Commit Graph

73 Commits

Author SHA1 Message Date
Bagatur
a9c5b4bcea
Bagatur/clarifai update (#7324)
This PR improves upon the Clarifai LangChain integration with improved docs, errors, args and the addition of embedding model support in LancChain for Clarifai's embedding models and an overview of the various ways you can integrate with Clarifai added to the docs.

---------

Co-authored-by: Matthew Zeiler <zeiler@clarifai.com>
2023-07-07 02:23:20 -04:00
John Landahl
e047541b5f
Corrected a typo in elasticsearch.ipynb (#7318)
Simple typo fix
2023-07-07 01:35:32 -04:00
hayao-k
c23e16c459
docs: Fixed typos in Amazon Kendra Retriever documentation (#7261)
## Description
Fixed to the official service name Amazon Kendra.

## Tag maintainer
@baskaryan
2023-07-06 11:56:52 -04:00
zhaoshengbo
e8f24164f0
Improve the alibaba cloud opensearch vector store documentation (#6964)
Based on user feedback, we have improved the Alibaba Cloud OpenSearch
vector store documentation.

Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>
2023-07-06 09:47:49 -04:00
Shantanu Nair
f773c21723
Update supabase match_docs ddl and notebook to use expected id type (#7257)
- Description: Switch supabase match function DDL to use expected uuid
type instead of bigint
- Issue: https://github.com/hwchase17/langchain/issues/6743,
https://github.com/hwchase17/langchain/issues/7179
  - Tag maintainer:  @rlancemartin, @eyurtsev
  - Twitter handle: https://twitter.com/ShantanuNair
2023-07-06 09:22:41 -04:00
Mike Nitsenko
d669b9ece9
Document loader for Cube Semantic Layer (#6882)
### Description

This pull request introduces the "Cube Semantic Layer" document loader,
which demonstrates the retrieval of Cube's data model metadata in a
format suitable for passing to LLMs as embeddings. This enhancement aims
to provide contextual information and improve the understanding of data.

Twitter handle:
@the_cube_dev

---------

Co-authored-by: rlm <pexpresss31@gmail.com>
2023-07-05 15:18:12 -07:00
Tom
e533da8bf2
Adding Marqo to vectorstore ecosystem (#7068)
This PR brings in a vectorstore interface for
[Marqo](https://www.marqo.ai/).

The Marqo vectorstore exposes some of Marqo's functionality in addition
the the VectorStore base class. The Marqo vectorstore also makes the
embedding parameter optional because inference for embeddings is an
inherent part of Marqo.

Docs, notebook examples and integration tests included.

Related PR:
https://github.com/hwchase17/langchain/pull/2807

---------

Co-authored-by: Tom Hamer <tom@marqo.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-07-05 14:44:12 -07:00
Conrad Fernandez
6eff0fa2ca
Added documentation for add_texts function for Pinecone integration (#7134)
- Description: added some documentation to the Pinecone vector store
docs page.
- Issue: #7126 
- Dependencies: None
- Tag maintainer: @baskaryan 

I can add more documentation on the Pinecone integration functions as I
am going to go in great depth into this area. Just wanted to check with
the maintainers is if this is all good.
2023-07-05 13:11:37 -04:00
Prakul Agarwal
38f853dfa3
Fixed typos in MongoDB Atlas Vector Search documentation (#7174)
Fix for typos in MongoDB Atlas Vector Search documentation
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2023-07-05 12:48:00 -04:00
Raouf Chebri
6fc24743b7
Add pg_hnsw vectorstore integration (#6893)
Hi @rlancemartin, @eyurtsev!

