 44 | import os
 45 | import sys
 46 | import json
 47 | from dotenv import load_dotenv
 48 | from langchain_openai import ChatOpenAI
 49 | from langchain.text_splitter import RecursiveCharacterTextSplitter
 50 | from langchain_community.vectorstores import Chroma
 51 | from langchain.docstore.document import Document
 52 | from langchain_community.embeddings import HuggingFaceEmbeddings
 53 |   
 54 | class InitialiseRAG:
 55 |     def __init__(self):
 56 |         self.setup_environment()
 57 |         self.local_llm = self.setup_llm()
 58 |         self.vectorstore = None
 59 |         self.retriever = None
 60 | 
 61 |     def setup_environment(self):
 62 |         load_dotenv()
 63 |         os.environ['LANGCHAIN_TRACING_V2'] = 'true'
 64 |         os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'
 65 |         if "LANGCHAIN_API_KEY" not in os.environ:
 66 |             print("LANGCHAIN API Key missing from .env")
 67 |             sys.exit(1)
 68 | 
 69 |     def setup_llm(self):
 70 |         return ChatOpenAI(temperature=0, model="gpt-4-turbo", streaming=True)
 71 | 
 72 |     def load_documents(self, folder):
 73 |         doc_list = []
 74 |         for filename in os.listdir(folder):
 75 |             if filename.endswith(".json") and filename != "medication_table.json":
 76 |                 file_path = os.path.join(folder, filename)
 77 |                 try:
 78 |                     with open(file_path, 'r') as json_file:
 79 |                         dict = json.load(json_file)
 80 |                         for obj in dict.values():
 81 |                             obj = json.loads(obj)
 82 |                             doc = Document(**obj)
 83 |                             doc_list.append(doc)
 84 |                     print(f"Loaded Medication: {filename}")
 85 |                 except FileNotFoundError:
 86 |                     print(f"Error: Medication file not found: {filename}")
 87 |                 except json.JSONDecodeError:
 88 |                     print(f"Error: Invalid JSON format in medication file: {filename}")
 89 |         return doc_list
 90 | 
 91 |     def split_documents(self, doc_list):
 92 |         text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
 93 |             chunk_size=512, 
 94 |             chunk_overlap=64,
 95 |         )
 96 |         return text_splitter.split_documents(doc_list)
 97 | 
 98 |     def setup_embeddings(self):
 99 |         model_name = "Alibaba-NLP/gte-large-en-v1.5"
100 |         model_kwargs = {'device': 'cuda', "trust_remote_code": True}
101 |         return HuggingFaceEmbeddings(
102 |             model_name=model_name,
103 |             model_kwargs=model_kwargs,
104 |         )
105 | 
106 |     def setup_vectorstore(self, doc_splits):
107 |         hf = self.setup_embeddings()
108 |         if os.path.exists("./chroma_db"):
109 |             self.vectorstore = Chroma(persist_directory="./chroma_db", collection_name="rag-chroma", embedding_function=hf)
110 |             print("Vector database loaded")
111 |         else:
112 |             self.vectorstore = Chroma.from_documents(
113 |                 documents=doc_splits,
114 |                 collection_name="rag-chroma",
115 |                 embedding=hf,
116 |                 persist_directory="./chroma_db",
117 |             )
118 |             self.vectorstore.persist()
119 |             print("Vector database created")
120 | 
121 |     def setup_retriever(self):
122 |         self.retriever = self.vectorstore.as_retriever()
123 | 
124 |     def run(self):
125 |         folder = "testdata/NHSmed"
126 |         doc_list = self.load_documents(folder)
127 |         doc_splits = self.split_documents(doc_list)
128 |         self.setup_vectorstore(doc_splits)
129 |         self.setup_retriever()
130 | 
131 | 
132 | ##################
133 | ### AGENT STATE ##
134 | ##################
135 | # We will define a graph.
136 | # A state object passes around to each node.
137 | # Our state will be a list of messages.
138 | # Each node in our graph will append to it.
