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https://api.github.com/repos/langchain-ai/langchain/issues/10268
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| 1,883,115,087 |
I_kwDOIPDwls5wPg5P
| 10,268 |
Our whole business is down. please help!! HuggingFaceBgeEmbeddings(model_name="BAAI/bge-large-en")
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[] | null | 5 | 2023-09-06T03:31:13 | 2023-12-25T16:02:50 | null |
NONE
| null |
### System Info
Our startup using this line all across out scripts.
`HuggingFaceBgeEmbeddings(model_name="BAAI/bge-large-en")`
Today, someone updated langchain, everything has stopped, please help!!
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
We see if is in the docs?
https://api.python.langchain.com/en/latest/embeddings/langchain.embeddings.self_hosted_hugging_face.SelfHostedHuggingFaceEmbeddings.html
### Expected behavior
Should import and work.
|
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PR_kwDOIPDwls5Zom7j
| 10,267 |
add konko chat_model files
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[] | null | 2 | 2023-09-06T03:17:14 | 2023-09-08T17:29:31 | 2023-09-08T17:00:55 |
CONTRIBUTOR
| null |
_Thank you to the LangChain team for the great project and in advance for your review. Let me know if I can provide any other additional information or do things differently in the future to make your lives easier 🙏 _
@hwchase17 please let me know if you're not the right person to review 😄
This PR enables LangChain to access the Konko API via the chat_models API wrapper.
Konko API is a fully managed API designed to help application developers:
1. Select the right LLM(s) for their application
2. Prototype with various open-source and proprietary LLMs
3. Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructure
_Note on integration tests:_
We added 14 integration tests. They will all fail unless you export the right API keys. 13 will pass with a KONKO_API_KEY provided and the other one will pass with a OPENAI_API_KEY provided. When both are provided, all 14 integration tests pass. If you would like to test this yourself, please let me know and I can provide some temporary keys.
### Installation and Setup
1. **First you'll need an API key**
2. **Install Konko AI's Python SDK**
1. Enable a Python3.8+ environment
`pip install konko`
3. **Set API Keys**
**Option 1:** Set Environment Variables
You can set environment variables for
1. KONKO_API_KEY (Required)
2. OPENAI_API_KEY (Optional)
In your current shell session, use the export command:
`export KONKO_API_KEY={your_KONKO_API_KEY_here}`
`export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional`
Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.
**Option 2:** Set API Keys Programmatically
If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:
```python
konko.set_api_key('your_KONKO_API_KEY_here')
konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional
```
### Calling a model
Find a model on the [[Konko Introduction page](https://docs.konko.ai/docs#available-models)](https://docs.konko.ai/docs#available-models)
For example, for this [[LLama 2 model](https://docs.konko.ai/docs/meta-llama-2-13b-chat)](https://docs.konko.ai/docs/meta-llama-2-13b-chat). The model id would be: `"meta-llama/Llama-2-13b-chat-hf"`
Another way to find the list of models running on the Konko instance is through this [[endpoint](https://docs.konko.ai/reference/listmodels)](https://docs.konko.ai/reference/listmodels).
From here, we can initialize our model:
```python
chat_instance = ChatKonko(max_tokens=10, model = 'meta-llama/Llama-2-13b-chat-hf')
```
And run it:
```python
msg = HumanMessage(content="Hi")
chat_response = chat_instance([msg])
```
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| 1,883,078,790 |
I_kwDOIPDwls5wPYCG
| 10,266 |
ModuleNotFoundError: No module named 'langchain.schema.document'; 'langchain.schema' is not a package
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[] | null | 4 | 2023-09-06T02:53:08 | 2023-12-14T16:05:38 | 2023-12-14T16:05:37 |
NONE
| null |
### System Info
Since we updated to the latest langchain we are also getting this issue. We are a startup, just launched, updated yesterday now all our apps don't work. First customer annoyed.
**Please, please help!**
Name: langchain
Version: 0.0.281
` data_state_nsw_legisation_index_instance = FAISS.load_local("data_indexes/federal/federal_legislativeinstruments_inforce_index", embeddings)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/vectorstores/faiss.py", line 472, in load_local
docstore, index_to_docstore_id = pickle.load(f)
ModuleNotFoundError: No module named 'langchain.schema.document'; 'langchain.schema' is not a package`
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
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- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
data_state_nsw_legisation_runner = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0,
openai_api_key=openai_api_key_value),
chain_type="stuff",
retriever=data_state_nsw_legisation_index_instance.as_retriever())
### Expected behavior
yesterday before we updated is was working perfectly, we updated to the latest version and it all stopped :(
**Please help!** the team is scrambling
|
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| 1,883,076,458 |
PR_kwDOIPDwls5ZohJi
| 10,265 |
Don't hardcode PGVector distance strategies
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[] | null | 2 | 2023-09-06T02:51:18 | 2023-09-07T02:25:46 | 2023-09-06T22:20:44 |
CONTRIBUTOR
| null |
- Description: Remove hardcoded/duplicated distance strategies in the PGVector store.
- Issue: NA
- Dependencies: NA
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: @archmonkeymojo
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I_kwDOIPDwls5wPVRe
| 10,263 |
HuggingFaceBgeEmbeddings Error, Please help!
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[] | null | 6 | 2023-09-06T02:42:31 | 2023-12-18T23:47:49 | 2023-12-18T23:47:48 |
NONE
| null |
### System Info
Yesterday is works, someone accidentally update langchain now the whole platform is down.
We built the whole platform using his code all over the place. Now nothing works.
We have around 50 models. All our models are build like this and we just went live as a startup.
**We are scrabbling here guys. Please help us.**
`
from langchain.embeddings import HuggingFaceBgeEmbeddings
embeddings = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-large-en")
news_instance = FAISS.load_local("federal_legislativeinstruments_inforce_index", embeddings)
data_state_nsw_legisation_index_instance = FAISS.load_local("data_indexes/federal/federal_legislativeinstruments_inforce_index", embeddings)
data_state_nsw_legisation_runner = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0,
openai_api_key=openai_api_key_value),
chain_type="stuff",
retriever=data_state_nsw_legisation_index_instance.as_retriever())
`
Please, please help. How to we refactor so it works. How team is going crazy to get it live again, Our very first customers are ringing us complaining. Please help.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings import HuggingFaceBgeEmbeddings
embeddings = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-large-en")
news_instance = FAISS.load_local("federal_legislativeinstruments_inforce_index", embeddings)
data_state_nsw_legisation_index_instance = FAISS.load_local("data_indexes/federal/federal_legislativeinstruments_inforce_index", embeddings)
data_state_nsw_legisation_runner = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0,
openai_api_key=openai_api_key_value),
chain_type="stuff",
retriever=data_state_nsw_legisation_index_instance.as_retriever())
### Expected behavior
How load the embedding like yesterday and all the time before.
|
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PR_kwDOIPDwls5Zoe3f
| 10,262 |
Fix bug in MarkdownHeaderTextSplitter for codeblock
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[] | null | 9 | 2023-09-06T02:41:12 | 2023-10-06T01:34:43 | 2023-10-06T01:34:43 |
CONTRIBUTOR
| null |
- Description: The previous version of the MarkdownHeaderTextSplitter did not take into account the possibility of '#' appearing within code blocks, which caused segmentation anomalies in these situations. This PR has fixed this issue.
- Issue:
- Dependencies: No
- Tag maintainer:
- Twitter handle:
cc @baskaryan @eyurtsev @rlancemartin
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PR_kwDOIPDwls5ZoSzZ
| 10,261 |
docs: `portkey` update
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[] | null | 2 | 2023-09-06T01:57:24 | 2023-09-08T15:25:29 | 2023-09-08T13:37:47 |
COLLABORATOR
| null |
Added the `Portkey` description. Fixed a title in the nested document (and nested navbar).
|
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PR_kwDOIPDwls5ZoLid
| 10,260 |
Updated Additional Resources section of documentation
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[] | null | 2 | 2023-09-06T01:11:50 | 2023-09-06T22:10:43 | 2023-09-06T22:10:43 |
CONTRIBUTOR
| null |
- Description: Updated Additional Resources section of documentation and added to YouTube videos with excellent playlist of Langchain content from Sam Witteveen
- Issue: None -- updating documentation
- Dependencies: None
- Tag maintainer: @baskaryan
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PR_kwDOIPDwls5Znsc7
| 10,258 |
docs: fix `tools/sqlite` page
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[] | null | 3 | 2023-09-05T23:10:36 | 2023-09-08T15:25:46 | 2023-09-08T13:42:09 |
COLLABORATOR
| null |
The `/docs/integrations/tools/sqlite` page is not about the tool integrations.
I've moved it into `/docs/use_cases/sql/sqlite`.
`vercel.json` modified
As a result two pages now under the `/docs/use_cases/sql/` folder. So the `sql` root page moved down together with `sqlite` page.
|
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| 10,257 |
Bagatur/split sql
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[] | null | 1 | 2023-09-05T22:32:14 | 2023-09-06T23:19:23 | 2023-09-06T23:19:22 |
COLLABORATOR
| null |
Split sql use case into directory so we can add other structured data pages
|
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I_kwDOIPDwls5wOWYy
| 10,256 |
SystemMessage in create_pandas_dataframe_agent
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[] | null | 4 | 2023-09-05T22:30:56 | 2023-10-08T03:26:47 | null |
NONE
| null |
### System Info
For some reason SystemMessage does not work for me (agent ignores it). Here is my code:
```
system_message = SystemMessage(content="write response in uppercase")
agent_kwargs = {
"system_message": system_message,
}
agent_func = create_pandas_dataframe_agent(
llm,
df,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
agent_kwargs=agent_kwargs,
)
```
I tried to do with system_message directly but agent still ignores SystemMessage:
```
system_message = SystemMessage(content="write response in uppercase")
agent_func = create_pandas_dataframe_agent(
llm,
df,
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
system_message=system_message,
)
```
Also, I tried to use `system_message.context` instead of `system_message` but still no luck.
Langchain version is 0.0.281
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
let me know if additional info is needed
### Expected behavior
create_pandas_dataframe_agent should work with SystemMessage
|
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| 1,882,761,368 |
PR_kwDOIPDwls5ZncAh
| 10,255 |
docs: `integrations/memory` consistency
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[] | null | 9 | 2023-09-05T21:42:19 | 2023-10-01T02:27:24 | 2023-09-30T23:35:55 |
COLLABORATOR
| null |
- updated titles and descriptions of the `integrations/memory` notebooks into consistent and laconic format;
- removed `docs/extras/integrations/memory/motorhead_memory_managed.ipynb` file as a duplicate of the `docs/extras/integrations/memory/motorhead_memory.ipynb`;
- added `integrations/providers` Integration Cards for `dynamodb`, `motorhead`.
- updated `integrations/providers/redis.mdx` with links
- renamed several notebooks; updated `vercel.json` to reroute new names.
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I_kwDOIPDwls5wOH17
| 10,254 |
Add Support of Azure Cognitive Search for SelfQueryRetriever
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[] | null | 3 | 2023-09-05T21:30:47 | 2023-11-29T18:28:11 | null |
NONE
| null |
### Feature request
Azure Cognitive Search already has [hybrid search functionality](https://python.langchain.com/docs/integrations/vectorstores/azuresearch#perform-a-hybrid-search) and it makes sense to add support of SelfQueryRetriever as well
### Motivation
Azure Cognitive Search is production ready solution.
### Your contribution
I can help with testing of this feature
|
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PR_kwDOIPDwls5ZnWcF
| 10,253 |
Deny creating files as a result of test runs.
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[] | null | 1 | 2023-09-05T21:16:31 | 2023-09-06T15:15:18 | 2023-09-06T15:15:16 |
COLLABORATOR
| null |
A test file was accidentally dropping a `results.json` file in the current working directory as a result of running `make test`.
This is undesirable, since we don't want to risk accidentally adding stray files into the repo if we run tests locally and then do `git add .` without inspecting the file list very closely.
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PR_kwDOIPDwls5ZnOYh
| 10,252 |
Disable loading jinja2 `PromptTemplate` from file.
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[] | null | 5 | 2023-09-05T20:52:04 | 2023-11-17T16:36:59 | 2023-10-10T15:15:42 |
COLLABORATOR
| null |
jinja2 templates are not sandboxed and are at risk for arbitrary code execution. To mitigate this risk:
- We no longer support loading jinja2-formatted prompt template files.
- `PromptTemplate` with jinja2 may still be constructed manually, but the class carries a security warning reminding the user to not pass untrusted input into it.
