From 3cd5106e8fe2faf0f21d372d96bed1674f2812a4 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Sun, 18 May 2025 16:12:57 -0700
Subject: [PATCH 01/40] update product recommendation example description

---
 README.md                                 | 2 +-
 examples/product_recommendation/README.md | 6 ++----
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 8b3cb3a2..3c7a9a93 100644
--- a/README.md
+++ b/README.md
@@ -137,7 +137,7 @@ It defines an index flow like this:
 | [Docs to Knowledge Graph](examples/docs_to_knowledge_graph) | Extract relationships from Markdown documents and build a knowledge graph |
 | [Embeddings to Qdrant](examples/text_embedding_qdrant) | Index documents in a Qdrant collection for semantic search |
 | [FastAPI Server with Docker](examples/fastapi_server_docker) | Run the semantic search server in a Dockerized FastAPI setup | 
-| [Product Recommendation](examples/product_recommendation) | Build real-time product recommendations with LLM and graph database| 
+| [Recommendation Engine with Knowledge Graph](examples/product_taxonomy_knowledge_graph) | Build real-time product recommendations with LLM and knowledge graph | 
 | [Image Search with Vision API](examples/image_search_example) | Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend|
 
 More coming and stay tuned 👀!
diff --git a/examples/product_recommendation/README.md b/examples/product_recommendation/README.md
index 96565782..24da1f06 100644
--- a/examples/product_recommendation/README.md
+++ b/examples/product_recommendation/README.md
@@ -1,8 +1,6 @@
-# Build Real-Time Recommendation Engine with LLM and Graph Database
+# Build Real-Time Recommendation Engine with LLM and Knowledge Graph
 
-We will build a real-time product recommendation engine with LLM and graph database. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). 
-
-We will use Graph to explore the relationships between products that can be further used for product recommendations or labeling.
+We will process a list of products and use LLM to extract the taxonomy and complimentary taxonomy for each product and find connections between products.
 
 Please drop [CocoIndex on Github](https://github.com/cocoindex-io/cocoindex) a star to support us and stay tuned for more updates. Thank you so much 🥥🤗. [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 

From e32cfea69db9d46698dbcb663198f6040292a862 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Sun, 18 May 2025 16:54:12 -0700
Subject: [PATCH 02/40] rename folder

---
 examples/product_recommendation/README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/product_recommendation/README.md b/examples/product_recommendation/README.md
index 24da1f06..c554655b 100644
--- a/examples/product_recommendation/README.md
+++ b/examples/product_recommendation/README.md
@@ -1,6 +1,8 @@
 # Build Real-Time Recommendation Engine with LLM and Knowledge Graph
 
-We will process a list of products and use LLM to extract the taxonomy and complimentary taxonomy for each product and find connections between products.
+We will build a real-time product recommendation engine with LLM and knowledge graph. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). 
+
+We will use Knowledge Graph to explore the relationships between products that can be further used for product recommendations or labeling.
 
 Please drop [CocoIndex on Github](https://github.com/cocoindex-io/cocoindex) a star to support us and stay tuned for more updates. Thank you so much 🥥🤗. [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 

From dddee93334a7ad8121f84d35e6f31adc01e68adb Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Sun, 18 May 2025 16:54:40 -0700
Subject: [PATCH 03/40] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3c7a9a93..b09e7621 100644
--- a/README.md
+++ b/README.md
@@ -137,7 +137,7 @@ It defines an index flow like this:
 | [Docs to Knowledge Graph](examples/docs_to_knowledge_graph) | Extract relationships from Markdown documents and build a knowledge graph |
 | [Embeddings to Qdrant](examples/text_embedding_qdrant) | Index documents in a Qdrant collection for semantic search |
 | [FastAPI Server with Docker](examples/fastapi_server_docker) | Run the semantic search server in a Dockerized FastAPI setup | 
-| [Recommendation Engine with Knowledge Graph](examples/product_taxonomy_knowledge_graph) | Build real-time product recommendations with LLM and knowledge graph | 
+| [Product Recommendation with Knowledge Graph](examples/product_recommendation) | Build real-time product recommendations with LLM and knowledge graph | 
 | [Image Search with Vision API](examples/image_search_example) | Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend|
 
 More coming and stay tuned 👀!

From e43a28ebe676ee1584819513d576b4e1b8e6f341 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Sun, 18 May 2025 16:56:18 -0700
Subject: [PATCH 04/40] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b09e7621..8b3cb3a2 100644
--- a/README.md
+++ b/README.md
@@ -137,7 +137,7 @@ It defines an index flow like this:
 | [Docs to Knowledge Graph](examples/docs_to_knowledge_graph) | Extract relationships from Markdown documents and build a knowledge graph |
 | [Embeddings to Qdrant](examples/text_embedding_qdrant) | Index documents in a Qdrant collection for semantic search |
 | [FastAPI Server with Docker](examples/fastapi_server_docker) | Run the semantic search server in a Dockerized FastAPI setup | 
-| [Product Recommendation with Knowledge Graph](examples/product_recommendation) | Build real-time product recommendations with LLM and knowledge graph | 
+| [Product Recommendation](examples/product_recommendation) | Build real-time product recommendations with LLM and graph database| 
 | [Image Search with Vision API](examples/image_search_example) | Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend|
 
 More coming and stay tuned 👀!

From ade9afb527f9d7dc8afc14c07419764865ff4031 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Sun, 18 May 2025 17:07:15 -0700
Subject: [PATCH 05/40] Update README.md

---
 examples/product_recommendation/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/product_recommendation/README.md b/examples/product_recommendation/README.md
index c554655b..51552a6b 100644
--- a/examples/product_recommendation/README.md
+++ b/examples/product_recommendation/README.md
@@ -1,6 +1,6 @@
-# Build Real-Time Recommendation Engine with LLM and Knowledge Graph
+# Build Real-Time Recommendation Engine with LLM and Graph Database
 
-We will build a real-time product recommendation engine with LLM and knowledge graph. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). 
+We will build a real-time product recommendation engine with LLM and graph database. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). 
 
 We will use Knowledge Graph to explore the relationships between products that can be further used for product recommendations or labeling.
 

From 2bead8794e35eb0eebe2628927f86b6b94a1d0a3 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Sun, 18 May 2025 17:07:46 -0700
Subject: [PATCH 06/40] Update README.md

---
 examples/product_recommendation/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/product_recommendation/README.md b/examples/product_recommendation/README.md
index 51552a6b..96565782 100644
--- a/examples/product_recommendation/README.md
+++ b/examples/product_recommendation/README.md
@@ -2,7 +2,7 @@
 
 We will build a real-time product recommendation engine with LLM and graph database. In particular, we will use LLM to understand the category (taxonomy) of a product. In addition, we will use LLM to enumerate the complementary products - users are likely to buy together with the current product (pencil and notebook). 
 
-We will use Knowledge Graph to explore the relationships between products that can be further used for product recommendations or labeling.
+We will use Graph to explore the relationships between products that can be further used for product recommendations or labeling.
 
 Please drop [CocoIndex on Github](https://github.com/cocoindex-io/cocoindex) a star to support us and stay tuned for more updates. Thank you so much 🥥🤗. [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 

From a37465081314654d65508856fd1378dcdfc5bb1b Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 17:49:08 -0700
Subject: [PATCH 07/40] update text_embedding with new query handler

---
 examples/text_embedding/README.md | 5 ++++-
 examples/text_embedding/main.py   | 1 -
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 1e999882..fc821c6c 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -1,4 +1,7 @@
-# Build text embedding and semantic search 🔍
+Build text embedding and semantic search based on local files.
+
+In this example, we will build a text embedding index and a semantic search flow based on local markdown files.
+
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cocoindex-io/cocoindex/blob/main/examples/text_embedding/Text_Embedding.ipynb)
 [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
diff --git a/examples/text_embedding/main.py b/examples/text_embedding/main.py
index e69e1e7c..581f62c6 100644
--- a/examples/text_embedding/main.py
+++ b/examples/text_embedding/main.py
@@ -42,7 +42,6 @@ def text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoind
                 field_name="embedding",
                 metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])
 
-
 def search(pool: ConnectionPool, query: str, top_k: int = 5):
     # Get the table name, for the export target in the text_embedding_flow above.
     table_name = cocoindex.utils.get_target_storage_default_name(text_embedding_flow, "doc_embeddings")

From 3df050d01251413fe920c22cfa04a59bd90bac31 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:14:44 -0700
Subject: [PATCH 08/40] Update main.py

---
 examples/text_embedding/main.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/text_embedding/main.py b/examples/text_embedding/main.py
index 581f62c6..e69e1e7c 100644
--- a/examples/text_embedding/main.py
+++ b/examples/text_embedding/main.py
@@ -42,6 +42,7 @@ def text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoind
                 field_name="embedding",
                 metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])
 
+
 def search(pool: ConnectionPool, query: str, top_k: int = 5):
     # Get the table name, for the export target in the text_embedding_flow above.
     table_name = cocoindex.utils.get_target_storage_default_name(text_embedding_flow, "doc_embeddings")

From 0471abec519d46af2902862d75fc0dd402d99e32 Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:01:29 -0700
Subject: [PATCH 09/40] Update README.md

---
 examples/text_embedding/README.md | 22 +++++-----------------
 1 file changed, 5 insertions(+), 17 deletions(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index fc821c6c..9ddc9d7b 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -1,27 +1,15 @@
-Build text embedding and semantic search based on local files.
-
-In this example, we will build a text embedding index and a semantic search flow based on local markdown files.
-
+# Build text embedding and semantic search 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cocoindex-io/cocoindex/blob/main/examples/text_embedding/Text_Embedding.ipynb)
-[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
-In this example, we will build index flow from text embedding from local markdown files, and query the index.
 
-We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
+In this example, we will build a text embedding index and a semantic search flow based on local markdown files.
 
-## Steps:
-🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)
 
-### Indexing Flow:
 <img width="461" alt="Screenshot 2025-05-19 at 5 48 28 PM" src="https://github.com/user-attachments/assets/b6825302-a0c7-4b86-9a2d-52da8286b4bd" />
 
-1. We will ingest from a list of local files.
-2. For each file, perform chunking (Recursive Split) and then embeddings. 
-3. We will save the embeddings and the metadata in Postgres with PGVector.
-   
-### Query:
-We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
-
+We will ingest from a list of local files. For each file, perform chunking (Recursive Split) and then embeddings. 
+We will save the embeddings and the metadata in Postgres with PGVector.
+And then add a simpler query handler for semantic search.
 
 ## Prerequisite
 

From c3c09b0d1cab00c4971365e4b2d1b99ac53d4b81 Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:02:01 -0700
Subject: [PATCH 10/40] Update README.md

---
 examples/text_embedding/README.md | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 9ddc9d7b..01db5953 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -7,9 +7,10 @@ In this example, we will build a text embedding index and a semantic search flow
 
 <img width="461" alt="Screenshot 2025-05-19 at 5 48 28 PM" src="https://github.com/user-attachments/assets/b6825302-a0c7-4b86-9a2d-52da8286b4bd" />
 
-We will ingest from a list of local files. For each file, perform chunking (Recursive Split) and then embeddings. 
-We will save the embeddings and the metadata in Postgres with PGVector.
-And then add a simpler query handler for semantic search.
+- We will ingest from a list of local files.
+- For each file, perform chunking (Recursive Split) and then embeddings. 
+- We will save the embeddings and the metadata in Postgres with PGVector.
+- And then add a simpler query handler for semantic search.
 
 ## Prerequisite
 

From bab25a4db17b2d79bbe0d39b422eeab312a79d71 Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:11:22 -0700
Subject: [PATCH 11/40] Update README.md

---
 examples/text_embedding/README.md | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 01db5953..618f854c 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -1,16 +1,21 @@
-# Build text embedding and semantic search 
+# Build text embedding and semantic search 🔍
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cocoindex-io/cocoindex/blob/main/examples/text_embedding/Text_Embedding.ipynb)
+[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
+In this example, we will build index flow from text embedding from local markdown files. And build semantic search with simple query handler.
 
-In this example, we will build a text embedding index and a semantic search flow based on local markdown files.
-
+We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
 <img width="461" alt="Screenshot 2025-05-19 at 5 48 28 PM" src="https://github.com/user-attachments/assets/b6825302-a0c7-4b86-9a2d-52da8286b4bd" />
 
-- We will ingest from a list of local files.
-- For each file, perform chunking (Recursive Split) and then embeddings. 
-- We will save the embeddings and the metadata in Postgres with PGVector.
-- And then add a simpler query handler for semantic search.
+Steps:
+1. We will ingest from a list of local files.
+2. For each file, perform chunking (Recursive Split) and then embeddings. 
+3. We will save the embeddings and the metadata in Postgres with PGVector.
+4. And then add a simpler query handler for semantic search.
+
+🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)
+
 
 ## Prerequisite
 

From 4f0b607ffb95a8eb805757f8c2fcfe7868927228 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:20:44 -0700
Subject: [PATCH 12/40] Update README.md

---
 examples/text_embedding/README.md | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 618f854c..aba3e16d 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -2,17 +2,20 @@
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cocoindex-io/cocoindex/blob/main/examples/text_embedding/Text_Embedding.ipynb)
 [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
-In this example, we will build index flow from text embedding from local markdown files. And build semantic search with simple query handler.
+In this example, we will build index flow from text embedding from local markdown files. And provide an simple example to query the index.
 
 We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
 <img width="461" alt="Screenshot 2025-05-19 at 5 48 28 PM" src="https://github.com/user-attachments/assets/b6825302-a0c7-4b86-9a2d-52da8286b4bd" />
 
 Steps:
-1. We will ingest from a list of local files.
-2. For each file, perform chunking (Recursive Split) and then embeddings. 
-3. We will save the embeddings and the metadata in Postgres with PGVector.
-4. And then add a simpler query handler for semantic search.
+- Indexing Flow:
+    1. We will ingest from a list of local files.
+    2. For each file, perform chunking (Recursive Split) and then embeddings. 
+    3. We will save the embeddings and the metadata in Postgres with PGVector.
+
+- Query:
+1. We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
 
 🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)
 

From 9ffe7ed4a73363abfdebaa5bd349249c94c83c1c Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:21:39 -0700
Subject: [PATCH 13/40] Update README.md

---
 examples/text_embedding/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index aba3e16d..809b2de7 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -2,7 +2,7 @@
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cocoindex-io/cocoindex/blob/main/examples/text_embedding/Text_Embedding.ipynb)
 [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
-In this example, we will build index flow from text embedding from local markdown files. And provide an simple example to query the index.
+In this example, we will build index flow from text embedding from local markdown files, and query the index.
 
 We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 

From 631cad75c4625b8fc6f68736d6dbb0235575e18f Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:29:22 -0700
Subject: [PATCH 14/40] Update README.md

---
 examples/text_embedding/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 809b2de7..32c91d1b 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -15,7 +15,7 @@ Steps:
     3. We will save the embeddings and the metadata in Postgres with PGVector.
 
 - Query:
-1. We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
+We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
 
 🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)
 

From 8008d85d91b6c02c5691fe6ecc01dfebe3f1d3d3 Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Mon, 19 May 2025 18:23:39 -0700
Subject: [PATCH 15/40] Update README.md

---
 examples/text_embedding/README.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 32c91d1b..f5b39f97 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -6,18 +6,18 @@ In this example, we will build index flow from text embedding from local markdow
 
 We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
-<img width="461" alt="Screenshot 2025-05-19 at 5 48 28 PM" src="https://github.com/user-attachments/assets/b6825302-a0c7-4b86-9a2d-52da8286b4bd" />
-
-Steps:
-- Indexing Flow:
-    1. We will ingest from a list of local files.
-    2. For each file, perform chunking (Recursive Split) and then embeddings. 
-    3. We will save the embeddings and the metadata in Postgres with PGVector.
+## Steps:
+🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)
 
-- Query:
-We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
+### Indexing Flow:
+<img width="461" alt="Screenshot 2025-05-19 at 5 48 28 PM" src="https://github.com/user-attachments/assets/b6825302-a0c7-4b86-9a2d-52da8286b4bd" />
 
-🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)
+1. We will ingest from a list of local files.
+2. For each file, perform chunking (Recursive Split) and then embeddings. 
+3. We will save the embeddings and the metadata in Postgres with PGVector.
+   
+### Query:
+1. We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
 
 
 ## Prerequisite

From 2aa1bc8c3048d4ae1e9367aeb67cea5ba3e80c41 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 19:13:27 -0700
Subject: [PATCH 16/40] qdrant

---
 examples/text_embedding_qdrant/README.md      | 16 ++++---
 examples/text_embedding_qdrant/main.py        | 43 +++++++++++--------
 examples/text_embedding_qdrant/pyproject.toml |  2 +-
 3 files changed, 36 insertions(+), 25 deletions(-)

diff --git a/examples/text_embedding_qdrant/README.md b/examples/text_embedding_qdrant/README.md
index 5e2ea059..55091faf 100644
--- a/examples/text_embedding_qdrant/README.md
+++ b/examples/text_embedding_qdrant/README.md
@@ -1,6 +1,10 @@
 ## Description
+# Build text embedding and semantic search 🔍 with Qdrant
 
-Example to build a vector index in Qdrant based on local files.
+[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
+
+In this example, we will build index flow from text embedding from local markdown files, and query the index.
+We will use **Qdrant** as the vector database.
 