139 | 
140 | from typing import Annotated, Sequence, TypedDict, List
141 | 
142 | from langchain_core.messages import BaseMessage
143 | from langgraph.graph.message import add_messages
144 | 
145 | 
146 | class AgentState(TypedDict):
147 |     # The add_messages function defines how an update should be processed
148 |     # Default is to replace. add_messages says "append"
149 |     messages: Annotated[Sequence[BaseMessage], add_messages]
150 |     rewrite_question: BaseMessage # store, not append, the reworded question for access without indexing
151 |     documents: List[Document] # same for retrieved documents
152 |     # failed: int # default failed, updated by grader
153 |         
154 | from typing import Annotated, Literal, Sequence, TypedDict
155 | 
156 | from langchain_core.messages import BaseMessage, HumanMessage
157 | from langchain_core.pydantic_v1 import BaseModel, Field
158 | from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
159 | 
160 | #############
161 | ### EDGES####
162 | #############
163 |     
164 | def verify_question(state) -> Literal["rewrite", "reject"]:
165 |     """
166 |     Determines whether the initial question is relevant to capabilities of chatbot.
167 | 
168 |     Args:
169 |         state (messages): The current state
170 | 
171 |     Returns:
172 |         str: A decision for whether the question is relevant or not
173 |     """
174 | 
175 |     print("---CHECK RELEVANCE---")
176 | 
177 |     # Data model
178 |     class grade(BaseModel):
179 |         """Binary score for relevance check."""
180 | 
181 |         binary_score: str = Field(description="Relevance score 'yes' or 'no'")
182 | 
183 |     # LLM
184 |     model = RAGSystem.local_llm
185 | 
186 |     # LLM with tool and validation
187 |     llm_with_tool = model.with_structured_output(grade)
188 | 
189 |     # Prompt
190 |     prompt = PromptTemplate(
191 |         template="""You are a grader assessing relevance of the user question to the capabilities of this medical chatbot\n 
192 |         Here is the user question: {question} \n
193 |         If the question contains queries about medication or medication related information such as treatment options or side effects, then grade it as relevant\n
194 |         Give a binary score 'yes' or 'no' score to indicate whether the question is relevant to medications""",
195 |         input_variables=["question"],
196 |     )
197 | 
198 |     # Chain
199 |     chain = prompt | llm_with_tool
200 | 
201 |     messages = state["messages"]
202 |     question = messages[-1]["content"] # most recent user query
203 | 
204 |     scored_result = chain.invoke({"question": question})
205 | 
206 |     score = scored_result.binary_score
207 | 
208 |     if score == "yes":
209 |         print("---DECISION: QUESTION RELEVANT---")
210 |         return "rewrite"
211 | 
212 |     else:
213 |         print("---DECISION: QUESTION NOT RELEVANT---")
214 |         return "reject"
215 | 
216 | ############
217 | ### NODES ##
218 | ############
219 | 
220 | # print docs helper
221 | def pretty_print_docs(docs):
222 |     print(
223 |         f"\n{'-' * 100}\n".join(
224 |             [
225 |                 f"Document {i+1}:\n\n{d.page_content}\nMetadata: {d.metadata}"
226 |                 for i, d in enumerate(docs)
227 |             ]
228 |         )
229 |     )
230 | 
231 | from langchain.retrievers.multi_query import MultiQueryRetriever
232 | from langchain.chains.query_constructor.base import AttributeInfo
233 | from langchain.retrievers.self_query.base import SelfQueryRetriever
234 | 
235 | def retrieve(state):
236 |     """
237 |     Retrieve documents
238 | 
239 |     Args:
240 |         state (dict): The current graph state
241 | 
242 |     Returns:
243 |         state (dict): New key added to state, documents, that contains retrieved documents
244 |     """
245 |     print("---RETRIEVE---")
246 |     question = state["rewrite_question"]
247 |     messages = state["messages"]
248 | 
249 |     # Query Expansion Retrieval
250 |     multi_retriever = MultiQueryRetriever.from_llm(
251 |     retriever=RAGSystem.retriever, llm=RAGSystem.local_llm
252 |     )
253 |     
254 |     # Self Query + Filtered Search
255 |     metadata_field_info = [
256 |         AttributeInfo(
257 |             name="med_name",
258 |             description="Name of the medication. If a common brand name exists it may be in brackets after",
259 |             type="string"
260 |         ),
261 |         AttributeInfo(
262 |             name="document_description", 
263 |             description="The specific topics about the medication",
