it fixes two issues:
### YGPTs are broken #25575
```
File ....conda/lib/python3.11/site-packages/langchain_community/embeddings/yandex.py:211, in _make_request(self, texts, **kwargs)
..
--> 211 res = stub.TextEmbedding(request, metadata=self._grpc_metadata) # type: ignore[attr-defined]
AttributeError: 'YandexGPTEmbeddings' object has no attribute '_grpc_metadata'
```
My gut feeling that #23841 is the cause.
I have to drop leading underscore from `_grpc_metadata` for quickfix,
but I just don't know how to do it _pydantic_ enough.
### minor issue:
if we use `api_key`, which is not the best practice the code fails with
```
File ~/git/...../python3.11/site-packages/langchain_community/embeddings/yandex.py:119, in YandexGPTEmbeddings.validate_environment(cls, values)
...
AttributeError: 'tuple' object has no attribute 'append'
```
- Added new integration test. But it requires YGPT env available and
active account. I don't know how int tests dis\enabled in CI.
- added small unit tests with mocks. Should be fine.
---------
Co-authored-by: mikhail-khludnev <mikhail_khludnev@rntgroup.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
Support passing extra params when executing UC functions:
The params should be a dictionary with key EXECUTE_FUNCTION_ARG_NAME,
the assumption is that the function itself doesn't use such variable
name (starting and ending with double underscores), and if it does we
raise Exception.
If invalid params passing to the execute_statement, we raise Exception
as well.
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
adds an init method to ChatDeepInfra to set the model_name attribute
accordings to the argument
### Issue
currently, the model_name specified by the user during initialization of
the ChatDeepInfra class is never set. Therefore, it always chooses the
default model (meta-llama/Llama-2-70b-chat-hf, however probably since
this is deprecated it always uses meta-llama/Llama-3-70b-Instruct). We
stumbled across this issue and fixed it as proposed in this pull
request. Feel free to change the fix according to your coding guidelines
and style, this is just a proposal and we want to draw attention to this
problem.
### Dependencies
no additional dependencies required
Feel free to contact me or @timo282 and @finitearth if you have any
questions.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Added Azure Search Access Token Authentication instead of API KEY auth.
Fixes Issue: https://github.com/langchain-ai/langchain/issues/24263
Dependencies: None
Twitter: @levalencia
@baskaryan
Could you please review? First time creating a PR that fixes some code.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR introduces adjustments to ensure compatibility with the recently
released preview version of [TiDB Serverless Vector
Search](https://tidb.cloud/ai), aiming to prevent user confusion.
- TiDB Vector now supports vector indexing with cosine and l2 distance
strategies, although inner_product remains unsupported.
- Changing the distance strategy is currently not supported, so the test
cased should be adjusted.
updated stop and request_timeout so they aliased to stop_sequences, and
timeout respectively. Added test that both continue to set the same
underlying attributes.
Related to
[20085](https://github.com/langchain-ai/langchain/issues/20085)
Co-authored-by: ccurme <chester.curme@gmail.com>
Description: Send both the query and query_embedding to the Databricks
index for hybrid search.
Issue: When using hybrid search with non-Databricks managed embedding we
currently don't pass both the embedding and query_text to the index.
Hybrid search requires both of these. This change fixes this issue for
both `similarity_search` and `similarity_search_by_vector`.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Refactor PebbloSafeLoader**
- Created `APIWrapper` and moved API logic into it.
- Moved helper functions to the utility file.
- Created smaller functions and methods for better readability.
- Properly read environment variables.
- Removed unused code.
**Issue:** NA
**Dependencies:** NA
**tests**: Updated
- [x] NatbotChain: move to community, deprecate langchain version.
Update to use `prompt | llm | output_parser` instead of LLMChain.
- [x] LLMMathChain: deprecate + add langgraph replacement example to API
ref
- [x] HypotheticalDocumentEmbedder (retriever): update to use `prompt |
llm | output_parser` instead of LLMChain
- [x] FlareChain: update to use `prompt | llm | output_parser` instead
of LLMChain
- [x] ConstitutionalChain: deprecate + add langgraph replacement example
to API ref
- [x] LLMChainExtractor (document compressor): update to use `prompt |
llm | output_parser` instead of LLMChain
- [x] LLMChainFilter (document compressor): update to use `prompt | llm
| output_parser` instead of LLMChain
- [x] RePhraseQueryRetriever (retriever): update to use `prompt | llm |
output_parser` instead of LLMChain
- **Description:** Standardize SparkLLM, include:
- docs, the issue #24803
- to support stream
- update api url
- model init arg names, the issue #20085
- In the in ` embedding-3 ` and later models of Zhipu AI, it is
supported to specify the dimensions parameter of Embedding. Ref:
https://bigmodel.cn/dev/api#text_embedding-3 .
