Replace this entire comment with:
- **Description:** VertexAI models are now GA, moved away from using
preview ones from the SDK
- **Issue:** #13606
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
**Description:**
Repair Wikipedia document loader `load_max_docs` and improve test
coverage.
**Issue:**
The Wikipedia document loader was not respecting the `load_max_docs`
paramater (not reported) and would always return a maximum of 10
documents. This is because the API wrapper (in `utilities/wikipedia.py`)
wasn't passing `top_k_results` to the underlying [Wikipedia
library](https://wikipedia.readthedocs.io/en/latest/code.html#module-wikipedia).
By default this library returns 10 results.
The default number of results for the document loader has been reduced
from 100 to 25. This is because loading 100 results takes a very long
time and is an inconvenient default. It should possibly be 10.
In addition, the documentation for the loader reported that there was a
hard limit (300) on the number of documents returned. In actuality 300
is the maximum Wikipedia query character length set by the API wrapper.
Tests have been added for the document loader (previously missing) and
to test the correct numbers of documents are being returned by each
class, both by default, and when overridden. Also repaired is the
`assert_docs` test which has been updated to correctly test for the
default metadata (which includes `source` in recent releases).
**Dependencies:**
nil
**Tag maintainer:**
@leo-gan
**Twitter handle:**
@queenvictoria
### **Description:**
Previously `python_repl` was a built-in tool, but now it has been moved
to `langchain_experimental`.
When I use `load_tools` I get an error:
```python
In [1]: from langchain.agents import load_tools
In [2]: load_tools(["python_repl"])
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[2], line 1
----> 1 load_tools(["python_repl"])
File ~/workspace/langchain/libs/langchain/langchain/agents/load_tools.py:530, in load_tools(tool_names, llm, callbacks, **kwargs)
528 tool_names.extend(requests_method_tools)
529 elif name in _BASE_TOOLS:
--> 530 tools.append(_BASE_TOOLS[name]())
531 elif name in _LLM_TOOLS:
532 if llm is None:
File ~/workspace/langchain/libs/langchain/langchain/agents/load_tools.py:84, in _get_python_repl()
83 def _get_python_repl() -> BaseTool:
---> 84 raise ImportError(
85 "This tool has been moved to langchain experiment. "
86 "This tool has access to a python REPL. "
87 "For best practices make sure to sandbox this tool. "
88 "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md "
89 "To keep using this code as is, install langchain experimental and "
90 "update relevant imports replacing 'langchain' with 'langchain_experimental'"
91 )
ImportError: This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental'
```
In this case, it will be very confusing. I think it is no longer a
built-in tool now, so it can be removed from `_BASE_TOOLS`
### **Issue:**
https://github.com/langchain-ai/langchain/issues/13858,
https://github.com/langchain-ai/langchain/issues/13859,
https://github.com/langchain-ai/langchain/issues/13856
### **Twitter handle:**
[lin_bob57617](https://twitter.com/lin_bob57617)
The `integrations/vectorstores/matchingengine.ipynb` example has the
"Google Vertex AI Vector Search" title. This place this Title in the
wrong order in the ToC (it is sorted by the file name).
- Renamed `integrations/vectorstores/matchingengine.ipynb` into
`integrations/vectorstores/google_vertex_ai_vector_search.ipynb`.
- Updated a correspondent comment in docstring
- Rerouted old URL to a new URL
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
It was :
`from langchain.schema.prompts import BasePromptTemplate`
but because of the breaking change in the ns, it is now
`from langchain.schema.prompt_template import BasePromptTemplate`
This bug prevents building the API Reference for the langchain_experimental
There are the following main changes in this PR:
1. Rewrite of the DocugamiLoader to not do any XML parsing of the DGML
format internally, and instead use the `dgml-utils` library we are
separately working on. This is a very lightweight dependency.
2. Added MMR search type as an option to multi-vector retriever, similar
to other retrievers. MMR is especially useful when using Docugami for
RAG since we deal with large sets of documents within which a few might
be duplicates and straight similarity based search doesn't give great
results in many cases.
We are @docugami on twitter, and I am @tjaffri
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
- **Description:** Adds a retriever implementation for [Knowledge Bases
for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/), a
new service announced at AWS re:Invent, shortly before this PR was
opened. This depends on the `bedrock-agent-runtime` service, which will
be included in a future version of `boto3` and of `botocore`. We will
open a follow-up PR documenting the minimum required versions of `boto3`
and `botocore` after that information is available.
