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PR_kwDOIPDwls5dTUVM
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Move embedding name to metadata
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Instead of tags. Easier to filter
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Add default value
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5dTGYI
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`baichuan_secret_key` use pydantic.types.SecretStr & Add Baichuan tests
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CONTRIBUTOR
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### Description
- `baichuan_secret_key` use pydantic.types.SecretStr
- Add Baichuan tests
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PR_kwDOIPDwls5dSzJU
| 12,030 |
bump 318
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COLLABORATOR
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PR_kwDOIPDwls5dSvia
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add _acall method to YandexGPT
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CONTRIBUTOR
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
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See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
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- **Description:** Add async support for YandexGPT LLM model
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I_kwDOIPDwls50Yk26
| 12,028 |
create_conversational_retrieval_agent with BedrockChat models
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NONE
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### Feature request
Hi, I'd love if we could create conversational retrieval agents using BedrockChat LLMs!
### Motivation
This feature would be very useful for many users.
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PR_kwDOIPDwls5dSmI4
| 12,027 |
Fix langsmith walkthrough doc dataset
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CONTRIBUTOR
| null |
- **Description:** the walkthrough notebook for langsmith created a dataset with an unequal number of input questions and reference answers.
- **Issue:** when the dataset was created with a python `zip` iteration, an off-by-one error was created which meant the last few questions had incorrect reference answers
- **Dependencies:** none
- **Tag maintainer:** Not sure
- **Twitter handle:** none
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PR_kwDOIPDwls5dRoJ2
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QA Privacy Nit
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[] | null | 1 | 2023-10-19T13:29:37 | 2023-10-19T13:43:47 | 2023-10-19T13:43:47 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls50XHUN
| 12,024 |
how can i get only 1 or 2 words output from my llm?
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NONE
| null |
### Feature request
We want some output parser or feature that will restrict llm to generate specifi number of words.in json or any format.
some times user want 10 lines output,sometime only 2 words etc
so this is very helpful feature .thanks
### Motivation
i want to build the next word auto suggetion model using llm,but my llm is giving me crazy output every time
instead of giving only 1 words
example: my input is
input: what is
output : your name
input: good
output: morning
input: what is the meaninig
output: of
so it will give me only 1 word suggetion,insted of giving all other stuff. want to restrict llm for specific words.
### Your contribution
will help with promt.but few quantised llm ar not good with giving only one word
|
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PR_kwDOIPDwls5dRPgT
| 12,023 |
Elasticsearch Query Retriever: Use match + fuzziness for LIKE
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CONTRIBUTOR
| null |
Updated the elasticsearch self query retriever to use the match clause for LIKE operator instead of the non-analyzed fuzzy search clause.
Other small updates include:
- fixing the stack inference integration test where the index's default pipeline didn't use the inference pipeline created
- adding a user-agent to the old implementation to track usage
- improved the documentation for ElasticsearchStore filters
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PR_kwDOIPDwls5dQ8Yp
| 12,022 |
Add Tencent Hunyuan chat model
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[] | null | 4 | 2023-10-19T12:09:02 | 2023-11-21T09:24:12 | 2023-10-19T19:10:12 |
CONTRIBUTOR
| null |
### Description:
The Tencent Hunyuan model, developed by Tencent, is a large language model by robust Chinese text generation capabilities, adeptness in logical reasoning within complex contexts, and reliable task execution proficiency.For more information, see [https://cloud.tencent.com/document/product/1729](https://cloud.tencent.com/document/product/1729)
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| 1,952,005,615 |
PR_kwDOIPDwls5dQu8U
| 12,021 |
Add example of retriever usage with SingleStoreDB vector store
|
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[] | null | 1 | 2023-10-19T11:41:43 | 2023-10-19T13:52:57 | 2023-10-19T13:48:36 |
CONTRIBUTOR
| null |
Added a notebook with examples of the creation of a retriever from the SingleStoreDB vector store, and further usage.
|
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I_kwDOIPDwls50VXZv
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Issue:The completion operation does not work with the specified model for azure openai api
|
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[] | null | 5 | 2023-10-19T09:49:05 | 2023-10-24T12:32:39 | null |
NONE
| null |
### Issue you'd like to raise.
```
import os
from langchain.llms import AzureOpenAI
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-07-01-preview"
os.environ["OPENAI_API_BASE"] = "https://myurlid.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "my key"
llm = AzureOpenAI(
deployment_name="gpt35",
openai_api_version="2023-07-01-preview",
)
# Run the LLM
llm("Tell me a joke")
print(llm)
```
this is my demo code, try run it show error info:
```
$ /bin/python /home/good/langchain/cookbook/test/azure_hello.py
Traceback (most recent call last):
File "/home/good/langchain/cookbook/test/azure_hello.py", line 22, in <module>
llm("Tell me a joke")
File "/home/good/.local/lib/python3.9/site-packages/langchain/llms/base.py", line 866, in __call__
self.generate(
File "/home/good/.local/lib/python3.9/site-packages/langchain/llms/base.py", line 646, in generate
output = self._generate_helper(
File "/home/good/.local/lib/python3.9/site-packages/langchain/llms/base.py", line 534, in _generate_helper
raise e
File "/home/good/.local/lib/python3.9/site-packages/langchain/llms/base.py", line 521, in _generate_helper
self._generate(
File "/home/good/.local/lib/python3.9/site-packages/langchain/llms/openai.py", line 401, in _generate
response = completion_with_retry(
File "/home/good/.local/lib/python3.9/site-packages/langchain/llms/openai.py", line 115, in completion_with_retry
return _completion_with_retry(**kwargs)
File "/home/good/.local/lib/python3.9/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/home/good/.local/lib/python3.9/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/home/good/.local/lib/python3.9/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
File "/usr/lib64/python3.9/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/usr/lib64/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/home/good/.local/lib/python3.9/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/home/good/.local/lib/python3.9/site-packages/langchain/llms/openai.py", line 113, in _completion_with_retry
return llm.client.create(**kwargs)
File "/home/good/.local/lib/python3.9/site-packages/openai/api_resources/completion.py", line 25, in create
return super().create(*args, **kwargs)
File "/home/good/.local/lib/python3.9/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 155, in create
response, _, api_key = requestor.request(
File "/home/good/.local/lib/python3.9/site-packages/openai/api_requestor.py", line 299, in request
resp, got_stream = self._interpret_response(result, stream)
File "/home/good/.local/lib/python3.9/site-packages/openai/api_requestor.py", line 710, in _interpret_response
self._interpret_response_line(
File "/home/good/.local/lib/python3.9/site-packages/openai/api_requestor.py", line 775, in _interpret_response_line
raise self.handle_error_response(
openai.error.InvalidRequestError: The completion operation does not work with the specified model, gpt-35-turbo. Please choose different model and try again. You can learn more about which models can be used with each operation here: https://go.microsoft.com/fwlink/?linkid=2197993.
```
$ python -V
Python 3.9.9
I'm sure my Azure key and other information are correct, because they work normally elsewhere.
### Suggestion:
_No response_
|
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| 1,951,581,376 |
I_kwDOIPDwls50UsTA
| 12,018 |
self._scrape in lazy_load method is not taken any parameters except path given instantiating WebBaseLoader
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open
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[] | null | 1 | 2023-10-19T08:23:38 | 2023-10-19T08:30:15 | null |
NONE
| null |
### System Info
langchain 0.0.312
Python 3.11.6
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from bs4 import SoupStrainer
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import CharacterTextSplitter
def load_html():
only_body = SoupStrainer('body')
loader = WebBaseLoader(['https://example.com/'], bs_kwargs={'parse_only': only_body})
docs = loader.load()
text_splitter = CharacterTextSplitter(
separator = "\n",
chunk_size = 300,
chunk_overlap = 50,
length_function = len,
)
print(text_splitter.split_documents(docs))
# -> get all texts in html, not filtered by bs_kwargs passed in WebBaseLoader
```
### Expected behavior
expected filtered texts passed by parse_only in bs_kwargs when instantiate WebBaseLoader.
https://github.com/langchain-ai/langchain/blob/12f8e87a0e89a8ff50fc7dbab612ac6770f3d258/libs/langchain/langchain/document_loaders/web_base.py#L245
in lazy_load method, self._scrape is called with path but not other parameters
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| 1,951,580,025 |
PR_kwDOIPDwls5dPR7u
| 12,017 |
Interntional Talk Like a Pirate Day is September 19
|
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[] | null | 3 | 2023-10-19T08:22:54 | 2023-10-19T19:29:54 | 2023-10-19T19:29:53 |
NONE
| null |
Interntional Talk Like a Pirate Day is September 19.
There is a concern of causing confusion for prompting beginner.
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Interntional Talk Like a Pirate Day is September 19
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PR_kwDOIPDwls5dO1dC
| 12,015 |
Fixed some grammatical and Exception types issues
|
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CONTRIBUTOR
| null |
Fixed some grammatical issues and Exception types.
@baskaryan , @eyurtsev
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PR_kwDOIPDwls5dOx2J
| 12,014 |
fix: superfluous List Parser doc
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[] | null | 3 | 2023-10-19T06:57:21 | 2023-10-19T07:14:51 | 2023-10-19T07:14:38 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** This PR fixes the superfluous [List Parser documentation](https://python.langchain.com/docs/modules/model_io/output_parsers/comma_separated) that unnecessary modules are imported inside the example,
- **Issue:** N/A,
- **Dependencies:** N/A,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** @dosuken123
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
|
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I_kwDOIPDwls50T2S9
| 12,013 |
Issue: How to Automatically Generate Embeddings for Updated Documents in a Confluence Space and Enable Real-Time Question Answering on the Updated Data?