 ## Pre-requisites
 
@@ -57,13 +61,13 @@ python main.py
 ```
 
 ## CocoInsight
-
-CocoInsight is in Early Access now (Free) 😊 You found us! A quick 3 minute video tutorial about CocoInsight: [Watch on YouTube](https://youtu.be/ZnmyoHslBSc?si=pPLXWALztkA710r9).
-
-Run CocoInsight to understand your RAG data pipeline:
+I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline. 
+It just connects to your local CocoIndex server, with Zero pipeline data retention. Run following command to start CocoInsight:
 
 ```bash
 python main.py cocoindex server -ci
 ```
 
-Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).
+Open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).
+
+
diff --git a/examples/text_embedding_qdrant/main.py b/examples/text_embedding_qdrant/main.py
index 57f27a45..fd15ba73 100644
--- a/examples/text_embedding_qdrant/main.py
+++ b/examples/text_embedding_qdrant/main.py
@@ -1,21 +1,22 @@
 from dotenv import load_dotenv
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Filter, FieldCondition, MatchValue
 
 import cocoindex
 
 
-def text_to_embedding(text: cocoindex.DataSlice) -> cocoindex.DataSlice:
+@cocoindex.transform_flow()
+def text_to_embedding(text: cocoindex.DataSlice[str]) -> cocoindex.DataSlice[list[float]]:
     """
     Embed the text using a SentenceTransformer model.
     This is a shared logic between indexing and querying, so extract it as a function.
     """
     return text.transform(
         cocoindex.functions.SentenceTransformerEmbed(
-            model="sentence-transformers/all-MiniLM-L6-v2"
-        )
-    )
+            model="sentence-transformers/all-MiniLM-L6-v2"))
 
 
-@cocoindex.flow_def(name="TextEmbedding")
+@cocoindex.flow_def(name="TextEmbeddingWithQdrant")
 def text_embedding_flow(
     flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
 ):
@@ -57,28 +58,34 @@ def text_embedding_flow(
     )
 
 
-query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
-    name="SemanticsSearch",
-    flow=text_embedding_flow,
-    target_name="doc_embeddings",
-    query_transform_flow=text_to_embedding,
-    default_similarity_metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY,
-)
-
-
 @cocoindex.main_fn()
 def _run():
+    # Initialize Qdrant client
+    client = QdrantClient(host="localhost", port=6333)
+    
     # Run queries in a loop to demonstrate the query capabilities.
     while True:
         try:
             query = input("Enter search query (or Enter to quit): ")
             if query == "":
                 break
-            results, _ = query_handler.search(query, 10, "text_embedding")
+            
+            # Get the embedding for the query
+            query_embedding = text_to_embedding.eval(query)
+            
+            # Search in Qdrant
+            search_results = client.search(
+                collection_name="cocoindex",
+                query_vector=("text_embedding", query_embedding),
+                limit=10
+            )
+            
             print("\nSearch results:")
-            for result in results:
-                print(f"[{result.score:.3f}] {result.data['filename']}")
-                print(f"    {result.data['text']}")
+            for result in search_results:
+                score = result.score
+                payload = result.payload
+                print(f"[{score:.3f}] {payload['filename']}")
+                print(f"    {payload['text']}")
                 print("---")
             print()
         except KeyboardInterrupt:
diff --git a/examples/text_embedding_qdrant/pyproject.toml b/examples/text_embedding_qdrant/pyproject.toml
index 25b2663c..70454200 100644
--- a/examples/text_embedding_qdrant/pyproject.toml
+++ b/examples/text_embedding_qdrant/pyproject.toml
@@ -3,7 +3,7 @@ name = "text-embedding-qdrant"
 version = "0.1.0"
 description = "Simple example for cocoindex: build embedding index based on local text files."
 requires-python = ">=3.10"
-dependencies = ["cocoindex>=0.1.39", "python-dotenv>=1.0.1"]
+dependencies = ["cocoindex>=0.1.39", "python-dotenv>=1.0.1", "qdrant-client>=1.6.0"]
 
 [tool.setuptools]
 packages = []

From c76db2dd9f4ed5d39f35d52801a3497abecb2c6c Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 19:15:44 -0700
Subject: [PATCH 17/40] Update README.md

---
 examples/text_embedding/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index f5b39f97..1e999882 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -17,7 +17,7 @@ We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 3. We will save the embeddings and the metadata in Postgres with PGVector.
    
 ### Query:
-1. We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
+We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
 
 
 ## Prerequisite

From f5e965df731d3ee0f667d388d56a0cfaceeda167 Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Mon, 19 May 2025 19:22:36 -0700
Subject: [PATCH 18/40] Update README.md

---
 examples/text_embedding_qdrant/README.md | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/examples/text_embedding_qdrant/README.md b/examples/text_embedding_qdrant/README.md
index 55091faf..17fb118b 100644
--- a/examples/text_embedding_qdrant/README.md
+++ b/examples/text_embedding_qdrant/README.md
@@ -1,14 +1,28 @@
-## Description
 # Build text embedding and semantic search 🔍 with Qdrant
 
 [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
-In this example, we will build index flow from text embedding from local markdown files, and query the index.
-We will use **Qdrant** as the vector database.
+CocoIndex supports Qdrant natively - [documentation](https://cocoindex.io/docs/ops/storages#qdrant). In this example, we will build index flow from text embedding from local markdown files, and query the index. We will use **Qdrant** as the vector database.
+
+We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
+
+<img width="1227" alt="coco feat qdrant" src="https://github.com/user-attachments/assets/a7b69fc1-d975-4a73-8677-d1315b0f06e7" />
+
+## Steps:
+### Indexing Flow:
+<img width="480" alt="Screenshot 2025-05-19 at 7 19 50 PM" src="https://github.com/user-attachments/assets/44d47b5e-b49b-4f05-9a00-dcb8027602a1" />
+
+1. We will ingest from a list of local files.
+2. For each file, perform chunking (Recursive Split) and then embeddings. 
+3. We will save the embeddings and the metadata in Postgres with PGVector.
+   
+### Query:
+We will be use Qdrant client to query the index, reusing the embedding operation in the indexing flow.
+
 
 ## Pre-requisites
 
-- [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
+- [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one. Even the target store is Qdrant, CocoIndex uses Postgress to track the data lineage for incremental processing.
 
 - Run Qdrant.
 

From eed7a258a510ba7b5be28ca46eed51b92d96214b Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Mon, 19 May 2025 19:25:08 -0700
Subject: [PATCH 19/40] Update README.md

---
 examples/text_embedding_qdrant/README.md | 70 ++++++++++++------------
 1 file changed, 35 insertions(+), 35 deletions(-)

diff --git a/examples/text_embedding_qdrant/README.md b/examples/text_embedding_qdrant/README.md
index 17fb118b..bb497f6a 100644
--- a/examples/text_embedding_qdrant/README.md
+++ b/examples/text_embedding_qdrant/README.md
@@ -6,7 +6,7 @@ CocoIndex supports Qdrant natively - [documentation](https://cocoindex.io/docs/o
 
 We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
-<img width="1227" alt="coco feat qdrant" src="https://github.com/user-attachments/assets/a7b69fc1-d975-4a73-8677-d1315b0f06e7" />
+<img width="860" alt="Screenshot 2025-05-19 at 7 24 13 PM" src="https://github.com/user-attachments/assets/a9deecfa-dd94-4b97-a1b1-90488d8178df" />
 
 ## Steps:
 ### Indexing Flow:
@@ -26,53 +26,53 @@ We will be use Qdrant client to query the index, reusing the embedding operation
 
 - Run Qdrant.
 
-```bash
-docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
-```
+   ```bash
+   docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
+   ```
 
 - [Create a collection](https://qdrant.tech/documentation/concepts/vectors/#named-vectors) to export the embeddings to.
 
-```bash
-curl  -X PUT \
-  'http://localhost:6333/collections/cocoindex' \
-  --header 'Content-Type: application/json' \
-  --data-raw '{
-  "vectors": {
-    "text_embedding": {
-      "size": 384,
-      "distance": "Cosine"
-    }
-  }
-}'
-```
-
-You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.
+   ```bash
+   curl  -X PUT \
+     'http://localhost:6333/collections/cocoindex' \
+     --header 'Content-Type: application/json' \
+     --data-raw '{
+     "vectors": {
+       "text_embedding": {
+         "size": 384,
+         "distance": "Cosine"
+       }
+     }
+   }'
+   ```
+
+   You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.
 
 ## Run
 
-Install dependencies:
+- Install dependencies:
 
-```bash
-pip install -e .
-```
+   ```bash
+   pip install -e .
+   ```
 
-Setup:
+- Setup:
 
-```bash
-python main.py cocoindex setup
-```
+   ```bash
+   python main.py cocoindex setup
+   ```
 
-Update index:
+- Update index:
 
-```bash
-python main.py cocoindex update
-```
+   ```bash
+   python main.py cocoindex update
+   ```
 
-Run:
+- Run:
 
-```bash
-python main.py
-```
+   ```bash
+   python main.py
+   ```
 
 ## CocoInsight
 I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline. 

From 12fc9be73573cc85d3298d078e6e13e2c8ceef04 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 20:57:29 -0700
Subject: [PATCH 20/40] Update README.md

---
 examples/text_embedding_qdrant/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/text_embedding_qdrant/README.md b/examples/text_embedding_qdrant/README.md
index bb497f6a..55a7c20f 100644
--- a/examples/text_embedding_qdrant/README.md
+++ b/examples/text_embedding_qdrant/README.md
@@ -4,7 +4,7 @@
 
 CocoIndex supports Qdrant natively - [documentation](https://cocoindex.io/docs/ops/storages#qdrant). In this example, we will build index flow from text embedding from local markdown files, and query the index. We will use **Qdrant** as the vector database.
 
-We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
+We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
 <img width="860" alt="Screenshot 2025-05-19 at 7 24 13 PM" src="https://github.com/user-attachments/assets/a9deecfa-dd94-4b97-a1b1-90488d8178df" />
 

From 407eda24d51664cf871170ef605934bb92a7bab7 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 21:00:34 -0700
Subject: [PATCH 21/40] Update README.md

---
 examples/text_embedding/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 1e999882..63291a15 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -4,7 +4,7 @@
 
 In this example, we will build index flow from text embedding from local markdown files, and query the index.
 
-We appreicate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
+We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
 ## Steps:
 🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)

From 44d93dde6aa9e5ac8ba6bf2cf017a1f28c74e8bc Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Mon, 19 May 2025 21:06:21 -0700
Subject: [PATCH 22/40] Update README.md

---
 examples/text_embedding_qdrant/README.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/examples/text_embedding_qdrant/README.md b/examples/text_embedding_qdrant/README.md
index 55a7c20f..3f91dc95 100644
--- a/examples/text_embedding_qdrant/README.md
+++ b/examples/text_embedding_qdrant/README.md
@@ -6,23 +6,23 @@ CocoIndex supports Qdrant natively - [documentation](https://cocoindex.io/docs/o
 
 We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
-<img width="860" alt="Screenshot 2025-05-19 at 7 24 13 PM" src="https://github.com/user-attachments/assets/a9deecfa-dd94-4b97-a1b1-90488d8178df" />
+<img width="860" alt="CocoIndex supports Qdrant" src="https://github.com/user-attachments/assets/a9deecfa-dd94-4b97-a1b1-90488d8178df" />
 
-## Steps:
-### Indexing Flow:
-<img width="480" alt="Screenshot 2025-05-19 at 7 19 50 PM" src="https://github.com/user-attachments/assets/44d47b5e-b49b-4f05-9a00-dcb8027602a1" />
+## Steps
+### Indexing Flow
+<img width="480" alt="Index flow for text embedding" src="https://github.com/user-attachments/assets/44d47b5e-b49b-4f05-9a00-dcb8027602a1" />
 
-1. We will ingest from a list of local files.
-2. For each file, perform chunking (Recursive Split) and then embeddings. 
+1. We will ingest a list of local files.
+2. For each file, perform chunking (recursively split) and then embedding. 
 3. We will save the embeddings and the metadata in Postgres with PGVector.
    
-### Query:
-We will be use Qdrant client to query the index, reusing the embedding operation in the indexing flow.
+### Query
+We use Qdrant client to query the index, and reuse the embedding operation in the indexing flow.
 
 
 ## Pre-requisites
 
-- [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one. Even the target store is Qdrant, CocoIndex uses Postgress to track the data lineage for incremental processing.
+- [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one. Although the target store is Qdrant, CocoIndex uses Postgress to track the data lineage for incremental processing.
 
 - Run Qdrant.
 

From 689d359d495eb72bad88ae686e750699e8936824 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 21:07:00 -0700
Subject: [PATCH 23/40] Update README.md

---
 examples/text_embedding/README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/text_embedding/README.md b/examples/text_embedding/README.md
index 63291a15..2dd1dbb8 100644
--- a/examples/text_embedding/README.md
+++ b/examples/text_embedding/README.md
@@ -6,18 +6,18 @@ In this example, we will build index flow from text embedding from local markdow
 
 We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
-## Steps:
+## Steps
 🌱 A detailed step by step tutorial can be found here: [Get Started Documentation](https://cocoindex.io/docs/getting_started/quickstart)
 
-### Indexing Flow:
+### Indexing Flow
 <img width="461" alt="Screenshot 2025-05-19 at 5 48 28 PM" src="https://github.com/user-attachments/assets/b6825302-a0c7-4b86-9a2d-52da8286b4bd" />
 
-1. We will ingest from a list of local files.
-2. For each file, perform chunking (Recursive Split) and then embeddings. 
+1. We will ingest a list of local files.
