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https://api.github.com/repos/langchain-ai/langchain/issues/9815
|
https://api.github.com/repos/langchain-ai/langchain
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| 1,868,314,208 |
I_kwDOIPDwls5vXDZg
| 9,815 |
ValidationError: 2 validation errors for PydanticOutputFunctionsParser
|
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open
| false | null |
[] | null | 12 | 2023-08-27T01:01:05 | 2023-12-18T12:51:09 | null |
NONE
| null |
### System Info
I was just trying to run the Tagging tutorial (no code modification on colab).
https://python.langchain.com/docs/use_cases/tagging
And on the below code part,
```chain = create_tagging_chain_pydantic(Tags, llm)```
I got this error.
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
[<ipython-input-8-4724aee0c891>](https://localhost:8080/#) in <cell line: 1>()
----> 1 chain = create_tagging_chain_pydantic(Tags, llm)
2 frames
[/usr/local/lib/python3.10/dist-packages/pydantic/v1/main.py](https://localhost:8080/#) in __init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 2 validation errors for PydanticOutputFunctionsParser
pydantic_schema
subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)
pydantic_schema
value is not a valid dict (type=type_error.dict)
```
Is this a bug?
langchain version
```
!pip show langchain
Name: langchain
Version: 0.0.274
Summary: Building applications with LLMs through composability
Home-page: https://github.com/langchain-ai/langchain
Author:
Author-email:
License: MIT
Location: /usr/local/lib/python3.10/dist-packages
Requires: aiohttp, async-timeout, dataclasses-json, langsmith, numexpr, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity
Required-by:
```
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just by running the colab notebook on the tagging tutorial.
No modification applied.
https://python.langchain.com/docs/use_cases/tagging
### Expected behavior
Finishing running the notebook without any issues.
|
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PR_kwDOIPDwls5Y3Boj
| 9,814 |
Update SeleniumURLLoader to use webdriver Service in favor of deprecated executable_path parameter
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| false | null |
[] | null | 2 | 2023-08-27T00:30:07 | 2023-08-30T02:45:19 | 2023-08-30T02:45:18 |
CONTRIBUTOR
| null |
Description: This commit uses the new Service object in Selenium webdriver as executable_path has been [deprecated and removed in selenium version 4.11.2](https://github.com/SeleniumHQ/selenium/commit/9f5801c82fb3be3d5850707c46c3f8176e3ccd8e)
Issue: https://github.com/langchain-ai/langchain/issues/9808
Tag Maintainer: @eyurtsev
Note -
First attempt was to create a Service object and pass empty if executable_path not provided as such:
`def _get_driver(self) -> Union["Chrome", "Firefox"]:
"""Create and return a WebDriver instance based on the specified browser.
Raises:
ValueError: If an invalid browser is specified.
Returns:
Union[Chrome, Firefox]: A WebDriver instance for the specified browser.
"""
if self.browser.lower() == "chrome":
from selenium.webdriver import Chrome
from selenium.webdriver.chrome.options import Options as ChromeOptions
from selenium.webdriver.chrome.service import Service
chrome_options = ChromeOptions()
chrome_service = Service()
for arg in self.arguments:
chrome_options.add_argument(arg)
if self.headless:
chrome_options.add_argument("--headless")
chrome_options.add_argument("--no-sandbox")
if self.binary_location is not None:
chrome_options.binary_location = self.binary_location
if self.executable_path is None:
chrome_service.path = self.executable_path
return Chrome(options=chrome_options, service=chrome_service)
elif self.browser.lower() == "firefox":
from selenium.webdriver import Firefox
from selenium.webdriver.firefox.options import Options as FirefoxOptions
from selenium.webdriver.firefox.service import Service
firefox_options = FirefoxOptions()
firefox_service = Service()
for arg in self.arguments:
firefox_options.add_argument(arg)
if self.headless:
firefox_options.add_argument("--headless")
if self.binary_location is not None:
firefox_options.binary_location = self.binary_location
if self.executable_path is None:
firefox_service.path = self.executable_path
return Firefox(options=firefox_options, service=firefox_service)
else:
raise ValueError("Invalid browser specified. Use 'chrome' or 'firefox'.")`
However in testing I found this crashes when executable_path is not passed in with the follow Exception: NoSuchDriverException(f"Unable to locate or obtain driver for {options.capabilities['browserName']}"). This updated pull request revises to only pass Service object when executable_path is provided
|
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| 1,868,301,552 |
PR_kwDOIPDwls5Y3AQS
| 9,813 |
Update SeleniumURLLoader to use webdriver Service in favor of deprecated executable_path parameter
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| false | null |
[] | null | 1 | 2023-08-27T00:00:42 | 2023-08-27T00:12:36 | 2023-08-27T00:12:36 |
CONTRIBUTOR
| null |
Description: This commit uses the new Service object in Selenium webdriver as executable_path has been [deprecated and removed in selenium version 4.11.2](https://github.com/SeleniumHQ/selenium/commit/9f5801c82fb3be3d5850707c46c3f8176e3ccd8e)
Issue: https://github.com/langchain-ai/langchain/issues/9808
Tag Maintainer: @eyurtsev
Note -
First attempt was to create a Service object and pass empty if executable_path not provided as such:
`def _get_driver(self) -> Union["Chrome", "Firefox"]:
"""Create and return a WebDriver instance based on the specified browser.
Raises:
ValueError: If an invalid browser is specified.
Returns:
Union[Chrome, Firefox]: A WebDriver instance for the specified browser.
"""
if self.browser.lower() == "chrome":
from selenium.webdriver import Chrome
from selenium.webdriver.chrome.options import Options as ChromeOptions
from selenium.webdriver.chrome.service import Service
chrome_options = ChromeOptions()
chrome_service = Service()
for arg in self.arguments:
chrome_options.add_argument(arg)
if self.headless:
chrome_options.add_argument("--headless")
chrome_options.add_argument("--no-sandbox")
if self.binary_location is not None:
chrome_options.binary_location = self.binary_location
if self.executable_path is None:
chrome_service.path = self.executable_path
return Chrome(options=chrome_options, service=chrome_service)
elif self.browser.lower() == "firefox":
from selenium.webdriver import Firefox
from selenium.webdriver.firefox.options import Options as FirefoxOptions
from selenium.webdriver.firefox.service import Service
firefox_options = FirefoxOptions()
firefox_service = Service()
for arg in self.arguments:
firefox_options.add_argument(arg)
if self.headless:
firefox_options.add_argument("--headless")
if self.binary_location is not None:
firefox_options.binary_location = self.binary_location
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firefox_service.path = self.executable_path
return Firefox(options=firefox_options, service=firefox_service)
else:
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However in testing I found this crashes when executable_path is not passed in with the follow Exception: NoSuchDriverException(f"Unable to locate or obtain driver for {options.capabilities['browserName']}"). This updated pull request revises to only pass Service object when executable_path is provided
|
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| 1,868,296,846 |
I_kwDOIPDwls5vW_KO
| 9,812 |
Renaming RetrievalQAWithSources to RetrievalQAWithCitations
|
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[] | null | 2 | 2023-08-26T23:33:34 | 2023-12-02T16:04:52 | 2023-12-02T16:04:51 |
CONTRIBUTOR
| null |
### Feature request
Hey, I'm trying to gather some empirical evidence that RetrievalQAWithSources chain often hallucinates to returns all sources rather than cite them.
Current Issue:
Assuming you have a retriever that returns 4 sources, RetrievalQAWithSources gets confused with the below prompt:
Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES").
and usually returns all the 4 sources that were fetched by the retriever.
Possible Solution:
Given the following extracted parts of a long document and a question, create a final answer and also include all references to cite your answer in CITATIONS.
### Motivation
Having it named as "SOURCES" often leads to confusion in the prompt.
"cite" involves referring to, acknowledging, or using a source or example to support a claim, idea, or statement.
### Your contribution
I can create a PR to carry forward this change, across the various places we use Citations. Please let me know the kind of evidence or information I'll have to present to make my case for it.
|
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I_kwDOIPDwls5vW-q2
| 9,811 |
ImportError: cannot import name 'StreamlitCallbackHandler' from 'langchain.callbacks'
|
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[] | null | 3 | 2023-08-26T23:22:26 | 2023-09-19T02:01:31 | 2023-08-27T05:15:58 |
NONE
| null |
### System Info
**Background**
I'm trying to run the Streamlit Callbacks example
https://python.langchain.com/docs/integrations/callbacks/streamlit
My code is copy + paste but I get an error from the LangChain library import.
**Error**
```
ImportError: cannot import name 'StreamlitCallbackHandler' from 'langchain.callbacks' (/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/langchain/callbacks/__init__.py)
```
**What I tried**
- Upgrading LangChain `pip install langchain --upgrade`
- Reinstall `pip uninstall langchain` then `pip install langchain`
**Version**
langchain 0.0.274
streamlit 1.24.1
**System**
Mac M2 Ventura –13.4.1
### Who can help?
@hwchase17
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Run the code snippet `streamlit run app.py`
2. Error message
**Run the code with `streamlit run app.py`**
```
import streamlit as st
from langchain.llms import OpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import StreamlitCallbackHandler
from dotenv import find_dotenv, load_dotenv
load_dotenv(find_dotenv())
st_callback = StreamlitCallbackHandler(st.container())
llm = OpenAI(temperature=0, streaming=True)
tools = load_tools(["ddg-search"])
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
if prompt := st.chat_input():
st.chat_message("user").write(prompt)
with st.chat_message("assistant"):
st_callback = StreamlitCallbackHandler(st.container())
response = agent.run(prompt, callbacks=[st_callback])
st.write(response)
```
### Expected behavior
Should look like the tutorial
<img width="844" alt="image" src="https://github.com/langchain-ai/langchain/assets/94336773/f5a5f166-4013-4c2b-9776-b268156c41f8">
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| 1,868,289,453 |
PR_kwDOIPDwls5Y2-E_
| 9,810 |
add gmail loader
|
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[] | null | 1 | 2023-08-26T22:52:58 | 2023-08-28T00:18:10 | 2023-08-28T00:18:09 |
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PR_kwDOIPDwls5Y28CT
| 9,809 |
Update SeleniumURLLoader to use webdriver Service in favor of deprecated executable_path parameter
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[] | null | 1 | 2023-08-26T21:55:21 | 2023-08-26T23:50:40 | 2023-08-26T23:50:40 |
CONTRIBUTOR
| null |
Description: This commit uses the new Service object in Selenium webdriver as executable_path has been [deprecated and removed in selenium version 4.11.2](https://github.com/SeleniumHQ/selenium/commit/9f5801c82fb3be3d5850707c46c3f8176e3ccd8e)
Issue: [9808](https://github.com/langchain-ai/langchain/issues/9808)
Tag Maintainer: @eyurtsev
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| 1,868,270,215 |
I_kwDOIPDwls5vW4qH
| 9,808 |
Issue: Selenium Webdriver parameter executable_path deprecated
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[] | null | 4 | 2023-08-26T21:15:49 | 2023-12-02T16:04:57 | 2023-12-02T16:04:56 |
CONTRIBUTOR
| null |
### System Info
langchain version 0.0.273
selenium version 4.11.2
In version 4.11.2, [Selenium fully deprecated and removed executable_path parameter from webdriver](https://github.com/SeleniumHQ/selenium/commit/9f5801c82fb3be3d5850707c46c3f8176e3ccd8e) in favor of using Service class object to pass in path/executable_path parameter. This results in a crash when using SeleniumURLLoader with executable_path upon upgrading selenium to 4.11.2.
### Who can help?
@eyurtsev
### 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
Upgrade selenium to version 4.11.2. Load SeleniumURLLoader with executable_path parameter.
### Expected behavior
Expected Result: SeleniumURLLoader is instantiated
Actual Result: ERROR: WebDriver.__init__() got an unexpected keyword argument 'executable_path'
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| 1,868,269,407 |
I_kwDOIPDwls5vW4df
| 9,807 |
It should be possible to pass client to SagemakerEndpointEmbeddings & SagemakerEndpoint class
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[] | null | 1 | 2023-08-26T21:11:58 | 2023-12-02T16:05:02 | 2023-12-02T16:05:01 |
NONE
| null |
### System Info
LangChain 0.0.274, Python
### Who can help?
@hwchase17 @agola
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] 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
When initializing the the SagemakerEndpointEmbeddings & SagemakerEndpoint class, I pass the client but still get the error: Could not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid. (type=value_error)
content_handler = ContentHandler_LLM()
embeddings = SagemakerEndpoint(
client= get_sagemaker_client(),
endpoint_name= endpointName,
model_kwargs = model_kwargs ,
content_handler=content_handler,
)
content_handler = ContentHandler_Embeddings()
embeddings = SagemakerEndpointEmbeddings(
client= get_sagemaker_client(),
endpoint_name= endpointName,
content_handler=content_handler,
)
As you can see from the documentation below, the validate_environment function does not use the client that I am passing and instead tries creating its own client which causes the issue:
SOURCE: https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html#SagemakerEndpointEmbeddings
SOURCE: https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html#SagemakerEndpoint
### Expected behavior
Passing the client works with BedrockEmbeddings class. The validate_environment function checks if there is value in client, then it just returns the existing values. You can see from below snippet in the class:
SOURCE: https://api.python.langchain.com/en/latest/_modules/langchain/embeddings/bedrock.html
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that AWS credentials to and python package exists in environment."""
if values["client"] is not None:
return values
Please fix this in both the SagemakerEndpointEmbeddings and SagemakerEndpoint classes to check if value already has client then just return the values as done in BedrockEmbeddings class above.
Meanwhile, is there a work around for this?
