Commit Graph

37 Commits

Author SHA1 Message Date
Joseph McElroy
2a06e7b216
ElasticsearchStore: improve error logging for adding documents (#9648)
Not obvious what the error is when you cannot index. This pr adds the
ability to log the first errors reason, to help the user diagnose the
issue.

Also added some more documentation for when you want to use the
vectorstore with an embedding model deployed in elasticsearch.

Credit: @elastic and @phoey1
2023-08-23 07:04:09 -07:00
Leonid Ganeline
e1f4f9ac3e
docs: integrations/providers (#9631)
Added missed pages for `integrations/providers` from `vectorstores`.
Updated several `vectorstores` notebooks.
2023-08-22 20:28:11 -07:00
Matthew Zeiler
949b2cf177
Improvements to the Clarifai integration (#9290)
- Improved docs
- Improved performance in multiple ways through batching, threading,
etc.
 - fixed error message 
 - Added support for metadata filtering during similarity search.

@baskaryan PTAL
2023-08-21 12:53:36 -07:00
ricki-epsilla
66a47d9a61
add Epsilla vectorstore (#9239)
[Epsilla](https://github.com/epsilla-cloud/vectordb) vectordb is an
open-source vector database that leverages the advanced academic
parallel graph traversal techniques for vector indexing.
This PR adds basic integration with
[pyepsilla](https://github.com/epsilla-cloud/epsilla-python-client)(Epsilla
vectordb python client) as a vectorstore.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-21 12:51:15 -07:00
Daniel Chalef
1d55141c50
zep/new ZepVectorStore (#9159)
- new ZepVectorStore class
- ZepVectorStore unit tests
- ZepVectorStore demo notebook
- update zep-python to ~1.0.2

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-16 00:23:07 -07:00
Xiaoyu Xee
b30f449dae
Add dashvector vectorstore (#9163)
## Description
Add `Dashvector` vectorstore for langchain

- [dashvector quick
start](https://help.aliyun.com/document_detail/2510223.html)
- [dashvector package description](https://pypi.org/project/dashvector/)

## How to use
```python
from langchain.vectorstores.dashvector import DashVector

dashvector = DashVector.from_documents(docs, embeddings)
```

---------

Co-authored-by: smallrain.xuxy <smallrain.xuxy@alibaba-inc.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-15 16:19:30 -07:00
Bagatur
bfbb97b74c
Bagatur/deeplake docs fixes (#9275)
Co-authored-by: adilkhan <adilkhan.sarsen@nu.edu.kz>
2023-08-15 15:56:36 -07:00
Hech
4b505060bd
fix: max_marginal_relevance_search and docs in Dingo (#9244) 2023-08-15 01:06:06 -07:00
Joseph McElroy
eac4ddb4bb
Elasticsearch Store Improvements (#8636)
Todo:
- [x] Connection options (cloud, localhost url, es_connection) support
- [x] Logging support
- [x] Customisable field support
- [x] Distance Similarity support 
- [x] Metadata support
  - [x] Metadata Filter support 
- [x] Retrieval Strategies
  - [x] Approx
  - [x] Approx with Hybrid
  - [x] Exact
  - [x] Custom 
  - [x] ELSER (excluding hybrid as we are working on RRF support)
- [x] integration tests 
- [x] Documentation

👋 this is a contribution to improve Elasticsearch integration with
Langchain. Its based loosely on the changes that are in master but with
some notable changes:

## Package name & design improvements
The import name is now `ElasticsearchStore`, to aid discoverability of
the VectorStore.

```py
## Before
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch, ElasticKnnSearch

## Now
from langchain.vectorstores.elasticsearch import ElasticsearchStore
```

## Retrieval Strategy support
Before we had a number of classes, depending on the strategy you wanted.
`ElasticKnnSearch` for approx, `ElasticVectorSearch` for exact / brute
force.

