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| 2,021,147,121 |
I_kwDOIPDwls54eEHx
| 14,129 |
OpenSearchVectorSearch fails to detect AOSS is enabled when using RequestAWS4SignerAuth from opensearch-py
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[] | null | 3 | 2023-12-01T15:53:57 | 2024-01-09T10:12:53 | 2024-01-09T10:12:53 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.335
macOS 14.1.1 / Windows 10.0.19045
Python 3.11.6 / 3.10.9
### Who can help?
@naveentatikonda
### 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
Install dependencies
```shell
pip install langchain opensearch-py boto3
```
Import packages
```python
import boto3
from langchain.embeddings import FakeEmbeddings
from langchain.vectorstores.opensearch_vector_search import OpenSearchVectorSearch
from opensearchpy import RequestsAWSV4SignerAuth, RequestsHttpConnection
```
Create AOSS auth
```python
boto_session = boto3.Session()
aoss_auth = RequestsAWSV4SignerAuth(credentials=boto_session.get_credentials(), region=boto_session.region_name, service="aoss")
```
Create AOSS vector search class
```python
embeddings = FakeEmbeddings(size=42)
database = OpenSearchVectorSearch(
opensearch_url="https://some_aoss_endpoint:443",
http_auth=aoss_auth,
connection_class=RequestsHttpConnection,
index_name="test",
embedding_function=embeddings,
```
Check if AOSS vector search identifies itself as AOSS
```python
database.is_aoss
```
Returns **False**
### Expected behavior
AOSS should correctly identify itself as AOSS given the configuration above, which means that `database.is_aoss` should return **True** in this case.
The issue is related to how the internal `_is_aoss_enabled` method works:
```python
def _is_aoss_enabled(http_auth: Any) -> bool:
"""Check if the service is http_auth is set as `aoss`."""
if (
http_auth is not None
and hasattr(http_auth, "service")
and http_auth.service == "aoss"
):
return True
return False
```
The `RequestsAWSV4SignerAuth` does not expose the _service_ property directly, but via the _signer_ attribute. In order to respect that, `is_aoss_enabled` should be adapted as following:
```python
def _is_aoss_enabled(http_auth: Any) -> bool:
"""Check if the service set via http_auth equals `aoss`."""
if http_auth is not None:
if hasattr(http_auth, "service") and http_auth.service == "aoss":
return True
elif hasattr(http_auth, "signer") and http_auth.signer.service == "aoss":
return True
else:
return False
return False
```
|
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I_kwDOIPDwls54d4fu
| 14,128 |
AttributeError: 'NotImplementedType' object has no attribute 'get_input_schema'
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[] | null | 4 | 2023-12-01T15:26:49 | 2023-12-20T05:11:23 | null |
NONE
| null |
### System Info
Using latest docker versions for langchain templates
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
First, following the quickstart for Templates until it gets to LangSmith deployment:
1. `pip install -U langchain-cli`
2. `langchain app new my-app`
3. `cd my-app`
4. `langchain app add pirate-speak`
Then, since I don't have access to LangServe, follow the template README for docker deployment:
5. `docker build . -t my-langserve-app`
6. `docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -p 8080:8080 my-langserve-app`
Results in error:
```
Traceback (most recent call last):
File "/usr/local/bin/uvicorn", line 8, in <module>
sys.exit(main())
^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1157, in __call__
return self.main(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1078, in main
rv = self.invoke(ctx)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/uvicorn/main.py", line 416, in main
run(
File "/usr/local/lib/python3.11/site-packages/uvicorn/main.py", line 587, in run
server.run()
File "/usr/local/lib/python3.11/site-packages/uvicorn/server.py", line 61, in run
return asyncio.run(self.serve(sockets=sockets))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/asyncio/runners.py", line 190, in run
return runner.run(main)
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/asyncio/base_events.py", line 653, in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/uvicorn/server.py", line 68, in serve
config.load()
File "/usr/local/lib/python3.11/site-packages/uvicorn/config.py", line 467, in load
self.loaded_app = import_from_string(self.app)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/uvicorn/importer.py", line 21, in import_from_string
module = importlib.import_module(module_str)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 940, in exec_module
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "/code/app/server.py", line 14, in <module>
add_routes(app, NotImplemented)
File "/usr/local/lib/python3.11/site-packages/langserve/server.py", line 627, in add_routes
input_type_ = _resolve_model(runnable.get_input_schema(), "Input", model_namespace)
^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'NotImplementedType' object has no attribute 'get_input_schema'
```
I also tried the `research-assistant` template and got a similar `get_input_schema` error.
Am I missing a setup step?
### Expected behavior
Deploying the solution locally
|
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I_kwDOIPDwls54dQLT
| 14,127 |
Volc Engine MaaS has wrong entry in LLM type to class dict (causing SpaCy to not work with LangChain anymore)
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[] | null | 4 | 2023-12-01T13:58:13 | 2023-12-04T08:58:10 | 2023-12-04T08:58:10 |
NONE
| null |
### System Info
* Windows 11 Home (build 22621.2715)
* Python 3.12.0
* Clean virtual environment using Poetry with following dependencies:
```
python = "3.12.0"
langchain = "0.0.344"
spacy = "3.7.2"
spacy-llm = "0.6.4"
```
### Who can help?
@h3l As the creator of the pull request where VolcEngine was introduced
@baskaryan As tag handler of that pull request
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Anything that triggers spaCy's registry to make an inventory, for example:
```python
import spacy
spacy.blank("en")
```
With the last part of the Traceback being:
```
File "PROJECT_FOLDER\.venv\Lib\site-packages\langchain\llms\__init__.py", line 699, in __getattr__
k: v() for k, v in get_type_to_cls_dict().items()
^^^
File "PROJECT_FOLDER\.venv\Lib\site-packages\langchain_core\load\serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "PROJECT_FOLDER\.venv\Lib\site-packages\pydantic\v1\main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for VolcEngineMaasLLM
__root__
Did not find volc_engine_maas_ak, please add an environment variable `VOLC_ACCESSKEY` which contains it, or pass `volc_engine_maas_ak` as a named parameter. (type=value_error)
```
#### What I think causes this
I am quite certain that this is caused by [`langchain.llms.__init__.py:869 (for commit b161f30)`](https://github.com/langchain-ai/langchain/blob/b161f302ff56a14d8d0331cbec4a3efa23d06e1a/libs/langchain/langchain/llms/__init__.py#L869C51-L869C51):
```python
def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
return {
"ai21": _import_ai21,
"aleph_alpha": _import_aleph_alpha,
"amazon_api_gateway": _import_amazon_api_gateway,
...
"qianfan_endpoint": _import_baidu_qianfan_endpoint,
"yandex_gpt": _import_yandex_gpt,
# Line below is the only that actually calls the import function, returning a class instead of an import function
"VolcEngineMaasLLM": _import_volcengine_maas(),
}
```
The Volc Engine Maas LLM is the only in this dict to actually call the import function, while all other entries only the function itself, and do not call it.
### Expected behavior
Class to type dict only returns import functions, not actual classes:
```python
def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
return {
"ai21": _import_ai21,
"aleph_alpha": _import_aleph_alpha,
"amazon_api_gateway": _import_amazon_api_gateway,
...
"qianfan_endpoint": _import_baidu_qianfan_endpoint,
"yandex_gpt": _import_yandex_gpt,
# What I think would be correct (now without function call)
"VolcEngineMaasLLM": _import_volcengine_maas,
}
```
Unfortunately I don't have time to put in a PR myself, but I hope this helps finding the solution!
|
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| 14,126 |
Improve indexing performance for Postgres (remote database) for refresh
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CONTRIBUTOR
| null |
**Description:** By combining the document timestamp refresh within a single call to update(), this enables batching of multiple documents in a single SQL statement. This is important for non-local databases where tens of milliseconds has a huge impact on performance when doing document-by-document SQL statements.
**Issue:** #11935
**Dependencies:** None
**Tag maintainer:** @eyurtsev
|
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| 2,020,718,789 |
PR_kwDOIPDwls5g4-dX
| 14,125 |
Support of custom hugging face inference endpoints url
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[] | null | 1 | 2023-12-01T11:43:27 | 2023-12-04T20:08:51 | 2023-12-04T20:08:51 |
CONTRIBUTOR
| null |
- **Description:** to support not only publicly available Hugging Face endpoints, but also protected ones (created with "Inference Endpoints" Hugging Face feature), I have added ability to specify custom api_url. But if not specified, default behaviour won't change
- **Issue:** #9181,
- **Dependencies:** no extra dependencies
|
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I_kwDOIPDwls54cAjQ
| 14,124 |
Specify a custom tokenizer for the ConversationSummaryBufferMemory
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[] | null | 1 | 2023-12-01T10:43:31 | 2023-12-01T10:45:49 | null |
NONE
| null |
### Feature request
Currently, the ConversationSummaryBufferMemory only supports the GPT2TokenizerFast and there is no option to pass a custom tokenizer. Different models have different corresponding tokenizers, so it makes sense to allow the option to specify a custom tokenizer.
The tokenizer fetching is implemented in langchain/schema/language_model.py:
@lru_cache(maxsize=None) # Cache the tokenizer
def get_tokenizer() -> Any:
try:
from transformers import GPT2TokenizerFast
except ImportError:
raise ImportError(
"Could not import transformers python package. "
"This is needed in order to calculate get_token_ids. "
"Please install it with `pip install transformers`."
)
# create a GPT-2 tokenizer instance
return GPT2TokenizerFast.from_pretrained("gpt2")
### Motivation
Using the exact tokenizer which the model uses will help in being more accurate in the context summarization cutoff.
### Your contribution
I could suggest a solution.
|
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How can we add Prompt in RetrievalQA
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[] | null | 1 | 2023-12-01T10:41:14 | 2023-12-01T10:48:43 | null |
NONE
| null |
qa = RetrievalQA.from_chain_type(llm=llm, retriever=docsearch.as_retriever(
search_kwargs={"k": 1}), verbose=True)
How can we add the prompts like systemPromptTemplate and humanPromptTemplate in the above code.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5g4SZz
| 14,121 |
fix: set maxsplit when parse python function docstring
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[] | null | 2 | 2023-12-01T09:55:41 | 2023-12-01T18:46:54 | 2023-12-01T18:46:53 |
CONTRIBUTOR
| null |
Description
when the desc of arg in python docstring contains ":", the `_parse_python_function_docstring` will raise **ValueError: too many values to unpack (expected 2)**.
A sample desc would be:
"""
Args:
error_arg: this is an arg with an additional ":" symbol
"""
So, set `maxsplit` parameter to fix it.
|
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I_kwDOIPDwls54bkKN
| 14,120 |
Expose Amazon Bedrock amazon-bedrock-invocationMetrics in response
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[] | null | 3 | 2023-12-01T09:41:44 | 2023-12-03T19:05:08 | null |
NONE
| null |
### Feature request
Amazon Bedrock now provides some useful metrics when it has finished:
`"completion":"Response from Bedrock...","stop_reason":"stop_sequence","stop":"\n\nHuman:","amazon-bedrock-invocationMetrics":{"inputTokenCount":16,"outputTokenCount":337,"invocationLatency":4151,"firstByteLatency":136}}`
It would be good to expose these via LangChain, similar to how OpenAI API provides them.
### Motivation
To make it easier to calculate the cost of Amazon Bedrock queries.
### Your contribution
I'm happy to test any solution. I'd look to existing developers to guide us how best to expose them.
|
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| 2,020,436,621 |
I_kwDOIPDwls54bWqN
| 14,119 |
Trouble Connecting Langchain Chroma to Existing ChromaDB on Port 8001
|
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[] | null | 1 | 2023-12-01T09:09:40 | 2023-12-01T09:17:27 | null |
NONE
| null |
I am currently operating a chroma docker on port 8001 and using it for my node js application with the URL CHROMA_URI=http://localhost:8001/, which allows me to read the vectors.
However, when I try to use the same chroma_URI with langchain chroma, I encounter an error message. The error message reads: **{"lc":1,"type":"not_implemented","id":["langchain","vectorstores","chroma","Chroma"]}**.
The above screenshots are my codes.
does anyone face this issue?
|
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| 2,020,424,087 |
PR_kwDOIPDwls5g39xA
| 14,118 |
ignore the first turn to apply "history" mechanism
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[] | null | 4 | 2023-12-01T09:01:29 | 2023-12-13T01:31:02 | null |
NONE
| null |
This will generate a meaningless string "system: " for generating condense question; this increases the probability to make an improper condense question and misunderstand user's question. Below is a case
- Original Question: Can you explain the arguments of Meilisearch?
- Condense Question
- What are the benefits of using Meilisearch? (by CodeLlama)
- What are the reasons for using Meilisearch? (by GPT-4)
The condense questions (not matter from CodeLlam or GPT-4) are different from the original one.
By checking the content of each dialogue turn, generating history string only when the dialog content is not empty.
Since there is nothing before first turn, the "history" mechanism will be ignored at the very first turn.
Doing so, the condense question will be "What are the arguments for using Meilisearch?".
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls54bJgS
| 14,117 |
Cost doesnt work when used inside Tool
|
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[] | null | 2 | 2023-12-01T08:37:47 | 2023-12-01T13:40:19 | 2023-12-01T13:40:18 |
CONTRIBUTOR
| null |
### System Info
- Python 3.9.13
- langchain==0.0.344
- langchain-core==0.0.8
### Who can help?
@agola11
@hwchase17
@agola11
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Define a Custom Tool which useses some sort of OpenAI Model. For example GPT 3.5
2. Define a Tool agent which uses this Tool
3. Call the Agent inside a with Block with `get_openai_callback()`
4. The agent should now call the tool which uses an OpenAI Model
5. Calculate the Total Cost. By using the `get_openai_callback` variable. The cost does only include tokens used by the tool agent
### Expected behavior
It should Include all Cost. Including the one from the tool
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| 2,020,257,158 |
I_kwDOIPDwls54aq2G
| 14,114 |
Google Cloud Vertex AI Throws AttributeError: partially initialized module 'vertexai' has no attribute 'init'
|
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[] | null | 2 | 2023-12-01T07:13:02 | 2023-12-01T19:51:02 | null |
NONE
| null |
### System Info
I was just trying to run the LLMs tutorial.
```
from langchain.llms import VertexAI
llm = VertexAI()
print(llm("What are some of the pros and cons of Python as a programming language?"))
```
I got this error.
```
File "C:\Users\******\anaconda3\envs\vertex-ai\Lib\site-packages\langchain_core\load\serializable.py", line 97, in __init__
super().__init__(**kwargs)
File "C:\Users\******\anaconda3\envs\vertex-ai\Lib\site-packages\pydantic\v1\main.py", line 339, in __init__
values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\******\anaconda3\envs\vertex-ai\Lib\site-packages\pydantic\v1\main.py", line 1102, in validate_model
values = validator(cls_, values)
^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\******\anaconda3\envs\vertex-ai\Lib\site-packages\langchain\llms\vertexai.py", line 249, in validate_environment
cls._try_init_vertexai(values)
File "C:\Users\******\anaconda3\envs\vertex-ai\Lib\site-packages\langchain\llms\vertexai.py", line 216, in _try_init_vertexai
init_vertexai(**params)
File "C:\Users\******\anaconda3\envs\vertex-ai\Lib\site-packages\langchain\utilities\vertexai.py", line 42, in init_vertexai
import vertexa
```
I try to install an older version of pydantic like pip install pydantic == 1.10 or 1.10.10, but still error about "validation_error" .
### 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
Anaconda Navigator
1.Python version == 3.12.0
2.Langchain version == 0.0.344
3.Google-cloud-aiplatform version == 1.36.4
4.Pydantic version == 2.5.2
### Expected behavior
Running code without any issues.
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| 2,020,249,681 |
I_kwDOIPDwls54apBR
| 14,113 |
Issue:How to get all the Pages of Confluence Spaces.
|
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open
| false | null |
[] | null | 9 | 2023-12-01T07:07:47 | 2024-01-11T13:25:41 | null |
NONE
| null |
### Issue you'd like to raise.
I am in need to get all the pages and page id of a particular Confluence Spaces, How can I get all the page id?
### Suggestion:
_No response_
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| 2,020,223,271 |
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| 14,112 |
How to connect an LLM Chain to Metaphor
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[] | null | 1 | 2023-12-01T06:47:10 | 2023-12-01T06:57:52 | null |
NONE
| null |
### Issue you'd like to raise.
I want to connect my ConversationChain to Metaphor. Please help me on how to do it! Thanks
### Suggestion:
_No response_
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| 14,111 |
The _convert_message_to_dict function of the ErnieBotChat module does not process the SystemMessage, resulting in an error message.
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The _convert_message_to_dict function of the ErnieBotChat module does not process the SystemMessage, resulting in an error message.