- Add test case for `embedding-3` model by assigning dimensions.
This PR gets rid `root_validators(allow_reuse=True)` logic used in
EdenAI Tool in preparation for pydantic 2 upgrade.
- add another test to secret_from_env_factory
Change all usages of __fields__ with get_fields adapter merged into
langchain_core.
Code mod generated using the following grit pattern:
```
engine marzano(0.1)
language python
`$X.__fields__` => `get_fields($X)` where {
add_import(source="langchain_core.utils.pydantic", name="get_fields")
}
```
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
- **Description:** Instantiating `GPT4AllEmbeddings` with no
`gpt4all_kwargs` argument raised a `ValidationError`. Root cause: #21238
added the capability to pass `gpt4all_kwargs` through to the `GPT4All`
instance via `Embed4All`, but broke code that did not specify a
`gpt4all_kwargs` argument.
- **Issue:** #25119
- **Dependencies:** None
- **Twitter handle:** [`@metadaddy`](https://twitter.com/metadaddy)
**Description:**
In this PR, I am adding three stock market tools from
financialdatasets.ai (my API!):
- get balance sheets
- get cash flow statements
- get income statements
Twitter handle: [@virattt](https://twitter.com/virattt)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- description: I remove the limitation of mandatory existence of
`QIANFAN_AK` and default model name which langchain uses cause there is
already a default model nama underlying `qianfan` SDK powering langchain
component.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- **Description:**
Support ChatMlflow.bind_tools method
Tested in Databricks:
<img width="836" alt="image"
src="https://github.com/user-attachments/assets/fa28ef50-0110-4698-8eda-4faf6f0b9ef8">
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
This PR adds annotations in comunity package.
Annotations are only strictly needed in subclasses of BaseModel for
pydantic 2 compatibility.
This PR adds some unnecessary annotations, but they're not bad to have
regardless for documentation pages.
- **Title:** [PebbloSafeLoader] Implement content-size-based batching in
the classification flow(loader/doc API)
- **Description:**
- Implemented content-size-based batching in the loader/doc API, set to
100KB with no external configuration option, intentionally hard-coded to
prevent timeouts.
- Remove unused field(pb_id) from doc_metadata
- **Issue:** NA
- **Dependencies:** NA
- **Add tests and docs:** Updated
Description: The old method will be discontinued; use the official SDK
for more model options.
Issue: None
Dependencies: None
Twitter handle: None
Co-authored-by: trumanyan <trumanyan@tencent.com>
## Description
This PR:
- Fixes the validation error in `FastEmbedEmbeddings`.
- Adds support for `batch_size`, `parallel` params.
- Removes support for very old FastEmbed versions.
- Updates the FastEmbed doc with the new params.
Associated Issues:
- Resolves#24039
- Resolves #https://github.com/qdrant/fastembed/issues/296
Thank you for contributing to LangChain!
- [x] **PR title**: "community:add Yi LLM", "docs:add Yi Documentation"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** This PR adds support for the Yi model to LangChain.
- **Dependencies:**
[langchain_core,requests,contextlib,typing,logging,json,langchain_community]
- **Twitter handle:** 01.AI
- [x] **Add tests and docs**: I've added the corresponding documentation
to the relevant paths
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Description:
- This PR adds a self query retriever implementation for SAP HANA Cloud
Vector Engine. The retriever supports all operators except for contains.
