first seen here at LlamaIndex π.
Special thanks to the teams behind LlamaIndex
components, HuggingFace
for embedding models, PyMuPDF
for document parsing, and PostgreSQL
for database management.
- Introduction π
- Features π
- Installation π§
- Detailed Usage Guide π
- Configuration Settings βοΈ
- PostgreSQL Quick Start π
- RAG Implementation Insights with LlamaIndex π§
- Videos
βΆοΈ
LlamaIndex is a data retrieval and analysis tool to for process and query large text datasets with advanced machine learning models and database technologies using RAG (Retrieval-Augmented Generation).
- Data Processing π: Efficient document loading with
PyMuPDFReader
, and optimized data handling usingSentenceSplitter
. - Advanced Query Capabilities π: Deep text understanding with
LlamaCPP
, and natural language querying viaQueryBundle
. - Flexible Data Storage ποΈ: Effective vector management in PostgreSQL databases with
PGVectorStore
. - Command Line Interface π: Simplified command-line interface with clear operation logging.
- Environment Configuration π: Set up
LLAMA_MODEL_PATH
,DOCUMENT_PATH
,DB_PASSWORD
. - Database Initialization π οΈ: Initialize PostgreSQL with
PGVectorStore
, and connect usingpsycopg2
.
Install psycopg2
with pip install psycopg2
for PostgreSQL interaction in Python.
- Connection Issues: Check server status, credentials, and firewall settings.
- Performance Bottlenecks: Analyze queries with
EXPLAIN
and optimize indexing. - Locks and Deadlocks: Monitor and manage database locks.
Install necessary Python packages, set up environment variables, and configure PostgreSQL.
- Load documents using
PyMuPDFReader
. - Parse text with
SentenceSplitter
.
- Experimented with various models on Hugging Face, including
BAAI/bge-small-en-v1.5
anddbmdz/bert-base-german-cased
. - Final selection:
TheBloke/em_german_leo_mistral-GGUF
for German content. - Switch models in
HuggingFaceEmbedding
instantiation.
- Initialize PostgreSQL database and connect using
psycopg2
. - Manage document embeddings with
PGVectorStore
.
- Write your query as a string.
- Generate query embeddings.
- Retrieve and rank documents with
VectorDBRetriever
andRetrieverQueryEngine
.
- LlamaCPP Model Settings:
model_path
,temperature
,max_new_tokens
,context_window
,model_kwargs
. - Database Connection Settings:
db_name
,host
,password
,port
,user
. - Vector Store Configuration:
table_name
,embed_dim
. - Document Processing and Query Settings:
chunk_size
,similarity_top_k
.
- Start with
psql -U username -d dbname
. - Connect to a database with
\c dbname
. - Execute SQL files with
\i path/to/file.sql
. - Exit with
\q
.
- Create/Delete Database:
CREATE DATABASE dbname;
,DROP DATABASE dbname;
- Create User:
CREATE USER username WITH PASSWORD 'password';
- Grant Privileges:
GRANT ALL PRIVILEGES ON DATABASE dbname TO username;
- List Databases/Tables:
\l
,\dt
- Display Table Structure:
\d tablename
- Run a Query:
SELECT * FROM tablename;
CREATE EXTENSION IF NOT EXISTS PGVectorStore;
Retrieval-Augmented Generation (RAG) in LlamaIndex enhances data retrieval with a combination of retrieval-based and generative AI models.
- Retrieval System: Uses
PGVectorStore
for vector-based retrieval. - Generative System: Uses models like
LlamaCPP
for generating coherent responses.
- Document Chunking and Embedding with
SentenceSplitter
andHuggingFaceEmbedding
. - Query Processing: Embed queries and match with document embeddings.
- Contextualization and Generation with
LlamaCPP
.
- Chunk Size Optimization: Explore different chunk sizes.
- Contextual Metadata Enhancement: Add semantic tags and thematic links.
- Model Experimentation and Tuning: Continue exploring models for multilingual content.
- Jerry LiuβLlamaIndex β Practical Data Considerations for building Production-Ready LLM Applications
- Building Production-Ready RAG Applications: Jerry Liu
- High-performance RAG with LlamaIndex
- Lessons Learned on LLM RAG Solutions