264 |             type="string"
265 |         ),
266 |         AttributeInfo(
267 |             name="page_description", 
268 |             description="What conditions the medication is used to treat. May contain alternated brand names",
269 |             type="string"
270 |         ),
271 |     ]
272 |     document_content_description = "Information about a specific medication"
273 |     self_retriever = SelfQueryRetriever.from_llm(
274 |         RAGSystem.local_llm,
275 |         RAGSystem.vectorstore,
276 |         document_content_description,
277 |         metadata_field_info,
278 |     )
279 |     
280 |     # Run Retrievers
281 |     multi_documents = multi_retriever.invoke(question.content)
282 |     print(f"\n Query Expansion: {len(multi_documents)} Documents Returned \n")
283 |     #pretty_print_docs(multi_documents)
284 |     self_documents = self_retriever.invoke(question.content)
285 |     print(f"\n Self Query + Filter: {len(self_documents)} Documents Returned \n")
286 |     #pretty_print_docs(self_documents)
287 |     
288 |     # Combine docs and deduplicate
289 |     documents = []
290 |     metadata_set = set()
291 |     for doc in multi_documents + self_documents:
292 |         if doc.page_content not in metadata_set:
293 |             metadata_set.add(doc.page_content)
294 |             documents.append(doc)
295 |     print(f"\n UNIQUE DOCUMENTS: {len(metadata_set)} \n")
296 |     return {"documents": documents, "rewrite_question": question, "messages": messages}
297 | 
298 | def reject(state):
299 |     """
300 |     Reject the question, offer a list of capabilities and signpost to key resources
301 | 
302 |     Args:
303 |         state (messages): The current state
304 | 
305 |     Returns:
306 |         dict: The updated state with the reject response appended to messages
307 |     """
308 |     print("---REJECT QUERY---")
309 |     question = state["rewrite_question"]
310 |     if question == None: # if rejected immediately then get original question
311 |         messages = state["messages"]
312 |         question = messages[-1]["content"] 
313 | 
314 |     msg = [
315 |         HumanMessage(
316 |             content=f""" \n 
317 |     Reject the following question politely. Inform the user that you are a chatbot only capable of answering questions using official NHS guidance on medications, their side effects, interactions, dosage, administration, lifestyle considerations, efficacy and monitoring. \n
318 |     Here is the question:
319 |     \n ------- \n
320 |     {question} 
321 |     \n ------- \n
322 |     Use the question to give specific reasons why you are unable to answer. \n
323 |     Always provide this link to the NHS website so they can search for relevant information theirself https://www.nhs.uk/medicines/. \n
324 |     If the question is asking for specific medical advice, advise them to speak to a healthcare professional, call 111 for immediate advice or call 999 in an emergency.
325 |     """,
326 |         )
327 |     ]
328 | 
329 |     model = RAGSystem.local_llm
330 |     response = model.invoke(msg)
331 |     return {"messages": [response]}
332 | 
333 | 
334 | def rewrite(state):
335 |     """
336 |     Transform the query to produce a better question.
337 | 
338 |     Args:
339 |         state (messages): The current state
340 | 
341 |     Returns:
342 |         dict: The updated state with re-phrased question
343 |     """
344 | 
345 |     print("---TRANSFORM QUERY---")
346 |     messages = state["messages"]
347 |     question = messages[-1]["content"] # latest user query
348 | 
349 |     msg = [
350 |         HumanMessage(
351 |             content=f""" \n 
352 |     Look at the question and try to reason about the underlying semantic intent / meaning. Pay particular attention to any key medical terms \n 
353 |     Here is the initial question:
354 |     \n ------- \n
355 |     {question} 
356 |     \n ------- \n
357 |     Correct any spelling errors in medication names and formulate an improved question for searching medical information.
358 |     Only respond with the reworded question, with no other preamble or conversation: """,
359 |         )
360 |     ]
361 | 
362 |     model = RAGSystem.local_llm
363 |     response = model.invoke(msg)
364 |     print(response)
365 |     return {
366 |         "messages": [response], 
367 |         "rewrite_question" : response, # access from state without indexing
368 |     }
369 |     
370 | # UNUSED
371 | def grade_documents(state):
372 |     """
373 |     Determines whether the retrieved documents are relevant to the question.
