Hi there:
As I implement the AnalyticDB VectorStore use two table to store the
document before. It seems just use one table is a better way. So this
commit is try to improve AnalyticDB VectorStore implementation without
affecting user behavior:
**1. Streamline the `post_init `behavior by creating a single table with
vector indexing.
2. Update the `add_texts` API for document insertion.
3. Optimize `similarity_search_with_score_by_vector` to retrieve results
directly from the table.
4. Implement `_similarity_search_with_relevance_scores`.
5. Add `embedding_dimension` parameter to support different dimension
embedding functions.**
Users can continue using the API as before.
Test cases added before is enough to meet this commit.
## DocArray as a Retriever
[DocArray](https://github.com/docarray/docarray) is an open-source tool
for managing your multi-modal data. It offers flexibility to store and
search through your data using various document index backends. This PR
introduces `DocArrayRetriever` - which works with any available backend
and serves as a retriever for Langchain apps.
Also, I added 2 notebooks:
DocArray Backends - intro to all 5 currently supported backends, how to
initialize, index, and use them as a retriever
DocArray Usage - showcasing what additional search parameters you can
pass to create versatile retrievers
Example:
```python
from docarray.index import InMemoryExactNNIndex
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import DocArrayRetriever
# define document schema
class MyDoc(BaseDoc):
description: str
description_embedding: NdArray[1536]
embeddings = OpenAIEmbeddings()
# create documents
descriptions = ["description 1", "description 2"]
desc_embeddings = embeddings.embed_documents(texts=descriptions)
docs = DocList[MyDoc](
[
MyDoc(description=desc, description_embedding=embedding)
for desc, embedding in zip(descriptions, desc_embeddings)
]
)
# initialize document index with data
db = InMemoryExactNNIndex[MyDoc](docs)
# create a retriever
retriever = DocArrayRetriever(
index=db,
embeddings=embeddings,
search_field="description_embedding",
content_field="description",
)
# find the relevant document
doc = retriever.get_relevant_documents("action movies")
print(doc)
```
#### Who can review?
@dev2049
---------
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
This PR refactors the ArxivAPIWrapper class making
`doc_content_chars_max` parameter optional. Additionally, tests have
been added to ensure the functionality of the doc_content_chars_max
parameter.
Fixes#6027 (issue)
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# Introduces embaas document extraction api endpoints
In this PR, we add support for embaas document extraction endpoints to
Text Embedding Models (with LLMs, in different PRs coming). We currently
offer the MTEB leaderboard top performers, will continue to add top
embedding models and soon add support for customers to deploy thier own
models. Additional Documentation + Infomation can be found
[here](https://embaas.io).
While developing this integration, I closely followed the patterns
established by other langchain integrations. Nonetheless, if there are
any aspects that require adjustments or if there's a better way to
present a new integration, let me know! :)
Additionally, I fixed some docs in the embeddings integration.
Related PR: #5976
#### Who can review?
DataLoaders
- @eyurtsev
Adds a new parameter `relative_chunk_overlap` for the
`SentenceTransformersTokenTextSplitter` constructor. The parameter sets
the chunk overlap using a relative factor, e.g. for a model where the
token limit is 100, a `relative_chunk_overlap=0.5` implies that
`chunk_overlap=50`
Tag maintainers/contributors who might be interested:
@hwchase17, @dev2049
#### What I do
Adding embedding api for
[DashScope](https://help.aliyun.com/product/610100.html), which is the
DAMO Academy's multilingual text unified vector model based on the LLM
base. It caters to multiple mainstream languages worldwide and offers
high-quality vector services, helping developers quickly transform text
data into high-quality vector data. Currently supported languages
include Chinese, English, Spanish, French, Portuguese, Indonesian, and
more.
#### Who can review?
Models
- @hwchase17
- @agola11
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Inspired by the filtering capability available in ChromaDB, added the
same functionality to the FAISS vectorestore as well. Since FAISS does
not have an inbuilt method of filtering used the approach suggested in
this [thread](https://github.com/facebookresearch/faiss/issues/1079)
Langchain Issue inspiration:
https://github.com/hwchase17/langchain/issues/4572
- [x] Added filtering capability to semantic similarly and MMR
- [x] Added test cases for filtering in
`tests/integration_tests/vectorstores/test_faiss.py`
#### Who can review?
Tag maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
- @hwchase17
This PR updates the Vectara integration (@hwchase17 ):
* Adds reuse of requests.session to imrpove efficiency and speed.