- Description: Adding HNSW extension support for Postgres. Similar to
pgvector vectorstore, with 3 differences
      1. it uses HNSW extension for exact and ANN searches, 
      2. Vectors are of type array of real
      3. Only supports L2
      
- Dependencies: [HNSW](https://github.com/knizhnik/hnsw) extension for
Postgres
  
  - Example:
  ```python
    db = HNSWVectoreStore.from_documents(
      embedding=embeddings,
      documents=docs,
      collection_name=collection_name,
      connection_string=connection_string
  )
  
  query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] =
db.similarity_search_with_score(query)
  ```

The example notebook is in the PR too.
2023-07-05 08:10:10 -07:00
Lance Martin
9ca4c54428
Minor updates to notebook for MultiQueryRetriever (#7102)
* Add an easier-to-run example.
* Add logging per https://github.com/hwchase17/langchain/pull/6891.
* Updated params per https://github.com/hwchase17/langchain/pull/5962.

---------

Co-authored-by: R. Lance Martin <rlm@Rs-MacBook-Pro.local>
Co-authored-by: Lance Martin <lance@langchain.dev>
2023-07-03 17:32:50 -07:00
rjarun8
e2d61ab85a
Add SpacyEmbeddings class (#6967)
- Description: Added a new SpacyEmbeddings class for generating
embeddings using the Spacy library.
- Issue: Sentencebert/Bert/Spacy/Doc2vec embedding support #6952
- Dependencies: This change requires the Spacy library and the
'en_core_web_sm' Spacy model.
- Tag maintainer: @dev2049
- Twitter handle: N/A

This change includes a new SpacyEmbeddings class, but does not include a
test or an example notebook.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-03 09:38:31 -06:00
adam91holt
80e86b602e
Remove duplicate mongodb integration doc (#7006) 2023-07-03 02:23:33 -06:00
Johnny Lim
a081e419a0
Fix sample in FAISS section (#7050)
This PR fixes a sample in the FAISS section in the reference docs.
2023-07-03 02:18:32 -06:00
Leonid Ganeline
200be43da6
added Brave Search document_loader (#6989)
- Added `Brave Search` document loader.
- Refactored BraveSearch wrapper
- Added a Jupyter Notebook example
- Added `Ecosystem/Integrations` BraveSearch page 

Please review:
  - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
2023-07-02 19:01:24 -07:00
Sergey Kozlov
6d15854cda
Add JSON Lines support to JSONLoader (#6913)
**Description**:

The JSON Lines format is used by some services such as OpenAI and
HuggingFace. It's also a convenient alternative to CSV.

This PR adds JSON Lines support to `JSONLoader` and also updates related
tests.

**Tag maintainer**: @rlancemartin, @eyurtsev.

PS I was not able to build docs locally so didn't update related
section.
2023-07-02 12:32:41 -07:00
Ofer Mendelevitch
153b56d19b
Vectara upd2 (#6506)
Update to Vectara integration 
- By user request added "add_files" to take advantage of Vectara
capabilities to process files on the backend, without the need for
separate loading of documents and chunking in the chain.
- Updated vectara.ipynb example notebook to be broader and added testing
of add_file()
 
  @hwchase17 - project lead

---------

Co-authored-by: rlm <pexpresss31@gmail.com>
2023-07-02 12:15:50 -07:00
Leonid Ganeline
77ae8084a0
docstrings document_loaders 1 (#6847)
- Updated docstrings in `document_loaders`
- several code fixes.
- added `docs/extras/ecosystem/integrations/airtable.md`

@rlancemartin, @eyurtsev
2023-07-02 12:13:04 -07:00
Stefano Lottini
8d2281a8ca
Second Attempt - Add concurrent insertion of vector rows in the Cassandra Vector Store (#7017)
Retrying with the same improvements as in #6772, this time trying not to
mess up with branches.

@rlancemartin doing a fresh new PR from a branch with a new name. This
should do. Thank you for your help!

---------

Co-authored-by: Jonathan Ellis <jbellis@datastax.com>
Co-authored-by: rlm <pexpresss31@gmail.com>
2023-07-01 11:09:52 -07:00
Matt Robinson
0498dad562
feat: enable UnstructuredEmailLoader to process attachments (#6977)
### Summary

Updates `UnstructuredEmailLoader` so that it can process attachments in
addition to the e-mail content. The loader will process attachments if
the `process_attachments` kwarg is passed when the loader is
instantiated.

