feat: Adding OpenAI Compatible Prompts API

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

llama_stack/providers/registry/vector_io.py

@@ -404,6 +404,60 @@ That means you'll get fast and efficient vector retrieval.
 - Easy to use
 - Fully integrated with Llama Stack
 
+There are three implementations of search for PGVectoIndex available:
+
+1. Vector Search:
+- How it works:
+  - Uses PostgreSQL's vector extension (pgvector) to perform similarity search
+  - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics
+  - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance
+
+-Characteristics:
+  - Semantic understanding - finds documents similar in meaning even if they don't share keywords
+  - Works with high-dimensional vector embeddings (typically 768, 1024, or higher dimensions)
+  - Best for: Finding conceptually related content, handling synonyms, cross-language search
+
+2. Keyword Search
+- How it works:
+  - Uses PostgreSQL's full-text search capabilities with tsvector and ts_rank
+  - Converts text to searchable tokens using to_tsvector('english', text). Default language is English.
+  - Eg. SQL query: SELECT document, ts_rank(tokenized_content, plainto_tsquery('english', %s)) AS score
+
+- Characteristics:
+  - Lexical matching - finds exact keyword matches and variations
+  - Uses GIN (Generalized Inverted Index) for fast text search performance
+  - Scoring: Uses PostgreSQL's ts_rank function for relevance scoring
+  - Best for: Exact term matching, proper names, technical terms, Boolean-style queries
+
+3. Hybrid Search
+- How it works:
+  - Combines both vector and keyword search results
+  - Runs both searches independently, then merges results using configurable reranking
+
+- Two reranking strategies available:
+    - Reciprocal Rank Fusion (RRF) - (default: 60.0)
+    - Weighted Average - (default: 0.5)
+
+- Characteristics:
+  - Best of both worlds: semantic understanding + exact matching
+  - Documents appearing in both searches get boosted scores
+  - Configurable balance between semantic and lexical matching
+  - Best for: General-purpose search where you want both precision and recall
+
+4. Database Schema
+The PGVector implementation stores data optimized for all three search types:
+CREATE TABLE vector_store_xxx (
+    id TEXT PRIMARY KEY,
+    document JSONB,                    -- Original document
+    embedding vector(dimension),        -- For vector search
+    content_text TEXT,                 -- Raw text content
+    tokenized_content TSVECTOR          -- For keyword search
+);
+
+-- Indexes for performance
+CREATE INDEX content_gin_idx ON table USING GIN(tokenized_content);  -- Keyword search
+-- Vector index created automatically by pgvector
+
 ## Usage
 
 To use PGVector in your Llama Stack project, follow these steps:
@@ -412,6 +466,25 @@ To use PGVector in your Llama Stack project, follow these steps:
 2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector).
 3. Start storing and querying vectors.
 
+## This is an example how you can set up your environment for using PGVector
+
+1. Export env vars:
+```bash
+export ENABLE_PGVECTOR=true
+export PGVECTOR_HOST=localhost
+export PGVECTOR_PORT=5432
+export PGVECTOR_DB=llamastack
+export PGVECTOR_USER=llamastack
+export PGVECTOR_PASSWORD=llamastack
+```
+
+2. Create DB:
+```bash
+psql -h localhost -U postgres -c "CREATE ROLE llamastack LOGIN PASSWORD 'llamastack';"
+psql -h localhost -U postgres -c "CREATE DATABASE llamastack OWNER llamastack;"
+psql -h localhost -U llamastack -d llamastack -c "CREATE EXTENSION IF NOT EXISTS vector;"
+```
+
 ## Installation
 
 You can install PGVector using docker:
@@ -449,6 +522,7 @@ Weaviate supports:
 - Metadata filtering
 - Multi-modal retrieval
 
+
 ## Usage
 
 To use Weaviate in your Llama Stack project, follow these steps: