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I_kwDOIPDwls5t-y_R
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NameError: name 'partition_docx' is not defined
|
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[] | null | 2 | 2023-08-10T13:19:48 | 2023-08-10T15:19:12 | 2023-08-10T15:19:11 |
NONE
| null |
### System Info
I have checked the issue tracker for similar issues but did not find any. Therefore, I am creating a new one.
I was trying to load a sample document using *UnstructuredFileLoader* from *langchain.document_loaders*. I was using T4 GPU on Google Colab.
I think the issue might be related to some recent upgrades and maybe the solution is trivial. However, I am in a bit of a hurry as I have to submit my project report to my professor :disappointed:
### 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
```
loader = UnstructuredFileLoader("/content/FAQ-ShelfLifeItem.docx")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
docs = text_splitter.split_documents(documents)
hf_embedding = HuggingFaceInstructEmbeddings()
```
### Expected behavior
I would expect no errors like:
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
[<ipython-input-14-79f2c22bc34d>](https://localhost:8080/#) in <cell line: 2>()
1 loader = UnstructuredFileLoader("/content/FAQ-ShelfLifeItem.docx")
----> 2 documents = loader.load()
3 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
4 docs = text_splitter.split_documents(documents)
5
2 frames
[/usr/local/lib/python3.10/dist-packages/unstructured/partition/auto.py](https://localhost:8080/#) in partition(filename, content_type, file, file_filename, url, include_page_breaks, strategy, encoding, paragraph_grouper, headers, skip_infer_table_types, ssl_verify, ocr_languages, pdf_infer_table_structure, xml_keep_tags, data_source_metadata, **kwargs)
170 **kwargs,
171 )
--> 172 elif filetype == FileType.MD:
173 elements = partition_md(
174 filename=filename,
NameError: name 'partition_docx' is not defined
```
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openai.error.InvalidRequestError: The completion operation does not work with the specified model, gpt-35-turbo-16k. 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.
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[] | null | 3 | 2023-08-10T12:42:46 | 2023-11-23T16:06:40 | 2023-11-23T16:06:39 |
NONE
| null |
Traceback (most recent call last):
File "D:\Corent\AI\LangChain\azure\azure_connection.py", line 14, in <module>
print(llm("Tell me joke"))
^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\llms\base.py", line 802, in __call__
self.generate(
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\llms\base.py", line 598, in generate
output = self._generate_helper(
^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\llms\base.py", line 504, in _generate_helper
raise e
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\llms\base.py", line 491, in _generate_helper
self._generate(
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\llms\openai.py", line 384, in _generate
response = completion_with_retry(
^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\llms\openai.py", line 116, in completion_with_retry
return _completion_with_retry(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 314, in iter
return fut.result()
^^^^^^^^^^^^
File "C:\Users\donbosco\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "C:\Users\donbosco\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py", line 401, in __get_result
raise self._exception
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\tenacity\__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\langchain\llms\openai.py", line 114, in _completion_with_retry
return llm.client.create(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_resources\completion.py", line 25, in create
return super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_requestor.py", line 298, in request
resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_requestor.py", line 700, in _interpret_response
self._interpret_response_line(
File "D:\Corent\AI\LangChain\azure\venv\Lib\site-packages\openai\api_requestor.py", line 763, 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-16k. 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.
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PR_kwDOIPDwls5XonqP
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Update google_cloud_enterprise_search.py to support structured data sources
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Added the capability to handles structured data from google enterprise search,
- Issue: Retriever failed when underline search engine was integrated with structured data,
- Dependencies: google-api-core
- Tag maintainer: @jarokaz
- Twitter handle: anifort
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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I_kwDOIPDwls5t93uZ
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Streaming ConversationalRetrievalQaChain
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NONE
| null |
### Issue you'd like to raise
### Suggestion:
_No response_
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I_kwDOIPDwls5t9en2
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Functional difference between GraphCypherQAChain and GraphQAChain
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[] | null | 5 | 2023-08-10T09:50:43 | 2023-11-16T16:05:29 | 2023-11-16T16:05:28 |
NONE
| null |
### Feature request
What is the exact difference between the two graph based chains in langchain 1 )GraphCypherQAChain and 2)GraphQAChain. What are the pros and cons of each, and when to use one over another?
### Motivation
What is the exact difference between the two graph based chains in langchain 1 )GraphCypherQAChain and 2)GraphQAChain. What are the pros and cons of each, and when to use one over another?
### Your contribution
What is the exact difference between the two graph based chains in langchain 1 )GraphCypherQAChain and 2)GraphQAChain. What are the pros and cons of each, and when to use one over another?
|
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| 1,844,760,734 |
PR_kwDOIPDwls5XndSc
| 9,034 |
Fix a broken code block display
|
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[] | null | 1 | 2023-08-10T09:17:26 | 2023-08-11T01:58:56 | 2023-08-10T14:39:01 |
CONTRIBUTOR
| null |
- Description: Fix a broken code block in this page: https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/
- Issue: N/A
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: yaotti
## before
<img width="985" alt="image" src="https://github.com/yaotti/langchain/assets/18807/4707fff8-ca64-4144-b1d4-74f641b26f1a">
## after
<img width="987" alt="image" src="https://github.com/yaotti/langchain/assets/18807/d011384f-60a3-416d-99cf-5a29203c5b2b">
|
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| 1,844,699,377 |
I_kwDOIPDwls5t8-Dx
| 9,033 |
Issue: make command is not working after installing langchain
|
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[] | null | 6 | 2023-08-10T08:39:47 | 2023-12-01T16:08:23 | 2023-12-01T16:08:22 |
NONE
| null |
### Issue you'd like to raise.
I've been trying to install langchain using `pip install langchain[all]` in the conda environment where it already had packages to run hugging face text-generation-inference module.
Let me know how to handle this issue. While running make command i'm facing this error.
```python
Traceback (most recent call last):
File "/home/sri/miniconda3/envs/text-generation-inference/bin/make", line 5, in <module>
from scripts.proto import main
ModuleNotFoundError: No module named 'scripts'
```
### Suggestion:
_No response_
|
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| 1,844,689,666 |
PR_kwDOIPDwls5XnN9C
| 9,032 |
Implement .transform() in RunnablePassthrough()
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[] | null | 1 | 2023-08-10T08:33:23 | 2023-08-10T17:41:20 | 2023-08-10T17:41:19 |
COLLABORATOR
| null |
- This ensures passthrough doesnt break streaming
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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| 1,844,686,751 |
I_kwDOIPDwls5t86-f
| 9,031 |
Issue: How to build a prompt that include SystemMessage, vector store context and final question
|
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[] | null | 4 | 2023-08-10T08:31:32 | 2023-11-16T16:05:50 | 2023-11-16T16:05:49 |
NONE
| null |
### Issue you'd like to raise.
Hello guys !
I would like to build a Prompt which include a System message, Context data from a vector_store and the final question. What is the best way to do that?
Right now, I'm doing this:
```
def get_prompt(system_prompt):
prompt = SystemMessage(content=system_prompt)
new_prompt = (
prompt
+ "--- \n\n" + "le contexte:" + "\n"
+ "{context}"
+ '\n\n --- \n\n ' + "la question: \n\n"
+ "{question}"
)
return PromptTemplate(
template=new_prompt, input_variables=["context", "question"]
)
```
But get this error: **KeyError: 'template'**
Is there a way to do what I want? I'm searching for a solution where I don't need to add the context myself, because it should already be managed by the vector store retriever
### Suggestion:
_No response_
|
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| 1,844,566,660 |
PR_kwDOIPDwls5XmzXi
| 9,029 |
Agent vector store tool doc
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[] | null | 1 | 2023-08-10T07:16:28 | 2023-08-11T01:52:50 | 2023-08-10T18:13:12 |
CONTRIBUTOR
| null |
I was initially confused weather to use create_vectorstore_agent or create_vectorstore_router_agent due to lack of documentation so I created a simple documentation for each of the function about their different usecase.
Replace this comment with:
- Description: Added the doc_strings in create_vectorstore_agent and create_vectorstore_router_agent to point out the difference in their usecase
- Tag maintainer: @rlancemartin, @eyurtsev
|
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| 1,844,513,797 |
I_kwDOIPDwls5t8QwF
| 9,028 |
The "search bar" and "mendable chat" should also follow the dark theme.
|
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[] | null | 4 | 2023-08-10T06:38:18 | 2023-12-05T04:41:49 | 2023-12-05T04:41:49 |
CONTRIBUTOR
| null |
## Issue with current `dark theme`:
Recently the documentation has been much improvised aesthetically and functionally. But when we switch to the dark mode, the search bar **on the top right** and the **mendable chat screen** remain in the light theme.
I think, making them match the dark theme, will even out an overall experience ✌🏻
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PR_kwDOIPDwls5Xmjfl
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Adding new Github functions for reading pull requests
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CONTRIBUTOR
| null |
The Github utilities are fantastic, so I'm adding support for deeper interaction with pull requests. Agents should read "regular" comments and review comments, and the content of PR files (with summarization or `ctags` abbreviations).
Progress:
- [x] Add functions to read pull requests and the full content of modified files.
- [x] Function to use Github's built in code / issues search.
Out of scope:
- Smarter summarization of file contents of large pull requests (`tree` output, or ctags).
- Smarter functions to checkout PRs and edit the files incrementally before bulk committing all changes.
- Docs example for creating two agents:
- One watches issues: For every new issue, open a PR with your best attempt at fixing it.
- The other watches PRs: For every new PR && every new comment on a PR, check the status and try to finish the job.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5XmZSb
| 9,026 |
Corrected grammatical errors and spelling mistakes in the index.mdx file.
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[] | null | 1 | 2023-08-10T05:34:35 | 2023-08-10T17:17:10 | 2023-08-10T17:17:10 |
CONTRIBUTOR
| null |
Expressing gratitude to the creator for crafting this remarkable application. 🙌, Would like to Enhance grammar and spelling in the documentation for a polished reader experience.
Your feedback is valuable as always
@baskaryan , @hwchase17 , @eyurtsev
|
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PR_kwDOIPDwls5XmUu4
| 9,025 |
Improved grobid documentation
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[] | null | 1 | 2023-08-10T05:09:34 | 2023-08-10T14:47:23 | 2023-08-10T14:47:22 |
CONTRIBUTOR
| null |
- Description: Improvement in the Grobid loader documentation, typos and suggesting to use the docker image instead of installing Grobid in local (the documentation was also limited to Mac, while docker allow running in any platform)
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @whitenoise
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PR_kwDOIPDwls5XmTMR
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Bedrock embeddings async methods
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[] | null | 2 | 2023-08-10T05:00:56 | 2023-08-10T21:21:04 | 2023-08-10T21:21:03 |
CONTRIBUTOR
| null |
## Description
This PR adds the `aembed_query` and `aembed_documents` async methods for improving the embeddings generation for large documents. The implementation uses asyncio tasks and gather to achieve concurrency as there is no bedrock async API in boto3.
