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PR_kwDOIPDwls5Z6dXQ
| 10,383 |
fix(DynamoDBChatMessageHistory): correct delete_item method call
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[] | null | 3 | 2023-09-08T20:59:16 | 2023-09-11T23:16:21 | 2023-09-11T23:16:20 |
CONTRIBUTOR
| null |
**Description**:
Fixed a bug introduced in version 0.0.281 in `DynamoDBChatMessageHistory` where `self.table.delete_item(self.key)` produced a TypeError: `TypeError: delete_item() only accepts keyword arguments`. Updated the method call to `self.table.delete_item(Key=self.key)` to resolve this issue.
Please see also [the official AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/dynamodb/table/delete_item.html#) on this **delete_item** method - only `**kwargs` are accepted.
See also the PR, which introduced this bug: https://github.com/langchain-ai/langchain/pull/9896#discussion_r1317899073
Please merge this, I rely on this delete dynamodb item functionality (because of GDPR considerations).
**Dependencies**:
None
**Tag maintainer**:
@hwchase17 @joshualwhite
**Twitter handle**:
[@BenjaminLinnik](https://twitter.com/BenjaminLinnik)
- [x] Linting (`make lint`)
- [x] Formatting (`make format`)
- [x] Tests (`make test`)
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PR_kwDOIPDwls5Z6NhY
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Rewrite Retrieval docs page
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[] | null | 1 | 2023-09-08T20:20:18 | 2023-09-14T15:38:34 | 2023-09-14T15:38:34 |
COLLABORATOR
| null |
Using https://colab.research.google.com/drive/1pXsjcOFkrOKXUMwD5SIoed0Ct8DYtly1?authuser=1#scrollTo=wTC9cRDiZZ-t
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`chat_loaders` refactoring
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COLLABORATOR
| null |
Replaced unnecessary namespace renaming
`from langchain.chat_loaders import base as chat_loaders`
with
`from langchain.chat_loaders.base import BaseChatLoader, ChatSession`
and simplified correspondent types.
@eyurtsev
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Bagatur/add konko chat model
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Resolves issue DOC: Incorrect and confusing documentation of AIMessag…
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[] | null | 2 | 2023-09-08T16:52:31 | 2023-09-08T22:53:08 | 2023-09-08T22:53:08 |
CONTRIBUTOR
| null |
Resolves issue DOC: Incorrect and confusing documentation of AIMessagePromptTemplate and HumanMessagePromptTemplate #10378
- Description: Revised docstrings to correctly and clearly document each PromptTemplate
- Issue: #10378
- Dependencies: N/A
- Tag maintainer: @baskaryan
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I_kwDOIPDwls5wiMPW
| 10,378 |
DOC: Incorrect and confusing documentation of AIMessagePromptTemplate and HumanMessagePromptTemplate
|
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[] | null | 2 | 2023-09-08T16:43:51 | 2023-12-18T23:47:12 | 2023-12-18T23:47:12 |
CONTRIBUTOR
| null |
### Issue with current documentation:
[AIMessagePromptTemplate documentation](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.AIMessagePromptTemplate.html#langchain-prompts-chat-aimessageprompttemplate) incorrectly and confusingly describes the message as "... This is a message that is not sent to the user."
[HumanMessagePromptTemplate documentation](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.HumanMessagePromptTemplate.html#langchain-prompts-chat-humanmessageprompttemplate) incorrectly and confusingly describes the message as "... This is a message that is sent to the user."
Compare to the documentation for [AIMessage](https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.AIMessage.html#langchain-schema-messages-aimessage) and [HumanMessage](https://api.python.langchain.com/en/latest/schema/langchain.schema.messages.HumanMessage.html#langchain-schema-messages-humanmessage), which correctly and clearly describe each message as "A message from an AI" and "A message from a human." respectively.
### Idea or request for content:
AIMessagePromptTemplate should be described as "AI message prompt template. This is a message that is sent to the user from the AI."
HumanMessagePromptTemplate should be described as "Human message prompt template. This is a message that is sent from the user to the AI."
These are clear, concise and consistent with documentation of the message schema.
I will submit a PR with revised docstrings for each class. This should, then, be reflected in the API reference documentation upon next build.
|
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I_kwDOIPDwls5wh-of
| 10,377 |
Couldn't import BaseMessageConverter from
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[] | null | 5 | 2023-09-08T16:06:52 | 2023-09-09T00:46:51 | 2023-09-09T00:46:51 |
NONE
| null |
### System Info
Python 3.10.8 running on mac mini in VS Code
### Who can help?
@asai95
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
attempt to import basemessage converter following customer storage guide here - https://python.langchain.com/docs/integrations/memory/sql_chat_message_history
Enter from langchain.memory.chat_message_histories.sql import BaseMessageConverter
Actual behavior - ImportError: cannot import name 'BaseMessageConverter' from 'langchain.memory.chat_message_histories.sql' (/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/langchain/memory/chat_message_histories/sql.py)
### Expected behavior
Being able to import the BaseMessageConverter
|
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PR_kwDOIPDwls5Z5F3z
| 10,376 |
Support multiple errors
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[] | null | 1 | 2023-09-08T16:04:26 | 2023-09-08T16:07:16 | 2023-09-08T16:07:15 |
COLLABORATOR
| null |
in on_retry
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I_kwDOIPDwls5whzGM
| 10,375 |
DOC: How to properly initialize Function Agent as a Tool for other Agent
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[] | null | 2 | 2023-09-08T15:33:54 | 2023-12-18T23:47:18 | 2023-12-18T23:47:17 |
NONE
| null |
### Issue with current documentation:
I would like to get clarification of proper setting of Function Agents as Tools for other Agents.
In [Agent Tools Docs](https://python.langchain.com/docs/modules/agents/tools/) said that **tools can be even other agents but there are lack of examples**
I have implemented the following code with 2 approaches - Inheriting from the `BaseTool` interface to convert a Function Agent into a Tool object and I have also tried `Tool.from_function()` approach. My goal is to pre-define custom business formulas to be executed precisely as required and OpenAI Functions suit my needs well. For this purpose, I must use the Function Agent and I have followed this [Custom functions with OpenAI Functions Agent guide](https://python.langchain.com/docs/modules/agents/how_to/custom-functions-with-openai-functions-agent). However, there's a need to use other types of tools as well, requiring the initialization of different Agent types to invoke them. I'm seeking a solution where I can have a centralized Agent capable of invoking other agents. Currently, I can see only a solution using Classification Chain model for this purpose as provided below cases don't work
### Current Behavior
The current structure of the code exhibits the following issues:
- The `function_agent` executes FOO OpenAI Function and other tasks perfectly.
- The `main_agent` throws errors and occasionally manages to execute the FOO OpenAI function. Not sure why it's able to run successfully once in time.
### Desired Behavior
I would like the `functions_agent` to function seamlessly within the `main_agent` so that it performs the same well.
I believe there might be an issue with how the `message query` is being passed to OpenAI Python Functions in the two distinct approaches outlined below. Can you please offer guidance on the correct method to initialize the Function agent as a tool for another agent or suggest an alternative approach to achieve the desired behavior. Thank you
```python
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.agents import AgentType, Tool
from pydantic import BaseModel, Field
from langchain.tools import BaseTool
from typing import Type
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
def foo(target):
"""Useful when you want to get current mean and std
"""
return {"TARGET": target, "number": 123}
class FooFuncInput(BaseModel):
"""Inputs parsed from LLM for 'foo' function"""
target: str = Field(description="Allows for manual interaction using either hands or legs. "
"allowable values: {`hands`, `legs`} ")
class FooFuncTool(BaseTool):
name = "foo"
description = f"""
Calculate {name} business function.
"""
args_schema: Type[BaseModel] = FooFuncInput
return_direct = True
verbose = False
def _run(self, target: str):
response = foo(target)
return response
def _arun(self):
raise NotImplementedError("foo does not support async")
def get_function_tools():
tools = [
FooFuncTool(),
]
return tools
functions_agent = initialize_agent(
tools=get_function_tools(),
llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
max_iterations=1,
early_stopping_method="generate"
)
class AGENT_FUNCTION_TOOL(BaseTool):
name = "AGENT_FUNCTION_TOOL"
description = """
Useful for when you need to calculate business functions
Names of allowable business functions: `ATT`, `DDT`, `FOO`
"""
return_direct = True
verbose = True
def _run(self, query: str):
response = functions_agent.run(query)
return response
def _arun(self):
raise NotImplementedError("AGENT_FUNCTION_TOOL does not support async")
tools = [
AGENT_FUNCTION_TOOL(),
# Tool.from_function(
# func=functions_agent.run,
# name="Defined Business functions agent",
# description="""
# Useful for when you need to calculate business functions
# Names of allowable business functions: `ATT`, `DDT`, `FOO`
# """),
]
assistant_target = 'business management'
agent_kwargs = \
{'prefix': f'You are friendly {assistant_target} assistant.'
f'questions related to {assistant_target}. You have access to the following tools:'}
main_agent = initialize_agent(
tools=tools,
llm=ChatOpenAI(model="gpt-3.5-turbo", temperature=0),
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
max_iterations=1,
early_stopping_method="generate",
agent_kwargs=agent_kwargs
)
message = 'calculate business function `FOO` and do it with bare hands'
main_agent.run(message)
```
Also I have tried using `Tool.from_function()` method as explained on [reddit can an agent be a tool thread](https://www.reddit.com/r/LangChain/comments/13618qu/can_an_agent_be_a_tool/) but it looks like `message` query is not properly passed to OpenAI python Functions also.
```python
Tool.from_function(
func=functions_agent.run,
name="Defined Business functions agent",
description="""
Useful for when you need to calculate business functions
Names of allowable business functions: `ATT`, `DDT`, `FOO`
"""),
```
---------
Executed script using `main_agent` with `AGENT_FUNCTION_TOOL`, class implemented from `BaseTool` langchain interface
---------
Executed script using `main_agent` with `Tool.from_function()`
---------
Executing script using `functions_agent` and desired behavior:
### Idea or request for content:
How to properly use Function Agents as Tools which consequently can be used by other Agents.