With `ElasticsearchStore` we have retrieval strategies:

### Approx Example
Default strategy for the vast majority of developers who use
Elasticsearch will be inferring the embeddings from outside of
Elasticsearch. Uses KNN functionality of _search.

```py
        texts = ["foo", "bar", "baz"]
       docsearch = ElasticsearchStore.from_texts(
            texts,
            FakeEmbeddings(),
            es_url="http://localhost:9200",
            index_name="sample-index"
        )
        output = docsearch.similarity_search("foo", k=1)
```

### Approx, with hybrid
Developers who want to search, using both the embedding and the text
bm25 match. Its simple to enable.

```py
 texts = ["foo", "bar", "baz"]
       docsearch = ElasticsearchStore.from_texts(
            texts,
            FakeEmbeddings(),
            es_url="http://localhost:9200",
            index_name="sample-index",
            strategy=ElasticsearchStore.ApproxRetrievalStrategy(hybrid=True)
        )
        output = docsearch.similarity_search("foo", k=1)
```

### Approx, with `query_model_id`
Developers who want to infer within Elasticsearch, using the model
loaded in the ml node.

This relies on the developer to setup the pipeline and index if they
wish to embed the text in Elasticsearch. Example of this in the test.

```py
 texts = ["foo", "bar", "baz"]
       docsearch = ElasticsearchStore.from_texts(
            texts,
            FakeEmbeddings(),
            es_url="http://localhost:9200",
            index_name="sample-index",
            strategy=ElasticsearchStore.ApproxRetrievalStrategy(
                query_model_id="sentence-transformers__all-minilm-l6-v2"
            ),
        )
        output = docsearch.similarity_search("foo", k=1)
```

### I want to provide my own custom Elasticsearch Query
You might want to have more control over the query, to perform
multi-phase retrieval such as LTR, linearly boosting on document
parameters like recently updated or geo-distance. You can do this with
`custom_query_fn`

```py
        def my_custom_query(query_body: dict, query: str) -> dict:
            return {"query": {"match": {"text": {"query": "bar"}}}}

        texts = ["foo", "bar", "baz"]
        docsearch = ElasticsearchStore.from_texts(
            texts, FakeEmbeddings(), **elasticsearch_connection, index_name=index_name
        )
        docsearch.similarity_search("foo", k=1, custom_query=my_custom_query)

```

### Exact Example
Developers who have a small dataset in Elasticsearch, dont want the cost
of indexing the dims vs tradeoff on cost at query time. Uses
script_score.

```py
        texts = ["foo", "bar", "baz"]
       docsearch = ElasticsearchStore.from_texts(
            texts,
            FakeEmbeddings(),
            es_url="http://localhost:9200",
            index_name="sample-index",
            strategy=ElasticsearchStore.ExactRetrievalStrategy(),
        )
        output = docsearch.similarity_search("foo", k=1)
```

### ELSER Example
Elastic provides its own sparse vector model called ELSER. With these
changes, its really easy to use. The vector store creates a pipeline and
index thats setup for ELSER. All the developer needs to do is configure,
ingest and query via langchain tooling.

```py
texts = ["foo", "bar", "baz"]
       docsearch = ElasticsearchStore.from_texts(
            texts,
            FakeEmbeddings(),
            es_url="http://localhost:9200",
            index_name="sample-index",
            strategy=ElasticsearchStore.SparseVectorStrategy(),
        )
        output = docsearch.similarity_search("foo", k=1)

```

## Architecture
In future, we can introduce new strategies and allow us to not break bwc
as we evolve the index / query strategy.

