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| 1,943,639,624 |
I_kwDOIPDwls5z2ZZI
| 11,814 |
Create retriever for Outline to ask questions on knowledge base
|
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[] | null | 1 | 2023-10-15T01:58:24 | 2023-11-28T20:58:07 | 2023-11-27T02:56:13 |
CONTRIBUTOR
| null |
### Feature request
A retriever for documents from [Outline](https://github.com/outline/outline).
The API has a search endpoint which allows this to be possible: https://www.getoutline.com/developers#tag/Documents/paths/~1documents.search/post
The implementation will be similar to the Wikipedia retriever:
https://python.langchain.com/docs/integrations/retrievers/wikipedia
### Motivation
Outline is an open source project that let's you create a knowledge base, like a wiki. Creating a retriever for Outline will let your team interact with your knowledge base using an LLM.
### Your contribution
PR will be coming soon.
|
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I_kwDOIPDwls5z2SGI
| 11,813 |
SQL DB Tools sql_db_schema bad input
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[] | null | 0 | 2023-10-14T23:56:03 | 2023-10-14T23:58:09 | null |
NONE
| null |
### System Info
Langchain 0.0.314
When LLM returns with action input parameter without space in-between two tables, call fails. Certain LLMs dont follow "Table1, Table2" format and returns with "Table1,Table2"
This is easily addressed by stripping out all spaces and split with ","
<code>
class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
"""Tool for getting metadata about a SQL database."""
name: str = "sql_db_schema"
description: str = """
Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables.
Example Input: "table1, table2, table3"
"""
def _run(
self,
table_names: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Get the schema for tables in a comma-separated list."""
return self.db.get_table_info_no_throw(table_names.split(", "))
</code>
Replace return line with the following:
<code>
import re
.
.
.
def_run(
.
.
.
return self.db.get_table_info_no_throw(re.sub(" *","",table_names).split(","))
</code>
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This can be reproduced with Claude V2
### Expected behavior
sql_db_schema to not fail
|
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PR_kwDOIPDwls5c0A4t
| 11,812 |
bearly tool
|
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[] | null | 1 | 2023-10-14T20:27:12 | 2023-10-14T23:03:59 | 2023-10-14T23:03:58 |
COLLABORATOR
| null | null |
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| 1,943,495,649 |
I_kwDOIPDwls5z12Ph
| 11,811 |
Add support for many of the querying endpoints with serpapi
|
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[] | null | 1 | 2023-10-14T19:16:42 | 2023-10-14T19:18:55 | null |
CONTRIBUTOR
| null |
### Feature request
We propose the addition of some of the querying endpoints supported by serpapi that are not suported on langchain at the moment. Some examples, include querying google job, google trends and google finance etc.###
### Motivation
The motivation for this feature proposal is to enhance the querying capabilities of Langchain by incorporating support for various querying endpoints provided by serpapi. The proposed feature aims to improve Langchain's querying capabilities by enabling users to interact with serpapi supported endpoints seamlessly.
### Your contribution
Me and 3 others intend to submit a pull request for this issue at some point in November for a school project. Looking forward to contributing.
|
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I_kwDOIPDwls5z1Y1B
| 11,810 |
Issue: `AsyncChromiumLoader` RuntimeError: asyncio.run() cannot be called from a running event loop
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[] | null | 1 | 2023-10-14T16:12:24 | 2023-10-14T16:17:42 | null |
NONE
| null |
### Issue you'd like to raise.
Looking to use `AsyncChromiumLoader` to scrape URLs but I keep running into the following error:
```python
loader = AsyncChromiumLoader(["https://www.espn.com/nfl/story/_/id/38652877/lions-rb-jahmyr-gibbs-wr-amon-ra-st-brown-return-vs-bucs"])
loader.load()
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[17], line 2
1 loader = AsyncChromiumLoader(["https://www.espn.com/nfl/story/_/id/38652877/lions-rb-jahmyr-gibbs-wr-amon-ra-st-brown-return-vs-bucs"])
----> 2 loader.load()
File /opt/conda/lib/python3.10/site-packages/langchain/document_loaders/chromium.py:90, in AsyncChromiumLoader.load(self)
81 def load(self) -> List[Document]:
82 """
83 Load and return all Documents from the provided URLs.
84
(...)
88
89 """
---> 90 return list(self.lazy_load())
File /opt/conda/lib/python3.10/site-packages/langchain/document_loaders/chromium.py:77, in AsyncChromiumLoader.lazy_load(self)
66 """
67 Lazily load text content from the provided URLs.
68
(...)
74
75 """
76 for url in self.urls:
---> 77 html_content = asyncio.run(self.ascrape_playwright(url))
78 metadata = {"source": url}
79 yield Document(page_content=html_content, metadata=metadata)
File /opt/conda/lib/python3.10/asyncio/runners.py:33, in run(main, debug)
9 """Execute the coroutine and return the result.
10
11 This function runs the passed coroutine, taking care of
(...)
30 asyncio.run(main())
31 """
32 if events._get_running_loop() is not None:
---> 33 raise RuntimeError(
34 "asyncio.run() cannot be called from a running event loop")
36 if not coroutines.iscoroutine(main):
37 raise ValueError("a coroutine was expected, got {!r}".format(main))
RuntimeError: asyncio.run() cannot be called from a running event loop
```
I am running langchain 0.0.314. I looked through [the documentation](https://python.langchain.com/docs/integrations/document_loaders/async_html) which wasn't helpful. I found [this stackoverflow thread](https://stackoverflow.com/questions/55409641/asyncio-run-cannot-be-called-from-a-running-event-loop-when-using-jupyter-no) which suggests adding an `await` but that seems useful if we are directly using the asychio class.
It's worth noting that I run into a similar error when using the `PlaywrightURLLoader` and I run into the following error:
```python
loader = PlaywrightURLLoader(["https://www.espn.com/nfl/story/_/id/38652877/lions-rb-jahmyr-gibbs-wr-amon-ra-st-brown-return-vs-bucs"])
loader.load()
---------------------------------------------------------------------------
Error Traceback (most recent call last)
Cell In[186], line 2
1 loader = PlaywrightURLLoader(["https://www.espn.com/nfl/story/_/id/38652877/lions-rb-jahmyr-gibbs-wr-amon-ra-st-brown-return-vs-bucs"])
----> 2 loader.load()
File /opt/conda/lib/python3.10/site-packages/langchain/document_loaders/url_playwright.py:154, in PlaywrightURLLoader.load(self)
150 from playwright.sync_api import sync_playwright
152 docs: List[Document] = list()
--> 154 with sync_playwright() as p:
155 browser = p.chromium.launch(headless=self.headless)
156 for url in self.urls:
File /opt/conda/lib/python3.10/site-packages/playwright/sync_api/_context_manager.py:47, in PlaywrightContextManager.__enter__(self)
45 self._own_loop = True
46 if self._loop.is_running():
---> 47 raise Error(
48 """It looks like you are using Playwright Sync API inside the asyncio loop.
49 Please use the Async API instead."""
50 )
52 # Create a new fiber for the protocol dispatcher. It will be pumping events
53 # until the end of times. We will pass control to that fiber every time we
54 # block while waiting for a response.
55 def greenlet_main() -> None:
Error: It looks like you are using Playwright Sync API inside the asyncio loop.
Please use the Async API instead.
```
### Suggestion:
_No response_
|
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AttributeError: 'LlamaForCausalLM' object has no attribute 'is_quantized'
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[] | null | 4 | 2023-10-14T13:46:33 | 2023-10-16T23:54:22 | 2023-10-16T23:54:22 |
NONE
| null |
### System Info
LangChain: langchain-0.0.314
Python: Anaconda Python 3.9.18
X86
RTX3080 Laptop (16G)
CUDA 11.8
cuDNN 8.9.5
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1.git clone https://github.com/ymcui/Chinese-LLaMA-Alpaca-2.git
2.cd Chinese-LLaMA-Alpaca-2/scripts/langchain
3.python langchain_sum.py --model_path chinese-alpaca-2-7b-hf --file_path doc.txt --chain_type refine
(langchain) zhanghui@zhanghui-OMEN-by-HP-Laptop-17-ck0xxx:~/Chinese-LLaMA-Alpaca-2/scripts/langchain$ python langchain_sum.py --model_path chinese-alpaca-2-7b-hf --file_path doc.txt --chain_type refine
/home/zhanghui/anaconda3/envs/langchain/lib/python3.9/site-packages/langchain/__init__.py:39: UserWarning: Importing HuggingFacePipeline from langchain root module is no longer supported.
warnings.warn(
loading LLM...
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████| 2/2 [00:03<00:00, 1.86s/it]
/home/zhanghui/anaconda3/envs/langchain/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
warnings.warn(
/home/zhanghui/anaconda3/envs/langchain/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
warnings.warn(
Traceback (most recent call last):
File "/home/zhanghui/Chinese-LLaMA-Alpaca-2/scripts/langchain/langchain_sum.py", line 50, in <module>
model = HuggingFacePipeline.from_model_id(model_id=model_path,
File "/home/zhanghui/anaconda3/envs/langchain/lib/python3.9/site-packages/langchain/llms/huggingface_pipeline.py", line 112, in from_model_id
model.is_quantized
File "/home/zhanghui/anaconda3/envs/langchain/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1614, in __getattr__
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'LlamaForCausalLM' object has no attribute 'is_quantized'
### Expected behavior
|
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| 1,943,257,033 |
PR_kwDOIPDwls5czNe9
| 11,808 |
test_stream_log_retriever Unit Test + Tool names fix
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[] | null | 3 | 2023-10-14T13:16:34 | 2023-12-16T20:28:31 | 2023-10-17T01:46:20 |
CONTRIBUTOR
| null |
## Description
| Tool | Original Tool Name |
|-----------------------------|---------------------------|
| open-meteo-api | Open Meteo API |
| news-api | News API |
| tmdb-api | TMDB API |
| podcast-api | Podcast API |
| golden_query | Golden Query |
| dall-e-image-generator | Dall-E Image Generator |
| twilio | Text Message |
| searx_search_results | Searx Search Results |
| dataforseo | DataForSeo Results JSON |
When using these tools through `load_tools`, I encountered the following validation error:
```console
openai.error.InvalidRequestError: 'TMDB API' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.0.name'
```
In order to avoid this error, I replaced spaces with hyphens in the tool names:
| Tool | Corrected Tool Name |
|-----------------------------|---------------------------|
| open-meteo-api | Open-Meteo-API |
| news-api | News-API |
| tmdb-api | TMDB-API |
| podcast-api | Podcast-API |
| golden_query | Golden-Query |
| dall-e-image-generator | Dall-E-Image-Generator |
| twilio | Text-Message |
| searx_search_results | Searx-Search-Results |
| dataforseo | DataForSeo-Results-JSON |
This correction resolved the validation error.