- **Issue:** N/A
- **Dependencies:** `boto3>=1.33.2, botocore>=1.33.2`
- **Tag maintainer:** @baskaryan
- **Twitter handles:** `@pjain7` `@dead_letter_q`
This PR includes a documentation notebook under
`docs/docs/integrations/retrievers`, which I (@dlqqq) have verified
independently.
EDIT: `bedrock-agent-runtime` service is now included in
`boto3>=1.33.2`:
5cf793f493
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Addressing incorrect order being sent to callbacks / tracers, due to the
nature of threading
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Add arg to omit streamed_output list, in cases where final_output is
enough this saves bandwidth
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
This PR rearranges the docstring for the `AstraDB` vector store class so
as to have all useful information in the _class_ docstring for ease of
reading.
(incidentally, due to an oversight, the docstring that was in the
constructor ended up buried below some lines of code, thereby
disappearing altogether from accessibility. Apologies.)
- **Description:** Updates to `AnthropicFunctions` to be compatible with
the OpenAI `function_call` functionality.
- **Issue:** The functionality to indicate `auto`, `none` and a forced
function_call was not completely implemented in the existing code.
- **Dependencies:** None
- **Tag maintainer:** @baskaryan , and any of the other maintainers if
needed.
- **Twitter handle:** None
I have specifically tested this functionality via AWS Bedrock with the
Claude-2 and Claude-Instant models.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** We are adding functionality to extract message
content from the `attributedBody` field of the database, in case the
content is not in the `text` field.
- **Issue:** Closes#13326 and #10680
- **Dependencies:** None.
- **Tag maintainer:** @eyurtsev, @hwchase17
---------
Co-authored-by: onotate <johnp.pham@mail.utoronto.ca>
- **Description:** Previously `MarkdownHeaderTextSplitter` did not
consider tilde-fenced code blocks
(https://spec.commonmark.org/0.30/#fenced-code-blocks). This PR fixes
that.
````md
# Bug caused by previous implementation:
~~~py
foo()
# This is a comment that would be considered header
bar()
~~~
````
- **Tag maintainer:** @baskaryan
Several bug fixes:
- emails: instead of `bcc` the `cc` is used.
- errors in the truncation descriptions
- no truncation of the `message_search`
Several updates:
- generalized UTC format
- truncation limit can be changed now in _call()
Fixes#13407.
This workaround consists in letting the RunnableLambda create its
self.afunc from its self.func when self.afunc is not provided; the
change has no dependency.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Nuno Campos <nuno@langchain.dev>
```
---- chunk 1
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]}
---- chunk 2
{'messages': [FunctionMessage(content="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”", name='Search')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”")]}
---- chunk 3
{'actions': [AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]}
---- chunk 4
{'messages': [FunctionMessage(content='25 years', name='Search')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]), observation='25 years')]}
---- chunk 5
{'actions': [AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]}
---- chunk 6
{'messages': [FunctionMessage(content='Answer: 3.991298452658078', name='Calculator')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')]}
---- chunk 7
{'messages': [AIMessage(content="Leonardo DiCaprio's current girlfriend is the Italian model Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 power is approximately 3.99.")],
'output': "Leonardo DiCaprio's current girlfriend is the Italian model "
'Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 '
'power is approximately 3.99.'}
---- final
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]),
AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]),
AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}}),
FunctionMessage(content="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”", name='Search'),
AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}}),
FunctionMessage(content='25 years', name='Search'),
AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}}),
FunctionMessage(content='Answer: 3.991298452658078', name='Calculator'),
AIMessage(content="Leonardo DiCaprio's current girlfriend is the Italian model Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 power is approximately 3.99.")],
'output': "Leonardo DiCaprio's current girlfriend is the Italian model "
'Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 '
'power is approximately 3.99.',
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”"),
AgentStep(action=AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]), observation='25 years'),
AgentStep(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')]}
```
- **Description:** Existing model used for Prompt Injection is quite
outdated but we fine-tuned and open-source a new model based on the same
model deberta-v3-base from Microsoft -
[laiyer/deberta-v3-base-prompt-injection](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection).
It supports more up-to-date injections and less prone to
false-positives.
- **Dependencies:** No
- **Tag maintainer:** -
- **Twitter handle:** @alex_yaremchuk
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** The experimental package needs to be compatible with
the usage of importing agents
For example, if i use `from langchain.agents import
create_pandas_dataframe_agent`, running the program will prompt the
following information:
```
Traceback (most recent call last):
File "/Users/dongwm/test/main.py", line 1, in <module>
from langchain.agents import create_pandas_dataframe_agent
File "/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 87, in __getattr__
raise ImportError(
ImportError: create_pandas_dataframe_agent has been moved to langchain experimental. See https://github.com/langchain-ai/langchain/discussions/11680 for more information.
Please update your import statement from: `langchain.agents.create_pandas_dataframe_agent` to `langchain_experimental.agents.create_pandas_dataframe_agent`.
```
But when I changed to `from langchain_experimental.agents import
create_pandas_dataframe_agent`, it was actually wrong:
```python
Traceback (most recent call last):
File "/Users/dongwm/test/main.py", line 2, in <module>
from langchain_experimental.agents import create_pandas_dataframe_agent
ImportError: cannot import name 'create_pandas_dataframe_agent' from 'langchain_experimental.agents' (/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain_experimental/agents/__init__.py)
```
I should use `from langchain_experimental.agents.agent_toolkits import
create_pandas_dataframe_agent`. In order to solve the problem and make
it compatible, I added additional import code to the
langchain_experimental package. Now it can be like this Used `from
langchain_experimental.agents import create_pandas_dataframe_agent`
- **Twitter handle:** [lin_bob57617](https://twitter.com/lin_bob57617)
- **Description:** Adds a tqdm progress bar to OllamaEmbeddings when
embedding a list.
- **Issue:** Related to #13637, but extended to Ollama.
- **Dependencies:** `tqdm` made a necessary dependency.
Thanks to @ugm2 for helping identify a common problem. Embeddings take a
very long time to finish on local machines, and require a progress bar
to help identify if one should even attempt the workload.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Added a line to pass the tenant parameter to
add_data_object
- **Issue:** An extra line added from the fix for #9956
- **Dependencies:** n/a
- **Tag maintainer:** @baskaryan
Tested locally, works as expected with the line change.
---------
Co-authored-by: Simon Dai <simon6752@gmail.com>
Description: Some Elastic indexes do not return a 'metadata' field in
'_source'. However, prior to this PR, the code assumed there always is a
'metadata' field. This PR adds support for cases where the field is
missing by adding it manually.
Issue: #13869
**Description:**
This PR adds Databricks Vector Search as a new vector store in
LangChain.
- [x] Add `DatabricksVectorSearch` in `langchain/vectorstores/`
- [x] Unit tests
- [x] Add
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
as a new optional dependency
We ran the following checks:
- `make format` passed ✅
- `make lint` failed but the failures were caused by other files
+ Files touched by this PR passed the linter ✅
- `make test` passed ✅
- `make coverage` failed but the failures were caused by other files.
Tests added by or related to this PR all passed
+ langchain/vectorstores/databricks_vector_search.py test coverage 94% ✅
- `make spell_check` passed ✅
The example notebook and updates to the [provider's documentation
page](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/providers/databricks.md)
will be added later in a separate PR.
**Dependencies:**
Optional dependency:
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This pull request addresses an issue found in the example code within
the docstring of `libs/core/langchain_core/runnables/passthrough.py`
The original code snippet caused a `NameError` due to the missing import
of `RunnableLambda`. The error was as follows:
```
12 return "completion"
13
---> 14 chain = RunnableLambda(fake_llm) | {
15 'original': RunnablePassthrough(), # Original LLM output
16 'parsed': lambda text: text[::-1] # Parsing logic
NameError: name 'RunnableLambda' is not defined
```
To resolve this, I have modified the example code to include the
necessary import statement for `RunnableLambda`. Additionally, I have
adjusted the indentation in the code snippet to ensure consistency and
readability.
The modified code now successfully defines and utilizes
`RunnableLambda`, ensuring that users referencing the docstring will
have a functional and clear example to follow.
There are no related GitHub issues for this particular change.
Modified Code:
```python
from langchain_core.runnables import RunnablePassthrough, RunnableParallel
from langchain_core.runnables import RunnableLambda
runnable = RunnableParallel(
origin=RunnablePassthrough(),
modified=lambda x: x+1
)
runnable.invoke(1) # {'origin': 1, 'modified': 2}
def fake_llm(prompt: str) -> str: # Fake LLM for the example
return "completion"
chain = RunnableLambda(fake_llm) | {
'original': RunnablePassthrough(), # Original LLM output
'parsed': lambda text: text[::-1] # Parsing logic
}
chain.invoke('hello') # {'original': 'completion', 'parsed': 'noitelpmoc'}
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
