Full Code of jlonge4/local_llama for AI

Repository: jlonge4/local_llama
Branch: main
Commit: 50ca6a875687
Files: 6
Total size: 26.6 KB

Directory structure:
gitextract_soot6vu5/

├── LICENSE
├── README.md
├── deprecated/
│   └── local_llama_v2.py
├── local_llama_chat.py
├── local_llama_v3.py
└── requirements.txt

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FILE CONTENTS
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================================================
FILE: LICENSE
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================================================
FILE: README.md
================================================
# Local Llama

This project enables you to chat with your PDFs, TXT files, or Docx files entirely offline, free from OpenAI dependencies. It's an evolution of the gpt_chatwithPDF project, now leveraging local LLMs for enhanced privacy and offline functionality.

## Features

- Offline operation: Run in airplane mode
- Local LLM integration: Uses Ollama for improved performance
- Multiple file format support: PDF, TXT, DOCX, MD
- Persistent vector database: Reusable indexed documents
- Streamlit-based user interface

## New Updates

- Ollama integration for significant performance improvements
- Uses nomic-embed-text and llama3:8b models (can be changed to your liking)
- Upgraded to Haystack 2.0
- Persistent Chroma vector database to enable re-use of previously updloaded docs

## Installation

1. Install Ollama from https://ollama.ai/download
2. Clone this repository
3. Install dependencies:
   ```
   pip install -r requirements.txt
   ```
4. Pull required Ollama models:
   ```
   ollama pull nomic-embed-text
   ollama pull llama3:8b
   ```

## Usage

1. Start the Ollama server:
   ```
   ollama serve
   ```
2. Run the Streamlit app:
   ```
   python -m streamlit run local_llama_v3.py
   ```
3. Upload your documents and start chatting!

## How It Works

1. Document Indexing: Uploaded files are processed, split, and embedded using Ollama.
2. Vector Storage: Embeddings are stored in a local Chroma vector database.
3. Query Processing: User queries are embedded and relevant document chunks are retrieved.
4. Response Generation: Ollama generates responses based on the retrieved context and chat history.


## License

This project is licensed under the Apache 2.0 License.

## Acknowledgements

- Ollama team for their excellent local LLM solution
- Haystack for providing the RAG framework
- The-Bloke for the GGUF models


================================================
FILE: deprecated/local_llama_v2.py
================================================
from llama_cpp import Llama
import streamlit as st
from langchain.llms.base import LLM
from typing import Optional, List, Mapping, Any
from haystack.pipelines import Pipeline
from haystack.pipelines.standard_pipelines import DocumentSearchPipeline
from haystack.document_stores import FAISSDocumentStore
from haystack.nodes import EmbeddingRetriever, TextConverter, FileTypeClassifier, PDFToTextConverter, MarkdownConverter, DocxToTextConverter, PreProcessor
import os

MODEL_NAME = 'llama-2-7b-chat.Q4_K_M.gguf'
MODEL_PATH = "Model Path"
# Number of threads to use
NUM_THREADS = 8

def get_doc_store():
    try:
        document_store = FAISSDocumentStore.load(index_path="my_index.faiss", config_path="my_config.json")
    except:
        document_store = FAISSDocumentStore(embedding_dim=768)
        document_store.save(index_path="my_index.faiss", config_path="my_config.json")
    return document_store

def get_context(query):
    document_store = get_doc_store()
    retriever = EmbeddingRetriever(
            document_store=document_store,
            embedding_model="sentence-transformers/msmarco-bert-base-dot-v5",
            model_format="sentence_transformers"
    )
    pipe = DocumentSearchPipeline(retriever)
    top_k = 1
    answer = pipe.run(
                query=query,
                params={
                    "Retriever": {
                        "top_k": top_k,
                    },
                }
            )
    # st.write(answer['documents'])
    return answer['documents']


def indexing_pipe(filename):
    document_store = get_doc_store()
    file_type_classifier = FileTypeClassifier()

    text_converter = TextConverter()
    pdf_converter = PDFToTextConverter()
    md_converter = MarkdownConverter()
    docx_converter = DocxToTextConverter()
    preprocessor = PreProcessor(
            clean_empty_lines=True,
            clean_whitespace=True,
            clean_header_footer=True,
            split_by="word",
            split_length=300,
            split_overlap=20,
            split_respect_sentence_boundary=True,
        )
    
    retriever = EmbeddingRetriever(
        document_store=document_store,
        embedding_model="sentence-transformers/msmarco-bert-base-dot-v5",
        model_format="sentence_transformers"
    )

