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Dockerfile

@@ -1,20 +0,0 @@
-FROM python:3.13.5-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -1,20 +0,0 @@
----
-title: Search Engine LLM
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
-license: apache-2.0
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).

app.py

@@ -0,0 +1,238 @@
+import hashlib
+import os
+
+import streamlit as st
+from dotenv import load_dotenv
+from langchain.agents import create_agent
+from langchain_community.callbacks.streamlit import StreamlitCallbackHandler
+from langchain_community.tools import ArxivQueryRun, DuckDuckGoSearchRun, WikipediaQueryRun
+from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper
+from langchain_chroma import Chroma
+from langchain_core.documents import Document
+from langchain_core.tools import tool
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from pypdf import PdfReader 
+
+
+load_dotenv()
+
+if os.getenv("HF_TOKEN"):
+    os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
+
+
+@st.cache_resource
+def get_embeddings():
+    return HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+
+def build_retriever(uploaded_files):
+    documents = []
+
+    for uploaded_file in uploaded_files:
+        file_bytes = uploaded_file.getvalue()
+
+        if uploaded_file.type == "application/pdf" or uploaded_file.name.lower().endswith(".pdf"):
+            reader = PdfReader(uploaded_file)
+            for page_number, page in enumerate(reader.pages, start=1):
+                page_text = page.extract_text() or ""
+                if page_text.strip():
+                    documents.append(
+                        Document(
+                            page_content=page_text,
+                            metadata={"source": uploaded_file.name, "page": page_number},
+                        )
+                    )
+        else:
+            text = file_bytes.decode("utf-8", errors="ignore")
+            if text.strip():
+                documents.append(
+                    Document(
+                        page_content=text,
+                        metadata={"source": uploaded_file.name},
+                    )
+                )
+
+    if not documents:
+        return None
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=500)
+    splits = text_splitter.split_documents(documents)
+    vectorstore = Chroma.from_documents(documents=splits, embedding=get_embeddings())
+    return vectorstore.as_retriever(search_kwargs={"k": 4})
+
+
+def uploaded_files_signature(uploaded_files):
+    digest = hashlib.sha256()
+    for uploaded_file in uploaded_files:
+        digest.update(uploaded_file.name.encode("utf-8"))
+        digest.update(uploaded_file.getvalue())
+    return digest.hexdigest()
+
+
+def create_documents_tool(retriever):
+    @tool("uploaded_documents")
+    def uploaded_documents(query: str) -> str:
+        """Search the uploaded documents for information relevant to the user's question."""
+        docs = retriever.invoke(query)
+        if not docs:
+            return "No relevant uploaded document content was found."
+
+        chunks = []
+        for doc in docs:
+            source = doc.metadata.get("source", "uploaded document")
+            page = doc.metadata.get("page")
+            label = f"{source}, page {page}" if page else source
+            chunks.append(f"Source: {label}\n{doc.page_content}")
+
+        return "\n\n".join(chunks)
+
+    return uploaded_documents
+
+
+arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+arxiv = ArxivQueryRun(api_wrapper=arxiv_wrapper)
+
+wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200)
+wiki = WikipediaQueryRun(api_wrapper=wiki_wrapper)
+
+search = DuckDuckGoSearchRun(name="Search")
+
+
+def default_messages():
+    return [
+        {
+            "role": "assistant",
+            "content": "Hi, choose tools from the sidebar and ask me anything.",
+        }
+    ]
+
+
+st.set_page_config(page_title="LangChain Enhanced Tools Chat", page_icon="🔎")
+st.title("🔎 LangChain Chat with Selectable Tools")
+st.write(
+    "Choose the tools you want to enable, then ask questions in the chat. "
+    "When document chat is enabled, upload files in the sidebar first."
+)
+
+with st.sidebar:
+    st.header("Settings")
+    api_key = os.getenv("GROQ_API_KEY")
+    if api_key:
+        st.success("Groq API key loaded from .env.")
+    else:
+        st.warning("GROQ_API_KEY is missing from .env or the environment.")
+
+    st.header("Tools")
+    use_search = st.checkbox("Search", value=True)
