reworked document adding and most of the project
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# Local RAG Setup
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Minimal RAG implementation with LangChain, FAISS, and support for either Ollama or OpenAI (API-key needed).
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Minimal RAG implementation with LangChain, FAISS, and either **Ollama** (local) or **OpenAI** (API key). A web chat UI is included.
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## Dependencies
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---
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- `langchain` - Core framework
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- `langchain-community` - Loaders, vectorstores
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- `langchain-ollama` - Ollama integration
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- `langchain-openai` - OpenAI integration
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- `langchain-text-splitters` - Text splitting
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- `langchain-huggingface` - HuggingFace embeddings
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- `faiss-cpu` - Vector search
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- `sentence-transformers` - Embeddings
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- `pypdf` - PDF loading
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- `fastapi` - Web server
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- `uvicorn` - ASGI server
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## What you need (before you start)
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## Installation
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- **Python 3.10 or newer** ([python.org](https://www.python.org/downloads/))
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- **Git** (optional, only if you clone the project)
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- Either:
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- **Ollama** installed and running ([ollama.com](https://ollama.com)), with at least one model pulled, **or**
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- An **OpenAI API key** (if you use OpenAI in the chat)
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---
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## Install dependencies (step by step)
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Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/) if you do not have Conda yet.
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All commands below assume your terminal is open **in the project folder** (the folder that contains `requirements.txt`).
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```bash
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conda create -n local_rag python=3.10 -y
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conda activate local_rag
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pip install --upgrade pip
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pip install -r requirements.txt
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```
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## Setup
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Use `conda activate local_rag` in every new terminal session before running `python` or `uvicorn` for this project.
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### Ollama (optional)
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### OpenAI (only if you use the OpenAI provider in the chat)
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In the same terminal **before** starting the server:
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```bash
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export OPENAI_API_KEY="your-key-here"
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```
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On Windows (Command Prompt): `set OPENAI_API_KEY=your-key-here`
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---
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## Run Ollama (only if you use Ollama)
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In a **separate** terminal:
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```bash
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ollama serve
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ollama pull mistral
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```
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### OpenAI (optional)
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Set the API key when using OpenAI:
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In another terminal, pull a model once (example):
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```bash
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export OPENAI_API_KEY="your-key"
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ollama pull gpt-oss:20b
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```
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## Add Documents
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The model name must match what you configure in `server.py` (see [Configuration reference](#configuration-reference)).
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**Option 1:** Add PDFs from a folder via script. Edit `DATA_ROOT` in [add_pdfs.py](add_pdfs.py) to point at your folder, then run:
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---
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## Build the vector store from a folder of PDFs
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The project includes [add_pdfs.py](add_pdfs.py). It finds every **`.pdf`** file under a folder you choose (including subfolders), then chunks, embeds, and saves to FAISS.
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**Two modes** (set in the script):
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| Setting | Behavior |
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|---------|----------|
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| `CLEAR_VECTORSTORE_FIRST = True` | Deletes the existing vector store folder, then builds a **new** index from the PDFs under `DATA_ROOT`. Use this for a full rebuild. |
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| `CLEAR_VECTORSTORE_FIRST = False` | Keeps the current index (if it exists) and **merges** chunks from the PDFs under `DATA_ROOT` into it. Use this to add another batch of PDFs without wiping what you already indexed. |
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**Steps:**
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1. Open [add_pdfs.py](add_pdfs.py) in a text editor.
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2. Set **`DATA_ROOT`** to the folder that contains your PDFs (absolute path or path relative to how you run the script).
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3. Set **`CLEAR_VECTORSTORE_FIRST`** to `True` (fresh index) or `False` (append to existing store).
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4. Optionally set **`VECTORSTORE_PATH`** (default: `./vectorstore`). It must match **`VECTORSTORE_PATH`** in [server.py](server.py) so the chat loads the same index.
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5. From the project folder, with `conda activate local_rag` (or your chosen env name):
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```bash
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python add_pdfs.py
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```
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The script clears the existing vector store and indexes all PDFs recursively. Supports `.pdf`, `.txt`, `.md`.
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Indexing can take a long time for many large PDFs. When it finishes, you should see `Vector store saved to ...`.
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**Option 2:** Use `local_rag.py` programmatically:
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**Note:** This script only indexes **PDF** files. To add `.txt` or `.md` files, use the Python snippet below or call `add_documents` yourself.
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---
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## Add more documents later (alternative to add_pdfs)
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You can also merge files by hand with a short script (any mix of supported types):
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```python
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from local_rag import LocalRAG
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rag = LocalRAG()
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rag.add_documents(["path/to/doc1.pdf", "path/to/doc2.txt"])
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rag = LocalRAG(vectorstore_path="./vectorstore") # same path as server.py
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rag.add_documents([
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"path/to/new1.pdf",
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"path/to/notes.txt",
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])
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```
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## Chat GUI
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`add_documents` merges new chunks into the existing FAISS store and saves it again—the same behavior as [add_pdfs.py](add_pdfs.py) with `CLEAR_VECTORSTORE_FIRST = False`.
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Start the server:
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---
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## Swap or experiment with different vector stores
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The vector index is stored on disk under the folder given by **`VECTORSTORE_PATH`** (default `./vectorstore`). That folder contains files such as `index.faiss` and `index.pkl`.
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**To use a different index:**
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1. Set **`VECTORSTORE_PATH`** in both [server.py](server.py) and any script you use to build the index (e.g. [add_pdfs.py](add_pdfs.py)) to the **same** path, e.g. `./vectorstore_experiment`.
