#8: rename ailab-llama-search to ailab-llamaindex-search

.env.template

@@ -43,8 +43,8 @@ DB_NAME=
 # TABLE_NAME=
 # COLUMN=
 
-# ailab-llama-search parameters. Adapt to your needs.
+# ailab-llamaindex-search parameters. Adapt to your needs.
 # Ask your cloud admin for the secrets.
 LLAMAINDEX_DB_EMBED_MODEL_PARAMS='{"model": "text-embedding-ada-002", "deployment_name": "ada", "api_key": "<azure_openai_api_key>", "api_version": "2023-07-01-preview", "azure_endpoint": "<azure_openai_endpoint>"}'
 LLAMAINDEX_DB_VECTOR_STORE_PARAMS='{"database": "llamaindex_db_legacy", "host": "<postgres_host>", "password": "<postgres_password>", "port": "5432", "user": "<postgres_user>", "embed_dim": 1536}'
-LLAMAINDEX_DB_TRANS_PATHS={"id": "node/metadata/id", "chunk_id": "node/metadata/chunk_id", "url": "node/metadata/url", "title": "node/metadata/title", "subtitle": "node/metadata/subtitle", "tokens_count": "node/metadata/tokens_count", "last_updated": "node/metadata/last_updated", "score": "node/metadata/score", "llama_id": "node/id_", "llama_score": "score", "content": "node/text"}
+LLAMAINDEX_DB_TRANS_PATHS={"id": "node/metadata/id", "chunk_id": "node/metadata/chunk_id", "url": "node/metadata/url", "title": "node/metadata/title", "subtitle": "node/metadata/subtitle", "tokens_count": "node/metadata/tokens_count", "last_updated": "node/metadata/last_updated", "score": "node/metadata/score", "llamaindex_id": "node/id_", "llamaindex_score": "score", "content": "node/text"}

ailab-llama-search/README.md → ailab-llamaindex-search/README.md

@@ -2,18 +2,18 @@
 
 ## Overview
 
-The `ailab-llama-search` package facilitates querying our custom index built using LlamaIndex and PostgresSQL.
+The `ailab-llamaindex-search` package facilitates querying our custom index built using LlamaIndex and PostgresSQL.
 
 ## Installation
 
 ```bash
-pip install git+https://github.com/ai-cfia/llamaindex-db.git@main#subdirectory=ailab-llama-search
+pip install git+https://github.com/ai-cfia/llamaindex-db.git@main#subdirectory=ailab-llamaindex-search
 ```
 
