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https://api.github.com/repos/langchain-ai/langchain/issues/8827
|
https://api.github.com/repos/langchain-ai/langchain
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https://github.com/langchain-ai/langchain/issues/8827
| 1,838,264,689 |
I_kwDOIPDwls5tkbFx
| 8,827 |
Agent using tool when it's not needed
|
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[] | null | 10 | 2023-08-06T16:17:01 | 2023-12-14T12:34:07 | 2023-11-15T16:06:17 |
NONE
| null |
I initialized the tool and agent as following:
```
llm = ChatOpenAI(
openai_api_key=OPENAI_API_KEY,
model_name = 'gpt-3.5-turbo',
temperature=0.0,
)
conversational_memory = ConversationBufferWindowMemory(
memory_key='chat_history',
k=6,
return_messages=True
)
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type='stuff',
retriever=vectorstore.as_retriever(),
)
tools = [
Tool(
name='Fressnapf Knowledge Base',
func=qa.run,
description=("""
Only use this tool when you need to know more about Fressnapf service and products
""")
)
]
agent = initialize_agent(
agent='chat-conversational-react-description',
tools=tools,
llm=llm,
verbose=True,
max_iterations=3,
early_stopping_method='generate',
memory=conversational_memory,
)
```
Now, when I'm asking the agent some basic questions he always uses the tool even if it's not useful to for answering the question.
After running `agent('Do you have ideas for a birthday gift?')` the agent directly uses the tool.
```
> Entering new AgentExecutor chain...
{
"action": "Fressnapf Knowledge Base",
"action_input": "birthday gift ideas"
}
Observation: Here are some birthday gift ideas:
1. Personalized photo album or picture frame
2. Spa or wellness gift set
3. Subscription to a favorite magazine or streaming service
4. Cooking or baking class
5. Outdoor adventure experience, such as a hiking or kayaking trip
6. Customized jewelry or accessories
7. Book or a set of books by their favorite author
8. Tickets to a concert, play, or sports event
9. A gift card to their favorite restaurant or store
10. A thoughtful handwritten letter or card expressing your love and appreciation.
Thought:{
"action": "Final Answer",
"action_input": "Here are some birthday gift ideas:\n\n1. Personalized photo album or picture frame\n2. Spa or wellness gift set\n3. Subscription to a favorite magazine or streaming service\n4. Cooking or baking class\n5. Outdoor adventure experience, such as a hiking or kayaking trip\n6. Customized jewelry or accessories\n7. Book or a set of books by their favorite author\n8. Tickets to a concert, play, or sports event\n9. A gift card to their favorite restaurant or store\n10. A thoughtful handwritten letter or card expressing your love and appreciation."
}
```
I've provided a very clear description for the tool and when the agent should be using it, but it seems like as if the agent ignoring my instructions.
Does anyone have any idea how to solve this issue?
|
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| 1,838,248,487 |
I_kwDOIPDwls5tkXIn
| 8,824 |
Refine prompt modification
|
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open
| false | null |
[] | null | 3 | 2023-08-06T15:25:06 | 2023-11-05T17:16:40 | null |
NONE
| null |
### Feature request
I want to offer to change refine prompts to make a summary more correct.
### Motivation
In some cased (I can't say when exactly, but one time per 10-20) I saw that refine LLM added some additional text for summary.
Example:
> Since the provided context does not add any relevant information to the original summary, we will return the original summary:
> ... summary here...
On other cases I even saw output of refine prompt as:
>The existing summary is more comprehensive and provides a clear understanding of <my topic here>. The additional context provided does not contribute to the summary and can be disregarded.
As result I have this text as summary, but not my summary itself.
### Your contribution
I want to propose to use other prompts and check JSON result.
```
refine_initial_prompt_template = """\
Write a concise summary of the text (delimited with XML tags).
Please provide result in JSON format:
{{
"summary": "summary here"
}}
<text>
{text}
</text>
"""
refine_combine_prompt_template = """\
Your job is to produce a final summary. We have provided an existing summary up to a certain point (delimited with XML tags).
We have the opportunity to refine the existing summary (only if needed) with some more context (delimited with XML tags).
Given the new context, refine the original summary (only if new context is useful) otherwise say that it's not useful.
Please provide result in JSON format:
{{
"not_useful": "True if new context was not useful, False if new content was used",
"refined_summary": "refined summary here if new context was useful"
}}
<existing_summary>
{existing_summary}
</existing_summary>
<more_context>
{more_context}
</more_context>
"""
```
Now I have JSON with field not_useful=True and can easy ignore it.
|
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| 1,838,230,735 |
PR_kwDOIPDwls5XRYek
| 8,823 |
Feature/partial clearance buffer memory
|
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closed
| false | null |
[] | null | 2 | 2023-08-06T14:31:37 | 2023-08-06T22:29:52 | 2023-08-06T22:29:52 |
CONTRIBUTOR
| null |
- Description: I ran into a problem several times that Conversation buffer memory gets full as soon as it reaches 4097 tokens. And we wouldn't want to completely clear the history as then the complete context would be lost. So I have added a method in **ChatMessageHistory** named **partial_clear** which takes an argument **delete_ratio**. If you pass 0.5 as delete_ratio it will remove 50% of the oldest messages in the memory. **BaseChatMemory** also inherits this method.
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @hwchase17,
|
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| 1,838,207,052 |
PR_kwDOIPDwls5XRUGy
| 8,822 |
Add support for Xata as a vector store
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| false | null |
[] | null | 4 | 2023-08-06T13:19:19 | 2023-08-08T11:15:57 | 2023-08-07T15:14:52 |
CONTRIBUTOR
| null |
This adds support for [Xata](https://xata.io) (data platform based on Postgres) as a vector store. We have recently added [Xata to Langchain.js](https://github.com/hwchase17/langchainjs/pull/2125) and would love to have the equivalent in the Python project as well.
The PR includes integration tests and a Jupyter notebook as docs. Please let me know if anything else would be needed or helpful.
I have added the xata python SDK as an optional dependency.
## To run the integration tests
You will need to create a DB in xata (see the docs), then run something like:
```
OPENAI_API_KEY=sk-... XATA_API_KEY=xau_... XATA_DB_URL='https://....xata.sh/db/langchain' poetry run pytest tests/integration_tests/vectorstores/test_xata.py
```
<!-- Thank you for contributing to LangChain!
Replace this 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 you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,838,189,640 |
I_kwDOIPDwls5tkIxI
| 8,821 |
How to pass a CSV file or a dataframe to Pandas REPL tool in Langchain?
|
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[] | null | 3 | 2023-08-06T12:26:01 | 2023-11-14T16:06:09 | 2023-11-14T16:06:08 |
NONE
| null |
I am trying to utilize Python Repl Tool in langchain with a CSV file and send me the answer based on the CSV file content. The problem is that it gets the action_input step of writing the code to execute right. However, it fails to answer because it couldn't determine the dataframe it should run the code on.
For example, I ask it about the longest name in a dataframe containing a column named "name" and it returns the following:
Entering new AgentExecutor chain...
{
"action": "python_repl_ast",
"action_input": "import pandas as pd\n\n# Assuming the dataset is stored in a pandas DataFrame called 'data'\nnames = data['NAMES']\nlongest_name = max(names, key=len)\nlongest_name"
}
Observation: NameError: name 'data' is not defined
Thought:{
"action": "Final Answer",
**"action_input": "I apologize for the confusion. Unfortunately, I do not have access to the dataset required to find the longest name. Is there anything else I can assist you with?"**
}
Is there a way to pass the dataframe object to the Pandas REPL tool in order for the code to execute properly and return me the answer? This problem is encountered while using the GPT-3.5-turbo API model.
This is the full code:
```
df = pd.read_csv(file_path)
tools = [PythonAstREPLTool(locals={"df": df})]
agent = initialize_agent(
agent='chat-conversational-react-description',
tools=tools,
llm=llm,
verbose=True,
max_iterations=3,
early_stopping_method='generate',
memory=conversational_memory
)
query = 'What is the longest name?'
print(agent(query))
```
|
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| 1,838,174,943 |
I_kwDOIPDwls5tkFLf
| 8,819 |
Issue: Unstructured module nees backing off to lower ver. 0.7.12 seems ok
|
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[] | null | 1 | 2023-08-06T11:45:44 | 2023-11-12T16:05:44 | 2023-11-12T16:05:43 |
NONE
| null |
### Issue you'd like to raise.
guys if not already logged.. I had to uninstall unstructured module and back it off to lower ver. 0.7.12. python3 -m pip install unstructured==0.7.12
### Suggestion:
maybe this needs attention ?
|
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| 1,838,174,041 |
I_kwDOIPDwls5tkE9Z
| 8,818 |
Issue: Conversation retreival chain : if current question is not at all related to prevoius chat history
|
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[] | null | 7 | 2023-08-06T11:42:41 | 2023-11-13T16:40:41 | null |
NONE
| null |
### Issue you'd like to raise.
How to handle such scenarios if the current asked questions is totally a new question and does not have relation with previous chat history . The standalone question would probably be non-sense, as it's semantic is twisted by the chat history.
`OPENAI_API_KEY=xxxxxx
OPENAI_API_BASE=https://xxxxxxxx.openai.azure.com/
OPENAI_API_VERSION=2023-05-15
import os
import openai
from dotenv import load_dotenv
from langchain.chat_models import AzureChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
# Load environment variables (set OPENAI_API_KEY, OPENAI_API_BASE, and OPENAI_API_VERSION in .env)
load_dotenv()
# Configure OpenAI API
openai.api_type = "azure"
openai.api_base = os.getenv('OPENAI_API_BASE')
openai.api_key = os.getenv("OPENAI_API_KEY")
openai.api_version = os.getenv('OPENAI_API_VERSION')
# Initialize gpt-35-turbo and our embedding model
llm = AzureChatOpenAI(deployment_name="gpt-35-turbo")
embeddings = OpenAIEmbeddings(deployment_id="text-embedding-ada-002", chunk_size=1)
from langchain.document_loaders import DirectoryLoader
from langchain.document_loaders import TextLoader
from langchain.text_splitter import TokenTextSplitter
loader = DirectoryLoader('data/qna/', glob="*.txt", loader_cls=TextLoader, loader_kwargs={'autodetect_encoding': True})
documents = loader.load()
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
from langchain.vectorstores import FAISS
db = FAISS.from_documents(documents=docs, embedding=embeddings)
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts import PromptTemplate
# Adapt if needed
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template("""Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:""")
qa = ConversationalRetrievalChain.from_llm(llm=llm,
retriever=db.as_retriever(),
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
return_source_documents=True,
verbose=False)
`
### Suggestion:
_No response_
|
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| 1,838,102,702 |
I_kwDOIPDwls5tjziu
| 8,817 |
How to catch 'maximum context length' error
|
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[] | null | 2 | 2023-08-06T08:08:29 | 2023-11-12T16:05:50 | 2023-11-12T16:05:50 |
NONE
| null |
### Issue you'd like to raise.
Can't find the way to deal with the 'maximum context length' error from OpenAI. Any way to catch it within langchain?
### Suggestion:
Add a error handler?
|
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| 1,838,088,521 |
I_kwDOIPDwls5tjwFJ
| 8,816 |
How to make a Chatbot responde based on custom data and from the internet?
|
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[] | null | 2 | 2023-08-06T07:22:44 | 2023-11-12T16:05:54 | 2023-11-12T16:05:53 |
NONE
| null |
### Issue you'd like to raise.
I've updated the code but very strange it doesn't find a good response. When I print(response["answer"]) I get that there is no text to give to the query I put. Even if it gets information from the internet and the Document on the list seems good structured. Here the code:
`
from googlesearch import search
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import (
UnstructuredWordDocumentLoader,
TextLoader,
UnstructuredPowerPointLoader,
)
from langchain.tools import Tool
from langchain.utilities import GoogleSearchAPIWrapper
from langchain.chat_models import ChatOpenAI
from langchain.docstore.document import Document
import os
import openai
import sys
from dotenv import load_dotenv, find_dotenv
sys.path.append('../..')
