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PR_kwDOIPDwls5U_CaH
| 7,389 |
Try er/redis index retrieval rect
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CONTRIBUTOR
| null |
Replace this comment with:
- Description: Changed the code to return id of relevant documents retrieved from redis vectorstore
- Issue: the issue # it returns id to the user for downstream operations
- Tag maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5U-soR
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[Breaking] Update Evaluation Functionality
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[] | null | 2 | 2023-07-08T03:40:32 | 2023-07-13T09:13:08 | 2023-07-13T09:13:06 |
COLLABORATOR
| null |
- Migrate from deprecated langchainplus_sdk to `langsmith` package
- Update the `run_on_dataset()` API to use an eval config
- Update a number of evaluators, as well as the loading logic
- Update docstrings / reference docs
- Update tracer to share single HTTP session
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PR_kwDOIPDwls5U-NRI
| 7,386 |
Fix HF endpoint returns blank for text-generation
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[] | null | 6 | 2023-07-07T23:44:35 | 2023-07-11T07:06:12 | 2023-07-11T07:06:05 |
CONTRIBUTOR
| null |
Description: Current `_call` function in the `langchain.llms.HuggingFaceEndpoint` class truncates response when `task=text-generation`. Same error discussed a few days ago on Hugging Face: https://huggingface.co/tiiuae/falcon-40b-instruct/discussions/51
Issue: Fixes #7353
Tag maintainer: @hwchase17 @baskaryan @hinthornw
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I_kwDOIPDwls5q9j76
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Add the `name` and `arguments` from `function_call` to `on_llm_new_token` when `streaming=True`
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[] | null | 6 | 2023-07-07T23:14:25 | 2023-09-25T09:45:57 | 2023-09-25T09:45:56 |
NONE
| null |
### Feature request
Add the `name` and `arguments` from `function_call` to `on_llm_new_token` when `streaming=True`.
Now it's getting called with an empty token several times and no way to retrieve the `function_call['arguments']`.
We need to add this to every llm/
### Motivation
I'm streaming my calls (user get's to see the output realtime) to an llm and I've decided to use function calls so I get a structured output. I want to show the user the results (one argument is `message`) but I cannot cause only the tokens from `content` are streamed.
Using the plain openai api I can do this.
### Your contribution
I can contribute a PR to a model and some tests, but I need guidance, as what the API should be. As I understand only `chat_models.openai` should be modified.
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I_kwDOIPDwls5q9jcf
| 7,384 |
ChromaDB score goes the wrong way
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NONE
| null |
### System Info
> langchain==0.0.227
> langchainplus-sdk==0.0.20
> chromadb==0.3.26
### 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
I'm sorry, but I don't have the time to carve out a MRE right now. My take is that it's still better to report it than not to.
### Expected behavior
`similarity_search_with_score` returns the distance as expected, but `similarity_search_with_relevance_scores` gives the same values, so that the closest distances return the smallest values, even though the output of the latter function is supposed to be higher for vectors that are closer:
> `similarity_search_with_relevance_scores`
> Return docs and relevance scores in the range [0, 1].
> 0 is dissimilar, 1 is most similar.
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PR_kwDOIPDwls5U-IBA
| 7,383 |
Make Ref Example Threadsafe
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[] | null | 2 | 2023-07-07T23:08:12 | 2023-07-08T04:50:43 | 2023-07-08T04:50:42 |
COLLABORATOR
| null |
Have noticed transient ref example misalignment. I believe this is caused by the logic of assigning an example within the thread executor rather than before.
|
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PR_kwDOIPDwls5U-Fi8
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Update pinecone.ipynb
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[] | null | 2 | 2023-07-07T22:52:40 | 2023-07-08T05:48:04 | 2023-07-08T05:48:04 |
CONTRIBUTOR
| null |
Fix typo
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PR_kwDOIPDwls5U-Cya
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Allow passing custom prompts to GraphIndexCreator
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[] | null | 2 | 2023-07-07T22:41:28 | 2023-07-08T18:01:36 | 2023-07-08T05:47:54 |
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!
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_kwDOIPDwls5U-Bib
| 7,380 |
[feat]: Adding memory to Sql Database Chain
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[] | null | 8 | 2023-07-07T22:35:36 | 2023-07-11T15:01:13 | 2023-07-11T15:01:05 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: SQL Database Chain does not allow to pass in memory to its chain.
- Issue: This has been requested in issue #6918
- Tag maintainer: @hwchase17
|
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PR_kwDOIPDwls5U951O
| 7,379 |
Add new types of document transformers
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CONTRIBUTOR
| null |
- Description: Add two new document transformers that translates documents into different languages and converts documents into q&a format to improve vector search results. Uses OpenAI function calling via the [doctran](https://github.com/psychic-api/doctran/tree/main) library.
- Issue: N/A
- Dependencies: `doctran = "^0.0.5"`
- Tag maintainer: @rlancemartin @eyurtsev @hwchase17
- Twitter handle: @psychicapi or @jfan001
Notes
- Adheres to the `DocumentTransformer` abstraction set by @dev2049 in #3182
- refactored `EmbeddingsRedundantFilter` to put it in a file under a new `document_transformers` module
- Added basic docs for `DocumentInterrogator`, `DocumentTransformer` as well as the existing `EmbeddingsRedundantFilter`
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PR_kwDOIPDwls5U9t8v
| 7,378 |
update databerry-chaindesk redirect
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[] | null | 1 | 2023-07-07T21:33:02 | 2023-07-07T23:11:47 | 2023-07-07T23:11:46 |
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PR_kwDOIPDwls5U9XZt
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Update the API Reference link in Streamlit integration docs
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[] | null | 3 | 2023-07-07T20:52:57 | 2023-07-07T21:35:58 | 2023-07-07T21:35:58 |
CONTRIBUTOR
| null |
This page:
https://python.langchain.com/docs/modules/callbacks/integrations/streamlit
Has a bad API Reference link currently. This PR fixes it to the correct link.
Also updates the embedded app link to https://langchain-mrkl.streamlit.app/ (better name) which is hosted in langchain-ai/streamlit-agent repo
cc @baskaryan @agola11
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| 1,794,271,625 |
I_kwDOIPDwls5q8mmJ
| 7,376 |
Passing VectorStoreRetriever arguments to VectorStoreIndexWrapper.query()
|
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[] | null | 1 | 2023-07-07T20:39:29 | 2023-10-14T20:09:57 | 2023-10-14T20:09:56 |
NONE
| null |
### Feature request
I want to pass arguments to the retriever used in the `VectorStoreIndexWrapper` `query` and `query_with_sources` methods. Right now, those methods don't have any means of passing `VectorStoreRetriever` arguments into `vectorstore.as_retriever()`:
```
langchain/indexes/vectorstore.py
def query(
self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
) -> str:
"""Query the vectorstore."""
llm = llm or OpenAI(temperature=0)
chain = RetrievalQA.from_chain_type(
--->llm, retriever=self.vectorstore.as_retriever(), **kwargs
)
return chain.run(question)
def query_with_sources(
self, question: str, llm: Optional[BaseLanguageModel] = None, **kwargs: Any
) -> dict:
"""Query the vectorstore and get back sources."""
llm = llm or OpenAI(temperature=0)
chain = RetrievalQAWithSourcesChain.from_chain_type(
--->llm, retriever=self.vectorstore.as_retriever(), **kwargs
)
return chain({chain.question_key: question})
```
### Motivation
I can't input `VectorStoreRetriever` arguments such as `search_type` or `search_kwargs` into `VectorStoreIndexWrapper.query()`, but I would be able to do that via `VectorStore.as_retriever()`, which `query()` and `query_with_sources()` use anyway.
### Your contribution
If someone isn't already working on this, I can make this change and submit a PR.
|
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| 1,794,215,961 |
I_kwDOIPDwls5q8ZAZ
| 7,374 |
How to load Vicuna-7b, Vicuna13-b and Falcon LLM's from langchain through ConversationalRetrievalChain function?
|
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[] | null | 0 | 2023-07-07T20:17:44 | 2023-07-07T20:26:58 | 2023-07-07T20:20:24 |
NONE
| null |
### Issue with current documentation:
Below is the code which i'm using for another model
```
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
import llama_cpp
# create a chatbot chain. Memory is managed externally.
qa = ConversationalRetrievalChain.from_llm(
llm=load_model(model_id="TheBloke/orca_mini_v2_7B-GGML", device_type="mps", model_basename="orca-mini-v2_7b.ggmlv3.q4_0.bin"),#ChatOpenAI(model_name=llm_name, temperature=0),
chain_type=chain_type,
retriever=retriever,
return_source_documents=True,
return_generated_question=True,
)
```
I'm loading orca mini model with model_id and model_basename. How to load Vicuna7b, Vicuna13b and Falcon LLM's? And how to change the device type from mps to Cuda?
### Idea or request for content:
_No response_
|
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| 1,794,191,212 |
I_kwDOIPDwls5q8S9s
| 7,373 |
Fortune Reporter trying to get in touch with Chase
|
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[] | null | 1 | 2023-07-07T20:07:36 | 2023-07-10T16:26:16 | 2023-07-10T16:26:05 |
NONE
| null |
### Issue you'd like to raise.
@hwchase17 There's been a reporter from Fortune who are in their words: "launching our A.I. 50 list later this month. LangChain is being considered for the list, and while I've tried to get in touch with Harrison and Ankush in as many ways as possible, I haven't been able to."
They've been trying to get in touch with @hwchase17 for weeks now.
Search for **bdanweiss** on the Discord group.
### Suggestion:
_No response_
|
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| 1,794,125,262 |
PR_kwDOIPDwls5U8tWe
| 7,372 |
Fix buggy output from GraphQAChain
|
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[] | null | 3 | 2023-07-07T19:34:58 | 2023-07-07T21:19:53 | 2023-07-07T21:19:53 |
CONTRIBUTOR
| null |
fixes https://github.com/hwchase17/langchain/issues/7289
A simple fix of the buggy output of `graph_qa`. If we have several entities with triplets then the last entry of `triplets` for a given entity merges with the first entry of the `triplets` of the next entity.
## Example
```
from langchain.llms import OpenAI
from langchain.indexes import GraphIndexCreator
from langchain.chains import GraphQAChain
from langchain.prompts import PromptTemplate
import os
text = "Apple announced the Vision Pro in 2023."
openai_api_key = os.getenv("OPENAI_API_KEY")
index_creator = GraphIndexCreator(llm=OpenAI(openai_api_key=openai_api_key, temperature=0))
graph = index_creator.from_text(text)
chain = GraphQAChain.from_llm(
OpenAI(temperature=0, openai_api_key=openai_api_key),
graph=graph,
verbose=True
)
chain.run("When did Apple announce the Vision Pro?")
# output pre-fix
Entities Extracted:
Apple, Vision Pro
Full Context:
Apple announced Vision ProVision Pro was announced in 2023
> Finished chain.
# output post-fix
Entities Extracted:
Apple, Vision Pro
Full Context:
Apple announced Vision Pro
Vision Pro was announced in 2023
> Finished chain.
```
@baskaryan
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| 1,794,046,628 |
PR_kwDOIPDwls5U8brw
| 7,371 |
vscode test
|
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[] | null | 1 | 2023-07-07T18:44:21 | 2023-07-07T19:11:27 | 2023-07-07T19:11:27 |
NONE
| null |
pr description
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PR_kwDOIPDwls5U8XDC
| 7,370 |
Harrison/add to imports
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[] | null | 1 | 2023-07-07T18:30:42 | 2023-07-07T20:27:45 | 2023-07-07T20:27:45 |
COLLABORATOR
| null |
pgvector cleanup
|
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I_kwDOIPDwls5q7qXp
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from langchain.llms import OpenAI causing ImportError: cannot import name 'Protocol'
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[] | null | 2 | 2023-07-07T18:30:00 | 2023-10-14T20:10:02 | 2023-10-14T20:10:02 |
NONE
| null |
### System Info
from typing_extensions import Protocol
from langchain.llms import OpenAI
llm = OpenAI(model_name='text-davinci-003', temperature=0.7, max_tokens=512)
print(llm)
-----------------------------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-28-c0d04f63c5e1> in <module>
1 get_ipython().system('pip install typing-extensions==4.3.0')
2 from typing_extensions import Protocol
----> 3 from langchain.llms import OpenAI
4 llm = OpenAI(model_name='text-davinci-003', temperature=0.7, max_tokens=512)
5 print(llm)
~\anaconda3\envs\GPTLLM\lib\site-packages\langchain\__init__.py in <module>
1 """Main entrypoint into package."""