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[] | null | 1 | 2023-10-19T06:09:01 | 2023-10-19T06:21:21 | null |
NONE
| null |
### Issue you'd like to raise.
I'm currently working on accessing a Confluence space using Langchain and performing question answering on its data. The embeddings of this data are stored in a Chromadb vector database once I provide user name,API keyand Space key.
However, I'm looking for a way to automatically generate embeddings for any documents that change in real-time within the Confluence space and enable real-time question answering on the updated data. Any suggestions or solutions on how to achieve this would be greatly appreciated!
### Suggestion:
_No response_
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| 12,012 |
Issue:doubt about generating embeddings for both PDF documents and Confluence content and storing them in a single 'embeddings' folder
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NONE
| null |
### Issue you'd like to raise.
**Below code is for generating embeddings from pdf**
loader = PyPDFLoader(f"{file_path}")
document = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents=document)
embedding = OpenAIEmbeddings()
**Below code is for generating embeddings from confluence**
embedding = OpenAIEmbeddings()
loader = ConfluenceLoader(
url=confluence_url,
username=username,
api_key=api_key
)
for space_key in space_key:
documents.extend(loader.load(space_key=space_key,limit=100))
# Split the texts
text_splitter = CharacterTextSplitter(chunk_size=6000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=10, encoding_name="cl100k_base")
texts = text_splitter.split_documents(texts)
I would like to inquire about the feasibility of generating embeddings for both PDF documents and Confluence content and storing them in a single 'embeddings' folder. This would allow us to have the flexibility to perform question answering from either Confluence or multiple PDF sources without switching between different folders. Can you provide guidance on how to achieve this integrated storage approach?
### Suggestion:
_No response_
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PR_kwDOIPDwls5dOcnv
| 12,011 |
Integrate E2B's data analysis/code interpreter
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[] | null | 3 | 2023-10-19T05:52:58 | 2023-10-24T23:04:03 | 2023-10-24T23:04:02 |
CONTRIBUTOR
| null |
This PR adds a data [E2B's](https://e2b.dev/) analysis/code interpreter sandbox as a tool
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TypeError: UnstructuredYoloXModel.initialize() got an unexpected keyword argument 'extract_images_in_pdf' while running Semi_structured_multi_modal_RAG_LLaMA2.ipynb
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open
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[] | null | 8 | 2023-10-19T05:14:22 | 2023-10-24T12:45:42 | null |
NONE
| null |
### System Info
Hi, I am facing an issue when attempting to run the "Semi_structured_multi_modal_RAG_LLaMA2.ipynb" notebook from the cookbook.

**Environment Details**
Langchain Version: 0.0.317
I would appreciate any assistance in resolving this issue. Thank you.
### 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
TypeError: UnstructuredYoloXModel.initialize() got an unexpected keyword argument 'extract_images_in_pdf'
### Expected behavior
The notebook should run without any issues and produce the expected output as documented in the cookbook
|
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| 1,951,202,063 |
PR_kwDOIPDwls5dOARr
| 12,009 |
fix typo in multi_language.ipynb
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[] | null | 2 | 2023-10-19T04:48:13 | 2023-10-19T06:33:26 | 2023-10-19T06:33:26 |
CONTRIBUTOR
| null |
exprience -> experience
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| 1,951,178,914 |
I_kwDOIPDwls50TKCi
| 12,008 |
KayAiRetriever: without metadata filtering, wrong results
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[] | null | 8 | 2023-10-19T04:29:06 | 2023-10-24T15:12:17 | 2023-10-24T15:12:17 |
NONE
| null |
### System Info
Hello! Even though [API](https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.kay.KayAiRetriever.html) mentions a metadata param, it's not found in [code](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/retrievers/kay.py#L9).
Without metadata filtering, querying “Tell me about the returns of Palantir Technologies Inc.” returns docs with 'company_name': 'ETSY INC'.
Thank you
### Who can help?
@eyurtsev?
### 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
from langchain.retrievers import KayAiRetriever
retriever = KayAiRetriever.create(dataset_id="company", data_types=["10-K"], num_contexts=1)
retriever.get_relevant_documents(“Tell me about the returns of Palantir Technologies Inc.?")
### Expected behavior
[Document(page_content="Company Name: ETSY INC \n Company Industry: SERVICES-BUSINESS SERVICES, NEC \n Form Title: 10-K 2020-FY \n Form Section: Risk Factors \n Text: Any events causing ... abroad.", metadata={'chunk_type': 'text', 'chunk_years_mentioned': [], 'company_name': 'PALANTIR TECHNOLOGIES INC', 'company_sic_code_description': 'SERVICES-BUSINESS SERVICES, NEC', 'data_source': '10-K', 'data_source_link': 'https://www.sec.gov/Archives/edgar/data/1370637/000137063721000012', 'data_source_publish_date': '2020-01-01T00:00:00Z', 'data_source_uid': '0001370637-21-000012', 'title': 'ETSY INC | 10-K 2020-FY '})]
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| 1,951,166,863 |
PR_kwDOIPDwls5dN4s1
| 12,007 |
LLaMA2 SQL cookbook clean
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COLLABORATOR
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| 1,951,117,494 |
I_kwDOIPDwls50S7C2
| 12,005 |
langchain answers change drastically
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[] | null | 9 | 2023-10-19T03:18:24 | 2023-10-19T19:24:59 | null |
NONE
| null |
i have 1 folder called with data, in it there are 2 .txt files which are obama.txt and trump.txt, each file contains summary of each person from wikipedia and on the root of the folder, i have anthropic.py and below is the code
```
from langchain.document_loaders import DirectoryLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.embeddings import BedrockEmbeddings
from langchain.llms.bedrock import Bedrock
boto3_bedrock = boto3.client("bedrock-runtime")
llm = Bedrock(
model_id="anthropic.claude-v2",
client=boto3_bedrock,
model_kwargs={"max_tokens_to_sample": 200},
)
bedrock_embeddings = BedrockEmbeddings(
model_id="amazon.titan-embed-text-v1", client=boto3_bedrock
)
loader = DirectoryLoader("./data/", glob="*.txt")
index = VectorstoreIndexCreator(embedding=bedrock_embeddings).from_loaders([loader])
print(index.query("who is george washington", llm=llm))
```
as you can see i am using anthropic.claude-v2 LLM, and as for the query, i asked "who is george washington." during my first run, i got a response that describes who is george washington which shouldnt have happened because the code says to look for context provided only in obama.txt and trump.txt and george washington is not mentioned in either of them. However, when i re-run the code for the second time with **no changes** anywhere, i got a response saying that it does not know who is george washington which is what should have happened. Why is this the case? Why the answers are different drastically when i run the code with 0 change? I attached a screenshot of the terminal output

### Suggestion:
_No response_
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| 1,951,079,170 |
I_kwDOIPDwls50SxsC
| 12,004 |
Is there any way to stream output for VLLM and Together ai?
|
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[] | null | 1 | 2023-10-19T02:37:53 | 2023-10-19T02:47:12 | null |
NONE
| null |
### Issue you'd like to raise.
I have checked the documentation, although both are supported in langchain, but I could not find a way for streaming output.
### Suggestion:
_No response_
|
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| 1,951,017,778 |
PR_kwDOIPDwls5dNYd-
| 12,003 |
Fix Fireworks Callbacks
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[] | null | 2 | 2023-10-19T01:30:36 | 2023-10-19T06:33:10 | 2023-10-19T06:33:09 |
COLLABORATOR
| null |
I may be missing something but it seems like we inappropriately overrode the 'stream()' method, losing callbacks in the process. I don't think (?) it gave us anything in this case to customize it here?
See new trace:
https://smith.langchain.com/public/fbb82825-3a16-446b-8207-35622358db3b/r
and confirmed it streams.
Also fixes the stopwords issues from #12000
|
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| 1,951,006,205 |
I_kwDOIPDwls50Sf39
| 12,002 |
MultiRetrievalQAChain return source documents
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[] | null | 2 | 2023-10-19T01:16:15 | 2023-10-24T02:05:32 | null |
NONE
| null |
hi team,
Can I return source documents when using MultiRetrievalQAChain?
I want to fetch the metadata of source.
thx
|
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I_kwDOIPDwls50Sdkk
| 12,001 |
Overriding VectorstoreIndexCreator() embedding
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[] | null | 4 | 2023-10-19T01:04:25 | 2023-10-19T03:07:29 | 2023-10-19T03:07:29 |
NONE
| null |
This is the default class of VectorstoreIndexCreator
```
class VectorstoreIndexCreator(
*,
vectorstore_cls: type[VectorStore] = Chroma,
embedding: Embeddings = OpenAIEmbeddings,
text_splitter: TextSplitter = _get_default_text_splitter,
vectorstore_kwargs: dict = dict
)
```
the default is to use OpenAIEmbeddings as its embedding. What i'm trying to do is to use BedrockEmbeddings, below is my code
```
loaders = TextLoader('data.txt')
index = VectorstoreIndexCreator(embedding=BedrockEmbeddings).from_loaders([loaders])
```
The error that i got
```
VectorstoreIndexCreator(embedding=BedrockEmbeddings).from_loaders([loaders])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for VectorstoreIndexCreator
embedding
instance of Embeddings expected (type=type_error.arbitrary_type; expected_arbitrary_type=Embeddings)
```
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PR_kwDOIPDwls5dNPxi
| 12,000 |
Fireworks: plumb stop words through for streaming
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[] | null | 2 | 2023-10-19T00:43:00 | 2023-10-19T03:24:25 | 2023-10-19T03:24:10 |
CONTRIBUTOR
| null |
Fireworks was not plumbing through stop words for streaming (was for non streaming). Note that there is this other PR for the same llm that does not solve this problem and also seems to be missing stop words in some cases (I will leave a comment in the PR): https://github.com/langchain-ai/langchain/pull/11489
For now, this is a pretty bad issue preventing us from using non-chat models in fireworks so I appreciate a quick merge for this one. We are not blocked since we temporarily forked langchain but would like to unfork quickly.