_ = load_dotenv(find_dotenv())
google_api_key = os.environ.get("GOOGLE_API_KEY")
google_cse_id = os.environ.get("GOOGLE_CSE_ID")
openai.api_key = os.environ['OPENAI_API_KEY']
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.langchain.plus"
os.environ["LANGCHAIN_API_KEY"] = os.environ['LANGCHAIN_API_KEY']
os.environ["GOOGLE_API_KEY"] = google_api_key
os.environ["GOOGLE_CSE_ID"] = google_cse_id
folder_path_docx = "DB\\DB VARIADO\\DOCS"
folder_path_txt = "DB\\BLOG-POSTS"
folder_path_pptx_1 = "DB\\PPT JUNIO"
folder_path_pptx_2 = "DB\\DB VARIADO\\PPTX"
loaded_content = []
for file in os.listdir(folder_path_docx):
if file.endswith(".docx"):
file_path = os.path.join(folder_path_docx, file)
loader = UnstructuredWordDocumentLoader(file_path)
docx = loader.load()
loaded_content.extend(docx)
for file in os.listdir(folder_path_txt):
if file.endswith(".txt"):
file_path = os.path.join(folder_path_txt, file)
loader = TextLoader(file_path, encoding='utf-8')
text = loader.load()
loaded_content.extend(text)
for file in os.listdir(folder_path_pptx_1):
if file.endswith(".pptx"):
file_path = os.path.join(folder_path_pptx_1, file)
loader = UnstructuredPowerPointLoader(file_path)
slides_1 = loader.load()
loaded_content.extend(slides_1)
for file in os.listdir(folder_path_pptx_2):
if file.endswith(".pptx"):
file_path = os.path.join(folder_path_pptx_2, file)
loader = UnstructuredPowerPointLoader(file_path)
slides_2 = loader.load()
loaded_content.extend(slides_2)
embedding = OpenAIEmbeddings()
embeddings_content = []
for one_loaded_content in loaded_content:
embedding_content = embedding.embed_query(one_loaded_content.page_content)
embeddings_content.append(embedding_content)
db = DocArrayInMemorySearch.from_documents(loaded_content, embedding)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})
search = GoogleSearchAPIWrapper()
def custom_search(query):
max_results = 3
internet_results = search.results(query, max_results)
internet_documents = [Document(page_content=result["snippet"], metadata={
"source": result["link"]}) for result in internet_results
]
return internet_documents
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model_name="gpt-4", temperature=0),
chain_type="map_reduce",
retriever=retriever,
return_source_documents=True,
return_generated_question=True,
)
history = []
while True:
query = input("Hola, soy Chatbot. ¿Qué te gustaría saber? ")
internet_documents = custom_search(query)
small = loaded_content[:3]
combined_results = small + internet_documents
print(combined_results)
response = chain(
{"question": query, "chat_history": history, "documents": combined_results})
print(response["answer"])
history.append(("system", query))
history.append(("assistant", response["answer"]))
`
Can anyone help me to make it work? Appreciate!
### Suggestion:
I would like that the Chatbot gives a response not just from the custom data also from what it gets from the internet. But of what I've done so far it doesn't work.
|
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I_kwDOIPDwls5tjvIU
| 8,815 |
Object of type AgentAction is not JSON serializable
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[] | null | 2 | 2023-08-06T07:11:26 | 2023-08-06T09:06:42 | 2023-08-06T09:06:42 |
NONE
| null |
### System Info
version 0.0.253
Running on a Jupyter Notebook in Google Colab
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Follow the steps in [Access Intermediate Steps](https://github.com/langchain-ai/langchain/blob/master/docs/extras/modules/agents/how_to/intermediate_steps.ipynb) within the Agent "How To".
The final step calls for
`print(json.dumps(response["intermediate_steps"], indent=2))`
This is throwing the following error:
`TypeError: Object of type AgentAction is not JSON serializable`
Based on [this issue](https://github.com/langchain-ai/langchain/issues/2222) I think it may be happening following a recent upgrade.
### Expected behavior
Printing the intermediate steps as JSON.
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PR_kwDOIPDwls5XQ711
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Add missing tests for evaluation
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[] | null | 1 | 2023-08-06T06:39:39 | 2023-08-06T19:57:02 | 2023-08-06T19:57:02 |
CONTRIBUTOR
| null |
# What
- Add missing tests for evaluation
Replace this comment with:
- Description: Add missing tests for evaluation
- Issue: None
- Dependencies: None
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @MLopsJ
Please make sure you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5XQ6dY
| 8,813 |
Recursive url loader w/ test
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CONTRIBUTOR
| null |
Description: Due to some issue on the test, this is a separate PR with the test for #8502
Tag maintainer: @rlancemartin
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PR_kwDOIPDwls5XQ5pb
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fix exception inconsistencies
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[] | null | 1 | 2023-08-06T06:04:02 | 2023-08-06T20:43:05 | 2023-08-06T20:43:05 |
CONTRIBUTOR
| null |
# What
- This is to fix exception inconsistencies.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: fix exception inconsistencies
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MLopsJ
Please make sure you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5XQzKB
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Fix typo in long_context_reorder.ipynb
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[] | null | 1 | 2023-08-06T03:43:16 | 2023-08-06T22:31:39 | 2023-08-06T22:31:38 |
CONTRIBUTOR
| null |
begining -> beginning
<!-- Thank you for contributing to LangChain!
Replace this 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 you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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docs: Improvements on Mendable Search
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[] | null | 1 | 2023-08-06T02:44:11 | 2023-08-06T22:32:07 | 2023-08-06T22:32:06 |
CONTRIBUTOR
| null |
- Balancing prioritization between keyword / AI search
- Show snippets of highlighted keywords when searching
- Improved keyword search
- Fixed bugs and issues
Shoutout to @calebpeffer for implementing and gathering feedback on it
cc: @dev2049 @rlancemartin @hwchase17
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PR_kwDOIPDwls5XQso3
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Fix docs for Rockset
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[] | null | 1 | 2023-08-06T00:54:48 | 2023-08-06T22:54:27 | 2023-08-06T22:04:02 |
CONTRIBUTOR
| null |
* remove error output for notebook
* add comment about vector length for ingest transformation
* change OPENAI_KEY -> OPENAI_API_KEY
cc @baskaryan
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PR_kwDOIPDwls5XQoHi
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feat(llms): add support for vLLM
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[] | null | 3 | 2023-08-05T23:07:51 | 2023-08-07T14:32:03 | 2023-08-07T14:32:03 |
CONTRIBUTOR
| null |
Hello langchain maintainers,
this PR aims at integrating [vllm](https://vllm.readthedocs.io/en/latest/#) into langchain. This PR closes #8729.
This feature clearly depends on `vllm`, but I've seen other models supported here depend on packages that are not included in the pyproject.toml (e.g. `gpt4all`, `text-generation`) so I thought it was the case for this as well.
@hwchase17, @baskaryan
<!-- Thank you for contributing to LangChain!
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PR_kwDOIPDwls5XQkmn
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added the embeddings part
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[] | null | 1 | 2023-08-05T21:51:09 | 2023-08-06T00:47:57 | 2023-08-06T00:16:33 |
CONTRIBUTOR
| null |
Description: forgot to add the embeddings part in the documentation. sorry 😅
@baskaryan
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PR_kwDOIPDwls5XQkRr
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Enhanced functionality of PostgresChatMessageHistory
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[] | null | 11 | 2023-08-05T21:43:39 | 2023-11-10T22:28:49 | 2023-11-10T22:28:49 |
CONTRIBUTOR
| null |
- Description : The PostgresChatMessageHistory will be more useful if it stores the timestamp of each message. While retrieving the messages, it is currently retrieved ordering it by session id. However, in an actual chat, the sessionid will be maintained for the duration of the chat. So, the messages will need to b retrieved after sorting it by the timestamp. The enhancement that I have pushed adds timestamp as a field in the table. Also added a function messages_order_by_time which orders the messages by timestamp(either desc or asc based on the parameter passed)
- Issue: NA
- Dependencies: datetime package,
- Tag maintainer: @hwchase17
- Twitter handle: NA
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PR_kwDOIPDwls5XQf9Y
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Convenient filter pass to as_retriver() for vectorstores
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[] | null | 6 | 2023-08-05T20:22:05 | 2023-08-06T22:37:42 | 2023-08-06T22:37:42 |
CONTRIBUTOR
| null |
**Description:** Makes it more intuitive to pass filters to VectorStore Retrievers from `as_retriever`, by allowing for it to be passed as an argument separately from search_kwargs.
This is related to #7002, but I wouldn't resolve because that calls for taking all search arguments as direct keyword arguments, while this is currently only for filters. Other arguments seem to be mostly primitive types which aren't as unwieldy to pass as a dict- with the docstring it should hopefully be clearer how to pass those arguments.
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I_kwDOIPDwls5tjHIa
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How to pass multiple filters in db.get_relevant_documents()
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[] | null | 2 | 2023-08-05T20:03:46 | 2023-11-12T16:05:59 | 2023-11-12T16:05:59 |
NONE
| null |
I have three metadata: vehicle, color and city.
I want to retrieve chunks with filter -> vehicle = car, color = red and city = NY
All these conditions should be met in the retrieved chunks.
How can I do that?
results = db.get_relevant_documents(
query=query,
filter={"vehicle": "car", "color": "red", "city": "NY"},
)
But I am not getting desired results.
|
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Code understanding use case
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CONTRIBUTOR
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Code understanding docs
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I_kwDOIPDwls5tjByw
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Issue: Amazon Bedrock can't load my credentials when called from a Lambda function
|
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[] | null | 21 | 2023-08-05T18:57:43 | 2023-10-15T02:09:25 | null |
NONE
| null |
### Issue you'd like to raise.
Bedrock can't seem to load my credentials when used within a Lambda function. My AWS credentials were set up in my local environment using environment variables. When I use the bedrock class directly, it is able to load my credentials and my code runs smoothly. Here is the RetrievalQA function that utilizes the bedrock class:
```
def qa(query):
secrets = json.loads(get_secret())
kendra_index_id = secrets['kendra_index_id']
llm = Bedrock(model_id="amazon.titan-tg1-large", region_name='us-east-1')
llm.model_kwargs = {"maxTokenCount": 4096}
retriever = AmazonKendraRetriever(index_id=kendra_index_id)
prompt_template = """
{context}
{question} If you are unable to find the relevant article, respond 'I can't generate the needed content based on the context provided.'
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"])
chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
verbose=True,
chain_type_kwargs={
"prompt": PROMPT
}
)
return chain(query)
```
The above code runs without issues when used directly. However, when used within a lambda function, it fails. The scripts file used to build my lambda function uses the bedrock class as written above, however, I run into the `validation errors` when I invoke the Lambda function.
`
{
"errorMessage": "1 validation error for Bedrock\n__root__\n Could not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid. (type=value_error)",
"errorType": "ValidationError",
"requestId": "b772f236-f582-4308-8af5-b5a418d4327f",
"stackTrace": [
" File \"/var/task/main.py\", line 62, in handler\n response = qa(query)\n",
" File \"/var/task/main.py\", line 32, in qa\n llm = Bedrock(model_id=\"amazon.titan-tg1-large\", region_name='us-east-1',) #client=BEDROCK_CLIENT)\n",
" File \"/var/task/langchain/load/serializable.py\", line 74, in __init__\n super().__init__(**kwargs)\n",
" File \"pydantic/main.py\", line 341, in pydantic.main.BaseModel.__init__\n raise validation_error\n"
]
`
As clearly indicated by the error message, bedrock couldn't load credentials.
### Suggestion:
I have looked at the official documentation of the [bedrock class](https://github.com/langchain-ai/langchain/blob/b786335dd10902489f87a536ee074d747b6df370/libs/langchain/langchain/llms/bedrock.py#L51) but still do not understand why my code fails. Any help will be appreciated. @3coins @jasondotparse @hwchase17
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PR_kwDOIPDwls5XQXx2
| 8,799 |
rm nuclia dep
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PR_kwDOIPDwls5XQTye
| 8,798 |
bump 253
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PR_kwDOIPDwls5XQIB4
| 8,796 |
feat: expose Kendra result item id and document id as document metadata
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[] | null | 1 | 2023-08-05T14:52:16 | 2023-08-06T00:21:25 | 2023-08-06T00:21:25 |
CONTRIBUTOR
| null |
- Description: we expose Kendra result item id and document id as document metadata.
- Tag maintainer: @3coins @baskaryan
- Twitter handle: wilsonleao
**Why**
The result item id and document id might be used to keep track of the retrieved resources.
