Step-by-Step Guide to Implement Local Document Retrieval with Transformer Models

Table of Contents

• Install Required Python Libraries
• Set Up OLama on Local Machine
• Define Local Document Directory
• Download Embedding Model
• Connect OLama Query Engine
• Begin Querying Documents
• Generate New Content from Documents
• Conclusion and Next Steps

Install Required Python Libraries for Local OLama

The first step to setting up OLama locally is to install the required Python libraries. The main ones we need are Llama Index, Transformers, and SentenceTransformers. Llama Index allows us to index and search local documents. Transformers provides access to transformer models like OLama. SentenceTransformers gives us sentence embeddings for semantic search.

We can install these libraries via pip as follows:

Set Up OLama on Local Machine

With the libraries installed, the next step is to set up OLama itself. OLama is the open source implementation of Anthropic's Constitutional AI assistant. It allows us to run AI models like Claude locally.

To set up OLama, we need to install it and launch the server. We can install via pip:

pip install olama

Then to start the server with the LLMA-2 model, we run:

olama run llama2

Define Local Document Directory

Now we need to point OLama at the directory containing our local documents for indexing. This will allow us to search and retrieve information from these files.

Llama Index provides a simple API for this. We create a SimpleDirectoryReader pointing to our docs folder:

Download Embedding Model

To power semantic search, we need an embedding model to encode our documents into vector representations. While we could use OpenAI's embedding, to keep things fully local we'll download a model from Hugging Face.

Llama Index has a handy API for this. We can download and load a model like BLOOM or BLOOM-560M-Encoder:

Connect OLama Query Engine

Now we need to connect our indexed documents and embedding model to OLama so we can query everything. Llama Index provides query packs for this.

We can download the OLama query pack and load it:

Begin Querying Documents

With everything set up, we can now start querying our documents! The query engine provides a simple API for this.

We can pass a query and it will search over the indexed docs, return relevant passages, and send to OLama to generate a final response:

Generate New Content from Documents

In addition to querying, we can leverage OLama to generate new content based on our documents. This unlocks creative applications!

We simply pass a generative prompt to the query engine leveraging the indexed data:

Conclusion

Setting up local document search with OLama provides a powerful capability - no API keys or internet required! We walked through installing OLama, indexing local docs, downloading embeddings, connecting the query engine, querying, and creative generation.

From here there are many directions to explore like fine-tuning models, improving retrieval, and building applications on top of the query engine.

FAQ

Q: What transformer models can I use for local document retrieval?
A: You can use models like OLama and Anthropic's Claude to power local document retrieval without needing access to external APIs.

Q: How long does it take to process documents?
A: Processing time depends on your machine's CPU and RAM capacity and the number/size of documents. Expect longer times for large document sets.

Q: Can I create new content from my documents?
A: Yes, you can use retrieval augmented generation to ask the model to generate tweets, summaries, and other content derived from your private documents.