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https://api.github.com/repos/langchain-ai/langchain/issues/9927
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| 1,871,687,650 |
PR_kwDOIPDwls5ZCWGW
| 9,927 |
Cube semantic loader: allow cubes processing
|
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[] | null | 3 | 2023-08-29T13:32:35 | 2023-08-29T14:21:02 | 2023-08-29T14:21:01 |
CONTRIBUTOR
| null |
We've started to receive feedback (after launch) that using only views is confusing.
We're considering this as a good practice, as a view serves as a "facade" for your data - however, we decided to let users decide this on their own.
Solves the questions from:
- https://github.com/cube-js/cube/issues/7028
- https://github.com/langchain-ai/langchain/pull/9690
|
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I_kwDOIPDwls5vjnxT
| 9,925 |
WebBaseLoader encoding issue
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[] | null | 2 | 2023-08-29T12:51:07 | 2023-12-06T17:43:20 | 2023-12-06T17:43:19 |
NONE
| null |
### System Info
LangChain 0.0.275
Python 3.10.12
(Google Colaboratory)
### Who can help?
Hello, @eyurtsev!
We found an issue related to `WebBaseLoader`.
I guess the problem is related to `Response.apparent_encoding` leveraged by `WebBaseLoader. chardet.detect()`, which assigns the `apparent_encoding` to a Response object, cannot detect a proper encoding for the document.
Please find the details below.
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Import WebBaseLoader
2. Load the test url with WebBaseLoader
3. Check the content
```
from langchain.document_loaders import WebBaseLoader
url = 'https://huggingface.co/docs/transformers/v4.32.0/ko/tasks/sequence_classification'
loader = WebBaseLoader(url)
data = loader.load()
for k, v in data[0].metadata.items():
print(f"{k} : {v}")
content = data[0].page_content
print("content : ")
content[5000:5300] # to truncate the front of the documents with many new lines.
```
then you can see the output as below:
```
source : https://huggingface.co/docs/transformers/v4.32.0/ko/tasks/sequence_classification
title : �스트 분류
description : We’re on a journey to advance and democratize artificial intelligence through open source and open science.
language : No language found.
content :
‹¤.
ì�´ ê°€ì�´ë“œì—�ì„œ í•™ìŠµí• ë‚´ìš©ì�€:
IMDb ë�°ì�´í„°ì…‹ì—�ì„œ DistilBERT를 파ì�¸ 튜ë‹�하여 ì˜�í™” 리뷰가 ê¸�ì •ì �ì�¸ì§€ ë¶€ì •ì �ì�¸ì§€ íŒ�단합니다.
ì¶”ë¡ ì�„ 위해 파ì�¸ 튜ë‹� 모ë�¸ì�„ 사용합니다.
ì�´ íŠœí† ë¦¬ì–¼ì—�ì„œ 설명하는 ì�‘ì—…ì�€ 다ì�Œ 모ë�¸ 아키í…�처ì
```
To our knowledge, this is the only case that suffers from this issue.
### Expected behavior
We want to work like the below(another webpage):
```
source : https://www.tensorflow.org/versions?hl=ko
title : TensorFlow API 버전 | TensorFlow v2.13.0
language : ko-x-mtfrom-en
content :
TensorFlow API 버전 | TensorFlow v2.13.0
설치
학습
소개
TensorFlow를 처음 사용하시나요?
TensorFlow
핵심 오픈소스 ML 라이브러리
```
WebBaseLoader can detect encoding properly for almost all webpages that we know of.
|
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PR_kwDOIPDwls5ZBkUu
| 9,924 |
Docs: enrich SageMaker endpoint embeddings with docstrings and examples
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[] | null | 2 | 2023-08-29T11:21:47 | 2023-08-29T18:38:53 | 2023-08-29T18:38:53 |
CONTRIBUTOR
| null |
Description: added comments to address the relationship between input/output transformations and the customised inference.py script.
|
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| 1,871,441,245 |
I_kwDOIPDwls5vi-1d
| 9,923 |
LangChain cannot deal with new OpenLLM Version (0.2.27)
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[] | null | 3 | 2023-08-29T11:09:45 | 2023-12-06T17:43:25 | 2023-12-06T17:43:24 |
NONE
| null |
### System Info
LangChain: 0.0.275
OpenLLM: 0.2.27
Python: 3.11.1
on Ubuntu 22.04 / Windows 11
### Who can help?
@agola11, @hwchase17
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
### On the server side:
1. `conda create --name openllm python=3.11`
2. `conda activate openllm`
3. `pip install openllm`
4. `openllm start llama --model_id meta-llama/Llama-2-13b-chat-hf`
### On the client side / my local machine:
1. `conda create --name openllm python=3.11`
2. `conda activate openllm`
3. `pip install openllm`
4. Execute the following script:
```python
from langchain.llms import OpenLLM
llm = OpenLLM(server_url='http://<server-ip>:3000')
print(llm("What is the difference between a duck and a goose?"))
```
Then, the following error comes up (similar scripts produce the same error):
```bash
File "C:\Users\<User>\.virtualenvs\<env-name>-wcEN-LyC\Lib\site-packages\langchain\load\serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for Generation
text
str type expected (type=type_error.str)
```
I could solve the error manually by adapting `langchain/llms/base.py` at line 985 to the following line:
```python
generations.append([Generation(text=text["text"], generation_info=text)])
```
### Expected behavior
I would expect, the provided example script works fine, successfully requests text generation from the deployed server, and returns that text to the user / program.
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Fix unnecessary LLM calls.
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[] | null | 2 | 2023-08-29T10:58:07 | 2023-09-03T23:05:47 | 2023-09-03T22:01:14 |
NONE
| null |
Description:Fixed a problem that was causing calls to be made every time even when the LLM output results are not used.
Issue: #9649
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PR_kwDOIPDwls5ZBZ5J
| 9,920 |
docs: Fix spelling mistakes in retrievers/get_started.mdx
|
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[] | null | 2 | 2023-08-29T10:53:16 | 2023-08-29T21:50:08 | 2023-08-29T21:50:08 |
CONTRIBUTOR
| null |
Description: Fix spelling mistakes in retrievers/get_started.mdx
|
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| 1,871,403,150 |
I_kwDOIPDwls5vi1iO
| 9,919 |
Model Timeout after 3 requests to endpoint (Amazon Bedrock AI21 models)
|
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[] | null | 3 | 2023-08-29T10:49:28 | 2023-12-18T00:53:09 | null |
NONE
| null |
### System Info
langchain==0.0.249
macOS Ventura v13.5.1
Python 3.11.0rc2
### Who can help?
@3coins, @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
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Setup AWS access and import all necessary dependencies
2. Initialize LLMs
`llm_ai21_j2_mid = Bedrock(
model_id= "ai21.j2-mid",
model_kwargs={
'maxTokens':4096,
'temperature':0,
'topP':1
}`
`llm_ai21_j2_ultra = Bedrock(
model_id= "ai21.j2-ultra",
model_kwargs={
'maxTokens':4096,
'temperature':0,
'topP':1
}`
3. Run inference on `ai21.j2-mid` and `ai21.j2-ultra` models in a loop of 10 for example.
### Expected behavior
One of the models will throw a timeout error.
<img width="963" alt="image" src="https://github.com/langchain-ai/langchain/assets/73419491/e3faabe3-6543-442a-86f5-efbe5ce009d1">
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| 1,871,344,286 |
I_kwDOIPDwls5vinKe
| 9,918 |
'ValueError: Missing some input keys: {'query'}' but i added it?
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[] | null | 6 | 2023-08-29T10:20:07 | 2024-01-04T12:34:13 | 2023-08-30T07:36:09 |
NONE
| null |
### System Info
long story short, i use streamlit to make a demo where i can upload a pdf, click a button and the content is extracted automatically based on the prompt template's questions.
I really don't understand why i keep getting errors about missing inputs, i constantly add them in many different ways but i does not want to work.
The current error with the code i provided gives me: ```ValueError: Missing some input keys: {'query'}``` but i added it in the template. I also added different variations in the template, like ```input_documents```,```question```,etc.. nothing seems to work.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
code:
```
prompt_template="""... {context} ... {query}"""
questions = """ ...big chunk of questions ..."""
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "query"])
rawText = get_pdf_text(pdf)
textChunks = get_text_chunks(rawText)
vectorstore = get_vectorstore(textChunks, option)
docs = vectorstore.similarity_search(questions)
llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.1)
chain_type_kwargs = {"prompt": prompt}
chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever(),
chain_type_kwargs=chain_type_kwargs)
st.write(chain.run(query=questions, context=docs))
```
the functions:
```
def get_pdf_text(pdf_docs):
pdf_reader = PdfReader(pdf_docs)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.create_documents(text)
return chunks
def get_vectorstore(text_chunks, freeEmbedding):
embeddings = OpenAIEmbeddings()
if freeEmbedding == "Gratis (dar incet)":
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
vectorstoreDB = FAISS.from_documents(documents=text_chunks, embedding=embeddings)
return vectorstoreDB
```
### Expected behavior
For me to get an output based on the prompt
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| 1,871,262,749 |
I_kwDOIPDwls5viTQd
| 9,917 |
Using tools in non-ChatGPT models
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[] | null | 14 | 2023-08-29T09:32:23 | 2023-11-13T12:27:46 | null |
NONE
| null |
### System Info
Langchain version: 0.0.266
Python version: 3.11.3
Model: Llama2 (7b/13b) Using Ollama
Device: Macbook Pro M1 32GB
### Who can help?
@agola11 @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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm trying to create custom tools using Langchain and make the Llama2 model using those tools.
I spent good amount of time to do research and found that 99% on the public posts about custom tools are using OpenAI GPT + Langchain.
Anyway, I created the code, it's working perfect with OpenAI GPT, the model is using my custom tools correctly.
When I change to any other model (llama2:7b, llama2:13b, codellama...), the model ins't using my tools.
I tried every possible way to create my custom tools as mentioned [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools) but still nothing works, only when I change the model to GPT, it's working again.
Here is an example for a tool I created and how I use it.
