The previous API of the `_execute()` function had a few rough edges that
this PR addresses:
- The `fetch` argument was type-hinted as being able to take any string,
but any string other than `"all"` or `"one"` would `raise ValueError`.
The new type hints explicitly declare that only those values are
supported.
- The return type was type-hinted as `Sequence` but using `fetch =
"one"` would actually return a single result item. This was incorrectly
suppressed using `# type: ignore`. We now always return a list.
- Using `fetch = "one"` would return a single item if data was found, or
an empty *list* if no data was found. This was confusing, and we now
always return a list to simplify.
- The return type was `Sequence[Any]` which was a bit difficult to use
since it wasn't clear what one could do with the returned rows. I'm
making the new type `Dict[str, Any]` that corresponds to the column
names and their values in the query.
I've updated the use of this method elsewhere in the file to match the
new behavior.
continuation of PR #8550
@hwchase17 please see and merge. And also close the PR #8550.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Instead of:
```
client = Client()
with collect_runs() as cb:
chain.invoke()
run = cb.traced_runs[0]
client.get_run_url(run)
```
it's
```
with tracing_v2_enabled() as cb:
chain.invoke()
cb.get_run_url()
```
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Similarly to Vertex classes, PaLM classes weren't marked as
serialisable. Should be working fine with LangSmith.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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This PR uses 2 dedicated LangChain warnings types for deprecations
(mirroring python's built in deprecation and pending deprecation
warnings).
These deprecation types are unslienced during initialization in
langchain achieving the same default behavior that we have with our
current warnings approach. However, because these warnings have a
dedicated type, users will be able to silence them selectively (I think
this is strictly better than our current handling of warnings).
The PR adds a deprecation warning to llm symbolic math.
---------
Co-authored-by: Predrag Gruevski <2348618+obi1kenobi@users.noreply.github.com>
- Also move RunnableBranch to its own file
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### Description
renamed several repository links from `hwchase17` to `langchain-ai`.
### Why
I discovered that the README file in the devcontainer contains an old
repository name, so I took the opportunity to rename the old repository
name in all files within the repository, excluding those that do not
require changes.
### Dependencies
none
### Tag maintainer
@baskaryan
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
**Description:** Adds streaming and many more sampling parameters to the
DeepSparse interface
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Fix a code injection vuln by adding one more keyword
into the filtering list
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:**
- **Twitter handle:**
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Passes through dict input and assigns additional keys
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<img width="1728" alt="Screenshot 2023-09-28 at 20 15 01"
src="https://github.com/langchain-ai/langchain/assets/56902/ed0644c3-6db7-41b9-9543-e34fce46d3e5">
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Suppress warnings in interactive environments that can arise from users
relying on tab completion (without even using deprecated modules).
jupyter seems to filter warnings by default (at least for me), but
ipython surfaces them all
- **Description:** A Document Loader for MongoDB
- **Issue:** n/a
- **Dependencies:** Motor, the async driver for MongoDB
- **Tag maintainer:** n/a
- **Twitter handle:** pigpenblue
Note that an initial mongodb document loader was created 4 months ago,
but the [PR ](https://github.com/langchain-ai/langchain/pull/4285)was
never pulled in. @leo-gan had commented on that PR, but given it is
extremely far behind the master branch and a ton has changed in
Langchain since then (including repo name and structure), I rewrote the
branch and issued a new PR with the expectation that the old one can be
closed.
Please reference that old PR for comments/context, but it can be closed
in favor of this one. Thanks!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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```
ChatPromptTemplate(messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='You are a nice assistant.')), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], template='{question}'))])
| RunnableLambda(lambda x: x)
| {
chat: FakeListChatModel(responses=["i'm a chatbot"]),
llm: FakeListLLM(responses=["i'm a textbot"])
}
```
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- **Description:**
be able to use langchain with other version than tiktoken 0.3.3 i.e
0.5.1
- **Issue:**
cannot installed the conda-forge version since it applied all optional
dependency:
https://github.com/conda-forge/langchain-feedstock/pull/85
replace "^0.3.2" by "">=0.3.2,<0.6.0" and "^3.9" by python=">=3.9"
Tested with python 3.10, langchain=0.0.288 and tiktoken==0.5.0
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
As of now, when instantiating and during inference, `LlamaCppEmbeddings`
outputs (a lot of) verbose when controlled from Langchain binding - it
is a bit annoying when computing the embeddings of long documents, for
instance.
This PR adds `verbose` for `LlamaCppEmbeddings` objects to be able
**not** to print the verbose of the model to `stderr`. It is natively
supported by `llama-cpp-python` and directly passed to the library – the
PR is hence very small.
