**Description:** Update to the docstring for class RunnableSerializable,
method configurable_alternatives
**Issue:** [Add in code documentation to core Runnable methods
#18804](https://github.com/langchain-ai/langchain/issues/18804)
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
In this small PR I added the `template_tool_response` arg to the
`create_json_chat` function, so that users can customize this prompt in
case of need.
Thanks for your reviews!
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
Add our solar chat models, available model choices:
* solar-1-mini-chat
* solar-1-mini-translate-enko
* solar-1-mini-translate-koen
More documents and pricing can be found at
https://console.upstage.ai/services/solar.
The references to our solar model can be found at
* https://arxiv.org/abs/2402.17032
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Adds support for `with_structured_output` to Cohere,
which supports single function calling.
---------
Co-authored-by: BeatrixCohere <128378696+BeatrixCohere@users.noreply.github.com>
This PR allows to calculate token usage for prompts and completion
directly in the generation method of BedrockChat. The token usage
details are then returned together with the generations, so that other
downstream tasks can access them easily.
This allows to define a callback for tokens tracking and cost
calculation, similarly to what happens with OpenAI (see
[OpenAICallbackHandler](https://api.python.langchain.com/en/latest/_modules/langchain_community/callbacks/openai_info.html#OpenAICallbackHandler).
I plan on adding a BedrockCallbackHandler later.
Right now keeping track of tokens in the callback is already possible,
but it requires passing the llm, as done here:
https://how.wtf/how-to-count-amazon-bedrock-anthropic-tokens-with-langchain.html.
However, I find the approach of this PR cleaner.
Thanks for your reviews. FYI @baskaryan, @hwchase17
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] **PR title**: "community: fix baidu qianfan missing stop
parameter"
- [x] **PR message**:
- **Description: Baidu Qianfan lost the stop parameter when requesting
service due to extracting it from kwargs. This bug can cause the agent
to receive incorrect results
---------
Co-authored-by: ligang33 <ligang33@baidu.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Bug fixes in this PR:
* allows for other params such as "message" not just the input param to
the prompt for the cohere tools agent
* fixes to documents kwarg from messages
* fixes to tool_calls API call
---------
Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com>
- **Issue:** When passing an empty list to MergerRetriever it fails with
error: ValueError: max() arg is an empty sequence
- **Description:** We have a use case where we dynamically select
retrievers and use MergerRetriever for merging the output of the
retrievers. We faced this issue when the retriever_docs list is empty.
Adding a default 0 for cases when retriever_docs is an empty list to
avoid "ValueError: max() arg is an empty sequence". Also, changed to use
map() which is more than twice as fast compared to the current
implementation.
```
import timeit
# Sample retriever_docs with varying lengths of sublists
retriever_docs = [[i for i in range(j)] for j in range(1, 1000)]
# First code snippet
code1 = '''
max_docs = max(len(docs) for docs in retriever_docs)
'''
# Second code snippet
code2 = '''
max_docs = max(map(len, retriever_docs), default=0)
'''
# Benchmarking
time1 = timeit.timeit(stmt=code1, globals=globals(), number=10000)
time2 = timeit.timeit(stmt=code2, globals=globals(), number=10000)
# Output
print(f"Execution time for code snippet 1: {time1} seconds")
print(f"Execution time for code snippet 2: {time2} seconds")
```
- **Dependencies:** none
The previous version didn't had Voyage rerank in the init file
- [ ] **PR title**: langchain_voyageai reranker is not working
- [ ] **PR message**:
- **Description:** This fix let you run reranker from voyage
- **Issue:** Was not able to run reranker from voyage
@efriis
#### Description
Fixed the following error with `rerank` method from `CohereRerank`:
```
---> [79](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:79) results = self.client.rerank(
[80](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:80) query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
[81](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:81) )
[82](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:82) result_dicts = []
[83](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:83) for res in results.results:
TypeError: BaseCohere.rerank() takes 1 positional argument but 4 positional arguments (and 2 keyword-only arguments) were given
```
This was easily fixed going from this:
```
def rerank(
self,
documents: Sequence[Union[str, Document, dict]],
query: str,
*,
model: Optional[str] = None,
top_n: Optional[int] = -1,
max_chunks_per_doc: Optional[int] = None,
) -> List[Dict[str, Any]]:
...