2
----> 3 from importlib import metadata
4 from typing import Optional
5
~\anaconda3\envs\GPTLLM\lib\importlib\metadata\__init__.py in <module>
15 import collections
16
---> 17 from . import _adapters, _meta
18 from ._collections import FreezableDefaultDict, Pair
19 from ._functools import method_cache, pass_none
~\anaconda3\envs\GPTLLM\lib\importlib\metadata\_meta.py in <module>
----> 1 from typing import Any, Dict, Iterator, List, Protocol, TypeVar, Union
2
3
4 _T = TypeVar("_T")
5
ImportError: cannot import name 'Protocol'
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.llms import OpenAI
llm = OpenAI(model_name='text-davinci-003', temperature=0.7, max_tokens=512)
print(llm)
### Expected behavior
OpenAI
Params:{model_name, temperature, max_tokens}
|
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I_kwDOIPDwls5q7pHa
| 7,368 |
conversationalRetrievalChain - how to improve accuracy
|
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[] | null | 3 | 2023-07-07T18:26:31 | 2023-10-16T16:06:15 | 2023-10-16T16:06:14 |
NONE
| null |
I am using conversational retrieval chain with memory, but I am getting incorrect answers for trivial questions.
Any suggestions what can I do to improve the accuracy of the output?
#memory = ConversationEntityMemory(llm=llm, return_messages=True)
memory=ConversationBufferMemory(memory_key="chat_history",output_key='answer')
chain = ConversationalRetrievalChain.from_llm(
llm = llm,
retriever=retriever,
memory=memory,
get_chat_history=lambda h :h,
return_source_documents=True)
|
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| 1,794,014,938 |
PR_kwDOIPDwls5U8Ujj
| 7,367 |
feat: Add `UnstructuredTSVLoader`
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CONTRIBUTOR
| null |
### Summary
Adds an `UnstructuredTSVLoader` for TSV files. Also updates the doc strings for `UnstructuredCSV` and `UnstructuredExcel` loaders.
### Testing
```python
from langchain.document_loaders.tsv import UnstructuredTSVLoader
loader = UnstructuredTSVLoader(
file_path="example_data/mlb_teams_2012.csv", mode="elements"
)
docs = loader.load()
```
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
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| 1,794,008,740 |
I_kwDOIPDwls5q7mak
| 7,366 |
use youtube chapter as hints and metadata in the youtube loader
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[] | null | 9 | 2023-07-07T18:19:36 | 2023-10-09T17:05:41 | null |
CONTRIBUTOR
| null |
### Feature request
When using the youtube loader. I think it would be useful to take into account the chapters if present.
1. The chapter timecode could be used to know when to chunk. Any chunk inside a chapter timeframe could also contain the same "youtube_chapter_title" metadata.
2. The name of the chapter could added directly inside the transcript. For example as a markdown header. This could be useful for LLM to maintain context over time.
### Motivation
There are useful information present in the youtube chapter title and timecodes that could be of use to LLMs.
Summarizing transcripts would probably be of higher quality if headers are present rather than a huge wall of text.
Adding metadata is always a win.
### Your contribution
Unfortunately not able to help for the time being but wanted to get the idea out there.
|
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I_kwDOIPDwls5q7apQ
| 7,365 |
ConversationBufferWindowMemory returns empty string on empty history instead of empty array.
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[] | null | 7 | 2023-07-07T17:56:12 | 2023-12-05T12:07:46 | 2023-10-14T20:10:11 |
NONE
| null |
### System Info
Python 3.10
Langchain 0.0.226
Windows 11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import os, langchain
os.environ['SERPAPI_API_KEY'] = ""
os.environ['OPENAI_API_KEY'] = ""
from langchain.chains import LLMChain
from langchain.agents import ConversationalChatAgent, SelfAskWithSearchChain, AgentExecutor
from langchain.memory import ConversationBufferWindowMemory
from langchain.tools import Tool
from langchain.llms import OpenAI
conversation_buffer_window_memory: ConversationBufferWindowMemory = ConversationBufferWindowMemory(
input_key="input", memory_key="chat_history")
search = langchain.SerpAPIChain()
self_ask_and_search = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search, verbose=True)
tools = [
Tool(
name="Search",
func=self_ask_and_search.run,
description="useful for when you need to answer questions about current events",
)
]
prompt = ConversationalChatAgent.create_prompt(
tools, input_variables=["input", "chat_history", "agent_scratchpad"]
)
llm_chain = LLMChain(
llm=OpenAI(temperature=0), prompt=prompt)
agent = ConversationalChatAgent(
llm_chain=llm_chain, tools=tools, verbose=True)
agent_executor = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, memory=conversation_buffer_window_memory)
agent_executor.run('what is the capital of texas?')
```
returns the following error:
(<class 'ValueError'>, ValueError('variable chat_history should be a list of base messages, got '), <traceback object at 0x0000027BA6366E40>)
### Expected behavior
Return the LLM result while updating the memory mechanism.
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PR_kwDOIPDwls5U8DX3
| 7,364 |
Added support for stability API for bedrock
|
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NONE
| null |
## Summary
This PR adds support for stability AI in bedrock. Implementation is going to return the image in base64 format as text string.
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PR_kwDOIPDwls5U71-m
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Upgrade the AwaDB from 0.3.5 to 0.3.6
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CONTRIBUTOR
| null |
Upgrade the AwaDB from 0.3.5 to 0.3.6
1. Support get, delete and update batch documents
2. Add the implementation of the max_marginal_relevance_search
3. Change the default vectors' distance computation type from L2 to InnerProduct
Please review @rlancemartin, @eyurtsev @hwchase17. Thanks!
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PR_kwDOIPDwls5U7zIF
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Unset Cache in Tests
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COLLABORATOR
| null |
This is impacting other unit tests that use callbacks since the cache is still set (just empty)
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PR_kwDOIPDwls5U7u5_
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Added support for stability API for bedrock
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NONE
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<!-- Thank you for contributing to LangChain!
This pull request is to add support for stability AI models for bedrock.
Replace this comment with:
- Description: Added support for bedrock stability AI models
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: @3coins
- 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!
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_kwDOIPDwls5U7trY
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docs: `dependents` update 4
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[] | null | 2 | 2023-07-07T16:41:20 | 2023-07-07T18:35:43 | 2023-07-07T17:22:31 |
COLLABORATOR
| null |
Updated links and counters of the `dependents` page.
@baskaryan
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PR_kwDOIPDwls5U7sli
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update extension name
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CONTRIBUTOR
| null |
hi @rlancemartin ,
We had a new deployment and the `pg_extension` creation command was updated from `CREATE EXTENSION pg_embedding` to `CREATE EXTENSION embedding`.
https://github.com/neondatabase/neon/pull/4646
The extension not made public yet. No users will be affected by this. Will be public next week.
Please let me know if you have any questions.
Thank you in advance 🙏
|
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PR_kwDOIPDwls5U7prv
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Update Pinecone Upsert method usage
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CONTRIBUTOR
| null |
Description: Refactor the upsert method in the Pinecone class to allow for additional keyword arguments. This change adds flexibility and extensibility to the method, allowing for future modifications or enhancements. The upsert method now accepts the `**kwargs` parameter, which can be used to pass any additional arguments to the Pinecone index. This change has been made in both the `upsert` method in the `Pinecone` class and the `upsert` method in the `similarity_search_with_score` class method. Falls in line with the usage of the upsert method in [Pinecone-Python-Client](https://github.com/pinecone-io/pinecone-python-client/blob/4640c4cf27b1ab935e76929c46d230dea9fe5506/pinecone/index.py#L73)
Issue: [This feature request in Pinecone Repo](https://github.com/pinecone-io/pinecone-python-client/issues/184)
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- Memory: @hwchase17
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PR_kwDOIPDwls5U7ls0
| 7,357 |
Explicitly list requires_reference in function
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CONTRIBUTOR
| null |
### Description
Created a Loader to get a list of specific logs from Datadog Logs.
### Dependencies
`datadog_api_client` is required.
### Tag maintainer
@rlancemartin, @eyurtsev
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
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change embedding to None
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bump 227
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[] | null | 1 | 2023-07-07T15:34:51 | 2023-07-07T15:52:36 | 2023-07-07T15:52:35 |
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| 1,793,680,431 |
I_kwDOIPDwls5q6WQv
| 7,353 |
HuggingFaceEndpoint Class Bug
|
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[] | null | 1 | 2023-07-07T14:49:27 | 2023-07-11T07:06:07 | 2023-07-11T07:06:07 |
NONE
| null |
### System Info
the provided class
[langchain/langchain/llms/huggingface_endpoint.py](https://github.com/hwchase17/langchain/blob/370becdfc2dea35eab6b56244872001116d24f0b/langchain/llms/huggingface_endpoint.py)
class HuggingFaceEndpoint(LLM):
has a bug.
it should be
```python
if self.task == "text-generation":
# Text generation return includes the starter text.
text = generated_text[0]["generated_text"]
```
not
```python
text = generated_text[0]["generated_text"][len(prompt) :]
```
the current class will likely just return a 0.
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
llm= HuggingFaceEndpoint(endpoint_url=os.getenv('ENDPOINT_URL'),task="text-generation",
model_kwargs={"temperature":0.7, "max_length":512})
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
```
### Expected behavior
the output is 0
|
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PR_kwDOIPDwls5U7DO9
| 7,352 |
openai fn update nb
|
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[] | null | 1 | 2023-07-07T14:26:07 | 2023-07-07T15:52:23 | 2023-07-07T15:52:22 |
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PR_kwDOIPDwls5U63e6
| 7,349 |
Update README.md
|
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[] | null | 1 | 2023-07-07T13:51:48 | 2023-07-07T13:57:36 | 2023-07-07T13:51:59 |
NONE
| 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!
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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| 1,793,567,578 |
PR_kwDOIPDwls5U6ywI
| 7,348 |
update MosaicML inputs and outputs
|
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[] | null | 3 | 2023-07-07T13:37:40 | 2023-07-07T21:23:11 | 2023-07-07T21:23:11 |
CONTRIBUTOR
| null |
As of today (July 7, 2023), the [MosaicML API](https://docs.mosaicml.com/en/latest/inference.html#text-completion-requests) uses `"inputs"` for the prompt
but we have
https://github.com/hwchase17/langchain/blob/0ed2da70200ada39f5319f604b4c5248bc98e81b/langchain/llms/mosaicml.py#L125
Furthermore, the output should be inside the `"outputs"` key
but we have
https://github.com/hwchase17/langchain/blob/0ed2da70200ada39f5319f604b4c5248bc98e81b/langchain/llms/mosaicml.py#L163-L170
This PR adds support for this new format.