    # indexing pipeline
    p = Pipeline()
    p.add_node(component=file_type_classifier, name="FileTypeClassifier", inputs=["File"])
    p.add_node(component=text_converter, name="TextConverter", inputs=["FileTypeClassifier.output_1"])
    p.add_node(component=pdf_converter, name="PdfConverter", inputs=["FileTypeClassifier.output_2"])
    p.add_node(component=md_converter, name="MarkdownConverter", inputs=["FileTypeClassifier.output_3"])
    p.add_node(component=docx_converter, name="DocxConverter", inputs=["FileTypeClassifier.output_4"])
    p.add_node(
        component=preprocessor,
        name="Preprocessor",
        inputs=["TextConverter", "PdfConverter", "MarkdownConverter", "DocxConverter"],
    )
    p.add_node(component=retriever,name='Retriever', inputs=['Preprocessor'])
    p.add_node(component=document_store, name="DocumentStore", inputs=["Retriever"])

    os.makedirs("uploads", exist_ok=True)
    # Save the file to disk
    file_path = os.path.join("uploads", filename.name)
    with open(file_path, "wb") as f:
        f.write(file.getbuffer())

    # Run pipeline on document and add metadata to include doc name
    p.run(file_paths=['uploads/{0}'.format(filename.name)], meta={"document_name": filename.name})

    # Once documents are ran through the pipeline, use this to add embeddings to the datastore
    document_store.save(index_path="my_index.faiss", config_path="my_config.json")
    print(f'Docs match embedding count: {document_store.get_document_count() == document_store.get_embedding_count()}')


class CustomLLM(LLM):
    model_name = MODEL_NAME

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        context = get_context(prompt)
        p = f'Based on the context \n{context} \nAnswer the question {prompt}'
        prompt_length = len(p)
        llm = Llama(model_path=MODEL_PATH, n_threads=NUM_THREADS, n_ctx=2048,)
        output = llm(p, max_tokens=4016, stop=["Human:"], echo=True)['choices'][0]['text']
        # only return newly generated tokens by slicing list to include words after the original prompt
        response = output[prompt_length:]
        st.session_state.messages.append({"role": "user", "content": prompt})
        st.session_state.messages.append({"role": "assistant", "content": response})

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        return {"name_of_model": self.model_name}

    @property
    def _llm_type(self) -> str:
        return "custom"
    

def clear_convo():
    st.session_state['messages'] = []


def init():
    st.set_page_config(page_title='Local Llama', page_icon=':robot_face: ')
    st.sidebar.title('Local Llama')
    if 'messages' not in st.session_state:
        st.session_state['messages'] = []


if __name__ == '__main__':
    init()


    @st.cache_resource
    def get_llm():
        llm = CustomLLM()
        return llm

    clear_button = st.sidebar.button("Clear Conversation", key="clear", on_click=clear_convo)
    file = st.file_uploader("Choose a file to index...", type=['docx', 'pdf', 'txt'])
    clicked = st.button('Upload File', key='Upload')
    if file and clicked:
        with st.spinner('Wait for it...'):
            document_store = indexing_pipe(file)
        st.success('Indexed {0}! Refresh to update indexes.'.format(file.name))

    user_input = st.chat_input("Say something")

    if user_input:
        llm = get_llm()
        llm._call(prompt=user_input)

    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

================================================
FILE: local_llama_chat.py
================================================
from llama_cpp import Llama
import streamlit as st
from langchain.llms.base import LLM
from typing import Optional, List, Mapping, Any

MODEL_NAME = 'llama-2-7b-chat.Q4_K_M.gguf'
MODEL_PATH = "model path"
# Number of threads to use
NUM_THREADS = 8

class CustomLLM(LLM):
    model_name = MODEL_NAME

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        p = f"Human: {prompt} Assistant: "
        prompt_length = len(p)
        llm = Llama(model_path=MODEL_PATH, n_threads=NUM_THREADS)
        output = llm(p, max_tokens=512, stop=["Human:"], echo=True)['choices'][0]['text']
        # only return newly generated tokens by slicing list to include words after the original prompt
        response = output[prompt_length:]
        st.session_state.messages.append({"role": "user", "content": prompt})
        st.session_state.messages.append({"role": "assistant", "content": response})

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        return {"name_of_model": self.model_name}

    @property
    def _llm_type(self) -> str:
        return "custom"


def clear_convo():
    st.session_state['messages'] = []


def init():
    st.set_page_config(page_title='Local Llama', page_icon=':robot_face: ')
    st.sidebar.title('Local Llama')
    if 'messages' not in st.session_state:
        st.session_state['messages'] = []


if __name__ == '__main__':
    init()


    @st.cache_resource
    def get_llm():
        llm = CustomLLM()
        return llm

    clear_button = st.sidebar.button("Clear Conversation", key="clear", on_click=clear_convo)

    user_input = st.chat_input("Say something")

    if user_input:
        llm = get_llm()
        llm._call(prompt=user_input)

