**Description:**
Add cost calculation for fine tuned models (new and legacy), this is
required after OpenAI added new models for fine tuning and separated the
costs of I/O for fine tuned models.
Also I updated the relevant unit tests
see https://platform.openai.com/docs/guides/fine-tuning for more
information.
issue: https://github.com/langchain-ai/langchain/issues/11715
- **Issue:** 11715
- **Twitter handle:** @nirkopler
- replace `requests` package with `langchain.requests`
- add `_acall` support
- add `_stream` and `_astream`
- freshen up the documentation a bit
- update vendor doc
Allows for passing arguments into the LLM chains used by the
GraphCypherQAChain. This is to address a request by a user to include
memory in the Cypher creating chain. Will keep the prompt variables
as-is to be backward compatible. But, would be a good idea to deprecate
them and use the **kwargs variables. Added a test case.
In general, I think it would be good for any chain to automatically pass
in a readonlymemory(of its input) to its subchains whilist allowing for
an override. But, this would be a different change.
- **Description:**
Add missing apostrophe in `user's` in stuff_prompt's system_template.
The first sentence in the system template went from:
> Use the following pieces of context to answer the users question.
to
> Use the following pieces of context to answer the user's question.
- **Issue:**
- **Dependencies:** none
- **Tag maintainer:** @baskaryan
- **Twitter handle:** ojohnnyo
- This is used internally to gather aggregate usage metrics for the
LangChain integrations
- Note: This cannot be added to some of the Vertex AI integrations at
this time because the SDK doesn't allow overriding the
[`ClientInfo`](https://googleapis.dev/python/google-api-core/latest/client_info.html#module-google.api_core.client_info)
- Added to:
- BigQuery
- Google Cloud Storage
- Document AI
- Vertex AI Model Garden
- Document AI Warehouse
- Vertex AI Search
- Vertex AI Matching Engine (Cloud Storage Client)
@baskaryan, @eyurtsev, @hwchase17
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** In the max_marginal_relevance_search function of the
ElasticsearchStore vector store, the name of the field corresponding to
the vector embedding of the document is hard coded in the delete
statement that drops the field from the document metadata. This results
in an exception if the vector embedding field is customized. This PR
changes the hard-coded "vector" into the vector_query_field variable.
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
Co-authored-by: Shilong Dai <sdai@viperfish.net>
**Description: Allow to inject boto3 client for Cross account access
type of scenarios in using SagemakerEndpointEmbeddings and also updated
the documentation for same in the sample notebook**
**Issue:SagemakerEndpointEmbeddings cross account capability #10634
#10184**
Dependencies: None
Tag maintainer:
Twitter handle:lethargicoder
Co-authored-by: Vikram(VS) <vssht@amazon.com>
- **Description:** sqlalchemy create_engine() does not take into account
connect_args which are mandatory for managed PGSQL instances on cloud
providers (ssl_context for example).
Also re-enabled create_vector_extension at post_init for using pgvector
class seamlessly
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Sami Bargaoui <bargaoui.sam@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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If non-pickleable objects (like locks) get passed to the tracing
callback, they'll fail in the deepcopy. Fallback to a shallow copy in
these instances .
We don't use any of the new functionality at the moment. Just making
sure we don't fall back on versions and fail to benefit from new
patches. This is an easy upgrade and it's always harder to upgrade
across multiple major versions at once.
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Adding Tavily Search API as a tool. I will be the maintainer and
assaf_elovic is the twitter handler.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Current ChatTongyi is not compatible with DashScope API, which will
cause error when passing api key to chat model directly.
- **Description:** Update tongyi.py to be compatible with DashScope API.
Specifically, update parameter name "dashscope_api_key" to "api_key".
- **Issue:** None.
- **Dependencies:** Nothing new, Tongyi would require DashScope as
before.
- **Description:** Implementing the Google Scholar Tool as requested in
PR #11505. The tool will be using the [serpapi python
package](https://serpapi.com/integrations/python#search-google-scholar).
The main idea of the tool will be to return the results from a Google
Scholar search given a query as an input to the tool.
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17
- Fixes error:
```
ValueError: "GoogleVertexAISearchRetriever" object has no field "_serving_config"
```
Introduced in #11736
@baskaryan, @eyurtsev, @hwchase17 if you could review and merge quickly,
that would be appreciated :)
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- **Description:** The return info in the documentation for
similarity_search_by_vector and similarity_search_with_relevance_scores
is wrong
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This reverts commit a46eef64a7.
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- **Description:** Provide a way to use different text for embedding.
- For example, if you are ingesting stack-overflow Q&As for RAG, you
would want to embed the questions and return the answer(s) for the hits.
With this change, the consumer of langchain can implement that easily.
- I noticed the similar function is added on faiss.py with #1912 which
was for performance reason, but I see the same function can be used to
achieve what I thought. So instead of changing Document class to have
embedding_content, I mimicked the implementation of faiss.py.
- The test should provide some guidance on how to use it. It would be
more intuitive if I just pass texts and embedding_texts as separate
arguments, but I chose to use `zip`-ed object for the consistency with
faiss.py implementation.