374 | 
375 |     Args:
376 |         state (dict): The current graph state
377 | 
378 |     Returns:
379 |         state (dict): Updates documents key with only filtered relevant documents
380 |     """
381 |     # class for output
382 |     class GradeDocuments(BaseModel):
383 |         """Binary score for relevance check on retrieved documents."""
384 | 
385 |         binary_score: str = Field(
386 |             description="Documents are relevant to the question, 'yes' or 'no'"
387 |         )
388 | 
389 |     # LLM with function call
390 |     llm = RAGSystem.local_llm
391 |     structured_llm_grader = llm.with_structured_output(GradeDocuments)
392 | 
393 |     # Prompt
394 |     system = """You are a grader assessing relevance of a retrieved document to a user question. \n 
395 |         If the document contains keyword(s) or semantic meaning related to the question, grade it as relevant. \n
396 |         Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question."""
397 |     grade_prompt = ChatPromptTemplate.from_messages(
398 |         [
399 |             ("system", system),
400 |             ("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),
401 |         ]
402 |     )
403 |     
404 |     retrieval_grader = grade_prompt | structured_llm_grader
405 | 
406 |     print("---CHECK DOCUMENT RELEVANCE TO QUESTION---")
407 |     
408 |     question = state["rewrite_question"]
409 |     documents = state["documents"]
410 |     failed = state["failed"]
411 |     messages = state["messages"]
412 | 
413 |     # Score each doc
414 |     filtered_docs = []
415 |     for d in documents:
416 |         score = retrieval_grader.invoke(
417 |             {"question": question, "document": d.page_content}
418 |         )
419 |         grade = score.binary_score
420 |         if grade == "yes":
421 |             print("---GRADE: DOCUMENT RELEVANT---")
422 |             print(d)
423 |             filtered_docs.append(d)
424 |             failed = 0 # mark as not failed if ANY doc is relevant
425 |         else:
426 |             print("---GRADE: DOCUMENT NOT RELEVANT---")
427 |             print(d)
428 |             continue
429 |     return {"documents": filtered_docs, "rewrite_question": question, "failed": failed, "messages": messages}
430 | 
431 | from langchain_community.document_compressors import FlashrankRerank
432 | 
433 | def rank_documents(state):
434 |     """
435 |     Reranks the documents according to relevance to original question 
436 | 
437 |     Args:
438 |         state (dict): The current graph state
439 | 
440 |     Returns:
441 |         state (dict): Updates documents key with only top relevant documents
442 |     """
443 |     
444 |     print("---RANKING DOCUMENT RELEVANCE TO QUESTION---")
445 |     
446 |     question = state["rewrite_question"]
447 |     documents = state["documents"]
448 |     messages = state["messages"]
449 |     # Use FlashrankRerank to rank and return top n relevant documents
450 |     reranker = FlashrankRerank(top_n=4)
451 |     reranked_docs = reranker.compress_documents(documents=documents, query=question.content) 
452 | 
453 |     print(f"\n Returning top {len(reranked_docs)} out of {len(documents)} Documents \n")
454 | 
455 |     return {"documents": reranked_docs, "rewrite_question": question, "messages": messages}
456 | 
457 | 
458 | def generate(state):
459 |     """
460 |     Generate answer
461 | 
462 |     Args:
463 |         state (messages): The current state
464 | 
465 |     Returns:
466 |          dict: The updated state with re-phrased question
467 |     """
468 |     print("---GENERATE---")
469 |     
470 |     messages = state["messages"]
471 |     docs = state["documents"]
472 |     question = state["rewrite_question"]
473 |     
474 |     doctext = ""
475 |     for doc in docs:
476 |         doctext += f"{doc.page_content}\n\n"
477 |     
478 |     pretty_print_docs(docs)
479 |     
480 |     # Prompt 
481 |     prompt = ChatPromptTemplate.from_messages([
482 |         ("human", """You are an assistant for answering questions about medications.