Requesting review: @baskaryan et al
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PR_kwDOIPDwls5dNNIP
| 11,999 |
a bug fix for sagemaker endpoint streaming.
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[] | null | 3 | 2023-10-19T00:31:01 | 2023-10-21T23:56:05 | 2023-10-21T23:29:14 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** bug fix for sagemakerendpoint streaming.
- **Issue:** the issue # it fixes sagemakerendpoint streaming modified line 341-350 to reflect. I've discovered a bug in the code from the recent sagemaker endpoint streaming.
Fixed it based on AWS code found here (https://aws.amazon.com/blogs/machine-learning/elevating-the-generative-ai-experience-introducing-streaming-support-in-amazon-sagemaker-hosting/)
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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Streaming not working when routing between Runnables in LCEL
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NONE
| null |
### System Info
langchain_version: "0.0.306"
library: "langchain"
library_version: "0.0.306"
platform: "Windows-10-10.0.22621-SP0"
py_implementation: "CPython"
runtime: "python"
runtime_version: "3.9.0rc2"
sdk_version: "0.0.41"
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Streaming the response of a simple route created among 2 Runnables doesn't work. When I stream each Runnable independelty, it streams perfectly.
When I create a route for those Runnables to choose which one of them should be the next step, it doesn't stream the result.
Here's the example:
```
# ----------------- Runnable 1 -----------------
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
def _combine_documents(docs, document_prompt = DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"):
doc_strings = [format_document(doc, document_prompt) for doc in docs]
return document_separator.join(doc_strings)
def _format_chat_history(chat_history: List[Tuple]) -> str:
buffer = ""
for dialogue_turn in chat_history:
human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
return buffer
_inputs = RunnableMap({
"standalone_question": {
"question": lambda x: x["question"],
"chat_history": lambda x: _format_chat_history(x['chat_history'])
} | PromptFactory.CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0, streaming=True) | StrOutputParser(),
})
_context = {
"context": itemgetter("standalone_question") | vector_retriever | _combine_documents,
"question": lambda x: x["standalone_question"]
}
Runnable1 = _inputs | _context | PromptFactory.PROMPT | ChatOpenAI(temperature=0, streaming=True)
# ----------------- Stream the response: -----------------
for s in Runnable1.stream({"question": "Hello!", "chat_history": []}):
print(s, end="", flush=True)
```
The above stream works
```
# ----------------- Runnable 2 -----------------
Runnable2 = RunnableMap({
"response": {
"question": lambda x: x["question"],
"chat_history": lambda x: _format_chat_history(x['chat_history']),
"context": lambda x: QueriesContext.get_result()
} | PromptTemplate.from_template(PromptFactory.followup_context_template) | ChatOpenAI(temperature=0, streaming=True) | StrOutputParser(),
})
# ----------------- Stream the response: -----------------
for s in Runnable2.stream({"question": "Hello!", "chat_history": [], "context": ['initialvalue']}):
print(s, end="", flush=True)
```
The above stream also works
```
def get_result(text):
return ["newvalue"]
router_chain = PromptTemplate.from_template(PromptFactory.router_template_test) | ChatOpenAI(temperature=0, streaming=True) | StrOutputParser()
def route(info):
if "runnable1" in info["topic"].lower():
return Runnable1
elif "runnable2" in info["topic"].lower():
return Runnable2
else:
raise Exception("Invalid topic")
full_runnable_router_chain = {
"topic": router_chain,
"question": lambda x: x["question"],
"chat_history": lambda x: x["chat_history"],
"context": lambda x: x["context"]
} | RunnableLambda(route)
# ----------------- Stream the response: -----------------
for s in full_runnable_router_chain.stream({"question": "Hello!", "chat_history": [], "context": ['initialvalue']}):
print(s.content, end="", flush=True)
```
The above stream doesn't stream
### Expected behavior
Stream from "full_runnable_router_chain"
|
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Harrison/lcel configuration
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PR_kwDOIPDwls5dMQ7Q
| 11,995 |
Update LLaMA2 SQL notebook
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| 11,994 |
Add Alpha Vantage API Tool
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NONE
| null |
### Feature request
We propose the integration of a new tool into Langchain that will provide comprehensive support for queries on the AlphaVantage Trading API. AlphaVantage offers a wide range of financial data and services, and this integration will enhance Langchain's capabilities for financial data analysis.
Here is the list of AlphaVantage APIs that will be integrated into the new tool:
- [TIME_SERIES_DAILY](https://www.alphavantage.co/documentation/#daily)
- [TIME_SERIES_WEEKLY](https://www.alphavantage.co/documentation/#weekly)
- [Quote Endpoint](https://www.alphavantage.co/documentation/#latestprice)
- [Search Endpoint](https://www.alphavantage.co/documentation/#symbolsearch)
- [Market News & Sentiment](https://www.alphavantage.co/documentation/#news-sentiment)
- [Top Gainers, Losers, and Most Actively Traded Tickers (US Market)](https://www.alphavantage.co/documentation/#gainer-loser)
### Motivation
The integration of AlphaVantage Trading API support in Langchain will provide users with access to a wealth of financial data, enabling them to perform in-depth analysis, develop trading strategies, and make informed financial decisions, all with real-time information
### Your contribution
I am a University of Toronto Student, working in a group and plan to submit a PR for this issue in November
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| 11,993 |
caps not title for cookbooks descriptions
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add cookbook readme
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PR_kwDOIPDwls5dLXhp
| 11,991 |
[Draft] - Including cookbook for SQL + RAG
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Add more security notes
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Add more security notes
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Add security notes to agent toolkits
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[] | null | 1 | 2023-10-18T18:05:34 | 2023-10-23T14:04:59 | 2023-10-18T18:36:30 |
COLLABORATOR
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Add more security notes to agent toolkits.
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PR_kwDOIPDwls5dKkN3
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docstrings added
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[] | null | 2 | 2023-10-18T16:36:43 | 2023-10-18T17:06:39 | 2023-10-18T17:05:50 |
COLLABORATOR
| null |
Added docstrings. Some docsctrings formatting.
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PR_kwDOIPDwls5dKiWf
| 11,987 |
added rrf argument in ApproxRetrievalStrategy class __init__()
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[] | null | 7 | 2023-10-18T16:32:51 | 2023-10-27T18:53:20 | 2023-10-27T18:53:19 |
CONTRIBUTOR
| null |
- **Description: To handle the hybrid search with RRF(Reciprocal Rank Fusion) in the Elasticsearch, rrf argument was added for adjusting 'rank_constant' and 'window_size' to combine multiple result sets with different relevance indicators into a single result set. (ref: https://www.elastic.co/kr/blog/whats-new-elastic-enterprise-search-8-9-0),
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** No dependencies changed,
- **Tag maintainer:** @baskaryan,
Nice to meet you,
I'm a newbie for contributions and it's my first PR.
I only changed the langchain/vectorstores/elasticsearch.py file.
I did make format&lint
I got this message,
```shell
make lint_diff
./scripts/check_pydantic.sh .
./scripts/check_imports.sh
poetry run ruff .
[ "langchain/vectorstores/elasticsearch.py" = "" ] || poetry run black langchain/vectorstores/elasticsearch.py --check
All done! ✨ 🍰 ✨
1 file would be left unchanged.
[ "langchain/vectorstores/elasticsearch.py" = "" ] || poetry run mypy langchain/vectorstores/elasticsearch.py
langchain/__init__.py: error: Source file found twice under different module names: "mvp.nlp.langchain.libs.langchain.langchain" and "langchain"
Found 1 error in 1 file (errors prevented further checking)
make: *** [lint_diff] Error 2
```
Thank you
|
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PR_kwDOIPDwls5dKdZW
| 11,986 |
bump 317
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[] | null | 1 | 2023-10-18T16:24:47 | 2023-10-18T16:25:19 | 2023-10-18T16:25:18 |
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| 1,950,139,790 |
I_kwDOIPDwls50PMWO
| 11,985 |
Issue: Which memory type i need to use for db-backed history
|
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[] | null | 5 | 2023-10-18T16:15:42 | 2023-10-19T15:22:23 | null |
NONE
| null |
### Issue you'd like to raise.
I want to interact with my database so I'm using SQLDatabaseChain and SQL Agent to convert natural language query to sql query then execute on the database.
So I want to save the chat history here so that if a user asks anything related previous question/answer it should pick and then answer.
So my doubt is here what memory type I need to use.
Can you show me code example.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5dKUtT
| 11,984 |
fix chroma from_texts bug
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I_kwDOIPDwls50O7yk
| 11,983 |
ParentDocumentRetriever is incompatible with MultiVectorRetriever
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[] | null | 4 | 2023-10-18T15:48:45 | 2023-11-29T09:34:15 | null |
CONTRIBUTOR
| null |
### System Info
**Description:**
It's not possible to use the ParentDocumentRetriever and MultiVectorRetriever at the same time.
But, it's a good idea to generate multiple vector for one fragment, then manage the life cycle of all vertions the ParentDocumentRetriever.
I think, the `MultiVectorRetriever` is not a good idea. I think it's better to create a MultiVectorStore to be compatible with all vectorstore interface. Then, it's possible to get a Retriever with vs.as_retriever()
I started to implement this scenario, but, because all my pull-request were never reads, I prefer not to submit any code and to maintain it endlessly.
### Who can help?
@eyurtsev
### 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
MultiVectorRetriever need a VectorStore.