Additionally, a unit test, `tests/unit_tests/schema/runnable/test_runnable.py::test_stream_log_retriever`, was failing at random. Upon further investigation, I confirmed that the failure was not related to the above-mentioned changes. The `stream_log` variable was generating the order of logs in two ways at random The reason for this behavior is unclear, but in the assertion, I included both possible orders to account for this variability.
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| 1,943,251,646 |
I_kwDOIPDwls5z06q-
| 11,807 |
Issue: : Issue with HuggingFacePipeline while trying to use a Falcon model for chat with text files
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open
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[] | null | 1 | 2023-10-14T12:59:34 | 2023-10-14T13:04:31 | null |
NONE
| null |
### Issue you'd like to raise.
I'm encountering an error while trying to use a Falcon model to chat with a bunch of text files. I've downloaded the model locally on my Google Drive, and everything seems fine up to this point. However, when I try to define the LLM model in Langchain using the following code:
```
from langchain.llms import HuggingFacePipeline
llm = HuggingFacePipeline.from_model_id(
model_id='/content/gdrive/MyDrive/Colab Notebooks/falcon model/898df1396f35e447d5fe44e0a3ccaaaa69f30d36',
task="text-generation",
model_kwargs={"temperature": 0, "max_length": 64},
)
```
I encounter the following error:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-31-902f2455d555> in <cell line: 3>()
1 from langchain.llms import HuggingFacePipeline
2
----> 3 llm = HuggingFacePipeline.from_model_id(
4 model_id='/content/gdrive/MyDrive/Colab Notebooks/falcon model/898df1396f35e447d5fe44e0a3ccaaaa69f30d36',
5 task="text-generation",
AttributeError: 'FalconForCausalLM' object has no attribute 'is_quantized'
```
the whole code
```
device = torch.device('cuda')
checkpoint = "tiiuae/falcon-7b"
print(f"Checkpoint path: {checkpoint}") # Add this line for debugging
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path="/content/gdrive/MyDrive/Colab Notebooks/falcon model/898df1396f35e447d5fe44e0a3ccaaaa69f30d36")
base_model = AutoModel.from_pretrained(
pretrained_model_name_or_path='/content/gdrive/MyDrive/Colab Notebooks/falcon model/898df1396f35e447d5fe44e0a3ccaaaa69f30d36',
torch_dtype=torch.float32
)
loader = DirectoryLoader('/content/gdrive/MyDrive/Colab Notebooks/chatbot 3.0/', glob="./*.txt", loader_cls=TextLoader)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=500)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(cache_folder='/content/gdrive/MyDrive/Colab Notebooks/falcon model/898df1396f35e447d5fe44e0a3ccaaaa69f30d36',)
from langchain.llms import HuggingFacePipeline
llm = HuggingFacePipeline.from_model_id(
model_id='/content/gdrive/MyDrive/Colab Notebooks/falcon model/898df1396f35e447d5fe44e0a3ccaaaa69f30d36',
task="text-generation",
model_kwargs={"temperature": 0, "max_length": 64},
)
```
### Suggestion:
_No response_
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| 1,943,235,474 |
PR_kwDOIPDwls5czKDU
| 11,806 |
Fixed cyclomatic complexity of get function
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[] | null | 1 | 2023-10-14T12:28:42 | 2023-10-15T02:12:35 | null |
NONE
| null |
Splitted get function in 3 functions to fix it cyclomatic complexity from C to B and A
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I_kwDOIPDwls5z0i1k
| 11,805 |
DOC: LangChain Cookbook RAG typo
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[] | null | 3 | 2023-10-14T09:54:28 | 2023-10-15T14:39:12 | 2023-10-15T14:36:37 |
NONE
| null |
### Issue with current documentation:
[Link](https://python.langchain.com/docs/expression_language/cookbook/retrieval#with-memory-and-returning-source-documents)
Hi, I've been exploring the Cookbook RAG (link provided above) and noticed that `expanded_memory` is not defined anywhere in the code snippet(**With Memory and returning source documents**). I was wondering if a portion of the code is missing or if it's just a typo. Thank you in advance for looking over my issue!
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5cy0_5
| 11,804 |
Minor typo fix
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[] | null | 2 | 2023-10-14T09:17:33 | 2023-10-20T21:11:54 | 2023-10-20T21:11:53 |
CONTRIBUTOR
| null |
remove redundant a
langchain > LangChain
|
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I_kwDOIPDwls5z0XiA
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Issue: unit tests, a quick question.
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[] | null | 1 | 2023-10-14T09:11:36 | 2023-10-14T09:15:48 | null |
NONE
| null |
### Issue you'd like to raise.
Sorry to raise this as an issue, but I have a quick question. I am preparing come code for `langchain` and noticed that most of the code I wish to build on lacks unit tests. I was considering first submitting a PR of purely unit tests for the existing code I wish to use in order to keep subsequent PRs more digestible. _Is submitting a PR of just unit tests acceptable?_
### Suggestion:
_No response_
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PR_kwDOIPDwls5cypXD
| 11,802 |
Previous version was not respecting the max_token_limit field.
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[] | null | 1 | 2023-10-14T07:14:45 | 2023-10-14T07:14:55 | null |
NONE
| null |
**Description:** The new version now pops the buffer when the messages are requested, keeping the original messages safe inside the chat history.
The scenario where the previous version did not work for me was with DynamoDB memory implementation, since I was loading the chat history on initialization only and never called the save_context method before my first llm call.
This version now also keeps the original chat history intact, which was desirable for my case.
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I_kwDOIPDwls5z0GIm
| 11,801 |
Add support for AWS Kendra `ScoreConfidence` filter.
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[] | null | 2 | 2023-10-14T07:10:08 | 2023-12-03T07:07:44 | null |
NONE
| null |
### Feature request
- If user wants to get content from document which are having confidence HIGH or HIGHEST then with help of this feature Kendra will return only more relevance records.
### Motivation
- Sometimes kendra return content with LOW `ScoreConfidence` which may lead to inaccurate results.
### Your contribution
- I've checked the `_get_relevant_documents` method from `AmazonKendraRetriever` class but it is only returning top_k docs not actually filtering based on `ScoreConfidence`.
- With the help of `boto3` we can achieve that.
- I would like to do more research and open PR for it.
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PR_kwDOIPDwls5cygPz
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`_dalle_image_url` returns list of urls if n>1
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[] | null | 5 | 2023-10-14T05:16:02 | 2023-10-30T05:43:59 | 2023-10-29T21:23:24 |
CONTRIBUTOR
| null |
- **Description:** Updated the `_dalle_image_url` method to return a list of URLs if self.n>1,
- **Issue:** #10691,
- **Dependencies:** unsure,
- **Tag maintainer:** @eyurtsev,
- **Twitter handle:** @silvhua
I am new to development and do not know how to do any of the make format, make lint and make test steps or how to test the code locally. I would appreciate help with that.
|
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PR_kwDOIPDwls5cyexg
| 11,799 |
Fix import in Doc Notebook for textgen webui integration
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[] | null | 4 | 2023-10-14T05:04:57 | 2023-11-01T23:39:20 | 2023-11-01T23:39:20 |
NONE
| null |
- **Description:** Fix the integration library import for the Oobagooba Textgeneration webui
Self explanatory fix - it seems like the paths were changed.
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RM snippets
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[] | null | 1 | 2023-10-14T04:55:00 | 2023-10-15T19:21:00 | 2023-10-15T19:20:59 |
COLLABORATOR
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customize rtd build
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[] | null | 1 | 2023-10-14T02:49:18 | 2023-10-14T02:50:23 | 2023-10-14T02:50:23 |
COLLABORATOR
| null |
customize readthedocs config so that we can parallelize the api docs build
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fix typos in docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb
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[] | null | 1 | 2023-10-14T02:33:39 | 2023-10-17T02:07:04 | 2023-10-17T02:07:04 |
CONTRIBUTOR
| null |
trasform -> transform
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fix typos in cookbook/Semi_structured_and_multi_modal_RAG.ipynb
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[] | null | 1 | 2023-10-14T02:17:26 | 2023-10-17T02:07:21 | 2023-10-17T02:07:21 |
CONTRIBUTOR
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I have fixed some typos in file `cookbook/Semi_structured_and_multi_modal_RAG.ipynb`. I kindly request the repo maintainers to review and merge it. Thanks!
|
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fix typos in cookbook/learned_prompt_optimization.ipynb
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CONTRIBUTOR
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I have fixed some typos in file `cookbook/learned_prompt_optimization.ipynb`. I kindly request the repo maintainers to review and merge it. Thanks!
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fix typos in cookbook/self_query_hotel_search.ipynb
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CONTRIBUTOR
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I have fixed some typos in file `cookbook/self_query_hotel_search.ipynb`. I kindly request the repo maintainers to review and merge it. Thanks!
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I have fixed some typos in file `cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb`. I kindly request the repo maintainers to review and merge it. Thanks!
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[] | null | 1 | 2023-10-14T02:14:07 | 2023-10-17T02:10:40 | 2023-10-17T02:10:40 |
CONTRIBUTOR
| null |
I have fixed some typos in file `cookbook/sales_agent_with_context.ipynb`. I kindly request the repo maintainers to review and merge it. Thanks!
|
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| 1,942,824,316 |
PR_kwDOIPDwls5cyF2X
| 11,789 |
docs `safety` update
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[] | null | 3 | 2023-10-14T00:43:57 | 2023-10-17T15:31:00 | 2023-10-17T02:14:21 |
COLLABORATOR
| null |
The current ToC on the index page and on navbar don't match. Page titles and Titles in ToC doesn't match
Changes:
- made ToCs equal
- made titles equal
- updated some page formattings.
|
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PR_kwDOIPDwls5cyDP1
| 11,788 |
pipe default key
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I_kwDOIPDwls5zzGTO
| 11,787 |
feat: add NASA API tool
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[] | null | 8 | 2023-10-13T23:29:42 | 2023-11-27T15:47:10 | null |
CONTRIBUTOR
| null |
### Feature request
The feature proposal is to integrate a new Langchain tool that would provide support for queries on [https://api.nasa.gov/](NASA APIs)
Here is the list of NASA APIs that will be integrated into the new tool:
- APOD
- Asteroids NeoWs
- Earth
- EONET
- EPIC
- GeneLab
- Insight
- Mars Rover Photos
- NASA Image and Video Library
- TechTransfer
- TLE API
- Vesta/Moon/Mars Trek WMTS
### Motivation
Currently, Langchain does not provide access to a large data pool of reliable information related to astronomy. The integration of a NASA API tool would allow users to retrieve updated and accurate data related to satellite imagery, mission data and other space-related information. In cases where accuracy of data is critical, the NASA API tool may also be preferred over its GPT-generated counterpart.
### Your contribution
I am part of a group of 3 other students that will be able to submit a PR for this issue sometime in November.
|
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I_kwDOIPDwls5zzFsU
| 11,786 |
MultiPromptChain does not use its assigned destination chains
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[] | null | 3 | 2023-10-13T23:23:40 | 2023-10-13T23:53:37 | null |
NONE
| null |
### System Info
Python Version: 3.11.0
LangChain Version: 0.0.264
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
### define a custom chain:
```
class BlastAPIChain(Chain):
prompt: BasePromptTemplate
llm: BaseLanguageModel
@property
def input_keys(self) -> List[str]:
return ["prompt"]
@property
def output_keys(self) -> List[str]:
return ["response"]
@property
def _chain_type(self) -> str:
return "custom_chain"
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None
) -> Dict[str, str]:
'''
The primary method for this chain.
:param inputs: a dictionary containing the input keys and values.
:param run_manager: an optional callback manager for the chain run.
'''
# some custom logic goes here, but is omitted now
response = 'some custom logic would go here'
return {"response": response,}
```
### setup an instance of your custom chain
```
blast_api_chain_description = {
"name": "blast_api_chain",
"description": "lalalala, ",
"template": """ lalalala """,
}
blast_api_chain_prompt = PromptTemplate(
template=blast_api_chain_description["template"],
input_variables=[]
)
blast_api_chain = BlastAPIChain(llm=llm, verbose=True, memory=memory, prompt=blast_api_chain_prompt)
```
### add the destination chains to destination_chains
```
DESTINATION_CHAINS['blast_api_chain'] = blast_api_chain
```
### create a multi-prompt-chain from the destination chain
```
master_chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=DESTINATION_CHAINS,
default_chain=default_chain,
verbose=True,
)
```
### observe the issue: the blast_router_chain destination has been reassigned
```
print (master_chain.__dict__['destination_chains']['blast_api_chain'])
print (DESTINATION_CHAINS['blast_api_chain'])
```
### Expected behavior
I expect that after defining `master_chain` with the code below, the `destination_chains` attribute should hold all of the same chains that are defined in `DESTINATION_CHAINS`.
```
master_chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=DESTINATION_CHAINS,
default_chain=default_chain,
verbose=True,
)
```
However, this is not the case. Instead, the chains in DESTINATION_CHAINS have been over-written with different objects.
This can be seen here;
You can see that my custom BlastAPIChain instance has been replaced with an LLMChain instance. How am I supposed to route traffic to my custom chain?
|
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| 1,942,722,039 |
PR_kwDOIPDwls5cxzBL
| 11,785 |
update
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[] | null | 2 | 2023-10-13T22:25:49 | 2023-10-17T02:18:58 | 2023-10-17T02:18:58 |
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| null | null |
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PR_kwDOIPDwls5cxeVE
| 11,783 |
Create pattern_using_functions.c
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[] | null | 1 | 2023-10-13T20:45:32 | 2023-11-02T00:20:14 | 2023-11-02T00:20:14 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,942,600,243 |
PR_kwDOIPDwls5cxbuT
| 11,782 |
fix recent docs integrations file loc
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[] | null | 1 | 2023-10-13T20:36:43 | 2023-10-13T20:58:28 | 2023-10-13T20:58:27 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5cxXoy
| 11,781 |
fix upstash test import
|
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[] | null | 1 | 2023-10-13T20:20:01 | 2023-10-13T20:31:38 | 2023-10-13T20:31:36 |
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I_kwDOIPDwls5zyQLQ
| 11,780 |
Issue: <add_texts method in AzureSearch class throwing a metadata error>
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[] | null | 9 | 2023-10-13T19:51:52 | 2023-10-14T07:52:38 | null |
NONE
| null |
### Issue you'd like to raise.
my Azure search index has 4 fields
id, user_id, content, content_vector.
I am trying to add a conversation to the azure search vector store for a user_id ="msunkara"
The code is given below:
memory_vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=memory_index_name,
embedding_function=embeddings.embed_query)
history = []
user_id = "msunkara"
session_id = "1"
query = Hi
res.content = Hi, How can I assist you today?
history.append("User: "+ query + " Assistant: " + res.content)
memory_id = session_id + user_id
metadata = [{"user_id": user_id}]
memory_vector_store.add_texts(history,keys = [memory_id],metadatas=metadata)
The above code is throwing the below error:
Result: Failure Exception: HttpResponseError: () The request is invalid. Details: The property 'metadata' does not exist on type 'search.documentFields'. Make sure to only use property names that are defined by the type.
Help me solve this problem
### Suggestion:
_No response_
|
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| 1,942,541,488 |
PR_kwDOIPDwls5cxO9g
| 11,779 |
Move EverlyAI python notebook to the right location
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[] | null | 3 | 2023-10-13T19:41:18 | 2023-10-13T20:34:28 | 2023-10-13T20:34:28 |
CONTRIBUTOR
| null |
Hi,
After submitting https://github.com/langchain-ai/langchain/pull/11357, we realized that the notebooks are moved to a new location. Sending a new PR to update the doc.
|
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| 1,942,541,303 |
I_kwDOIPDwls5zyNP3
| 11,778 |
LLMRouterChain should easily support multiple inputs
|
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[] | null | 3 | 2023-10-13T19:41:14 | 2023-10-19T13:04:33 | null |
NONE
| null |
### Feature request
As it stands, `LLMRouterChain` basically only supports a single input. Here is the template:
https://github.com/langchain-ai/langchain/blob/v0.0.314/libs/langchain/langchain/chains/router/multi_prompt_prompt.py
While it is currently supported to define multiple input variables on a `PromptTemplate`, the `LLMRouterChain` basically doesn't support it because of the way the output from the LLM call is handled.
```
{{{{
"destination": string \\ name of the prompt to use or "DEFAULT"
"next_inputs": string \\ a potentially modified version of the original input
}}}}
```
With a lot of massaging, you can get to a place where an LLM causes `next_inputs` to be a comma-separated dict-like object but, down the chain (pun intended), you end up with an explosion because the way inputs are passed to chains doesn't handle this.
Overall, this implementation is odd. It expects the LLM to massage the original input(s) and then wants to use that output directly in future things.
Wouldn't it make more sense to simply ask the LLM to reason out which destination to send to (alone) and then take the original inputs and pass them along?
This current implementation is combining two things into one:
* a routing decision
* paraphrasing user input
The whole purpose of Langchain is to chain things, so why wouldn't you have "paraphrase/rephrase user input" as a chain step, instead of smushing it into the router implementation?
### Motivation
I have a use case where various inputs are collected separately and need to be maintained separately for a custom-tuned LLM later on. This caused me to attempt to use `LLMRouterChain` with multiple inputs, whereby I hit the roadblock described above.
It doesn't seem that wild to want to be able to make a routing decision based on input A, input B, or multiple inputs, and subsequently send all of those inputs to different templates later.
### Your contribution
I don't really have sufficient coding skills to help. I could maybe make some diagrams that help illustrate how this could work.
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PR_kwDOIPDwls5cxCOP
| 11,777 |
rm duplicate zilliz import
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I_kwDOIPDwls5zxRKe
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Issue: Many unit tests failing when running on Windows
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[] | null | 3 | 2023-10-13T17:01:59 | 2023-12-21T21:45:43 | 2023-12-21T21:45:43 |
NONE
| null |
### Issue you'd like to raise.
I tried to follow the contributing Quick Start [here](https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md#-quick-start), but I'm running into issues when I run the unit tests using `make test`
Here are the failing tests:
```
FAILED tests/unit_tests/agents/test_agent_iterator.py::test_agent_iterator_stopped_early - AssertionError: assert 'curses foiled again' == 'Agent stopped due to iteration limit or time limit.'
FAILED tests/unit_tests/agents/test_agent_iterator.py::test_agent_iterator_reset - assert 0.0 > 0.0
FAILED tests/unit_tests/callbacks/test_callback_manager.py::test_callback_manager_with_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
FAILED tests/unit_tests/storage/test_filesystem.py::test_yield_keys - AssertionError: assert ['key1', 'subdir\\key2'] == ['key1', 'subdir/key2']
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_absolute - re.error: incomplete escape \U at position 53
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_parent_dir - re.error: incomplete escape \U at position 69
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_for_symlink_outside_root - re.error: incomplete escape \U at position 64
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_for_symlink_inside_root - langchain.tools.file_management.utils.FileValidationError: Path symlink_inside_root is outside of the allowed direc...
FAILED tests/unit_tests/tools/requests/test_tool.py::test_requests_get_tool - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
FAILED tests/unit_tests/tools/requests/test_tool.py::test_requests_post_tool - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
FAILED tests/unit_tests/tools/requests/test_tool.py::test_requests_patch_tool - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
FAILED tests/unit_tests/tools/requests/test_tool.py::test_requests_put_tool - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
FAILED tests/unit_tests/tools/requests/test_tool.py::test_requests_delete_tool - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/agents/test_agent_iterator.py::test_agent_async_iterator_stopped_early - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/agents/test_agent_iterator.py::test_agent_async_iterator_with_callbacks - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/agents/test_agent_iterator.py::test_agent_async_iterator_output_structure - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/callbacks/test_callback_manager.py::test_callback_manager_with_async_with_running_loop - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/callbacks/test_callback_manager.py::test_async_callback_manager - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/callbacks/test_callback_manager.py::test_async_callback_manager_sync_handler - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/chains/test_sequential.py::test_simple_sequential_functionality_with_callbacks[False] - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/chains/test_sequential.py::test_simple_sequential_functionality_with_callbacks[True] - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/indexes/test_indexing.py::test_abatch - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/output_parsers/test_json.py::test_partial_text_json_output_parser_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/output_parsers/test_json.py::test_partial_functions_json_output_parser_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/output_parsers/test_json.py::test_partial_text_json_output_parser_diff_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/output_parsers/test_json.py::test_partial_functions_json_output_parser_diff_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/retrievers/test_base.py::test_fake_retriever_v1_upgrade_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/retrievers/test_base.py::test_fake_retriever_v1_with_kwargs_upgrade_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/retrievers/test_base.py::test_fake_retriever_v2_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/retrievers/self_query/test_base.py::test__aget_relevant_documents - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_locals.py::test_put_get_async[<lambda>-foo-foo] - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_locals.py::test_put_get_async[<lambda>-input1-output1] - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_passthrough_tap_async - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_with_config - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_default_method_implementations - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_prompt - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_prompt_with_chat_model - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_prompt_with_llm - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_stream_log_retriever - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_prompt_with_llm_and_async_lambda - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_router_runnable - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_higher_order_lambda_runnable - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_map_astream - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_map_astream_iterator_input - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_deep_astream - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_deep_astream_assign - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_runnable_sequence_atransform - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_llm_with_fallbacks[llm_with_fallbacks] - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_llm_with_fallbacks[llm_with_multi_fallbacks] - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_llm_with_fallbacks[llm_chain_with_fallbacks] - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_async_retrying - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_seq_abatch_return_exceptions - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_runnable_branch_ainvoke - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_runnable_branch_ainvoke_callbacks - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_runnable_branch_abatch - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_tool_from_runnable - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_runnable_gen - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/schema/runnable/test_runnable.py::test_runnable_gen_transform - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/smith/evaluation/test_runner_utils.py::test_arun_on_dataset - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_base.py::test_create_async_tool - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_base.py::test_async_exception_handling_bool - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_base.py::test_async_exception_handling_str - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_base.py::test_async_exception_handling_callable - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_base.py::test_async_exception_handling_non_tool_exception - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_zapier.py::test_apreview - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_zapier.py::test_arun - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/test_zapier.py::test_alist - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
ERROR tests/unit_tests/tools/shell/test_shell.py::test_shell_tool_arun - pytest_socket.SocketBlockedError: A test tried to use socket.socket.