- Issue: N/A
- Dependencies: no new dependencies added
**Add tests and docs:**
Added integration tests to:
libs/community/tests/unit_tests/query_constructors/test_hanavector.py
**Documentation for self query retriever:**
/docs/integrations/retrievers/self_query/hanavector_self_query.ipynb
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Expanded the chat model functionality to support tools
in the 'baichuan.py' file. Updated module imports and added tool object
handling in message conversions. Additional changes include the
implementation of tool binding and related unit tests. The alterations
offer enhanced model capabilities by enabling interaction with tool-like
objects.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- [x] **PR title**:
community: Add OCI Generative AI tool and structured output support
- [x] **PR message**:
- **Description:** adding tool calling and structured output support for
chat models offered by OCI Generative AI services. This is an update to
our last PR 22880 with changes in
/langchain_community/chat_models/oci_generative_ai.py
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** NA
- [x] **Add tests and docs**:
1. we have updated our unit tests
2. we have updated our documentation under
/docs/docs/integrations/chat/oci_generative_ai.ipynb
- [x] **Lint and test**: `make format`, `make lint` and `make test` we
run successfully
---------
Co-authored-by: RHARPAZ <RHARPAZ@RHARPAZ-5750.us.oracle.com>
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
**Description:**
- This PR exposes some functions in VDMS vectorstore, updates VDMS
related notebooks, updates tests, and upgrade version of VDMS (>=0.0.20)
**Issue:** N/A
**Dependencies:**
- Update vdms>=0.0.20
Fixes for Eden AI Custom tools and ChatEdenAI:
- add missing import in __init__ of chat_models
- add `args_schema` to custom tools. otherwise '__arg1' would sometimes
be passed to the `run` method
- fix IndexError when no human msg is added in ChatEdenAI
Thank you for contributing to LangChain!
**Description:**
This PR allows users of `langchain_community.llms.ollama.Ollama` to
specify the `auth` parameter, which is then forwarded to all internal
calls of `requests.request`. This works in the same way as the existing
`headers` parameters. The auth parameter enables the usage of the given
class with Ollama instances, which are secured by more complex
authentication mechanisms, that do not only rely on static headers. An
example are AWS API Gateways secured by the IAM authorizer, which
expects signatures dynamically calculated on the specific HTTP request.
**Issue:**
Integrating a remote LLM running through Ollama using
`langchain_community.llms.ollama.Ollama` only allows setting static HTTP
headers with the parameter `headers`. This does not work, if the given
instance of Ollama is secured with an authentication mechanism that
makes use of dynamically created HTTP headers which for example may
depend on the content of a given request.
**Dependencies:**
None
**Twitter handle:**
None
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Added [ScrapingAnt](https://scrapingant.com/) Web Loader integration.
ScrapingAnt is a web scraping API that allows extracting web page data
into accessible and well-formatted markdown.
Description: Added ScrapingAnt web loader for retrieving web page data
as markdown
Dependencies: scrapingant-client
Twitter: @WeRunTheWorld3
---------
Co-authored-by: Oleg Kulyk <oleg@scrapingant.com>
This PR is under WIP and adds the following functionalities:
- [X] Supports tool calling across the langchain ecosystem. (However
streaming is not supported)
- [X] Update documentation
- [ ] **Community**: "Retrievers: Product Quantization"
- [X] This PR adds Product Quantization feature to the retrievers to the
Langchain Community. PQ is one of the fastest retrieval methods if the
embeddings are rich enough in context due to the concepts of
quantization and representation through centroids
- **Description:** Adding PQ as one of the retrievers
- **Dependencies:** using the package nanopq for this PR
- **Twitter handle:** vishnunkumar_
- [X] **Add tests and docs**: If you're adding a new integration, please
include
- [X] Added unit tests for the same in the retrievers.
- [] Will add an example notebook subsequently
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/ -
done the same
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- This PR adds vector search filtering for Azure Cosmos DB Mongo vCore
and NoSQL.
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
### Description
This pull request added new document loaders to load documents of
various formats using [Dedoc](https://github.com/ispras/dedoc):
- `DedocFileLoader` (determine file types automatically and parse)
- `DedocPDFLoader` (for `PDF` and images parsing)
- `DedocAPIFileLoader` (determine file types automatically and parse
using Dedoc API without library installation)
[Dedoc](https://dedoc.readthedocs.io) is an open-source library/service
that extracts texts, tables, attached files and document structure
(e.g., titles, list items, etc.) from files of various formats. The
library is actively developed and maintained by a group of developers.
`Dedoc` supports `DOCX`, `XLSX`, `PPTX`, `EML`, `HTML`, `PDF`, images
and more.
Full list of supported formats can be found
[here](https://dedoc.readthedocs.io/en/latest/#id1).
For `PDF` documents, `Dedoc` allows to determine textual layer
correctness and split the document into paragraphs.
### Issue
This pull request extends variety of document loaders supported by
`langchain_community` allowing users to choose the most suitable option
for raw documents parsing.