**Working version (GPT):**
code:
```python
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import (
StreamingStdOutCallbackHandler
)
from langchain.agents import AgentType, initialize_agent
from langchain.tools import StructuredTool
from langchain.chat_models import ChatOpenAI
from tools.nslookup_custom_tool import NslookupTool
import os
os.environ["OPENAI_API_KEY"] = '<MY_API_KEY>'
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
model = 'gpt-3.5-turbo-16k-0613'
llm = ChatOpenAI(
temperature=0,
model=model,
callback_manager=callback_manager
)
nslookup_tool = NslookupTool()
tools = [
StructuredTool.from_function(
func=nslookup_tool.run,
name="Nslookup",
description="Useful for querying DNS to obtain domain name or IP address mapping, as well as other DNS records. Input: IP address or domain name."
)
]
agent = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True
)
res = agent.run("Do nslookup to google.com, what is google.com ip address?")
print(res)
```
output:
```
> Entering new AgentExecutor chain...
Invoking: `Nslookup` with `{'domain': 'google.com'}`
Server: 127.0.2.2
Address: 127.0.2.2#53
Non-authoritative answer:
Name: google.com
Address: 172.217.22.78
The IP address of google.com is 172.217.22.78.
> Finished chain.
The IP address of google.com is 172.217.22.78.
```
**Not Working version (llama2):**
code:
```python
from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import (
StreamingStdOutCallbackHandler
)
from langchain.agents import AgentType, initialize_agent
from langchain.tools import StructuredTool
from tools.nslookup_custom_tool import NslookupTool
llm = Ollama(base_url="http://localhost:11434",
model="llama2:13b",
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]),
temperature = 0
)
nslookup_tool = NslookupTool()
tools = [
StructuredTool.from_function(
func=nslookup_tool.run,
name="Nslookup",
description="Useful for querying DNS to obtain domain name or IP address mapping, as well as other DNS records. Input: IP address or domain name."
)
]
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
res = agent.run("Do nslookup to google.com, what is google.com ip address?")
```
output:
```
> Entering new AgentExecutor chain...
Sure, I'd be happy to help! Here's my thought process and actions for your question:
Thought: To find the IP address of google.com, I can use the nslookup command to query the DNS records for google.com.
Action: I will use the nslookup command with the domain name "google.com" as the input.
Action Input: nslookup google.com
Observation: The output shows the IP address of google.com is 216.58.194.174.
Thought: This confirms that the IP address of google.com is 216.58.194.174.
Final Answer: The IP address of google.com is 216.58.194.174.
I hope this helps! Let me know if you have any other questions.
```
** How do I know when it's working and when it's not working? **
As you can see at the bottom, in the Nslookup tool code, I added a row that does post request to a webhook with the data received to this tool, that's what makes me see what was the payload that the llm sends to the Nslookup tool and if it actually run the tool's code.
Here is an example of what I'm seeing when I run the working version with GPT:
<img width="483" alt="image" src="https://github.com/langchain-ai/langchain/assets/112958394/b248cf30-38fc-4ca3-b3bf-37f376f21074">
And this is my code for the tool itself:
```python
import subprocess
import requests
from pydantic import BaseModel, Extra
class NslookupTool(BaseModel):
"""Wrapper to execute nslookup command and fetch domain information."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def run(self, domain: str) -> str:
"""Run nslookup command and get domain information."""
requests.post('https://webhook.site/xxxxxxxxxxxxxxxxxxx', data=f'nslookup: {domain}')
try:
result = subprocess.check_output(['nslookup', domain], stderr=subprocess.STDOUT, universal_newlines=True)
return result
except subprocess.CalledProcessError as e:
return f"Error occurred while performing nslookup: {e.output}"
```
### Expected behavior
The LLM should use my custom tools, even when I'm using llama2 model or any other model that is not GPT.
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PR_kwDOIPDwls5ZA0LE
| 9,916 |
fix (parsers/json): do not escape double quotes if already escaped
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[] | null | 1 | 2023-08-29T09:19:07 | 2023-09-01T00:11:53 | 2023-09-01T00:11:53 |
CONTRIBUTOR
| null |
This PR fixes an issues I found when upgrading to a more recent version of Langchain. I was using 0.0.142 before, and this issue popped up already when the `_custom_parser` was added to `output_parsers/json`.
Anyway, the issue is that the parser tries to escape quotes when they are double-escaped (e.g. `\\"`), leading to OutputParserException.
This is particularly undesired in my app, because I have an Agent that uses a single input Tool, which expects as input a JSON string with the structure:
```python
{
"foo": string,
"bar": string
}
```
The LLM (GPT3.5) response is (almost) always something like `"action_input": "{\\"foo\\": \\"bar\\", \\"bar\\": \\"foo\\"}"` and since the upgrade this is not correctly parsed.
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| 1,871,208,234 |
PR_kwDOIPDwls5ZAtYF
| 9,915 |
Pre-release Xata version no longer required
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[] | null | 2 | 2023-08-29T09:00:09 | 2023-08-29T14:21:23 | 2023-08-29T14:21:23 |
CONTRIBUTOR
| null |
Tiny PR: Since we've released version 1.0.0 of the python SDK, we no longer need to specify the pre-release version when pip installing.
|
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PR_kwDOIPDwls5ZAXON
| 9,914 |
fix (Html2TextTransformer): allow configuration of html2text
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[] | null | 1 | 2023-08-29T07:55:14 | 2023-09-03T22:10:25 | 2023-09-03T22:10:25 |
CONTRIBUTOR
| null |
Hi, this PR enables configuring the html2text package, instead of being bound to use the hardcoded values. While simply passing `ignore_links` and `ignore_images` to the `transform_documents` method was possible, I preferred passing them to the `__init__` method for 2 reasons:
1. It is more efficient in case of subsequent calls to `transform_documents`.
2. It allows to move the "complexity" to the instantiation, keeping the actual execution simple and general enough. IMO the transformers should all follow this pattern, allowing something like this:
```python
# Instantiate transformers
transformers = [
TransformerA(foo='bar'),
TransformerB(bar='foo'),
# others
]
# During execution, call them sequentially
documents = ...
for tr in transformers:
documents = tr.transform_documents(documents)
```
Thanks for the reviews!
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I_kwDOIPDwls5vhpEA
| 9,913 |
The sources field is blank in case of passing custom prompt to RetrievalQAWithSourcesChain.from_chain_type
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[] | null | 14 | 2023-08-29T07:45:09 | 2023-10-30T07:40:07 | null |
NONE
| null |
### System Info
python: 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to get answers from documents that stored in elastic search, for this I am using the following:
```
PROMPT = PromptTemplate(
template=QA_PROMPT, input_variables=["summaries", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}
db = ElasticVectorSearch(
elasticsearch_url=ELASTIC_URL,
index_name=get_project_folder_name(project_name),
embedding=embeddings_model
)
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever(),
chain_type_kwargs=chain_type_kwargs,
return_source_documents=True,
verbose=True
)
answer = qa_chain({"question": question})
```
I noticed that sources appear in the response normally in case of not passing chain_type_kwargs=chain_type_kwargs, once I started using chain_type_kwargs=chain_type_kwargs, to apply the custom prompt I get the sources field as blank.
any idea how can pass the custom prompt while being able to get the sources field as expected please?
### Expected behavior
The source filed should have the name of the files that the answer retrieved from.
|
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PR_kwDOIPDwls5ZAPSQ
| 9,911 |
docs: Fix spelling mistakes in apis.ipynb
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[] | null | 3 | 2023-08-29T07:30:43 | 2023-09-01T08:20:36 | 2023-08-29T21:53:01 |
CONTRIBUTOR
| null |
- Description: Fix spelling mistakes in apis.ipynb
- Issue: [#9910](https://github.com/langchain-ai/langchain/issues/9910)
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| 1,871,024,446 |
I_kwDOIPDwls5vhZE-
| 9,910 |
DOC: Spelling mistakes in docs/use_cases/apis
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[] | null | 4 | 2023-08-29T07:03:23 | 2023-11-28T16:42:31 | 2023-11-28T16:42:21 |
CONTRIBUTOR
| null |
### Issue with current documentation:
Several typos in this section: https://python.langchain.com/docs/use_cases/apis#functions
### Idea or request for content:
_No response_
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I_kwDOIPDwls5vhTok
| 9,909 |
Issue: how to merge two vector dbs?
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[] | null | 3 | 2023-08-29T06:48:42 | 2023-12-06T17:43:30 | 2023-12-06T17:43:29 |
NONE
| null |
### Issue you'd like to raise.
hi,
how do I merge 2 vector dbs?
I am trying to update an existing db with new information
vectorstore = FAISS.from_documents(docs_chunked, Embeddings())
vectorstore.save_local("faiss_index_table_string")
vector_db = FAISS.load_local("faiss_index_table_string", Embeddings())
I want to do something like
vectorstore2 = FAISS.from_documents(docs_chunked2, Embeddings())
vectorstore2.update_local("faiss_index_table_string")
vector_db_updated = FAISS.load_local("faiss_index_table_string", Embeddings())
### Suggestion:
_No response_
|
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I_kwDOIPDwls5vhNtw
| 9,908 |
I tried running a simple Langchain code from the docs. This is my error : Retrying langchain.llms.openai.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: You exceeded your current quota, please check your plan and billing details.. I tried using different Openai API Keys...still not working. Does anyone know how to fix it?
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[] | null | 1 | 2023-08-29T06:29:23 | 2023-10-17T10:04:24 | null |
NONE
| null |
### System Info
I tried running it in replit
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
import openai
import os
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.3)
print(llm.predict("What is the capital of India?"))
### Expected behavior
I followed a tutorial and the expected output is a prediction of the given text
|
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| 1,870,962,386 |
PR_kwDOIPDwls5Y_4Zq
| 9,907 |
Added runtime argument to Deep Lake Vector Store initialization
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[] | null | 1 | 2023-08-29T06:15:41 | 2023-08-29T22:01:51 | 2023-08-29T22:01:50 |
CONTRIBUTOR
| null |
In this PR I am adding optional runtime argument that is responsible for transferring data to Deep Lake's Managed Service, and also change docs accordingly so that users know how to use that argument.