`<rant>`
It is beyond me, why they apparently have versioned their API (endpoints look like `https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict`; note the `v1` in there), but just keep changing the API without upping the version number. There is a good reason for
https://github.com/hwchase17/langchain/blob/0ed2da70200ada39f5319f604b4c5248bc98e81b/langchain/llms/mosaicml.py#L160-L161
`</rant>`
CC @hwchase17, @baskaryan
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| 1,793,525,647 |
I_kwDOIPDwls5q5weP
| 7,347 |
map_reduce and refine not working with RetrievalQA chain
|
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[] | null | 9 | 2023-07-07T13:09:59 | 2023-11-14T16:08:01 | 2023-11-14T16:08:00 |
NONE
| null |
### System Info
Langchain Version - 0.0.201
Platform - Windows 11
Python - 3.10.11
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. load some text documents to a vector store, i used deeplake
2. load the db
3. call the function, summarizer(db,"Summarize the mentions of google according to their AI program")(defined in attached file)
4. run for chain_type as stuff, it will work, for map_reduce it will fail in retrieval QA Bot
[main.zip](https://github.com/hwchase17/langchain/files/11982265/main.zip)
### Expected behavior
it should work for all the chain types and give results
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| 1,793,511,650 |
I_kwDOIPDwls5q5tDi
| 7,346 |
Issue: When implementing Cypher Search using neo4j environment
|
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NONE
| null |
### Issue you'd like to raise.
python environment：
neo4j 5.0.0
neobolt 1.7.17
neo4j version: neo4j-35-apoc:v20220808
报错：
File "D:\Anaconda3\envs\langchain\lib\site-packages\neo4j_sync\io_bolt3.py", line 200, in run
raise ConfigurationError(
neo4j.exceptions.ConfigurationError: Database name parameter for selecting database is not supported in Bolt Protocol Version(3, 0). Database name 'neo4
### Suggestion:
_No response_
|
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| 1,793,476,844 |
I_kwDOIPDwls5q5kjs
| 7,345 |
Load OPEN API yaml file to vertex ai LLM model
|
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[] | null | 1 | 2023-07-07T12:36:51 | 2023-10-14T20:10:17 | 2023-10-14T20:10:16 |
NONE
| null |
### Feature request
I am looking for loading OPEN API yaml file into vertex ai llm model. Langchain provides that for OPENAI but not for vertex ai. This is how I currently write code in Open AI. I want a similar functionality for Vertex AI
spec = OpenAPISpec.from_file("sample.yaml")
openai_fns, call_api_fn = openapi_spec_to_openai_fn(spec)
### Motivation
I am creating on a GENAI chatbot for my company where customers can ask questions specific to our product. I need to return the answers of those queries using our internal API's. To query those API's, I need to know which API to call and the API parameters filled as per the user query. For that, I need vertex ai function calling support to query it.
Is there already a way in vertex ai which does this? Kindly help me on this.
### Your contribution
I can help on the issue if anything is required.
|
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| 1,793,440,622 |
PR_kwDOIPDwls5U6XHQ
| 7,344 |
Add context to an output parsing error on Pydantic schema to improve exception handling
|
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[] | null | 2 | 2023-07-07T12:10:03 | 2023-07-07T18:49:38 | 2023-07-07T18:49:37 |
CONTRIBUTOR
| null |
## Changes
- [X] Fill the `llm_output` param when there is an output parsing error in a Pydantic schema so that we can get the original text that failed to parse when handling the exception
## Background
With this change, we could do something like this:
```
output_parser = PydanticOutputParser(pydantic_object=pydantic_obj)
chain = ConversationChain(..., output_parser=output_parser)
try:
response: PydanticSchema = chain.predict(input=input)
except OutputParserException as exc:
logger.error(
'OutputParserException while parsing chatbot response: %s', exc.llm_output,
)
```
<!-- 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!
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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| 1,793,386,363 |
I_kwDOIPDwls5q5Od7
| 7,343 |
Issue: openai.error.RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization
|
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[] | null | 18 | 2023-07-07T11:34:51 | 2023-10-22T20:00:13 | null |
NONE
| null |
### Issue you'd like to raise.
I want to download a large file, 5,000,000 characters, and I get an error: openai.error.RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-DOqIVFPozlLEOcvlTbpvpcKt on tokens per min. Limit: 150000 / min. Current: 0 / min. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.
My code:
`class Agent:
def __init__(self, openai_api_key: str | None = None) -> None:
self.key = openai_api_key
self.embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key, request_timeout=120, max_retries=10)
self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
self.llm = ChatOpenAI(temperature=0, openai_api_key=openai_api_key, max_tokens=500, model_name="gpt-3.5-turbo-16k")
self.chat_history = None
self.chain = None
self.db = None
def ask(self, question: str) -> str:
response = self.chain({"question": question, "chat_history": self.chat_history})
response = response["answer"].strip()
self.chat_history.append((question, response))
return response
def ingest(self, file_path) -> None:
loader = TextLoader(file_path, encoding="utf-8")
documents = loader.load()
splitted_documents = self.text_splitter.split_documents(documents)
if self.db is None:
self.db = FAISS.from_documents(splitted_documents, self.embeddings)
self.chain = ConversationalRetrievalChain.from_llm(self.llm, self.db.as_retriever())
self.chat_history = []
else:
self.db.add_documents(splitted_documents)
def forget(self) -> None:
self.db = None
self.chain = None
self.chat_history = None`
Is there a solution to this problem?
### Suggestion:
_No response_
|
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| 1,793,380,496 |
PR_kwDOIPDwls5U6J00
| 7,342 |
added index retreival from redis search
|
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CONTRIBUTOR
| null |
Replace this comment with:
- Description: change consists retrieval of document id from redis search for semantic search of redis vectorstore
- Issue: It helps to gather the document id for further processing of the filtered data in redis database
- Dependencies: no dependancies
- Tag maintainer: @rlancemartin, @eyurtsev
|
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| 1,793,313,012 |
I_kwDOIPDwls5q48j0
| 7,341 |
Create a built in translator for SelfQueryRetriever for Elasticsearch
|
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[] | null | 2 | 2023-07-07T10:53:34 | 2023-11-21T16:06:47 | 2023-11-21T16:06:45 |
NONE
| null |
### Feature request
Inferring query filters from natural language is powerful. The SelfQuery retriever is a great implementation but is not yet compatible with Elasticseach.
### Motivation
Choosing Elasticsearch as a vector store is interesting in terms of hybrid search.
It also makes sense when you have an established infrastructure and technical expertise.
### Your contribution
cc: @jeffvestal, @derickson
|
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I_kwDOIPDwls5q44A5
| 7,340 |
langchain + gpt4all
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[] | null | 6 | 2023-07-07T10:43:13 | 2023-11-28T16:09:46 | 2023-11-28T16:09:45 |
NONE
| null |
### System Info
LangChain v0.0.225, Ubuntu 22.04.2 LTS, Python 3.10
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm executing [https://github.com/hwchase17/langchain/blob/master/docs/extras/modules/model_io/models/llms/integrations/gpt4all.ipynb](url) code on my local machine, IDE is VSCode
Getting this error - **AttributeError: 'Model' object has no attribute '_ctx'**
### Expected behavior
|
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| 1,793,287,416 |
I_kwDOIPDwls5q42T4
| 7,339 |
Issue: Whisper terminates YouTube transcriptions.
|
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[] | null | 3 | 2023-07-07T10:39:03 | 2023-10-14T20:10:22 | 2023-10-14T20:10:21 |
NONE
| null |
### Issue you'd like to raise.
Hello everyone,
I've tried numerous approaches, but every time I attempt to transcribe a video from Google, the Whisper task gets terminated.
Does anyone have any suggestions?
```
def youtube_transcript(query: str) -> str:
# Use the URL of the YouTube video you want to download
youtube_url = [get_input_writing(query)]
# -------------
# Directory to save audio files
save_dir = "/home/ds_logos_2/transcripts"
# Transcribe the videos to text
loader = GenericLoader(YoutubeAudioLoader(youtube_url, save_dir), OpenAIWhisperParser())
docs = loader.load()
# Combine doc
combined_docs = [doc.page_content for doc in docs]
text = " ".join(combined_docs)
# Save the transcription to a text file
with open('/home/davesoma/transcripts/transcript.txt', 'w') as f:
f.write(text)
```
```
[youtube] -hxeDjAxvJ8: Downloading webpage
[youtube] -hxeDjAxvJ8: Downloading ios player API JSON
[youtube] -hxeDjAxvJ8: Downloading android player API JSON
[youtube] -hxeDjAxvJ8: Downloading m3u8 information
[info] -hxeDjAxvJ8: Downloading 1 format(s): 140
[download] Destination: /home/davesoma/ds_logos_2/transcripts/Marc Andreessen： Future of the Internet, Technology, and AI ｜ Lex Fridman Podcast #386.m4a
[download] 100% of 177.41MiB in 00:00:18 at 9.52MiB/s
[FixupM4a] Correcting container of "/home/davesoma/ds_logos_2/transcripts/Marc Andreessen： Future of the Internet, Technology, and AI ｜ Lex Fridman Podcast #386.m4a"
[ExtractAudio] Not converting audio /home/davesoma/ds_logos_2/transcripts/Marc Andreessen： Future of the Internet, Technology, and AI ｜ Lex Fridman Podcast #386.m4a; fil
e is already in target format m4a Transcribing part 1!
Killed
```
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I_kwDOIPDwls5q4ydD
| 7,338 |
Add feature to get document ID from redis after redis search document retrieval.
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[] | null | 2 | 2023-07-07T10:29:51 | 2023-10-06T16:04:59 | 2023-10-06T16:04:49 |
CONTRIBUTOR
| null |
### Feature request
I have been trying to delete custom redis data using langchain. But the search feature only returns the document and the metadata with similarity score. Hence I modified the code to return the document ID along with the document data. The id will be helpful for manual deletion of certain elements.
### Motivation
The retrieval of document ID from the redis semantic search will help me to systematically modify the wrong data with right ones after filtering it using LLMs.
### Your contribution
Yes I have fixed the redis part to retrieve the document ID from semantic search. I will fix the issue if the pull request is allowed.
|
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I_kwDOIPDwls5q4qQg
| 7,337 |
Working of ConversationBufferMemory in the context of document based question answering
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[] | null | 1 | 2023-07-07T10:12:08 | 2023-10-14T20:10:27 | 2023-10-14T20:10:26 |
NONE
| null |
### Issue you'd like to raise.
I am doing the pdf question answering using the below code.
Note: I am integrated the ConversationBufferMemory for keeping my chat in the memory
```
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import Cohere
import os
os.environ["COHERE_API_KEY"] = "cohere key"
model = Cohere(model="command-xlarge")
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
pdf_path = "file.pdf"
loader = PyPDFLoader(pdf_path)
pages = loader.load_and_split()
vectordb = Chroma.from_documents(pages, embedding=embeddings,
persist_directory=".")
vectordb.persist()
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
pdf_qa = ConversationalRetrievalChain.from_llm(model, vectordb.as_retriever(), memory=memory)
while True:
query = input("Enter the question\n")
result = pdf_qa({"question": query})
print("Answer:")
print(result["answer"])
```
Actually what happening here?
I am observing that memory is not keeping updated here, because if I am asking any question related to the previous context, it is unable to answer.
Is my method is correct?
### Suggestion:
_No response_
|
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| 7,336 |
Issue: question about agents
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[] | null | 6 | 2023-07-07T09:49:22 | 2023-10-19T16:06:33 | 2023-10-19T16:06:32 |
NONE
| null |
I want pass "chat_history" to the agents like :
```python
...
agent = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)
response = agent.run({"input": {"chat_history": _message_to_tuple(history), "question": query}})
```
but got an error. how can i pass 'chat_history' to the agent?
### Suggestion:
_No response_
|
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| 7,335 |
bump 226
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[] | null | 1 | 2023-07-07T09:47:39 | 2023-07-07T09:59:14 | 2023-07-07T09:59:13 |
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Qdrant: Ability to add vectors to already existing collection & set vector size
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NONE
| null |
Problem:
Whenever we add vectors to an existing qdrant collection that collection gets deleted and re-created from scratch if we use for example `VectorstoreIndexCreator()`.
This PR solves this issue and adds further automatisation abilities without breaking the already existing behavior.
In addition to that this PR adds the ability to configure a fixed vector size, eliminating the need to make a single embedding just to figure out the vector size and further reducing api usage resulting in reduced costs for end-users.
So this PR introduces the following two flags:
`recreate_collection = Optional[bool]`
`vector_size = Optional[int]`
How this solution works:
If recreate_collection=False, it checks if collection exists, if not, continue like before this PR ((re)create collection)
if collection exists, and recreate_collection=False, and init_from=None then do not execute recreate_collection()
VectorstoreIndexCreator code example (after PR):
```python
VectorstoreIndexCreator(
vectorstore_cls=Qdrant,
text_splitter=RecursiveCharacterTextSplitter(
chunk_size=int(os.environ.get('CHUNK_SIZE_TOKENS', 400)),
separators=sepList,
chunk_overlap=0,
length_function=get_tokens,
),
vectorstore_kwargs=dict(
host=os.environ.get('QDRANT_HOST', 'qdrant'),
port=int(os.environ.get('QDRANT_PORT', '6333')),
grpc_port=int(os.environ.get('QDRANT_GRPC_PORT', '6334')),
prefer_grpc=bool(os.environ.get('QDRANT_PREFER_GRPC', 'True')),
collection_name=self.request.collection_name,
recreate_collection=False, # <-- this is new, default value is True
vector_size=int(os.environ.get('VECTOR_DIMENSIONS', '1536')), # <-- this is new, default value is None
),
).from_documents(self.documents)
```
--
Maintainer responsibilities:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5U5ZQX
| 7,333 |
Add upserting and deletion capability to PGVector
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NONE
| null |
Currently, the PGVector vectorstore only supports adding embeddings to the store. This PR adds upserting and deletion capability to the store, so you can update existing embeddings if the underlying text have changed. This is useful if you have indexed a set of documents but some of the documents are being changed and you want to keep the index up-to-date.
To use this feature, simply provided ids for all texts/or documents. The pgvector notebook has been updated accordingly.
The fixes issue #6866.
👀 @rlancemartin, @eyurtsev
In case you want to announce on Twitter, my Twitter handle is @angelol
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PR_kwDOIPDwls5U5Kyi
| 7,332 |
Bug fix related to base vectorstore's metadata.
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[] | null | 4 | 2023-07-07T08:49:01 | 2023-08-16T04:04:24 | 2023-07-12T01:15:28 |
NONE
| null |
If I use Pinecone to create a vectorbase, by calling from_documents:
```python
vectorstore = Pinecone.from_documents(
documents=source_chunks, embedding=embedding, index_name='apple')
```
the vectors of the same source, have the same content.
Issue also described here: https://stackoverflow.com/questions/76401057/why-does-pinecone-repeatedly-return-the-same-result-from-my-series-of-documents
This happens because when I create documents like this:
```python
source_chunks = []
for source in docs:
for chunk in splitter.split_text(source.page_content):
source_chunks.append(
Document(page_content=chunk, metadata=source.metadata))
```
the source.metadata is not copied by default. To enforce that, I need to use `Document(page_content=chunk, metadata=source.metadata.copy()))`.
Although this is a user implementation issue, such a thing can happen quite often. To prevent this, we can make sure that the metadata dict is copied by default.
After wasting 2 hours of my time trying to find out why am I getting dublicate responses on similarity search, I realized that this should be addressed.
Let me know what you think.
Thanks!
|
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AzureChatOpenAI raises The API deployment for this resource does not exist when used with ConversationalRetrievalChain
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[] | null | 2 | 2023-07-07T08:41:48 | 2023-07-10T11:11:16 | 2023-07-10T11:11:16 |
NONE
| null |
### System Info
Hello,
I am trying to use AzureChatOpenAI to develop a QA system with memory. For that purpose, I have the following code:
```python
import faiss
import pickle
from langchain.chat_models import AzureChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.schema import HumanMessage
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
# Load the LangChain.
index = faiss.read_index("docs.index")
with open("faiss_store.pkl", "rb") as f:
store = pickle.load(f)
AZURE_BASE_URL = "{MY_BASE_URL}.openai.azure.com/"
AZURE_OPENAI_API_KEY = "MY_API_KEY"
DEPLOYMENT_NAME = "chat"
llm = AzureChatOpenAI(
openai_api_base=AZURE_BASE_URL,
openai_api_version="2023-03-15-preview",
deployment_name="chat",
openai_api_key=AZURE_OPENAI_API_KEY,
openai_api_type="azure",
temperature=0.01
)
retriever = store.as_retriever()
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory
)
user_input = "Question?"
result = qa({"question": user_input})
print(result)
```
This code is raising the following error:
```
openai.error.InvalidRequestError: The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again.
```
I have tried using AzureChatOpenAI in other ways and it is working without any problem, using the same deployed model in Azure:
```python
AZURE_BASE_URL = "{MY_BASE_URL}.openai.azure.com/"
AZURE_OPENAI_API_KEY = "MY_API_KEY"
DEPLOYMENT_NAME = "chat"
llm = AzureChatOpenAI(
openai_api_base=AZURE_BASE_URL,
openai_api_version="2023-03-15-preview",
deployment_name="chat",
openai_api_key=AZURE_OPENAI_API_KEY,
openai_api_type="azure",
temperature=0.01
)
chain = load_qa_chain(llm=llm, chain_type="map_reduce")
```
Therefore, the problem is not about the deployment I made in Azure, it seems to work fine in other situations.
Am I missing something when using ConversationalRetrievalChain with AzureChatOpenAI? I have tried so many things and nothing seems to work.
Thanks in advance for any help.
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Declare the LLM using AzureChatOpenAI:
```python
AZURE_BASE_URL = "{MY_BASE_URL}.openai.azure.com/"
AZURE_OPENAI_API_KEY = "MY_API_KEY"
DEPLOYMENT_NAME = "chat"
llm = AzureChatOpenAI(
openai_api_base=AZURE_BASE_URL,
openai_api_version="2023-03-15-preview",
deployment_name="chat",
openai_api_key=AZURE_OPENAI_API_KEY,
openai_api_type="azure",
temperature=0.01
)
```
2. Declare the ConversationBufferMemory:
```python
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
```
3. Load FAISS index:
```python
# Load the LangChain.
index = faiss.read_index("docs.index")
with open("faiss_store.pkl", "rb") as f:
store = pickle.load(f)
```
4. Declare and run the ConversationalRetrievalChain:
```python
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory
)
user_input = "Question?"
result = qa({"question": user_input})
print(result)
```
### Expected behavior
Error raised:
```
openai.error.InvalidRequestError: The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again.
```
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Output parser redirect
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COLLABORATOR
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Related to ##7311
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PR_kwDOIPDwls5U4zo9
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Add local support for audio models
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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!
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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Link to correct paper
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CONTRIBUTOR
| null |
The documentation talked about the ReAct framework, but linked to the MRKL Systems paper. Now linking to the correct paper.
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Conversation Summary Buffer Memory does not accept a system message
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[] | null | 7 | 2023-07-07T07:27:42 | 2023-12-30T18:31:29 | 2023-12-30T16:07:28 |
NONE
| null |
### System Info
I intend to use the conversation summary buffer memory with ChatOpenAI in a conversation chain. For the chat, there's a need to set the system message to instruct and give appropriate personality to the chat assistant. However, system message is not supported to be inserted in the memory either via the save_context (the documented way) or the memory.chat_memory.message.insert()
The summary of the chat itself seems to use the system message to send the summary. This makes Conversation Summary Buffer Memory incompatible with ChatOpenAI.
### Who can help?
@hwchase17 @agola11
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Using either of the two methods to use the system message
1. memory.chat_memory.messages.insert(0, system_message)
2. memory.save_context({"input": SystemMessage(content=system_message)}, {"output": ""})
### Expected behavior
We should be able to use any memory with ChatOpenAI as these need to be modular but ConversationSummaryBufferMemory seems incompatible with it due to system message.
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Marqo Vector Store Examples & Type Hints
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[] | null | 2 | 2023-07-07T07:16:09 | 2023-07-07T08:11:20 | 2023-07-07T08:11:20 |
CONTRIBUTOR
| null |
This PR improves the example notebook for the Marqo vectorstore implementation by adding a new RetrievalQAWithSourcesChain example. The `embedding` parameter in `from_documents` has its type updated to `Union[Embeddings, None]` and a default parameter of None because this is ignored in Marqo.
This PR also upgrades the Marqo version to 0.11.0 to remove the device parameter after a breaking change to the API.
Related to #7068 @tomhamer @hwchase17
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[] | null | 3 | 2023-07-07T06:06:08 | 2023-12-30T16:07:34 | 2023-12-30T16:07:33 |
NONE
| null |
I've got a GoogleDriveLoader implemented with a exponential backoff and a sleep function to try further mitigate rate limits, but I still get rate limit errors from Google.
Even though I've added a time.sleep(5) statement, I assume it only takes effect before each attempt to load all the documents, not between individual API calls within the load() method.
```
google_loader = GoogleDriveLoader(
folder_id="xxxxxxxxx",
credentials_path="credentials.json",
token_path="token.json",
file_types=["document", "sheet", "pdf"],
file_loader_cls=UnstructuredFileIOLoader,
recursive=True,
verbose=True,
)
@retry(
stop=stop_after_attempt(7), wait=wait_exponential(multiplier=2, min=60, max=300)
)
def load_documents():
time.sleep(5) # delay for 5 seconds
return google_loader.load()
try:
google_docs = load_documents()
except:
logging.error("Exceeded retry attempts for Google API rate limit.")
raise
```
The exception output:
```
ERROR:root:Exceeded retry attempts for Google API rate limit.
IndexError: list index out of range
```
Stacktrace:
```
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/A_xx?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/A_xx?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/A_xx?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/BF%20IS?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/BF%20BS%20?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/Reporting%20IS?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/Reporting%20BS?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/Statistics?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/Val_Summary?alt=json
DEBUG:googleapiclient.discovery:URL being requested: GET https://sheets.googleapis.com/v4/spreadsheets/xxxxx-xxxxx/values/Val_Workings?alt=json
ERROR:root:Exceeded retry attempts for Google API rate limit.
Traceback (most recent call last):
File "/Users/chris/Repositories/xxxx/.venv/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/Users/chris/Repositories/xxxx/ingest.py", line 44, in load_documents
return google_loader.load()
File "/Users/chris/Repositories/xxxx/.venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 347, in load
return self._load_documents_from_folder(
File "/Users/chris/Repositories/xxxx/.venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 248, in _load_documents_from_folder
returns.extend(self._load_sheet_from_id(file["id"])) # type: ignore
File "/Users/chris/Repositories/xxxx/.venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 173, in _load_sheet_from_id
header = values[0]
IndexError: list index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/chris/Repositories/xxxx/ingest.py", line 68, in <module>
ingest_docs()
File "/Users/chris/Repositories/xxxx/ingest.py", line 47, in ingest_docs
google_docs = load_documents()
File "/Users/chris/Repositories/xxxx/.venv/lib/python3.10/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/Users/chris/Repositories/xxxx/.venv/lib/python3.10/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/Users/chris/Repositories/xxxx/.venv/lib/python3.10/site-packages/tenacity/__init__.py", line 326, in iter
raise retry_exc from fut.exception()
tenacity.RetryError: RetryError[<Future at 0x142dfab30 state=finished raised IndexError>]
```
### Suggestion:
Due to the recursive function, and the use case for most people being to load a large Drive folder, would it be possible to implement a rate limiter into the loader itself to slow down the individual API calls?
Alternatively, does anyone have any recommendations on how to better implement an exponential backoff?
|
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Bagatur/clarifai update
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[] | null | 1 | 2023-07-07T06:02:29 | 2023-07-07T06:23:22 | 2023-07-07T06:23:21 |
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!
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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|
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I_kwDOIPDwls5q3FJd
| 7,322 |
What is the best size for a chunk
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[] | null | 2 | 2023-07-07T05:42:08 | 2023-10-14T20:10:37 | 2023-10-14T20:10:36 |
NONE
| null |
### Issue you'd like to raise.
I used RecursiveCharacterTextSplitter.from_tiktoken_encoder to split a document, and if I set chunk_size to 2000, OpenAI cannot answer my question by the documents, if I set chunk_size to 500, OpenAI can work very well. I want to know, As a rule of thumb, what is the best size for a chunk
### Suggestion:
_No response_
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[RFC] add interface for evaluating messages
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[] | null | 2 | 2023-07-07T05:38:20 | 2023-07-17T04:11:08 | 2023-07-17T04:11:08 |
COLLABORATOR
| null |
still a wip
<!-- 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!
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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| 7,320 |
docs: `ecosystem/integration` update
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[] | null | 6 | 2023-07-07T05:09:35 | 2023-07-26T17:14:22 | 2023-07-26T17:14:21 |
COLLABORATOR
| null |
The example notebooks with `CallbackHandlers` examples were in the `ecosystem/integration` folder.
They should be in the `modules/callbacks/integrations` folder.
- Moved notebooks. See ^.
- Created correspondent `ecosystem/integration` pages in place of examples. Now no Jupyter Notebooks are here, but only .md and .mdx .
- Added descriptions to the Integration Cards.
@baskaryan
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PR_kwDOIPDwls5U4CDx
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Add Xorbits Dataframe as a Document Loader
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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!
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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- [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source computing framework that makes it easy to scale data science and machine learning workloads in parallel. Xorbits can leverage multi cores or GPUs to accelerate computation on a single machine, or scale out up to thousands of machines to support processing terabytes of data.
- This PR added support for the Xorbits document loader, which allows langchain to leverage Xorbits to parallelize and distribute the loading of data.
- Dependencies: This change requires the Xorbits library to be installed in order to be used.
`pip install xorbits`
- Request for review: @rlancemartin, @eyurtsev
- Twitter handle: https://twitter.com/Xorbitsio
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PR_kwDOIPDwls5U4A9i
| 7,318 |
Corrected a typo in elasticsearch.ipynb
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[] | null | 2 | 2023-07-07T04:34:14 | 2023-07-07T05:35:33 | 2023-07-07T05:35:32 |
CONTRIBUTOR
| null |
Simple typo fix
|
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PR_kwDOIPDwls5U3qss
| 7,316 |
docs : add cnosdb to Ecosystem Integrations
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[] | null | 2 | 2023-07-07T03:09:24 | 2023-07-07T05:35:23 | 2023-07-07T05:35:22 |
CONTRIBUTOR
| null |
- Implement a `from_cnosdb` method for the `SQLDatabase` class
- Write CnosDB documentation and add it to Ecosystem Integrations
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I_kwDOIPDwls5q2DOx
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Zapier Toolkit and Function Agents not compatible
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[] | null | 2 | 2023-07-07T01:38:10 | 2023-10-14T20:10:42 | 2023-10-14T20:10:41 |
NONE
| null |
### System Info
Langchain version: v0.0.225
Python Version: 3.10
Deployed and running on AWS Lambda deployed with x86_64 architecture.
### Who can help?
@jacoblee93
### Information
- [x] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
def callChatModel(input, token):
print('Executing with input:', input)
llm = ChatOpenAI(model="gpt-3.5-turbo-0613",
temperature=0)`
history = DynamoDBChatMessageHistory(table_name="MemoryPy",
session_id=token)
memory = ConversationBufferWindowMemory(
k=20, memory_key='chat_history', chat_memory=history, input_key="input", return_messages=True)
zapier = ZapierNLAWrapper(zapier_nla_oauth_access_token=token)
zapier_toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)
tools = load_tools(["serpapi"], llm=llm) + zapier_toolkit.tools
print(tools)
agent = initialize_agent(
tools, llm,
agent=AgentType.OPENAI_FUNCTIONS,
memory=memory,
verbose=True,
handle_parsing_errors=True,
)
resp = agent.run(input=input)
return resp
Input to the chat model is
`Executing with input: Look up a basic fact about the sun, no more than one sentence. Send this fact to <email>@gmail.com `
Execution logs from CloudWatch:
```
[1m> Entering new chain...[0m
[ERROR] InvalidRequestError: 'Gmail: Send Email' does not match '^[a-zA-Z0-9_-]{1,64}<!--EndFragment-->
</body>
</html> - 'functions.1.name'Traceback (most recent call last): File "/var/task/handler.py", line 152, in handle_chat chatResp = callChatModel(message, token) File "/var/task/handler.py", line 92, in callChatModel resp = agent.run(input=input) File "/var/task/langchain/chains/base.py", line 320, in run return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ File "/var/task/langchain/chains/base.py", line 181, in __call__ raise e File "/var/task/langchain/chains/base.py", line 175, in __call__ self._call(inputs, run_manager=run_manager) File "/var/task/langchain/agents/agent.py", line 987, in _call next_step_output = self._take_next_step( File "/var/task/langchain/agents/agent.py", line 792, in _take_next_step output = self.agent.plan( File "/var/task/langchain/agents/openai_functions_agent/base.py", line 210, in plan predicted_message = self.llm.predict_messages( File "/var/task/langchain/chat_models/base.py", line 398, in predict_messages return self(messages, stop=_stop, **kwargs) File "/var/task/langchain/chat_models/base.py", line 348, in __call__ generation = self.generate( File "/var/task/langchain/chat_models/base.py", line 124, in generate raise e File "/var/task/langchain/chat_models/base.py", line 114, in generate self._generate_with_cache( File "/var/task/langchain/chat_models/base.py", line 261, in _generate_with_cache return self._generate( File "/var/task/langchain/chat_models/openai.py", line 371, in _generate response = self.completion_with_retry(messages=message_dicts, **params) File "/var/task/langchain/chat_models/openai.py", line 319, in completion_with_retry return _completion_with_retry(**kwargs) File "/var/task/tenacity/__init__.py", line 289, in wrapped_f return self(f, *args, **kw) File "/var/task/tenacity/__init__.py", line 379, in __call__ do = self.iter(retry_state=retry_state) File "/var/task/tenacity/__init__.py", line 314, in iter return fut.result() File "/var/lang/lib/python3.10/concurrent/futures/_base.py", line 451, in result return self.__get_result() File "/var/lang/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/var/task/tenacity/__init__.py", line 382, in __call__ result = fn(*args, **kwargs) File "/var/task/langchain/chat_models/openai.py", line 317, in _completion_with_retry return self.client.create(**kwargs) File "/var/task/openai/api_resources/chat_completion.py", line 25, in create return super().create(*args, **kwargs) File "/var/task/openai/api_resources/abstract/engine_api_resource.py", line 153, in create response, _, api_key = requestor.request( File "/var/task/openai/api_requestor.py", line 298, in request resp, got_stream = self._interpret_response(result, stream) File "/var/task/openai/api_requestor.py", line 700, in _interpret_response self._interpret_response_line( File "/var/task/openai/api_requestor.py", line 763, in _interpret_response_line raise self.handle_error_response( | [ERROR] InvalidRequestError: 'Gmail: Send Email' does not match '^[a-zA-Z0-9_-]{1,64}<!--EndFragment-->
</body>
</html> - 'functions.1.name' Traceback (most recent call last): File "/var/task/handler.py", line 152, in handle_chat chatResp = callChatModel(message, token) File "/var/task/handler.py", line 92, in callChatModel resp = agent.run(input=input) File "/var/task/langchain/chains/base.py", line 320, in run return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ File "/var/task/langchain/chains/base.py", line 181, in __call__ raise e File "/var/task/langchain/chains/base.py", line 175, in __call__ self._call(inputs, run_manager=run_manager) File "/var/task/langchain/agents/agent.py", line 987, in _call next_step_output = self._take_next_step( File "/var/task/langchain/agents/agent.py", line 792, in _take_next_step output = self.agent.plan( File "/var/task/langchain/agents/openai_functions_agent/base.py", line 210, in plan predicted_message = self.llm.predict_messages( File "/var/task/langchain/chat_models/base.py", line 398, in predict_messages return self(messages, stop=_stop, **kwargs) File "/var/task/langchain/chat_models/base.py", line 348, in __call__ generation = self.generate( File "/var/task/langchain/chat_models/base.py", line 124, in generate raise e File "/var/task/langchain/chat_models/base.py", line 114, in generate self._generate_with_cache( File "/var/task/langchain/chat_models/base.py", line 261, in _generate_with_cache return self._generate( File "/var/task/langchain/chat_models/openai.py", line 371, in _generate response = self.completion_with_retry(messages=message_dicts, **params) File "/var/task/langchain/chat_models/openai.py", line 319, in completion_with_retry return _completion_with_retry(**kwargs) File "/var/task/tenacity/__init__.py", line 289, in wrapped_f return self(f, *args, **kw) File "/var/task/tenacity/__init__.py", line 379, in __call__ do = self.iter(retry_state=retry_state) File "/var/task/tenacity/__init__.py", line 314, in iter return fut.result() File "/var/lang/lib/python3.10/concurrent/futures/_base.py", line 451, in result return self.__get_result() File "/var/lang/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result raise self._exception File "/var/task/tenacity/__init__.py", line 382, in __call__ result = fn(*args, **kwargs) File "/var/task/langchain/chat_models/openai.py", line 317, in _completion_with_retry return self.client.create(**kwargs) File "/var/task/openai/api_resources/chat_completion.py", line 25, in create return super().create(*args, **kwargs) File "/var/task/openai/api_resources/abstract/engine_api_resource.py", line 153, in create response, _, api_key = requestor.request( File "/var/task/openai/api_requestor.py", line 298, in request resp, got_stream = self._interpret_response(result, stream) File "/var/task/openai/api_requestor.py", line 700, in _interpret_response self._interpret_response_line( File "/var/task/openai/api_requestor.py", line 763, in _interpret_response_line raise self.handle_error_response(
-- | --
```
### Expected behavior
I'd expect the agent chain to just execute
|
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PR_kwDOIPDwls5U3TBs
| 7,314 |
Support streaming for LLM and Chat Models calls with n>1 and batch calls
|
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[] | null | 3 | 2023-07-07T01:32:09 | 2023-10-02T19:30:24 | 2023-10-02T19:30:24 |
CONTRIBUTOR
| null |
Mirror PR: https://github.com/hwchase17/langchainjs/pull/1760
This also adds support for multiple prompts in ChatOpenAI LLM
Fixes LC-98
|
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| 7,313 |
Fix: OpenAIFunctionsAgent often returns python as a tool in planning, when argument parsing fails
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[] | null | 10 | 2023-07-07T01:29:57 | 2023-09-18T23:17:17 | 2023-09-18T23:17:17 |
CONTRIBUTOR
| null |
- Description: With OpenAIFunctionsAgent, often a plan includes `python` as a tool, whose `arguments` is a `str` of the code to execute. In such a case, `_parse_ai_message` fails due to `JSONDecodeError`. This PR does a hack to fix the problem by creating `{"__arg1": arguments}` following the existing hack. I'm unsure when the arguments become string, so I put a condition to do that only for `python` as a tool.
- Tag maintainer: @hinthornw
- Twitter handle: mapped
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Refac package version check
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[] | null | 1 | 2023-07-07T00:04:31 | 2023-07-07T05:21:54 | 2023-07-07T05:21:53 |
COLLABORATOR
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I_kwDOIPDwls5q1yim
| 7,311 |
DOC: Broken Python Information Link in Langchain Documentation
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[] | null | 2 | 2023-07-07T00:02:09 | 2023-07-07T16:31:22 | 2023-07-07T16:31:22 |
NONE
| null |
### Issue with current documentation:
Python Documentation for Output Parser Unavailable
URL: https://python.langchain.com/docs/modules/prompts/output_parsers.html
### Idea or request for content:
I am currently taking the "Langchain/lesson/2/models, prompts, and parsers" course from deeplearning.ai. While working on the course material, I encountered difficulties with the output parser in Python. To seek assistance and better understand the usage of the output parser, I attempted to access the documentation for the Python implementation. However, I received a "page not found" error when trying to access the Python documentation.