### Dependencies
The PR added a new (optional) dependency `dedoc>=2.2.5` ([library
documentation](https://dedoc.readthedocs.io)) to the
`extended_testing_deps.txt`
### Twitter handle
None
### Add tests and docs
1. Test for the integration:
`libs/community/tests/integration_tests/document_loaders/test_dedoc.py`
2. Example notebook:
`docs/docs/integrations/document_loaders/dedoc.ipynb`
3. Information about the library:
`docs/docs/integrations/providers/dedoc.mdx`
### Lint and test
Done locally:
- `make format`
- `make lint`
- `make integration_tests`
- `make docs_build` (from the project root)
---------
Co-authored-by: Nasty <bogatenkova.anastasiya@mail.ru>
- **Description:** Add a DocumentTransformer for executing one or more
`LinkExtractor`s and adding the extracted links to each document.
- **Issue:** n/a
- **Depedencies:** none
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
- **Description:** `QianfanChatEndpoint` When using tool result to
answer questions, the content of the tool is required to be in Dict
format. Of course, this can require users to return Dict format when
calling the tool, but in order to be consistent with other Chat Models,
I think such modifications are necessary.
Regardless of whether `embedding_func` is set or not, the 'text'
attribute of document should be assigned, otherwise the `page_content`
in the document of the final search result will be lost
xfailing some sql tests that do not currently work on sqlalchemy v1
#22207 was very much not sqlalchemy v1 compatible.
Moving forward, implementations should be compatible with both to pass
CI
The `MongoDBStore` can manage only documents.
It's not possible to use MongoDB for an `CacheBackedEmbeddings`.
With this new implementation, it's possible to use:
```python
CacheBackedEmbeddings.from_bytes_store(
underlying_embeddings=embeddings,
document_embedding_cache=MongoDBByteStore(
connection_string=db_uri,
db_name=db_name,
collection_name=collection_name,
),
)
```
and use MongoDB to cache the embeddings !
- **Description:**
- Updated checksum in doc metadata
- Sending checksum and removing actual content, while sending data to
`pebblo-cloud` if `classifier-location `is `pebblo-cloud` in
`/loader/doc` API
- Adding `pb_id` i.e. pebblo id to doc metadata
- Refactoring as needed.
- Sending `content-checksum` and removing actual content, while sending
data to `pebblo-cloud` if `classifier-location `is `pebblo-cloud` in
`prmopt` API
- **Issue:** NA
- **Dependencies:** NA
- **Tests:** Updated
- **Docs** NA
---------
Co-authored-by: dristy.cd <dristy@clouddefense.io>
**Description:**
**TextEmbed** is a high-performance embedding inference server designed
to provide a high-throughput, low-latency solution for serving
embeddings. It supports various sentence-transformer models and includes
the ability to deploy image and text embedding models. TextEmbed offers
flexibility and scalability for diverse applications.
- **PyPI Package:** [TextEmbed on
PyPI](https://pypi.org/project/textembed/)
- **Docker Image:** [TextEmbed on Docker
Hub](https://hub.docker.com/r/kevaldekivadiya/textembed)
- **GitHub Repository:** [TextEmbed on
GitHub](https://github.com/kevaldekivadiya2415/textembed)
**PR Description**
This PR adds functionality for embedding documents and queries using the
`TextEmbedEmbeddings` class. The implementation allows for both
synchronous and asynchronous embedding requests to a TextEmbed API
endpoint. The class handles batching and permuting of input texts to
optimize the embedding process.
**Example Usage:**
```python
from langchain_community.embeddings import TextEmbedEmbeddings
# Initialise the embeddings class
embeddings = TextEmbedEmbeddings(model="your-model-id", api_key="your-api-key", api_url="your_api_url")
# Define a list of documents
documents = [
"Data science involves extracting insights from data.",
"Artificial intelligence is transforming various industries.",
"Cloud computing provides scalable computing resources over the internet.",
"Big data analytics helps in understanding large datasets.",
"India has a diverse cultural heritage."
]
# Define a query
query = "What is the cultural heritage of India?"
# Embed all documents
document_embeddings = embeddings.embed_documents(documents)
# Embed the query
query_embedding = embeddings.embed_query(query)
# Print embeddings for each document
for i, embedding in enumerate(document_embeddings):
print(f"Document {i+1} Embedding:", embedding)
# Print the query embedding
print("Query Embedding:", query_embedding)
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
- **Description:** Add a `KeybertLinkExtractor` for graph vectorstores.
This allows extracting links from keywords in a Document and linking
nodes that have common keywords.
- **Issue:** None
- **Dependencies:** None.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description:** This allows extracting links between documents with
common named entities using [GLiNER](https://github.com/urchade/GLiNER).