* Utilizes Vectara's low-level API (instead of standard API) to better
match user's specific chunking with LangChain
* Now add_texts puts all the texts into a single Vectara document so
indexing is much faster.
* updated variables names from alpha to lambda_val (to be consistent
with Vectara docs) and added n_context_sentence so it's available to use
if needed.
* Updates to documentation and tests
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Unstructured XML Loader
Adds an `UnstructuredXMLLoader` class for .xml files. Works with
unstructured>=0.6.7. A plain text representation of the text with the
XML tags will be available under the `page_content` attribute in the
doc.
### Testing
```python
from langchain.document_loaders import UnstructuredXMLLoader
loader = UnstructuredXMLLoader(
"example_data/factbook.xml",
)
docs = loader.load()
```
## Who can review?
@hwchase17
@eyurtsev
Added AwaDB vector store, which is a wrapper over the AwaDB, that can be
used as a vector storage and has an efficient similarity search. Added
integration tests for the vector store
Added jupyter notebook with the example
Delete a unneeded empty file and resolve the
conflict(https://github.com/hwchase17/langchain/pull/5886)
Please check, Thanks!
@dev2049
@hwchase17
---------
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#### Before submitting
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---------
Co-authored-by: ljeagle <vincent_jieli@yeah.net>
Co-authored-by: vincent <awadb.vincent@gmail.com>
Based on the inspiration from the SQL chain, the following three
parameters are added to Graph Cypher Chain.
- top_k: Limited the number of results from the database to be used as
context
- return_direct: Return database results without transforming them to
natural language
- return_intermediate_steps: Return intermediate steps
"One Retriever to merge them all, One Retriever to expose them, One
Retriever to bring them all and in and process them with Document
formatters."
Hi @dev2049! Here bothering people again!
I'm using this simple idea to deal with merging the output of several
retrievers into one.
I'm aware of DocumentCompressorPipeline and
ContextualCompressionRetriever but I don't think they allow us to do
something like this. Also I was getting in trouble to get the pipeline
working too. Please correct me if i'm wrong.
This allow to do some sort of "retrieval" preprocessing and then using
the retrieval with the curated results anywhere you could use a
retriever.
My use case is to generate diff indexes with diff embeddings and sources
for a more colorful results then filtering them with one or many
document formatters.
I saw some people looking for something like this, here:
https://github.com/hwchase17/langchain/issues/3991
and something similar here:
https://github.com/hwchase17/langchain/issues/5555
This is just a proposal I know I'm missing tests , etc. If you think
this is a worth it idea I can work on tests and anything you want to
change.
Let me know!
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR adds a Baseten integration. I've done my best to follow the
contributor's guidelines and add docs, an example notebook, and an
integration test modeled after similar integrations' test.
Please let me know if there is anything I can do to improve the PR. When
it is merged, please tag https://twitter.com/basetenco and
https://twitter.com/philip_kiely as contributors (the note on the PR
template said to include Twitter accounts)
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Fixes#3983
Mimicing what we do for saving and loading VectorDBQA chain, I added the
logic for RetrievalQA chain.
Also added a unit test. I did not find how we test other chains for
their saving and loading functionality, so I just added a file with one
test case. Let me know if there are recommended ways to test it.
#### Before submitting
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Tag maintainers/contributors who might be interested:
@dev2049
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Added `SingleStoreDB` vector store, which is a wrapper over the
SingleStore DB database, that can be used as a vector storage and has an
efficient similarity search.
- Added integration tests for the vector store
- Added jupyter notebook with the example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Implementation of similarity_search_with_relevance_scores for quadrant
vector store.
As implemented the method is also compatible with other capacities such
as filtering.
Integration tests updated.
#### Who can review?
Tag maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
### Summary
Adds an `UnstructuredCSVLoader` for loading CSVs. One advantage of using
`UnstructuredCSVLoader` relative to the standard `CSVLoader` is that if
you use `UnstructuredCSVLoader` in `"elements"` mode, an HTML
representation of the table will be available in the metadata.
#### Who can review?
@hwchase17
@eyurtsev
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17 - project lead
- @agola11
---------
Co-authored-by: Yessen Kanapin <yessen@deepinfra.com>
Fixes # 5807
Realigned tests with implementation.