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2. Rebuild the index (run `add_pdfs.py` or `add_documents`) so that folder is created.
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3. **Restart** the web server so it loads the new path at startup.
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**Tips:**
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- Keep multiple copies of the folder (e.g. `vectorstore_backup`, `vectorstore_papers_only`) and swap `VECTORSTORE_PATH` to switch between them.
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- If you change **chunk size**, **embedding model**, or **FAISS** usage in code, treat the old index as incompatible: use a new `VECTORSTORE_PATH` or delete the old folder and rebuild.
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---
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## Run the chat web app
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With the Conda environment activated (`conda activate local_rag`) and (if needed) `OPENAI_API_KEY` set:
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```bash
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uvicorn server:app --reload
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```
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Open [http://localhost:8000](http://localhost:8000). The chat UI provides:
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Open [http://127.0.0.1:8000](http://127.0.0.1:8000) or [http://localhost:8000](http://localhost:8000).
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- **Provider switch** – Toggle between Ollama and OpenAI without restart (OpenAI requires `OPENAI_API_KEY`)
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- **Conversation history** – Multi-turn chat with context
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- **Markdown** – Assistant replies rendered as markdown (headings, code, lists, links)
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- Use the **LLM provider** dropdown: **Ollama** or **OpenAI** (OpenAI only works if the server was started with a valid `OPENAI_API_KEY`).
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- You need a **non-empty vector store** (see above) for answers to work.
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Ensure the vector store is populated and at least one provider (Ollama or OpenAI) is configured.
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---
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## API
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## API (short reference)
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- `POST /api/chat` – `{ "message": "...", "history": [...], "llm_provider": "ollama"|"openai" }`
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- `GET /api/providers` – `{ "ollama": true, "openai": true|false }`
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- `GET /api/health` – Health and vectorstore status
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| Endpoint | Purpose |
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|----------|---------|
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| `POST /api/chat` | Body: `message`, optional `history`, optional `llm_provider` (`ollama` or `openai`) |
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| `GET /api/providers` | Which providers are available (`openai` false if no API key at startup) |
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| `GET /api/health` | Server and whether a vector store is loaded |
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## How it works
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---
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1. **Load documents** – PDFs or text via PyPDFLoader / TextLoader
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2. **Chunk** – RecursiveCharacterTextSplitter (2000 chars, 400 overlap)
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3. **Embed** – sentence-transformers/all-MiniLM-L6-v2
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4. **Store** – FAISS vector store (similarity search with scores)
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5. **Query** – Retrieve chunks, optionally rephrase with conversation history, generate answer with selected LLM
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## How it works (high level)
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1. **Load documents** – PDFs via `PyPDFLoader`, text via `TextLoader`.
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2. **Chunk** – `RecursiveCharacterTextSplitter` (defaults in [local_rag.py](local_rag.py)).
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3. **Embed** – Hugging Face `sentence-transformers/all-MiniLM-L6-v2`.
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4. **Store** – FAISS; retrieval uses `similarity_search_with_score`.
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5. **Query** – Optional rephrase with chat history, retrieval, then answer from the LLM.
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---
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## Configuration reference (what to edit)
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These are the main places to change behavior without restructuring the app.
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### [server.py](server.py)
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| What | Where |
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|------|--------|
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| Ollama model name | `OLLAMA_MODEL = "..."` |
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| OpenAI model name | `OPENAI_MODEL = "..."` |
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| Where the FAISS index is loaded from | `VECTORSTORE_PATH = "./vectorstore"` (must match your indexing script) |
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### [local_rag.py](local_rag.py) – `LocalRAG.__init__`
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| What | Where (approx.) |
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|------|------------------|
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| Default vector store folder | Parameter `vectorstore_path="./vectorstore"` |
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| Embedding model | `HuggingFaceEmbeddings(model_name="sentence-transformers/...")` |
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| Chunk size and overlap | Module-level `CHUNK_SIZE` and `CHUNK_OVERLAP` (used by `RecursiveCharacterTextSplitter` when adding documents) |
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| Default Ollama / OpenAI model strings | Parameters `ollama_model`, `openai_model`, `ollama_base_url` |
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Changing the embedding model or chunk settings requires **rebuilding** the vector store (old index is not compatible).
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### [local_rag.py](local_rag.py) – `query_with_history`
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| What | Where |
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|------|--------|
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| Default number of chunks retrieved (`k`) | Module-level `RETRIEVAL_K` (overrides: pass `k=` to `query` / `query_with_history`) |
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| Extra text appended only to the **FAISS query** (biases retrieval, not the final answer phrasing) | `QUERY_ADDITIONAL_INSTRUCTIONS` (concatenated to the search query before embedding) |
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| **Rephrase** prompt (standalone question when there is chat history) | String `rephrase_prompt = f"""..."""` inside `query_with_history` |
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| **Answer** prompt – opening instructions only | Module-level `ANSWER_PROMPT` (edit the role / style lines). The block from chat history through `Answer:` is built in `query_with_history` |
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### [add_pdfs.py](add_pdfs.py)
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| What | Where |
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|------|--------|
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| Folder to scan for PDFs | `DATA_ROOT = Path("...")` |
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| Output vector store folder | `VECTORSTORE_PATH = "./vectorstore"` (keep in sync with `server.py`) |
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| Wipe index vs merge | `CLEAR_VECTORSTORE_FIRST = True` (delete and rebuild) or `False` (append to existing index) |
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---
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## Dependencies (for developers)
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See [requirements.txt](requirements.txt) for the full list (LangChain, FAISS, sentence-transformers, FastAPI, uvicorn, etc.).
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