update code base embedding with query handling

examples/code_embedding/README.md

@@ -1,52 +1,71 @@
-# Build embedding index for codebase
+# Build real-time index for codebase 
+[![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
-![Build embedding index for codebase](https://cocoindex.io/blogs/assets/images/cover-9bf0a7cff69b66a40918ab2fc1cea0c7.png)
+CocoIndex provides built-in support for code base chunking, using Tree-sitter to keep syntax boundary. In this example, we will build real-time index for codebase using CocoIndex. 
 
-In this example, we will build an embedding index for a codebase using CocoIndex. CocoIndex provides built-in support for code base chunking, with native Tree-sitter support. [Tree-sitter](https://en.wikipedia.org/wiki/Tree-sitter_%28parser_generator%29) is a parser generator tool and an incremental parsing library, it is available in Rust 🦀 - [GitHub](https://github.com/tree-sitter/tree-sitter). CocoIndex has built-in Rust integration with Tree-sitter to efficiently parse code and extract syntax trees for various programming languages.
+We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/cocoindex) if this is helpful.
 
+![Build embedding index for codebase](https://github.com/user-attachments/assets/6dc5ce89-c949-41d4-852f-ad95af163dbd)
+
+[Tree-sitter](https://en.wikipedia.org/wiki/Tree-sitter_%28parser_generator%29) is a parser generator tool and an incremental parsing library. It is available in Rust 🦀 - [GitHub](https://github.com/tree-sitter/tree-sitter). CocoIndex has built-in Rust integration with Tree-sitter to efficiently parse code and extract syntax trees for various programming languages. Check out the list of supported languages [here](https://cocoindex.io/docs/ops/functions#splitrecursively) - in the `language` section.
 
-Please give [Cocoindex on Github](https://github.com/cocoindex-io/cocoindex) a star to support us if you like our work. Thank you so much with a warm coconut hug 🥥🤗. [![GitHub](https://img.shields.io/github/stars/cocoindex-io/cocoindex?color=5B5BD6)](https://github.com/cocoindex-io/cocoindex)
 
 ## Tutorials
-- Blog with step by step tutorial [here](https://cocoindex.io/blogs/index-code-base-for-rag).
-- Video walkthrough [here](https://youtu.be/G3WstvhHO24?si=Bnxu67Ax5Lv8b-J2)
+- Step by step tutorial - Check out the [blog](https://cocoindex.io/blogs/index-code-base-for-rag).
+- Video tutorial - [Youtube](https://youtu.be/G3WstvhHO24?si=Bnxu67Ax5Lv8b-J2).
+
+## Steps
+
+### Indexing Flow
+<p align='center'>
+  <img width="434" alt="Screenshot 2025-05-19 at 10 14 36 PM" src="https://github.com/user-attachments/assets/3a506034-698f-480a-b653-22184dae4e14" />
+</p>
+
+1. We will ingest CocoIndex codebase.
+2. For each file, perform chunking (Tree-sitter) 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.
 
 
 ## Prerequisite
 [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
 
 ## Run
 
-Install dependencies:
-```bash
-pip install -e .
-```
-
-Setup:
+- Install dependencies:
+  ```bash
+  pip install -e .
+  ```
 
-```bash
-python main.py cocoindex setup
-```
+- Setup:
 
-Update index:
+  ```bash
+  python main.py cocoindex setup
+  ```
 
-```bash
-python main.py cocoindex update
-```
+- Update index:
+
+  ```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 the 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).
+
+<img width="1305" alt="Chunking Visualization" src="https://github.com/user-attachments/assets/8e83b9a4-2bed-456b-83e5-b5381b28b84a" />
+

examples/code_embedding/main.py

@@ -1,5 +1,5 @@
 from dotenv import load_dotenv
-
+from psycopg_pool import ConnectionPool
 import cocoindex
 import os
 
@@ -8,7 +8,8 @@ def extract_extension(filename: str) -> str:
     """Extract the extension of a filename."""
     return os.path.splitext(filename)[1]
 
-def code_to_embedding(text: cocoindex.DataSlice) -> cocoindex.DataSlice:
+@cocoindex.transform_flow()
+def code_to_embedding(text: cocoindex.DataSlice[str]) -> cocoindex.DataSlice[list[float]]:
     """
     Embed the text using a SentenceTransformer model.
     """
@@ -24,7 +25,7 @@ def code_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoind
     data_scope["files"] = flow_builder.add_source(
         cocoindex.sources.LocalFile(path="../..",
                                     included_patterns=["*.py", "*.rs", "*.toml", "*.md", "*.mdx"],
-                                    excluded_patterns=[".*", "target", "**/node_modules"]))
+                                    excluded_patterns=["**/.*", "target", "**/node_modules"]))
     code_embeddings = data_scope.add_collector()
 
     with data_scope["files"].row() as file:
@@ -47,26 +48,40 @@ def code_embedding_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoind
                 metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY)])
 
 
-query_handler = cocoindex.query.SimpleSemanticsQueryHandler(
-    name="SemanticsSearch",
-    flow=code_embedding_flow,
-    target_name="code_embeddings",
-    query_transform_flow=code_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 code_embedding_flow above.
+    table_name = cocoindex.utils.get_target_storage_default_name(code_embedding_flow, "code_embeddings")
+    # Evaluate the transform flow defined above with the input query, to get the embedding.
+    query_vector = code_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, code, embedding <=> %s::vector AS distance
+                FROM {table_name} ORDER BY distance LIMIT %s
+            """, (query_vector, top_k))
+            return [
+                {"filename": row[0], "code": row[1], "score": 1.0 - row[2]}
+                for row in cur.fetchall()
+            ]
 
 @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)
+            # 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.data['filename']}")
-                print(f"    {result.data['code']}")
+                print(f"[{result['score']:.3f}] {result['filename']}")
+                print(f"    {result['code']}")
                 print("---")
             print()
         except KeyboardInterrupt: