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
- **Description:** Add a flag to determine whether to show progress bar
- **Issue:** n/a
- **Dependencies:** n/a
- **Twitter handle:** n/a
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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
- **Description:** Search has a limit of 500 results, playlistItems
doesn't. Added a class in except clause to catch another common error.
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** @TupleType
---------
Co-authored-by: asi-cider <88270351+asi-cider@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:** This PR introduces a change to the
`cypher_generation_chain` to dynamically concatenate inputs. This
improvement aims to streamline the input handling process and make the
method more flexible. The change involves updating the arguments
dictionary with all elements from the `inputs` dictionary, ensuring that
all necessary inputs are dynamically appended. This will ensure that any
cypher generation template will not require a new `_call` method patch.
**Issue:** This PR fixes issue #24260.
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 Riza Python/JS code execution tool
- **Issue:** N/A
- **Dependencies:** an optional dependency on the `rizaio` pypi package
- **Twitter handle:** [@rizaio](https://x.com/rizaio)
[Riza](https://riza.io) is a safe code execution environment for
agent-generated Python and JavaScript that's easy to integrate into
langchain apps. This PR adds two new tool classes to the community
package.
- **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**: Mask API key for ChatOpenAi based chat_models
(openai, azureopenai, anyscale, everlyai).
Made changes to all chat_models that are based on ChatOpenAI since all
of them assumes that openai_api_key is str rather than SecretStr.
- **Issue:**: #12165
- **Dependencies:** N/A
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** N/A
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
- Updated the format for the 'Action' section in the planner prompt to
ensure it must be one of the tools without additional words. Adjusted
the phrasing from "should be" to "must be" for clarity and
enforceability.
- Corrected the tool appending logic in the
`_create_api_controller_agent` function to ensure that
`RequestsDeleteToolWithParsing` and `RequestsPatchToolWithParsing` are
properly added to the tools list for "DELETE" and "PATCH" operations.
**Issue:** #24382
**Dependencies:** None
**Twitter handle:** @lunara_x
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: [PebbloSafeLoader] Rename loader type and add
SharePointLoader to supported loaders
- **Description:** Minor fixes in the PebbloSafeLoader:
- Renamed the loader type from `remote_db` to `cloud_folder`.
- Added `SharePointLoader` to the list of loaders supported by
PebbloSafeLoader.
- **Issue:** NA
- **Dependencies:** NA
- [x] **Add tests and docs**: NA
### Description
Missing "stream" parameter. Without it, you'd never receive a stream of
tokens when using stream() or astream()
### Issue
No existing issue available
**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: When SQLDatabase.from_databricks is ran from a Databricks
Workflow job, line 205 (default_host = context.browserHostName) throws
an ``AttributeError`` as the ``context`` object has no
``browserHostName`` attribute. The fix handles the exception and sets
the ``default_host`` variable to null
---------
Co-authored-by: lmorosdb <lmorosdb>
Co-authored-by: Eugene Yurtsev <eugene@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>
On using TavilySearchAPIRetriever with any conversation chain getting
error :
`TypeError: Client.__init__() got an unexpected keyword argument
'api_key'`
It is because the retreiver class is using the depreciated `Client`
class, `TavilyClient` need to be used instead.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
**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.
Thank you for contributing to LangChain!
- [ ] **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"
- [ ] **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.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** This pull request introduces two new methods to the
Langchain Chroma partner package that enable similarity search based on
image embeddings. These methods enhance the package's functionality by
allowing users to search for images similar to a given image URI. Also
introduces a notebook to demonstrate it's use.
- **Issue:** N/A
- **Dependencies:** None
- **Twitter handle:** @mrugank9009
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
In some lines its trying to read a key that do not exists yet. In this
cases I changed the direct access to dict.get() method
Thank you for contributing to LangChain!
- [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/
**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
Thank you for contributing to LangChain!
- [ ] **HuggingFaceEndpoint**: "Skip Login to HuggingFaceHub"
- Where: langchain, community, llm, huggingface_endpoint
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Skip login to huggingface hub when when
`huggingfacehub_api_token` is not set. This is needed when using custom
`endpoint_url` outside of HuggingFaceHub.
- **Issue:** the issue # it fixes
https://github.com/langchain-ai/langchain/issues/20342 and
https://github.com/langchain-ai/langchain/issues/19685
- **Dependencies:** None
- [ ] **Add tests and docs**:
1. Tested with locally available TGI endpoint
2. Example Usage
```python
from langchain_community.llms import HuggingFaceEndpoint
llm = HuggingFaceEndpoint(
endpoint_url='http://localhost:8080',
server_kwargs={
"headers": {"Content-Type": "application/json"}
}
)
resp = llm.invoke("Tell me a joke")
print(resp)
```
Also tested against HF Endpoints
```python
from langchain_community.llms import HuggingFaceEndpoint
huggingfacehub_api_token = "hf_xyz"
repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
llm = HuggingFaceEndpoint(
huggingfacehub_api_token=huggingfacehub_api_token,
repo_id=repo_id,
)
resp = llm.invoke("Tell me a joke")
print(resp)
```
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, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
If you use `refresh_schema=False`, then the metadata constraint doesn't
exist. ATM, we used default `None` in the constraint check, but then
`any` fails because it can't iterate over None value
Thank you for contributing to LangChain!
**Description**:
This PR fixes a bug described in the issue in #24064, when using the
AzureSearch Vectorstore with the asyncronous methods to do search which
is also the method used for the retriever. The proposed change includes
just change the access of the embedding as optional because is it not
used anywhere to retrieve documents. Actually, the syncronous methods of
retrieval do not use the embedding neither.
With this PR the code given by the user in the issue works.
```python
vectorstore = AzureSearch(
azure_search_endpoint=os.getenv("AI_SEARCH_ENDPOINT_SECRET"),
azure_search_key=os.getenv("AI_SEARCH_API_KEY"),
index_name=os.getenv("AI_SEARCH_INDEX_NAME_SECRET"),
fields=fields,
embedding_function=encoder,
)
retriever = vectorstore.as_retriever(search_type="hybrid", k=2)
await vectorstore.avector_search("what is the capital of France")
await retriever.ainvoke("what is the capital of France")
```
**Issue**:
The Azure Search Vectorstore is not working when searching for documents
with asyncronous methods, as described in issue #24064
**Dependencies**:
There are no extra dependencies required for this change.
---------
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Description: ImagePromptTemplate for Multimodal llms like llava when
using Ollama
Twitter handle: https://x.com/a7ulr
Details:
When using llava models / any ollama multimodal llms and passing images
in the prompt as urls, langchain breaks with this error.
```python
image_url_components = image_url.split(",")
^^^^^^^^^^^^^^^^^^^^
AttributeError: 'dict' object has no attribute 'split'
```
From the looks of it, there was bug where the condition did check for a
`url` field in the variable but missed to actually assign it.
This PR fixes ImagePromptTemplate for Multimodal llms like llava when
using Ollama specifically.
@hwchase17