```
Here is the complete log:
[test-log.txt](https://github.com/langchain-ai/langchain/files/12895952/test-log.txt)
### Suggestion:
_No response_
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PR_kwDOIPDwls5cwbZQ
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Add python,pandas,xorbits,spark agents to experimental
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[] | null | 2 | 2023-10-13T16:44:57 | 2023-10-16T03:20:45 | 2023-10-13T21:36:44 |
COLLABORATOR
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See for contex https://github.com/langchain-ai/langchain/discussions/11680
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bump 314
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[] | null | 4 | 2023-10-13T16:35:06 | 2023-11-04T04:21:52 | 2023-10-27T16:35:06 |
NONE
| null |
### System Info
Using `langchain==0.0.300`
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I tried running the [ElasticsearchDatabaseChain](https://python.langchain.com/docs/use_cases/qa_structured/sql#elastic-search) on a simple Elasticsearch database that contains one index as defined below, but I always get a `SerializationError` due to some faulty json.
Here are the details:
1. Get a local Elasticsearch instance running
```
docker run -p 9200:9200 -e "discovery.type=single-node" -e "xpack.security.enabled=false" -e "xpack.security.http.ssl.enabled=false" docker.elastic.co/elasticsearch/elasticsearch:8.10.2
```
2. Create the index mapping as follows
```bash
curl --request PUT \
--url 'http://localhost:9200/products-index?pretty=' \
--header 'content-type: application/json' \
--header 'user-agent: vscode-restclient' \
--data '{"settings": {"index.mapping.coerce": false},"mappings": {"properties": {"name": { "type": "text" },"price": { "type": "double" },"reviews": { "type": "text" },"createdAt": { "type": "date", "format": "epoch_second" }},"dynamic": true}}'
```
3. Bulk insert some data
```bash
curl --request POST \
--url http://localhost:9200/_bulk \
--header 'content-type: application/x-ndjson' \
--header 'user-agent: vscode-restclient' \
--data '{ "index": { "_index": "products-index" } }{ "name": "Bulk Item 666", "price": 666, "reviews": ["Good"], "createdAt": 1697213529 }{ "index": { "_index": "products-index" } }{ "name": "Bulk Item 123", "price": 123, "reviews": ["Good"], "createdAt": 1697213529 }'
```
4. Create and run the chain
```python
from elasticsearch import Elasticsearch
from langchain import OpenAI
from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain
db = Elasticsearch("http://localhost:9200")
chain = ElasticsearchDatabaseChain.from_llm(
llm=OpenAI(),
database=db,
verbose=True,
)
result = chain.run(prompt)
print(result)
```
<details>
<summary>Traceback (most recent call last):</summary>
> File "/home/sindeavour/Documents/github/sinaapps/datavacuum-old/ai-service/src/routes/v1/elastic.py", line 82, in elastic
result = chain.run(prompt)
^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/langchain/chains/base.py", line 487, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/langchain/chains/base.py", line 292, in __call__
raise e
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/langchain/chains/base.py", line 286, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/langchain/chains/elasticsearch_database/base.py", line 127, in _call
indices_info = self._get_indices_infos(indices)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/langchain/chains/elasticsearch_database/base.py", line 100, in _get_indices_infos
hits = self.database.search(
^^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/elasticsearch/_sync/client/utils.py", line 414, in wrapped
return api(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/elasticsearch/_sync/client/__init__.py", line 3924, in search
return self.perform_request( # type: ignore[return-value]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/elasticsearch/_sync/client/_base.py", line 285, in perform_request
meta, resp_body = self.transport.perform_request(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/elastic_transport/_transport.py", line 347, in perform_request
data = self.serializers.loads(raw_data, meta.mimetype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/elastic_transport/_serializer.py", line 196, in loads
return self.get_serializer(mimetype).loads(data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/sindeavour/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/elastic_transport/_serializer.py", line 95, in loads
raise SerializationError(
elastic_transport.SerializationError: Unable to deserialize as JSON: b'{"took":1,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":4,"relation":"eq"},"max_score":1.0,"hits":[{"_index":"products-index","_id":"1","_score":1.0,"_source":{\n "name": "Samsung S22 Ultra",\n "price": 975.99,\n "reviews": ["Good"],\n "createdAt": 1697145247 // epoch_second\n}},{"_index":"products-index","_id":"pRG9JYsB0QTitqW3X0qT","_score":1.0,"_source":{\n "name": "iPhone 14 Pro",\n "price": 1099,\n "reviews": ["Good"],\n "createdAt": 1697145249 // epoch_second\n}},{"_index":"products-index","_id":"phG9JYsB0QTitqW3ZEq5","_score":1.0,"_source":{ "name": "Bulk Item 666", "price": 666, "reviews": ["Good"], "createdAt": 1697145250 }}]}}'
</details>
```
elastic_transport.SerializationError: Unable to deserialize as JSON: b'{"took":0,"timed_out":false,"_shards":{"total":1,"successful":1,"skipped":0,"failed":0},"hits":{"total":{"value":4,"relation":"eq"},"max_score":1.0,"hits":[{"_index":"products-index","_id":"1","_score":1.0,"_source":{\n "name": "Samsung S22 Ultra",\n "price": 975.99,\n "reviews": ["Good"],\n "createdAt": 1697145247 // epoch_second\n}},{"_index":"products-index","_id":"pRG9JYsB0QTitqW3X0qT","_score":1.0,"_source":{\n "name": "iPhone 14 Pro",\n "price": 1099,\n "reviews": ["Good"],\n "createdAt": 1697145249 // epoch_second\n}},{"_index":"products-index","_id":"phG9JYsB0QTitqW3ZEq5","_score":1.0,"_source":{ "name": "Bulk Item 666", "price": 666, "reviews": ["Good"], "createdAt": 1697145250 }}]}}'
```
If you look closely, you can see that there is an invalid comment `// epoch_second` in multiple places in the generated json, hence why the parsing fails.
### Expected behavior
The chain should run without issues when executing `chain.run(prompt)`
|
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| 1,942,255,454 |
I_kwDOIPDwls5zxHde
| 11,770 |
Video imagery to text (Closed Captioning)
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open
| false | null |
[] | null | 7 | 2023-10-13T16:29:52 | 2023-11-30T04:53:58 | null |
NONE
| null |
### Feature request
Implement a feature using Langchain's image_captions.py and audio_speech_to_text.py to produce .srt files. This system will provide both subtitles and visual scene descriptions, essentially creating closed captioning.
### Motivation
Current closed captioning primarily captures spoken content, omitting crucial visual elements of videos. This lack of comprehensive description hinders a full understanding, especially for the hearing-impaired community. Our goal is to elevate video accessibility by integrating both visual and auditory components in closed captioning, ensuring a more inclusive viewing experience.
### Your contribution
We are students at the University of Toronto, interested in contributing to Langchain for a school project. We would like to submit a PR that implements this feature sometime in November.
|
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I_kwDOIPDwls5zwmDE
| 11,769 |
When using Streaming and AsyncCallbackHandler, sometimes it is not possible to obtain the complete token concatenation string.
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[] | null | 2 | 2023-10-13T14:56:55 | 2023-10-17T10:17:06 | null |
NONE
| null |
### System Info
I encountered a very strange problem when using AsyncCallbackHandler. Most of the time, it works normally, but occasionally strange problems occur. Therefore, I logged the entire call process in each function, and finally learned from the logs that under normal circumstances, the logs are on_ llm_ start, then the API call log for calling openai, and finally on_llm_ end, but occasionally this is not the case, and in exceptional cases, the log order is on_ llm_ start, on_ llm_ end, followed by the API call log of OpenAI, which occasionally prevents me from obtaining the complete token concatenated string because I need to record the complete answer of AI each time. Therefore, I checked the source code and temporarily did not find how the while loop broke out. While not self. queue. empty() or not self. done.is_ Set () This needs to satisfy both that the queue is not empty and that done has been set. Where are the specific issues and I hope to receive answers
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [X] Async
### Reproduction
class AsyncIteratorCallbackHandler(AsyncCallbackHandler):
"""Callback handler that returns an async iterator."""
queue: asyncio.Queue[str]
done: asyncio.Event
@property
def always_verbose(self) -> bool:
return True
def __init__(self) -> None:
self.queue = asyncio.Queue()
self.done = asyncio.Event()
async def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
# If two calls are made in a row, this resets the state
self.done.clear()
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
if token is not None and token != "":
self.queue.put_nowait(token)
async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
self.done.set()
async def on_llm_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
self.done.set()
# TODO implement the other methods
async def aiter(self) -> AsyncIterator[str]:
while not self.queue.empty() or not self.done.is_set():
# Wait for the next token in the queue,
# but stop waiting if the done event is set
done, other = await asyncio.wait(
[
# NOTE: If you add other tasks here, update the code below,
# which assumes each set has exactly one task each
asyncio.ensure_future(self.queue.get()),
asyncio.ensure_future(self.done.wait()),
],
return_when=asyncio.FIRST_COMPLETED,
)
# Cancel the other task
if other:
other.pop().cancel()
# Extract the value of the first completed task
token_or_done = cast(Union[str, Literal[True]], done.pop().result())
# If the extracted value is the boolean True, the done event was set
if token_or_done is True:
break
# Otherwise, the extracted value is a token, which we yield
yield token_or_done
### Expected behavior
AsyncCallbackHandler can execute strictly in the order of on_llm_start, openai call(on_llm_new_token), and on_llm_end each time.
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PR_kwDOIPDwls5cvSB7
| 11,768 |
adding parameter for changing the language in SpacyEmbeddings
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[] | null | 3 | 2023-10-13T13:10:38 | 2024-01-09T11:42:25 | 2024-01-09T11:42:15 |
NONE
| null |
- **Description:** Added the parameter for a possibility to change a language model in SpacyEmbeddings. The default value is still the same: "en_core_web_sm", so it shouldn't affect a code which previously did not specify this parameter, but it is not hard-coded anymore and easy to change in case you want to use it with other languages or models.