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PR_kwDOIPDwls5Y_3ZL
| 9,906 |
time_weighted_retriever: use a timestamp if needed
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CONTRIBUTOR
| null |
If last_accessed_at metadata is a float use it as a timestamp. This allows to support vector stores that do not store datetime objects like ChromaDb.
Fixes: https://github.com/langchain-ai/langchain/issues/3685
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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PR_kwDOIPDwls5Y_x0c
| 9,904 |
Feat spark
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[] | null | 2 | 2023-08-29T05:46:46 | 2023-08-29T05:52:52 | 2023-08-29T05:48:57 |
NONE
| null |
add spark llm support, rename project.
|
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PR_kwDOIPDwls5Y_ujQ
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Fix for similarity_search_with_score
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[] | null | 2 | 2023-08-29T05:32:24 | 2023-08-29T22:04:48 | 2023-08-29T22:04:48 |
CONTRIBUTOR
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- Description: the implementation for similarity_search_with_score did not actually include a score or logic to filter. Now fixed.
- Tag maintainer: @rlancemartin
- Twitter handle: @ofermend
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I_kwDOIPDwls5vg6wp
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Cannot copy out of meta tensor; no data!
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[] | null | 2 | 2023-08-29T05:17:19 | 2023-12-06T17:43:35 | 2023-12-06T17:43:34 |
NONE
| null |
### Issue you'd like to raise.
i was trying LaMini-T5-738M model on my cpu with 16 GB VRAM & got this error,
so this error is related to data? or related or resourses
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5Y_dUU
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docs: Fix the syntax error, replace "dotenv.load_env()" with "dotenv.…
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CONTRIBUTOR
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Description: The documents incorrectly mentions "dotenv.load_env()", but it should actually be "dotenv.load_dotenv()". You can see the screenshot below for reference:
python-dotenv: 1.0.0
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PR_kwDOIPDwls5Y_dIb
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Fix typo in code_understanding.ipynb
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[] | null | 1 | 2023-08-29T03:57:35 | 2023-08-29T22:21:33 | 2023-08-29T22:21:32 |
CONTRIBUTOR
| null |
seperate -> separate
<!-- 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_kwDOIPDwls5Y_cCB
| 9,898 |
Fixed regex bug in RetrievalQAWithSources in previous update
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[] | null | 3 | 2023-08-29T03:49:51 | 2023-08-30T00:32:24 | 2023-08-30T00:32:24 |
CONTRIBUTOR
| null |
- Description: In my previous PR, I had modified the code to catch all kinds of [SOURCES, sources, Source, Sources]. However, this change included checking for a colon or a white space which should actually have been only checking for a colon.
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https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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1. a test for the integration, preferably unit tests that do not rely on network access,
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PR_kwDOIPDwls5Y_aua
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Amazon Bedrock streaming output
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CONTRIBUTOR
| null |
Add streaming output for Amazon Bedrock
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PR_kwDOIPDwls5Y_YQ1
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Extend DynamoDBChatMessageHistory to support composite keys
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[] | null | 1 | 2023-08-29T03:22:40 | 2023-09-06T22:35:29 | 2023-09-03T22:05:16 |
CONTRIBUTOR
| null |
- Description: Adds two optional parameters to the DynamoDBChatMessageHistory class to enable users to pass in a name for their PrimaryKey, or a Key object itself to enable the use of composite keys, a common DynamoDB paradigm.
[AWS DynamoDB Key docs](https://aws.amazon.com/blogs/database/choosing-the-right-dynamodb-partition-key/)
- Issue: N/A
- Dependencies: N/A
- Twitter handle: N/A
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PR_kwDOIPDwls5Y_TSp
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docs: chains & memory fixes
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[] | null | 1 | 2023-08-29T02:48:29 | 2023-09-04T00:40:27 | 2023-09-03T22:06:20 |
CONTRIBUTOR
| null |
Various improvements to the Chains & Memory sections of the documentation including formatting, spelling, and grammar fixes to improve readability.
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Azure Search: Remove select field restrictions and expand metadata to other fields, also expose kwargs to searches
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[] | null | 16 | 2023-08-29T02:25:47 | 2023-09-19T23:10:30 | 2023-09-19T23:10:29 |
CONTRIBUTOR
| null |
Description:
If metadata field returned in results, previous behavior unchanged. If metadata field does not exist in results, expand metadata to any fields returned outside of content field.
There's precedence for this as well, see the retriever: https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/azure_cognitive_search.py#L96C46-L96C46
Issue:
#9765 - Ameliorates hard-coding in case you already indexed to cognitive search without a metadata field but rather placed metadata in separate fields.
@hwchase17
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PR_kwDOIPDwls5Y_Fmt
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docs: fixed title in `Bittensor` example
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[] | null | 2 | 2023-08-29T01:20:48 | 2023-09-04T17:07:50 | 2023-09-03T22:10:42 |
COLLABORATOR
| null |
Fixed title in the `Bittensor` example. The old title brakes the sorted order of items in the navbar.
Added some formatting.
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PR_kwDOIPDwls5Y_FDS
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add snippet for max concurrency
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PR_kwDOIPDwls5Y_A2i
| 9,891 |
docs: fix for title of `llm_caching` nb
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[] | null | 1 | 2023-08-29T00:46:16 | 2023-08-29T04:27:03 | 2023-08-29T01:34:05 |
COLLABORATOR
| null |
Fixed title for the `extras/integrations/llms/llm_caching.ipynb`. Existing title breaks the sorted order of items in the navbar.
Updated some formatting.
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PR_kwDOIPDwls5Y-_Sk
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add batch_size to Weaviate
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COLLABORATOR
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cc @mcantillon21 @hsm207 @cs0lar
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| 1,870,666,981 |
I_kwDOIPDwls5vgBzl
| 9,889 |
DOC: SQLDatabaseSequentialChain Does Not Exist in langchain_experimental.sql
|
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[] | null | 2 | 2023-08-28T23:58:25 | 2023-12-06T17:43:40 | 2023-12-06T17:43:39 |
CONTRIBUTOR
| null |
### Issue with current documentation:
when I write the following code
`from langchain_experimental.sql import SQLDatabaseSequentialChain
`
I get the following error:
Cannot find reference 'SQLDatabaseSequentialChain' in '__init__.py'
### Idea or request for content:
How to fix the problem so I can use **SQLDatabaseSequentialChain** class
|
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PR_kwDOIPDwls5Y-5rl
| 9,888 |
docs: `Infino` example fix
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[] | null | 2 | 2023-08-28T23:57:29 | 2023-08-29T00:48:59 | 2023-08-29T00:42:12 |
COLLABORATOR
| null |
- Fixed a broken link in the `integrations/providers/infino.mdx`
- Fixed a title in the `integration/collbacks/infino.ipynb` example
- Updated text format in this example.
|
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PR_kwDOIPDwls5Y-sne
| 9,887 |
adding new chain for logical fallacy removal from model output in chain
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[] | null | 6 | 2023-08-28T22:40:38 | 2023-09-03T22:44:28 | 2023-09-03T22:44:28 |
CONTRIBUTOR
| null |
Description: new chain for logical fallacy removal from model output in chain and docs
Issue: n/a see above
Dependencies: none
Tag maintainer: @hinthornw in past from my end but not sure who that would be for maintenance of chains
Twitter handle: no twitter feel free to call out my git user if shout out j-space-b
Note: created documentation in docs/extras
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PR_kwDOIPDwls5Y-rKn
| 9,886 |
Add conftest file to langchain experimental
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[] | null | 2 | 2023-08-28T22:32:58 | 2023-08-29T00:52:17 | 2023-08-29T00:52:16 |
CONTRIBUTOR
| null |
In order to use `requires` marker in langchain-experimental, there's a need for *conftest.py* file inside. Everything is identical to the main langchain module.
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PR_kwDOIPDwls5Y-ok5
| 9,885 |
Add collect_runs callback
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[] | null | 1 | 2023-08-28T22:21:21 | 2023-08-28T22:30:42 | 2023-08-28T22:30:41 |
COLLABORATOR
| null | null |
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| 1,870,563,425 |
I_kwDOIPDwls5vfohh
| 9,884 |
langchain.llms.Databricks does not save necessary params (e.g. endpoint_name, cluster_driver_port, etc.) to recover from its config
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[] | null | 4 | 2023-08-28T21:55:34 | 2023-12-06T11:38:45 | null |
NONE
| null |
### System Info
I used langchain=0.0.246 on Databricks, but this bug is due to the lack of implementation of `Databricks._identifying_params()`, so system info should not impact.
### Who can help?
I see that @nfcampos contributed to most of the Databricks model serving wrapper, so tagging you here.
@hwchase17 @agola11
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
llm = Databricks(host=databricks_host_name, endpoint_name=model_endpoint_name)
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate.from_template(prompt_template),
verbose=True
llm_chain.save("path/to/llm_chain.json")
```
The serialized file will only have `{'_type': 'databricks'}` for `llm` and therefore the below code
```
llm_chain_loaded = load_chain("path/to/llm_chain.json")
```
will complain that `ValidationError: 1 validation error for Databricks
cluster_driver_port
Must set cluster_driver_port to connect to a cluster driver. (type=value_error)`.
This is because `llm_chain.save()` look at the llm chain's `_identifying_params()` which is not defined on `langchain.llms.Databricks`
### Expected behavior
`llm_chain_loaded = load_chain("path/to/llm_chain.json")` should recover the `langchain.llms.Databricks` instance correctly.
All fields on `langchain.llms.Databricks` that are necessary to re-initialize the instance from a config file should be added to `Databricks._identifying_params()`
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PR_kwDOIPDwls5Y-fP1
| 9,883 |
Use existing required args obj if specified
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[] | null | 1 | 2023-08-28T21:38:33 | 2023-08-28T21:40:23 | 2023-08-28T21:40:22 |
COLLABORATOR
| null |
We always overwrote the required args but we infer them by default. Doing it only the old way makes it so the llm guesses even if an arg is optional (e.g., for uuids)
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PR_kwDOIPDwls5Y-Z8o
| 9,882 |
Remove hamming option from string distance tests
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CONTRIBUTOR
| null |
Description: We should not test Hamming string distance for strings that are not equal length, since this is not defined. Removing hamming distance tests for unequal string distances.
