passage finder version

app.py

@@ -1,167 +1,86 @@
 import gradio as gr
-import os
-from langchain_core.messages import HumanMessage    
-import src.srf_bot as sb
-import prompts.system_prompts as sp
+from langchain_core.messages import HumanMessage
+import src.passage_finder as pf
 
+# Initialize PassageFinder
+passage_finder = pf.PassageFinder()
 
+def respond(message):
+    config = passage_finder.get_configurable()
+    results = passage_finder.graph.invoke({"messages": [HumanMessage(content=message)]}, config)
+    
+    documents = results.get('documents', [])
+    
+    output = []
+    for doc in documents:
+        quotes = doc.metadata.get('matched_quotes', [])
+        publication = doc.metadata.get('publication_name', 'Unknown Publication')
+        chapter = doc.metadata.get('chapter_name', 'Unknown Chapter')
+        full_passage = doc.metadata.get('highlighted_content', '')
+        
+        quote_text = "\n".join([f"• \"{q.quote}\"" for q in quotes])
+        output.append({
+            "quotes": quote_text,
+            "reference": f"{publication}: {chapter}",
+            "full_passage": full_passage
+        })
+    
+    return output
 
-# Initialize chatbot
-chatbot = sb.SRFChatbot()
-
-# Define the respond function
-def respond(query, history):
-    formatted_query = [HumanMessage(content=query)]
-    # Invoke the graph with properly formatted input
-    result = chatbot.graph.invoke({"messages": formatted_query}, chatbot.config)
-    # Get the passages from the graph and append to history if documents exist
-    state = chatbot.graph.get_state(config=chatbot.config).values
-    documents = state.get("documents")
-    passages = ''
-    if documents and len(documents) > 0:
-        for d in documents:
-            passages += f'<b>{d.metadata["publication_name"]} - {d.metadata["chapter_name"]}</b>\n{d.page_content}\n\n'
-        history.append((f'Passages: {query}', passages))
-    # Extract the assistant's response and append to history
-    response = result["messages"][-1].content
-    system_message_dropdown = state.get("system_message_dropdown")
-    history.append((query, f"<i>[{system_message_dropdown}]</i>\n" + response))
-    return history
-
+def process_input(message):
+    results = respond(message)
+    html_output = "<div class='response-container'>"
+    for result in results:
+        html_output += f"""
+        <div class='result-item'>
+            <h3 class='reference'>{result['reference']}</h3>
+            <div class='quotes'>{result['quotes'].replace("• ", "<br>• ")}</div>
+            <details>
+                <summary>Show full passage</summary>
+                <div class='full-passage'>{result['full_passage']}</div>
+            </details>
+        </div>
+        """
+    html_output += "</div>"
+    return html_output
 
-# Gradio interface with black and grey color scheme
 with gr.Blocks(css="""
-    .gradio-container {
-        background-color: #F0F0F0; 
-        font-family: 'Arial', sans-serif;
-    }
-    h1, h2, p {
-        color: black;
-    }
-    h1 {
-        font-size: 32px;
-        text-align: left;
-    }
-    h2 {
-        font-size: 24px;
-    }
-    p {
-        font-size: 18px;
-        margin-bottom: 15px;
-    }
-    .gr-button {
-        background-color: #333333; 
-        color: white;
-        font-size: 18px;
-        padding: 10px;
-    }
-    .gr-textbox textarea {
-        font-size: 18px;
-        color: black;
-    }
-    .gr-dropdown {
-        font-size: 18px;
-        color: black;
-    }
-    .source-box {
-        background-color: white; 
-        padding: 10px; 
-        border-radius: 8px; 
-        margin-top: 20px;
-        color: black;
-        border: 1px solid #D0D0D0;
-    }
-      
-
-    /* Dark mode specific styles */
-    @media (prefers-color-scheme: dark) {
-        .gradio-container {
-            background-color: #1e1e1e; /* Dark background */
-            color: white; /* Light text color for contrast */
-        }
-        h1, h2, p {
-            color: white; /* Light text for headings */
-        }
-        .gr-textbox textarea {
-            background-color: #333333; /* Dark background for text area */
-            color: white; /* Light text color */
-        }
-        .gr-button {
-            background-color: #555555; /* Darker button for dark mode */
-            color: white;
-        }
-        .gr-dropdown {
-            background-color: #333333; /* Dark dropdown background */
-            color: white;
-        }
-        .source-box {
-            background-color: #333333; /* Dark background for sources box */
-            color: white;
-            border: 1px solid #555555; /* Lighter border for visibility */
-        }
-    }
-
-    @media (max-width: 600px) {
-        .gr-row { flex-direction: column !important; }
-        .gr-column { width: 100% !important; }
-    }
+    body { background-color: #f0f0f0; }
+    .gradio-container { background-color: #ffffff; }
+    .response-container { border: 1px solid #e0e0e0; border-radius: 8px; padding: 20px; background-color: #f9f9f9; }
+    .result-item { margin-bottom: 20px; background-color: white; padding: 15px; border-radius: 5px; box-shadow: 0 2px 5px rgba(0,0,0,0.1); }
+    .reference { color: #2c3e50; margin-bottom: 10px; }
+    .quotes { font-style: italic; margin-bottom: 10px; }
+    .full-passage { margin-top: 10px; padding: 10px; background-color: #f0f0f0; border-radius: 5px; }
+    details summary { cursor: pointer; color: #3498db; font-weight: bold; }
+    details summary:hover { text-decoration: underline; }
 """) as demo:
+    gr.Markdown("# SRF Teachings Chatbot")
+    gr.Markdown("Ask questions about Self-Realization Fellowship teachings and receive responses with relevant quotes.")
     
