**Description:** Invoke callback prior to yielding token for llama.cpp
**Issue:** [Callback for on_llm_new_token should be invoked before the
token is yielded by the model
#16913](https://github.com/langchain-ai/langchain/issues/16913)
**Dependencies:** None
```python
from langchain.agents import tool
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
return len(word)
tools = [get_word_length]
llm_with_tools = llm.bind_tools(tools)
llm_with_tools.invoke("how long is the word chrysanthemum")
```
currently raises
```
AttributeError: 'dict' object has no attribute 'model_dump'
```
Same with `.with_structured_output`
```python
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
"""An answer to the user question along with justification for the answer."""
answer: str
justification: str
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
```
This appears to fix.
For prompt templates with only 1 variable (common in e.g.,
MessageGraph), it's convenient to wrap the incoming object in the
variable before formatting.
The downside of this, of course, would be that some number of
invocations will successfully format when the user may have intended to
format it properly before
This is a basic VectorStore implementation using an in-memory dict to
store the documents.
It doesn't need any extra/optional dependency as it uses numpy which is
already a dependency of langchain.
This is useful for quick testing, demos, examples.
Also it allows to write vendor-neutral tutorials, guides, etc...
Classes and functions defined in __init__.py are not parsed into the API
Reference.
For example:
- libs/core/langchain_core/messages/__init__.py : AnyMessage,
MessageLikeRepresentation, get_buffer_string(), messages_from_dict(),
...
Opinionated: __init__.py is not a typical place to define artifacts.
Moved artifacts from __init__ into utils.py.
Added `MessageLikeRepresentation` to __all__ since it is used outside of
`messages`, for example, in
`libs/core/langchain_core/language_models/base.py`
Added `_message_from_dict` to __all__ since it is used outside of
`messages`(???) I would add `message_from_dict` (without underscore) as
an alias. Please, advise.
Covered by tests in
`libs/core/tests/unit_tests/language_models/chat_models/test_base.py`,
`libs/core/tests/unit_tests/language_models/llms/test_base.py` and
`libs/core/tests/unit_tests/runnables/test_runnable_events.py`
**Description:**
Currently, `CacheBackedEmbeddings` computes vectors for *all* uncached
documents before updating the store. This pull request updates the
embedding computation loop to compute embeddings in batches, updating
the store after each batch.
I noticed this when I tried `CacheBackedEmbeddings` on our 30k document
set and the cache directory hadn't appeared on disk after 30 minutes.
The motivation is to minimize compute/data loss when problems occur:
* If there is a transient embedding failure (e.g. a network outage at
the embedding endpoint triggers an exception), at least the completed
vectors are written to the store instead of being discarded.
* If there is an issue with the store (e.g. no write permissions), the
condition is detected early without computing (and discarding!) all the
vectors.
**Issue:**
Implements enhancement #18026.
**Testing:**
I was unable to run unit tests; details in [this
post](https://github.com/langchain-ai/langchain/discussions/15019#discussioncomment-8576684).
---------
Signed-off-by: chrispy <chrispy@synopsys.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
## Description
Semantic Cache can retrieve noisy information if the score threshold for
the value is too low. Adding the ability to set a `score_threshold` on
cache construction can allow for less noisy scores to appear.
- [x] **Add tests and docs**
1. Added tests that confirm the `score_threshold` query is valid.
- [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/
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Classes and functions defined in __init__.py are not parsed into the API
Reference.
For example: libs/core/langchain_core/globals/__init__.py :
`set_verbose` `get_llm_cache`, `set_llm_cache`, ...
And the whole `langchain_core.globals` namespace is not visible in the
API Reference. The refactoring is just file renaming.
- **Description:** Enhanced the `BaseChatModel` to support an
`Optional[Union[bool, BaseCache]]` type for the `cache` attribute,
allowing for both boolean flags and custom cache implementations.