### Testing

```python

file_path = "fake-email-attachment.eml"
loader = UnstructuredEmailLoader(
    file_path, mode="elements", process_attachments=True
)
docs = loader.load()
docs[-1]
```

### Reviewers

-  @rlancemartin 
-  @eyurtsev
- @hwchase17
2023-07-01 06:09:26 -07:00
Zander Chase
b0859c9b18
Add New Retriever Interface with Callbacks (#5962)
Handle the new retriever events in a way that (I think) is entirely
backwards compatible? Needs more testing for some of the chain changes
and all.

This creates an entire new run type, however. We could also just treat
this as an event within a chain run presumably (same with memory)

Adds a subclass initializer that upgrades old retriever implementations
to the new schema, along with tests to ensure they work.

First commit doesn't upgrade any of our retriever implementations (to
show that we can pass the tests along with additional ones testing the
upgrade logic).

Second commit upgrades the known universe of retrievers in langchain.

- [X] Add callback handling methods for retriever start/end/error (open
to renaming to 'retrieval' if you want that)
- [X] Update BaseRetriever schema to support callbacks
- [X] Tests for upgrading old "v1" retrievers for backwards
compatibility
- [X] Update existing retriever implementations to implement the new
interface
- [X] Update calls within chains to .{a]get_relevant_documents to pass
the child callback manager
- [X] Update the notebooks/docs to reflect the new interface
- [X] Test notebooks thoroughly


Not handled:
- Memory pass throughs: retrieval memory doesn't have a parent callback
manager passed through the method

---------

Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
2023-06-30 14:44:03 -07:00
Daniel Chalef
b26cca8008
Zep Authentication (#6728)
## Description: Add Zep API Key argument to ZepChatMessageHistory and
ZepRetriever
- correct docs site links
- add zep api_key auth to constructors

ZepChatMessageHistory: @hwchase17, 
ZepRetriever: @rlancemartin, @eyurtsev
2023-06-30 14:24:26 -07:00
Davis Chase
f780678910
Add back in clickhouse mongo vecstore notebooks (#6949) 2023-06-29 19:21:47 -07:00
Kacper Łukawski
140ba682f1
Support named vectors in Qdrant (#6871)
# Description

This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.

## Example

```python
Qdrant.from_documents(
    docs,
    embeddings,
    location=":memory:",
    collection_name="my_documents",
    vector_name="custom_vector",
)
```

### Issue: #2594 

Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
2023-06-29 15:14:22 -07:00
corranmac
20c6ade2fc
Grobid parser for Scientific Articles from PDF (#6729)
### Scientific Article PDF Parsing via Grobid

`Description:`
This change adds the GrobidParser class, which uses the Grobid library
to parse scientific articles into a universal XML format containing the
article title, references, sections, section text etc. The GrobidParser
uses a local Grobid server to return PDFs document as XML and parses the
XML to optionally produce documents of individual sentences or of whole
paragraphs. Metadata includes the text, paragraph number, pdf relative
bboxes, pages (text may overlap over two pages), section title
(Introduction, Methodology etc), section_number (i.e 1.1, 2.3), the
title of the paper and finally the file path.
      
Grobid parsing is useful beyond standard pdf parsing as it accurately
outputs sections and paragraphs within them. This allows for
post-fitering of results for specific sections i.e. limiting results to
the methodology section or results. While sections are split via
headings, ideally they could be classified specifically into
introduction, methodology, results, discussion, conclusion. I'm
currently experimenting with chatgpt-3.5 for this function, which could
later be implemented as a textsplitter.

`Dependencies:`
For use, the grobid repo must be cloned and Java must be installed, for
colab this is:

```
!apt-get install -y openjdk-11-jdk -q
!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
!git clone https://github.com/kermitt2/grobid.git
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
os.chdir('grobid')
!./gradlew clean install
```

Once installed the server is ran on localhost:8070 via
```
get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
```

@rlancemartin, @eyurtsev

Twitter Handle: @Corranmac

Grobid Demo Notebook is
[here](https://colab.research.google.com/drive/1X-St_mQRmmm8YWtct_tcJNtoktbdGBmd?usp=sharing).

