<Community>: Add Initial Support for TiDB Vector Store #15796
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
The latest updates on your projects. Learn more about Vercel for Git ↗︎
|
…gchain into tidb_vectorestore
|
@IANTHEREAL feel free to ping when the PR is ready for review |
|
@baskaryan The TiDB Vector feature is expected to release a preview version around the end of January. I will make the final adjustments then and invite you to review it 😊 |
|
Deployment failed with the following error: |
dosubot
bot
added
size:XXL
|
Thank you for your patience with this long-standing PR. I'm excited to tell that the TiDB Vector feature is now in the release pipeline, with most tests completed. We're targeting a release in the last two weeks of March. And. the feature has been deployed in our staging environment for this integration review with LangChain. I'll share access credentials with reviewers shortly. Your review would be greatly appreciated, thanks in advance! @baskaryan @hwchase17 |
|
Recently, there have been more and more code conflicts. If possible, please help review this PR. @baskaryan @hwchase17 And I've DM'd the access method for the tidb vector staging environment to @baskaryan, hope it's useful for this PR reviewing |
|
This is great @baskaryan can you please assist ? |
baskaryan
approved these changes
Mar 8, 2024
dosubot
bot
added
the
lgtm
|
Thanks for reviewing @baskaryan @AV25242 |
This was referenced Mar 8, 2024
gkorland
pushed a commit
to FalkorDB/langchain
that referenced
this pull request
Mar 30, 2024
…n-ai#15796) This pull request introduces initial support for the TiDB vector store. The current version is basic, laying the foundation for the vector store integration. While this implementation provides the essential features, we plan to expand and improve the TiDB vector store support with additional enhancements in future updates. Upcoming Enhancements: * Support for Vector Index Creation: To enhance the efficiency and performance of the vector store. * Support for max marginal relevance search. * Customized Table Structure Support: Recognizing the need for flexibility, we plan for more tailored and efficient data store solutions. Simple use case exmaple ```python from typing import List, Tuple from langchain.docstore.document import Document from langchain_community.vectorstores import TiDBVectorStore from langchain_openai import OpenAIEmbeddings db = TiDBVectorStore.from_texts( embedding=embeddings, texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'], table_name="tidb_vector_langchain", connection_string=tidb_connection_url, distance_strategy="cosine", ) query = "Can you tell me about Alexandra?" docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query) for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80) ```
hinthornw
pushed a commit
that referenced
this pull request
Apr 26, 2024
This pull request introduces initial support for the TiDB vector store.
The current version is basic, laying the foundation for the vector store
integration. While this implementation provides the essential features,
we plan to expand and improve the TiDB vector store support with
additional enhancements in future updates.
Upcoming Enhancements:
* Support for Vector Index Creation: To enhance the efficiency and
performance of the vector store.
* Support for max marginal relevance search.
* Customized Table Structure Support: Recognizing the need for
flexibility, we plan for more tailored and efficient data store
solutions.
Simple use case exmaple
```python
from typing import List, Tuple
from langchain.docstore.document import Document
from langchain_community.vectorstores import TiDBVectorStore
from langchain_openai import OpenAIEmbeddings
db = TiDBVectorStore.from_texts(
embedding=embeddings,
texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'],
table_name="tidb_vector_langchain",
connection_string=tidb_connection_url,
distance_strategy="cosine",
)
query = "Can you tell me about Alexandra?"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
```
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This pull request introduces initial support for the TiDB vector store. The current version is basic, laying the foundation for the vector store integration. While this implementation provides the essential features, we plan to expand and improve the TiDB vector store support with additional enhancements in future updates.
Upcoming Enhancements:
Simple use case exmaple