Implemented logic within chat model methods to utilize the provided
custom cache implementation effectively. This change aims to provide
more flexibility in caching strategies for chat models.
- **Issue:** Implements enhancement request #17242.
- **Dependencies:** No additional dependencies required for this change.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Changing OpenAIAssistantRunnable.create_assistant to send the `file_ids`
parameter to openai.beta.assistants.create
Co-authored-by: Frederico Wu <fred.diaswu@coxautoinc.com>
**Description**:
this PR enable VectorStore autoconfiguration for Infinispan: if
metadatas are only of basic types, protobuf
config will be automatically generated for the user.
When creating a new index, if we use a retrieval strategy that expects a
model to be deployed in Elasticsearch, check if a model with this name
is indeed deployed before creating an index. This lowers the probability
to get into a state in which an index was created with a faulty model
ID, which cannot be overwritten any more (the index has to manually be
deleted).
Add `keep_alive` parameter to control how long the model will stay
loaded into memory with Ollama。
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Issue : For functions which have an argument with the name 'title', the
convert_pydantic_to_openai_function generates an incorrect output and
omits the argument all together. This is because the _rm_titles function
removes all instances of the the key 'title' from the output.
Description : Updates the _rm_titles function to check the presence of
the 'type' key as well before removing the 'title' key. As the title key
that we wish to omit always has a type key along with it.
Potential gap if there is a function defined which has both title and
key as argument names, in which case this would fail. Maybe we could set
a filter on the function argument names and reject those with keyword
argument names.
No dependencies. Passed all tests.
- [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:** 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!
- [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, hwchase17.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** There was no formatter for mistral models for Azure
ML endpoints. Adding that, plus a configurable timeout (it was hard
coded before)
- **Dependencies:** none
- **Twitter handle:** @tjaffri @docugami
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.
- [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.
add **kwargs in add_documents for upsert, to make it use for other
argument also.
Lets use this, it was unused as of now.
- [ ] **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.
Co-authored-by: Rohit Gupta <rohit.gupta2@walmart.com>
## Description
This PR modifies the settings in `libs/langchain/dev.Dockerfile` to
ensure that the `text-splitters` directory is copied before the poetry
installation process begins.
Without this modification, the `docker build` command fails for
`dev.Dockerfile`, preventing the setup of some development environments,
including `.devcontainer`.
## Bug Details
### Repro
Run the following command:
```bash
docker build -f libs/langchain/dev.Dockerfile .
```
### Current Behavior
The docker build command fails, raising the following error:
```
...
=> [langchain-dev-dependencies 4/5] COPY libs/community/ ../community/ 0.4s
=> ERROR [langchain-dev-dependencies 5/5] RUN poetry install --no-interaction --no-ansi --with dev,test,docs 1.1s
------
> [langchain-dev-dependencies 5/5] RUN poetry install --no-interaction --no-ansi --with dev,test,docs:
#13 0.970
#13 0.970 Directory ../text-splitters does not exist
------
executor failed running [/bin/sh -c poetry install --no-interaction --no-ansi --with dev,test,docs]: exit code: 1
```
### Expected Behavior
The `docker build` command successfully completes without the poetry
error.
### Analysis
The error occurs because the `text-splitters` directory is not copied
into the build environment, unlike the other packages under the `libs`
directory. I suspect that the `COPY` setting was overlooked since
`text-splitters` was separated in a recent PR.
## Fix
Add the following lines to the `libs/langchain/dev.Dockerfile`:
```dockerfile
# Copy the text-splitters library for installation
COPY libs/text-splitters/ ../text-splitters/
```
- **Description:** Tests fail to do value lookup because it does not
specify the index name
- **Issue:** the issue # Failing integration test
- [x] **Add tests and docs**: Tests now pass
- [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/
The root run id (~trace id's) is useful for assigning feedback, but the
current recommended approach is to use callbacks to retrieve it, which
has some drawbacks:
1. Doesn't work for streaming until after the first event
2. Doesn't let you call other endpoints with the same trace ID in
parallel (since you have to wait until the call is completed/started to
use
This PR lets you provide = "run_id" in the runnable config.