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PR_kwDOIPDwls5Z46HS
| 10,374 |
Add `Runnable.astream_log()`
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COLLABORATOR
| null |
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5Z43lq
| 10,373 |
bump 285
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PR_kwDOIPDwls5Z4rqP
| 10,371 |
add missing verb
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CONTRIBUTOR
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PR_kwDOIPDwls5Z4nBQ
| 10,370 |
Adding headers for accessing pdf file url
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[] | null | 1 | 2023-09-08T14:35:11 | 2023-09-26T19:20:34 | 2023-09-13T23:09:38 |
CONTRIBUTOR
| null |
- Description: Set up 'file_headers' params for accessing pdf file url
- Tag maintainer: @hwchase17
✅ make format, make lint, make test
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PR_kwDOIPDwls5Z4d_L
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Adding file_headers parameters for accessing pdf file path(url)
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CONTRIBUTOR
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- Description: Set up 'file_headers' params for accessing pdf file url
- Tag maintainer: @hwchase17
✅ make format, make lint, make test
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PR_kwDOIPDwls5Z4ds9
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VertexAI now allows to tune codey models
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CONTRIBUTOR
| null |
Description: VertexAI now supports to tune codey models, I adapted the Vertex AI LLM wrapper accordingly https://cloud.google.com/vertex-ai/docs/generative-ai/models/tune-code-models
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I_kwDOIPDwls5whAJt
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Why did you broke Redis completely in a minor version change
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[] | null | 4 | 2023-09-08T13:30:27 | 2023-09-10T06:59:18 | 2023-09-10T06:59:17 |
NONE
| null |
### System Info
Hi there :wave: ,
I am wondering why now redis is completely broken (most of the paramters names have been changed) and you did this is a minor version change - doesn't make any sense - and I'd like to know how to pass `k` to the retriever now and how to get the `k`; previous I could just do `retriever.k`
Thanks,
Fra
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just try to use redis now
### Expected behavior
I wouldn't except BREAKING changes from a minor version change
|
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| 1,887,656,311 |
PR_kwDOIPDwls5Z4Flu
| 10,365 |
Add Language Detection
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[] | null | 1 | 2023-09-08T13:06:57 | 2023-09-08T13:07:45 | 2023-09-08T13:07:45 |
CONTRIBUTOR
| null |
**!! Should wait for https://github.com/langchain-ai/langchain/pull/10181 to be merged, so that the example with TTS component is added !!**
### Description
Adds language detection component based on [langdetect](https://github.com/Mimino666/langdetect/tree/master/langdetect) library. It implements naive Bayesian filter and is able to recognize >50 languages. This module can be especially useful together with components that require selection of the language (e.g. text-2-speech or data-anonymizer)
### Twitter handle
@deepsense_ai, @matt_wosinski
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PR_kwDOIPDwls5Z3kCA
| 10,362 |
fix ChatMessageChunk concat error
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NONE
| null |
- Description: fix ChatMessageChunk '+' error, add __add__ method for ChatMessageChunk
- Issue: ChatMessageChunk has required field "role", however it's use BaseMessageChunk.__add__method which do not handle role field
```python
# BaseMessageChunk
def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
if isinstance(other, BaseMessageChunk):
# If both are (subclasses of) BaseMessageChunk,
# concat into a single BaseMessageChunk
# this line will raise error for role not set
return self.__class__(
content=self.content + other.content,
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
else:
raise TypeError(
'unsupported operand type(s) for +: "'
f"{self.__class__.__name__}"
f'" and "{other.__class__.__name__}"'
)
````
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I_kwDOIPDwls5wfw5G
| 10,361 |
DOC: What is the Difference between OpenAIAgent and agent=AgentType.OPENAI_FUNCTIONS
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[] | null | 2 | 2023-09-08T10:16:55 | 2023-12-18T23:47:22 | 2023-12-18T23:47:22 |
NONE
| null |
### Issue with current documentation:
Overall there are so many terms on top of each other and I do understand we are trading off growing fast to having clean, set terms but lets discuss about currently what is the common way to define an AGENT that uses openAI for reasoning engine, and answers questions using tools that we define.
What is the AgentExecutor if we can also run agent.run()
What is AgentExecutorIterator
We can define an agent like this:
```
agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS,
agent_kwargs=agent_kwargs, verbose=True, return_intermediate_steps=True,)
```
But also we can define an agent like this: [(From this documentation: Custom LLM Agent)](https://python.langchain.com/docs/modules/agents/how_to/custom_llm_agent)
```
llm = OpenAI(temperature=0)
llm_chain = LLMChain(llm=llm, prompt=prompt)
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
```
There are also ChatAgent, OpenAIAgent and more[ Classes](https://js.langchain.com/docs/api/agents/classes/)
I would like to have a custom agent that I use with openai api and I will give custom tools like weather functions or finance functions etc. What is the cleanest way to do this currently ? (8 September 2023)
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5Z3FGz
| 10,360 |
Modified chain datatype to Chain to support for ConversationalRetrievalChain, etc
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NONE
| null |
**Description:**
In the class ConstitutionalChain currently supports LLMChain when using from_llm.
When we try to use it with other type of chains such as ConversationalRetrievalChain whose superclass isn't LLMChain it gives a type error.
As both the class's superclass was Chain, when replaced with it, it worked as supposed to.
**Issue:**
Found it to be relevant to
[https://github.com/langchain-ai/langchain/issues/5881](https://github.com/langchain-ai/langchain/issues/5881)
[https://github.com/langchain-ai/langchain/issues/1904](https://github.com/langchain-ai/langchain/issues/1904)
**Dependencies:**
None
**Tag maintainer:**
@wnmurphy
@hwchase17
**Twitter handle:**
Linkdin: www.linkedin.com/in/prar
**How I used it**
```
qa_chain = ConversationalRetrievalChain.from_llm(llm, db.as_retriever(),
memory=memory, condense_question_prompt=qa_prompt)
constitutional_chain = ConstitutionalChain.from_llm(
llm=llm,
chain=qa_chain,
constitutional_principles=list(principles),
)
```
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| 10,359 |
Own Api LLM
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[] | null | 2 | 2023-09-08T10:00:51 | 2023-12-18T23:47:27 | 2023-12-18T23:47:27 |
NONE
| null |
### Feature request
The Project [Basaran](https://github.com/hyperonym/basaran) lets you host an api that is similar to the OpenAI api, but with a self hosted llm. Support for such custom API LLMs would be great
### Motivation
Further democritizig LLMs
### Your contribution
I could test it
|
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| 1,887,295,643 |
I_kwDOIPDwls5wfdib
| 10,357 |
Issue: The mapreduce chain doesn't generate full response.
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[] | null | 2 | 2023-09-08T09:26:17 | 2023-12-18T23:47:33 | 2023-12-18T23:47:32 |
NONE
| null |
### mapreduce chain doesn't provide full response.
I'm currently using mapreduce chain to do news summarization. But the output doesn't provide full response. Is there any way to fix this issue? I'm following the guide of langchain api mapreduce chain example.
|
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I_kwDOIPDwls5wfKGl
| 10,355 |
pydantic 2.0 support
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[] | null | 1 | 2023-09-08T08:33:35 | 2023-09-08T08:49:07 | 2023-09-08T08:49:07 |
NONE
| null |
### Feature request
Please update dependencies to support installing `pydantic>=2.0. Now there's a conflict, for example pip resolver gives following error:
```
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
langchainplus-sdk 0.0.20 requires pydantic<2,>=1, but you have pydantic 2.3.0 which is incompatible.
langchain 0.0.228 requires pydantic<2,>=1, but you have pydantic 2.3.0 which is incompatible.
```
### Motivation
pydantic 1.* is outdated. Starting new project and use old syntax which will be deprecated soon is unpleasant
### Your contribution
-
|
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PR_kwDOIPDwls5Z2kdj
| 10,354 |
ENH: Add `llm_kwargs` for Xinference LLMs
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CONTRIBUTOR
| null |
- This pr adds `llm_kwargs` to the initialization of Xinference LLMs (integrated in #8171 ).
- With this enhancement, users can not only provide `generate_configs` when calling the llms for generation but also during the initialization process. This allows users to include custom configurations when utilizing LangChain features like LLMChain.
- It also fixes some format issues for the docstrings.
|
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| 1,887,127,834 |
I_kwDOIPDwls5we0ka
| 10,353 |
ConversationalRetrievalChain is not adding chat_history to new message
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[] | null | 1 | 2023-09-08T07:36:09 | 2023-12-18T23:47:37 | 2023-12-18T23:47:37 |
NONE
| null |
### Issue you'd like to raise.
I am using ConversationalRetrievalChain for document Q/A bot and updating chat_history = [] with every message however I noticed this chat_history string is never added to final inference string. In _call method of BaseConversationalRetrievalChain class, even when "if chat_history_str:" condition is true, new_question is never updated with the chat_history_str.
### Suggestion:
_No response_
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I_kwDOIPDwls5weqVh
| 10,352 |
quip doc loader
|
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[] | null | 5 | 2023-09-08T07:07:49 | 2023-12-18T23:47:43 | 2023-12-18T23:47:42 |
CONTRIBUTOR
| null |
### Feature request
like https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/document_loaders/confluence.py, we need a loader for quip document, please follow https://github.com/quip/quip-api/tree/master/python
### Motivation
I have a lot of documents in quip, want to load these quip documents.
### Your contribution
I like to contribute this idea if no one to take.
|
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| 10,351 |
Add json evaluators
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[] | null | 1 | 2023-09-08T04:39:35 | 2023-12-08T00:35:36 | 2023-12-08T00:35:36 |
COLLABORATOR
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PR_kwDOIPDwls5Z1ceh
| 10,350 |
Feature/adding csharp support to textsplitter
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[] | null | 3 | 2023-09-08T03:40:29 | 2023-09-08T23:02:47 | 2023-09-08T23:01:06 |
CONTRIBUTOR
| null |
**Description:** Adding C# language support for `RecursiveCharacterTextSplitter`
**Issue:** N/A
**Dependencies:** N/A
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PR_kwDOIPDwls5Z1X16
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Feature/add deepeval
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[] | null | 4 | 2023-09-08T03:04:43 | 2023-09-09T20:28:17 | 2023-09-09T20:28:17 |
CONTRIBUTOR
| null |
Description: Adding `DeepEval` - which provides an opinionated framework for testing and evaluating LLMs
Issue: Missing Deepeval
Dependencies: Optional DeepEval dependency
Tag maintainer: @baskaryan (not 100% sure)
Twitter handle: https://twitter.com/ColabDog
✅ `make format`
✅`make lint` and
✅`make test`
If you're adding a new integration, please include:
✅ a test for the integration, preferably unit tests that do not rely on network access,
✅ an example notebook showing its use. These live is docs/extras directory.
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PR_kwDOIPDwls5Z1Jo4
| 10,348 |
Add progress bar + runner fixes
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[] | null | 1 | 2023-09-08T01:17:42 | 2023-09-08T14:45:29 | 2023-09-08T14:45:28 |
COLLABORATOR
| null |
- Add progress bar to eval runs
- Use thread pool for concurrency
- Update some error messages
- Friendlier project name
- Print out quantiles of the final stats
Closes LS-902
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PR_kwDOIPDwls5Z0i84
| 10,347 |
Stop generating undesired tokens from GPT4All
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[] | null | 3 | 2023-09-07T22:08:25 | 2023-11-29T02:50:52 | 2023-11-29T02:50:51 |
NONE
| null |
@baskaryan In this PR, I made a small modification in gpt4all.py in the project folder libs/langchain, more precisely in the method `_call`. I added a unit test for the additional function `remove_stop_words` in libs/lanchain/llms/utils.py
It fixes the issue https://github.com/langchain-ai/langchain/issues/10345
There is no additional packages.
By executing `make format`, `make lint` and `make test` locally, it works.
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PR_kwDOIPDwls5Z0e6d
| 10,346 |
Bagatur/multilingual anon
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[] | null | 1 | 2023-09-07T21:45:19 | 2023-09-07T22:15:09 | 2023-09-07T22:15:09 |
COLLABORATOR
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I_kwDOIPDwls5wcw5A
| 10,345 |
Issue: GPT4All LLM continue to generate undesired tokens even if stop attribute has been specified
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[] | null | 5 | 2023-09-07T21:33:08 | 2023-11-15T21:51:45 | null |
NONE
| null |
### Issue you'd like to raise.
I was led to use GPT4All for building a personal chatbot. It generated undesired output. More precisely, it generates the all conversation, after writing the first prompt. For solving this problem, I instantiated GPT4All with a not empty list value for attribute `stop` :
`
llm = GPT4All(
model=local_path,
callbacks=callbacks,
verbose=True,
streaming=True,
stop=["System:"],
)
`
However, it continues to generate these undesired output.
### Suggestion:
Finally I may have found a small fix to solve this problem for this type of LLM. I intent to create a PR very soon, if you agree with this.
|
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| 1,886,576,310 |
PR_kwDOIPDwls5Z0Z_y
| 10,344 |
Fix colab link(missing graph in url) and comment to match the code fo…
|
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[] | null | 2 | 2023-09-07T21:19:16 | 2023-09-07T22:59:09 | 2023-09-07T21:57:28 |
CONTRIBUTOR
| null |
- Description: Fixing Colab broken link and comment correction to align with the code that uses Warren Buffet for wiki query
- Issue: None open
- Dependencies: none
- Tag maintainer: n/a
- Twitter handle: Not a PR change but: kcocco
|
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PR_kwDOIPDwls5Z0J45
| 10,342 |
split lcel cookbook
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[] | null | 1 | 2023-09-07T20:08:42 | 2023-09-07T21:56:39 | 2023-09-07T21:56:38 |
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PR_kwDOIPDwls5Z0Hwv
| 10,341 |
Reduce default num retries
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[] | null | 2 | 2023-09-07T20:00:06 | 2023-11-22T22:42:03 | 2023-11-22T22:42:03 |
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PR_kwDOIPDwls5ZzwyF
| 10,340 |
eugene/automaton variant 3
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[] | null | 1 | 2023-09-07T18:26:37 | 2023-09-07T21:07:02 | null |
COLLABORATOR
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Rewrite with more runnable stuff
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I_kwDOIPDwls5wb2y0
| 10,339 |
Streaming Turns on Verbose mode for AgentType.CONVERSATIONAL_REACT_DESCRIPTION
|
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[] | null | 3 | 2023-09-07T17:56:38 | 2023-12-14T16:04:47 | 2023-12-14T16:04:47 |
NONE
| null |
### System Info
With Streaming turned on, verbose mode is turned on.
Am I doing something wrong?
Python 3.10.9
Name: langchain
Version: 0.0.284
llm2 = ChatOpenAI(
temperature=0,
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()],
model="gpt-4-0613",
)
agent = initialize_agent(
tools,
llm2,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=memory,
verbose=False,
)
Thought: Do I need to use a tool? Yes
Action: Search for Property Owners
Action Input: 5954A Bartonsville RoadDo I need to use a tool? No
AI: The house at 5954A Bartonsville Road is owned by Rainbown Johonson.The house at 5954A Bartonsville Road is owned by Rainbown Johonson.
Thought: Do I need to use a tool? Yes
Action: Search for Property Owners
Action Input: 1163 Annamarie WayDo I need to use a tool? Yes
Action: Search for People
Action Input: Randolph HillDo I need to use a tool? No
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
llm2 = ChatOpenAI(
temperature=0,
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()],
model="gpt-4-0613",
)
agent = initialize_agent(
tools,
llm2,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
memory=memory,
#verbose=False,
)
print(
agent.run(
input="Who owns the house at the following address 5954A Bartonsville Road?"
)
)
### Expected behavior
I do not expect verbose output.
|
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| 1,886,322,923 |
I_kwDOIPDwls5wbwDr
| 10,338 |
DOC: LLMSingleActionAgent vs. Agent
|
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[] | null | 2 | 2023-09-07T17:33:37 | 2023-12-14T16:04:52 | 2023-12-14T16:04:52 |
NONE
| null |
### Issue with current documentation:
Hello.
I think there is little to no explanation as to which are the differences between `LLMSingleActionAgent` and `Agent` classes, and which is suitable for what scenario. Both classes inherit from `BaseSingleActionAgent`.
Thanks in advance.
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5wbrBi
| 10,337 |
TextGen parameter to silence the print in terminal
|
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[] | null | 2 | 2023-09-07T17:16:09 | 2023-12-07T16:14:14 | 2023-12-07T16:14:07 |
NONE
| null |
### Feature request
I'd like a way to silence TextGen's output in my terminal. For example, a parameter would be perfect.
Looking at TextGen's source code, I see this line `print(prompt + result)`.
If I remove this line, then I get the desired effect where nothing is printed to my terminal.
### Motivation
I'm working on an app with lots of logging and thousands of requests are sent via TextGen.
My output is very noisy with every prompt and result printed, and it's troublesome to scroll through it all. I only care about the result I receive.
### Your contribution
My suggestion is to add a parameter flag to TextGen such that I can control the print (on or off), instead of it being hardcoded in.
There are 2 areas where I'd like to see it changed:
1. When streaming is enabled `print(prompt + combined_text_output)`
2. When streaming is disabled `print(prompt + result)`
|
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| 1,886,296,572 |
I_kwDOIPDwls5wbpn8
| 10,336 |
Issue with Few Short prompt when passed to agent
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[] | null | 36 | 2023-09-07T17:11:20 | 2023-10-03T17:02:39 | null |
NONE
| null |
### System Info
I have used the SemanticSimilarityExampleSelector and created a prompt. When I try to pass this to agent it fails saying: **ValueError: Saving an example selector is not currently supported**
ValueError: Saving an example selector is not currently supported
to create prompt I have used https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/similarity
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use the above code provided along with prompt selector provided in the original docuementation
### Expected behavior
To produce the output for the question asked
|
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PR_kwDOIPDwls5ZzaBc
| 10,335 |
supabase embedding usage fix
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[] | null | 1 | 2023-09-07T17:00:49 | 2023-09-07T17:06:45 | 2023-09-07T17:04:49 |
COLLABORATOR
| null |
Should be calling Embeddings.embed_query instead of embed_documents when searching
|
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| 1,886,256,568 |
PR_kwDOIPDwls5ZzUog
| 10,334 |
nuclia db nit
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[] | null | 1 | 2023-09-07T16:48:52 | 2023-09-07T16:49:01 | 2023-09-07T16:48:57 |
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| null | null |
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| 1,886,206,628 |
I_kwDOIPDwls5wbTqk
| 10,333 |
PineconeHynridSearchRetriever missing several args and not returning ids of vectors when adding texts
|
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[] | null | 2 | 2023-09-07T16:19:08 | 2023-12-14T16:04:58 | 2023-12-14T16:04:57 |
CONTRIBUTOR
| null |
### System Info
There are a few differences between the PineconeHybridSearchRetriever and base Pinecone retriever making it difficult to switch to the former.
@hw
You can pass search_kwargs to pinecone index.as_retriver(search_kwargs:{filter: 'value'}) because it inherits from base VectorStore which has the as_retriever method with search_kwarg args.
But it is not clear from the documentation of PineconeHybridSearchRetriever, which inherits from BaseRetriever, how to pass those same arguments.
Also, the add_text method in Pinecone class returns the IDs, whereas in HybridSearch it does not.
`[[docs]](https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html#langchain.vectorstores.pinecone.Pinecone.add_texts) def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
batch_size: int = 32,
embedding_chunk_size: int = 1000,
**kwargs: Any,
....
....
for i in range(0, len(texts), embedding_chunk_size):
chunk_texts = texts[i : i + embedding_chunk_size]
chunk_ids = ids[i : i + embedding_chunk_size]
chunk_metadatas = metadatas[i : i + embedding_chunk_size]
embeddings = self._embed_documents(chunk_texts)
async_res = [
self._index.upsert(
vectors=batch,
namespace=namespace,
async_req=True,
**kwargs,
)
for batch in batch_iterate(
batch_size, zip(chunk_ids, embeddings, chunk_metadatas)
)
]
[res.get() for res in async_res]
return ids`
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create pinecone index and use as_retriever with passing search_kwargs('filter':{key:value})
Vs
Create PineconeHybridSearchRetriever, there is no way to pass filters
### Expected behavior
Pass search_kwargs in the class instantiation of PineconeHybridSearchRetriever so it can be used downstream in conversationalretrievalchain
|
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| 1,886,166,026 |
PR_kwDOIPDwls5ZzA0i
| 10,332 |
Adding Self-querying for Vectara
|
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[] | null | 2 | 2023-09-07T15:53:10 | 2023-09-07T17:24:50 | 2023-09-07T17:24:50 |
CONTRIBUTOR
| null |
- Description: Adding support for self-querying to Vectara integration
- Issue: per customer request
- Tag maintainer: @rlancemartin @baskaryan
- Twitter handle: @ofermend
Also updated some documentation, added self-query testing, and a demo notebook with self-query example.
|
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nuclia nb nit
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[] | null | 1 | 2023-09-07T15:49:23 | 2023-09-07T15:49:34 | 2023-09-07T15:49:33 |
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PR_kwDOIPDwls5Zy7T9
| 10,330 |
bump 284
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[] | null | 1 | 2023-09-07T15:39:48 | 2023-09-07T15:45:43 | 2023-09-07T15:45:42 |
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| 10,329 |
Response is sometimes in different language
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[] | null | 1 | 2023-09-07T14:13:38 | 2023-12-14T16:05:03 | 2023-12-14T16:05:02 |
NONE
| null |
### System Info
Got no system info to share.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I just want to say that what you developed is awesome, but I have small bug to report, sometimes when I am chatting the response will be in french/spanish although training documentation and question asked is in English.
How to reproduce:
Say hello to bot, he will respond information is not in the given context.
Ask him some question about docs it learned from in English, answer will be in another language French/Spanish/etc.
Here is the code.
```
for message in st.session_state.messages:
with st.chat_message(message["role"]:
st.markdown(message["content"])
if prompt := st.chat_input("State your question"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
llm = ChatOpenAI(temperature=0.5, max_tokens=1000,
model_name="gpt-3.5-turbo")
conversation = ConversationalRetrievalChain.from_llm(
llm, vector_store.as_retriever())
final= conversation ({"question": prompt, "chat_history": [
(message["role"], message["content"]) for message in st.session_state.messages]})
with st.chat_message("assistant):
message_placeholder = st.empty()
final_response= final["answer"]
message_placeholder.markdown(final_response)
message_placeholder.markdown(final_response)
st.session_state.messages.append(
{"role": "assistant", "content": final_response})
```
### Expected behavior
Answer should be in same language question was asked.
|
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PR_kwDOIPDwls5ZyB07
| 10,327 |
Multilingual anonymization
|
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[] | null | 1 | 2023-09-07T13:14:26 | 2023-09-07T21:42:24 | 2023-09-07T21:42:24 |
CONTRIBUTOR
| null |
### Description
Add multiple language support to Anonymizer
PII detection in Microsoft Presidio relies on several components - in addition to the usual pattern matching (e.g. using regex), the analyser uses a model for Named Entity Recognition (NER) to extract entities such as:
- `PERSON`
- `LOCATION`
- `DATE_TIME`
- `NRP`
- `ORGANIZATION`
[[Source]](https://github.com/microsoft/presidio/blob/main/presidio-analyzer/presidio_analyzer/predefined_recognizers/spacy_recognizer.py)
To handle NER in specific languages, we utilize unique models from the `spaCy` library, recognized for its extensive selection covering multiple languages and sizes. However, it's not restrictive, allowing for integration of alternative frameworks such as [Stanza](https://microsoft.github.io/presidio/analyzer/nlp_engines/spacy_stanza/) or [transformers](https://microsoft.github.io/presidio/analyzer/nlp_engines/transformers/) when necessary.
### Future works
- **automatic language detection** - instead of passing the language as a parameter in `anonymizer.anonymize`, we could detect the language/s beforehand and then use the corresponding NER model. We have discussed this internally and @mateusz-wosinski-ds will look into a standalone language detection tool/chain for LangChain :smile:
### Twitter handle
@deepsense_ai / @MaksOpp
### Tag maintainer
@baskaryan @hwchase17 @hinthornw
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PR_kwDOIPDwls5Zx4AZ
| 10,326 |
Enhancement: add parameter boto3_session for AWS DynamoDB cross account use cases
|
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[] | null | 2 | 2023-09-07T12:50:26 | 2023-09-08T00:57:10 | 2023-09-07T21:58:29 |
CONTRIBUTOR
| null |
- Description: to allow boto3 assume role for AWS cross account use cases to read and update the chat history,
- Issue: use case I faced in my company,
- Dependencies: no
- Tag maintainer: @baskaryan ,
- Twitter handle: @tmin97
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Running SQLDatabaseChain return records that does not match the query
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[] | null | 6 | 2023-09-07T11:33:14 | 2023-12-30T16:02:24 | null |
NONE
| null |
### System Info
Running SQLDatabaseChain with LangChain version 0.0.271 and SQLite return records that does not match the query.
Using SQLDatabaseChain with verbose set to true I am getting these in the console:
```sql
SQLQuery:SELECT id, sale_type, sold_date, property_type, address, city, state_or_province, zip_or_postal_code, price, beds, baths, location, square_feet, lot_size, year_built, day_on_market, usd_per_square_feet, hoa_per_month, url, latitude, longitude FROM properties WHERE city = 'New York' AND price <= 900000 AND property_type = 'House' AND beds >= 2 ORDER BY price DESC LIMIT 5;
```
```
SQLResult:
Answer:[{"id":3,"sale_type":"MLS Listing","sold_date":"None","property_type":"Condo/Co-op","address":"225 Fifth Ave Ph -H","city":"New York","state_or_province":"NY","zip_or_postal_code":10010,"price":3495000,"beds":3,"baths":3.0,"location":"NoMad","square_feet":"1987","lot_size":31106,"year_built":1907,"day_on_market":1,"usd_per_square_feet":"1987","hoa_per_month":"HOAMONTH","url":"https://www.redfin.com/NY/New-York/225-5th-Ave-10010/unit-H/home/174298359","latitude":40.7437447,"longitude":-73.9875513},{"id":2,"sale_type":"MLS Listing","sold_date":"None","property_type":"Condo/Co-op","address":"416 W 52nd St #520","city":"New York","state_or_province":"NY","zip_or_
```
The SQLResult does not match the SQLQuery. The SQLResult contains a property with a price of 3495000 which is higher than the 900000 filter in the SQLQuery. Also property_type "Condo/Co-op" does not match "house" from the query. Note that for any reason the results string is truncated at "zip_or_" around 770 characters.
When I execute a copy/paste of the SQL query from SQLQuery in console directly in the database it returns me 0 result. which what it should return. For info the "price" column is an INTEGER.
This is the code :
```python
def ask_question():
data = request.get_json()
question = data['question']
question = QUERY.format(question=question)
text = db_chain.run(question)
```
Did I miss something ?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run an SQLDatabaseChain with verbose to True
Compare the SQLquery and the SQLresult from the console. The result is not what is expected with the query.
### Expected behavior
The SQL result from the console should match the SQL query.
|
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PR_kwDOIPDwls5ZxP5x
| 10,324 |
Multilingual anonymization
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[] | null | 1 | 2023-09-07T10:49:53 | 2023-09-07T11:00:00 | 2023-09-07T10:50:52 |
CONTRIBUTOR
| null |
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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I_kwDOIPDwls5wYbbk
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sqlite3.OperationalError: too many SQL variables
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[] | null | 6 | 2023-09-07T09:11:01 | 2023-12-14T16:05:07 | 2023-12-14T16:05:06 |
NONE
| null |
### Issue you'd like to raise.
I have pdf which has more than 2000 pages. file read successfully, but vector db was not created and getting this error. please suggest any solution.
**complete error:**
embeddings
results = cur.execute(sql, params).fetchall()
sqlite3.OperationalError: too many SQL variables
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5Zwepi
| 10,320 |
Fixed link to colab notebook
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[] | null | 2 | 2023-09-07T08:54:44 | 2023-09-07T15:42:05 | 2023-09-07T15:42:05 |
CONTRIBUTOR
| null |
@hinthornw small fix to anonymizer documentation
|
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PR_kwDOIPDwls5Zwbja
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added ContentFormatter escape special characters for message content
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[] | null | 4 | 2023-09-07T08:48:37 | 2023-10-06T01:02:30 | 2023-10-06T01:02:30 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: for azure ml chat endpoint added support for ContentFormatter escape special characters for message content,
- Issue: escape special character for message content,
- Dependencies: not required any
- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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I_kwDOIPDwls5wYIKH
| 10,318 |
Langchain doesnt work with Databricks Model serving asking generate (str type expected (type=type_error.str)
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[] | null | 7 | 2023-09-07T08:28:28 | 2023-12-05T12:55:20 | null |
NONE
| null |
### System Info
I have created a LLama 7b model and in Databricks i have used model serving options. When i test model it works in databricks, but when i use the same model to connect with Langchain i get this error.
this works in Databricks model endpoint:
Json input:
{
"dataframe_split": {
"columns": [
"prompt",
"temperature",
"max_tokens"
],
"data": [
[
"what is ML?",
0.5,
100
]
]
}
}
code:-
from langchain.llms import Databricks
llm = Databricks(endpoint_name="databricks-llama-servingmodel", model_kwargs={"temperature": 0.1, "max_tokens": 100})
llm ("who are you")
Error:-
ValidationError: 1 validation error for Generation
text
str type expected (type=type_error.str)
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Any model you take LLM and use the below notebook
https://github.com/databricks/databricks-ml-examples/blob/master/llm-models/llamav2/llamav2-7b/02_mlflow_logging_inference.py (for model serving)
Try using model serving in Langchain as show in the code and official langchain documents
from langchain.llms import Databricks
llm = Databricks(endpoint_name="databricks-llama-servingmodel", model_kwargs={"temperature": 0.1, "max_tokens": 100})
llm ("who are you")
### Expected behavior
llm("who are you")
expected output:- I am language model
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https://api.github.com/repos/langchain-ai/langchain/issues/10317
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| 1,885,368,696 |
I_kwDOIPDwls5wYHF4
| 10,317 |
Issue: How to customize the question_generator_chain in ConversationalRetrievalChain
|
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[] | null | 2 | 2023-09-07T08:25:40 | 2023-12-08T04:55:32 | 2023-12-08T04:55:26 |
NONE
| null |
### Issue you'd like to raise.
I am writing a ConversationalRetrievalChain with a question_generator_chain. When writing a prompt for condensing the chat_history, I found it doesn't work well with the whole {chat_history}. I guess if I only give all questions asked by "human:", it will perform better. But I don't know how to do that.
The original prompt is:
Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, if the follow up question is already a standalone question, just return the follow up question.
Chat History:
{chat_history}
Follow Up Question: {question}
Standalone question:
What I want may looks like:
.........
Question asked:
{chat_history.human}
Follow Up Question: {question}
Standalone question:
Thanks for attention
### Suggestion:
_No response_
|
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| 1,885,260,192 |
I_kwDOIPDwls5wXsmg
| 10,316 |
Final answer streaming problem
|
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[] | null | 16 | 2023-09-07T07:14:40 | 2024-01-05T17:54:39 | null |
NONE
| null |
### System Info
hi,
I am unable to stream the final answer from llm chain to chianlit UI.
langchain==0.0.218
Python 3.9.16
here are the details:
https://github.com/Chainlit/chainlit/issues/313
is this implemented? - https://github.com/langchain-ai/langchain/pull/1222/
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
the code given above can reproduce the behaviour
### Expected behavior
expected behaviour : the final answer from llm chain to be streamed properly to chianlit UI
|
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I_kwDOIPDwls5wXmw6
| 10,315 |
Issue: CustomCallBackHandlers for catch all intermediate steps
|
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[] | null | 2 | 2023-09-07T06:57:57 | 2023-12-14T16:05:12 | 2023-12-14T16:05:12 |
NONE
| null |
### Issue you'd like to raise.
I want to create a callback handler to monitor all tokens in all intermediate steps and output catch and return when the response has "AI:". How can I achieve that using the following custom callback handler?
```
import sys
from typing import Any, Dict, List, Optional
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["AI", ":"]
class CustomCallBackHandler(StreamingStdOutCallbackHandler):
"""Callback handler for streaming in agents.
Only works with agents using LLMs that support streaming.
Only the final output of the agent will be streamed.
"""
def append_to_last_tokens(self, token: str) -> None:
self.last_tokens.append(token)
self.last_tokens_stripped.append(token.strip())
if len(self.last_tokens) > len(self.answer_prefix_tokens):
self.last_tokens.pop(0)
self.last_tokens_stripped.pop(0)
def check_if_answer_reached(self) -> bool:
if self.strip_tokens:
return self.last_tokens_stripped == self.answer_prefix_tokens_stripped
else:
return self.last_tokens == self.answer_prefix_tokens
def __init__(
self,
*,
answer_prefix_tokens: Optional[List[str]] = None,
strip_tokens: bool = True,
stream_prefix: bool = False
) -> None:
self.collected_tokens = []
"""Instantiate FinalStreamingStdOutCallbackHandler.
Args:
answer_prefix_tokens: Token sequence that prefixes the answer.
Default is ["Final", "Answer", ":"]
strip_tokens: Ignore white spaces and new lines when comparing
answer_prefix_tokens to last tokens? (to determine if answer has been
reached)
stream_prefix: Should answer prefix itself also be streamed?
"""
super().__init__()
if answer_prefix_tokens is None:
self.answer_prefix_tokens = DEFAULT_ANSWER_PREFIX_TOKENS
else:
self.answer_prefix_tokens = answer_prefix_tokens
if strip_tokens:
self.answer_prefix_tokens_stripped = [
token.strip() for token in self.answer_prefix_tokens
]
else:
self.answer_prefix_tokens_stripped = self.answer_prefix_tokens
self.last_tokens = [""] * len(self.answer_prefix_tokens)
self.last_tokens_stripped = [""] * len(self.answer_prefix_tokens)
self.strip_tokens = strip_tokens
self.stream_prefix = stream_prefix
self.answer_reached = False
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
self.answer_reached = False
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Run on new LLM token. Only available when streaming is enabled."""
# Remember the last n tokens, where n = len(answer_prefix_tokens)
self.append_to_last_tokens(token)
# Check if the last n tokens match the answer_prefix_tokens list ...
if self.check_if_answer_reached():
self.answer_reached = True
if self.stream_prefix:
for t in self.last_tokens:
self.collected_tokens.append(t)
return
# ... if yes, then collect tokens from now on
if self.answer_reached:
self.collected_tokens.append(token)
def get_collected_tokens(self) -> str:
"""Return the collected tokens as a single string."""
return ''.join(self.collected_tokens)
```
### Suggestion:
_No response_
|
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| 1,885,210,992 |
I_kwDOIPDwls5wXglw
| 10,314 |
Langchain Quickstart is not working for me
|
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[] | null | 2 | 2023-09-07T06:38:07 | 2023-09-09T21:29:36 | 2023-09-09T21:29:36 |
NONE
| null |
### System Info
Apple Macbook M1 Pro
python: 3.11.2
langchain: 0.0.283
pydantic: 2.3.0
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. pip3.11 install langchain
2. run python3.11 in terminal
3. execute `from langchain.llms import OpenAI` which produce following error for me
```
Python 3.11.2 (main, Feb 16 2023, 02:55:59) [Clang 14.0.0 (clang-1400.0.29.202)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from langchain.llms import OpenAI
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/homebrew/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/opt/homebrew/lib/python3.11/site-packages/langchain/agents/__init__.py", line 31, in <module>
from langchain.agents.agent import (
File "/opt/homebrew/lib/python3.11/site-packages/langchain/agents/agent.py", line 14, in <module>
from langchain.agents.agent_iterator import AgentExecutorIterator
File "/opt/homebrew/lib/python3.11/site-packages/langchain/agents/agent_iterator.py", line 30, in <module>
from langchain.tools import BaseTool
File "/opt/homebrew/lib/python3.11/site-packages/langchain/tools/__init__.py", line 25, in <module>
from langchain.tools.arxiv.tool import ArxivQueryRun
File "/opt/homebrew/lib/python3.11/site-packages/langchain/tools/arxiv/tool.py", line 8, in <module>
from langchain.utilities.arxiv import ArxivAPIWrapper
File "/opt/homebrew/lib/python3.11/site-packages/langchain/utilities/__init__.py", line 7, in <module>
from langchain.utilities.apify import ApifyWrapper
File "/opt/homebrew/lib/python3.11/site-packages/langchain/utilities/apify.py", line 3, in <module>
from langchain.document_loaders import ApifyDatasetLoader
File "/opt/homebrew/lib/python3.11/site-packages/langchain/document_loaders/__init__.py", line 76, in <module>
from langchain.document_loaders.embaas import EmbaasBlobLoader, EmbaasLoader
File "/opt/homebrew/lib/python3.11/site-packages/langchain/document_loaders/embaas.py", line 54, in <module>
class BaseEmbaasLoader(BaseModel):
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/main.py", line 204, in __new__
fields[ann_name] = ModelField.infer(
^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 488, in infer
return cls(
^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 419, in __init__
self.prepare()
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 539, in prepare
self.populate_validators()
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 801, in populate_validators
*(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/validators.py", line 696, in find_validators
yield make_typeddict_validator(type_, config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/validators.py", line 585, in make_typeddict_validator
TypedDictModel = create_model_from_typeddict(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/annotated_types.py", line 35, in create_model_from_typeddict
return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/main.py", line 972, in create_model
return type(__model_name, __base__, namespace)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/main.py", line 204, in __new__
fields[ann_name] = ModelField.infer(
^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 488, in infer
return cls(
^^^^
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 419, in __init__
self.prepare()
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 534, in prepare
self._type_analysis()
File "/opt/homebrew/lib/python3.11/site-packages/pydantic/fields.py", line 638, in _type_analysis
elif issubclass(origin, Tuple): # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/[email protected]/3.11.2_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/typing.py", line 1551, in __subclasscheck__
return issubclass(cls, self.__origin__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: issubclass() arg 1 must be a class
```
### Expected behavior
To be able to use langchain
|
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| 1,885,171,818 |
I_kwDOIPDwls5wXXBq
| 10,313 |
apply embeddings to pandas dataframe
|
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[] | null | 7 | 2023-09-07T06:02:33 | 2023-12-14T16:05:18 | 2023-12-14T16:05:17 |
NONE
| null |
### System Info
jupyter notebook, RTX 3090
### Who can help?
@agola11 @hwchase17 @ey
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.embeddings import SentenceTransformerEmbeddings
embedding=lambda x: x['combined_info'].apply(lambda text: embeddings.embed_documents(text))
```
does not work
Any workarounds on it?
### Expected behavior
outputs embeddings
|
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I_kwDOIPDwls5wXJgm
| 10,312 |
DOC: <Observation Streaming Using Call Back>
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[] | null | 4 | 2023-09-07T04:57:35 | 2023-12-30T18:21:43 | 2023-12-14T16:05:21 |
NONE
| null |
### Issue with current documentation:
When I am using a tool with `return_direct = True` , the observation step will provide the answer. Is there a way to stream that observation from the intermediate steps using callbacks. I found no information regarding this. It is much appreciated if we can add something regarding that as well.
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5wXE-a
| 10,311 |
Issue: Obeservation is capping out when passing to Action Input [CONVERSATIONAL_REACT_DESCRIPTION]
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[] | null | 2 | 2023-09-07T04:34:10 | 2023-12-14T16:05:27 | 2023-12-14T16:05:26 |
NONE
| null |
### Issue you'd like to raise.
When working with the `CONVERSATIONAL_REACT_DESCRIPTION`, I found Observation was capped out when passing to the action input. For example I have retrieved a JsonArray by using a structured tool but when the agent passes it to the final tool jsonArray was capped out. Can I handle it?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5wW_oZ
| 10,310 |
DOC: Action Input and Final Answer
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[] | null | 2 | 2023-09-07T04:03:13 | 2023-09-15T03:15:07 | 2023-09-15T03:15:07 |
NONE
| null |
### Issue with current documentation:
I wasn't able to find any information related to the customization of Action Input and Final Answer customization. It is much-appreciated if the documentation can provide some info regarding that as well.
### Idea or request for content:
_No response_
|
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| 10,309 |
Errors about ErnieBotChat
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[] | null | 2 | 2023-09-07T03:46:18 | 2023-09-07T04:09:47 | 2023-09-07T04:09:46 |
NONE
| null |
### Issue you'd like to raise.
my test code
```
import unittest
from langchain.chat_models import ErnieBotChat
from langchain.schema import (
HumanMessage
)
class TestErnieBotCase(unittest.TestCase):
def test_ernie_bot(self):
chat_llm = ErnieBotChat(
ernie_client_id="xxx",
ernie_client_secret="xxx"
)
result = chat_llm.generate(messages=[HumanMessage(content="请列出清朝的所有皇帝的姓名和年号")])
print(result)
if __name__ == '__main__':
unittest.main()
```
it's return error：
```
Error
Traceback (most recent call last):
File "/Users/keigo/Workspace/study/langchain/tests/test_erniebot.py", line 14, in test_ernie_bot
result = chat_llm.generate(messages=[HumanMessage(content="请列出清朝的所有皇帝的姓名和年号")])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/keigo/Workspace/study/langchain/venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 309, in generate
raise e
File "/Users/keigo/Workspace/study/langchain/venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 299, in generate
self._generate_with_cache(
File "/Users/keigo/Workspace/study/langchain/venv/lib/python3.11/site-packages/langchain/chat_models/base.py", line 446, in _generate_with_cache
return self._generate(
^^^^^^^^^^^^^^^
File "/Users/keigo/Workspace/study/langchain/venv/lib/python3.11/site-packages/langchain/chat_models/ernie.py", line 157, in _generate
"messages": [_convert_message_to_dict(m) for m in messages],
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/keigo/Workspace/study/langchain/venv/lib/python3.11/site-packages/langchain/chat_models/ernie.py", line 157, in <listcomp>
"messages": [_convert_message_to_dict(m) for m in messages],
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/keigo/Workspace/study/langchain/venv/lib/python3.11/site-packages/langchain/chat_models/ernie.py", line 31, in _convert_message_to_dict
raise ValueError(f"Got unknown type {message}")
ValueError: Got unknown type ('content', '请列出清朝的所有皇帝的姓名和年号')
```
in ernie.py file function _convert_message_to_dict,the variable message is of type tuple。
### Suggestion:
_No response_
|
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I_kwDOIPDwls5wW7IM
| 10,308 |
Issue: create_pandas_dataframe_agent is hard to work with llama models
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[] | null | 11 | 2023-09-07T03:36:15 | 2024-01-05T07:11:45 | null |
NONE
| null |
### Issue you'd like to raise.
Langchain pandas agents (create_pandas_dataframe_agent ) is hard to work with llama models. (the same scripts work well with gpt3.5.)
I am trying to use local model Vicuna 13b v1.5 (LLaMa2 based) to create a local Question&Answer system.
it works well dealing with doc QAs.
but it doesn't work well dealing with pandas data (by calling create_pandas_dataframe_agent ). Any suggestions for me ?
my calling code:
```
df=pd.read_excel(CSV_NAME)
pd_agent = create_pandas_dataframe_agent(
ChatOpenAI(temperature=0,model_name=set_model),
df,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
agent_executor_kwargs={"handle_parsing_errors":True}
)
pd_agent.run(query)
```
example: when I asking How many events occurred after May 20, 2023?
the final answer would be :
````From the data provided, we can find the GUID of the event, and then filter out the data after May 20, 2023 based on the date. The following is the Python code implementation:
```python
import pandas as pd
#Read data
data = pd.read_csv('your_data_file.csv')
# Filter the data in the date range
filtered_data = data[data['date'] >= '2023-05-20']
# Extract the GUID of the event
guids = filtered_data[filtered_data['element'] == 'events']['id'].unique()
# Output the GUID of the event
print(guids)
```
Please replace `your_data_file.csv` with your data file name. This code will output the GUID of the event after May 20, 2023.
````
it seems the chain did not proceed to the last step. any suggestions for me ?
1. other than create_pandas_dataframe_agent , is there other chain or agent can I try?
2. if i need to overwrite some methods, which method should I edit?
the only similar example I found is written by kvnsng https://github.com/langchain-ai/langchain/issues/7709#issuecomment-1653833036
### Suggestion:
_No response_
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PR_kwDOIPDwls5ZvOli
| 10,307 |
docstrings: `chat_loaders`
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[] | null | 1 | 2023-09-07T03:26:42 | 2023-09-08T02:55:34 | 2023-09-08T02:35:35 |
COLLABORATOR
| null |
Updated docstrings. Made them consistent across the module.
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Bagatur/myscale vector sql
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PR_kwDOIPDwls5ZuwOE
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Supabase vector self querying retriever
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[] | null | 5 | 2023-09-07T00:06:38 | 2023-09-07T22:03:27 | 2023-09-07T22:03:26 |
CONTRIBUTOR
| null |
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
## Description
Adds Supabase Vector as a self-querying retriever.
- Designed to be backwards compatible with existing `filter` logic on `SupabaseVectorStore`.
- Adds new filter `postgrest_filter` to `SupabaseVectorStore` `similarity_search()` methods
- Supports entire PostgREST [filter query language](https://postgrest.org/en/stable/references/api/tables_views.html#read) (used by self-querying retriever, but also works as an escape hatch for more query control)
- `SupabaseVectorTranslator` converts Langchain filter into the above PostgREST query
- Adds Jupyter Notebook for the self-querying retriever
- Adds tests
## Tag maintainer
@hwchase17
## Twitter handle
[@ggrdson](https://twitter.com/ggrdson)
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PR_kwDOIPDwls5ZutKE
| 10,303 |
add in, nin for pinecone
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[] | null | 4 | 2023-09-06T23:42:15 | 2023-10-06T01:31:14 | 2023-10-06T01:31:10 |
CONTRIBUTOR
| null |
Description: Adds the in and nin comparators for pinecone seen [here](https://docs.pinecone.io/docs/metadata-filtering#metadata-query-language)
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PR_kwDOIPDwls5ZusBf
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docs `integrations/embeddings` consistency
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[] | null | 1 | 2023-09-06T23:33:16 | 2023-09-08T02:55:55 | 2023-09-08T02:53:33 |
COLLABORATOR
| null |
Updated `integrations/embeddings`: fixed titles; added links, descriptions
Updated `integrations/providers`.
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Bagatur/nuclia vector
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| 1,884,882,502 |
PR_kwDOIPDwls5ZurVe
| 10,300 |
Change StructuredTool's ainvoke to await
|
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[] | null | 2 | 2023-09-06T23:28:10 | 2023-09-08T02:54:53 | 2023-09-08T02:54:53 |
CONTRIBUTOR
| null |
Fixes #10080. StructuredTool's `ainvoke` doesn't `await`.
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|
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| 1,884,829,458 |
PR_kwDOIPDwls5ZugCF
| 10,299 |
add use case nb position
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[] | null | 1 | 2023-09-06T22:17:17 | 2023-09-06T22:46:34 | 2023-09-06T22:46:34 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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If you're adding a new integration, please include:
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|
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| 1,884,823,731 |
PR_kwDOIPDwls5Zue0b
| 10,298 |
Fixed qa_citations.ipynb documentation error
|
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[] | null | 2 | 2023-09-06T22:10:36 | 2023-11-01T23:45:58 | 2023-11-01T23:45:58 |
NONE
| null |
Issue: #8593
Description: Documentation error fixed. create_citation_fuzzy_match_chain expects dictionary with a specific key-value pair, not just any dictionary like the documentation showed.
Dependencies: None
Twitter handle: @ayushzenith
Tag maintainer: @hwchase17
|
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PR_kwDOIPDwls5ZuQVQ
| 10,297 |
Enhancements and bug fixes for `LLMonitorCallbackHandler`
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[] | null | 2 | 2023-09-06T21:10:06 | 2023-09-08T22:56:42 | 2023-09-08T22:56:42 |
CONTRIBUTOR
| null |
Hi @baskaryan,
I've made updates to LLMonitorCallbackHandler to address a few bugs reported by users
These changes don't alter the fundamental behavior of the callback handler.
Thanks you!
|
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PR_kwDOIPDwls5Zth4w
| 10,295 |
Strip text splits
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[] | null | 2 | 2023-09-06T18:32:27 | 2023-09-06T21:06:13 | 2023-09-06T21:06:12 |
CONTRIBUTOR
| null |
#10085
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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
|
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I_kwDOIPDwls5wUPLD
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`langchain/document_loaders/s3_directory.py` S3DirectoryLoader has 3 bugs
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[] | null | 2 | 2023-09-06T16:20:11 | 2023-12-14T16:05:33 | 2023-12-14T16:05:32 |
NONE
| null |
### System Info
langchain==0.0.281
boto3==1.28.41
```python
s3 = boto3.resource(
"s3",
region_name=self.region_name,
api_version=self.api_version,
use_ssl=self.use_ssl,
verify=self.verify,
endpoint_url=self.endpoint_url,
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key,
aws_session_token=self.aws_session_token,
config=self.boto_config,
)
```
line 117 should be `config=self.boto_config` not `boto_config=self.boto_config`.
```python
for obj in bucket.objects.filter(Prefix=self.prefix):
if obj.get()["ContentLength"] == 0:
continue
loader = S3FileLoader(
self.bucket,
obj.key,
region_name=self.region_name,
api_version=self.api_version,
use_ssl=self.use_ssl,
verify=self.verify,
endpoint_url=self.endpoint_url,
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key,
aws_session_token=self.aws_session_token,
config=self.boto_config,
)
docs.extend(loader.load())
```
before line 122 should add the condition if object is a directory, and line 135 `boto_config` again.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
unnecessary
### Expected behavior
unnecessary
|
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| 1,884,299,594 |
PR_kwDOIPDwls5ZssR1
| 10,293 |
Update rwkv.py
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[] | null | 2 | 2023-09-06T15:48:14 | 2023-09-06T20:50:21 | 2023-09-06T20:50:21 |
CONTRIBUTOR
| null |
I have updated the code to ensure consistent error handling for ImportError. Instead of relying on ValueError as before, I've followed the standard practice of raising ImportError while also including detailed error messages. This modification improves code clarity and explicitly indicates that any issues are related to module imports.
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PR_kwDOIPDwls5Zse7i
| 10,292 |
Add notebook example to use sqlite-vss as a vector store.
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[] | null | 2 | 2023-09-06T15:14:15 | 2023-09-06T20:47:00 | 2023-09-06T20:47:00 |
CONTRIBUTOR
| null |
Follow-up PR for https://github.com/langchain-ai/langchain/pull/10047, simply adding a notebook quickstart example for the vector store with SQLite, using the class SQLiteVSS.
Maintainer tag @baskaryan
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PR_kwDOIPDwls5ZsR0l
| 10,291 |
Added the Polars dataframe agent
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CONTRIBUTOR
| null |
## Description
Added the Polars dataframe agent.
This is a dataframe agent and can be used in the same way as the Pandas dataframe agent.
(Polars is a DataFrame interface on top of an OLAP Query Engine implemented in Rust.)
## Issue
https://github.com/langchain-ai/langchain/issues/4620
## Dependencies
polars (https://pola-rs.github.io/polars-book/user-guide/)
## Tag maintainer
N/A
## Twitter handle
N/A
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PR_kwDOIPDwls5ZsRKb
| 10,290 |
Add LCEL cookbook examples
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[] | null | 1 | 2023-09-06T14:44:40 | 2023-09-06T20:50:44 | 2023-09-06T20:50:43 |
COLLABORATOR
| null |
1. For passing config to runnable lambda
2. For branching and merging
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PR_kwDOIPDwls5ZsRH_
| 10,289 |
Fix langchain lint on `master`.
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PR_kwDOIPDwls5ZsO6P
| 10,288 |
lint fix
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PR_kwDOIPDwls5ZsIgQ
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bump 283
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closed
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[] | null | 1 | 2023-09-06T14:26:41 | 2023-09-06T14:33:04 | 2023-09-06T14:33:03 |
COLLABORATOR
| null | null |
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| 1,883,976,481 |
PR_kwDOIPDwls5Zrlvh
| 10,286 |
Update base.py
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[] | null | 2 | 2023-09-06T13:04:18 | 2023-11-27T21:15:12 | 2023-11-27T21:15:11 |
NONE
| null |
Return both SQL Query and final answer
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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I_kwDOIPDwls5wStrN
| 10,285 |
using `pip install langchain[all]` in a `venv` installs langchain 0.0.74
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[] | null | 1 | 2023-09-06T12:53:05 | 2023-09-26T19:17:13 | 2023-09-26T19:17:13 |
CONTRIBUTOR
| null |
### Discussed in https://github.com/langchain-ai/langchain/discussions/10068
<div type='discussions-op-text'>
<sup>Originally posted by **aMahanna** August 31, 2023</sup>
Hi!
Python version used: `3.10`
Virtual Environment Tool used: `venv`
I am trying to understand why `pip install langchain[all]` is installing LangChain `0.0.74`, as opposed to the latest version of LangChain.
Based on the installation output, I can see the installation of external modules, and a series of `Using cached langchain-X-py3-none-any.whl` logs, where `X` descends from `0.0.278` all the way to `0.0.74`
Perhaps I missed the documentation that speaks to this behaviour? I have been relying on the [installation.mdx](https://github.com/langchain-ai/langchain/blob/master/docs/snippets/get_started/installation.mdx) guide for this.
<details>
<summary>Output: `pip install "langchain[all]"`</summary>
```
❯ pip install "langchain[all]"
Collecting langchain[all]
Using cached langchain-0.0.278-py3-none-any.whl (1.6 MB)
Collecting async-timeout<5.0.0,>=4.0.0
Using cached async_timeout-4.0.3-py3-none-any.whl (5.7 kB)
Collecting langsmith<0.1.0,>=0.0.21
Using cached langsmith-0.0.30-py3-none-any.whl (35 kB)
Collecting numpy<2,>=1
Using cached numpy-1.25.2-cp310-cp310-macosx_11_0_arm64.whl (14.0 MB)
Collecting tenacity<9.0.0,>=8.1.0
Using cached tenacity-8.2.3-py3-none-any.whl (24 kB)
Collecting numexpr<3.0.0,>=2.8.4
Using cached numexpr-2.8.5-cp310-cp310-macosx_11_0_arm64.whl (90 kB)
Collecting SQLAlchemy<3,>=1.4
Using cached SQLAlchemy-2.0.20-cp310-cp310-macosx_11_0_arm64.whl (2.0 MB)
Collecting dataclasses-json<0.6.0,>=0.5.7
Using cached dataclasses_json-0.5.14-py3-none-any.whl (26 kB)
Collecting aiohttp<4.0.0,>=3.8.3
Using cached aiohttp-3.8.5-cp310-cp310-macosx_11_0_arm64.whl (343 kB)
Collecting pydantic<3,>=1
Using cached pydantic-2.3.0-py3-none-any.whl (374 kB)
Collecting requests<3,>=2
Using cached requests-2.31.0-py3-none-any.whl (62 kB)
Collecting PyYAML>=5.3
Using cached PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl (169 kB)
Collecting lark<2.0.0,>=1.1.5
Using cached lark-1.1.7-py3-none-any.whl (108 kB)
Collecting arxiv<2.0,>=1.4
Using cached arxiv-1.4.8-py3-none-any.whl (12 kB)
Collecting html2text<2021.0.0,>=2020.1.16
Using cached html2text-2020.1.16-py3-none-any.whl (32 kB)
Collecting jq<2.0.0,>=1.4.1
Using cached jq-1.5.0-cp310-cp310-macosx_11_0_arm64.whl (370 kB)
Collecting azure-identity<2.0.0,>=1.12.0
Using cached azure_identity-1.14.0-py3-none-any.whl (160 kB)
Collecting deeplake<4.0.0,>=3.6.8
Using cached deeplake-3.6.22.tar.gz (538 kB)
Preparing metadata (setup.py) ... done
Collecting torch<3,>=1
Using cached torch-2.0.1-cp310-none-macosx_11_0_arm64.whl (55.8 MB)
Collecting huggingface_hub<1,>=0
Using cached huggingface_hub-0.16.4-py3-none-any.whl (268 kB)
Collecting tiktoken<0.4.0,>=0.3.2
Using cached tiktoken-0.3.3-cp310-cp310-macosx_11_0_arm64.whl (706 kB)
Collecting transformers<5,>=4
Using cached transformers-4.32.1-py3-none-any.whl (7.5 MB)
Collecting openai<1,>=0
Using cached openai-0.27.10-py3-none-any.whl (76 kB)
Collecting pinecone-client<3,>=2
Using cached pinecone_client-2.2.2-py3-none-any.whl (179 kB)
Collecting aleph-alpha-client<3.0.0,>=2.15.0
Using cached aleph_alpha_client-2.17.0-py3-none-any.whl (41 kB)
Collecting azure-ai-formrecognizer<4.0.0,>=3.2.1
Using cached azure_ai_formrecognizer-3.3.0-py3-none-any.whl (297 kB)
Collecting wikipedia<2,>=1
Using cached wikipedia-1.4.0.tar.gz (27 kB)
Preparing metadata (setup.py) ... done
Collecting manifest-ml<0.0.2,>=0.0.1
Using cached manifest_ml-0.0.1-py2.py3-none-any.whl (42 kB)
Collecting momento<2.0.0,>=1.5.0
Using cached momento-1.9.1-py3-none-any.whl (134 kB)
Collecting pexpect<5.0.0,>=4.8.0
Using cached pexpect-4.8.0-py2.py3-none-any.whl (59 kB)
Collecting azure-cognitiveservices-speech<2.0.0,>=1.28.0
Using cached azure_cognitiveservices_speech-1.31.0-py3-none-macosx_11_0_arm64.whl (6.4 MB)
Collecting gptcache>=0.1.7
Using cached gptcache-0.1.40-py3-none-any.whl (124 kB)
Collecting azure-cosmos<5.0.0,>=4.4.0b1
Using cached azure_cosmos-4.5.0-py3-none-any.whl (226 kB)
Collecting requests-toolbelt<2.0.0,>=1.0.0
Using cached requests_toolbelt-1.0.0-py2.py3-none-any.whl (54 kB)
Collecting pyowm<4.0.0,>=3.3.0
Using cached pyowm-3.3.0-py3-none-any.whl (4.5 MB)
Collecting amadeus>=8.1.0
Using cached amadeus-8.1.0.tar.gz (39 kB)
Preparing metadata (setup.py) ... done
Collecting networkx<3.0.0,>=2.6.3
Using cached networkx-2.8.8-py3-none-any.whl (2.0 MB)
Collecting qdrant-client<2.0.0,>=1.3.1
Using cached qdrant_client-1.4.0-py3-none-any.whl (132 kB)
Collecting langkit<0.1.0,>=0.0.6
Using cached langkit-0.0.17-py3-none-any.whl (754 kB)
Collecting jinja2<4,>=3
Using cached Jinja2-3.1.2-py3-none-any.whl (133 kB)
Collecting openlm<0.0.6,>=0.0.5
Using cached openlm-0.0.5-py3-none-any.whl (10 kB)
Collecting faiss-cpu<2,>=1
Using cached faiss_cpu-1.7.4-cp310-cp310-macosx_11_0_arm64.whl (2.7 MB)
Collecting psycopg2-binary<3.0.0,>=2.9.5
Using cached psycopg2_binary-2.9.7-cp310-cp310-macosx_11_0_arm64.whl (2.5 MB)
Collecting pinecone-text<0.5.0,>=0.4.2
Using cached pinecone_text-0.4.2-py3-none-any.whl (17 kB)
Collecting opensearch-py<3.0.0,>=2.0.0
Using cached opensearch_py-2.3.1-py2.py3-none-any.whl (327 kB)
Collecting weaviate-client<4,>=3
Using cached weaviate_client-3.23.2-py3-none-any.whl (108 kB)
Collecting sentence-transformers<3,>=2
Using cached sentence_transformers-2.2.2-py3-none-any.whl
Collecting google-search-results<3,>=2
Using cached google_search_results-2.4.2.tar.gz (18 kB)
Preparing metadata (setup.py) ... done
Collecting pytesseract<0.4.0,>=0.3.10
Using cached pytesseract-0.3.10-py3-none-any.whl (14 kB)
Collecting singlestoredb<0.8.0,>=0.7.1
Using cached singlestoredb-0.7.1-cp36-abi3-macosx_10_9_universal2.whl (196 kB)
Collecting marqo<2.0.0,>=1.2.4
Using cached marqo-1.2.4-py3-none-any.whl (32 kB)
Collecting nomic<2.0.0,>=1.0.43
Using cached nomic-1.1.14.tar.gz (31 kB)
Preparing metadata (setup.py) ... done
Collecting pypdf<4.0.0,>=3.4.0
Using cached pypdf-3.15.4-py3-none-any.whl (272 kB)
Collecting langchain[all]
Using cached langchain-0.0.277-py3-none-any.whl (1.6 MB)
Using cached langchain-0.0.276-py3-none-any.whl (1.6 MB)
Using cached langchain-0.0.275-py3-none-any.whl (1.6 MB)
Using cached langchain-0.0.274-py3-none-any.whl (1.6 MB)
Using cached langchain-0.0.273-py3-none-any.whl (1.6 MB)
Using cached langchain-0.0.272-py3-none-any.whl (1.6 MB)
Collecting google-api-core<3.0.0,>=2.11.1
Using cached google_api_core-2.11.1-py3-none-any.whl (120 kB)
Collecting langchain[all]
Using cached langchain-0.0.271-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.270-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.269-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.268-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.267-py3-none-any.whl (1.5 MB)
Collecting openapi-schema-pydantic<2.0,>=1.2
Using cached openapi_schema_pydantic-1.2.4-py3-none-any.whl (90 kB)
Collecting langchain[all]
Using cached langchain-0.0.266-py3-none-any.whl (1.5 MB)
Collecting pydantic<2,>=1
Using cached pydantic-1.10.12-cp310-cp310-macosx_11_0_arm64.whl (2.5 MB)
Collecting langchain[all]