Resolves #4394.
Resolves: CVE-2023-36281
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I_kwDOIPDwls5wNn0W
| 10,251 |
Error when Instantiating GPT4AllEmbeddings
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[] | null | 2 | 2023-09-05T19:44:51 | 2023-11-19T20:42:12 | 2023-09-18T17:24:24 |
NONE
| null |
### System Info
langchain 0.0.281, pydantic 1.10.9, gpt4all 1.0.9, Linux Gardua(Arch), Python 3.11.5
### 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
This is my code:
```python
from langchain.embeddings import GPT4AllEmbeddings
gpt4all_embd = GPT4AllEmbeddings()
```
I get this error:
Found model file at /home/chigoma333/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin
Invalid model file
Traceback (most recent call last):
File "/home/chigoma333/Desktop/Program/test.py", line 3, in <module>
gpt4all_embd = GPT4AllEmbeddings()
^^^^^^^^^^^^^^^^^^^
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for GPT4AllEmbeddings
__root__
Unable to instantiate model (type=value_error)
### Expected behavior
I expected the GPT4AllEmbeddings instance to be created successfully without errors.
|
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PR_kwDOIPDwls5Zm8ro
| 10,250 |
Final poetry action fix: manually recreate softlinks broken by caching.
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[] | null | 1 | 2023-09-05T19:44:09 | 2023-09-05T19:47:59 | 2023-09-05T19:47:58 |
COLLABORATOR
| null |
It seems the caching action was not always correctly recreating softlinks. At first glance, the softlinks it created seemed fine, but they didn't always work. Possibly hitting some kind of underlying bug, but not particularly worth debugging in depth -- we can manually create the soft links we need.
|
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Install dev, lint, test, typing extra deps for linting steps.
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[] | null | 2 | 2023-09-05T19:04:54 | 2023-09-06T15:15:29 | 2023-09-06T15:15:28 |
COLLABORATOR
| null |
`mypy` cannot type-check code that relies on dependencies that aren't installed.
Eventually we'll probably want to install as many optional dependencies as possible. However, the full "extended deps" setup for langchain creates a 3GB cache file and takes a while to unpack and install. We'll probably want something a bit more targeted.
This is a first step toward something better.
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PR_kwDOIPDwls5ZmqFD
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Fix SQL search_path for Trino query engine
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[] | null | 2 | 2023-09-05T18:36:25 | 2023-09-07T13:16:41 | 2023-09-06T23:19:38 |
CONTRIBUTOR
| null |
This PR replaces the generic `SET search_path TO` statement by `USE` for the Trino dialect since Trino does not support `SET search_path`. Official Trino documentation can be found [here](https://trino.io/docs/current/sql/use.html).
With this fix, the `SQLdatabase` will now be able to set the current schema and execute queries using the Trino engine. It will use the catalog set as default by the connection uri.
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PR_kwDOIPDwls5ZmenS
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Update amazon_comprehend_chain.ipynb
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[] | null | 2 | 2023-09-05T17:56:21 | 2023-09-06T22:38:37 | 2023-09-06T22:38:37 |
CONTRIBUTOR
| null |
Huggingface, HuggingFace -> Hugging Face
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If you're adding a new integration, please include:
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PR_kwDOIPDwls5ZmbIX
| 10,245 |
Extract abstracts from Pubmed articles, even if they have no extra label
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CONTRIBUTOR
| null |
### Description
This pull request involves modifications to the extraction method for abstracts/summaries within the PubMed utility. A condition has been added to verify the presence of unlabeled abstracts. Now an abstract will be extracted even if it does not have a subtitle. In addition, the extraction of the abstract was extended to books.
### Issue
The PubMed utility occasionally returns an empty result when extracting abstracts from articles, despite the presence of an abstract for the paper on PubMed. This issue arises due to the varying structure of articles; some articles follow a "subtitle/label: text" format, while others do not include subtitles in their abstracts. An example of the latter case can be found at: [https://pubmed.ncbi.nlm.nih.gov/37666905/](url)
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- 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!
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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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2. an example notebook showing its use. These live is docs/extras directory.
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PR_kwDOIPDwls5ZmZlA
| 10,244 |
Re-enable poetry binary caching with fix and more logging.
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[] | null | 2 | 2023-09-05T17:38:15 | 2023-09-05T18:03:05 | 2023-09-05T18:03:04 |
COLLABORATOR
| null |
- Revert "Temporarily disable step that seems to be transiently failing. (#10234)"
- Refresh shell hashtable and show poetry/python location and version.
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I_kwDOIPDwls5wM-dq
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Issue: Lower requirements for PyYAML to 5.3.1 due to Cython bug
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closed
| false | null |
[] | null | 0 | 2023-09-05T17:33:43 | 2023-09-18T15:13:05 | 2023-09-18T15:13:05 |
NONE
| null |
### Issue you'd like to raise.
PyYAML has [known issues](https://github.com/yaml/pyyaml/issues/724) with version 5.4.1 and several other libraries are rolling back requirements to allow compatibility.
### Suggestion:
Lower requirement to allow PyYAML 5.3.1
|
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| 1,882,420,114 |
PR_kwDOIPDwls5ZmRwK
| 10,242 |
RL Chain with VowpalWabbit
|
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[] | null | 9 | 2023-09-05T17:13:48 | 2023-10-06T01:40:33 | 2023-10-06T01:07:23 |
CONTRIBUTOR
| null |
- Description: This PR adds a new chain `rl_chain.PickBest` for learned prompt variable injection, detailed description and usage can be found in the example notebook added. It essentially adds a [VowpalWabbit](https://github.com/VowpalWabbit/vowpal_wabbit) layer before the llm call in order to learn or personalize prompt variable selections.
Most of the code is to make the API simple and provide lots of defaults and data wrangling that is needed to use Vowpal Wabbit, so that the user of the chain doesn't have to worry about it.
- Dependencies: [vowpal-wabbit-next](https://pypi.org/project/vowpal-wabbit-next/),
- sentence-transformers (already a dep)
- numpy (already a dep)
- tagging @ataymano who contributed to this chain
- Tag maintainer: @baskaryan
- Twitter handle: @olgavrou
Added example notebook and unit tests
|
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I_kwDOIPDwls5wM2xy
| 10,241 |
Error message when using force_recreate on Qdrant
|
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[] | null | 2 | 2023-09-05T17:12:31 | 2023-12-13T16:05:58 | 2023-12-13T16:05:57 |
NONE
| null |
### System Info
```
langchain[qdrant]==0.0.281
qdrant-client==1.4.0
```
Qdrant server v1.4.1
### Who can help?
@agola11 @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I started seeing this issue after using `force_recreate=True`, so for example this is the method I am using to populate my collection:
```python
qdrant.from_documents(
docs,
url=self.settings.QDRANT_API_URL,
collection_name=collection,
embedding=self.embeddings,
force_recreate=True,
replication_factor=3,
timeout=60
)
```
And I keep getting the following error messages in my logs:
```
[2023-09-05T16:33:45.308Z WARN storage::content_manager::consensus_manager] Failed to apply collection meta operation entry with user error: Wrong input: Replica 6838680705292431 of shard 4 has state Some(Active), but expected Some(Initializing)
```
I found where this error is being raised on Qdrant: https://github.com/qdrant/qdrant/blob/383fecf64b6d97e4718deb2bf0f46422060e7e52/lib/collection/src/collection.rs#L339
I understand the issue might be in the Qdrant client or the Qdrant server itself.
### Expected behavior
This message doesn't make sense, and the data is being stored without any issues.
|
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I_kwDOIPDwls5wMxwt
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[tool_name] is not a valid tool, try one of [tool_name]
|
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[] | null | 4 | 2023-09-05T16:58:01 | 2023-12-15T07:40:36 | null |
NONE
| null |
### Issue you'd like to raise.
I am using Opensearch as vector database in langchain to store the documents and using tools to integrate it with agent to look for the information when we pass any query and to use GPT model to get the enhanced response.
I am facing tool not valid issue while running the code. Any suggestions to fix the error.
Error: Observation:[faq] is not a valid tool, try one of [faq].
Code:
import re
from langchain import OpenAI, PromptTemplate, VectorDBQA, LLMChain
from langchain.agents import Tool, initialize_agent, AgentExecutor, AgentOutputParser, LLMSingleActionAgent
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import ConversationChain, RetrievalQA
from langchain.chains.conversation.memory import ConversationBufferMemory, ConversationSummaryBufferMemory,
CombinedMemory
import pypdf
from langchain.prompts import StringPromptTemplate
from langchain.schema import HumanMessage, AgentAction, AgentFinish
from langchain.text_splitter import CharacterTextSplitter
from langchain.tools import BaseTool
from langchain.memory import ConversationBufferWindowMemory
import os
from typing import List, Union, Optional
from langchain.memory import ConversationSummaryBufferMemory
from langchain.vectorstores import Chroma, OpenSearchVectorSearch
os.environ['OPENAI_API_KEY'] = "api_key"
embeddings = OpenAIEmbeddings()
llm = OpenAI(temperature=0.7)
docsearch = OpenSearchVectorSearch(
index_name="customer_data",
embedding_function=embeddings,
opensearch_url="host_url",
)
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch.as_retriever(),
)
tools = [
Tool(
name="faq",
func=qa.run(),
description="Useful when you have to answer FAQ's"
)]
template = """Your are a support representative, Customers reaches you for queries. refer the tools and answer.
You have access to the following tools to answer the question.
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, can 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.
Begin!
Previous conversation history:
{history}
New Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
prompt = CustomPromptTemplate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps", "history"]
)
# Custom Output Parser. The output parser is responsible for parsing the LLM output into AgentAction and AgentFinish. This usually depends heavily on the prompt used. This is where you can change the parsing to do retries, handle whitespace, etc
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
print(match)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
output_parser = CustomOutputParser()
# Set up LLM !
llm = OpenAI(temperature=0, model_name='gpt-3.5-turbo')
# Define the stop sequence. This is important because it tells the LLM when to stop generation. This depends heavily on the prompt and model you are using. Generally, you want this to be whatever token you use in the prompt to denote the start of an Observation (otherwise, the LLM may hallucinate an observation for you).
# Set up the Agent
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
# Agent Executors take an agent and tools and use the agent to decide which tools to call and in what order.
convo_memory = ConversationBufferMemory(
memory_key="chat_history_lines",
input_key="input"
)
summary_memory = ConversationSummaryBufferMemory(llm=llm, memory_key="chat_history", input_key="input")
memory = CombinedMemory(memories=[convo_memory, summary_memory], memory_key="story")
agent_executor = AgentExecutor.from_agent_and_tools(
agent=agent,
tools=tools,
verbose=True,
memory=memory
)
si = input("Human: ")
agent_executor.run({'history': memory, 'input': si})
### Suggestion:
_No response_
|
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| 1,882,240,252 |
PR_kwDOIPDwls5ZlqRk
| 10,239 |
Run CI when CI infra itself has changed.
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[] | null | 1 | 2023-09-05T15:48:45 | 2023-09-05T17:08:21 | 2023-09-05T17:08:19 |
COLLABORATOR
| null |
Make sure that changes to CI infrastructure get tested on CI before being merged.
Without this PR, changes to the poetry setup action don't trigger a CI run and in principle could break `master` when merged.
|
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PR_kwDOIPDwls5ZlqD8
| 10,238 |
Add WatsonX support
|
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NONE
| null |
It is a connector to use a LLM from WatsonX.
It requires python SDK "ibm-generative-ai"
(It might not be perfect since it is my first PR on a public repository 😄)
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PR_kwDOIPDwls5ZlpHM
| 10,237 |
Revert "Temporarily disable step that seems to be transiently failing."
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[] | null | 1 | 2023-09-05T15:45:18 | 2023-09-05T17:38:51 | 2023-09-05T17:38:48 |
COLLABORATOR
| null |
This reverts commit 068ebe4ed44a2223d032dda1500baa5aef92b705.
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PR_kwDOIPDwls5ZlgM8
| 10,236 |
Implement NucliaDB vector store
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[] | null | 2 | 2023-09-05T15:20:27 | 2023-09-07T07:42:38 | 2023-09-06T23:26:15 |
CONTRIBUTOR
| null |
# Description
This pull request allows to use the [NucliaDB](https://docs.nuclia.dev/docs/docs/nucliadb/intro) as a vector store in LangChain.
It works with both a [local NucliaDB instance](https://docs.nuclia.dev/docs/docs/nucliadb/deploy/basics) or with [Nuclia Cloud](https://nuclia.cloud).