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PR_kwDOIPDwls5U2zB4
| 7,310 |
adding max_marginal_relevance_search method to MongoDBAtlasVectorSearch
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CONTRIBUTOR
| null |
Adding a maximal_marginal_relevance method to the MongoDBAtlasVectorSearch vectorstore enhances the user experience by providing more diverse search results
Issue: #7304
@rlancemartin, @eyurtsev --> Please make sure that I'm using correctly the maximal_marginal_relevance (from utils).
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I_kwDOIPDwls5q1brY
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Llama_index model as a tool for lang chain
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[] | null | 5 | 2023-07-06T22:34:36 | 2023-07-09T20:42:18 | 2023-07-09T20:42:18 |
NONE
| null |
### System Info
I got error when try to load custom LLM for Llama-Index
```
# setup prompts - specific to StableLM
from llama_index.prompts.prompts import SimpleInputPrompt
system_prompt = """<|SYSTEM|># StableLM Tuned (Alpha version)
- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
- StableLM will refuse to participate in anything that could harm a human.
"""
# This will wrap the default prompts that are internal to llama-index
query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
import torch
llm = HuggingFaceLLM(
context_window=4096,
max_new_tokens=256,
generate_kwargs={"temperature": 0.7, "do_sample": False, "return_dict_in_generate":True},
system_prompt=system_prompt,
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name="StabilityAI/stablelm-tuned-alpha-3b",
model_name="StabilityAI/stablelm-tuned-alpha-3b",
device_map="auto",
stopping_ids=[50278, 50279, 50277, 1, 0],
tokenizer_kwargs={"max_length": 4096},
)
# load in HF embedding model from langchain
embed_model = LangchainEmbedding(HuggingFaceEmbeddings())
service_context = ServiceContext.from_defaults(chunk_size=1024, llm=llm,embed_model=embed_model)
documents = SimpleDirectoryReader('data\\abnamro').load_data()
index = VectorStoreIndex.from_documents(documents, service_context=service_context,show_progress=True)
from langchain.agents import Tool
tools = [
Tool(
name="LlamaIndex",
func=lambda q: str(index.as_query_engine(
retriever_mode="embedding",
verbose=True,
service_context=service_context
).query(q)),
description="useful for when you want to answer questions about finance. The input to this tool should be a complete english sentence.",
return_direct=True,
),
]
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.agents import initialize_agent
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent_executor = initialize_agent(
tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,memory=memory
)
agent_executor.run(input="What is inflation in the Czech Republic?")
```
got
```
ValidationError Traceback (most recent call last)
Cell In[13], line 1
----> 1 agent_executor = initialize_agent(
2 tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,memory=memory
3 )
4 agent_executor.run(input="What is inflation in the Czech Republic?")
File F:\anaconda\lib\site-packages\langchain\agents\initialize.py:57, in initialize_agent(tools, llm, agent, callback_manager, agent_path, agent_kwargs, tags, **kwargs)
55 agent_cls = AGENT_TO_CLASS[agent]
56 agent_kwargs = agent_kwargs or {}
---> 57 agent_obj = agent_cls.from_llm_and_tools(
58 llm, tools, callback_manager=callback_manager, **agent_kwargs
59 )
60 elif agent_path is not None:
61 agent_obj = load_agent(
62 agent_path, llm=llm, tools=tools, callback_manager=callback_manager
63 )
File F:\anaconda\lib\site-packages\langchain\agents\conversational\base.py:115, in ConversationalAgent.from_llm_and_tools(cls, llm, tools, callback_manager, output_parser, prefix, suffix, format_instructions, ai_prefix, human_prefix, input_variables, **kwargs)
105 cls._validate_tools(tools)
106 prompt = cls.create_prompt(
107 tools,
108 ai_prefix=ai_prefix,
(...)
113 input_variables=input_variables,
114 )
--> 115 llm_chain = LLMChain(
116 llm=llm,
117 prompt=prompt,
118 callback_manager=callback_manager,
119 )
120 tool_names = [tool.name for tool in tools]
121 _output_parser = output_parser or cls._get_default_output_parser(
122 ai_prefix=ai_prefix
123 )
File F:\anaconda\lib\site-packages\langchain\load\serializable.py:74, in Serializable.__init__(self, **kwargs)
73 def __init__(self, **kwargs: Any) -> None:
---> 74 super().__init__(**kwargs)
75 self._lc_kwargs = kwargs
File F:\anaconda\lib\site-packages\pydantic\main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for LLMChain
llm
value is not a valid dict (type=type_error.dict)
```
### Who can help?
@hwchase17 @agola11
### 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
Connect LlamaIndex with Langchain
### Expected behavior
Load custom LLM
|
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PR_kwDOIPDwls5U2sj0
| 7,308 |
Update audio.py added local execution version
|
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CONTRIBUTOR
| null |
To allow users to easily execute models for audio conversion locally I am adding a class on document_loaders.parsers.audio called OpenAIWhisperParserLocal() Takes two optional arguments: device and lang_model.
It checks if there is a GPU available and automatically select the best model based on the memory available.
<!-- 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!
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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wip: add caching option for telegram
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CONTRIBUTOR
| null |
todo: write proper description and test this out.
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!
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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| 1,792,305,201 |
PR_kwDOIPDwls5U2dAz
| 7,306 |
Solving anthropic packaging version issue
|
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[] | null | 2 | 2023-07-06T21:45:51 | 2023-07-06T23:38:43 | 2023-07-06T23:35:43 |
CONTRIBUTOR
| null |
- Description: Solving, anthropic packaging version issue by clearing the mixup from package.version that is being confused with version from - importlib.metadata.version.
- Issue: it fixes the issue #7283
- Maintainer: @hwchase17
The following change has been explained in the comment - https://github.com/hwchase17/langchain/issues/7283#issuecomment-1624328978
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I_kwDOIPDwls5q08yD
| 7,304 |
MongoDBAtlasVectorSearch vectorstore - add maximal_marginal_relevance method
|
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[] | null | 2 | 2023-07-06T21:24:25 | 2023-10-12T16:05:25 | 2023-10-12T16:05:24 |
CONTRIBUTOR
| null |
### Feature request
It would be nice to add a maximal_marginal_relevance to the MongoDBAtlasVectorSearch vectorstore
### Motivation
This will bring help users to get more diverse results than the ones only based on the relevance score
### Your contribution
I'll write a PR
|
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I_kwDOIPDwls5q07X3
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Evaluation returns 'INCORRECT' when ground truth is empty and prediction is empty
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[] | null | 1 | 2023-07-06T21:20:21 | 2023-10-12T16:05:30 | 2023-10-12T16:05:29 |
NONE
| null |
### System Info
Langchain version: 0.0.225
Python version: 3.8.5
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When ground truth and model prediction are both empty strings, the evaluation model returns 'INCORRECT'. I expect the evalution to return 'CORRECT'
I ran the below piece of code.
```
llm_bit = AzureChatOpenAI(deployment_name='gpt-4-32k', temperature=0)
test_gt = [{'question': 'What is the name of the company?', 'gt': 'Company A'}, {'question': 'What is the name of the building', 'gt': ''}]
test_output = [{'question': 'What is the name of the company?', 'prediction': 'Company A'}, {'question': 'What is the name of the building', 'prediction': ''}]
eval_chain = QAEvalChain.from_llm(llm_bit)
temp = eval_chain.evaluate(
test_gt, test_output, question_key="question", answer_key="gt", prediction_key="prediction"
)
temp
```
### Expected behavior
Received output: [{'text': 'CORRECT'}, {'text': 'INCORRECT'}]
Expected output: [{'text': 'CORRECT'}, {'text': 'CORRECT'}]
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| 1,792,242,224 |
I_kwDOIPDwls5q03Iw
| 7,302 |
DOC: <ValueError: OpenAIChat currently only supports single prompt, got ["Use the following portion of a long document to see if any of the text is relevant to answer the question."]'DOC: ' prefix>
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[] | null | 3 | 2023-07-06T21:08:00 | 2023-11-24T16:08:09 | 2023-11-24T16:08:08 |
NONE
| null |
### Issue with current documentation:
I got this code that i want to move to the model gpt3.5-turbo since its 10 times cheaper than the text-davinci-003 but i get this error
```
ValueError: OpenAIChat currently only supports single prompt, got ["Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nW H I T E PA P E R\nTHE FUTURE OF ART\nRelease\nV 3 . 1\nTable of Contents\n01 Abstract 02 Riwa Tech\nPg 3\nPg
4\n03 Market research 04 Technology\nPg 5\nPg 7\n05 Why we do this 06 How Riwa Tech works\nPg 12\nPg 14\n07 The future 08 Team 09 Coin distribution 10 Business model 11 Timeline\nPg 20\nPg 21\nPg 22\nPg 24\nPg 26\n2\nAbstract\nArt and antiques have always been an integral part of the global economy, and\nthis remains true
today. With the rise of digital platforms and technologies,\ntransaction methods have been revolutionized, but challenges such as\nprovenance, authentication, protection and preservation of cultural heritage\npersist. This white paper proposes integrating blockchain technology to improve\nthe industry's landscape and protect its unique value. Blockchain can provide\nsecure, transparent, and tamper-proof records for art and antiques, addressing\nnumerous issues. By combining traditional values and innovative technology, we\nQuestion: hi\nRelevant text, if any:", 'Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nTag. The owners can know the current location anytime they want.\nBlockchain\nAppreciation, trading and collecting of artworks are gradually becoming a part of\npeople’s life pursuits. In the development of the art market industry, collectibles\nlack clear records of transactions and evidence systems that can be veri\x00ed,\nmaking it almost impossible to determine the source information of collectibles.\nCollectibles do not have an “ID” system, resulting in no records for artworks. This\nlack of traceability in the industry can easily lead to counterfeiters taking\nadvantage of the situation, resulting in a proliferation of counterfeit artworks and\naffecting the development of the industry.\nOwners who deposit collectibles to Riwa’s ecosystem will get NFTs backed by the\ncollectible. The NFT smart contract will inherit the basic and anti-counterfeit\ndetails. For every future transaction of
the collectible, the smart contract will\nQuestion: hi\nRelevant text, if any:', "Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nRiwa's advanced 3D technology to create 3D NFT collections and establish their\nown virtual spaces for exhibitions and other purposes.\n20\nTeam\nVIONA ZHANG Founder & chairman Experienced in commercializing artists and artworks, with a successful record in corporate and brand management.\nPIERRE BRUNETOT CEO Ex-CEO and founder of Sante Nature with extensive experience in marketing and strategy.\nYINJI DAI Co-Founder
& Sales Manager Manager of the Asia Region and Co-founder. Over 17 years of experience in art and antiques industry management.\nAASHIR IFTIKHAR Chief Technical Of\x00cer at HashPotato Over 3 years of experience in mobile application development. Expert in Full Stack development.\nEDOUARD BRUNETOT COO CEO of Cobound helps
businesses grow through marketing, sales, and customer service tools.\nFABIEN CERCLET Sales manager Over 7 years in blockchain tech & economics, established strong marketing foundation.\n21\nCoin distribution\nInitial Coin Offering (ICO)\nQuestion: hi\nRelevant text, if any:", "Use the following portion of a long document
to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nand transaction reliability of artworks within the market.\n1. Riwa dual anti-counterfeiting\n1.1 Electronic Tag (E-Tag) management system\nRiwa's E-Tag technology management system is ef\x00cient, accurate and reliable.\nThe system can automatically read real-time artwork\ninformation and\ndynamically track and detect artwork locations through an electronic map,\nimproving the timeliness and accuracy of issue detection. Each Riwa E-Tag has a\nunique identity code assigned to the artwork or antique it represents, and the\ntags are physically
non-replicable and indestructible. With large storage capacity,\nlong service life, and adaptability to indoor and outdoor environments, Riwa's E-\nTag also allows contactless information collection, pollution resistance, and high\nreliability.\n7\nUsers can access the Riwa system by sensing an item's E-Tag with a smartphone,\nobtaining detailed features, inspection count, origin, ownership change records,\nQuestion: hi\nRelevant text, if any:"]
```
I'm using RetrievalQAWithSourcesChain and FAISS, this is the code
```python
import os
from langchain.document_loaders import UnstructuredURLLoader
from langchain.text_splitter import CharacterTextSplitter
import pickle
import faiss
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chains.question_answering import load_qa_chain
from langchain import OpenAI
os.environ["OPENAI_API_KEY"] = "Apikey"
urls = [
'https://riwa.nftify.network/collection/riwa-nft'
]
loaders = UnstructuredURLLoader(urls=urls)
data = loaders.load()
docs = text_splitter.split_documents(data)
embeddings = OpenAIEmbeddings()
vectorStore_openAI = FAISS.from_documents(docs, embeddings)
with open("faiss_store_openai.pkl", "wb") as f:
pickle.dump(vectorStore_openAI, f)
with open("faiss_store_openai.pkl", "rb") as f:
VectorStore = pickle.load(f)
llm=OpenAI(temperature=0,model_name="gpt-3.5-turbo",max_tokens=32)
chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=VectorStore.as_retriever())
question=input("What question you want to ask? : ")
print(chain({"question": str(question)}, return_only_outputs=True)["answer"])
```
I would really appreciate if someone could give me some guidance, i've been blocked in this problem for a while
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5U2Hs5
| 7,301 |
Add support for filters and namespaces in similarity search in Pinecone similarity_score_threshold
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[] | null | 2 | 2023-07-06T20:46:52 | 2023-07-07T19:03:59 | 2023-07-07T19:03:59 |
CONTRIBUTOR
| null |
At the moment, pinecone vectorStore does not support filters and namespaces when using similarity_score_threshold search type.
