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https://api.github.com/repos/langchain-ai/langchain/issues/11925
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https://api.github.com/repos/langchain-ai/langchain
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https://github.com/langchain-ai/langchain/pull/11925
| 1,947,732,958
|
PR_kwDOIPDwls5dCLCY
| 11,925
|
Sitemap specify default filter url (Fixes: CVE-2023-46229)
|
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| 2023-10-19T17:42:18
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|
COLLABORATOR
| null |
Specify default filter URL in sitemap loader and add a security note
Fixes: CVE-2023-46229
|
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https://api.github.com/repos/langchain-ai/langchain/issues/11924
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https://github.com/langchain-ai/langchain/issues/11924
| 1,947,709,822
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I_kwDOIPDwls50F7F-
| 11,924
|
Is there a way to obtain `finish_reason` value from OpenAI response when using `load_qa_with_sources_chain`
|
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| 2023-10-17T15:32:03
| 2023-10-17T15:45:23
| null |
NONE
| null |
### Issue you'd like to raise.
I am calling LLM `ChatOpenAI` models through `load_qa_with_sources_chain` as shown in the following code snippet.
```python
llm=ChatOpenAI(
model_name=....,
temperature=0,
openai_api_key=...,
max_tokens=...,
)
chain = load_qa_with_sources_chain(
llm, chain_type=chain_type, verbose=verbose, prompt=prompt
)
response = chain(
{"input_documents": docs, "question": query},
)
```
Is there an easy way to obtain somehow the raw response of `OpenAI` for debugging purposes? I am especially interested in the `finish_reason` value (that is retrieved in the `LLMResult` object) to know if the OpenAI response is complete or not!
Thank you in advance for your help
### Suggestion:
_No response_
|
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https://api.github.com/repos/langchain-ai/langchain/issues/11923
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https://github.com/langchain-ai/langchain/pull/11923
| 1,947,647,794
|
PR_kwDOIPDwls5dB4_L
| 11,923
|
Add Baichuan chat model
|
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| 2023-10-17T18:30:58
| 2023-10-17T18:30:58
|
CONTRIBUTOR
| null |
Description: A large language models developed by Baichuan Intelligent Technology，https://www.baichuan-ai.com/home
Issue: None
Dependencies: None
Tag maintainer:
Twitter handle:
|
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https://api.github.com/repos/langchain-ai/langchain/issues/11922
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https://github.com/langchain-ai/langchain/issues/11922
| 1,947,647,017
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I_kwDOIPDwls50Frwp
| 11,922
|
Langchain SQLDatabaseToolkit providing incorrect results. it's faking the results using the top k rows
|
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| 2023-10-17T15:02:23
| 2023-10-17T16:31:42
| null |
NONE
| null |
### System Info
Here chain example:
Thought:I can query the 'information_enquiry' table to find out who is assigned to a job.
Action: SQL Query
Action Input: SELECT name FROM nformation_enquiry WHERE job_id = '123'
Observation: SQL Query is not a valid tool, try one of [sql_db_query, sql_db_schema, sql_db_list_tables, sql_db_query_checker].
Thought:I made a mistake in the action. I should use the `sql_db_query` tool to execute the SQL query.
Action: sql_db_query
Action Input: SELECT name FROM customer_information_enquiry WHERE job_no = '123'
Observation:
Thought:I have the information about the person assigned to the job.
Final Answer: The job was assigned to Helena. --> this is wrong , it faked the first row
I have instructed prompt :
Begin!
Question: Who is assigned to 123?
Thought: I need to find the status of a specific job.
Action: SQL Query
Action Input: SELECT name FROM information_enquiry WHERE job_no ='123'
Observation:
Thought: I don't have the information about the job status.
Final Answer: information not found
but it's not working
### 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
llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0)
sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm)
sql_toolkit.get_tools()
sqldb_agent = create_sql_agent(
llm=llm,
toolkit=sql_toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True
)
formatted_prompt = updated_prompt.format(question=query)
result = sqldb_agent.run(formatted_prompt)
sqldb_agent.run(updated_prompt.format(
question="Who is assigned to the job 123"))
### Expected behavior
it should return 'information not found'
|
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| 1,947,643,981
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I_kwDOIPDwls50FrBN
| 11,921
|
Issue: How to modify the actual prompt to add in new input parameters of the open ai functions agent, so that during the running of the agent, we pass those parameters also
|
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| 2023-10-17T15:31:44
| null |
NONE
| null |
### Issue you'd like to raise.
I want to modify the prompt of the open ai function agent, so i can add more parameters that i can pass as inputs during execution
initialize_agent(tools=tool_items,
llm=llm,
agent=AgentType.OPENAI_FUNCTIONS)
and during execution such as
result = agent_chain.run({"input": "Whats the distance between Malmo and Stockholm"})
i want to be able to pass several input parameters so that for example it becomes like this
agent_chain.run({"input": "Whats the distance between Malmo and Stockholm","language":"French"}) etc
I have been able to do that successfully for other chains, but not for this one
How do i achieve this
### Suggestion:
_No response_
|
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| 1,947,606,825
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PR_kwDOIPDwls5dBwKK
| 11,920
|
Add lock for using global config enum weak map
|
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| 2023-10-17T14:44:26
| 2023-10-17T14:50:36
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|
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| 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),
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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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https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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| 1,947,569,327
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I_kwDOIPDwls50FYyv
| 11,919
|
ConversationTokenBufferMemory doesn't work with db based chat history
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[] | null | 3
| 2023-10-17T14:28:09
| 2023-10-18T13:22:45
| null |
NONE
| null |
### System Info
Langchain version: 0.0.272
Python Version: 3.11.0
### Who can help?
@hwchase17
@ag
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The `save_context` method of the `ConversationTokenBufferMemory` does pop operation directly on `chat_memory.messages` [link to code](https://github.com/langchain-ai/langchain/blob/31f264169db4ab23689f2e179983f1cfdfd1a33a/libs/langchain/langchain/memory/token_buffer.py#L48-L49)
It works only with in-memory memory and not with db-backed memory like `PostgresChatMessageHistory` because the `buffer`( `self.chat_memory.messages` ) is immutable
```python
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
"""Save context from this conversation to buffer. Pruned."""
super().save_context(inputs, outputs)
# Prune buffer if it exceeds max token limit
buffer = self.chat_memory.messages
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
if curr_buffer_length > self.max_token_limit:
pruned_memory = []
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
```
### Expected behavior
The `ChatMessageHistory` should work like an interface with`ConversationTokenBufferMemory`.
Step to resolve
- remove direct calls to `self.chat_memory.messages`
- add a private variable to hold chat history
|
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I_kwDOIPDwls50EocX
| 11,917
|
langchain s3 loader not able to load files from subfolders
|
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[] | null | 3
| 2023-10-17T13:00:11
| 2023-10-17T14:09:53
| null |
NONE
| null |
we are trying to use langchain s3 loader to load files from the bucket using python, once we create any subfolder we are getting no such directory error. also we are not able to load the files from those sub folders.
How to fix these errors?
|
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| 1,947,286,923
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PR_kwDOIPDwls5dAplJ
| 11,916
|
Export merge_configs function
|
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| 2023-10-17T12:23:13
| 2023-10-17T14:36:13
| 2023-10-17T14:36:12
|
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/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5c_5wu
| 11,915
|
Anonymizer small fixes
|
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| 2023-10-17T10:16:59
| 2023-10-17T17:27:29
| 2023-10-17T17:27:29
|
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/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50DXi9
| 11,914
|
Support for OpenAI quotas
|
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| 2023-10-17T10:02:45
| 2023-10-17T10:09:43
| null |
NONE
| null |
### System Info
Python 3.11
Lanchain 315
### Who can help?
@hwchase17
@agola11
### 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
When OpenAI quotas are reached (or no payment method is defined), requests should not be retried but should raise an appropriate error.
Error in console :
```
Retrying langchain.chat_models.openai.acompletion_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..
```
Equivalent TS issue : https://github.com/langchain-ai/langchainjs/issues/1929
Fix in TS land : https://github.com/langchain-ai/langchainjs/pull/1934
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| 1,946,778,393
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PR_kwDOIPDwls5c-64l
| 11,910
|
Add validation for configurable keys passed to .with_config()
|
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| 2023-10-17T07:51:01
| 2023-10-17T14:34:51
| 2023-10-17T14:34:49
|
COLLABORATOR
| null |
- Fix some typing issues found while doing that
<!-- Thank you for contributing to LangChain!
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| 1,946,732,038
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PR_kwDOIPDwls5c-w_o
| 11,909
|
Ensure that configurable fields with enums support deduplication
|
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|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls50CHEB
| 11,908
|
How to use Multiple Retrieaval Sources and Added Memory at SQLDatabaseChain
|
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NONE
| null |
### Discussed in https://github.com/langchain-ai/langchain/discussions/11846
<div type='discussions-op-text'>
<sup>Originally posted by **Marbyun** October 16, 2023</sup>
Hi folks!
i have case to create chatbot that will use 2 sources as the dataset (text file and sqlite). I use Multiple Retrieaval Sources [document](https://python.langchain.com/docs/use_cases/question_answering/multiple_retrieval) as my main code. But the code can not do continuous conversation like this:
> q:'what's name of employee id xx.xx'
> a:'his name is xxx xxx'
> q:'what's his email?'
> a:'his email is xxx@email.com'
and then i get code from this [update](https://github.com/langchain-ai/langchain/pull/8597/files) by @keenborder786 that can perfomance my q&a, but rigth now i get confuse how to combine it? can you guys help me please, i am very new in here...
> Ps:
> - I have 2 sources Text file have data about company profile and SQLite have employee data
> - Need to combine the sources and can do continuous conversation</div>
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@dosu-bot
When I use chromadb instead of deeplake, my code worked fine but with deeplake, I am facing this error when I use deeplake for some reason despite everything is the exact same thing:
creating embeddings: 12%|██████████▏ | 3/26 [00:02<00:17, 1.32it/s]Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for text-embedding-ada-002 in organization org-m0YReKtLXxUATOVCwzcBNfqm on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for text-embedding-ada-002 in organization org-m0YReKtLXxUATOVCwzcBNfqm on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..
Below is my code:
from langchain.chat_models import ChatOpenAI
from langchain.prompts.prompt import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Qdrant
from langchain.callbacks import StreamingStdOutCallbackHandler
from langchain.embeddings import OpenAIEmbeddings
from langchain.callbacks import get_openai_callback
from langchain.vectorstores import Chroma
from langchain.vectorstores import DeepLake
from langchain.document_loaders import DirectoryLoader, PyPDFLoader
from dotenv import load_dotenv
import time
import warnings
# warnings.filterwarnings("ignore")
load_dotenv()
directory_path = "C:\\Users\\Asus\\Documents\\Vendolista"
pdf_loader = DirectoryLoader(directory_path,
glob="**/*.pdf",
show_progress=True,
use_multithreading=True,
silent_errors=True,
loader_cls = PyPDFLoader)
documents = pdf_loader.load()
print(str(len(documents))+ " documents loaded")
def print_letter_by_letter(text):
for char in text:
print(char, end='', flush=True)
time.sleep(0.02)
# Create embeddings
def langchain(customer_prompt, chat_history):
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
chunk_overlap=80,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
#save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
# Create a custom prompt for your use case
prompt_template = """
Answer the Question as a AI assistant that is answering based on the documents only. If the question is unrelated
then say "sorry this question is completely not related. If you think it is, email the staff
and they will get back to you: yazanrisheh@hotmail.com." Do not ever answer with "I don't know" to any question.