- **Issue:** None
- **Dependencies:** None
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
- Added masking of the API Keys for the modules:
- `langchain/chat_models/openai.py`
- `langchain/llms/openai.py`
- `langchain/llms/google_palm.py`
- `langchain/chat_models/google_palm.py`
- `langchain/llms/edenai.py`
- Updated the modules to utilize `SecretStr` from pydantic to securely
manage API key.
- Added unit/integration tests
- `langchain/chat_models/asure_openai.py` used the `open_api_key` that
is derived from the `ChatOpenAI` Class and it was assuming
`openai_api_key` is a str so we changed it to expect `SecretStr`
instead.
**Issue:** https://github.com/langchain-ai/langchain/issues/12165 ,
**Dependencies:** none,
**Tag maintainer:** @eyurtsev
---------
Co-authored-by: HassanA01 <anikeboss@gmail.com>
Co-authored-by: Aneeq Hassan <aneeq.hassan@utoronto.ca>
Co-authored-by: kristinspenc <kristinspenc2003@gmail.com>
Co-authored-by: faisalt14 <faisalt14@gmail.com>
Co-authored-by: Harshil-Patel28 <76663814+Harshil-Patel28@users.noreply.github.com>
Co-authored-by: kristinspenc <146893228+kristinspenc@users.noreply.github.com>
Co-authored-by: faisalt14 <90787271+faisalt14@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
- Updated constructors in PyPDFParser and PyPDFLoader to handle
`extraction_mode` and additional kwargs, aligning with the capabilities
of `PageObject.extract_text()` from pypdf.
- Added `test_pypdf_loader_with_layout` along with a corresponding
example text file to validate layout extraction from PDFs.
**Issue:** fixes#19735
**Dependencies:** This change requires updating the pypdf dependency
from version 3.4.0 to at least 4.0.0.
Additional changes include the addition of a new test
test_pypdf_loader_with_layout and an example text file to ensure the
functionality of layout extraction from PDFs aligns with the new
capabilities.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:** At the moment neo4j wrapper is using setVectorProperty,
which is deprecated
([link](https://neo4j.com/docs/operations-manual/5/reference/procedures/#procedure_db_create_setVectorProperty)).
I replaced with the non-deprecated version.
Neo4j recently introduced a new cypher method to associate embeddings
into relations using "setRelationshipVectorProperty" method. In this PR
I also implemented a new method to perform this association maintaining
the same format used in the "add_embeddings" method which is used to
associate embeddings into Nodes.
I also included a test case for this new method.
Thank you for contributing to LangChain!
- [X] *ApertureDB as vectorstore**: "community: Add ApertureDB as a
vectorestore"
- **Description:** this change provides a new community integration that
uses ApertureData's ApertureDB as a vector store.
- **Issue:** none
- **Dependencies:** depends on ApertureDB Python SDK
- **Twitter handle:** ApertureData
- [X] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
Integration tests rely on a local run of a public docker image.
Example notebook additionally relies on a local Ollama server.
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
All lint tests pass.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Gautam <gautam@aperturedata.io>
**Description:**
Databricks Vector Search recently added support for hybrid
keyword-similarity search.
See [usage
examples](https://docs.databricks.com/en/generative-ai/create-query-vector-search.html#query-a-vector-search-endpoint)
from their documentation.
This PR updates the Langchain vectorstore interface for Databricks to
enable the user to pass the *query_type* parameter to
*similarity_search* to make use of this functionality.
By default, there will not be any changes for existing users of this
interface. To use the new hybrid search feature, it is now possible to
do
```python
# ...
dvs = DatabricksVectorSearch(index)
dvs.similarity_search("my search query", query_type="HYBRID")
```
Or using the retriever:
```python
retriever = dvs.as_retriever(
search_kwargs={
"query_type": "HYBRID",
}
)
retriever.invoke("my search query")
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
You.com is releasing two new conversational APIs — Smart and Research.
This PR:
- integrates those APIs with Langchain, as an LLM
- streaming is supported
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description:** Spell check fixes for docs, comments, and a couple of
strings. No code change e.g. variable names.
**Issue:** none
**Dependencies:** none
**Twitter handle:** hmartin
This adds an extractor interface and an implementation for HTML pages.
Extractors are used to create GraphVectorStore Links on loaded content.
**Twitter handle:** cbornet_
**Description:** Fix for source path mismatch in PebbloSafeLoader. The
fix involves storing the full path in the doc metadata in VectorDB
**Issue:** NA, caught in internal testing
**Dependencies:** NA
**Add tests**: Updated tests
This PR introduces a GraphStore component. GraphStore extends
VectorStore with the concept of links between documents based on
document metadata. This allows linking documents based on a variety of
techniques, including common keywords, explicit links in the content,
and other patterns.