+2. For each file, perform chunking (recursively split) and then embedding. 
 3. We will save the embeddings and the metadata in Postgres with PGVector.
    
-### Query:
-We will match against user-provided text by a SQL query, reusing the embedding operation in the indexing flow.
+### Query
+We will match against user-provided text by a SQL query, and reuse the embedding operation in the indexing flow.
 
 
 ## Prerequisite

From eb56fe2e12880c3e48e250c175508c219abfb138 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 21:21:25 -0700
Subject: [PATCH 24/40] Update main.py

---
 examples/text_embedding_qdrant/main.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/examples/text_embedding_qdrant/main.py b/examples/text_embedding_qdrant/main.py
index fd15ba73..7bc81edb 100644
--- a/examples/text_embedding_qdrant/main.py
+++ b/examples/text_embedding_qdrant/main.py
@@ -4,6 +4,10 @@
 
 import cocoindex
 
+# Define Qdrant connection constants
+QDRANT_URL = "http://localhost:6333"
+QDRANT_COLLECTION = "cocoindex"
+
 
 @cocoindex.transform_flow()
 def text_to_embedding(text: cocoindex.DataSlice[str]) -> cocoindex.DataSlice[list[float]]:
@@ -51,7 +55,7 @@ def text_embedding_flow(
     doc_embeddings.export(
         "doc_embeddings",
         cocoindex.storages.Qdrant(
-            collection_name="cocoindex", grpc_url="http://localhost:6334/"
+            collection_name=QDRANT_COLLECTION, grpc_url=QDRANT_URL
         ),
         primary_key_fields=["id"],
         setup_by_user=True,
@@ -61,7 +65,7 @@ def text_embedding_flow(
 @cocoindex.main_fn()
 def _run():
     # Initialize Qdrant client
-    client = QdrantClient(host="localhost", port=6333)
+    client = QdrantClient(url=QDRANT_URL)
     
     # Run queries in a loop to demonstrate the query capabilities.
     while True:
@@ -75,7 +79,7 @@ def _run():
             
             # Search in Qdrant
             search_results = client.search(
-                collection_name="cocoindex",
+                collection_name=QDRANT_COLLECTION,
                 query_vector=("text_embedding", query_embedding),
                 limit=10
             )

From e69c212426719be5cc73d6f6f4c5fe15d948b25e Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 21:44:49 -0700
Subject: [PATCH 25/40] Update main.py

---
 examples/text_embedding_qdrant/main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/text_embedding_qdrant/main.py b/examples/text_embedding_qdrant/main.py
index 7bc81edb..20341e25 100644
--- a/examples/text_embedding_qdrant/main.py
+++ b/examples/text_embedding_qdrant/main.py
@@ -5,7 +5,7 @@
 import cocoindex
 
 # Define Qdrant connection constants
-QDRANT_URL = "http://localhost:6333"
+QDRANT_URL = "http://localhost:6334"
 QDRANT_COLLECTION = "cocoindex"
 
 

From 9272a8a1438dd4edc2700614f348733eb4cb5b96 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 22:59:28 -0700
Subject: [PATCH 26/40] Update main.py

---
 examples/text_embedding_qdrant/main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/text_embedding_qdrant/main.py b/examples/text_embedding_qdrant/main.py
index 20341e25..7bc81edb 100644
--- a/examples/text_embedding_qdrant/main.py
+++ b/examples/text_embedding_qdrant/main.py
@@ -5,7 +5,7 @@
 import cocoindex
 
 # Define Qdrant connection constants
-QDRANT_URL = "http://localhost:6334"
+QDRANT_URL = "http://localhost:6333"
 QDRANT_COLLECTION = "cocoindex"
 
 

From 79679140ad8a8929111bdbb732b6602aa65362e4 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Mon, 19 May 2025 23:29:59 -0700
Subject: [PATCH 27/40] Update main.py

---
 examples/text_embedding_qdrant/main.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/examples/text_embedding_qdrant/main.py b/examples/text_embedding_qdrant/main.py
index 7bc81edb..b2892c43 100644
--- a/examples/text_embedding_qdrant/main.py
+++ b/examples/text_embedding_qdrant/main.py
@@ -5,7 +5,7 @@
 import cocoindex
 
 # Define Qdrant connection constants
-QDRANT_URL = "http://localhost:6333"
+QDRANT_GRPC_URL = "http://localhost:6334"
 QDRANT_COLLECTION = "cocoindex"
 
 
@@ -55,7 +55,7 @@ def text_embedding_flow(
     doc_embeddings.export(
         "doc_embeddings",
         cocoindex.storages.Qdrant(
-            collection_name=QDRANT_COLLECTION, grpc_url=QDRANT_URL
+            collection_name=QDRANT_COLLECTION, grpc_url=QDRANT_GRPC_URL
         ),
         primary_key_fields=["id"],
         setup_by_user=True,
@@ -65,7 +65,7 @@ def text_embedding_flow(
 @cocoindex.main_fn()
 def _run():
     # Initialize Qdrant client
-    client = QdrantClient(url=QDRANT_URL)
+    client = QdrantClient(url=QDRANT_GRPC_URL, prefer_grpc=True)
     
     # Run queries in a loop to demonstrate the query capabilities.
     while True:
@@ -77,13 +77,11 @@ def _run():
             # Get the embedding for the query
             query_embedding = text_to_embedding.eval(query)
             
-            # Search in Qdrant
             search_results = client.search(
                 collection_name=QDRANT_COLLECTION,
                 query_vector=("text_embedding", query_embedding),
                 limit=10
             )
-            
             print("\nSearch results:")
             for result in search_results:
                 score = result.score

From 3577694bf5efadd8cb5bcfad244da3a5bf45a5e6 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 14:25:53 -0700
Subject: [PATCH 28/40] upgrade query handling for pdf embedding

---
 examples/pdf_embedding/README.md |  3 +-
 examples/pdf_embedding/main.py   | 70 +++++++++++++++++++++++---------
 2 files changed, 52 insertions(+), 21 deletions(-)

diff --git a/examples/pdf_embedding/README.md b/examples/pdf_embedding/README.md
index 3dde765d..dbee1145 100644
--- a/examples/pdf_embedding/README.md
+++ b/examples/pdf_embedding/README.md
@@ -1,4 +1,5 @@
-Simple example for cocoindex: build embedding index based on local files.
+# Build embedding index from PDF files and query with natural language
+[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
 ## Prerequisite
 [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
diff --git a/examples/pdf_embedding/main.py b/examples/pdf_embedding/main.py
index 00b1ae51..7928a16f 100644
--- a/examples/pdf_embedding/main.py
+++ b/examples/pdf_embedding/main.py
@@ -1,16 +1,21 @@
+import cocoindex
+import os
 import tempfile
+from typing import List, Dict, Any
 
 from dotenv import load_dotenv
+from marker.config.parser import ConfigParser
 from marker.converters.pdf import PdfConverter
 from marker.models import create_model_dict
 from marker.output import text_from_rendered
-from marker.config.parser import ConfigParser
+from psycopg_pool import ConnectionPool
+from jinja2 import Template
 
-import cocoindex
 
 class PdfToMarkdown(cocoindex.op.FunctionSpec):
     """Convert a PDF to markdown."""
 
+
 @cocoindex.op.executor_class(gpu=True, cache=True, behavior_version=1)
 class PdfToMarkdownExecutor:
     """Executor for PdfToMarkdown."""
@@ -30,14 +35,17 @@ def __call__(self, content: bytes) -> str:
             return text
 
 
-def text_to_embedding(text: cocoindex.DataSlice) -> cocoindex.DataSlice:
+@cocoindex.transform_flow()
+def text_to_embedding(text: cocoindex.DataSlice[str]) -> cocoindex.DataSlice[list[float]]:
     """
     Embed the text using a SentenceTransformer model.
+    This is a shared logic between indexing and querying, so extract it as a function.
     """
     return text.transform(
         cocoindex.functions.SentenceTransformerEmbed(
             model="sentence-transformers/all-MiniLM-L6-v2"))
 
+
 @cocoindex.flow_def(name="PdfEmbedding")
 def pdf_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
     """
@@ -45,7 +53,7 @@ def pdf_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoinde
     """
     data_scope["documents"] = flow_builder.add_source(cocoindex.sources.LocalFile(path="pdf_files", binary=True))
 
-    doc_embeddings = data_scope.add_collector()
+    pdf_embeddings = data_scope.add_collector()
 
     with data_scope["documents"].row() as doc:
         doc["markdown"] = doc["content"].transform(PdfToMarkdown())
@@ -55,12 +63,12 @@ def pdf_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoinde
 
         with doc["chunks"].row() as chunk:
             chunk["embedding"] = chunk["text"].call(text_to_embedding)
-            doc_embeddings.collect(id=cocoindex.GeneratedField.UUID,
+            pdf_embeddings.collect(id=cocoindex.GeneratedField.UUID,
                                    filename=doc["filename"], location=chunk["location"],
                                    text=chunk["text"], embedding=chunk["embedding"])
 
-    doc_embeddings.export(
-        "doc_embeddings",
+    pdf_embeddings.export(
+        "pdf_embeddings",
         cocoindex.storages.Postgres(),
         primary_key_fields=["id"],
         vector_indexes=[
@@ -68,31 +76,53 @@ def pdf_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoinde
                 field_name="embedding",
                 metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])
 
-query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
-    name="SemanticsSearch",
-    flow=pdf_embedding_flow,
-    target_name="doc_embeddings",
-    query_transform_flow=text_to_embedding,
-    default_similarity_metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)
+
+def search(pool: ConnectionPool, query: str, top_k: int = 5):
+    # Get the table name, for the export target in the pdf_embedding_flow above.
+    table_name = cocoindex.utils.get_target_storage_default_name(pdf_embedding_flow, "pdf_embeddings")
+    # Evaluate the transform flow defined above with the input query, to get the embedding.
+    query_vector = text_to_embedding.eval(query)
+    # Run the query and get the results.
+    with pool.connection() as conn:
+        with conn.cursor() as cur:
+            cur.execute(f"""
+                SELECT filename, text, embedding <=> %s::vector AS distance
+                FROM {table_name} ORDER BY distance LIMIT %s
+            """, (query_vector, top_k))
+            return [
+                {"filename": row[0], "text": row[1], "score": 1.0 - row[2]}
+                for row in cur.fetchall()
+            ]
+
+
+# Define the search results template using Jinja2
+SEARCH_RESULTS_TEMPLATE = Template("""
+Search results:
+{% for result in results %}
+[{{ "%.3f"|format(result.score) }}] {{ result.filename }}
+    {{ result.text }}
+---
+{% endfor %}
+""")
+
 
 @cocoindex.main_fn()
 def _run():
+    # Initialize the database connection pool.
+    pool = ConnectionPool(os.getenv("COCOINDEX_DATABASE_URL"))
     # Run queries in a loop to demonstrate the query capabilities.
     while True:
         try:
             query = input("Enter search query (or Enter to quit): ")
             if query == '':
                 break
-            results, _ = query_handler.search(query, 10)
-            print("\nSearch results:")
-            for result in results:
-                print(f"[{result.score:.3f}] {result.data['filename']}")
-                print(f"    {result.data['text']}")
-                print("---")
-            print()
+            # Run the query function with the database connection pool and the query.
+            results = search(pool, query)
+            print(SEARCH_RESULTS_TEMPLATE.render(results=results))
         except KeyboardInterrupt:
             break
 
+
 if __name__ == "__main__":
     load_dotenv(override=True)
     _run()

From 9741fa8a6b055ab4da6ca09be8cf05b3fdcf311d Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 14:30:07 -0700
Subject: [PATCH 29/40] Update README.md

---
 examples/pdf_embedding/README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/examples/pdf_embedding/README.md b/examples/pdf_embedding/README.md
index dbee1145..bea0684e 100644
--- a/examples/pdf_embedding/README.md
+++ b/examples/pdf_embedding/README.md
@@ -1,6 +1,16 @@
 # Build embedding index from PDF files and query with natural language
 [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
+
+In this example, we will build index flow from text embedding from local PDF files, and query the index.
+
+We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
+
+## Steps
+### Indexing Flow
+
+
+
 ## Prerequisite
 [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
 

From 818b8060903d25ecdba83e27d05f08b95a2ce4c9 Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Tue, 20 May 2025 14:33:34 -0700
Subject: [PATCH 30/40] Update README.md

---
 examples/pdf_embedding/README.md | 50 ++++++++++++++++++--------------
 1 file changed, 29 insertions(+), 21 deletions(-)

diff --git a/examples/pdf_embedding/README.md b/examples/pdf_embedding/README.md
index bea0684e..32b6e178 100644
--- a/examples/pdf_embedding/README.md
+++ b/examples/pdf_embedding/README.md
@@ -9,6 +9,16 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 ## Steps
 ### Indexing Flow
 
+<img width="662" alt="PDF indexing flow" src="https://github.com/user-attachments/assets/5e132dd9-7120-4b28-bc57-88d6b5583ef4" />
+1. We will ingest a list of PDF files
+2. For each file
+   - convert it to markdown
+   - perform chunking (recursively split) and then embed each chunk.
+3. We will save the embeddings and the metadata in Postgres with PGVector.
+
+### Query
+We will match against user-provided text by a SQL query, and reuse the embedding operation in the indexing flow.
+
 
 
 ## Prerequisite
@@ -16,37 +26,35 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 
 ## Run
 
-Install dependencies:
-
-```bash
-pip install -e .
-```
+- Install dependencies:
+  
+  ```bash
+  pip install -e .
+  ```
 
-Setup:
+- Setup:
 
-```bash
-python main.py cocoindex setup
-```
+  ```bash
+  python main.py cocoindex setup
+  ```
 
-Update index:
+- Update index:
 
-```bash
-python main.py cocoindex update
-```
+  ```bash
+  python main.py cocoindex update
+  ```
 
-Run:
+- Run:
 
-```bash
-python main.py
-```
+  ```bash
+  python main.py
+  ```
 
 ## CocoInsight 
-CocoInsight is in Early Access now (Free) 😊 You found us! A quick 3 minute video tutorial about CocoInsight: [Watch on YouTube](https://youtu.be/ZnmyoHslBSc?si=pPLXWALztkA710r9).
-
-Run CocoInsight to understand your RAG data pipeline:
+I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline. It just connects to your local CocoIndex server, with Zero pipeline data retention. Run following command to start CocoInsight:
 