You either give an answer or mention it's not related.
Text: {context}
Question: {question}
Answer :
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
memory = ConversationBufferMemory(llm=llm, memory_key='chat_history', input_key='question', output_key='answer', return_messages=False)
conversation = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=new_knowledge_base.as_retriever(),
memory=memory,
chain_type="stuff",
verbose=True,
combine_docs_chain_kwargs={"prompt":PROMPT}
)
return conversation({"question": customer_prompt, "chat_history": memory})
def main():
chat_history = []
while True:
customer_prompt = input("Ask me anything about the files (type 'exit' to quit): ")
if customer_prompt.lower() in ["exit"] and len(customer_prompt) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
if customer_prompt:
with get_openai_callback() as cb:
response = (langchain(customer_prompt, chat_history))
print(response['answer'])
print(cb)
if __name__ == "__main__":
main()
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I_kwDOIPDwls50BjW3
| 11,906
|
Enable token usage count for AWS Bedrock
|
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[] | null | 4
| 2023-10-17T05:16:38
| 2023-12-27T11:29:50
| null |
NONE
| null |
### Feature request
There should be a callback handler like [OpenAICallbackHandler](https://github.com/langchain-ai/langchain/blob/31f264169db4ab23689f2e179983f1cfdfd1a33a/libs/langchain/langchain/callbacks/openai_info.py#L120) for AWS Bedrock models, so that we can easily get the token usage and monitor cost.
It looks like the AWS Bedrock API doesn't return with token usage. Unlike OpenAI.
Can we have similar feature, perhaps [using the internal Anthropic tokenizer to count](https://github.com/langchain-ai/langchain/blob/31f264169db4ab23689f2e179983f1cfdfd1a33a/libs/langchain/langchain/llms/bedrock.py#L405)?
### Motivation
Cost monitoring
### Your contribution
PR
|
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I_kwDOIPDwls50BSCl
| 11,905
|
Repetitive answer and not getting source of documents
|
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| 2023-10-17T04:03:05
| 2023-10-17T04:14:05
| null |
NONE
| null |
@dosu-bot
Below is my code in python about creating a Q & A using Langchain with openai API. I have 3 issues I want to fix:
1) The answer is always being repeated twice.
2) I am using ConversationalRetrievalChain from LangChain therefore, I want to retrieve the source of the document when I get my answer.
3) I want to change the chain_type = "stuff" into Map Re Rank.
from langchain.chat_models import ChatOpenAI
from langchain.prompts.prompt import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Qdrant
from langchain.callbacks import StreamingStdOutCallbackHandler
from langchain.embeddings import OpenAIEmbeddings
from langchain.callbacks import get_openai_callback
from langchain.vectorstores import Chroma
from langchain.document_loaders import DirectoryLoader, PyPDFLoader
from dotenv import load_dotenv
import time
import warnings
# warnings.filterwarnings("ignore")
load_dotenv()
def print_letter_by_letter(text):
for char in text:
print(char, end='', flush=True)
time.sleep(0.02)
# Create embeddings
def langchain(customer_prompt, chat_history):
directory_path = "C:\\Users\\Asus\\Documents\\Vendolista"
pdf_loader = DirectoryLoader(directory_path,
glob="**/*.pdf",
show_progress=True,
use_multithreading=True,
silent_errors=True,
loader_cls = PyPDFLoader)
documents = pdf_loader.load()
print(str(len(documents))+ " documents loaded")
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
chunk_overlap=80,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
#save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
# Create a custom prompt for your use case
prompt_template = """
Answer the Question as a AI assistant that is answering based on the documents only. If the question is unrelated
then say "sorry this question is completely not related. If you think it is, email the staff
and they will get back to you: yazanrisheh@hotmail.com." Do not ever answer with "I don't know" to any question.
You either give an answer or mention it's not related.
Text: {context}
Question: {question}
Answer :
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
memory = ConversationBufferMemory(llm=llm, memory_key='chat_history', input_key='question', output_key='answer', return_messages=True)
conversation = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=new_knowledge_base.as_retriever(),
memory=memory,
chain_type="stuff",
combine_docs_chain_kwargs={"prompt":PROMPT}
)
return conversation({"question": customer_prompt, "chat_history": memory})
def main():
chat_history = []
while True:
customer_prompt = input("Ask me anything about the files (type 'exit' to quit): ")
if customer_prompt.lower() in ["exit"] and len(customer_prompt) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
if customer_prompt:
with get_openai_callback() as cb:
response = langchain(customer_prompt, chat_history)
print(response['answer'])
print(cb)
if __name__ == "__main__":
main()
|
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| 1,946,482,425
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PR_kwDOIPDwls5c97AK
| 11,904
|
Flesh out semi-structured cookbook
|
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| 2023-10-17T03:49:45
| 2023-10-18T11:43:27
| 2023-10-17T03:50:15
|
COLLABORATOR
| null | null |
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| 1,946,480,896
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PR_kwDOIPDwls5c96qo
| 11,903
|
Supported RetryOutputParser & RetryWithErrorOutputParser max_retries
|
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| 2023-10-17T03:47:55
| 2023-10-19T06:57:17
| 2023-10-19T06:57:17
|
CONTRIBUTOR
| null |
Description: Supported RetryOutputParser & RetryWithErrorOutputParser max_retries
- max_retries: Maximum number of retries to parser.
Issue: None
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle:
|
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PR_kwDOIPDwls5c9y-f
| 11,901
|
Revert "Add baichuan model"
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| 2023-10-17T03:00:58
| 2023-10-17T03:01:13
| 2023-10-17T03:01:12
|
COLLABORATOR
| null |
cc @cloudscool, apologies your PR wasn't actually passing CI
|
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Add security notices to toolkits
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[] | null | 1
| 2023-10-17T02:45:52
| 2023-10-17T15:45:11
| 2023-10-17T15:45:10
|
COLLABORATOR
| null |
This adds security notices to toolkits init, and to several toolkits.
We'll need to continue documenting the rest of the toolkits.
|
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| 1,946,420,866
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PR_kwDOIPDwls5c9uFT
| 11,899
|
Add deprecation warnings
|
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| 2023-10-17T02:35:58
| 2023-10-17T14:59:41
| 2023-10-17T14:59:39
|
COLLABORATOR
| null |
Add deprecation warnings
|
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I_kwDOIPDwls50Atyt
| 11,898
|
logger.info(output.decode().strip()) of common.py will raise a mistake when use convert_office_doc to convert .doc file to .docx file because it is not encode by 'utf-8'
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[] | null | 0
| 2023-10-17T01:16:58
| 2023-10-23T06:38:25
| null |
NONE
| null |
### Issue with current documentation:
I want to load doc type file use UnstructuredFileLoader, andI installed all require library, but I got this mistake:
Traceback (most recent call last):
File "D:\soft\Anaconda3\lib\site-packages\unstructured\partition\doc.py", line 67, in partition_doc
convert_office_doc(
File "D:\soft\Anaconda3\lib\site-packages\unstructured\partition\common.py", line 294, in convert_office_doc
logger.info(output.decode().strip())
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xd0 in position 42: invalid continuation byte.
Then I see the code of function convert_office_doc that can not change encoding type, this mistake is 'utf-8' is not valid encoding type, so I changed this to 'gb2312', it worked.
So, whether a parameter should be added for the encoding type when reading the file?
### Idea or request for content:
So, whether a parameter should be added for the encoding type when reading the file?
For example, UnstructuredFileLoader(file_path, file_name, coding='utf-8').
|
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PR_kwDOIPDwls5c9Wht
| 11,897
|
fix: use jsonb instead of json for pgvector
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| 2023-10-17T00:27:23
| 2023-10-18T16:33:01
| null |
NONE
| null |
## Description
When trying to integrate pgvector as a vector store with langchain, I got the following error:
```
2023-10-16 18:27:54.326 EDT [87858] ERROR: operator does not exist: json @> unknown at character 106
2023-10-16 18:27:54.326 EDT [87858] HINT: No operator matches the given name and argument types. You might need to add explicit type casts.
2023-10-16 18:27:54.326 EDT [87858] STATEMENT:
SELECT *, embedding <=> $1 as "_distance"
FROM langchain_pg_embedding
WHERE cmetadata @> $2
ORDER BY "_distance" ASC
LIMIT $3;
```
## The problem
From my brief research, it appears that the `@>` operator only works with jsonb. So I'm trying to figure out how this ever worked in the first place, but I'm not a postgres expert.
## Testing / Reproduced
To test this, I ran the following against my database after langchain created it and this seemed to fix my issue:
```sql
ALTER TABLE langchain_pg_embedding ALTER COLUMN cmetadata SET DATA TYPE jsonb;
```
I'm using this python version of langchain for running my document loaders and storing my embeddings in PG, but then I have a chat client that's using the langchain-js lib to query it, so I happened to then go see if pgvector had the same problem over there, and it doesn't.
https://github.com/langchain-ai/langchainjs/blob/aa9b618786757c301e93caeb3253c56b56d05602/langchain/src/vectorstores/pgvector.ts#L244C38-L244C43
Let me know if the fix isn't as simple as this or if there's a better solution that should be considered like type casting or something else?
cc:
@baskaryan, @eyurtsev, @hwchase17
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PR_kwDOIPDwls5c891p
| 11,896
|
Use traceable context
|
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| 2023-10-16T22:22:11
| 2023-10-17T22:10:21
| 2023-10-17T22:10:20
|
COLLABORATOR
| null |
Example
```
from langchain.schema.runnable import RunnableLambda
from langsmith import traceable
chain = RunnableLambda(lambda x: x)
@traceable(run_type = "chain")
def my_traceable(a):
chain.invoke(a)
my_traceable(5)
```
Would have a nested result.
This would NOT work for interleaving chains and traceables. E.g., things like thiswould still not work well
```
from langchain.schema.runnable import RunnableLambda
from langsmith import traceable
@traceable()
def other_traceable(a):
return a
def foo(x):
return other_traceable(x)
chain = RunnableLambda(foo)
@traceable(run_type = "chain")
def my_traceable(a):
chain.invoke(a)
my_traceable(5)
```
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| 1,946,172,223
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PR_kwDOIPDwls5c84WO
| 11,895
|
Add LCEL to chain doc
|
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| 2023-10-16T23:44:14
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I_kwDOIPDwls50ABzG
| 11,894
|
LangChain with Stable Diffusion
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[] | null | 8
| 2023-10-16T21:53:29
| 2023-12-03T12:42:44
| null |
NONE
| null |
### Feature request
combines LangChain with Stable Diffusion to generate a text-related image
### Motivation
The motivation behind this feature proposal is to enhance the capabilities of LangChain by integrating it with Stable Diffusion to enable the generation of images based on text inputs. This integration serves several purposes:
Enriched User Experience: The ability to generate images from text can significantly enrich user experiences in various applications. It can be applied to chatbots, content generation, creative tools, and more.
Creative Content Generation: This feature can empower users to generate creative content, artwork, or visualizations based on their textual ideas or descriptions.