-    # Title
-    gr.Markdown("# SRF Chatbot")
+    with gr.Row():
+        input_text = gr.Textbox(
+            placeholder="Ask about the meaning of life, spirituality, or any other topic...",
+            label="Your Question"
+        )
+        submit_btn = gr.Button("Submit", variant="primary")
     
-    with gr.Row(elem_classes="gr-row"):
-        with gr.Column(scale=4, elem_classes="gr-column"):
-            # Chatbot interface
-            chatbot_output = gr.Chatbot(height=600)  # Increased height for longer chat interface
-            user_input = gr.Textbox(placeholder="Type your question here...", label="Your Question", value="What is the meaning of life?")
-            submit_button = gr.Button("Submit")
-        
-        with gr.Column(scale=1, elem_classes="gr-column"):
-            # Dropdown to select system prompts
-            system_prompt_dropdown = gr.Dropdown(
-                choices=list(sp.system_prompt_templates.keys()),
-                label="Select Chatbot Instructions",
-                value=list(sp.system_prompt_templates.keys())[0],
-                elem_classes="gr-dropdown"
-            )
-            # Display the selected system prompt
-            system_prompt_display = gr.Textbox(
-                value=sp.system_prompt_templates[list(sp.system_prompt_templates.keys())[0]],
-                label="Current Chatbot Instructions",
-                lines=5,
-                interactive=False
-            )
-            
-            # Sources box (Now white, matching the other boxes)
-            gr.Markdown("""
-            <div class="source-box">
-                <strong>Available sources:</strong>
-                <ul>
-                    <li>Journey to Self-Realization</li>
-                    <li>The Second Coming of Christ</li>
-                    <li>Autobiography of a Yogi</li>
-                </ul>
-            </div>
-            """)
+    output_area = gr.HTML()
     
-    # Update system prompt display when a new prompt is selected
-    system_prompt_dropdown.change(
-        fn=chatbot.reset_system_prompt,
-        inputs=[system_prompt_dropdown],
-        outputs=[system_prompt_display]
-    )
+    gr.Markdown("### Sources")
+    gr.Textbox(value="Journey to Self Realization, Second Coming of Christ, and Autobiography of a Yogi", 
+               label="Available Sources", interactive=False)
     
-    # Submit button logic to handle chatbot conversation
-    submit_button.click(
-        fn=respond,
-        inputs=[user_input, chatbot_output],
-        outputs=[chatbot_output]
-    )
+    submit_btn.click(process_input, inputs=input_text, outputs=output_area)
 
-# Access the secrets
-username = os.getenv("USERNAME")
-password = os.getenv("PASSWORD")
+    gr.Examples(
+        examples=[
+            "What is the meaning of life?",
+            "Importance of good posture",
+            "How can I find inner peace?",
+            "What does Paramahansa Yogananda say about meditation?",
+        ],
+        inputs=input_text,
+    )
 