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PR_kwDOIPDwls5g3A82
| 14,110 |
fix: use async_embed_with_retry in _aget_len_safe_embeddings
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[] | null | 1 | 2023-12-01T05:51:53 | 2023-12-01T18:47:07 | 2023-12-01T18:47:07 |
CONTRIBUTOR
| null |
**Description**
`embed_with_retry` is for sync operations and not for async operations.
Use `async_embed_with_retry` for appropriate async operations.
I'm using `OpenAIEmbedding(http_client=httpx.AsyncClient())` with only async operations.
However, I got an error when I use `embedding.aembed_documents` because `embed_with_retry` uses sync OpenAI client with async http client.
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PR_kwDOIPDwls5g2_Jx
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Multi-modal RAG on slide decks cookbook
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COLLABORATOR
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I_kwDOIPDwls54Z6pY
| 14,108 |
DOC: Link to subscribe to the "newsletter" on Community page is no longer valid
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[] | null | 1 | 2023-12-01T04:52:55 | 2023-12-01T04:59:00 | null |
CONTRIBUTOR
| null |
### Issue with current documentation:
The link to subscribe to the "newsletter" at the bottom of Community page (`/docs/docs/community.md`) is no longer available.
As the image above indicates, the "form has moved to a new address", and the new address (https://form.typeform.com/to/KjZB1auB?typeform-source=6w1pwbss0py.typeform.com) gives "This typeform is now closed", as the image shown below:
### Idea or request for content:
If the newsletter is still operating, replace the current link with a new one where people can subscribe to it.
If the newsletter is no longer running, replace the text with something else (eg. the link to langchain blogs).
|
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I_kwDOIPDwls54ZuI9
| 14,107 |
Issue: openai.ChatCompletion is no longer supported in openai>=1.0.0
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[] | null | 4 | 2023-12-01T03:57:52 | 2023-12-11T05:58:02 | 2023-12-11T05:58:02 |
NONE
| null |
### Issue you'd like to raise.
i am using load_qa_chain()
chain=load_qa_chain(OpenAI(temperature=0.25,model_name="gpt-3.5-turbo-1106"), chain_type="stuff", prompt=PROMPT)
i got the following error
"""
APIRemovedInV1:
You tried to access openai.ChatCompletion, but this is no longer supported in openai>=1.0.0 - see the README at
https://github.com/openai/openai-python for the API.
You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface.
Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`
A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742
"""
and the line that raised the error is
anaconda3/envs/Doc2/lib/site-packages/langchain/llms/openai.py:115
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5g2b3Q
| 14,106 |
Update invalid link
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[] | null | 2 | 2023-12-01T03:26:20 | 2023-12-01T16:47:39 | 2023-12-01T16:47:39 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5g2byF
| 14,105 |
Added an IMDb toolkit
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[] | null | 3 | 2023-12-01T03:25:55 | 2023-12-06T23:09:39 | null |
NONE
| null |
Adds support for an IMDb toolkit. Agents should now be able to retrieve up-to-date information from IMDb.
Issue #11926
Dependencies
- Cinemagoer (https://github.com/cinemagoer/cinemagoer)
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StarCoder LLM Wrapper
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[] | null | 4 | 2023-12-01T02:34:47 | 2023-12-04T19:18:08 | 2023-12-04T19:18:07 |
NONE
| null |
**Description:**
An attempted LLM Wrapper implementing StarCoder https://github.com/bigcode-project/starcoder
**Issue:**
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templates[patch]: opensearch readme update
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[] | null | 1 | 2023-12-01T02:22:04 | 2023-12-01T16:48:01 | 2023-12-01T16:48:00 |
COLLABORATOR
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PR_kwDOIPDwls5g2Nny
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docs[patch]: Update facebook.ipynb
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[] | null | 2 | 2023-12-01T02:13:35 | 2023-12-01T16:49:56 | 2023-12-01T16:49:56 |
CONTRIBUTOR
| null |
### Description
Openai version 1.0.0 and later no longer supports the usage of camel case, So [the APIs](https://github.com/openai/openai-python/blob/main/api.md#finetuning) needs to be modified.
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
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PR_kwDOIPDwls5g2GqQ
| 14,100 |
Support passing parameters to `llms.Databricks` and `llms.Mlflow`
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[] | null | 1 | 2023-12-01T01:39:47 | 2023-12-02T03:27:18 | 2023-12-02T03:27:18 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
Before, we need to use `params` to pass extra parameters:
```python
from langchain.llms import Databricks
Databricks(..., params={"temperature": 0.0})
```
Now, we can directly specify extra params:
```python
from langchain.llms import Databricks
Databricks(..., temperature=0.0)
```
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docs[patch]: Update discord.ipynb
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[] | null | 1 | 2023-12-01T01:21:35 | 2023-12-01T16:54:32 | 2023-12-01T16:54:31 |
CONTRIBUTOR
| null |
### Description
Now if `example` in Message is False, it will not be displayed. Update the output in this document.
```python
In [22]: m = HumanMessage(content="Text")
In [23]: m
Out[23]: HumanMessage(content='Text')
In [24]: m = HumanMessage(content="Text", example=True)
In [25]: m
Out[25]: HumanMessage(content='Text', example=True)
```
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
|
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Runnablemap different input for each chain
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[] | null | 3 | 2023-12-01T01:09:07 | 2023-12-01T01:26:15 | null |
NONE
| null |
### Issue you'd like to raise.
langchain_core.runnables.base.RunnableMap
Im trying use this and i want to know is there any way that i can give different input for each chain
### Suggestion:
_No response_
|
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[Nits] Evaluation - Some Rendering Improvements
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[] | null | 1 | 2023-12-01T01:02:01 | 2023-12-01T17:06:08 | 2023-12-01T17:06:07 |
COLLABORATOR
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- Improve rendering of aggregate results at the end
- flatten reference if present
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local docs build <5s
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langchain[patch]: release 0.0.344
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side by side style
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[] | null | 1 | 2023-11-30T23:39:15 | 2023-12-01T00:58:47 | 2023-12-01T00:54:40 |
COLLABORATOR
| null |
Extra changes are from merging in master - should just be the why.ipynb and columns.js
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Update metaphor_search.ipynb
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[] | null | 1 | 2023-11-30T23:30:20 | 2024-01-18T00:16:06 | 2023-12-01T00:34:05 |
CONTRIBUTOR
| null |
- **Description:** Touch up of the documentation page for Metaphor Search Tool integration. Removes documentation for old built-in tool wrapper.
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Template for Ollama + Multi-query retriever
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add streaming to huggingfacepipeline models
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[] | null | 1 | 2023-11-30T21:31:52 | 2023-12-13T01:32:47 | null |
NONE
| null |
**Description:**
This PR includes modifications to the `HuggingFacePipeline` class in the `langchain` library. Changes include the removal of unused imports, a minor cleanup in the code for better readability, and the addition of an `_astream` method for asynchronous streaming support. The `_astream` method integrates the `TextIteratorStreamer` from the `transformers` library and ensures inputs and the model are on the same device (cuda). Additionally, a print statement has been added in the generation loop for debugging purposes.
**Issue:**
N/A
**Dependencies:**
- `transformers` library
**Tag maintainer:**
@baskaryan, @eyurtsev, @hwchase17 for review.
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Bagatur/lcel why doc
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[] | null | 1 | 2023-11-30T21:26:41 | 2023-12-02T00:13:32 | 2023-12-02T00:13:31 |
COLLABORATOR
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls54XwON
| 14,088 |
Pydantic error field required (type=value_error.missing) in LLMChainExtractor or ContextualCompressionRetriever
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[] | null | 3 | 2023-11-30T20:53:24 | 2023-11-30T22:24:23 | 2023-11-30T22:24:23 |
NONE
| null |
### System Info
langchain 0.0.343
langchain-core 0.0.7
### 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
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
retriever = AzureCognitiveSearchRetriever(content_key="content", top_k=5)
llm = AzureChatOpenAI(
openai_api_version="2023-05-15",
azure_deployment="larger-chat-35",
)
template = """
I want you to act as a research assistant. you will answer questions about the context.
Context: {context}
Question: {question}?
"""
prompt = PromptTemplate(
input_variables=["question", "context"],
template=template,
)
compressor = LLMChainExtractor.from_llm(llm)
compression_retriever = ContextualCompressionRetriever(base_compressors=compressor,
base_retriever=retriever)
```
### Expected behavior
I'd expect the compression_retriever to be instantiated. But instead I receive the following error:
```
pydantic.v1.error_wrappers.ValidationError: 1 validation error for ContextualCompressionRetriever
base_compressor
field required (type=value_error.missing)
```
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Mask API key Acree LLM
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CONTRIBUTOR
| null |
Description: Add masking of API Key for Acree LLM when printed.