Enhanced Language Model Integration: By integrating with Stable Diffusion, LangChain can harness the power of state-of-the-art generative models to create meaningful images that complement the textual context.
Research and Innovation: This integration can also serve as a research platform to explore the synergy between language models and generative image models, advancing the field of AI and creative content generation.
### Your contribution
Identify Relevant Repositories, Submit a Pull Request, Engage with the Community
|
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I_kwDOIPDwls5z_und
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|
API deployment not found when using Azure with embeddings
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[] | null | 3
| 2023-10-16T21:00:04
| 2023-10-17T14:16:56
| 2023-10-17T14:16:56
|
NONE
| null |
### System Info
I'm using jupyter notebook and Azure OpenAI
Python 3.11.5
langchain==0.0.315
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I got the error:
InvalidRequestError: The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again.
When I run
```
from langchain.embeddings.openai import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
sentence1 = "i like dogs"
embedding1 = embedding.embed_query(sentence1)
```
But if I run - not using Lanchain - it works fine:
```
response = openai.Embedding.create(
input="Your text string goes here",
model="text-embedding-ada-002",
engine="embeddingstest"
)
embeddings = response['data'][0]['embedding']
```
### Expected behavior
I would expect the embeddings of my string.
|
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PR_kwDOIPDwls5c8kaE
| 11,892
|
feat(llm): add together.xyz as an LLM provider
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[] | null | 2
| 2023-10-16T20:57:55
| 2023-10-17T04:22:06
| 2023-10-17T00:08:05
|
CONTRIBUTOR
| null |
- **Description:** added together.xyz as an LLM provider,
- **Issues:** fix some linting issues
- twitter handle @jilijeanlouis
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access, ✅
2. an example notebook showing its use. It lives in `docs/extras` directory. ❌ => classic LLM provider
|
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PR_kwDOIPDwls5c8c5q
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|
fix: more robust check whether the HF model is quantized
|
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[] | null | 1
| 2023-10-16T20:36:27
| 2023-10-16T23:54:21
| 2023-10-16T23:54:21
|
CONTRIBUTOR
| null |
Removes the check of `model.is_quantized` and adds more robust way of checking for 4bit and 8bit quantization in the `huggingface_pipeline.py` script. I had to make the original change on the outdated version of `transformers`, because the models had this property before. Seems redundant now.
Fixes: https://github.com/langchain-ai/langchain/issues/11809 and https://github.com/langchain-ai/langchain/issues/11759
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|
added Vertex examples as attributes
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|
CONTRIBUTOR
| null |
- **Description:** added examples to Vertex chat models as optional class attributes, so that a model with examples can be used inside a chain
- **Twitter handle:** lkuligin
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PR_kwDOIPDwls5c8ZS9
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|
add how to configure documentation
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|
COLLABORATOR
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| 1,946,025,528
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PR_kwDOIPDwls5c8XmL
| 11,888
|
add LCEL to retriever doc
|
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PR_kwDOIPDwls5c8UQZ
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|
Add option to RetrievalQA chains to use provided documents in call() and acall() methods
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CONTRIBUTOR
| null |
#### Description
This PR adds an option to the RetrievalQA chains to use provided documents in `_call()` and`_acall()` methods like: `qa_chain({"query": question, "from_documents": documents})` where documents is `List[Document]`
Currently there is no way to reuse documents found from a `similarity_search` in a `RetrievalQA`. This PR enables the following sequence:
1. User embeds query and gets documents
2. User implements desired logic based on documents
3. User calls `RetrievalQA` without needing to re-embed query to find relevant documents.
This option will also be helpful if the user wants to gather documents in any custom way, like combining from different sources.
#### Usage
Example Usage:
```
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vector_store.as_retriever(),
)
documents = vector_store.similarity_search("Is the sky blue?")
if "blue" in documents[0].page_content:
# Reuse documents in LLM call since they passed our condition
response = qa_chain({"query": question, "from_documents": documents})
#True
response["source_documents"] == documents
```
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PR_kwDOIPDwls5c8Tzk
| 11,886
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Add deps and minor cleaning to cookbooks
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PR_kwDOIPDwls5c8TZA
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|
added a multiturn search based on Vertex AI Search
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CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** Added a retriever based on multi-turn Vertex AI Search
- **Twitter handle:** lkuligin
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PR_kwDOIPDwls5c8G0C
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|
Fix issues in runnable docs examples
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|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls5z_MHr
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HumanMessage error expecting a str type in content
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NONE
| null |
### System Info
Python 3.11.6
Langchain 0.0.315
Device name Precision7760
Processor 11th Gen Intel(R) Core(TM) i9-11950H @ 2.60GHz 2.61 GHz
Installed RAM 32.0 GB (31.2 GB usable)
Device ID 049EB0D9-D534-47A1-9F59-62B1F3D578D4
Product ID 00355-60713-95419-AAOEM
System type 64-bit operating system, x64-based processor
Pen and touch No pen or touch input is available for this display
Edition Windows 11 Pro
Version 22H2
Installed on 10/7/2023
OS build 22621.2428
Experience Windows Feature Experience Pack 1000.22674.1000.0
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.schema import AIMessage, HumanMessage
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain
chat = ChatOpenAI()
memory = ConversationBufferMemory()
conversation = ConversationChain(llm=chat, memory=memory)
user_input="Hola atenea yo me llamo franks y estoy interesado en adquirir un vehículo"
response = conversation.run([HumanMessage(content=str(user_input))])
print(response)
I am getting this errors:
Traceback (most recent call last):
File "F:\Audi\Chatbot_Demo\mini_example.py", line 12, in <module>
response = conversation.run([HumanMessage(content=str(user_input))])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 503, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 310, in __call__
final_outputs: Dict[str, Any] = self.prep_outputs(
^^^^^^^^^^^^^^^^^^
File "E:\Python\Python311\Lib\site-packages\langchain\chains\base.py", line 406, in prep_outputs
self.memory.save_context(inputs, outputs)
File "E:\Python\Python311\Lib\site-packages\langchain\memory\chat_memory.py", line 36, in save_context
self.chat_memory.add_user_message(input_str)
File "E:\Python\Python311\Lib\site-packages\langchain\schema\chat_history.py", line 46, in add_user_message
self.add_message(HumanMessage(content=message))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "E:\Python\Python311\Lib\site-packages\langchain\load\serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "E:\Python\Python311\Lib\site-packages\pydantic\v1\main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for HumanMessage
content
**str type expected (type=type_error.str)**
### Expected behavior
The argument passed to HumanMessage is a str, nevertheless an error is produced (as it wasn't).
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PR_kwDOIPDwls5c7_kh
| 11,881
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Add security.md
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Add security markdown file
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PR_kwDOIPDwls5c79pT
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Add LCEL to output parser doc
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I_kwDOIPDwls5z_DDx
| 11,879
|
No current event loop in thread 'MainThread' error while using SitemapLoader()
|
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NONE
| null |
### System Info
I am uing the code provided in the Chat-Langchain implementation
```python
def load_langchain_docs():
return SitemapLoader(
"https://python.langchain.com/sitemap.xml",
filter_urls=["https://python.langchain.com/"],
parsing_function=langchain_docs_extractor,
default_parser="lxml",
bs_kwargs={
"parse_only": SoupStrainer(
name=("article", "title", "html", "lang", "content")
),
},
meta_function=metadata_extractor,
).load()
```
When Fetcing pages complete the error comes.
```
Fetching pages: 100%|###################################################################################################################| 944/944 [04:17<00:00, 3.66it/s]
Exception ignored in: <function _ProactorBasePipeTransport.__del__ at 0x00000202B4D78CA0>
Traceback (most recent call last):
File "C:\Users\meetg\AppData\Local\Programs\Python\Python39\lib\asyncio\proactor_events.py", line 116, in __del__
self.close()
File "C:\Users\meetg\AppData\Local\Programs\Python\Python39\lib\asyncio\proactor_events.py", line 108, in close
self._loop.call_soon(self._call_connection_lost, None)
File "C:\Users\meetg\AppData\Local\Programs\Python\Python39\lib\asyncio\base_events.py", line 746, in call_soon
self._check_closed()
File "C:\Users\meetg\AppData\Local\Programs\Python\Python39\lib\asyncio\base_events.py", line 510, in _check_closed
raise RuntimeError('Event loop is closed')
RuntimeError: Event loop is closed
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ C:\python\Projects\Langchain\save_langchain_docs.py: │
│ 201 in <module> │
│ │
│ 198 │ working_memory.save(documents=docs_transformed) │
│ 199 │
│ 200 if __name__ == "__main__": │
│ ❱ 201 │ ingest_docs() │
│ 202 │
│ │
│ C:\python\Projects\Langchain\save_langchain_docs.py: │
│ 182 in ingest_docs │
│ │
│ 179 def ingest_docs(): │
│ 180 │ docs_from_documentation = load_langchain_docs() │
│ 181 │ logger.info(f"Loaded {len(docs_from_documentation)} docs from documentation") │
│ ❱ 182 │ docs_from_api = load_api_docs() │
│ 183 │ logger.info(f"Loaded {len(docs_from_api)} docs from API") │
│ 184 │ │
│ 185 │ text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=200) │
│ │
│ C:\python\Projects\Langchain\save_langchain_docs.py: │
│ 159 in load_api_docs │
│ │
│ 156 │
│ 157 │
│ 158 def load_api_docs(): │
│ ❱ 159 │ return RecursiveUrlLoader( │
│ 160 │ │ url="https://api.python.langchain.com/en/latest/", │
│ 161 │ │ max_depth=8, │
│ 162 │ │ extractor=simple_extractor, │
│ │
│ C:\python\Projects\Gesture │
│ Scrolling\keyvenv\lib\site-packages\langchain\document_loaders\recursive_url_loader.py:112 in │
│ __init__ │
│ │
│ 109 │ │ self.timeout = timeout │
│ 110 │ │ self.prevent_outside = prevent_outside if prevent_outside is not None else True │
│ 111 │ │ self.link_regex = link_regex │
│ ❱ 112 │ │ self._lock = asyncio.Lock() if self.use_async else None │
│ 113 │ │ self.headers = headers │
│ 114 │ │ self.check_response_status = check_response_status │
│ 115 │
│ │
│ C:\Users\meetg\AppData\Local\Programs\Python\Python39\lib\asyncio\locks.py:81 in __init__ │
│ │
│ 78 │ │ self._waiters = None │
│ 79 │ │ self._locked = False │
│ 80 │ │ if loop is None: │
│ ❱ 81 │ │ │ self._loop = events.get_event_loop() │
│ 82 │ │ else: │
│ 83 │ │ │ self._loop = loop │
│ 84 │ │ │ warnings.warn("The loop argument is deprecated since Python 3.8, " │
│ │
│ C:\Users\meetg\AppData\Local\Programs\Python\Python39\lib\asyncio\events.py:642 in │
│ get_event_loop │
│ │
│ 639 │ │ │ self.set_event_loop(self.new_event_loop()) │
│ 640 │ │ │
│ 641 │ │ if self._local._loop is None: │
│ ❱ 642 │ │ │ raise RuntimeError('There is no current event loop in thread %r.' │
│ 643 │ │ │ │ │ │ │ % threading.current_thread().name) │
│ 644 │ │ │
│ 645 │ │ return self._local._loop │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: There is no current event loop in thread 'MainThread'.