Also reinforced folder unicity for the test_faiss_local_save_load test
using date-time suffix
#### Before submitting
- Integration test updated
- formatting and linting ok (locally)
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17 - project lead
VectorStores / Retrievers / Memory
-@dev2049
- Remove the client implementation (this breaks backwards compatibility
for existing testers. I could keep the stub in that file if we want, but
not many people are using it yet
- Add SDK as dependency
- Update the 'run_on_dataset' method to be a function that optionally
accepts a client as an argument
- Remove the langchain plus server implementation (you get it for free
with the SDK now)
We could make the SDK optional for now, but the plan is to use w/in the
tracer so it would likely become a hard dependency at some point.
Aviary is an open source toolkit for evaluating and deploying open
source LLMs. You can find out more about it on
[http://github.com/ray-project/aviary). You can try it out at
[http://aviary.anyscale.com](aviary.anyscale.com).
This code adds support for Aviary in LangChain. To minimize
dependencies, it connects directly to the HTTP endpoint.
The current implementation is not accelerated and uses the default
implementation of `predict` and `generate`.
It includes a test and a simple example.
@hwchase17 and @agola11 could you have a look at this?
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Token text splitter for sentence transformers
The current TokenTextSplitter only works with OpenAi models via the
`tiktoken` package. This is not clear from the name `TokenTextSplitter`.
In this (first PR) a token based text splitter for sentence transformer
models is added. In the future I think we should work towards injecting
a tokenizer into the TokenTextSplitter to make ti more flexible.
Could perhaps be reviewed by @dev2049
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Unstructured Excel Loader
Adds an `UnstructuredExcelLoader` class for `.xlsx` and `.xls` files.
Works with `unstructured>=0.6.7`. A plain text representation of the
Excel file will be available under the `page_content` attribute in the
doc. If you use the loader in `"elements"` mode, an HTML representation
of the Excel file will be available under the `text_as_html` metadata
key. Each sheet in the Excel document is its own document.
### Testing
```python
from langchain.document_loaders import UnstructuredExcelLoader
loader = UnstructuredExcelLoader(
"example_data/stanley-cups.xlsx",
mode="elements"
)
docs = loader.load()
```
## Who can review?
@hwchase17
@eyurtsev
# Chroma update_document full document embeddings bugfix
Chroma update_document takes a single document, but treats the
page_content sting of that document as a list when getting the new
document embedding.
This is a two-fold problem, where the resulting embedding for the
updated document is incorrect (it's only an embedding of the first
character in the new page_content) and it calls the embedding function
for every character in the new page_content string, using many tokens in
the process.
Fixes#5582
Co-authored-by: Caleb Ellington <calebellington@Calebs-MBP.hsd1.ca.comcast.net>
# Fix Qdrant ids creation
There has been a bug in how the ids were created in the Qdrant vector
store. They were previously calculated based on the texts. However,
there are some scenarios in which two documents may have the same piece
of text but different metadata, and that's a valid case. Deduplication
should be done outside of insertion.
It has been fixed and covered with the integration tests.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Similar to #1813 for faiss, this PR is to extend functionality to pass
text and its vector pair to initialize and add embeddings to the
PGVector wrapper.
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @dev2049
# Support Qdrant filters
Qdrant has an [extensive filtering
system](https://qdrant.tech/documentation/concepts/filtering/) with rich
type support. This PR makes it possible to use the filters in Langchain
by passing an additional param to both the
`similarity_search_with_score` and `similarity_search` methods.
## Who can review?
@dev2049 @hwchase17
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add batching to Qdrant
Several people requested a batching mechanism while uploading data to
Qdrant. It is important, as there are some limits for the maximum size
of the request payload, and without batching implemented in Langchain,
users need to implement it on their own. This PR exposes a new optional
`batch_size` parameter, so all the documents/texts are loaded in batches
of the expected size (64, by default).
The integration tests of Qdrant are extended to cover two cases:
1. Documents are sent in separate batches.
2. All the documents are sent in a single request.
# What does this PR do?
Bring support of `encode_kwargs` for ` HuggingFaceInstructEmbeddings`,
change the docstring example and add a test to illustrate with
`normalize_embeddings`.
Fixes#3605
(Similar to #3914)
Use case:
```python
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceInstructEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
```
# Creates GitHubLoader (#5257)
GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.
Fixes#5257
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fix for `update_document` Function in Chroma
## Summary
This pull request addresses an issue with the `update_document` function
in the Chroma class, as described in
[#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947).
The issue was identified as an `AttributeError` raised when calling
`update_document` due to a missing corresponding method in the
`Collection` object. This fix refactors the `update_document` method in
`Chroma` to correctly interact with the `Collection` object.