But MultiVectorRetriever IS NOT a VectorStore.
### Expected behavior
An API to create multiple vectors for the same fragment, and, at the same time, a solution to manage all the vectors with the lifecycle of the original document.
If I remove/update the original document, all the associated vectors must be removed/updated.
|
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| 1,950,052,502 |
I_kwDOIPDwls50O3CW
| 11,982 |
ParentDocumentRetriever: parent_splitter and ids are incompatible
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CONTRIBUTOR
| null |
### System Info
**description**
With parent_splitter, it's not possible to know the number of IDs before the split.
So, it's not possible to know the ID of each fragment.
Then, it's not possible to manage the life cycle of the fragment because it's impossible to know the list of IDs associated with the original big document.
### Who can help?
@eyurtsev
### 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
```
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import ParentDocumentRetriever
from langchain.schema import Document
from langchain.storage import InMemoryStore
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores.chroma import Chroma
vectorstore = Chroma(
collection_name="full_documents",
embedding_function=OpenAIEmbeddings()
)
store = InMemoryStore()
docs = [Document(page_content=txt, metadata={"id": id}) for txt, id in [("aaaaaa", 1), ("bbbbbb", 2)]]
ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
id_key="id",
parent_splitter=RecursiveCharacterTextSplitter(
chunk_size = 2,
chunk_overlap = 0,
length_function = len,
add_start_index = True,
),
child_splitter=RecursiveCharacterTextSplitter(
chunk_size = 1,
chunk_overlap = 0,
length_function = len,
add_start_index = True,
),
).add_documents(docs,ids=[doc.metadata["id"] for doc in docs])
```
Produce:
```
ValueError: Got uneven list of documents and ids. If `ids` is provided, should be same length as `documents`.
```
### Expected behavior
No error.
|
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PR_kwDOIPDwls5dJiiE
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bearly api key docs
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I_kwDOIPDwls50ON5S
| 11,980 |
how to connect to sql view in database
|
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[] | null | 9 | 2023-10-18T14:22:33 | 2024-01-15T17:59:07 | null |
NONE
| null |
### System Info
latest version of langchain. python=3.11.4
### 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
How to connect to SQL view in the database, instead of using the all the tables from the DB, as the DB is huge(6Gb), I'm unable to get any out a correct output, I'm getting token error and when it is performing any join its unable to fetch a correct answer, but when I using a selected table its working efficiently in terms of accuracy and time taking.
Can I get a appropriate method by which I can deal with the huge data without getting token or parser errors.
and how to work with the SQL views in the database.
here is the code for the connection string I have tried which includes the tables and its working fine,
from urllib.parse import quote_plus
driver = 'ODBC Driver 17 for SQL Server'
host = '########'
user = '#####'
database = 'HR_Git'
password = '#########'
encoded_password = quote_plus(password)
db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{encoded_password}@{host}/{database}?driver={quote_plus(driver)}", include_tables = ['EGV_emp_departments'], sample_rows_in_table_info=2)
Thank you.
### Expected behavior
How to work with the SQL views inside the database and way to avoid token and parser error.
|
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| 1,949,879,048 |
PR_kwDOIPDwls5dJgwx
| 11,979 |
DeepSparse LangChain
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Tests and documentation for DeepSparse LangChain integration
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| 1,949,786,869 |
I_kwDOIPDwls50N2L1
| 11,978 |
UnstructuredEmailLoader just returning the first element
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[] | null | 4 | 2023-10-18T13:40:37 | 2023-10-19T06:05:51 | 2023-10-19T06:05:51 |
NONE
| null |
# Issue:
UnstructuredEmailLoader only returning first or no `element` of the mail irrespective of the `mode`.
### System Info
langchain version: 0.0.316
unstructured version: 0.10.18
### Who can help?
@eyurtsev
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is the [link](https://colab.research.google.com/drive/105l79buRBNsWelBIec0E1yUaowyfbVju?usp=sharing) to the code to regenerate the reponse
### Expected behavior
It should have returned the email's message text as the output with it's metadata
|
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| 1,949,759,853 |
I_kwDOIPDwls50Nvlt
| 11,977 |
Specific word crashing LLMChain
|
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[] | null | 1 | 2023-10-18T13:27:44 | 2023-10-18T13:33:50 | null |
NONE
| null |
### Issue you'd like to raise.
I am using the LLMChain with the ConversationBufferMemory and it works pretty well. There is a case where the chain throws this exception: ValueError: unexpected '{' in field name. This happens only when I use the word "field" in my question. The code I have written is down below:
FUNCTION TO RETRIEVE LLMCHAIN:
```python
def get_llm_chain(OPENAI_API_KEY, context):
global glob_llm_chain
if glob_llm_chain is None:
llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY, model_name="gpt-4")
sysprompttemplate = PromptTemplate.from_template("""Given the following extracted parts of a long document and a question, create a final answer in english with references SOURCES.
If you dont know the answer, just say that you don't know. Dont try to make up an answer. ALWAYS return a SOURCES part in your answer.
You also have the ability to remember the previous conversation you had with the human. Answer the question based on context provided.
============
{context}
============""").format(context=context)
messages = [ SystemMessagePromptTemplate.from_template(sysprompttemplate), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{question}")]
prompt = ChatPromptTemplate.from_messages(messages=messages)
MEMORY = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm_chain = LLMChain(prompt=prompt,
llm=llm,
verbose=True,
memory=MEMORY)
glob_llm_chain = llm_chain
return glob_llm_chain
else:
return glob_llm_chain
```
FUNCTION THAT RETURNS THE ANSWER
```python
def get_answer(question):
embeddings = OpenAIEmbeddings()
milvus_connection_properties = get_milvus_connection_properties()
vector_store = retrieve_colection(embeddings, "langchain", milvus_connection_properties)
docs = vector_store.similarity_search(question)
context = get_full_context(docs)
llm_chain = get_llm_chain(OPENAI_API_KEY, context)
#result = llm_chain.predict(input={"question": question, "context":context})
result = llm_chain({"question":question})
return result
```
So, for example when I ask: What is a Field Dependency Map? I get:
```
ERROR:root:unexpected '{' in field name
Traceback (most recent call last):
File "/home/ardit/projects/evolutivoAI/langchain/app.py", line 29, in <module>
st.session_state.results = get_answer(question)
^^^^^^^^^^^^^^^^^^^^
File "/home/ardit/projects/evolutivoAI/langchain/scripts/retrieval.py", line 68, in get_answer
llm_chain = get_llm_chain(OPENAI_API_KEY, context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ardit/projects/evolutivoAI/langchain/scripts/retrieval.py", line 41, in get_llm_chain
messages = [ SystemMessagePromptTemplate.from_template(sysprompttemplate), MessagesPlaceholder(variable_name="chat_history"), HumanMessagePromptTemplate.from_template("{question}")]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ardit/projects/evolutivoAI/langchain/.venv/lib/python3.11/site-packages/langchain/prompts/chat.py", line 151, in from_template
prompt = PromptTemplate.from_template(template, template_format=template_format)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ardit/projects/evolutivoAI/langchain/.venv/lib/python3.11/site-packages/langchain/prompts/prompt.py", line 204, in from_template
input_variables = {
^
File "/home/ardit/projects/evolutivoAI/langchain/.venv/lib/python3.11/site-packages/langchain/prompts/prompt.py", line 204, in <setcomp>
input_variables = {
^
ValueError: unexpected '{' in field name
```
Any suggestion why this happens?
|
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| 1,949,413,017 |
I_kwDOIPDwls50Ma6Z
| 11,976 |
Issue: Doubt about Confluence Loader
|
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[] | null | 1 | 2023-10-18T10:32:57 | 2023-10-18T13:00:26 | 2023-10-18T13:00:26 |
NONE
| null |
### Issue you'd like to raise.
Hi!
I was taking a look to the Confluence integration at https://python.langchain.com/docs/integrations/document_loaders/confluence , and in our team we have the following doubt:
from langchain.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(
url="https://yoursite.atlassian.com/wiki",
username="me",
api_key="12345"
)
documents = loader.load(space_key="SPACE", include_attachments=True, limit=100, max_pages=800)
Can we fetch multiple space and generate embeddings at a time and do question answering from either of the space fetched by confluence loader?
### Suggestion:
_No response_
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ValueError: Missing some input keys: {'chat_history'} when adding memory to GraphCypherQAChain
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[] | null | 6 | 2023-10-18T10:23:57 | 2023-11-06T14:46:44 | 2023-11-06T14:46:44 |
NONE
| null |
### System Info
Langchain version: v0.0.316
I am following the langchain documentation to add memory to the chat with LLMChain: https://python.langchain.com/docs/modules/memory/adding_memory
```py
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
CYPHER_GENERATION_TEMPLATE = """Task: Generate Cypher statement to query a graph database.
Instructions: ...
Schema:
{schema}
The question is:
{question}
Chat History:
{chat_history}
"""
CYPHER_GENERATION_PROMPT = PromptTemplate(
input_variables=["schema", "question", "chat_history"],
template=CYPHER_GENERATION_TEMPLATE,
)
chain = GraphCypherQAChain.from_llm(
AzureChatOpenAI(
deployment_name=OPENAI_DEPLOYMENT_NAME,
model_name=OPENAI_MODEL_NAME,
openai_api_base=OPENAI_API_BASE,
openai_api_version=OPENAI_API_VERSION,
openai_api_key=OPENAI_API_KEY,
openai_api_type=OPENAI_API_TYPE,
temperature=0
),
graph=graph, verbose=True,
return_intermediate_steps=False,
cypher_prompt=CYPHER_GENERATION_PROMPT,
include_types=property_include_list,
memory=memory,
)
return chain.run(question)
```
So, when I call the `chain.run()` I get the error:
```bash
> Entering new GraphCypherQAChain chain...