```
### 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
- [x] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [x] Async
### Reproduction
By executing the function `load_langchain_docs()` from the above code snippet, error will come.
### Expected behavior
All the pages should be scraped and loaded to `Document` class
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Add security notice to file management tool
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Add security notice to file management tool
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PR_kwDOIPDwls5c70gB
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Accept uuid argument when calling weaviate.from_documents
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[] | null | 2
| 2023-10-16T18:41:16
| 2023-12-01T17:09:54
| null |
NONE
| null |
The `weaviate.from_documents` api should accept the `uuid` argument.
<!-- 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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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5c7r3j
| 11,875
|
Add LCEL to prompt doc
|
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I_kwDOIPDwls5z-r89
| 11,874
|
KuzuGraph not working
|
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open
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| 2023-10-16T18:10:46
| 2023-11-03T03:19:58
| null |
NONE
| null |
### System Info
Name: kuzu
Version: 0.0.10 (latest)
Name: langchain
Version: 0.0.311
### 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 using the example from langchain documentation:
https://python.langchain.com/docs/use_cases/graph/graph_kuzu_qa
This line fails:
graph = KuzuGraph(db)
F```
ile [~/.local/lib/python3.10/site-packages/langchain/graphs/kuzu_graph.py:71](https://file+.vscode-resource.vscode-cdn.net/home/steve/Projects/homelab/homeapi/notebooks/~/.local/lib/python3.10/site-packages/langchain/graphs/kuzu_graph.py:71), in KuzuGraph.refresh_schema(self)
69 for table in rel_tables:
70 current_table_schema = {"properties": [], "label": table["name"]}
---> 71 properties_text = self.conn._connection.get_rel_property_names(
72 table["name"]
73 ).split("\n")
74 for i, line in enumerate(properties_text):
75 # The first 3 lines defines src, dst and name, so we skip them
76 if i < 3:
AttributeError: 'kuzu._kuzu.Connection' object has no attribute 'get_rel_property_names'
```
### Expected behavior
should work like example
|
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| 1,945,756,011
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I_kwDOIPDwls5z-eFr
| 11,873
|
Denodo connector for langchain
|
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[] | null | 0
| 2023-10-16T17:37:46
| 2023-10-16T17:39:52
| null |
NONE
| null |
### Feature request
connect langchain to denodo database. while support for mysql, postgres etc exists, there is currently none for more niche platforms like denodo. Somthing like this
### Motivation
many organizations now use denodo for storing massive datasets , providing denodo connectivity will greatly enhance the reach of llamaindex and add immense value
### Your contribution
I can perform thorough testing of the feature and then update the documentation.
|
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| 1,945,755,085
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PR_kwDOIPDwls5c7dHd
| 11,872
|
update CONTRIBUTING.md
|
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| 2023-10-16T17:37:10
| 2023-10-16T21:22:43
| 2023-10-16T21:21:37
|
COLLABORATOR
| null |
Adding description of the `View deployment` button on the PR page. This nice feature was not documented.
|
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| 1,945,627,705
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I_kwDOIPDwls5z9-w5
| 11,871
|
Dead Links in Retrieval-Augmented Generation (RAG) Tutorial
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open
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[] | null | 1
| 2023-10-16T16:19:47
| 2023-10-16T16:22:35
| null |
NONE
| null |
### System Info
N/A (issue pertains to the web tutorial, [Retrieval-augmented generation (RAG)](https://python.langchain.com/docs/use_cases/question_answering/#quickstart))
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Navigate to [Retrieval-augmented generation (RAG)](https://python.langchain.com/docs/use_cases/question_answering/#quickstart)].
2. Attempt to access the links provided below:
- "Open in Colab" link, which is intended to direct users to a Colab notebook: [Open in Colab](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/question_answering/qa.ipynb)
- "See details in here on the multi-vector retriever for this purpose" link, which should provide more information on the multi-vector retriever: [See details in here on the multi-vector retriever for this purpose](https://python.langchain.com/docs/use_cases/question_answering/docs/modules/data_connection/retrievers/multi_vector)
3. Observe that these links do not lead to the intended resources and instead result in 404 errors or lead to pages that no longer exist.
### Expected behavior
All links in the tutorial lead to active, relevant pages.
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| 1,945,609,594
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PR_kwDOIPDwls5c69QU
| 11,870
|
Update SQL cookbook
|
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COLLABORATOR
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| 1,945,552,068
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PR_kwDOIPDwls5c6wke
| 11,869
|
Add security considerations
|
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| 2023-10-16T15:43:24
| 2023-10-16T16:23:50
| 2023-10-16T16:23:49
|
COLLABORATOR
| null |
Add security considerations to existing graph tools.
|
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| 1,945,531,386
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PR_kwDOIPDwls5c6r4S
| 11,868
|
Fix missing word
|
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| 2023-10-16T15:35:33
| 2023-10-17T00:10:32
| 2023-10-17T00:10:32
|
CONTRIBUTOR
| null |
- **Description:** added one missing word to a doc,
- **Dependencies:** N/A
|
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I_kwDOIPDwls5z9bCF
| 11,867
|
createJsonAgent returns answer based on top 5 data only
|
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[] | null | 1
| 2023-10-16T15:15:52
| 2023-10-16T15:22:17
| null |
NONE
| null |
### System Info
Subject: Bug Report - Langchain API Query Limitation
I am currently utilizing the following code snippet:
```typescript
let data: JsonObject;
try {
const jsonFile = await fs.readFileSync(
'./src/openAI/data/formattedResults.json',
'utf8',
);
data = JSON.parse(jsonFile) as JsonObject;
if (!data) {
throw new Error('Failed to load JSON spec');
}
} catch (e) {
console.error(e);
return;
}
const toolkit = new JsonToolkit(new JsonSpec(data));
const executor = await createJsonAgent(model, toolkit);
const res = await executor.call({ input: question }).catch((err) => {
console.log(err);
});
```
While interacting with the Langchain API, I noticed an issue. When I asked any question, it only returned the top few projects as answers. For instance, when I queried the agent, 'How many projects are there?' it correctly identified that there are 289 projects. However, it incorrectly responded with 'There are 5 projects' instead of providing the accurate count.
Upon further investigation, I realized that the Langchain API appears to be limiting the results to the top 5 data entries, rather than considering the entire dataset of 289 projects. This issue is affecting the accuracy of the responses.
I kindly request assistance in resolving this limitation so that the Langchain API can process and return the complete dataset accurately. Please advise on the necessary steps to address this issue and ensure that all relevant data is considered in responses to queries.
Thank you for your prompt attention to this matter.
### Who can help?
@eyurtsev
@hwchase17
@agola11
@dosu-beta
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
let data: JsonObject;
try {
const jsonFile = await fs.readFileSync(
'./src/openAI/data/formattedResults.json',
'utf8',
);
data = JSON.parse(jsonFile) as JsonObject;
if (!data) {
throw new Error('Failed to load JSON spec');
}
} catch (e) {
console.error(e);
return;
}
const toolkit = new JsonToolkit(new JsonSpec(data));
const executor = await createJsonAgent(model, toolkit);
const res = await executor.call({ input: question }).catch((err) => {
console.log(err);
});
<img width="735" alt="image" src="https://github.com/langchain-ai/langchain/assets/82230052/a0d70ea4-b236-4bb7-9450-52838aff97c5">
Totally there were 289 projects
<img width="178" alt="image" src="https://github.com/langchain-ai/langchain/assets/82230052/881bff40-0ca4-4729-83c5-83f2fe982fe9">
### Expected behavior
The agent should take into consideration all the data, rather than top few data
|
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PR_kwDOIPDwls5c6dUK
| 11,866
|
Fix: Sitemap Document Loader Tests and Documentation
|
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| 2023-10-16T15:10:55
| 2023-10-17T00:42:10
| 2023-10-17T00:42:10
|
CONTRIBUTOR
| null |
**Description:**
While working on the Docusaurus site loader #9138, I noticed some outdated docs and tests for the Sitemap Loader.
**Issue:**
This is tangentially related to #6691 in reference to doc links. I plan on digging in to a few of these issue when I find time next.
|
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I_kwDOIPDwls5z9MNd
| 11,865
|
load_qa_chain succeeds where load_qa_with_sources_chain fails to return result
|
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[] | null | 1
| 2023-10-16T14:55:08
| 2023-10-16T15:03:15
| null |
NONE
| null |
### System Info
Windows 10
Python 3.10.11
I am using FAISS as the vectorstore, HuggingFaceEmbeddings, and the llm is HuggingFaceHub with google/flan-t5-small. There are no changes to any of these elements when running with and without 'with_sources'.
All libraries were installed within the last week.
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
embeddings = HuggingFaceEmbeddings()
db = FAISS.load_local("FAISS-DB", embeddings)
llm = HuggingFaceHub(repo_id="google/flan-t5-small", model_kwargs={"temperature": 0.2, "max_length": 256})
query = "what does the company sodexo provide?"
similarity_docs = db.similarity_search(query)
chain = load_qa_chain(llm, chain_type="stuff")
results = chain.run(input_documents=similarity_docs, question=query)
print(query)
print(results)
similarity_docs = db.similarity_search(query)
chain = load_qa_with_sources_chain (llm, chain_type="stuff")
results = chain.run(input_documents=similarity_docs, question=query)
print(query)
print(results)
------------------------------
Output:
what does the company sodexo provide?
integrated food, facilities management and other services
what does the company sodexo provide?
0
[similarity_docs.txt](https://github.com/langchain-ai/langchain/files/12918308/similarity_docs.txt)
### Expected behavior
The expected result was the same answer with the source, similar to {'answer': 'integrated food, facilities management and other services', 'source': 'Inputs\\bodyExtract_71756.txt'}
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| 1,945,393,137
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PR_kwDOIPDwls5c6MqB
| 11,864
|
Update llama.cpp integration
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| 2023-11-01T23:32:16
| 2023-11-01T23:32:02
|
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| null |
<!--
- **Description:** removed redondant link, replaced it with Meta's LLaMA repo, add resources for models' hardware requirements,
- **Issue:** None,
- **Dependencies:** None,
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- **Twitter handle:** @ElliotAlladaye
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| 1,945,260,211
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PR_kwDOIPDwls5c5vp_
| 11,863
|
Fix HuggingFace notebook link
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[] | null | 3
| 2023-10-16T13:46:20
| 2023-10-17T01:03:47
| 2023-10-17T01:03:47
|
CONTRIBUTOR
| null |
- **Description:** While reading the docs (https://python.langchain.com/docs/integrations/providers/huggingface), I noticed the notebook linked in https://python.langchain.com/docs/use_cases/evaluation/huggingface_datasets.html was giving back 404. I made a search in the docs to see whether it was available, so this PR updates the link in the docs.
- **Issue:** I haven't opened an issue for this change.
- **Dependencies:** -
- **Tag maintainer:** -,
- **Twitter handle:** -
|
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PR_kwDOIPDwls5c5ZSv
| 11,862
|
Fix spelling typos in learned_prompt_optimization.ipynb
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| 2023-10-16T12:56:24
| 2023-10-17T01:01:48
| 2023-10-17T01:01:48
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CONTRIBUTOR
| null |
This patch fixes some spelling typo in learned_prompt_optimization.ipynb.