The value of `verbose` is `True` by default, following the way it is
defined in [`LlamaCpp` (`llamacpp.py`
#L136-L137)](c87e9fb2ce/libs/langchain/langchain/llms/llamacpp.py (L136-L137))
## Issue
_No issue linked_
## Dependencies
_No additional dependency needed_
## To see it in action
```python
from langchain.embeddings import LlamaCppEmbeddings
MODEL_PATH = "<path_to_gguf_file>"
if __name__ == "__main__":
llm_embeddings = LlamaCppEmbeddings(
model_path=MODEL_PATH,
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
verbose=False,
)
```
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Description
Adds logic for NotionDBLoader to correctly populate `last_edited_time`
and `created_time` fields from [page
properties](https://developers.notion.com/reference/page#property-value-object).
There are no relevant tests for this code to be updated.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Based on the customers' requests for native langchain integration,
SearchApi is ready to invest in AI and LLM space, especially in
open-source development.
- This is our initial PR and later we want to improve it based on
customers' and langchain users' feedback. Most likely changes will
affect how the final results string is being built.
- We are creating similar native integration in Python and JavaScript.
- The next plan is to integrate into Java, Ruby, Go, and others.
- Feel free to assign @SebastjanPrachovskij as a main reviewer for any
SearchApi-related searches. We will be glad to help and support
langchain development.
- **Description:**
- Make running integration test for opensearch easy
- Provide a way to use different text for embedding: refer to #11002 for
more of the use case and design decision.
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
Both black and mypy expect a list of files or directories as input.
As-is the Makefile computes a list files changed relative to the last
commit; these are passed to black and mypy in the `format_diff` and
`lint_diff` targets. This is done by way of the Makefile variable
`PYTHON_FILES`. This is to save time by skipping running mypy and black
over the whole source tree.
When no changes have been made, this variable is empty, so the call to
black (and mypy) lacks input files. The call exits with error causing
the Makefile target to error out with:
```bash
$ make format_diff
poetry run black
Usage: black [OPTIONS] SRC ...
One of 'SRC' or 'code' is required.
make: *** [format_diff] Error 1
```
This is unexpected and undesirable, as the naive caller (that's me! 😄 )
will think something else is wrong. This commit smooths over this by
short circuiting when `PYTHON_FILES` is empty.
- **Description:** The types of 'destination_chains' and 'default_chain'
in 'MultiPromptChain' were changed from 'LLMChain' to 'Chain'. and
removed variables declared overlapping with the parent class
- **Issue:** When a class that inherits only Chain and not LLMChain,
such as 'SequentialChain' or 'RetrievalQA', is entered in
'destination_chains' and 'default_chain', a pydantic validation error is
raised.
- - codes
```
retrieval_chain = ConversationalRetrievalChain(
retriever=doc_retriever,
combine_docs_chain=combine_docs_chain,
question_generator=question_gen_chain,
)
destination_chains = {
'retrieval': retrieval_chain,
}
main_chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain,
verbose=True,
)
```
✅ `make format`, `make lint` and `make test`
## Description
Expanded the upper bound for `networkx` dependency to allow installation
of latest stable version. Tested the included sample notebook with
version 3.1, and all steps ran successfully.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds support for the `$vectorSearch` operator for
MongoDBAtlasVectorSearch, which was announced at .Local London
(September 26th, 2023). This change maintains breaks compatibility
support for the existing `$search` operator used by the original
integration (https://github.com/langchain-ai/langchain/pull/5338) due to
incompatibilities in the Atlas search implementations.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
We noticed that as we have been moving developers to the new
`ElasticsearchStore` implementation, we want to keep the
ElasticVectorSearch class still available as developers transition
slowly to the new store.
To speed up this process, I updated the blurb giving them a better
recommendation of why they should use ElasticsearchStore.
Description: Add "source" metadata to OutlookMessageLoader
This pull request adds the "source" metadata to the OutlookMessageLoader
class in the load method. The "source" metadata is required when
indexing with RecordManager in order to sync the index documents with a
source.
Issue: None
Dependencies: None
Twitter handle: @ATelders
Co-authored-by: Arthur Telders <arthur.telders@roquette.com>
- **Description:** Bedrock updated boto service name to
"bedrock-runtime" for the InvokeModel and InvokeModelWithResponseStream
APIs. This update also includes new model identifiers for Titan text,
embedding and Anthropic.
Co-authored-by: Mani Kumar Adari <maniadar@amazon.com>
The key of stopping strings used in text-generation-webui api is
[`stopping_strings`](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples/api-example.py#L51),
not `stop`.