-# Launch the interface
-demo.launch(share=True, auth=(username, password), debug=True)
+demo.launch()

app2.py → app_old.py

@@ -4,22 +4,16 @@ from langchain_core.messages import HumanMessage
 import src.srf_bot as sb
 import prompts.system_prompts as sp
 
+
+
 # Initialize chatbot
 chatbot = sb.SRFChatbot()
 
 # Define the respond function
-def respond(query, history, custom_instructions, use_custom_instructions, selected_system_prompt):
+def respond(query, history):
     formatted_query = [HumanMessage(content=query)]
-    
-    # Set the system instructions based on user input or dropdown
-    if use_custom_instructions:
-        chatbot.config["system_prompt"] = custom_instructions  # Use custom instructions
-    else:
-        chatbot.config["system_prompt"] = sp.system_prompt_templates[selected_system_prompt]  # Use selected from dropdown
-    
     # Invoke the graph with properly formatted input
     result = chatbot.graph.invoke({"messages": formatted_query}, chatbot.config)
-    
     # Get the passages from the graph and append to history if documents exist
     state = chatbot.graph.get_state(config=chatbot.config).values
     documents = state.get("documents")
@@ -28,12 +22,10 @@ def respond(query, history, custom_instructions, use_custom_instructions, select
         for d in documents:
             passages += f'<b>{d.metadata["publication_name"]} - {d.metadata["chapter_name"]}</b>\n{d.page_content}\n\n'
         history.append((f'Passages: {query}', passages))
-    
     # Extract the assistant's response and append to history
     response = result["messages"][-1].content
     system_message_dropdown = state.get("system_message_dropdown")
     history.append((query, f"<i>[{system_message_dropdown}]</i>\n" + response))
-    
     return history
 
 
@@ -79,31 +71,33 @@ with gr.Blocks(css="""
         color: black;
         border: 1px solid #D0D0D0;
     }
+      
 
+    /* Dark mode specific styles */
     @media (prefers-color-scheme: dark) {
         .gradio-container {
-            background-color: #1e1e1e;
-            color: white;
+            background-color: #1e1e1e; /* Dark background */
+            color: white; /* Light text color for contrast */
         }
         h1, h2, p {
-            color: white;
+            color: white; /* Light text for headings */
         }
         .gr-textbox textarea {
-            background-color: #333333;
-            color: white;
+            background-color: #333333; /* Dark background for text area */
+            color: white; /* Light text color */
         }
         .gr-button {
-            background-color: #555555;
+            background-color: #555555; /* Darker button for dark mode */
             color: white;
         }
         .gr-dropdown {
-            background-color: #333333;
+            background-color: #333333; /* Dark dropdown background */
             color: white;
         }
         .source-box {
-            background-color: #333333;
+            background-color: #333333; /* Dark background for sources box */
             color: white;
-            border: 1px solid #555555;
+            border: 1px solid #555555; /* Lighter border for visibility */
         }
     }
 
@@ -131,7 +125,6 @@ with gr.Blocks(css="""
                 value=list(sp.system_prompt_templates.keys())[0],
                 elem_classes="gr-dropdown"
             )
-            
             # Display the selected system prompt
             system_prompt_display = gr.Textbox(
                 value=sp.system_prompt_templates[list(sp.system_prompt_templates.keys())[0]],
@@ -140,20 +133,7 @@ with gr.Blocks(css="""
                 interactive=False
             )
             
-            # Custom instructions input
-            custom_instructions = gr.Textbox(
-                placeholder="Write your own instructions here...",
-                label="Or Write Your Own Instructions",
-                lines=5
-            )
-            
-            # Toggle between using dropdown or custom instructions
-            use_custom_instructions = gr.Checkbox(
-                label="Use Custom Instructions",
-                value=False
-            )
-            
-            # Sources box
+            # Sources box (Now white, matching the other boxes)
             gr.Markdown("""
             <div class="source-box">
                 <strong>Available sources:</strong>
@@ -164,10 +144,10 @@ with gr.Blocks(css="""
                 </ul>
             </div>
             """)
-
+    
     # Update system prompt display when a new prompt is selected
     system_prompt_dropdown.change(
-        fn=lambda x: sp.system_prompt_templates[x],
+        fn=chatbot.reset_system_prompt,
         inputs=[system_prompt_dropdown],
         outputs=[system_prompt_display]
     )
@@ -175,7 +155,7 @@ with gr.Blocks(css="""
     # Submit button logic to handle chatbot conversation
     submit_button.click(
         fn=respond,
-        inputs=[user_input, chatbot_output, custom_instructions, use_custom_instructions, system_prompt_dropdown],
+        inputs=[user_input, chatbot_output],
         outputs=[chatbot_output]
     )
 