Using cached langchain-0.0.265-py3-none-any.whl (1.5 MB)
Collecting anthropic<0.4,>=0.3
Using cached anthropic-0.3.11-py3-none-any.whl (796 kB)
Collecting xinference<0.0.7,>=0.0.6
Using cached xinference-0.0.6-py3-none-any.whl (65 kB)
Collecting spacy<4,>=3
Using cached spacy-3.6.1-cp310-cp310-macosx_11_0_arm64.whl (6.6 MB)
Collecting langchain[all]
Using cached langchain-0.0.264-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.263-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.262-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.261-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.260-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.259-py3-none-any.whl (1.5 MB)
Using cached langchain-0.0.258-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.257-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.256-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.255-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.254-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.253-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.252-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.251-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.250-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.249-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.248-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.247-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.246-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.245-py3-none-any.whl (1.4 MB)
Collecting awadb<0.4.0,>=0.3.3
Using cached awadb-0.3.10-cp310-cp310-macosx_13_0_arm64.whl (1.6 MB)
Collecting langchain[all]
Using cached langchain-0.0.244-py3-none-any.whl (1.4 MB)
Collecting cohere<4,>=3
Using cached cohere-3.10.0.tar.gz (15 kB)
Preparing metadata (setup.py) ... done
Collecting langchain[all]
Using cached langchain-0.0.243-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.242-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.240-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.239-py3-none-any.whl (1.4 MB)
Using cached langchain-0.0.238-py3-none-any.whl (1.3 MB)
Using cached langchain-0.0.237-py3-none-any.whl (1.3 MB)
Collecting langsmith<0.0.11,>=0.0.10
Using cached langsmith-0.0.10-py3-none-any.whl (27 kB)
Collecting langchain[all]
Using cached langchain-0.0.236-py3-none-any.whl (1.3 MB)
Using cached langchain-0.0.235-py3-none-any.whl (1.3 MB)
Collecting langsmith<0.0.8,>=0.0.7
Using cached langsmith-0.0.7-py3-none-any.whl (26 kB)
Collecting langchain[all]
Using cached langchain-0.0.234-py3-none-any.whl (1.3 MB)
Collecting langsmith<0.0.6,>=0.0.5
Using cached langsmith-0.0.5-py3-none-any.whl (25 kB)
Collecting langchain[all]
Using cached langchain-0.0.233-py3-none-any.whl (1.3 MB)
Collecting beautifulsoup4<5,>=4
Using cached beautifulsoup4-4.12.2-py3-none-any.whl (142 kB)
Collecting langchain[all]
Using cached langchain-0.0.232-py3-none-any.whl (1.3 MB)
Using cached langchain-0.0.231-py3-none-any.whl (1.3 MB)
Collecting langchainplus-sdk<0.0.21,>=0.0.20
Using cached langchainplus_sdk-0.0.20-py3-none-any.whl (25 kB)
Collecting langchain[all]
Using cached langchain-0.0.230-py3-none-any.whl (1.3 MB)
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Collecting blis<0.8.0,>=0.7.8
Using cached blis-0.7.10-cp310-cp310-macosx_11_0_arm64.whl (1.1 MB)
Collecting confection<1.0.0,>=0.0.1
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Collecting cffi>=1.12
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Collecting hyperframe<7,>=6.0
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Collecting hpack<5,>=4.0
Using cached hpack-4.0.0-py3-none-any.whl (32 kB)
Collecting anyio<5.0,>=3.0
Using cached anyio-4.0.0-py3-none-any.whl (83 kB)
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Using cached pycparser-2.21-py2.py3-none-any.whl (118 kB)
Installing collected packages: tokenizers, sqlitedict, safetensors, faiss-cpu, cymem, xmltodict, wasabi, validators, urllib3, uritemplate, typing-extensions, tqdm, SQLAlchemy, spacy-loggers, spacy-legacy, soupsieve, sniffio, smart-open, six, regex, PyYAML, pyparsing, pycparser, pyasn1, protobuf, packaging, numpy, mypy-extensions, murmurhash, more-itertools, MarkupSafe, loguru, langcodes, joblib, jaraco.context, idna, hyperframe, hpack, h11, grpcio, fsspec, exceptiongroup, dnspython, dill, click, charset-normalizer, certifi, catalogue, cachetools, async-timeout, wolframalpha, typing-inspect, typer, torch, srsly, rsa, requests, redis, python-dateutil, pydantic, pyasn1-modules, preshed, nltk, marshmallow, jinja2, httplib2, h2, grpcio-tools, googleapis-common-protos, filelock, elastic-transport, cffi, blis, beautifulsoup4, anyio, wikipedia, tiktoken, pinecone-client, pathy, manifest-ml, huggingface_hub, httpcore, google-auth, elasticsearch, dataclasses-json, cryptography, confection, transformers, thinc, langchain, httpx, google-auth-httplib2, google-api-core, authlib, weaviate-client, spacy, google-api-python-client, qdrant-client
DEPRECATION: sqlitedict is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559
Running setup.py install for sqlitedict ... done
DEPRECATION: SQLAlchemy is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559
Running setup.py install for SQLAlchemy ... done
DEPRECATION: wikipedia is being installed using the legacy 'setup.py install' method, because it does not have a 'pyproject.toml' and the 'wheel' package is not installed. pip 23.1 will enforce this behaviour change. A possible replacement is to enable the '--use-pep517' option. Discussion can be found at https://github.com/pypa/pip/issues/8559
Running setup.py install for wikipedia ... done
Successfully installed MarkupSafe-2.1.3 PyYAML-6.0.1 SQLAlchemy-1.4.49 anyio-4.0.0 async-timeout-4.0.3 authlib-1.2.1 beautifulsoup4-4.12.2 blis-0.7.10 cachetools-5.3.1 catalogue-2.0.9 certifi-2023.7.22 cffi-1.15.1 charset-normalizer-3.2.0 click-8.1.7 confection-0.1.1 cryptography-41.0.3 cymem-2.0.7 dataclasses-json-0.5.14 dill-0.3.7 dnspython-2.4.2 elastic-transport-8.4.0 elasticsearch-8.9.0 exceptiongroup-1.1.3 faiss-cpu-1.7.4 filelock-3.12.3 fsspec-2023.6.0 google-api-core-2.11.1 google-api-python-client-2.70.0 google-auth-2.22.0 google-auth-httplib2-0.1.0 googleapis-common-protos-1.60.0 grpcio-1.57.0 grpcio-tools-1.57.0 h11-0.14.0 h2-4.1.0 hpack-4.0.0 httpcore-0.17.3 httplib2-0.22.0 httpx-0.24.1 huggingface_hub-0.16.4 hyperframe-6.0.1 idna-3.4 jaraco.context-4.3.0 jinja2-3.1.2 joblib-1.3.2 langchain-0.0.74 langcodes-3.3.0 loguru-0.7.0 manifest-ml-0.0.1 marshmallow-3.20.1 more-itertools-10.1.0 murmurhash-1.0.9 mypy-extensions-1.0.0 nltk-3.8.1 numpy-1.25.2 packaging-23.1 pathy-0.10.2 pinecone-client-2.2.2 preshed-3.0.8 protobuf-4.24.2 pyasn1-0.5.0 pyasn1-modules-0.3.0 pycparser-2.21 pydantic-1.10.12 pyparsing-3.1.1 python-dateutil-2.8.2 qdrant-client-0.11.10 redis-4.6.0 regex-2023.8.8 requests-2.31.0 rsa-4.9 safetensors-0.3.3 six-1.16.0 smart-open-6.3.0 sniffio-1.3.0 soupsieve-2.4.1 spacy-3.6.1 spacy-legacy-3.0.12 spacy-loggers-1.0.4 sqlitedict-2.1.0 srsly-2.4.7 thinc-8.1.12 tiktoken-0.4.0 tokenizers-0.13.3 torch-1.13.1 tqdm-4.66.1 transformers-4.32.1 typer-0.9.0 typing-extensions-4.7.1 typing-inspect-0.9.0 uritemplate-4.1.1 urllib3-1.26.16 validators-0.21.0 wasabi-1.1.2 weaviate-client-3.23.2 wikipedia-1.4.0 wolframalpha-5.0.0 xmltodict-0.13.0
[notice] A new release of pip is available: 23.0.1 -> 23.2.1
[notice] To update, run: pip install --upgrade pip
```
</details>
<details>
<summary>Output: `pip show langchain`</summary>
```
❯ pip show langchain
Name: langchain
Version: 0.0.74
Summary: Building applications with LLMs through composability
Home-page: https://www.github.com/hwchase17/langchain
Author:
Author-email:
License: MIT
Location: /Users/amahanna/Desktop/temp/.venv/lib/python3.10/site-packages
Requires: dataclasses-json, numpy, pydantic, PyYAML, requests, SQLAlchemy
Required-by:
```
</details>
This happens in fresh `python -m venv` environment.
</div>
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PR_kwDOIPDwls5ZrYYM
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Vaidehi2027 patch 1
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[] | null | 3 | 2023-09-06T12:30:36 | 2023-09-08T13:37:01 | 2023-09-08T13:37:01 |
NONE
| null |
<!-- 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/hwchase17/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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5Zq5fW
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Doc: openai_functions_agent.mdx import
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[] | null | 2 | 2023-09-06T11:04:34 | 2023-09-07T01:39:35 | 2023-09-06T21:00:39 |
CONTRIBUTOR
| null |
Fix the import in docmention
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[create_csv_agent] Change dataframe as according to the prompt, and also save when required
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| null |
### Feature request
Currently there isn't any feature which we can use to actually manipulate/change and save the data frame.
**Examples:**
If I say rename columns to Column1, Column2, Column3.
Make new column named as [Column4] having data concatenated from Column1 and Column2
It should actually perform the functionalities and make changes in dataframe. And once user is done with its prompt there should be a function or a specific prompt which user can use to actually save manipulated dataframe as CSV.
```
agent = create_pandas_dataframe_agent(
ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),
df,
return_intermediate_steps=True,
verbose=True,
)
```
Using above code it returns good analytics and actual code which can be used to perform functionalities but can't actually manipulate exactly what is said to it. PLUS we cant save dataframe.
### Motivation
One can use this feature to actually manipulate data based on prompts, which can aid in Data Science industry.
### Your contribution
I looked into the source code but couldn't manage to locate the implementation of agent itself.
|
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PR_kwDOIPDwls5ZqrWF
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Added Hugging face inference api to embed documents without locally d…
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[] | null | 3 | 2023-09-06T10:23:35 | 2023-09-08T08:54:48 | 2023-09-06T21:55:48 |
CONTRIBUTOR
| null |
…ownloading the model
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: Added Hugging Face Inference API support for embedding small documents without downloading the model locally ,
- Issue: the issue # it fixes (if applicable),
- Dependencies: There are no external dependencies as it depends on embedding model of langchain as requested by @hwchase17 ,
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PR_kwDOIPDwls5Zqm5e
| 10,279 |
fix llm_inputs duplication problem in intermediate_steps in SQLDatabaseChain
|
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CONTRIBUTOR
| null |
Use `.copy()` to fix the bug that the first `llm_inputs` element is overwritten by the second `llm_inputs` element in `intermediate_steps`.