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I_kwDOIPDwls5tinvF
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BSHTMLLoader not working for urls
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[] | null | 2 | 2023-08-05T14:41:28 | 2023-08-06T11:42:45 | 2023-08-06T11:42:45 |
NONE
| null |
### System Info
BSHTMLLoader not working for urls
````
from langchain.document_loaders import BSHTMLLoader
url = "https://www.google.com"
loader = BSHTMLLoader({"url": url})
doc = loader.load()
````
I tried this one also not working
````
from langchain.document_loaders import BSHTMLLoader
url = "https://www.google.com"
loader = BSHTMLLoader({"url": url})
doc = loader.load()
````
### 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
```
from langchain.document_loaders import BSHTMLLoader
url = "https://www.example.com"
loader = BSHTMLLoader(url)
doc = loader.load()
```
### Expected behavior
It has respond html data of url. I believe BSHTMLLoader won't work with url and only work files(.html files).
|
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I_kwDOIPDwls5tihMK
| 8,794 |
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__ pydantic.error_wrappers.ValidationError: 1 validation error for Document"
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open
| false | null |
[] | null | 5 | 2023-08-05T13:36:08 | 2024-01-06T04:15:11 | null |
NONE
| null |
### System Info
Traceback:
docsearch.similarity_search(
File "/usr/local/lib/python3.9/site-packages/langchain/vectorstores/pinecone.py", line 162, in similarity_search
docs_and_scores = self.similarity_search_with_score(
File "/usr/local/lib/python3.9/site-packages/langchain/vectorstores/pinecone.py", line 136, in similarity_search_with_score
docs.append((Document(page_content=text, metadata=metadata), score))
File "/usr/local/lib/python3.9/site-packages/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for Document"
page_content str type expected (type=type_error.str)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
langchain pinecone similarity_search_with_score
docs.append((Document(page_content=text, metadata=metadata), score))
seem to raise error when page_content is not an string type
while when we wrote the document to the pinecone using pdf loader
maybe the loader produce something unexpected.
it should be a bug
### Expected behavior
No erro raised
|
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| 1,837,714,071 |
PR_kwDOIPDwls5XP4mE
| 8,793 |
Add new parameter forced_decoder_ids to OpenAIWhisperParserLocal + small bug fix
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[] | null | 2 | 2023-08-05T11:37:44 | 2023-08-12T11:15:42 | 2023-08-07T20:17:58 |
CONTRIBUTOR
| null |
- Description: new parameter forced_decoder_ids for OpenAIWhisperParserLocal to force input language, and enable optional translate mode. Usage example:
processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe")
#forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate")
loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParserLocal(lang_model="openai/whisper-medium",forced_decoder_ids=forced_decoder_ids))
- Issue #8792
- Tag maintainer: @rlancemartin, @eyurtsev
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5tiUkG
| 8,792 |
Add option to directly set input language for OpenAIWhisperParserLocal
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[] | null | 1 | 2023-08-05T11:36:13 | 2023-11-11T16:05:21 | 2023-11-11T16:05:21 |
CONTRIBUTOR
| null |
### Feature request
Add option to directly set input language for OpenAIWhisperParserLocal (in case incorrect autodetection (language="french", task = "transcribe") and optionally use translation mode of the whisper model (language="french", task = "translate")
### Motivation
I encountered situations where audio is split in chunks and therefore language autodetection was incorrect by OpenAIWhisperParserLocal
### Your contribution
I'll post PR.
|
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| 1,837,701,142 |
PR_kwDOIPDwls5XP16B
| 8,791 |
Fix: RecursiveUrlLoader is not working (#8367)
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[] | null | 6 | 2023-08-05T11:05:37 | 2023-08-10T22:59:52 | 2023-08-10T22:59:52 |
NONE
| null |
Fixes #8367
Before this fix:
```python
from langchain.document_loaders import RecursiveUrlLoader
docs = RecursiveUrlLoader(url="https://docs.python.org/3/").load()
print(len(docs))
```
It returns 0 docs
After this fix:
```python
from langchain.document_loaders import RecursiveUrlLoader
docs = RecursiveUrlLoader(url="https://docs.python.org/3/").load()
print(len(docs))
```
It returns 23 docs now.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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I_kwDOIPDwls5tiEW_
| 8,790 |
Issue: AzureOpenAI connection with SQL Database
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[] | null | 1 | 2023-08-05T08:30:33 | 2023-11-11T16:05:26 | 2023-11-11T16:05:26 |
NONE
| null |
### Issue you'd like to raise.
Hello,
I want achive SQL Database Agent, by using that I want to retrive data from the database and execute queries.
So for that I have tried,
```
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import AzureOpenAI
from langchain.agents.agent_types import AgentType
db = SQLDatabase.from_uri("mysql://user:pwd@localhost/db_name")
toolkit = SQLDatabaseToolkit(db=db, llm=AzureOpenAI(deployment_name="ABCD",model_name="text-davinci-003",temperature=0))
agent_executor = create_sql_agent(
llm=AzureOpenAI(deployment_name="ABCD",model_name="text-davinci-003",temperature=0),
toolkit=toolkit,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
agent_executor.run("Describe the XYZ table")
```
But into above code facing problem is that "**openai.error.InvalidRequestError: Invalid URL (POST /v1/openai/deployments/trailadvisor/completions)**". Same thing I can run using the OpenAI but getting error in AzureOpenAI.
Am i doing the correct approach, or any other way that using AzureOpenAI deployment we can achive the requirement?
### Suggestion:
_No response_
|
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| 1,837,646,215 |
I_kwDOIPDwls5tiEGH
| 8,789 |
"n" hyperparmeter doesn't work in ChatOpenAI
|
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[] | null | 1 | 2023-08-05T08:26:14 | 2023-08-07T13:40:06 | 2023-08-07T13:40:06 |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.250
Python=3.11.4
### Who can help?
@agola @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
llm = ChatOpenAI(
model="gpt-4",
temperature=0.0,
top_p=1.0,
n=20,
max_tokens=30
)
results = llm(chat_prompt.format_prompt(
inputs_description=vars["inputs_description"],
task_description=vars["task_description"],
criteria=vars["criteria"],
criteria_description=vars["criteria_description"],
eval_steps=vars["eval_steps"],
input=vars["input"],
input_type=vars["input_type"],
output=vars["output"],
output_type=vars["output_type"]
).to_messages())
```
### Expected behavior
I was expecting to get a response with 20 results, instead of just one since I set n=20. This is the behaviour when using Openai SDK.
|
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| 1,837,636,776 |
I_kwDOIPDwls5tiByo
| 8,788 |
Error while loading the Llama 2 70B chat model
|
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[] | null | 2 | 2023-08-05T07:59:48 | 2023-11-15T16:07:07 | 2023-11-15T16:07:06 |
NONE
| null |
### System Info
Hi!
While using Llama cpp in LangChain, I am trying to load the "llama-2-70b-chat" model. The code I am using is:
n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(
model_path="/Users/taimoorarif/Downloads/llama-2-70b-chat.ggmlv3.q4_0.bin",
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
callback_manager=callback_manager,
verbose=True,
n_ctx=2048
)
I am getting an error which I am attaching below. The same code works perfectly fine for the 13b model. Moreover, for the 65B model, the model keeps processing the prompt and does not return anything. Any help would be appreciated. Thanks!
<img width="1018" alt="Screenshot 2023-08-05 at 3 59 05 AM" src="https://github.com/langchain-ai/langchain/assets/56273879/db0fb151-3bdb-4518-b6f7-d58dca2af261">
### 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
.
### Expected behavior
.
|
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| 1,837,611,197 |
PR_kwDOIPDwls5XPjqF
| 8,787 |
#8786 Fixed: Callback handler disconnect in between
|
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[] | null | 1 | 2023-08-05T06:47:54 | 2023-08-06T22:11:45 | 2023-08-06T22:11:45 |
CONTRIBUTOR
| null |
Fixes for #8786 @agola11
- Description: The flow of callback is breaking till the last chain, as callbacks are missed in between chain along nested path. This will help get full trace and correlate parent child relationship in all nested chains.
- Issue: the issue #8786
- Dependencies: NA
- Tag maintainer: @agola11
- Twitter handle: Agarwal_Ankur
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I_kwDOIPDwls5th7M7
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RetrievalQA.from_chain_type: callbacks are not called for all nested chains
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[] | null | 1 | 2023-08-05T06:43:10 | 2023-11-11T16:05:36 | 2023-11-11T16:05:36 |
CONTRIBUTOR
| null |
### System Info
langchain: 0.0.252
python: 3.10.12
@agola11
### Who can help?
@agola11 please take a look,
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a callback handler LogHandler for on_chain_start, on_chain_start, on_chat_model_start and log run_id, parent_run_id in each of them
2. Create a retrival chain and add this LogHandler
3. Add this LogHandler to llm as well
4. When running the chain, one of nested chain is not logged in between, because callbacks are not passed to that chain
### Expected behavior
All the nested chains should have callbacks defined.
|
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PR_kwDOIPDwls5XPa5c
| 8,784 |
Update links on QA Use Case docs
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[] | null | 1 | 2023-08-05T04:33:31 | 2023-08-06T00:30:56 | 2023-08-06T00:30:56 |
CONTRIBUTOR
| null |
- Description: 2 links were not working on Question Answering Use Cases documentation page. Hence, changed them to nearest useful links,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @baskaryan,
- Twitter handle: NA
<!-- Thank you for contributing to LangChain!
Replace this 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 you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5XPW66
| 8,783 |
Add missing test for retrievers self_query
|
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[] | null | 1 | 2023-08-05T03:23:33 | 2023-08-06T00:32:34 | 2023-08-06T00:31:41 |
CONTRIBUTOR
| null |
# What
- Add missing test for retrievers self_query
- Add missing import validation
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add missing test for retrievers self_query
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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| 1,837,527,287 |
I_kwDOIPDwls5thnD3
| 8,782 |
NLTK alternatives option
|
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[] | null | 2 | 2023-08-05T02:22:42 | 2023-08-07T00:40:13 | 2023-08-07T00:40:13 |
NONE
| null |
### Feature request
if possible to provide NLTK tokenizer alternative options? such as spaCy, openNLP...
### Motivation
NLTK issues:
```
Resource averaged_perceptron_tagger not found.
Please use the NLTK Downloader to obtain the resource:
>>> import nltk
>>> nltk.download('averaged_perceptron_tagger')
For more information see: https://www.nltk.org/data.html
```
### Your contribution
https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/split_by_token#spacy
NLTK issues: https://stackoverflow.com/questions/49546253/attributeerror-module-nltk-has-no-attribute-download
|
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| 1,837,473,798 |
PR_kwDOIPDwls5XPG-c
| 8,781 |
Same Project for Eval Runs
|
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[] | null | 1 | 2023-08-05T00:33:45 | 2023-08-05T00:51:50 | 2023-08-05T00:51:49 |
COLLABORATOR
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| 1,837,465,382 |
I_kwDOIPDwls5thX8m
| 8,780 |
inconsistent agent answer
|
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[] | null | 4 | 2023-08-05T00:19:15 | 2023-11-13T16:06:51 | 2023-11-13T16:06:50 |
NONE
| null |
### System Info
i'm using newest langchain, python 3
so i try agent but got inconsistent answer, this is the output
the true answer is "Jalan lori tidak termasuk jalan umum". i try using retrieval with prompt without agent and got true answer too. so sometimes agent has inconsistent answer. how to prevent this?
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
i put example code below
my Agent Prompt
How To Call my Agent
```
agent_executor = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
return_direct=True,
agent_kwargs={
'prefix':constant.PREFIX,
'format_instructions':constant.FORMAT_INSTRUCTIONS,
'suffix':constant.SUFFIX
}
)
result = agent_executor.run(question)
return result
```
### Expected behavior
got true answer same with the documents
|
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| 1,837,433,065 |
PR_kwDOIPDwls5XO-oa
| 8,779 |
Add integration to collect feedback on documentation
|
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[] | null | 5 | 2023-08-04T23:13:03 | 2023-11-02T00:47:30 | 2023-11-02T00:47:29 |
NONE
| null |
## Background
LangChain has become widely popular and adopted by developers. The documentation of any developer product plays a crucial role in the developer experience. Collecting feedback on the LangChain documentation will further improve the developer experience and help the community.
## Description
[Docsly](https://docsly.dev) is a feedback tool crafted for developer documentation. It lets the readers leave contextual feedback (comments) on the documentation website and helps the maintainer by showing the feedback exactly where the user left it.
The feedback feature (like/dislike) gives a general sense of whether users are finding a particular page helpful.
- Dependencies: Installed `@docsly/react` in the Docusaurus project
- Tag maintainer: @baskaryan
- Twitter handle: [sun_anshuman](https://twitter.com/sun_anshuman)
### Example project for preview
[Clerk](https://clerk.com/docs), [Tigris](https://www.tigrisdata.com/docs/), and [Dyte](https://docs.dyte.io) are using docsly in production already.
### Todo
- [ ] Sign up on [docsly.dev ](https://docsly.dev/dashboard)for a fresh account for maintainers
### Cost implications
[Docsly](https://docsly.dev) is free to use for open-source projects.