# Dependencies
It requires an up-to-date version of the `nuclia` Python package.
@rlancemartin, @eyurtsev, @hinthornw, please review it when you have a moment :)
Note: our Twitter handler is `@NucliaAI`
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I_kwDOIPDwls5wL6hO
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ConfigError: Field 'app' Not Yet Prepared with ForwardRef Error When Initializing VespaRetriever
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[] | null | 3 | 2023-09-05T15:07:29 | 2023-09-14T07:18:23 | 2023-09-14T07:18:23 |
NONE
| null |
### System Info
Hello team,
I'm currently facing an issue while trying to use VespaRetriever within the context of langchain: '0.0.281' .
Vespa has been successfully deployed on my local environment, and queries are functioning correctly
```
from langchain.retrievers.vespa_retriever import VespaRetriever
vespa_query_body = {
'yql': 'select * from sources * where userQuery()',
'query': 'what keeps planes in the air',
'ranking': 'native_rank',
'type': 'all',
'hits': 10
}
vespa_content_field = "body"
retriever = VespaRetriever(app=app, body=query, content_field=vespa_content_field)
```
````
---------------------------------------------------------------------------
ConfigError Traceback (most recent call last)
Cell In[34], line 11
3 vespa_query_body = {
4 'yql': 'select * from sources * where userQuery()',
5 'query': 'what keeps planes in the air',
(...)
8 'hits': 10
9 }
10 vespa_content_field = "body"
---> 11 retriever = VespaRetriever(app=app, body=query, content_field=vespa_content_field)
File ~/miniconda3/lib/python3.10/site-packages/langchain/load/serializable.py:75, in Serializable.__init__(self, **kwargs)
74 def __init__(self, **kwargs: Any) -> None:
---> 75 super().__init__(**kwargs)
76 self._lc_kwargs = kwargs
File ~/miniconda3/lib/python3.10/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__()
File ~/miniconda3/lib/python3.10/site-packages/pydantic/main.py:1076, in pydantic.main.validate_model()
File ~/miniconda3/lib/python3.10/site-packages/pydantic/fields.py:860, in pydantic.fields.ModelField.validate()
ConfigError: field "app" not yet prepared so type is still a ForwardRef, you might need to call VespaRetriever.update_forward_refs().
````
```
from vespa.application import Vespa
VespaRetriever.update_forward_refs()
````
````
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[37], line 3
1 from vespa.application import Vespa
----> 3 VespaRetriever.update_forward_refs()
File ~/miniconda3/lib/python3.10/site-packages/pydantic/main.py:815, in pydantic.main.BaseModel.update_forward_refs()
File ~/miniconda3/lib/python3.10/site-packages/pydantic/typing.py:554, in pydantic.typing.update_model_forward_refs()
File ~/miniconda3/lib/python3.10/site-packages/pydantic/typing.py:520, in pydantic.typing.update_field_forward_refs()
File ~/miniconda3/lib/python3.10/site-packages/pydantic/typing.py:66, in pydantic.typing.evaluate_forwardref()
File ~/miniconda3/lib/python3.10/typing.py:694, in ForwardRef._evaluate(self, globalns, localns, recursive_guard)
689 if self.__forward_module__ is not None:
690 globalns = getattr(
691 sys.modules.get(self.__forward_module__, None), '__dict__', globalns
692 )
693 type_ = _type_check(
--> 694 eval(self.__forward_code__, globalns, localns),
695 "Forward references must evaluate to types.",
696 is_argument=self.__forward_is_argument__,
697 allow_special_forms=self.__forward_is_class__,
698 )
699 self.__forward_value__ = _eval_type(
700 type_, globalns, localns, recursive_guard | {self.__forward_arg__}
701 )
702 self.__forward_evaluated__ = True
File <string>:1
NameError: name 'Vespa' is not defined
```
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The same example here https://python.langchain.com/docs/integrations/retrievers/vespa where I have a local url that works correctly
### Expected behavior
Retrieve indexed document on vespa.ia
|
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PR_kwDOIPDwls5ZlVfu
| 10,234 |
Temporarily disable step that seems to be transiently failing.
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[] | null | 1 | 2023-09-05T14:52:16 | 2023-09-05T14:55:49 | 2023-09-05T14:55:48 |
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PR_kwDOIPDwls5ZlUtN
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bump 282
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[] | null | 1 | 2023-09-05T14:50:16 | 2023-09-05T14:58:02 | 2023-09-05T14:58:01 |
COLLABORATOR
| null | null |
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| 1,882,060,479 |
I_kwDOIPDwls5wLfa_
| 10,232 |
WatsonX LLM support
|
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[] | null | 1 | 2023-09-05T14:07:32 | 2023-09-05T15:48:37 | 2023-09-05T15:48:37 |
NONE
| null |
### Feature request
Add WatsonX (IBM) connector for LLM
### Motivation
Working at IBM, I think it could be great to have it integrated to easily use langchain with Watsonx
### Your contribution
I have implemented and tested a small connector for watsonX
|
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I_kwDOIPDwls5wLaN5
| 10,230 |
Language parameter in OpoenAI() is not working anymore!
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[] | null | 6 | 2023-09-05T13:56:32 | 2023-12-18T23:47:53 | 2023-12-18T23:47:52 |
NONE
| null |
### System Info
All latest versions
### Who can help?
@agola
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The parameter language="es" in OpenAI() is not working anymore. And I have to present it tomorrow at the university! I can't find any solution for this. Nowhere.
Pretty simple code that was working perfectly and now not anymore:
`model= OpenAI(temperature=0, language="es")`
Now I'm getting this error message:
`C:\Users\zaesa\anaconda3\Lib\site-packages\langchain\utils\utils.py:155: UserWarning: WARNING! language is not default parameter.
language was transferred to model_kwargs.
Please confirm that language is what you intended.
warnings.warn( `
How to solve it?
### Expected behavior
It should just search in spanish pages and answer in spanish. It was pretty simple. Now it just doesn't work anymore. Any help will be really apprecaited.
|
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PR_kwDOIPDwls5ZkkAc
| 10,229 |
adding language as parameter to NLTK text splitter
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[] | null | 3 | 2023-09-05T12:48:32 | 2023-09-09T00:59:23 | 2023-09-09T00:59:23 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
- Description:
Adding language as parameter to NLTK, by default it is only using English. This will help using NLTK splitter for other languages. Change is simple, via adding language as parameter to NLTKTextSplitter and then passing it to nltk "sent_tokenize".
- Issue: N/A
- Dependencies: N/A
|
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I_kwDOIPDwls5wKmFZ
| 10,226 |
pydantic.v1.error_wrappers.ValidationError: 1 validation error for LlamaCpp __root__ Could not load Llama model from path: D:\model.safetensors. Received error (type=value_error)
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[] | null | 2 | 2023-09-05T11:56:03 | 2023-10-27T09:54:11 | null |
NONE
| null |
### Issue you'd like to raise.
Trying to load the llama 2 7b model which is in D drive, but I'm constantly getting errors.
This is my code
`
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
MODEL_PATH = "D:\model.safetensors"
def load_model():
"""Loads Llama model"""
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
Llama_model = LlamaCpp(
model_path=MODEL_PATH,
temperature=0.5,
max_tokens=2000,
top_p=1,
callback_manager=callback_manager,
verbose=True
)
return Llama_model
llm = load_model()
model_prompt = """
Question:What is the largest planet discovered so far?
"""
response = llm(model_prompt)
print(response)
`
This is the error
"
PS D:\Python Projects\python> python learn.py
gguf_init_from_file: invalid magic number 00029880
error loading model: llama_model_loader: failed to load model from D:\model.safetensors
llama_load_model_from_file: failed to load model
Traceback (most recent call last):
File "D:\Python Projects\python\learn.py", line 24, in <module>
llm = load_model()
File "D:\Python Projects\python\learn.py", line 12, in load_model
Llama_model = LlamaCpp(
File "C:\Users\krish\anaconda3\envs\newlang\lib\site-packages\langchain\load\serializable.py", line 75, in __init__
super().__init__(**kwargs)
File "C:\Users\krish\anaconda3\envs\newlang\lib\site-packages\pydantic\v1\main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for LlamaCpp
__root__
Could not load Llama model from path: D:\model.safetensors. Received error (type=value_error)
"
Please help.
### Suggestion:
_No response_
|
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| 10,225 |
create_sql_agent has incorrect behaviour when it queries agaisnt Google BigQuery
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[] | null | 8 | 2023-09-05T11:35:05 | 2023-12-19T00:48:43 | 2023-12-19T00:48:42 |
NONE
| null |
### System Info
OS: Ubuntu 22.04.3 LTS
SQLAlchemy==1.4.49
langchain==0.0.281
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.llms import VertexAI
from langchain.sql_database import SQLDatabase
project = "AAAA"
dataset = "HHHH"
sqlalchemy_url = f'bigquery://{project}/{dataset}'
db = SQLDatabase.from_uri(sqlalchemy_url)
llm = VertexAI(
model_name="text-bison@001",
max_output_tokens=256,
temperature=0.1,
top_p=0.8,
top_k=40,
verbose=True,
)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True,
max_execution_time=60,
)
question = "What is the total <numbers> in 2023?" #replace number to the target metric
response = agent_executor.run(question)
```
The agent will return "(Background on this error at: https://sqlalche.me/e/14/4xp6) Error: (google.cloud.bigquery.dbapi.exceptions.DatabaseError) 400 Syntax error: Expected end of input but got identifier "Thought" at [4:1]"
And when I check the project history on GCP, I found the agent included the thought and action inside the query, so the executed query was like that:
```
SELECT SUM(AAA) AS total_AAA, SUM(BBB) AS total_BBB
FROM YOU_CANT_SEE_ME
WHERE Year = 2023
Thought: I should check the query before executing it.
Action: sql_db_query_checker
Action Input:
SELECT SUM(AAA) AS total_AAA, SUM(BBB) AS total_BBB
FROM YOU_CANT_SEE_ME
WHERE Year = 2023
```
It was stunning. Or was there anything I did wrong?
### Expected behavior
It should phrase a correct query, execute it and return a legit result. It works fine when I do not specify the year.
|
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| 1,881,785,169 |
I_kwDOIPDwls5wKcNR
| 10,224 |
Issue: How can we load multiple CSVs
|
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[] | null | 2 | 2023-09-05T11:32:25 | 2023-12-13T16:06:13 | 2023-12-13T16:06:12 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Is there a feature in langchain through which we can load multiple CSVs with different headers??
Right now in CSVLoader we can upload only single CSV.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5wKbnm
| 10,223 |
What is the difference between Pandas Data frame agent, CSV agent and SQL Agent?
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[] | null | 4 | 2023-09-05T11:30:53 | 2023-12-14T16:05:58 | 2023-12-14T16:05:57 |
NONE
| null |
### Issue you'd like to raise.
What is the difference between Pandas Data frame agent, CSV agent and SQL Agent?
Can you brief each and when to use ?
### Suggestion:
_No response_
|
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from ._pydantic_core import ( ModuleNotFoundError: No module named 'pydantic_core._pydantic_core'
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[] | null | 1 | 2023-09-05T11:26:34 | 2023-09-05T11:54:29 | 2023-09-05T11:54:29 |
NONE
| null |
### Issue you'd like to raise.
I'm trying to run the local llama 2-7b version. I've installed llama-cpp-python and all other requirements. When I run the code I constantly get the error which says
Here is my code
`
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from pydantic import *
MODEL_PATH = "/D:/llama2-7b.bin"
def load_model():
"""Loads Llama model"""
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
Llama_model = LlamaCpp(
model_path=MODEL_PATH,
temperature=0.5,
max_tokens=2000,
top_p=1,
callback_manager=callback_manager,
verbose=True
)
return Llama_model
llm = load_model()
model_prompt = """
Question:What is the largest planet discovered so far?