### Maintainers
@agola11
@aarora79
### Open questions
To avoid throttling from the Bedrock API, should there be an option to limit the concurrency of the calls?
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PR_kwDOIPDwls5XmQjh
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fix faiss vectorstore Delete by ID method
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[] | null | 5 | 2023-08-10T04:44:48 | 2023-08-16T07:30:50 | 2023-08-16T07:28:09 |
NONE
| null |
Description: FAISS shift the index after removing ids when IndexFlatL2 or IndexFlatIP is used, so `index_to_docstore_id` need to update.
|
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I_kwDOIPDwls5t7zwz
| 9,022 |
ElasticKnnSearch: ValueError: Either `query_vector` or `model_id` must be provided, but not both.
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[] | null | 4 | 2023-08-10T04:31:24 | 2023-09-19T15:36:38 | 2023-09-19T15:36:37 |
NONE
| null |
### System Info
LangChain 0.0.260, Mac M2, Miniforge3, Python 3.9
### Who can help?
@ves
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain import ElasticVectorSearch
from elasticsearch import Elasticsearch
model_name = "sentence-transformers/all-mpnet-base-v2"
embedding = HuggingFaceEmbeddings(model_name=model_name)
es_url = "http://localhost:9221"
es = Elasticsearch(es_url)
# prepare db
texts = ["This is a test document", "This is another test document"]
metadatas = [{}, {}]
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.create_documents(texts, metadatas=metadatas)
ElasticVectorSearch.from_documents(documents, embedding, index_name="bug_demo", elasticsearch_url=es_url)
# chat with db
memory = ConversationBufferMemory(memory_key="chat_history", output_key="answer", return_messages=True)
db = ElasticKnnSearch(embedding=embedding, es_connection=es, index_name="bug_demo")
llm_model="gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_model, temperature=0)
chain = ConversationalRetrievalChain.from_llm(
llm,
db.as_retriever(),
memory=memory,
return_source_documents=True
)
user_input = "What is love?"
output = chain({"question": user_input})
```
### Expected behavior
The previous code used to work correctly with langchain 0.0.245.
After merge https://github.com/langchain-ai/langchain/pull/8180, it produces this error:
```
>>> output = chain({"question": user_input})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/chains/base.py", line 258, in __call__
raise e
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/chains/base.py", line 252, in __call__
self._call(inputs, run_manager=run_manager)
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/chains/conversational_retrieval/base.py", line 135, in _call
docs = self._get_docs(new_question, inputs, run_manager=_run_manager)
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/chains/conversational_retrieval/base.py", line 287, in _get_docs
docs = self.retriever.get_relevant_documents(
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/schema/retriever.py", line 193, in get_relevant_documents
raise e
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/schema/retriever.py", line 186, in get_relevant_documents
result = self._get_relevant_documents(
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/vectorstores/base.py", line 504, in _get_relevant_documents
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/vectorstores/elastic_vector_search.py", line 472, in similarity_search
results = self.knn_search(query=query, k=k, **kwargs)
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/vectorstores/elastic_vector_search.py", line 520, in knn_search
knn_query_body = self._default_knn_query(
File "/Users/miguel/miniforge3/envs/fenonto_qa3/lib/python3.9/site-packages/langchain/vectorstores/elastic_vector_search.py", line 460, in _default_knn_query
raise ValueError(
ValueError: Either `query_vector` or `model_id` must be provided, but not both.
```
My guess is, after refactoring, knn_search no longer takes into account the embedding parameter. If so it would use it to create the query_vector. @jeffvestal any clue?
BTW, I have also tried preparing the db using the new ElasticKnnSearch#from_texts method.
```python
from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain import ElasticVectorSearch
from elasticsearch import Elasticsearch
model_name = "sentence-transformers/all-mpnet-base-v2"
embedding = HuggingFaceEmbeddings(model_name=model_name)
es_url = "http://localhost:9221"
es = Elasticsearch(es_url)
index_name = "test_bug3"
# prepare db
texts = ["This is a test document", "This is another test document"]
metadatas = [{}, {}]
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.create_documents(texts, metadatas=metadatas)
knnvectorsearch = ElasticKnnSearch.from_texts(
texts=texts,
embedding=embedding,
index_name= index_name,
vector_query_field='vector',
query_field='text',
dims=768,
es_connection=es
)
# Test `add_texts` method
texts2 = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knnvectorsearch.add_texts(texts2)
# chat with db
memory = ConversationBufferMemory(memory_key="chat_history", output_key="answer", return_messages=True)
db = ElasticKnnSearch(embedding=embedding, es_connection=es, index_name="index_name")
llm_model="gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_model, temperature=0)
chain = ConversationalRetrievalChain.from_llm(
llm,
db.as_retriever(),
memory=memory,
return_source_documents=True
)
user_input = "Who is Janine?"
output = chain({"question": user_input})
```
But I get the same error.
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I_kwDOIPDwls5t7yhP
| 9,021 |
Typing issue: Undocumented parameter `client`
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[] | null | 2 | 2023-08-10T04:24:33 | 2023-11-16T16:05:23 | 2023-11-16T16:05:22 |
NONE
| null |
### System Info
Python type checking complains about a parameter `client` with a simple setup like this:
<img width="170" alt="Screenshot 2023-08-10 at 12 20 53 PM" src="https://github.com/langchain-ai/langchain/assets/1828968/355100b6-11dc-4f73-beec-8059aba004f9">
<img width="534" alt="Screenshot 2023-08-10 at 12 03 14 PM" src="https://github.com/langchain-ai/langchain/assets/1828968/9eb22e4e-d78e-4b5b-ae98-6e649bffc14f">
According to documentation, no fields are required to initialize the model:
https://python.langchain.com/docs/modules/model_io/models/chat/
### 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
Version: langchain = "^0.0.250".
Python 3.11.4
Install Pylance. Use VSCode to run typecheck.
### Expected behavior
It should pass type checking based on documentation.
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PR_kwDOIPDwls5XmMu1
| 9,020 |
Adding ChatLiteLLM model
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[] | null | 7 | 2023-08-10T04:21:22 | 2023-08-14T14:43:41 | 2023-08-14T14:43:40 |
CONTRIBUTOR
| null |
Description: Adding a langchain integration for the LiteLLM library
Tag maintainer: @hwchase17, @baskaryan
Twitter handle: @krrish_dh / @Berri_AI
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I_kwDOIPDwls5t7skp
| 9,019 |
Duplicate ids passed into langchain\vectorstores\fais.__add will result in a mismatch between faiss and document's index
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[] | null | 4 | 2023-08-10T03:52:27 | 2023-11-17T16:05:59 | 2023-11-17T16:05:58 |
NONE
| null |
### System Info
windows 10
python 3.10.12
### Who can help?
@eyurtsev There is a BUG in the def __add function in langchain\vectorstores\faiss.py: When the parameter ids is provided and there are duplicates in it, index_to_docstore_id will report an error and the program will terminate. But the text vector embedding has been added to the index. This causes the id in the index to be inconsistent with the id in index_to_docstore_id, not aligned, and the text cannot be matched correctly.
langchain\vectorstores\faiss.py 中 def __add 函数存在BUG:
当参数 ids 已提供, 且其中存在重复时, index_to_docstore_id 新增会报错,程序运行终止. 但是文本向量 embedding 已经添加至 index 中.导致 index 中的 id 与 index_to_docstore_id 的中 id 对应不一致,文本无法正确匹配
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [x] Document Loaders
- [x] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
None
### Expected behavior
None
|
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| 1,844,332,543 |
I_kwDOIPDwls5t7kf_
| 9,018 |
ImportError when importing MultiQueryRetriever
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[] | null | 1 | 2023-08-10T03:04:59 | 2023-08-11T06:13:41 | 2023-08-11T06:13:41 |
NONE
| null |
### System Info
# System Info:
Langchain==0.0.260
Python==3.10.10
### Who can help?
@agola11
### 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using the following script from official documentation:
```python
from langchain.chat_models import ChatOpenAI
from langchain.retrievers.multi_query import MultiQueryRetriever
question = "What are the approaches to Task Decomposition?"
llm = ChatOpenAI(temperature=0)
retriever_from_llm = MultiQueryRetriever.from_llm(
retriever=vectordb.as_retriever(), llm=llm
)
```
ImportError arise:
```bash
File /opt/conda/envs/gpt-env/lib/python3.10/site-packages/langchain/retrievers/multi_query.py:6
2 from typing import List
4 from pydantic import BaseModel, Field
----> 6 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
7 from langchain.chains.llm import LLMChain
8 from langchain.llms.base import BaseLLM
ImportError: cannot import name 'CallbackManagerForRetrieverRun' from 'langchain.callbacks.manager' (/opt/conda/envs/gpt-env/lib/python3.10/site-packages/langchain/callbacks/manager.py)
```
### Expected behavior
There should be no issue when importing MultiQueryRetriever and using the script from the official documentation.
|
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| 1,844,278,830 |
I_kwDOIPDwls5t7XYu
| 9,017 |
For the Visual currently it doesn't display or show tabular format with scroll bar
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[] | null | 4 | 2023-08-10T01:55:13 | 2023-11-17T16:06:07 | 2023-11-17T16:06:06 |
NONE
| null |
### Feature request
It would be good it create_pandas_dataframe_agent could return a table with scroll bar . Instead of us adding html code on the top of the generated code
### Motivation
Being able to capture table data is one key element of visualizations, currently create_pandas_dataframe_agent doesn't generate a table visualization with scroll bar
### Your contribution
I can try, but might need help
|
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| 1,844,253,726 |
PR_kwDOIPDwls5XlwUX
| 9,016 |
make runnable dir
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| 1,844,233,539 |
PR_kwDOIPDwls5XlsBA
| 9,015 |
Bagatur/filter metadata
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I_kwDOIPDwls5t7EdM
| 9,014 |
WebResearchRetriever - RuntimeError: asyncio.run() cannot be called from a running event loop
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[] | null | 6 | 2023-08-10T00:04:39 | 2024-01-17T19:07:42 | 2023-11-22T16:07:08 |
NONE
| null |
### System Info
Langchain version 0.0.260, Python
### Who can help?
@agola11
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Both chunks of code gave me the error.
