# Work with Chromia through the Python client · Semantic search on Chromia using Vector DB · Learn Chromia > This lesson shows you how to upload your movie data to your Chromia chain and run semantic vector searches against it with the Postchain Python client. Source: https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/python-client/ Semantic search on Chromia using Vector DBLesson 14 of 15 1. [01Searching movies semantically on Chromia](https://chromia.com/learn/vector-db-movie-demo/introduction/) Module 1: Project setup 1. [02Overview](https://chromia.com/learn/vector-db-movie-demo/setup/) 2. [03Choose an embedding model](https://chromia.com/learn/vector-db-movie-demo/setup/embedding-model/) 3. [04Configure your Rell module](https://chromia.com/learn/vector-db-movie-demo/setup/configure.rell/) 4. [05Deploy your Rell module](https://chromia.com/learn/vector-db-movie-demo/setup/deploy-rell-module/) 5. [06Finalize your Python environment](https://chromia.com/learn/vector-db-movie-demo/setup/finalize-python-env/) Module 2: Running the data pipeline 1. [07Overview](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/) 2. [08Preprocess movie data](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/preprocess/) 3. [09Generate sentence embeddings](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/generate-embeddings/) 4. [10Store vectors on the blockchain](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/upload-vectors/) 1. [11Search the vector database](https://chromia.com/learn/vector-db-movie-demo/search/) Module 4: A deep dive into the code 1. [12Overview](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/) 2. [13Link your data to the Vector DB with Rell](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/rell-interface/) 3. [14Work with Chromia through the Python client](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/python-client/) 1. [15Use cases and extensions](https://chromia.com/learn/vector-db-movie-demo/use-cases/) [Learn](https://chromia.com/learn/)[Semantic search on Chromia using Vector DB](https://chromia.com/learn/vector-db-movie-demo/) 14 / 15Module 4: A deep dive into the codeAdvanced # Work with Chromia through the Python client This lesson shows you how to upload your movie data to your Chromia chain and run semantic vector searches against it with the [Postchain Python client](https://docs.chromia.com/build/clients/postchain-clients/python-client/). The steps are: * Set up the Python client * Send movie data to the chain in batches through the `add_movies` operation * Turn a **user query** into a vector with the **same model** you used for the upload * Search the chain through **Rell query templates** Complete [the setup module](https://chromia.com/learn/vector-db-movie-demo/setup/) first if your `.env` file isn't configured or the required dependencies aren't installed. ## 1. Initialize the Python client Load your environment variables and create a client that points to your deployed chain: python ``` from postchain_client_py import BlockchainClient from dotenv import load_dotenv import os, json load_dotenv() async def get_client(): settings = { "node_url_pool": json.loads(os.getenv("NODE_URL_POOL", "[]")), "blockchain_rid": os.getenv("BLOCKCHAIN_RID") } return await BlockchainClient.create(settings) ``` ## 2. Upload movies and vectors You send movie data to the chain through the `add_movies` operation defined in your Rell module. It takes a **list of flat movie structs**, and each struct holds the movie's metadata plus its vector as a string. A single movie entry looks like this. Every field is a string except `wiki_id`, which is an integer: python ``` [ 12345, "The Matrix", "A man discovers reality is a simulation...", "1999-03-31", "466000000", "136", "English", "USA", "Science Fiction, Action", "[0.1, 0.2, ...]" ] ``` Send the entries in batches: python ``` from postchain_client_py.blockchain_client import Operation, Transaction from secp256k1 import PrivateKey async def store_batch(client, batch): private_key = PrivateKey(bytes.fromhex(os.getenv("PRIV_KEY")), raw=True) public_key = private_key.pubkey.serialize() operation = Operation(op_name="add_movies", args=[batch]) transaction = Transaction( operations=[operation], signers=[public_key], blockchain_rid=os.getenv("BLOCKCHAIN_RID") ) signed = await client.sign_transaction(transaction, private_key.private_key) receipt = await client.send_transaction(signed, do_status_polling=True) if receipt.status != 0: raise Exception(f"Upload failed: {receipt.message}") ``` Each entry matches the `movie_input` struct in Rell. Internally, the operation saves both the metadata and the vectors by calling `store_vectors(...)`. ## 3. Run a semantic search The query template `get_movies_with_distance` in your Rell module returns each matching movie's metadata together with its vector distance. Call it from Python: python ``` async def search_movies(query_vector, max_results=10): payload = { "context": 0, "q_vector": query_vector, "max_distance": "1.0", "max_vectors": max_results, "query_template": {"type": "get_movies_with_distance"} } client = await get_client() return await client.query("query_closest_objects", payload) ``` ## 4. Filter search results by genre To restrict results to a genre, call the `get_movies_with_filter` template instead. It behaves the same way but takes one extra argument: python ``` async def search_with_filter(query_vector, genre, max_results=10): payload = { "context": 0, "q_vector": query_vector, "max_distance": "1.0", "max_vectors": max_results, "query_template": { "type": "get_movies_with_filter", "args": {"genre_filter": genre} } } client = await get_client() return await client.query("query_closest_objects", payload) ``` ## 5. Create the query vector Generate the query vector with the **same model** you chose during preprocessing, for example: python ``` from sentence_transformers import SentenceTransformer import os model = SentenceTransformer(os.getenv("EMBEDDING_MODEL")) def create_query_vector(prompt): raw_vector = model.encode(prompt) return "[" + ", ".join(f"{float(v)}" for v in raw_vector) + "]" ``` Ensure model consistency The embedding model for your queries must be **exactly the same** as the one you used to create the vectors uploaded to Chromia. If the dimensionality or encoding differs, queries either fail or return irrelevant matches. ## 6. Interpret the results A query returns a list of matching movies. Each item includes the full metadata and a similarity score: python ``` { "movie": { "wiki_id": 12345, "title": "The Matrix", "plot": "...", ... }, "distance": "0.042" } ``` * `movie` holds all the Rell metadata for the matched entry * `distance` measures how far the stored vector is from your query vector; the smaller the value, the more similar the movie ## Summary * Call `add_movies` to upload movie metadata and vectors together as a batch operation * Retrieve movies by vector similarity, optionally narrowed down by genre * Use the same embedding model throughout to get accurate results Written for this site from Chromia’s public course. Code samples are the course’s own. [← Previous**Link your data to the Vector DB with Rell**](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/rell-interface/)[Next →**Use cases and extensions**](https://chromia.com/learn/vector-db-movie-demo/use-cases/) On this page * [1. Initialize the Python client](#1-initialize-the-python-client) * [2. Upload movies and vectors](#2-upload-movies-and-vectors) * [3. Run a semantic search](#3-run-a-semantic-search) * [4. Filter search results by genre](#4-filter-search-results-by-genre) * [5. Create the query vector](#5-create-the-query-vector) * [6. Interpret the results](#6-interpret-the-results) * [Summary](#summary)