if len(documents) == 0: # to avoid empty api call
return []
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
model = model or self.model
top_n = top_n if (top_n is None or top_n > 0) else self.top_n
results = self.client.rerank(
query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
)
result_dicts = []
for res in results:
result_dicts.append(
{"index": res.index, "relevance_score": res.relevance_score}
)
return result_dicts
```
to this:
```
def rerank(
self,
documents: Sequence[Union[str, Document, dict]],
query: str,
*,
model: Optional[str] = None,
top_n: Optional[int] = -1,
max_chunks_per_doc: Optional[int] = None,
) -> List[Dict[str, Any]]:
...
if len(documents) == 0: # to avoid empty api call
return []
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
model = model or self.model
top_n = top_n if (top_n is None or top_n > 0) else self.top_n
results = self.client.rerank(
query=query, documents=docs, model=model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc <-------------
)
result_dicts = []
for res in results.results: <-------------
result_dicts.append(
{"index": res.index, "relevance_score": res.relevance_score}
)
return result_dicts
```
#### Unit & Integration tests
I added a unit test to check the behaviour of `rerank`. Also fixed the
original integration test which was failing.
#### Format & Linting
Everything worked properly with `make lint_diff`, `make format_diff` and
`make format`. However I noticed an error coming from other part of the
library when doing `make lint`:
```
(langchain-py3.9) ➜ langchain git:(master) make format
[ "." = "" ] || poetry run ruff format .
1636 files left unchanged
[ "." = "" ] || poetry run ruff --select I --fix .
(langchain-py3.9) ➜ langchain git:(master) make lint
./scripts/check_pydantic.sh .
./scripts/lint_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run ruff format . --diff
1636 files already formatted
[ "." = "" ] || poetry run ruff --select I .
[ "." = "" ] || mkdir -p .mypy_cache && poetry run mypy . --cache-dir .mypy_cache
langchain/agents/openai_assistant/base.py:252: error: Argument "file_ids" to "create" of "Assistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]" [arg-type]
langchain/agents/openai_assistant/base.py:374: error: Argument "file_ids" to "create" of "AsyncAssistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]" [arg-type]
Found 2 errors in 1 file (checked 1634 source files)
make: *** [Makefile:65: lint] Error 1
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Due to changes in the OpenAI SDK, the previous method of setting the
OpenAI proxy in ChatOpenAI no longer works. This PR fixes this issue,
making the previous way of setting the OpenAI proxy in ChatOpenAI
effective again.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This is a follow up to #18371. These are the changes:
- New **Azure AI Services** toolkit and tools to replace those of
**Azure Cognitive Services**.
- Updated documentation for Microsoft platform.
- The image analysis tool has been rewritten to use the new package
`azure-ai-vision-imageanalysis`, doing a proper replacement of
`azure-ai-vision`.
These changes:
- Update outdated naming from "Azure Cognitive Services" to "Azure AI
Services".
- Update documentation to use non-deprecated methods to create and use
agents.
- Removes need to depend on yanked python package (`azure-ai-vision`)
There is one new dependency that is needed as a replacement to
`azure-ai-vision`:
- `azure-ai-vision-imageanalysis`. This is optional and declared within
a function.
There is a new `azure_ai_services.ipynb` notebook showing usage; Changes
have been linted and formatted.
I am leaving the actions of adding deprecation notices and future
removal of Azure Cognitive Services up to the LangChain team, as I am
not sure what the current practice around this is.
---
If this PR makes it, my handle is @galo@mastodon.social
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description**: `bigdl-llm` library has been renamed to
[`ipex-llm`](https://github.com/intel-analytics/ipex-llm). This PR
migrates the `bigdl-llm` integration to `ipex-llm` .
- **Issue**: N/A. The original PR of `bigdl-llm` is
https://github.com/langchain-ai/langchain/pull/17953
- **Dependencies**: `ipex-llm` library
- **Contribution maintainer**: @shane-huang
Updated doc: docs/docs/integrations/llms/ipex_llm.ipynb
Updated test:
libs/community/tests/integration_tests/llms/test_ipex_llm.py
- **Description:** Add support for Intel Lab's [Visual Data Management
System (VDMS)](https://github.com/IntelLabs/vdms) as a vector store
- **Dependencies:** `vdms` library which requires protobuf = "4.24.2".
There is a conflict with dashvector in `langchain` package but conflict
is resolved in `community`.
- **Contribution maintainer:** [@cwlacewe](https://github.com/cwlacewe)
- **Added tests:**
libs/community/tests/integration_tests/vectorstores/test_vdms.py
- **Added docs:** docs/docs/integrations/vectorstores/vdms.ipynb
- **Added cookbook:** cookbook/multi_modal_RAG_vdms.ipynb
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
If you use an embedding dist function in an eval loop, you get warned
every time. Would prefer to just check once and forget about it.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- .stream() and .astream() call on_llm_new_token, removing the need for
subclasses to do so. Backwards compatible because now we don't pass
run_manager into ._stream and ._astream
- .generate() and .agenerate() now handle `stream: bool` kwarg for
_generate and _agenerate. Subclasses handle this arg by delegating to
._stream(), now one less thing they need to do. Backwards compat because
this is an optional arg that we now never pass to the subclasses
- .generate() and .agenerate() now inspect callback handlers to decide
on a default value for stream:bool if not passed in. This auto enables
streaming when using astream_events and astream_log
- as a result of these three changes any usage of .astream_events and
.astream_log should now yield chat model stream events
- In future PRs we can update all subclasses to reflect these two things
now handled by base class, but in meantime all will continue to work
* **Description**: add `None` type for `file_path` along with `str` and
`List[str]` types.
* `file_path`/`filename` arguments in `get_elements_from_api()` and
`partition()` can be `None`, however, there's no `None` type hint for
`file_path` in `UnstructuredAPIFileLoader` and `UnstructuredFileLoader`
currently.
* calling the function with `file_path=None` is no problem, but my IDE
annoys me lol.
* **Issue**: N/A
* **Dependencies**: N/A
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Updates Meilisearch vectorstore for compatibility
with v1.6 and above. Adds embedders settings and embedder_name which are
now required.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
This PR adds a slightly more helpful message to a Tool Exception
```
# current state
langchain_core.tools.ToolException: Too many arguments to single-input tool
# proposed state
langchain_core.tools.ToolException: Too many arguments to single-input tool. Consider using a StructuredTool instead.
```
**Issue:** Somewhat discussed here 👉#6197
**Dependencies:** None
**Twitter handle:** N/A
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
As mentioned in #18322, the current PydanticOutputParser won't work for
anyone trying to parse to pydantic v2 models. This PR adds a separate
`PydanticV2OutputParser`, as well as a `langchain_core.pydantic_v2`
namespace that will fail on import to any projects using pydantic<2.
Happy to update the docs for output parsers if this is something we're
interesting in adding.
On a separate note, I also updated `check_pydantic.sh` to detect
pydantic imports with leading whitespace and excluded the internal
namespaces. That change can be separated into its own PR if needed.
---------
Co-authored-by: Jan Nissen <jan23@gmail.com>
Added example to the docstring of the "bind" method of Runnable. This
makes it easier to understand the purpose of the method when reviewing
in code editors. E.g. VS Code below.
<img width="833" alt="Screenshot 2024-03-27 at 16 24 18"
src="https://github.com/langchain-ai/langchain/assets/45722942/ad022d4e-7bc0-4f4b-aa7a-838f1816cc52">
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
PebbloSafeLoader: Add support for non-file-based Document Loaders
This pull request enhances PebbloSafeLoader by introducing support for
several non-file-based Document Loaders. With this update,
PebbloSafeLoader now seamlessly integrates with the following loaders:
- GoogleDriveLoader
- SlackDirectoryLoader
- Unstructured EmailLoader
**Issue:** NA
**Dependencies:** - None
**Twitter handle:** @Raj__725
---------
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Patch potential XML vulnerability CVE-2024-1455
This patches a potential XML vulnerability in the XMLOutputParser in
langchain-core. The vulnerability in some situations could lead to a
denial of service attack.
At risk are users that:
1) Running older distributions of python that have older version of
libexpat
2) Are using XMLOutputParser with an agent
3) Accept inputs from untrusted sources with this agent (e.g., endpoint
on the web that allows an untrusted user to interact wiith the parser)
Introduction
[Intel® Extension for
Transformers](https://github.com/intel/intel-extension-for-transformers)
is an innovative toolkit designed to accelerate GenAI/LLM everywhere
with the optimal performance of Transformer-based models on various
Intel platforms
Description
adding ITREX runtime embeddings using intel-extension-for-transformers.
added mdx documentation and example notebooks
added embedding import testing.
---------
Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] **PR title**: "experimental: Enhance LLMGraphTransformer with
async processing and improved readability"
- [x] **PR message**:
- **Description:** This pull request refactors the `process_response`
and `convert_to_graph_documents` methods in the LLMGraphTransformer
class to improve code readability and adds async versions of these
methods for concurrent processing.
The main changes include:
- Simplifying list comprehensions and conditional logic in the
process_response method for better readability.
- Adding async versions aprocess_response and
aconvert_to_graph_documents to enable concurrent processing of
documents.
These enhancements aim to improve the overall efficiency and
maintainability of the `LLMGraphTransformer` class.
- **Issue:** N/A
- **Dependencies:** No additional dependencies required.
- **Twitter handle:** @jjovalle99
- [x] **Add tests and docs**: N/A (This PR does not introduce a new
integration)
- [x] **Lint and test**: Ran make format, make lint, and make test from
the root of the modified package(s). All tests pass successfully.
Additional notes:
- The changes made in this PR are backwards compatible and do not
introduce any breaking changes.
- The PR touches only the `LLMGraphTransformer` class within the
experimental package.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Update Azure Document Intelligence implementation by
Microsoft team and RAG cookbook with Azure AI Search
---------
Co-authored-by: Lu Zhang (AI) <luzhan@microsoft.com>
Co-authored-by: Yateng Hong <yatengh@microsoft.com>
Co-authored-by: teethache <hongyateng2006@126.com>
Co-authored-by: Lu Zhang <44625949+luzhang06@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Implemented try-except block for
`GCSDirectoryLoader`. Reason: Users processing large number of
unstructured files in a folder may experience many different errors. A
try-exception block is added to capture these errors. A new argument
`use_try_except=True` is added to enable *silent failure* so that error
caused by processing one file does not break the whole function.
- **Issue:** N/A
- **Dependencies:** no new dependencies
- **Twitter handle:** timothywong731
---------
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:** Adding oracle autonomous database document loader
integration. This will allow users to connect to oracle autonomous
database through connection string or TNS configuration.
https://www.oracle.com/autonomous-database/
- **Issue:** None
- **Dependencies:** oracledb python package
https://pypi.org/project/oracledb/
- **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.
Unit test and doc are added.
- [ ] **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.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Currently the semantic_configurations are not used
when creating an AzureSearch instance, instead creating a new one with
default values. This PR changes the behavior to use the passed
semantic_configurations if it is present, and the existing default
configuration if not.
---------
Co-authored-by: Adam Law <adamlaw@microsoft.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:
- 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.
DefusedXML is causing parsing errors on previously functional code with
the 0.7.x versions. These do not seem to support newer version of python
well. 0.8.x has only been released as rc, so we're not going to to use
it in the core package
* Adds support for `additional_kwargs` in `get_cohere_chat_request`
* This functionality passes in Cohere SDK specific parameters from
`BaseMessage` based classes to the API
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [x] **Add len() implementation to Chroma**: "package: community"
- [x] **PR message**:
- **Description:** add an implementation of the __len__() method for the
Chroma vectostore, for convenience.
- **Issue:** no exposed method to know the size of a Chroma vectorstore
- **Dependencies:** None
- **Twitter handle:** lowrank_adrian
- [x] **Add tests and docs**
- [x] **Lint and test**
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Be more explicit with the `model_kwargs` and
`encode_kwargs` for `HuggingFaceEmbeddings`.
- **Issue:** -
- **Dependencies:** -
I received some reports by my users that they didn't realise that you
could change the default `batch_size` with `HuggingFaceEmbeddings`,
which may be attributed to how the `model_kwargs` and `encode_kwargs`
don't give much information about what you can specify.
I've added some parameter names & links to the Sentence Transformers
documentation to help clear it up. Let me know if you'd rather have
Markdown/Sphinx-style hyperlinks rather than a "bare URL".
- Tom Aarsen
So this arose from the
https://github.com/langchain-ai/langchain/pull/18397 problem of document
loaders not supporting `pathlib.Path`.
This pull request provides more uniform support for Path as an argument.
The core ideas for this upgrade:
- if there is a local file path used as an argument, it should be
supported as `pathlib.Path`
- if there are some external calls that may or may not support Pathlib,
the argument is immidiately converted to `str`
- if there `self.file_path` is used in a way that it allows for it to
stay pathlib without conversion, is is only converted for the metadata.
Twitter handle: https://twitter.com/mwmajewsk
### Issue
Recently, the new `allow_dangerous_deserialization` flag was introduced
for preventing unsafe model deserialization that relies on pickle
without user's notice (#18696). Since then some LLMs like Databricks
requires passing in this flag with true to instantiate the model.
However, this breaks existing functionality to loading such LLMs within
a chain using `load_chain` method, because the underlying loader
function
[load_llm_from_config](f96dd57501/libs/langchain/langchain/chains/loading.py (L40))
(and load_llm) ignores keyword arguments passed in.
### Solution
This PR fixes this issue by propagating the
`allow_dangerous_deserialization` argument to the class loader iff the
LLM class has that field.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Create a Class which allows to use the "text2vec" open source embedding
model.
It should install the model by running 'pip install -U text2vec'.
Example to call the model through LangChain:
from langchain_community.embeddings.text2vec import Text2vecEmbeddings
embedding = Text2vecEmbeddings()
bookend.embed_documents([
"This is a CoSENT(Cosine Sentence) model.",
"It maps sentences to a 768 dimensional dense vector space.",
])
bookend.embed_query(
"It can be used for text matching or semantic search."
)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
## Description
This PR proposes a modification to the `libs/langchain/dev.Dockerfile`
configuration to copy the `libs/langchain/poetry.lock` into the working
directory. The change aims to address the issue where the Poetry install
command, the last command in the `dev.Dockerfile`, takes excessively
long hours, and to ensure the reproducibility of the poetry environment
in the devcontainer.
## Problem
The `dev.Dockerfile`, prepared for development environments such as
`.devcontainer`, encounters an unending dependency resolution when
attempting the Poetry installation.
### Steps to Reproduce
Execute the following build command:
```bash
docker build -f libs/langchain/dev.Dockerfile .
```
### Current Behavior
The Docker build process gets stuck at the following step, which, in my
experience, did not conclude even after an entire night:
```
=> [langchain-dev-dependencies 4/6] COPY libs/community/ ../community/ 0.9s
=> [langchain-dev-dependencies 5/6] COPY libs/text-splitters/ ../text-splitters/ 0.0s
=> [langchain-dev-dependencies 6/6] RUN poetry install --no-interaction --no-ansi --with dev,test,docs 12.3s
=> => # Updating dependencies
=> => # Resolving dependencies...
