feat: Adding OpenAI Compatible Prompts API

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

docs/source/contributing/testing/record-replay.md

@@ -40,18 +40,15 @@ The system patches OpenAI and Ollama client methods to intercept calls before th
 
 ### Storage Architecture
 
-Recordings use a two-tier storage system optimized for both speed and debuggability:
+Recordings are stored as JSON files in the recording directory. They are looked up by their request hash.
 
 ```
 recordings/
-├── index.sqlite          # Fast lookup by request hash
 └── responses/
     ├── abc123def456.json  # Individual response files
     └── def789ghi012.json
 ```
 
-**SQLite index** enables O(log n) hash lookups and metadata queries without loading response bodies.
-
 **JSON files** store complete request/response pairs in human-readable format for debugging.
 
 ## Recording Modes
@@ -166,8 +163,8 @@ This preserves type safety - when replayed, you get the same Pydantic objects wi
 Control recording behavior globally:
 
 ```bash
-export LLAMA_STACK_TEST_INFERENCE_MODE=replay
-export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings
+export LLAMA_STACK_TEST_INFERENCE_MODE=replay   # this is the default
+export LLAMA_STACK_TEST_RECORDING_DIR=/path/to/recordings   # default is tests/integration/recordings
 pytest tests/integration/
 ```
 

docs/source/distributions/self_hosted_distro/nvidia.md

@@ -50,6 +50,7 @@ The following models are available by default:
 - `meta/llama-3.2-11b-vision-instruct `
 - `meta/llama-3.2-90b-vision-instruct `
 - `meta/llama-3.3-70b-instruct `
+- `nvidia/vila `
 - `nvidia/llama-3.2-nv-embedqa-1b-v2 `
 - `nvidia/nv-embedqa-e5-v5 `
 - `nvidia/nv-embedqa-mistral-7b-v2 `

docs/source/providers/vector_io/remote_pgvector.md

@@ -12,6 +12,60 @@ 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:
@@ -20,6 +74,25 @@ 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:

docs/source/providers/vector_io/remote_weaviate.md

@@ -17,6 +17,7 @@ Weaviate supports:
 - Metadata filtering
 - Multi-modal retrieval
 
+
 ## Usage
 
 To use Weaviate in your Llama Stack project, follow these steps:

docs/source/references/llama_stack_client_cli_reference.md

@@ -478,7 +478,6 @@ llama-stack-client scoring_functions list
 ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓
 ┃ identifier                                 ┃ provider_id  ┃ description                                                   ┃ type             ┃
 ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩
-│ basic::bfcl                                │ basic        │ BFCL complex scoring                                          │ scoring_function │
 │ basic::docvqa                              │ basic        │ DocVQA Visual Question & Answer scoring function              │ scoring_function │
 │ basic::equality                            │ basic        │ Returns 1.0 if the input is equal to the target, 0.0          │ scoring_function │
 │                                            │              │ otherwise.                                                    │                  │