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- **Description:** Changed data type from `text` to `json` in xata for
improved performance. Also corrected the `additionalKwargs` key in the
`messages()` function to `additional_kwargs` to adhere to `BaseMessage`
requirements.
- **Issue:** The Chathisroty.messages() will return {} of
`additional_kwargs`, as the name is wrong for `additionalKwargs` .
- **Dependencies:** N/A
- **Tag maintainer:** N/A
- **Twitter handle:** N/A
My PR is passing linting and testing before submitting.
This adds `input_schema` and `output_schema` properties to all
runnables, which are Pydantic models for the input and output types
respectively. These are inferred from the structure of the Runnable as
much as possible, the only manual typing needed is
- optionally add type hints to lambdas (which get translated to
input/output schemas)
- optionally add type hint to RunnablePassthrough
These schemas can then be used to create JSON Schema descriptions of
input and output types, see the tests
- [x] Ensure no InputType and OutputType in our classes use abstract
base classes (replace with union of subclasses)
- [x] Implement in BaseChain and LLMChain
- [x] Implement in RunnableBranch
- [x] Implement in RunnableBinding, RunnableMap, RunnablePassthrough,
RunnableEach, RunnableRouter
- [x] Implement in LLM, Prompt, Chat Model, Output Parser, Retriever
- [x] Implement in RunnableLambda from function signature
- [x] Implement in Tool
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Adds LangServe package
* Integrate Runnables with Fast API creating Server and a RemoteRunnable
client
* Support multiple runnables for a given server
* Support sync/async/batch/abatch/stream/astream/astream_log on the
client side (using async implementations on server)
* Adds validation using annotations (relying on pydantic under the hood)
-- this still has some rough edges -- e.g., open api docs do NOT
generate correctly at the moment
* Uses pydantic v1 namespace
Known issues: type translation code doesn't handle a lot of types (e.g.,
TypedDicts)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
The current behaviour just calls the handler without awaiting the
coroutine, which results in exceptions/warnings, and obviously doesn't
actually execute whatever the callback handler does
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- **Description:** Prompt wrapping requirements have been implemented on
the service side of AWS Bedrock for the Anthropic Claude models to
provide parity between Anthropic's offering and Bedrock's offering. This
overnight change broke most existing implementations of Claude, Bedrock
and Langchain. This PR just steals the the Anthropic LLM implementation
to enforce alias/role wrapping and implements it in the existing
mechanism for building the request body. This has also been tested to
fix the chat_model implementation as well. Happy to answer any further
questions or make changes where necessary to get things patched and up
to PyPi ASAP, TY.
- **Issue:** No issue opened at the moment, though will update when
these roll in.
- **Dependencies:** None
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description:
NotionDB supports a number of common property types. I have found three
common types that are not included in notiondb loader. When programs
loaded them with notiondb, which will cause some metadata information
not to be passed to langchain. Therefore, I added three common types:
- date
- created_time
- last_edit_time.
### Issue:
no
### Dependencies:
No dependencies added :)
### Tag maintainer:
@rlancemartin, @eyurtsev
### Twitter handle:
@BJTUTC
Reverts langchain-ai/langchain#8610
this is actually an oversight - this merges all dfs into one df. we DO
NOT want to do this - the idea is we work and manipulate multiple dfs
This removes the use of the intermediate df list and directly
concatenates the dataframes if path is a list of strings. The pd.concat
function combines the dataframes efficiently, making it faster and more
memory-efficient compared to appending dataframes to a list.
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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.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: this PR adds the support for arxiv identifier of the
ArxivAPIWrapper. I modified the `run()` and `load()` functions in
`arxiv.py`, using regex to recognize if the query is in the form of
arxiv identifier (see
[https://info.arxiv.org/help/find/index.html](https://info.arxiv.org/help/find/index.html)).
If so, it will directly search the paper corresponding to the arxiv
identifier. I also modified and added tests in `test_arxiv.py`.
- Issue: #9047
- Dependencies: N/A
- Tag maintainer: N/A
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The new Fireworks and FireworksChat implementations are awesome! Added
in this PR https://github.com/langchain-ai/langchain/pull/11117 thank
you @ZixinYang
However, I think stop words were not plumbed correctly. I've made some
simple changes to do that, and also updated the notebook to be a bit
clearer with what's needed to use both new models.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
**Description:**
As long as `enforce_stop_tokens` returns a first occurrence, we can
speed up the execution by setting the optional `maxsplit` parameter to
1.
Tag maintainer:
@agola11
@hwchase17
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** New metadata fields were added to
`unstructured==0.10.15`, and our hosted api has been updated to reflect
this. When users call `partition_via_api` with an older version of the
library, they'll hit a parsing error related to the new fields.
Description
* Refactor Fireworks within Langchain LLMs.
* Remove FireworksChat within Langchain LLMs.
* Add ChatFireworks (which uses chat completion api) to Langchain chat
models.
* Users have to install `fireworks-ai` and register an api key to use
the api.
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin @baskaryan
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:**: Adds LLM as a judge as an eval chain
- **Tag maintainer:** @hwchase17
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This enables bulk args like `chunk_size` to be passed down from the
ingest methods (from_text, from_documents) to be passed down to the bulk
API.
This helps alleviate issues where bulk importing a large amount of
documents into Elasticsearch was resulting in a timeout.
Contribution Shoutout
- @elastic
- [x] Updated Integration tests
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Sometimes you don't want the LLM to be aware of the whole graph schema,
and want it to ignore parts of the graph when it is constructing Cypher
statements.
- **Description**: Adding retrievers for [kay.ai](https://kay.ai) and
SEC filings powered by Kay and Cybersyn. Kay provides context as a
service: it's an API built for RAG.
- **Issue**: N/A
- **Dependencies**: Just added a dep to the
[kay](https://pypi.org/project/kay/) package
- **Tag maintainer**: @baskaryan @hwchase17 Discussed in slack
- **Twtter handle:** [@vishalrohra_](https://twitter.com/vishalrohra_)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The huggingface pipeline in langchain (used for locally hosted models)
does not support batching. If you send in a batch of prompts, it just
processes them serially using the base implementation of _generate:
https://github.com/docugami/langchain/blob/master/libs/langchain/langchain/llms/base.py#L1004C2-L1004C29
This PR adds support for batching in this pipeline, so that GPUs can be
fully saturated. I updated the accompanying notebook to show GPU batch
inference.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
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Closes#8842
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- Description: fix `ChatMessageChunk` concat error
- Issue: #10173
- Dependencies: None
- Tag maintainer: @baskaryan, @eyurtsev, @rlancemartin
- Twitter handle: None
---------
Co-authored-by: wangshuai.scotty <wangshuai.scotty@bytedance.com>
Co-authored-by: Nuno Campos <nuno@boringbits.io>
### Description
This PR makes the following changes to OpenSearch:
1. Pass optional ids with `from_texts`
2. Pass an optional index name with `add_texts` and `search` instead of
using the same index name that was used during `from_texts`
### Issue
https://github.com/langchain-ai/langchain/issues/10967
### Maintainers
@rlancemartin, @eyurtsev, @navneet1v
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
LLMRails Embedding Integration
This PR provides integration with LLMRails. Implemented here are:
langchain/embeddings/llm_rails.py
docs/extras/integrations/text_embedding/llm_rails.ipynb
Hi @hwchase17 after adding our vectorstore integration to langchain with
confirmation of you and @baskaryan, now we want to add our embedding
integration
---------
Co-authored-by: Anar Aliyev <aaliyev@mgmt.cloudnet.services>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds support for gradient.ai's embedding model.
This will remain a Draft, as the code will likely be refactored with the
`pip install gradientai` python sdk.
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@baskaryan, @eyurtsev, @hwchase17.
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- **Description:** a fix for `index`.
- **Issue:** Not applicable.
- **Dependencies:** None
- **Tag maintainer:**
- **Twitter handle:** richarddwang
# Problem
Replication code
```python
from pprint import pprint
from langchain.embeddings import OpenAIEmbeddings
from langchain.indexes import SQLRecordManager, index
from langchain.schema import Document
from langchain.vectorstores import Qdrant
from langchain_setup.qdrant import pprint_qdrant_documents, create_inmemory_empty_qdrant
# Documents
metadata1 = {"source": "fullhell.alchemist"}
doc1_1 = Document(page_content="1-1 I have a dog~", metadata=metadata1)
doc1_2 = Document(page_content="1-2 I have a daugter~", metadata=metadata1)
doc1_3 = Document(page_content="1-3 Ahh! O..Oniichan", metadata=metadata1)
doc2 = Document(page_content="2 Lancer died again.", metadata={"source": "fate.docx"})
# Create empty vectorstore
collection_name = "secret_of_D_disk"
vectorstore: Qdrant = create_inmemory_empty_qdrant()
# Create record Manager
import tempfile
from pathlib import Path
record_manager = SQLRecordManager(
namespace="qdrant/{collection_name}",
db_url=f"sqlite:///{Path(tempfile.gettempdir())/collection_name}.sql",
)
record_manager.create_schema() # 必須
sync_result = index(
[doc1_1, doc1_2, doc1_2, doc2],
record_manager,
vectorstore,
cleanup="full",
source_id_key="source",
)
print(sync_result, end="\n\n")
pprint_qdrant_documents(vectorstore)
```
<details>
<summary>Code of helper functions `pprint_qdrant_documents` and
`create_inmemory_empty_qdrant`</summary>
```python
def create_inmemory_empty_qdrant(**from_texts_kwargs):
# Qdrant requires vector size, which can be only know after applying embedder
vectorstore = Qdrant.from_texts(["dummy"], location=":memory:", embedding=OpenAIEmbeddings(), **from_texts_kwargs)
dummy_document_id = vectorstore.client.scroll(vectorstore.collection_name)[0][0].id
vectorstore.delete([dummy_document_id])
return vectorstore
def pprint_qdrant_documents(vectorstore, limit: int = 100, **scroll_kwargs):
document_ids, documents = [], []
for record in vectorstore.client.scroll(
vectorstore.collection_name, limit=100, **scroll_kwargs
)[0]:
document_ids.append(record.id)
documents.append(
Document(
page_content=record.payload["page_content"],
metadata=record.payload["metadata"] or {},
)
)
pprint_documents(documents, document_ids=document_ids)
def pprint_document(document: Document = None, document_id=None, return_string=False):
displayed_text = ""
if document_id:
displayed_text += f"Document {document_id}:\n\n"
displayed_text += f"{document.page_content}\n\n"
metadata_text = pformat(document.metadata, indent=1)
if "\n" in metadata_text:
displayed_text += f"Metadata:\n{metadata_text}"
else:
displayed_text += f"Metadata:{metadata_text}"
if return_string:
return displayed_text
else:
print(displayed_text)
def pprint_documents(documents, document_ids=None):
if not document_ids:
document_ids = [i + 1 for i in range(len(documents))]
displayed_texts = []
for document_id, document in zip(document_ids, documents):
displayed_text = pprint_document(
document_id=document_id, document=document, return_string=True
)
displayed_texts.append(displayed_text)
print(f"\n{'-' * 100}\n".join(displayed_texts))
```
</details>
You will get
```
{'num_added': 3, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}
Document 1b19816e-b802-53c0-ad60-5ff9d9b9b911:
1-2 I have a daugter~
Metadata:{'source': 'fullhell.alchemist'}
----------------------------------------------------------------------------------------------------
Document 3362f9bc-991a-5dd5-b465-c564786ce19c:
1-1 I have a dog~
Metadata:{'source': 'fullhell.alchemist'}
----------------------------------------------------------------------------------------------------
Document a4d50169-2fda-5339-a196-249b5f54a0de:
1-2 I have a daugter~
Metadata:{'source': 'fullhell.alchemist'}
```
This is not correct. We should be able to expect that the vectorsotre
now includes doc1_1, doc1_2, and doc2, but not doc1_1, doc1_2, and
doc1_2.
# Reason
In `index`, the original code is
```python
uids = []
docs_to_index = []
for doc, hashed_doc, doc_exists in zip(doc_batch, hashed_docs, exists_batch):
if doc_exists:
# Must be updated to refresh timestamp.
record_manager.update([hashed_doc.uid], time_at_least=index_start_dt)
num_skipped += 1
continue
uids.append(hashed_doc.uid)
docs_to_index.append(doc)
```
In the aforementioned example, `len(doc_batch) == 4`, but
`len(hashed_docs) == len(exists_batch) == 3`. This is because the
deduplication of input documents [doc1_1, doc1_2, doc1_2, doc2] is
[doc1_1, doc1_2, doc2]. So `index` insert doc1_1, doc1_2, doc1_2 with
the uid of doc1_1, doc1_2, doc2.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR makes `ChatAnthropic.anthropic_api_key` a `pydantic.SecretStr`
to avoid inadvertently exposing API keys when the `ChatAnthropic` object
is represented as a str.
**Description**
Fixes broken link to `CONTRIBUTING.md` in `libs/langchain/README.md`.
Because`libs/langchain/README.md` was copied from the top level README,
and because the README contains a link to `.github/CONTRIBUTING.md`, the
copied README's link relative path must be updated. This commit fixes
that link.
**Description:**
Default refine template does not actually use the refine template
defined above, it uses a string with the variable name.
@baskaryan, @eyurtsev, @hwchase17
- chat vertex async
- vertex stream
- vertex full generation info
- vertex use server-side stopping
- model garden async
- update docs for all the above
in follow up will add
[] chat vertex full generation info
[] chat vertex retries
[] scheduled tests