```
### Expected Behavior
The Docker build completes in a realistic timeframe. By applying this
PR, the build finishes within a few minutes.
### Analysis
The complexity of LangChain's dependencies has reached a point where
Poetry is required to resolve dependencies akin to threading a needle.
Consequently, poetry install fails to complete in a practical timeframe.
## Solution
The solution for dependency resolution is already recorded in
`libs/langchain/poetry.lock`, so we can use it. When copying
`project.toml` and `poetry.toml`, the `poetry.lock` located in the same
directory should also be copied.
```diff
# Copy only the dependency files for installation
-COPY libs/langchain/pyproject.toml libs/langchain/poetry.toml ./
+COPY libs/langchain/pyproject.toml libs/langchain/poetry.toml libs/langchain/poetry.lock ./
```
## Note
I am not intimately familiar with the historical context of the
`dev.Dockerfile` and thus do not know why `poetry.lock` has not been
copied until now. It might have been an oversight, or perhaps dependency
resolution used to complete quickly even without the `poetry.lock` file
in the past. However, if there are deliberate reasons why copying
`poetry.lock` is not advisable, please just close this PR.
Description:
this change fixes the pydantic validation error when looking up from
GPTCache, the `ChatOpenAI` class returns `ChatGeneration` as response
which is not handled.
use the existing `_loads_generations` and `_dumps_generations` functions
to handle it
Trace
```
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/development/scripts/chatbot-postgres-test.py", line 90, in <module>
print(llm.invoke("tell me a joke"))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 166, in invoke
self.generate_prompt(
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 544, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 408, in generate
raise e
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 398, in generate
self._generate_with_cache(
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 585, in _generate_with_cache
cache_val = llm_cache.lookup(prompt, llm_string)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_community/cache.py", line 807, in lookup
return [
^
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_community/cache.py", line 808, in <listcomp>
Generation(**generation_dict) for generation_dict in json.loads(res)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/load/serializable.py", line 120, in __init__
super().__init__(**kwargs)
File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for Generation
type
unexpected value; permitted: 'Generation' (type=value_error.const; given=ChatGeneration; permitted=('Generation',))
```
Although I don't seem to find any issues here, here's an
[issue](https://github.com/zilliztech/GPTCache/issues/585) raised in
GPTCache. Please let me know if I need to do anything else
Thank you
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Few-Shot prompt template may use a `SemanticSimilarityExampleSelector`
that in turn uses a `VectorStore` that does I/O operations.
So to work correctly on the event loop, we need:
* async methods for the `VectorStore` (OK)
* async methods for the `SemanticSimilarityExampleSelector` (this PR)
* async methods for `BasePromptTemplate` and `BaseChatPromptTemplate`
(future work)
This is a small breaking change but I think it should be done as:
* No external dependency needs to be installed anymore for the default
to work
* It is vendor-neutral
Fixing some issues for AzureCosmosDBSemanticCache
- Added the entry for "AzureCosmosDBSemanticCache" which was missing in
langchain/cache.py
- Added application name when creating the MongoClient for the
AzureCosmosDBVectorSearch, for tracking purposes.
@baskaryan, can you please review this PR, we need this to go in asap.
These are just small fixes which we found today in our testing.
- **Description:** The `semantic_hybrid_search_with_score_and_rerank`
method of `AzureSearch` contains a hardcoded field name "metadata" for
the document metadata in the Azure AI Search Index. Adding such a field
is optional when creating an Azure AI Search Index, as other snippets
from `AzureSearch` test for the existence of this field before trying to
access it. Furthermore, the metadata field name shouldn't be hardcoded
as "metadata" and use the `FIELDS_METADATA` variable that defines this
field name instead. In the current implementation, any index without a
metadata field named "metadata" will yield an error if a semantic answer
is returned by the search in
`semantic_hybrid_search_with_score_and_rerank`.