- I plan to make similar pull request for OpenSearch.
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Adding Pydantic v2 support for OpenAPI Specs
- **Issue:**
- OpenAPI spec support was disabled because `openapi-schema-pydantic`
doesn't support Pydantic v2:
#9205
- Caused errors in `get_openapi_chain`
- This may be the cause of #9520.
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** kreneskyp
The root cause was that `openapi-schema-pydantic` hasn't been updated in
some time but
[openapi-pydantic](https://github.com/mike-oakley/openapi-pydantic)
forked and updated the project.
Updated the elasticsearch self query retriever to use the match clause
for LIKE operator instead of the non-analyzed fuzzy search clause.
Other small updates include:
- fixing the stack inference integration test where the index's default
pipeline didn't use the inference pipeline created
- adding a user-agent to the old implementation to track usage
- improved the documentation for ElasticsearchStore filters
### Description:
To provide an eas llm service access methods in this pull request by
impletementing `PaiEasEndpoint` and `PaiEasChatEndpoint` classes in
`langchain.llms` and `langchain.chat_models` modules. Base on this pr,
langchain users can build up a chain to call remote eas llm service and
get the llm inference results.
### About EAS Service
EAS is a Alicloud product on Alibaba Cloud Machine Learning Platform for
AI which is short for AliCloud PAI. EAS provides model inference
deployment services for the users. We build up a llm inference services
on EAS with a general llm docker images. Therefore, end users can
quickly setup their llm remote instances to load majority of the
hugginface llm models, and serve as a backend for most of the llm apps.
### Dependencies
This pr does't involve any new dependencies.
---------
Co-authored-by: 子洪 <gaoyihong.gyh@alibaba-inc.com>
Description: Supported RetryOutputParser & RetryWithErrorOutputParser
max_retries
- max_retries: Maximum number of retries to parser.
Issue: None
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle:
We now require uses to have the pip package `llmonitor` installed. It
allows us to have cleaner code and avoid duplicates between our library
and our code in Langchain.
FAISS does not implement embeddings method and use embed_query to
embedding texts which is wrong for some embedding models.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
feat: Raise KeyError when 'prompt' key is missing in JSON response
This commit updates the error handling in the code to raise a KeyError
when the 'prompt' key is not found in the JSON response. This change
makes the code more explicit about the nature of the error, helping to
improve clarity and debugging.
@baskaryan, @eyurtsev.
I may be missing something but it seems like we inappropriately overrode
the 'stream()' method, losing callbacks in the process. I don't think
(?) it gave us anything in this case to customize it here?
See new trace:
https://smith.langchain.com/public/fbb82825-3a16-446b-8207-35622358db3b/r
and confirmed it streams.
Also fixes the stopwords issues from #12000
- **Description:** According to the document
https://cloud.baidu.com/doc/WENXINWORKSHOP/s/clntwmv7t, add ERNIE-Bot-4
model support for ErnieBotChat.
- **Dependencies:** Before using the ERNIE-Bot-4, you should have the
model's access authority.
By default replace input_variables with the correct value
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.dict() is a Pydantic method that cannot raise exceptions, as it is used
eg. in `__eq__`
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Type hinting `*args` as `List[Any]` means that each positional argument
should be a list. Type hinting `**kwargs` as `Dict[str, Any]` means that
each keyword argument should be a dict of strings.
This is almost never what we actually wanted, and doesn't seem to be
what we want in any of the cases I'm replacing here.
- Update Zep Memory and Retriever docstrings
- Zep Memory Retriever: Add support for native MMR
- Add MMR example to existing ZepRetriever Notebook
@baskaryan
Example
```
from langchain.schema.runnable import RunnableLambda
from langsmith import traceable
chain = RunnableLambda(lambda x: x)
@traceable(run_type = "chain")
def my_traceable(a):
chain.invoke(a)
my_traceable(5)
```
Would have a nested result.
This would NOT work for interleaving chains and traceables. E.g., things
like thiswould still not work well
```
from langchain.schema.runnable import RunnableLambda
from langsmith import traceable
@traceable()
def other_traceable(a):
return a
def foo(x):
return other_traceable(x)
chain = RunnableLambda(foo)
@traceable(run_type = "chain")
def my_traceable(a):
chain.invoke(a)
my_traceable(5)
```
Minor lint dependency version upgrade to pick up latest functionality.
Ruff's new v0.1 version comes with lots of nice features, like
fix-safety guarantees and a preview mode for not-yet-stable features:
https://astral.sh/blog/ruff-v0.1.0
- **Description:** Chroma >= 0.4.10 added support for batch sizes
validation of add/upsert. This batch size is dependent on the SQLite
limits of the target system and varies. In this change, for
Chroma>=0.4.10 batch splitting was added as the aforementioned
validation is starting to surface in the Chroma community (users using
LC)
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** t_azarov
Description: A large language models developed by Baichuan Intelligent
Technology，https://www.baichuan-ai.com/home
Issue: None
Dependencies: None
Tag maintainer:
Twitter handle:
This adds security notices to toolkits init, and to several toolkits.
We'll need to continue documenting the rest of the toolkits.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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