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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+2401.08406v3.pdf filter=lfs diff=lfs merge=lfs -text

2401.08406v3.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d14009d8b28062d838bb11ac5cdfb2a3ce5135511bfb4fe343037d3b2309010
+size 1786632

app.py

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+import os
+import gc
+import re
+import uuid
+import subprocess
+import requests
+from dotenv import load_dotenv
+
+os.environ["HF_HOME"] = "weights"
+os.environ["TORCH_HOME"] = "weights"
+import streamlit as st
+
+from llama_index.core import Settings
+from llama_index.llms.ollama import Ollama
+from llama_index.core import PromptTemplate
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core import VectorStoreIndex
+from llama_index.core.storage.storage_context import StorageContext
+
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from llama_index.embeddings.langchain import LangchainEmbedding
+
+from rag_101.retriever import (
+    load_embedding_model,
+    load_reranker_model
+)
+
+# setup the llm
+ollama_url = 'http://localhost:11434/api/chat'
+
+llm = Ollama(model="mistral:instruct", url=ollama_url ,request_timeout=1000.0)
+
+# TODO: setup the embedding model
+lc_embedding_model = load_embedding_model()
+embed_model = LangchainEmbedding(lc_embedding_model)
+
+
+# utility functions
+def parse_github_url(url):
+    pattern = r"https://github\.com/([^/]+)/([^/]+)"
+    match = re.match(pattern, url)
+    return match.groups() if match else (None, None)
+
+
+def clone_repo(repo_url):
+    try:
+        result = subprocess.run(["git", "clone", repo_url], check=True, text=True, capture_output=True)
+        print(result.stdout)
+        return result
+    except subprocess.CalledProcessError as e:
+        print(f"Error occurred: {e.stderr}")
+        raise e
+
+
+def validate_owner_repo(owner, repo):
+    return bool(owner) and bool(repo)
+
+
+if "id" not in st.session_state:
+    st.session_state.id = uuid.uuid4()
+    st.session_state.file_cache = {}
+
+session_id = st.session_state.id
+client = None
+
+
+def reset_chat():
+    st.session_state.messages = []
+    st.session_state.context = None
+    gc.collect()
+
+
+with st.sidebar:
+
+    # input for Github URL
+    github_url = st.text_input("Github Repository URL")
+
+    # button to load and process the github repository
+    process_button = st.button("Load")
+
+    message_container = st.empty()  # placeholder for dynamic messages
+
+    if process_button and github_url:
+        owner, repo = parse_github_url(github_url)
+
+        if validate_owner_repo(owner, repo):
+            with st.spinner(f"Loading {repo} repository by {owner}..."):
+                try:
+                    input_dir_path = f"./{repo}"
+
+                    if not os.path.exists((input_dir_path)):
+                        clone_repo(github_url)
+
+                    if os.path.exists(input_dir_path):
+                        loader = SimpleDirectoryReader(
+                            input_dir=input_dir_path,
+                            required_exts=[".py", ".ipynb", ".js", ".ts", ".md"],
+                            recursive=True
+                        )
+                    else:
+                        st.error('Error occurred while cloning the repo, carefully check the URL')
+                        st.stop()
+
+                    docs = loader.load_data()
+
+                    # TODO: ====== Create vector store and upload data ======
+                    Settings.embed_model = embed_model
+                    index = VectorStoreIndex.from_documents(docs)
+
+                    # setup a query engine
+                    Settings.llm = llm
+                    query_engine = index.as_query_engine(streaming=True, similarity_top_k=4)
+
+                    # customize prompt template
+                    qa_prompt_tmpl_str = (
+                        "Context information is below.\n"
+                        "---------------------\n"
+                        "{context_str}\n"
+                        "---------------------\n"
+                        "Given the context information above I want you to think step by step to answer the query in a crisp manner, in case you don't know the answer say 'I don't know!'.\n"
+                        "Query: {query_str}\n"
+                        "Answer: "
+                    )
+                    qa_prompt_tmpl_str = PromptTemplate(qa_prompt_tmpl_str)
+
+                    query_engine.update_prompts(
+                        {"response_synthesizer:text_qa_template": qa_prompt_tmpl_str}
+                    )
+
+                    if docs:
+                        message_container.success("Data loaded successfully!!")
+                    else:
+                        message_container.write(
+                            "No Data found, check if repository is not empty!!"
+                        )
+                    st.session_state.query_engine = query_engine
+
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+                    st.stop()
+
+                st.success("Ready to chat!")
+        else:
+            st.error('Invalid owner or repo')
+            st.stop()
+
+col1, col2 = st.columns([6, 1])
+
+with col1:
+    st.header(f"Chat with your code! </>")
+
+with col2:
+    st.button("Clear ↺", on_click=reset_chat)
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    reset_chat()
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+
+# Accept user input
+# TODO: old one
+if prompt := st.chat_input("What's up?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+
+        # context = st.session_state.context
+        query_engine = st.session_state.query_engine
+
+        # Simulate stream of response with milliseconds delay
+        streaming_response = query_engine.query(prompt)
+
+        for chunk in streaming_response.response_gen:
+            full_response += chunk
+            message_placeholder.markdown(full_response + "▌")
+
+        # full_response = query_engine.query(prompt)
+
+        message_placeholder.markdown(full_response)
+        # st.session_state.context = ctx
+
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+# todo: new one
+# prompt = st.chat_input("What's up?")
+# if prompt:
+#     # Add user message to chat history
+#     st.session_state.messages.append({"role": "user", "content": prompt})
+#
+#     # Display user message in chat message container
+#     with st.chat_message("user"):
+#         st.markdown(prompt)
+#
+#     # Display assistant response in chat message container
+#     with st.chat_message("assistant"):
+#         message_placeholder = st.empty()
+#         full_response = ""
+#
+#     # context
+#     query_engine = st.session_state.query_engine
+#
+#     # simulate stream of response with milliseconds delay
+#     try:
+#         # Construct the request payload
+#         payload = {
+#             "message": prompt,
+#             "model": "mistral:instruct"
+#         }
+#
+#         # Send the request
+#         response = requests.post(ollama_url, json=payload)
+#
+#         # Check for HTTP errors
+#         response.raise_for_status()
+#
+#         # Print the full response to debug
+#         response_json = response.json()
+#         print(response_json)
+#
+#         # Process the response
+#         if "response_gen" in response_json:
+#             for chunk in response_json["response_gen"]:
+#                 full_response += chunk
+#                 message_placeholder.markdown(full_response + "▌")
+#             message_placeholder.markdown(full_response)
+#
+#             # add assistant response to chat history
+#             st.session_state.messages.append({"role": "assistant", "content": full_response})
+#         else:
+#             st.error("Unexpected response format: 'response_gen' key not found")
+#
+#     except requests.exceptions.HTTPError as e:
+#         st.error(f"HTTP error: {e}")
+
+