"""
response = llm(model_prompt)
print(response)
`
This is the error
`
PS D:\Python Projects\python> python learn.py
Traceback (most recent call last):
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\pydantic_v1\__init__.py", line 15, in <module>
from pydantic.v1 import * # noqa: F403
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\pydantic\__init__.py", line 3, in <module>
import pydantic_core
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\pydantic_core\__init__.py", line 6, in <module>
from ._pydantic_core import (
ModuleNotFoundError: No module named 'pydantic_core._pydantic_core'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\Python Projects\python\learn.py", line 1, in <module>
from langchain.llms import LlamaCpp
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\agents\__init__.py", line 31, in <module>
from langchain.agents.agent import (
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\agents\agent.py", line 14, in <module>
from langchain.agents.agent_iterator import AgentExecutorIterator
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\agents\agent_iterator.py", line 21, in <module>
from langchain.callbacks.manager import (
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\callbacks\__init__.py", line 10, in <module>
from langchain.callbacks.aim_callback import AimCallbackHandler
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\callbacks\aim_callback.py", line 5, in <module>
from langchain.schema import AgentAction, AgentFinish, LLMResult
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\schema\__init__.py", line 3, in <module>
from langchain.schema.cache import BaseCache
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\schema\cache.py", line 6, in <module>
from langchain.schema.output import Generation
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\schema\output.py", line 7, in <module>
from langchain.load.serializable import Serializable
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\load\serializable.py", line 4, in <module>
from langchain.pydantic_v1 import BaseModel, PrivateAttr
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\langchain\pydantic_v1\__init__.py", line 17, in <module>
from pydantic import * # noqa: F403
^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\pydantic\__init__.py", line 3, in <module>
import pydantic_core
File "C:\Users\krish\anaconda3\envs\lang\Lib\site-packages\pydantic_core\__init__.py", line 6, in <module>
from ._pydantic_core import (
ModuleNotFoundError: No module named 'pydantic_core._pydantic_core'
PS D:\Python Projects\python>
`
I tried installing pydantic but to no avail.
Please help
Python version = 3.11.4
pip version =23.2.1
### Suggestion:
_No response_
|
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| 1,881,690,807 |
PR_kwDOIPDwls5ZjzT0
| 10,220 |
Fix usage of AsyncHtmlLoader with an already running event loop
|
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[] | null | 1 | 2023-09-05T10:34:41 | 2023-09-05T14:25:30 | 2023-09-05T14:25:29 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,881,536,484 |
I_kwDOIPDwls5wJffk
| 10,219 |
Please add support URL parse for BaijiaHao
|
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[] | null | 3 | 2023-09-05T09:04:40 | 2023-12-13T16:06:23 | 2023-12-13T16:06:22 |
NONE
| null |
### System Info
windows
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.document_loaders import UnstructuredURLLoader
from langchain.chains import LLMRequestsChain
from langchain.chains.llm_requests import DEFAULT_HEADERS
from langchain.requests import TextRequestsWrapper
from bs4 import BeautifulSoup
# loader = UnstructuredURLLoader(
# urls=["https://baijiahao.baidu.com/s?id=1776165472932985664"],
# show_progress_bar=True,
# )
# data = loader.load()
# print(data)
url = "https://baijiahao.baidu.com/s?id=1776165472932985664"
a = TextRequestsWrapper(headers=DEFAULT_HEADERS)
res = a.get(url)
# extract the text from the html
soup = BeautifulSoup(res, "html.parser")
res = soup.get_text()
print(res)
```
I can not get the content from this url, while all other urls are OK.
### Expected behavior
Please take a look at this url
|
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https://api.github.com/repos/langchain-ai/langchain/issues/10219/timeline
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not_planned
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https://github.com/langchain-ai/langchain/issues/10218
| 1,881,424,933 |
I_kwDOIPDwls5wJEQl
| 10,218 |
Error failed to index when using ElasticsearchStore.from_documents
|
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closed
| false | null |
[] | null | 18 | 2023-09-05T07:59:38 | 2023-09-14T02:31:09 | 2023-09-05T11:55:21 |
NONE
| null |
### System Info
Python : v3.10.10
Langchain : v0.0.281
Elasticsearch : v8.9.0
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I was following this documentation https://python.langchain.com/docs/integrations/vectorstores/elasticsearch
my script was
```
# GENERATE INDEXING
loader = TextLoader("models/state_of_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = ElasticsearchStore.from_documents(
docs,
embeddings,
es_url="http://localhost:9200",
index_name="test-basic",
es_user=os.environ.get("ELASTIC_USERNAME"),
es_password=os.environ.get("ELASTIC_PASSWORD"),
)
```
but it raising an error when indexing the document
```
Created a chunk of size 132, which is longer than the specified 100
Created a chunk of size 107, which is longer than the specified 100
Created a chunk of size 103, which is longer than the specified 100
Created a chunk of size 104, which is longer than the specified 100
Error adding texts: 336 document(s) failed to index.
First error reason: failed to parse
Traceback (most recent call last):
File "D:\Project\elastic-langchain\main.py", line 31, in <module>
db = ElasticsearchStore.from_documents(
File "D:\Project\elastic-langchain\.venv\lib\site-packages\langchain\vectorstores\elasticsearch.py", line 1027, in from_documents
elasticsearchStore.add_documents(documents)
File "D:\Project\elastic-langchain\.venv\lib\site-packages\langchain\vectorstores\base.py", line 101, in add_documents
return self.add_texts(texts, metadatas, **kwargs)
File "D:\Project\elastic-langchain\.venv\lib\site-packages\langchain\vectorstores\elasticsearch.py", line 881, in add_texts
raise e
File "D:\Project\elastic-langchain\.venv\lib\site-packages\langchain\vectorstores\elasticsearch.py", line 868, in add_texts
success, failed = bulk(
File "D:\Project\elastic-langchain\.venv\lib\site-packages\elasticsearch\helpers\actions.py", line 521, in bulk
for ok, item in streaming_bulk(
File "D:\Project\elastic-langchain\.venv\lib\site-packages\elasticsearch\helpers\actions.py", line 436, in streaming_bulk
for data, (ok, info) in zip(
File "D:\Project\elastic-langchain\.venv\lib\site-packages\elasticsearch\helpers\actions.py", line 355, in _process_bulk_chunk
yield from gen
File "D:\Project\elastic-langchain\.venv\lib\site-packages\elasticsearch\helpers\actions.py", line 274, in _process_bulk_chunk_success
raise BulkIndexError(f"{len(errors)} document(s) failed to index.", errors)
elasticsearch.helpers.BulkIndexError: 336 document(s) failed to index.
```
### Expected behavior
It can indexing and not raising any errror
|
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| 1,881,407,285 |
I_kwDOIPDwls5wI_81
| 10,217 |
Issue: Timeout parameter in the AzureMLEndpointClient cannot be modified
|
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closed
| false | null |
[] | null | 2 | 2023-09-05T07:49:27 | 2023-12-13T16:06:28 | 2023-12-13T16:06:27 |
NONE
| null |
### Issue you'd like to raise.
class AzureMLEndpointClient(object):
"""AzureML Managed Endpoint client."""
def __init__(
self, endpoint_url: str, endpoint_api_key: str, deployment_name: str = ""
) -> None:
"""Initialize the class."""
if not endpoint_api_key or not endpoint_url:
raise ValueError(
"""A key/token and REST endpoint should
be provided to invoke the endpoint"""
)
self.endpoint_url = endpoint_url
self.endpoint_api_key = endpoint_api_key
self.deployment_name = deployment_name
def call(self, body: bytes, **kwargs: Any) -> bytes:
"""call."""
# The azureml-model-deployment header will force the request to go to a
# specific deployment. Remove this header to have the request observe the
# endpoint traffic rules.
headers = {
"Content-Type": "application/json",
"Authorization": ("Bearer " + self.endpoint_api_key),
}
if self.deployment_name != "":
headers["azureml-model-deployment"] = self.deployment_name
req = urllib.request.Request(self.endpoint_url, body, headers)
response = urllib.request.urlopen(req, timeout=kwargs.get("timeout", 50))
result = response.read()
return result
I am using this class in order to call a AzureML Endpoint and i am not able to pass the timeout as a parameter anywhere in the function call.
### Suggestion:
_No response_
|
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https://github.com/langchain-ai/langchain/pull/10216
| 1,881,383,793 |
PR_kwDOIPDwls5Ziw2C
| 10,216 |
Fixing issue #9656
|
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[] | null | 1 | 2023-09-05T07:34:57 | 2023-09-06T15:44:18 | 2023-09-06T15:44:18 |
CONTRIBUTOR
| null |
@hwchase17 @eyurtsev @baskaryan - Great progress on Langchain. Really impressed with the work. Someone had raised this issue a couple of weeks back. Simple fix.
Fixing error in prompt template code. Issue #9656 (https://github.com/langchain-ai/langchain/issues/9656)
|
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| 1,881,374,483 |
I_kwDOIPDwls5wI38T
| 10,215 |
Errors about ErnieBotChat using agent
|
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[] | null | 2 | 2023-09-05T07:28:55 | 2023-12-13T16:06:33 | 2023-12-13T16:06:32 |
NONE
| null |
### System Info
LangChain version：0.0271
python version：3.9
transformers：4.30.2
linux
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
model = ErnieBotChat(model="ERNIE-Bot")
tools = load_tools(["llm-math", "wikipedia"], llm=model)
agent = initialize_agent(
tools,
model,
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True)
result = agent("300的25%是多少？")
print(result)
### Expected behavior
Error：
ValueError: Got unknown type content='Answer the following questions as best you can. You have access to the following tools:\n\nCalculator: Useful for when you need to answer questions about math.\nWikipedia: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, facts, historical events, or other subjects. Input should be a search query.\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the "action" field are: Calculator, Wikipedia\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{\n "action": $TOOL_NAME,\n "action_input": $INPUT\n}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin! Reminder to always use the exact characters `Final Answer` when responding.' additional_kwargs={}
When I was running ErnieBotChat and trying to join agent, an error occurred, as shown above. How to solve this? thank you!
|
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| 1,881,361,310 |
PR_kwDOIPDwls5ZisAd
| 10,214 |
add where_document filter for chroma
|
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[] | null | 6 | 2023-09-05T07:20:30 | 2023-09-08T23:50:34 | 2023-09-08T23:50:30 |
CONTRIBUTOR
| null |
- Description: add where_document filter parameter in Chroma
- Issue: [10082](https://github.com/langchain-ai/langchain/issues/10082)
- Dependencies: no
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: no
@hwchase17
|
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| 1,881,312,976 |
PR_kwDOIPDwls5ZihtW
| 10,213 |
Fixed incorrect prompt formatting for chat based ReAct agent
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[] | null | 1 | 2023-09-05T06:47:23 | 2023-09-05T06:53:14 | 2023-09-05T06:51:35 |
NONE
| null |
# Fixed incorrect prompt formatting for chat based ReAct agent
Fixes #5390
Created two new classes (ReActStringPromptValue and ReActPromptTemplate) in order to handle correct prompt formatting for chat based models. Unit tests are also included.
Format/lint/tests already run.
My twitter handle: @gultepeoguz
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PR_kwDOIPDwls5Zif-j
| 10,212 |
update(llms/TGI): Allow None as temperature value
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[] | null | 2 | 2023-09-05T06:41:14 | 2023-09-05T08:07:58 | 2023-09-05T08:07:58 |
CONTRIBUTOR
| null |
Text Generation Inference's client permits the use of a None temperature as seen [here](https://github.com/huggingface/text-generation-inference/blob/033230ae667101d2d8d8bcd4952442fa348ef951/clients/python/text_generation/client.py#L71C9-L71C20). While I haved dived into TGI's server code and don't know about the implications of using None as a temperature setting, I think we should grant users the option to pass None as a temperature parameter to TGI.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5ZiXdA
| 10,211 |
Param name change
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[] | null | 4 | 2023-09-05T06:11:24 | 2023-11-13T06:44:18 | 2023-11-13T06:44:17 |
NONE
| null |
- Description: Changed parameter name 'model_name' to 'model_name_or_path' of HuggingFaceEmbeddings / HuggingFaceInstructEmbeddings / HuggingFaceBgeEmbeddings modules since it can take and process local model files as input, without any change of code!
- Issue: Prevent confusion of users
- Dependencies: Not at all!
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I_kwDOIPDwls5wIQQ3
| 10,210 |
Pydantic issue
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[] | null | 3 | 2023-09-05T05:21:26 | 2023-09-05T06:14:19 | 2023-09-05T06:14:19 |
NONE
| null |
### System Info
Kaggle notebook
### Who can help?
@agola11 @hw
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
'''
I installed:
```
pip install -qq -U langchain tiktoken pypdf chromadb faiss-gpu unstructured openai
pip install -qq -U transformers InstructorEmbedding sentence_transformers pydantic==1.9.0
pip uninstall pydantic-settings
pip uninstall inflect
pip install pydantic-settings
pip install inflect
```
but getting error:
```
PydanticImportError: `BaseSettings` has been moved to the `pydantic-settings` package. See https://docs.pydantic.dev/2.3/migration/#basesettings-has-moved-to-pydantic-settings for more details.
