Wisdom

app.py

@@ -10,7 +10,7 @@
 # Set OpenAI API key
 openai.api_key = st.secrets["OPENAI_API_KEY"]
 
-st.header("MBAGPT: Chatting with Multiple Data Sources")
+st.header("BusinessGPT: By YoussefMadkour")
 
 # Initialize embeddings
 embeddings = OpenAIEmbeddings()
@@ -19,6 +19,11 @@
 buffettDB = Chroma(persist_directory=os.path.join('db', 'buffett'), embedding_function=embeddings)
 buffett_retriever = buffettDB.as_retriever(search_kwargs={"k": 3})
 
+
+# Load the Buffett and Branson databases
+hormoziDB = Chroma(persist_directory=os.path.join('db', 'hormozi'), embedding_function=embeddings)
+hormozi_retriever = hormoziDB.as_retriever(search_kwargs={"k": 3})
+
 bransonDB = Chroma(persist_directory=os.path.join('db', 'branson'), embedding_function=embeddings)
 branson_retriever = bransonDB.as_retriever(search_kwargs={"k": 3})
 
@@ -44,12 +49,11 @@ def construct_messages(history):
 # Define handler functions for each category
 def hormozi_handler(query):
     print("Using Hormozi handler...")
-    # Perform semantic search and format results
-    search_results = semantic_search(query, top_k=3)
-    context = ""
-    for i, (title, snippet) in enumerate(search_results):
-        context += f"Snippet from: {title}\n {snippet}\n\n"
+    # Get relevant documents from Buffett's database
+    relevant_docs = hormozi_retriever.get_relevant_documents(query)
 
+    # Use the provided function to prepare the context
+    context = get_page_contents(relevant_docs)
     # Generate human prompt template and convert to API message format
     query_with_context = human_template.format(query=query, context=context)
 
@@ -96,7 +100,7 @@ def route_by_category(query, category):
     if category == "0":
         return hormozi_handler(query)
     elif category == "1":
-        return buffet_handler(query)
+        return buffett_handler(query)
     elif category == "2":
         return branson_handler(query)
     elif category == "3":

docs/branson/hormozi

@@ -0,0 +1 @@
+

indexing.py

@@ -15,20 +15,25 @@
 
 buffett_loader = DirectoryLoader('./docs/buffett/', glob="*.pdf")
 branson_loader = DirectoryLoader('./docs/branson/', glob="*.pdf")
+hormozi_loader = DirectoryLoader('./docs/hormozi/', glob="*.pdf")
 
 buffett_docs = buffett_loader.load()
 branson_docs = branson_loader.load()
+hormozi_docs = hormozi_loader.load()
 
 embeddings = OpenAIEmbeddings()
 text_splitter = CharacterTextSplitter(chunk_size=250, chunk_overlap=8)
 
 # Split documents and generate embeddings
 buffett_docs_split = text_splitter.split_documents(buffett_docs)
 branson_docs_split = text_splitter.split_documents(branson_docs)
+hormozi_docs_split = text_splitter.split_documents(hormozi_docs)
 
 # Create Chroma instances and persist embeddings
 buffettDB = Chroma.from_documents(buffett_docs_split, embeddings, persist_directory=os.path.join(persist_directory, 'buffett'))
 buffettDB.persist()
 
 bransonDB = Chroma.from_documents(branson_docs_split, embeddings, persist_directory=os.path.join(persist_directory, 'branson'))
 bransonDB.persist()
+hormoziDB = Chroma.from_documents(hormozi_docs_split, embeddings, persist_directory=os.path.join(persist_directory, 'hormozi'))
+bransonDB.persist()

requirements.txt

@@ -4,4 +4,5 @@ requests==2.28.2
 pinecone-client==2.2.1
 langchain==0.0.168
 tiktoken==0.4.0
-retrying==1.3.4
+retrying==1.3.4
+chromadb == 0.3.26