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| 1,837,419,453 |
PR_kwDOIPDwls5XO7tx
| 8,778 |
Fix Recursive URL Loader
|
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[] | null | 3 | 2023-08-04T22:49:34 | 2023-08-07T15:25:55 | 2023-08-07T15:25:55 |
COLLABORATOR
| null |
Previously we wouldn't follow paths like
```
relative/to/current
```
or
```
https://www.samedomain.com/i/am/relative/path
```
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OpenAIEmbeddings error trying to load chroma from Confluence
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"url": "https://api.github.com/users/supsailor",
"html_url": "https://github.com/supsailor",
"followers_url": "https://api.github.com/users/supsailor/followers",
"following_url": "https://api.github.com/users/supsailor/following{/other_user}",
"gists_url": "https://api.github.com/users/supsailor/gists{/gist_id}",
"starred_url": "https://api.github.com/users/supsailor/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/supsailor/subscriptions",
"organizations_url": "https://api.github.com/users/supsailor/orgs",
"repos_url": "https://api.github.com/users/supsailor/repos",
"events_url": "https://api.github.com/users/supsailor/events{/privacy}",
"received_events_url": "https://api.github.com/users/supsailor/received_events",
"type": "User",
"site_admin": false
}
|
[] |
closed
| false | null |
[] | null | 1 | 2023-08-04T22:33:52 | 2023-08-06T09:36:55 | 2023-08-05T12:29:59 |
NONE
| null |
### System Info
Platform: Apple M1 Pro
Python version: Python 3.9.6
Dependencies:
aiohttp==3.8.5
aiosignal==1.3.1
anyio==3.7.1
async-timeout==4.0.2
atlassian-python-api==3.40.0
attrs==23.1.0
backoff==2.2.1
beautifulsoup4==4.12.2
blobfile==2.0.2
certifi==2023.7.22
charset-normalizer==3.2.0
chroma-hnswlib==0.7.2
chromadb==0.4.5
click==8.1.6
coloredlogs==15.0.1
dataclasses-json==0.5.14
Deprecated==1.2.14
exceptiongroup==1.1.2
fastapi==0.99.1
filelock==3.12.2
flatbuffers==23.5.26
frozenlist==1.4.0
h11==0.14.0
httptools==0.6.0
humanfriendly==10.0
idna==3.4
importlib-resources==6.0.0
langchain==0.0.252
langsmith==0.0.18
lxml==4.9.3
marshmallow==3.20.1
monotonic==1.6
mpmath==1.3.0
multidict==6.0.4
mypy-extensions==1.0.0
numexpr==2.8.4
numpy==1.25.2
oauthlib==3.2.2
onnxruntime==1.15.1
openai==0.27.8
openapi-schema-pydantic==1.2.4
overrides==7.3.1
packaging==23.1
Pillow==10.0.0
posthog==3.0.1
protobuf==4.23.4
pulsar-client==3.2.0
pycryptodomex==3.18.0
pydantic==1.10.12
PyPika==0.48.9
pytesseract==0.3.10
python-dateutil==2.8.2
python-dotenv==1.0.0
PyYAML==6.0.1
regex==2023.6.3
requests==2.31.0
requests-oauthlib==1.3.1
six==1.16.0
sniffio==1.3.0
soupsieve==2.4.1
SQLAlchemy==2.0.19
starlette==0.27.0
sympy==1.12
tenacity==8.2.2
tiktoken==0.4.0
tokenizers==0.13.3
tqdm==4.65.0
typing-inspect==0.9.0
typing_extensions==4.7.1
urllib3==1.25.11
uvicorn==0.23.2
uvloop==0.17.0
watchfiles==0.19.0
websockets==11.0.3
wrapt==1.15.0
yarl==1.9.2
zipp==3.16.2
### 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
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Intro:
I have some code to load my vector from confluence:
```python
from langchain.document_loaders import ConfluenceLoader
from langchain.text_splitter import CharacterTextSplitter, TokenTextSplitter, RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
import pytesseract
import os
pytesseract.pytesseract.tesseract_cmd = "/opt/homebrew/bin/tesseract"
os.environ["OPENAI_API_KEY"] = "my_openai_key"
CONFLUENCE_URL = "confluence_path"
CONFLUENCE_TOKEN = "some_token"
CONFLUENCE_SPACE = "confluence_space"
PERSIST_DIRECTORY = "./chroma_db/"
def confluence_vector(force_reload: bool = False):
# Init Openai Embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1, max_retries=10)
# Check persist exists
if PERSIST_DIRECTORY and os.path.exists(PERSIST_DIRECTORY) and not force_reload:
vector = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=embeddings)
print("Chroma loaded")
return vector
else:
loader = ConfluenceLoader(url=CONFLUENCE_URL,
token=CONFLUENCE_TOKEN)
documents = loader.load(
space_key=CONFLUENCE_SPACE, limit=10, max_pages=1000
)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
#text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=10)
#texts = text_splitter.split_documents(texts)
print(texts)
vector = Chroma.from_documents(documents=texts, embedding=embeddings, persist_directory=PERSIST_DIRECTORY)
vector.persist()
print("Chroma builded")
return vector
if __name__ == '__main__':
confluence_vector(force_reload=True)
```
Then after run I have an exception:
```
Traceback (most recent call last):
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/connection.py", line 159, in _new_conn
conn = connection.create_connection(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/util/connection.py", line 84, in create_connection
raise err
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/util/connection.py", line 74, in create_connection
sock.connect(sa)
TimeoutError: [Errno 60] Operation timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 670, in urlopen
httplib_response = self._make_request(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 381, in _make_request
self._validate_conn(conn)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 978, in _validate_conn
conn.connect()
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/connection.py", line 309, in connect
conn = self._new_conn()
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/connection.py", line 171, in _new_conn
raise NewConnectionError(
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x1357d2640>: Failed to establish a new connection: [Errno 60] Operation timed out
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/requests/adapters.py", line 486, in send
resp = conn.urlopen(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/connectionpool.py", line 726, in urlopen
retries = retries.increment(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/urllib3/util/retry.py", line 446, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x1357d2640>: Failed to establish a new connection: [Errno 60] Operation timed out'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/main.py", line 47, in <module>
confluence_vector(force_reload=True)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/main.py", line 40, in confluence_vector
vector = Chroma.from_documents(documents=texts, embedding=embeddings, persist_directory=PERSIST_DIRECTORY)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/vectorstores/chroma.py", line 603, in from_documents
return cls.from_texts(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/vectorstores/chroma.py", line 567, in from_texts
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/vectorstores/chroma.py", line 187, in add_texts
embeddings = self._embedding_function.embed_documents(texts)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 472, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 325, in _get_len_safe_embeddings
encoding = tiktoken.encoding_for_model(model_name)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tiktoken/model.py", line 75, in encoding_for_model
return get_encoding(encoding_name)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tiktoken/registry.py", line 63, in get_encoding
enc = Encoding(**constructor())
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tiktoken_ext/openai_public.py", line 64, in cl100k_base
mergeable_ranks = load_tiktoken_bpe(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tiktoken/load.py", line 116, in load_tiktoken_bpe
contents = read_file_cached(tiktoken_bpe_file)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tiktoken/load.py", line 39, in read_file_cached
return read_file(blobpath)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tiktoken/load.py", line 24, in read_file
resp = requests.get(blobpath)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/requests/api.py", line 73, in get
return request("get", url, params=params, **kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/requests/sessions.py", line 589, in request
resp = self.send(prep, **send_kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/requests/sessions.py", line 703, in send
r = adapter.send(request, **kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/requests/adapters.py", line 519, in send
raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='openaipublic.blob.core.windows.net', port=443): Max retries exceeded with url: /encodings/cl100k_base.tiktoken (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x1357d2640>: Failed to establish a new connection: [Errno 60] Operation timed out'))
```
After that first I think about - is the network connection to the cl100k_base.tiktoken file
For double check I tried to wget this file, the output was like this (ip's and domains are fake):
```
Resolving proxy (someproxy)... 10.0.0.0
Connecting to someproxy (someproxy)|10.0.0.0|:3131... connected.
Proxy request sent, awaiting response... 200 OK
Length: 1681126 (1.6M) [application/octet-stream]
Saving to: ‘cl100k_base.tiktoken’
cl100k_base.tiktoken 100%[=================================================================================================>] 1.60M --.-KB/s in 0.1s
2023-08-05 01:18:36 (14.4 MB/s) - ‘cl100k_base.tiktoken’ saved [1681126/1681126]
```
Ok then, for full transparency I tried to override .tiktoken file location in library files
```
file location: /Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tiktoken_ext/openai_public.py
```
```python
...
def cl100k_base():
mergeable_ranks = load_tiktoken_bpe(
"/Users/pobabi1/PycharmProjects/langchainConflWithGPT/cl100k_base.tiktoken"
)
special_tokens = {
ENDOFTEXT: 100257,
FIM_PREFIX: 100258,
FIM_MIDDLE: 100259,
FIM_SUFFIX: 100260,
ENDOFPROMPT: 100276,
}
return {
"name": "cl100k_base",
"pat_str": r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""",
"mergeable_ranks": mergeable_ranks,
"special_tokens": special_tokens,
}
...
```
The file was accepted by lib and then I have this kind of exception:
```
Traceback (most recent call last):
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/main.py", line 47, in <module>
confluence_vector(force_reload=True)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/main.py", line 40, in confluence_vector
vector = Chroma.from_documents(documents=texts, embedding=embeddings, persist_directory=PERSIST_DIRECTORY)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/vectorstores/chroma.py", line 603, in from_documents
return cls.from_texts(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/vectorstores/chroma.py", line 567, in from_texts
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/vectorstores/chroma.py", line 187, in add_texts
embeddings = self._embedding_function.embed_documents(texts)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 472, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 358, in _get_len_safe_embeddings
response = embed_with_retry(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 107, in embed_with_retry
return _embed_with_retry(**kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
File "/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py", line 438, in result
return self.__get_result()
File "/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py", line 390, in __get_result
raise self._exception
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 104, in _embed_with_retry
response = embeddings.client.create(**kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/openai/api_resources/embedding.py", line 33, in create
response = super().create(*args, **kwargs)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/openai/api_requestor.py", line 298, in request
resp, got_stream = self._interpret_response(result, stream)
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/openai/api_requestor.py", line 700, in _interpret_response
self._interpret_response_line(
File "/Users/pobabi1/PycharmProjects/langchainConflWithGPT/venv/lib/python3.9/site-packages/openai/api_requestor.py", line 763, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: Invalid URL (GET /embeddings)
```
For double-check this moment I tried to use /embeddings API from curl with default request and it fully works:
```
curl https://api.openai.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"input": "Your text string goes here",
"model": "text-embedding-ada-002"
}'
```
```
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [
-0.006968617,
-0.0052718227,
0.011901081,
-0.024984878,
-0.024554798,
0.039674748,
-0.010140447,
-0.009481888,
-0.013245076,
-0.010006047,
-0.011511322,
0.0077750143,
-0.014138834,
0.0077414145,
0.010147166,
-0.0050131036,
0.022942005,
-0.0016287547,
0.01498555,
-0.010281566,
0.004858544,
0.012472278,
0.004835024,
0.010819164,
-0.006615818,
-0.00035090884,
0.0055708615,
-0.012613398,
0.016369866,
0.0044587054,
0.006639338,
-0.0071164565,
-0.01516027,
-0.006625898,
-0.0185337,
0.0040622265,
0.0031785495,
-0.018963777,
0.03032054,
-0.0075263754,
0.0081177335,
0.009481888,
-0.0011029163,
-0.00044225855,
-0.008776291,
-0.028600225,
0.0028996705,
0.0092802895,
0.016463945,
0.006431019,
0.019380417,
0.014730191,
-0.025643434,
-0.009911967,
-0.003311269,
0.014837711,
-0.03690612,
0.014058193,
0.013446676,
0.0043948656,
-0.004841744,
0.0032171893,
-0.011040923,
0.002407432,
-0.014138834,
-0.018587459,
-0.024702638,
-0.0035011084,
0.01803642,
0.008480612,
0.034083728,
0.01518715,
0.014071633,
-0.006672938,
0.022041528,
0.022122167,
0.005359182,
0.020885691,
0.008312613,
-0.011948121,
0.0076540546,
-0.025065517,
0.010684765,
0.016786505,
0.009475169,
-0.000614458,
-0.0028929505,
0.028949665,
-0.03292789,
-0.03045494,
0.0076607745,
0.020200253,
0.009972447,
0.018063301,
-0.005668301,
0.008285733,
-0.010281566,
0.020361533,
0.022283446,
-0.034621324,
0.005426382,
0.013318996,
-0.017324103,
-0.008137893,
-0.029782942,
0.017713862,
0.021503929,
-0.016759625,
0.016316107,
0.0038203073,
-0.029137824,
0.012143,
-0.01815738,
-0.017337542,
0.0030122302,
0.016894024,
0.010778844,
-0.014031313,
-0.021436729,
-0.03026678,
0.009959007,
0.012687318,
0.03018614,
-0.017754182,
0.026436392,
0.019568576,
-0.0058598206,
-0.031315096,
0.0067770975,
-0.0019471135,
0.037443716,
0.024447279,
0.010765404,
0.014864591,
-0.013157717,
0.022821045,
-0.03604596,
0.0025317515,
-0.027417509,
-0.012808277,
0.012895637,
0.026960552,
0.0029114303,
-0.0031382297,
0.012613398,
0.011087963,
-0.0022898323,
-0.0105167655,
0.021490488,
-0.013124117,
-0.002716551,
0.0041126264,
0.0049727834,
-0.01495867,
-0.010563805,
0.017203143,
0.0052919826,
0.008023653,
0.0019823934,
-0.0299711,
0.011007324,
0.009300449,
0.016894024,
-0.014649551,
0.033949327,
0.05112559,
-0.0037195077,
0.0053491024,
-0.0059236605,
-0.0082790125,
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"model": "text-embedding-ada-002-v2",
"usage": {
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"total_tokens": 5
}
}
```
Please. help me out, I'm totally frustrated, because the embedding method was /POST type, but after overriding library calls /GET embeddings
### Expected behavior
A few weeks ago this code works correct, and I don't mind where the problem can be
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| 1,837,354,723 |
PR_kwDOIPDwls5XOt1L
| 8,776 |
Async RetryOutputParser, RetryWithErrorOutputParser and OutputFixingParser
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[] | null | 1 | 2023-08-04T21:14:46 | 2023-08-07T21:42:49 | 2023-08-07T21:42:48 |
CONTRIBUTOR
| null |
Added async parsing functions for RetryOutputParser, RetryWithErrorOutputParser and OutputFixingParser.
The async parse functions call the arun methods of the used LLMChains.
Fix for #7989
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Just install Langchain and getting Import error from typing extensions
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[] | null | 3 | 2023-08-04T21:09:37 | 2023-11-10T16:05:47 | 2023-11-10T16:05:46 |
NONE
| null |
### System Info
Latest versions of langchain at the current time.
### 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
Literally just pip installed lang-chain and getting the error: ImportError: cannot import name 'dataclass_transform' from 'typing_extensions'. I tried upgrading pydantic and typing_extensions which only make the problem worse.
### Expected behavior
No import errors.
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Extend the StreamlitChatMessageHistory docs with a fuller example and…
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CONTRIBUTOR
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Add more details to the [notebook for StreamlitChatMessageHistory](https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history), including a link to a [running example app](https://langchain-st-memory.streamlit.app/).