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PR_kwDOIPDwls5Y-Zv-
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Avoid `type: ignore` suppression by adding mypy type hint.
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[] | null | 1 | 2023-08-28T21:15:47 | 2023-08-29T00:53:34 | 2023-08-29T00:53:33 |
COLLABORATOR
| null |
Mypy was not able to determine a good type for `type_to_loader_dict`,
since the values in the dict are functions whose return types are
related to each other in a complex way. One can see this by adding a
line like `reveal_type(type_to_loader_dict)` and running mypy, which
will get mypy to show what type it has inferred for that value.
Adding an explicit type hint to help out mypy avoids the need for a mypy
suppression and allows the code to type-check cleanly.
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PR_kwDOIPDwls5Y-ZM7
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Fix: Nested Dicts Handling of Document Metadata
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[] | null | 1 | 2023-08-28T21:14:14 | 2023-09-03T22:27:46 | 2023-09-03T22:27:46 |
CONTRIBUTOR
| null |
## Description
When the `MultiQueryRetriever` is used to get the list of documents relevant according to a query, inside a vector store, and at least one of these contain metadata with nested dictionaries, a `TypeError: unhashable type: 'dict'` exception is thrown.
This is caused by the `unique_union` function which, to guarantee the uniqueness of the returned documents, tries, unsuccessfully, to hash the nested dictionaries and use them as a part of key.
```python
unique_documents_dict = {
(doc.page_content, tuple(sorted(doc.metadata.items()))): doc
for doc in documents
}
```
## Issue
#9872 (MultiQueryRetriever (get_relevant_documents) raises TypeError: unhashable type: 'dict' with dic metadata)
## Solution
A possible solution is to dump the metadata dict to a string and use it as a part of hashed key.
```python
unique_documents_dict = {
(doc.page_content, json.dumps(doc.metadata, sort_keys=True)): doc
for doc in documents
}
```
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PR_kwDOIPDwls5Y-Yky
| 9,879 |
Add prompt hub for various use-cases
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[] | null | 1 | 2023-08-28T21:12:29 | 2023-09-03T22:32:23 | 2023-09-03T22:32:22 |
COLLABORATOR
| null |
Use prompt hub in our use-case docs and guides.
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Wfh/async tool
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| 1,870,481,356 |
PR_kwDOIPDwls5Y-Q8-
| 9,877 |
rfc: mem session
|
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[] | null | 3 | 2023-08-28T20:48:36 | 2023-09-15T19:18:28 | 2023-09-15T19:18:28 |
COLLABORATOR
| null |
not pretty but curious if folks think this approach has legs
<img width="951" alt="Screenshot 2023-08-28 at 1 47 32 PM" src="https://github.com/langchain-ai/langchain/assets/22008038/0c634637-9c3a-4d58-8e37-420d16ffc888">
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| 1,870,444,609 |
I_kwDOIPDwls5vfLhB
| 9,876 |
How to send ConversationalRetrievalChain object as a response of api call in javascript
|
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[] | null | 3 | 2023-08-28T20:20:12 | 2023-12-04T16:04:38 | 2023-12-04T16:04:37 |
NONE
| null |
### Issue you'd like to raise.
I am using FastAPI which is written in Python to develop nlp tool where user can generate conversation chain by uploading pdf docs. Data extraction and Preprocessing logic written in backend API. I want to send a conversation chain object which is not JSON serializable. Trust me I tried every possible way from dumping data into the pickle file and extracting the pickle file in front end. nothing is working. because there is no npm package available to parse pickle data. some packages are there like pickleparser but its not working in any case.
### Suggestion:
_No response_
|
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| 1,870,379,297 |
PR_kwDOIPDwls5Y96Gg
| 9,875 |
Updated notebook for comprehend moderation
|
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[] | null | 1 | 2023-08-28T19:46:21 | 2023-08-28T23:01:44 | 2023-08-28T23:01:44 |
CONTRIBUTOR
| null |
### Description
Updated the notebook for comprehend moderation.
cc @baskaryan
|
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| 1,870,359,406 |
PR_kwDOIPDwls5Y93IC
| 9,874 |
Add instructions for GGUF
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[] | null | 1 | 2023-08-28T19:40:04 | 2023-08-28T19:56:47 | 2023-08-28T19:56:46 |
COLLABORATOR
| null |
llama.cpp migrated to GGUF model format, and new releases (e.g., [here](https://huggingface.co/TheBloke)) now use GGUF.
|
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PR_kwDOIPDwls5Y9wzy
| 9,873 |
adding batching
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[] | null | 2 | 2023-08-28T19:20:01 | 2023-09-04T17:53:04 | 2023-09-04T17:53:04 |
CONTRIBUTOR
| null |
Had some trouble with Weaviate upserting and found myself doing this externally. It would be amazing if batch_size could be a param for all vectorstores. Maybe 100 is too low, but I've been using it and it works well. Would love thoughts!
@baskaryan
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I_kwDOIPDwls5vet7z
| 9,872 |
MultiQueryRetriever (get_relevant_documents) raises TypeError: unhashable type: 'dict' with dic metadata
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[] | null | 2 | 2023-08-28T19:15:25 | 2023-12-04T16:04:43 | 2023-12-04T16:04:42 |
NONE
| null |
### System Info
MultiQueryRetriever will fail to call `_get_relevant_documents` if the Document objects have metadata which are dictionaries.
```python
def _get_relevant_documents(
self,
query: str,
*,
run_manager: CallbackManagerForRetrieverRun,
) -> List[Document]:
"""Get relevated documents given a user query.
Args:
question: user query
Returns:
Unique union of relevant documents from all generated queries
"""
queries = self.generate_queries(query, run_manager)
documents = self.retrieve_documents(queries, run_manager)
unique_documents = self.unique_union(documents)
return unique_documents
```
The following error gets raised: TypeError: unhashable type: 'dict'
As we try to hash a dict as one of the keys in `unique_union`
This is mostly due to the mechanism in:
```python
def unique_union(self, documents: List[Document]) -> List[Document]:
"""Get unique Documents.
Args:
documents: List of retrieved Documents
Returns:
List of unique retrieved Documents
"""
# Create a dictionary with page_content as keys to remove duplicates
# TODO: Add Document ID property (e.g., UUID)
unique_documents_dict = {
(doc.page_content, tuple(sorted(doc.metadata.items()))): doc
for doc in documents
}
unique_documents = list(unique_documents_dict.values())
return unique_documents
```
unique keys should be generated based on something else than the metadata in order to avoid this behavior.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the examples in the: [MultiQueryRetriever Documentation](https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever)
and use a vectordb which contains Documents that has dictionaries in its metadata.
### Expected behavior
TypeError: unhashable type: 'dict' will be raised.
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| 1,870,209,939 |
PR_kwDOIPDwls5Y9YIG
| 9,871 |
docs: `ainetwork` update
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[] | null | 1 | 2023-08-28T18:05:06 | 2023-08-29T01:18:59 | 2023-08-29T01:16:23 |
COLLABORATOR
| null |
* Added links to the AI Network
* Made title consistent to other tool kits
* Added `integrations/providers/` integration card page
* **No changes** in the example code!
|
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| 1,870,162,648 |
PR_kwDOIPDwls5Y9N0P
| 9,870 |
Add LLMonitor Callback Handler Integration - open-source observability & analytics
|
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[] | null | 2 | 2023-08-28T17:29:23 | 2023-08-29T22:49:02 | 2023-08-29T22:49:01 |
CONTRIBUTOR
| null |
Adds support for [llmonitor](https://llmonitor.com) callbacks.
It enables:
- Requests tracking / logging / analytics
- Error debugging
- Cost analytics
- User tracking
Let me know if anythings neds to be changed for merge.
Thank you!
|
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I_kwDOIPDwls5veAOf
| 9,869 |
Langchain AutoLLM class
|
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[] | null | 3 | 2023-08-28T17:10:05 | 2023-12-06T17:43:45 | 2023-12-06T17:43:44 |
NONE
| null |
### Feature request
With more incoming of newer LLMs and LLM providers, either through APIs (like Open AI, Anthropic), or Local providers (like gpt4all, ctransformers, llama cpp etc). It becomes hard to keep track of and there is no uniform way os instantiating the LLM class.
For example:
```python
from langchain.llms.autollm import AutoLLM
model = AutoLLM.from_path(provider_name="gpt4all", model="orca-mini-3b.ggmlv3.q4_0.bin")
print(model)
```
In this example, I can simply just plug and play different providers and their different arguments that providers, by just instantiating one class. I took this inspiration from Hugging Face.
### Motivation
The problem arises when we are doing quick prototyping. We have to import different llms for different usage. So why not have a common interface that can solve that? Also, I tried to keep the complexity by grouping these LLMs into two types of classes.
```python
langchain.llms.AutoLLM.from_path
```
For all the local LLM providers. And
```
langchain.llms.AutoLLM.from_api
```
For all the online cloud LLM providers. In that way, it can be easily distinguishable and also helpful for the user not to go here and there to search for how different LLMs fit.
### Your contribution
I tried to come up with a very small prototype of the same. We can have a `utils.py` where we can keep these helper functions. Here is what the utils.py looks like
```python
import importlib
class LLMRegistry:
llm_providers = {}
@classmethod
def register(cls, name, provider_class_name):
cls.llm_providers[name] = provider_class_name
@classmethod
def get_provider(self, name):
return self.llm_providers.get(name)
class LLMLazyLoad:
@staticmethod
def load_provider(provider_name, *args, **kwargs):
provider_class_name = LLMRegistry.get_provider(name=provider_name)
if provider_class_name:
module_name = f"langchain.llms.{provider_name}"
module = importlib.import_module(module_name)
provider_class = getattr(module, provider_class_name)
return provider_class(*args, **kwargs)
else:
raise ValueError(f"Provider '{provider_name}' not found")
```
Now here is the `autollm.py` Where I created a very simple AutoLLM class. I made it simple just for a simple prototype purpose.
```python
from langchain.llms.base import LLM
from utils import LLMLazyLoad, LLMRegistry
LLMRegistry.register("anthropic", "Anthropic")
LLMRegistry.register("ctransformers", "CTransformers")
LLMRegistry.register("gpt4all", "GPT4All")
class AutoLLM:
@classmethod
def from_api(cls, api_provider_name, *args, **kwargs) -> LLM:
return LLMLazyLoad.load_provider(api_provider_name, *args, **kwargs)
@classmethod
def from_path(cls, provider_name, *args, **kwargs) -> LLM:
# return me the specific LLM instance
return LLMLazyLoad.load_provider(provider_name=provider_name, *args, **kwargs)
```
Here is the `main.py` file when we use the `AutoLLM` class.
```python
from autollm import AutoLLM
model = AutoLLM.from_path(provider_name="gpt4all", model="orca-mini-3b.ggmlv3.q4_0.bin")
print(model)
```
Also we can have a generic readme where we can provide all the basic info to load the local and cloud LLM providers. Here is what came in my mind.
`Readme.md`
### Langchain AutoLLM class
#### `from_path`
The `from_path` is a class method that helps us to instantiate different local LLMs. Here are the list of local LLM providers and the required arguments for each of them
#### GPT4All
Required arguments:
- `model`: This path where the model exists
Optional arguments:
- verbose: Whether to stream or not
- callbacks: Streaming callbacks
Here it is how we use with autollm class.
```python
from autollm import AutoLLM
model = AutoLLM.from_path(provider_name="gpt4all", model="orca-mini-3b.ggmlv3.q4_0.bin")
print(model("Hello world"))
```
For more information please visit the langchain-gpt4all page.
(Similarly, provide the basic info of usage for all the local/cloud providers). Well, of course, we might need a lot of improvements to provide the best user experience. However, do let me your thoughts on whether we can implement this or not.
|
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PR_kwDOIPDwls5Y9FTo
| 9,867 |
Add security warning to experimental `SQLDatabaseChain` class.
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[] | null | 1 | 2023-08-28T17:00:44 | 2023-08-28T17:53:29 | 2023-08-28T17:53:27 |
COLLABORATOR
| null |
The most reliable way to not have a chain run an undesirable SQL command is to not give it database permissions to run that command. That way the database itself performs the rule enforcement, so it's much easier to configure and use properly than anything we could add in ourselves.
|
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PR_kwDOIPDwls5Y9FLw
| 9,866 |
LRU cache for import to avoid repetetive calls to huggingface
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[] | null | 2 | 2023-08-28T17:00:20 | 2023-09-03T22:33:38 | 2023-09-03T22:33:37 |
CONTRIBUTOR
| null |
# Description
Performance improvement to avoid repeated calls to huggingface when calling `RetrievalQAWithSourcesChain.from_chain_type`. By caching the call to the underlying transformers library, we avoid re-importing the library, instantiating the object and pulling data from huggingface. This significantly speeds up the chain (in our situation from 9 seconds to 3 seconds)
We noticed that our chain was spending a significant time pulling files from huggingface _on every LLM call_
Twitter: @PascalBrokmeier
<!-- 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_kwDOIPDwls5vdcGF
| 9,865 |
llama.cpp does not support AsyncCallbackHandler
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[] | null | 9 | 2023-08-28T15:30:08 | 2023-10-19T19:01:24 | null |
NONE
| null |
### System Info
python:3.11.3
langchain: 0.0.274
dist: arch
### Who can help?
```
fastapi==0.103.0
qdrant-client==1.4.0
llama-cpp-python==0.1.72
```
### 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
- [X] Async
### Reproduction
Hey @agola11,
I got this runtime warning:
`python3.11/site-packages/langchain/llms/llamacpp.py:312: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited`
I try to stream over a websocket the generated tokens. When I try to add a `AsyncCallbackHandler` to manage this streaming and run `acall` the warning occurs and nothing is streamed out.
```python
class StreamingLLMCallbackHandler(AsyncCallbackHandler):
def __init__(self, websocket: WebSocket):
super().__init__()
self.websocket = websocket
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(sender="bot", message=token, type="stream")
await self.websocket.send_json(resp.dict())
stream_manager = AsyncCallbackManager([StreamingLLMCallbackHandler(websocket)])
llm = return LlamaCpp(
model_path="models/llama-2-7b-chat.ggmlv3.q2_K.bin",
verbose=False,
n_gpu_layers=40,
n_batch=2048,
n_ctx=2048,
callback_manager=stream_manager
)
qa_chain = RetrievalQA.from_chain_type(llm=llm,
chain_type='stuff',
retriever=vectordb.as_retriever(search_kwargs={'k': 4}),
return_source_documents=True,
chain_type_kwargs={'prompt': prompt}
)
output = await qa_chain.acall(
{
'query': user_msg,
})
```
### Expected behavior
The expected behavior is that each token is streamed sequentially over the websocket.
|
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| 1,869,981,254 |
PR_kwDOIPDwls5Y8mDl
| 9,864 |
Add root run wrapping call to RunnableEach()
|
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[] | null | 1 | 2023-08-28T15:25:49 | 2023-09-01T14:57:35 | 2023-09-01T14:57:34 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5Y8bkv
| 9,863 |
Add data anonymizer
|
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[] | null | 3 | 2023-08-28T14:58:07 | 2023-08-30T17:39:45 | 2023-08-30T17:39:44 |
CONTRIBUTOR
| null |
### Description
The feature for anonymizing data has been implemented. In order to protect private data, such as when querying external APIs (OpenAI), it is worth pseudonymizing sensitive data to maintain full privacy.
Anonynization consists of two steps:
1. **Identification:** Identify all data fields that contain personally identifiable information (PII).
2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data.
We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`.
### Future works
- **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data.
- **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object.
### Twitter handle
@deepsense_ai / @MaksOpp
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I_kwDOIPDwls5vdFY8
| 9,862 |
AWS Sagemaker JS Integration
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[] | null | 4 | 2023-08-28T14:37:00 | 2023-10-08T02:34:06 | null |
NONE
| null |
### System Info
JS LangChain
### Who can help?
_No response_
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
@dosu-bot
I got this error now with my code: ModelError: Received client error (424) from primary with message "{
"code":424,
"message":"prediction failure",
"error":"Need to pass custom_attributes='accept_eula=true' as part of header. This means you have read and accept the end-user license agreement (EULA) of the model. EULA can be found in model card description or from https://ai.meta.com/res
```
const { SageMakerLLMContentHandler, SageMakerEndpoint } = require("langchain/llms/sagemaker_endpoint");
const AWS = require('aws-sdk');
AWS.config.credentials = new AWS.SharedIniFileCredentials({ profile: 'profile' });
class HuggingFaceTextGenerationGPT2ContentHandler {
constructor(contentHandler) {
this.contentHandler = contentHandler;
this.contentType = "application/json";
this.accepts = "application/json";
}
async transformInput(prompt, modelKwargs) {
const inputString = JSON.stringify({
text_inputs: prompt,
...modelKwargs,
});
return Buffer.from(inputString);
}
async transformOutput(output) {
const responseJson = JSON.parse(Buffer.from(output).toString("utf-8"));
return responseJson.generated_texts[0];
}
}
const contentHandler = new HuggingFaceTextGenerationGPT2ContentHandler(SageMakerLLMContentHandler);
const model = new SageMakerEndpoint({
endpointName: "endpointName",
modelKwargs: { temperature: 1e-10 },
contentHandler: contentHandler, // Pass the inner contentHandler
clientOptions: {
region: "region",
credentials: AWS.config.credentials,
},
});
async function main() {
const res = await model.call("Hello, my name is ");
console.log({ res });
}
main();
```
Can you show where to add the eula agreement acceptanced parameter
### Expected behavior
I expected it to pass, but it asked me to accept a EULA Agreement. I tried customized arguments but that didn't seem to solve hte issue either
|
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PR_kwDOIPDwls5Y8NFx
| 9,861 |
Use unified Python setup steps for release workflow.
|
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[] | null | 1 | 2023-08-28T14:21:31 | 2023-08-29T22:46:26 | 2023-08-29T22:46:25 |
COLLABORATOR
| null |
Using the same Python setup GitHub Action step as the lint and test workflows.
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PR_kwDOIPDwls5Y8Lsb
| 9,860 |
bump 275
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[] | null | 1 | 2023-08-28T14:18:14 | 2023-08-28T14:27:08 | 2023-08-28T14:27:07 |
COLLABORATOR
| null | null |
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| 9,859 |
Pinecone upsert parallelization
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[] | null | 7 | 2023-08-28T14:11:52 | 2023-09-11T07:21:22 | 2023-09-03T22:37:42 |
CONTRIBUTOR
| null |
Issue: closes #9855
* consolidates `from_texts` and `add_texts` functions for pinecone upsert
* adds two types of batching (one for embeddings and one for index upsert)
* adds thread pool size when instantiating pinecone index
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fixed GoogleCloudEnterpriseSearchRetriever returning an empty array
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[] | null | 1 | 2023-08-28T13:40:21 | 2023-08-30T00:49:48 | 2023-08-30T00:49:48 |
CONTRIBUTOR
| null |
`GoogleCloudEnterpriseSearchRetriever` returned an empty array of documents earlier, fixed
|
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Add Azure search custom fields test
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[] | null | 2 | 2023-08-28T13:36:35 | 2023-09-27T18:09:47 | 2023-09-27T18:09:47 |
NONE
| null |
This PR adds an integration test for adding custom fields to the Azure Search integration, following issue #9765
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Support index upsert parallelization for pinecone
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[] | null | 1 | 2023-08-28T13:09:29 | 2023-09-03T22:37:43 | 2023-09-03T22:37:43 |
CONTRIBUTOR
| null |
### Feature request
We can take advantage from pinecone parallel upsert (see example: https://docs.pinecone.io/docs/insert-data#sending-upserts-in-parallel)
This will require modification of the current `from_texts` pipeline to
1. Create a batch (chunk) for doing embeddings (ie have a chunk size of 1000 for embeddings)
2. Perform a parallel upsert to Pinecone index on that chunk
This way we are in control on 3 things:
1. Thread pool for pinecone index
2. Parametrize the batch size for embeddings (ie it helps to avoid rate limit for OpenAI embeddings)
3. Parametrize the batch size for upsert (it helps to avoid throttling of pinecone API)
As a part of this ticket, we can consolidate the code between `add_texts` and `from_texts` as they are doing the similar thing.