It only changed messages, no logic changed.
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PR_kwDOIPDwls5c5AWo
| 11,860
|
default user agent is added in recursive url loader header
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NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Experiencing a 403 Forbidden error when using the RecursiveUrlLoader on certain websites, and that this issue is resolved when we manually set a User-Agent in the headers. The WebBaseLoader in LangChain has a default User-Agent set in the session headers but RecursiveUrlLoader doesn't have default User-Agent. Updated RecursiveUrlLoader code to include default user-agent, if it is not provided.
- **Issue:** This fixes 403 Forbidden error on certain websites. This code update will solve the issue mentioned in the link https://github.com/langchain-ai/langchain/issues/11540,https://github.com/langchain-ai/langchain/issues/11541.
- **Dependencies:** fake_useragent library is needed,
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17,
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| 1,945,007,015
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I_kwDOIPDwls5z7nOn
| 11,859
|
Issue: When using ChatGLM, I can't connect to the endpoint_url.
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| 2023-10-16T11:35:37
| 2023-10-18T07:40:13
| null |
NONE
| null |
### Issue you'd like to raise.
When I use the chatglm code from the official documentation, I can't connect to the endpoint URL provided there.
The official website is: https://python.langchain.com/docs/integrations/llms/chatglm
The specific code is as follows：
from langchain.llms import ChatGLM
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
template = """{question}"""
prompt = PromptTemplate(template=template, input_variables=["question"])
endpoint_url = "http://127.0.0.1:8000"
llm = ChatGLM(
endpoint_url=endpoint_url,
max_token=80000,
history=[["我将从美国到中国来旅游，出行前希望了解中国的城市", "欢迎问我任何问题。"]],
top_p=0.9,
model_kwargs={"sample_model_args": False},
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "北京和上海两座城市有什么不同？"
llm_chain.run(question)
The code is encountering the following error：
ValueError: Error raised by inference endpoint: HTTPConnectionPool(host='127.0.0.1', port=8000): Max retries exceeded with
url: / (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x00000163A6691F70>: Failed to establish a new connection: [WinError 10061] 由于目标计算机积极拒绝，无法连接。'))
I have tried many methods I found online, but they didn't have much effect. I'm not sure if it's necessary to open a specific server through cmd to connect to 127.0.0.1:8000.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5c4rsh
| 11,858
|
lancedb integration improvements
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| 2023-10-16T11:03:17
| 2024-01-17T17:08:59
| 2024-01-17T17:08:59
|
NONE
| null |
- **Description:**
Hi, I'm from lancedb team.
- Improves lancedb integration by making it easier to use - now you don't require to create tables manually and pass it in the constructor, although that is still backwards compatible.
- Bug fix - pandas was being used even though its not a dependency for lancedb or langchain
- **Tag maintainer:**
@baskaryan
- **Twitter handle:** @loldedxd
|
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I_kwDOIPDwls5z7IJS
| 11,857
|
Not answering correctly according to prompt and writes same answer 3-4 times
|
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[] | null | 7
| 2023-10-16T10:23:37
| 2023-10-16T11:47:30
| 2023-10-16T11:47:30
|
NONE
| null |
This is my current code and it does run however it answers completely differently sometimes and it also writes the answer 3 or 4 times. Please fix it for me and if possible, fix my templates to make my output consistent
from dotenv import load_dotenv
import csv
import PyPDF2
from PyPDF2 import PdfReader
from langchain.document_loaders import DirectoryLoader, PyPDFLoader, PyPDFDirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
import time
from langchain.vectorstores import Qdrant
from langchain.vectorstores import Chroma
from langchain.vectorstores import deeplake
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.callbacks import StreamingStdOutCallbackHandler
import pandas as pd
from docx import Document
from nltk.tokenize import sent_tokenize, word_tokenize
from collections import Counter
from nltk.corpus import stopwords
import os
def print_letter_by_letter(text):
for char in text:
print(char, end='', flush=True)
time.sleep(0.02)
def main():
load_dotenv()
my_activeloop_org_id = "yazanrisheh"
my_activeloop_dataset_name = "langchain_course_customer_support"
dataset_path = f"hub://{my_activeloop_org_id}/{my_activeloop_dataset_name}"
# directory_path = input("Copy your directory path here or upload a file: ")
directory_path = "C:\\Users\\Asus\\Documents\\Vendolista"
# pdf_loader = DirectoryLoader(directory_path,
# glob="**/*.pdf",
# show_progress=True,
# use_multithreading=True,
# silent_errors=True,
# loader_cls = PyPDFLoader)
pdf_loader = PyPDFDirectoryLoader(directory_path)
documents = pdf_loader.load()
print(str(len(documents))+ " documents loaded")
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
chunk_overlap=100,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
# save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
# weird_knowledge_base = deeplake(chunks, dataset_path=dataset_path, embedding=embeddings)
# knowledge_base = Qdrant(documents, embeddings)
p_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
If the Standalone question is empty or cannot be generated, use the follow up question as Standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
#CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
CONDENSE_QUESTION_PROMPT = PromptTemplate(input_variables=["chat_history","question"],
template=p_template)
memory = ConversationBufferMemory(memory_key="chat_history",input_key="question",output_key='answer',return_messages=True)
chatTemplate = """
Important: You are an intelligent chatbot designed to help agents by answering questions only on Enterprise services & activities.
Answer the question only if there is information in the chat history(delimited by ) and context(delimited by ) below.
If context is not empty and answer cannot be determined from context, say "I cannot detemine the answer from context".
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Do not print your answer starting with "Answer:"
{context} ----------- {chat_history} ----------- Question: {question} Answer: Answer the question only if there is information based on the chat history(delimited by ) and context(delimited by ) below. 1. If context is not empty and answer cannot be determined from context, say "I cannot detemine the answer from context". 2. If you don't know the answer, just say that you don't know, don't try to make up an answer. 3. Do not print your answer starting with "Answer:"
"""
promptHist = PromptTemplate(
input_variables=["context", "question", "chat_history"],
template=chatTemplate
)
qa = ConversationalRetrievalChain.from_llm(
llm = llm,
retriever = new_knowledge_base.as_retriever(),
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
verbose=True,
memory=memory,
condense_question_llm=llm,
return_generated_question=False,
combine_docs_chain_kwargs={"prompt": promptHist},
return_source_documents=False,
)
while True:
question = input("Ask me anything about the files (type 'exit' to quit): ")
if question.lower() in ["exit"] and len(question) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
if question:
# chat_history = []
with get_openai_callback() as cb:
response = qa({"question": question}, return_only_outputs = False)
# chat_history.append(('user', question))
# chat_history.append(('AI', response))
print("AI:", response)
print(cb)
if __name__ == '__main__':
main()
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| 1,944,857,017
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I_kwDOIPDwls5z7Cm5
| 11,856
|
Issue: Not clear through docs: how we can pass variale to the input_variable in prompt
|
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[] | null | 4
| 2023-10-16T10:11:06
| 2023-10-17T19:45:24
| null |
NONE
| null |
### Issue you'd like to raise.
hi @dosu-bot , @dosu-beta :
I am not able to figure out how we can pass variale to the input_variable in prompt :
prompt_with_loader = PromptTemplate(
input_variables=["query", "username", "password"],
template=
"""
Based on the instruction you are tasked to construct a Python script that performs the following action: {query}. Utilize the specifics in the given API documentation to understand the required endpoint, method, and parameters.
Credentials for authentication:
Username: {username}
Password: {password}
Please create a Python script using the 'requests' library to execute the desired action. Ensure the script includes:
- Parsing of the necessary details from the API specification
- Basic Authentication with the provided username and password (encoded in Base 64)
- Proper use of the relevant HTTP method, headers, body, and parameters as per the API specification
- print the response of the api
- Error handling for the request
"""
)
chain_type_kwargs = {"prompt": prompt_with_loader} ,
**qa_chain = RetrievalQAWithSourcesChain.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
chain_type_kwargs=chain_type_kwargs,
return_source_documents=False,
verbose=True
)**. // when i am goining to hit this block it gives me this ERRR : document_variable_name summaries was not found in llm_chain input_variables: ['query', 'username', 'password'] (type=value_error)
pls can you guide us here
### Suggestion:
_No response_
|
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| 1,944,786,305
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I_kwDOIPDwls5z6xWB
| 11,855
|
ConversationalRetrievalChain error
|
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[] | null | 2
| 2023-10-16T09:34:13
| 2023-10-16T09:49:44
| null |
NONE
| null |
Can someone help me fix this code please.
My error: Traceback (most recent call last):
File "C:\Users\Asus\Documents\Vendolista\app2.py", line 130, in <module>
main()
File "C:\Users\Asus\Documents\Vendolista\app2.py", line 99, in main
qa = ConversationalRetrievalChain.from_llm(
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 356, in from_llm
return cls(
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\load\serializable.py", line 75, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for ConversationalRetrievalChain
retriever
value is not a valid dict (type=type_error.dict)
PS C:\Users\Asus\Documents\Vendolista> python app2.py
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 33/33 [00:02<00:00, 11.17it/s]
87 documents loaded
Traceback (most recent call last):
File "C:\Users\Asus\Documents\Vendolista\app2.py", line 130, in <module>
main()
File "C:\Users\Asus\Documents\Vendolista\app2.py", line 99, in main
qa = ConversationalRetrievalChain.from_llm(
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\conversational_retrieval\base.py", line 356, in from_llm
return cls(
File "C:\Users\Asus\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\load\serializable.py", line 75, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for ConversationalRetrievalChain
retriever
value is not a valid dict (type=type_error.dict)
My code:
from dotenv import load_dotenv
import csv
import PyPDF2
from PyPDF2 import PdfReader
from langchain.document_loaders import DirectoryLoader, PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
import time
from langchain.vectorstores import Qdrant
from langchain.vectorstores import Chroma
from langchain.vectorstores import deeplake
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.callbacks import StreamingStdOutCallbackHandler
import pandas as pd
from docx import Document
from nltk.tokenize import sent_tokenize, word_tokenize
from collections import Counter
from nltk.corpus import stopwords
import os
def print_letter_by_letter(text):
for char in text:
print(char, end='', flush=True)
time.sleep(0.02)
def main():
load_dotenv()
my_activeloop_org_id = "yazanrisheh"
my_activeloop_dataset_name = "langchain_course_customer_support"
dataset_path = f"hub://{my_activeloop_org_id}/{my_activeloop_dataset_name}"
# directory_path = input("Copy your directory path here or upload a file: ")
directory_path = "C:\\Users\\Asus\\Documents\\Vendolista"
pdf_loader = DirectoryLoader(directory_path,
glob="**/*.pdf",
show_progress=True,
use_multithreading=True,
silent_errors=True,
loader_cls = PyPDFLoader)
documents = pdf_loader.load()
print(str(len(documents))+ " documents loaded")
llm = ChatOpenAI(temperature = 0, model_name='gpt-3.5-turbo', callbacks=[StreamingStdOutCallbackHandler()], streaming = True)
# Split into chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=800,
chunk_overlap=100,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
persist_directory = "C:\\Users\\Asus\\OneDrive\\Documents\\Vendolista"
knowledge_base = Chroma.from_documents(chunks, embeddings, persist_directory = persist_directory)
# save to disk
knowledge_base.persist()
#To delete the DB we created at first so that we can be sure that we will load from disk as fresh db
knowledge_base = None
new_knowledge_base = Chroma(persist_directory = persist_directory, embedding_function = embeddings)
# weird_knowledge_base = deeplake(chunks, dataset_path=dataset_path, embedding=embeddings)
# knowledge_base = Qdrant(documents, embeddings)
p_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
If the Standalone question is empty or cannot be generated, use the follow up question as Standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
#CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
CONDENSE_QUESTION_PROMPT = PromptTemplate(input_variables=["chat_history","question"],
template=p_template)
memory = ConversationBufferMemory(memory_key="chat_history",input_key="question",output_key='answer',return_messages=True)
chatTemplate = """
Important: You are an intelligent chatbot designed to help agents by answering questions only on Enterprise services & activities.
