https://github.com/ydb-platform/langchain-ydb
LangChain Integration package for YDB
https://github.com/ydb-platform/langchain-ydb
Last synced: 12 months ago
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LangChain Integration package for YDB
- Host: GitHub
- URL: https://github.com/ydb-platform/langchain-ydb
- Owner: ydb-platform
- License: apache-2.0
- Created: 2025-04-02T11:54:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-06T17:21:12.000Z (about 1 year ago)
- Last Synced: 2025-06-06T18:28:10.907Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 507 KB
- Stars: 10
- Watchers: 4
- Forks: 1
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# langchain-ydb
---
[](https://github.com/ydb-platform/langchain-ydb/blob/main/LICENSE)
[](https://badge.fury.io/py/langchain-ydb)
[](https://github.com/ydb-platform/langchain-ydb/actions/workflows/tests.yml)
[](https://github.com/ydb-platform/langchain-ydb/actions/workflows/lint.yml)
LangChain's YDB integration (langchain-ydb) provides vector capabilities for working with [YDB](https://ydb.tech/).
## Getting Started
### Setting Up YDB
Launch a YDB Docker container with:
```shell
docker run -d -p 2136:2136 --name ydb-langchain -e YDB_USE_IN_MEMORY_PDISKS=true -h localhost ydbplatform/local-ydb:trunk
```
### Installing the Package
Install `langchain-ydb` package with:
```bash
pip install -U langchain-ydb
```
VectorStore works along with an embedding model, here using `langchain-openai` as example.
```shell
pip install langchain-openai
export OPENAI_API_KEY=...
```
## Work with YDB Vector Store
### Creating a Vector Store
```python
from langchain_openai import OpenAIEmbeddings
from langchain_ydb.vectorstores import YDB, YDBSearchStrategy, YDBSettings
settings = YDBSettings(
table="ydb_example",
strategy=YDBSearchStrategy.COSINE_SIMILARITY,
)
vector_store = YDB(
OpenAIEmbeddings(),
config=settings,
)
```
### Add items to vector store
Once you have created your vector store, you can interact with it by adding and deleting different items.
Prepare documents to work with:
```python
from uuid import uuid4
from langchain_core.documents import Document
document_1 = Document(
page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
metadata={"source": "tweet"},
)
document_2 = Document(
page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata={"source": "news"},
)
document_3 = Document(
page_content="Building an exciting new project with LangChain - come check it out!",
metadata={"source": "tweet"},
)
document_4 = Document(
page_content="Robbers broke into the city bank and stole $1 million in cash.",
metadata={"source": "news"},
)
document_5 = Document(
page_content="Wow! That was an amazing movie. I can't wait to see it again.",
metadata={"source": "tweet"},
)
document_6 = Document(
page_content="Is the new iPhone worth the price? Read this review to find out.",
metadata={"source": "website"},
)
document_7 = Document(
page_content="The top 10 soccer players in the world right now.",
metadata={"source": "website"},
)
document_8 = Document(
page_content="LangGraph is the best framework for building stateful, agentic applications!",
metadata={"source": "tweet"},
)
document_9 = Document(
page_content="The stock market is down 500 points today due to fears of a recession.",
metadata={"source": "news"},
)
document_10 = Document(
page_content="I have a bad feeling I am going to get deleted :(",
metadata={"source": "tweet"},
)
documents = [
document_1,
document_2,
document_3,
document_4,
document_5,
document_6,
document_7,
document_8,
document_9,
document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]
```
You can add items to your vector store by using the `add_documents` function.
```python
vector_store.add_documents(documents=documents, ids=uuids)
```
### Delete items from vector store
You can delete items from your vector store by ID using the `delete` function.
```python
vector_store.delete(ids=[uuids[-1]])
```
### Query vector store
Once your vector store has been created and relevant documents have been added, you will likely want to query it during the execution of your chain or agent.
#### Query directly
**Similarity search**:
A simple similarity search can be performed as follows:
```python
results = vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy", k=2
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
```
**Similarity search with score**
You can also perform a search with a score:
```python
results = vector_store.similarity_search_with_score("Will it be hot tomorrow?", k=3)
for res, score in results:
print(f"* [SIM={score:.3f}] {res.page_content} [{res.metadata}]")
```
#### Filtering
You can search with filters as described below:
```python
results = vector_store.similarity_search_with_score(
"What did I eat for breakfast?",
k=4,
filter={"source": "tweet"},
)
for res, _ in results:
print(f"* {res.page_content} [{res.metadata}]")
```
#### Query by turning into retriever
You can also transform the vector store into a retriever for easier usage in your chains.
Here's how to transform your vector store into a retriever and then invoke the retriever with a simple query and filter.
```python
retriever = vector_store.as_retriever(
search_kwargs={"k": 2},
)
results = retriever.invoke(
"Stealing from the bank is a crime", filter={"source": "news"}
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
```