- **Issue:** At Barcelona Supercomputing Center in Aina project (https://github.com/projecte-aina), a project for Catalan Language Models and Resources, we would like to use Langchain for one of our current projects and we would like to comment that Langchain, while being a very powerful and useful open-source tool, is pretty much focused on English language. We would like to contribute to make it a bit more adaptable for using with other languages.
- **Dependencies:** This change requires the Spacy library and a language model, specified in the model parameter.
- **Tag maintainer:** @dev2049
- **Twitter handle:** @projecte_aina
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I_kwDOIPDwls5zvYUq
| 11,767 |
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')).
|
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[] | null | 1 | 2023-10-13T11:51:17 | 2023-10-13T12:01:12 | null |
NONE
| null |
### System Info
llm = ChatOpenAI(model_name="gpt-4", temperature=0.5, request_timeout=300)
qa_chain = ConversationalRetrievalChain.from_llm(
verbose=True,
llm=llm,
retriever=retriever,
return_source_documents=True,
combine_docs_chain_kwargs={"prompt": prompt},
rephrase_question=False,
)
given code I get error:
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response')).
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
llm = ChatOpenAI(model_name="gpt-4", temperature=0.5, request_timeout=300)
qa_chain = ConversationalRetrievalChain.from_llm(
verbose=True,
llm=llm,
retriever=retriever,
return_source_documents=True,
combine_docs_chain_kwargs={"prompt": prompt},
rephrase_question=False,
)
### Expected behavior
successfully call OpenAI embedding api
|
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| 1,941,798,898 |
PR_kwDOIPDwls5cuywE
| 11,766 |
Recursive loader user agent header
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[] | null | 1 | 2023-10-13T11:50:15 | 2023-10-16T12:09:28 | 2023-10-16T11:36:29 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
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- **Issue:** Fixes the following issue raised. https://github.com/langchain-ai/langchain/issues/11541 ,
- **Dependencies:** Needed fake_useragent python library . pip install fake_useragent,
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17,
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| 1,941,723,919 |
PR_kwDOIPDwls5cuiRw
| 11,764 |
Include information on the tools for creating gbnf grammar files in the llama-cpp notebook
|
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[] | null | 2 | 2023-10-13T10:57:00 | 2023-10-17T03:28:33 | 2023-10-17T03:28:32 |
CONTRIBUTOR
| null |
Hi,
I recently experimented with grammar-based sampling and discovered two methods for speeding up the creation of gbnf grammar files:
1. [Online grammar generator app](https://github.com/ggerganov/llama.cpp/discussions/2494) introduced [here](https://github.com/ggerganov/llama.cpp/discussions/2494)
2. [Script](https://github.com/ggerganov/llama.cpp/blob/master/examples/json-schema-to-grammar.py) for parsing json schema to gbnf grammar
I believe it is a good idea to include the information that leads to them in the `llama-cpp` notebook.
***
Codespell check fails but due to the unrelated script
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| 1,941,719,788 |
I_kwDOIPDwls5zvErs
| 11,763 |
Add a new vector store hippo to langchain
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open
| false | null |
[] | null | 0 | 2023-10-13T10:54:02 | 2023-10-13T10:56:10 | null |
CONTRIBUTOR
| null |
### Feature request
I would like to propose a new vector database to be included in the Langchain ecosystem to expand Langchain's influence. Below is the official link to our vector database.
https://www.transwarp.cn/starwarp
### Motivation
I hope that everyone can more conveniently use Hippo + Langchain to build AI Agents.
### Your contribution
Integrating the Hippo vector database into the Langchain ecosystem can expand Langchain's influence.
|
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PR_kwDOIPDwls5cufpJ
| 11,762 |
Use a less specific return type for | on Runnables
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[] | null | 1 | 2023-10-13T10:48:24 | 2023-10-15T20:15:07 | 2023-10-15T20:15:06 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 1,941,702,031 |
PR_kwDOIPDwls5cudcK
| 11,761 |
Revert "New add Baichuan Model"
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[] | null | 3 | 2023-10-13T10:41:51 | 2023-10-19T03:20:14 | 2023-10-13T15:23:15 |
COLLABORATOR
| null |
Reverts langchain-ai/langchain#11714
This has linting and formatting issues, plus it's added to chat models folder but doesn't subclass Chat Model base class
|
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| 1,941,683,584 |
I_kwDOIPDwls5zu72A
| 11,760 |
Use conversation history with MultiRetrievalQAChain
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[] | null | 0 | 2023-10-13T10:28:58 | 2023-10-13T10:31:06 | null |
NONE
| null |
### Feature request
It would be great to make it possible to use multiple retrievers, LLM, and conversation history together.
### Motivation
Currently, when using `MultiRetrievalQAChain.fromLLMAndRetrievers`, you can provide an LLM and multiple retrievers for context. If there was a possibility to add a conversation/chat history to this flow, it could help further understand the context, especially in cases where there was a previous conversation between a user and a human agent/bot.
### Your contribution
I'm not familiar with Python but could provide help with the TypeScript implementation on top of the base logic.
|
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| 11,759 |
AttributeError: 'BloomForCausalLM' object has no attribute 'is_quantized'
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[] | null | 5 | 2023-10-13T09:35:49 | 2023-10-16T20:36:36 | null |
NONE
| null |
### System Info
Google Colab
langchain==0.0.313
transformers==4.34.0
Python 3.10.12
[Dependencies](https://pastebin.com/raw/T1BqzVq1)
I am trying to download huggingface model and feed them to the chain. It was working prior to few weeks. I have tried to change model id to other models.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
!pip install langchain transformers
from langchain import HuggingFacePipeline
llm = HuggingFacePipeline.from_model_id(model_id = "bigscience/bloom-560m", task = "text-generation")
```
### Expected behavior
Successful execution
## Screenshot
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I_kwDOIPDwls5zugp7
| 11,758 |
DOC:**Page Not Found**
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[] | null | 1 | 2023-10-13T09:16:52 | 2023-11-27T11:37:00 | null |
NONE
| null |
### Issue with current documentation:
From the page
https://python.langchain.com/docs/integrations/platforms/aws
If you select See a [usage example.](https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example) the following message is generated:
**Page Not Found**
We could not find what you were looking for.
Please contact the owner of the site that linked you to the original URL and let them know their link is broken.
https://python.langchain.com/docs/integrations/llms/amazon_api_gateway_example
### Idea or request for content:
_No response_
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| 1,941,426,457 |
PR_kwDOIPDwls5cthO9
| 11,756 |
allow passing HTTP headers in AzureOpenAI class
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[] | null | 3 | 2023-10-13T07:47:38 | 2023-10-13T09:50:39 | null |
NONE
| null |
- **Description:** Some organisations expose Azure OpenAI endpoints via some proxies that need additional HTTP headers. This PR supports for that by allowing the user to set custom headers when initialising the AzureOpenAI class. These custom headers can be used when making requests.
- **Issue:** it aims to fix the issue #11593
- **Dependencies:** -
|
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I_kwDOIPDwls5ztzEg
| 11,755 |
Adapt to openai-python v1.0.0 Beta
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[] | null | 10 | 2023-10-13T07:18:30 | 2023-11-09T00:25:09 | null |
NONE
| null |
### Feature request
will you update to follow the update of https://github.com/openai/openai-python/discussions/631 ?
### Motivation
Many issues in langchain point that when calling async api and using openai LLM(Chains like MapReduceDocumentsChain), it will raise '[Error Response payload is not complete](https://github.com/langchain-ai/langchain/issues/9187#top)' or [TimeoutError](https://github.com/langchain-ai/langchain/issues/7148)
After discussion, they all point to [an openai issue](https://github.com/openai/openai-python/issues/574), which will be fix in v1.0.0(now v1.0.0 beta is avaliable)
So if you can adapt to their updates, this critical issue can be fixed
### Your contribution
none
|
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PR_kwDOIPDwls5ctVLo
| 11,754 |
Supported OutputFixingParser max_retries
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[] | null | 2 | 2023-10-13T07:07:07 | 2023-10-17T02:25:48 | 2023-10-17T02:25:47 |
CONTRIBUTOR
| null |
Description: Supported OutputFixingParser max_retries
- max_retries: Maximum number of retries to parser.
Issue: None
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle: @JohnMai95
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Modify spelling errors in the comments section of the baichuan_baichuaninc_endpoint.py file
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[] | null | 2 | 2023-10-13T06:56:11 | 2023-10-17T02:27:36 | 2023-10-17T02:27:36 |
CONTRIBUTOR
| null |
Defautlt -->Default
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| 1,941,321,112 |
I_kwDOIPDwls5ztjWY
| 11,751 |
Question (Urgent): Agent with multiple tools doesn't work as expected and also caused infinite loop.
|
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[] | null | 3 | 2023-10-13T06:26:38 | 2023-12-22T11:37:00 | null |
NONE
| null |
### Issue you'd like to raise.
I am trying to build an agent with multiple tools. Based on the query agent may need to execute single or multiple tools. Some of the tools may ask clarification question to the user. In that case agent should add the question to the final answer along with other tools response. But it wont work as expected. Also It executes the same tool again and again with similar kind of input if the tools response not satisfactory to it.
My agent has 4 tools. I have added portion of the code and a screenshot for better understanding.
I have also tried to modify the prefix, suffix and format_instruction in so many ways but no luck. Also I know return_direct=true will return the tools response to the user directly but this is not my case as this flag won't execute multiple tools.
**Expectation:** Agent should return the tools clarification question to the user along with other tools response instead of answering on it's own and execute same tool for similar kind of input. Also Agent should reply proper answer if the Query not related to tools. Eg: If the query is just "Hello" it produce error.
```
FORMAT_INSTRUCTIONS = """Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question"""
PREFIX_PROMPT = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
SUFFIX_PROMPT = """Begin!