```
Even though i have chromadb installed:
```
ImportError: Could not import chromadb python package. Please install it with `pip install chromadb`.
```
```
from langchain.document_loaders import PyPDFLoader, DirectoryLoader, PDFMinerLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma, FAISS
import os
persist_directory = "db"
def main():
for root, dirs, files in os.walk("docs"):
for file in files:
if file.endswith(".pdf"):
print(file)
loader = PyPDFLoader(os.path.join(root, file))
documents = loader.load()
print("splitting into chunks")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
#create embeddings here
print("Loading sentence transformers model")
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
#create vector store here
print(f"Creating embeddings. May take some minutes...")
db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
db.persist()
db=None
print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
if __name__ == "__main__":
main()
```
### Expected behavior
shows results
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| 1,881,171,623 |
PR_kwDOIPDwls5ZiDXi
| 10,209 |
Fixed Import Error type in base.py
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[] | null | 4 | 2023-09-05T04:40:38 | 2023-09-13T19:12:58 | 2023-09-13T19:12:58 |
CONTRIBUTOR
| null |
I have revamped the code to ensure uniform error handling for ImportError. Instead of the previous reliance on ValueError, I have adopted the conventional practice of raising ImportError and providing informative error messages. This change enhances code clarity and clearly signifies that any problems are associated with module imports.
CC: @baskaryan, @eyurtsev, @rlancemartin.
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| 1,881,157,181 |
PR_kwDOIPDwls5ZiATu
| 10,208 |
Update image_captions.py
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CONTRIBUTOR
| null |
enabled ImageCaptionLoader to accept bytes or filepath as input, if you input bytes the metadata states that bytes were input or if file path it shows the file path, pretty straightforward
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: enabled ImageCaptionLoader to accept bytes or filepath as input, if you input bytes the metadata states that bytes were input or if file path it shows the file path,
- Issue: n/a
- Dependencies: n/a,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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I_kwDOIPDwls5wH9Kl
| 10,207 |
langchain/vectorstores/milvus.py _load function need timeout parameter
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[] | null | 4 | 2023-09-05T03:46:17 | 2024-01-08T06:57:51 | null |
NONE
| null |
### System Info
0.0.274
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
def _load(self) -> None:
"""Load the collection if available."""
from pymilvus import Collection
if isinstance(self.col, Collection) and self._get_index() is not None:
self.col.load(timeout=5)
### Expected behavior
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/milvus.py#L387C3-L387C3
<img width="563" alt="ab96a8450969eaeae4eb91cf582828cf" src="https://github.com/langchain-ai/langchain/assets/16218592/2976b0de-fc41-46aa-88eb-fd807e3b6b57">
<img width="350" alt="26f720d9fa837900f91c8ff59d6fd815" src="https://github.com/langchain-ai/langchain/assets/16218592/740e0f7d-da60-4903-8d46-30e5e6c3faa2">
<img width="576" alt="02eee59006291866c52eda92921c9281" src="https://github.com/langchain-ai/langchain/assets/16218592/4de8f6dd-b154-4407-9153-b860a1f9a332">
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reopened
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https://api.github.com/repos/langchain-ai/langchain/issues/10205
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| 1,881,063,737 |
I_kwDOIPDwls5wHsE5
| 10,205 |
Issue: SQLDatabaseToolkit inconsistency
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[] | null | 4 | 2023-09-05T02:15:33 | 2023-12-07T02:53:55 | 2023-12-07T02:53:48 |
NONE
| null |
I have the following code (see below). I have two prompts. One works ok (p1), and the other (p2) throws following error (complete error below):
`OutputParserException: Could not parse LLM output: I don't know how to answer the question because I don't have access to the casos_perfilados_P2 table.`
`p1 : Seleccionar los campos linea, nivel y genero, que contengan el valor F en genero y el valor D2 en el campo nse. Limitar el número de registros a 3. Armar un dataframe de pandas con el resultado anterior. `
`p2 : Seleccionar los campos linea y fecha, donde el campo fecha es mayor o igual a 2023-02-18 y menor o igual a 2023-02-24. Agrupar y sumar los valores del campo linea por el campo fecha. Armar un dataframe de pandas con el resultado anterior. `
...Why p2 has "access" issues while p1 doesn't...any clues ?
```python
# google
import vertexai
# Alchemy
from sqlalchemy import *
from sqlalchemy.schema import *
# Langchain
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.callbacks import StreamlitCallbackHandler
from langchain.llms import VertexAI
from langchain.agents.agent_types import AgentType
from langchain.agents import initialize_agent
from langchain.tools.python.tool import PythonREPLTool
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools import Tool
# Streamlit
import streamlit as st
# Settings
PROJECT_ID = "xxxxxxx"
REGION = "xxxxxxx"
dataset = "xxxxxxx"
# Initialize Vertex AI SDK
vertexai.init(project=PROJECT_ID, location=REGION)
# BQ db
sqlalchemy_url = f'bigquery://{PROJECT_ID}/{dataset}'
db = SQLDatabase.from_uri(sqlalchemy_url)
# llm
llm = VertexAI(
model_name="text-bison@001",
max_output_tokens=1024,
temperature=0,
top_p=0.8,
top_k=40,
verbose=True
)
# SQL Agent
sql_agent = create_sql_agent(
llm=llm,
toolkit=SQLDatabaseToolkit(db=db, llm=llm),
verbose=True,
top_k=1000,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)
# Python Agent
python_agent = create_python_agent(
llm,
tool=PythonREPLTool(),
verbose=True
)
# Main Agent
agent = initialize_agent(
tools=[
Tool(
name="SQLAgent",
func=sql_agent.run,
description="""Useful to execute sql commands""",
),
Tool(
name="PythonAgent",
func=python_agent.run,
description="""Useful to run python commands""",
),
],
llm=llm,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True,
verbose=True,
)
if prompt := st.chat_input():
st.chat_message("user").write(prompt)
with st.chat_message("assistant"):
st_callback = StreamlitCallbackHandler(st.container())
response = agent.run(prompt)
st.write(response)
```
```bash
> Entering new AgentExecutor chain...
Action: sql_db_list_tables
Action Input:
Observation: casos_perfilados_P2
Thought:2023-09-04 19:23:34.050 Uncaught app exception
Traceback (most recent call last):
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "/home/flow/Documentos/genai/chat1.py", line 91, in <module>
response = agent.run(prompt)
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 475, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
raise e
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 276, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/agents/agent.py", line 1036, in _call
next_step_output = self._take_next_step(
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/agents/agent.py", line 891, in _take_next_step
observation = tool.run(
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/tools/base.py", line 351, in run
raise e
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/tools/base.py", line 323, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/tools/base.py", line 493, in _run
self.func(
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 475, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
raise e
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 276, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/agents/agent.py", line 1036, in _call
next_step_output = self._take_next_step(
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/agents/agent.py", line 844, in _take_next_step
raise e
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/agents/agent.py", line 833, in _take_next_step
output = self.agent.plan(
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/agents/agent.py", line 457, in plan
return self.output_parser.parse(full_output)
File "/home/flow/anaconda3/envs/llm/lib/python3.10/site-packages/langchain/agents/mrkl/output_parser.py", line 52, in parse
raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Could not parse LLM output: `I don't know how to answer the question because I don't have access to the casos_perfilados_P2 table.`
```
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| 1,881,049,958 |
PR_kwDOIPDwls5ZhpxB
| 10,204 |
add NumberedListOutputParser to output_parser init
|
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[] | null | 2 | 2023-09-05T01:55:18 | 2023-09-05T08:12:42 | 2023-09-05T08:12:42 |
CONTRIBUTOR
| null |
`from langchain.output_parsers import NumberedListOutputParser` did not work, needed to add it to the init file
|
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| 1,881,038,241 |
PR_kwDOIPDwls5ZhnWF
| 10,203 |
Update tool.py
|
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[] | null | 2 | 2023-09-05T01:38:37 | 2023-09-08T05:04:56 | 2023-09-08T05:04:56 |
CONTRIBUTOR
| null |
Fixed the description of tool QuerySQLCheckerTool, the last line of the string description had the old name of the tool 'sql_db_query', this caused the models to sometimes call the non-existent tool
The issue was not numerically identified.
No dependencies
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5ZhiYp
| 10,202 |
Better ls info
|
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[] | null | 1 | 2023-09-05T01:03:28 | 2023-09-05T01:21:16 | 2023-09-05T01:21:15 |
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I_kwDOIPDwls5wHYE0
| 10,201 |
create_extraction_chain does not work with other LLMs? Replicate models fails to load
|
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[] | null | 12 | 2023-09-05T00:08:26 | 2023-12-27T06:09:09 | null |
NONE
| null |
### System Info
LangChain 0.0.281
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use this lines of code
```
schema = {
"properties": {
"visit": {"type": "string"},
"date": {"type": "string"},
"gender": {"type": "string"},
"age": {"type": "integer"},
}
}
inp = """This 23-year-old white female presents with complaint of allergies.
She used to have allergies when she lived in Seattle but she thinks they are worse here.
In the past, she has tried Claritin, and Zyrtec. Both worked for short time but then seemed to lose effectiveness. """
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
input={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
chain = create_extraction_chain(schema, llm)
chain.run(inp)
```
---------------------------------------------------------------------------
```
OutputParserException Traceback (most recent call last)
[<ipython-input-9-5e77f11609b2>](https://localhost:8080/#) in <cell line: 72>()
70 )
71 chain = create_extraction_chain(schema, llm)
---> 72 chain.run(inp)["data"]
8 frames
[/usr/local/lib/python3.10/dist-packages/langchain/output_parsers/openai_functions.py](https://localhost:8080/#) in parse_result(self, result)
21 generation = result[0]
22 if not isinstance(generation, ChatGeneration):
---> 23 raise OutputParserException(
24 "This output parser can only be used with a chat generation."
25 )
OutputParserException: This output parser can only be used with a chat generation.
```
### Expected behavior
Structrued JSON based on schema
|
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PR_kwDOIPDwls5ZhXZp
| 10,200 |
add streaming capability to FakeMessagesListChatModel
|
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[] | null | 4 | 2023-09-04T23:38:08 | 2023-11-29T15:32:26 | null |
CONTRIBUTOR
| null |
Attempt to add streaming capability to `FakeMessagesListChatModel`.
This is an extension of the work from #10152 and #10116
Not sure I've implemented this correctly, but it is working in my testing.
@hwchase17 happy to make any adjustments if the PR needs improvement.
|
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PR_kwDOIPDwls5ZhKp4
| 10,199 |
redis self-query
|
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[] | null | 2 | 2023-09-04T22:00:20 | 2023-09-08T23:43:17 | 2023-09-08T23:43:16 |
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| null | null |
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Adding custom tools to SQL Agent
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[] | null | 2 | 2023-09-04T21:39:46 | 2023-09-06T20:38:52 | 2023-09-04T22:28:28 |
CONTRIBUTOR
| null |
Changes in:
- `create_sql_agent` function so that user can easily add custom tools as complement for the toolkit.
- updating **sql use case** notebook to showcase 2 examples of extra tools.
Motivation for these changes is having the possibility of including domain expert knowledge to the agent, which improves accuracy and reduces time/tokens.
|
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PR_kwDOIPDwls5Zg_aT
| 10,197 |
Update docs w/ prompt hub
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[] | null | 1 | 2023-09-04T20:55:28 | 2023-10-11T20:56:49 | 2023-09-04T21:09:08 |
COLLABORATOR
| null |
Small updates to docs
|
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PR_kwDOIPDwls5Zg36l
| 10,196 |
OpenAIEmbeddings: Add optional an optional parameter to skip empty embeddings
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[] | null | 3 | 2023-09-04T20:11:23 | 2023-09-21T23:47:22 | 2023-09-04T21:10:37 |
CONTRIBUTOR
| null |
## Description
### Issue
This pull request addresses a lingering issue identified in PR #7070. In that previous pull request, an attempt was made to address the problem of empty embeddings when using the `OpenAIEmbeddings` class. While PR #7070 introduced a mechanism to retry requests for embeddings, it didn't fully resolve the issue as empty embeddings still occasionally persisted.
### Problem
In certain specific use cases, empty embeddings can be encountered when requesting data from the OpenAI API. In some cases, these empty embeddings can be skipped or removed without affecting the functionality of the application. However, they might not always be resolved through retries, and their presence can adversely affect the functionality of applications relying on the `OpenAIEmbeddings` class.