## Changes
1. Fixed the `update_document` method in the `Chroma` class to correctly
call methods on the `Collection` object.
2. Added the corresponding test `test_chroma_update_document` in
`tests/integration_tests/vectorstores/test_chroma.py` to reflect the
updated method call.
3. Added an example and explanation of how to use the `update_document`
function in the Jupyter notebook tutorial for Chroma.
## Test Plan
All existing tests pass after this change. In addition, the
`test_chroma_update_document` test case now correctly checks the
functionality of `update_document`, ensuring that the function works as
expected and updates the content of documents correctly.
## Reviewers
@dev2049
This fix will ensure that users are able to use the `update_document`
function as expected, without encountering the previous
`AttributeError`. This will enhance the usability and reliability of the
Chroma class for all users.
Thank you for considering this pull request. I look forward to your
feedback and suggestions.
# Add Momento as a standard cache and chat message history provider
This PR adds Momento as a standard caching provider. Implements the
interface, adds integration tests, and documentation. We also add
Momento as a chat history message provider along with integration tests,
and documentation.
[Momento](https://www.gomomento.com/) is a fully serverless cache.
Similar to S3 or DynamoDB, it requires zero configuration,
infrastructure management, and is instantly available. Users sign up for
free and get 50GB of data in/out for free every month.
## Before submitting
✅ We have added documentation, notebooks, and integration tests
demonstrating usage.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Add Multi-CSV/DF support in CSV and DataFrame Toolkits
* CSV and DataFrame toolkits now accept list of CSVs/DFs
* Add default prompts for many dataframes in `pandas_dataframe` toolkit
Fixes#1958
Potentially fixes#4423
## Testing
* Add single and multi-dataframe integration tests for
`pandas_dataframe` toolkit with permutations of `include_df_in_prompt`
* Add single and multi-CSV integration tests for csv toolkit
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Add C Transformers for GGML Models
I created Python bindings for the GGML models:
https://github.com/marella/ctransformers
Currently it supports GPT-2, GPT-J, GPT-NeoX, LLaMA, MPT, etc. See
[Supported
Models](https://github.com/marella/ctransformers#supported-models).
It provides a unified interface for all models:
```python
from langchain.llms import CTransformers
llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')
print(llm('AI is going to'))
```
It can be used with models hosted on the Hugging Face Hub:
```py
llm = CTransformers(model='marella/gpt-2-ggml')
```
It supports streaming:
```py
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = CTransformers(model='marella/gpt-2-ggml', callbacks=[StreamingStdOutCallbackHandler()])
```
Please see [README](https://github.com/marella/ctransformers#readme) for
more details.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add Joplin document loader
[Joplin](https://joplinapp.org/) is an open source note-taking app.
Joplin has a [REST API](https://joplinapp.org/api/references/rest_api/)
for accessing its local database. The proposed `JoplinLoader` uses the
API to retrieve all notes in the database and their metadata. Joplin
needs to be installed and running locally, and an access token is
required.
- The PR includes an integration test.
- The PR includes an example notebook.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Beam
Calls the Beam API wrapper to deploy and make subsequent calls to an
instance of the gpt2 LLM in a cloud deployment. Requires installation of
the Beam library and registration of Beam Client ID and Client Secret.
Additional calls can then be made through the instance of the large
language model in your code or by calling the Beam API.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Vectara Integration
This PR provides integration with Vectara. Implemented here are:
* langchain/vectorstore/vectara.py
* tests/integration_tests/vectorstores/test_vectara.py
* langchain/retrievers/vectara_retriever.py
And two IPYNB notebooks to do more testing:
* docs/modules/chains/index_examples/vectara_text_generation.ipynb
* docs/modules/indexes/vectorstores/examples/vectara.ipynb
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add MosaicML inference endpoints
This PR adds support in langchain for MosaicML inference endpoints. We
both serve a select few open source models, and allow customers to
deploy their own models using our inference service. Docs are here
(https://docs.mosaicml.com/en/latest/inference.html), and sign up form
is here (https://forms.mosaicml.com/demo?utm_source=langchain). I'm not
intimately familiar with the details of langchain, or the contribution
process, so please let me know if there is anything that needs fixing or
this is the wrong way to submit a new integration, thanks!
I'm also not sure what the procedure is for integration tests. I have
tested locally with my api key.
## Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR introduces a new module, `elasticsearch_embeddings.py`, which
provides a wrapper around Elasticsearch embedding models. The new
ElasticsearchEmbeddings class allows users to generate embeddings for
documents and query texts using a [model deployed in an Elasticsearch
cluster](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-model-ref.html#ml-nlp-model-ref-text-embedding).
### Main features:
1. The ElasticsearchEmbeddings class initializes with an Elasticsearch
connection object and a model_id, providing an interface to interact
with the Elasticsearch ML client through
[infer_trained_model](https://elasticsearch-py.readthedocs.io/en/v8.7.0/api.html?highlight=trained%20model%20infer#elasticsearch.client.MlClient.infer_trained_model)
.
2. The `embed_documents()` method generates embeddings for a list of
documents, and the `embed_query()` method generates an embedding for a
single query text.
3. The class supports custom input text field names in case the deployed
model expects a different field name than the default `text_field`.
4. The implementation is compatible with any model deployed in
Elasticsearch that generates embeddings as output.
### Benefits:
1. Simplifies the process of generating embeddings using Elasticsearch
models.
2. Provides a clean and intuitive interface to interact with the
Elasticsearch ML client.
3. Allows users to easily integrate Elasticsearch-generated embeddings.
Related issue https://github.com/hwchase17/langchain/issues/3400
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Implementation is similar to search_distance and where_filter
# adds 'additional' support to Weaviate queries
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call
different inference endpoints directly via HTTP. It implements the
OpenAI Completion class so that it can be used as a drop-in replacement
for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added
code.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add Mastodon toots loader.
Loader works either with public toots, or Mastodon app credentials. Toot
text and user info is loaded.
I've also added integration test for this new loader as it works with
public data, and a notebook with example output run now.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Enhance the code to support SSL authentication for Elasticsearch when
using the VectorStore module, as previous versions did not provide this
capability.
@dev2049
---------
Co-authored-by: caidong <zhucaidong1992@gmail.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Let user inspect the token ids in addition to getting th enumber of tokens
---------
Co-authored-by: Zach Schillaci <40636930+zachschillaci27@users.noreply.github.com>
### Submit Multiple Files to the Unstructured API
Enables batching multiple files into a single Unstructured API requests.
Support for requests with multiple files was added to both
`UnstructuredAPIFileLoader` and `UnstructuredAPIFileIOLoader`. Note that
if you submit multiple files in "single" mode, the result will be
concatenated into a single document. We recommend using this feature in
"elements" mode.
### Testing
The following should load both documents, using two of the example docs
from the integration tests folder.
```python
from langchain.document_loaders import UnstructuredAPIFileLoader
file_paths = ["examples/layout-parser-paper.pdf", "examples/whatsapp_chat.txt"]
loader = UnstructuredAPIFileLoader(
file_paths=file_paths,
api_key="FAKE_API_KEY",
strategy="fast",
mode="elements",
)
docs = loader.load()
```
# Adds "IN" metadata filter for pgvector to all checking for set
presence
PGVector currently supports metadata filters of the form:
```
{"filter": {"key": "value"}}
```
which will return documents where the "key" metadata field is equal to
"value".
This PR adds support for metadata filters of the form:
```
{"filter": {"key": { "IN" : ["list", "of", "values"]}}}
```
Other vector stores support this via an "$in" syntax. I chose to use
"IN" to match postgres' syntax, though happy to switch.
Tested locally with PGVector and ChatVectorDBChain.
@dev2049
---------
Co-authored-by: jade@spanninglabs.com <jade@spanninglabs.com>
# Powerbi API wrapper bug fix + integration tests
- Bug fix by removing `TYPE_CHECKING` in in utilities/powerbi.py
- Added integration test for power bi api in
utilities/test_powerbi_api.py
- Added integration test for power bi agent in
agent/test_powerbi_agent.py
- Edited .env.examples to help set up power bi related environment
variables
- Updated demo notebook with working code in
docs../examples/powerbi.ipynb - AzureOpenAI -> ChatOpenAI
Notes:
Chat models (gpt3.5, gpt4) are much more capable than davinci at writing
DAX queries, so that is important to getting the agent to work properly.
Interestingly, gpt3.5-turbo needed the examples=DEFAULT_FEWSHOT_EXAMPLES
to write consistent DAX queries, so gpt4 seems necessary as the smart
llm.
Fixes#4325
## Before submitting
Azure-core and Azure-identity are necessary dependencies
check integration tests with the following:
`pytest tests/integration_tests/utilities/test_powerbi_api.py`
`pytest tests/integration_tests/agent/test_powerbi_agent.py`
You will need a power bi account with a dataset id + table name in order
to test. See .env.examples for details.
