Update SDK version to 1.43.2 and remove outdated app documentation

README.md

@@ -4,7 +4,7 @@ emoji: 🌍
 colorFrom: pink
 colorTo: indigo
 sdk: streamlit
-sdk_version: 1.42.0
+sdk_version: 1.43.2
 app_file: app.py
 pinned: false
 license: mit

app_documentation.md

@@ -1,127 +0,0 @@
-# Langchain Search Assistant
-
-## Overview
-This application is a powerful research assistant built with Langchain that can search across multiple knowledge sources including Wikipedia, arXiv, and the web via DuckDuckGo. It leverages Groq's LLM capabilities to provide intelligent, context-aware responses to user queries.
-
-## Features
-- **Multi-source search**: Access information from Wikipedia, arXiv scientific papers, and web results
-- **Conversational memory**: Retains context from previous interactions
-- **Streaming responses**: See the AI's response generated in real-time
-- **User-friendly interface**: Clean Streamlit UI for easy interaction
-
-## Technical Components
-- **LLM**: Groq's Llama3-8b-8192 model (with fallback support for Ollama models)
-- **Embeddings**: Hugging Face's all-MiniLM-L6-v2
-- **Search Tools**:
-  - Wikipedia API
-  - arXiv API
-  - DuckDuckGo Search
-- **Framework**: Langchain for agent orchestration
-- **Frontend**: Streamlit
-
-## Project Structure
-- **app.py**: Main application file containing the Streamlit UI and Langchain integration
-- **requirements.txt**: Dependencies required to run the application
-- **README.md**: Project metadata and description for Hugging Face Spaces
-- **tools_agents.ipynb**: Jupyter notebook demonstrating how to use Langchain tools and agents
-- **.github/workflows/main.yaml**: GitHub Actions workflow for deploying to Hugging Face Spaces
-- **.gitattributes**: Git LFS configuration for handling large files
-- **.gitignore**: Standard Python gitignore file
-- **LICENSE**: MIT License file
-- **app_documentation.md**: This comprehensive documentation file
-
-## Implementation Details
-
-### LLM Integration
-The application uses Groq's API to access the Llama3-8b-8192 model with streaming capability:
-
-```python
-llm = ChatGroq(
-    groq_api_key = st.session_state.api_key, 
-    model_name = "Llama3-8b-8192", 
-    streaming = True
-)
-```
-
-Alternative local models can also be configured with Ollama:
-```python
-#llm = ChatOllama(base_url=OLLAMA_WSL_IP, model="llama3.1", streaming=True)
-```
-
-### Search Tools Configuration
-The app configures three primary search tools:
-
-1. **Wikipedia Search**:
-```python
-api_wrapper_wiki = WikipediaAPIWrapper(top_k_results = 3, doc_content_chars_max=10000)
-wiki = WikipediaQueryRun(api_wrapper=api_wrapper_wiki)
-wiki_tool= Tool(
-    name = "Wikipedia",
-    func = wiki.run,
-    description = "This tool uses the Wikipedia API to search for a topic."
-)
-```
-
-2. **arXiv Search**:
-```python
-api_wrapper_arxiv = ArxivAPIWrapper(top_k_results = 5, doc_content_chars_max=10000)
-arxiv = ArxivQueryRun(api_wrapper=api_wrapper_arxiv)
-arxiv_tool = Tool(
-    name = "arxiv",
-    func = arxiv.run,
-    description = "Searches arXiv for papers matching the query.",
-)
-```
-
-3. **DuckDuckGo Web Search**:
-```python
-api_wrapper_ddg = DuckDuckGoSearchAPIWrapper(region="us-en", time="y", max_results=10)
-ddg = DuckDuckGoSearchResults(
-    api_wrapper=api_wrapper_ddg,
-    output_format="string",
-    handle_tool_error=True,
-    handle_validation_error=True)
-ddg_tool = Tool(
-    name = "DuckDuckGo_Search",
-    func = ddg.run,
-    description = "Searches for search queries using the DuckDuckGo Search engine."
-)
-```
-
-### Agent Configuration
-The system uses the CHAT_CONVERSATIONAL_REACT_DESCRIPTION agent type with a conversational memory buffer:
-
-```python
-memory = ConversationBufferWindowMemory(k=5, memory_key="chat_history", return_messages=True)
-
-search_agent = initialize_agent(
-    tools = tools,
-    llm = llm,
-    agent = AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
-    max_iterations = 10,
-    memory = memory,
-    handle_parsing_errors = True)
-```
-
-## Setup Requirements
-1. Groq API key
-2. Hugging Face token (for embeddings)
-3. Python environment with required dependencies
-
-## Installation Instructions
-Install the required packages using:
-
-```bash
-pip install -r requirements.txt
-```
-
-Required packages include:
-- arxiv
-- wikipedia
-- langchain, langchain-community, langchain-huggingface, langchain-groq
-- openai
-- duckduckgo-search
-- ollama, langchain-ollama (for local model support)
-
-## Environment Variables
-Create a `.env` file with the following variables: