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
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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
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Please make sure your PR is passing linting and testing before
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tests, lint, etc:
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1. a test for the integration, preferably unit tests that do not rely on
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2. an example notebook showing its use. It lives in `docs/extras`
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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!
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- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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maintainer (see below),
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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!
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- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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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>
- **Description:** Added a retriever for the Outline API to ask
questions on knowledge base
- **Issue:** resolves#11814
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
- **Description:** Simple change, I just added title metadata to
GoogleDriveLoader for optional File Loaders
- **Dependencies:** no dependencies
- **Tag maintainer:** @hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR provides idiomatic implementations for the exact-match and the
semantic LLM caches using Astra DB as backend through the database's
HTTP JSON API. These caches require the `astrapy` library as dependency.
Comes with integration tests and example usage in the `llm_cache.ipynb`
in the docs.
@baskaryan this is the Astra DB counterpart for the Cassandra classes
you merged some time ago, tagging you for your familiarity with the
topic. Thank you!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds a chat message history component that uses Astra DB for
persistence through the JSON API.
The `astrapy` package is required for this class to work.
I have added tests and a small notebook, and updated the relevant
references in the other docs pages.
(@rlancemartin this is the counterpart of the Cassandra equivalent class
you so helpfully reviewed back at the end of June)
Thank you!
- **Description:** This commit fixed the problem that Redis vector store
will change the value of a metadata from 0 to empty when saving the
document, which should be an un-intended behavior.
- **Issue:** N/A
- **Dependencies:** N/A
**Description:** Currently, if we pass in a ToolMessage back to the
chain, it crashes with error
`Got unsupported message type: `
This fixes it.
Tested locally
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** BaseStringMessagePromptTemplate.from_template was
passing the value of partial_variables into cls(...) via **kwargs,
rather than passing it to PromptTemplate.from_template. Which resulted
in those *partial_variables being* lost and becoming required
*input_variables*.
Co-authored-by: Josep Pon Farreny <josep.pon-farreny@siemens.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fix some circular deps:
- move PromptValue into top level module bc both PromptTemplates and
OutputParsers import
- move tracer context vars to `tracers.context` and import them in
functions in `callbacks.manager`
- add core import tests
- **Description:** We need to update the Dockerfile for templates to
also copy your README.md. This is because poetry requires that a readme
exists if it is specified in the pyproject.toml
Changes:
- remove langchain_core/schema since no clear distinction b/n schema and
non-schema modules
- make every module that doesn't end in -y plural
- where easy have 1-2 classes per file
- no more than one level of nesting in directories
- only import from top level core modules in langchain
<!-- 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:** fix a bug that prevented as_retriever() in Vectara to
use the desired input arguments
- **Issue:** as_retriever did not pass the arguments properly
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ofermend
I encountered this during summarization with VertexAI. I was receiving
an INVALID_ARGUMENT error, as it was trying to send a list of about
17000 single characters.
The [count_tokens
method](https://github.com/googleapis/python-aiplatform/blob/main/vertexai/language_models/_language_models.py#L658)
made available by Google takes in a list of prompts. It does not fail
for small texts, but it does for longer documents because the argument
list will be exceeding Googles allowed limit. Enforcing the list type
makes it work successfully.
This change will cast the input text to count to a list of that single
text so that the input format is always correct.
[Twitter](https://www.x.com/stijn_tratsaert)
- **Description:** ERNIE-Bot-Chat-4 Large Language Model adds the
ability of `Function Calling` by passing parameters through the
`functions` parameter in the request. To simplify function calling for
ERNIE-Bot-Chat-4, the `create_ernie_fn_chain()` function has been added.
The definition and usage of the `create_ernie_fn_chain()` function is
similar to that of the `create_openai_fn_chain()` function.
Examples as the follows:
```
import json
from langchain.chains.ernie_functions import (
create_ernie_fn_chain,
)
from langchain.chat_models import ErnieBotChat
from langchain.prompts import ChatPromptTemplate
def get_current_news(location: str) -> str:
"""Get the current news based on the location.'
Args:
location (str): The location to query.
Returs:
str: Current news based on the location.
"""
news_info = {
"location": location,
"news": [
"I have a Book.",
"It's a nice day, today."
]
}
return json.dumps(news_info)
def get_current_weather(location: str, unit: str="celsius") -> str:
"""Get the current weather in a given location
Args:
location (str): location of the weather.
unit (str): unit of the tempuature.
Returns:
str: weather in the given location.
"""
weather_info = {
"location": location,
"temperature": "27",
"unit": unit,
"forecast": ["sunny", "windy"],
}
return json.dumps(weather_info)
llm = ErnieBotChat(model_name="ERNIE-Bot-4")
prompt = ChatPromptTemplate.from_messages(
[
("human", "{query}"),
]
)
chain = create_ernie_fn_chain([get_current_weather, get_current_news], llm, prompt, verbose=True)
res = chain.run("北京今天的新闻是什么？")
print(res)
```
The running results of the above program are shown below：
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 北京今天的新闻是什么？
> Finished chain.
{'name': 'get_current_news', 'thoughts': '用户想要知道北京今天的新闻。我可以使用get_current_news工具来获取这些信息。', 'arguments': {'location': '北京'}}
```
- **Description:** during search with DeepLake some people are facing
backwards compatibility issues, this PR fixes it by making search
accessible for the older datasets
---------
Co-authored-by: adolkhan <adilkhan.sarsen@alumni.nu.edu.kz>
- **Description:**
- Fixes a `key_prefix` bug where passing it in on
`Redis.from_existing(...)` did not work properly. Updates doc strings
accordingly.
- Updates Redis filter classes logic with best practices on typing,
string formatting, and handling "empty" filters.
- Fixes a bug that would prevent multiple tag filters from being applied
together in some scenarios.
- Added a whole new filter unit testing module. Also updated code
formatting for a number of modules that were failing the `make`
commands.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @tchutch94
In the `FORMAT_INSTRUCTIONS` template, 4 curly braces (escaping) are
used to get single curly brace after formatting:
```
"{{{ ... }}}}" -> format_instructions.format() -> "{{ ... }}" -> template.format() -> "{ ... }".
```
Tool's `args_schema` string contains single braces `{ ... }`, and is
also transformed to `{{{{ ... }}}}` form. But this is not really correct
since there is only one `format()` call:
```
"{{{{ ... }}}}" -> template.format() -> "{{ ... }}".
```
As a result we get double curly braces in the prompt:
````
Respond to the human as helpfully and accurately as possible. You have access to the following tools:
foo: Test tool FOO, args: {{'tool_input': {{'type': 'string'}}}} # <--- !!!
...
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
````
This PR fixes curly braces escaping in the `args_schema` to have single
braces in the final prompt:
````
Respond to the human as helpfully and accurately as possible. You have access to the following tools:
foo: Test tool FOO, args: {'tool_input': {'type': 'string'}} # <--- !!!
...
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
````
---------
Co-authored-by: Sergey Kozlov <sergey.kozlov@ludditelabs.io>
Hi 👋 We are working with Llama2 on Bedrock, and would like to add it to
Langchain. We saw a [pull
request](https://github.com/langchain-ai/langchain/pull/13322) to add it
to the `llm.Bedrock` class, but since it concerns a chat model, we would
like to add it to `BedrockChat` as well.
- **Description:** Add support for Llama2 to `BedrockChat` in
`chat_models`
- **Issue:** the issue # it fixes (if applicable)
[#13316](https://github.com/langchain-ai/langchain/issues/13316)
- **Dependencies:** any dependencies required for this change `None`
- **Tag maintainer:** /
- **Twitter handle:** `@SimonBockaert @WouterDurnez`
---------
Co-authored-by: wouter.durnez <wouter.durnez@showpad.com>
Co-authored-by: Simon Bockaert <simon.bockaert@showpad.com>
- **Description:** This change adds an agent to the Azure Cognitive
Services toolkit for identifying healthcare entities
- **Dependencies:** azure-ai-textanalytics (Optional)
---------
Co-authored-by: James Beck <James.Beck@sa.gov.au>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Hi!
This short PR aims at:
* Fixing `OpenAIEmbeddings`' check on `chunk_size` when used with Azure
OpenAI (thus with openai < 1.0). Azure OpenAI embeddings support at most
16 chunks per batch, I believe we are supposed to take the min between
the passed value/default value and 16, not the max - which, I suppose,
was introduced by accident while refactoring the previous version of
this check from this other PR of mine: #10707
* Porting this fix to the newest class (`AzureOpenAIEmbeddings`) for
openai >= 1.0
This fixes#13539 (closed but the issue persists).
@baskaryan @hwchase17
- **Description:** There are several mistakes in the sample code in the
doc-string of `DashVector` class, and this pull request aims to correct
them.
The correction code has been tested against latest version (at the time
of creation of this pull request) of: `langchain==0.0.336`
`dashvector==1.0.6` .
- **Issue:** No issue is created for this.
- **Dependencies:** No dependency is required for this change,
<!-- - **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below), -->
- **Twitter handle:** `zeyanglin`
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- **Description:** AstraDB is going to deprecate the `$similarity`
projection property in favor of the ´includeSimilarity´ option flag. I
moved all the queries to the new format.
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
Added a `search_kwargs` field to BingSearchAPIWrapper in
`bing_search.py,` enabling users to include extra keyword arguments in
Bing search queries. This update, like specifying language preferences,
adds more customization to searches. The `search_kwargs` seamlessly
merge with standard parameters in `_bing_search_results` method.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Fix Astra integration tests that are failing. The
`delete` always return True as the deletion is successful if no errors
are thrown. I aligned the test to verify this behaviour
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The issue was accuring because of `openai` update in Completions. its
not accepting `api_key` and 'api_base' args.
The fix is we check for the openai version and if ats v1 then remove
these keys from args before passing them to `Compilation.create(...)`
when sending from `VLLMOpenAI`
Fixed: #13507
@eyu
@efriis
@hwchase17
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** In this pull request, we address an issue related to
assigning a schema to the SQLDatabase class when utilizing an Oracle
database. The current implementation encounters a bug where, upon
attempting to execute a query, the alter session parse is not
appropriately defined for Oracle, leading to an error,
- **Issue:** #7928,
- **Dependencies:** No dependencies,
- **Tag maintainer:** @baskaryan,
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This change allows for the `MWDumpLoader` to load all
namespaces including custom by default instead of only loading the
[default
namespaces](https://www.mediawiki.org/wiki/Help:Namespaces#Localisation).
- **Tag maintainer:** @hwchase17
**Description:**
This commit adds embedchain retriever along with tests and docs.
Embedchain is a RAG framework to create data pipelines.
**Twitter handle:**
- [Taranjeet's twitter](https://twitter.com/taranjeetio) and
[Embedchain's twitter](https://twitter.com/embedchain)
**Reviewer**
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
Enhance the functionality of YoutubeLoader to enable the translation of
available transcripts by refining the existing logic.
**Issue:**
Encountering a problem with YoutubeLoader (#13523) where the translation
feature is not functioning as expected.
Tag maintainers/contributors who might be interested:
@eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
BUG: langchain.agents.openai_assistant has a reference as
`from langchain_experimental.openai_assistant.base import
OpenAIAssistantRunnable`
should be
`from langchain.agents.openai_assistant.base import
OpenAIAssistantRunnable`
This prevents building of the API Reference docs
## Update 2023-09-08
This PR now supports further models in addition to Lllama-2 chat models.
See [this comment](#issuecomment-1668988543) for further details. The
title of this PR has been updated accordingly.
## Original PR description
This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to
serve Llama-2 chat models (like `LlamaCPP`,
`HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`,
converts a list of chat messages into the [required Llama-2 chat prompt
format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and
forwards the formatted prompt as `str` to the wrapped `LLM`. Usage
example:
```python
# uses a locally hosted Llama2 chat model
llm = HuggingFaceTextGenInference(
inference_server_url="http://127.0.0.1:8080/",
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
)
# Wrap llm to support Llama2 chat prompt format.
# Resulting model is a chat model
model = Llama2Chat(llm=llm)
messages = [
SystemMessage(content="You are a helpful assistant."),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{text}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt, memory=memory)
# use chat model in a conversation
# ...
```
Also part of this PR are tests and a demo notebook.
- Tag maintainer: @hwchase17
- Twitter handle: `@mrt1nz`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Added a method `fetch_valid_documents` to
`WebResearchRetriever` class that will test the connection for every url
in `new_urls` and remove those that raise a `ConnectionError`.
- **Issue:** [Previous
PR](https://github.com/langchain-ai/langchain/pull/13353),
- **Dependencies:** None,
- **Tag maintainer:** @efriis
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
This PR adds an option to allow unsigned requests to the Neptune
database when using the `NeptuneGraph` class.
```python
graph = NeptuneGraph(
host='<my-cluster>',
port=8182,
sign=False
)
```
Also, added is an option in the `NeptuneOpenCypherQAChain` to provide
additional domain instructions to the graph query generation prompt.
This will be injected in the prompt as-is, so you should include any
provider specific tags, for example `<instructions>` or `<INSTR>`.
```python
chain = NeptuneOpenCypherQAChain.from_llm(
llm=llm,
graph=graph,
extra_instructions="""
Follow these instructions to build the query:
1. Countries contain airports, not the other way around
2. Use the airport code for identifying airports
"""
)
```
**Description**
MongoDB drivers are used in various flavors and languages. Making sure
we exercise our due diligence in identifying the "origin" of the library
calls makes it best to understand how our Atlas servers get accessed.