 ```
 python main.py cocoindex server -ci
 ```
 
-Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).
\ No newline at end of file
+Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).

From b5fbae66375543552af98de0689e21100cbed213 Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Tue, 20 May 2025 14:34:24 -0700
Subject: [PATCH 31/40] Update README.md

---
 examples/pdf_embedding/README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/pdf_embedding/README.md b/examples/pdf_embedding/README.md
index 32b6e178..e041ed84 100644
--- a/examples/pdf_embedding/README.md
+++ b/examples/pdf_embedding/README.md
@@ -10,6 +10,7 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 ### Indexing Flow
 
 <img width="662" alt="PDF indexing flow" src="https://github.com/user-attachments/assets/5e132dd9-7120-4b28-bc57-88d6b5583ef4" />
+
 1. We will ingest a list of PDF files
 2. For each file
    - convert it to markdown

From 3ab5ddce0e761f14ea374e36e4752efb9e775f1f Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 14:47:10 -0700
Subject: [PATCH 32/40] Update README.md

---
 examples/pdf_embedding/README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/examples/pdf_embedding/README.md b/examples/pdf_embedding/README.md
index e041ed84..d738f7db 100644
--- a/examples/pdf_embedding/README.md
+++ b/examples/pdf_embedding/README.md
@@ -2,7 +2,7 @@
 [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
 
-In this example, we will build index flow from text embedding from local PDF files, and query the index.
+In this example, we will build index flow for text embedding from local PDF files, and query the index.
 
 We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
@@ -11,9 +11,9 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 
 <img width="662" alt="PDF indexing flow" src="https://github.com/user-attachments/assets/5e132dd9-7120-4b28-bc57-88d6b5583ef4" />
 
-1. We will ingest a list of PDF files
-2. For each file
-   - convert it to markdown
+1. We will ingest a list of PDF files.
+2. For each file:
+   - convert it to markdown, and then
    - perform chunking (recursively split) and then embed each chunk.
 3. We will save the embeddings and the metadata in Postgres with PGVector.
 

From ff43f46f241bbbba06ca364a3d0396839ffe539d Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 16:49:16 -0700
Subject: [PATCH 33/40] Update main.py

---
 examples/pdf_embedding/main.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/examples/pdf_embedding/main.py b/examples/pdf_embedding/main.py
index 928a66ff..a623c152 100644
--- a/examples/pdf_embedding/main.py
+++ b/examples/pdf_embedding/main.py
@@ -62,7 +62,7 @@ def pdf_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoinde
             language="markdown", chunk_size=2000, chunk_overlap=500)
 
         with doc["chunks"].row() as chunk:
-            chunk["embedding"] = chunk["text"].call(text_to_embedding)
+            chunk["embedding"] = text_to_embedding(chunk["text"])
             pdf_embeddings.collect(id=cocoindex.GeneratedField.UUID,
                                    filename=doc["filename"], location=chunk["location"],
                                    text=chunk["text"], embedding=chunk["embedding"])
@@ -107,6 +107,8 @@ def search(pool: ConnectionPool, query: str, top_k: int = 5):
 
 
 def _main():
+    # Initialize the database connection pool.
+    pool = ConnectionPool(os.getenv("COCOINDEX_DATABASE_URL"))
     # Run queries in a loop to demonstrate the query capabilities.
     while True:
         try:

From 55ebc4af2d44119e90b2d54ccaab93470b24b26c Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 16:58:43 -0700
Subject: [PATCH 34/40] google drive example update query handler

---
 examples/gdrive_text_embedding/README.md | 54 +++++++++++-------
 examples/gdrive_text_embedding/main.py   | 73 +++++++++++++++---------
 2 files changed, 79 insertions(+), 48 deletions(-)

diff --git a/examples/gdrive_text_embedding/README.md b/examples/gdrive_text_embedding/README.md
index 73167ab8..3c41f1ab 100644
--- a/examples/gdrive_text_embedding/README.md
+++ b/examples/gdrive_text_embedding/README.md
@@ -1,6 +1,21 @@
-This example builds embedding index based on Google Drive files.
-It continuously updates the index as files are added / updated / deleted in the source folders:
-it keeps the index in sync with the source folders effortlessly.
+# Build Google Drive text embedding and semantic search 🔍
+[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
+
+In this example, we will build an embedding index based on Google Drive files and perform semantic search.
+
+It continuously updates the index as files are added / updated / deleted in the source folders. It keeps the index in sync with the source folders in real-time.
+
+We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
+
+## Steps
+
+### Indexing Flow
+1. We will ingest files from Google Drive folders.
+2. For each file, perform chunking (recursively split) and then embedding.
+3. We will save the embeddings and the metadata in Postgres with PGVector.
+   
+### Query
+We will match against user-provided text by a SQL query, and reuse the embedding operation in the indexing flow.
 
 ## Prerequisite
 
@@ -25,32 +40,31 @@ Before running the example, you need to:
 
 ## Run
 
-Install dependencies:
-
-```sh
-pip install -e .
-```
+- Install dependencies:
 
-Setup:
+    ```sh
+    pip install -e .
+    ```
 
-```sh
-cocoindex setup main.py
-```
+- Setup:
 
-Run:
+    ```sh
+    cocoindex setup main.py
+    ```
 
-```sh
-python main.py
-```
+- Run:
+        
+    ```sh
+    python main.py
+    ```
 
 During running, it will keep observing changes in the source folders and update the index automatically.
 At the same time, it accepts queries from the terminal, and performs search on top of the up-to-date index.
 
 
 ## CocoInsight 
-CocoInsight is in Early Access now (Free) 😊 You found us! A quick 3 minute video tutorial about CocoInsight: [Watch on YouTube](https://youtu.be/ZnmyoHslBSc?si=pPLXWALztkA710r9).
-
-Run CocoInsight to understand your RAG data pipeline:
+I used CocoInsight (Free beta now) to troubleshoot the index generation and understand the data lineage of the pipeline. 
+It just connects to your local CocoIndex server, with Zero pipeline data retention. Run following command to start CocoInsight:
 