prompts/quote_finder_prompts.py

@@ -3,6 +3,7 @@ from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTempla
 system_template = '''The following are passages from the books and teachings of Paramhansa Yogananda, his disciples, or other
                 sources related to the Self-Realization Fellowship / Yogoda Satsanga Society.
                 Extract the most relevant quotes from the given passage that directly address the user's original query and are the most helpful and/or insipriational to the user. 
+                Quotes can be up to three sentences long.
                 Ensure that quotes are verbatim and provide specific information related to the query.
 '''
 

requirements.txt

@@ -1,7 +1,8 @@
 aiofiles==23.2.1
-aiohappyeyeballs==2.4.0
-aiohttp==3.10.5
+aiohappyeyeballs==2.4.3
+aiohttp==3.10.8
 aiosignal==1.3.1
+altair==5.4.1
 annotated-types==0.7.0
 anyio==4.6.0
 appnope==0.1.4
@@ -14,6 +15,8 @@ attrs==24.2.0
 babel==2.16.0
 beautifulsoup4==4.12.3
 bleach==6.1.0
+blinker==1.8.2
+cachetools==5.5.0
 certifi==2024.8.30
 cffi==1.17.1
 charset-normalizer==3.3.2
@@ -21,7 +24,7 @@ click==8.1.7
 comm==0.2.2
 contourpy==1.3.0
 cycler==0.12.1
-debugpy==1.8.5
+debugpy==1.8.6
 decorator==5.1.1
 defusedxml==0.7.1
 distro==1.9.0
@@ -30,21 +33,24 @@ fastapi==0.115.0
 fastjsonschema==2.20.0
 ffmpy==0.4.0
 filelock==3.16.1
-fonttools==4.53.1
+fonttools==4.54.1
 fqdn==1.5.1
 frozenlist==1.4.1
 fsspec==2024.9.0
-gradio==4.44.0
+fuzzywuzzy==0.18.0
+gitdb==4.0.11
+GitPython==3.1.43
+gradio==4.44.1
 gradio_client==1.3.0
 greenlet==3.1.1
-grpcio==1.66.1
-grpcio-tools==1.66.1
+grpcio==1.66.2
+grpcio-tools==1.66.2
 h11==0.14.0
 h2==4.1.0
 hpack==4.0.0
 httpcore==1.0.5
 httpx==0.27.2
-huggingface-hub==0.25.0
+huggingface-hub==0.25.1
 hyperframe==6.0.1
 idna==3.10
 importlib_resources==6.4.5
@@ -73,14 +79,15 @@ jupyterlab_pygments==0.3.0
 jupyterlab_server==2.27.3
 jupyterlab_widgets==3.0.13
 kiwisolver==1.4.7
-langchain==0.3.0
-langchain-core==0.3.5
-langchain-openai==0.2.0
+langchain==0.3.1
+langchain-core==0.3.7
+langchain-openai==0.2.1
 langchain-qdrant==0.1.4
 langchain-text-splitters==0.3.0
-langgraph==0.2.23
-langgraph-checkpoint==1.0.10
-langsmith==0.1.125
+langgraph==0.2.31
+langgraph-checkpoint==1.0.14
+langsmith==0.1.129
+Levenshtein==0.26.0
 markdown-it-py==3.0.0
 MarkupSafe==2.1.5
 matplotlib==3.9.2
@@ -89,6 +96,7 @@ mdurl==0.1.2
 mistune==3.0.2
 msgpack==1.1.0
 multidict==6.1.0
+narwhals==1.9.0
 nbclient==0.10.0
 nbconvert==7.16.4
 nbformat==5.10.4
@@ -96,7 +104,7 @@ nest-asyncio==1.6.0
 notebook==7.2.2
 notebook_shim==0.2.4
 numpy==1.26.4
-openai==1.47.0
+openai==1.50.2
 orjson==3.10.7
 overrides==7.7.0
 packaging==24.1
@@ -108,46 +116,53 @@ pillow==10.4.0
 platformdirs==4.3.6
 portalocker==2.10.1
 prometheus_client==0.21.0
-prompt_toolkit==3.0.47
+prompt_toolkit==3.0.48
 protobuf==5.28.2
 psutil==6.0.0
 ptyprocess==0.7.0
 pure_eval==0.2.3
+pyarrow==17.0.0
 pycparser==2.22
 pydantic==2.9.2
 pydantic_core==2.23.4
+pydeck==0.9.1
 pydub==0.25.1
 Pygments==2.18.0
 pyparsing==3.1.4
 python-dateutil==2.9.0.post0
 python-dotenv==1.0.1
 python-json-logger==2.0.7
-python-multipart==0.0.10
+python-Levenshtein==0.26.0
+python-multipart==0.0.12
 pytz==2024.2
 PyYAML==6.0.2
 pyzmq==26.2.0
-qdrant-client==1.11.2
+qdrant-client==1.11.3
+RapidFuzz==3.10.0
 referencing==0.35.1
 regex==2024.9.11
 requests==2.32.3
 rfc3339-validator==0.1.4
 rfc3986-validator==0.1.1
-rich==13.8.1
+rich==13.9.1
 rpds-py==0.20.0
-ruff==0.6.7
+ruff==0.6.8
 semantic-version==2.10.0
 Send2Trash==1.8.3
 shellingham==1.5.4
 six==1.16.0
+smmap==5.0.1
 sniffio==1.3.1
 soupsieve==2.6
 SQLAlchemy==2.0.35
 stack-data==0.6.3
-starlette==0.38.5
+starlette==0.38.6
+streamlit==1.38.0
 tenacity==8.5.0
 terminado==0.18.1
 tiktoken==0.7.0
 tinycss2==1.3.0
+toml==0.10.2
 tomlkit==0.12.0
 tornado==6.4.1
 tqdm==4.66.5
@@ -155,14 +170,14 @@ traitlets==5.14.3
 typer==0.12.5
 types-python-dateutil==2.9.0.20240906
 typing_extensions==4.12.2
-tzdata==2024.1
+tzdata==2024.2
 uri-template==1.3.0
 urllib3==2.2.3
-uvicorn==0.30.6
+uvicorn==0.31.0
 wcwidth==0.2.13
 webcolors==24.8.0
 webencodings==0.5.1
 websocket-client==1.8.0
 websockets==12.0
 widgetsnbextension==4.0.13
-yarl==1.11.1
+yarl==1.13.1