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| 1,868,242,221 |
PR_kwDOIPDwls5Y21RZ
| 9,805 |
bump 274
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[] | null | 1 | 2023-08-26T19:09:33 | 2023-08-26T19:16:27 | 2023-08-26T19:16:26 |
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| 1,868,188,378 |
I_kwDOIPDwls5vWkra
| 9,804 |
DOC: How to Connect Database Agent with SqlServer
|
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[] | null | 6 | 2023-08-26T17:45:27 | 2023-12-26T16:02:07 | null |
CONTRIBUTOR
| null |
### Issue with current documentation:
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.chat_models import ChatOpenAI
db = SQLDatabase.from_uri("sqlite:///../../../../../notebooks/Chinook.db")
toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0))
# how to connect to sql server instead of sqlite???
### Idea or request for content:
I need to connect with my sqlserver database, but the documention does not explain how to connect langchain database agent to sqlserver.
|
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I_kwDOIPDwls5vWiCn
| 9,803 |
GoogleDriveLoader - AttributeError: 'Credentials' object has no attribute 'with_scopes'
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[] | null | 2 | 2023-08-26T17:11:56 | 2023-08-26T19:09:12 | 2023-08-26T19:09:12 |
NONE
| null |
### System Info
Google Auth version 2.22.0
Python version 3.8.8
langchain version: 0.0.273
### Who can help?
@eyurtsev the GoogleDriveLoader does not seem to be working as expected. I get a error of AttributeError: 'Credentials' object has no attribute 'with_scopes'.
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I run the code below (and many other versions of the Google Drive code) to import data from Google Drive. However, no matter what I try I get an error of "AttributeError: 'Credentials' object has no attribute 'with_scopes'". The troubleshooting seems to indicate incompatibility with version of Google Auth and Python, but the versions are compatible. I have gone through all the documentation and tutorials online about connecting Google Drive to Langchain, and I have complete and verified every step multiple times. I am at a loss of what else to try to get langchain to connect to Google Drive. Thanks in advance for your help!!!
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.document_loaders import GoogleDriveLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
folder_id = "1ExuF7GUaeDzJpuDn8ThH6t8LBcmjAKE_"
loader = GoogleDriveLoader(
folder_id=folder_id,
recursive=False
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=4000, chunk_overlap=0, separators=[" ", ",", "\n"]
)
texts = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
while True:
query = input("> ")
answer = qa.run(query)
print(answer)
### Expected behavior
I expect langchain to be able to connect to me Google Drive files and folders.
|
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| 1,868,175,495 |
I_kwDOIPDwls5vWhiH
| 9,802 |
Issue: vector store supports custom database connections?
|
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[] | null | 1 | 2023-08-26T17:04:57 | 2023-10-28T07:03:20 | 2023-10-28T07:03:20 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
May I inquire whether the vector stroe accommodates the provision for customized database connection pooling?
Typically, opening a database connection is an expensive operation, especially if the database is remote. Pooling keeps the connections active so that, when a connection is later requested, one of the active ones is used in preference to having to create another one.
### Suggestion:
Maybe it could be like this?
```python
engine = sqlalchemy.create_engine(connection_string, pool_size=10)
conn = engine.connect()
store = PGVector(
custom_connection=conn,
embedding_function=embeddings
)
```
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PR_kwDOIPDwls5Y2l4i
| 9,801 |
Adds dataview fields and tags to metadata #9800
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[] | null | 3 | 2023-08-26T16:18:04 | 2023-09-04T12:48:52 | 2023-09-03T22:56:49 |
CONTRIBUTOR
| null |
Description: Adds tags and dataview fields to ObsidianLoader doc metadata.
- Issue: #9800, #4991
- Dependencies: none
- Tag maintainer: My best guess is @hwchase17 looking through the git logs
- Twitter handle: I don't use twitter, sorry!
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I_kwDOIPDwls5vWcxF
| 9,800 |
Add support tags and dataview fields to ObsidianLoader
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[] | null | 2 | 2023-08-26T16:02:26 | 2023-12-02T16:05:07 | 2023-12-02T16:05:06 |
CONTRIBUTOR
| null |
### Feature request
Obsidian markdown documents frequently have additional metadata beyond what is in the frontmatter: tags within the document, and (for many users) dataview plugin values.
Add support for this.
### Motivation
Surfacing tags and dataview fields would unlock more abilities for self-querying obsidian data (https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/chroma_self_query)
### Your contribution
I plan to make a PR for this.
|
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| 9,798 |
Issue: RuntimeWarning: coroutine 'AsyncCallbackHandler.on_chat_model_start' was never awaited
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[] | null | 3 | 2023-08-26T12:46:53 | 2023-12-02T16:05:12 | 2023-12-02T16:05:11 |
NONE
| null |
### Issue you'd like to raise.
Hello, I tried to use AsyncFinalIteratorCallbackHandler (which inherits from AsyncCallbackHandler) to implement async streaming, then encountered following issue:
libs/langchain/langchain/callbacks/manager.py:301: RuntimeWarning: coroutine 'AsyncCallbackHandler.on_chat_model_start' was never awaited
getattr(handler, event_name)(*args, **kwargs)
following is the demo:
`
import os
import asyncio
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.agents import AgentType
from langchain.agents import initialize_agent
from langchain.callbacks.streaming_aiter_final_only import AsyncFinalIteratorCallbackHandler
os.environ["OPENAI_API_KEY"] = "<your openai key>"
async_streaming_handler = AsyncFinalIteratorCallbackHandler()
memory = ConversationBufferMemory(
memory_key="chat_history", return_messages=True)
llm = ChatOpenAI(temperature=0, streaming=True, model="gpt-3.5-turbo-16k-0613")
agent_chain = initialize_agent(
[], llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
async def streaming():
result = agent_chain.run(
'how to gain good reviews from customer?', callbacks=[async_streaming_handler])
print(f"result: {result}")
while True:
token = await async_streaming_handler.queue.get()
print(f"async token: {token}")
await asyncio.sleep(0.1)
asyncio.run(streaming())
`
how can I deal with it, could anyone help me?
### Suggestion:
_No response_
|
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| 1,868,070,708 |
PR_kwDOIPDwls5Y2UsN
| 9,797 |
Add ChatGLM for llm and chat_model by using ChatGLM API
|
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[] | null | 8 | 2023-08-26T11:50:52 | 2023-11-16T07:05:42 | 2023-09-19T23:17:07 |
CONTRIBUTOR
| null |
using sample:
```
endpoint_url = API URL
ChatGLM_llm = ChatGLM(
endpoint_url=endpoint_url,
api_key=Your API Key by ChatGLM
)
print(ChatGLM_llm("hello"))
```
```
model = ChatChatGLM(
chatglm_api_key="api_key",
chatglm_api_base="api_base_url",
model_name="model_name"
)
chain = LLMChain(llm=model)
```
Description: The call of ChatGLM has been adapted.
Issue: The call of ChatGLM has been adapted.
Dependencies: Need python package `zhipuai` and `aiostream`
Tag maintainer: @baskaryan
Twitter handle: None
I remove the compatibility test for pydantic version 2, because pydantic v2 can't not pickle classmethod,but BaseModel use @root_validator is a classmethod decorator.
|
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PR_kwDOIPDwls5Y2IPU
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Add ChatGLM API for llm and chat_model
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[] | null | 2 | 2023-08-26T10:09:54 | 2023-08-26T11:49:03 | 2023-08-26T11:49:03 |
CONTRIBUTOR
| null |
using sample:
```
endpoint_url = API URL
ChatGLM_llm = ChatGLM(
endpoint_url=endpoint_url,
api_key=Your API Key by ChatGLM
)
print(ChatGLM_llm("hello"))
```
```
model = ChatChatGLM(
chatglm_api_key="api_key",
chatglm_api_base="api_base_url",
model_name="model_name"
)
chain = LLMChain(llm=model)
```
Description: The call of ChatGLM has been adapted.
Issue: The call of ChatGLM has been adapted.
Dependencies: Need python package `zhipuai` and `aiostream`
Tag maintainer: None
Twitter handle: None
|
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PR_kwDOIPDwls5Y2DGV
| 9,794 |
docs: Fix a spelling mistake in adding_memory.ipynb
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[] | null | 2 | 2023-08-26T08:50:05 | 2023-08-26T19:04:43 | 2023-08-26T19:04:43 |
CONTRIBUTOR
| null |
# Description
This pull request fixes a small spelling mistake found while reading docs.
|
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PR_kwDOIPDwls5Y18cC
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Bugfix/jsonloader metadata
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[] | null | 1 | 2023-08-26T07:24:09 | 2023-09-03T23:01:43 | 2023-09-03T23:01:43 |
CONTRIBUTOR
| null |
Hi,
- Description:
- Solves the issue #6478.
- Includes some additional rework on the `JSONLoader` class:
- Getting metadata is decoupled from `_get_text`
- Validating metadata_func is perform now by `_validate_metadata_func`, instead of `_validate_content_key`
- Issue: #6478
- Dependencies: NA
- Tag maintainer: @hwchase17
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I_kwDOIPDwls5vVpwL
| 9,792 |
Issue: what's the difference between this with RASA
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NONE
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PR_kwDOIPDwls5Y16QZ
| 9,791 |
docs: misc retrievers fixes
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[] | null | 1 | 2023-08-26T06:50:29 | 2023-09-04T06:16:09 | 2023-09-04T03:26:49 |
CONTRIBUTOR
| null |
Various miscellaneous fixes to most pages in the 'Retrievers' section of the documentation:
- "VectorStore" and "vectorstore" changed to "vector store" for consistency
- Various spelling, grammar, and formatting improvements for readability
|
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PR_kwDOIPDwls5Y15YR
| 9,790 |
fix multipart email body retrieval
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[] | null | 2 | 2023-08-26T06:36:50 | 2023-09-03T23:04:36 | 2023-09-03T23:04:36 |
CONTRIBUTOR
| null |
Description:
Gmail message retrieval in GmailGetMessage and GmailSearch returned an empty string when encountering multipart emails. This change correctly extracts the email body for multipart emails.
Dependencies: None
@hwchase17 @vowelparrot
|
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PR_kwDOIPDwls5Y140P
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Fixed Typos and grammatical issues in document files
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[] | null | 5 | 2023-08-26T06:28:26 | 2023-09-03T23:09:14 | 2023-09-03T23:09:14 |
CONTRIBUTOR
| null |
Fixed typos and grammatical issues in document files.
@baskaryan , @eyurtsev
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PR_kwDOIPDwls5Y14Hd
| 9,787 |
Update mrkl docs
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[] | null | 2 | 2023-08-26T06:17:30 | 2023-09-03T23:09:35 | 2023-09-03T23:09:35 |
NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- Dependencies: any dependencies required for this change,
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If you're adding a new integration, please include:
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I_kwDOIPDwls5vVjKN
| 9,786 |
GPT4All callup failure
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[] | null | 2 | 2023-08-26T05:44:11 | 2023-12-18T23:48:24 | 2023-12-18T23:48:23 |
NONE
| null |
### System Info
Colab environment
LangChain version: 0.0.152
### Who can help?
_No response_
### Information
- [X] 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
Please follow the tutorial [here](https://learn.activeloop.ai/courses/take/langchain/multimedia/46317672-using-the-open-source-gpt4all-model-locally) and run the code below to reproduce
```
template = """Question: {question}
Answer: Let's answer in two sentence while being funny."""
prompt = PromptTemplate(template=template, input_variables=["question"])
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = GPT4All(model="./models/ggml-model-q4_0.bin", callback_manager=callback_manager, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What happens when it rains somewhere?"
llm_chain.run(question)
```
### Expected behavior
TypeError: GPT4All.generate() got an unexpected keyword argument 'n_ctx'
|
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PR_kwDOIPDwls5Y1wme
| 9,785 |
Update json_loader.py: encoding bug
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[] | null | 1 | 2023-08-26T04:11:56 | 2023-09-03T23:16:03 | 2023-09-03T23:16:02 |
CONTRIBUTOR
| null |
JSONLoader.load does not specify `encoding` in `self.file_path.read_text()` as `self.file_path.open()`
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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:
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PR_kwDOIPDwls5Y1sFj
| 9,784 |
typo: funtions --> functions
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[] | null | 2 | 2023-08-26T02:58:45 | 2023-08-26T18:47:47 | 2023-08-26T18:47:47 |
CONTRIBUTOR
| null |
Minor typo in the extractions use-case
|
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| 1,867,861,639 |
I_kwDOIPDwls5vVU6H
| 9,783 |
document loader google drive for javascript version
|
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[] | null | 2 | 2023-08-26T02:19:29 | 2023-12-02T16:05:22 | 2023-12-02T16:05:21 |
NONE
| null |
### Feature request
Hi, Actually exists a document loader for Google Drive in the Python version, I would like to propose this feature in the javascript version, it is important to our company.
### Motivation
Hi, Actually exists a document loader for Google Drive in the Python version, I would like to propose this feature in the javascript version, it is important to our company.
### Your contribution
no :( I don´t have the knowledge
|
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PR_kwDOIPDwls5Y1oTy
| 9,782 |
docs: Fix `CustomLLM` and `Question_answering` docs
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[] | null | 1 | 2023-08-26T01:55:49 | 2023-09-05T14:38:50 | 2023-09-03T23:15:46 |
CONTRIBUTOR
| null |
### Description
- Update `CustomLLM._call`: Corrected the _call method in CustomLLM to include **kwargs, ensuring consistency with parent class.
- Update `Question_answering`: To fix `Page not found` error
- https://python.langchain.com/docs/use_cases/code -> https://python.langchain.com/docs/use_cases/code_understanding
### Issue
N/A
### Dependencies
N/A
### Tag maintainer
N/A
### Twitter handle
N/A
|
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PR_kwDOIPDwls5Y1hrc
| 9,781 |
Issue #2241 Support nested code blocks in agent response JSON.
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[] | null | 4 | 2023-08-26T00:41:19 | 2023-11-10T22:03:54 | 2023-11-10T22:03:54 |
CONTRIBUTOR
| null |
Resolves https://github.com/langchain-ai/langchain/issues/2241.
ConversationalChatAgent prompt contains JSON examples nested in triple-ticked codeblocks.
When response contains a triple-ticked codeblock, this breaks the response.
PR updates prompt to use quadruple-ticked code blocks, enabling responses to include code examples in markdown.
cc: @hwchase17 @agola11
|
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I_kwDOIPDwls5vUzgs
| 9,780 |
RetrievalQA for document comparison is not working for similar type of questions.
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[] | null | 2 | 2023-08-25T21:54:12 | 2023-12-01T16:06:23 | 2023-12-01T16:06:22 |
NONE
| null |
### System Info
pydantic==1.10.12
langchain==0.0.271
System: MacOS Ventura 13.5 (22G74)
Python 3.9.6
### Who can help?
@hwchase17 @agola11
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here is the main python file:
```
from dotenv import load_dotenv
load_dotenv()
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyPDFLoader
from langchain.agents import Tool
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from pydantic import BaseModel, Field
class DocumentInput(BaseModel):
question: str = Field()
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
tools = []
files = [
{
"name": "alphabet-earnings",
"path": "https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf",
},
{
"name": "tesla-earnings",
"path": "https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update",
},
]
for file in files:
print(f"Loading {file['name']} with path {file['path']}")
loader = PyPDFLoader(file["path"])
pages = loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(pages)
embeddings = OpenAIEmbeddings()
retriever = FAISS.from_documents(docs, embeddings).as_retriever()
# Wrap retrievers in a Tool
tools.append(
Tool(
args_schema=DocumentInput,
name=file["name"],
description=f"useful when you want to answer questions about {file['name']}",
func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever),
)
)
agent = initialize_agent(
agent=AgentType.OPENAI_FUNCTIONS,
tools=tools,
llm=llm,
verbose=True,
)
comparison = agent({"input": "which one has higher earning?"})
print(comparison)
print('-------------------------------')
comparison = agent({"input": "did alphabet or tesla have more revenue?"})
print(comparison)
```
The python version is 3.9.6 and pydantic==1.10.12 and langchain==0.0.271
Run the code using python main.py
Here is the output:
```
Loading alphabet-earnings with path https://abc.xyz/investor/static/pdf/2023Q1_alphabet_earnings_release.pdf
Loading tesla-earnings with path https://digitalassets.tesla.com/tesla-contents/image/upload/IR/TSLA-Q1-2023-Update
> Entering new AgentExecutor chain...
Which companies' earnings are you referring to?
> Finished chain.
{'input': 'which one has higher earning?', 'output': "Which companies' earnings are you referring to?"}
-------------------------------
> Entering new AgentExecutor chain...
Invoking: `alphabet-earnings` with `{'question': 'revenue'}`
{'query': 'revenue', 'result': 'The revenue for Alphabet Inc. for the quarter ended March 31, 2023, was $69,787 million.'}
Invoking: `tesla-earnings` with `{'question': 'revenue'}`
{'query': 'revenue', 'result': 'Total revenue for Q1-2023 was $23.3 billion.'}Alphabet Inc. had more revenue than Tesla. Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million, while Tesla's total revenue for Q1-2023 was $23.3 billion.
> Finished chain.
{'input': 'did alphabet or tesla have more revenue?', 'output': "Alphabet Inc. had more revenue than Tesla. Alphabet's revenue for the quarter ended March 31, 2023, was $69,787 million, while Tesla's total revenue for Q1-2023 was $23.3 billion."}
```
### Expected behavior
I expect that for the question 'which one has higher earning?' also gives a good answer just like it did when the question asked was 'did alphabet or tesla have more revenue?' instead.
I was following this guide: https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit
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| 9,779 |
Code LLaMA in code understanding use case
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[] | null | 1 | 2023-08-25T21:12:03 | 2023-08-25T21:24:39 | 2023-08-25T21:24:38 |
COLLABORATOR
| null |
Update Code Understanding use case doc w/ Code-llama.
|
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| 1,867,657,651 |
I_kwDOIPDwls5vUjGz
| 9,778 |
Pass headers arg (requests library) to loaders that fetch from web
|
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[] | null | 1 | 2023-08-25T20:39:29 | 2023-12-01T16:06:27 | 2023-12-01T16:06:27 |
CONTRIBUTOR
| null |
### Feature request
Some urls will return status code 403 if the right headers are not passed to the requests.get(path) function. My workaround was to provide the headers of that website to get the approval from the server. Would be great to be able to pass headers to PyMuPDFLoader and all the other web based document_loaders
headers = {
"User-Agent": "Chrome/109.0.5414.119",
"Referer": "https://www.ncbi.nlm.nih.gov" if 'ncbi' in self.file_path else None
}
r = requests.get(self.file_path, headers=headers)
the execution would be PyMuPDF(path, headers).load() and if it detects that headers is provided itll provide it downstream to 'get'
### Motivation
Some urls will return status code 403 if the right headers are not passed to the requests.get(path) function. My workaround was to provide the headers of that website to get the approval from the server. Would be great to be able to pass headers to PyMuPDFLoader and all the other web based document_loaders
Mainly an issue with websites like NCBI
### Your contribution
Not experienced enough
|
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I_kwDOIPDwls5vUTMH
| 9,776 |
ConversationChain sends malformatted requests to server
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[] | null | 3 | 2023-08-25T19:37:16 | 2023-12-07T16:06:30 | 2023-12-07T16:06:29 |
NONE
| null |
### System Info
LangChain version 0.0.273
Python version 3.8.10
Ubuntu 20.04.5 LTS
### Who can help?
@hwchase17
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the following code:
```
MY_MODEL_NAME="SUBSTITUTE_THIS_WITH_YOUR_OWN_MODEL_FOR_REPRODUCTION"
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
llm = ChatOpenAI(temperature=0.1, model=MY_MODEL_NAME)
memory = ConversationBufferMemory(return_messages=True)
conversation = ConversationChain(llm=llm, memory=memory)
message1 = "Howdy there"
response1 = conversation(message1)
print(response1)
message2 = "How's it going?"
response2 = conversation(message2)
print(response2)
```
Inspect the requests sent to the server. They will resemble the following packets received by my own server:
request1:
```
'messages': [{'role': 'user', 'content': 'The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n\nCurrent conversation:\n[]\nHuman: Howdy there\nAI:'}]
```
request2:
```
'messages': [{'role': 'user', 'content': "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n\nCurrent conversation:\n[HumanMessage(content='Howdy there', additional_kwargs={}, example=False), AIMessage(content='Howdy! How can I help you today?\\n', additional_kwargs={}, example=False)]\nHuman: How's it going?\nAI:"}]
```
Note that these requests are malformatted.
### Expected behavior
There are two issues.
First, the `messages` packet is clearly malformatted, containing HumanMessage and AIMessage strings.
Second, the `messages` packet only has one conversation turn, and there appears to be no options within the ConversationChain class to allow for multiple turns.
This is particularly problematic as the ConversationChain class requires the user to know what turn tokens are appropriate to use. The user cannot and should not be expected to have knowledge of how the model was trained: there should be an option to leave this up to the server to decide.
My expected (and required for my product) behavior is for the two requests to be formatted as follows.
request1:
```
'messages': [{'role': 'user', 'content': 'Howdy there'}]
```
request2:
```
'messages': [{'role': 'user', 'content': 'Howdy there'}, {'role': 'assistant', 'content': 'Howdy! How can I help you today?\\n'}, {'role': 'user', 'content': "How's it going?"}]
```
Ultimately it is confusing why `conversation(message1); conversation(message2);` sends a different request to the server back-end than `llm([HumanMessage(content=message1), AIMessage(content=response1), HumanMessage(content=message2)])` does.
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PR_kwDOIPDwls5Y0b_8
| 9,775 |
fixed regex to match sources for all cases, also includes source
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[] | null | 5 | 2023-08-25T18:16:29 | 2023-08-26T01:10:33 | 2023-08-26T01:10:33 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: Updated the regex to handle all the different cases for string matching (SOURCES, sources, Sources),
- Issue: https://github.com/langchain-ai/langchain/issues/9774
- Dependencies: N/A
- 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_kwDOIPDwls5vT2Jl
| 9,774 |
The RetrievalQAWithSourcesChain with the ExamplePrompt doesn't always return SOURCES as part of it's answers.
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[] | null | 3 | 2023-08-25T17:57:20 | 2023-12-02T16:05:32 | 2023-12-02T16:05:31 |
CONTRIBUTOR
| null |
https://github.com/langchain-ai/langchain/blob/709a67d9bfcff475356924d8461140052dd418f7/libs/langchain/langchain/chains/qa_with_sources/base.py#L123
I've noticed that the retrieval qa chain doesn't always return SOURCES, it sometimes returns "Sources", "sources" or "source".
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I_kwDOIPDwls5vTqGy
| 9,773 |
Issue: SagemakerEndpoint does not support async calls
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[] | null | 1 | 2023-08-25T17:21:24 | 2023-12-01T16:06:42 | 2023-12-01T16:06:42 |
NONE
| null |
### Issue you'd like to raise.
Right now, the Sagemaker Inference Endpoint LLM does not allow for async requests and it's a performance bottleneck.
I have an API gateway set up such that I have a restful api endpoint backed by the sagemaker inference endpoint.
In an ideal world:
1. Langchain should allow for arbitrary http requests to a backend LLM of our choice fronted by your LLM interfaces. This way, we can standardize async calls for this sort of flow.
2. SagemakerEndpoint should allow for async requests.
Is this feasible?
Does this exist at the moment?
### Suggestion:
_No response_
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Cassandra support for LLM cache (exact-match and semantic)
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[] | null | 6 | 2023-08-25T16:35:37 | 2023-09-14T00:27:13 | 2023-09-04T03:27:02 |
CONTRIBUTOR
| null |
This PR implements two new classes in the cache module: `CassandraCache` and `CassandraSemanticCache`, similar in structure and functionality to their Redis counterpart: providing a cache for the response to a (prompt, llm) pair.
Integration tests are included. Moreover, linting and type checks are all passing on my machine.
Dependencies: the `pyproject.toml` and `poetry.lock` have the newest version of cassIO (the very same as in the Cassandra vector store metadata PR, submitted as #9280).
If I may suggest, this issue and #9280 might be reviewed together (as they bring the same poetry changes along), so I'm tagging @baskaryan who already helped out a little with poetry-related conflicts there. (Thank you!)
I'd be happy to add a short notebook if this is deemed necessary (but it seems to me that, contrary e.g. to vector stores, caches are not covered in specific notebooks).
Thank you!
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PR_kwDOIPDwls5Yzk8C
| 9,771 |
fix broken wandb link in debugging page
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[] | null | 1 | 2023-08-25T15:25:36 | 2023-08-25T22:34:08 | 2023-08-25T22:34:08 |
CONTRIBUTOR
| null |
- Description: Fix broken hyperlink in debugging page
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PR_kwDOIPDwls5YzeYm
| 9,770 |
Add neo4j vector support
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[] | null | 5 | 2023-08-25T15:04:30 | 2023-08-29T14:54:21 | 2023-08-29T14:54:20 |
CONTRIBUTOR
| null |
Neo4j has added vector index integration just recently. To allow both ingestion and integrating it as vector RAG applications, I wrapped it as a vector store as the implementation is completely different from `GraphCypherQAChain`. Here, we are not generating any Cypher statements at query time, we are simply doing the vector similarity search using the new vector index as if we were dealing with a vector database.
Todo:
* [x] Tests
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PR_kwDOIPDwls5Yzaad
| 9,769 |
Add Notebook demonstrating how to use Poetry
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[] | null | 1 | 2023-08-25T14:52:12 | 2023-09-03T23:25:00 | 2023-09-03T23:25:00 |
CONTRIBUTOR
| null |
-It can be useful to understand how to load modified code from a custom fork
- Using Poetry as a dependency and virtualenv manager is helpful, but requires a different workflow within a Jupyter Notebook context
[Link to rendered Notebook for easier review.](https://github.com/langchain-ai/langchain/blob/dc331e33695137affbe4ded1f5bf2b7633f4df15/docs/extras/integrations/vectorstores/pineconepoetry.ipynb)
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: Add a new example Jupyter Notebook demonstrating how to work with Poetry to load modified library code, such as langchain, from a branch of a fork
- Dependencies: None, the Notebook fetches the data it needs remotely at runtime,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: zackproser
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_kwDOIPDwls5YzZPj
| 9,768 |
Fixing broken links to Moderation and Constitutional chain
|
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[] | null | 2 | 2023-08-25T14:49:02 | 2023-08-25T22:19:32 | 2023-08-25T22:19:32 |
CONTRIBUTOR
| null |
- Description: Fixing broken links for Moderation and Constitutional chain
- Issue: N/A
- Twitter handle: MonamiSharma
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I_kwDOIPDwls5vSnFi
| 9,767 |
PySpark error message and token limits in spark_sql
|
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[] | null | 1 | 2023-08-25T14:22:11 | 2023-12-01T16:06:48 | 2023-12-01T16:06:47 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
The issue with PySpark messages is that they could become quite lengthy and after some iterations they could potentially causing problems with token limits. In this context, I would like to initiate a discussion about this topic and explore potential solutions.
### Suggestion:
My suggestion would be to generate a summary of the error message before returning. I am currently not deep enough into the langchain topic and maybe there are better options so feel free to comment.
It is regarding following part [langchain/libs/langchain/langchain/utilities/spark_sql.py](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/utilities/spark_sql.py):
```python
def run_no_throw(self, command: str, fetch: str = "all") -> str:
"""Execute a SQL command and return a string representing the results.
If the statement returns rows, a string of the results is returned.
If the statement returns no rows, an empty string is returned.
If the statement throws an error, the error message is returned.
"""
try:
return self.run(command, fetch)
except Exception as e:
"""Format the error message"""
return f"Error: {e}"
```
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PR_kwDOIPDwls5YzOva
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Utility to run commands in docker containers as isolated execution environment.
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NONE
| null |
**DRAFT**
Description: It is a wrapper around docker python library, allowing to pull/build docker images with ability to run and execute command in a container. It is a low-level building block which allows to isolate execution and constrain resources - should allow to improve security a bit after integration with things like PythonREPL.
Dependencies: docker
Twitter handle: @deepsense_ai
**WIP**: For now I think integration tests are required. I run tests outside langchain so I need yet to see if they pass & can run on CI.
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I_kwDOIPDwls5vSlLP
| 9,765 |
AzureSearch.py is using constant field names instead of ours
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[] | null | 12 | 2023-08-25T14:18:44 | 2023-12-18T23:48:28 | 2023-12-18T23:48:27 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.273
### Who can help?
@hwchase17
### 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
I am trying to use Azure Cognitive Search retriever, however it fails because our fields are different:
Our index looks like this:
Our code:
```
llm = AzureChatOpenAI(
openai_api_base=config['AZURE_OPENAI_ENDPOINT'],
openai_api_version=config['AZURE_OPENAI_API_VERSION'],
deployment_name=config['OPENAI_DEPLOYMENT_NAME'],
openai_api_key=config['AZURE_OPENAI_API_KEY'],
openai_api_type=config['OPENAI_API_TYPE'],
model_name=config['OPENAI_MODEL_NAME'],
temperature=0)
embeddings = OpenAIEmbeddings(
openai_api_base=config['AZURE_OPENAI_ENDPOINT'],
openai_api_type="azure",
deployment=config['AZURE_OPENAI_EMBEDDING_DEPLOYED_MODEL_NAME'],
openai_api_key=config['AZURE_OPENAI_API_KEY'],
model=config['AZURE_OPENAI_EMBEDDING_DEPLOYED_MODEL_NAME'],
chunk_size=1)
fields = [
SimpleField(
name="id",
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name="text",
type=SearchFieldDataType.String,
searchable=True,
),
SearchField(
name="embedding",
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=1536,
vector_search_configuration="default",
)
]
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=config['AZURE_SEARCH_SERVICE_ENDPOINT'],
azure_search_key=config['AZURE_SEARCH_ADMIN_KEY'],
index_name=config['AZURE_SEARCH_VECTOR_INDEX_NAME'],
embedding_function=embeddings.embed_query,
fields=fields,
)
retriever = vector_store.as_retriever(search_type="similarity", kwargs={"k": 3})
# Creating instance of RetrievalQA
chain = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
# Generating response to user's query
response = chain({"query": config['question']})
```
I traced it all down to the function: vector_search_with_score in azuresearch.py
```
results = self.client.search(
search_text="",
vectors=[
Vector(
value=np.array(
self.embedding_function(query), dtype=np.float32
).tolist(),
k=k,
fields=FIELDS_CONTENT_VECTOR,
)
],
select=[FIELDS_ID, FIELDS_CONTENT, FIELDS_METADATA],
filter=filters,
)
```
The code is trying to use FIELDS_CONTENT_VECTOR which is a constant and its not our field name.
I guess some other issues may arise with other parts of the code where constants are used.
Why do we have different field names?
We are using Microsoft examples to setup all azure indexing, indexers, skillsets and datasources:
https://github.com/Azure/cognitive-search-vector-pr/blob/main/demo-python/code/azure-search-vector-ingestion-python-sample.ipynb
and their open ai embedding generator deployed as an azure function:
https://github.com/Azure-Samples/azure-search-power-skills/blob/main/Vector/EmbeddingGenerator/README.md
I wrote a blog post series about this
https://medium.com/python-in-plain-english/elevate-chat-ai-applications-mastering-azure-cognitive-search-with-vector-storage-for-llm-a2082f24f798
### Expected behavior
I should be able to define the fields we want to use, but the code uses constants
|
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| 1,867,102,659 |
PR_kwDOIPDwls5YzFIc
| 9,764 |
Add EdenAI Tools
|
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[] | null | 2 | 2023-08-25T13:54:35 | 2023-09-01T15:26:57 | 2023-09-01T15:26:56 |
CONTRIBUTOR
| null |
This PR follows the Eden AI (LLM + embeddings) integration. #8633
We added different Tools to empower agents with new capabilities :
- text: explicit content detection
- image: explicit content detection
- image: object detection
- OCR: invoice parsing
- OCR: ID parsing
- audio: speech to text
- audio: text to speech
We plan to add more in the future (like translation, language detection, + others).
Usage:
```python
llm=EdenAI(feature="text",provider="openai", params={"temperature" : 0.2,"max_tokens" : 250})
tools = [
EdenAiTextModerationTool(providers=["openai"],language="en"),
EdenAiObjectDetectionTool(providers=["google","api4ai"]),
EdenAiTextToSpeechTool(providers=["amazon"],language="en",voice="MALE"),
EdenAiExplicitImageTool(providers=["amazon","google"]),
EdenAiSpeechToTextTool(providers=["amazon"]),
EdenAiParsingIDTool(providers=["amazon","klippa"],language="en"),
EdenAiParsingInvoiceTool(providers=["amazon","google"],language="en"),
]
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
return_intermediate_steps=True,
)
result = agent_chain(""" i have this text : 'i want to slap you'
first : i want to know if this text contains explicit content or not .
second : if it does contain explicit content i want to know what is the explicit content in this text,
third : i want to make the text into speech .
if there is URL in the observations , you will always put it in the output (final answer) .
""")
```
output:
> Entering new AgentExecutor chain...
> I need to extract the information from the ID and then convert it to text and then to speech
> Action: edenai_identity_parsing
> Action Input: "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg"
> Observation: last_name :
> value : ANGELA
> given_names :
> value : GREENE
> birth_place :
> birth_date :
> value : 2000-11-09
> issuance_date :
> expire_date :
> document_id :
> issuing_state :
> address :
> age :
> country :
> document_type :
> value : DRIVER LICENSE FRONT
> gender :
> image_id :
> image_signature :
> mrz :
> nationality :
> Thought: I now need to convert the information to text and then to speech
> Action: edenai_text_to_speech
> Action Input: "Welcome Angela Greene!"
> Observation: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5yHAJjf657u7Z1lFTBMoXGBuw1VYmyno-3TAiPeUcVlQXPueJ-ymZXmwaITmGOfH7HipZngZBziofRAFdhMYbIjYhegu5jS7TxHwRuox32A__&Key-Pair-Id=K1F55BTI9AHGIK
> Thought: I now know the final answer
> Final Answer: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y
>
> Finished chain.
Other examples are available in the jupyter notebook.
This PR is made in parallel with EdenAI LLM update #8963
I apologize for the messy PR. While working in implementing Tools we realized there was a few problems we needed to fix on LLM as well.
Ping: @hwchase17, @baskaryan
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PR_kwDOIPDwls5Yy-lM
| 9,763 |
fix call _get_keys for custom_evaluator
|
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[] | null | 4 | 2023-08-25T13:36:21 | 2023-08-30T13:35:30 | 2023-08-30T13:35:23 |
CONTRIBUTOR
| null |
In the function _load_run_evaluators the function _get_keys was not called if only custom_evaluators parameter is used
- Description: In the function _load_run_evaluators the function _get_keys was not called if only custom_evaluators parameter is used,
- Issue: no issue created for this yet,
- Dependencies: None,
- Tag maintainer: @vowelparrot,
- Twitter handle: Buckler89
|
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| 1,867,071,726 |
I_kwDOIPDwls5vSUDu
| 9,762 |
Streaming doesn't work correctly with caching
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[] | null | 4 | 2023-08-25T13:35:30 | 2023-12-01T16:06:53 | 2023-12-01T16:06:52 |
NONE
| null |
### System Info
LangChain version : 0.0.273
Python version : 3.10.8
Platform : macOS 13.5.1
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import langchain
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.cache import InMemoryCache
langchain.llm_cache = InMemoryCache()
llm = ChatOpenAI(model_name="gpt-3.5-turbo", streaming=True, callbacks=[StreamingStdOutCallbackHandler()])
resp = llm.predict("Tell me a joke")
resp1 = llm.predict("Tell me a joke")
```
output:
```
Sure, here's a classic one for you:
Why don't scientists trust atoms?
Because they make up everything!
```
### Expected behavior
I'd expect both responses to be streamed to stdout but as the second one is coming from the memory cache, the callback handler `on_llm_new_token` is never called and thus the second response never printed.
I guess `on_llm_new_token` should be called once with the full response loaded from cache to ensure a consistent behavior between new and cached responses.
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| 1,867,063,910 |
I_kwDOIPDwls5vSSJm
| 9,761 |
SelfQueryRetriever creates attributes that do not exist
|
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[] | null | 5 | 2023-08-25T13:31:11 | 2024-01-12T11:56:38 | 2023-12-01T16:06:57 |
NONE
| null |
### System Info
langchain==0.0.191
llama-cpp-python==0.1.78
chromadb==0.3.22
python3.10
wizard-vicuna-13B.ggmlv3.q4_0.bin
### Who can help?
@hwchase17 @agol
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have a slightly modified version of privateGPT running. I am facing a weird issue where the SelfQueryRetriever is creating attributes that do not exist in my ChromaDB. This is crashing the app when running the RetrievalQA with error `chromadb.errors.NoDatapointsException: No datapoints found for the supplied filter`. I have provided a list of the attributes that exists in my DB, but still the SelfQueryRetreiever is creating filters on metadata that does not exist.
To reproduce the problem, use the wizard-vicuna-13B.ggmlv3.q4_0.bin model provided by TheBloke/wizard-vicuna-13B-GGML on HuggingFace and run the below code. I don't think the choice of model has an impact here. The issue I am facing is the creation of metadata filters that do not exist.
Is there a way to limit the attributes allowed by the SelfQueryRetriever?
```python
import logging
import click
import torch
from auto_gptq import AutoGPTQForCausalLM
from huggingface_hub import hf_hub_download
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.llms import HuggingFacePipeline, LlamaCpp
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
import time
# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
LlamaForCausalLM,
LlamaTokenizer,
pipeline,
)
from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME
SEED = 42
def load_model(device_type, model_id, model_basename=None, local_model: bool = False, local_model_path: str = None):
"""
Select a model for text generation using the HuggingFace library.
If you are running this for the first time, it will download a model for you.
subsequent runs will use the model from the disk.
Args:
device_type (str): Type of device to use, e.g., "cuda" for GPU or "cpu" for CPU.
model_id (str): Identifier of the model to load from HuggingFace's model hub.
model_basename (str, optional): Basename of the model if using quantized models.
Defaults to None.
Returns:
HuggingFacePipeline: A pipeline object for text generation using the loaded model.
Raises:
ValueError: If an unsupported model or device type is provided.
"""
if local_model:
logging.info(f'Loading local model at {local_model_path}')
else:
logging.info(f"Loading Model: {model_id}, on: {device_type}")
logging.info("This action can take a few minutes!")
if model_basename is not None:
# if "ggml" in model_basename:
if ".ggml" in model_basename:
logging.info("Using Llamacpp for GGML quantized models")
if local_model:
model_path = local_model_path
else:
model_path = hf_hub_download(repo_id=model_id, filename=model_basename)
max_ctx_size = 2048
kwargs = {
"model_path": model_path,
"n_ctx": max_ctx_size,
"max_tokens": max_ctx_size,
}
if device_type.lower() == "mps":
kwargs["n_gpu_layers"] = 1000
if device_type.lower() == "cuda":
kwargs['seed'] = SEED
kwargs["n_gpu_layers"] = 40
return LlamaCpp(**kwargs)
else:
# The code supports all huggingface models that ends with GPTQ and have some variation
# of .no-act.order or .safetensors in their HF repo.
logging.info("Using AutoGPTQForCausalLM for quantized models")
if ".safetensors" in model_basename:
# Remove the ".safetensors" ending if present
model_basename = model_basename.replace(".safetensors", "")
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
logging.info("Tokenizer loaded")
model = AutoGPTQForCausalLM.from_quantized(
model_id,
model_basename=model_basename,
use_safetensors=True,
trust_remote_code=True,
device="cuda:0",
use_triton=False,
quantize_config=None,
)
elif (
device_type.lower() == "cuda"
): # The code supports all huggingface models that ends with -HF or which have a .bin
# file in their HF repo.
logging.info("Using AutoModelForCausalLM for full models")
tokenizer = AutoTokenizer.from_pretrained(model_id)
logging.info("Tokenizer loaded")
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
trust_remote_code=True,
# max_memory={0: "15GB"} # Uncomment this line with you encounter CUDA out of memory errors
)
model.tie_weights()
else:
logging.info("Using LlamaTokenizer")
tokenizer = LlamaTokenizer.from_pretrained(model_id)
model = LlamaForCausalLM.from_pretrained(model_id)
# Load configuration from the model to avoid warnings
generation_config = GenerationConfig.from_pretrained(model_id)
# see here for details:
# https://huggingface.co/docs/transformers/
# main_classes/text_generation#transformers.GenerationConfig.from_pretrained.returns
# Create a pipeline for text generation
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_length=2048,
temperature=0,
top_p=0.95,
repetition_penalty=1.15,
generation_config=generation_config,
)
local_llm = HuggingFacePipeline(pipeline=pipe)
logging.info("Local LLM Loaded")
return local_llm
# chose device typ to run on as well as to show source documents.
@click.command()
@click.option(
"--device_type",
default="cuda" if torch.cuda.is_available() else "cpu",
type=click.Choice(
[
"cpu",
"cuda",
"ipu",
"xpu",
"mkldnn",
"opengl",
"opencl",
"ideep",
"hip",
"ve",
"fpga",
"ort",
"xla",
"lazy",
"vulkan",
"mps",
"meta",
"hpu",
"mtia",
],
),
help="Device to run on. (Default is cuda)",
)
@click.option(
"--show_sources",
"-s",
is_flag=True,
help="Show sources along with answers (Default is False)",
)
@click.option(
"--local_model",
"-lm",
is_flag=True,
help="Use local model (Default is False)",
)
@click.option(
"--local_model_path",
"-lmp",
default=None,
help="Path to local model. (Default is None)",
)
def main(device_type, show_sources, local_model: bool = False, local_model_path: str = None):
"""
This function implements the information retrieval task.
1. Loads an embedding model, can be HuggingFaceInstructEmbeddings or HuggingFaceEmbeddings
2. Loads the existing vectorestore that was created by inget.py
3. Loads the local LLM using load_model function - You can now set different LLMs.
4. Setup the Question Answer retreival chain.
5. Question answers.
"""
logging.info(f"Running on: {device_type}")
logging.info(f"Display Source Documents set to: {show_sources}")
embeddings = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": device_type})
# uncomment the following line if you used HuggingFaceEmbeddings in the ingest.py
# embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)
# load the vectorstore
db = Chroma(
persist_directory=PERSIST_DIRECTORY,
embedding_function=embeddings,
client_settings=CHROMA_SETTINGS,
)
template = """Use the following pieces of context to answer the question at the end. If you don't know the answer,\
just say that you don't know, don't try to make up an answer.
{context}
{history}
Question: {question}
Helpful Answer:"""
prompt = PromptTemplate(input_variables=["history", "context", "question"], template=template)
memory = ConversationBufferMemory(input_key="question", memory_key="history")
llm = load_model(
device_type, model_id=MODEL_ID, model_basename=MODEL_BASENAME, local_model=local_model,
local_model_path=local_model_path)
metadata_field_info = [
AttributeInfo(
name='country',
description='The country name.',
type='string'
),
AttributeInfo(
name='source',
description='Filename and location of the source file.',
type='string'
)
]
retriever = SelfQueryRetriever.from_llm(
llm=llm,
vectorstore=db,
document_contents='News, policies, and laws for various countries.',
metadata_field_info=metadata_field_info,
verbose=True,
)
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
chain_type_kwargs={"prompt": prompt, "memory": memory},
)
# Interactive questions and answers
while True:
query = input("\nEnter a query: ")
if query == "exit":
break
# Get the answer from the chain
start = time.time()
res = qa(query)
answer, docs = res["result"], res["source_documents"]
# Print the result
print(f'Time: {time.time() - start}')
print("\n\n> Question:")
print(query)
print("\n> Answer:")
print(answer)
if show_sources: # this is a flag that you can set to disable showing answers.
# # Print the relevant sources used for the answer
print("----------------------------------SOURCE DOCUMENTS---------------------------")
for document in docs:
print("\n> " + document.metadata["source"] + ":")
print(document.page_content)
print("----------------------------------SOURCE DOCUMENTS---------------------------")
if __name__ == "__main__":
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(message)s", level=logging.INFO
)
main()
```
```bash
2023-08-25 12:53:40,256 - INFO - run_localGPT.py:209 - Running on: cuda
2023-08-25 12:53:40,256 - INFO - run_localGPT.py:210 - Display Source Documents set to: True
2023-08-25 12:53:40,397 - INFO - SentenceTransformer.py:66 - Load pretrained SentenceTransformer: hkunlp/instructor-large
load INSTRUCTOR_Transformer
max_seq_length 512
2023-08-25 12:53:42,924 - INFO - __init__.py:88 - Running Chroma using direct local API.
2023-08-25 12:53:42,929 - WARNING - __init__.py:43 - Using embedded DuckDB with persistence: data will be stored in: /home/waleedalfaris/localGPT/DB
2023-08-25 12:53:42,934 - INFO - ctypes.py:22 - Successfully imported ClickHouse Connect C data optimizations
2023-08-25 12:53:42,937 - INFO - json_impl.py:45 - Using python library for writing JSON byte strings
2023-08-25 12:53:47,543 - INFO - duckdb.py:460 - loaded in 129337 embeddings
2023-08-25 12:53:47,545 - INFO - duckdb.py:472 - loaded in 1 collections
2023-08-25 12:53:47,546 - INFO - duckdb.py:89 - collection with name langchain already exists, returning existing collection
2023-08-25 12:53:47,546 - INFO - run_localGPT.py:50 - Loading local model at /home/waleedalfaris/localGPT/models/wizard-vicuna-13B.ggmlv3.q4_0.bin
2023-08-25 12:53:47,546 - INFO - run_localGPT.py:53 - This action can take a few minutes!
2023-08-25 12:53:47,546 - INFO - run_localGPT.py:58 - Using Llamacpp for GGML quantized models
ggml_init_cublas: found 1 CUDA devices:
Device 0: Tesla T4, compute capability 7.5
llama.cpp: loading model from /home/waleedalfaris/localGPT/models/wizard-vicuna-13B.ggmlv3.q4_0.bin
llama_model_load_internal: format = ggjt v3 (latest)
llama_model_load_internal: n_vocab = 32000
llama_model_load_internal: n_ctx = 2048
llama_model_load_internal: n_embd = 5120
llama_model_load_internal: n_mult = 256
llama_model_load_internal: n_head = 40
llama_model_load_internal: n_head_kv = 40
llama_model_load_internal: n_layer = 40
llama_model_load_internal: n_rot = 128
llama_model_load_internal: n_gqa = 1
llama_model_load_internal: rnorm_eps = 5.0e-06
llama_model_load_internal: n_ff = 13824
llama_model_load_internal: freq_base = 10000.0
llama_model_load_internal: freq_scale = 1
llama_model_load_internal: ftype = 2 (mostly Q4_0)
llama_model_load_internal: model size = 13B
llama_model_load_internal: ggml ctx size = 0.11 MB
llama_model_load_internal: using CUDA for GPU acceleration
llama_model_load_internal: mem required = 669.91 MB (+ 1600.00 MB per state)
llama_model_load_internal: allocating batch_size x (640 kB + n_ctx x 160 B) = 480 MB VRAM for the scratch buffer
llama_model_load_internal: offloading 40 repeating layers to GPU
llama_model_load_internal: offloaded 40/43 layers to GPU
llama_model_load_internal: total VRAM used: 7288 MB
llama_new_context_with_model: kv self size = 1600.00 MB
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | VSX = 0 |
Enter a query: what is the penalty for cybercrime in the United Arab Emirates?
llama_print_timings: load time = 786.98 ms
llama_print_timings: sample time = 158.14 ms / 196 runs ( 0.81 ms per token, 1239.39 tokens per second)
llama_print_timings: prompt eval time = 83050.84 ms / 920 tokens ( 90.27 ms per token, 11.08 tokens per second)
llama_print_timings: eval time = 22099.62 ms / 195 runs ( 113.33 ms per token, 8.82 tokens per second)
llama_print_timings: total time = 105962.51 ms
query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='country', value='United Arab Emirates'), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='penalty', value='cybercrime')]) limit=None
Traceback (most recent call last):
File "/home/waleedalfaris/localGPT/run_localGPT.py", line 302, in <module>
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
return self.main(*args, **kwargs)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "/home/waleedalfaris/localGPT/run_localGPT.py", line 279, in main
query = input("\nEnter a query: ")
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 140, in __call__
raise e
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 134, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py", line 119, in _call
docs = self._get_docs(question)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py", line 181, in _get_docs
return self.retriever.get_relevant_documents(question)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/retrievers/self_query/base.py", line 90, in get_relevant_documents
docs = self.vectorstore.search(new_query, self.search_type, **search_kwargs)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/vectorstores/base.py", line 81, in search
return self.similarity_search(query, **kwargs)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 182, in similarity_search
docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 230, in similarity_search_with_score
results = self.__query_collection(
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/utils.py", line 53, in wrapper
return func(*args, **kwargs)
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/langchain/vectorstores/chroma.py", line 121, in __query_collection
return self._collection.query(
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/chromadb/api/models/Collection.py", line 219, in query
return self._client._query(
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/chromadb/api/local.py", line 408, in _query
uuids, distances = self._db.get_nearest_neighbors(
File "/home/waleedalfaris/localGPT/venv/lib/python3.10/site-packages/chromadb/db/clickhouse.py", line 576, in get_nearest_neighbors
raise NoDatapointsException(
chromadb.errors.NoDatapointsException: No datapoints found for the supplied filter {"$and": [{"country": {"$eq": "United Arab Emirates"}}, {"penalty": {"$eq": "cybercrime"}}]}
2023-08-25 13:05:24,584 - INFO - duckdb.py:414 - Persisting DB to disk, putting it in the save folder: /home/waleedalfaris/localGPT/DB
```
### Expected behavior
Result of SelfQueryRetriever should only contain filters country with a value of United Arab Emirates and query should not be blank. Should have an ouptut similar to `query='cybercrime penalty' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='country', value='United Arab Emirates') limit=None`
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I_kwDOIPDwls5vSJsQ
| 9,760 |
JavaScript LangChain Qdrant semantic search results: pageContent in each Document is undefined
|
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[] | null | 3 | 2023-08-25T13:08:16 | 2023-08-25T13:27:22 | 2023-08-25T13:27:06 |
NONE
| null |
### System Info
- LangChain version: 0.0.105
- Platform: Macbook Pro M1 - Mac OS Ventura
- Node.js version: v18.17.1
- qdrant/js-client-rest: 1.4.0
### Who can help?
@hwchase17
@agola11
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Related Components:**
- Vector Stores / Retrievers
- JavaScript LangChain Qdrant Wrapper
**Information:**
The issue arises when attempting to perform a semantic search using the Qdrant wrapper in LangChain through JavaScript. The provided code snippet is as follows:
```javascript
const embeddings = new OpenAIEmbeddings({ openAIApiKey: process.env.OPENAI_API_KEY })
const vectorStore = await QdrantVectorStore.fromExistingCollection(
embeddings,
{
url: process.env.QDRANT_URL,
collectionName: process.env.QDRANT_COLLECTION_NAME
})
const results = await vectorStore.similaritySearch("some query", 4)
```
The problem is that the `results` list of Documents contains undefined `pageContent`, while the metadata is fetched correctly. Strangely, when performing the same operation using the Python LangChain Qdrant wrapper, the `page_content` and `metadata` are both retrieved from the same Qdrant vectorstore correctly.
**Reproduction:**
To reproduce the issue, follow these steps:
1. Use the provided code snippet to perform a semantically search using the JavaScript LangChain Qdrant wrapper.
2. Examine the `results` list of Documents and observe that the `pageContent` property is undefined.
3. Compare the results with the results from the python equivalent code snippet:
```python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Qdrant
from qdrant_client import QdrantClient
qdrant_client = QdrantClient(
api_key=os.getenv("QDRANT_API_KEY"),
url=os.getenv("QDRANT_URL")
)
# get existing Qdrant vectorstore
vectorstore = Qdrant(
embeddings=openai_embeddings,
client=qdrant_client,
collection_name=os.getenv("QDRANT_COLLECTION_NAME"),
)
vectorstore.similarity_search(query='some query', k=4)
```
Please assist in resolving this discrepancy in the behavior between the two wrappers.
### Expected behavior
The expected behavior is that when performing a semantically search using the JavaScript LangChain Qdrant wrapper, the `results` list of Documents should contain valid `pageContent` along with correct metadata, similar to the behavior in the Python LangChain Qdrant wrapper.
Expected result (works with the python Qdrant wrapper):
```bash
[Document(page_content='\n Some content of a document ', metadata={'source': 'https://some.source.com', 'title': 'some title'})
...
]
```
Actual result:
```bash
[Document(page_content=undefined, metadata={'source': 'https://some.source.com', 'title': 'some title'})
...
]
```
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| 1,867,001,703 |
PR_kwDOIPDwls5Yyu-c
| 9,759 |
Synthetic data generation
|
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[] | null | 7 | 2023-08-25T12:50:21 | 2023-09-19T23:40:50 | 2023-09-19T23:29:52 |
CONTRIBUTOR
| null |
### Description
Implements synthetic data generation with the fields and preferences given by the user. Adds showcase notebook.
Corresponding prompt was proposed for langchain-hub.
### Example
```
output = chain({"fields": {"colors": ["blue", "yellow"]}, "preferences": {"style": "Make it in a style of a weather forecast."}})
print(output)
# {'fields': {'colors': ['blue', 'yellow']},
'preferences': {'style': 'Make it in a style of a weather forecast.'},
'text': "Good morning! Today's weather forecast brings a beautiful combination of colors to the sky, with hues of blue and yellow gently blending together like a mesmerizing painting."}
```
### Twitter handle
@deepsense_ai @matt_wosinski
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I_kwDOIPDwls5vSBgX
| 9,758 |
Issue: To be able to store Weaviate (for that matter any vector store) Vector Store in REDIS
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[] | null | 10 | 2023-08-25T12:46:04 | 2023-12-03T16:04:41 | 2023-12-03T16:04:40 |
NONE
| null |
### Issue you'd like to raise.
Hello,
have two separate docker images as follows.
1. for the purpose of Loading documents and then tokenizing and creating embeddings and storing in Vector store DB like this `vector_store_db:Weaviate = weaviateInstance.from_documents(documents, self.embeddings, by_text=False) `
2. Another docker image running FASTAPI which receives the actual Query. Now we want to be ablet to store the `vector_store_db` (created by the first docker image) in Redis Store so that the second docker image can get the `vector_store_db` from REDIS and execute the Query against it by invoking function like `similar_doc=vector_store_db.similarity_search("Question ?",k=1)`
we tried number of options to be able to store the `vector_store_db` ( which is of type Weaviate as per the documentation here [https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html#langchain.vectorstores.weaviate.Weaviate.from_texts](url) ) REDIS , but we are getting SERIALIZATION issues. We tried ` pickle, dill and json` and NO luck yet.
And then we came across the issue link [https://github.com/langchain-ai/langchain/issues/9288](url) and we tried the `dumps()` option and our relevant code snippet looks like this
```vs_redis_obj = dumps(vector_store_db)
redis_client = redis.Redis(host='localhost', port=6379, encoding="utf-8", decode_responses=True)
redis_client.set("ourkey", vs_serialized)
vs_obj:Weaviate = redis_client.get("ourkey")
#Start: sample code for Querying the Vector store DB
similar_doc=vector_store_db.similarity_search("Who is trying to invade earth?",k=1)
```
but we are getting the error `Error :'str' object has no attribute 'similarity_search'`
Basically we kind of get why the error is coming because of the following....
1. when we store the object `vector_store_db` in REDIS its getting serialized to its `str` equivalent.
2. So when we do redis.get() we get the vector_store_db to be its `str` equivalent. And this is the reason why our call to `similarity_search()` fails .
Any ideas how can we fix this please.
Basically we need to be able to make the vector_store_db (created by one docker image) to another docker image through REDIS,
Any help / suggestion is much appreciated and thanks in advance.
### Suggestion:
_No response_
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I_kwDOIPDwls5vR_Y_
| 9,757 |
Custom Map-Reduce chain
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[] | null | 2 | 2023-08-25T12:39:58 | 2023-12-01T16:07:08 | 2023-12-01T16:07:07 |
NONE
| null |
### Feature request
Hey wanted to request a feature within the map-reduce chain where a person can send his own list of chunks data of a corpus instead of creating chunks of data based on sending a textsplitter and a corpus.
### Motivation
Sometime one might see map-reduce cases where one wants to use their own chunks of data and don't want to split a data corpus based on sending a textsplitter or character splitter.
### Your contribution
I can work on it by raising a PR.
|
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PR_kwDOIPDwls5Yyp2K
| 9,756 |
docs: Fix `CustomLLM` and `QA over Documents` docs
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[] | null | 1 | 2023-08-25T12:33:34 | 2023-08-26T01:36:47 | 2023-08-26T01:36:17 |
CONTRIBUTOR
| null |
### Description
- Update `CustomLLM._call`: Corrected the _call method in CustomLLM to include **kwargs, ensuring consistency with parent class.
- Update `QA over Documents`: To fix `Page not found` error
- https://python.langchain.com/docs/use_cases/tabular -> https://python.langchain.com/docs/use_cases/sql
- https://python.langchain.com/docs/use_cases/code -> https://python.langchain.com/docs/use_cases/code_understanding
### Issue
N/A
### Dependencies
N/A
### Tag maintainer
N/A
### Twitter handle
N/A
|
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PR_kwDOIPDwls5YyMU4
| 9,755 |
typo in locall llms fixed
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[] | null | 1 | 2023-08-25T11:07:50 | 2023-09-04T03:29:42 | 2023-09-04T03:29:41 |
CONTRIBUTOR
| null |
Hi,
I noticed a typo in the local_llms.ipynb file and fixed it. The word challenge is without 'a' in the original file.
@baskaryan , @eyurtsev
Thanks.
|
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PR_kwDOIPDwls5YyEoG
| 9,754 |
Add 'download_dir' argument to VLLM
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[] | null | 2 | 2023-08-25T10:41:43 | 2023-09-04T17:53:49 | 2023-09-04T17:53:49 |
CONTRIBUTOR
| null |
- Description:
Add a 'download_dir' argument to VLLM model (to change the cache download directotu when retrieving a model from HF hub)
- Issue:
On some remote machine, I want the cache dir to be in a volume where I have space (models are heavy nowadays). Sometimes the default HF cache dir might not be what we want.
- Dependencies:
None
|
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PR_kwDOIPDwls5Yx4q6
| 9,753 |
Nc/runnables seqmap tags
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[] | null | 2 | 2023-08-25T10:04:21 | 2023-09-01T17:53:11 | 2023-09-01T17:53:10 |
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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I_kwDOIPDwls5vRCK3
| 9,752 |
Issue: can i load my local model by chain = LLMChain(llm=chat, prompt=chat_prompt)
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[] | null | 4 | 2023-08-25T09:51:51 | 2023-12-01T16:07:13 | 2023-12-01T16:07:12 |
NONE
| null |
### Issue you'd like to raise.
can i load my local model by chain = LLMChain(llm=chat, prompt=chat_prompt)
### Suggestion:
_No response_
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PR_kwDOIPDwls5Yxs5n
| 9,751 |
bump 273
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I_kwDOIPDwls5vQv5I
| 9,750 |
Prolonged execution times when using ConversationChain and ChatOpenAI
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[] | null | 4 | 2023-08-25T09:08:55 | 2023-12-27T16:05:53 | 2023-12-27T16:05:52 |
NONE
| null |
### System Info
## Description:
### Context:
I'm using LangChain to develop an application that interacts with the gpt-3.5-turbo-16k model to handle long chains of up to 16384 tokens.
### Problem:
While the first message processes quickly **(specially if i have not previues context in the inputs)**, from the second message onward, I experience excessively long execution times, exceeding 5 minutes. On occasions, I receive timeout errors exceeding 10 minutes like the following:
`Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised Timeout: Request timed out: HTTPSConnectionPool(host='api.openai.com', port=443): Read timed out. (read timeout=600).`
#### **It's worth noting that when using the OpenAI API directly with the same context and length, the response is almost immediate.**
### Relevant Code:
```
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from langchain.chat_models import ChatOpenAI
from langchain.memory.entity import ConversationEntityMemory
def create_conversation_chain(inputs, num_msgs=3):
"""
Creates the base instance for the conversation with the llm and the memory
:param num_msgs: Number of messages to include in the memory buffer
:return: The conversation chain instance
"""
load_dotenv()
llm = ChatOpenAI(
temperature=0,
model_name=MODEL,
verbose=False,
)
memory = ConversationEntityMemory(
llm=llm,
k=num_msgs,
)
if inputs:
for inp in inputs:
memory.save_context(inp[0], inp[1])
conversation = ConversationChain(
llm=llm,
memory=memory,
prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
verbose=True,
)
return conversation
conversation = create_conversation_chain(inputs=self.input_msgs_entries, num_msgs=num_msgs_to_include)
ans = self.conversation.predict(input=msg)
```
Feel free to send me questions about my code if you need to know something else, but essentialy that is what I have
### Additional Details:
1. Operating System: Windows 10
2. Python Version: 3.10
3. LangChain Version: 0.0.271
4. I've tried with the streaming=True parameter cause I saw that in other issue, but the results remain the same.
### Request:
Could you help me identify and address the cause of these prolonged execution times when using LangChain, especially compared to direct use of the OpenAI API?
Thank you very much for your help!! ^^
### Who can help?
@hwchase17 @agola11
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
## Steps to Reproduce the Behavior:
1. Setup Environment:
> - Install LangChain version 0.0.271 on a Windows 10 (but i tryed it in ubuntu and same problems) machine with Python 3.10.
> - gpt-3.5-turbo-16k model.
2. Implement the Conversation Chain:
> - Utilize the create_conversation_chain function provided in the initial description.
3. Provide Context:
> - Use a context (inputs) that is long enough to approach the 6000 to 9000 (context + msg) tokens but i am also getting this time consuming responses with lower bounds, I let you bellow one file with text (it is spanish text) that I use as an input to call gpt-3.5-turbo-16k.
4. Initialize and Predict:
> - Call the function:
> `conversation = create_conversation_chain(inputs=input_msgs_entries, num_msgs=num_msgs_to_include_in_buffer)`
> - Then, predict using:
> `ans = conversation.predict(input=msg)`
5. Observe Delay:
> - Note that while the first message processes quickly **(specially if i have not previues context in the inputs)**, subsequent messages experience prolonged execution times, sometimes exceeding 10 minutes.
> - Occasionally, timeout errors might occur, indicating a failure in the request due to excessive waiting time.
6. Compare with Direct OpenAI API:
> - Directly send the same context and message to the OpenAI API, without using LangChain.
> - Observe that the response is almost immediate, highlighting the difference in performance.
7. Test with Streaming:
> - Set the streaming=True parameter and observe that the prolonged execution times persist.
[test_random_conv_text.txt](https://github.com/langchain-ai/langchain/files/12437477/test_random_conv_text.txt)
### Expected behavior
## Expected Behavior:
When utilizing the create_conversation_chain function with the gpt-3.5-turbo-16k model to handle chains close to 16384 tokens:
1. **Consistent Performance:** The execution times for each message, regardless of it being the first or subsequent ones, should be relatively consistent and not show drastic differences.
2. **Reasonable Response Times:** Even for longer contexts approaching the model's token limit, the response time should be within a reasonable range, certainly not exceeding 10 minutes for a single prediction.
3. **No Timeout Errors:** The system should handle the requests efficiently, avoiding timeout errors, especially if the direct OpenAI API call with the same context responds almost immediately.
4. **Memory Efficiency:** The memory management system, especially when handling the context and inputs, should efficiently store and retrieve data without causing significant delays.
|
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| 1,866,651,760 |
I_kwDOIPDwls5vQthw
| 9,749 |
DirectoryLoader for different file types
|
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[] | null | 3 | 2023-08-25T09:03:10 | 2023-12-14T16:06:33 | 2023-12-14T16:06:32 |
NONE
| null |
### Discussed in https://github.com/langchain-ai/langchain/discussions/9605
<div type='discussions-op-text'>
<sup>Originally posted by **nima-cp** August 22, 2023</sup>
Hello everyone, I wanna have a Q&A over some documents including pdf, xml and csv. I'm having some difficulty to write a DirectoryLoader for different types of files in a folder. I'm using Chroma and couldn't find a way to solve this. However, I found this in TS documentation:
```typescript
const directoryLoader = new DirectoryLoader(filePath, {
'.pdf': (path) => new PDFLoader(path, { splitPages: true }),
'.docx': (path) => new DocxLoader(path),
'.json': (path) => new JSONLoader(path, '/texts'),
'.jsonl': (path) => new JSONLinesLoader(path, '/html'),
'.txt': (path) => new TextLoader(path),
'.csv': (path) => new CSVLoader(path, 'text'),
'.htm': (path) => new UnstructuredLoader(path),
'.html': (path) => new UnstructuredLoader(path),
'.ppt': (path) => new UnstructuredLoader(path),
'.pptx': (path) => new UnstructuredLoader(path),
});
```
How can I write the same in Python?
```python
loader = DirectoryLoader(
filePath,
glob="./*.pdf",
loader_cls=PyMuPDFLoader,
show_progress=True,
)
```</div>
|
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| 1,866,641,171 |
I_kwDOIPDwls5vQq8T
| 9,748 |
BambooHR Tool
|
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[] | null | 16 | 2023-08-25T08:56:25 | 2023-12-01T16:07:18 | 2023-12-01T16:07:17 |
NONE
| null |
### Feature request
I would like to have BambooHR Tool here(https://python.langchain.com/docs/integrations/tools/).
To make it possible to request information about employees within the company, their vacations, and so on.
### Motivation
BambooHR is quite a popular service, so I believe this tool will be used a lot.
### Your contribution
I am willing to contribute by coding a portion, but I am hesitant to code everything as it may be too much. It would be great if other enthusiasts could also join in.
I've already found the BambooHR OpenAPI file
[bamboo_openapi.json.zip](https://github.com/langchain-ai/langchain/files/12437406/bamboo_openapi.json.zip)
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| 9,747 |
Issue: LLM Multiple access problem
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[] | null | 4 | 2023-08-25T08:42:02 | 2023-12-01T16:07:23 | 2023-12-01T16:07:22 |
NONE
| null |
Hi, I would like to build a chat bot to support multple users to access the chat bot.
Since the llm model size is very big, my VRAM can only load only one copy of LLM.
I would like to know if there is any way to load the model once and multiple access concurrently.
Here is what I just tried.
I tried to create two threads, and each thread run the llm model with different prompt.
Unfortunately, the responses are very strange. The r1 and r2 are gibberish code.
If I remove one of the thread, the response is good.
`
from langchain.llms import CTransformers
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import yaml
import os
import threading
import datetime
import time
def job1():
print("job1: ", datetime.datetime.now())
question1 = "Please introduce the history of China"
r1 = llm(question1)
print("r1:", r1)
def job2():
print("job2: ", datetime.datetime.now())
question2 = "Please introduce the history of The United States"
r2 = llm(question2)
print("r2:", r2)
// load the model once
llm = LlamaCpp(
model_path="/workspace/test/llama-2-7b-combined/ggml-model-q6_K.bin",
n_gpu_layers=20,
n_batch=128,
n_ctx=2048,
temperature=0.1,
max_tokens=512,
)
t1 = threading.Thread(target=job1)
t2 = threading.Thread(target=job2)
t1.start()
t2.start()
t1.join()
t2.join()
print("Done.")
`
### Suggestion:
_No response_
|
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| 9,746 |
Add `optional_variables` for templates and make `format_document` customizable
|
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[] | null | 0 | 2023-08-25T07:46:15 | 2023-08-28T08:18:56 | 2023-08-28T08:18:56 |
CONTRIBUTOR
| null |
### Feature request
1. Ideally, current `input_variables` should be separated into `required_variables` and `allowed_variables`
2. `allowed_variables` should consist of `required_variables` + `optional_variables`
3. Current implementation of `format_document` requires some overhaul, as suggested by #7239. Since `format_document` is part of the schema, it should either be a class or, at least, its formatting and validation parts should be separated.
```python
# from:
def format_document(doc: Document, prompt: BasePromptTemplate) -> str:
base_info = {"page_content": doc.page_content, **doc.metadata}
missing_metadata = set(prompt.input_variables).difference(base_info)
if len(missing_metadata) > 0:
required_metadata = [
iv for iv in prompt.input_variables if iv != "page_content"
]
raise ValueError(
f"Document prompt requires documents to have metadata variables: "
f"{required_metadata}. Received document with missing metadata: "
f"{list(missing_metadata)}."
)
document_info = {k: base_info[k] for k in prompt.input_variables}
return prompt.format(**document_info)
# into (assumes required_variables is input_variables - optional_variables, backward compatible, not ideal or elegant so far):
def _validate_document(doc: Document, prompt: BasePromptTemplate) -> None:
base_info = {"page_content": doc.page_content, **doc.metadata}
missing_metadata = set(prompt.required_variables).difference(base_info)
if missing_metadata:
raise ValueError(
f"Document prompt requires documents to have metadata variables: "
f"{prompt.required_variables}. Received document with missing metadata: "
f"{list(missing_metadata)}."
)
def _format_document(doc: Document, prompt: BasePromptTemplate) -> None:
base_info = {"page_content": doc.page_content, **doc.metadata}
document_info = {k: base_info[k] for k in prompt.input_variables} # or allowed_variables
return prompt.format(**document_info)
def format_document(doc: Document, prompt: BasePromptTemplate, validation_function: Callable = _validate_document, formatting_function: Callable = _format_document, **kwargs) -> str:
_validate_document(doc, prompt)
return _format_document(doc, prompt, **kwargs) # format_kwargs?
```
### Motivation
Given that both `f-string` and `jinja2` support some control logic, it seems quite logical to allow optional variables, or to make `format_document` function more customizable.
### Your contribution
I'd like to work on it, but I believe there's a need for further discussion.
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| 1,866,480,477 |
PR_kwDOIPDwls5Yw8ik
| 9,745 |
Fix: when only config custom evaluators, the keys(input_key,prediction_key,reference_key) config will be lost
|
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[] | null | 3 | 2023-08-25T07:17:30 | 2023-08-31T04:43:28 | 2023-08-31T04:43:28 |
NONE
| null |
* Description: When only use custom evaluators, Will meet ` Could not map run {which} with multiple keys {source} \n Please manually specify a {which}_key` ERROR from `ChainStringRunMapper`. Because the configuration of the keys(input_key,prediction_key,reference_key) are initialized only when `config.evaluators` are configured. So `ChainStringRunMapper` can't get the keys configuration.
* Issue: None
* Dependencies: None
* Maintainer: @hinthornw @baskaryan
* Twitter handle: @_LiuHu
|
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| 1,866,443,166 |
I_kwDOIPDwls5vP6me
| 9,744 |
unexpected behavior: retriever.get_relevant_documents
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[] | null | 2 | 2023-08-25T06:54:26 | 2023-12-01T16:07:28 | 2023-12-01T16:07:27 |
NONE
| null |
### System Info
As stated in the title, the query is returning, but not the relevant documents. The code snippet below illustrates the issue:
```python
query = 'building bridges'
filter = Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='construction_material', value='steel')
limit = None
[]
```
I know that the documents and data are stored correctly because the query I am using works fine with similarity_search, and it returns the appropriate text. After splitting the PDF, I had to recreate the metadata and added it along with the documents. The meta_data field prints off without any problems when I access it in the similarity_search as well.
### Who can help?
@hwchase17
@agola11
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
**### Steps to Reproduce the Behavior:**
1. **Load PDF:** Begin by loading the PDF file that you will be working with.
2. **Append page_content:** Next, append the content from the PDF pages into an empty string called text.
3. **Split text Recursively:** Split the text string recursively to segment the content.
4. **Create Metadata for Split Text:** Use the following function to create metadata for the split text.
```python
def create_metadata(title: str, section: int, material: str) -> dict:
metadata = {
"title": title,
"section": section,
"material": material,
}
return metadata
```
5. **Loop Over Split Text:** Iterate through the split text, appending custom metadata to a list.
6. **Add Docs, Embeddings, Metadata:** Utilize the Chroma.from_texts method with the following parameters:
```python
vectordb = Chroma.from_texts(
texts=docs,
embedding=embedding,
persist_directory=persist_directory,
metadatas=metadatas_list,
)
```
**Proceed with SelfQueryRetriever:** Finally, proceed to use the SelfQueryRetriever.from_llm method as described in the documentation.
---------------------------------------------------------------------------------------------------------------------
### Note:
Everything works as intended with the similarity_search. The SelfQueryRetriever is returning as expected minus the relevant documents.
My suspicion is that the issue may be related to the Documents() class, but I recreated the object/class without any success regarding output. It should still function properly if the data is inserting fine into the database and all the other queries are working fine. What has lead me to this place early on is an issue arises with PDFs when they are split; thus, the workarounds are either:
**Appending into an Empty String:** This is necessary because metadata becomes distorted, and page break behavior takes precedence over separators and chunk size.
**Converting PDF to Image and Then to Text:** The process is PDF -> IMG -> Tesseract -> Text, which still requires metadata to be recreated.
### Expected behavior
Expected behavior:
Output the query and the data related to it, not just the query.
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PR_kwDOIPDwls5YwrWX
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feature: add verbosity to create_qa_with_sources_chain
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[] | null | 4 | 2023-08-25T06:27:31 | 2023-09-04T03:42:20 | 2023-09-04T03:42:20 |
CONTRIBUTOR
| null |
Adds a verbose parameter to the create_qa_with_sources_chain and create_qa_with_structure_chain functions
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PR_kwDOIPDwls5Ywmjm
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Fixed some Error Caused links and Grammatical error in multiple files
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[] | null | 1 | 2023-08-25T06:11:52 | 2023-08-26T00:52:27 | 2023-08-26T00:52:27 |
CONTRIBUTOR
| null |
Fixed some Error Caused links and Grammatical errors in multiple documentation files.
@baskaryan , @eyurtsev
Thanks
Aashish Saini
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multivector notebook
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PR_kwDOIPDwls5YwM2y
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Fix QuestionListOutputParser
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| false | null |
[] | null | 2 | 2023-08-25T04:36:12 | 2023-08-25T08:47:17 | 2023-08-25T08:47:17 |
CONTRIBUTOR
| null |
This PR fixes `QuestionListOutputParser` text splitting.
`QuestionListOutputParser` incorrectly splits numbered list text into lines. If text doesn't end with `\n` , the regex doesn't capture the last item. So it always returns `n - 1` items, and `WebResearchRetriever.llm_chain` generates less queries than requested in the search prompt.
How to reproduce:
```python
from langchain.retrievers.web_research import QuestionListOutputParser
parser = QuestionListOutputParser()
good = parser.parse(
"""1. This is line one.
2. This is line two.
""" # <-- !
)
bad = parser.parse(
"""1. This is line one.
2. This is line two.""" # <-- No new line.
)
assert good.lines == ['1. This is line one.\n', '2. This is line two.\n'], good.lines
assert bad.lines == ['1. This is line one.\n', '2. This is line two.'], bad.lines
```
NOTE: Last item will not contain a line break but this seems ok because the items are stripped in the `WebResearchRetriever.clean_search_query()`.
|
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| 1,866,264,892 |
PR_kwDOIPDwls5YwMw7
| 9,737 |
add twitter chat loader doc
|
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[] | null | 1 | 2023-08-25T04:35:39 | 2023-08-25T04:55:23 | 2023-08-25T04:55:22 |
COLLABORATOR
| null | null |
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| 1,866,229,858 |
I_kwDOIPDwls5vPGhi
| 9,735 |
Issue: Error in Similarity Search with MongoDB Vector Store in Langchain
|
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open
| false | null |
[] | null | 2 | 2023-08-25T03:52:38 | 2023-10-27T00:59:05 | null |
NONE
| null |
### Issue you'd like to raise.
I've been using Langchain to connect with the MongoDB vector store. While the file upload functionality works seamlessly, I encounter an error when trying to use the similarity search feature. Here's the error message I receive:
### Suggestion:
_No response_
|
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| 1,866,199,668 |
PR_kwDOIPDwls5Yv-zH
| 9,734 |
docs: misc modelIO fixes
|
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[] | null | 1 | 2023-08-25T03:14:06 | 2023-09-04T06:16:22 | 2023-09-04T03:33:20 |
CONTRIBUTOR
| null |
Various improvements to the Model I/O section of the documentation
- Changed "Chat Model" to "chat model" in a few spots for internal consistency
- Minor spelling & grammar fixes to improve readability & comprehension
|
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| 1,866,186,574 |
I_kwDOIPDwls5vO79O
| 9,733 |
AWS Sagemaker
|
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[] | null | 9 | 2023-08-25T02:57:42 | 2023-12-01T16:07:32 | 2023-12-01T16:07:32 |
NONE
| null |
### System Info
Hi, I'm getting the following error with Langchain integration with AWS Sagemaker:
`ValueError: Error raised by inference endpoint: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (424) from primary with message "{
"code":424,
"message":"prediction failure",
"error":"Input payload must contain a 'inputs' key and optionally a 'parameters' key containing a dictionary of parameters."
}".`
I've tried adding a custom attribute to accept any relevant terms in order to run my model, but I'm still having issues. See below for my initialization of the model:
chain = load_qa_chain(
llm=SagemakerEndpoint(
endpoint_name="endpointname"
credentials_profile_name="profilename",
region_name="region",
model_kwargs={"temperature": 1e-10},
endpoint_kwargs={"CustomAttributes": "accept_eula=true"},
content_handler=content_handler,
),
prompt=PROMPT,
)
### Who can help?
_No response_
### Information
- [X] 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
chain = load_qa_chain(
llm=SagemakerEndpoint(
endpoint_name="endpointname"
credentials_profile_name="profilename",
region_name="region",
model_kwargs={"temperature": 1e-10},
endpoint_kwargs={"CustomAttributes": "accept_eula=true"},
content_handler=content_handler,
),
prompt=PROMPT,
)
### Expected behavior
error described above
|
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| 1,866,073,423 |
I_kwDOIPDwls5vOgVP
| 9,732 |
`Chatbots` use case example is not working
|
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[] | null | 3 | 2023-08-25T00:48:21 | 2023-12-02T16:05:42 | 2023-12-02T16:05:41 |
NONE
| null |
### System Info
Langchain version: 0.0.271
Platform: Ubuntu 20.04
Device: Nvidia-T4
Python version: 3.9.17
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from typing import Any, Dict, List, Optional
from langchain.pydantic_v1 import Field, root_validator
from langchain.llms import VLLM
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
class MyVLLM(VLLM):
dtype: str = 'auto'
vllm_kwargs: Dict[str, Any] = Field(default_factory=dict)
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that python package exists in environment."""
try:
from vllm import LLM as VLLModel
except ImportError:
raise ImportError(
"Could not import vllm python package. "
"Please install it with `pip install vllm`."
)
values["client"] = VLLModel(
model=values["model"],
tensor_parallel_size=values["tensor_parallel_size"],
trust_remote_code=values["trust_remote_code"],
dtype=values["dtype"],
**values['vllm_kwargs']
)
return values
llm = MyVLLM(model="tiiuae/falcon-7b",
trust_remote_code=True, # mandatory for hf models
max_new_tokens=128,
top_k=10,
top_p=0.95,
temperature=0.8,
dtype='float16',
vllm_kwargs = {'gpu_memory_utilization': 0.98},
callbacks=[StreamingStdOutCallbackHandler()]
)
# Prompt
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"You are a nice chatbot having a conversation with a human."
),
# The `variable_name` here is what must align with memory
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}")
]
)
# Notice that we `return_messages=True` to fit into the MessagesPlaceholder
# Notice that `"chat_history"` aligns with the MessagesPlaceholder name
memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True)
conversation = LLMChain(
llm=llm,
prompt=prompt,
verbose=True,
memory=memory
)
```
### Expected behavior
I am following the `Chatbots` example [here](https://python.langchain.com/docs/use_cases/chatbots).
It's not working as expected. The responses returned are weird that not just a single LLM response is there but also some human responses. What is happening there?
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PR_kwDOIPDwls5YvYlp
| 9,731 |
Hub Push Ergonomics
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[] | null | 1 | 2023-08-24T23:32:56 | 2023-08-25T01:06:29 | 2023-08-25T00:41:55 |
COLLABORATOR
| null |
👋 me again
Improves the hub pushing experience, returning a url instead of just a commit hash.