Answer the question only if there is information in the chat history(delimited by ) and context(delimited by ) below.
If context is not empty and answer cannot be determined from context, say "I cannot detemine the answer from context".
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Do not print your answer starting with "Answer:"
{context} ----------- {chat_history} ----------- Question: {question} Answer: Answer the question only if there is information based on the chat history(delimited by ) and context(delimited by ) below. 1. If context is not empty and answer cannot be determined from context, say "I cannot detemine the answer from context". 2. If you don't know the answer, just say that you don't know, don't try to make up an answer. 3. Do not print your answer starting with "Answer:"
"""
promptHist = PromptTemplate(
input_variables=["context", "question", "chat_history"],
template=chatTemplate
)
qa = ConversationalRetrievalChain.from_llm(
llm = llm,
retriever = new_knowledge_base,
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
verbose=False,
memory=memory,
condense_question_llm=llm,
return_generated_question=True,
combine_docs_chain_kwargs={"prompt": promptHist},
return_source_documents=True,
)
while True:
question = input("Ask me anything about the files (type 'exit' to quit): ")
if question.lower() in ["exit"] and len(question) == 4:
end_chat = "Thank you for visiting us! Have a nice day"
print_letter_by_letter(end_chat)
break
if question:
# chat_history = []
# with get_openai_callback() as cb:
response = qa({"question": question}, return_only_outputs = True)
# chat_history.append(('user', question))
# chat_history.append(('AI', response))
print("AI:", response)
if __name__ == '__main__':
main()
|
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| 1,944,679,764
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I_kwDOIPDwls5z6XVU
| 11,854
|
S3 Directory Loader not working as expected
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open
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[] | null | 3
| 2023-10-16T08:37:20
| 2023-10-16T09:34:50
| null |
CONTRIBUTOR
| null |
### System Info
langchain : 0.0.306
python : 3.10.12
platform : Ubuntu
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`loader = S3DirectoryLoader(
bucket="", prefix="data/",
aws_access_key_id="",
aws_secret_access_key="")
data = loader.load()`
It should have load all the files with in the bucket from a given prefix which may contain multiple sub-folders but it does not
### Expected behavior
It should have loaded all the files as list of Document of langchain schema , but it does not
|
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| 1,944,674,010
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I_kwDOIPDwls5z6V7a
| 11,853
|
ConfluenceLoader's keep_newlines is not passed correctly
|
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open
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| 2023-10-16T08:34:03
| 2023-10-16T08:39:35
| null |
NONE
| null |
### System Info
LangChain version: v0.0.314
### Who can help?
@eyurtsev @baskaryan
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(url="xxx", session=get_session())
docs = loader.load(page_ids=["xxx"], keep_newlines=True)
for doc in docs:
print(doc.page_content)
have_newlines = "\n" in doc.page_content
print(f"{have_newlines=}")
```
read the source code, I found the `keep_newlines` arguments are not passed to `process_page` function when load with `cql` and `page_ids`
file: `langchain.document_loaders.confluence.py`
function: `Confluence.load`
lines: 308-353
<img width="781" alt="image" src="https://github.com/langchain-ai/langchain/assets/19882756/1d11d7dd-f883-4782-8b5d-3365ca2dff87">
<img width="821" alt="image" src="https://github.com/langchain-ai/langchain/assets/19882756/b87f3a16-65a4-490c-851f-0c51a534d171">
### Expected behavior
The `keep_newlines` parameter is passed correctly and empty lines are correctly retained
|
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| 1,944,652,671
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PR_kwDOIPDwls5c3qa5
| 11,852
|
Encapsulate alicloud pai-eas access method for chatmodels and llms
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[] | null | 2
| 2023-10-16T08:22:23
| 2023-10-19T07:20:19
| 2023-10-19T07:20:19
|
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/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
### Description:
To provide an eas llm service access methods in this pull request by impletementing `PaiEasEndpoint` and `PaiEasChatEndpoint` classes in `langchain.llms` and `langchain.chat_models` modules. Base on this pr, langchain users can build up a chain to call remote eas llm service and get the llm inference results.
### About EAS Service
EAS is a Alicloud product on Alibaba Cloud Machine Learning Platform for AI which is short for AliCloud PAI. EAS provides model inference deployment services for the users. We build up a llm inference services on EAS with a general llm docker images. Therefore, end users can quickly setup their llm remote instances to load majority of the hugginface llm models, and serve as a backend for most of the llm apps.
### Dependencies
This pr does't involve any new dependencies.
|
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| 1,944,587,356
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PR_kwDOIPDwls5c3cVq
| 11,850
|
bump 315
|
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| 2023-10-16T07:46:02
| 2023-10-16T07:50:55
| 2023-10-16T07:50:54
|
COLLABORATOR
| null | null |
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| 1,944,575,643
|
PR_kwDOIPDwls5c3ZzH
| 11,849
|
Adapt to the latest version of Alibaba Cloud OpenSearch vector store API
|
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| 2023-10-16T07:38:54
| 2023-10-17T01:07:24
| 2023-10-17T01:07:24
|
CONTRIBUTOR
| null |
Hello Folks,
Alibaba Cloud OpenSearch has released a new version of the vector storage engine, which has significantly improved performance compared to the previous version. At the same time, the sdk has also undergone changes, requiring adjustments alibaba opensearch vector store code to adapt.
This PR includes:
Adapt to the latest version of Alibaba Cloud OpenSearch API.
More comprehensive unit testing.
Improve documentation.
I have read your contributing guidelines. And I have passed the tests below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
|
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PR_kwDOIPDwls5c3Wa8
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|
Revised notebook and add delete to MyScale vector store
|
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[] | null | 2
| 2023-10-16T07:30:30
| 2023-11-10T03:07:31
| 2023-10-17T18:42:22
|
CONTRIBUTOR
| null |
- **Description:**
- Add `.delete` to myscale vector store.
- Revised vector store notebooks
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @myscaledb @mpsk_liu
|
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| 1,944,554,193
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I_kwDOIPDwls5z54rR
| 11,847
|
Tagging documentation does not work with LlamaCpp or Ollama
|
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[] | null | 2
| 2023-10-16T07:27:07
| 2023-10-17T17:33:28
| null |
NONE
| null |
### System Info
python = "=3.10.12"
llama-cpp-python = "=0.2.11"
langchain = "=0.0.313"
### Who can help?
@hwchase17
@agola11
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the [tagging docs]() with either `Ollama` or `llama-cpp-python` shown below.
```python
from langchain.chains import create_tagging_chain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.llms import LlamaCpp
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
model_using = [YOUR MODEL PATH] / "llama-2-7b-chat.Q4_0.gguf"
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(
model_path=model_using,
n_gpu_layers=32,
n_batch=512,
f16_kv=True,
callback_manager=callback_manager,
verbose=True,
n_ctx=512,
seed=42,
)
# Schema
schema = {
"properties": {
"sentiment": {"type": "string"},
"aggressiveness": {"type": "integer"},
"language": {"type": "string"},
}
}
chain = create_tagging_chain(schema, llm)
inp = "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!"
print(chain.run(inp))
```
Gives an error that OpenAI functions are not available.
```
Traceback (most recent call last):
File "/Users/jak121/Git_repos/jk/20231016_langchain_tagging_issue.py", line 34, in <module>
print(chain.run(inp))
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/chains/base.py", line 501, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
raise e
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/chains/base.py", line 300, in __call__
self._call(inputs, run_manager=run_manager)
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/chains/llm.py", line 93, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/chains/llm.py", line 103, in generate
return self.llm.generate_prompt(
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/llms/base.py", line 498, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/llms/base.py", line 647, in generate
output = self._generate_helper(
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/llms/base.py", line 535, in _generate_helper
raise e
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/llms/base.py", line 522, in _generate_helper
self._generate(
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/llms/base.py", line 1044, in _generate
self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/llms/llamacpp.py", line 291, in _call
for chunk in self._stream(
File "/Users/jak121/Git_repos/llm/env/lib/python3.10/site-packages/langchain/llms/llamacpp.py", line 343, in _stream
result = self.client(prompt=prompt, stream=True, **params)
TypeError: Llama.__call__() got an unexpected keyword argument 'functions'
ggml_metal_free: deallocating
```
### Expected behavior
As per the docs: ` {'sentiment': 'positive', 'language': 'Spanish'}`
|
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I_kwDOIPDwls5z5ljC
| 11,845
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Issue: Getting Error while integrating confluence attachment
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[] | null | 1
| 2023-10-16T06:34:51
| 2023-10-16T06:42:21
| null |
NONE
| null |
### Issue you'd like to raise.
I'm encountering an error while trying to integrate confluenece attachment by the keyword include_attachments=True
Created a chunk of size 494, which is longer than the specified 100
Created a chunk of size 230, which is longer than the specified 100
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Created a chunk of size 127, which is longer than the specified 100
Created a chunk of size 306, which is longer than the specified 100
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Below is my code:
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texts = text_splitter.split_documents(documents)
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=10, encoding_name="cl100k_base")
texts = text_splitter.split_documents(texts)
### Suggestion:
_No response_
|
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I_kwDOIPDwls5z5aCT
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Issue: Getting Error while integrating confluence attachment
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NONE
| null |
### Issue you'd like to raise.
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Below is my code:
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=10, encoding_name="cl100k_base")
texts = text_splitter.split_documents(texts)
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_No response_
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I_kwDOIPDwls5z5VjR
| 11,843
|
In the "Dynamic Few-Shot Prompting" documentation, it is mentioned that when example_selector selects a sample for matching, it can cause the few-shot examples to lose effectiveness because the selected sample may not be relevant to the given input.
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### Issue with current documentation:
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.prompts import (
FewShotChatMessagePromptTemplate,
SemanticSimilarityExampleSelector,
ChatPromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
{"input": "2+4", "output": "6"},
{"input": "What did the cow say to the moon?", "output": "nothing at all"},
{
"input": "Write me a poem about the moon",
"output": "One for the moon, and one for me, who are we to talk about the moon?",
},
]
example_selector = SemanticSimilarityExampleSelector.from_examples(
examples,
OpenAIEmbeddings(),
Chroma,
k=1
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
example_selector=example_selector,
example_prompt=ChatPromptTemplate.from_messages(
[("human", "{input}"), ("ai", "{output}")]
),
)
final_prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a wondrous wizard of math."),
few_shot_prompt,
("human", "{input}"),
]
)
# 由于example_selector已经选择了一个样本进行匹配，这将导致少样本示例失效，因为选择的样本可能与给定的输入不相关。
print(final_prompt.format(input="What did the cow say to the moon?"))