Chunk 1
`from langchain.chains import RetrievalQAWithSourcesChain
user_input = "How do LLM Powered Autonomous Agents work?"
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=web_research_retriever)
result = qa_chain({"question": user_input})
result`
Chunk 2
'import logging
logging.basicConfig()
logging.getLogger("langchain.retrievers.web_research").setLevel(logging.INFO)
user_input = "How do LLM Powered Autonomous Agents work?"
docs = web_research_retriever.get_relevant_documents(user_input)
docs'
Both gave me this error
`[/usr/lib/python3.10/asyncio/runners.py](https://localhost:8080/#) in run(main, debug)
31 """
32 if events._get_running_loop() is not None:
---> 33 raise RuntimeError(
34 "asyncio.run() cannot be called from a running event loop")
35
RuntimeError: asyncio.run() cannot be called from a running event loop`
### Expected behavior
In both cases I just wanted it to produce website text with a source.
|
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PR_kwDOIPDwls5Xlgef
| 9,013 |
Add optional model kwargs to ChatAnthropic to allow overrides
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closed
| false | null |
[] | null | 2 | 2023-08-09T23:31:26 | 2023-08-10T00:34:00 | 2023-08-10T00:34:00 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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| 1,844,166,468 |
I_kwDOIPDwls5t679E
| 9,012 |
DOC: API Reference: Classes are not sorted
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[] | null | 7 | 2023-08-09T23:14:10 | 2023-11-14T19:03:46 | 2023-11-14T19:03:36 |
COLLABORATOR
| null |
### Issue with current documentation:
DOC: API Reference: Classes are not sorted in the root page. It is really hard to search for unsorted items.
For example:
```
...
agents.agent_iterator.AgentExecutorIterator(...)
agents.agent.ExceptionTool
agents.agent.LLMSingleActionAgent
agents.tools.InvalidTool
agents.schema.AgentScratchPadChatPromptTemplate
agents.agent_types.AgentType(value[, names, ...])
agents.xml.base.XMLAgent
agents.xml.base.XMLAgentOutputParser
...
```
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5t64Ig
| 9,011 |
Conversational agent returning action plan as response after 0.0.260 release
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[] | null | 3 | 2023-08-09T22:54:46 | 2023-11-19T16:05:37 | 2023-11-19T16:05:36 |
NONE
| null |
### System Info
After I upgraded my Langchain to version 0.0.260, the ConversationalChatAgent returns the action plan as the chat response.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce: request something that will make a ConversationalChatAgent call a tool.
### Expected behavior
The agent should pass the action and action_input to the tool and not return it as a final response to the user.
|
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PR_kwDOIPDwls5XlFKa
| 9,010 |
Add logprobs to SamplingParameters in vllm
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[] | null | 2 | 2023-08-09T21:36:52 | 2023-08-09T22:48:30 | 2023-08-09T22:48:30 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
This PR aims at amending #8806 , that I opened a few days ago, adding the extra `logprobs` parameter that I accidentally forgot
|
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| 1,844,040,504 |
PR_kwDOIPDwls5XlCAv
| 9,009 |
import airbyte loaders
|
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[] | null | 1 | 2023-08-09T21:26:56 | 2023-08-09T21:51:17 | 2023-08-09T21:51:16 |
COLLABORATOR
| null | null |
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| 1,844,024,765 |
PR_kwDOIPDwls5Xk-lL
| 9,007 |
Locals in config
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[] | null | 4 | 2023-08-09T21:14:26 | 2023-08-23T17:25:13 | 2023-08-23T17:24:28 |
COLLABORATOR
| null |
Alternate implementation of #8772. just a proof of concept, im sure there's lots of broken stuff
|
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| 1,843,990,957 |
PR_kwDOIPDwls5Xk3Ma
| 9,006 |
Suppress divide by zero wranings for cosine similarity
|
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[] | null | 1 | 2023-08-09T20:48:35 | 2023-08-09T22:56:52 | 2023-08-09T22:56:51 |
COLLABORATOR
| null |
Suppress run time warnings for divide by zero as the downstream code handles the scenario (handling inf and nan)
|
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| 1,843,990,236 |
I_kwDOIPDwls5t6Q7c
| 9,005 |
Issue: UnicodeDecodeError when running .txt files using TextLoader
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[] | null | 3 | 2023-08-09T20:48:00 | 2023-11-16T16:06:42 | 2023-11-16T16:06:41 |
NONE
| null |
### Issue you'd like to raise.
Hello - I receive a UnicodeDecodeError when running the below code:
from dotenv import load_dotenv
load_dotenv()
from langchain.llms import OpenAI
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import RetrievalQA
llm = OpenAI(temperature=0.1)
from langchain.document_loaders import TextLoader
loader = TextLoader("./Training/test2.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(search_kwargs={"k": 1}))
query = "How much did the judge fine Twitter?"
qa.run(query)
The test2.txt is a 1,600-word UTF-8 encoded file. Here is the error text that I receive:
**Traceback (most recent call last):
File "C:\Users\Admin\PycharmProjects\pythonProject\Misc\Testing.py", line 17, in <module>
documents = loader.load()
^^^^^^^^^^^^^
File "C:\Users\Admin\PycharmProjects\pythonProject\Misc\venv\Lib\site-packages\langchain\document_loaders\text.py", line 18, in load
text = f.read()
^^^^^^^^
File "C:\Users\Admin\AppData\Local\Programs\Python\Python311\Lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 1079: character maps to <undefined>**
Any advice would be greatly appreciated.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5Xk060
| 9,004 |
ChatPromptTemplate pending deprecation proposal
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[] | null | 1 | 2023-08-09T20:40:37 | 2023-08-10T18:40:57 | 2023-08-10T18:40:56 |
COLLABORATOR
| null |
Pending deprecations for ChatPromptTemplate proposals
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| 1,843,966,554 |
PR_kwDOIPDwls5Xkx7d
| 9,003 |
expose llm_chain to create_openai_fn_chain
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[] | null | 3 | 2023-08-09T20:29:52 | 2023-08-12T16:53:18 | 2023-08-12T16:53:14 |
CONTRIBUTOR
| null |
I made this so people could wrap LLM Chains and optionally pass those into create_openai_fn_chain. Very simple, and non-breaking change.
@baskaryan
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| 1,843,964,132 |
PR_kwDOIPDwls5XkxZr
| 9,002 |
bump 260
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[] | null | 1 | 2023-08-09T20:28:03 | 2023-08-09T20:40:50 | 2023-08-09T20:40:49 |
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I_kwDOIPDwls5t6J3Y
| 9,001 |
Chat LLM adds an extra sentence in front of SQL queries, produces Output Parsing Error
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[] | null | 3 | 2023-08-09T20:26:30 | 2023-10-26T19:20:31 | null |
NONE
| null |
### System Info
python==3.11
langchain==0.0.246
### 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
1. db_chain = SQLDatabaseChain.from_llm(chat_llm, db, use_query_checker=True, verbose=True)#, query_prompt=PROMPT)
2. chat_llm=AzureChatOpenAI(deployment_name="gpt-35-turbo-16k",
model_name="gpt-35-turbo-16k" )
3. tools=[Tool(
func=db_chain.run,
name="Database Search",
description="useful for when you need to lookup specific queries on the ATLASIQ Schema"
)]
4. prefix = """ You are an expert Snowflake SQL data analyst, who writes queries with perfect syntax,
and performs necessary computations on that data in the AtlasIQ Schema. Your goal is to answer the following questions as best you can.
When there are multiple results for the same quantity, return all of them. DO NOT hallucinate an answer if there is no result."""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"],
)
memory = ConversationBufferMemory(memory_key="chat_history")
llm_chain = LLMChain(llm=chat_llm, prompt=prompt)#, callbacks=[custom_handler.short_chain(
new_zero_agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(
agent=new_zero_agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=True)
agent_chain.run(query)
### Expected behavior
I expect the query to be synthesized and executed but instead, I get an unnecessary explanation of why the query is correct "The original query does not contain any of the mentioned mistakes. Therefore, here is the reproduced original query:
SELECT SUM(total_operating_expenses)
FROM expense_income_table
WHERE market = 'Seattle'
AND period = '202305'The original query provided does not contain any of the mentioned mistakes. Hence, the original query is reproduced below:
```sql
SELECT SUM(total_operating_expenses)
FROM expense_income_table
WHERE market = 'Seattle"<- which causes the output parsing error
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PR_kwDOIPDwls5XkrQP
| 9,000 |
Fix guard-import throwing module not found error
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[] | null | 2 | 2023-08-09T20:11:40 | 2023-08-09T20:13:42 | 2023-08-09T20:13:42 |
CONTRIBUTOR
| null |
- Description:Fix guard-import throwing module not found error
- Issue: n/a
- Dependencies: n/a
- Tag maintainer: @rlancemartin @eyurtsev
- Twitter handle: @pelaseyed
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I_kwDOIPDwls5t6BPB
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DOC: What are the default values to RecursiveCharacterTextSplitter
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[] | null | 0 | 2023-08-09T20:03:15 | 2023-08-09T20:06:51 | 2023-08-09T20:06:51 |
NONE
| null |
### Issue with current documentation:
I am using the default configuration for RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter()
chunks = text_splitter.create_documents([full_text])
However, I would like to know what values are being used in the defaults. I would like to explicitly pass values, because for certain documents it seems to be not splitting and downstream I am getting from OpenAI's embeddings endpoint `This model's maximum context length is 8191 tokens, however you requested 11451 tokens (11451 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.`
### Idea or request for content:
I would like to know what values are being used as default for RecursiveCharacterTextSplitter
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PR_kwDOIPDwls5XknN2
| 8,998 |
Fix airbyte loader
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COLLABORATOR
| null |
Fix airbyte loader
https://github.com/langchain-ai/langchain/issues/8996
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PR_kwDOIPDwls5XkltJ
| 8,997 |
Move graph use case to "more" section
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[] | null | 3 | 2023-08-09T19:51:36 | 2023-08-15T00:20:39 | 2023-08-15T00:20:38 |
COLLABORATOR
| null |
Clean `use_cases` by moving the `GraphDB` to `integrations`.
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I_kwDOIPDwls5t56k0
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`AirbyteStripeLoader` throws an error
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[] | null | 5 | 2023-08-09T19:43:49 | 2023-08-09T20:19:05 | 2023-08-09T20:14:45 |
CONTRIBUTOR
| null |
### System Info
When importing the `AirbyteStripeLoader` introduced in v0.0.259 it throws an `module not found error`.
```
from libs.langchain.langchain.utils.utils import guard_import
ModuleNotFoundError: No module named 'libs'
```
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.document_loaders.airbyte import AirbyteStripeLoader
config = {
# your stripe configuration
}
loader = AirbyteStripeLoader(config=config, stream_name="invoices") # check the documentation linked above for a list of all streams
```
### Expected behavior
Import the document should not throw an error.
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PR_kwDOIPDwls5XkgK0
| 8,995 |
Updating port and ssl use in sample notebook
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[] | null | 4 | 2023-08-09T19:33:26 | 2023-08-10T00:08:48 | 2023-08-10T00:08:48 |
CONTRIBUTOR
| null |
## Description
This PR updates the sample notebook to use the default port (8182) and the ssl for the Neptune database connection.
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DirectoryLoader slicing
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[] | null | 3 | 2023-08-09T19:24:26 | 2023-08-09T23:05:25 | 2023-08-09T23:05:16 |
CONTRIBUTOR
| null |
DirectoryLoader can now return a random sample of files in a directory.
Parameters added are:
sample_size
randomize_sample
sample_seed
@rlancemartin, @eyurtsev
|
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PR_kwDOIPDwls5XkWfG
| 8,993 |
docstrings cleanup
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[] | null | 2 | 2023-08-09T19:02:45 | 2023-08-09T23:00:35 | 2023-08-09T22:49:06 |
COLLABORATOR
| null |
Added/Updated docstrings
@baskaryan
|
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PR_kwDOIPDwls5XkRyl
| 8,992 |
Add docs community page
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PR_kwDOIPDwls5XkPzr
| 8,991 |
Include tokens from function output in streaming callbacks
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NONE
| null |
Description
---
This adds a small bit of logic so that `on_new_llm_token` callbacks will be properly passed a valid token (instead of `''`) when a streaming model is called as part of a function call or tool run.
Issue
---
This is a *partial* fix for #7385, having only implemented this logic for `ChatOpenAI`. As @rasca mentions in that issue:
> We need to add this to every llm/
I haven't gone this far, as I don't have reliable (or any) access to many of the LLMs that are integrated, but am using OpenAI's models for my own development.
Dependencies
---
There are no extra dependencies required for this fix.
Tag Maintainer
---
I have a feeling this one should probably be looked at by @agola11.
Twitter Handle
---
What's Twitter? 🤣
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5t5iEA
| 8,990 |
run() got an unexpected keyword argument 'name'
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[] | null | 7 | 2023-08-09T18:31:56 | 2023-08-13T16:01:55 | 2023-08-13T16:01:55 |
NONE
| null |
### Issue you'd like to raise.
Hey y'all. i have this code. i wrote it one month back and when i am running it now, it is giving me an error.
**CODE**:
<code>import os
from langchain.llms import OpenAI
from langchain import PromptTemplate
from langchain.chains import LLMChain
from constant import openai_key
import streamlit as st
os.environ['OPENAI_API_KEY'] = openai_key
llm = OpenAI(temperature=0.8)
multiple_inputs = PromptTemplate(
input_variables=["name", "age", "location"],
template="My name is {name}, I am {age} years old and live in {location}."
)
chain = LLMChain(llm=llm, prompt=multiple_inputs)
output = chain.run(
name="John",
age=30,
location="New York"
)
print(output)
</code>
**ERROR**:
<code>Traceback (most recent call last):
File ".\questions.py", line 19, in <module>
output = chain.run(
TypeError: run() got an unexpected keyword argument 'name'</code>
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5XkMAQ
| 8,989 |
Update quickstart.mdx
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openai adapters
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[] | null | 1 | 2023-08-09T18:23:59 | 2023-08-10T23:08:51 | 2023-08-10T23:08:50 |
COLLABORATOR
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| 1,843,784,776 |
I_kwDOIPDwls5t5exI
| 8,987 |
Issue: ConversationRetrievalChain" is not accessedPylance
|
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[] | null | 2 | 2023-08-09T18:21:55 | 2023-11-16T16:06:44 | 2023-11-16T16:06:43 |
NONE
| null |
### Issue you'd like to raise.
```from langchain.chains import ConversationRetrievalChain```
this is what I have and i am using the latest langchain version
### Suggestion:
_No response_
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| 1,843,782,612 |
I_kwDOIPDwls5t5ePU
| 8,986 |
Out of dataset answer and reference link provided for RAG example
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[] | null | 3 | 2023-08-09T18:20:23 | 2023-11-16T16:06:02 | 2023-11-16T16:06:01 |
NONE
| null |
### System Info
Ubuntu 22
Langchain version
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Current behaviour:
I'm using the [RAG example](https://github.com/pinecone-io/examples/blob/master/docs/langchain-retrieval-augmentation.ipynb) and feeding my own database of [1 football article](https://www.bbc.co.uk/sport/football/65984561).
The Pinecone DB is a brand new database and only contains vectors from the football article.
When I do qa_with_sources(query="Who is Sachin Tendulkar") it provides me an answer and a link as a reference. This is not the expected behavior.
I have not fed any article about Sachin Tendulkar to the database. How and why/where from is it getting the answer and the link?
Now, If I add more articles only about football, push the vector count in the database to around 90. And then I ask the same question, query="Who is Sachin Tendulkar", it is not able to give the answer, which is the expected behavior.
I wonder if the fullness of the vector db makes it more accurate? Has anyone else seen this?
Repro:
Create a new Vector DB on pinecone. Use [this example](https://github.com/pinecone-io/examples/blob/master/docs/langchain-retrieval-augmentation.ipynb) to feed in a [football](https://www.bbc.com/sport/football/65984561) article.
Run query="Who is Sachin Tendulkar". Note the result contains a reference and an answer. (Unexpected)
Now, create a more full db, with more articles and ask the same query. Note that the results is empty as expected.
### Expected behavior
Since the database does not contain any article or mention of Sachin Tendulkar, it should not provide any answer, and instead say "This is not mentioned in the database".
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| 1,843,767,365 |
I_kwDOIPDwls5t5ahF
| 8,985 |
Issue in running ConversationalRetrievalChain query across multiple Opensearch indices with wildcard specification
|
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[] | null | 5 | 2023-08-09T18:08:27 | 2023-11-29T16:08:15 | 2023-11-29T16:08:14 |
NONE
| null |
### System Info
Langchain version: 0.0.257
Python version: 3.11
Opensearch-py version: 2.3.0
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have this `Opensearch Vector DB `and I maintain multiple indices that start with "index-" (for example index-pdf, index-html). I have indexed sets of documents to each of the indices using Langchain's OpenSearchVectorSearch.from_documents() function.
Now, I want to run some queries which I want them to be run across multiple indices. An example would be "What is the title of each document?". When I execute below code, it either just outputs answer from first or last matching index, or says it cannot find the answer. Here is my current code:
```
from langchain.vectorstores import OpenSearchVectorSearch
from langchain.chains import RetrievalQA, ConversationalRetrievalChain
import os
from langchain.embeddings import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
embeddings = OpenAIEmbeddings()
def get_llm(model):
llm = ChatOpenAI(model_name=model.lower(), verbose=False, temperature=0)
return llm
docsearch = OpenSearchVectorSearch(opensearch_url="http://localhost:9200",
index_name="index-*",
embedding_function=embeddings)
chain = ConversationalRetrievalChain.from_llm(
llm=get_llm("gpt-3.5-turbo"),
retriever=docsearch.as_retriever(),
)
result = chain({'question': 'What is the title of each document?', "chat_history": []})
response = result['answer']
print(response)
```
The response I get is either of the format "The document provided does not list different titles..."
### Expected behavior
Response should be span across multiple indices
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| 1,843,758,645 |
I_kwDOIPDwls5t5YY1
| 8,984 |
pydantic upgrade
|
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[] | null | 6 | 2023-08-09T18:01:33 | 2023-11-24T16:07:25 | 2023-11-24T16:07:24 |
NONE
| null |
### Feature request
Many packages use pydantic versions that are much more recent than the one in langchain.
### Motivation
It is very difficult to use langchain with other packages that use recent versions of pydantic
### Your contribution
Just wanting to signal this as a needed feature
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I_kwDOIPDwls5t5U2a
| 8,983 |
Kernel crash when using llama2 70b on langchain with llama.cpp
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[] | null | 3 | 2023-08-09T17:50:43 | 2023-11-16T16:06:21 | 2023-11-16T16:06:20 |
NONE
| null |
### System Info
Mac OS 14.0
M1 Max 64GB ram
VSCode 1.80.2
Jupyter Notebook
Python 3.11.4
Llama-cpp-python using `!CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install --upgrade llama-cpp-python`
### Who can help?
@hwchase17 @agol
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- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.llms import LlamaCpp
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = LlamaCpp(
model_path="./llama2_70b_chat_uncensored.ggmlv3.q5_K_S.bin",
n_gpu_layers=n_gpu_layers,
n_gqa=8,
n_batch=n_batch,
n_ctx=2048,
f16_kv=True,
callback_manager=callback_manager,
verbose=True,
)
prompt = """
Question: A rap battle between Stephen Colbert and John Oliver
"""
llm(prompt)
```
Error log:
```
10:30:36.498 [error] Disposing session as kernel process died ExitCode: undefined, Reason: 0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
llama.cpp: loading model from ./llama2_70b_chat_uncensored.ggmlv3.q5_K_S.bin
llama_model_load_internal: warning: assuming 70B model based on GQA == 8
llama_model_load_internal: format = ggjt v3 (latest)
llama_model_load_internal: n_vocab = 32000
llama_model_load_internal: n_ctx = 2048
llama_model_load_internal: n_embd = 8192
llama_model_load_internal: n_mult = 7168
llama_model_load_internal: n_head = 64
llama_model_load_internal: n_head_kv = 8
llama_model_load_internal: n_layer = 80
llama_model_load_internal: n_rot = 128
llama_model_load_internal: n_gqa = 8
llama_model_load_internal: rnorm_eps = 1.0e-06
llama_model_load_internal: n_ff = 28672
llama_model_load_internal: freq_base = 10000.0
llama_model_load_internal: freq_scale = 1
llama_model_load_internal: ftype = 16 (mostly Q5_K - Small)
llama_model_load_internal: model size = 70B
llama_model_load_internal: ggml ctx size = 0.21 MB
llama_model_load_internal: mem required = 46046.21 MB (+ 640.00 MB per state)
llama_new_context_with_model: kv self size = 640.00 MB
ggml_metal_init: allocating
ggml_metal_init: using MPS
ggml_metal_init: loading '~/anaconda3/envs/llama-cpp-venv/lib/python3.11/site-packages/llama_cpp/ggml-metal.metal'
ggml_metal_init: loaded kernel_add 0x126d41720
ggml_metal_init: loaded kernel_add_row 0x126d41130
ggml_metal_init: loaded kernel_mul 0x126d4e410
ggml_metal_init: loaded kernel_mul_row 0x126d4e710
ggml_metal_init: loaded kernel_scale 0x126d55390
ggml_metal_init: loaded kernel_silu 0x126d538c0
ggml_metal_init: loaded kernel_relu 0x126d56130
ggml_metal_init: loaded kernel_gelu 0x126d568e0
ggml_metal_init: loaded kernel_soft_max 0x126d56d00
ggml_metal_init: loaded kernel_diag_mask_inf 0x126d56f70
ggml_metal_init: loaded kernel_get_rows_f16 0x126d57af0
ggml_metal_init: loaded kernel_get_rows_q4_0 0x126d581f0
ggml_metal_init: loaded kernel_get_rows_q4_1 0x126d58780
ggml_metal_init: loaded kernel_get_rows_q2_K 0x111ce66e0
ggml_metal_init: loaded kernel_get_rows_q3_K 0x126d589f0
ggml_metal_init: loaded kernel_get_rows_q4_K 0x126d58c60
ggml_metal_init: loaded kernel_get_rows_q5_K 0x126d59830
ggml_metal_init: loaded kernel_get_rows_q6_K 0x126d59d80
ggml_metal_init: loaded kernel_rms_norm 0x130328d00
ggml_metal_init: loaded kernel_norm 0x1303284a0
ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x13032a1c0
ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x126d59ff0
ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x126d5a260
ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x126d5b0c0
ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x126d5b640
ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x126d5bc50
ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x126d5c240
ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x126d5c960
ggml_metal_init: loaded kernel_rope 0x13032af10
ggml_metal_init: loaded kernel_alibi_f32 0x13032b180
ggml_metal_init: loaded kernel_cpy_f32_f16 0x13032b3f0
ggml_metal_init: loaded kernel_cpy_f32_f32 0x13032b660
ggml_metal_init: loaded kernel_cpy_f16_f16 0x13032d380
ggml_metal_init: recommendedMaxWorkingSetSize = 49152.00 MB
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: maxTransferRate = built-in GPU
llama_new_context_with_model: max tensor size = 205.08 MB
ggml_metal_add_buffer: allocated 'data ' buffer, size = 36864.00 MB, offs = 0
ggml_metal_add_buffer: allocated 'data ' buffer, size = 8603.55 MB, offs = 38439649280, (45468.00 / 49152.00)
ggml_metal_add_buffer: allocated 'eval ' buffer, size = 24.00 MB, (45492.00 / 49152.00)
ggml_metal_add_buffer: allocated 'kv ' buffer, size = 642.00 MB, (46134.00 / 49152.00)
ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 456.00 MB, (46590.00 / 49152.00)
ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 304.00 MB, (46894.00 / 49152.00)
10:30:36.498 [info] Dispose Kernel process 40138.
10:30:36.498 [error] Raw kernel process exited code: undefined
10:30:36.499 [error] Error in waiting for cell to complete [Error: Canceled future for execute_request message before replies were done
at t.KernelShellFutureHandler.dispose (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:2:32375)
at ~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:2:51427
at Map.forEach (<anonymous>)
at v._clearKernelState (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:2:51412)
at v.dispose (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:2:44894)
at ~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:24:113024
at re (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:2:1587343)
at Cv.dispose (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:24:113000)
at Ev.dispose (~/.vscode/extensions/ms-toolsai.jupyter-2023.6.1101941928-darwin-arm64/out/extension.node.js:24:120283)
at process.processTicksAndRejections (node:internal/process/task_queues:96:5)]
10:30:36.499 [warn] Cell completed with errors {
message: 'Canceled future for execute_request message before replies were done'
}
10:30:36.499 [info] End cell 21 execution @ 1691602236499, started @ 1691602225152, elapsed time = 11.347s
```
Tested with llama.cpp sample and 70b model works directly without langchain. The problem only occurs when using langchain to prompt to llama.cpp & the 70b model.
### Expected behavior
Kernel should not crash.
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PR_kwDOIPDwls5Xj-U5
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Use Embeddings in pinecone
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COLLABORATOR
| null |
cc @eyurtsev @olivier-lacroix @jamescalam
redo of #2741
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Add convenience methods to ConversationBufferMemory and ConversationB…
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[] | null | 2 | 2023-08-09T17:07:14 | 2023-08-10T22:45:30 | 2023-08-10T22:45:30 |
CONTRIBUTOR
| null |
Add convenience methods to `ConversationBufferMemory` and `ConversationBufferWindowMemory` to get buffer either as messages or as string.
Helps when `return_messages` is set to `True` but you want access to the messages as a string, and vice versa.
@hwchase17
One use case: Using a `MultiPromptRouter` where `default_chain` is `ConversationChain`, but destination chains are `LLMChains`. Injecting chat memory into prompts for destination chains prints a stringified `List[Messages]` in the prompt, which creates a lot of noise. These convenience methods allow caller to choose either as needed.
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Add redis storage
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| null |
Add a redis implementation of a BaseStore
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I_kwDOIPDwls5t4_Md
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Force LangChain Agent to Use a Tool
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[] | null | 2 | 2023-08-09T16:48:11 | 2023-08-22T13:32:38 | 2023-08-14T14:48:04 |
NONE
| null |
### Issue you'd like to raise.
How do you force a langchain agent to use a tool and not use any information outside the tool? Currently, if the question is not related to the Tool it uses its own data to generate answer which I don't want it to be.
Use Case: I have an index which has a lot of documents. I want to make sure the agent will use the index ALWAYS to get information. If the question is not in the index it should return "I don't know" or similar.
P.S I am using OPENAI_FUNCTION agent.
### Suggestion:
_No response_
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issues loading the prompt with load_prompt
|
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[] | null | 1 | 2023-08-09T16:45:57 | 2023-08-09T16:47:49 | 2023-08-09T16:47:49 |
NONE
| null |
### System Info
colab
```
!pip install -q langchain tiktoken openai chromadb
```
### Who can help?
@eyurtsev @aga
### 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
```
%%writefile adjective_joke_prompt.json
{
# what type of prompt. Currently supports "prompt" and "few_shot"
"_type": "prompt",
# the input variables used in the template
"input_variables": ["adjective", "content"],
# the template text of the prompt, including variable placeholders
"template": "Tell me a {{ adjective }} joke about {{ content }}",
# alternatively the template text can be loaded from a file
"template_path": "adjective_joke_prompt_template.txt"
# NOTE: both "template" and "template_path" cannot be used at the same time!
# the format of the template
"template_format": "jinja2",
# currently only the "RegexParser" is supported for "output_parser"
# this is example of a date parser
"output_parser": {
"_type": "regex_parser",
"regex": "(\\d{4})-(\\d{2})-(\\d{2})",
"output_keys": ["year", "month", "day"]
}
}
```
```
# load the prompt using a file
prompt_template = load_prompt("adjective_joke_prompt.json")
# create a prompt using the variables
prompt_template.format(adjective="funny", content="chickens")
```
gives error:
```
---------------------------------------------------------------------------
JSONDecodeError Traceback (most recent call last)
[<ipython-input-41-7755c281a03b>](https://localhost:8080/#) in <cell line: 2>()
1 # load the prompt using a file
----> 2 prompt_template = load_prompt("adjective_joke_prompt.json")
3
4 # create a prompt using the variables
5 prompt_template.format(adjective="funny", content="chickens")
5 frames
[/usr/lib/python3.10/json/decoder.py](https://localhost:8080/#) in raw_decode(self, s, idx)
351 """
352 try:
--> 353 obj, end = self.scan_once(s, idx)
354 except StopIteration as err:
355 raise JSONDecodeError("Expecting value", s, err.value) from None
JSONDecodeError: Expecting property name enclosed in double quotes: line 3 column 2 (char 4)
```
### Expected behavior
gives response based on template
|
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| 1,843,649,007 |
PR_kwDOIPDwls5Xjtcw
| 8,977 |
scheduled gha fix
|
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[] | null | 1 | 2023-08-09T16:43:29 | 2023-08-09T16:44:26 | 2023-08-09T16:44:25 |
COLLABORATOR
| null | null |
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| 1,843,646,644 |
PR_kwDOIPDwls5Xjs9P
| 8,976 |
Add embeddings cache
|
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[] | null | 3 | 2023-08-09T16:41:44 | 2023-08-10T15:15:32 | 2023-08-10T15:15:30 |
COLLABORATOR
| null |
This PR adds the ability to temporarily cache or persistently store embeddings.
A notebook has been included showing how to set up the cache and how to use it
with a vectorstore.
|
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| 1,843,641,608 |
I_kwDOIPDwls5t470I
| 8,975 |
Issue: Can't pass embedded documents with chat
|
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[] | null | 0 | 2023-08-09T16:38:34 | 2023-08-09T17:38:23 | 2023-08-09T17:38:23 |
NONE
| null |
### Issue you'd like to raise.
I am trying to have a conversation with my documents like this
```typescript
const index = client.Index(indexName); //pinecode index
const queryEmbedding = await new OpenAIEmbeddings().embedQuery(question);
const queryResponse = await index.query({
queryRequest: { topK: 10, vector: queryEmbedding, includeMetadata: true },
});
const llm = new OpenAI();
const chatHistory = new ChatMessageHistory();
const memory = new BufferMemory({ chatHistory, inputKey: "my_chat_history", memoryKey: "chat_history" });
const chain = new ConversationChain({ llm, memory, verbose: true });
const concatenatedPageContent = queryResponse.matches.map(match => (<any>match?.metadata)?.pageContent).join(" ");
const result = await chain.call({
input_documents: [new Document({ pageContent: concatenatedPageContent })],
input: question,
});
```
But gettiing
```
Error: Missing value for input history
```
It seems to be impossible to marry embedded docs with chatting
|
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| 1,843,635,976 |
PR_kwDOIPDwls5XjqoF
| 8,974 |
skip over empty google spreadsheets
|
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[] | null | 2 | 2023-08-09T16:34:57 | 2023-08-09T23:05:36 | 2023-08-09T23:05:03 |
CONTRIBUTOR
| null |
- Description: Allow GoogleDriveLoader to handle empty spreadsheets
- Issue: Currently GoogleDriveLoader will crash if it tries to load a spreadsheet with an empty sheet
- Dependencies: n/a
- Tag maintainer: @rlancemartin, @eyurtsev
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I_kwDOIPDwls5t4zz7
| 8,973 |
Human as a Tool in Production
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[] | null | 2 | 2023-08-09T16:15:19 | 2023-11-15T16:05:23 | 2023-11-15T16:05:22 |
NONE
| null |
### Issue you'd like to raise.
Can we use Human as a tool in product?
def get_input() -> str:
print("Insert your text. Enter 'q' or press Ctrl-D (or Ctrl-Z on Windows) to end.")
contents = []
while True:
try:
line = input()
except EOFError:
break
if line == "q":
break
contents.append(line)
return "\n".join(contents)
# You can modify the tool when loading
tools = load_tools(["human", "ddg-search"], llm=math_llm, input_func=get_input)
what to do with input() in get_input() function if we need to use this tool in production?
reference:-https://python.langchain.com/docs/integrations/tools/human_tools
### Suggestion:
_No response_
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| 1,843,600,490 |
I_kwDOIPDwls5t4xxq
| 8,972 |
qa_with_source it returns all documents, not just the ones used.
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[] | null | 1 | 2023-08-09T16:09:42 | 2023-11-15T16:05:34 | 2023-11-15T16:05:33 |
CONTRIBUTOR
| null |
### Feature request
### First
The aim of qa_with_sources is to find only the documents used in the answer.
The attribute `return_source_documents` in `qa_with_sources` chain, returns all the documents. Not just the documents used to provide the answer.
I think it's not necessary because `retriever.get_relevant_documents(question)` returns the same documents list.
It has no added value, and this is not in the spirit of the chain.
I propose to add an attribute `return_used_documents` or change the semantic of `return_source_documents` to limit the result to the documents used to provide the answer.
### Second
With a long list of documents, with a big URL for each document (like a document come from google drive or Microsoft Azure), the number of tokens used is exploding.
The recursive map-reduce must be activated. At each reduction, **some URLs disappear**, and space for documents is shrinking.
### Motivation
When we use the qa_with_source, we want to be able to justify the response.
Actually, we can return a correct list of url, but not the list of associated documents.
Sometime, when the original document is split into multiple documents, all part have the same URL. It's not possible to find the corresponding documents with a list of URL.
### Your contribution
I propose a new chain [qa_with_reference](https://github.com/langchain-ai/langchain/pull/7278) without these problems, but for the moment, nothing is moving.
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PR_kwDOIPDwls5XjfXQ
| 8,971 |
BagelDB (bageldb.ai), VectorStore integration.
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[] | null | 11 | 2023-08-09T16:01:45 | 2023-08-10T23:48:37 | 2023-08-10T23:48:37 |
CONTRIBUTOR
| null |
- **Description**: [BagelDB](bageldb.ai) a collaborative vector database. Integrated the bageldb PyPi package with langchain with related tests and code.
- **Issue**: Not applicable.
- **Dependencies**: `betabageldb` PyPi package.
- **Tag maintainer**: @rlancemartin, @eyurtsev, @baskaryan
- **Twitter handle**: bageldb_ai (https://twitter.com/BagelDB_ai)
We ran `make format`, `make lint` and `make test` locally.
Followed the contribution guideline thoroughly https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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| 1,843,546,427 |
PR_kwDOIPDwls5XjW7H
| 8,970 |
Fixed wrong paper reference
|
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[] | null | 1 | 2023-08-09T15:45:24 | 2023-08-09T20:17:47 | 2023-08-09T20:17:47 |
CONTRIBUTOR
| null |
The ReAct reference references to MRKL paper. Corrected so that it points to the actual ReAct paper #8964.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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2. an example notebook showing its use.
Maintainer responsibilities:
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| 1,843,326,275 |
I_kwDOIPDwls5t3u1D
| 8,969 |
Bugs in `GoogleDriveLoader.file_loader_cls`
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[] | null | 2 | 2023-08-09T14:06:18 | 2023-09-19T08:31:39 | 2023-09-19T08:31:38 |
CONTRIBUTOR
| null |
### System Info
langchain version 0.0.259
### Who can help?
@hwchase17 @baskaryan @eyurtsev
## Issue with `GoogleDriveLoader.file_loader_cls` accepting only classes with constructors that receive a `file` parameter.
The current behavior of `GoogleDriveLoader.file_loader_cls` is that it only accepts a class with a constructor that
takes a `file` parameter. For instance, it can accept the class `UnstructuredFileIOLoader`, and
typically other classes like `NotebookLoader` as well. However, when attempting to use
the `NotebookLoader` class with the following code:
```python
from langchain.document_loaders import NotebookLoader
file_id = "1Hrrf3b4cgjwuKEt1wQUgRtipxqyprKaU"
loader = GoogleDriveLoader(
file_ids=[file_id],
file_loader_cls=NotebookLoader,
file_loader_kwargs={"mode": "elements"},
)
loader.load()
```
An exception is thrown: `TypeError: NotebookLoader.__init__() got an unexpected keyword argument 'file'`.
## Issue with `GoogleDriveLoader` and `import PyPDF2`
If the `file_loader_cls` is not set explicitly, the code attempts to execute `import PyPDF2`.
However, the code is only designed to handle PDF files. Additionally, the dependencies
of `PyPDF2` are not declared in the `pyproject.toml` file. Currently, only `pypdf` is
declared as a dependency. To address this, it is recommended to update the code to utilize `pypdf`
instead of `PyPDF2`. Otherwise, an exception will be raised.
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders import NotebookLoader
file_id = "1Hrrf3b4cgjwuKEt1wQUgRtipxqyprKaU" # Link to colab file
loader = GoogleDriveLoader(
file_ids=[file_id],
file_loader_cls=NotebookLoader,
file_loader_kwargs={"mode": "elements"},
)
loader.load()
### Expected behavior
The code should be modified to accept the provided code without any exceptions.
I propose a solution with [a pull request](https://github.com/langchain-ai/langchain/pull/5135),
with a reimplementation of this class.
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I_kwDOIPDwls5t3urI
| 8,968 |
Extend google drive loader
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[] | null | 1 | 2023-08-09T14:06:01 | 2023-09-05T14:42:04 | 2023-09-05T14:42:04 |
CONTRIBUTOR
| null |
### Feature request
- The google drive loader, can manage only GDoc and GSheet.
- The code can load others type of files, but only with one other loader (`file_loader_cls`).
- It's not possible to request Google Drive to find some files with criteria.
- A Google Drive tools is not exist now.
- The can not manage the Google shortcut
- Can not use the description metadata of Google file
- Load only the description for a snippet of document
- It's not possible to set the advanced parameters like
- corpora, driveId, fields, includeItemsFromAllDrives, includeLabels, includePermissionsForView, orderBy, pageSize, spaces, supportsAllDrives,
- Select if return the URL of the document if for view or download
- It's not possible to return
- For GSheet: mode `single` or `elements`
- For GSlide: mode `single`, `elements` or ̀slide`
- It's not possible to use a fine filter (to refuse some document during the load)
- It's not possible to lazy loading if case of import a long list of documents and save memory
- It's not possible to use a standardized environment variable to manage the authentification (like all others technologies)
[LINK](https://github.com/langchain-ai/langchain/pull/5135)
### Motivation
All my company's documents are on GDrive, with more or less complex organizations and formats
(in the organization of CVs, customer documents, etc.).
The actual implementation of `GoogleDriveLoader` is very limited.
We tag all CVs with #cv. This allows us to quickly search for skills using Google Drive search.
Since the directory structure is complex, this approach requires only a single query
(instead of one query per subdirectory).
We utilize Google Drive searches for various purposes.
### Your contribution
@hwchase17 @baskaryan
For the last 10 weeks, I've been offering a pull-request number
[5135](https://github.com/langchain-ai/langchain/pull/5135) that nobody is taking up.
I've had various commitments, but they've never been kept.
My [proposition](https://github.com/langchain-ai/langchain/pull/5135) resolves all these features, and maintains the compatibility with the current version
(with many deprecated warning).
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| 1,843,325,349 |
I_kwDOIPDwls5t3uml
| 8,967 |
Issue: re-implement pivot files to facilitate the integration of new functions
|
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[] | null | 1 | 2023-08-09T14:05:53 | 2023-11-15T16:07:03 | 2023-11-15T16:07:02 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
@hwchase17
The code utilizes certain pivot files responsible for the comprehensive integration of classes (__init__.py, load_tools.py, etc.).
Currently, maintaining an up-to-date pull request for these files poses challenges, as they undergo updates with each new version. This necessitates frequent rebasing until the code is approved.
In my opinion, it would be more advantageous to establish a mechanism that prevents alterations to these files (via naming conventions, abstract classes, etc.). This way, incorporating a new feature would involve solely the addition of new files without requiring modifications to existing ones. Such an approach could serve as the foundation for implementing plugins.
What are your thoughts on this idea?
### Suggestion:
_No response_
|
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| 1,843,265,073 |
I_kwDOIPDwls5t3f4x
| 8,966 |
OpenAIEmbeddings expects client parameter
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[] | null | 1 | 2023-08-09T13:38:54 | 2023-11-15T16:05:26 | 2023-11-15T16:05:25 |
NONE
| null |
### System Info
langchain: 0.0.257
Python: 3.11.4
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to create an object for `OpenAIEmbeddings` as described [here](https://python.langchain.com/docs/integrations/text_embedding/azureopenai), but the constructor expects a `client `parameter which is documented nowhere.
<img width="591" alt="image" src="https://github.com/langchain-ai/langchain/assets/33937977/6d78c87b-bdcb-4c84-82d8-2c171739d47d">
### Expected behavior
Initialize the object without passing a client
|
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| 1,843,172,117 |
PR_kwDOIPDwls5XiF54
| 8,965 |
Enhance regex of structured_chat agents' output parser
|
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[] | null | 2 | 2023-08-09T12:50:46 | 2023-08-10T21:26:08 | 2023-08-10T21:26:08 |
CONTRIBUTOR
| null |
Current regex only extracts agent's action between '` ``` ``` `', this commit will extract action between both '` ```json ``` `' and '` ``` ``` `'
This is very similar to #7511
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
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Maintainer responsibilities:
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| 1,843,168,836 |
I_kwDOIPDwls5t3IZE
| 8,964 |
Wrong paper reference
|
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[] | null | 2 | 2023-08-09T12:49:03 | 2023-08-20T05:26:14 | 2023-08-20T05:26:13 |
NONE
| null |
In the docs it seems the ReAct reference references to the wrong paper.