Couple considerations:
1. For batch calls, we split the trace up into separate trees (to permit
better rendering). We keep the provided run ID for the first one and
generate a unique one for other elements of the batch.
2. For nested calls, the provided ID is ONLY used on the top root/trace.
### Example Usage
```
chain.invoke("foo", {"run_id": uuid.uuid4()})
```
Classes are missed in __all__ and in different places of __init__.py
- BaichuanLLM
- ChatDatabricks
- ChatMlflow
- Llamafile
- Mlflow
- Together
Added classes to __all__. I also sorted __all__ list.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: deprecate DocugamiLoader"
- [x] **PR message**: Deprecate the langchain_community and use the
docugami_langchain DocugamiLoader
---------
Co-authored-by: Kenzie Mihardja <kenzie28@cs.washington.edu>
## Description
* In memory cache easily gets out of sync with the server cache, so we
will remove it entirely to reduce the issues around invalidated caches.
## Dependencies
None
- [x] 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/
Co-authored-by: Erick Friis <erick@langchain.dev>
## Description
Returning the embedding is not necessary in the vector search
functionality unless specified as a debugging step. This change defaults
the behavior such that the server _only_ returns the embedding key if
explicitly requested, such as in the case of
`max_marginal_relevance_search`.
- [x] **Add tests and docs**: If you're adding a new integration, please
include
* Added `test_from_documents_no_embedding_return`
- [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/
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- Description:
- Updated the import path for `StreamingStdOutCallbackHandler` in the
streaming response example within `huggingface_endpoint.py`. This change
corrects the import statement to reflect the actual location of
`StreamingStdOutCallbackHandler` in
`langchain_core.callbacks.streaming_stdout`.
- Issue:
- None
- Dependencies:
- No additional dependencies are required for this change.
- Twitter handle:
- None
## Note:
I have tested this change locally and confirmed that the
`StreamingStdOutCallbackHandler` works as expected with the updated
import path. This PR does not require the addition of new tests since it
is a correction to documentation/examples rather than functional code.
- [x] **Support for translation**: "community: Add support for
translation in `HuggingFacePipeline`"
- [x] **Add support for translation in `HuggingFacePipeline`**:
- **Description:** Add support for translation in `HuggingFacePipeline`,
which earlier used to support only text summarization and generation.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** None
- Description:
- This pull request is to fix a bug where page numbers were not set
correctly. In the current code, all chunks share the same metadata
object doc_metadata, so the page number is set with the same value for
all documents. To fix this, I changed to using separate metadata objects
for each chunk.
- Issue:
- None
- Dependencies:
- No additional dependencies are required for this change.
- Twitter handle:
- @eycjur
- Test
- Even if it's not a bug, there are cases where everything ends up with
the same number of pages, so it's very difficult for me to write
integration tests.
**Description:**
#18040 forces `fastembed>2.0`, and this causes dependency conflicts with
the new `unstructured` package (different `onnxruntime`). There may be
other dependency conflicts.. The only way to use
`langchain-community>=0.0.28` is rollback to `unstructured 0.10.X`. But
new `unstructured` contains many fixes.
This PR allows to use both `fastembed` `v1` and `v2`.
How to reproduce:
`pyproject.toml`:
```toml
[tool.poetry]
name = "depstest"
version = "0.0.0"
description = "test"
authors = ["<dev@example.org>"]
[tool.poetry.dependencies]
python = ">=3.10,<3.12"
langchain-community = "^0.0.28"
fastembed = "^0.2.0"
unstructured = {extras = ["pdf"], version = "^0.12"}
```
```bash
$ poetry lock
```
Co-authored-by: Sergey Kozlov <sergey.kozlov@ludditelabs.io>
- **Description:** This modification addresses the issue of mutable
default parameters in functions. In the original code, the `chunks`
parameter is defaulted to a list containing an empty dictionary, which
is mutable. Since default parameters in Python are evaluated only once
at function definition time, modifications to the parameter would
persist across future calls. By changing the default to `None` and
checking/initializing within the function, a new list is created for
each call, thus avoiding potential issues.