483 |          Rely heavily on the following pieces of retrieved context to answer the question.
484 |          In your response do not use the phrase "in the provided context", instead say "on the NHS website"
485 |          If you don't know the answer, just say that you are unable to find any specific information from the NHS Medicines website, but offer adjacent relevant advice from the provided context.
486 |          Question: {question}
487 |          Context: {context}
488 |          Answer: """),])
489 | 
490 |     # LLM
491 |     llm = RAGSystem.local_llm
492 | 
493 |     # Post-processing
494 |     # def format_docs(docs):
495 |     #     return "\n\n".join(doc.page_content for doc in docs)
496 | 
497 |     # Chain
498 |     rag_chain = prompt | llm #| StrOutputParser()
499 | 
500 |     # Run
501 |     response = rag_chain.invoke({"context": doctext, "question": question})
502 |     return {"messages": [response],"documents": docs, "rewrite_question": question,}
503 | 
504 | 
505 | #############
506 | ### GRAPH ###
507 | #############
508 | 
509 | from langgraph.graph import END, StateGraph
510 | 
511 | # Setup LLM and vectorDB
512 | RAGSystem = InitialiseRAG()
513 | RAGSystem.run()
514 | 
515 | # Define a new graph
516 | workflow = StateGraph(AgentState)
517 | 
518 | # Define the nodes we will cycle between
519 | workflow.add_node("rewrite", rewrite)
520 | workflow.add_node("reject", reject)
521 | workflow.add_node("retrieve", retrieve)
522 | workflow.add_node("rank_documents", rank_documents)
523 | workflow.add_node("generate", generate)  # Generating a response after we know the documents are relevant
524 | 
525 | # Verify first then reject or rewrite for retrieval
526 | workflow.set_conditional_entry_point(
527 |     verify_question,
528 |     {
529 |         "rewrite": "rewrite",
530 |         "reject": "reject",
531 |     },
532 | )
533 | 
534 | # Then send to retrieve
535 | workflow.add_edge("rewrite", "retrieve")
536 | 
537 | # Grade retrieved documents
538 | workflow.add_edge("retrieve", "rank_documents")
539 | 
540 | workflow.add_edge("rank_documents", "generate")
541 | 
542 | # Finish after generate
543 | workflow.add_edge("generate", END)
544 | 
545 | # Finish after reject
546 | workflow.add_edge("reject", END)
547 | 
548 | # Compile
549 | graph = workflow.compile()
550 | 
551 | # Test w/ console output
552 | def test_run():
553 |     import pprint
554 |     inputs = {
555 |         "messages": [
556 |             ("user", ""),
557 |         ]
558 |     }
559 |     for output in graph.stream(inputs):
560 |         for key, value in output.items():
561 |             pprint.pprint(f"Output from node '{key}':")
562 |             pprint.pprint("---")
563 |             pprint.pprint(value, indent=2, width=80, depth=None)
564 |             message_content = value['messages'][0].content
565 |         pprint.pprint("\n---\n")
566 |     print(message_content)
567 |     
568 | # Run Flask
569 | if __name__ == '__main__':
570 |   app.run(debug=False)
571 |

--------------------------------------------------------------------------------
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287 | 
288 |     e) Convey the object code using peer-to-peer transmission, provided
289 |     you inform other peers where the object code and Corresponding
290 |     Source of the work are being offered to the general public at no
291 |     charge under subsection 6d.
292 | 
293 |   A separable portion of the object code, whose source code is excluded
294 | from the Corresponding Source as a System Library, need not be
295 | included in conveying the object code work.
296 | 
297 |   A "User Product" is either (1) a "consumer product", which means any
298 | tangible personal property which is normally used for personal, family,
299 | or household purposes, or (2) anything designed or sold for incorporation
300 | into a dwelling.  In determining whether a product is a consumer product,
301 | doubtful cases shall be resolved in favor of coverage.  For a particular
302 | product received by a particular user, "normally used" refers to a
303 | typical or common use of that class of product, regardless of the status
304 | of the particular user or of the way in which the particular user
305 | actually uses, or expects or is expected to use, the product.  A product
306 | is a consumer product regardless of whether the product has substantial
307 | commercial, industrial or non-consumer uses, unless such uses represent
308 | the only significant mode of use of the product.