---------

Co-authored-by: rlm <pexpresss31@gmail.com>
2023-06-29 14:29:29 -07:00
Yaohui Wang
9d1bd18596
feat (documents): add LarkSuite document loader (#6420)
<!--
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### Summary

This PR adds a LarkSuite (FeiShu) document loader. 
> [LarkSuite](https://www.larksuite.com/) is an enterprise collaboration
platform developed by ByteDance.

### Tests

- an integration test case is added
- an example notebook showing usage is added. [Notebook
preview](https://github.com/yaohui-wyh/langchain/blob/master/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb)

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### Who can review?

- PTAL @eyurtsev @hwchase17

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  - @agola11

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---------

Co-authored-by: Yaohui Wang <wangyaohui.01@bytedance.com>
2023-06-27 23:08:05 -07:00
Jingsong Gao
a435a436c1
feat(document_loaders): add tencent cos directory and file loader (#6401)
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- add tencent cos directory and file support for document-loader

#### Before submitting

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#### Who can review?

@eyurtsev
2023-06-27 23:07:20 -07:00
Lance Martin
3f9900a864
Create MultiQueryRetriever (#6833)
Distance-based vector database retrieval embeds (represents) queries in
high-dimensional space and finds similar embedded documents based on
"distance". But, retrieval may produce difference results with subtle
changes in query wording or if the embeddings do not capture the
semantics of the data well. Prompt engineering / tuning is sometimes
done to manually address these problems, but can be tedious.

The `MultiQueryRetriever` automates the process of prompt tuning by
using an LLM to generate multiple queries from different perspectives
for a given user input query. For each query, it retrieves a set of
relevant documents and takes the unique union across all queries to get
a larger set of potentially relevant documents. By generating multiple
perspectives on the same question, the `MultiQueryRetriever` might be
able to overcome some of the limitations of the distance-based retrieval
and get a richer set of results.

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-06-27 22:59:40 -07:00
Tim Asp
3ca1a387c2
Web Loader: Add proxy support (#6792)
Proxies are helpful, especially when you start querying against more
anti-bot websites.

[Proxy
services](https://developers.oxylabs.io/advanced-proxy-solutions/web-unblocker/making-requests)
(of which there are many) and `requests` make it easy to rotate IPs to
prevent banning by just passing along a simple dict to `requests`.

CC @rlancemartin, @eyurtsev
2023-06-27 22:27:49 -07:00
Matt Robinson
dd2a151543
Docs/unstructured api key (#6781)
### Summary

The Unstructured API will soon begin requiring API keys. This PR updates
the Unstructured integrations docs with instructions on how to generate
Unstructured API keys.

### Reviewers

@rlancemartin
@eyurtsev
@hwchase17
2023-06-27 16:54:15 -07:00
Matt Robinson
b24472eae3
feat: Add UnstructuredOrgModeLoader (#6842)
### Summary

Adds `UnstructuredOrgModeLoader` for processing
[Org-mode](https://en.wikipedia.org/wiki/Org-mode) documents.

### Testing

```python
from langchain.document_loaders import UnstructuredOrgModeLoader

loader = UnstructuredOrgModeLoader(
    file_path="example_data/README.org", mode="elements"
)
docs = loader.load()
print(docs[0])
```

### Reviewers

- @rlancemartin
- @eyurtsev
- @hwchase17
2023-06-27 16:34:17 -07:00
Cristóbal Carnero Liñán
e494b0a09f
feat (documents): add a source code loader based on AST manipulation (#6486)
#### Summary

A new approach to loading source code is implemented:

Each top-level function and class in the code is loaded into separate
documents. Then, an additional document is created with the top-level
code, but without the already loaded functions and classes.

This could improve the accuracy of QA chains over source code.