## Who can review?
@hwchase17
@vowelparrot
---------
Co-authored-by: aditya-pethe <adityapethe1@gmail.com>
# Add bs4 html parser
* Some minor refactors
* Extract the bs4 html parsing code from the bs html loader
* Move some tests from integration tests to unit tests
# Remove unnecessary comment
Remove unnecessary comment accidentally included in #4800
## Before submitting
- no test
- no document
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
# Improve the Chroma get() method by adding the optional "include"
parameter.
The Chroma get() method excludes embeddings by default. You can
customize the response by specifying the "include" parameter to
selectively retrieve the desired data from the collection.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fix Telegram API loader + add tests.
I was testing this integration and it was broken with next error:
```python
message_threads = loader._get_message_threads(df)
KeyError: False
```
Also, this particular loader didn't have any tests / related group in
poetry, so I added those as well.
@hwchase17 / @eyurtsev please take a look on this fix PR.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Cassandra support for chat history
### Description
- Store chat messages in cassandra
### Dependency
- cassandra-driver - Python Module
## Before submitting
- Added Integration Test
## Who can review?
@hwchase17
@agola11
# Your PR Title (What it does)
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Fixes # (issue)
## Before submitting
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## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
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Tracing / Callbacks
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Async
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DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
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Co-authored-by: Jinto Jose <129657162+jj701@users.noreply.github.com>
# Fix DeepLake Overwrite Flag Issue
Fixes Issue #4682: essentially, setting overwrite to False in the
DeepLake constructor still triggers an overwrite, because the logic is
just checking for the presence of "overwrite" in kwargs. The fix is
simple--just add some checks to inspect if "overwrite" in kwargs AND
kwargs["overwrite"]==True.
Added a new test in
tests/integration_tests/vectorstores/test_deeplake.py to reflect the
desired behavior.
Co-authored-by: Anirudh Suresh <ani@Anirudhs-MBP.cable.rcn.com>
Co-authored-by: Anirudh Suresh <ani@Anirudhs-MacBook-Pro.local>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add summarization task type for HuggingFace APIs
Add summarization task type for HuggingFace APIs.
This task type is described by [HuggingFace inference
API](https://huggingface.co/docs/api-inference/detailed_parameters#summarization-task)
My project utilizes LangChain to connect multiple LLMs, including
various HuggingFace models that support the summarization task.
Integrating this task type is highly convenient and beneficial.
Fixes#4720
# Add GraphQL Query Support
This PR introduces a GraphQL API Wrapper tool that allows LLM agents to
query GraphQL databases. The tool utilizes the httpx and gql Python
packages to interact with GraphQL APIs and provides a simple interface
for running queries with LLM agents.
@vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Parameterize Redis vectorstore index
Redis vectorstore allows for three different distance metrics: `L2`
(flat L2), `COSINE`, and `IP` (inner product). Currently, the
`Redis._create_index` method hard codes the distance metric to COSINE.
I've parameterized this as an argument in the `Redis.from_texts` method
-- pretty simple.
Fixes#4368
## Before submitting
I've added an integration test showing indexes can be instantiated with
all three values in the `REDIS_DISTANCE_METRICS` literal. An example
notebook seemed overkill here. Normal API documentation would be more
appropriate, but no standards are in place for that yet.
## Who can review?
Not sure who's responsible for the vectorstore module... Maybe @eyurtsev
/ @hwchase17 / @agola11 ?
Thanks to @anna-charlotte and @jupyterjazz for the contribution! Made
few small changes to get it across the finish line
---------
Signed-off-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Co-authored-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: Saba Sturua <45267439+jupyterjazz@users.noreply.github.com>
# ODF File Loader
Adds a data loader for handling Open Office ODT files. Requires
`unstructured>=0.6.3`.
### Testing
The following should work using the `fake.odt` example doc from the
[`unstructured` repo](https://github.com/Unstructured-IO/unstructured).
```python
from langchain.document_loaders import UnstructuredODTLoader
loader = UnstructuredODTLoader(file_path="fake.odt", mode="elements")
loader.load()
loader = UnstructuredODTLoader(file_path="fake.odt", mode="single")
loader.load()
```
Fixes#4153
If the sender of a message in a group chat isn't in your contact list,
they will appear with a ~ prefix in the exported chat. This PR adds
support for parsing such lines.