In this PR, I've implemented that. It passes all the kwargs except "score_threshold" as that is not a supported argument for method "similarity_search_with_score".
Tag maintainers/contributors who might be interested:
@dev2049 @baskaryan @rlancemartin @eyurtsev
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Working cpal
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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!
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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langchain.document_loaders.generic GenericLoader not working on Azure OpenAI - InvalidRequestError: Resource Not Found, cannot detect declared resource
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[] | null | 4 | 2023-07-06T19:16:57 | 2023-10-23T08:09:45 | null |
NONE
| null |
### System Info
langchain=0.0.225, python=3.9.17, openai=0.27.8
openai.api_type = "azure", openai.api_version = "2023-05-15"
api_base, api_key, deployment_name environment variables all configured.
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behavior:
Note: This code is pulled directly from document loaders chapter of Langchain Chat With Your Data course with Harrison Chase and Andrew Ng. It downloads an audio file of a public youtube video and generates a transcript.
1. In a Jupyter notebook, configure your Azure OpenAI environment variables and add this code:
```
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import OpenAIWhisperParser
from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
```
2. Create and run a new cell with this inside:
```
url="<https://www.youtube.com/watch?v=jGwO_UgTS7I>"
save_dir="docs/youtube/"
loader = GenericLoader( YoutubeAudioLoader([url],save_dir), OpenAIWhisperParser() )
docs = loader.load()
```
4. At the transcribing step, it will fail on "InvalidRequestError".
Successfully executes the following steps:
```
[youtube] Extracting URL: https://www.youtube.com/watch?v=jGwO_UgTS7I
[youtube] jGwO_UgTS7I: Downloading webpage
[youtube] jGwO_UgTS7I: Downloading ios player API JSON
[youtube] jGwO_UgTS7I: Downloading android player API JSON
[youtube] jGwO_UgTS7I: Downloading m3u8 information
[info] jGwO_UgTS7I: Downloading 1 format(s): 140
[download] docs/youtube//Stanford CS229： Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018).m4a has already been downloaded
[download] 100% of 69.76MiB
[ExtractAudio] Not converting audio docs/youtube//Stanford CS229： Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018).m4a; file is already in target format m4a
Transcribing part 1!
```
```
InvalidRequestError Traceback (most recent call last)
Cell In[14], line 8
3 save_dir="docs/youtube/"
4 loader = GenericLoader(
5 YoutubeAudioLoader([url],save_dir),
6 OpenAIWhisperParser()
7 )
----> 8 docs = loader.load()
File /usr/local/lib/python3.9/site-packages/langchain/document_loaders/generic.py:90, in GenericLoader.load(self)
88 def load(self) -> List[Document]:
89 """Load all documents."""
---> 90 return list(self.lazy_load())
File /usr/local/lib/python3.9/site-packages/langchain/document_loaders/generic.py:86, in GenericLoader.lazy_load(self)
84 """Load documents lazily. Use this when working at a large scale."""
85 for blob in self.blob_loader.yield_blobs():
---> 86 yield from self.blob_parser.lazy_parse(blob)
File /usr/local/lib/python3.9/site-packages/langchain/document_loaders/parsers/audio.py:51, in OpenAIWhisperParser.lazy_parse(self, blob)
49 # Transcribe
50 print(f"Transcribing part {split_number+1}!")
---> 51 transcript = openai.Audio.transcribe("whisper-1", file_obj)
53 yield Document(
54 page_content=transcript.text,
55 metadata={"source": blob.source, "chunk": split_number},
56 )
File /usr/local/lib/python3.9/site-packages/openai/api_resources/audio.py:65, in Audio.transcribe(cls, model, file, api_key, api_base, api_type, api_version, organization, **params)
55 requestor, files, data = cls._prepare_request(
56 file=file,
57 filename=file.name,
(...)
62 **params,
63 )
64 url = cls._get_url("transcriptions")
---> 65 response, _, api_key = requestor.request("post", url, files=files, params=data)
66 return util.convert_to_openai_object(
67 response, api_key, api_version, organization
68 )
File /usr/local/lib/python3.9/site-packages/openai/api_requestor.py:298, in APIRequestor.request(self, method, url, params, headers, files, stream, request_id, request_timeout)
277 def request(
278 self,
279 method,
(...)
286 request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
287 ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]:
288 result = self.request_raw(
289 method.lower(),
290 url,
(...)
296 request_timeout=request_timeout,
297 )
--> 298 resp, got_stream = self._interpret_response(result, stream)
299 return resp, got_stream, self.api_key
File /usr/local/lib/python3.9/site-packages/openai/api_requestor.py:700, in APIRequestor._interpret_response(self, result, stream)
692 return (
693 self._interpret_response_line(
694 line, result.status_code, result.headers, stream=True
695 )
696 for line in parse_stream(result.iter_lines())
697 ), True
698 else:
699 return (
--> 700 self._interpret_response_line(
701 result.content.decode("utf-8"),
702 result.status_code,
703 result.headers,
704 stream=False,
705 ),
706 False,
707 )
File /usr/local/lib/python3.9/site-packages/openai/api_requestor.py:763, in APIRequestor._interpret_response_line(self, rbody, rcode, rheaders, stream)
761 stream_error = stream and "error" in resp.data
762 if stream_error or not 200 <= rcode < 300:
--> 763 raise self.handle_error_response(
764 rbody, rcode, resp.data, rheaders, stream_error=stream_error
765 )
766 return resp
InvalidRequestError: Resource not found
```
Usually, with "resource not found" errors, the message will tell you to input api_key or deployment_name. I'm not sure what this means, as none of the Loader methods take in these as params.
### Expected behavior
Expected behavior is to finish four parts of transcription and "load" as doc in docs variable.
|
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PR_kwDOIPDwls5U1sK_
| 7,297 |
rm side nav
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[] | null | 1 | 2023-07-06T19:08:13 | 2023-07-06T19:19:31 | 2023-07-06T19:19:30 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5U1pzz
| 7,296 |
handle missing finish_reason
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[] | null | 2 | 2023-07-06T18:59:55 | 2023-08-31T17:00:58 | 2023-07-06T19:13:52 |
CONTRIBUTOR
| null |
In some cases, the OpenAI response is missing the `finish_reason` attribute. It seems to happen when using Ada or Babbage and `stream=true`, but I can't always reproduce it. This change just gracefully handles the missing key.
@hwchase17 @baskaryan
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PR_kwDOIPDwls5U1j9a
| 7,295 |
Wrapper function for OpenAI Function in LLMChain
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[] | null | 13 | 2023-07-06T18:37:36 | 2023-07-18T18:50:38 | 2023-07-12T01:17:06 |
NONE
| null |
- Description: This pull request does two things: 1) Add wrapper function for pydantic model, JSON Schema, and dictionary to function calls in LLM Chain. 2) Add an additional class for the output parser class so pydantic model returns a string that could be added to AIMessage for memory storage.
- Issue: #6933
- Dependencies: N/A
- Tag maintainer: @baskaryan
- Twitter handle: #7t_eric
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PR_kwDOIPDwls5U1dU1
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Update docs for whylabs (langkit) callback handler
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[] | null | 2 | 2023-07-06T18:13:10 | 2023-07-06T21:21:29 | 2023-07-06T21:21:28 |
CONTRIBUTOR
| null |
- Description: Update docs for whylabs callback handler
- Issue: none
- Dependencies: none
- Tag maintainer: @agola11
- Twitter handle: @useautomation @whylabs
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PR_kwDOIPDwls5U1ctR
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updated `huggingface_hub` examples
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[] | null | 2 | 2023-07-06T18:10:45 | 2023-07-06T19:38:11 | 2023-07-06T19:04:38 |
COLLABORATOR
| null |
Added examples for models:
- Google `Flan`
- TII `Falcon`
- Salesforce `XGen`
@hwchase17, @baskaryan
|
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| 1,791,972,879 |
I_kwDOIPDwls5qz1YP
| 7,291 |
OpenAI Chat agents don't make use of OpenAI API `messages` parameter.
|
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[] | null | 3 | 2023-07-06T17:42:57 | 2023-10-14T20:10:48 | 2023-10-14T20:10:46 |
NONE
| null |
### System Info
Langchain: 0.0.225
OS: Arch Linux
Python: 3.11
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use any of langchain's openai chat agents with the memory/chat history feature results in the chat history/memory being sent to the openai API in the SYSTEM message, and with incorrect roles specified.
### Expected behavior
While that might be appropriate for certain types of message (Maybe compressed or summarized from older conversations), I expected the chat history memory to be utilizing openai's [messages](https://platform.openai.com/docs/api-reference/chat#chat/create-messages) parameter.
It's much easier to parse (If ever needed) since it's an array of messages. This is related to https://github.com/hwchase17/langchain/issues/7285 which is an even bigger issue that addressing this one could resolve.