309 | 
310 |   "Installation Information" for a User Product means any methods,
311 | procedures, authorization keys, or other information required to install
312 | and execute modified versions of a covered work in that User Product from
313 | a modified version of its Corresponding Source.  The information must
314 | suffice to ensure that the continued functioning of the modified object
315 | code is in no case prevented or interfered with solely because
316 | modification has been made.
317 | 
318 |   If you convey an object code work under this section in, or with, or
319 | specifically for use in, a User Product, and the conveying occurs as
320 | part of a transaction in which the right of possession and use of the
321 | User Product is transferred to the recipient in perpetuity or for a
322 | fixed term (regardless of how the transaction is characterized), the
323 | Corresponding Source conveyed under this section must be accompanied
324 | by the Installation Information.  But this requirement does not apply
325 | if neither you nor any third party retains the ability to install
326 | modified object code on the User Product (for example, the work has
327 | been installed in ROM).
328 | 
329 |   The requirement to provide Installation Information does not include a
330 | requirement to continue to provide support service, warranty, or updates
331 | for a work that has been modified or installed by the recipient, or for
332 | the User Product in which it has been modified or installed.  Access to a
333 | network may be denied when the modification itself materially and
334 | adversely affects the operation of the network or violates the rules and
335 | protocols for communication across the network.
336 | 
337 |   Corresponding Source conveyed, and Installation Information provided,
338 | in accord with this section must be in a format that is publicly
339 | documented (and with an implementation available to the public in
340 | source code form), and must require no special password or key for
341 | unpacking, reading or copying.
342 | 
343 |   7. Additional Terms.
344 | 
345 |   "Additional permissions" are terms that supplement the terms of this
346 | License by making exceptions from one or more of its conditions.
347 | Additional permissions that are applicable to the entire Program shall
348 | be treated as though they were included in this License, to the extent
349 | that they are valid under applicable law.  If additional permissions
350 | apply only to part of the Program, that part may be used separately
351 | under those permissions, but the entire Program remains governed by
352 | this License without regard to the additional permissions.
353 | 
354 |   When you convey a copy of a covered work, you may at your option
355 | remove any additional permissions from that copy, or from any part of
356 | it.  (Additional permissions may be written to require their own
357 | removal in certain cases when you modify the work.)  You may place
358 | additional permissions on material, added by you to a covered work,
359 | for which you have or can give appropriate copyright permission.
360 | 
361 |   Notwithstanding any other provision of this License, for material you
362 | add to a covered work, you may (if authorized by the copyright holders of
363 | that material) supplement the terms of this License with terms:
364 | 
365 |     a) Disclaiming warranty or limiting liability differently from the
366 |     terms of sections 15 and 16 of this License; or
367 | 
368 |     b) Requiring preservation of specified reasonable legal notices or
369 |     author attributions in that material or in the Appropriate Legal
370 |     Notices displayed by works containing it; or
371 | 
372 |     c) Prohibiting misrepresentation of the origin of that material, or
373 |     requiring that modified versions of such material be marked in
374 |     reasonable ways as different from the original version; or
375 | 
376 |     d) Limiting the use for publicity purposes of names of licensors or
377 |     authors of the material; or
378 | 
379 |     e) Declining to grant rights under trademark law for use of some
380 |     trade names, trademarks, or service marks; or
381 | 
382 |     f) Requiring indemnification of licensors and authors of that
383 |     material by anyone who conveys the material (or modified versions of
384 |     it) with contractual assumptions of liability to the recipient, for
385 |     any liability that these contractual assumptions directly impose on
386 |     those licensors and authors.
387 | 
388 |   All other non-permissive additional terms are considered "further
389 | restrictions" within the meaning of section 10.  If the Program as you
390 | received it, or any part of it, contains a notice stating that it is
391 | governed by this License along with a term that is a further
392 | restriction, you may remove that term.  If a license document contains
393 | a further restriction but permits relicensing or conveying under this
394 | License, you may add to a covered work material governed by the terms
395 | of that license document, provided that the further restriction does
396 | not survive such relicensing or conveying.