 ```sh
 cocoindex server -ci main.py
@@ -62,4 +76,4 @@ You can also add a `-L` flag to make the server keep updating the index to refle
 cocoindex server -ci -L main.py
 ```
 
-Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).
\ No newline at end of file
+Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).
diff --git a/examples/gdrive_text_embedding/main.py b/examples/gdrive_text_embedding/main.py
index 7e37ca7e..b612e2d5 100644
--- a/examples/gdrive_text_embedding/main.py
+++ b/examples/gdrive_text_embedding/main.py
@@ -1,9 +1,19 @@
 from dotenv import load_dotenv
-
+from psycopg_pool import ConnectionPool
 import cocoindex
 import datetime
 import os
 
+@cocoindex.transform_flow()
+def text_to_embedding(text: cocoindex.DataSlice[str]) -> cocoindex.DataSlice[list[float]]:
+    """
+    Embed the text using a SentenceTransformer model.
+    This is a shared logic between indexing and querying, so extract it as a function.
+    """
+    return text.transform(
+        cocoindex.functions.SentenceTransformerEmbed(
+            model="sentence-transformers/all-MiniLM-L6-v2"))
+
 @cocoindex.flow_def(name="GoogleDriveTextEmbedding")
 def gdrive_text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
     """
@@ -27,9 +37,7 @@ def gdrive_text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope:
             language="markdown", chunk_size=2000, chunk_overlap=500)
 
         with doc["chunks"].row() as chunk:
-            chunk["embedding"] = chunk["text"].transform(
-                cocoindex.functions.SentenceTransformerEmbed(
-                 model="sentence-transformers/all-MiniLM-L6-v2")) 
+            chunk["embedding"] = text_to_embedding(chunk["text"])
             doc_embeddings.collect(filename=doc["filename"], location=chunk["location"],
                                    text=chunk["text"], embedding=chunk["embedding"])
 
@@ -42,33 +50,42 @@ def gdrive_text_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope:
                 field_name="embedding",
                 metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])
 
-query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
-    name="SemanticsSearch",
-    flow=gdrive_text_embedding_flow,
-    target_name="doc_embeddings",
-    query_transform_flow=lambda text: text.transform(
-        cocoindex.functions.SentenceTransformerEmbed(
-            model="sentence-transformers/all-MiniLM-L6-v2")),
-    default_similarity_metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)
+def search(pool: ConnectionPool, query: str, top_k: int = 5):
+    # Get the table name, for the export target in the gdrive_text_embedding_flow above.
+    table_name = cocoindex.utils.get_target_storage_default_name(gdrive_text_embedding_flow, "doc_embeddings")
+    # Evaluate the transform flow defined above with the input query, to get the embedding.
+    query_vector = text_to_embedding.eval(query)
+    # Run the query and get the results.
+    with pool.connection() as conn:
+        with conn.cursor() as cur:
+            cur.execute(f"""
+                SELECT filename, text, embedding <=> %s::vector AS distance
+                FROM {table_name} ORDER BY distance LIMIT %s
+            """, (query_vector, top_k))
+            return [
+                {"filename": row[0], "text": row[1], "score": 1.0 - row[2]}
+                for row in cur.fetchall()
+            ]
 
 def _main():
-    # Use a `FlowLiveUpdater` to keep the flow data updated.
-    with cocoindex.FlowLiveUpdater(gdrive_text_embedding_flow):
-        # Run queries in a loop to demonstrate the query capabilities.
-        while True:
-            try:
-                query = input("Enter search query (or Enter to quit): ")
-                if query == '':
-                    break
-                results, _ = query_handler.search(query, 10)
-                print("\nSearch results:")
-                for result in results:
-                    print(f"[{result.score:.3f}] {result.data['filename']}")
-                    print(f"    {result.data['text']}")
-                    print("---")
-                print()
-            except KeyboardInterrupt:
+    # Initialize the database connection pool.
+    pool = ConnectionPool(os.getenv("COCOINDEX_DATABASE_URL"))
+    # Run queries in a loop to demonstrate the query capabilities.
+    while True:
+        try:
+            query = input("Enter search query (or Enter to quit): ")
+            if query == '':
                 break
+            # Run the query function with the database connection pool and the query.
+            results = search(pool, query)
+            print("\nSearch results:")
+            for result in results:
+                print(f"[{result['score']:.3f}] {result['filename']}")
+                print(f"    {result['text']}")
+                print("---")
+            print()
+        except KeyboardInterrupt:
+            break
 
 if __name__ == "__main__":
     load_dotenv()

From 6e72fb268762c0816723bef3c0109629abd8681b Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Tue, 20 May 2025 17:07:48 -0700
Subject: [PATCH 35/40] Update README.md

---
 examples/gdrive_text_embedding/README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/examples/gdrive_text_embedding/README.md b/examples/gdrive_text_embedding/README.md
index 3c41f1ab..3d79dcb9 100644
--- a/examples/gdrive_text_embedding/README.md
+++ b/examples/gdrive_text_embedding/README.md
@@ -10,6 +10,8 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 ## Steps
 
 ### Indexing Flow
+<img width="801" alt="Google Drive File Ingestion" src="https://github.com/user-attachments/assets/bc772e1e-d7a0-46de-b57c-290a78c128ac" />
+
 1. We will ingest files from Google Drive folders.
 2. For each file, perform chunking (recursively split) and then embedding.
 3. We will save the embeddings and the metadata in Postgres with PGVector.
@@ -77,3 +79,5 @@ cocoindex server -ci -L main.py
 ```
 
 Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).
+
+<img width="1316" alt="Screenshot 2025-05-20 at 5 06 31 PM" src="https://github.com/user-attachments/assets/0ed848db-3cc3-43d3-8cb8-35069f503288" />

From b46cf771562ae075c76859f20c8dfecbe2aad8e0 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 17:17:13 -0700
Subject: [PATCH 36/40] Update README.md

---
 examples/gdrive_text_embedding/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/gdrive_text_embedding/README.md b/examples/gdrive_text_embedding/README.md
index 3d79dcb9..6f7bbb79 100644
--- a/examples/gdrive_text_embedding/README.md
+++ b/examples/gdrive_text_embedding/README.md
@@ -80,4 +80,4 @@ cocoindex server -ci -L main.py
 
 Then open the CocoInsight UI at [https://cocoindex.io/cocoinsight](https://cocoindex.io/cocoinsight).
 
-<img width="1316" alt="Screenshot 2025-05-20 at 5 06 31 PM" src="https://github.com/user-attachments/assets/0ed848db-3cc3-43d3-8cb8-35069f503288" />
+<img width="1316" alt="Use CocoInsight to understand the data of the pipeline" src="https://github.com/user-attachments/assets/0ed848db-3cc3-43d3-8cb8-35069f503288" />

From b8745127ddce2dd319980f8ab54da58e71893f57 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 17:27:41 -0700
Subject: [PATCH 37/40] rename  to image_search (to be more consistent with
 other examples)

---
 README.md                                           |   2 +-
 .../{image_search_example => image_search}/.env     |   0
 .../README.md                                       |   0
 .../frontend/.gitignore                             |   0
 .../frontend/index.html                             |   0
 .../frontend/package-lock.json                      |   0
 .../frontend/package.json                           |   0
 .../frontend/src/App.jsx                            |   0
 .../frontend/src/main.jsx                           |   0
 .../frontend/src/style.css                          |   0
 .../frontend/vite.config.js                         |   0
 .../img/cat1.jpeg                                   | Bin
 .../img/dog1.jpeg                                   | Bin
 .../img/elephant1.jpg                               | Bin
 .../img/giraffe.jpg                                 | Bin
 .../{image_search_example => image_search}/main.py  |   0
 .../requirements.txt                                |   0
 17 files changed, 1 insertion(+), 1 deletion(-)
 rename examples/{image_search_example => image_search}/.env (100%)
 rename examples/{image_search_example => image_search}/README.md (100%)
 rename examples/{image_search_example => image_search}/frontend/.gitignore (100%)
 rename examples/{image_search_example => image_search}/frontend/index.html (100%)
 rename examples/{image_search_example => image_search}/frontend/package-lock.json (100%)
 rename examples/{image_search_example => image_search}/frontend/package.json (100%)
 rename examples/{image_search_example => image_search}/frontend/src/App.jsx (100%)
 rename examples/{image_search_example => image_search}/frontend/src/main.jsx (100%)
 rename examples/{image_search_example => image_search}/frontend/src/style.css (100%)
 rename examples/{image_search_example => image_search}/frontend/vite.config.js (100%)
 rename examples/{image_search_example => image_search}/img/cat1.jpeg (100%)
 rename examples/{image_search_example => image_search}/img/dog1.jpeg (100%)
 rename examples/{image_search_example => image_search}/img/elephant1.jpg (100%)
 rename examples/{image_search_example => image_search}/img/giraffe.jpg (100%)
 rename examples/{image_search_example => image_search}/main.py (100%)
 rename examples/{image_search_example => image_search}/requirements.txt (100%)

diff --git a/README.md b/README.md
index 8b3cb3a2..007586e7 100644
--- a/README.md
+++ b/README.md
@@ -138,7 +138,7 @@ It defines an index flow like this:
 | [Embeddings to Qdrant](examples/text_embedding_qdrant) | Index documents in a Qdrant collection for semantic search |
 | [FastAPI Server with Docker](examples/fastapi_server_docker) | Run the semantic search server in a Dockerized FastAPI setup | 
 | [Product Recommendation](examples/product_recommendation) | Build real-time product recommendations with LLM and graph database| 
-| [Image Search with Vision API](examples/image_search_example) | Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend|
+| [Image Search with Vision API](examples/image_search) | Generates detailed captions for images using a vision model, embeds them, enables live-updating semantic search via FastAPI and served on a React frontend|
 
 More coming and stay tuned 👀!
 
diff --git a/examples/image_search_example/.env b/examples/image_search/.env
similarity index 100%
rename from examples/image_search_example/.env
rename to examples/image_search/.env
diff --git a/examples/image_search_example/README.md b/examples/image_search/README.md
similarity index 100%
rename from examples/image_search_example/README.md
rename to examples/image_search/README.md
diff --git a/examples/image_search_example/frontend/.gitignore b/examples/image_search/frontend/.gitignore
similarity index 100%
rename from examples/image_search_example/frontend/.gitignore
rename to examples/image_search/frontend/.gitignore
diff --git a/examples/image_search_example/frontend/index.html b/examples/image_search/frontend/index.html
similarity index 100%
rename from examples/image_search_example/frontend/index.html
rename to examples/image_search/frontend/index.html
diff --git a/examples/image_search_example/frontend/package-lock.json b/examples/image_search/frontend/package-lock.json
similarity index 100%
rename from examples/image_search_example/frontend/package-lock.json
rename to examples/image_search/frontend/package-lock.json
diff --git a/examples/image_search_example/frontend/package.json b/examples/image_search/frontend/package.json
similarity index 100%
rename from examples/image_search_example/frontend/package.json
rename to examples/image_search/frontend/package.json
diff --git a/examples/image_search_example/frontend/src/App.jsx b/examples/image_search/frontend/src/App.jsx
similarity index 100%
rename from examples/image_search_example/frontend/src/App.jsx
rename to examples/image_search/frontend/src/App.jsx
diff --git a/examples/image_search_example/frontend/src/main.jsx b/examples/image_search/frontend/src/main.jsx
similarity index 100%
rename from examples/image_search_example/frontend/src/main.jsx
rename to examples/image_search/frontend/src/main.jsx
diff --git a/examples/image_search_example/frontend/src/style.css b/examples/image_search/frontend/src/style.css
similarity index 100%
rename from examples/image_search_example/frontend/src/style.css
rename to examples/image_search/frontend/src/style.css
diff --git a/examples/image_search_example/frontend/vite.config.js b/examples/image_search/frontend/vite.config.js
similarity index 100%
rename from examples/image_search_example/frontend/vite.config.js
rename to examples/image_search/frontend/vite.config.js
diff --git a/examples/image_search_example/img/cat1.jpeg b/examples/image_search/img/cat1.jpeg
similarity index 100%
rename from examples/image_search_example/img/cat1.jpeg
rename to examples/image_search/img/cat1.jpeg
diff --git a/examples/image_search_example/img/dog1.jpeg b/examples/image_search/img/dog1.jpeg
similarity index 100%
rename from examples/image_search_example/img/dog1.jpeg
rename to examples/image_search/img/dog1.jpeg
diff --git a/examples/image_search_example/img/elephant1.jpg b/examples/image_search/img/elephant1.jpg
similarity index 100%
rename from examples/image_search_example/img/elephant1.jpg
rename to examples/image_search/img/elephant1.jpg
diff --git a/examples/image_search_example/img/giraffe.jpg b/examples/image_search/img/giraffe.jpg
similarity index 100%
rename from examples/image_search_example/img/giraffe.jpg
rename to examples/image_search/img/giraffe.jpg
diff --git a/examples/image_search_example/main.py b/examples/image_search/main.py
similarity index 100%
rename from examples/image_search_example/main.py
rename to examples/image_search/main.py
diff --git a/examples/image_search_example/requirements.txt b/examples/image_search/requirements.txt
similarity index 100%
rename from examples/image_search_example/requirements.txt
rename to examples/image_search/requirements.txt