For instance, having this script:

```
class MyClass:
    def __init__(self, name):
        self.name = name

    def greet(self):
        print(f"Hello, {self.name}!")

def main():
    name = input("Enter your name: ")
    obj = MyClass(name)
    obj.greet()

if __name__ == '__main__':
    main()
```

The loader will create three documents with this content:

First document:
```
class MyClass:
    def __init__(self, name):
        self.name = name

    def greet(self):
        print(f"Hello, {self.name}!")
```

Second document:
```
def main():
    name = input("Enter your name: ")
    obj = MyClass(name)
    obj.greet()
```

Third document:
```
# Code for: class MyClass:

# Code for: def main():

if __name__ == '__main__':
    main()
```

A threshold parameter is added to control whether small scripts are
split in this way or not.

At this moment, only Python and JavaScript are supported. The
appropriate parser is determined by examining the file extension.

#### Tests

This PR adds:

- Unit tests
- Integration tests

#### Dependencies

Only one dependency was added as optional (needed for the JavaScript
parser).

#### Documentation

A notebook is added showing how the loader can be used.

#### Who can review?

@eyurtsev @hwchase17

---------

Co-authored-by: rlm <pexpresss31@gmail.com>
2023-06-27 15:58:47 -07:00
WaseemH
7ac9b22886
RecusiveUrlLoader to RecursiveUrlLoader (#6787) 2023-06-26 23:12:14 -07:00
Leonid Ganeline
49c864fa18
docs: vectorstore upgrades 2 (#6796)
updated vectorstores/ notebooks; added new integrations into
ecosystem/integrations/
@dev2049
@rlancemartin, @eyurtsev
2023-06-26 22:55:04 -07:00
Chris Pappalardo
70f7c2bb2e
align chroma vectorstore get with chromadb to enable where filtering (#6686)
allows for where filtering on collection via get

- Description: aligns langchain chroma vectorstore get with underlying
[chromadb collection
get](https://github.com/chroma-core/chroma/blob/main/chromadb/api/models/Collection.py#L103)
allowing for where filtering, etc.
  - Issue: NA
  - Dependencies: none
  - Tag maintainer: @rlancemartin, @eyurtsev
  - Twitter handle: @pappanaka
2023-06-26 10:51:20 -07:00
Pau Ramon Revilla
87802c86d9
Added a MHTML document loader (#6311)
MHTML is a very interesting format since it's used both for emails but
also for archived webpages. Some scraping projects want to store pages
in disk to process them later, mhtml is perfect for that use case.

This is heavily inspired from the beautifulsoup html loader, but
extracting the html part from the mhtml file.

---------

Co-authored-by: rlm <pexpresss31@gmail.com>
2023-06-25 13:12:08 -07:00
Matt Robinson
be68f6f8ce
feat: Add UnstructuredRSTLoader (#6594)
### Summary

Adds an `UnstructuredRSTLoader` for loading
[reStructuredText](https://en.wikipedia.org/wiki/ReStructuredText) file.

### Testing

```python
from langchain.document_loaders import UnstructuredRSTLoader

loader = UnstructuredRSTLoader(
    file_path="example_data/README.rst", mode="elements"
)
docs = loader.load()
print(docs[0])
```

### Reviewers

- @hwchase17 
- @rlancemartin 
- @eyurtsev
2023-06-25 12:41:57 -07:00
Davis Chase
f1e1ac2a01
chroma nb close img tag (#6669) 2023-06-23 15:41:54 -07:00
Jeff Huber
2acf109c4b
update chroma notebook (#6664)
@rlancemartin I updated the notebook for Chroma to hopefully be a lot
easier for users.
2023-06-23 15:03:06 -07:00
Piyush Jain
b1de927f1b
Kendra retriever api (#6616)
## Description
Replaces [Kendra
Retriever](https://github.com/hwchase17/langchain/blob/master/langchain/retrievers/aws_kendra_index_retriever.py)
with an updated version that uses the new [retriever
API](https://docs.aws.amazon.com/kendra/latest/dg/searching-retrieve.html)
which is better suited for retrieval augmented generation (RAG) systems.

