chat with groq

.gitignore

@@ -23,3 +23,14 @@ txt/*.txt
 # exclude json files
 #freud_index/*.json
 
+# exclude .env file
+.env
+
+# exlude .venv folder
+.venv/
+
+# .streamlit folder
+.streamlit/
+
+
+

app.py

@@ -4,6 +4,12 @@ from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.core import Settings
 from llama_index.retrievers.bm25 import BM25Retriever
 from llama_index.core.retrievers import QueryFusionRetriever
+from litellm import completion
+import os
+from dotenv import load_dotenv
+from llama_index.core.settings import Settings
+from llama_index.core.llms import ChatMessage, MessageRole
+from llama_index.llms.groq import Groq
 
 # Page config
 st.set_page_config(
@@ -12,20 +18,52 @@ st.set_page_config(
     layout="wide"
 )
 
-# Title
-st.title("Freud Works Hybrid Search")
-st.markdown("""
-This demo allows you to search through Freud's complete works using a hybrid approach combining:
-- BM25 (keyword-based search)
-- Vector search (semantic similarity)
-""")
+# Load environment variables
+load_dotenv()
+
+# Initialize session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+if "sources" not in st.session_state:
+    st.session_state.sources = {}
+if "system_prompt" not in st.session_state:
+    st.session_state.system_prompt = """You are Sigmund Freud, speaking from your historical context and perspective. As the founder of psychoanalysis, you should:
+
+1. Only engage with topics related to:
+   - Psychoanalysis and its theories
+   - Dreams and their interpretation
+   - The unconscious mind
+   - Human sexuality and development
+   - Your published works and case studies
+   - Your historical context and contemporaries
+
+2. Politely decline to answer:
+   - Questions about events after your death in 1939
+   - Medical advice or diagnosis
+   - Topics outside your expertise or historical context
+   - Personal matters unrelated to psychoanalysis
+
+3. Maintain your characteristic style:
+   - Speak with authority on psychoanalytic matters
+   - Use psychoanalytic terminology when appropriate
+   - Reference your own works and theories
+   - Interpret questions through a psychoanalytic lens
+
+If a question is inappropriate or outside your scope, explain why you cannot answer it from your perspective as Freud."""
+
+# Configure LlamaIndex settings
+Settings.llm = Groq(
+    model="llama3-8b-8192",
+    api_key=os.getenv("GROQ_API_KEY"),
+    max_tokens=6000,
+    context_window=6000
+)
 
 @st.cache_resource
 def load_indices():
     """Load the index and create retrievers"""
     # Load embeddings
     embed_model = HuggingFaceEmbedding(model_name="multi-qa-MiniLM-L6-cos-v1")
-    Settings.llm = None
     Settings.embed_model = embed_model
     
     # Load index
@@ -42,7 +80,7 @@ def load_indices():
     hybrid_retriever = QueryFusionRetriever(
         [vector_retriever, bm25_retriever],
         similarity_top_k=10,
-        num_queries=1,  # set this to 1 to disable query generation
+        num_queries=1,
         mode="reciprocal_rerank",
         use_async=True,
         verbose=True,
@@ -53,64 +91,198 @@ def load_indices():
 # Load indices
 index, vector_retriever, bm25_retriever, hybrid_retriever = load_indices()
 
-# Search interface
-search_query = st.text_input("Enter your search query:", placeholder="e.g. Oedipus complex")
-
-# Add top_k selector
-top_k = st.slider("Number of results to return:", min_value=1, max_value=20, value=10)
-
-# Update retrievers with new top_k
-vector_retriever.similarity_top_k = top_k
-bm25_retriever.similarity_top_k = top_k
-hybrid_retriever.similarity_top_k = top_k
-
-# Search type selector
-search_type = st.radio(
-    "Select search method:",
-    ["Hybrid", "Vector", "BM25"],
-    horizontal=True,
-    help="""
-    - **BM25**: Keyword-based search that works best for exact matches and specific terms. Similar to traditional search engines.
-    - **Vector**: Semantic search that understands the meaning of your query, even if it uses different words than the source text.
-    - **Hybrid**: Combines both approaches for better overall results, balancing exact matches with semantic understanding.
-    """
-)
+# Function to process chat with RAG
+def chat_with_rag(message, history, retriever):
+    # Get context from the index if RAG is enabled
+    if st.session_state.get('use_rag', True):
+        nodes = retriever.retrieve(message)
+        # sort nodes by score
+        nodes = sorted(nodes, key=lambda x: x.score, reverse=True)
+        # nodes up to slider value
+        nodes = nodes[:st.session_state.get('num_chunks', 1)]
+        context = "\n\n".join([node.text for node in nodes])
+        system_prompt = f"""{st.session_state.system_prompt}
 
-if search_query:
-    with st.spinner('Searching...'):
-        if search_type == "Hybrid":
-            nodes = hybrid_retriever.retrieve(search_query)
-        elif search_type == "Vector":
-            nodes = vector_retriever.retrieve(search_query)
-        else:  # BM25
-            nodes = bm25_retriever.retrieve(search_query)
-            
-        # Display results
-        st.subheader(f"Search Results")
+        Use the following passages from my writings to inform your response:
         
-        for i, node in enumerate(nodes, 1):
-            # Create a preview of the text (first 200 characters)
-            preview = node.text[:200] + "..." if len(node.text) > 200 else node.text
+        {context}
+        """
+        
+        # Store sources in session state for this message
+        # Calculate the correct message index (total number of messages)
+        message_index = len(st.session_state.messages)
+        st.session_state.sources[message_index] = nodes
+    else:
+        system_prompt = st.session_state.system_prompt
+        nodes = []
+
+    # Prepare messages for the API call
+    messages = [ChatMessage(role=MessageRole.SYSTEM, content=system_prompt)]
+    for h in history:
+        role = MessageRole.ASSISTANT if h["role"] == "assistant" else MessageRole.USER
+        messages.append(ChatMessage(role=role, content=h["content"]))
+    messages.append(ChatMessage(role=MessageRole.USER, content=message))
+    
+    # Call Groq via LiteLLM (replace with LlamaIndex's Groq)
+    response = Settings.llm.chat(messages)
+    assistant_response = response.message.content
+    
+    return assistant_response
+
+# Move the title to the top, before tabs
+st.title("Freud Explorer")
+
+# Add tab selection
+tab2, tab1 = st.tabs(["Chat", "Search"])
+
+with tab1:
+    st.title("Freud Works Hybrid Search")
+    st.markdown("""
+    This demo allows you to search through Freud's complete works using a hybrid approach combining:
+    - BM25 (keyword-based search)
+    - Vector search (semantic similarity)
+    """)
+    
+    # Search interface
+    search_query = st.text_input("Enter your search query:", placeholder="e.g. Oedipus complex")
+
+    # Add top_k selector
+    top_k = st.slider("Number of results to return:", min_value=1, max_value=20, value=10)
+
+    # Update retrievers with new top_k
+    vector_retriever.similarity_top_k = top_k
+    bm25_retriever.similarity_top_k = top_k
+    hybrid_retriever.similarity_top_k = top_k
+
+    # Search type selector
+    search_type = st.radio(
+        "Select search method:",
+        ["Hybrid", "Vector", "BM25"],
+        horizontal=True,
+        help="""
+        - **BM25**: Keyword-based search that works best for exact matches and specific terms.
+        - **Vector**: Semantic search that understands the meaning of your query.
+        - **Hybrid**: Combines both approaches for better overall results.
+        """
+    )
+
+    if search_query:
+        with st.spinner('Searching...'):
+            if search_type == "Hybrid":
+                nodes = hybrid_retriever.retrieve(search_query)
+            elif search_type == "Vector":
+                nodes = vector_retriever.retrieve(search_query)
+            else:  # BM25
+                nodes = bm25_retriever.retrieve(search_query)
             
-            # Format score to 3 decimal places
-            score = f"{node.score:.3f}" if hasattr(node, 'score') else "N/A"
+            # Display results
+            st.subheader(f"Search Results")
             
-            # Create expandable container with new title format
-            with st.expander(f"Result {i} (score: {score})\n\n{preview}", expanded=False):
-                st.markdown(node.text)
-                if node.metadata:
-                    st.markdown("---")
-                    st.markdown("**Source:**")
-                    st.json(node.metadata)
-
-# Add sidebar with information
-with st.sidebar:
-    st.header("About")
-    st.markdown("""
-    This demo searches through Freud's complete works using:
+            for i, node in enumerate(nodes, 1):
+                preview = node.text[:200] + "..." if len(node.text) > 200 else node.text
+                score = f"{node.score:.3f}" if hasattr(node, 'score') else "N/A"
+                
+                with st.expander(f"Result {i} (score: {score})\n\n{preview}", expanded=False):
+                    st.markdown(node.text)
+                    if node.metadata:
+                        st.markdown("---")
+                        st.markdown("**Source:**")
+                        st.json(node.metadata)
+
+    # Add sidebar with information
+    with st.sidebar:
+        st.header("About")
+        st.markdown("""
+        This demo searches through Freud's complete works using:
+        
+        - **BM25**: Traditional keyword-based search
+        - **Vector Search**: Semantic similarity using embeddings
+        - **Hybrid**: Combines both approaches
+        """)
+
+with tab2:
+    st.header("Chat with Freud's Works")
     