### Solution
To provide a more robust solution for handling empty embeddings, we propose the introduction of an optional parameter, `skip_empty`, in the `OpenAIEmbeddings` class. When set to `True`, this parameter will enable the behavior of automatically skipping empty embeddings, ensuring that problematic empty embeddings do not disrupt the processing flow. The developer will be able to optionally toggle this behavior if needed without disrupting the application flow.
## Changes Made
- Added an optional parameter, `skip_empty`, to the `OpenAIEmbeddings` class.
- When `skip_empty` is set to `True`, empty embeddings are automatically skipped without causing errors or disruptions.
### Example Usage
```python
from openai.embeddings import OpenAIEmbeddings
# Initialize the OpenAIEmbeddings class with skip_empty=True
embeddings = OpenAIEmbeddings(api_key="your_api_key", skip_empty=True)
# Request embeddings, empty embeddings are automatically skipped. docs is a variable containing the already splitted text.
results = embeddings.embed_documents(docs)
# Process results without interruption from empty embeddings
```
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PR_kwDOIPDwls5Zgyb4
| 10,195 |
Adding a Verbose Hook
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NONE
| null |
Adding a Verbose Hook, so verbose output wont only be printed, but also handled if chosen to
solution to #5448 (Issue: Get Verbose messages from chain)
Usage:
```python
def handle_verbose(verbose_output, *args, **kwargs):
print("$$$$$$$ HANDLEING VERBOSE OUTPUT $$$$$$")
print(verbose_output) # Handle verbose here
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$", args, kwargs)
worker = LLMChain(prompt=prompt,llm=llm, verbose=verbose, verbose_hook=handle_verbose)
# Verbose messages will be sent to hook
```
Usefull when you want to include verbose output, for example in a custom debugger gui, logger, or for any other usage.
( I've only changed the on_text event, which includes the final (green) prompt before it is sent to the llm, if you like it you can extend to use in other places. )
Hope you find it useful as I do
Have a good one and all the best!
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5ZgnDF
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model garden nit
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PR_kwDOIPDwls5ZgeTG
| 10,193 |
Fix S3DirectoryLoader exception
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[] | null | 2 | 2023-09-04T17:57:17 | 2023-09-05T07:37:13 | 2023-09-04T22:59:23 |
CONTRIBUTOR
| null |
#9304 introduced a critical bug. The S3DirectoryLoader fails completely because boto3 checks the naming of kw arguments and one of the args is badly named (very sorry for that)
cc @baskaryan
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| 1,880,552,977 |
PR_kwDOIPDwls5Zf-NG
| 10,191 |
Fixed some grammatical typos in doc files
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[] | null | 1 | 2023-09-04T15:56:12 | 2023-09-04T17:48:09 | 2023-09-04T17:48:09 |
CONTRIBUTOR
| null |
Fixed some grammatical typos in doc files
CC: @baskaryan, @eyurtsev, @rlancemartin.
|
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PR_kwDOIPDwls5Zf6L-
| 10,189 |
bump 281
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[] | null | 1 | 2023-09-04T15:45:40 | 2023-09-04T15:51:51 | 2023-09-04T15:51:50 |
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I_kwDOIPDwls5wFqlM
| 10,188 |
How to develop an efficient question answering solution acting as a customer assistant based on RAG or Fine tuning?
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[] | null | 3 | 2023-09-04T15:44:40 | 2023-10-22T14:36:17 | null |
NONE
| null |
### System Info
0.0.274
### Who can help?
@hwchase17 @agola11 @eyurtsev
### 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
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.llms import VertexAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
import os
PROMPT= """
You are a customer service assistant.
Your task is to respond to user questions.
Start by greeting the user and introducing yourself as BotAssistant, and end with a polite closing. If you don't know the answer, suggest contacting customer service at 3232. Always conclude with: "I hope I've answered your request
Query: """
llm_model=VertexAI(
model_name="text-bison@001",
max_output_tokens=1024,
temperature=0.1,
top_p=0.8,
top_k=40,
verbose=True,
)
conversation = ConversationChain(
llm=llm_model,
verbose=True,
memory=ConversationBufferMemory(),
)
qst="I have an issue with my order; I received the wrong item. What should I do?"
conversation.predict(input=PROMPT+qst)
```
### Expected behavior
I want to develop an llm that acts as a customer assistant( to use for E comerce), responding to user queries using a JSON dataset containing question-answer pairs,possibly incorporating pdf documents of terms and conditions and offer descriptions. How can I effectively use Retrieval Augmented Generation to address this challenge? Is fine-tuning a recommended approach?
Sometimes, the answers to queries are indirect, and they may include links to previously provided answers on the same topic.do you think that a graph representation of the questions answers pairs dataset is relevant?
Any help,links would be appreciated,
|
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| 1,880,407,689 |
I_kwDOIPDwls5wFL6J
| 10,187 |
Adopt Microsoft's Classy-fire classification approach
|
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[] | null | 3 | 2023-09-04T14:28:14 | 2023-12-13T16:06:43 | 2023-12-13T16:06:42 |
NONE
| null |
### Feature request
Adopt [Classy-fire](https://github.com/microsoft/classy-fire)
### Motivation
See above link on benefits of the approach
### Your contribution
Can adapt classy-fire for easier integration as requested
|
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| 1,880,395,414 |
I_kwDOIPDwls5wFI6W
| 10,186 |
Issue: RedisVectorStoreRetriever not accessible
|
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[] | null | 4 | 2023-09-04T14:21:34 | 2023-09-12T22:29:54 | 2023-09-12T22:29:54 |
NONE
| null |
### Issue you'd like to raise.
After PR [#8612](https://github.com/langchain-ai/langchain/pull/8612), access to [RedisVectorStoreRetriever](https://github.com/langchain-ai/langchain/blob/27944cb611ee8face34fbe764c83e37841f96eb7/libs/langchain/langchain/vectorstores/redis/base.py#L1293) has been removed
### Suggestion:
Include **RedisVectorStoreRetriever** import in [redis/__init__.py](https://github.com/langchain-ai/langchain/blob/27944cb611ee8face34fbe764c83e37841f96eb7/libs/langchain/langchain/vectorstores/redis/__init__.py) on line 1
current: `from .base import Redis`
suggestion update: `from .base import Redis, RedisVectorStoreRetriever`
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I_kwDOIPDwls5wFEsH
| 10,185 |
Add option to disable question augmentation in ConversationalRetrievalQAChain
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[] | null | 1 | 2023-09-04T14:11:55 | 2023-12-11T16:04:43 | 2023-12-11T16:04:42 |
NONE
| null |
I think it'd better if there was a flag in the ConversationalRetreivalQAChain() where we can choose to not select the chain of question rephrasing before generation. Can this be considered as an issue and dealt with accordingly?
_Originally posted by @AshminJayson in https://github.com/langchain-ai/langchain/issues/4076#issuecomment-1705339045_
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I_kwDOIPDwls5wFEer
| 10,184 |
Issue: How to configure Amazon SageMaker endpoint
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[] | null | 3 | 2023-09-04T14:11:27 | 2023-12-25T16:08:45 | 2023-12-25T16:08:44 |
NONE
| null |
### Issue you'd like to raise.
#1. Open terminal, install bedrock specific version boto3 SDK,langchain
curl -sS https://d2eo22ngex1n9g.cloudfront.net/Documentation/SDK/bedrock-python-sdk.zip > sdk.zip
sudo yum install unzip -y
unzip sdk.zip -d sdk
pip install --no-build-isolation --force-reinstall ./sdk/awscli-*-py3-none-any.whl ./sdk/boto3-*-py3-none-any.whl ./sdk/botocore-*-py3-none-any.whl
pip install --quiet langchain==0.0.249
#pip install 'jupyter-ai>=1.0,<2.0' # If you use JupyterLab 3 pip install jupyter-ai # If you use JupyterLab 4
#2. change the default token count to 1024
vi ~/anaconda3/lib/python3.11/site-packages/langchain/llms/sagemaker_endpoint.py
Insert below lines after body = self.content_handler.transform_input(prompt, _model_kwargs)
parameters={"max_new_tokens": 1024, "top_p": 0.9, "temperature": 0.6, "return_full_text": True}
t = json.loads(body)
t["parameters"] = parameters
body = json.dumps(t)
Insert the line CustomAttributes='accept_eula=true’, between Accept=accepts, and **_endpoint_kwargs,
#3. aws configure default profile, make sure the aksk has enough permissions(SageMakerFullAccess)
aws configure
#4.run %%ai in *.ipynb file on ec2 instead of SageMaker notebook instance / SageMaker Studio [also can run in VSCODE] after making sure your Amazon SageMaker endpoint is health
%load_ext jupyter_ai
%%ai sagemaker-endpoint:jumpstart-dft-meta-textgeneration-llama-2-7b --region-name=us-east-1 --request-schema={"inputs":"<prompt>"} --response-path=[0]['generation']
write somthing on Humor
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5ZfXFG
| 10,183 |
Update Banana.dev docs to latest correct usage
|
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[] | null | 3 | 2023-09-04T14:10:35 | 2023-09-06T14:46:22 | 2023-09-06T14:46:17 |
CONTRIBUTOR
| null |
- Description: this PR updates all Banana.dev-related docs to match the latest client usage. The code in the docs before this PR were out of date and would never run.
- Issue: [#6404](https://github.com/langchain-ai/langchain/issues/6404)
- Dependencies: -
- Tag maintainer:
- Twitter handle: [BananaDev_ ](https://twitter.com/BananaDev_ )
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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I_kwDOIPDwls5wFDof
| 10,182 |
Issue: how to configure Amazon Bedrock
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[] | null | 3 | 2023-09-04T14:09:32 | 2023-12-13T16:06:47 | 2023-12-13T16:06:47 |
NONE
| null |
### Issue you'd like to raise.
how to configure Amazon Bedrock step by step:
Answers:
#1. Open terminal, install bedrock specific version boto3 SDK,langchain
curl -sS https://d2eo22ngex1n9g.cloudfront.net/Documentation/SDK/bedrock-python-sdk.zip > sdk.zip
sudo yum install unzip -y
unzip sdk.zip -d sdk
pip install --no-build-isolation --force-reinstall ./sdk/awscli-*-py3-none-any.whl ./sdk/boto3-*-py3-none-any.whl ./sdk/botocore-*-py3-none-any.whl
pip install --quiet langchain==0.0.249
#pip install 'jupyter-ai>=1.0,<2.0' # If you use JupyterLab 3 pip install jupyter-ai # If you use JupyterLab 4
#2. change the default token count to 2048
vi ~/anaconda3/lib/python3.11/site-packages/langchain/llms/bedrock.py
change this line: input_body["max_tokens_to_sample"] = 2048
#3. aws configure default profile, make sure the aksk has enough permissions(BedrockFullAccess)
aws configure
#4.run %%ai in *.ipynb file on ec2/local machine [also can run in VSCODE] instead of SageMaker notebook instance / SageMaker Studio
%load_ext jupyter_ai
%%ai bedrock:anthropic.claude-v2
Write something about Amazon
### Suggestion:
_No response_
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PR_kwDOIPDwls5ZfRLT
| 10,181 |
Integration with ElevenLabs text to speech
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[] | null | 4 | 2023-09-04T13:57:28 | 2023-09-13T05:56:54 | 2023-09-13T05:56:53 |
CONTRIBUTOR
| null |
- Description: adds integration with ElevenLabs text-to-speech [component](https://github.com/elevenlabs/elevenlabs-python) in the similar way it has been already done for [azure cognitive services](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/tools/azure_cognitive_services/text2speech.py)
- Dependencies: elevenlabs
- Twitter handle: @deepsense_ai, @matt_wosinski
- Future plans: refactor both implementations in order to avoid dumping speech file, but rather to keep it in memory.
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PR_kwDOIPDwls5Ze2rY
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MyScale hot fix on type check
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[] | null | 1 | 2023-09-04T12:55:23 | 2023-09-05T02:22:17 | 2023-09-04T15:40:58 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
Previous PR #9353 has incomplete type checks and deprecation warnings. This PR will fix those type check and add deprecation warning to myscale vectorstore
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I_kwDOIPDwls5wEJPw
| 10,179 |
Multiple bases have instance lay-out conflict
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[] | null | 7 | 2023-09-04T12:03:49 | 2023-12-06T08:21:24 | 2023-09-28T07:59:35 |
NONE
| null |
### System Info
torch = "2.0.1"
transformers = "4.31.0"
langchain= "0.0.251"
kor= "0.13.0"
openai= "0.27.8"
pydantic= "1.10.8"
python_version = "3.9"
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from db.tools import DbTools
from langchain.chat_models import ChatOpenAI
from kor.nodes import Object, Text, Number
from kor import create_extraction_chain, Object, Text
llm = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0,
max_tokens=2000,
frequency_penalty=0,
presence_penalty=0,
openai_api_key = "" ,
top_p=1.0,
)
### Expected behavior
Hello I encountered the following error when trying to import the langchain.schema module:
Traceback (most recent call last):
File "/home/alpha/platform/shared/libpython/computation/fine_instruction_GPT.py", line 2, in <module>
from langchain.chat_models import ChatOpenAI
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/agents/__init__.py", line 31, in <module>
from langchain.agents.agent import (
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/agents/agent.py", line 15, in <module>
from langchain.agents.agent_iterator import AgentExecutorIterator
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/agents/agent_iterator.py", line 21, in <module>
from langchain.callbacks.manager import (
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/callbacks/__init__.py", line 10, in <module>
from langchain.callbacks.aim_callback import AimCallbackHandler
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/callbacks/aim_callback.py", line 5, in <module>
from langchain.schema import AgentAction, AgentFinish, LLMResult
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/schema/__init__.py", line 4, in <module>
from langchain.schema.memory import BaseChatMessageHistory, BaseMemory
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/schema/memory.py", line 7, in <module>
from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
File "/home/alpha/.local/share/virtualenvs/platform-_bj8LfuO/lib/python3.9/site-packages/langchain/schema/messages.py", line 147, in <module>
class HumanMessageChunk(HumanMessage, BaseMessageChunk):
File "pydantic/main.py", line 367, in pydantic.main.ModelMetaclass.__new__
File "/usr/lib/python3.9/abc.py", line 85, in __new__
cls = super().__new__(mcls, name, bases, namespace, **kwargs)
TypeError: multiple bases have instance lay-out conflict
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I_kwDOIPDwls5wEHYh
| 10,178 |
An error occurred: not enough values to unpack (expected 2, got 1)
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[] | null | 2 | 2023-09-04T11:59:08 | 2023-12-11T16:04:53 | 2023-12-11T16:04:52 |
NONE
| null |
### System Info
langchain 0.0.279
### Who can help?
@agola11
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
def ask_local_vector_db(question):
# old docsearch_db
docs = docsearch_db.similarity_search(question, k=10)
pretty_print_docs(docs)
print("**************************************************")
cleaned_matches = []
total_toknes = 0
# print(docs)
for context in docs:
cleaned_context = context.page_content.replace('\n', ' ').strip()
cleaned_context = f"{cleaned_context}"
tokens = tokenizers.encode(cleaned_context, add_special_tokens=False)
if total_toknes + len(tokens) <= (1536 * 8):
cleaned_matches.append(cleaned_context)
total_toknes += len(tokens)
else:
break
# 将清理过的匹配项组合合成一个字符串
combined_text = " ".join(cleaned_matches)
answer = local_chain.predict(combined_text=combined_text, human_input=question)
return answer
# 创建工具列表
tools = [
Tool(
name="Google_Search",
func=GoogleSerper_search.run,
description="""
当你用本地向量数据库问答后说无法找到答案的之后，你可以使用互联网搜索引擎工具进行信息查询,尝试直接找到问题答案。
注意你需要提出非常有针对性准确的问题。
""",
),
Tool(
name="Local_Search",
func=ask_local_vector_db,
description="""
你可以首先通过本地向量数据知识库尝试寻找问答案。
注意你需要提出非常有针对性准确的问题
"""
)
]
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
}
memory = ConversationBufferMemory(memory_key="memory", return_messages=True)
# 初始化agent代理
agent_open_functions = initialize_agent(
tools,
llm=llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs=agent_kwargs,
memory=memory,
max_iterations=10,
early_stopping_method="generate",
handle_parsing_errors=True, # 初始化代理并处理解析错误
callbacks=[handler],
)
### Expected behavior
请输入您的问题（纯文本格式），换行输入 n 以结束：
本地搜索gpt
n
===============Thinking===================
> Entering new AgentExecutor chain...
Invoking: `Local_Search` with `gpt`
An error occurred: not enough values to unpack (expected 2, got 1)
请输入您的问题（纯文本格式），换行输入 n 以结束：
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| 1,880,011,755 |
PR_kwDOIPDwls5ZeGh7
| 10,177 |
Resolve: VectorSearch enabled SQLChain?
|
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[] | null | 2 | 2023-09-04T10:48:22 | 2023-09-13T02:53:07 | 2023-09-07T00:08:12 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Squashed from #7454 with updated features
We have separated the `SQLDatabseChain` from `VectorSQLDatabseChain` and put everything into `experimental/`.
Below is the original PR message from #7454.
-------
Hello from [MyScale](https://myscale.com/) AI team! 😊👋
We have been working on features to fill up the gap among SQL, vector search and LLM applications. Some inspiring works like self-query retrievers for VectorStores (for example [Weaviate](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/weaviate_self_query.html) and [others](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query.html)) really turn those vector search databases into a powerful knowledge base! 🚀🚀
We are thinking if we can merge all in one, like SQL and vector search and LLMChains, making this SQL vector database memory as the only source of your data. Here are some benefits we can think of for now, maybe you have more 👀:
With ALL data you have: since you store all your pasta in the database, you don't need to worry about the foreign keys or links between names from other data source.
Flexible data structure: Even if you have changed your schema, for example added a table, the LLM will know how to JOIN those tables and use those as filters.
SQL compatibility: We found that vector databases that supports SQL in the marketplace have similar interfaces, which means you can change your backend with no pain, just change the name of the distance function in your DB solution and you are ready to go!
### Issue resolved:
- [Feature Proposal: VectorSearch enabled SQLChain?](https://github.com/hwchase17/langchain/issues/5122)
### Change made in this PR:
- An improved schema handling that ignore `types.NullType` columns
- A SQL output Parser interface in `SQLDatabaseChain` to enable Vector SQL capability and further more
- A Retriever based on `SQLDatabaseChain` to retrieve data from the database for RetrievalQAChains and many others
- Allow `SQLDatabaseChain` to retrieve data in python native format
- Includes PR #6737
- Vector SQL Output Parser for `SQLDatabaseChain` and `SQLDatabaseChainRetriever`
- Prompts that can implement text to VectorSQL
- Corresponding unit-tests and notebook
### Twitter handle:
- @MyScaleDB
### Tag Maintainer:
Prompts / General: @hwchase17, @baskaryan
DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
### Dependencies:
No dependency added
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| 1,879,932,593 |
PR_kwDOIPDwls5Zd1G1
| 10,176 |
added support for stop_sequences params on Vertex AI
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[] | null | 7 | 2023-09-04T10:01:59 | 2023-10-09T19:15:02 | 2023-10-09T19:15:02 |
CONTRIBUTOR
| null |
Added a native support for stop words (`stop_sequences`), now exposed by Vertex AI:
https://github.com/googleapis/python-aiplatform/releases/tag/v1.31.0
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PR_kwDOIPDwls5ZdaVO
| 10,175 |
Add async support to SelfQueryRetriever
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[] | null | 4 | 2023-09-04T08:49:11 | 2023-10-06T01:54:22 | 2023-10-06T01:54:22 |
CONTRIBUTOR
| null |
### Description
SelfQueryRetriever is missing async support, so I am adding it.
I also removed deprecated predict_and_parse method usage here, and added some tests.
### Issue
N/A
### Tag maintainer
Not yet
### Twitter handle
N/A
|
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| 1,879,766,310 |
PR_kwDOIPDwls5ZdRJn
| 10,174 |
fix ChatMessageChunk concat error
|
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
- Description: fix `ChatMessageChunk` concat error
- Issue: #10173
- Dependencies: None
- Tag maintainer: @baskaryan, @eyurtsev, @rlancemartin
- Twitter handle: None
|
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I_kwDOIPDwls5wCu6d
| 10,173 |
ChatMessageChunk concat error
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[] | null | 0 | 2023-09-04T08:22:02 | 2023-10-27T02:15:23 | 2023-10-27T02:15:23 |
CONTRIBUTOR
| null |
### System Info
- LangChain: 0.0.279
- Python: 3.11.4
- Platform: Linux and MacOS
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.schema.messages import ChatMessageChunk
message = ChatMessageChunk(role="User", content="I am") + ChatMessageChunk(role="User", content=" indeed.")
```
Here is the error info:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[2], line 1
----> 1 message = ChatMessageChunk(role="User", content="I am") + ChatMessageChunk(role="User", content=" indeed.")
File ~/pyenv/venv/lib/python3.11/site-packages/langchain/schema/messages.py:120, in BaseMessageChunk.__add__(self, other)
115 def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore
116 if isinstance(other, BaseMessageChunk):
117 # If both are (subclasses of) BaseMessageChunk,
118 # concat into a single BaseMessageChunk
--> 120 return self.__class__(
121 content=self.content + other.content,
122 additional_kwargs=self._merge_kwargs_dict(
123 self.additional_kwargs, other.additional_kwargs
124 ),
125 )
126 else:
127 raise TypeError(
128 'unsupported operand type(s) for +: "'
129 f"{self.__class__.__name__}"
130 f'" and "{other.__class__.__name__}"'
131 )
File ~/pyenv/venv/lib/python3.11/site-packages/langchain/load/serializable.py:74, in Serializable.__init__(self, **kwargs)
73 def __init__(self, **kwargs: Any) -> None:
---> 74 super().__init__(**kwargs)
75 self._lc_kwargs = kwargs
File ~/pyenv/venv/lib/python3.11/site-packages/pydantic/v1/main.py:341, in BaseModel.__init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 1 validation error for ChatMessageChunk
role
field required (type=value_error.missing
```
### Expected behavior
Expected output:
```python
ChatMessageChunk(content='I am indeed.', additional_kwargs={}, role='User')
```
|
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| 1,879,747,922 |
I_kwDOIPDwls5wCq1S
| 10,172 |
RecursiveUrlLoader doesn't include root URL content
|
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[] | null | 2 | 2023-09-04T08:12:04 | 2023-12-11T16:04:58 | 2023-12-11T16:04:57 |
NONE
| null |
### System Info
```
url = "https://www.wsj.com"
loader = RecursiveUrlLoader(url=url, max_depth=2, extractor=lambda x: Soup(x, "html.parser").text)
docs = loader.load()
```
Docs don't have any data related to `url`. The problem is related to DFS in the codebase. It doesn't handle the root case.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To produce the issue executed the provided code snippet.
### Expected behavior
RecursiveUrlLoader should hold the the given `url` data along with it's child.
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| 1,879,709,967 |
I_kwDOIPDwls5wChkP
| 10,170 |
Can I get the token usage for each tool in agent?
|
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[] | null | 2 | 2023-09-04T07:47:46 | 2023-12-11T16:05:03 | 2023-12-11T16:05:02 |
NONE
| null |
HI team,
I am using get_openai_callback to fetch the total token usage for agent.
Is there any idea to get the token usage for each tool?
Thanks
|
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| 10,169 |
Add a clear() method to VectorStoreRetrieverMemory with FAISS vector store
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[] | null | 1 | 2023-09-04T07:41:47 | 2023-10-06T08:01:35 | 2023-10-06T08:01:35 |
NONE
| null |
**- Description:** In constructing the VectorStoreRetrieverMemory with InMemoryDocstore() like faiss vectorstore, it may also need to clear the chat_history memory like ConversationBufferMemory. To clear the memory without reconstruct a new memory, I add a clear() method to VectorStoreRetrieverMemory make it reusable in the same session, and can use memory.clear() to empty the memory vectorstore. < Only fot the FAISS vectorstore>
**- Issue:** the issue #5817 how to clear VectorStoreRetrieverMemory?
**- Dependencies:** None
**- Tag maintainer:**
**- Twitter handle:** @932196e6a7d042d
**- Usecase: **
```
from langchain.memory import VectorStoreRetrieverMemory
from langchain.prompts import PromptTemplate
from langchain.docstore import InMemoryDocstore
from langchain.vectorstores import FAISS
import faiss
embedding_size = 1536 # Dimensions of the OpenAIEmbeddings
index = faiss.IndexFlatL2(embedding_size)
embedding_fn = OpenAIEmbeddings().embed_query
vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {})
retriever = vectorstore.as_retriever(search_kwargs=dict(k= 3)) #set the vector store args
memory = VectorStoreRetrieverMemory(retriever=retriever)
# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "that's good to know"})
memory.save_context({"input": "My favorite sport is soccer"}, {"output": "..."})
memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"})
# Query the relevance history
memory.load_memory_variables({"prompt": "How's the weather "})
## return {'history': "input: My favorite food is pizza\noutput: that's good to know"}
#Clear
memory.clear()
memory.load_memory_variables({"prompt": "How's the weather "})
## return {"history":}
```
@baskaryan
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PR_kwDOIPDwls5ZdAoD
| 10,168 |
Fixed Import Error type
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[] | null | 1 | 2023-09-04T07:34:25 | 2023-09-04T15:43:29 | 2023-09-04T15:43:29 |
CONTRIBUTOR
| null |
I have restructured the code to ensure uniform handling of ImportError. In place of previously used ValueError, I've adopted the standard practice of raising ImportError with explanatory messages. This modification enhances code readability and clarifies that any problems stem from module importation.