**Note**: This change requires the latest version (1.26.159) of boto3 to
work. `pip install -U boto3` to upgrade the boto3 version.

cc @hupe1980
cc @dev2049
2023-06-23 14:59:35 -07:00
Ikko Eltociear Ashimine
73da193a4b
Fix typo in myscale_self_query.ipynb (#6601) 2023-06-23 14:57:12 -07:00
Lance Martin
c2b25c17c5
Recursive URL loader (#6455)
We may want to process load all URLs under a root directory.

For example, let's look at the [LangChain JS
documentation](https://js.langchain.com/docs/).

This has many interesting child pages that we may want to read in bulk.

Of course, the `WebBaseLoader` can load a list of pages. 

But, the challenge is traversing the tree of child pages and actually
assembling that list!
 
We do this using the `RecusiveUrlLoader`.

This also gives us the flexibility to exclude some children (e.g., the
`api` directory with > 800 child pages).
2023-06-23 13:09:00 -07:00
Lance Martin
393f469eb3
Create merge loader that combines documents from a set of loaders (#6659)
Simple utility loader that combines documents from a set of specified
loaders.
2023-06-23 13:02:48 -07:00
Lance Martin
6e69bfbb28
Loader for OpenCityData and minor cleanups to Pandas, Airtable loaders (#6301)
Many cities have open data portals for events like crime, traffic, etc.

Socrata provides an API for many, including SF (e.g., see
[here](https://dev.socrata.com/foundry/data.sfgov.org/tmnf-yvry)).

This is a new data loader for city data that uses Socrata API.
2023-06-22 22:20:42 -07:00
Christoph Kahl
9d42621fa4
added redis method to delete entries by keys (#6222)
In addition to my last pr (return keys of added entries), we also need a
method to delete the entries by keys.

@dev2049
2023-06-22 13:26:47 -07:00
Harrison Chase
a9108c1809
add mongo (HOLD) (#6437)
do not merge in
2023-06-22 11:08:12 -07:00
Lance Martin
30f7288082
MD header text splitter returns Documents (#6571)
Return `Documents` from MD header text splitter to simplify UX.

Updates the test as well as example notebooks.
2023-06-22 09:25:38 -07:00
minhajul-clarifai
6e57306a13
Clarifai integration (#5954)
# Changes
This PR adds [Clarifai](https://www.clarifai.com/) integration to
Langchain. Clarifai is an end-to-end AI Platform. Clarifai offers user
the ability to use many types of LLM (OpenAI, cohere, ect and other open
source models). As well, a clarifai app can be treated as a vector
database to upload and retrieve data. The integrations includes:
- Clarifai LLM integration: Clarifai supports many types of language
model that users can utilize for their application
- Clarifai VectorDB: A Clarifai application can hold data and
embeddings. You can run semantic search with the embeddings

#### Before submitting
- [x] Added integration test for LLM 
- [x] Added integration test for VectorDB 
- [x] Added notebook for LLM 
- [x] Added notebook for VectorDB 

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-06-22 08:00:15 -07:00
Andrey E. Vedishchev
a2a0715bd4
Minor Grammar Fixes in Docs and Comments (#6536)
Just some grammar fixes: I found "retriver" instead of "retriever" in
several comments across the documentation and in the comments. I fixed
it.


Co-authored-by: andrey.vedishchev <andrey.vedishchev@rgigroup.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-06-21 09:53:31 -07:00
dirtysalt
57cc3d1d3d
[Feature][VectorStore] Support StarRocks as vector db (#6119)
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Fixes # (issue)

#### Before submitting

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Here are some examples to use StarRocks as vectordb

```
from langchain.vectorstores import StarRocks
from langchain.vectorstores.starrocks import StarRocksSettings

embeddings = OpenAIEmbeddings()

# conifgure starrocks settings
settings = StarRocksSettings()
settings.port = 41003
settings.host = '127.0.0.1'
settings.username = 'root'
settings.password = ''
settings.database = 'zya'

# to fill new embeddings
docsearch = StarRocks.from_documents(split_docs, embeddings, config = settings)   


# or to use already-built embeddings in database.
docsearch = StarRocks(embeddings, settings)
```

#### Who can review?

Tag maintainers/contributors who might be interested:

@dev2049 

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---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-06-21 09:02:33 -07:00