### Feature
Using FAISS on a retrievalQA task, I found myself wanting to allow in
multiple sources. From what I understood, the filter feature takes in a
dict of form {key: value} which then will check in the metadata for the
exact value linked to that key.
I added some logic to be able to pass a list which will be checked
against instead of an exact value. Passing an exact value will also
work.
Here's an example of how I could then use it in my own project:
```
pdfs_to_filter_in = ["file_A", "file_B"]
filter_dict = {
"source": [f"source_pdfs/{pdf_name}.pdf" for pdf_name in pdfs_to_filter_in]
}
retriever = db.as_retriever()
retriever.search_kwargs = {"filter": filter_dict}
```
I added an integration test based on the other ones I found in
`tests/integration_tests/vectorstores/test_faiss.py` under
`test_faiss_with_metadatas_and_list_filter()`.
It doesn't feel like this is worthy of its own notebook or doc, but I'm
open to suggestions if needed.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR adds Rockset as a vectorstore for langchain.
[Rockset](https://rockset.com/blog/introducing-vector-search-on-rockset/)
is a real time OLAP database which provides a fast and efficient vector
search functionality. Further since it is entirely schemaless, it can
store metadata in separate columns thereby allowing fast metadata
filters during vector similarity search (as opposed to storing the
entire metadata in a single JSON column). It currently supports three
distance functions: `COSINE_SIMILARITY`, `EUCLIDEAN_DISTANCE`, and
`DOT_PRODUCT`.
This PR adds `rockset` client as an optional dependency.
We would love a twitter shoutout, our handle is
https://twitter.com/RocksetCloud
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR adds `KuzuGraph` and `KuzuQAChain` for interacting with [Kùzu
database](https://github.com/kuzudb/kuzu). Kùzu is an in-process
property graph database management system (GDBMS) built for query speed
and scalability. The `KuzuGraph` and `KuzuQAChain` provide the same
functionality as the existing integration with NebulaGraph and Neo4j and
enables query generation and question answering over Kùzu database.
A notebook example and a simple test case have also been added.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This addresses #6291 adding support for using Cassandra (and compatible
databases, such as DataStax Astra DB) as a [Vector
Store](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor(ANN)+Vector+Search+via+Storage-Attached+Indexes).
A new class `Cassandra` is introduced, which complies with the contract
and interface for a vector store, along with the corresponding
integration test, a sample notebook and modified dependency toml.
Dependencies: the implementation relies on the library `cassio`, which
simplifies interacting with Cassandra for ML- and LLM-oriented
workloads. CassIO, in turn, uses the `cassandra-driver` low-lever
drivers to communicate with the database. The former is added as
optional dependency (+ in `extended_testing`), the latter was already in
the project.
Integration testing relies on a locally-running instance of Cassandra.
[Here](https://cassio.org/more_info/#use-a-local-vector-capable-cassandra)
a detailed description can be found on how to compile and run it (at the
time of writing the feature has not made it yet to a release).
During development of the integration tests, I added a new "fake
embedding" class for what I consider a more controlled way of testing
the MMR search method. Likewise, I had to amend what looked like a
glitch in the behaviour of `ConsistentFakeEmbeddings` whereby an
`embed_query` call would have bypassed storage of the requested text in
the class cache for use in later repeated invocations.
@dev2049 might be the right person to tag here for a review. Thank you!
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Hello Folks,
Thanks for creating and maintaining this great project. I'm excited to
submit this PR to add Alibaba Cloud OpenSearch as a new vector store.
OpenSearch is a one-stop platform to develop intelligent search
services. OpenSearch was built based on the large-scale distributed
search engine developed by Alibaba. OpenSearch serves more than 500
business cases in Alibaba Group and thousands of Alibaba Cloud
customers. OpenSearch helps develop search services in different search
scenarios, including e-commerce, O2O, multimedia, the content industry,
communities and forums, and big data query in enterprises.
OpenSearch provides the vector search feature. In specific scenarios,
especially test question search and image search scenarios, you can use
the vector search feature together with the multimodal search feature to
improve the accuracy of search results.
This PR includes:
A AlibabaCloudOpenSearch class that can connect to the Alibaba Cloud
OpenSearch instance.
add embedings and metadata into a opensearch datasource.
querying by squared euclidean and metadata.
integration tests.
ipython notebook and docs.
I have read your contributing guidelines. And I have passed the tests
below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
---------
Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>
Already supported in the reverse operation in
`_convert_message_to_dict()`, this just provides parity.
@hwchase17
@agola11
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fix issue #6380
<!-- Remove if not applicable -->
Fixes#6380 (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
---------
Co-authored-by: HubertKl <HubertKl>
1. Introduced new distance strategies support: **DOT_PRODUCT** and
**EUCLIDEAN_DISTANCE** for enhanced flexibility.
2. Implemented a feature to filter results based on metadata fields.
3. Incorporated connection attributes specifying "langchain python sdk"
usage for enhanced traceability and debugging.