Issue: https://github.com/langchain-ai/langchain/issues/12165
Dependencies: None
Tag maintainer: @eyurtsev
|
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core[patch]: release 0.0.8
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I_kwDOIPDwls54XhK4
| 14,085 |
RunnableWithFallbacks should have a parameter to choose whether to raise first error or last error
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[] | null | 1 | 2023-11-30T20:12:47 | 2023-11-30T20:15:05 | null |
NONE
| null |
### Feature request
The `invoke()` method for RunnableWithFallbacks is only raising the first_error caught in the runnables. I would like to have an option to choose whether to raise the first error or last error.
Can this be a parameter for `invoke()`?
### Motivation
When using fallbacks, the first error could be related to the first Runnable. However, the error I'm actually interested to handle is the last error from the end of my fallbacks.
The last error is the most downstream error from my chains, and it's the error I want to handle in my app business logic.
### Your contribution
N/A
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`BaseOutputParser` breaks pydantic `PrivateAttr`
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[] | null | 4 | 2023-11-30T19:56:09 | 2023-12-02T18:33:06 | 2023-12-02T18:33:06 |
CONTRIBUTOR
| null |
### System Info
langchain== 0.0.340
pydantic==2.4.2
pydantic_core==2.10.1
### Who can help?
@hwchase17 @eyur
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.schema import BaseOutputParser
from pydantic import PrivateAttr
class MyParser(BaseOutputParser[list[int]]):
_list: list[int] = PrivateAttr(default_factory=list)
def parse(self, text: str) -> list[int]:
return self._list
parser = MyParser()
assert parser.parse("test") == []
```
### Expected behavior
I expect `PrivateAttr` to still work within `BaseOutputParser`.
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I_kwDOIPDwls54XYDC
| 14,083 |
in AWS langchain Bedrock, how do I set temperature?
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[] | null | 17 | 2023-11-30T19:51:15 | 2023-12-01T01:16:45 | null |
NONE
| null |
### System Info
in AWS langchain Bedrock, how do I set temperature?
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Tried putting it as a parameter of Bedrock and gave error that such parameter does not exist. Then put is as paramenters of kwargs and it had no effect
### Expected behavior
Expected this to be a direct parameter of Bedrock, but it seems that it is not
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I_kwDOIPDwls54XT8n
| 14,082 |
Combine langchain retrievers
|
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[] | null | 8 | 2023-11-30T19:39:09 | 2023-12-06T09:30:57 | null |
NONE
| null |
### Issue you'd like to raise.
I am using ConversationalRetrievalChain. I have created two vector stores and I want the query from ConversationalRetrievalChain to be compared against both vector stores and results from both vector stores to be used to create the final answer.
So I have decided to create two retrievers
retriever1 = vectorstore1.as_retriever()
retriever2 = vectorstore2.as_retriever()
How can I now override the Retriever class so that when the query is compared against my custom_retriever, it is compared against documents from both retrievers, and documents from both retrievers are used to create the prompt.
Note: i don't want to merge the vectorstores because that messes up the similarity search.
### Suggestion:
_No response_
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| 2,019,317,039 |
I_kwDOIPDwls54XFUv
| 14,080 |
Random Seed when using LLM Agent
|
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open
| false | null |
[] | null | 1 | 2023-11-30T18:56:50 | 2023-11-30T19:06:42 | null |
NONE
| null |
### System Info
Mac OS
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When I am using Agent, random seed doesn't work.