-    - **BM25**: Traditional keyword-based search
-    - **Vector Search**: Semantic similarity using embeddings
-    - **Hybrid**: Combines both approaches
-    """)
+    # Create containers in the right order
+    chat_container = st.container()
+    input_container = st.container()
+    options_container = st.container()
+    
+    # System prompt editor in an expander with help text above
+    with options_container:
+        st.info("💡 The system prompt defines the AI's persona and behavior. It's like giving stage directions to an actor.")
+        with st.expander("System Prompt"):
+            st.text_area(
+                "Edit System Prompt",
+                value=st.session_state.system_prompt,
+                height=100,
+                help="This prompt sets the AI's personality and behavior. When RAG is enabled, relevant passages will be automatically added after this prompt.",
+                key="system_prompt",
+                on_change=lambda: setattr(st.session_state, 'system_prompt', st.session_state.system_prompt)
+            )
+        
+        # Put the RAG toggle, chunks slider, and clear button in a horizontal layout
+        col1, col2, col3 = st.columns([2, 2, 1])
+        with col1:
+            st.session_state.use_rag = st.toggle(
+                "Enable RAG (Retrieval Augmented Generation)", 
+                value=st.session_state.get('use_rag', True),
+                key='rag_toggle'
+            )
+        with col2:
+            if st.session_state.use_rag:
+                num_chunks = st.slider(
+                    "Number of chunks to retrieve",
+                    min_value=1,
+                    max_value=3,
+                    value=st.session_state.get('num_chunks', 1),
+                    key='num_chunks_slider'
+                )
+        with col3:
+            if st.button("Clear Chat", use_container_width=True):
+                st.session_state.messages = []
+                st.session_state.sources = {}
+                st.rerun()
+    
+    # Display chat messages in the chat container
+    with chat_container:
+        for i, message in enumerate(st.session_state.messages):
+            with st.chat_message(message["role"]):
+                st.markdown(message["content"])
+                if (message["role"] == "assistant" and 
+                    i in st.session_state.sources and 
+                    st.session_state.sources[i]):
+                    with st.expander("View Sources"):
+                        nodes = st.session_state.sources[i]
+                        for j, node in enumerate(nodes, 1):
+                            st.markdown(f"**Source {j}:**")
+                            st.markdown(node.text)
+                            if node.metadata:
+                                st.markdown("---")
+                                st.markdown("**Metadata:**")
+                                st.json(node.metadata)
+    
+    # Chat input at the bottom
+    with input_container:
+        if prompt := st.chat_input("What would you like to know about Freud's works?", key="chat_input"):
+            st.session_state.messages.append({"role": "user", "content": prompt})
+            with chat_container:
+                with st.chat_message("user"):
+                    st.markdown(prompt)
+
+            with chat_container:
+                with st.chat_message("assistant"):
+                    with st.spinner("Thinking..."):
+                        response = chat_with_rag(
+                            prompt, 
+                            st.session_state.messages[:-1],
+                            hybrid_retriever if st.session_state.use_rag else None
+                        )
+                        st.markdown(response)
+                        st.session_state.messages.append({"role": "assistant", "content": response})
+            
+            st.rerun()
+
+if __name__ == "__main__":
+    pass  # Remove the duplicate title
     

epub2txt-all.py

@@ -596,7 +596,7 @@ def main():
           sys.stderr.write('Error converting {!r}:\n'.format(filename))
           raise
         if len(txt.strip()) > 0:
-          if out is None:
+          if out is None: 
             out = open(args.outfile, "w", encoding="utf-8") if args.outfile != '-' else sys.stdout
           out.write(txt)
           out.flush()

requirements.txt

@@ -1,5 +1,11 @@
 streamlit
+python-dotenv
+litellm
+langchain
+chromadb
+tiktoken
 llama-index
 sentence-transformers 
 llama-index-retrievers-bm25
 llama-index-embeddings-huggingface
+llama-index-llms-groq