## Credit
On release, could you credit @elastic and @phoey1 please? Thank you!

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-14 23:42:35 -07:00
Bagatur
8cb2594562
Bagatur/dingo (#9079)
Co-authored-by: gary <1625721671@qq.com>
2023-08-11 10:54:45 -07:00
Josh Phillips
5fc07fa524
change id column type to uuid to match function (#7456)
The table creation process in these examples commands do not match what
the recently updated functions in these example commands is looking for.
This change updates the type in the table creation command.
Issue Number for my report of the doc problem #7446
@rlancemartin and @eyurtsev I believe this is your area
Twitter: @j1philli

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-10 16:57:19 -07:00
Bidhan Roy
02430e25b6
BagelDB (bageldb.ai), VectorStore integration. (#8971)
- **Description**: [BagelDB](bageldb.ai) a collaborative vector
database. Integrated the bageldb PyPi package with langchain with
related tests and code.

  - **Issue**: Not applicable.
  - **Dependencies**: `betabageldb` PyPi package.
  - **Tag maintainer**: @rlancemartin, @eyurtsev, @baskaryan
  - **Twitter handle**: bageldb_ai (https://twitter.com/BagelDB_ai)
  
We ran `make format`, `make lint` and `make test` locally.

Followed the contribution guideline thoroughly
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md

---------

Co-authored-by: Towhid1 <nurulaktertowhid@gmail.com>
2023-08-10 16:48:36 -07:00
Molly Cantillon
99b5a7226c
Weaviate: adding auth example + fixing spelling in ReadME (#8939)
Added basic auth example to Weaviate notebook @baskaryan
2023-08-08 16:24:17 -07:00
Seif
6327eecdaf
Fix typo in Vectara docs (#8925)
Fixed a typo in the Vectara docs description.
2023-08-08 10:11:07 -07:00
Ash Vardanian
1f9124ceaa
Add: USearch Vector Store (#8835)
## Description

I am excited to propose an integration with USearch, a lightweight
vector-search engine available for both Python and JavaScript, among
other languages.

## Dependencies

It introduces a new PyPi dependency - `usearch`. I am unsure if it must
be added to the Poetry file, as this would make the PR too clunky.
Please let me know.

## Profiles

- Maintainers: @ashvardanian @davvard
- Twitter handles: @ashvardanian @unum_cloud

---------

Co-authored-by: Davit Vardanyan <78792753+davvard@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-07 20:41:00 -07:00
Tudor Golubenco
aeaef8f3a3
Add support for Xata as a vector store (#8822)
This adds support for [Xata](https://xata.io) (data platform based on
Postgres) as a vector store. We have recently added [Xata to
Langchain.js](https://github.com/hwchase17/langchainjs/pull/2125) and
would love to have the equivalent in the Python project as well.

The PR includes integration tests and a Jupyter notebook as docs. Please
let me know if anything else would be needed or helpful.

I have added the xata python SDK as an optional dependency.

## To run the integration tests

You will need to create a DB in xata (see the docs), then run something
like:

```
OPENAI_API_KEY=sk-... XATA_API_KEY=xau_... XATA_DB_URL='https://....xata.sh/db/langchain'  poetry run pytest tests/integration_tests/vectorstores/test_xata.py
```

<!-- Thank you for contributing to LangChain!

Replace this comment with:
  - Description: a description of the change, 
  - Issue: the issue # it fixes (if applicable),
  - Dependencies: any dependencies required for this change,
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  - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Philip Krauss <35487337+philkra@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-07 08:14:52 -07:00
fqassemi
485d716c21
Feature faiss delete (#8135)
<!-- Thank you for contributing to LangChain!

Replace this comment with:
- Description: docstore had two main method: add and search, however,
dealing with docstore sometimes requires deleting an entry from
docstore. So I have added a simple delete method that deletes items from
docstore. Additionally, I have added the delete method to faiss
vectorstore for the very same reason.
  - Issue: NA
  - Dependencies: NA
  - Tag maintainer:  @rlancemartin, @eyurtsev
- Twitter handle: we announce bigger features on Twitter. If your PR
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Please make sure you're PR is passing linting and testing before
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If you're adding a new integration, please include:
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  2. an example notebook showing its use.