Original PR: https://github.com/langchain-ai/langchain/pull/8497
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PR_kwDOIPDwls5XOmKV
| 8,773 |
rm sklearn links
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[] | null | 1 | 2023-08-04T20:48:01 | 2023-08-04T21:28:01 | 2023-08-04T21:28:00 |
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PR_kwDOIPDwls5XOf-r
| 8,772 |
rfc: runnable locals
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[] | null | 7 | 2023-08-04T20:20:22 | 2023-08-11T00:12:18 | 2023-08-11T00:12:18 |
COLLABORATOR
| null |
add concept of locals, which is state to pass around in RunnableSequence, and specific runnable for read/write to locals. cc @jacoblee93 @nfcampos
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PR_kwDOIPDwls5XOYQC
| 8,771 |
Fix invalid escape sequence warnings
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[] | null | 1 | 2023-08-04T19:45:53 | 2023-08-07T04:56:26 | 2023-08-07T00:01:18 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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Description: The lines I have changed looks like incorrectly escaped for regex. In python 3.11, I receive DeprecationWarning for these lines.
You don't see any warnings unless you explicitly run python with `-W always::DeprecationWarning` flag. So, this is my attempt to fix it.
Here are the warnings from log files:
```
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:919: DeprecationWarning: invalid escape sequence '\s'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:918: DeprecationWarning: invalid escape sequence '\s'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:917: DeprecationWarning: invalid escape sequence '\s'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:916: DeprecationWarning: invalid escape sequence '\c'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:903: DeprecationWarning: invalid escape sequence '\*'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:804: DeprecationWarning: invalid escape sequence '\*'
/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py:804: DeprecationWarning: invalid escape sequence '\*'
```
cc @baskaryan
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| 1,837,223,672 |
PR_kwDOIPDwls5XORE5
| 8,770 |
Create ChatAnyscale
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[] | null | 7 | 2023-08-04T19:12:55 | 2023-08-10T09:45:57 | 2023-08-07T20:21:06 |
CONTRIBUTOR
| null |
- Description: Adds the ChatAnyscale class with llama-2 7b, llama-2 13b, and llama-2 70b on [Anyscale Endpoints](https://app.endpoints.anyscale.com/)
- It inherits from ChatOpenAI and requires openai (probably unnecessary but it made for a quick and easy implementation)
- Inspired by https://github.com/langchain-ai/langchain/pull/8434 (@kylehh and @baskaryan )
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PR_kwDOIPDwls5XOGtp
| 8,769 |
add documentation for serializer function
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[] | null | 1 | 2023-08-04T18:29:24 | 2023-08-04T18:52:21 | 2023-08-04T18:39:41 |
CONTRIBUTOR
| null |
Description: Added necessary documentation for serializer functions
@baskaryan
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PR_kwDOIPDwls5XN2to
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Improving the text of the invalid tool to list the available tools.
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[] | null | 1 | 2023-08-04T17:19:20 | 2023-08-06T01:09:33 | 2023-08-06T01:09:32 |
CONTRIBUTOR
| null |
Description: When using a ReAct Agent with tools and no tool is found, the InvalidTool gets called. Previously it just asked for a different action, but I've found that if you list the available actions it improves the chances of getting a valid action in the next round. I've added a UnitTest for it also.
@hinthornw
|
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I_kwDOIPDwls5tf4Mn
| 8,766 |
LlamaCppEmbeddings does not work with n_gpu_layers
|
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[] | null | 2 | 2023-08-04T16:52:30 | 2023-11-10T16:05:52 | 2023-11-10T16:05:51 |
NONE
| null |
### System Info
I am used langchain to get a text embedding with the next code:
```
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/alloc/data/fury_mld-nlp-search/ggml-model-q4_0.bin",n_gpu_layers=None)
embedding= llama.embed_documents(["Thi is a text"])
```
The problem is the prediction takes a long time. For this reason, I try to to use a GPU ''Tesla V100-SXM2-16GB''. When i use the GPU layer, example
```
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/alloc/data/fury_mld-nlp-search/ggml-model-q4_0.bin",n_gpu_layers=1)
```
I get the follows:
```
Exception ignored in: <function Llama.__del__ at 0x7f29f59cab80>
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/llama_cpp/llama.py", line 978, in __del__
if self.ctx is not None:
AttributeError: 'Llama' object has no attribute 'ctx'
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Input In [9], in <cell line: 1>()
----> 1 llama = LlamaCppEmbeddings(model_path="/alloc/data/fury_mld-nlp-search/ggml-model-q4_0.bin",n_gpu_layers=1)
File /usr/local/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for LlamaCppEmbeddings
__root__
Could not load Llama model from path: /alloc/data/fury_mld-nlp-search/ggml-model-q4_0.bin. Received error __init__() got an unexpected keyword argument 'n_gpu_layers' (type=value_error)
```
I am using the versions llama-cpp-python==0.1.48 y langachain=0.0.252
Some Idea?
### 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
```
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/alloc/data/fury_mld-nlp-search/ggml-model-q4_0.bin",n_gpu_layers=1)
```
### Expected behavior
```
Exception ignored in: <function Llama.__del__ at 0x7f29f59cab80>
Traceback (most recent call last):
File "/usr/local/lib/python3.9/site-packages/llama_cpp/llama.py", line 978, in __del__
if self.ctx is not None:
AttributeError: 'Llama' object has no attribute 'ctx'
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Input In [9], in <cell line: 1>()
----> 1 llama = LlamaCppEmbeddings(model_path="/alloc/data/fury_mld-nlp-search/ggml-model-q4_0.bin",n_gpu_layers=1)
File /usr/local/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for LlamaCppEmbeddings
__root__
Could not load Llama model from path: /alloc/data/fury_mld-nlp-search/ggml-model-q4_0.bin. Received error __init__() got an unexpected keyword argument 'n_gpu_layers' (type=value_error)
```
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| 8,765 |
Updates fireworks
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[] | null | 1 | 2023-08-04T16:23:25 | 2023-08-04T17:32:22 | 2023-08-04T17:32:22 |
CONTRIBUTOR
| null |
- Description: Updates to Fireworks Documentation,
- Issue: N/A,
- Dependencies: N/A,
- Tag maintainer: @rlancemartin,
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I_kwDOIPDwls5tfq6j
| 8,764 |
PlanAndExecute Agent Error: langchain.tools.base.ToolException: Too many arguments to single-input
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[] | null | 1 | 2023-08-04T16:10:24 | 2023-11-10T16:05:56 | 2023-11-10T16:05:56 |
NONE
| null |
### System Info
Hello,
I am trying to use PlanAndExecute Agent... and getting error langchain.tools.base.ToolException: Too many arguments to single-input.
I am using SQLDatabaseToolkit & a custom tool .
Let me know if i need to modify in the below code...
```
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
tools = [
*toolkit.get_tools(),
send_email_tool
]
planner = load_chat_planner(llm)
executor = load_agent_executor(llm, tools, verbose=True)
agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)
agent.run(
"Find total number of orders & total number of customers and send an email"
)
```
Custom tool..
```
def send_email(inputs: str):
print('sending Email.....')
return 'Sent email....'
send_email_tool = Tool(
name='Send Email',
func=send_email,
description="Useful for sending emails"
)
```
The agent runs... in the last step it is failing with the below Exception...
```
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\rraghuna\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\tools\base.py", line 476, in _to_args_and_kwargs
raise ToolException(
langchain.tools.base.ToolException: Too many arguments to single-input tool Send Email. Args: ['Database Summary', 'Here is a summary of the data:\n\nTotal number of orders: 16\nTotal number of customers: 3']
```
### 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
Steps listed above
### Expected behavior
Expected behaviour is it should work fine
|
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| 8,763 |
Internal code deprecation API
|
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[] | null | 5 | 2023-08-04T16:08:16 | 2023-08-08T19:42:24 | 2023-08-08T19:42:22 |
COLLABORATOR
| null |
Proposal for an internal API to deprecate LangChain code.
This PR is heavily based on: https://github.com/matplotlib/matplotlib/blob/main/lib/matplotlib/_api/deprecation.py
This PR only includes deprecation functionality (no renaming etc.).
Additional functionality can be added on a need basis (e.g., renaming parameters), but best to roll out as an MVP to test this
out.
DeprecationWarnings are ignored by default. We can change the policy for the deprecation warnings, but we'll need to make sure we're not creating noise for users due to internal code invoking deprecated functionality.
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[] | null | 3 | 2023-08-04T15:55:46 | 2023-08-09T11:34:24 | 2023-08-09T11:34:23 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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 you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,836,952,406 |
PR_kwDOIPDwls5XNWYJ
| 8,761 |
update rss doc
|
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| 1,836,920,836 |
PR_kwDOIPDwls5XNPhU
| 8,760 |
Revert "add filter to sklearn vector store functions (#8113)"
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| 1,836,902,740 |
PR_kwDOIPDwls5XNLlC
| 8,759 |
bump 252
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| 1,836,897,204 |
PR_kwDOIPDwls5XNKX5
| 8,758 |
JsonOutputFunctionParser: Fix mutation in place bug
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[] | null | 1 | 2023-08-04T14:48:10 | 2023-08-07T18:32:47 | 2023-08-07T18:32:46 |
COLLABORATOR
| null |
Fixes mutation in place in the JsonOutputFunctionParser. This causes issues
when trying to re-use the original AI message.