This works with existing Documents, so it’s easy to extend existing
VectorStores to be used as GraphStores. The interface can be implemented
for any Vector Store technology that supports metadata, not only graph
DBs.
When retrieving documents for a given query, the first level of search
is done using classical similarity search. Next, links may be followed
using various traversal strategies to get additional documents. This
allows documents to be retrieved that aren’t directly similar to the
query but contain relevant information.
2 retrieving methods are added to the VectorStore ones :
* traversal_search which gets all linked documents up to a certain depth
* mmr_traversal_search which selects linked documents using an MMR
algorithm to have more diverse results.
If a depth of retrieval of 0 is used, GraphStore is effectively a
VectorStore. It enables an easy transition from a simple VectorStore to
GraphStore by adding links between documents as a second step.
An implementation for Apache Cassandra is also proposed.
See
https://github.com/datastax/ragstack-ai/blob/main/libs/knowledge-store/notebooks/astra_support.ipynb
for a notebook explaining how to use GraphStore and that shows that it
can answer correctly to questions that a simple VectorStore cannot.
**Twitter handle:** _cbornet
This PR rolls out part of the new proposed interface for vectorstores
(https://github.com/langchain-ai/langchain/pull/23544) to existing store
implementations.
The PR makes the following changes:
1. Adds standard upsert, streaming_upsert, aupsert, astreaming_upsert
methods to the vectorstore.
2. Updates `add_texts` and `aadd_texts` to be non required with a
default implementation that delegates to `upsert` and `aupsert` if those
have been implemented. The original `add_texts` and `aadd_texts` methods
are problematic as they spread object specific information across
document and **kwargs. (e.g., ids are not a part of the document)
3. Adds a default implementation to `add_documents` and `aadd_documents`
that delegates to `upsert` and `aupsert` respectively.
4. Adds standard unit tests to verify that a given vectorstore
implements a correct read/write API.
A downside of this implementation is that it creates `upsert` with a
very similar signature to `add_documents`.
The reason for introducing `upsert` is to:
* Remove any ambiguities about what information is allowed in `kwargs`.
Specifically kwargs should only be used for information common to all
indexed data. (e.g., indexing timeout).
*Allow inheriting from an anticipated generalized interface for indexing
that will allow indexing `BaseMedia` (i.e., allow making a vectorstore
for images/audio etc.)
`add_documents` can be deprecated in the future in favor of `upsert` to
make sure that users have a single correct way of indexing content.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description:** Enhance JiraAPIWrapper to accept the 'cloud'
parameter through an environment variable. This update allows more
flexibility in configuring the environment for the Jira API.
- **Twitter handle:** Andre_Q_Pereira
---------
Co-authored-by: André Quintino <andre.quintino@tui.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This PR adds a `SingleStoreDBSemanticCache` class that implements a
cache based on SingleStoreDB vector store, integration tests, and a
notebook example.
Additionally, this PR contains minor changes to SingleStoreDB vector
store:
- change add texts/documents methods to return a list of inserted ids
- implement delete(ids) method to delete documents by list of ids
- added drop() method to drop a correspondent database table
- updated integration tests to use and check functionality implemented
above
CC: @baskaryan, @hwchase17
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
enviroment -> environment
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- **Description:** Fix some issues in MiniMaxChat
- Fix `minimax_api_host` not in `values` error
- Remove `minimax_group_id` from reading environment variables, the
`minimax_group_id` no longer use in MiniMaxChat
- Invoke callback prior to yielding token, the issus #16913
This change adds a new message type `RemoveMessage`. This will enable
`langgraph` users to manually modify graph state (or have the graph
nodes modify the state) to remove messages by `id`
Examples:
* allow users to delete messages from state by calling
```python
graph.update_state(config, values=[RemoveMessage(id=state.values[-1].id)])
```
* allow nodes to delete messages
```python
graph.add_node("delete_messages", lambda state: [RemoveMessage(id=state[-1].id)])
```
Thank you for contributing to LangChain!
- [x] **PR title**: "community: update docs and add tool to init.py"
- [x] **PR message**:
- **Description:** Fixed some errors and comments in the docs and added
our ZenGuardTool and additional classes to init.py for easy access when
importing
- **Question:** when will you update the langchain-community package in
pypi to make our tool available?
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Thank you for review!
---------
Co-authored-by: Baur <baur.krykpayev@gmail.com>