- **Issue:** https://github.com/langchain-ai/langchain/issues/18731
- **Prior fix to this bug:** This bug was fixed in this PR
https://github.com/langchain-ai/langchain/pull/15642 by adding a check
for the existence of the metadata field named `FIELDS_METADATA` and
retrieving a value for the key called "key" in that metadata if it
exists. If the field named `FIELDS_METADATA` was not present, an empty
string was returned. This fix was removed in this PR
https://github.com/langchain-ai/langchain/pull/15659 (see
ed1ffca911#).
@lz-chen: could you confirm this wasn't intentional?
- **New fix to this bug:** I believe there was an oversight in the logic
of the fix from
[#1564](https://github.com/langchain-ai/langchain/pull/15642) which I
explain below.
The `semantic_hybrid_search_with_score_and_rerank` method creates a
dictionary `semantic_answers_dict` with semantic answers returned by the
search as follows.
5c2f7e6b2b/libs/community/langchain_community/vectorstores/azuresearch.py (L574-L581)
The keys in this dictionary are the unique document ids in the index, if
I understand the [documentation of semantic
answers](https://learn.microsoft.com/en-us/azure/search/semantic-answers)
in Azure AI Search correctly. When the method transforms a search result
into a `Document` object, an "answer" key is added to the document's
metadata. The value for this "answer" key should be the semantic answer
returned by the search from this document, if such an answer is
returned. The match between a `Document` object and the semantic answers
returned by the search should be done through the unique document id,
which is used as a key for the `semantic_answers_dict` dictionary. This
id is defined in the search result's field named `FIELDS_ID`. I added a
check to avoid any error in case no field named `FIELDS_ID` exists in a
search result (which shouldn't happen in theory).
A benefit of this approach is that this fix should work whether or not
the Azure AI Search Index contains a metadata field.
@levalencia could you confirm my analysis and test the fix?
@raunakshrivastava7 do you agree with the fix?
Thanks for the help!
Description: Added support for AI21 Labs model - Segmentation, as a Text
Splitter
Dependencies: ai21, langchain-text-splitter
Twitter handle: https://github.com/AI21Labs
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Prem SDK integration in LangChain
This PR adds the integration with [PremAI's](https://www.premai.io/)
prem-sdk with langchain. User can now access to deployed models
(llms/embeddings) and use it with langchain's ecosystem. This PR adds
the following:
### This PR adds the following:
- [x] Add chat support
- [X] Adding embedding support
- [X] writing integration tests
- [X] writing tests for chat
- [X] writing tests for embedding
- [X] writing unit tests
- [X] writing tests for chat
- [X] writing tests for embedding
- [X] Adding documentation
- [X] writing documentation for chat
- [X] writing documentation for embedding
- [X] run `make test`
- [X] run `make lint`, `make lint_diff`
- [X] Final checks (spell check, lint, format and overall testing)
---------
Co-authored-by: Anindyadeep Sannigrahi <anindyadeepsannigrahi@Anindyadeeps-MacBook-Pro.local>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** PgVector class always runs "create extension" on init
and this statement crashes on ReadOnly databases (read only replicas).
but wierdly the next create collection etc work even in readOnly
databases
- **Dependencies:** no new dependencies
- **Twitter handle:** @VenOmaX666
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Thank you for contributing to LangChain!
When run command langchain app new my-app, i get this error:
File
"/home/mauricio/.local/lib/python3.8/site-packages/langchain_cli/utils/pyproject.py",
line 15, in <module>
pyproject_toml: Path, local_editable_dependencies: Iterable[tuple[str,
Path]]
TypeError: 'type' object is not subscriptable
This PR fix the error.
**Description:**
- minor PR to speed up onboarding by not trying to add a dataset, if a
model is already present.
- replace batch publish API with streaming when single events are
published.
**Dependencies:** any dependencies required for this change
**Twitter handle:** behalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
**Description:**
Expanding version in all the Confluence API calls so to get when the
page was last modified/created in all cases.
**Issue:** #12812
**Twitter handle:** zzste