---------
Co-authored-by: sixiang <sixiang@lixiang.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Update docstring of Together class to show example and
update API URL
**Issue:** Improves usability
**Dependencies:** None
**Lint and test**: `make format`, `make lint` and `make test` were run
- Description: [a description of the change] Add in code documentation
to core Runnable with_fallbacks method (docs only)
- Issue: the issue #18804
@eyurtsev PTAL
**Description:** Many LLM steps complete in sub-second duration, which
can lead to non-collection of duration field for Fiddler. This PR
updates duration from seconds to milliseconds.
**Issue:** [INTERNAL] FDL-17568
**Dependencies:** NA
**Twitter handle:** behalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
- **Description:** Handling fallbacks when calling async streaming for a
LLM that doesn't support it.
- **Issue:** #18920
- **Twitter handle:**@maximeperrin_
---------
Co-authored-by: Maxime Perrin <mperrin@doing.fr>
**Description:** This PR adds updates the fiddler events schema to also
pass user feedback, and llm status to fiddler
**Tickets:** [INTERNAL] FDL-17559
**Dependencies:** NA
**Twitter handle:** behalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
- **Description:** This modification adds pydantic input definition for
sql_database tools. This helps for function calling capability in
LangGraph. Since actions nodes will usually check for the args_schema
attribute on tools, This update should make these tools compatible with
it (only implemented on the InfoSQLDatabaseTool)
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** juanfe8881
poetry can't reliably handle resolving the number of optional "extended
test" dependencies we have. If we instead just rely on pip to install
extended test deps in CI, this isn't an issue.
Description: Added support for batching when using AI21 Embeddings model
Twitter handle: https://github.com/AI21Labs
---------
Co-authored-by: Asaf Gardin <asafg@ai21.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** add async tests, add tokenize support
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:**
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally -> ✅
Please make sure integration_tests passing locally -> ✅
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR makes the following updates in the pgvector database:
1. Use JSONB field for metadata instead of JSON
2. Update operator syntax to include required `$` prefix before the
operators (otherwise there will be name collisions with fields)
3. The change is non-breaking, old functionality is still the default,
but it will emit a deprecation warning
4. Previous functionality has bugs associated with comparisons due to
casting to text (so lexical ordering is used incorrectly for numeric
fields)
5. Adds an a GIN index on the JSONB field for more efficient querying
**PR message**: ***Delete this entire checklist*** and replace with
- **Description:** [a description of the change](docs: Add in code
documentation to core Runnable assign method)
- **Issue:** the issue #18804
- **Description:** When calling the `_stream_log_implementation` from
the `astream_log` method in the `Runnable` class, it is not handing over
the `kwargs` argument. Therefore, even if i want to customize APIHandler
and implement additional features with additional arguments, it is not
possible. Conversely, the `astream_events` method normally handing over
the `kwargs` argument.
- **Issue:** https://github.com/langchain-ai/langchain/issues/19054
- **Dependencies:**
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
Co-authored-by: hyungwookyang <hyungwookyang@worksmobile.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.
- **Description:** This change fixes a bug where attempts to load data
from Notion using the NotionDBLoader resulted in a 400 Bad Request
error. The issue was traced to the unconditional addition of an empty
'filter' object in the request payload, which Notion's API does not
accept. The modification ensures that the 'filter' object is only
included in the payload when it is explicitly provided and not empty,
thus preventing the 400 error from occurring.
- **Issue:** Fixes
[#18009](https://github.com/langchain-ai/langchain/issues/18009)
- **Dependencies:** None
- **Twitter handle:** @gunnzolder
Co-authored-by: Anton Parkhomenko <anton@merge.rocks>
This PR adds `batch as completed` method to the standard Runnable
interface. It takes in a list of inputs and yields the corresponding
outputs as the inputs are completed.