Goes with hub sdk 0.1.8
|
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PR_kwDOIPDwls5YvXbb
| 9,730 |
rfc: runnable mem
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[] | null | 2 | 2023-08-24T23:24:33 | 2023-09-07T21:36:00 | 2023-09-07T21:36:00 |
COLLABORATOR
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I_kwDOIPDwls5vOO55
| 9,728 |
Langchain agent doesn't complete reasoning sequence stops halfway and can't use structured tools
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[] | null | 2 | 2023-08-24T23:02:33 | 2023-11-30T16:05:51 | 2023-11-30T16:05:50 |
NONE
| null |
### System Info
langchain==0.0.271
### Who can help?
@hwchase17 @agola11
### Information
- [X] 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
from langchain import PromptTemplate
from langchain.agents import AgentType
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from api.utils.agent.tools import view_capabilities_tool
load_dotenv()
system_message = "You are required to use only the tools provided to answer a query.If no given tool can help,"\
" truthfully tell the user that you are unable to help them.Always end reply with see ya!."\
"Query: {query}"
prompt_template = PromptTemplate(
template=system_message,
input_variables=["query"],
)
capabilities = view_capabilities_tool.CapabilitiesTool()
llm = ChatOpenAI(temperature=0)
agent_chain = initialize_agent(
[capabilities],
llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
agent_kwargs={
"system_message": system_message
}
)
response = agent_chain.run(input="What can you do")
print(response)
capabilities_tool:
from typing import Type
from langchain.tools import BaseTool
from pydantic import BaseModel, BaseSettings
class CapabilitiesToolSchema(BaseModel):
pass
class CapabilitiesTool(BaseTool, BaseSettings):
name = "capabilities_tool"
description = """Tool that shows what you are capable of doing."""
args_schema: Type[CapabilitiesToolSchema] = CapabilitiesToolSchema
def _run(
self,
) -> str:
body = ("I can help you out with"
"\nAdding a site\nRemoving a site\nAdding an interest\nRemoving an interest\nViewing your details\n "
"Opt out")
return body
### Expected behavior
The model is meant to go through the whole reasoning process, select a tool and wait for the response for that tool called.
Instead the agent just stops at """Action_Input: ...""" every time. The model doesnt use any tool given, sometimes it would give this steps:
> Entering new AgentExecutor chain...
I can use the capabilities_tool to see what I am capable of doing. Let me check.
> Finished chain.
I can use the capabilities_tool to see what I am capable of doing. Let me check.
|
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| 1,866,001,085 |
PR_kwDOIPDwls5YvUD8
| 9,727 |
Enable grammars for JSON, list w/ LLaMA
|
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[] | null | 2 | 2023-08-24T23:01:16 | 2023-08-29T15:23:49 | 2023-08-29T15:23:49 |
COLLABORATOR
| null |
Takes feedback from [here](https://github.com/abetlen/llama-cpp-python/issues/637#issuecomment-1692459836).
And builds on [here](https://github.com/langchain-ai/langchain/pull/9712).
I needed to modify `json.gbnf` to get it to work properly (else, it does not stop, and repeats newlines).
I check it in temporarily, but it's not obvious it should actually live in this location.
|
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| 1,865,993,227 |
I_kwDOIPDwls5vOMwL
| 9,726 |
Pgvector support to filter by List
|
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[] | null | 3 | 2023-08-24T22:50:34 | 2023-12-11T16:05:58 | 2023-12-11T16:05:57 |
NONE
| null |
### Feature request
Hi there,
Currently using PGVector you can pass a filter object. But this filter object only allows key value pairs.
`Dict[str, str].
`
I am requesting the ability to also send a list of strings for easy filter across many pieces of data.
`Dict[str, list[str]]
`
### Motivation
I have a lot of Data with Metadata that contains content type.
I would like to provide a list of content types to PGvector and allow it to filter and return content from multiple types.
### Your contribution
N/A
|
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| 1,865,972,423 |
PR_kwDOIPDwls5YvN_R
| 9,725 |
mv chat history
|
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[] | null | 1 | 2023-08-24T22:25:33 | 2023-08-30T04:41:33 | 2023-08-30T04:41:32 |
COLLABORATOR
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| 1,865,945,832 |
PR_kwDOIPDwls5YvIMw
| 9,724 |
feat(llms): add missing params to huggingface text-generation
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[] | null | 5 | 2023-08-24T21:57:30 | 2023-09-01T20:19:07 | 2023-09-01T20:16:28 |
CONTRIBUTOR
| null |
This small PR aims at supporting the following missing parameters in the `HuggingfaceTextGen` LLM:
- `return_full_text` - sometimes useful for completion tasks
- `do_sample` - quite handy to control the randomness of the model.
- `watermark`
@hwchase17 @baskaryan
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PR_kwDOIPDwls5YvC8c
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remove Value error "pyspark is not installed. Please install it with `pip i…
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[] | null | 4 | 2023-08-24T21:37:09 | 2023-08-27T16:30:23 | 2023-08-25T05:18:56 |
CONTRIBUTOR
| null |
Description: You cannot execute spark_sql with versions prior to 3.4 due to the introduction of pyspark.errors in version 3.4.
And if you are below you get 3.4 "pyspark is not installed. Please install it with pip nstall pyspark" which is not helpful. Also if you not have pyspark installed you get already the error in init. I would return all errors. But if you have a different idea feel free to comment.
Issue: None
Dependencies: None
Maintainer:
|
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PR_kwDOIPDwls5YvCJC
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fix integration deps
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[] | null | 1 | 2023-08-24T21:34:15 | 2023-08-24T22:06:54 | 2023-08-24T22:06:53 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5Yu2Cm
| 9,721 |
rm google api core
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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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| 1,865,802,598 |
PR_kwDOIPDwls5Yuoz3
| 9,719 |
Xata as a chat message memory store
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[] | null | 2 | 2023-08-24T20:02:13 | 2023-08-25T00:37:47 | 2023-08-25T00:37:47 |
CONTRIBUTOR
| null |
This adds Xata as a memory store also to the python version of LangChain, similar to the [one for LangChain.js](https://github.com/hwchase17/langchainjs/pull/2217).
I have added a Jupyter Notebook with a simple and a more complex example using an agent.
To run the integration test, you need to execute something like:
```
XATA_API_KEY='xau_...' XATA_DB_URL="https://demo-uni3q8.eu-west-1.xata.sh/db/langchain" poetry run pytest tests/integration_tests/memory/test_xata.py
```
Where `langchain` is the database you create in Xata.
<!-- 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_kwDOIPDwls5Yun9y
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rfc/wip Bagatur/new msg
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[] | null | 1 | 2023-08-24T19:59:06 | 2023-09-04T17:53:28 | 2023-09-04T17:53:28 |
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| 1,865,791,331 |
I_kwDOIPDwls5vNbdj
| 9,717 |
Langchain QA over large documents results in Rate limit errors
|
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[] | null | 6 | 2023-08-24T19:54:13 | 2023-12-12T16:33:44 | 2023-08-29T01:03:05 |
NONE
| null |
### System Info
Langchain Version: 0.0.258
Python Version 3.8.10
Ubuntu 20.04.5 LTS
We are following the instructions from the blog posted at https://python.langchain.com/docs/use_cases/question_answering/
We find that this works on small documents/directories. However when we run it on larger data sets, we get rate limit errors as below:
```
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-RVhTeiJGcKtuLfYUSO6rLABk on tokens per min. Limit: 1000000 / min. Current: 899517 / min. Contact us through our help center at help.openai.com if you continue to have issues..
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-RVhTeiJGcKtuLfYUSO6rLABk on tokens per min. Limit: 1000000 / min. Current: 887493 / min. Contact us through our help center at help.openai.com if you continue to have issues..
```
Since we have a paid account with OpenAI we doubt we are running into any actual issues with OpenAI. Looking at their dashboird, we see we are well under any limits.
Our full code that demonstrates this issue is posted below:
```
import os
import sys
import environ
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
env = environ.Env()
environ.Env.read_env()
# Load API key and document location
OPENAI_API_KEY = env('OPENAI_API_KEY')
if OPENAI_API_KEY == "":
print("Missing OpenAPI key")
exit()
print("Using OpenAPI with key ["+OPENAI_API_KEY+"]")
path = sys.argv[1]
if path == "":
print("Missing document path")
exit()
# Document loading
loader = DirectoryLoader(path, glob="*")
data = loader.load()
# Text splitting
text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500, chunk_overlap = 0)
all_splits = text_splitter.split_documents(data)
# Create retriever
vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
# Connect to LLM for generation
template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.
Always say "thanks for asking!" at the end of the answer.
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
qa_chain = RetrievalQA.from_chain_type(
llm,
retriever=vectorstore.as_retriever(),
chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}
)
# prompt loop
def get_prompt():
print("Type 'exit' to quit")
while True:
prompt = input("Enter a prompt: ")
if prompt.lower() == 'exit':
print('Exiting...')
break
else:
try:
result = qa_chain({"query": prompt})
print(result["result"])
except Exception as e:
print(e)
get_prompt()
```
### Who can help?
@eyurtsev @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
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
You can run the code posted above against any document/directory that is large... try with something over 50MB
### Expected behavior
A correct chat reply
|
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PR_kwDOIPDwls5Yukxn
| 9,716 |
change error "pyspark is not installed. Please install it with `pip i…
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[] | null | 1 | 2023-08-24T19:48:47 | 2023-08-24T21:28:08 | 2023-08-24T21:27:57 |
CONTRIBUTOR
| null |
Description: The issue is you can not run spark_sql with a version below 3.4, because pyspark.errors is added in 3.4.
And if you are below you get 3.4 "pyspark is not installed. Please install it with `pip nstall pyspark`" which is not helpful. Also if you not have pyspark installed you get already the error in __init__. I would return all errors. But if you have a different idea feel free to comment.
Issue: None
Dependencies: None
Maintainer:
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I_kwDOIPDwls5vNMWp
| 9,715 |
Issue: pandas agent tries to create new variables but returns along the lines of "the top 10 are {top_10_df}"
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[] | null | 5 | 2023-08-24T19:07:43 | 2023-10-30T10:31:59 | null |
NONE
| null |
### Issue you'd like to raise.
It looks like it's creating new dataframes to answer complex questions, but in the answer it provides it keeps referencing the variable name rather than the printed variable.
Does the current agent not have the ability to create and use new variables?
Note: I'm using the CSV agent as a tool within another agent
### Suggestion:
Will the CSV agent having the create file tool alleviate this?
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PR_kwDOIPDwls5Yt_Pp
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Add Code LLaMA to code QA use case
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[] | null | 2 | 2023-08-24T17:42:32 | 2023-08-24T18:03:36 | 2023-08-24T18:03:36 |
COLLABORATOR
| null |
Use [Ollama integration](https://ollama.ai/blog/run-code-llama-locally).
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PR_kwDOIPDwls5YtqBL
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feat: grammar-based sampling in llama-cpp
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CONTRIBUTOR
| null |
## Description
The following PR enables the [grammar-based sampling](https://github.com/ggerganov/llama.cpp/tree/master/grammars) in llama-cpp LLM.
In short, loading file with formal grammar definition will constrain model outputs. For instance, one can force the model to generate valid JSON or generate only python lists.
In the follow-up PR we will add:
* docs with some description why it is cool and how it works
* maybe some code sample for some task such as in llama repo
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PR_kwDOIPDwls5Ytndd
| 9,711 |
Nc/runnables retry
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[] | null | 3 | 2023-08-24T16:24:45 | 2023-09-01T14:52:22 | 2023-09-01T14:52:20 |
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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sort api classes
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COLLABORATOR
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Chat Loaders
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[] | null | 1 | 2023-08-24T15:29:06 | 2023-08-25T00:23:28 | 2023-08-25T00:23:27 |
COLLABORATOR
| null |
Still working out interface/notebooks + need discord data dump to test out things other than copy+paste
Update:
- Going to remove the 'user_id' arg in the loaders themselves and just standardize on putting the "sender" arg in the extra kwargs. Then can provide a utility function to map these to ai and human messages
- Going to move the discord one into just a notebook since I don't have a good dump to test on and copy+paste maybe isn't the greatest thing to support in v0
- Need to do more testing on slack since it seems the dump only includes channels and NOT 1 on 1 convos
-
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ParentDocumentRetriever doesn't implement BaseRetriever._aget_relevant_documents
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[] | null | 5 | 2023-08-24T15:28:16 | 2023-12-01T16:07:43 | 2023-12-01T16:07:42 |
NONE
| null |
### System Info
LangChain Version: 0.0.271, Python 3.11
Like the title says, _aget_relevant_documents isn't implemented in ParentDocumentRetriever so async calls are not working. It throws an NotImplementedError in BaseRetriever:
```
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
) -> List[Document]:
"""Asynchronously get documents relevant to a query.
Args:
query: String to find relevant documents for
run_manager: The callbacks handler to use
Returns:
List of relevant documents
"""
raise NotImplementedError()
```
### Who can help?
@hwchase17 @agola11
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just do a chain.acall from a chain using documents, (like a ConverstionalRetrievalChain), and it should trigger this error.
### Expected behavior
Should be able to call the chain async when using ParentDocumentRetriever.
|
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PR_kwDOIPDwls5YtQVM
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Qdrant Client: Expose instance for creating client
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[] | null | 3 | 2023-08-24T15:23:58 | 2023-08-28T13:31:00 | 2023-08-28T13:30:59 |
COLLABORATOR
| null |
Expose classmethods to convenient initialize the vectostore.
The purpose of this PR is to make it easy for users to initialize an empty vectorstore that's properly pre-configured without having to index documents into it via `from_documents`.
This will make it easier for users to rely on the following indexing code: https://github.com/langchain-ai/langchain/pull/9614
to help manage data in the qdrant vectorstore.
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Bagatur/redis refactor
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[] | null | 1 | 2023-08-24T15:04:51 | 2023-08-25T08:30:34 | 2023-08-25T08:30:34 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
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