src/passage_finder.py

@@ -51,7 +51,7 @@ class ToolManager:
 
     def add_tools(self):
         @tool
-        def vector_search(query: str, k: int = 5) -> list[Document]:
+        def vector_search(query: str, k: int = 10) -> list[Document]:
             """Useful for simple queries. This tool will search a vector database for passages from the teachings of Paramhansa Yogananda and other publications from the Self Realization Fellowship (SRF).
             The user has the option to specify the number of passages they want the search to return, otherwise the number of passages will be set to the default value."""
             retriever = self.vectorstore.as_retriever(search_kwargs={"k": k})
@@ -59,7 +59,7 @@ class ToolManager:
             return documents
              
         @tool
-        def multiple_query_vector_search(query: str, k: int = 5) -> list[Document]:
+        def multiple_query_vector_search(query: str, k: int = 10) -> list[Document]:
             """Useful when the user's query is vague, complex, or involves multiple concepts. 
             This tool will write multiple versions of the user's query and search the vector database for relevant passages. 
             Use this tool when the user asks for an in depth answer to their question."""
@@ -102,7 +102,7 @@ class BasicToolNode:
 
 # Create the Pydantic Model for the quote finder
 class Quote(BaseModel):
-    '''Most relevant quotes to the user's query strictly pulled verbatim from the context provided.'''
+    '''Most relevant quotes to the user's query strictly pulled verbatim from the context provided. Quotes can be up to three sentences long.'''
     quote: str
 
 class QuoteList(BaseModel):
@@ -147,7 +147,7 @@ class QuoteFinder:
                 # Replace the matched text with highlighted version
                 highlighted_content = re.sub(
                     escaped_match,
-                    f"<b>{best_match}</b>",
+                    f"<mark>{best_match}</mark>",
                     highlighted_content,
                     flags=re.IGNORECASE
                 )