From 2402c78afc875678c0b85ad3379062bc80e11b56 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 18:53:53 -0700
Subject: [PATCH 38/40] update query handling for image search

---
 examples/image_search/README.md      | 12 ++-------
 examples/image_search/main.py        | 40 ++++++++++++++++++----------
 examples/image_search/pyproject.toml |  9 +++++++
 3 files changed, 37 insertions(+), 24 deletions(-)
 create mode 100644 examples/image_search/pyproject.toml

diff --git a/examples/image_search/README.md b/examples/image_search/README.md
index 3299ab45..51ce32c6 100644
--- a/examples/image_search/README.md
+++ b/examples/image_search/README.md
@@ -2,7 +2,7 @@
 
 ![image](https://github.com/user-attachments/assets/3a696344-c9b4-46e8-9413-6229dbb8672a)
 
-- QDrant for Vector Storage
+- Qdrant for Vector Storage
 - Ollama Gemma3 (Image to Text)
 - CLIP ViT-L/14 - Embeddings Model
 - Live Update
@@ -13,7 +13,7 @@ docker run -d --name qdrant -p 6334:6334 qdrant/qdrant:latest
 export COCOINDEX_DATABASE_URL="postgres://cocoindex:cocoindex@localhost/cocoindex"
 ```
 
-## Create QDrant Collection
+## Create Qdrant Collection
 ```
 curl -X PUT
   'http://localhost:6333/collections/image_search' \
@@ -26,7 +26,6 @@ curl -X PUT
       }
     }
   }'
-
 ```
 
 ## Run Ollama
@@ -35,13 +34,6 @@ ollama pull gemma3
 ollama serve
 ```
 
-## Create virtual environment and install dependencies
-```
-python -m venv .venv
-source .venv/bin/activate
-pip install -r requirements.txt
-```
-
 ### Place your images in the `img` directory.
 - No need to update manually. CocoIndex will automatically update the index as new images are added to the directory.
 
diff --git a/examples/image_search/main.py b/examples/image_search/main.py
index 7ea2e9eb..abbc7443 100644
--- a/examples/image_search/main.py
+++ b/examples/image_search/main.py
@@ -7,9 +7,11 @@
 from fastapi import FastAPI, Query
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
+from qdrant_client import QdrantClient
 
 OLLAMA_URL = "http://localhost:11434/api/generate"
 OLLAMA_MODEL = "gemma3"
+QDRANT_GRPC_URL = os.getenv("QDRANT_GRPC_URL", "http://localhost:6334/")
 
 # 1. Extract caption from image using Ollama vision model
 @cocoindex.op.function(cache=True, behavior_version=1)
@@ -42,7 +44,12 @@ def get_image_caption(img_bytes: bytes) -> str:
 
 
 # 2. Embed the caption string
-def caption_to_embedding(caption: cocoindex.DataSlice) -> cocoindex.DataSlice:
+@cocoindex.transform_flow()
+def caption_to_embedding(caption: cocoindex.DataSlice[str]) -> cocoindex.DataSlice[list[float]]:
+    """
+    Embed the caption using a CLIP model.
+    This is shared logic between indexing and querying.
+    """
     return caption.transform(
         cocoindex.functions.SentenceTransformerEmbed(
             model="clip-ViT-L-14",
@@ -70,7 +77,7 @@ def image_object_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope:
         "img_embeddings",
         cocoindex.storages.Qdrant(
             collection_name="image_search",
-            grpc_url=os.getenv("QDRANT_GRPC_URL", "http://localhost:6334/"),
+            grpc_url=QDRANT_GRPC_URL,
         ),
         primary_key_fields=["id"],
         setup_by_user=True,
@@ -93,26 +100,31 @@ def image_object_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope:
 def startup_event():
     load_dotenv()
     cocoindex.init()
-    app.state.query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
-        name="ImageObjectSearch",
-        flow=image_object_embedding_flow,
-        target_name="img_embeddings",
-        query_transform_flow=caption_to_embedding,
-        default_similarity_metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY,
+    # Initialize Qdrant client
+    app.state.qdrant_client = QdrantClient(
+        url=QDRANT_GRPC_URL,
+        prefer_grpc=True
     )
     app.state.live_updater = cocoindex.FlowLiveUpdater(image_object_embedding_flow)
     app.state.live_updater.start()
 
 @app.get("/search")
 def search(q: str = Query(..., description="Search query"), limit: int = Query(5, description="Number of results")):
-    query_handler = app.state.query_handler
-    results, _ = query_handler.search(q, limit, "embedding")
+    # Get the embedding for the query
+    query_embedding = caption_to_embedding.eval(q)
+    
+    # Search in Qdrant
+    search_results = app.state.qdrant_client.search(
+        collection_name="image_search",
+        query_vector=("embedding", query_embedding),
+        limit=limit
+    )
+    
+    # Format results
     out = []
-    for result in results:
-        row = dict(result.data)
-        # Only include filename and score
+    for result in search_results:
         out.append({
-            "filename": row["filename"],
+            "filename": result.payload["filename"],
             "score": result.score
         })
     return {"results": out}
diff --git a/examples/image_search/pyproject.toml b/examples/image_search/pyproject.toml
new file mode 100644
index 00000000..ac010d53
--- /dev/null
+++ b/examples/image_search/pyproject.toml
@@ -0,0 +1,9 @@
+[project]
+name = "image-search"
+version = "0.1.0"
+description = "Simple example for cocoindex: build embedding index based on images."
+requires-python = ">=3.11"
+dependencies = ["cocoindex>=0.1.42", "python-dotenv>=1.0.1", "fastapi>=0.100.0"]
+
+[tool.setuptools]
+packages = []

From ac43f786930c229754a6d6e633dc2e489018b5a2 Mon Sep 17 00:00:00 2001
From: Linghua Jin <linghua@cocoindex.io>
Date: Tue, 20 May 2025 19:44:54 -0700
Subject: [PATCH 39/40] Update README.md

---
 examples/image_search/README.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/examples/image_search/README.md b/examples/image_search/README.md
index 51ce32c6..16c91205 100644
--- a/examples/image_search/README.md
+++ b/examples/image_search/README.md
@@ -9,16 +9,15 @@
 
 ## Make sure Postgres and Qdrant are running
 ```
-docker run -d --name qdrant -p 6334:6334 qdrant/qdrant:latest
+docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
 export COCOINDEX_DATABASE_URL="postgres://cocoindex:cocoindex@localhost/cocoindex"
 ```
 
 ## Create Qdrant Collection
 ```
-curl -X PUT
-  'http://localhost:6333/collections/image_search' \
-  --header 'Content-Type: application/json' \
-  --data-raw '{
+curl -X PUT 'http://localhost:6333/collections/image_search' \
+  -H 'Content-Type: application/json' \
+  -d '{
     "vectors": {
       "embedding": {
         "size": 768,

From b645bb14b6d0478877b52e707b6491a60a3d927a Mon Sep 17 00:00:00 2001
From: LJ <linghua@cocoindex.io>
Date: Tue, 20 May 2025 21:56:46 -0700
Subject: [PATCH 40/40] Update README.md

---
 examples/image_search/README.md | 28 +++++++++++++++++-----------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/examples/image_search/README.md b/examples/image_search/README.md
index 16c91205..0aa2a7c9 100644
--- a/examples/image_search/README.md
+++ b/examples/image_search/README.md
@@ -38,17 +38,23 @@ ollama serve
 
 
 ## Run Backend
-```
-cocoindex setup main.py
-uvicorn main:app --reload --host 0.0.0.0 --port 8000
-```
-
-## Run Frontend
-```
-cd frontend
-npm install
-npm run dev
-```
+- Install dependencies:
+  ```
+  pip install -e .
+  ```
+
+- Run Backend
+  ```
+  cocoindex setup main.py
+  uvicorn main:app --reload --host 0.0.0.0 --port 8000
+  ```
+
+- Run Frontend
+  ```
+  cd frontend
+  npm install
+  npm run dev
+  ```
 
 Go to `http://localhost:5174` to search.