Traceback (most recent call last):
File "/home/sudobhat/workspaces/llm/openai-chatgpt-sample-code/neo4j-use-case/neo4j_query_filtered_schema.py", line 171, in <module>
answer = get_openai_answer(question)
File "/home/sudobhat/workspaces/llm/openai-chatgpt-sample-code/neo4j-use-case/neo4j_query_filtered_schema.py", line 162, in get_openai_answer
return chain.run(question)
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 501, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
raise e
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 300, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/graph_qa/cypher.py", line 185, in _call
generated_cypher = self.cypher_generation_chain.run(
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 501, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
inputs = self.prep_inputs(inputs)
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 439, in prep_inputs
self._validate_inputs(inputs)
File "/home/sudobhat/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 191, in _validate_inputs
raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {'chat_history'}
```
This is being done almost exactly as in the documentation. Is this a bug or am I missing something?
I also tried an approach with partial variables like this, by looking at the answer in this issue https://github.com/langchain-ai/langchain/issues/8746:
```
CYPHER_GENERATION_PROMPT = PromptTemplate(
input_variables=["schema", "question"],
template=CYPHER_GENERATION_TEMPLATE,
partial_variables={"chat_history": chat_history}
)
```
this does not throw any error, but when I print the final prompt, there is nothing in the chat history.
Also, it seems to work for normal LLmChain, but not GraphCypherQAChain
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Run the code provided in the documentation: https://python.langchain.com/docs/modules/memory/adding_memory, but for GraphCypherQAChain
### Expected behavior
The expected behavior is no error thrown like: ValueError: Missing some input keys: {'chat_history'} when chat_history is passed in the prompt template.
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| 1,949,335,904 |
I_kwDOIPDwls50MIFg
| 11,974 |
SeleniumURLLoader in Docker Container
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[] | null | 5 | 2023-10-18T09:52:41 | 2023-11-29T12:12:51 | null |
NONE
| null |
### Issue you'd like to raise.
Hi developers, I am trying to run SeleniumURLLoader inside a Docker container.
When I tried to do a web-scraping on the URLs I am met with this error:
`ERROR MESSAGE: Message: Service /root/.cache/selenium/chromedriver/linux64/118.0.5993.70/chromedriver unexpectedly exited. Status code was: 127`
I tried running `update apt-get` and installed all the necessary dependencies, as well as running this command in the Dockerfile:
`RUN chmod +x /root/.cache/selenium/chromedriver/linux64/118.0.5993.70/chromedriver` but I was met with this error:
`chmod: cannot access '/root/.cache/selenium/chromedriver/linux64/118.0.5993.70/chromedriver': No such file or directory`
Any advice would be greatly appreciated.
### Suggestion:
_No response_
|
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| 1,949,323,320 |
PR_kwDOIPDwls5dHm4U
| 11,973 |
Make prompt validation opt-in
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COLLABORATOR
| null |
By default replace input_variables with the correct value
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5dHTVs
| 11,972 |
Add some transformers and a transformers pipeline
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[] | null | 3 | 2023-10-18T09:01:24 | 2023-11-28T07:12:25 | 2023-11-28T07:12:25 |
CONTRIBUTOR
| null |
**Description:**
As with `document_compressors`, it seems important to provide a pipeline for transformers.
The algorithm is different of `DocumentCompressorPipeline`. The idea is to have several transformers to apply to the same document, in order to have several embeddings for the same fragment (via a `ParentDocumentRetriever`). You therefore need to retrieve all the transformations, rather than chaining them together as `DocumentCompressorPipeline` does.
- `DocumentTransformerPipeline` is used when importing data into a vector store, to have multiple embedding for the same fragment.
- `DocumentCompressorPipelinè is used when reading documents, to filter them.
Combined with this [pull-request](https://github.com/langchain-ai/langchain/pull/11968), it's easy to chain transformations while maintaining the link with the original document.
I propose 3 more transformers, to generate a summary and ask questions about the document.
The last one is a simple transformer without transformation. A simple copy of input to output.
I must explain the usage.
1. You split a big document to fragments
2. For each fragment, you want to generate a summary
3. For each fragment, you want to generate 3 questions, and generate a document one for each question
4. Then, you want to save the original fragment, the summary and the 3 questions in a vectorstore
5. You can use `ParentDocumentRetriever` to maintain the association with the original fragment.
6. When you use the `ParentDocumentRetriever`, all the embedding from the summary, the questions or the orginal fragment can select the same fragment.
But, `BaseDocumentTransformer` invoke a list of transformer to apply. If I use it with only the `SummarizeTransformer` and `GenerateQuestionsTransformer`, the result is composed with the summary and questions. But the original fragment are not in the result.
So the ̀`CopyDocumentTransformer` can add the original fragment.
Then, with `ParentDocumentVectorStore`, you can write:
```
vs = ParentDocumentVectorStore(
vectorstore=vs,
docstore=self.docstore,
id_key=ID_KEY,
parent_transformer=RecursiveCharacterTextSplitter(),
child_transformer=DocumentTransformerPipeline(
transformers=[
GenerateQuestions.from_llm(llm),
SummarizeTransformer.from_llm(llm),
CopyDocumentTransformer(),
]
),
)
vs.add_documents(documents) # Split, summarize, ask questions and import
```
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ConversationalRetrievalChain : make the condense_question chain choose the SelfQueryRetriever kwargs
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[] | null | 3 | 2023-10-18T08:55:23 | 2023-10-18T10:12:21 | null |
NONE
| null |
### Feature request
Being able to set a SelfQueryRetriever's kwargs from the output of the Condese_question chain or any chain that runs just before.
### Motivation
The motivation behind this is enabling the ConversationalRetrievalChain based on a vectordb to tackle a big range of queries in a more specific way, for example:
- When asking about a specific topic, I want **k** kwarg to be no more than 10 and to have **lambda_mult** close to 0, meaning that documents taken from **fetch_k** should be similar.
- On the other hand when I am asking it to compare 2 entities I want **k** to be 20 for example and the **lambda_mult** to be really close to 1, meaning documents taken should have a little bit of variance
This will ensure that the passed context is good enough
### Your contribution
for now I am trying to output these variables from the condense_question sub chain, but I am not sure it's going to workout
|
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PR_kwDOIPDwls5dHM_H
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Ensure dict() does not raise not implemented error, which should instead be raised in our custom method save()
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COLLABORATOR
| null |
.dict() is a Pydantic method that cannot raise exceptions, as it is used eg. in `__eq__`
<!-- Thank you for contributing to LangChain!
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PR_kwDOIPDwls5dHJZu
| 11,969 |
Add ERNIE-Bot-4 model support for ErnieBotChat.
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[] | null | 2 | 2023-10-18T08:36:39 | 2023-10-18T21:55:29 | 2023-10-18T21:55:29 |
CONTRIBUTOR
| null |
- **Description:** According to the document https://cloud.baidu.com/doc/WENXINWORKSHOP/s/clntwmv7t, add ERNIE-Bot-4 model support for ErnieBotChat.
- **Dependencies:** Before using the ERNIE-Bot-4, you should have the model's access authority.
|
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PR_kwDOIPDwls5dHEbm
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ParentDocumentRetriever need splitter and not transformer
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CONTRIBUTOR
| null |
**Description:**
The current implementation of `ParentDocumentRetriever` need two splitter: `child_splitter` and `parent_splitter`.
But a splitter is a kind of `BaseDocumentTransformer`.
It is a limitation. If you want to apply transformations to the documents (for each fragment, generate questions, generate a summary, etc.) this is not possible.
I propose a new version of ParentDocumentRetriever (in the file parent_document_retriever_v2.py for the moment). This version waits `child_transformer` and `child_parent`. It's possible to use it with some splitter.
I must change the name of the attributs because it's now, some transformer. May be, it's possible to declare the `child_splitter` and `parent_splitter` to be compatible with the previous version. I can do it if you accept the principle of my pull-request.
The idea behind this is to improve RAGs, by increasing the versions of embeddings for each fragment.
With other pull-request, it may be possible to write:
```
vs = ParentDocumentVectorStore(
vectorstore=vs,
docstore=self.docstore,
id_key=CV_ID_KEY,
parent_transformer=RecursiveCharacterTextSplitter(),
child_transformer=DocumentTransformerPipeline(
transformers=[
GenerateQuestions.from_llm(llm),
SummarizeTransformer.from_llm(llm)
]
),
)
```
New version of parent_document_retriever to use "transformer" in place of "splitter"
**Tag maintainer:**
@baskaryan
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I_kwDOIPDwls50LXpr
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ISSUE: Not able to log CustomLLM using mlflow.pyfunc.log_model
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[] | null | 1 | 2023-10-18T08:11:09 | 2023-10-18T08:16:57 | null |
NONE
| null |
### System Info
LangChain version: 0.0.316
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.llms.base import LLM
class CustomLLM(LLM):
endpoint_name: str
token: str
@property
def _llm_type(self) -> str:
return "custom"
def _score_model(self, dataset, temperature = 0.75, max_new_tokens = 100):
url = f'https://XXXXXXXXXXX/{self.endpoint_name}/XXXXXX'
headers = {'Authorization': f'Bearer {self.token}', 'Content-Type': 'application/json'}
temp_dict = {'index': [0], 'columns': ['prompt', 'temperature', 'max_new_tokens'], 'data': [[dataset, temperature, max_new_tokens]]}
data_json = json.dumps({'dataframe_split': temp_dict}, allow_nan=True)
response = requests.request(method='POST', headers=headers, url=url, data=data_json)
return response.json()['predictions']['candidates'][0]
def _call(self, prompt: str, stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any):
if stop is not None: raise ValueError("stop kwargs are not permitted.")
