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**fully local rag agents with llama 3**
**overview**
retrieval-augmented generation (rag) is a powerful technique that combines the strengths of retrieval-based methods and generative models. in the context of conversational agents like llama 3, rag allows the system to retrieve relevant information from a database or knowledge base to enhance the responses generated by the language model.
**fully local rag agents**
fully local rag agents operate without relying on cloud services or external apis. instead, they use a local database or knowledge base to fetch relevant information, which then informs the response generation.
components of a fully local rag agent
1. **knowledge base**: a local database containing information that the rag agent can query. this can be structured as a vector database or a traditional database.
2. **retriever**: a component that takes a query and retrieves relevant information from the knowledge base.
3. **generator**: a language model (like llama 3) that generates text based on the retrieved information and the original query.
steps to create a fully local rag agent
1. **set up the environment**: ensure you have the necessary libraries.
2. **create a local knowledge base**: populate it with relevant information.
3. **implement the retriever**: create a function to retrieve information based on queries.
4. **integrate the generator**: use llama 3 to generate responses using the retrieved data.
5. **testing the rag agent**: run queries against your agent to see how it responds.
code example
below is a basic implementation of a fully local rag agent using python, assuming you have llama 3 installed and a local knowledge base set up.
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from transformers import llamatokenizer, llamaforcausallm
step 1: set up the environment
assume llama 3 is installed with transformers
tokenizer = llamatokenizer.from_pretrained("llama-3")
model = llamaforca ...
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