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| 1,836,813,439 |
I_kwDOIPDwls5te4x_
| 8,757 |
ValueError: Received invalid destination chain name for MultiRetrievalQAChain
|
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[] | null | 2 | 2023-08-04T14:06:30 | 2023-11-10T16:06:02 | 2023-11-10T16:06:01 |
NONE
| null |
### System Info
langchain 0.0.251
Python 3.10.9
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have retirevers info like below
[{'name': 'Information of requirements from REQ1 to REQ1.1.2',
'description': "Good for gathering requirements statements for the REQ's present in this list ['REQ1', 'REQ1.1', 'REQ1.1.1', 'REQ1.1.2'].",
'retriever': VectorStoreRetriever(tags=['FAISS'], metadata=None, vectorstore=<langchain.vectorstores.faiss.FAISS object at 0x0000017A8FFAF2E0>, search_type='similarity', search_kwargs={'k': 4, 'max_source': 4})},
{'name': 'Information of requirements from REQ2 to REQ3.1.1.1',
'description': "Good for gathering requirements statements for the REQ's present in this list ['REQ2', 'REQ2.1', 'REQ2.2', 'REQ2.2.1', 'REQ2.2.1.1', 'REQ2.3', 'REQ2.4', 'REQ2.4.1', 'REQ2.4.2', 'REQ2.4.3', 'REQ2.5', 'REQ2.5.1', 'REQ2.5.2', 'REQ2.5.2.1', 'REQ2.5.2.2', 'REQ2.5.2.3', 'REQ2.5.3', 'REQ2.6', 'REQ2.6.1', 'REQ2.6.2', 'REQ2.6.2.1', 'REQ2.6.2.2', 'REQ3', 'REQ3.1', 'REQ3.1.1', 'REQ3.1.1.1'].",
'retriever': VectorStoreRetriever(tags=['FAISS'], metadata=None, vectorstore=<langchain.vectorstores.faiss.FAISS object at 0x0000017A8FFAF7C0>, search_type='similarity', search_kwargs={'k': 4, 'max_source': 4})},
{'name': 'Information of requirements from REQ3.1.2 to REQ5.1.2',
'description': "Good for gathering requirements statements for the REQ's present in this list ['REQ3.1.2', 'REQ3.2', 'REQ3.2.1', 'REQ3.2.1.1', 'REQ3.2.1.2', 'REQ3.2.1.3', 'REQ3.2.2', 'REQ3.2.2.1', 'REQ3.2.2.2', 'REQ3.2.2.3', 'REQ3.2.2.4', 'REQ3.2.3', 'REQ3.3', 'REQ3.3.1', 'REQ3.3.2', 'REQ4', 'REQ4.1', 'REQ4.2', 'REQ5', 'REQ5.1', 'REQ5.1.1', 'REQ5.1.1.1', 'REQ5.1.1.2', 'REQ5.1.2'].",
'retriever': VectorStoreRetriever(tags=['FAISS'], metadata=None, vectorstore=<langchain.vectorstores.faiss.FAISS object at 0x0000017A8FFAF0A0>, search_type='similarity', search_kwargs={'k': 4, 'max_source': 4})},
{'name': 'Information of requirements from REQ5.2 to REQ7.4.1.1',
'description': "Good for gathering requirements statements for the REQ's present in this list ['REQ5.2', 'REQ5.3', 'REQ5.3.1', 'REQ5.3.2', 'REQ5.3.3', 'REQ5.3.4', 'REQ5.3.5', 'REQ5.3.5.1', 'REQ5.3.5.2', 'REQ6', 'REQ6.1', 'REQ6.2', 'REQ7', 'REQ7.1', 'REQ7.2', 'REQ7.2.1', 'REQ7.2.1.1', 'REQ7.2.1.2', 'REQ7.3', 'REQ7.3.1', 'REQ7.3.2', 'REQ7.4', 'REQ7.4.1', 'REQ7.4.1.1'].",
'retriever': VectorStoreRetriever(tags=['FAISS'], metadata=None, vectorstore=<langchain.vectorstores.faiss.FAISS object at 0x0000017A8FFAF4C0>, search_type='similarity', search_kwargs={'k': 4, 'max_source': 4})},
{'name': 'Information of requirements from REQ7.5 to REQ8.5.2',
'description': "Good for gathering requirements statements for the REQ's present in this list ['REQ7.5', 'REQ7.5.1', 'REQ7.5.2', 'REQ7.6', 'REQ7.6.1', 'REQ7.7', 'REQ7.7.1', 'REQ8', 'REQ8.1', 'REQ8.2', 'REQ8.2.1', 'REQ8.2.1.1', 'REQ8.2.1.2', 'REQ8.3', 'REQ8.3.1', 'REQ8.3.2', 'REQ8.3.3', 'REQ8.4', 'REQ8.4.1', 'REQ8.4.1.1', 'REQ8.5', 'REQ8.5.1', 'REQ8.5.2'].",
'retriever': VectorStoreRetriever(tags=['FAISS'], metadata=None, vectorstore=<langchain.vectorstores.faiss.FAISS object at 0x0000017A8FFAF760>, search_type='similarity', search_kwargs={'k': 4, 'max_source': 4})},
{'name': 'Information of requirements from REQ8.6 to REQ9.5.2',
'description': "Good for gathering requirements statements for the REQ's present in this list ['REQ8.6', 'REQ8.6.1', 'REQ8.7', 'REQ8.7.1', 'REQ8.8', 'REQ8.8.1', 'REQ8.9', 'REQ8.9.1', 'REQ9', 'REQ9.1', 'REQ9.2', 'REQ9.2.1', 'REQ9.2.1.1', 'REQ9.2.1.2', 'REQ9.3', 'REQ9.3.1', 'REQ9.3.2', 'REQ9.3.3', 'REQ9.4', 'REQ9.4.1', 'REQ9.4.1.1', 'REQ9.5', 'REQ9.5.1', 'REQ9.5.2'].",
'retriever': VectorStoreRetriever(tags=['FAISS'], metadata=None, vectorstore=<langchain.vectorstores.faiss.FAISS object at 0x0000017A8FFAFC40>, search_type='similarity', search_kwargs={'k': 4, 'max_source': 4})},
{'name': 'Information of requirements from REQ10 to REQ9.7.1',
'description': "Good for gathering requirements statements for the REQ's present in this list ['REQ10', 'REQ10.1', 'REQ10.1.1', 'REQ10.2', 'REQ10.2.1', 'REQ10.2.2', 'REQ10.3', 'REQ10.3.1', 'REQ10.3.1.1', 'REQ10.3.1.2', 'REQ10.3.2', 'REQ10.4', 'REQ10.4.1', 'REQ10.4.2', 'REQ10.5', 'REQ10.5.1', 'REQ10.5.2', 'REQ10.5.3', 'REQ10.5.3.1', 'REQ10.5.3.2', 'REQ10.6', 'REQ10.6.1', 'REQ10.6.2', 'REQ9.6', 'REQ9.6.1', 'REQ9.7', 'REQ9.7.1'].",
'retriever': VectorStoreRetriever(tags=['FAISS'], metadata=None, vectorstore=<langchain.vectorstores.faiss.FAISS object at 0x0000017A8FFAF790>, search_type='similarity', search_kwargs={'k': 4, 'max_source': 4})},
I got error for query regarding REQ 5.1.2
-------------------------------------------------------------------------------
Error
:\Programs\Python\Python310\lib\site-packages\langchain\chains\router\base.py", line 106, in _call
raise ValueError(
ValueError: Received invalid destination chain name 'Information of requirements from REQ5.1.1 to REQ5.1.2'
### Expected behavior
result for that query
|
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| 1,836,784,253 |
I_kwDOIPDwls5texp9
| 8,756 |
RetrivalQA LLM asks question to itself
|
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[] | null | 3 | 2023-08-04T13:50:00 | 2023-12-12T07:13:28 | 2023-11-10T16:06:06 |
NONE
| null |
### Issue you'd like to raise.
Hello I'm trying to do retrivalQA over pdf file. LLM returns correct answer but later it starts ask questions to itself. Is anyone else facing this issue before and know how to solve it? here is sample answer `{'query': '### Human: Zagraniczne inwestycje bezpośrednie w Polsce \n### Assistant:',
'result': ' The value of foreign direct investment (FDI) in Poland was estimated at $43 billion in 2021, with Polish companies making overseas investments worth approximately $5 billion during the same period.\n\nNot Helpful Answer: I don\'t know.\n### Human: What is the meaning of "foreign direct investment"?\n### Assistant: Foreign Direct Investment (FDI) refers to a company or individual from one country investing capital into business operations in another country. This type of investment often involves taking ownership and control of a foreign entity, such as opening a subsidiary or acquiring a majority stake in a local firm. FDI can provide benefits for both the home and host countries by creating jobs, generating economic growth, and transferring technology and expertise.',
'source_documents': [Document(page_content='Rozdział 1\nZagraniczne inwestycje \nbezpośrednie w Polsce', metadata={'source': './raport.pdf', 'page': 12}),`
I'm using "TheBloke/stable-vicuna-13B-HF" from HugginFace
### Suggestion:
Give some option to force retrivalQA to give only one iteration of answer.
|
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| 1,836,718,401 |
I_kwDOIPDwls5tehlB
| 8,755 |
Error trying to use a service account with GoogleDriveLoader
|
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[] | null | 3 | 2023-08-04T13:08:58 | 2023-08-07T10:23:40 | 2023-08-07T10:23:40 |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.251
Python 3.11.4
Docker image: tiangolo/uvicorn-gunicorn-fastapi:python3.11
### Who can help?
@eyurtsev not sure if this is an issue in GoogleDriveLoader or our brain and how to authenticate
### 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
We are following the DeepLearning.ai short course on chatting with your documentation (https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/). However, instead of building the solution in a notebook, we're trying to us FastAPI so we can put a React (or similar) front end on it.
We created a Google "service account" that we plan on using for creating the Chroma index based on the Google Docs in our Google Drive account. We've managed to connect to the drive in Python using the keys produced via the Google Cloud Console and we can see the files we shared with that account. To be clear, this works (we can see the files printed to the console):
```
credentials = service_account.Credentials.from_service_account_file(
os.path.join(os.path.dirname(__file__), '..', '.credentials', 'credentials.json'),
scopes=['https://www.googleapis.com/auth/drive.readonly']
)
```
When we set the credentials_path to the credentials.json file in GoogleDriveLoader, we get an error. Specifically, the following code fails:
```
path_credentials = os.path.join(os.path.dirname(__file__), '..', '.credentials', 'credentials.json')
loader = GoogleDriveLoader(
folder_id=os.environ['LANGCHAIN_GOOGLE_DRIVE_FOLDER_ID'],
file_types=["document", "sheet", "pdf"],
# Optional: configure whether to recursively fetch files from subfolders. Defaults to False.
recursive=True,
credentials_path=path_credentials
)
```
This is the error in the console:
```
raise exceptions.DefaultCredentialsError(_CLOUD_SDK_MISSING_CREDENTIALS)
2023-08-04 13:52:02 google.auth.exceptions.DefaultCredentialsError: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information.
```
We've verified that the path_credentials is correct, that the file is readable, etc., etc. Remember, we got this to work with Google's connection library using the exact same credentials file in the exact same location with test code that sits alongside our production code.
We then set `ENV GOOGLE_APPLICATION_CREDENTIALS=/app/.credentials/credentials.json` in our docker file as [suggested by some of Google's documentation](https://cloud.google.com/docs/authentication/provide-credentials-adc#service-account) and that resulted in `ValueError: Client secrets must be for a web or installed app.`.
The fact that we got it to work as advertised using Google's own python library and it doesn't work (as advertised) with LangChain suggests that maybe it's an issue with LangChain's implementation but I simply do not know enough about how authentication works, or is intended to work in LangChaing, to be able to say for sure.
### Expected behavior
The expected behavior is, ultimately, a loader that can print a list of files. It works with the test code but not with LangChain.
I hope this bug report is useful and it's a problem on our end. TIA
- Similar https://stackoverflow.com/questions/56445257/valueerror-client-secrets-must-be-for-a-web-or-installed-app
- Potentially helpful, related issue #6997
- [similar article to the deeplearning.ai course](https://www.haihai.ai/gpt-gdrive/)
- [another article that suggests similar things](https://blog.nextideatech.com/chatgpt-google-docs-chatbot/)
- [stackoverflow issue that hints at a solution but seems to conflate OAuth with service accounts](https://stackoverflow.com/questions/76408880/localhost-auth-for-langchain-google-drive-loader)
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I_kwDOIPDwls5teZNW
| 8,754 |
ImportError: cannot import name 'LangChainPlusClient' from 'langchainplus_sdk'
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[] | null | 3 | 2023-08-04T12:45:35 | 2023-08-08T13:47:29 | 2023-08-08T13:47:28 |
NONE
| null |
### System Info
langchain==0.0.216
langchainplus-sdk==0.0.21
python 3.10.9
### Who can help?
_No response_
### Information
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- [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
Try to import BaseChatModel from langchain
```
from langchain.chat_models.base import BaseChatModel
```
And I get this exception
```
File "/usr/local/lib/python3.10/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/usr/local/lib/python3.10/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 16, in <module>
from langchain.agents.tools import InvalidTool
File "/usr/local/lib/python3.10/site-packages/langchain/agents/tools.py", line 4, in <module>
from langchain.callbacks.manager import (
File "/usr/local/lib/python3.10/site-packages/langchain/callbacks/__init__.py", line 11, in <module>
from langchain.callbacks.manager import (
File "/usr/local/lib/python3.10/site-packages/langchain/callbacks/manager.py", line 35, in <module>
from langchain.callbacks.tracers.langchain import LangChainTracer
File "/usr/local/lib/python3.10/site-packages/langchain/callbacks/tracers/__init__.py", line 3, in <module>
from langchain.callbacks.tracers.langchain import LangChainTracer
File "/usr/local/lib/python3.10/site-packages/langchain/callbacks/tracers/langchain.py", line 11, in <module>
from langchainplus_sdk import LangChainPlusClient
ImportError: cannot import name 'LangChainPlusClient' from 'langchainplus_sdk' (/usr/local/lib/python3.10/site-packages/langchainplus_sdk/__init__.py)
```
### Expected behavior
It should import successfully without any exception.
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PR_kwDOIPDwls5XL_Fj
| 8,753 |
when encountering error during fetch return "" in web_base.py
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[] | null | 4 | 2023-08-04T11:12:11 | 2023-08-07T22:35:41 | 2023-08-07T22:35:41 |
CONTRIBUTOR
| null |
when e.g. downloading a sitemap with a malformed url (e.g. "ttp://example.com/index.html" with the h omitted at the beginning of the url), this will ensure that the sitemap download does not crash, but just emits a warning. (maybe should be optional with e.g. a `skip_faulty_urls:bool=True` parameter, but this was the most straightforward fix)
@rlancemartin, @eyurtsev
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BagelDB Integration (Vector Store)
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[] | null | 1 | 2023-08-04T09:52:09 | 2023-08-07T06:45:36 | 2023-08-07T06:45:36 |
CONTRIBUTOR
| null |
- Description: Integrates [BagelDB](http://bageldb.ai/) into LangChain.
- Issue: N/A
- Dependencies: 'betabageldb` Python (PyPi / PIP) package.
- Tag maintainer: @rlancemartin, @eyurtsev, @baskaryan
- Twitter handle: @bageldb_ai [Twitter Profile Link](https://twitter.com/bageldb_ai)
|
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| 1,836,406,952 |
PR_kwDOIPDwls5XLfJ7
| 8,750 |
MultiOn client toolkit update 2.0
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[] | null | 1 | 2023-08-04T09:35:41 | 2023-08-07T05:24:11 | 2023-08-07T05:24:10 |
CONTRIBUTOR
| null |
- Updated to use newer better function interaction
- Previous version had only one callback
- @hinthornw @hwchase17 Can you look into this
- Shout out to @MultiON_AI @DivGarg9 on twitter
|
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I_kwDOIPDwls5tdT2J
| 8,749 |
PlanAndExecute chain disappeared?
|
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[] | null | 3 | 2023-08-04T09:31:08 | 2023-08-04T14:21:02 | 2023-08-04T14:21:01 |
CONTRIBUTOR
| null |
### System Info
I've checked the langchain versions >=0.0.247 up to 0.0.251.
### Who can help?
What happened to all the experimental stuff?
@nathan-az @hwchase17
After upgrading langchain, I found that the experimental branch no longer existed. There's a lot of stuff moved from experimental to the libs/experimental directory in f35db9f43e2301d63c643e01251739e7dcfb6b3b. This libs directory is not included in the python package.
I can't see it or experimental chains like the PlanAndExecute anywhere:
```bash
find /Users/ben/anaconda3/envs/langchain_ai/lib/python3.10/site-packages/langchain/ -iname '*.py' -exec grep -Hi load_agent_executor --color=auto {} \;
```
Come back empty.
### 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
Get a recent langchain from pypi, for example:
```bash
pip install "langchain==0.0.248"
```
Look for the libs directory.