Maintainer responsibilities:
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  - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
  - Models / Prompts: @hwchase17, @baskaryan
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  - Async: @agola11

If no one reviews your PR within a few days, feel free to @-mention the
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See contribution guidelines for more information on how to write/run
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 -->

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-08-06 15:46:30 -07:00
Kshitij Wadhwa
5f1aab5487
Fix docs for Rockset (#8807)
* remove error output for notebook
* add comment about vector length for ingest transformation
* change OPENAI_KEY -> OPENAI_API_KEY

cc @baskaryan
2023-08-06 15:04:01 -07:00
Bal Narendra Sapa
a22d502248
added the embeddings part (#8805)
Description: forgot to add the embeddings part in the documentation.
sorry 😅

@baskaryan
2023-08-05 17:16:33 -07:00
Bal Narendra Sapa
bd61757423
add documentation for serializer function (#8769)
Description: Added necessary documentation for serializer functions

@baskaryan
2023-08-04 14:39:40 -04:00
Bagatur
0d5a90f30a
Revert "add filter to sklearn vector store functions (#8113)" (#8760) 2023-08-04 08:13:32 -07:00
Ruiqi Guo
6aee589eec
Add ScaNN support in vectorstore. (#8251)
Description: Add ScaNN vectorstore to langchain.
ScaNN is a Open Source, high performance vector similarity library
optimized for AVX2-enabled CPUs.
https://github.com/google-research/google-research/tree/master/scann

- Dependencies: scann

Python notebook to illustrate the usage:
docs/extras/integrations/vectorstores/scann.ipynb
Integration test:
libs/langchain/tests/integration_tests/vectorstores/test_scann.py

@rlancemartin, @eyurtsev for review.

Thanks!
2023-08-03 23:41:30 -07:00
shibuiwilliam
0f0ccfe7f6
add filter to sklearn vector store functions (#8113)
# What
- This is to add filter option to sklearn vectore store functions

<!-- Thank you for contributing to LangChain!

Replace this comment with:
  - Description: Add filter to sklearn vectore store functions.
  - Issue: None
  - Dependencies: None
  - Tag maintainer: @rlancemartin, @eyurtsev
  - Twitter handle: @MlopsJ

If you're adding a new integration, please include:
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  2. an example notebook showing its use.

Maintainer responsibilities:
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  - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
  - Models / Prompts: @hwchase17, @baskaryan
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---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-08-03 23:06:41 -07:00
Ofer Mendelevitch
29f51055e8
Updates to Vectara documentation (#8699)
- Description: updates to Vectara documentation with more details on how
to get started.
- Issue: NA
- Dependencies: NA
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @vectara, @ofermend

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-03 20:21:17 -07:00
Lance Martin
59194c2214
Add summarization use-case (#8376)
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-02 14:25:11 -07:00
Jeff Huber
07d6d1ca38
fix error in chroma docker instructions (#8533)
This makes the Chroma instructions for Docker work! 


https://python.langchain.com/docs/integrations/vectorstores/chroma#basic-example-using-the-docker-container
2023-07-31 16:32:53 -07:00
Anubhav Bindlish
913a156cff
Minor improvements to rockset vectorstore (#8416)
This PR makes minor improvements to our python notebook, and adds
support for `Rockset` workspaces in our vectorstore client.

@rlancemartin, @eyurtsev

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-31 09:54:59 -07:00
Muhammed Al-Dulaimi
9975ba4124
Fix ChromaDB integration -> docker container instructions (#8447)
## Description
This PR handles modifying the Chroma DB integration's documentation.
It modifies the **Docker container** example to fix the instructions
mentioned in the documentation.
In the current documentation, the below `client.reset()` line causes a
runtime error:

```py
...
client = chromadb.HttpClient(settings=Settings(allow_reset=True))
client.reset()  # resets the database
collection = client.create_collection("my_collection")
...
```

`Exception: {"error":"ValueError('Resetting is not allowed by this
configuration')"}`