The actual ReAct paper is [this](https://arxiv.org/pdf/2210.03629.pdf)
https://github.com/langchain-ai/langchain/blob/b8df15cd647ca645ef16b2d66be271dc1f5187c1/docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx#L15
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PR_kwDOIPDwls5XiB8p
| 8,963 |
EdenAI LLM update. Add models name option
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[] | null | 8 | 2023-08-09T12:41:05 | 2023-09-01T19:11:34 | 2023-09-01T19:11:34 |
CONTRIBUTOR
| null |
This PR follows the **Eden AI (LLM + embeddings) integration**. #8633
We added an optional parameter to choose different AI models for providers (like 'text-bison' for provider 'google', 'text-davinci-003' for provider 'openai', etc.).
Usage:
```python
llm = EdenAI(
feature="text",
provider="google",
params={
"model": "text-bison", # new
"temperature": 0.2,
"max_tokens": 250,
},
)
```
You can also change the provider + model after initialization
```python
llm = EdenAI(
feature="text",
provider="google",
params={
"temperature": 0.2,
"max_tokens": 250,
},
)
prompt = """
hi
"""
llm(prompt, providers='openai', model='text-davinci-003') # change provider & model
```
The jupyter notebook as been updated with an example well.
Ping: @hwchase17, @baskaryan
|
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I_kwDOIPDwls5t3A_J
| 8,962 |
How to improve my embedding quality?
|
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[] | null | 2 | 2023-08-09T12:31:35 | 2023-11-24T19:36:17 | 2023-11-15T16:06:51 |
NONE
| null |
Now I am using Pinecone and langchain in my Project.
And I am using RecursiveCharacterTextSplitter when I embedding my bot based on my specific data.
And also I am using ConversationalRetrievalQAChain in chain.
These are my code.
```
const text_splitter = new RecursiveCharacterTextSplitter({
chunkSize: 1000,
chunkOverlap: 200,
});
const docs = await text_splitter.splitDocuments(rowDocs);
```
```
const chain = ConversationalRetrievalQAChain.fromLLM(
llm,
vectorStore.asRetriever(),
{
memory: new BufferMemory({
memoryKey: 'chat_history', // Must be set to "chat_history"
inputKey: 'question',
returnMessages: true,
}),
},
);
```
But my embedding quality is not good.
Sometimes my Bot gives strange answers to my questions. 😢
For example
I have already trained ChatBot using one docx(about Abrantes)
I have asked like that
Me: Please tell me about Abrantes
AI: Abrantes is a Project Controls Specialist ....
Me: What Project he managed?
AI: Ed has managed some .....
Who is Ed in this answer? 😣
There is no Ed in this docx too.
So how can I fix it?
|
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| 1,843,070,745 |
I_kwDOIPDwls5t2wcZ
| 8,961 |
Agents on a local llm with custom data
|
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[] | null | 3 | 2023-08-09T11:49:11 | 2023-11-28T16:08:55 | 2023-11-28T16:08:54 |
NONE
| null |
I'm implementing vectorstore agent on my custom data. Can I implement this with a local llm like gpt4all(GPT4All-J v1.3-groovy)
Can agents give better and expected answer when we use agents, or should go with better model like(gpt4, llama2)?
|
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| 1,843,037,826 |
PR_kwDOIPDwls5Xhov1
| 8,960 |
Update bge_huggingface.ipynb
|
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[] | null | 3 | 2023-08-09T11:28:41 | 2023-10-17T18:58:30 | 2023-10-17T18:58:29 |
CONTRIBUTOR
| null |
- Description: Considering the similarity computation method of [BGE](https://github.com/FlagOpen/FlagEmbedding) model is cosine similarity, set normalize_embeddings to be True.
- Tag maintainer: @baskaryan
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| 1,842,978,093 |
I_kwDOIPDwls5t2Z0t
| 8,959 |
Issue: How can I retrieve the action for a LLM agent?
|
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[] | null | 1 | 2023-08-09T10:52:59 | 2023-11-15T16:05:29 | 2023-11-15T16:05:28 |
NONE
| null |
### Issue you'd like to raise.
How can I retrieve the action for a LLM agent?
![Uploading image.png…]()
### Suggestion:
_No response_
|
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| 1,842,957,431 |
I_kwDOIPDwls5t2Ux3
| 8,958 |
Issue: FAISS taking long time to add to index for 30MB csv file
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[] | null | 1 | 2023-08-09T10:39:17 | 2023-11-15T16:10:55 | 2023-11-15T16:10:54 |
NONE
| null |
### Issue you'd like to raise.
FAISS is taking around 12hrs to create embedding and add it to index for 100000 rows csv file. is there any bulk load strategy for CSV files embedding
### Suggestion:
_No response_
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I_kwDOIPDwls5t2O8E
| 8,957 |
Embeddings are regenerated for entire vector db on updating collections
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[] | null | 1 | 2023-08-09T10:24:17 | 2023-08-31T14:33:51 | 2023-08-31T14:33:50 |
NONE
| null |
### System Info
macOS Ventura Version `13.4.1`
Python `3.11.4`
```
langchain==0.0.251
chromadb==0.4.5
```
### Who can help?
@hwchase17 @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a vector database and insert some items with relevant embeddings
2. Update vector database collection with some new docs
Python snippet to create/update vector db collection and it's embeddings:
```
documents = []
for row in get_data_rows():
documents.append(
Document(
page_content=row['full_text'],
metadata={
'id': int(row['id']),
'created_at': parser.parse(row['created_at']).timestamp(),
}
)
)
Chroma.from_documents(
documents,
embedding=OpenAIEmbeddings(),
collection_name='my_collection',
persist_directory=f'embeddings/my_collection'
)
```
### Expected behavior
Running above snippet in both scenarios should only call embeddings function for newly added or changed docs instead of everything?
|
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I_kwDOIPDwls5t2C30
| 8,956 |
lang chain connection with mongo DB
|
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[] | null | 10 | 2023-08-09T09:53:54 | 2023-11-21T06:03:17 | null |
NONE
| null |
### Issue you'd like to raise.
Can you please help me with connecting my LangChain agent to a MongoDB database?
I have connected my relational database below like
db = SQLDatabase.from_uri("mysql_db_url")
The above process is not working for Mongo Db because it is a NoSQL database
anyone can help me to do this ???????
Thank you .....
### Suggestion:
_No response_
|
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| 1,842,833,550 |
I_kwDOIPDwls5t12iO
| 8,955 |
LlamaCppEmbeddings not working with faiss
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[] | null | 1 | 2023-08-09T09:23:46 | 2023-08-11T03:17:28 | 2023-08-11T03:17:28 |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.251
Name: faiss-cpu
Version: 1.7.1
Name: llama-cpp-python
Version: 0.1.77
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import gradio as gr
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import HuggingFacePipeline
from langchain.document_loaders import DirectoryLoader
from langchain.document_loaders import UnstructuredWordDocumentLoader
from torch import cuda, bfloat16
from transformers import StoppingCriteria, StoppingCriteriaList
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import LlamaCppEmbeddings
template = """Question: {question}
Answer:"""
prompt = PromptTemplate(template=template, input_variables=["question"])
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
n_gpu_layers = 42 # Change this value based on your model and your GPU VRAM pool.
n_batch = 1024 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
embeddings = ubuntu(model_path="llama-2-7b-chat/7B/ggml-model-q4_0.bin",
n_gpu_layers=n_gpu_layers,
n_batch=n_batch)
llm = LlamaCpp(
model_path="llama-2-7b-chat/7B/ggml-model-q4_0.bin",
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
callback_manager=callback_manager,
verbose=True,
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
txt_loader = DirectoryLoader("doc", glob="./*.docx", loader_cls=UnstructuredWordDocumentLoader)
documents = txt_loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
all_splits = text_splitter.split_documents(documents)
vectorstore = FAISS.from_documents(all_splits, embeddings)
query = "How is it going?"
search = vectorstore.similarity_search(query, k=5)
template = '''Context: {context}
Based on Context provide me answer for following question
Question: {question}
Tell me the information about the fact. The answer should be from context only
do not use general knowledge to answer the query'''
prompt = PromptTemplate(input_variables=["context", "question"], template= template)
final_prompt = prompt.format(question=query, context=search)
result = llm_chain.run(final_prompt)
print(result)
```
I get error:
```
llama_tokenize_with_model: too many tokens
Traceback (most recent call last):
File "test_ggml.py", line 57, in <module>
vectorstore = FAISS.from_documents(all_splits, embeddings)
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/langchain/vectorstores/base.py", line 420, in from_documents
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/langchain/vectorstores/faiss.py", line 577, in from_texts
embeddings = embedding.embed_documents(texts)
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/langchain/embeddings/llamacpp.py", line 110, in embed_documents
embeddings = [self.client.embed(text) for text in texts]
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/langchain/embeddings/llamacpp.py", line 110, in <listcomp>
embeddings = [self.client.embed(text) for text in texts]
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/llama_cpp/llama.py", line 812, in embed
return list(map(float, self.create_embedding(input)["data"][0]["embedding"]))
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/llama_cpp/llama.py", line 776, in create_embedding
self.eval(tokens)
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/llama_cpp/llama.py", line 471, in eval
self.input_ids[self.n_tokens : self.n_tokens + n_tokens] = batch
ValueError: could not broadcast input array from shape (179,) into shape (0,)
Exception ignored in: <function Llama.__del__ at 0x7f68eedc9af0>
Traceback (most recent call last):
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/llama_cpp/llama.py", line 1508, in __del__
TypeError: 'NoneType' object is not callable
Exception ignored in: <function Llama.__del__ at 0x7f68eedc9af0>
Traceback (most recent call last):
File "/home/tupk/anaconda3/envs/nlp/lib/python3.8/site-packages/llama_cpp/llama.py", line 1508, in __del__
TypeError: 'NoneType' object is not callable
```
### Expected behavior
CppEmbedding can work well with faiss
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PR_kwDOIPDwls5Xg1Hp
| 8,954 |
add filter by metadata to annoy search
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[] | null | 2 | 2023-08-09T09:02:55 | 2023-09-20T17:48:22 | null |
CONTRIBUTOR
| null |
# What
- This is to add filter by metadata to annoy search with test.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add filter by metadata to annoy search
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MLOpsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5t1pzM
| 8,953 |
Issue: unclear processing of natural language query parsing + external api querying afterwards
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[] | null | 3 | 2023-08-09T08:51:52 | 2023-11-16T16:06:36 | 2023-11-16T16:06:35 |
NONE
| null |
### Issue you'd like to raise.
I believe one of the most popular directions for langchain-based applications will be to parse the query in a natural language and to use some external api for the response generation. However, as soon as this external API doesn't have an OpenAISchema-based specification, the easiest way to do it will be via chain parsing + request.
