```python
class UsageMetadata(TypedDict):
"""Usage metadata for a message, such as token counts.
Attributes:
input_tokens: (int) count of input (or prompt) tokens
output_tokens: (int) count of output (or completion) tokens
total_tokens: (int) total token count
"""
input_tokens: int
output_tokens: int
total_tokens: int
```
```python
class AIMessage(BaseMessage):
...
usage_metadata: Optional[UsageMetadata] = None
"""If provided, token usage information associated with the message."""
...
```
- **Description:** When I was running the sparkllm, I found that the
default parameters currently used could no longer run correctly.
- original parameters & values:
- spark_api_url: "wss://spark-api.xf-yun.com/v3.1/chat"
- spark_llm_domain: "generalv3"
```python
# example
from langchain_community.chat_models import ChatSparkLLM
spark = ChatSparkLLM(spark_app_id="my_app_id",
spark_api_key="my_api_key", spark_api_secret="my_api_secret")
spark.invoke("hello")
```
So I updated them to 3.5 (same as sparkllm official website). After the
update, they can be used normally.
- new parameters & values:
- spark_api_url: "wss://spark-api.xf-yun.com/v3.5/chat"
- spark_llm_domain: "generalv3.5"
This pull request addresses and fixes exception handling in the
UpstageLayoutAnalysisParser and enhances the test coverage by adding
error exception tests for the document loader. These improvements ensure
robust error handling and increase the reliability of the system when
dealing with external API calls and JSON responses.
### Changes Made
1. Fix Request Exception Handling:
- Issue: The existing implementation of UpstageLayoutAnalysisParser did
not properly handle exceptions thrown by the requests library, which
could lead to unhandled exceptions and potential crashes.
- Solution: Added comprehensive exception handling for
requests.RequestException to catch any request-related errors. This
includes logging the error details and raising a ValueError with a
meaningful error message.
2. Add Error Exception Tests for Document Loader:
- New Tests: Introduced new test cases to verify the robustness of the
UpstageLayoutAnalysisLoader against various error scenarios. The tests
ensure that the loader gracefully handles:
- RequestException: Simulates network issues or invalid API requests to
ensure appropriate error handling and user feedback.
- JSONDecodeError: Simulates scenarios where the API response is not a
valid JSON, ensuring the system does not crash and provides clear error
messaging.
**Description:**
- Added propagation of document metadata from O365BaseLoader to
FileSystemBlobLoader (O365BaseLoader uses FileSystemBlobLoader under the
hood).
- This is done by passing dictionary `metadata_dict`: key=filename and
value=dictionary containing document's metadata
- Modified `FileSystemBlobLoader` to accept the `metadata_dict`, use
`mimetype` from it (if available) and pass metadata further into blob
loader.
**Issue:**
- `O365BaseLoader` under the hood downloads documents to temp folder and
then uses `FileSystemBlobLoader` on it.
- However metadata about the document in question is lost in this
process. In particular:
- `mime_type`: `FileSystemBlobLoader` guesses `mime_type` from the file
extension, but that does not work 100% of the time.
- `web_url`: this is useful to keep around since in RAG LLM we might
want to provide link to the source document. In order to work well with
document parsers, we pass the `web_url` as `source` (`web_url` is
ignored by parsers, `source` is preserved)
**Dependencies:**
None
**Twitter handle:**
@martintriska1
Please review @baskaryan
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "Add CloudBlobLoader"
- community: Add CloudBlobLoader
- [ ] **PR message**: Add cloud blob loader
- **Description:**
Langchain provides several approaches to read different file formats:
Specific loaders (`CVSLoader`) or blob-compatible loaders
(`FileSystemBlobLoader`). The only implementation proposed for
BlobLoader is `FileSystemBlobLoader`.
Many projects retrieve files from cloud storage. We propose a new
implementation of `BlobLoader` to read files from the three cloud
storage systems. The interface is strictly identical to
`FileSystemBlobLoader`. The only difference is the constructor, which
takes a cloud "url" object such as `s3://my-bucket`, `az://my-bucket`,
or `gs://my-bucket`.
By streamlining the process, this novel implementation eliminates the
requirement to pre-download files from cloud storage to local temporary
files (which are seldom removed).
The code relies on the
[CloudPathLib](https://cloudpathlib.drivendata.org/stable/) library to
interpret cloud URLs. This has been added as an optional dependency.