Previous conversation history:
{chat_history}
Question: {input}
Thought:{agent_scratchpad}"""
class ChatService(RequestAccessorService):
def __init__(self, model: SenseChatModel, request: Request):
super().__init__(request)
self.model = model
async def execute_async(self):
tools = [
QATool(self.model, self),
RecordCreateTool(self.model, self),
RecordSearchTool(self.model, self),
AppCreateTool(self.model, self)
]
llm = ChatOpenAI(openai_api_key=self.model.openai_api_key, temperature=0)
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=PREFIX_PROMPT,
suffix=SUFFIX_PROMPT,
format_instructions=FORMAT_INSTRUCTIONS,
input_variables=["input", "chat_history", "agent_scratchpad"],
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True
)
print(agent.llm_chain.prompt.template)
result = await agent_chain.arun(input=self.model.query)
return result
```
**Query:** Give me the all product in the inventory and create a product named dell in inventory
**Output**: The output of the query. Please see the Action, Action Input and observation to understand the whole scenario.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5ztVcK
| 11,750 |
langchain agent如何知道选择哪个Tool?
|
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[] | null | 1 | 2023-10-13T05:25:20 | 2023-10-13T05:36:42 | null |
NONE
| null |
### Issue you'd like to raise.
_No response_
### Suggestion:
_No response_
|
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I_kwDOIPDwls5ztN6I
| 11,749 |
@root_validator error
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[] | null | 3 | 2023-10-13T04:51:51 | 2023-10-18T18:52:26 | 2023-10-13T05:15:14 |
NONE
| null |
### System Info
```sh
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/langchain/__init__.py", line 8, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/usr/local/lib/python3.8/dist-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import Agent
File "/usr/local/lib/python3.8/dist-packages/langchain/agents/agent.py", line 10, in <module>
from langchain.chains.base import Chain
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/__init__.py", line 2, in <module>
from langchain.chains.conversation.base import ConversationChain
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/conversation/base.py", line 7, in <module>
from langchain.chains.conversation.memory import ConversationBufferMemory
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/conversation/memory.py", line 7, in <module>
from langchain.chains.conversation.prompt import SUMMARY_PROMPT
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/conversation/prompt.py", line 2, in <module>
from langchain.prompts.prompt import PromptTemplate
File "/usr/local/lib/python3.8/dist-packages/langchain/prompts/__init__.py", line 2, in <module>
from langchain.prompts.base import BasePromptTemplate
File "/usr/local/lib/python3.8/dist-packages/langchain/prompts/base.py", line 35, in <module>
class BasePromptTemplate(BaseModel, ABC):
File "/usr/local/lib/python3.8/dist-packages/langchain/prompts/base.py", line 41, in BasePromptTemplate
@root_validator()
File "/usr/local/lib/python3.8/dist-packages/pydantic/deprecated/class_validators.py", line 228, in root_validator
raise PydanticUserError(
pydantic.errors.PydanticUserError: If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`. Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.
```
### Who can help?
@agola11
```python
import langchain
```
### 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
1. Install langchain latest version on Linux
2. import langchain
### Expected behavior
Nothing.
|
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| 1,941,094,880 |
PR_kwDOIPDwls5csZ7k
| 11,748 |
Minor fix
|
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[] | null | 1 | 2023-10-13T01:58:28 | 2023-10-13T05:36:31 | 2023-10-13T05:36:31 |
CONTRIBUTOR
| null |
changed > to over
|
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| 1,941,067,816 |
I_kwDOIPDwls5zslgo
| 11,747 |
Add Slack Toolkit
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[] | null | 3 | 2023-10-13T01:32:38 | 2023-10-15T09:12:26 | null |
CONTRIBUTOR
| null |
### Feature request
We would like to add Slack toolkit, which for a slack user, he can:
1. fetch information of workplaces (name, user type, time joined, etc.) he is a member of
2. fetch chat history across workplaces he is a member, and further process/analyze such data with LLM
3. fetch interaction (reply, mention, DM, etc) with other users across workplaces.
4. fetch a list of new activities across workplaces since last time login to slack platform,
5. and react to these activities.
Also, supports filters (by time, keyword etc) as appropriate for all the above functions
### Motivation
Currently lang-chain provides limited support for Slack, specifically, only supports loader of chat history zip file. On the other hand, slack platform analytics interface lacks aggregated information across workplaces; also most analytics info if provides are limited to owner/admin user type. Therefore, we want to implement the above feature:
1. Add-on current langchain toolkit: slack toolkit would be the one of the few, if not the first social media related toolkit.
2. Expose slack interface tools' functions to more user types: Only specific slack user type (workspace owner and/or admin) has access to downloading file option, along with most analytics tools embedded in slack user interface, which calls for a toolkit for other user types to access such information.
3. Better Info Aggregation: We notice that a user can be in multiple workspaces yet currently there lacks a way for user to get/compare information (real-time and logs) across workspaces they are member of. It would be nice for lang chain to provide a toolkit that provides user with such information upon request, so that the user can have all his/her user info across workspace at one place.
### Your contribution
We have started working on the feature and intend to submit PR in mid November.
|
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| 1,941,036,248 |
I_kwDOIPDwls5zsdzY
| 11,746 |
怎么控制langchain agent的意图分类的类别数？
|
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NONE
| null |
### Issue you'd like to raise.
_No response_
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5csDpX
| 11,745 |
fix tongyi import
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PR_kwDOIPDwls5cr_oA
| 11,744 |
mv self-query docs to integrations
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PR_kwDOIPDwls5cr7b-
| 11,743 |
mv semadb docs
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PR_kwDOIPDwls5cr6wy
| 11,742 |
clean up doc
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committed old doc in wrong place
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PR_kwDOIPDwls5cry15
| 11,741 |
use get_llm_cache and set_llm_cache
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PR_kwDOIPDwls5crxjb
| 11,740 |
fix(openllm): update with newer remote client implementation
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[] | null | 2 | 2023-10-12T22:55:59 | 2023-10-13T00:14:54 | 2023-10-13T00:01:19 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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cc @baskaryan
|
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| 1,940,902,588 |
I_kwDOIPDwls5zr9K8
| 11,739 |
BaseCallbackHandler on_llm_new_token not working with custom MultiRouteChain
|
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[] | null | 3 | 2023-10-12T22:41:51 | 2023-10-12T23:47:22 | null |
NONE
| null |
### System Info
Hi, I've implemented my own MultiRouteChain
### Who can help?
@UmerHA
@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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is the implementation of mycallbackhandler, taken from this documentation:
https://github.com/langchain-ai/langchain/issues/5433
class MyCallbackHandler(BaseCallbackHandler):
def on_llm_new_token(self, token, **kwargs) -> None:
# print every token on a new line
print(f"#{token}#")
This is part of the implementation of my classs:
class NestedRouterMultiType(MultiRouteChain):
@classmethod
def from_prompts_and_retrievers(
cls,
llm: BaseLanguageModel,
prompt_infos: List[Dict[str, str]],
retrieval_infos: List[Dict[str, Any]],
router_infos: List[Dict[str, Dict[str, List[Dict[str, Any]]]]],
default_chain: Optional[Chain] = None,
#We can include default retrievers, but for now it is useful to just use GPT as the default chain
return_source_documents: bool = False,
verbose: bool = False,
**kwargs: Any,
) -> NestedRouterMultiType:
"""Convenience constructor for instantiating from destination prompts and retrievers."""
# Combine prompt and retriever infos for destinations
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos + retrieval_infos + router_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_TYPE_ROUTER_TEMPLATE_TEST.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input", "context"],
output_parser=RouterOutputParserConversational(),
)
router_chain = LLMRouterChainConversational.from_llm(llm, router_prompt)
destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
input_variables = cls._extract_variables_from_template(prompt_template)
# Include here input variables input and history and test whether history is being passed correctly
#Deprecated line of code:
#prompt = PromptTemplate(template=prompt_template, input_variables=["question", "chat_history"])
prompt = PromptTemplate(template=prompt_template, input_variables=input_variables)
chain = UnifiedLLMChain(
prompt=prompt,
llm=llm,
verbose=verbose,
callbacks=[MyCallbackHandler()],
)
destination_chains[name] = chain
for r_info in retrieval_infos:
# refactor this to pass all arguments in PromptFactory
name = r_info["name"]
prompt = r_info.get("prompt") #try to retrieve a prompt, if empty, use None (not sure yet what this prompt is for)
retriever = r_info["retriever"]
condense_prompt_template = r_info["condense_prompt_template"]
prompt_template = r_info["prompt_template"]
# Here's where we create a Router that can route to ConversationalRetrievalChains
chain = ConversationalRetrievalChainHistory.from_llm(
llm,
retriever=retriever,
combine_docs_chain_kwargs={"prompt": prompt_template},
condense_question_prompt=condense_prompt_template,
return_source_documents = return_source_documents,
verbose=verbose,
callbacks=[MyCallbackHandler()],
)
destination_chains[name] = chain
### Expected behavior
I expect to print all tokens on each chain. The output is nonexistent. Either I'm missing something or it is a bug. The streaming parameter is set to True on BaseLanguageModel.
|
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PR_kwDOIPDwls5crsEd
| 11,738 |
reorder eval docs
|
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[] | null | 1 | 2023-10-12T22:25:36 | 2023-10-12T22:46:56 | 2023-10-12T22:46:55 |
COLLABORATOR
| null |
cc @leo-gan
|
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| 1,940,885,814 |
I_kwDOIPDwls5zr5E2
| 11,737 |
`extra_tools` argument in `create_pandas_dataframe_agent()` doesn't seem to be working
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[] | null | 2 | 2023-10-12T22:22:09 | 2023-12-05T04:54:09 | 2023-12-05T04:54:09 |
NONE
| null |
### System Info
Platform: Windows Server 2022
Python: 3.11.6
Langchain version: 0.0.306
### Who can help?
@agola11 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
### Creating a test tool
```py
from langchain.agents import Tool
tools = [
Tool(
name="test_tool",
func=print,
description="This is a test tool"
)
]
tools
```
```
[Tool(name='test_tool', description='This is a test tool', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, handle_tool_error=False, func=<built-in function print>, coroutine=None)]
```
### Setting up the pandas_dataframe_agent
```py
from langchain.agents import create_pandas_dataframe_agent
from langchain.llms import HuggingFacePipeline
import pandas as pd
llm = HuggingFacePipeline.from_model_id(
model_id="google/flan-t5-small", task="text2text-generation", device=0
)
agent = create_pandas_dataframe_agent(llm, pd.DataFrame(), verbose=True, extra_tools=tools)
agent.tools
```
```
[PythonAstREPLTool(name='python_repl_ast', description='A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.', args_schema=<class 'langchain.tools.python.tool.PythonInputs'>, return_direct=False, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, handle_tool_error=False, globals={}, locals={'df': Empty DataFrame
Columns: []
Index: []}, sanitize_input=True),
Tool(name='test_tool', description='This is a test tool', args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, handle_tool_error=False, func=<built-in function print>, coroutine=None)]
```
### Executing agent with debugging enabled
```py
import langchain
langchain.debug = True
agent.run('What is 2+2?')
```
```
[chain/start] [1:chain:AgentExecutor] Entering Chain run with input:
{
"input": "What is 2+2?"
}
[chain/start] [1:chain:AgentExecutor > 2:chain:LLMChain] Entering Chain run with input:
{
"input": "What is 2+2?",
"agent_scratchpad": "",
"stop": [
"\nObservation:",
"\n\tObservation:"
]
}
[llm/start] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:HuggingFacePipeline] Entering LLM run with input:
{
"prompts": [
"You are working with a pandas dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:\n\npython_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [python_repl_ast]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\n\nThis is the result of `print(df.head())`:\n\n\nBegin!\nQuestion: What is 2+2?"