This is due to the Chroma DB server needing to have the `allow_reset`
flag set to `true` there as well.
This is fixed by adding the `ALLOW_RESET=TRUE` to the `docker-compose`
file environment variable to the docker container before spinning it

## Issue
This fixes the runtime error that occurs when running the docker
container example code

## Tag Maintainer
@rlancemartin, @eyurtsev
2023-07-30 21:11:56 -07:00
Ludwig Hubert
08f5e6b801
Fix documentation for from_documents signature (#8482)
Docs for from_documents() were outdated as seen in
https://github.com/langchain-ai/langchain/issues/8457 .

fixes #8457 

<!-- Thank you for contributing to LangChain!

Replace this comment with:
  - Description: a description of the change, 
  - Issue: the issue # it fixes (if applicable),
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- Twitter handle: we announce bigger features on Twitter. If your PR
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Please make sure you're PR is passing linting and testing before
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  - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
  - Models / Prompts: @hwchase17, @baskaryan
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2023-07-30 13:24:44 -07:00
Muneeb Ahmad
4923cf029a
Added Proper Documentation for faiss-gpu Installation (#8492)
### Description
In the LangChain Documentation and Comments, I've Noticed that `pip
install faiss` was mentioned, instead of `pip install faiss-gpu`, since
installing `pip install faiss` results in an error. I've gone ahead and
updated the Documentation, and `faiss.ipynb`. This Change will ensure
ease of use for the end user, trying to install `faiss-gpu`.

### Issue: 
Documentation / Comments Related.

### Dependencies:
No Dependencies we're changed only updated the files with the wrong
reference.

### Tag maintainer:
 @rlancemartin, @eyurtsev (Thank You for your contributions 😄 )
2023-07-30 13:24:30 -07:00
William FH
b7c0eb9ecb
Wfh/ref links (#8454) 2023-07-29 08:44:32 -07:00
Zack Proser
3892cefac6
Minor fixes to enhance notebook usability: (#8389)
- Install langchain
- Set Pinecone API key and environment as env vars
- Create Pinecone index if it doesn't already exist
---
- Description: Fix a couple minor issues I came across when running this
notebook,
  - Issue: the issue # it fixes (if applicable),
  - Dependencies: none,
  - Tag maintainer: @rlancemartin @eyurtsev,
  - Twitter handle: @zackproser (certainly not necessary!)
2023-07-28 17:10:03 -07:00
Amélie
8ee56b9a5b
Feature: Add support for meilisearch vectorstore (#7649)
**Description:**

Add support for Meilisearch vector store.
Resolve #7603 

- No external dependencies added
- A notebook has been added

@rlancemartin

https://twitter.com/meilisearch

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-28 17:06:54 -07:00
Fabrizio Ruocco
ddc353a768
Azure Cognitive Search: Custom index and scoring profile support (#6843)
Description: Adding support for custom index and scoring profile support
in Azure Cognitive Search
@hwchase17

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-26 17:58:01 -07:00
Naveen Tatikonda
9cbefcc56c
[ OpenSearch ] : Add AOSS Support to OpenSearch (#8256)
### Description

This PR includes the following changes:

- Adds AOSS (Amazon OpenSearch Service Serverless) support to
OpenSearch. Please refer to the documentation on how to use it.
- While creating an index, AOSS only supports Approximate Search with
`nmslib` and `faiss` engines. During Search, only Approximate Search and
Script Scoring (on doc values) are supported.
- This PR also adds support to `efficient_filter` which can be used with
`faiss` and `lucene` engines.
- The `lucene_filter` is deprecated. Instead please use the
`efficient_filter` for the lucene engine.


Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
2023-07-25 23:59:36 -07:00
William FH
0a16b3d84b
Update Integrations links (#8206) 2023-07-24 21:20:32 -07:00
Bagatur
c8c8635dc9
mv module integrations docs (#8101) 2023-07-23 23:23:16 -07:00