I tried to implement it via a sequential chain, the example is provided below:
```
EXCHANGE_API_KEY = SOME_KEY
from langchain.chains import LLMRequestsChain, LLMChain, SimpleSequentialChain
from langchain.chains.openai_functions.openapi import get_openapi_chain
from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
template = """You help to convert currencies.
You need to convert the input phrase into the query like from=EUR&to=GBP&amount=100"
You need to extract these parameters yourself from the query in natural language.
Please, return the string like https://api.currencylayer.com/convert?from=EUR&to=GBP&amount=100, where you need to fill in "from", "to", and "amount" based on previous instructions"
"""
system_prompt = SystemMessagePromptTemplate.from_template(template)
human_message_prompt = HumanMessagePromptTemplate.from_template("{question}")
first_prompt = ChatPromptTemplate(
messages=[
system_prompt,
human_message_prompt
]
)
## this one above may be done nicer, via PromptTemplate, but I don't think it's necessary here
template_2 = """
Query api with {synopsis} and return api response
"""
llm = OpenAI(temperature=0)
convert_chain = LLMChain(llm=llm, prompt=first_prompt, output_key="synopsis")
system_prompt_2 = PromptTemplate(input_variables=["synopsis"], template=template_2)
chain = LLMRequestsChain(llm_chain=LLMChain(llm=OpenAI(temperature=0), prompt=system_prompt_2))
overall_chain = SimpleSequentialChain(chains=[convert_chain, chain], verbose=True)
question = "Convert 1000 American Dollars to euros please"
inputs = {"input": question,
#"url": "https://api.currencylayer.com/convert",
"headers": {"access_key": EXCHANGE_API_KEY}
}
overall_chain(inputs)
```
The code above fails due to _requests_ library stacktrace: `InvalidSchema: No connection adapters were found for 'System: [https://api.currencylayer.com/convert?from=USD&to=EUR&amount=1000'](https://api.currencylayer.com/convert?from=USD&to=EUR&amount=1000%27)`
It happens because the string "System: " is inevitably attached to the output of the first chain.
So, my questions are as follows:
1. Is the way I showed above right now the best option to interact with the APIs which have not been implemented yet to langchain and haven't got OpenAISchema-based docs?
2. If so, how to deal with my problem and strip the "System: " part for query? Is there a natural way to parse the natural language query into an API request via this chain?
### Suggestion:
I think there should be a very clear way and documentation of how to deal with external APIs after natural language parsing done by generative network
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PR_kwDOIPDwls5XgOtS
| 8,952 |
refactor(document_loaders): abstract page evaluation logic in PlaywrightURLLoader
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CONTRIBUTOR
| null |
This PR brings structural updates to `PlaywrightURLLoader`, aiming at making the code more readable and extensible through the abstraction of page evaluation logic. These changes also align this implementation with a similar structure used in LangChain.js.
The key enhancements include:
1. Introduction of 'PlaywrightEvaluator', an abstract base class for all evaluators.
2. Creation of 'UnstructuredHtmlEvaluator', a concrete class implementing 'PlaywrightEvaluator', which uses `unstructured` library for processing page's HTML content.
3. Extension of 'PlaywrightURLLoader' constructor to optionally accept an evaluator of the type 'PlaywrightEvaluator'. It defaults to 'UnstructuredHtmlEvaluator' if no evaluator is provided.
4. Refactoring of 'load' and 'aload' methods to use the 'evaluate' and 'evaluate_async' methods of the provided 'PageEvaluator' for page content handling.
This update brings flexibility to 'PlaywrightURLLoader' as it can now utilize different evaluators for page processing depending on the requirement. The abstraction also improves code maintainability and readability.
Please find the reference for LangChain.js implementation [here](https://github.com/hwchase17/langchainjs/blob/29f3ffdcb34acce1f1db78078f4e7d184eaec6b5/langchain/src/document_loaders/web/playwright.ts#L59).
Maintainers for review: @rlancemartin, @eyurtsev
Twitter: @ywkim
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PR_kwDOIPDwls5XgKRM
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bump 259
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I_kwDOIPDwls5t0wML
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schema consideration while generating query
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[] | null | 5 | 2023-08-09T06:00:14 | 2023-12-08T16:05:40 | 2023-12-08T16:05:39 |
NONE
| null |
### System Info
I am working with hugging face opensource models for SQL generation with langchain
Our model works without a connection db, but when I connect the db, the chain generates inaccurate answers and takes unknown columns.
I am using below models
wizard, alpaca,vicuna,falcon
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
db_chain = SQLDatabaseChain.from_llm(llm, db=db, verbose=True,prompt=PROMPT)
db_chain.run(input('enter the question:'))
### Expected behavior
I need to connect the database with my open-source model using Langchain
when I ask a question to my chain, the chain generates the correct SQL query with consideration of my database schema.
|
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I_kwDOIPDwls5t0odq
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Hope to get in touch
|
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[] | null | 4 | 2023-08-09T05:30:05 | 2023-08-20T19:42:46 | 2023-08-20T19:42:28 |
NONE
| null |
### Feature request
Dear [langchain](https://github.com/langchain-ai/langchain) developer,
Greetings! I am Jimmy, a community developer and volunteer at InternLM. Your work has been immensely beneficial to me, and I believe it can be effectively utilized in InternLM as well. Welcome to add Discord https://discord.gg/gF9ezcmtM3 . I hope to get in touch with you.
Best regards,
Jimmy
### Motivation
Hope to get in touch
### Your contribution
Hope to get in touch
|
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PR_kwDOIPDwls5XfwF8
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Update Key Check
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[] | null | 1 | 2023-08-09T05:04:49 | 2023-08-09T19:33:02 | 2023-08-09T19:33:01 |
COLLABORATOR
| null |
In eval loop. It needn't be done unless you are creating the corresponding evaluators
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I_kwDOIPDwls5tz9Rn
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DOC: Tabular doc not found
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[] | null | 2 | 2023-08-09T02:03:58 | 2023-11-21T02:13:34 | 2023-11-15T16:05:50 |
NONE
| null |
### Issue with current documentation:
got 404 error while looking for tabular documentation:
From link:
https://python.langchain.com/docs/use_cases/question_answering/
To link:
https://python.langchain.com/docs/use_cases/tabular
got 404:
### Idea or request for content:
_No response_
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`Brave Search` and `DataDog Logs` retrievers
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[] | null | 2 | 2023-08-09T01:22:09 | 2023-09-19T15:38:50 | 2023-09-19T15:38:50 |
COLLABORATOR
| null |
- Created two new retrievers: `Brave Search` and `DataDog Logs` based on `BaseLoaderAsRetriever`
- Created `BaseLoaderAsRetriever` with `QueryMixin`. This class is used to treat DocumentLoaders as Retrievers where DocumentLoader has query semantics.
- added retriever examples
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- changed `Arxiv` examples to address the above changes.
-
@rlancemartin, @eyurtsev
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PR_kwDOIPDwls5XfHAH
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Add id in error in tracer
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PR_kwDOIPDwls5XfGve
| 8,943 |
Open file in UTF-8 encoding (#6919)
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[] | null | 5 | 2023-08-09T00:53:38 | 2023-08-10T02:30:20 | 2023-08-10T00:54:21 |
CONTRIBUTOR
| null |
FileCallbackHandler cannot handle some language, for example: Chinese.
Open file using UTF-8 encoding can fix it.
@agola11
**Issue**: #6919
**Dependencies**: NO dependencies,
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PR_kwDOIPDwls5XfGS_
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document lcel fallbacks
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[] | null | 1 | 2023-08-09T00:50:02 | 2023-08-09T01:49:34 | 2023-08-09T01:49:33 |
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PR_kwDOIPDwls5XfAG2
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parent document retriever
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PR_kwDOIPDwls5Xe5yW
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Integrate Rockset as a chat history store
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[] | null | 1 | 2023-08-08T23:20:26 | 2023-08-09T01:54:08 | 2023-08-09T01:54:08 |
CONTRIBUTOR
| null |
Description: Adds Rockset as a chat history store
Dependencies: no changes
Tag maintainer: @hwchase17
This PR passes linting and testing.
I added a test for the integration and an example notebook showing its use.
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PR_kwDOIPDwls5XezBW
| 8,939 |
Weaviate: adding auth example + fixing spelling in ReadME
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[] | null | 2 | 2023-08-08T22:36:24 | 2023-08-08T23:24:18 | 2023-08-08T23:24:17 |
CONTRIBUTOR
| null |
Added basic auth example to Weaviate notebook @baskaryan
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PR_kwDOIPDwls5XeuB7
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add instructions on integrating Log10
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[] | null | 1 | 2023-08-08T22:11:49 | 2023-08-09T02:15:31 | 2023-08-09T02:15:31 |
CONTRIBUTOR
| null |
- Description: Instruction for integration with Log10: an [open source](https://github.com/log10-io/log10) proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls
- Tag maintainer: @baskaryan
- Twitter handle: @log10io @coffeephoenix
Several examples showing the integration included [here](https://github.com/log10-io/log10/tree/main/examples/logging) and in the PR
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PR_kwDOIPDwls5XetLj
| 8,937 |
📖 refactored `conversational_retrieval`
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[] | null | 6 | 2023-08-08T22:07:16 | 2023-10-10T21:24:08 | 2023-10-10T21:24:08 |
COLLABORATOR
| null |
Fixed #9133 issue.
1. Refactored `conversational_retrieval` by removing unnecessary nesting.
Current nesting makes `API Reference` unreadable (see the `description` column that is 6-char width):
It makes the full function name (with namespace) soo long, which makes the `description` column unreadable.
The resulting namespace+name is still too long.
2. renamed function `create_conversational_retrieval_agent` -> `create_agent`. Old name kept for backward compatibility
NOTE: The problem of too-long namespaces is also addressed in #8398 Please, review #8398 !!!!!
@baskaryan, @hinthorn
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