Add documentation notebook for `ElasticsearchRetriever`.
## Dependencies
- [ ] Release new `langchain-elasticsearch` version 0.2.0 that includes
`ElasticsearchRetriever`
**Description:** Circular dependencies when parsing references leading
to `RecursionError: maximum recursion depth exceeded` issue. This PR
address the issue by handling previously seen refs as in any typical DFS
to avoid infinite depths.
**Issue:** https://github.com/langchain-ai/langchain/issues/12163
**Twitter handle:** https://twitter.com/theBhulawat
- [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/
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Refactor code of FAISS vectorcstore and update the
related documentation.
Details:
- replace `.format()` with f-strings for strings formatting;
- refactor definition of a filtering function to make code more readable
and more flexible;
- slightly improve efficiency of
`max_marginal_relevance_search_with_score_by_vector` method by removing
unnecessary looping over the same elements;
- slightly improve efficiency of `delete` method by using set data
structure for checking if the element was already deleted;
**Issue:** fix small inconsistency in the documentation (the old example
was incorrect and unappliable to faiss vectorstore)
**Dependencies:** basic langchain-community dependencies and `faiss`
(for CPU or for GPU)
**Twitter handle:** antonenkodev
Issue : _call method of LLMRouterChain uses predict_and_parse, which is
slated for deprecation.
Description : Instead of using predict_and_parse, this replaces it with
individual predict and parse functions.
**Description:** Update the docstring of OpenAI, OpenAIEmbeddings and
ChatOpenAI classes
**Issue:** Update import module paths to the current LangChain API
**Dependencies:** None
**Lint and test**: `make format` and `make lint` were run
This incorporates the review comments from langchain-ai/langchain#18637
which I closed due to an issue I had in updating that pr branch
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Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:** Update AnthropicLLM deprecation message import path for
ChatAnthropic
**Issue:** Incorrect import path in deprecation message
**Dependencies:** None
**Lint and test**: `make format`, `make lint` and `make test` were run
This PR updates the on_tool_end handlers to return the raw output from the tool instead of casting it to a string.
This is technically a breaking change, though it's impact is expected to be somewhat minimal. It will fix behavior in `astream_events` as well.
Fixes the following issue #18760 raised by @eyurtsev
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
BasePDFLoader doesn't parse the suffix of the file correctly when
parsing S3 presigned urls. This fix enables the proper detection and
parsing of S3 presigned URLs to prevent errors such as `OSError: [Errno
36] File name too long`.
No additional dependencies required.
Deduplicate documents using MD5 of the page_content. Also allows for
custom deduplication with graph ingestion method by providing metadata
id attribute
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Adding an optional parameter `linearization_config`
to the `AmazonTextractPDFLoader` so the caller can define how the output
will be linearized, instead of forcing a predefined set of linearization
configs. It will still have a default configuration as this will be an
optional parameter.
- **Issue:** #17457
- **Dependencies:** The same ones that already exist for
`AmazonTextractPDFLoader`
- **Twitter handle:** [@lvieirajr19](https://twitter.com/lvieirajr19)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
*Description**: My previous
[PR](https://github.com/langchain-ai/langchain/pull/18521) was
mistakenly closed, so I am reopening this one. Context: AWS released two
Mistral models on Bedrock last Friday (March 1, 2024). This PR includes
some code adjustments to ensure their compatibility with the Bedrock
class.
---------
Co-authored-by: Anis ZAKARI <anis.zakari@hymaia.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Update azuresearch vectorstore from_texts() method to
include fields argument, necessary for creating an Azure AI Search index
with custom fields.
- **Issue:** Currently index fields are fixed to default fields if Azure
Search index is created using from_texts() method
- **Dependencies:** None
- **Twitter handle:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