return self._score_model(prompt)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"endpoint_name": self.endpoint_name, "token":self.token}
class MLflowQABot(mlflow.pyfunc.PythonModel):
def __init__(self, llm, retriever, chat_prompt):
# QABot class just call the custom LLM class and customize the output
self.qabot = QABot(llm, retriever, chat_prompt)
def predict(self, context, inputs):
questions = list(inputs['question'])
return [self.qabot.get_answer(q) for q in questions]
system_message_prompt = SystemMessagePromptTemplate.from_template(config['system_message_template'])
human_message_prompt = HumanMessagePromptTemplate.from_template(config['human_message_template'])
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# Working fine: mean able to log model when I change LLM to ChatOpenAI
# llm = ChatOpenAI(model_name=config['openai_chat_model'], temperature=config['temperature'])
llm = CustomLLM("llama2-7b", token)
model = MLflowQABot(llm, retriever, chat_prompt)
input_columns = [{"type": "string", "name": input_key} for input_key in qa_chain.input_keys]
with mlflow.start_run() as run:
mlflow_result = mlflow.pyfunc.log_model(
python_model = model,
extra_pip_requirements = ['langchain', 'tiktoken', 'openai',
'faiss-gpu', 'typing-inspect', 'typing_extensions'],
artifact_path = 'model',
#registered_model_name=config['registered_model_name'],
signature = infer_signature(input_columns, "This is prediction"))
```
### Expected behavior
### This is expected result.
``` python
(1) MLflow run
Logged [1 run](XXXXXb7d8e14c08964c988339b7c8a7) to an [experiment](XXXXXX80369415271497) in MLflow. [Learn more](https://docs.microsoft.com/azure/databricks/applications/mlflow/tracking#tracking-machine-learning-training-runs)
```
### But getting this error:
```python
---------------------------------------------------------------------------
PicklingError Traceback (most recent call last)
File <command-1869824020018208>, line 3
1 # persist model to mlflow
2 with mlflow.start_run() as run:
----> 3 mlflow_result = mlflow.pyfunc.log_model(
4 python_model = model,
5 extra_pip_requirements = ['langchain', 'tiktoken', 'openai',
6 'faiss-gpu', 'typing-inspect', 'typing_extensions'],
7 artifact_path = 'model',
8 #registered_model_name=config['registered_model_name'],
9 signature = signature)
File /databricks/python/lib/python3.10/site-packages/mlflow/pyfunc/__init__.py:1931, in log_model(artifact_path, loader_module, data_path, code_path, conda_env, python_model, artifacts, registered_model_name, signature, input_example, await_registration_for, pip_requirements, extra_pip_requirements, metadata)
1773 @format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="scikit-learn"))
1774 def log_model(
1775 artifact_path,
(...)
1788 metadata=None,
1789 ):
1790 """
1791 Log a Pyfunc model with custom inference logic and optional data dependencies as an MLflow
1792 artifact for the current run.
(...)
1929 metadata of the logged model.
1930 """
-> 1931 return Model.log(
1932 artifact_path=artifact_path,
1933 flavor=mlflow.pyfunc,
1934 loader_module=loader_module,
1935 data_path=data_path,
1936 code_path=code_path,
1937 python_model=python_model,
1938 artifacts=artifacts,
1939 conda_env=conda_env,
1940 registered_model_name=registered_model_name,
1941 signature=signature,
1942 input_example=input_example,
1943 await_registration_for=await_registration_for,
1944 pip_requirements=pip_requirements,
1945 extra_pip_requirements=extra_pip_requirements,
1946 metadata=metadata,
1947 )
File /databricks/python/lib/python3.10/site-packages/mlflow/models/model.py:572, in Model.log(cls, artifact_path, flavor, registered_model_name, await_registration_for, metadata, **kwargs)
566 if (
567 (tracking_uri == "databricks" or get_uri_scheme(tracking_uri) == "databricks")
568 and kwargs.get("signature") is None
569 and kwargs.get("input_example") is None
570 ):
571 _logger.warning(_LOG_MODEL_MISSING_SIGNATURE_WARNING)
--> 572 flavor.save_model(path=local_path, mlflow_model=mlflow_model, **kwargs)
573 mlflow.tracking.fluent.log_artifacts(local_path, mlflow_model.artifact_path)
574 try:
File /databricks/python/lib/python3.10/site-packages/mlflow/pyfunc/__init__.py:1759, in save_model(path, loader_module, data_path, code_path, conda_env, mlflow_model, python_model, artifacts, signature, input_example, pip_requirements, extra_pip_requirements, metadata, **kwargs)
1748 return _save_model_with_loader_module_and_data_path(
1749 path=path,
1750 loader_module=loader_module,
(...)
1756 extra_pip_requirements=extra_pip_requirements,
1757 )
1758 elif second_argument_set_specified:
-> 1759 return mlflow.pyfunc.model._save_model_with_class_artifacts_params(
1760 path=path,
1761 signature=signature,
1762 hints=hints,
1763 python_model=python_model,
1764 artifacts=artifacts,
1765 conda_env=conda_env,
1766 code_paths=code_path,
1767 mlflow_model=mlflow_model,
1768 pip_requirements=pip_requirements,
1769 extra_pip_requirements=extra_pip_requirements,
1770 )
File /databricks/python/lib/python3.10/site-packages/mlflow/pyfunc/model.py:189, in _save_model_with_class_artifacts_params(path, python_model, signature, hints, artifacts, conda_env, code_paths, mlflow_model, pip_requirements, extra_pip_requirements)
187 saved_python_model_subpath = "python_model.pkl"
188 with open(os.path.join(path, saved_python_model_subpath), "wb") as out:
--> 189 cloudpickle.dump(python_model, out)
190 custom_model_config_kwargs[CONFIG_KEY_PYTHON_MODEL] = saved_python_model_subpath
192 if artifacts:
File /databricks/python/lib/python3.10/site-packages/cloudpickle/cloudpickle_fast.py:57, in dump(obj, file, protocol, buffer_callback)
45 def dump(obj, file, protocol=None, buffer_callback=None):
46 """Serialize obj as bytes streamed into file
47
48 protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to
(...)
53 compatibility with older versions of Python.
54 """
55 CloudPickler(
56 file, protocol=protocol, buffer_callback=buffer_callback
---> 57 ).dump(obj)
File /databricks/python/lib/python3.10/site-packages/cloudpickle/cloudpickle_fast.py:602, in CloudPickler.dump(self, obj)
600 def dump(self, obj):
601 try:
--> 602 return Pickler.dump(self, obj)
603 except RuntimeError as e:
604 if "recursion" in e.args[0]:
PicklingError: Can't pickle <cyfunction bool_validator at 0x7f95076a4450>: it's not the same object as pydantic.validators.bool_validator
```
|
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| 1,949,124,994 |
PR_kwDOIPDwls5dG8HU
| 11,965 |
Sometime, the json generated with the llm is not correct. This pull request fix it
|
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[] | null | 2 | 2023-10-18T08:04:30 | 2023-11-10T15:48:23 | 2023-11-10T15:48:23 |
CONTRIBUTOR
| null |
** Description**
Help the llm to generate a correct json format.
This patch reduces the probability to generating this error.
*** Issue **
Sometime, the json generated with the llm is not correct. When the llm must generate a list of objects. The last item has an invalid comma.
```
{"a":["a",
"b", # Not, invalid comma at end
]
}
```
** Tag maintainer:***
@baskaryan
**Twitter handler:**
pprados
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PR_kwDOIPDwls5dG22S
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Update clarifai.mdx
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[] | null | 2 | 2023-10-18T07:55:06 | 2023-10-24T08:55:45 | 2023-10-18T20:05:59 |
CONTRIBUTOR
| null |
Corrected broken link
<!-- Thank you for contributing to LangChain!
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PR_kwDOIPDwls5dGwbc
| 11,963 |
Update clarifai.ipynb
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[] | null | 2 | 2023-10-18T07:41:59 | 2023-10-24T08:55:40 | 2023-10-18T20:05:43 |
CONTRIBUTOR
| null |
documents=docs not required when making a vector search on an existing Clarifai application
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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| 1,949,071,707 |
PR_kwDOIPDwls5dGwOj
| 11,962 |
chore: remove duplicated export variables
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CONTRIBUTOR
| null |
- **Description:** remove duplicated `__all__` variables
|
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I_kwDOIPDwls50K4fk
| 11,961 |
Issue: TypeError: TextGenerationModel.predict() got an unexpected keyword argument 'candidate_count'
|
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[] | null | 10 | 2023-10-18T07:06:38 | 2023-10-25T23:05:06 | null |
NONE
| null |
### Issue you'd like to raise.
I got this error when I built a ChatBot with Langchain using VertexAI. I'm seeing this error and couldn't find any details so far.
File /opt/conda/lib/python3.10/site-packages/langchain/llms/vertexai.py:100, in completion_with_retry.<locals>._completion_with_retry(*args, **kwargs)
98 @retry_decorator
99 def _completion_with_retry(*args: Any, **kwargs: Any) -> Any:
--> 100 return llm.client.predict(*args, **kwargs)
TypeError: TextGenerationModel.predict() got an unexpected keyword argument 'candidate_count'
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5dGZ77
| 11,960 |
bug fix for sagemakerendpoint streaming
|
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[] | null | 5 | 2023-10-18T06:38:38 | 2023-10-19T00:34:38 | 2023-10-19T00:20:07 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** bug fix for sagemakerendpoint streaming,
- **Issue:** the issue # bug fix for sagemakerendpoint streaming refer (https://aws.amazon.com/blogs/machine-learning/elevating-the-generative-ai-experience-introducing-streaming-support-in-amazon-sagemaker-hosting/),
- **Dependencies:** same as existing code,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,948,952,565 |
I_kwDOIPDwls50Kqf1
| 11,959 |
DOC: There is no documentation about how to interrupt the streaming generation once the model started generation.
