- **Description:** Adds a tqdm progress bar to OllamaEmbeddings when
embedding a list.
- **Issue:** Related to #13637, but extended to Ollama.
- **Dependencies:** `tqdm` made a necessary dependency.
Thanks to @ugm2 for helping identify a common problem. Embeddings take a
very long time to finish on local machines, and require a progress bar
to help identify if one should even attempt the workload.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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Adding rag-opensearch template.
---------
Signed-off-by: kalyanr <kalyan.ben10@live.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Current docs for adapters are in the `Guides/Adapters which is not a
good place.
- moved Adapters into `Integratons/Components/Adapters/
- simplified the OpenAI adapter notebook
- rerouted the old OpenAI adapter page URL to a new one.
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- **Description:** Added a line to pass the tenant parameter to
add_data_object
- **Issue:** An extra line added from the fix for #9956
- **Dependencies:** n/a
- **Tag maintainer:** @baskaryan
Tested locally, works as expected with the line change.
---------
Co-authored-by: Simon Dai <simon6752@gmail.com>
Description: Some Elastic indexes do not return a 'metadata' field in
'_source'. However, prior to this PR, the code assumed there always is a
'metadata' field. This PR adds support for cases where the field is
missing by adding it manually.
Issue: #13869
**Description:**
This PR adds Databricks Vector Search as a new vector store in
LangChain.
- [x] Add `DatabricksVectorSearch` in `langchain/vectorstores/`
- [x] Unit tests
- [x] Add
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
as a new optional dependency
We ran the following checks:
- `make format` passed ✅
- `make lint` failed but the failures were caused by other files
+ Files touched by this PR passed the linter ✅
- `make test` passed ✅
- `make coverage` failed but the failures were caused by other files.
Tests added by or related to this PR all passed
+ langchain/vectorstores/databricks_vector_search.py test coverage 94% ✅
- `make spell_check` passed ✅
The example notebook and updates to the [provider's documentation
page](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/providers/databricks.md)
will be added later in a separate PR.
**Dependencies:**
Optional dependency:
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- **Twitter handle:** we announce bigger features on Twitter. If your PR
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submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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1. a test for the integration, preferably unit tests that do not rely on
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The cookbook had some code to upload files, and wait for the processing
to finish.
This code is now moved to the `docugami` library so removing from the
cookbook to simplify.
Thanks @rlancemartin for suggesting this when working on evals.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
This pull request addresses an issue found in the example code within
the docstring of `libs/core/langchain_core/runnables/passthrough.py`
The original code snippet caused a `NameError` due to the missing import
of `RunnableLambda`. The error was as follows:
```
12 return "completion"
13
---> 14 chain = RunnableLambda(fake_llm) | {
15 'original': RunnablePassthrough(), # Original LLM output
16 'parsed': lambda text: text[::-1] # Parsing logic
NameError: name 'RunnableLambda' is not defined
```
To resolve this, I have modified the example code to include the
necessary import statement for `RunnableLambda`. Additionally, I have
adjusted the indentation in the code snippet to ensure consistency and
readability.
The modified code now successfully defines and utilizes
`RunnableLambda`, ensuring that users referencing the docstring will
have a functional and clear example to follow.
There are no related GitHub issues for this particular change.
Modified Code:
```python
from langchain_core.runnables import RunnablePassthrough, RunnableParallel
from langchain_core.runnables import RunnableLambda
runnable = RunnableParallel(
origin=RunnablePassthrough(),
modified=lambda x: x+1
)
runnable.invoke(1) # {'origin': 1, 'modified': 2}
def fake_llm(prompt: str) -> str: # Fake LLM for the example
return "completion"
chain = RunnableLambda(fake_llm) | {
'original': RunnablePassthrough(), # Original LLM output
'parsed': lambda text: text[::-1] # Parsing logic
}
chain.invoke('hello') # {'original': 'completion', 'parsed': 'noitelpmoc'}
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Added a retriever for the Outline API to ask
questions on knowledge base
- **Issue:** resolves#11814
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
- **Description:**
I encountered an issue while running the existing sample code on the
page https://python.langchain.com/docs/modules/agents/how_to/agent_iter
in an environment with Pydantic 2.0 installed. The following error was
triggered:
```python
ValidationError Traceback (most recent call last)
<ipython-input-12-2ffff2c87e76> in <cell line: 43>()
41
42 tools = [
---> 43 Tool(
44 name="GetPrime",
45 func=get_prime,
2 frames
/usr/local/lib/python3.10/dist-packages/pydantic/v1/main.py in __init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 1 validation error for Tool
args_schema
subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)
```
I have made modifications to the example code to ensure it functions
correctly in environments with Pydantic 2.0.
- **Description:** Simple change, I just added title metadata to
GoogleDriveLoader for optional File Loaders
- **Dependencies:** no dependencies
- **Tag maintainer:** @hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR provides idiomatic implementations for the exact-match and the
semantic LLM caches using Astra DB as backend through the database's
HTTP JSON API. These caches require the `astrapy` library as dependency.
Comes with integration tests and example usage in the `llm_cache.ipynb`
in the docs.
@baskaryan this is the Astra DB counterpart for the Cassandra classes
you merged some time ago, tagging you for your familiarity with the
topic. Thank you!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds a chat message history component that uses Astra DB for
persistence through the JSON API.
The `astrapy` package is required for this class to work.
I have added tests and a small notebook, and updated the relevant
references in the other docs pages.
(@rlancemartin this is the counterpart of the Cassandra equivalent class
you so helpfully reviewed back at the end of June)
Thank you!