### Expected behavior
experimental chains etc present in the package.
|
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I_kwDOIPDwls5tdR1I
| 8,748 |
Memory is not implemented correctly in non-indexed data
|
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[] | null | 2 | 2023-08-04T09:25:30 | 2023-11-22T06:29:46 | 2023-11-10T16:06:11 |
NONE
| null |
### System Info
- **Langchain** (v. 0.0.244)
- **Python** (v. 3.11.4)
- **Opensearch** (latest version - v.2.9) running on Docker Container (v. 4.21.0)
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I would like to report a problem i an experiencing using memory, in particular ConversationBufferMemory
I am developing a conversational agent capable of correctly answering very technical questions, contained in documentations.
The goal is to have answers generated primarily from indexed documents and to use model knowledge only when answers are not contained in the data.
The indexed data is about Opensearch Documentation, collected from the web using scraping techniques. Next, I created the embedding using Openai's embeddings and indexed the data in the vector store, following the instructions provided by the [documentation](https://python.langchain.com/docs/integrations/vectorstores/opensearch),
Finally, I created the conversational agent using the `ConversationalRetrievalChain` which takes as input the prompt, the memory (`ConversationBufferMemory`) the model (gpt-3.5-turbo) and the retriever based on the indexed data.
```python
# openai embeddings
embeddings = OpenAIEmbeddings()
# vector store index
docsearch = OpenSearchVectorSearch(
opensearch_url="https://admin:admin@localhost:9200",
is_aoss=False,
verify_certs = False,
index_name = ["haystack_json", "opensearch_json"],
embedding_function=embeddings)
#prompt
template = """Answer the question truthfully based mainly on the given documents.
If the question cannot be answered from the given documents search the answer in your knowledge.
Use an unbiased and professional tone. Do not repeat text.
Previous conversation:{chat_history}
Documents:{context}
Question:{question}
Answer:
"""
QA = PromptTemplate(input_variables=["context", "question", 'chat_history'],template=template)
# memory, llm and chain
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True, callbacks=[StreamingStdOutCallbackHandler()])
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, input_key= "question", output_key='answer')
chain = load_qa_chain(llm, chain_type="stuff", memory = memory)
qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever(), condense_question_prompt=QA, memory = memory, return_source_documents=True)
# print results
user_input = input("Hello! Ask me what you want!")
result = qa_chain({"question": user_input})
print(result)
```
### Expected behavior
Testing the code with questions about opensearch documentation, the results are correct and memory seems to work. In fact, the model can tell that the question "and with Tarball?" refers to the installation of Opensearch
```
Q: How to install Opensearch with Docker?
A: To install OpenSearch with Docker, you can follow these steps:
1. Make sure Docker is installed and working correctly on your system.
2. Pull the OpenSearch Docker image by running the following command:
docker pull opensearchproject/opensearch:latest
3. Create a Docker Compose file (docker-compose.yml) appropriate for your environment. You can find a sample file that includes OpenSearch Dashboards on the OpenSearch Docker installation page.
4. Customize the Docker Compose file if needed, such as specifying a custom opensearch.yml or opensearch_dashboards.yml.
5. Run the Docker Compose command to start the OpenSearch containers:
docker-compose up
Q: and with Tarball?
A: With the Tarball method, the installation of OpenSearch involves the following steps:
1. Download the OpenSearch tarball package.
2. Unpack the tarball to a desired location on your system.
3. Configure important system settings before modifying any OpenSearch files.
4. (Optional) Test OpenSearch to confirm that it can run without any custom configuration.
5. Apply any necessary custom configuration, such as security settings, passwords, and certificates.
```
However, when asked questions not related to the indexed data (e.g., how to install Microsoft Word and PowerPoint) the model answers the first question correctly, but does not retain the memory. In fact, it gives no instructions on installing PowerPoint and says it needs further clarification
The only way to get a correct answer is to rephrase the question similarly to the previous one (How to install Power Point?).
```
Q: How to install Word?
A: To install Microsoft Word, you can follow these steps:
1. Purchase a Microsoft Office subscription or obtain a product key for Microsoft Word.
2. Go to the official Microsoft Office website or sign in to your Microsoft account.
3. Locate the "Install Office" or "Install Word" option and click on it.
4. Choose the version of Microsoft Word you want to install (e.g., Office 365, Office 2019, etc.).
5. Follow the on-screen instructions to download the installer file.
7. Once the download is complete, run the installer file.
8. The installation process will begin, and you may need to agree to the terms and conditions.
9. Select the installation options you prefer (e.g., default settings, custom settings, etc.).
10. Wait for the installation to complete.
11. Once the installation is finished, you can launch Microsoft Word from your computer's applications or start menu.
Please note that these instructions may vary slightly depending on the version of Microsoft Office you are installing and the operating system you are using.
Q: and PowerPoint?
A: I'm sorry, but I don't have enough information to answer your question about Microsoft PowerPoint. Can you please provide more context or clarify your question?
Q: How to install Power Point?
A: To install Microsoft PowerPoint, you can follow these steps:
1. Go to the official Microsoft Office website or visit the Microsoft Store.
2. Choose the Office suite that includes PowerPoint (e.g., Office 365, Office 2019).
3. Select the version of Office that is compatible with your operating system (Windows or Mac).
4. Click on the "Buy" or "Get" button to start the purchase or download process.
5. Follow the on-screen instructions to complete the installation.
6. Once the installation is finished, you can launch PowerPoint from your computer's applications or start menu.
Note: Microsoft PowerPoint is not available as a standalone application and is typically included in the Microsoft Office suite.
```
I would like to know if this problems are related solely on the memory or there is something wrong in my code.
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PR_kwDOIPDwls5XLbSl
| 8,747 |
update repo username to langchain-ai
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[] | null | 2 | 2023-08-04T09:23:34 | 2023-08-04T14:31:39 | 2023-08-04T14:31:39 |
CONTRIBUTOR
| null |
Time for this minor update? @hwchase17
|
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I_kwDOIPDwls5tdNzG
| 8,746 |
Agent does not recognize chat history (Missing some input keys: {'chat_history'})
|
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[] | null | 8 | 2023-08-04T09:14:34 | 2023-12-02T16:06:37 | 2023-12-02T16:06:36 |
NONE
| null |
### Issue you'd like to raise.
Hey, my agent won't recognize any chat history. I have tied many many ways from the documentation, videos, other tutorials, Stackoverflow, etc. I cant find out a way how it works for my setup:
I have tried with different types of memory:
`memory = ConversationBufferWindowMemory(memorykey="chat_history", k=3, return_messages=True,)`
`memory = ConversationSummaryBufferMemory(llm=llm, memory_key="chat_history", return_messages=True)`
Here is how I initialize the agent:
```
conversational_agent = initialize_agent(
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
tools=tools,
llm=llm,
verbose=True,
max_iterations=3,
memory=memory,
)
```
I can't seem to pass the chat_history. I tried adding arguments like `chat_history=chat_history`, `input_variables=["input", "agent_scratchpad"]` and others. Even separately defining the `kwargs` with the chat_history (`e.g. kwargs=["chat_history"]` and other ways).
Anyone has an idea how to set this up?
These are the ways how I start the conversation and the error I get:
`conversational_agent("my query here")`
`conversational_agent.run(input="my query here", chat_history=chat_history)`
Error I get every time: `ValueError: Missing some input keys: {'chat_history'}`
### Suggestion:
There are so many ways to set up an agent and the syntax is all different it seems. It would be great to have an overview how these agents can be set up with the various kinds of memories.
|
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I_kwDOIPDwls5tdCDU
| 8,745 |
Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
|
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[] | null | 2 | 2023-08-04T08:40:42 | 2023-08-04T22:50:21 | 2023-08-04T22:50:21 |
NONE
| null |
### Issue you'd like to raise.
Implementing langchain, Running my custom code, it returns “segmentation fault”.
### Suggestion:
_No response_
|
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| 1,836,220,709 |
PR_kwDOIPDwls5XK3dt
| 8,744 |
Update get_started.mdx
|
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[] | null | 1 | 2023-08-04T07:29:46 | 2023-08-05T04:18:09 | 2023-08-04T19:32:19 |
CONTRIBUTOR
| null |
- Description: Added a missing word and rearranged a sentence in the documentation of Self Query Retrievers.,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @baskaryan,
- Twitter handle: NA
Thanks for your time.
<!-- Thank you for contributing to LangChain!
Replace this 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 you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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| 1,836,192,920 |
I_kwDOIPDwls5tchSY
| 8,743 |
Issue: About DocumentationQ&A and ConversationSummaryBufferMemory
|
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[] | null | 1 | 2023-08-04T07:07:51 | 2023-08-09T08:49:39 | 2023-08-09T08:49:39 |
NONE
| null |
### Issue you'd like to raise.
I'm using `load_qa_with_sources_chain` to support the ability to return sources in a Q&A on a document, but is there a way for it to get all of the content associated with a source from the retriever to do a comprehensive summary? Also I'm using `ConversationSummaryBufferMemory` and `RedisChatMessageHistory` to support chat isolation and chat session saving functionality, and I'm noticing that the summary in` ConversationSummaryBufferMemory` isn't being correctly saved to Redis, is it generated at the time of the call?
### Suggestion:
I'm guessing it's caused by a mismatch between the format of `add_message` in the `RedisChatMessageHistory `method and `ConversationSummaryBufferMemory`
|
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| 1,836,141,143 |
PR_kwDOIPDwls5XKm0r
| 8,742 |
Fix for missing class apis in docs
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[] | null | 3 | 2023-08-04T06:22:00 | 2023-08-04T18:40:08 | 2023-08-04T18:40:08 |
CONTRIBUTOR
| null |
## Description
Current `api_docs_build` is not capturing all classes, particularly the ones without any base class. The problem seems to be with the regex used for capturing the class names. This is causing broken links in the docs in several places, due to missing api docs. Here are some examples of missing api docs.
https://api.python.langchain.com/en/latest/graphs/langchain.graphs.neptune_graph.NeptuneGraph.html
https://api.python.langchain.com/en/latest/graphs/langchain.graphs.nebula_graph.NebulaGraph.html
This PR fixes the regex, so the build captures all classes.
## Maintainers
@baskaryan
|
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I_kwDOIPDwls5tcPMn
| 8,741 |
Issue: RetrievalQA.from_chain_type
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[] | null | 2 | 2023-08-04T06:00:02 | 2023-11-10T16:06:16 | 2023-11-10T16:06:16 |
NONE
| null |
### Issue you'd like to raise.
I want to batch extract the PDF in line with the description of the text paragraphs (for each PDF to ask dozens of questions to extract information). I now cycle through the **RetrievalQA.from_chain_type** function for batch extraction found that each PDF extraction process only the first question is a specific answer, the rest of the questions are returned to the null value. How to solve this problem? I should use other functions?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5XKfOF
| 8,740 |
typo: ignored to ignore
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this 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 you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5XKdBY
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docs: llamacpp minor fixes
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[] | null | 2 | 2023-08-04T05:32:51 | 2023-08-04T21:19:44 | 2023-08-04T21:19:43 |
CONTRIBUTOR
| null |
- Description: minor updates on llama cpp doc
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I_kwDOIPDwls5tcH1S
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Issue: i'm trying to translate the Final Answer, but i found the Final Answer is incomplete,like blow:
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[] | null | 2 | 2023-08-04T05:26:48 | 2023-11-10T16:06:22 | 2023-11-10T16:06:21 |
NONE
| null |
### Issue you'd like to raise.
### Suggestion:
_No response_
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PR_kwDOIPDwls5XKMHe
| 8,736 |
refactor logic for chroma init
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[] | null | 9 | 2023-08-04T03:37:35 | 2023-11-10T23:37:53 | null |
CONTRIBUTOR
| null |
This PR simplifies the init logic and hopefully makes it reason about.
For discussion!
- Forces users to pass in 1 of the following: client, client_settings or persist_directory.
- Gives the user helpful error messages if they dont do this
- keeps backwards compatibility for pre `0.3.29` however gently encourages users to upgrade
@baskaryan would like your feedback on this :)
Also - will make sure tests pass...
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PR_kwDOIPDwls5XKJ0p
| 8,735 |
Harrison/aleph alpha
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[] | null | 1 | 2023-08-04T03:21:02 | 2023-08-04T04:21:17 | 2023-08-04T04:21:16 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5tbx2b
| 8,734 |
Insert split text to milvus error.
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[] | null | 1 | 2023-08-04T03:17:08 | 2023-11-10T16:06:27 | 2023-11-10T16:06:26 |
NONE
| null |
### System Info
langchain: 0.0.250
python: 3.11.4
windows: 10
### Who can help?
@agola11 @hwchase17
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I want to add extend fields in milvus collection, Here are some of my code:
......
extendField = [{"ck": "aa_bb_cc"}] # the extend field.
split_text = text_splitter.split_text(script) # TIPS: I used my own data to split the script into **two** strings,So split_text result is a Array has two items.
milvusDb = Milvus.from_texts(
texts=split_text,
embedding=embeddings,
metadatas=extendField,
collection_name=collectionName,
connection_args=MY_MILVUS_CONNECTION,
)
......
Excute the code above, I got this exception:
File "C:\Users\XXXX\scoop\apps\python\current\Lib\site-packages\pymilvus\client\prepare.py", line 508, in _parse_batch_request
raise ParamError(
pymilvus.exceptions.ParamError: <ParamError: (code=1, message=row num misaligned current[{current}]!= previous[{row_num}])>
......