```Python
loader = CloudBlobLoader("s3://mybucket/id")
for blob in loader.yield_blobs():
print(blob)
```
- [X] **Dependencies:** CloudPathLib
- [X] **Twitter handle:** pprados
- [X] **Add tests and docs**: Add unit test, but it's easy to convert to
integration test, with some files in a cloud storage (see
`test_cloud_blob_loader.py`)
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
Hello from Paris @hwchase17. Can you review this PR?
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
This PR contains 4 added functions:
- max_marginal_relevance_search_by_vector
- amax_marginal_relevance_search_by_vector
- max_marginal_relevance_search
- amax_marginal_relevance_search
I'm no langchain expert, but tried do inspect other vectorstore sources
like chroma, to build these functions for SurrealDB. If someone has some
changes for me, please let me know. Otherwise I would be happy, if these
changes are added to the repository, so that I can use the orignal repo
and not my local monkey patched version.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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:https://github.com/arpitkumar980/langchain.git
- 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: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Fixed `AzureSearchVectorStoreRetriever` to account
for search_kwargs. More explanation is in the mentioned issue.
- **Issue:** #21492
---------
Co-authored-by: MAC <mac@MACs-MacBook-Pro.local>
Co-authored-by: Massimiliano Pronesti <massimiliano.pronesti@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [X] **PR title**: "docs: Chroma docstrings update"
- 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"
- [X] **PR message**:
- **Description:** Added and updated Chroma docstrings
- **Issue:** https://github.com/langchain-ai/langchain/issues/21983
- [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.
- only docs
- [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.
Description: This change adds args_schema (pydantic BaseModel) to
WikipediaQueryRun for correct schema formatting on LLM function calls
Issue: currently using WikipediaQueryRun with OpenAI function calling
returns the following error "TypeError: WikipediaQueryRun._run() got an
unexpected keyword argument '__arg1' ". This happens because the schema
sent to the LLM is "input: '{"__arg1":"Hunter x Hunter"}'" while the
method should be called with the "query" parameter.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added [Scrapfly](https://scrapfly.io/) Web Loader integration. Scrapfly
is a web scraping API that allows extracting web page data into
accessible markdown or text datasets.
- __Description__: Added Scrapfly web loader for retrieving web page
data as markdown or text.
- Dependencies: scrapfly-sdk
- Twitter: @thealchemi1st
---------
Co-authored-by: Bagatur <baskaryan@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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Updates Meilisearch vectorstore for compatibility
with v1.8. Adds [”showRankingScore”:
true”](https://www.meilisearch.com/docs/reference/api/search#ranking-score)
in the search parameters and replaces `_semanticScore` field with `
_rankingScore`
- [ ] **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.
**Description:**
- Extend AzureSearch with `maximal_marginal_relevance` (for vector and
hybrid search)
- Add construction `from_embeddings` - if the user has already embedded
the texts
- Add `add_embeddings`
- Refactor common parts (`_simple_search`, `_results_to_documents`,
`_reorder_results_with_maximal_marginal_relevance`)
- Add `vector_search_dimensions` as a parameter to the constructor to
avoid extra calls to `embed_query` (most of the time the user applies
the same model and knows the dimension)
**Issue:** none
**Dependencies:** none
- [x] **Add tests and docs**: The docstrings have been added to the new
functions, and unified for the existing ones. The example notebook is
great in illustrating the main usage of AzureSearch, adding the new
methods would only dilute the main content.
- [x] **Lint and test**
---------
Co-authored-by: Oleksii Pokotylo <oleksii.pokotylo@pwc.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Backwards compatible extension of the initialisation
interface of HanaDB to allow the user to specify
specific_metadata_columns that are used for metadata storage of selected
keys which yields increased filter performance. Any not-mentioned
metadata remains in the general metadata column as part of a JSON
string. Furthermore switched to executemany for batch inserts into
HanaDB.
**Issue:** N/A
**Dependencies:** no new dependencies added
**Twitter handle:** @sapopensource
---------
Co-authored-by: Martin Kolb <martin.kolb@sap.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Added extra functionality to `CharacterTextSplitter`,
`TextSplitter` classes.
The user can select whether to append the separator to the previous
chunk with `keep_separator='end' ` or else prepend to the next chunk.
Previous functionality prepended by default to next chunk.
**Issue:** Fixes#20908
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Integrate RankLLM reranker (https://github.com/castorini/rank_llm) into
LangChain
An example notebook is given in
`docs/docs/integrations/retrievers/rankllm-reranker.ipynb`
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Bug code**: In
langchain_community/document_loaders/csv_loader.py:100
- **Description**: currently, when 'CSVLoader' reads the column as None
in the 'csv' file, it will report an error because the 'CSVLoader' does
not verify whether the column is of str type and does not consider how
to handle the corresponding 'row_data' when the column is' None 'in the
csv. This pr provides a solution.