### Idea or request for content:
In the "Dynamic Few-Shot Prompting" documentation, it is mentioned that when example_selector selects a sample for matching, it can cause the few-shot examples to lose effectiveness because the selected sample may not be relevant to the given input.
https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat#dynamic-few-shot-prompting
|
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| 1,944,293,628
|
PR_kwDOIPDwls5c2cFX
| 11,842
|
update Weaviate to support multi tenancy
|
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| 2023-10-16T03:43:29
| 2023-10-19T07:49:39
| 2023-10-19T07:49:31
|
CONTRIBUTOR
| null |
- **Description:** update Weaviate to support multi tenancy
- **Issue:** 9956
- **Dependencies:**
- **Tag maintainer:** hwchase17
- **Twitter handle:** dsx1986_
|
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PR_kwDOIPDwls5c2XP1
| 11,840
|
Add a new vector store hippo for langchain #11763
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| 2023-10-16T03:17:48
| 2023-10-27T09:10:40
| 2023-10-27T08:30:12
|
CONTRIBUTOR
| null |
Issue: #11763
Dependencies: N/A
I have run 'make format' 'make lint' and 'make test'
<!-- 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),
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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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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls5z4j9F
| 11,839
|
QianfanChatEndpoint function call not work for agent.
|
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| 2023-10-16T02:16:39
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NONE
| null |
### System Info
I attempted to create an AI agent bot by utilizing the QianfanChatEndpoint. However, I encountered an issue when I attempted to define a tool for making calls.
Subsequent to consulting with a contributor from the QianfanChatEndpoint project, it was determined that it is imperative to merge the following pull request for resolution: [link to the GitHub pull request](https://github.com/langchain-ai/langchain/pull/11107). Once this fix is implemented, my agent will work fine with Function Call integration.
@hwchase17
@agola11
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
"""Example demo for agent.py
"""
import os
from typing import Any, Dict, List
from langchain.agents import tool
from langchain.agents.format_scratchpad import format_to_openai_functions
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.chat_models import QianfanChatEndpoint
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
# from langchain.tools.render import format_tool_to_openai_function
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are very powerful assistant, but bad at get"
"today's temperature of location.",
),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
@tool
def get_current_weather(location: str) -> str:
"""Returns current temperature of location."""
return "25"
tools = [get_current_weather]
if __name__ == "__main__":
os.environ["QIANFAN_AK"] = "ak"
os.environ["QIANFAN_SK"] = "sk"
llm = QianfanChatEndpoint(model="ERNIE-Bot") # 仅EB支持
llm_with_tools = llm.bind(
# functions=functions
functions=[
{
"name": "get_current_weather",
"description": "获得指定地点的天气",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "省，市名，例如：河北省，石家庄",
},
"unit": {"type": "string", "enum": ["摄氏度", "华氏度"]},
},
"required": ["location"],
},
}
]
)
p = {
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_to_openai_functions(
x["intermediate_steps"]
),
}
agent: Dict = p | prompt | llm_with_tools | OpenAIFunctionsAgentOutputParser()
# main loop
from langchain.schema.agent import AgentFinish
intermediate_steps: List[Any] = []
while True:
output = agent.invoke(
{"input": "上海市今天天气如何？", "intermediate_steps": intermediate_steps}
)
if isinstance(output, AgentFinish):
final_result = output.return_values["output"]
break
else:
print(output.tool, output.tool_input)
tool = {
"get_current_weather": get_current_weather,
}[output.tool]
observation = tool.run(output.tool_input)
# observation = "{\"temperature\": \"25\", \"description\": \"晴朗\"}"
intermediate_steps.append((output, observation))
print("result", final_result)
```
### Expected behavior
上海市今天气温25度
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| 1,944,082,144
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I_kwDOIPDwls5z4Fbg
| 11,838
|
AsyncMock not preserved when passed to custom Chain
|
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[] | null | 3
| 2023-10-15T22:27:00
| 2023-10-16T07:32:26
| null |
NONE
| null |
### System Info
Dependencies:
langchain == 0.0.285
pytest ~= 7.4.0
pytest-asyncio ~= 0.21.1
pytest-mock ~= 3.11.1
Using Python 3.11
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
The following Python script is self-contained. It features:
- A custom `Chain` implementation that uses an `LLMChain` internally, `bar`
- We don't care about its inputs or outputs
- We simply care that it takes an `LLMChain` and uses that to compute its output.
- A test (`async` using the `pytest-asyncio` library) where:
- I mocked the `LLMChain` using an `AsyncMock` with spec `LLMChain`
- I mocked the return value of the `arun` function in the `LLMChain`
### Self-contained Python Script
<details>
<summary>Self-contained Python Script</summary>
```py
from langchain.chains.base import Chain
from langchain import LLMChain
from typing import Any
from langchain.callbacks.manager import AsyncCallbackManagerForChainRun, CallbackManagerForChainRun
from unittest.mock import AsyncMock
from pydantic import BaseModel, Extra
class CustomChain(Chain):
bar: LLMChain
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
allow_population_by_field_name = True
@property
def input_schema(self) -> type[BaseModel]:
return BaseModel # we don't care about the inputs here
@property
def input_keys(self) -> list[str]:
return []
@property
def output_keys(self) -> list[str]:
return ["output"]
async def _acall(
self,
inputs: dict[str, Any],
run_manager: AsyncCallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
intermediary_result = await self.bar.arun(message="hello")
return {"output": intermediary_result}
def _call(self, inputs: dict[str, Any], run_manager: CallbackManagerForChainRun | None = None) -> dict[str, Any]:
raise NotImplementedError("Only supports async")
async def test_custom_chain():
bar = AsyncMock(LLMChain)
bar.arun.return_value = "Hi there!"
custom_chain = CustomChain(bar=bar)
await custom_chain._acall(inputs={})
bar.arun.assert_awaited_once()
```
</details>
### Observed Output
The test should naturally succeed. Problem is that _somewhere_ in the initialization of the `CustomChain`, `bar` is changed from being an `AsyncMock` to a `MagicMock`. This is the inexplicable bit to me.
Placing a debugger breakpoint right before the initialization of `CustomChain` in the test function:
```
custom_chain = CustomChain(bar=bar)
```
It shows that `bar` is `<AsyncMock spec='LLMChain' id='5218712656'>`, however from inside `CustomChain._acall` function, `self.bar` is a `<MagicMock name='mock._copy_and_set_values()' id='5221083792'>`.
<details>
<summary>Full test output</summary>
```
async def test_custom_chain():
bar = AsyncMock(LLMChain)
bar.arun.return_value = "Hi there!"
custom_chain = CustomChain(bar=bar)
> await custom_chain._acall(inputs={})
test/brain/test_foo.py:49:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = CustomChain(memory=None, callbacks=None, callback_manager=None, verbose=False, tags=None, metadata=None, bar=<MagicMock name='mock._copy_and_set_values()' id='5099464528'>)
inputs = {}, run_manager = None
async def _acall(
self,
inputs: dict[str, Any],
run_manager: AsyncCallbackManagerForChainRun | None = None,
) -> dict[str, Any]:
> intermediary_result = await self.bar.arun(message="hello")
E TypeError: object MagicMock can't be used in 'await' expression
test/brain/test_foo.py:37: TypeError
```
</details>
### Expected behavior
Naturally, I would expect the parameter to my custom chain to be exactly what I passed, unmodified at any point. Hence, I would expect the test and its assertion to succeed.
_Please note_: I tried reproducing the issue outside of LangChain, just by creating a similar class - just inheriting from a Pydantic `BaseModel` rather than LangChain's `Chain`. The test succeeded just as expected.
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| 1,944,073,141
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I_kwDOIPDwls5z4DO1
| 11,837
|
Use OpenAI ChatGPT Plugin via Python API fails with maximum number of tokens error
|
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| 2023-10-15T22:17:16
| null |
NONE
| null |
### System Info
0.0.314
### Who can help?
@hwchase17 @agola11 @eyurtsev
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.chat_models import ChatOpenAI
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.tools import AIPluginTool
tool = AIPluginTool.from_plugin_url("https://scholar-ai.net/.well-known/ai-plugin.json")
llm = ChatOpenAI(temperature=0, streaming=True, model_name="gpt-3.5-turbo-16k-0613", max_tokens=16000)
tools = [tool]
agent_chain = initialize_agent(
tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True
)
agent_chain.run("What are the antiviral effects of Sillymarin?")
```
And I get
```
InvalidRequestError: This model's maximum context length is 16385 tokens. However, you requested 17952 tokens (1855 in the messages, 97 in the functions, and 16000 in the completion). Please reduce the length of the messages, functions, or completion.
```
### Expected behavior
The idea is that should provide sensible information based on the Plugin Scholar AI.
It would be terrific, if you could help us here. 🙂
|
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I_kwDOIPDwls5z4Bvl
| 11,836
|
OpenAI API is extremely slow
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| 2024-01-03T22:43:00
| null |
CONTRIBUTOR
| null |
### System Info
LangChain Version: 0.0.314
Python 3.9.6
I have been using LangChain with OpenAI and FAISS for building RAG chatbots. Everything was working fine until last week.
I have noticed that for some reason, I have much higher response times. I have debugged the LangChain code and found that the reason is the LLM response itself. I am using OpenAI GPT-3.5-turbo, I haven't changed anything in the prompt being sent, but response time has increased dramatically. For example, a very simple prompt in September took 2 seconds to generate. Now the exact same prompt takes up to 14 seconds to get a response!
Does anybody have the same experience? Does anybody know why this can be happening?
Any help will be appreciated!
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Use OpenAI as an LLM
2. Observer response times
### Expected behavior
The LLM response should be much faster
|
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PR_kwDOIPDwls5c1q8T
| 11,835
|
Add LCEL to LLM intro
|
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| 2023-10-15T21:23:07
| 2023-10-15T21:59:46
| 2023-10-15T21:59:45
|
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/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5c1qFm
| 11,834
|
Add LCEL to chat model intro
|
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PR_kwDOIPDwls5c1m9W
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|
Update chat model output type
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COLLABORATOR
| null |
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Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
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PR_kwDOIPDwls5c1jDq
| 11,832
|
Update README.md
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| 2023-10-15T19:32:36
| 2023-11-13T07:15:19
| 2023-11-13T07:15:18
|
NONE
| null |
updated readme
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5c1YIP
| 11,831
|
directory_loader: Add "exclude_glob" option
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| 2023-10-15T16:53:24
| 2023-10-20T21:25:59
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NONE
| null |
Add an `exclude_glob` option to exclude some files or dirs from the main glob. Sample use case: a directory structure like this
```
main_directory/
backup/ # Don't want to load from here
file_directory_1/
file_directory_2/
...