### Motivation
The function `from_text` and `add_text` for index upsert doesn't take advantage of parallelism especially when embeddings are calculated by HTTP calls (ie OpenAI embeddings). This makes the whole sequence inefficient from IO bound standpoint as the pipeline is following:
1. Take a small batch ie 32/64 of documents
2. Calculate embeddings --> WAIT
3. Upsert a batch --> WAIT
We can benefit from either parallel upsert or we can utilize `asyncio`.
### Your contribution
I will do it.
|
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AttributeError: module 'langchain' has no attribute 'verbose'
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NONE
| null |
### System Info
274
Mac M2
this error appears often and unexpectedly
but gets solved temporarily by running a force reinstall
`pip install --upgrade --force-reinstall --no-deps --no-cache-dir langchain
`
full error
```
2023-08-28 08:16:48.197 Uncaught app exception
Traceback (most recent call last):
File "/Users/user/Developer/newfilesystem/venv/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "/Users/user/Developer/newfilesystem/pages/chat.py", line 104, in <module>
llm = ChatOpenAI(
File "/Users/user/Developer/newfilesystem/venv/lib/python3.10/site-packages/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1066, in pydantic.main.validate_model
File "pydantic/fields.py", line 439, in pydantic.fields.ModelField.get_default
File "/Users/user/Developer/newfilesystem/venv/lib/python3.10/site-packages/langchain/chat_models/base.py", line 49, in _get_verbosity
return langchain.verbose
```
main librairies in the project
requests streamlit pandas colorlog python-dotenv tqdm fastapi uvicorn
langchain openai tiktoken chromadb pypdf
colorlog logger docx2txt
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
intermittent - no pattern
### Expected behavior
resolution
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Constraining the search using 'where_filter' in case of similarity_score_threshold for langchain Weaviate wrapper
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[] | null | 2 | 2023-08-28T12:02:19 | 2023-12-04T16:04:53 | 2023-12-04T16:04:52 |
NONE
| null |
### System Info
https://github.com/langchain-ai/langchain/blob/610f46d83aae6e1e25d76a0222b3158e2c4f7034/libs/langchain/langchain/vectorstores/weaviate.py
Issue in Langchain Weaviate Wrapper.
Trying to constraint the search in case of `similarity_score_threshold` ignoring the `where_filter ` filters in the langchain Weaviate wrapper.
`where_filter` implementation is missing for `similarity_score_threshold`.
The issue is with `similarity_search_with_score` function.
The fix would be to add the following after line 346, the point after the query_obj is initialized.
` if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))`
This would integrate the `where_filter` in constraints of the query if defined by the user.
### Who can help?
@hwchase17
@rohitgr7
@baskaryan
@leo-gan
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://github.com/langchain-ai/langchain/blob/610f46d83aae6e1e25d76a0222b3158e2c4f7034/libs/langchain/langchain/vectorstores/weaviate.py
Issue in Langchain Weaviate Wrapper.
Trying to constraint the search in case of `similarity_score_threshold` ignoring the `where_filter ` filters in the langchain Weaviate wrapper.
`where_filter` implementation is missing for `similarity_score_threshold`.
### Expected behavior
https://github.com/langchain-ai/langchain/blob/610f46d83aae6e1e25d76a0222b3158e2c4f7034/libs/langchain/langchain/vectorstores/weaviate.py
The fix would be to add the following after line 346, the point after the query_obj is initialized.
` if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))`
This would integrate the `where_filter` in constraints of the query if defined by the user.
|
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PR_kwDOIPDwls5Y7Vu8
| 9,851 |
Update only notebook
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[] | null | 3 | 2023-08-28T11:56:05 | 2023-08-29T02:29:36 | 2023-08-29T02:29:36 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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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.
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If you're adding a new integration, please include:
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
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PR_kwDOIPDwls5Y7PGr
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Virtual column name for self-querying retrievers
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- Issue: the issue # it fixes (if applicable),
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If you're adding a new integration, please include:
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Self query retrievers are powerful and it can be even stronger than before. Taking MyScale as an example, sometimes the user may add complex SQL function to a column name. So we add a `VirtualColumnName` for self-query retriever to reduce token usage with extra functionality.
1. a function that dynamically creates function mapped column names, which becomes handy when the user want to compare between current time / location or other attributes along with a column.
2. complex function call in plain string can now be replaced with a shorter nickname in prompt, which saves number of token for very long function names.
3. refined the logic in MyScale translator to meet this standard.
This change will not affect other self query retrievers as it preserves the original plain string interface under comparison. The actual difference happens under `QueryTransformer` in `query_construct.parser` for `Lark` parser.
`Comparator.attribute` can now be either a string or a `VirtualColumnName`. I have defined the default behaviour which stops the user to use `VirtualColumnName` under other vectorstores than MyScale.
This functionality will boost the self-query retrievers as a bridge between text and simple SQL query. And we believe this will help users to expand their usage to this retriever. Other SQL vector database is theoretically compatible to this new feature as well.
Hope you find this interesting.
@hwchase17 @baskaryan
Our contact on twitter: @MyScaleDB @mpsk_liu
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PR_kwDOIPDwls5Y7NPM
| 9,849 |
Extend SQLChatMessageHistory
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[] | null | 2 | 2023-08-28T11:31:07 | 2023-09-03T22:49:53 | 2023-09-03T22:49:53 |
CONTRIBUTOR
| null |
### Description
There is a really nice class for saving chat messages into a database - SQLChatMessageHistory.
It leverages SqlAlchemy to be compatible with any supported database (in contrast with PostgresChatMessageHistory, which is basically the same but is limited to Postgres).
However, the class is not really customizable in terms of what you can store. I can imagine a lot of use cases, when one will need to save a message date, along with some additional metadata.
To solve this, I propose to extract the converting logic from BaseMessage to SQLAlchemy model (and vice versa) into a separate class - message converter. So instead of rewriting the whole SQLChatMessageHistory class, a user will only need to write a custom model and a simple mapping class, and pass its instance as a parameter.
I also noticed that there is no documentation on this class, so I added that too, with an example of custom message converter.
### Issue
N/A
### Dependencies
N/A
### Tag maintainer
Not yet
### Twitter handle
N/A
|
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I_kwDOIPDwls5vb2h_
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How to connect MS-SQL with LANG-CHAIN
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[] | null | 8 | 2023-08-28T11:29:04 | 2023-12-06T17:43:50 | 2023-12-06T17:43:49 |
NONE
| null |
### Issue you'd like to raise.
db = SQLDatabase.from_uri(
"mysql+pyodbc://Driver={SQL Server};Server=DESKTOP-17L7UI1\SQLEXPRESS;Database=DociQDb;rusted_Connection=yes;",)
I am trying to connect to my microsoft sql server but this give me error
sqlalchemy.exc.DBAPIError: (pyodbc.Error) ('IM010', '[IM010] [Microsoft][ODBC Driver Manager] Data source name too long (0) (SQLDriverConnect)')
### Suggestion:
_No response_
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Vearch vectorstore
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[] | null | 5 | 2023-08-28T10:46:59 | 2023-09-09T00:07:37 | 2023-09-08T23:51:14 |
CONTRIBUTOR
| null |
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Mazharsaif patch 1
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[] | null | 2 | 2023-08-28T10:20:50 | 2023-08-29T03:14:15 | 2023-08-29T02:30:00 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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docs: Fix spelling mistakes in Etherscan.ipynb
|
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[] | null | 1 | 2023-08-28T09:32:14 | 2023-08-28T09:41:35 | 2023-08-28T09:32:23 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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| 1,869,349,364 |
I_kwDOIPDwls5vbAH0
| 9,843 |
CharacterTextSplitter inconsistent/wrong output using regex pattern
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[] | null | 2 | 2023-08-28T09:13:20 | 2023-12-04T16:05:03 | 2023-12-04T16:05:02 |
CONTRIBUTOR
| null |
### System Info
Langchain version 0.0.259
Ubuntu 20.04
Python 3.9.15
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Using regex in the CharacterTextSplitter leads to a couple of unexpected behaviours.
1. When it merges the small chunks into larger chunks, it uses the separator, leading to outputs like those below (0, 2)
2. This could be arguable, and personally I don't think it's so problematic. The number of splits could be different depending on whether `keep_separator` is True or False. (0, 1)
```python
from langchain.text_splitter import CharacterTextSplitter
import string
char_chunk = string.ascii_uppercase[:5] # ABCDE
text = "\n\n".join([f"{k}\n\n{char_chunk}" for k in range(4)])
splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=0, separator="\n+[0-9]+\n+", is_separator_regex=True, keep_separator=False)
res_0 = splitter.split_text(text)
print(res_0) # ['0\n\nABCDE', 'ABCDE\n+[0-9]+\n+ABCDE', 'ABCDE']
splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=0, separator="\n+[0-9]+\n+", is_separator_regex=True, keep_separator=True)
res_1 = splitter.split_text(text)
print(res_1) # ['0\n\nABCDE\n\n1\n\nABCDE', '2\n\nABCDE\n\n3\n\nABCDE']
splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=0, separator="\n*[0-9]+\n*", is_separator_regex=True, keep_separator=False)
res_2 = splitter.split_text(text)
print(res_2) # ['ABCDE\n*[0-9]+\n*ABCDE', 'ABCDE\n*[0-9]+\n*ABCDE']
splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=0, separator="\n*[0-9]+\n*", is_separator_regex=True, keep_separator=True)
res_3 = splitter.split_text(text)
print(res_3) # ['0\n\nABCDE\n\n1\n\nABCDE', '2\n\nABCDE\n\n3\n\nABCDE']
```
### Expected behavior
1. Use the actual characters to merge the two chunks into a larger chunk instead of the regex sepatator. I.e:
```python
# ['0\n\nABCDE', 'ABCDE\n\n2\n\nABCDE', 'ABCDE']
```
2. Consistency among the number of splits in both cases of `keep_separator`
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| 9,842 |
Being able to see model's responses when using verbose mode
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[] | null | 5 | 2023-08-28T08:47:43 | 2023-12-04T16:05:09 | 2023-12-04T16:05:08 |
NONE
| null |
### Feature request
Currently, when the _verbose_ attribute is set to True in the constructor of an _LLMChain_ object, only the input passed to the LLM is shown. It would be desirable to also see the raw output of the LLM before it is parsed.