***Problem description:***
In [line 127](
https://github.com/langchain-ai/langchain/blob/c732d8fffd39d2b02bdc393c37d2ccdd48f7626d/libs/experimental/langchain_experimental/sql/base.py#L127C17-L127C17), the `llm_inputs` of the sql generation step is appended as the first element of `intermediate_steps`:
```
intermediate_steps.append(llm_inputs) # input: sql generation
```
However, `llm_inputs` is a mutable dict, it is updated in [line 179](https://github.com/langchain-ai/langchain/blob/master/libs/experimental/langchain_experimental/sql/base.py#L179) for the final answer step:
```
llm_inputs["input"] = input_text
```
Then, the updated `llm_inputs` is appended as another element of `intermediate_steps` in [line 180](https://github.com/langchain-ai/langchain/blob/c732d8fffd39d2b02bdc393c37d2ccdd48f7626d/libs/experimental/langchain_experimental/sql/base.py#L180):
```
intermediate_steps.append(llm_inputs) # input: final answer
```
As a result, the final `intermediate_steps` returned in [line 189](https://github.com/langchain-ai/langchain/blob/c732d8fffd39d2b02bdc393c37d2ccdd48f7626d/libs/experimental/langchain_experimental/sql/base.py#L189C43-L189C43) actually contains two same `llm_inputs` elements, i.e., the `llm_inputs` for the sql generation step overwritten by the one for final answer step by mistake. Users are not able to get the actual `llm_inputs` for the sql generation step from `intermediate_steps`
Simply calling `.copy()` when appending `llm_inputs` to `intermediate_steps` can solve this problem.
|
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I_kwDOIPDwls5wRGLB
| 10,278 |
Unable to Parse Final Answer through mrkl.output_parser
|
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[] | null | 1 | 2023-09-06T08:37:38 | 2023-12-13T16:05:33 | 2023-12-13T16:05:32 |
NONE
| null |
### System Info
I am running this code and getting below errro -
Code:
from langchain.agents import load_tools, tool, Tool, AgentType, initialize_agent
from langchain.llms import AzureOpenAI
from langchain import LLMMathChain
llm = AzureOpenAI(deployment_name=deployment_name, model_name=model_name, temperature=0)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
@tool
def get_word_length(word:str) -> int:
"""Returns the length of a word."""
try:
return (len(word))
except:
return 20
math_tool = Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math")
tools = [get_word_length,math_tool]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Tell me length of word - hippotamus + 5")
Error:
File ~\rhino\venv\lib\site-packages\langchain\agents\mrkl\output_parser.py:42, in MRKLOutputParser.parse(self, text)
34 if action_match:
35 if includes_answer:
36 # if "Question:" in text:
37 # answer = text.split('Question:')[0].strip()
(...)
40 # )
41 #else:
---> 42 raise OutputParserException(
43 f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
44 )
45 action = action_match.group(1).strip()
46 action_input = action_match.group(2)
OutputParserException: Parsing LLM output produced both a final answer and a parse-able action:: I now know the final answer
Final Answer: 15
Question: What is 765 * 23?
Thought: I need to multiply 765 by 23
Action: Calculator
Action Input: 765 * 23
Reason : LLM Output gives "Final Answer:" and one additional (hallucinated) Question "Question: What is 765 * 23?" as well, which caused this exception error.
Possible Fix - At line 32 of output_parser.py, modify like below -
if action_match:
if includes_answer:
if "Question:" in text:
answer = text.split('Question:')[0].strip()
return AgentFinish(
{"output": answer.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
)
else:
raise OutputParserException(
f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
)
Its fixing my reported issue. Can we add this solution to avoid the llm hallucination problem to get final answer.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am running this code and getting below error -
Code:
from langchain.agents import load_tools, tool, Tool, AgentType, initialize_agent
from langchain.llms import AzureOpenAI
from langchain import LLMMathChain
llm = AzureOpenAI(deployment_name=deployment_name, model_name=model_name, temperature=0)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
@tool
def get_word_length(word:str) -> int:
"""Returns the length of a word."""
try:
return (len(word))
except:
return 20
math_tool = Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math")
tools = [get_word_length,math_tool]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run("Tell me length of word - hippotamus + 5")
### Expected behavior
Error:
File ~\rhino\venv\lib\site-packages\langchain\agents\mrkl\output_parser.py:42, in MRKLOutputParser.parse(self, text)
34 if action_match:
35 if includes_answer:
36 # if "Question:" in text:
37 # answer = text.split('Question:')[0].strip()
(...)
40 # )
41 #else:
---> 42 raise OutputParserException(
43 f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
44 )
45 action = action_match.group(1).strip()
46 action_input = action_match.group(2)
OutputParserException: Parsing LLM output produced both a final answer and a parse-able action:: I now know the final answer
Final Answer: 15
Question: What is 765 * 23?
Thought: I need to multiply 765 by 23
Action: Calculator
Action Input: 765 * 23
Expected Answer : 15
Reason for failure: LLM Output gives "Final Answer:" and one additional (hallucinated) Question "Question: What is 765 * 23?" as well, which caused this exception error.
Possible Fix - At line 32 of output_parser.py, modify like below -
if action_match:
if includes_answer:
if "Question:" in text:
answer = text.split('Question:')[0].strip()
return AgentFinish(
{"output": answer.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
)
else:
raise OutputParserException(
f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
)
Its fixing my reported issue. Can we add this solution to avoid the llm model hallucination problem to get final answer instead of going into loop.
|
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| 1,883,494,749 |
I_kwDOIPDwls5wQ9ld
| 10,277 |
query database using natural language
|
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[] | null | 21 | 2023-09-06T08:16:51 | 2023-12-13T16:05:39 | 2023-12-13T16:05:38 |
NONE
| null |
### Issue you'd like to raise.
Where given a question in a natural language it has to convert to sql query and then operate on the sql data base then has to return the answer.
Here I want to catch sql query generated and also the reponse to the question
Earlier I did by using SQLDatabaseChain
But now I couldn't find this in your document.
So what are all supported here for my use case?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5wQ12M
| 10,276 |
GPU usage 0 even with n_gpu_layers.
|
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[] | null | 4 | 2023-09-06T07:56:48 | 2023-12-16T17:25:50 | null |
NONE
| null |
GPU usage doesn't change and my Local Llama model only runs under CPU
``I'm running a local llama 2 7b model(hugging face bloke model), on my local machine, everything works fine, except that only cpu works and GPU usage remains 0 throughout. I've included n_gpu_layers and other such options, but gpu just doesn't work for some reason.
device ram= 16gb
Vram = 6gb.
I can see 100% usage of my CPU, but nothing changes with respect to GPU usage.
This is my code in python.
`
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from transformers import AutoModelForCausalLM, AutoTokenizer
# MODEL_PATH = "D:\yarn-llama-2-7b-128k.Q2_K.gguf"
MODEL_PATH = "D:\llama-2-7b-chat.Q3_K_M.gguf"
def load_model():
"""Loads Llama model"""
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
Llama_model = LlamaCpp(
model_path=MODEL_PATH,
temperature=0.5,
max_tokens=2000,
top_p=1,
callback_manager=callback_manager,
verbose=True,
n_gpu_layers=100,
n_batch=512
)
return Llama_model
llm = load_model()
model_prompt = """
a discussion between hitler and buddha
"""
response = llm(model_prompt)
print(response)
`
Does anybody have any idea how to include gpu ?
### Suggestion:
_No response_
|
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| 10,275 |
Javelin
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[] | null | 15 | 2023-09-06T07:00:13 | 2023-09-30T05:58:00 | 2023-09-20T23:36:39 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
We are introducing the py integration to Javelin AI Gateway www.getjavelin.io. Javelin is an enterprise-scale fast llm router & gateway. Could you please review and let us know if there is anything missing.
Javelin AI Gateway wraps Embedding, Chat and Completion LLMs. Uses javelin_sdk under the covers (pip install javelin_sdk).
Author: Sharath Rajasekar, Twitter: @sharathr, @javelinai
Thanks!!
-->
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langchain cannot be installed in a new project
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[] | null | 3 | 2023-09-06T06:53:40 | 2023-09-25T14:34:05 | 2023-09-25T14:34:05 |
NONE
| null |
### System Info
npm --version
8.19.4
The langchain it's trying to install is 0.0.144
I think it's because chromadb recently released 1.5.7 and 1.5.8 which added a (conflicting) dep on cohere-ai. Pinning chromadb 1.5.6 works, but that's hardly satisfying.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
mkdir testproject && cd testproject && npm init -y && npm add langchain
### Expected behavior
Expected to install properly. Instead get
```
npm add langchain
npm ERR! code ERESOLVE
npm ERR! ERESOLVE unable to resolve dependency tree
npm ERR!
npm ERR! While resolving: [email protected]
npm ERR! Found: [email protected]
npm ERR! node_modules/cohere-ai
npm ERR! peerOptional cohere-ai@"^6.0.0" from [email protected]
npm ERR! node_modules/chromadb
npm ERR! chromadb@"^1.5.6" from the root project
npm ERR! peerOptional chromadb@"^1.5.3" from [email protected]
npm ERR! node_modules/langchain
npm ERR! langchain@"*" from the root project
npm ERR!
npm ERR! Could not resolve dependency:
npm ERR! peerOptional cohere-ai@"^5.0.2" from [email protected]
npm ERR! node_modules/langchain
npm ERR! langchain@"*" from the root project
npm ERR!
npm ERR! Fix the upstream dependency conflict, or retry
npm ERR! this command with --force, or --legacy-peer-deps
npm ERR! to accept an incorrect (and potentially broken) dependency resolution.
```
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PR_kwDOIPDwls5Zo4Wn
| 10,273 |
Allow config propagation, Add default lambda name, Improve ergonomics of config passed in
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[] | null | 1 | 2023-09-06T04:30:23 | 2023-09-06T12:54:39 | 2023-09-06T12:54:38 |
COLLABORATOR
| null |
Makes it easier to do recursion using regular python compositional patterns
```py
def lambda_decorator(func):
"""Decorate function as a RunnableLambda"""
return runnable.RunnableLambda(func)
@lambda_decorator
def fibonacci(a, config: runnable.RunnableConfig) -> int:
if a <= 1:
return a
else:
return fibonacci.invoke(
a - 1, config
) + fibonacci.invoke(a - 2, config)
fibonacci.invoke(10)
```
https://smith.langchain.com/public/cb98edb4-3a09-4798-9c22-a930037faf88/r
Also makes it more natural to do things like error handle and call other langchain objects in ways we probably don't want to support in `with_fallbacks()`
```py
@lambda_decorator
def handle_errors(a, config: runnable.RunnableConfig) -> int:
try:
return my_chain.invoke(a, config)
except MyExceptionType as exc:
return my_other_chain.invoke({"original": a, "error": exc}, config)
```
In this case, the next chain takes in the exception object. Maybe this could be something we toggle in `with_fallbacks` but I fear we'll get into uglier APIs + heavier cognitive load if we try to do too much there
|
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| 1,883,176,962 |
I_kwDOIPDwls5wPwAC
| 10,272 |
DOC: One link in Chain->Diff cll methods is not working
|
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[] | null | 2 | 2023-09-06T04:25:53 | 2023-12-13T16:05:43 | 2023-12-13T16:05:42 |
NONE
| null |
### Issue with current documentation:
[This](https://python.langchain.com/docs/modules/chains/how_to/call_methods#:~:text=See%20an%20example%20here.) link is not working.
<img width="1228" alt="image" src="https://github.com/langchain-ai/langchain/assets/84584929/19a6f523-8584-4338-80e0-ffe78c51714a">
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5wPnbo
| 10,270 |
Query data base using natural language
|
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[] | null | 1 | 2023-09-06T03:55:29 | 2023-09-06T08:44:16 | 2023-09-06T08:44:16 |
NONE
| null |
### Issue you'd like to raise.
Where given a question in a natural language it has to convert to sql query and then operate on the sql data base then has to return the answer.
Earlier I did by using **SQLDatabaseChain**
But now I couldn't find this in your document.
So what are all supported here for my use case?
### Suggestion:
_No response_
|
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