4. Expanded the suite of integration tests for improved code
reliability.
5. Updated the existing notebook with the usage example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
W.r.t recent changes, ChatPromptTemplate does not accepting partial
variables. This PR should fix that issue.
Fixes#6431
#### Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Throwing ToolException when incorrect arguments are passed to tools so
that that agent can course correct them.
# Incorrect argument count handling
I was facing an error where the agent passed incorrect arguments to
tools. As per the discussions going around, I started throwing
ToolException to allow the model to course correct.
## Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
VectorStores / Retrievers / Memory
- @dev2049
-->
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes: ChatAnthropic was mutating the input message list during
formatting which isn't ideal bc you could be changing the behavior for
other chat models when using the same input
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
#### Before submitting
Add memory support for `OpenAIFunctionsAgent` like
`StructuredChatAgent`.
#### Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes#5807 (issue)
#### Who can review?
Tag maintainers/contributors who might be interested: @dev2049
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
Similar as https://github.com/hwchase17/langchain/pull/5818
Added the functionality to save/load Graph Cypher QA Chain due to a user
reporting the following error
> raise NotImplementedError("Saving not supported for this chain
type.")\nNotImplementedError: Saving not supported for this chain
type.\n'
In LangChain, all module classes are enumerated in the `__init__.py`
file of the correspondent module. But some classes were missed and were
not included in the module `__init__.py`
This PR:
- added the missed classes to the module `__init__.py` files
- `__init__.py:__all_` variable value (a list of the class names) was
sorted
- `langchain.tools.sql_database.tool.QueryCheckerTool` was renamed into
the `QuerySQLCheckerTool` because it conflicted with
`langchain.tools.spark_sql.tool.QueryCheckerTool`
- changes to `pyproject.toml`:
- added `pgvector` to `pyproject.toml:extended_testing`
- added `pandas` to
`pyproject.toml:[tool.poetry.group.test.dependencies]`
- commented out the `streamlit` from `collbacks/__init__.py`, It is
because now the `streamlit` requires Python >=3.7, !=3.9.7
- fixed duplicate names in `tools`
- fixed correspondent ut-s
#### Who can review?
@hwchase17
@dev2049
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Added support to `search_by_vector` to Qdrant Vector store.
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
### Who can review
VectorStores / Retrievers / Memory
- @dev2049
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
-->
Hi there:
As I implement the AnalyticDB VectorStore use two table to store the
document before. It seems just use one table is a better way. So this
commit is try to improve AnalyticDB VectorStore implementation without
affecting user behavior:
**1. Streamline the `post_init `behavior by creating a single table with
vector indexing.
2. Update the `add_texts` API for document insertion.
3. Optimize `similarity_search_with_score_by_vector` to retrieve results
directly from the table.
4. Implement `_similarity_search_with_relevance_scores`.
5. Add `embedding_dimension` parameter to support different dimension
embedding functions.**
Users can continue using the API as before.
Test cases added before is enough to meet this commit.
## DocArray as a Retriever
[DocArray](https://github.com/docarray/docarray) is an open-source tool
for managing your multi-modal data. It offers flexibility to store and
search through your data using various document index backends. This PR
introduces `DocArrayRetriever` - which works with any available backend
and serves as a retriever for Langchain apps.
Also, I added 2 notebooks:
DocArray Backends - intro to all 5 currently supported backends, how to
initialize, index, and use them as a retriever
DocArray Usage - showcasing what additional search parameters you can
pass to create versatile retrievers
Example:
```python
from docarray.index import InMemoryExactNNIndex
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import DocArrayRetriever
# define document schema
class MyDoc(BaseDoc):
description: str
description_embedding: NdArray[1536]
embeddings = OpenAIEmbeddings()
# create documents
descriptions = ["description 1", "description 2"]
desc_embeddings = embeddings.embed_documents(texts=descriptions)
docs = DocList[MyDoc](
[
MyDoc(description=desc, description_embedding=embedding)
for desc, embedding in zip(descriptions, desc_embeddings)
]
)
# initialize document index with data
db = InMemoryExactNNIndex[MyDoc](docs)
# create a retriever
retriever = DocArrayRetriever(
index=db,
embeddings=embeddings,
search_field="description_embedding",
content_field="description",
)
# find the relevant document
doc = retriever.get_relevant_documents("action movies")
print(doc)
```
#### Who can review?
@dev2049
---------
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
This PR refactors the ArxivAPIWrapper class making
`doc_content_chars_max` parameter optional. Additionally, tests have
been added to ensure the functionality of the doc_content_chars_max
parameter.