    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

================================================
FILE: local_llama_v3.py
================================================
import os
from pathlib import Path

import streamlit as st
from haystack import Pipeline
from haystack.components.converters import PyPDFToDocument
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack_integrations.components.embedders.ollama import (
    OllamaDocumentEmbedder,
    OllamaTextEmbedder,
)
from haystack_integrations.components.retrievers.chroma import ChromaEmbeddingRetriever
from haystack_integrations.document_stores.chroma import ChromaDocumentStore
from ollama import generate

os.environ["HAYSTACK_TELEMETRY_ENABLED"] = "False"


def get_doc_store():
    return ChromaDocumentStore(
        collection_name="mydocs", persist_path="./vec-index", distance_function="cosine"
    )


def get_context(query):
    document_store = get_doc_store()

    query_pipeline = Pipeline()
    query_pipeline.add_component("text_embedder", OllamaTextEmbedder())
    query_pipeline.add_component(
        "retriever", ChromaEmbeddingRetriever(document_store=document_store, top_k=3)
    )

    query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
    result = query_pipeline.run({"text_embedder": {"text": query}})
    context = [doc.content for doc in result["retriever"]["documents"]]
    sources = [doc.meta["page_number"] for doc in result["retriever"]["documents"]]
    files = [doc.meta["file_path"] for doc in result["retriever"]["documents"]]
    final_context = [
        f"Context: {c} (Page: {s}, File: {f})"
        for c, s, f in zip(context, sources, files)
    ]
    # Uncomment for debug st.write(final_context)
    return final_context


def indexing_pipe(filename):
    document_store = get_doc_store()

    pipeline = Pipeline()
    pipeline.add_component("converter", PyPDFToDocument())
    pipeline.add_component(
        "cleaner",
        DocumentCleaner(
            remove_empty_lines=True,
            remove_extra_whitespaces=True,
            remove_repeated_substrings=True,
        ),
    )
    pipeline.add_component(
        "splitter",
        DocumentSplitter(split_by="word", split_length=300, split_overlap=15),
    )
    pipeline.add_component("embedder", OllamaDocumentEmbedder())
    pipeline.add_component("writer", DocumentWriter(document_store=document_store))

    pipeline.connect("converter", "cleaner")
    pipeline.connect("cleaner", "splitter")
    pipeline.connect("splitter", "embedder")
    pipeline.connect("embedder.documents", "writer")

    os.makedirs("uploads", exist_ok=True)
    # Save the file to disk
    file_path = os.path.join("uploads", filename.name)
    with open(file_path, "wb") as f:
        f.write(file.getbuffer())

    pipeline.run({"converter": {"sources": [Path(file_path)]}})


def invoke_ollama(user_input):
    st.session_state.messages.append({"role": "user", "content": user_input})
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    system = f"""You are a helpful assistant that answers users questions and chats. 
        History has been provided in the <history> tags. You must not mention your knowledge of the history to the user,
        only use it to answer follow up questions if needed.
        {{history}}
        {st.session_state.messages}
        {{history}}

        Context to help you answer user's questions have been provided in the <context> tags.
        {{context}}
        {get_context(user_input)}
        {{context}}
        Use ONLY the history and or context provided to answer the question.
        Use as few words as possible to accurately answer."""
    # Uncomment to make llama use a template {"answer": "the answer"}
    # Use the following template: {json.dumps(template)}."""
    prompt_wrapper = f"""You are a helpful assistant that answers users questions and chats. 
    Use the provided history and context to answer the question. 
    {{user_query}}
    {user_input}
    {{user_query}}
    Use as few words as possible to accurately answer, providing citations to the page number and file path from which your answer was synthesized."""

    data = {
        "prompt": prompt_wrapper,
        "model": "llama3:8b",
        "format": "json",
        "stream": True,
        "options": {"top_p": 0.05, "top_k": 5},
    }
    s = ""
    box = st.chat_message("assistant").empty()

    for part in generate(
        model=data["model"],
        prompt=data["prompt"],
        system=system,
        # Format seems equivelant to enforcing a template within the prompt
        # format=data["format"],
        options=data["options"],
        stream=data["stream"],
    ):
        s += part["response"]
        box.write(s)

    st.session_state.messages.append({"role": "assistant", "content": s})


def clear_convo():
    st.session_state["messages"] = []


def init():
    st.set_page_config(page_title="Local Llama", page_icon=":robot_face: ")
    st.sidebar.title("Local Llama")
    if "messages" not in st.session_state:
        st.session_state["messages"] = []


if __name__ == "__main__":
    init()

    clear_button = st.sidebar.button(
        "Clear Conversation", key="clear", on_click=clear_convo
    )
    file = st.file_uploader(
        "Choose a file to index...", type=["docx", "pdf", "txt", "md"]
    )

    # display on sidebar all files within uploads dir
    st.sidebar.markdown("## Uploaded Files")
    uploaded_files = os.listdir("uploads")
    for f in uploaded_files:
        st.sidebar.markdown(f)
    st.sidebar.info(
        """This application stores uploaded files in the 'uploads' directory upon upload and then indexes them into a 
                    locally persisted Chroma Document Store so that you may re-use your documentation as necessary."""
    )
    clicked = st.button("Upload File", key="Upload")
    if file and clicked:
        with st.spinner("Wait for it..."):
            indexing_pipe(file)
        st.success("Indexed {0}!".format(file.name))
    user_input = st.chat_input("Say something")

    if user_input:
        invoke_ollama(user_input=user_input)


================================================
FILE: requirements.txt
================================================
streamlit==1.31.0
chroma-haystack
pathlib
sentence-transformers
haystack-ai==2.2.4
ollama-haystack
pypdf
ollama