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!
- [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 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>
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!
### 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>
**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
This PR adds code to make sure that the correct base URL is being
created for the Azure Cognitive Search retriever. At the moment an
incorrect base URL is being generated. I think this is happening because
the original code was based on a depreciated API version. No
dependencies need to be added. I've also added more context to the test
doc strings.
I should also note that ACS is now Azure AI Search. I will open a
separate PR to make these changes as that would be a breaking change and
should potentially be discussed.
Twitter: @marlene_zw
- No new tests added, however the current ACS retriever tests are now
passing when I run them.
- Code was linted.
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** This commit introduces support for the newly
available GPU index types introduced in Milvus 2.4 within the LangChain
project's `milvus.py`. With the release of Milvus 2.4, a range of
GPU-accelerated index types have been added, offering enhanced search
capabilities and performance optimizations for vector search operations.
This update ensures LangChain users can fully utilize the new
performance benefits for vector search operations.
- Reference: https://milvus.io/docs/gpu_index.md
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This patch fixes the #18022 issue, converting the SimSIMD internal
zero-copy outputs to NumPy.
I've also noticed, that oftentimes `dtype=np.float32` conversion is used
before passing to SimSIMD. Which numeric types do LangChain users
generally care about? We support `float64`, `float32`, `float16`, and
`int8` for cosine distances and `float16` seems reasonable for
practically any kind of embeddings and any modern piece of hardware, so
we can change that part as well 🤗
- **Description:** Added support for lower-case and mixed-case names
The names for tables and columns previouly had to be UPPER_CASE.
With this enhancement, also lower_case and MixedCase are supported,
- **Issue:** N/A
- **Dependencies:** no new dependecies added
- **Twitter handle:** @sapopensource
Updated `HuggingFacePipeline` docs to be in sync with list of supported
tasks, including translation.
- [x] **PR title**: "community: Update docs for `HuggingFacePipeline`"
- 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**:
- **Description:** Update docs for `HuggingFacePipeline`, was earlier
missing `translation` as a valid task
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** None
- [x] **Add tests and docs**:
- [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/
**Description:**
This PR adds [Dappier](https://dappier.com/) for the chat model. It
supports generate, async generate, and batch functionalities. We added
unit and integration tests as well as a notebook with more details about
our chat model.
**Dependencies:**
No extra dependencies are needed.
DuckDB has a cosine similarity function along list and array data types,
which can be used as a vector store.
- **Description:** The latest version of DuckDB features a cosine
similarity function, which can be used with its support for list or
array column types. This PR surfaces this functionality to langchain.
- **Dependencies:** duckdb 0.10.0
- **Twitter handle:** @igocrite
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Update s3_file.py to use arguments **mode** and
**post_processors** from the base class **UnstructuredBaseLoader** to
include more metadata about the files from the S3 bucket such as
*'page_number', 'languages'* etc.
**Issue:** NA
**Dependencies:** None
**Twitter handle:** preak95
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Invoke callback prior to yielding token for BaseOpenAI
& OpenAIChat
**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
**Description:** Invoke callback prior to yielding token for Fireworks
**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
I have a small dataset, and I tried to use docarray:
``DocArrayHnswSearch ``. But when I execute, it returns:
```bash
raise ImportError(
ImportError: Could not import docarray python package. Please install it with `pip install "langchain[docarray]"`.
```
Instead of docarray it needs to be
```bash
docarray[hnswlib]
```
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
RecursiveUrlLoader does not currently provide an option to set
`base_url` other than the `url`, though it uses a function with such an
option.
For example, this causes it unable to parse the
`https://python.langchain.com/docs`, as it returns the 404 page, and
`https://python.langchain.com/docs/get_started/introduction` has no
child routes to parse.
`base_url` allows setting the `https://python.langchain.com/docs` to
filter by, while the starting URL is anything inside, that contains
relevant links to continue crawling.
I understand that for this case, the docusaurus loader could be used,
but it's a common issue with many websites.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**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
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...
**Description**:
this PR enable VectorStore autoconfiguration for Infinispan: if
metadatas are only of basic types, protobuf
config will be automatically generated for the user.
Add `keep_alive` parameter to control how long the model will stay
loaded into memory with Ollama。
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