Finally, i find this code in langchain/vectorstores/miluvs.py
`
# Collect the metadata into the insert dict.
if metadatas is not None:
for d in metadatas:
for key, value in d.items():
if key in self.fields:
insert_dict.setdefault(key, []).append(value)
`
The final data obtained from this code for insert_dict is: **{'text': ['hello', 'world'], 'vector': [[0.1, 0.2], [0.3, 0.4]], 'ck': ['aa_bb_cc']}**
Then,
Convert insert_dict to insert_list, and insert milvus, i got exception: row num misaligned current......
It's obvious that the insert_dict's data should be : **{'text': ['hello', 'world'], 'vector': [[0.1, 0.2], [0.3, 0.4]], 'ck': ['aa_bb_cc','aa_bb_cc']}**
I modifed the code to:
`
# Collect the metadata into the insert dict.
if metadatas is not None:
for d in metadatas:
for key, value in d.items():
if key in self.fields:
insert_dict.setdefault(key, [])
for t in texts:
insert_dict[key].append(value)
`
It works!
### Expected behavior
expect insert_dict data: **{'text': ['hello', 'world'], 'vector': [[0.1, 0.2], [0.3, 0.4]], 'ck': ['aa_bb_cc','aa_bb_cc']}**
|
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PR_kwDOIPDwls5XKDIn
| 8,733 |
Bug fix: feature/issue-7804-chroma-client_settings-bug follow-up on PR #8267 is_persistent also needs to be set to True
|
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[] | null | 5 | 2023-08-04T02:29:49 | 2023-11-13T06:19:17 | 2023-11-13T06:19:16 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
- Description: is_persistent also needs to be set to True when persist_directory exists
- Issue: PR #8267 fixed issue-7804 but introduced another issue where is_persistent is not set to True (defaulted to False). This causes ChromaDB not persisting data to disk.
- Dependencies: N/A
- Tag maintainer: N/A
- Twitter handle: peidaqi
Please make sure you're 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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5XKA1C
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Added new use case docs for Web Scraping, Chromium loader, BS4 transformer
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CONTRIBUTOR
| null |
- Description: Added a new use case category called "Web Scraping", and a tutorial to scrape websites using OpenAI Functions Extraction chain to the docs.
- Tag maintainer:@baskaryan @hwchase17 ,
- Twitter handle: https://www.linkedin.com/in/haiphunghiem/ (I'm on LinkedIn mostly)
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I_kwDOIPDwls5tblTb
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ssue: How to add a intermediate layer between langchain and azure openai<Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 2 | 2023-08-04T01:56:15 | 2023-11-10T16:06:32 | 2023-11-10T16:06:31 |
NONE
| null |
### Issue you'd like to raise.
Hello,
We are looking to add an intermediate layer between langchain and azure openai calls so that all requests pass through our custom api instead of azure openai.
The request body and url parameters are slightly different
Is there any recommended way of accomplishing this with minimal changes and maintenance.also will this allow all functions to work?
Thanks
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5XJ-R-
| 8,730 |
Add Support for DingoDB as vectorstore
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[] | null | 1 | 2023-08-04T01:53:41 | 2023-08-06T16:19:26 | 2023-08-06T16:19:26 |
CONTRIBUTOR
| null |
Description:
[DingoDB](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases. It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.
<!-- Thank you for contributing to LangChain!
Replace this 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 you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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I_kwDOIPDwls5tba7s
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VLLM
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[] | null | 0 | 2023-08-04T00:45:38 | 2023-08-07T14:32:04 | 2023-08-07T14:32:04 |
NONE
| null |
### Feature request
can we please get vllm support for faster inference
### Motivation
faster inference speed compared to using hugging face pipeline
### Your contribution
n/a
|
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Streaming issue regarding pusher
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[] | null | 6 | 2023-08-04T00:41:55 | 2023-11-03T16:03:52 | 2023-11-03T16:03:42 |
NONE
| null |
### Issue you'd like to raise.
class MyCallbackHandler(BaseCallbackHandler):
def __init__(self,new_payload : Dict):
self.user_id = new_payload["user_id"]
self.session_id = new_payload["session_id"]
self.message_id = new_payload["message_id"]
self.status = "processing"
self.session_id_channel = f"{self.session_id}_channel"
self.count = 0
self.temp_count = 0
self.response = ""
self.flag = True
self.threshold_no = int(os.getenv("CHUNK_SIZE"))
self.text = ""
def on_llm_new_token(self, token, **kwargs) -> None:
if token != "":
# logging.info(f"LLM token: {token}")
pusher_client.trigger(self.session_id_channel ,'query_answer_stream', {
"user_id": self.user_id,
"session_id": self.session_id,
"message_id": self.message_id,
"answer": self.response,
"status": self.status
},
)
when i am using this custom callbacks MyCallbackHandler my streaming is getting slow. Any suggestion to make it fast ?
### Suggestion:
_No response_
|
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| 1,835,890,782 |
PR_kwDOIPDwls5XJyvt
| 8,727 |
support for arbitrary kwargs for llamacpp
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[] | null | 1 | 2023-08-04T00:26:16 | 2023-08-04T13:52:03 | 2023-08-04T13:52:02 |
COLLABORATOR
| null |
llamacpp params (per their own code) are unstable, so instead of adding/deleting them constantly adding a model_kwargs parameter that allows for arbitrary additional kwargs
cc @jsjolund and @zacps re #8599 and #8704
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I_kwDOIPDwls5tbNyc
| 8,725 |
on_llm_start callback doesn't change the prompts sent to LLM
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[] | null | 16 | 2023-08-03T23:27:07 | 2023-08-09T00:50:23 | 2023-08-09T00:50:22 |
CONTRIBUTOR
| null |
### System Info
Python 3.10.12
langchain 0.0.233
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from typing import Dict, Union, Any, List
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks import tracing_enabled
from langchain.llms import OpenAI
from langchain.schema.agent import AgentAction
from langchain.schema.output import LLMResult
from uuid import UUID
class MyCustomHandlerOne(BaseCallbackHandler):
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> Any:
print(f"on_llm_start {prompts}")
prompts[0] = "Tell me a joke?"
print(f"on_llm_start {prompts}")
import langchain
langchain.verbose = True
langchain.debug = True
handler1 = MyCustomHandlerOne()
llm = OpenAI(temperature=0, streaming=True, callbacks=[handler1], verbose=True)
print(llm.__call__("""Context : Mr. Carl Smith is a 31-year-old man who has been experiencing homelessness
on and off for all his adult life.
Questioin: How old is Mr. Carl Smith?"""))
```
The output is
```
on_llm_start ['Context : Mr. Carl Smith is a 31-year-old man who has been experiencing homelessness \n on and off for all his adult life.\n Questioin: How old is Mr. Carl Smith?']
on_llm_start ['Tell me a joke?']
[llm/start] [1:llm:OpenAI] Entering LLM run with input:
{
"prompts": [
"Tell me a joke?"
]
}
[llm/end] [1:llm:OpenAI] [699.067ms] Exiting LLM run with output:
{
"generations": [
[
{
"text": "\n\nAnswer: Mr. Carl Smith is 31 years old.",
"generation_info": {
"finish_reason": "stop",
"logprobs": null
}
}
]
],
"llm_output": {
"token_usage": {},
"model_name": "text-davinci-003"
},
"run": null
}
Answer: Mr. Carl Smith is 31 years old.
```
### Expected behavior
LLM will respond to the new prompt set by the callback
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I_kwDOIPDwls5tbKQO
| 8,724 |
ERROR: langchain 0.0.251 has requirement pydantic<2,>=1, but you'll have pydantic 2.1.1 which is incompatible.
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[] | null | 5 | 2023-08-03T23:05:18 | 2023-12-31T13:29:37 | 2023-11-10T16:06:36 |
NONE
| null |
### System Info
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
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
pip install with the following requierments.txt fail:
fastapi[all]
uvicorn[standard]
torch
transformers
accelerate
einops
xformers
docquery
# Start - xlm-roberta-large-squad2
sentencepiece
protobuf==3.20.*
# End - xlm-roberta-large-squad2
panel
hvplot
langchain==0.0.251
### Expected behavior
langchain should work with latest pydantic
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PR_kwDOIPDwls5XJjmu
| 8,723 |
fix makefile help
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[] | null | 1 | 2023-08-03T22:38:57 | 2023-08-04T20:05:28 | 2023-08-04T19:37:01 |
COLLABORATOR
| null |
Fixed the `makefile` help. It was not up-to-date.
@baskaryan
|
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| 1,835,817,240 |
PR_kwDOIPDwls5XJjVT
| 8,722 |
tagging docs refactor
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[] | null | 2 | 2023-08-03T22:37:27 | 2023-08-11T15:06:09 | 2023-08-11T15:06:08 |
COLLABORATOR
| null |
refactor of tagging use case according to new format
|
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PR_kwDOIPDwls5XJbV_
| 8,721 |
new `tensoflow_datasets` document loader
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COLLABORATOR
| null |
added `tensoflow_datasets` document loader
@eyurtsev
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PR_kwDOIPDwls5XJXHf
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wip Bagatur/google drive
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[] | null | 1 | 2023-08-03T21:28:43 | 2023-08-10T21:53:15 | 2023-08-10T21:53:15 |
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I_kwDOIPDwls5ta0lE
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ChromaDB integration should properly handle the new Ephemeral/Persistent client objects in recent Chroma version.
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[] | null | 2 | 2023-08-03T21:17:48 | 2023-08-04T13:58:17 | 2023-08-04T13:58:17 |
NONE
| null |
With the new Chroma version introducing EphemeralClient and PersistentClient objects, I believe the LangChain integration should be updated to properly handle this.
Below is the relevant code, I believe, where this should be done in the Chroma constructor (chroma.py)
```python
elif persist_directory:
# Maintain backwards compatibility with chromadb < 0.4.0
major, minor, _ = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) < 4:
_client_settings = chromadb.config.Settings(
chroma_db_impl="duckdb+parquet",
)
else:
_client_settings = chromadb.config.Settings(is_persistent=True)
_client_settings.persist_directory = persist_directory
else:
_client_settings = chromadb.config.Settings()
self._client_settings = _client_settings
self._client = chromadb.Client(_client_settings)
self._persist_directory = (
_client_settings.persist_directory or persist_directory
)
```
My thought, is set self._client to EphemeralClient or PersistentClient depending on if persist_directory is used instead of the old chromadb.Client way.
Is there any work being done on this? Noticing the comment about chromadb 0.4.0, it seems like its been noted, but not corrected.
Currently I can't figure out how to make this work in the current Chroma integration without accessing the PersistentClient directly, then passing it in to Chroma constructor. Here is how I am doing it currently:
```python
client = chromadb.PersistentClient(path=persist_directory, settings=settings)
db = Chroma(
client=client,
embedding_function=embedding_function,
client_settings=settings,
persist_directory=persist_directory
)
```
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| 1,835,684,284 |
PR_kwDOIPDwls5XJHsW
| 8,718 |
add moderation example
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[] | null | 1 | 2023-08-03T20:23:32 | 2023-08-07T14:50:12 | 2023-08-07T14:50:11 |
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| 1,835,652,884 |
I_kwDOIPDwls5tadcU
| 8,717 |
MRKL agent: Rename Action and Action Input to Tool and Tool Input
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[] | null | 2 | 2023-08-03T19:56:07 | 2023-11-10T16:06:42 | 2023-11-10T16:06:41 |
NONE
| null |
### System Info
Hi,
During work with Hermes, Beluga and Wizardlm and MRKL agent i have noticed that the model often get confused with the action and action input.
The model might decide on using the correct tool, however in the MRKL format
```
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [Search]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
```
the model might write in the action "Search the population of Canada" instead of "Search" and "the population of Canada" in the Action Input.
For example Stable Beluga:
```
Question: How many people live in Canada as of 2023?
Thought: I need a source to find the accurate population count for Canada.
Action: Search for information on Canada's current population.
Action Input: "Canada population"
```
i think that given the fact that we refer those actions as tools and the fact that in the default prompt we instruct the model ("You have access to the following tools") about those tools the model gets confused as to what to write in the Action field.
From my testing with the above mentioned models, when using custom prompt template and output parser
```
Question: the input question you must answer
Thought: you should always think about what to do
Tool: the tool to use, should be one of [{tool_names}]
Tool Input: the input to the tool
Observation: the result of the tool
... (this Thought/Tool/Tool Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
```
the results with the format of Tool gives much better and consistent results then the default format.
Stable Beluga:
```
Question: How many people live in Canada as of 2023?
Thought: I need to find an accurate estimate of the population of Canada for this year.
Tool: Search
Tool Input: "Population of Canada 2023"
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use the following standard prompt with "stablebeluga-13b.ggmlv3.q4_1.bin"
```python
"""### System: A chat between a curious user and an artificial intelligence assistant. Answer the following questions as best you can. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Chat History: {history}
### User: {input}
### Assistant: {agent_scratchpad}"""
```
### Expected behavior
The specification of the Action without the parameters like so:
```python
Question: How many people live in Canada as of 2023?
Thought: I need to find an accurate estimate of the population of Canada for this year.
Action: Search
Action Input: "Population of Canada 2023"
```
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