397 | 
398 |   If you add terms to a covered work in accord with this section, you
399 | must place, in the relevant source files, a statement of the
400 | additional terms that apply to those files, or a notice indicating
401 | where to find the applicable terms.
402 | 
403 |   Additional terms, permissive or non-permissive, may be stated in the
404 | form of a separately written license, or stated as exceptions;
405 | the above requirements apply either way.
406 | 
407 |   8. Termination.
408 | 
409 |   You may not propagate or modify a covered work except as expressly
410 | provided under this License.  Any attempt otherwise to propagate or
411 | modify it is void, and will automatically terminate your rights under
412 | this License (including any patent licenses granted under the third
413 | paragraph of section 11).
414 | 
415 |   However, if you cease all violation of this License, then your
416 | license from a particular copyright holder is reinstated (a)
417 | provisionally, unless and until the copyright holder explicitly and
418 | finally terminates your license, and (b) permanently, if the copyright
419 | holder fails to notify you of the violation by some reasonable means
420 | prior to 60 days after the cessation.
421 | 
422 |   Moreover, your license from a particular copyright holder is
423 | reinstated permanently if the copyright holder notifies you of the
424 | violation by some reasonable means, this is the first time you have
425 | received notice of violation of this License (for any work) from that
426 | copyright holder, and you cure the violation prior to 30 days after
427 | your receipt of the notice.
428 | 
429 |   Termination of your rights under this section does not terminate the
430 | licenses of parties who have received copies or rights from you under
431 | this License.  If your rights have been terminated and not permanently
432 | reinstated, you do not qualify to receive new licenses for the same
433 | material under section 10.
434 | 
435 |   9. Acceptance Not Required for Having Copies.
436 | 
437 |   You are not required to accept this License in order to receive or
438 | run a copy of the Program.  Ancillary propagation of a covered work
439 | occurring solely as a consequence of using peer-to-peer transmission
440 | to receive a copy likewise does not require acceptance.  However,
441 | nothing other than this License grants you permission to propagate or
442 | modify any covered work.  These actions infringe copyright if you do
443 | not accept this License.  Therefore, by modifying or propagating a
444 | covered work, you indicate your acceptance of this License to do so.
445 | 
446 |   10. Automatic Licensing of Downstream Recipients.
447 | 
448 |   Each time you convey a covered work, the recipient automatically
449 | receives a license from the original licensors, to run, modify and
450 | propagate that work, subject to this License.  You are not responsible
451 | for enforcing compliance by third parties with this License.
452 | 
453 |   An "entity transaction" is a transaction transferring control of an
454 | organization, or substantially all assets of one, or subdividing an
455 | organization, or merging organizations.  If propagation of a covered
456 | work results from an entity transaction, each party to that
457 | transaction who receives a copy of the work also receives whatever
458 | licenses to the work the party's predecessor in interest had or could
459 | give under the previous paragraph, plus a right to possession of the
460 | Corresponding Source of the work from the predecessor in interest, if
461 | the predecessor has it or can get it with reasonable efforts.
462 | 
463 |   You may not impose any further restrictions on the exercise of the
464 | rights granted or affirmed under this License.  For example, you may
465 | not impose a license fee, royalty, or other charge for exercise of
466 | rights granted under this License, and you may not initiate litigation
467 | (including a cross-claim or counterclaim in a lawsuit) alleging that
468 | any patent claim is infringed by making, using, selling, offering for
469 | sale, or importing the Program or any portion of it.
470 | 
471 |   11. Patents.
472 | 
473 |   A "contributor" is a copyright holder who authorizes use under this
474 | License of the Program or a work on which the Program is based.  The
475 | work thus licensed is called the contributor's "contributor version".
476 | 
477 |   A contributor's "essential patent claims" are all patent claims
478 | owned or controlled by the contributor, whether already acquired or
479 | hereafter acquired, that would be infringed by some manner, permitted
480 | by this License, of making, using, or selling its contributor version,
481 | but do not include claims that would be infringed only as a
482 | consequence of further modification of the contributor version.  For
483 | purposes of this definition, "control" includes the right to grant
484 | patent sublicenses in a manner consistent with the requirements of
485 | this License.