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I_kwDOIPDwls5qzkMe
| 7,290 |
on_retriever_end() not called with ConversationalRetrievalChain
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[] | null | 12 | 2023-07-06T16:51:42 | 2024-01-18T03:36:06 | null |
CONTRIBUTOR
| null |
### System Info
LangChain: v0.0.225
OS: Ubuntu 22.04
### Who can help?
@agola11
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
### Code
```python
import langchain
from chromadb.config import Settings
from langchain.callbacks.streaming_stdout import BaseCallbackHandler
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import LlamaCpp
from langchain.memory import ConversationBufferMemory
from langchain.schema.document import Document
from langchain.vectorstores import Chroma
langchain.debug = True
class DocumentCallbackHandler(BaseCallbackHandler):
def on_retriever_end(
self,
documents: Sequence[Document],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
print(f"on_retriever_end() CALLED with {len(documents)} documents")
def setup():
llm = LlamaCpp(
model_path="models/GPT4All-13B-snoozy.ggml.q5_1.bin",
n_ctx=4096,
n_batch=8192,
callbacks=[],
verbose=False,
use_mlock=True,
n_gpu_layers=60,
n_threads=8,
)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
db = Chroma(
persist_directory="./db",
embedding_function=embeddings,
client_settings=Settings(
chroma_db_impl="duckdb+parquet",
persist_directory="./db",
anonymized_telemetry=False,
),
)
retriever = db.as_retriever(search_kwargs={"k": 4})
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
return ConversationalRetrievalChain.from_llm(
llm=llm, retriever=retriever, memory=memory, callbacks=[DocumentCallbackHandler()]
)
def main():
qa = setup()
while True:
question = input("\nEnter your question: ")
answer = qa(question)["answer"]
print(f"\n> Answer: {answer}")
if __name__ == "__main__":
main()
```
### Output
```
ggml_init_cublas: found 1 CUDA devices:
Device 0: Quadro RTX 6000
llama.cpp: loading model from models/GPT4All-13B-snoozy.ggml.q5_1.bin
llama_model_load_internal: format = ggjt v2 (pre #1508)
llama_model_load_internal: n_vocab = 32000
llama_model_load_internal: n_ctx = 4096
llama_model_load_internal: n_embd = 5120
llama_model_load_internal: n_mult = 256
llama_model_load_internal: n_head = 40
llama_model_load_internal: n_layer = 40
llama_model_load_internal: n_rot = 128
llama_model_load_internal: ftype = 9 (mostly Q5_1)
llama_model_load_internal: n_ff = 13824
llama_model_load_internal: n_parts = 1
llama_model_load_internal: model size = 13B
llama_model_load_internal: ggml ctx size = 0.09 MB
llama_model_load_internal: using CUDA for GPU acceleration
llama_model_load_internal: mem required = 2165.28 MB (+ 1608.00 MB per state)
llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer
llama_model_load_internal: offloading 40 repeating layers to GPU
llama_model_load_internal: offloading non-repeating layers to GPU
llama_model_load_internal: offloading v cache to GPU
llama_model_load_internal: offloading k cache to GPU
llama_model_load_internal: offloaded 43/43 layers to GPU
llama_model_load_internal: total VRAM used: 11314 MB
....................................................................................................
llama_init_from_file: kv self size = 3200.00 MB
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | VSX = 0 |
Enter your question: Should Hamlet end his life?
[chain/start] [1:chain:ConversationalRetrievalChain] Entering Chain run with input:
{
"question": "Should Hamlet end his life?",
"chat_history": []
}
[chain/start] [1:chain:ConversationalRetrievalChain > 3:chain:StuffDocumentsChain] Entering Chain run with input:
[inputs]
[chain/start] [1:chain:ConversationalRetrievalChain > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:
{
"question": "Should Hamlet end his life?",
"context": "Enter Hamlet.\n\nEnter Hamlet.\n\nEnter Hamlet.\n\nHaply the seas, and countries different,\n With variable objects, shall expel\n This something-settled matter in his heart,\n Whereon his brains still beating puts him thus\n From fashion of himself. What think you on't?\n Pol. It shall do well. But yet do I believe\n The origin and commencement of his grief\n Sprung from neglected love.- How now, Ophelia?\n You need not tell us what Lord Hamlet said.\n We heard it all.- My lord, do as you please;"
}
[llm/start] [1:chain:ConversationalRetrievalChain > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:LlamaCpp] Entering LLM run with input:
{
"prompts": [
"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\nEnter Hamlet.\n\nEnter Hamlet.\n\nEnter Hamlet.\n\nHaply the seas, and countries different,\n With variable objects, shall expel\n This something-settled matter in his heart,\n Whereon his brains still beating puts him thus\n From fashion of himself. What think you on't?\n Pol. It shall do well. But yet do I believe\n The origin and commencement of his grief\n Sprung from neglected love.- How now, Ophelia?\n You need not tell us what Lord Hamlet said.\n We heard it all.- My lord, do as you please;\n\nQuestion: Should Hamlet end his life?\nHelpful Answer:"
]
}
llama_print_timings: load time = 1100.49 ms
llama_print_timings: sample time = 13.20 ms / 17 runs ( 0.78 ms per token)
llama_print_timings: prompt eval time = 1100.33 ms / 208 tokens ( 5.29 ms per token)
llama_print_timings: eval time = 1097.70 ms / 16 runs ( 68.61 ms per token)
llama_print_timings: total time = 2270.30 ms
[llm/end] [1:chain:ConversationalRetrievalChain > 3:chain:StuffDocumentsChain > 4:chain:LLMChain > 5:llm:LlamaCpp] [2.27s] Exiting LLM run with output:
{
"generations": [
[
{
"text": " I'm sorry, I don't know the answer to that question.",
"generation_info": null
}
]
],
"llm_output": null,
"run": null
}
[chain/end] [1:chain:ConversationalRetrievalChain > 3:chain:StuffDocumentsChain > 4:chain:LLMChain] [2.27s] Exiting Chain run with output:
{
"text": " I'm sorry, I don't know the answer to that question."
}
[chain/end] [1:chain:ConversationalRetrievalChain > 3:chain:StuffDocumentsChain] [2.27s] Exiting Chain run with output:
{
"output_text": " I'm sorry, I don't know the answer to that question."
}
[chain/end] [1:chain:ConversationalRetrievalChain] [5.41s] Exiting Chain run with output:
{
"answer": " I'm sorry, I don't know the answer to that question."
}
> Answer: I'm sorry, I don't know the answer to that question.
```
### Expected behavior
I expect the `on_retriever_end()` callback to be called immediately after documents are retrieved. I'm not sure what I'm doing wrong.
|
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I_kwDOIPDwls5qzcBF
| 7,289 |
Input in GraphQAChain Prompt is Malformed When Only 1 Triplet Is Found
|
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[] | null | 2 | 2023-07-06T16:25:05 | 2023-07-07T21:19:54 | 2023-07-07T21:19:54 |
NONE
| null |
### System Info
Mac OS Ventura 13.3.1 (a)
Python 3.10.8
LangChain 0.0.224
### Who can help?
@hwchase17
@hinthornw
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Reproduction Steps:
1. Run the following
```
from langchain.llms import OpenAI
from langchain.indexes import GraphIndexCreator
from langchain.chains import GraphQAChain
from langchain.prompts import PromptTemplate
text = "Apple announced the Vision Pro in 2023."
index_creator = GraphIndexCreator(llm=OpenAI(openai_api_key='{OPEN_AI_KEY_HERE}', temperature=0))
graph = index_creator.from_text(text)
chain = GraphQAChain.from_llm(
OpenAI(temperature=0, openai_api_key='{OPEN_AI_KEY_HERE}'),
graph=graph,
verbose=True
)
chain.run("When did Apple announce the Vision Pro?")
```
2. Observe the "Full Context" output in your terminal and notice that the two triplets are concatenated onto a single line with no spacing in between them.
I believe the issue is in the code [here](https://github.com/hwchase17/langchain/blob/681f2678a357268c435c18f19323ccb50cac079c/langchain/chains/graph_qa/base.py#L80). When only 1 triplet is found in an iteration, `.join` does not add any `\n` characters, resulting in a context string with no separation between triplets.
### Expected behavior
Expected: A multi-line string with each triplet text on its own line (delimited by `"\n"`)
In the above repro steps, I would expect
```
Full Context:
Apple announced Vision Pro
Vision Pro was announced in 2023
```
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| 7,288 |
DOC: Search functionality: `Verified Sources:` fields unreadable
|
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[] | null | 1 | 2023-07-06T16:17:48 | 2023-10-05T22:06:37 | 2023-10-05T22:06:28 |
COLLABORATOR
| null |
### Issue with current documentation:
If we run a `Search` on the python.langchain.com/docs/ we got the result as several clickable fields. Those fields are the URLs of the found results. But the fields are too short to show the URLs. We cannot see what LangChain doc pages were found. We see just the start of the URL string, like `1. python.langchain.com/docs/module...` All found fields shows the same above text, which is useless.
### Idea or request for content:
Several ideas on how to fix it:
1. make the result fields longer and place them one after another in the list.
2. show the last part of the URL string not the start of the URL string.
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PR_kwDOIPDwls5U06vR
| 7,287 |
added pipeline tool for low code integration
|
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closed
| false | null |
[] | null | 4 | 2023-07-06T16:15:30 | 2023-12-18T00:05:01 | 2023-12-18T00:04:57 |
CONTRIBUTOR
| null |
## Description
added a pipeline tool consisting multiple steps that makes tool/chain reuse easier.
with more generic tools reused by domain task tools, metadata-driven agent-tool chain loading will be easier.
## Dependencies
None
## Maintainer
- Agents / Tools / Toolkits: @hinthornw
|
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| 1,791,854,231 |
PR_kwDOIPDwls5U06XD
| 7,286 |
Clarifai LangChain improvements and embeddings
|
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[] | null | 3 | 2023-07-06T16:14:17 | 2023-07-07T06:23:57 | 2023-07-07T06:23:57 |
CONTRIBUTOR
| null |
This PR improves upon the Clarifai LangChain integration with improved docs, errors, args and the addition of embedding model support in LancChain for Clarifai's embedding models and an overview of the various ways you can integrate with Clarifai added to the docs.
@hwchase17 please take a look.
Twitter: @clarifai
|
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| 1,791,828,808 |
I_kwDOIPDwls5qzSNI
| 7,285 |
Can't use human_prefix and ai_prefix with agent
|
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[] | null | 2 | 2023-07-06T15:57:25 | 2023-10-14T20:10:52 | 2023-10-14T20:10:51 |
NONE
| null |
### System Info
Langchain 0.0.223
Linux
Python 3.11
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
something like this
```python
chat_history = PostgresChatMessageHistory( # Just a slight mod of the postgres class for sorting the results by date
connection_string=config('SUPABASE_POSTGRES_CONNECT_STRING'),
session_id="58964243-23cd-41fe-ad05-ecbfd2a73202", # str(uuid.uuid4()),
table_name="chat_history"
)
memory = ConversationBufferMemory(
memory_key="chat_history",
chat_memory=chat_history,
return_messages=True,
human_prefix="USER", # This doesn't work.
ai_prefix="ASSISTANT",) # This doesn't work.
agent = ChatAgent(
name="Chat Assistant",
tools=_tools,
agent_type=AgentType.OPENAI_MULTI_FUNCTIONS,
llm=openai
)
```
If I look at what was prompted and in postgres, it always shows "Human" and "AI"
### Expected behavior
I expect USER and ASSISTANT to be used everywhere after I set it. I see this as especially important when using openai's chat endpoint since their models were trained using these tokens.
I also think it would be better to load the memory/chat history as the openai API provides parameters for (As a list of messages) instead of in the SYSTEM message, but perhaps that's for another issue.
|
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| 1,791,814,905 |
PR_kwDOIPDwls5U0x5j
| 7,284 |
add elasticknn to init
|
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[] | null | 1 | 2023-07-06T15:48:37 | 2023-07-06T15:58:25 | 2023-07-06T15:58:24 |
COLLABORATOR
| null | null |
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| 1,791,794,342 |
I_kwDOIPDwls5qzJym
| 7,283 |
anthropic_version = packaging.version.parse(version("anthropic")) AttributeError: module 'packaging' has no attribute 'version'
|
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[] | null | 5 | 2023-07-06T15:35:39 | 2023-10-14T20:10:57 | 2023-10-14T20:10:56 |
NONE
| null |
### System Info
When I initialise ChatAnthropic(), it got the error:
anthropic_version = packaging.version.parse(version("anthropic"))
AttributeError: module 'packaging' has no attribute 'version'
### Who can help?
@hwchase17 @agola11
### 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
from langchain.chat_models import ChatOpenAI, ChatAnthropic
llm = ChatAnthropic()
### Expected behavior
As shown above.
|
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| 1,791,761,739 |
I_kwDOIPDwls5qzB1L
| 7,282 |
MathpixPDFLoader doesn't work.
|
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[] | null | 5 | 2023-07-06T15:15:17 | 2023-10-07T17:05:46 | 2023-09-29T16:22:27 |
NONE
| null |
### System Info
Getting ''' ValueError: Unable to send PDF to Mathpix''' while using MathpixPDFLoader.
### 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
math_pix_loaded = MathpixPDFLoader(file_path)
load_list_mathpix = math_pix_loaded.load()
### Expected behavior
A list of pages to be returned.
|
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