**Description/Issue:**
When OpenAI calls a function with no args, the args are `""` rather than
`"{}"`. Then `json.loads("")` blows up. This PR handles it correctly.
**Dependencies:** None
This PR brings a few minor improvements to the docs, namely class/method
docstrings and the demo notebook.
- A note on how to control concurrency levels to tune performance in
bulk inserts, both in the class docstring and the demo notebook;
- Slightly increased concurrency defaults after careful experimentation
(still on the conservative side even for clients running on
less-than-typical network/hardware specs)
- renamed the DB token variable to the standardized
`ASTRA_DB_APPLICATION_TOKEN` name (used elsewhere, e.g. in the Astra DB
docs)
- added a note and a reference (add_text docstring, demo notebook) on
allowed metadata field names.
Thank you!
<!-- Thank you for contributing to LangChain!
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fix#13356
Add supports following properties for metadata to NotionDBLoader.
- `checkbox`
- `email`
- `number`
- `select`
There are no relevant tests for this code to be updated.
- **Description:** Adds `limit_to_domains` param to the APIChain based
tools (open_meteo, TMDB, podcast_docs, and news_api)
- **Issue:** I didn't open an issue, but after upgrading to 0.0.328
using these tools would throw an error.
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
**Note**: I included the trailing / simply because the docs here did
fc886cc303/docs/docs/use_cases/apis.ipynb (L246)
, but I checked the code and it is using `urlparse`. SoI followed the
docs since it comes down to stylee.
Hi,
this PR adds support for OpenAI API v1 for Azure OpenAI completion API.
@baskaryan @hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Bumps [pyarrow](https://github.com/apache/arrow) from 13.0.0 to 14.0.1.
<details>
<summary>Commits</summary>
<ul>
<li><a
href="ba53748361"><code>ba53748</code></a>
MINOR: [Release] Update versions for 14.0.1</li>
<li><a
href="529f3768fa"><code>529f376</code></a>
MINOR: [Release] Update .deb/.rpm changelogs for 14.0.1</li>
<li><a
href="b84bbcac64"><code>b84bbca</code></a>
MINOR: [Release] Update CHANGELOG.md for 14.0.1</li>
<li><a
href="f141709763"><code>f141709</code></a>
<a
href="https://redirect.github.com/apache/arrow/issues/38607">GH-38607</a>:
[Python] Disable PyExtensionType autoload (<a
href="https://redirect.github.com/apache/arrow/issues/38608">#38608</a>)</li>
<li><a
href="5a37e74198"><code>5a37e74</code></a>
<a
href="https://redirect.github.com/apache/arrow/issues/38431">GH-38431</a>:
[Python][CI] Update fs.type_name checks for s3fs tests (<a
href="https://redirect.github.com/apache/arrow/issues/38455">#38455</a>)</li>
<li><a
href="2dcee3f82c"><code>2dcee3f</code></a>
MINOR: [Release] Update versions for 14.0.0</li>
<li><a
href="297428cbf2"><code>297428c</code></a>
MINOR: [Release] Update .deb/.rpm changelogs for 14.0.0</li>
<li><a
href="3e9734f883"><code>3e9734f</code></a>
MINOR: [Release] Update CHANGELOG.md for 14.0.0</li>
<li><a
href="9f90995c8c"><code>9f90995</code></a>
<a
href="https://redirect.github.com/apache/arrow/issues/38332">GH-38332</a>:
[CI][Release] Resolve symlinks in RAT lint (<a
href="https://redirect.github.com/apache/arrow/issues/38337">#38337</a>)</li>
<li><a
href="bd61239a32"><code>bd61239</code></a>
<a
href="https://redirect.github.com/apache/arrow/issues/35531">GH-35531</a>:
[Python] C Data Interface PyCapsule Protocol (<a
href="https://redirect.github.com/apache/arrow/issues/37797">#37797</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/apache/arrow/compare/go/v13.0.0...go/v14.0.1">compare
view</a></li>
</ul>
</details>
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Hey @rlancemartin, @eyurtsev ,
I did some minimal changes to the `ElasticVectorSearch` client so that
it plays better with existing ES indices.
Main changes are as follows:
1. You can pass the dense vector field name into `_default_script_query`
2. You can pass a custom script query implementation and the respective
parameters to `similarity_search_with_score`
3. You can pass functions for building page content and metadata for the
resulting `Document`
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