- **Issue:** Fix#20699
- **thinking:**
1. Refer to the processing method for
'langchain_community/document_loaders/csv_loader.py:100' when **'v'**
equals'None', and apply the same method to '**k**'.
(Reference`csv.DictReader` ,**'k'** will only be None when `
len(columns) < len(number_row_data)` is established)
2. **‘k’** equals None only holds when it is the last column, and its
corresponding **'v'** type is a list. Therefore, I referred to the data
format in 'Document' and used ',' to concatenated the elements in the
list.(But I'm not sure if you accept this form, if you have any other
ideas, communicate)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Added revision_example prompt template to include the
revision request and revision examples in the revision chain.
**Issue:** Not Applicable
**Dependencies:** Not Applicable
**Twitter handle:** @nithinjp09
## Description
The existing public interface for `langchain_community.emeddings` is
broken. In this file, `__all__` is statically defined, but is
subsequently overwritten with a dynamic expression, which type checkers
like pyright do not support. pyright actually gives the following
diagnostic on the line I am requesting we remove:
[reportUnsupportedDunderAll](https://github.com/microsoft/pyright/blob/main/docs/configuration.md#reportUnsupportedDunderAll):
```
Operation on "__all__" is not supported, so exported symbol list may be incorrect
```
Currently, I get the following errors when attempting to use publicablly
exported classes in `langchain_community.emeddings`:
```python
import langchain_community.embeddings
langchain_community.embeddings.HuggingFaceEmbeddings(...) # error: "HuggingFaceEmbeddings" is not exported from module "langchain_community.embeddings" (reportPrivateImportUsage)
```
This is solved easily by removing the dynamic expression.
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:**
Fix ChatDatabricsk in case that streaming response doesn't have role
field in delta chunk
- [ ] **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, hwchase17.
---------
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
## 'raise_for_status' parameter of WebBaseLoader works in sync load but
not in async load.
In webBaseLoader:
Sync load is calling `_scrape` and has `raise_for_status` properly
handled.
```
def _scrape(
self,
url: str,
parser: Union[str, None] = None,
bs_kwargs: Optional[dict] = None,
) -> Any:
from bs4 import BeautifulSoup
if parser is None:
if url.endswith(".xml"):
parser = "xml"
else:
parser = self.default_parser
self._check_parser(parser)
html_doc = self.session.get(url, **self.requests_kwargs)
if self.raise_for_status:
html_doc.raise_for_status()
if self.encoding is not None:
html_doc.encoding = self.encoding
elif self.autoset_encoding:
html_doc.encoding = html_doc.apparent_encoding
return BeautifulSoup(html_doc.text, parser, **(bs_kwargs or {}))
```
Async load is calling `_fetch` but missing `raise_for_status` logic.
```
async def _fetch(
self, url: str, retries: int = 3, cooldown: int = 2, backoff: float = 1.5
) -> str:
async with aiohttp.ClientSession() as session:
for i in range(retries):
try:
async with session.get(
url,
headers=self.session.headers,
ssl=None if self.session.verify else False,
cookies=self.session.cookies.get_dict(),
) as response:
return await response.text()
```
Co-authored-by: kefan.you <darkfss@sina.com>
**Title**: "langchain: OpenAI Assistants v2 api support"
***Descriptions***
- [x] "attachments" support added along with backward compatibility of
"file_ids"
- [x] "tool_resources" support added while creating new assistant
- [ ] "tool_choice" parameter support
- [ ] Streaming support
- **Dependencies:** OpenAI v2 API (openai>=1.23.0)
- **Twitter handle:** @skanta_rath
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Updated docs to have an example to use Jamba instead of J2
---------
Co-authored-by: Asaf Gardin <asafg@ai21.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Tongyi uses different client for chat model and
vision model. This PR chooses proper client based on model name to
support both chat model and vision model. Reference [tongyi
document](https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api?spm=a2c4g.11186623.0.0.27404c9a7upm11)
for details.
```
from langchain_core.messages import HumanMessage
from langchain_community.chat_models import ChatTongyi
llm = ChatTongyi(model_name='qwen-vl-max')
image_message = {
"image": "https://lilianweng.github.io/posts/2023-06-23-agent/agent-overview.png"
}
text_message = {
"text": "summarize this picture",
}
message = HumanMessage(content=[text_message, image_message])
llm.invoke([message])
```
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** None