@baskaryan, @eyurtsev, @rlancemartin.
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PR_kwDOIPDwls5Zc-Eg
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Fixed Import Error
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[] | null | 1 | 2023-09-04T07:26:06 | 2023-09-04T07:32:09 | 2023-09-04T07:32:09 |
CONTRIBUTOR
| null |
I have restructured the code to ensure uniform handling of ImportError. In place of previously used ValueError, I've adopted the standard practice of raising ImportError with explanatory messages. This modification enhances code readability and clarifies that any problems stem from module importation.
@baskaryan, @eyurtsev, @rlancemartin.
|
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PR_kwDOIPDwls5Zc83X
| 10,166 |
fix 'Event loop is closed' error
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[] | null | 1 | 2023-09-04T07:22:12 | 2023-09-06T01:42:30 | 2023-09-06T01:42:30 |
NONE
| null |
- Description: asyncio.run() will automatically close the event loop and trigger an error when calling _ProactorBasePipeTransport.__del__, whereas asyncio.run_until_complete() will not.
- Issue: #10086
I closed this #10087
|
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PR_kwDOIPDwls5Zc6z-
| 10,165 |
Fix: the duplicate characters wrong results when using `pdfplumber loader`
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[] | null | 2 | 2023-09-04T07:15:47 | 2023-09-04T15:37:06 | 2023-09-04T15:37:00 |
CONTRIBUTOR
| null |
(Reopen PR #7706, hope this problem can fix.)
When using `pdfplumber`, some documents may be parsed incorrectly, resulting in **duplicated characters**.
Taking the [linked](https://bruusgaard.no/wp-content/uploads/2021/05/Datasheet1000-series.pdf) document as an example:
## Before
```python
from langchain.document_loaders import PDFPlumberLoader
pdf_file = 'file.pdf'
loader = PDFPlumberLoader(pdf_file)
docs = loader.load()
print(docs[0].page_content)
```
Results:
```
11000000 SSeerriieess
PPoorrttaabbllee ssiinnggllee ggaass ddeetteeccttoorrss ffoorr HHyyddrrooggeenn aanndd CCoommbbuussttiibbllee ggaasseess
TThhee RRiikkeenn KKeeiikkii GGPP--11000000 iiss aa ccoommppaacctt aanndd
lliigghhttwweeiigghhtt ggaass ddeetteeccttoorr wwiitthh hhiigghh sseennssiittiivviittyy ffoorr
tthhee ddeetteeccttiioonn ooff hhyyddrrooccaarrbboonnss.. TThhee mmeeaassuurreemmeenntt
iiss ppeerrffoorrmmeedd ffoorr tthhiiss ppuurrppoossee bbyy mmeeaannss ooff ccaattaallyyttiicc
sseennssoorr.. TThhee GGPP--11000000 hhaass aa bbuuiilltt--iinn ppuummpp wwiitthh
ppuummpp bboooosstteerr ffuunnccttiioonn aanndd aa ddiirreecctt sseelleeccttiioonn ffrroomm
aa lliisstt ooff 2255 hhyyddrrooccaarrbboonnss ffoorr eexxaacctt aalliiggnnmmeenntt ooff tthhee
ttaarrggeett ggaass -- OOnnllyy ccaalliibbrraattiioonn oonn CCHH iiss nneecceessssaarryy..
44
FFeeaattuurreess
TThhee RRiikkeenn KKeeiikkii 110000vvvvttaabbllee ssiinnggllee HHyyddrrooggeenn aanndd
CCoommbbuussttiibbllee ggaass ddeetteeccttoorrss..
TThheerree aarree 33 ssttaannddaarrdd mmooddeellss::
GGPP--11000000:: 00--1100%%LLEELL // 00--110000%%LLEELL ›› LLEELL ddeetteeccttoorr
NNCC--11000000:: 00--11000000ppppmm // 00--1100000000ppppmm ›› PPPPMM
ddeetteeccttoorr
DDiirreecctt rreeaaddiinngg ooff tthhee ccoonncceennttrraattiioonn vvaalluueess ooff
ccoommbbuussttiibbllee ggaasseess ooff 2255 ggaasseess ((55 NNPP--11000000))..
EEaassyy ooppeerraattiioonn ffeeaattuurree ooff cchhaannggiinngg tthhee ggaass nnaammee
ddiissppllaayy wwiitthh 11 sswwiittcchh bbuuttttoonn..
LLoonngg ddiissttaannccee ddrraawwiinngg ppoossssiibbllee wwiitthh tthhee ppuummpp
bboooosstteerr ffuunnccttiioonn..
VVaarriioouuss ccoommbbuussttiibbllee ggaasseess ccaann bbee mmeeaassuurreedd bbyy tthhee
ppppmm oorrddeerr wwiitthh NNCC--11000000..
www.bruusgaard.no [email protected] +47 67 54 93 30 Rev: 446-2
```
We can see that there are a large number of duplicated characters in the text, which can cause issues in subsequent applications.
## After
Therefore, based on the [solution](https://github.com/jsvine/pdfplumber/issues/71) provided by the `pdfplumber` source project. I added the `"dedupe_chars()"` method to address this problem. (Just pass the parameter `dedupe` to `True`)
```python
from langchain.document_loaders import PDFPlumberLoader
pdf_file = 'file.pdf'
loader = PDFPlumberLoader(pdf_file, dedupe=True)
docs = loader.load()
print(docs[0].page_content)
```
Results:
```
1000 Series
Portable single gas detectors for Hydrogen and Combustible gases
The Riken Keiki GP-1000 is a compact and
lightweight gas detector with high sensitivity for
the detection of hydrocarbons. The measurement
is performed for this purpose by means of catalytic
sensor. The GP-1000 has a built-in pump with
pump booster function and a direct selection from
a list of 25 hydrocarbons for exact alignment of the
target gas - Only calibration on CH is necessary.
4
Features
The Riken Keiki 100vvtable single Hydrogen and
Combustible gas detectors.
There are 3 standard models:
GP-1000: 0-10%LEL / 0-100%LEL › LEL detector
NC-1000: 0-1000ppm / 0-10000ppm › PPM
detector
Direct reading of the concentration values of
combustible gases of 25 gases (5 NP-1000).
Easy operation feature of changing the gas name
display with 1 switch button.
Long distance drawing possible with the pump
booster function.
Various combustible gases can be measured by the
ppm order with NC-1000.
www.bruusgaard.no [email protected] +47 67 54 93 30 Rev: 446-2
```
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| 1,879,529,359 |
PR_kwDOIPDwls5ZceZs
| 10,163 |
Fixed typo in get_started.mdx
|
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[] | null | 1 | 2023-09-04T05:21:50 | 2023-09-04T07:09:51 | 2023-09-04T07:09:51 |
CONTRIBUTOR
| null |
Fix typo: 'Whats up' -> 'What's up'
Thanks
CC: @baskaryan, @eyurtsev, @rlancemartin.
|
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| 1,879,488,217 |
PR_kwDOIPDwls5ZcVkH
| 10,162 |
Implemented Milvus translator for self-querying
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[] | null | 3 | 2023-09-04T04:33:08 | 2023-09-04T07:16:18 | 2023-09-04T07:16:18 |
CONTRIBUTOR
| null |
- Implemented the MilvusTranslator for self-querying using Milvus vector store
- Made unit tests to test its functionality
- Documented the Milvus self-querying
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I_kwDOIPDwls5wBnBS
| 10,161 |
How can I append suffix to the prompt when I am using agents to control the output language
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[] | null | 2 | 2023-09-04T04:09:02 | 2023-12-11T16:05:08 | 2023-12-11T16:05:07 |
NONE
| null |
### Issue you'd like to raise.
I would like to output the answer in a specific language (e.g. chinese) when I am using agent. But when I tried to do that with the code below, it gives me an error ```OutputParserException: Could not parse LLM output:```
```
# retrieval qa chain
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vector_store.as_retriever()
)
from langchain.agents import Tool
tools = [
Tool(
name='Knowledge Base',
func=qa.run,
description=(
'<some description>'
)
)
]
from langchain.agents import initialize_agent
agent = initialize_agent(
agent='chat-conversational-react-description',
tools=tools,
llm=llm,
verbose=True,
max_iterations=3,
early_stopping_method='generate',
memory=conversational_memory
)
prompt_prefix = f"""请用中文回答"""
agent.agent.llm_chain.prompt += prompt_prefix
query = "<some question>"
agent(query)
```
Anyone knows how to add custom prompt to agent to enforce the output language?
### Suggestion:
_No response_
|
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| 1,879,442,445 |
PR_kwDOIPDwls5ZcLsA
| 10,160 |
Fixed typo in bittensor.mdx
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[] | null | 1 | 2023-09-04T03:29:37 | 2023-09-04T04:49:33 | 2023-09-04T04:49:33 |
CONTRIBUTOR
| null |
Fixed Typo in bittenaor.mdx
@baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
|
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| 1,879,386,202 |
I_kwDOIPDwls5wBSha
| 10,159 |
HuggingFaceBgeEmbeddings error
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[] | null | 2 | 2023-09-04T02:12:44 | 2023-09-04T02:26:11 | 2023-09-04T02:25:21 |
NONE
| null |
### System Info
Windows 10
langchain 0.0.279
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-small-en"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
hf = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
### Expected behavior
from langchain.embeddings import HuggingFaceBgeEmbeddings
Couldn't find HuggingFaceBgeEmbeddings
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| 1,879,371,686 |
PR_kwDOIPDwls5Zb8ll
| 10,158 |
Fixed query/instruction typoes
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[] | null | 1 | 2023-09-04T01:55:03 | 2023-09-04T03:31:37 | 2023-09-04T03:31:37 |
CONTRIBUTOR
| null |
Fixed typoes in embedding parameters.
|
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https://api.github.com/repos/langchain-ai/langchain/issues/10157/events
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https://github.com/langchain-ai/langchain/pull/10157
| 1,879,370,782 |
PR_kwDOIPDwls5Zb8Yg
| 10,157 |
Enhance Google search tool SerpApi response
|
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"id": 5680700873,
"node_id": "LA_kwDOIPDwls8AAAABUpidyQ",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/auto:improvement",
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closed
| false | null |
[] | null | 1 | 2023-09-04T01:54:09 | 2023-09-04T06:01:08 | 2023-09-04T03:24:19 |
CONTRIBUTOR
| null |
Enhance SerpApi response which potential to have more relevant output.
<img width="345" alt="Screenshot 2023-09-01 at 8 26 13 AM" src="https://github.com/langchain-ai/langchain/assets/10222402/80ff684d-e02e-4143-b218-5c1b102cbf75">
Query: What is the weather in Pomfret?
**Before:**
> I should look up the current weather conditions.
...
Final Answer: The current weather in Pomfret is 73°F with 1% chance of precipitation and winds at 10 mph.
**After:**
> I should look up the current weather conditions.
...
Final Answer: The current weather in Pomfret is 62°F, 1% precipitation, 61% humidity, and 4 mph wind.
---
Query: Top team in english premier league?
**Before:**
> I need to find out which team is currently at the top of the English Premier League
...
Final Answer: Liverpool FC is currently at the top of the English Premier League.
**After:**
> I need to find out which team is currently at the top of the English Premier League
...
Final Answer: Man City is currently at the top of the English Premier League.
---
Query: Any upcoming events in Paris?
**Before:**
> I should look for events in Paris
Action: Search
...
Final Answer: Upcoming events in Paris this month include Whit Sunday & Whit Monday (French National Holiday), Makeup in Paris, Paris Jazz Festival, Fete de la Musique, and Salon International de la Maison de.
**After:**
> I should look for events in Paris
Action: Search
...
Final Answer: Upcoming events in Paris include Elektric Park 2023, The Aces, and BEING AS AN OCEAN.
|
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