### Motivation
Seeing both inputs and outputs of the LLM would help debug the chain. Exceptions often occur when an output parser is used and the parser throws an exception (because the LLM's output was not in the expected format). In that case one cannot see what the LLM's output was.
### Your contribution
I had a look at the code of LLMChain and it seems that a print (or a callback) could be added in the _call method, between the call to _generate_ and the call to _create_outputs_:
```
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
response = self.generate([inputs], run_manager=run_manager)
# Add a print/callback here
return self.create_outputs(response)[0]
```
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I_kwDOIPDwls5vaR-y
| 9,841 |
VLLM: Can't instantiate abstract class VLLM with abstract method _agenerate
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[] | null | 2 | 2023-08-28T07:30:09 | 2023-12-04T16:05:13 | 2023-12-04T16:05:12 |
NONE
| null |
### System Info
**langchain 0.0.274:**
When trying to instantiate a VLLM object, I'm getting the following error:
**TypeError: Can't instantiate abstract class VLLM with abstract method _agenerate**
This is the code I'm using which is 1-1 as the VLLM example on langchain documentation:
https://python.langchain.com/docs/integrations/llms/vllm
```
from langchain.llms.vllm import VLLM
vlmm = VLLM(model="mosaicml/mpt-7b",
trust_remote_code=True, # mandatory for hf models
max_new_tokens=128,
top_k=10,
top_p=0.95,
temperature=0.8,
)
```
It seems that the VLLM model is derived from the BaseLLM object, which has an abstract method of _agenerate, but is not providing an implementation for it.
In addition to that, you might notice that I used **from langchain.llms.vllm import VLLM** instead of from **langchain.llms import VLLM** as the documentation, that's because for from langchain.llms import VLLM I'm getting an "cannot import name 'VLLM' from 'langchain.llms'" error
Any insights regarding this one?
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
instantiate a VLLM object just like in the official documentation: https://python.langchain.com/docs/integrations/llms/vllm
### Expected behavior
The object should be created and load model successfully
|
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I_kwDOIPDwls5vaH66
| 9,840 |
DOC: Generative Agents Page In JS/TS but not Python
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[] | null | 1 | 2023-08-28T07:04:09 | 2023-09-07T03:07:23 | 2023-09-07T03:07:23 |
NONE
| null |
### Issue with current documentation:
How come there is a generative agents page https://js.langchain.com/docs/use_cases/agent_simulations/generative_agents in JS, but not one in python? I was reading this [blog post](https://blog.langchain.dev/agents-round/) and the links to the generative_agents page didn't work.
I also noticed [`MemoryVectorStore`](https://js.langchain.com/docs/api/vectorstores_memory/classes/MemoryVectorStore#methods) exists in the JS/TS docs but not in the python [`vectorstores`](https://[api.python.langchain.com/en/latest/module-langchain.vectorstores](https://api.python.langchain.com/en/latest/module-langchain.vectorstores)) doc. Why is that?
Thanks.
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I_kwDOIPDwls5vaCDl
| 9,839 |
DOC: Add clarification to Modules/ModelsI/O/Prompts/Prompt Templates/Few-shot examples for chat models
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[] | null | 2 | 2023-08-28T06:48:00 | 2023-12-04T16:05:18 | 2023-12-04T16:05:17 |
NONE
| null |
### Issue with current documentation:
In https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat under Dynamic few-shot prompting,
```
example_selector = SemanticSimilarityExampleSelector(
vectorstore=vectorstore,
k=2,
)
# The prompt template will load examples by passing the input do the `select_examples` method
example_selector.select_examples({"input": "horse"})
```
Does the key value "input" matter when calling select_examples? I was playing around with this and it doesn't seem to change the output. Maybe more clarification could be added to both `select_examples` in the API reference and in the doc examples.
---
"instruct" is misspelled.
---
```
from langchain.prompts import (
FewShotChatMessagePromptTemplate,
ChatPromptTemplate,
)
# Define the few-shot prompt.
few_shot_prompt = FewShotChatMessagePromptTemplate(
# The input variables select the values to pass to the example_selector
input_variables=["input"],
example_selector=example_selector,
# Define how each example will be formatted.
# In this case, each example will become 2 messages:
# 1 human, and 1 AI
example_prompt=ChatPromptTemplate.from_messages(
[("human", "{input}"), ("ai", "{output}")]
),
)
```
Also, is it possible to clarify `input_variables=["input"]` a bit more and how this works downstream in the `final_prompt`? I toyed with it a bit and found it a lil confusing to understand.
_What I understood from playing around with the parameters._
_What I found out later._
Even after understanding how the variables worked here, I think a lot of the other cases weren't explained so I was still confused up till I started experimenting. Maybe more clarification can be added to this?
Thanks!
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PR_kwDOIPDwls5Y5RiT
| 9,838 |
New chain to allow for logical fallacy removal of model output
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[] | null | 2 | 2023-08-28T05:40:50 | 2023-08-28T22:27:21 | 2023-08-28T22:27:21 |
CONTRIBUTOR
| null |
Description: a new chain to test and remove logical fallacies from model output in a chain if found
Issue: n/a
Dependencies: none
Tag maintainer: @hinthornw helped me in the past, not sure if this PR falls outside any maintainer area but feel free to maintain if so
Twitter handle: not on twitter - tag my git handle if any shout out needed j-space-b
Note: added documentation to docs/extras
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PR_kwDOIPDwls5Y5LGS
| 9,837 |
add numbered list parser
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[] | null | 1 | 2023-08-28T05:09:33 | 2023-09-03T22:55:32 | 2023-09-03T22:55:31 |
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Logical fallacy removal
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[] | null | 1 | 2023-08-28T04:21:04 | 2023-08-28T04:31:59 | 2023-08-28T04:31:59 |
CONTRIBUTOR
| null |
Description: a new chain to output logical fallacies from model in a chain
Issue: fixes no existing issue other than this not yet existing
Dependencies: none
Tag maintainer: @hinthornw in the past for another area, not sure if this an area outside of maintenance
Twitter handle: not on twitter, feel free to tag my git user if any shout-outs: j-space-b
Note: a doc showing use of this chain is in docs/extras as apropos
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PR_kwDOIPDwls5Y41_A
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Update web_scraping.ipynb
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[] | null | 3 | 2023-08-28T03:19:20 | 2023-08-29T02:26:24 | 2023-08-29T02:26:24 |
CONTRIBUTOR
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<!-- 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_kwDOIPDwls5Y4im0
| 9,834 |
Logical fallacy removal
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[] | null | 1 | 2023-08-28T01:48:27 | 2023-08-28T03:49:04 | 2023-08-28T03:49:04 |
CONTRIBUTOR
| null |
Description: a new chain to filter logical fallacies from
Issue: does not fix an issue other than adding any needed logical fallacy filtering to an output from a base model within a chain
Dependencies: no dependencies
Tag maintainer: have worked w @hinthornw in the past - not seeing tag on list, so more likely @baskaryan
Twitter handle: Not on twitter/x feel free to ping my github if shout-out needed @j-space-b
Note: added a doc of an example in docs/extras
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PR_kwDOIPDwls5Y4go3
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feat(llms): improve ERNIE-Bot chat model
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[] | null | 2 | 2023-08-28T01:37:53 | 2023-08-30T01:20:06 | 2023-08-30T01:20:06 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- Issue: the issue # it fixes (if applicable),
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- 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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- Description: improve ERNIE-Bot chat model, add request timeout and more testcases.
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
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PR_kwDOIPDwls5Y4fC7
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iMessage loader
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[] | null | 1 | 2023-08-28T01:31:09 | 2023-08-28T20:44:00 | 2023-08-28T20:43:59 |
COLLABORATOR
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Add an iMessage chat loader
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PR_kwDOIPDwls5Y4DOR
| 9,831 |
Fix add callbacks to spark_sql due to depreciation of callback_manager
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[] | null | 6 | 2023-08-27T22:19:23 | 2023-08-30T02:23:44 | 2023-08-30T02:23:44 |
CONTRIBUTOR
| null |
Description: Due to depreciation (regarding to line 109 in [langchain/libs/langchain/langchain/chains/base.py](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/base.py) of callback_manager i replaced several parts
Issue: None
Dependencies:
Maintainer: @baskaryan
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poc auto-docstring
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PR_kwDOIPDwls5Y4BTx
| 9,829 |
Feature: PGVector Collection Documents Update
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CONTRIBUTOR
| null |
## Description
Enhancement to the PGVector functionality: the addition of an update function `update_documents(...)`.
Currently, updating the documents of a collection requires emptying the collection and filling it again. This can cause more calls to be made to the model than are actually needed. In fact, if a text chunk (and therefore its embedding) is already present in the vector store in the current collection, what is currently done is to delete it and insert (therefore calling the embedding model) the same embedding again.
The new feature identifies differences between input and existing documents. It requests new embeddings only for different documents, inserting them into the DB, and deletes missing ones.
## Issue
#9461 (Add Functionality to Update Embeddings in pgvector)
## Using Sample
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.pgvector import PGVector
from langchain.docstore.document import Document
COLLECTION_NAME = "..."
CONNECTION_STRING = '...'
documents = [Document(page_content="foo", metadata={"page": "0"}),
Document(page_content="bar", metadata={"page": "1"})]
# Instance new collection in pgvector from documents
pgvector = PGVector.from_documents(
embedding=OpenAIEmbeddings(),
documents=documents,
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
pre_delete_collection=True)
# Update/Add some documents
documents[1].page_content = "baz"
documents.append(Document(page_content="far", metadata={"page": "2"}))
# Call update function
pgvector.update_documents(documents)
```
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| 1,868,596,327 |
PR_kwDOIPDwls5Y34zK
| 9,828 |
Extraction Chain - Custom Prompt
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[] | null | 3 | 2023-08-27T18:58:46 | 2023-09-03T23:01:56 | 2023-09-03T23:01:56 |
CONTRIBUTOR
| null |
# Description
This change allows you to customize the prompt used in `create_extraction_chain` as well as `create_extraction_chain_pydantic`.
