Description:
This PR adds embeddings for LocalAI (
https://github.com/go-skynet/LocalAI ), a self-hosted OpenAI drop-in
replacement. As LocalAI can re-use OpenAI clients it is mostly following
the lines of the OpenAI embeddings, however when embedding documents, it
just uses string instead of sending tokens as sending tokens is
best-effort depending on the model being used in LocalAI. Sending tokens
is also tricky as token id's can mismatch with the model - so it's safer
to just send strings in this case.
Partly related to: https://github.com/hwchase17/langchain/issues/5256
Dependencies: No new dependencies
Twitter: @mudler_it
---------
Signed-off-by: mudler <mudler@localai.io>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**PR Description:**
This pull request introduces several enhancements and new features to
the `CubeSemanticLoader`. The changes include the following:
1. Added imports for the `json` and `time` modules.
2. Added new constructor parameters: `load_dimension_values`,
`dimension_values_limit`, `dimension_values_max_retries`, and
`dimension_values_retry_delay`.
3. Updated the class documentation with descriptions for the new
constructor parameters.
4. Added a new private method `_get_dimension_values()` to retrieve
dimension values from Cube's REST API.
5. Modified the `load()` method to load dimension values for string
dimensions if `load_dimension_values` is set to `True`.
6. Updated the API endpoint in the `load()` method from the base URL to
the metadata endpoint.
7. Refactored the code to retrieve metadata from the response JSON.
8. Added the `column_member_type` field to the metadata dictionary to
indicate if a column is a measure or a dimension.
9. Added the `column_values` field to the metadata dictionary to store
the dimension values retrieved from Cube's API.
10. Modified the `page_content` construction to include the column title
and description instead of the table name, column name, data type,
title, and description.
These changes improve the functionality and flexibility of the
`CubeSemanticLoader` class by allowing the loading of dimension values
and providing more detailed metadata for each document.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Refactored `formatting.py`. The same as
https://github.com/langchain-ai/langchain/pull/7961#8098#8099
formatting.py is in the root code folder. This creates the
`langchain.formatting: Formatting` group on the API Reference navigation
ToC, on the same level as Chains and Agents which is incorrect.
Refactoring:
- moved formatting.py content into utils/formatting.py
- I did not add the backwards compatibility ref in the original
formatting.py. It seems unnecessary.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: In the llms/__init__.py, the key name is wrong for
mlflowaigateway. It should be mlflow-ai-gateway
- Issue: NA
- Dependencies: NA
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: na
Without this fix, when we run the code for mlflowaigateway, we will get
error as below
ValueError: Loading mlflow-ai-gateway LLM not supported
---------
Co-authored-by: rajib76 <rajib76@yahoo.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fixes an issue with the github tool where the API returned special
objects but the tool was expecting dictionaries.
Also added proper docstrings to the GitHubAPIWraper methods and a (very
basic) integration test.
Maintainer responsibilities:
- Agents / Tools / Toolkits: @hinthornw
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# What
- Add faiss vector search test for score threshold
- Fix failing faiss vector search test; filtering with list value is
wrong.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add faiss vector search test for score threshold; Fix
failing faiss vector search test; filtering with list value is wrong.
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before
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If you're adding a new integration, please include:
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2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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-->
Codespaces and devcontainer was broken by the [repo
restructure](https://github.com/langchain-ai/langchain/discussions/8043).
- Description: Add libs/langchain to container so it can be built
without error.
- Issue: -
- Dependencies: -
- Tag maintainer: @hwchase17 @baskaryan
- Twitter handle: @finnless
The failed build log says:
```
#10 [langchain-dev-dependencies 2/2] RUN poetry install --no-interaction --no-ansi --with dev,test,docs
#10 sha256:e850ee99fc966158bfd2d85e82b7c57244f47ecbb1462e75bd83b981a56a1929
2023-07-23 23:30:33.692Z: #10 0.827
#10 0.827 Directory libs/langchain does not exist
2023-07-23 23:30:33.738Z: #10 ERROR: executor failed running [/bin/sh -c poetry install --no-interaction --no-ansi --with dev,test,docs]: exit code: 1
```
The new pyproject.toml imports from libs/langchain:
77bf75c236/pyproject.toml (L14-L16)
But libs/langchain is never added to the dev.Dockerfile:
77bf75c236/libs/langchain/dev.Dockerfile (L37-L39)
Hopefully, this doesn't come across as nitpicky! That isn't the
intention. I only noticed it, because I enjoy reading the documentation
and when I hit a mental road bump it is usually due to a missing word or
something =)
@baskaryan
This bugfix PR adds kwargs support to Baseten model invocations so that
e.g. the following script works properly:
```python
chatgpt_chain = LLMChain(
llm=Baseten(model="MODEL_ID"),
prompt=prompt,
verbose=False,
memory=ConversationBufferWindowMemory(k=2),
llm_kwargs={"max_length": 4096}
)
```
Unexpectedly changed at
6792a3557d
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- Dependencies: any dependencies required for this change,
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(see below),
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gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
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-->
I guess `allowed_search_types` is unexpectedly changed in
6792a3557d,
so that we cannot specify `similarity_score_threshold` here.
```python
class VectorStoreRetriever(BaseRetriever):
...
allowed_search_types: ClassVar[Collection[str]] = (
"similarity",
"similarityatscore_threshold",
"mmr",
)
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
search_type = values["search_type"]
if search_type not in cls.allowed_search_types:
raise ValueError(...)
if search_type == "similarity_score_threshold":
... # UNREACHABLE CODE
```
VectorStores Maintainers: @rlancemartin @eyurtsev
- Description: Get SQL Cmd directly generated by SQL-Database Chain
without executing it in the DB engine.
- Issue: #4853
- Tag maintainer: @hinthornw,@baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