486 | 
487 |   Each contributor grants you a non-exclusive, worldwide, royalty-free
488 | patent license under the contributor's essential patent claims, to
489 | make, use, sell, offer for sale, import and otherwise run, modify and
490 | propagate the contents of its contributor version.
491 | 
492 |   In the following three paragraphs, a "patent license" is any express
493 | agreement or commitment, however denominated, not to enforce a patent
494 | (such as an express permission to practice a patent or covenant not to
495 | sue for patent infringement).  To "grant" such a patent license to a
496 | party means to make such an agreement or commitment not to enforce a
497 | patent against the party.
498 | 
499 |   If you convey a covered work, knowingly relying on a patent license,
500 | and the Corresponding Source of the work is not available for anyone
501 | to copy, free of charge and under the terms of this License, through a
502 | publicly available network server or other readily accessible means,
503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients.  "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 | 
513 |   If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
517 | or convey a specific copy of the covered work, then the patent license
518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 | 
521 |   A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License.  You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
531 | conveyed by you (or copies made from those copies), or (b) primarily
532 | for and in connection with specific products or compilations that
533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 | 
536 |   Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 | 
540 |   12. No Surrender of Others' Freedom.
541 | 
542 |   If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License.  If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all.  For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 | 
552 |   13. Use with the GNU Affero General Public License.
553 | 
554 |   Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work.  The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 | 
563 |   14. Revised Versions of this License.
564 | 
565 |   The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time.  Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 | 
570 |   Each version is given a distinguishing version number.  If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation.  If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 | 
579 |   If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 | 
584 |   Later license versions may give you additional or different
585 | permissions.  However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 | 
589 |   15. Disclaimer of Warranty.
590 | 
591 |   THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 | 
600 |   16. Limitation of Liability.
601 | 
602 |   IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 | 
612 |   17. Interpretation of Sections 15 and 16.
613 | 
614 |   If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 | 
621 |                      END OF TERMS AND CONDITIONS
622 | 
623 |             How to Apply These Terms to Your New Programs
624 | 
625 |   If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 | 
629 |   To do so, attach the following notices to the program.  It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 | 
634 |     NHS Medicines ChatBot is an LLM app that lets you talk to the NHS Medicines
635 |     Website.
636 | 
637 |     Copyright (C) 2024  Shyam Dhokia
638 | 
639 |     This program is free software: you can redistribute it and/or modify
640 |     it under the terms of the GNU General Public License as published by
641 |     the Free Software Foundation, either version 3 of the License, or
642 |     (at your option) any later version.
643 | 
644 |     This program is distributed in the hope that it will be useful,
645 |     but WITHOUT ANY WARRANTY; without even the implied warranty of
646 |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
647 |     GNU General Public License for more details.
648 | 
649 |     You should have received a copy of the GNU General Public License
650 |     along with this program.  If not, see <https://www.gnu.org/licenses/>.
651 | 
652 | Also add information on how to contact you by electronic and paper mail.
653 | 
654 |   If the program does terminal interaction, make it output a short
655 | notice like this when it starts in an interactive mode:
656 | 
657 |     NHS Medicines ChatBot  Copyright (C) 2024  Shyam Dhokia
658 |     This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
659 |     This is free software, and you are welcome to redistribute it
660 |     under certain conditions; type `show c' for details.
661 | 
662 | The hypothetical commands `show w' and `show c' should show the appropriate
663 | parts of the General Public License.  Of course, your program's commands
664 | might be different; for a GUI interface, you would use an "about box".
665 | 
666 |   You should also get your employer (if you work as a programmer) or school,
667 | if any, to sign a "copyright disclaimer" for the program, if necessary.
668 | For more information on this, and how to apply and follow the GNU GPL, see
669 | <https://www.gnu.org/licenses/>.
670 | 
671 |   The GNU General Public License does not permit incorporating your program
672 | into proprietary programs.  If your program is a subroutine library, you
673 | may consider it more useful to permit linking proprietary applications with
674 | the library.  If this is what you want to do, use the GNU Lesser General
675 | Public License instead of this License.  But first, please read
676 | <https://www.gnu.org/licenses/why-not-lgpl.html>.
677 |

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