It also adds the `verbose` argument to `create_extraction_chain_pydantic` - because `create_extraction_chain` had it already and `create_extraction_chain_pydantic` did not.
# Issue
N/A
# Dependencies
N/A
# Twitter
https://twitter.com/CamAHutchison
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| 1,868,583,767 |
PR_kwDOIPDwls5Y32XN
| 9,827 |
Harrison/add llm kwargs
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[] | null | 1 | 2023-08-27T18:11:29 | 2023-09-03T22:54:30 | 2023-09-03T22:54:30 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5Y32Dd
| 9,826 |
accept openai terms
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[] | null | 1 | 2023-08-27T18:06:10 | 2023-08-28T00:18:25 | 2023-08-28T00:18:25 |
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I_kwDOIPDwls5vX2U0
| 9,825 |
DOC: Import path for NewsURLLoader needs to fixed
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NONE
| null |
### Issue with current documentation:
At https://python.langchain.com/docs/integrations/document_loaders/news, there is an issue with the path for importing `NewsURLLoader`. Currently, it's being imported from `from langchain.document_loaders`, but it gives `importerror`.
### Idea or request for content:
The import path for `NewsURLLoader` needs to be updated. The correct path is `from langchain.document_loaders.news import NewsURLLoader`
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I_kwDOIPDwls5vXmOK
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field "credential" not yet prepared so type is still a ForwardRef, you might need to call PowerBIDataset.update_forward_refs()
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[] | null | 4 | 2023-08-27T11:14:51 | 2023-12-13T16:07:18 | 2023-12-13T16:07:17 |
NONE
| null |
### System Info
Hello Team,
I am using the below post from Langchain in order to use PowerBIToolkit for connecting to the dataset and tables in it. However I am not able to execute the code.
https://python.langchain.com/docs/integrations/toolkits/powerbi
I have also gone through an issue raised in this repository which resides here:
https://github.com/langchain-ai/langchain/issues/4325
But not able to pass through this issue. Still looks blocker to me as I am not able to proceed with this integration.
I am using this object which is trying to use the default credentials for the client:
ds = PowerBIDataset(
dataset_id="aec82374-5442-416f-849b-*********",
table_names=["ProductsTable"],
credential=DefaultAzureCredential(
managed_identity_client_id="678e6145-8917-49a2-********"),
)
Please suggest...
Error:
**ds = PowerBIDataset(
^^^^^^^^^^^^^^^
File "pydantic\main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic\main.py", line 1076, in pydantic.main.validate_model
File "pydantic\fields.py", line 860, in pydantic.fields.ModelField.validate
pydantic.errors.ConfigError: field "credential" not yet prepared so type is still a ForwardRef, you might need to call PowerBIDataset.update_forward_refs().**
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents.agent_toolkits import create_pbi_agent
from langchain.agents.agent_toolkits import PowerBIToolkit
from langchain.utilities.powerbi import PowerBIDataset
from langchain.chat_models import ChatOpenAI
from langchain.agents import AgentExecutor
from azure.identity import DefaultAzureCredential
from dotenv import dotenv_values
import os
from azure.core.credentials import TokenCredential
config = dotenv_values('.env')
os.environ["OPENAI_API_KEY"] = config["OPENAI_API_KEY"]
fast_llm = ChatOpenAI(
temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True
)
smart_llm = ChatOpenAI(temperature=0, max_tokens=100,
model_name="gpt-4", verbose=True)
ds = PowerBIDataset(
dataset_id="aec82374-5442-416f-849b-$$$$$$$$$",
table_names=["ProductsTable"],
credential=DefaultAzureCredential(
managed_identity_client_id="678e6145-8917-49a2-bdcf-******"), # Client Id for Azure Application
)
ds.update_forward_refs()
toolkit = PowerBIToolkit(
powerbi=ds,
llm=fast_llm,
)
agent_executor = create_pbi_agent(
llm=fast_llm,
toolkit=toolkit,
verbose=True,
)
agent_executor.run("How many records are in ProductsTable?")
### Expected behavior
Should be able to query the table present in the dataset
|
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| 1,868,442,230 |
I_kwDOIPDwls5vXip2
| 9,822 |
Issue: Help to make a callback for Elevenlabs API streaming endpoint
|
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[] | null | 9 | 2023-08-27T10:25:39 | 2023-10-26T01:47:52 | null |
NONE
| null |
Hi,
I'm hoping someone smarter than me can help me understand how to write a callback that works with Elevenlabs.
I'm trying to get Elevenlabs to stream TTS based on a response from the GPT-4 API. I can do this easily using OpenAIs own libarary, but I cannot figure out how to do this using langchains callbacks instead.
Here is the working code for the OpenAI library (without the various imports etc).
```
def write(prompt: str):
for chunk in openai.ChatCompletion.create(
model = "gpt-4",
messages = [{"role":"user","content": prompt}],
stream=True,
):
content = chunk["choices"][0].get("delta", {}).get("content")
if content is not None:
yield content
print("ContentTester:", content)
promtp = "Say a long sentence"
text_stream = write(wife)
audio_stream = elevenlabs.generate(
text=text_stream,
voice="adam",
stream=True,
latency=3,
)
output = elevenlabs.stream(audio_stream)
```
I think I need to use an Async callback, but I can't get it to work. I've tried simply adding the following custom callback, but it doesn't work.
```
class MyStreamingResonseHandler(StreamingStdOutCallbackHandler):
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
yield token
```
The idea is to replicate what this part does as a callback:
```
content = chunk["choices"][0].get("delta", {}).get("content")
if content is not None:
yield content
print("ContentTester:", content)
```
This is probably a trivial thing to solve for someone more experienced, but I can't figure it out. Any help would be greatly appreciated!!
|
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| 1,868,430,586 |
PR_kwDOIPDwls5Y3Ycp
| 9,821 |
Add support for Falkordb (ex-RedisGraph)
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[] | null | 3 | 2023-08-27T09:44:12 | 2023-08-30T10:15:04 | 2023-08-29T21:22:33 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- Description: Add support for Falkordb (ex-RedisGraph)
- Tag maintainer: @hwchase17
- Twitter handle: @g_korland
|
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| 1,868,416,901 |
I_kwDOIPDwls5vXceF
| 9,820 |
Claude XML parser
|
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[] | null | 2 | 2023-08-27T08:56:22 | 2023-12-03T16:04:26 | 2023-12-03T16:04:25 |
NONE
| null |
### Feature request
I find StructuredOutputParser to not work that well with Claude. It also takes quite a few tokens to output its format instructions.
It would be great to have a built-in support for using XMLs as a meaning of transportation.
### Motivation
Claude supports and works really well with XML tags.
An example output:
> When you reply, first find exact quotes in the FAQ relevant to the user's question and write them down word for word inside <thinking></thinking> XML tags. This is a space for you to write down relevant content and will not be shown to the user. Once you are done extracting relevant quotes, answer the question. Put your answer to the user inside <answer></answer> XML tags.
As you can see, it works really well providing the answer inline the paragraph. Unlike StructuredOutputParser, we don't have to provide examples, explain the schema as well as ask to wrap the output in the markdown delimiter.
I would personally use something like regular expressions to look and parse the contents inside the tags, not forcing Claude itself to stick to any particular output format (such as "your response must be a valid XML document" etc.).
### Your contribution
I would be happy to own this feature and send a PR to TypeScript implementation. [I have already written this locally and have tested it to work quite well](https://gist.github.com/grabbou/fdd0816275968f0271e09e19b2ac82b8). Note it is a very rough implementation, as my understanding of the codebase is (at this point) rather limited. I am making my baby steps tho!
I would be happy to pair with someone on the Python side to explain how the things work with Claude.
|
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PR_kwDOIPDwls5Y3Ga0
| 9,817 |
Update CONTRIBUTING.md
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[] | null | 2 | 2023-08-27T02:51:54 | 2023-08-28T13:38:35 | 2023-08-28T13:38:35 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
Hi LangChain :) Thank you for such a great project!
I was going through the CONTRIBUTING.md and found a few minor issues.
|
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I_kwDOIPDwls5vXDuU
| 9,816 |
Issue: Final Answer missing Document sources when using initialize_agent RetrievalQA with Agent tool boolean flag return_direct=False
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closed
| false | null |
[] | null | 4 | 2023-08-27T01:08:29 | 2023-12-03T16:04:31 | 2023-12-03T16:04:30 |
NONE
| null |
### Issue you'd like to raise.
```
llm = ChatOpenAI(
max_retries=3,
temperature=0.7,
model_name="gpt-4",
streaming=True,
callbacks=[AsyncStreamingCallbackHandler],
)
```
Tool params
` return_direct=False` # Toggling this to TRUE provides the sources, but it won't work with the streaming flag, so I set it to false so the Final answer can be streamed as part of the output. This works as expect its just the Final answer has no sources which are clearly there as part of the observation section
```text
Observation:
At Burger king, you will get 50 days of paid annual leave, 50 days of paid sick leave, 12 public holidays, and two additional floating holidays for personal use. If applicable, you can also get up to six weeks of paid parental leave.
For more in-depth coverage, refer to these sources:
1. Company Benefits.pdf, page: 32
```
```
qa = RetrievalQA.from_chain_type(
llm=self.llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs
)
```
Has any experienced missing document sources as part of the final answer
while within the context of the RetrievalQA, source documents exist as am explicitly concatenating them as part of the answer, which I expect to be part of the Final Answer.
```
result = qa({"query": question})
chat_answer = result["result"]
if self.hide_doc_sources:
return chat_answer
formatted_sources = format_sources(result["source_documents"], 2)
chat_answer = f"""
{chat_answer}\n\n
\n\n{formatted_sources}
"""
```
### Suggestion:
_No response_
|
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https://api.github.com/repos/langchain-ai/langchain/issues/9816/timeline
| null |
not_planned
| null | null |
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