]
}
```
### The prompt from the above log
```
You are working with a pandas dataframe in Python. The name of the dataframe is `df`.
You should use the tools below to answer the question posed of you:
python_repl_ast: A Python shell. Use this to execute python commands. Input should be a valid python command. When using this tool, sometimes output is abbreviated - make sure it does not look abbreviated before using it in your answer.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [python_repl_ast]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
This is the result of `print(df.head())`:
Begin!
Question: What is 2+2?
```
### Expected behavior
Where did my custom tool `test_tool` disappear? I expected it to show up after python_repl_ast?
|
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PR_kwDOIPDwls5crqRw
| 11,736 |
feat: Google Vertex AI Search Retriever - Add support for Website Data Stores
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[] | null | 6 | 2023-10-12T22:15:04 | 2023-10-19T18:33:09 | 2023-10-19T06:41:48 |
CONTRIBUTOR
| null |
- Only works for Data stores with Advanced Website Indexing
- https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features
- Minor restructuring - Follow up to #10513
- Remove outdated docs (readded in https://github.com/langchain-ai/langchain/pull/11620)
- Move legacy class into new py file to clean up the directory
- Shouldn't cause backwards compatibility issues as the import works the same way for users
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| 1,940,842,588 |
I_kwDOIPDwls5zruhc
| 11,735 |
Add option to specify prompt for SelfQueryRetriever
|
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[] | null | 2 | 2023-10-12T21:49:59 | 2023-10-14T17:56:07 | null |
CONTRIBUTOR
| null |
### Feature request
There is an option to specify the prompt sent to LLM and also the CONDENSE_QUESTION prompt. But there is no way to specify the prompt that is being used by SelfQueryRetriever. This can be helpful to provide additional context to the LLM in order to construct a proper query.
### Motivation
For my use case, the default prompt sent to LLM to generate a query doesn't work. Although the question I am asking the LLM is good, it is not able to figure the correct filter it should use. I think if I was able to modify the query, it could solve the issue and start returning correct queries.
### Your contribution
Not really.
|
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I_kwDOIPDwls5zrkQS
| 11,734 |
IndexError: pop from empty list - bug in ConversationTokenBufferMemory
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| false | null |
[] | null | 5 | 2023-10-12T21:11:56 | 2023-10-25T13:56:59 | null |
NONE
| null |
### System Info
Langchain version: 0.0.312
Python: 3.10.11
Platform: Ubuntu
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.memory import ConversationTokenBufferMemory
max_token_limit = 0
memory = ConversationTokenBufferMemory(
llm=llm, # Azure OpenAI GPT3.5
max_token_limit=max_token_limit,
return_messages=True,
)
context_pair = {
"inputs": {"input": "Example input message."},
"outputs": {"outputs": "Example output message."}
}
memory.save_context(**context_pair)
```
### Expected behavior
The above code produces an `IndexError: pop from empty list`
Due to some token budgeting we're doing, sometimes the space for the memory gets reduced to 0 (specifically when a user has a long message, we prioritize tokens for their message and the AI response). So we create memory with `max_token_limit=0`, and **ideally we'd like the memory to be pruned such that the buffer is empty.** However, we instead get the above error.
### Where the bug seems to be
The culprit seems to be in `memory.save_context()` , which we call directly in some cases or sometimes it happens internally like:
```
chat_chain = ConversationChain(
memory=memory, llm=llm, prompt=prompt_template
)
ai_response = chat_chain.predict(input=user_message) # internal call to save_context()
```
Within `save_context`, there's a loop:
```
while curr_buffer_length > self.max_token_limit:
pruned_memory.append(buffer.pop(0))
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
```
Oddly, when the buffer is an empty list, `curr_buffer_length` is 3 when it should be 0. So the loop continues and tries to pop from the empty buffer list and we get the above error.
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I_kwDOIPDwls5zrcMs
| 11,733 |
TypeError: BaseChatModel.__call__() missing 1 required positional argument: 'messages'
|
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| false | null |
[] | null | 1 | 2023-10-12T20:45:25 | 2023-10-12T20:53:40 | null |
NONE
| null |
### Issue you'd like to raise.
I am following the notebook in this github to create two agents with tools:
https://github.com/langchain-ai/langchain/blob/master/cookbook/two_agent_debate_tools.ipynb
I am also using AzureOpenAI instead of OpenAI directly because of corporate firewalls. I am getting this error:
TypeError Traceback (most recent call last)
Cell In[25], line 34
24 agent_description = AzureChatOpenAI(
25 openai_api_base=BASE_URL,
26 openai_api_version="2023-05-15",
(...)
29 openai_api_type="azure",
30 )(temperature=1.0)(agent_specifier_prompt).content
31 return agent_description
---> 34 agent_descriptions = {name: generate_agent_description(name) for name in names}
Cell In[25], line 34, in (.0)
24 agent_description = AzureChatOpenAI(
25 openai_api_base=BASE_URL,
26 openai_api_version="2023-05-15",
(...)
29 openai_api_type="azure",
30 )(temperature=1.0)(agent_specifier_prompt).content
31 return agent_description
---> 34 agent_descriptions = {name: generate_agent_description(name) for name in names}
Cell In[25], line 24, in generate_agent_description(name)
13 def generate_agent_description(name):
14 agent_specifier_prompt = [
...
29 openai_api_type="azure",
30 )(temperature=1.0)(agent_specifier_prompt).content
31 return agent_description
TypeError: BaseChatModel.__call__() missing 1 required positional argument: 'messages'
### Suggestion:
_No response_
|
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| 1,940,740,863 |
I_kwDOIPDwls5zrVr_
| 11,732 |
async SQLDatabaseChain
|
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| false | null |
[] | null | 4 | 2023-10-12T20:25:16 | 2023-12-21T14:49:09 | null |
NONE
| null |
### Feature request
Hello everyone!
Please when will we have SQLDatabaseChain async?
I believe it's crucial for all Chains to be async so that we can have a serverless service serving multiple clients.
Thank you!
### Motivation
I believe it's crucial for all Chains to be async so that we can have a serverless service serving multiple clients.
### Your contribution
I dont know how to contribute. Maybe I coult test your code.
|
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PR_kwDOIPDwls5crBpl
| 11,731 |
feat: serialize momento vector index metadata values to json
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[] | null | 2 | 2023-10-12T19:53:50 | 2023-10-12T21:04:15 | 2023-10-12T21:04:01 |
CONTRIBUTOR
| null |
**Description**:
To support a wider range of expected metadata values, we now serialize these values to JSON when storing in Momento Vector Index. Specifically, in the add method, values are dumped as JSON strings using `json.dumps`. During the search operation, we deserialize them using `json.loads`.
**Testing**:
We updated the integration tests to exercise this new behavior.
**Maintainers**:
@baskaryan who reviewed the original MVI PR.
**Twitter handle:**
@momentohq for Momento Vector Index vector store introduction
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PR_kwDOIPDwls5cq_YJ
| 11,730 |
Comprehend Moderation 0.2
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[] | null | 4 | 2023-10-12T19:45:59 | 2023-10-26T16:42:18 | 2023-10-26T16:42:18 |
CONTRIBUTOR
| null |
This PR replaces the previous `Intent` check with the new `Prompt Safety` check. The logic and steps to enable chain moderation via the Amazon Comprehend service, allowing you to detect and redact PII, Toxic, and Prompt Safety information in the LLM prompt or answer remains unchanged.
This implementation updates the code and configuration types with respect to `Prompt Safety`.
### Usage sample
```python
from langchain_experimental.comprehend_moderation import (BaseModerationConfig,
ModerationPromptSafetyConfig,
ModerationPiiConfig,
ModerationToxicityConfig
)
pii_config = ModerationPiiConfig(
labels=["SSN"],
redact=True,
mask_character="X"
)
toxicity_config = ModerationToxicityConfig(
threshold=0.5
)
prompt_safety_config = ModerationPromptSafetyConfig(
threshold=0.5
)
moderation_config = BaseModerationConfig(
filters=[pii_config, toxicity_config, prompt_safety_config]
)
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, #specify the configuration
client=comprehend_client, #optionally pass the Boto3 Client
verbose=True
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)
llm_chain = LLMChain(prompt=prompt, llm=llm)
chain = (
prompt
| comp_moderation_with_config
| {llm_chain.input_keys[0]: lambda x: x['output'] }
| llm_chain
| { "input": lambda x: x['text'] }
| comp_moderation_with_config
)
try:
response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})
except Exception as e:
print(str(e))
else:
print(response['output'])
```
### Output
```python
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running prompt safety Validation...
> Finished chain.
> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running prompt safety Validation...
> Finished chain.
Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like XXXXXXXXXXXX John Doe's phone number is (999)253-9876.
```
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| 1,940,641,679 |
PR_kwDOIPDwls5cq1DC
| 11,729 |
added candidate_count for Vertex models
|
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[] | null | 4 | 2023-10-12T19:19:02 | 2023-10-13T20:31:21 | 2023-10-13T20:31:21 |
CONTRIBUTOR
| null |
- **Description:** added support for `candidate_count` parameter on Vertex
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| 1,940,635,026 |
I_kwDOIPDwls5zq72S
| 11,728 |
Add support for themoviedb querying to get movie info
|
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[] | null | 3 | 2023-10-12T19:15:07 | 2023-10-16T04:21:47 | 2023-10-14T13:35:20 |
CONTRIBUTOR
| null |
### Feature request
The proposal is to enhance our Langchain by integrating support for querying The Movie Database (TMDb) using a Tool to retrieve detailed information about movies.
[https://developer.themoviedb.org/docs/getting-started](url)
[https://github.com/celiao/tmdbsimple/](url)
### Motivation
Allowing users to query The Movie Database (TMDb) would allow users to ask information regarding moives, and this tool will give the llms better/more reliable information to work with.
### Your contribution
We intend to submit a pull request for this issue at some point in November for a school project.
|
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PR_kwDOIPDwls5cqr4H
| 11,727 |
General anthropic functions, steps towards experimental integration tests
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[] | null | 1 | 2023-10-12T18:55:45 | 2023-10-13T16:48:26 | 2023-10-13T16:48:24 |
COLLABORATOR
| null |
To match change in js here https://github.com/langchain-ai/langchainjs/pull/2892
Some integration tests need a bit more work in experimental:
Pretty sure the sqldatabase ones are an actual regression or change in interface because it's returning a placeholder.
|
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PR_kwDOIPDwls5cqfUE
| 11,726 |
Add graceful fallback for ES vectorstore when content field is missing
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[] | null | 5 | 2023-10-12T18:18:47 | 2023-10-13T19:03:46 | 2023-10-13T19:03:33 |
CONTRIBUTOR
| null |
- **Description:**
- If the Elasticsearch field used for Langchain > Document.page_content is missing because the specific document is
somehow malformed fail gracefully.