 ## Usage
 
 ```python
-from ailab_llama_search import create_index_object, search
+from ailab_llamaindex_search import create_index_object, search
 
 # adapt these parameters to your needs
 embed_model_params = {
@@ -40,8 +40,8 @@ trans_paths = {
     "tokens_count": "node/metadata/tokens_count",
     "last_updated": "node/metadata/last_updated",
     "score": "node/metadata/score",
-    "llama_id": "node/id_",
-    "llama_score": "score",
+    "llamaindex_id": "node/id_",
+    "llamaindex_score": "score",
     "content": "node/text",
 }
 

ailab-llama-search/ailab_llama_search/__init__.py → ailab-llamaindex-search/ailab_llamaindex_search/__init__.py

@@ -6,8 +6,8 @@
 from llama_index.vector_stores.postgres import PGVectorStore
 
 
-class AilabLlamaSearchError(Exception):
-    """Generic Ailab llama search error."""
+class AilabLlamaIndexSearchError(Exception):
+    """Generic Ailab LlamaIndex search error."""
 
 
 def transform(node_dict: dict, paths: dict):
@@ -25,7 +25,7 @@ def search(
 ):
     if not query:
         logging.error("Empty search query received")
-        raise AilabLlamaSearchError("search query cannot be empty.")
+        raise AilabLlamaIndexSearchError("search query cannot be empty.")
 
     retriever = index.as_retriever(**search_params)
     nodes = retriever.retrieve(query)

ailab-llama-search/setup.py → ailab-llamaindex-search/setup.py

@@ -13,7 +13,7 @@ def read_requirements():
 
 
 setup(
-    name="ailab-llama-search",
+    name="ailab-llamaindex-search",
     version="0.1.0",
     packages=find_packages(),
     install_requires=read_requirements(),

ailab-llama-search/tests/test_ailab_llama_search.py → ailab-llamaindex-search/tests/test_ailab_llamaindex_search.py

@@ -1,8 +1,8 @@
 import unittest
 from unittest.mock import MagicMock, patch
 
-from ailab_llama_search import (
-    AilabLlamaSearchError,
+from ailab_llamaindex_search import (
+    AilabLlamaIndexSearchError,
     VectorStoreIndex,
     create_index_object,
     search,
@@ -49,20 +49,20 @@ def setUp(self):
         self.mock_index.as_retriever.return_value = self.mock_retriever
 
     def test_search_with_empty_query_error(self):
-        with self.assertRaises(AilabLlamaSearchError):
+        with self.assertRaises(AilabLlamaIndexSearchError):
             search("", self.mock_index)
 
-    @patch('ailab_llama_search.transform')
+    @patch('ailab_llamaindex_search.transform')
     def test_search_calls_transform_on_results(self, mock_transform):
         mock_transform.return_value = {'id': 1, 'name': 'Transformed Node'}
         results = search("test query", self.mock_index)
         self.assertTrue(mock_transform.called)
         self.assertEqual(len(results), 1)
         self.assertEqual(results[0], {'id': 1, 'name': 'Transformed Node'})
 
-    @patch('ailab_llama_search.AzureOpenAIEmbedding')
-    @patch('ailab_llama_search.PGVectorStore.from_params')
-    @patch('ailab_llama_search.VectorStoreIndex.from_vector_store')
+    @patch('ailab_llamaindex_search.AzureOpenAIEmbedding')
+    @patch('ailab_llamaindex_search.PGVectorStore.from_params')
+    @patch('ailab_llamaindex_search.VectorStoreIndex.from_vector_store')
     def test_create_index_object_initializes_correctly(self, mock_from_vector_store, mock_from_params, mock_azure_openai_embedding):
         mock_embed_model = MagicMock()
         mock_azure_openai_embedding.return_value = mock_embed_model

ailab-llama-search/tests/test_integration.py → ailab-llamaindex-search/tests/test_integration.py

@@ -2,7 +2,7 @@
 import os
 import unittest
 
-from ailab_llama_search import create_index_object, search
+from ailab_llamaindex_search import create_index_object, search
 from dotenv import load_dotenv
 
 

docs/puml/components.plantuml

@@ -13,16 +13,16 @@ title Component Diagram
 interface REST
 component "finesse-backend" as fb {
     package app as "flask app"
-    package ailab_llama_search 
-    note bottom of ailab_llama_search: pip install ailab-llama-search
+    package ailab_llamaindex_search 
+    note bottom of ailab_llamaindex_search: pip install ailab-llamaindex-search
 }
 database llamaIndexdb {
     package "vector store" as data_llamaindex
 }
 
 REST -- app
-app --> ailab_llama_search: uses
-ailab_llama_search --> data_llamaindex : reads
+app --> ailab_llamaindex_search: uses
+ailab_llamaindex_search --> data_llamaindex : reads
 
 legend
 <img:../img/logo.png{scale=0.2}>

notebooks/design.ipynb

@@ -16,7 +16,7 @@
    "source": [
     "## LlamaIndex integration in finesse\n",
     "\n",
-    "We expose the search functionality as a package (`ailab-llama-search`) for reusability. \n"
+    "We expose the search functionality as a package (`ailab-llamaindex-search`) for reusability. \n"
    ]
   },
   {