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[] | null | 15 | 2023-10-18T06:27:34 | 2023-11-22T14:42:44 | null |
NONE
| null |
### Issue with current documentation:
Currently in the streaming documentation page, there is no guidance on how to interrupt the streaming once the model start generation. I want to implement the same "Stop generation" button functionality in chatGPT web, which should stopped the streaming generation. I tried to use try except but it is not working.
### Idea or request for content:
There is no documentation about how to interrupt the streaming generation once the model started generation. Even an error happens during new token generation, the program will not stop running but raise an error. How to stop the generation if an error happened?
|
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| 1,948,740,305 |
I_kwDOIPDwls50J2rR
| 11,958 |
Issue: <ConversationBufferWindowMemory doesn't work with db based chat history>
|
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[] | null | 6 | 2023-10-18T03:44:58 | 2024-01-17T07:30:46 | null |
NONE
| null |
### Issue you'd like to raise.
I'm experimenting with natural language to SQL conversion using SQLDatabaseChain and SQLDatabaseAgent. In this experiment, I'm utilizing the ConversationBufferWindowMemory. However, I've encountered an issue where the memory is not functioning as expected. When I ask a question related to a previous question or answer, the chain/agent isn't handling memory and instead responds independently of the question asked based on memory.
How to fix this
@dosubot
### Suggestion:
_No response_
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| 1,948,721,975 |
I_kwDOIPDwls50JyM3
| 11,957 |
It was unexpected that I had to provide the accss_token when using QianfanLLMEndpoint
|
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[] | null | 2 | 2023-10-18T03:27:29 | 2023-10-18T08:45:56 | null |
NONE
| null |
### System Info
It was unexpected that I had to provide the accss_token when using QianfanLLMEndpoint
Name: langchain
Version: 0.0.312
Name: qianfan
Version: 0.0.6
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
python
from langchain.llms import QianfanLLMEndpoint
llm = QianfanLLMEndpoint(qianfan_ak=client_id,qianfan_sk=client_secret,model="ERNIE-Bot-turbo")
res = llm("hi")
print(res)
```
error msg:
860 if not isinstance(prompt, str):
861 raise ValueError(
862 "Argument `prompt` is expected to be a string. Instead found "
863 f"{type(prompt)}. If you want to run the LLM on multiple prompts, use "
864 "`generate` instead."
865 )
866 return (
--> 867 self.generate(
868 [prompt],
869 stop=stop,
870 callbacks=callbacks,
871 tags=tags,
872 metadata=metadata,
873 **kwargs,
874 )
875 .generations[0][0]
...
166 )
167 AuthManager().register(self._ak, self._sk, self._access_token)
168 else:
InvalidArgumentError: both ak and sk must be provided, otherwise access_token should be provided
### Expected behavior
Normal operation
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PR_kwDOIPDwls5dFUaV
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Bug fix tongyi.py to be compatible with DashScope API
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CONTRIBUTOR
| null |
Current ChatTongyi is not compatible with DashScope API, which will cause error when passing api key to chat model directly.
- **Description:** Update tongyi.py to be compatible with DashScope API. Specifically, update parameter name "dashscope_api_key" to "api_key".
- **Issue:** None.
- **Dependencies:** Nothing new, Tongyi would require DashScope as before.
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PR_kwDOIPDwls5dElMK
| 11,954 |
zep: Memory Retriever MMR Support & Docs Updates
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CONTRIBUTOR
| null |
- Update Zep Memory and Retriever docstrings
- Zep Memory Retriever: Add support for native MMR
- Add MMR example to existing ZepRetriever Notebook
@baskaryan
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PR_kwDOIPDwls5dERKG
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add step back notebook
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PR_kwDOIPDwls5dEOLi
| 11,952 |
Update `create_repo` CLI templates' GitHub Actions and ruff dependency.
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COLLABORATOR
| null |
This brings the templates closer to how we develop `langchain`: the same poetry and ruff versions, releasing on manual action instead of using a finicky `if` conditional trigger, etc. Also fixes minor lint and bugs in the workflows.
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PR_kwDOIPDwls5dEMfd
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Update to `actions/checkout@v4`.
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COLLABORATOR
| null |
We don't use any of the new functionality at the moment. Just making sure we don't fall back on versions and fail to benefit from new patches. This is an easy upgrade and it's always harder to upgrade across multiple major versions at once.
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PR_kwDOIPDwls5dEJvd
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Type hints on varargs and kwargs that take anything should be `Any`.
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[] | null | 1 | 2023-10-17T21:28:39 | 2023-10-18T01:31:48 | 2023-10-18T01:31:44 |
COLLABORATOR
| null |
Type hinting `*args` as `List[Any]` means that each positional argument should be a list. Type hinting `**kwargs` as `Dict[str, Any]` means that each keyword argument should be a dict of strings.
This is almost never what we actually wanted, and doesn't seem to be what we want in any of the cases I'm replacing here.
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PR_kwDOIPDwls5dEHYn
| 11,949 |
Update playwright documentation
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[] | null | 2 | 2023-10-17T21:23:40 | 2023-10-18T01:26:53 | 2023-10-18T01:22:26 |
COLLABORATOR
| null |
Add security note to playwright tool
|
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PR_kwDOIPDwls5dD78j
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[LLMonitorCallbackHandler] Refactor + add llmonitor-py dependency
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[] | null | 2 | 2023-10-17T20:58:02 | 2023-10-19T06:54:11 | 2023-10-19T06:54:11 |
CONTRIBUTOR
| null |
We now require uses to have the pip package `llmonitor` installed. It allows us to have cleaner code and avoid duplicates between our library and our code in Langchain.
|
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PR_kwDOIPDwls5dD6EY
| 11,947 |
Fix invalid kwarg error pointed out by mypy.
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COLLABORATOR
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PR_kwDOIPDwls5dD5Rj
| 11,946 |
Hub Runnable
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[] | null | 1 | 2023-10-17T20:51:34 | 2023-10-18T16:21:46 | 2023-10-18T16:21:45 |
COLLABORATOR
| null |
Adds `langchain.runnables.hub.HubRunnable` for pulling configurable objects from the hub
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I_kwDOIPDwls50H1b_
| 11,945 |
Streaming support with ConversationChain
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[] | null | 6 | 2023-10-17T20:36:50 | 2023-12-22T23:35:38 | null |
NONE
| null |
### System Info
langchain 0.0.316
langserve 0.0.10
python 3.11.4 on darwin
### Who can help?
@agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
Langserve example here (https://github.com/langchain-ai/langserve-launch-example/blob/main/langserve_launch_example/chain.py) in which I want to use ConversationChain instead of ChatOpenAI.
server.py
#!/usr/bin/env python
"""A server for the chain above."""
from fastapi import FastAPI
from langserve import add_routes
from chain import conversation_chain
app = FastAPI(title="My App")
add_routes(app, conversation_chain)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="localhost", port=8000)
chain.py
```
from langchain.llms import OpenAI`
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
template = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
{history}
Human: {input}
Assistant:"""
prompt = PromptTemplate(input_variables=["history", "input"], template=template)
conversation_chain = ConversationChain(
llm=OpenAI(temperature=0),
prompt=prompt,
verbose=True,
memory=ConversationBufferMemory(),
)
```
### Expected behavior
I was expecting to have a streaming response as the ChatOpenAI behavior. It seems to me that ConversationChain doesn't support streaming response.
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PR_kwDOIPDwls5dDuWo
| 11,944 |
Upgrade to newer black (23.10) and ruff (first 0.1.x!) versions.
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| null |
Minor lint dependency version upgrade to pick up latest functionality.
Ruff's new v0.1 version comes with lots of nice features, like fix-safety guarantees and a preview mode for not-yet-stable features: https://astral.sh/blog/ruff-v0.1.0
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| 1,948,163,377 |
PR_kwDOIPDwls5dDorK
| 11,943 |
Fix `invalid escape sequence` warnings by using raw strings for regexes.
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[] | null | 1 | 2023-10-17T20:06:01 | 2023-10-18T14:55:18 | 2023-10-18T14:55:17 |
COLLABORATOR
| null |
This code also generates warnings when our users' apps hit it, which is annoying and doesn't look great. Let's fix it.
|
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PR_kwDOIPDwls5dDhVh
| 11,942 |
Update security.md
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[] | null | 1 | 2023-10-17T19:44:51 | 2023-10-23T19:35:34 | 2023-10-23T19:35:33 |
COLLABORATOR
| null |
Update security.md
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PR_kwDOIPDwls5dDhVa
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SingleStoreDBChatMessageHistory: Add jupiter notebook with usage example
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CONTRIBUTOR
| null |
The Docs folder changed its structure, and the notebook example for SingleStoreDChatMessageHistory has not been copied to the new place due to a merge conflict. Adding the example to the correct place.
|
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| 1,948,106,971 |
PR_kwDOIPDwls5dDcbj
| 11,939 |
SQLDatabase async support
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CONTRIBUTOR
| null |
Add support for async sql database (by utilizing sqlalchemy apis). Could do with some more tests. But, would like some comments while i add tests.