```
(inspired by [LogSeq](https://logseq.com/) data structure)
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| 1,943,966,873
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I_kwDOIPDwls5z3pSZ
| 11,830
|
model_name is not passed as a argument to create method
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| 2023-10-15T16:52:02
| 2023-10-15T16:54:08
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NONE
| null |
https://github.com/langchain-ai/langchain/blob/a50630277295c3884be8e839b04718d0e99b4ea4/libs/langchain/langchain/chains/moderation.py#L93C43-L93C43
Code a line no. 93 needs to be updated as below
self.client.create(input = text, model = self.model_name)
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| 1,943,962,792
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I_kwDOIPDwls5z3oSo
| 11,829
|
return all documents in neo4j_vector
|
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[] | null | 1
| 2023-10-15T16:41:33
| 2023-10-15T16:46:42
| null |
NONE
| null |
### Feature request
similarly to:
```
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
collection = db.get()
```
where ```collection``` is a list of documents already in the vector store, is there a way to get that list from neo4j_vecror that was initialized from_documents() ?
### Motivation
This will allow to filter out existing documents when new ones are added in folder that is being used to build an expanding document vector store.
### Your contribution
i was thinkging using .query() as a workaround but not sure how a CYPHER query can return all possible documents currently in the store.
|
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| 1,943,958,270
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PR_kwDOIPDwls5c1WWd
| 11,828
|
Fix broken link on Retrievers state-of-the-union link
|
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[] | null | 4
| 2023-10-15T16:29:43
| 2023-10-19T09:09:42
| 2023-10-19T07:32:29
|
NONE
| null |
Thanks for building Langchain.
An small contribution:
- **Description:** Fix broken link for `modules/retrieval/retrievers` `state_of_the_union.txt` file in getting started section.
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** : N/A
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** @laura_uzcategui
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
|
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| 1,943,938,026
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PR_kwDOIPDwls5c1SZH
| 11,827
|
Update multi-modal notebooks
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COLLABORATOR
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I_kwDOIPDwls5z3cKO
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Issue: GPU is not used with Ctransformers even after specifying gpu_layers parameter
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NONE
| null |
### Issue you'd like to raise.
I have installed langchain and ctransformer using -
```
pip install langchain
pip install ctransformers[cuda]
```
I am trying following piece of code -
```
from langchain.llms import CTransformers
config = {'max_new_tokens': 512, 'repetition_penalty': 1.1, 'context_length': 8000, 'temperature':0, 'gpu_layers':50}
llm = CTransformers(model = "./codellama-7b.Q4_0.gguf", model_type = "llama", gpu_layers=50, config=config)
```
Here gpu_layers parameter is specified still gpu is not being used and complete load is on cpu.
Can someone please point out if there is any step missing.
### Suggestion:
_No response_
|
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| 1,943,896,799
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PR_kwDOIPDwls5c1KOO
| 11,825
|
missing wiki from the URL path
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| 2023-10-15T13:48:52
| 2023-10-16T10:03:45
| 2023-10-16T10:03:45
|
NONE
| null |
- Include `/wiki` to URL path
- My issue: [11842](https://github.com/langchain-ai/langchain/issues/11824)
|
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I_kwDOIPDwls5z3Xxu
| 11,824
|
Confluence document loader can not get the attachments
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[] | null | 1
| 2023-10-15T13:44:03
| 2023-10-16T10:03:34
| 2023-10-16T10:03:34
|
NONE
| null |
### System Info
Python 3.11.5
langchain==0.0.260
atlassian-python-api==3.41.2
### Who can help?
@eyurtsev
@hwchase17
### 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
My code:
`data = loader.load(page_ids=[page_id], include_attachments=True, limit=50)`
Error message:
`requests.exceptions.HTTPError: 404 Client Error: Not Found for url: ...`
### Expected behavior
The problem is the URL path is not vaild (you can read my post at atlassian developer community [here ](https://community.developer.atlassian.com/t/download-confluence-content-attachment-via-rest-api-get-404-error/73782))
My solution:
at document_loaders/confluence.py [line 542](https://github.com/langchain-ai/langchain/blob/a50630277295c3884be8e839b04718d0e99b4ea4/libs/langchain/langchain/document_loaders/confluence.py#L542C6-L542C6)
change
`absolute_url = self.base_url + attachment["_links"]["download"]`
to
`absolute_url = self.base_url + "/wiki" + attachment["_links"]["download"]`
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| 11,823
|
ValidationError: 1 validation error for ChatMessage
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open
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| 2023-10-15T12:14:33
| 2023-10-15T12:25:58
| null |
NONE
| null |
### System Info
window 、langchain-0.0.314、python 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run
```
from langchain.chains.summarize import load_summarize_chain
from langchain.chat_models import ChatOpenAI
from langchain import LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
)
api_base_url = "http://localhost:10860/v1"
api_key= "EMPTY"
LLM_MODEL = "/***/AquilaChat2-7B"
model = ChatOpenAI(
streaming=True,
verbose=True,
# callbacks=[callback],
openai_api_key=api_key,
openai_api_base=api_base_url,
model_name=LLM_MODEL
)
chain = load_summarize_chain(model,
chain_type="map_reduce",
verbose = True,
)
output_summary = chain.run(docs[:5])
print(output_summary)
``
### Expected behavior
nomal
https://blog.csdn.net/FrenzyTechAI/article/details/131524746
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| 1,943,864,974
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PR_kwDOIPDwls5c1D8p
| 11,822
|
Fixed a typo in anyscale.ipynb
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| 2023-10-15T12:14:18
| 2023-10-17T01:43:16
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|
CONTRIBUTOR
| null |
Fixed a typo :
"asyncrhonized" > "asynchronized"
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,943,863,813
|
PR_kwDOIPDwls5c1DtH
| 11,821
|
Fixed a typo in bittensor.ipynb
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| 2023-10-15T12:10:42
| 2023-10-17T01:43:29
| 2023-10-17T01:43:29
|
CONTRIBUTOR
| null |
Fixed a typo :
benifits -> benefits
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
|
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| 1,943,752,299
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PR_kwDOIPDwls5c0tLV
| 11,820
|
Add source for returning the source doc
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NONE
| null |
the RetrievalQAWithSourcesChain would fail without the source returned
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| 1,943,745,728
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I_kwDOIPDwls5z2zTA
| 11,819
|
Getting an error while importing MultiPromptChain
|
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open
| false
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[] | null | 5
| 2023-10-15T06:32:23
| 2023-11-22T18:22:45
| null |
NONE
| null |
### System Info
Langchain version: 0.0.313
Python version: Python 3
OS: Mac
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.chains.router.multi_prompt import MultiPromptChain
Assume have already installed langchain and other necessary libraries
Error I get:
cannot import name 'BasePromptTemplate' from 'langchain.schema'. I checked in the langchain.schema file. BasePromptTemplate is indeed not there. But then I don't know how to get that file. I see PromptTemplate file in the langchain.schema file
### Expected behavior
To import MultiPromptChain
|
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I_kwDOIPDwls5z2rJu
| 11,818
|
Issue: Getting Error while integrating confluence attachment
|
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open
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[] | null | 1
| 2023-10-15T04:59:22
| 2023-10-15T05:04:27
| null |
NONE
| null |
### Issue you'd like to raise.
I'm encountering an error while trying to integrate confluenece attachment using the keyword "include_attachments=True", without these keyword I am able to do question answering on Text content.
But I want to fetch attachment of the confluence space. Below is my code:
I encounter the following error:
### Suggestion:
_No response_
|
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I_kwDOIPDwls5z2qBm
| 11,817
|
Mutiple text_splitter ignore the chunk_size parameter
|
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[] | null | 2
| 2023-10-15T04:42:23
| 2023-10-16T07:10:32
| 2023-10-16T07:10:32
|
NONE
| null |
### System Info
langchain=0.0.294
python=3.10
### Who can help?
@hwchase17 @mmz-001 @baskaryan
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have tried multiple text splitters, but none of them seem to respect the chunk_size parameter. My document is a podcast transcript that I create from a simple text file that combines all transcript sentences into a single string.
The SpacyTextSplitter then formats the document into coherent sentences and paragraphs. But I end up with over five individual small documents, even when I test a small part of the transcript of only 13,000 characters.
The RecursiveCharacterTextSplitter and CharacterTextSplitter split the documents into simpler grouped sentences, but they also ignore the chunk_size parameter.
My test file
[3kiv08TDn4vywkNAdoT2In_short_v2.txt](https://github.com/langchain-ai/langchain/files/12908607/3kiv08TDn4vywkNAdoT2In_short_v2.txt)
```
import spacy
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter, SpacyTextSplitter
@app.post("/reduce_file/", response_model=ItemResponse)
async def reduce_file(item: Item_Reduce_FileURL):
logging.info("Entering function reduce_file")
try:
# Dynamic file type detection (from summarize_file)
response = requests.get(item.file_link)
if response.status_code != 200:
raise HTTPException(status_code=400, detail="Failed to download file")
content_type = response.headers.get('Content-Type')
file_extension = mimetypes.guess_extension(content_type)
if file_extension and file_extension.startswith('.'):
file_extension = file_extension[1:]
temp_file_name = f"temp_file.{file_extension}"
with open(temp_file_name, "wb") as f:
f.write(response.content)
# Load the document
file_loader = OnlinePDFLoader(file_path=temp_file_name) if file_extension == 'pdf' else UnstructuredAPIFileLoader(
file_path=temp_file_name,
infer_table_structure=True,
strategy="fast",
model_name="chipper",
mode="elements",
pdf_infer_table_structure=False,
ocr_languages=["eng"],
discard_invalid_pages=True,
encoding="utf_8",
api_key=unstructured_api_key
)
try:
doc = file_loader.load()
except Exception as load_exception:
logging.error(f"Error in file_loader.load(): {load_exception}")
return CustomError.handle_exception(load_exception, item, exception_type=str(type(load_exception)))
# Initialize text splitter and split the document (from reduce_pdf)
text_splitter = SpacyTextSplitter(
chunk_size=5000,
chunk_overlap=0,
length_function=len,
)
split_docs = text_splitter.split_documents(doc)
### Expected behavior
I expect only two documents since the chunk_size parameter is set to 10000 characters. I have tested with increasing and decreasing the chunk_size, and I continue to get a consistent amount of smaller documents of 1200 to 1500 characters.
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| 1,943,688,748
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I_kwDOIPDwls5z2lYs
| 11,816
|
Issue: Getting Error while integrating confluence attachment
|
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open
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[] | null | 2
| 2023-10-15T03:49:23
| 2023-10-15T05:08:26
| null |
NONE
| null |
### Issue you'd like to raise.
I'm encountering an error while trying to integrate confluenece attachment using the keyword "include_attachments=True", without these keyword I am able to do question answering on Text content.
But I want to fetch attachment of the confluence space. Below is my code:
I encounter the following error:
### Suggestion:
_No response_
|
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| 1,943,684,633
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I_kwDOIPDwls5z2kYZ
| 11,815
|
Issue: Similarity Search on Chroma does not retrieve relevant chunk for homogeneous document search
|
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open
| false
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[] | null | 1
| 2023-10-15T03:35:42
| 2023-10-15T03:41:46
| null |
NONE
| null |
### Issue you'd like to raise.
Hi, I have lots product factsheet in PDF format and would like to perform QnA on these documents. These documents have a common layout. I parsed these documents and stored them in ChromaDB. However, when I try to query the vector store for a specific details for ex. "What is the product category of product A?", it retrieved irrelevant chunks. What approach should be taken for such homogeneous documents QnA use cases?
### Suggestion:
_No response_
|
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