- **Tag maintainer:**
- @joemcelroy
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PR_kwDOIPDwls5cqZny
| 11,725 |
Update Documentation: Corrected Typos and Improved Clarity
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[] | null | 7 | 2023-10-12T18:02:11 | 2023-11-10T22:14:44 | 2023-11-10T22:14:44 |
CONTRIBUTOR
| null |
Commit Description:
In this update, several documentation sections were improved by correcting typos and enhancing overall clarity. The changes made include fixing spelling and grammar issues, refining sentence structures, and providing more informative content where necessary. These improvements aim to make the documentation more user-friendly and easier to understand.
Files changed for better clarity of text and typo fix : -
langchain\docs\docs\modules\agents\tools\index.mdx
langchain\docs\docs\modules\agents\toolkits\index.mdx
langchain\docs\docs\modules\agents\how_to\mrkl.mdx
langchain\docs\docs\modules\agents\how_to\custom_llm_chat_agent.mdx
langchain\docs\docs\modules\agents\how_to\custom_llm_agent.mdx
langchain\docs\docs\modules\agents\agent_types\index.mdx
Some examples of the fix : -
1. Added "For documentation on built-in toolkit integrations, visit" to the info box.
Corrected "convenience" to "convenient" for proper adjective usage.
2. Added "For documentation on built-in tool integrations, visit" to the info box.
Changed "Get started" to "Getting Started" for consistency.
Made the sentence structure in the "Getting Started" section more consistent and clear.
Capitalized "Getting Started" for section title consistency.
Clarified the loading of tools by rephrasing the sentence.
3. Added "This example uses" for clarity.
Added commas after "MRKL system" and "Chinook database" for better readability.
Clarified the setup instructions by breaking the sentence into two parts.
Capitalized "Using a Chat Model" for consistency.
4. "goes through" changed to "explains."
Added periods at the end of each bullet point for consistency.
Capitalized the "Agent" in "Custom LLM Chat Agent."
Added the word "components" to clarify the list.
Corrected the numbering of components.
5. "goes through" changed to "explains"
Added periods at the end of each bullet point for consistency.
Capitalized the "Agent" in "Custom LLM Agent."
Added the word "components" to clarify the list.
Made some minor wording adjustments for clarity and consistency.
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PR_kwDOIPDwls5cqIjd
| 11,723 |
add azure openai sched tests
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[] | null | 1 | 2023-10-12T17:11:25 | 2023-10-12T17:48:47 | 2023-10-12T17:48:46 |
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PR_kwDOIPDwls5cqIUy
| 11,722 |
Add `Memorize` tool
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[] | null | 3 | 2023-10-12T17:10:38 | 2023-11-08T00:09:50 | 2023-11-07T20:42:11 |
CONTRIBUTOR
| null |
- **Description:** Add `Memorize` tool
- **Tag maintainer:** @hwchase17
This PR added a new tool `Memorize` so that an agent can use it to fine-tune itself. This tool requires `TrainableLLM` introduced in #11721
DEMO: https://github.com/Preemo-Inc/gradient-langchain-tool-demo/tree/6a9003d5db8db03673ad8b205f4d00cf0d96b56b
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PR_kwDOIPDwls5cqIAW
| 11,721 |
Add `TrainableLLM`
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[] | null | 1 | 2023-10-12T17:09:27 | 2023-10-13T15:40:56 | 2023-10-13T00:38:34 |
CONTRIBUTOR
| null |
- **Description:** Add `TrainableLLM` for those LLM support fine-tuning
- **Tag maintainer:** @hwchase17
This PR added a new ABC `TrainableLLM` and let `GradientLLM` implement it
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I_kwDOIPDwls5zqCI1
| 11,720 |
Issue: <VectorStoreRetrieverMemory class doesn't support AzureSearchVectorStoreRetriever retriever>
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[] | null | 1 | 2023-10-12T16:49:35 | 2023-10-12T16:57:51 | null |
NONE
| null |
### Issue you'd like to raise.
The retriever object in the VectorStoreRetrieverMemory class is a VectorStoreRetriever and not AzureSearchVectorStoreRetriever.
The VectorStoreRetriever and AzureSearchVectorStoreRetriever are two different classes and doesn't support same methods.
As i want to use vector store backed memory retriever which has option for filtering with user_id metadata to maintain mutliple chat histories based on user. I require help in two things.
1. Support for AzureSearchVectorStoreRetriever object in VectorStoreRetrieverMemory
2. How to initialize the memory object that accepts azure vector store (with id, user_id, conversation, conversation_vector as fields in my case), I want to save_context with respect to user_id, the following example given in the
[](https://python.langchain.com/docs/modules/memory/types/vectorstore_retriever_memory)
memory.save_context({"input": "My favorite food is pizza"}, {"output": "that's good to know"}) doesn't say how.
similarly for the memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"]
### Suggestion:
As most organizations use cases would be at scale, examples that include how to maintain memory for different users (user_id being metadata) would help.
Add AzureSearchVectorStoreRetriever object to the VectorStoreRetrieverMemory class would also help.
If i am wrong in the above cases, please correct me and help me pointing out the resources to solve this.
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PR_kwDOIPDwls5cp-S9
| 11,719 |
Add real support for RetryParsers?
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[] | null | 8 | 2023-10-12T16:48:05 | 2023-12-19T18:24:35 | null |
CONTRIBUTOR
| null |
For retry parser to work, the prompt needs to be available. I don't see how this is hooked up in the current implementation of LLMChain. The LLMChain must provide the prompt and the corresponding generation to the appropriate output parser (I.e. the llm code must invoke parse_with_prompt). The real output parser could either use the base implementation of parse_with_prompt (i.e. simply call parse) or override it.
Don't think schema changes affects consumers(don't think a lot of people are subclassing output_parser).
Added test cases.
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PR_kwDOIPDwls5cp4pw
| 11,718 |
bump 313
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PR_kwDOIPDwls5cpY_l
| 11,717 |
Nc/why lcel
|
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[] | null | 1 | 2023-10-12T15:21:25 | 2023-10-12T16:52:22 | 2023-10-12T16:52:21 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5cpSMd
| 11,716 |
Add graph construction to neo4j docs
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[] | null | 2 | 2023-10-12T15:05:12 | 2023-10-12T18:37:43 | 2023-10-12T18:37:43 |
CONTRIBUTOR
| null |
Add graph construction section to Neo4j provider docs
|
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I_kwDOIPDwls5zpIFf
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Cost calculation is always zero with fine-tuned OpenAI models
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[] | null | 2 | 2023-10-12T14:46:31 | 2023-10-12T15:30:41 | null |
NONE
| null |
### System Info
LangChain 0.0.240
but code on latest master has same issue
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Use a fine-tuned OpenAI model, named like "ft:gpt-2.5-turbo-0613:orgname@12abCDEF"
`with get_openai_callback() as openai_callback:`
`openai_callback.total_cost`
The cost is zero.
### Expected behavior
The cost is returned.
The issue is in https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/callbacks/openai_info.py#L67
which expects fine-tuned models' names' to start with "ft-" (dash, should be colon)
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PR_kwDOIPDwls5co0R-
| 11,714 |
New add Baichuan Model
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[] | null | 4 | 2023-10-12T13:55:03 | 2023-10-13T06:21:28 | 2023-10-13T06:04:28 |
CONTRIBUTOR
| null |
Motivation and Context
At present, the Baichuan Large Language Model is relatively popular and efficient in performance. Due to widespread market recognition, this model has been added to enhance the scalability of Langchain's ability to access the big language model, so as to facilitate application access and usage for interested users.
System Info
langchain： 0.0.295
python：3.8.3
IDE：vs code
Description
Add the following files:
1. Add baichuan_baichuaninc_endpoint.py in the libs/langchain/langchain/chat_models
2. Modify the __init__.py file,which is located in the libs/langchain/langchain/chat_models/__init__.py：
a. Add "from langchain.chat_models.baichuan_baichuaninc_endpoint import BaichuanChatEndpoint"
b. Add "BaichuanChatEndpoint" In the file's __ All__ method
Your contribution
I am willing to help implement this feature and submit a PR, but I would appreciate guidance from the maintainers or community to ensure the changes are made correctly and in line with the project's standards and practices.
|
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| 1,939,947,832 |
PR_kwDOIPDwls5cob7M
| 11,713 |
Update zep_memory.ipynb
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[] | null | 1 | 2023-10-12T12:59:46 | 2023-10-12T14:41:20 | 2023-10-12T14:41:19 |
CONTRIBUTOR
| null |
fixed minor typos;
the your > your
on > upon
|
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PR_kwDOIPDwls5coT5H
| 11,712 |
Fix typos in docs/docs/use_cases/tagging.ipynb
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[] | null | 1 | 2023-10-12T12:38:55 | 2023-10-12T14:17:11 | 2023-10-12T14:17:10 |
CONTRIBUTOR
| null |
funtion -> function
|
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Add deploy command to repos generated by cli template
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[] | null | 1 | 2023-10-12T12:36:37 | 2023-10-12T14:09:22 | 2023-10-12T14:09:21 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,939,904,592 |
PR_kwDOIPDwls5coSec
| 11,710 |
Fix typos in docs/docs/use_cases/question_answering/code_understandin…
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[] | null | 1 | 2023-10-12T12:35:58 | 2023-10-12T14:17:24 | 2023-10-12T14:17:23 |
CONTRIBUTOR
| null |
herarchy -> hierarchy
|
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PR_kwDOIPDwls5coPzl
| 11,709 |
Fix typos in docs/docs/use_cases/question_answering/conversational_re…
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[] | null | 1 | 2023-10-12T12:31:31 | 2023-10-12T14:16:52 | 2023-10-12T14:16:52 |
CONTRIBUTOR
| null |
neccessary -> necessary
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| 1,939,890,616 |
PR_kwDOIPDwls5coPSO
| 11,708 |
Fix typos in docs/docs/use_cases/extraction.ipynb
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[] | null | 1 | 2023-10-12T12:30:38 | 2023-10-12T14:16:18 | 2023-10-12T14:16:17 |
CONTRIBUTOR
| null |
This PR has a number of typos correction. I kindly request the repo maintainers to review this PR and merge it.
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PR_kwDOIPDwls5coOjm
| 11,707 |
Fix typos in docs/docs/use_cases/chatbots.ipynb
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[] | null | 1 | 2023-10-12T12:28:43 | 2023-10-12T14:16:35 | 2023-10-12T14:16:35 |
CONTRIBUTOR
| null |
implemet -> implement
|
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