Fixes#6027 (issue)
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
## Add Solidity programming language support for code splitter.
Twitter: @0xjord4n_
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
VectorStores / Retrievers / Memory
- @dev2049
-->
# Introduces embaas document extraction api endpoints
In this PR, we add support for embaas document extraction endpoints to
Text Embedding Models (with LLMs, in different PRs coming). We currently
offer the MTEB leaderboard top performers, will continue to add top
embedding models and soon add support for customers to deploy thier own
models. Additional Documentation + Infomation can be found
[here](https://embaas.io).
While developing this integration, I closely followed the patterns
established by other langchain integrations. Nonetheless, if there are
any aspects that require adjustments or if there's a better way to
present a new integration, let me know! :)
Additionally, I fixed some docs in the embeddings integration.
Related PR: #5976
#### Who can review?
DataLoaders
- @eyurtsev
Adds a new parameter `relative_chunk_overlap` for the
`SentenceTransformersTokenTextSplitter` constructor. The parameter sets
the chunk overlap using a relative factor, e.g. for a model where the
token limit is 100, a `relative_chunk_overlap=0.5` implies that
`chunk_overlap=50`
Tag maintainers/contributors who might be interested:
@hwchase17, @dev2049
#### What I do
Adding embedding api for
[DashScope](https://help.aliyun.com/product/610100.html), which is the
DAMO Academy's multilingual text unified vector model based on the LLM
base. It caters to multiple mainstream languages worldwide and offers
high-quality vector services, helping developers quickly transform text
data into high-quality vector data. Currently supported languages
include Chinese, English, Spanish, French, Portuguese, Indonesian, and
more.
#### Who can review?
Models
- @hwchase17
- @agola11
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Inspired by the filtering capability available in ChromaDB, added the
same functionality to the FAISS vectorestore as well. Since FAISS does
not have an inbuilt method of filtering used the approach suggested in
this [thread](https://github.com/facebookresearch/faiss/issues/1079)
Langchain Issue inspiration:
https://github.com/hwchase17/langchain/issues/4572
- [x] Added filtering capability to semantic similarly and MMR
- [x] Added test cases for filtering in
`tests/integration_tests/vectorstores/test_faiss.py`
#### Who can review?
Tag maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
- @hwchase17
Fixes (not reported) an error that may occur in some cases in the
RecursiveCharacterTextSplitter.
An empty `new_separators` array ([]) would end up in the else path of
the condition below and used in a function where it is expected to be
non empty.
```python
if new_separators is None:
...
else:
# _split_text() expects this array to be non-empty!
other_info = self._split_text(s, new_separators)
```
resulting in an `IndexError`
```python
def _split_text(self, text: str, separators: List[str]) -> List[str]:
"""Split incoming text and return chunks."""
final_chunks = []
# Get appropriate separator to use
> separator = separators[-1]
E IndexError: list index out of range
langchain/text_splitter.py:425: IndexError
```
#### Who can review?
@hwchase17 @eyurtsev
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR updates the Vectara integration (@hwchase17 ):
* Adds reuse of requests.session to imrpove efficiency and speed.
* Utilizes Vectara's low-level API (instead of standard API) to better
match user's specific chunking with LangChain
* Now add_texts puts all the texts into a single Vectara document so
indexing is much faster.
* updated variables names from alpha to lambda_val (to be consistent
with Vectara docs) and added n_context_sentence so it's available to use
if needed.
* Updates to documentation and tests
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Unstructured XML Loader
Adds an `UnstructuredXMLLoader` class for .xml files. Works with
unstructured>=0.6.7. A plain text representation of the text with the
XML tags will be available under the `page_content` attribute in the
doc.
### Testing
```python
from langchain.document_loaders import UnstructuredXMLLoader
loader = UnstructuredXMLLoader(
"example_data/factbook.xml",
)
docs = loader.load()
```
## Who can review?
@hwchase17
@eyurtsev
Added AwaDB vector store, which is a wrapper over the AwaDB, that can be
used as a vector storage and has an efficient similarity search. Added
integration tests for the vector store
Added jupyter notebook with the example
Delete a unneeded empty file and resolve the
conflict(https://github.com/hwchase17/langchain/pull/5886)
Please check, Thanks!
@dev2049
@hwchase17
---------
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
VectorStores / Retrievers / Memory
- @dev2049
-->
---------
Co-authored-by: ljeagle <vincent_jieli@yeah.net>
Co-authored-by: vincent <awadb.vincent@gmail.com>
Based on the inspiration from the SQL chain, the following three
parameters are added to Graph Cypher Chain.
- top_k: Limited the number of results from the database to be used as
context
- return_direct: Return database results without transforming them to
natural language
- return_intermediate_steps: Return intermediate steps