Sample usage with SQLDatabaseChain as follows:
#!/usr/bin/env python3
import gevent.monkey
gevent.monkey.patch_all()
import asyncio
import asyncio_gevent
asyncio.set_event_loop_policy(asyncio_gevent.EventLoopPolicy())
import langchain
langchain.verbose=True
from langchain.utilities import SQLDatabase
from langchain.llms import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
async def say(what):
cnt = 0
while cnt < 5:
print(what)
await asyncio.sleep(1)
cnt += 1
async def do_db():
db = await SQLDatabase.from_uri_async("sqlite+aiosqlite:///Chinook.db")
llm = OpenAI(temperature=0, verbose=True)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
await db_chain.arun("How many employees are there?")
async def main():
task1 = asyncio.create_task(say("HELLO"))
task2 = asyncio.create_task(do_db())
asyncio.gather(task1, task2)
asyncio.run(main())
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Add documentation to tools
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[] | null | 1 | 2023-10-17T19:14:00 | 2023-10-18T01:28:00 | 2023-10-18T01:27:59 |
COLLABORATOR
| null |
Add security notes to tools
|
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| 1,948,013,473 |
I_kwDOIPDwls50HFOh
| 11,937 |
DOC: Agent & AgentExecutioner
|
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[] | null | 1 | 2023-10-17T18:29:39 | 2023-10-17T18:37:14 | null |
CONTRIBUTOR
| null |
### Issue with current documentation:
When looking at the documentation for agents, memory, and the Agent Executor, I noticed you pass in tools to both the agent and the executor. What's the purpose of passing the tools to both? Shouldn't you need to just pass it to one?
https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents#the-agent
**The Agent:**
`agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)`
**The Agent Executor:**
`agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True,
return_intermediate_steps=True)`
Perhaps I don't understand what AgentExecutor is doing under the hood? If someone could explain, that would be great.
### Idea or request for content:
_No response_
|
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| 11,936 |
Pydantic v2 support for OpenAPI Specs
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CONTRIBUTOR
| null |
- **Description:** Adding Pydantic v2 support for OpenAPI Specs
- **Issue:**
- OpenAPI spec support was disabled because `openapi-schema-pydantic` doesn't support Pydantic v2:
#9205
- Caused errors in `get_openapi_chain`
- This may be the cause of #9520.
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** kreneskyp
The root cause was that `openapi-schema-pydantic` hasn't been updated in some time but [openapi-pydantic](https://github.com/mike-oakley/openapi-pydantic) forked and updated the project.
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I_kwDOIPDwls50GvjA
| 11,935 |
Indexing API slow
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NONE
| null |
### System Info
I compared the speed of indexing with and without using the Indexing API, and I notice a significant difference. Using Indexing API is 30-50% slower. Also for experiment, I tried to index the exact same data twice, and the Indexing API is taking extremely long time for the second time. Any help would be appreciated. Thank you.
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
With Indexing API:
```
vdb = PGVector(
connection_string,
collection_name,
embedding_function
)
record_manager = SQLRecordManager(
namespace, engine=postgres_engine
)
record_manager.create_schema()
index(
doc_chunks,
record_manager,
vdb,
cleanup=None,
source_id_key="source",
)
```
Without Indexing API:
```
vdb = PGVector.from_texts(
texts
embedding
collection_name
connection_string
)
```
### Expected behavior
I expect the speeds are comparable with/without Indexing API.
|
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PR_kwDOIPDwls5dC0ml
| 11,934 |
Add security note to recursive url loader
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[] | null | 1 | 2023-10-17T17:34:30 | 2023-10-17T17:41:44 | 2023-10-17T17:41:43 |
COLLABORATOR
| null |
Add security note to recursive loader
|
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Enhancing Human-Level Interaction: Incorporating Greeting and Gratitude Modules into the Langchain SQLDatabaseToolkit
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[] | null | 1 | 2023-10-17T17:07:58 | 2023-10-17T17:10:14 | null |
NONE
| null |
### Feature request
My feature proposal involves the integration of both a greeting module and a gratitude module into the Langchain SQLDatabaseToolkit. The greeting module is designed to deliver an introductory message about the SQL-helpful bot, and the gratitude module aims to express appreciation when users interact with it.
### Motivation
The motivation behind this proposal is to enhance the user experience and make interactions with the SQLDatabaseToolkit more friendly and engaging. By adding these modules, we can create a welcoming and user-focused environment, improving user satisfaction and the overall utility of the toolkit.
I'm always frustrated when I encounter impersonal and uninspiring interactions with SQL database tools. By implementing these modules, we can address this issue and provide a more human-like and engaging experience for users.
### Your contribution
I am capable of improving and thoroughly testing the module currently
|
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| 1,947,877,543 |
PR_kwDOIPDwls5dCqs3
| 11,930 |
Add connection args for pgvector vector store
|
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CONTRIBUTOR
| null |
- **Description:** sqlalchemy create_engine() does not take into account connect_args which are mandatory for managed PGSQL instances on cloud providers (ssl_context for example).
Also re-enabled create_vector_extension at post_init for using pgvector class seamlessly
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17.
|
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I_kwDOIPDwls50GbPu
| 11,929 |
Langchain prompt not working as expected , it's not consistence and not able to understand examples
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[] | null | 2 | 2023-10-17T16:44:09 | 2023-10-17T16:53:01 | null |
NONE
| null |
### System Info
latest version of all modules
### Who can help?
here's my PROMPT and code:
from langchain.prompts.chat import ChatPromptTemplate
updated_prompt = ChatPromptTemplate.from_messages(
[
("system",
"""
You are a knowledgeable AI assistant specializing in extracting information from the 'inquiry' table in the MySQL Database.
Your primary task is to perform a single query on the schema of the 'inquiry' table and table and retrieve the data using SQL.
When formulating SQL queries, keep the following context in mind:
- Filter records based on exact column value matches.
- If the user inquires about the Status of the inquiry fetch all these columns: status, name, and time values, and inform the user about these specific values.
- Limit query results to a maximum of 3 unless the user specifies otherwise.
- Only query necessary columns.
- Avoid querying for non-existent columns.
- Place the 'ORDER BY' clause after 'WHERE.'
- Do not add a semicolon at the end of the SQL.
If the query results in an empty set, respond with "information not found"
Use this format:
Question: The user's query
Thought: Your thought process
Action: SQL Query
Action Input: SQL query
Observation: Query results
... (repeat for multiple queries)
Thought: Summarize what you've learned
Final Answer: Provide the final answer
Begin!
"""),
("user", "{question}\n ai: "),
]
)
llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0) # best result
sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm)
sql_toolkit.get_tools()
sqldb_agent = create_sql_agent(
llm=llm,
toolkit=sql_toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
)
sqldb_agent.run(updated_prompt.format(
question="What is the status of inquiry 123?"
))
### 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
here's my PROMPT and code:
from langchain.prompts.chat import ChatPromptTemplate
updated_prompt = ChatPromptTemplate.from_messages(
[
("system",
"""
You are a knowledgeable AI assistant specializing in extracting information from the 'inquiry' table in the MySQL Database.
Your primary task is to perform a single query on the schema of the 'inquiry' table and table and retrieve the data using SQL.
When formulating SQL queries, keep the following context in mind:
- Filter records based on exact column value matches.
- If the user inquires about the Status of the inquiry fetch all these columns: status, name, and time values, and inform the user about these specific values.
- Limit query results to a maximum of 3 unless the user specifies otherwise.
- Only query necessary columns.
- Avoid querying for non-existent columns.
- Place the 'ORDER BY' clause after 'WHERE.'
- Do not add a semicolon at the end of the SQL.
If the query results in an empty set, respond with "information not found"
Use this format:
Question: The user's query
Thought: Your thought process
Action: SQL Query
Action Input: SQL query
Observation: Query results
... (repeat for multiple queries)
Thought: Summarize what you've learned
Final Answer: Provide the final answer
Begin!
"""),
("user", "{question}\n ai: "),
]
)
llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0)
sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm)
sql_toolkit.get_tools()
sqldb_agent = create_sql_agent(
llm=llm,
toolkit=sql_toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
)
sqldb_agent.run(updated_prompt.format(
question="What is the status of inquiry 123?"
))
### Expected behavior
t should be comprehensible and generate succinct results
|
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| 1,947,830,968 |
PR_kwDOIPDwls5dCgdl
| 11,928 |
bump 316
|
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| false | null |
[] | null | 1 | 2023-10-17T16:38:51 | 2023-10-17T16:47:58 | 2023-10-17T16:47:57 |
COLLABORATOR
| null | null |
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| 1,947,798,052 |
I_kwDOIPDwls50GQok
| 11,927 |
Passing a JSON-Schema in create_json_agent
|
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| false | null |
[] | null | 2 | 2023-10-17T16:20:36 | 2023-10-27T10:06:44 | null |
NONE
| null |
### Issue you'd like to raise.
It seems the default prompts do not support passing a JSON-Schema in create_json_agent to provide description to each field and nested fields. Wanted to understand if this hasn't been deemed necessary?
In addition, if there is a list of JSON objects, with some fields in each JSON object having nested objects or arrays, is the JSON agent the right fit, or should one try to use custom agents (perhaps Pandas) for such cases?
### Suggestion:
_No response_
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| 11,926 |
Adding an IMDb tool
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[] | null | 2 | 2023-10-17T15:52:11 | 2023-11-28T00:49:16 | null |
NONE
| null |
### Feature request
A tool to allow agents to search and retrieve data from IMDb (https://www.imdb.com/).
### Motivation
IMDb is one of the largest movie databases available online. Adding this tool would allow agents to intelligently retrieve up-to-date movie information from IMDb, enhancing the experience of its users. For example, with this tool, users could utilize LangChain through easily accessible prompts to find movies based on favorite genres, relations to other movies, or other information such as actors and producers.
### Your contribution
If all goes well, a PR can be ready sometime in November with this feature implemented.
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