+    use_wiki = st.checkbox("Wikipedia", value=True)
+    use_arxiv = st.checkbox("Arxiv", value=True)
+    use_documents = st.checkbox("Uploaded documents", value=False)
+
+    uploaded_files = []
+    if use_documents:
+        uploaded_files = st.file_uploader(
+            "Add document/s",
+            type=["pdf", "txt", "md"],
+            accept_multiple_files=True,
+            help="Upload PDFs or text files to chat against them.",
+        )
+
+
+current_tool_selection = {
+    "search": use_search,
+    "wiki": use_wiki,
+    "arxiv": use_arxiv,
+    "documents": use_documents,
+}
+
+if "tool_selection" not in st.session_state:
+    st.session_state["tool_selection"] = current_tool_selection
+elif st.session_state["tool_selection"] != current_tool_selection:
+    st.session_state["tool_selection"] = current_tool_selection
+    st.session_state["messages"] = default_messages()
+    st.session_state["chat_memory"] = []
+    st.toast("Tool selection changed. Chat was reinitialized.")
+
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = default_messages()
+
+if "chat_memory" not in st.session_state:
+    st.session_state["chat_memory"] = []
+
+if "document_retriever_signature" not in st.session_state:
+    st.session_state["document_retriever_signature"] = None
+
+if "document_retriever" not in st.session_state:
+    st.session_state["document_retriever"] = None
+
+
+enabled_tools = []
+if use_search:
+    enabled_tools.append(search)
+if use_wiki:
+    enabled_tools.append(wiki)
+if use_arxiv:
+    enabled_tools.append(arxiv)
+
+if use_documents:
+    if uploaded_files:
+        signature = uploaded_files_signature(uploaded_files)
+        if signature != st.session_state["document_retriever_signature"]:
+            with st.sidebar.spinner("Indexing uploaded documents..."):
+                st.session_state["document_retriever"] = build_retriever(uploaded_files)
+                st.session_state["document_retriever_signature"] = signature
+
+        if st.session_state["document_retriever"]:
+            enabled_tools.append(create_documents_tool(st.session_state["document_retriever"]))
+            st.sidebar.success("Documents are ready for chat.")
+        else:
+            st.sidebar.warning("No readable text was found in the uploaded documents.")
+    else:
+        st.sidebar.info("Upload document/s to enable the document chat tool.")
+else:
+    st.session_state["document_retriever"] = None
+    st.session_state["document_retriever_signature"] = None
+
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+
+if prompt := st.chat_input(placeholder="What is machine learning?"):
+    if not api_key:
+        st.error("GROQ_API_KEY is missing. Add it to your environment or .env file.")
+        st.stop()
+
+    if not enabled_tools:
+        st.error("Select at least one tool from the sidebar before chatting.")
+        st.stop()
+
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.session_state["chat_memory"].append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+
+    llm = ChatGroq(groq_api_key=api_key, model_name="llama-3.1-8b-instant", streaming=True)
+    search_agent = create_agent(
+        model=llm,
+        tools=enabled_tools,
+        system_prompt=(
+            "You are a helpful assistant. Use only the enabled tools when they are useful. "
+            "If uploaded documents are enabled and the user asks about their files, use the "
+            "uploaded_documents tool before answering. Provide concise answers and mention "
+            "document sources when using uploaded document content."
+        ),
+    )
+
+    with st.chat_message("assistant"):
+        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+        result = search_agent.invoke(
+            {"messages": st.session_state["chat_memory"]},
+            config={"callbacks": [st_cb]},
+        )
+        response = result["messages"][-1].content
+        st.session_state.messages.append({"role": "assistant", "content": response})
+        st.session_state["chat_memory"].append({"role": "assistant", "content": response})
+        st.write(response)

requirements.txt

@@ -1,3 +1,15 @@
-altair
-pandas
-streamlit
+arxiv==3.0.0
+chromadb==1.5.7
+ddgs==9.13.1
+langchain==1.2.15
+langchain-chroma==1.1.0
+langchain-community==0.4.1
+langchain-core==1.2.30
+langchain-groq==1.1.2
+langchain-huggingface==1.2.1
+langchain-text-splitters==1.1.1
+pypdf==6.10.2
+python-dotenv==1.2.2
+sentence-transformers==5.4.1
+streamlit==1.56.0
+wikipedia==1.4.0

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))