model_kwargs = {
"seed": 2139
}
llm_agent = ChatOpenAI(temperature=0,
openai_api_key='sk-',
model_name='gpt-4-1106-preview',
model_kwargs=model_kwargs)
llm_cypher = ChatOpenAI(temperature=0,
openai_api_key='sk-',
model_name='gpt-4-1106-preview',
model_kwargs=model_kwargs)
llm_graph = ChatOpenAI(temperature=0,
openai_api_key='sk-',
model_name='gpt-4-1106-preview',
model_kwargs=model_kwargs)
tools = [
Tool(
name="GraphDB Search tool",
func=chain,
description=tool_description
)
]
chain = CustomGraphCypherQAChain.from_llm(
top_k=100,
llm=llm_graph,
return_direct=True, # this return the observation from cypher query directly without rethinking over observation
return_intermediate_steps=True, # this is only returned when it's call by chain() not by chain.run()
cypher_llm=llm_cypher,
graph=graph,
verbose=True,
cypher_prompt=CYPHER_GENERATION_PROMPT
)
def init_agent():
agent = initialize_agent(
tools,
llm_agent,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_errors=True,
handle_parsing_errors=True,
return_intermediate_steps=True,
max_iterations=10,
)
return agent
this agent don't reproduce for same input
### Expected behavior
I expect Agent keep resulting in same answer for the same input prompt
|
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| 2,019,294,151 |
I_kwDOIPDwls54W_vH
| 14,079 |
Support for Perplexity's PPLX models: `pplx-7b-online` and `pplx-70b-online`
|
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| false | null |
[] | null | 1 | 2023-11-30T18:41:11 | 2023-11-30T18:46:25 | null |
NONE
| null |
### Feature request
Support for Perplexity's new [PPLX models](https://blog.perplexity.ai/blog/introducing-pplx-online-llms).
### Motivation
Boosting online LLM support, particularly for Perplexity-based models, would be highly impactful and useful.
### Your contribution
Currently working on a PR!
|
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| 2,019,273,090 |
PR_kwDOIPDwls5g0CxF
| 14,078 |
Fix: (issue #14066) DOC: Summarization output broken
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[] | null | 1 | 2023-11-30T18:27:26 | 2023-12-03T18:13:57 | 2023-12-03T18:13:57 |
CONTRIBUTOR
| null |
- **Description:** : As described in the issue below,
https://python.langchain.com/docs/use_cases/summarization
I've modified the Python code in the above notebook to perform well.
I also modified the OpenAI LLM model to the latest version as shown below.
`gpt-3.5-turbo-16k --> gpt-3.5-turbo-1106`
This is because it seems to be a bit more responsive.
- **Issue:** : #14066
|
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| 2,019,257,671 |
I_kwDOIPDwls54W21H
| 14,077 |
MyCallbackHandler can't show logs explicitly
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| false | null |
[] | null | 1 | 2023-11-30T18:20:20 | 2023-11-30T18:25:42 | null |
NONE
| null |
### Issue you'd like to raise.
Hello everyone,
We are having request timeout on our calls and they are being getting retries after failing but we want to see how many times doing these retries and all their logs.
When the requests are timing out, nothing has been sending callbackhandler and we want to see how many retries had been done under the ChatOpenAI LLM model.
### Suggestion:
_No response_
|
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| 2,019,146,581 |
I_kwDOIPDwls54WbtV
| 14,074 |
Regardless of the text length, the QAGenerationChain consistently generates only one question.
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[] | null | 1 | 2023-11-30T17:21:42 | 2023-11-30T17:29:23 | null |
NONE
| null |
### System Info
Regardless of the text length, the QAGenerationChain consistently generates only one question.
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.chains import QAGenerationChain
from langchain.llms import OpenAI
# Initialize the language model
lm = OpenAI()
# Create the QA Generator Chain
qa_chain = QAGenerationChain.from_llm(llm=lm)
qa_chain.k = 4
# Example usage
context = """
Introduction
LangChain is a framework for developing applications powered by language models. It enables applications that:
Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)
This framework consists of several parts.
LangChain Libraries: The Python and JavaScript libraries. Contains interfaces and integrations for a myriad of components, a basic run time for combining these components into chains and agents, and off-the-shelf implementations of chains and agents.
LangChain Templates: A collection of easily deployable reference architectures for a wide variety of tasks.
LangServe: A library for deploying LangChain chains as a REST API.
LangSmith: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
LangChain Diagram
Together, these products simplify the entire application lifecycle:
Develop: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.
Productionize: Use LangSmith to inspect, test and monitor your chains, so that you can constantly improve and deploy with confidence.
Deploy: Turn any chain into an API with LangServe.
LangChain Libraries
The main value props of the LangChain packages are:
Components: composable tools and integrations for working with language models. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks
Off-the-shelf chains make it easy to get started. Components make it easy to customize existing chains and build new ones.
Get started
Here’s how to install LangChain, set up your environment, and start building.
We recommend following our Quickstart guide to familiarize yourself with the framework by building your first LangChain application.
Read up on our Security best practices to make sure you're developing safely with LangChain.
NOTE
These docs focus on the Python LangChain library. Head here for docs on the JavaScript LangChain library.
LangChain Expression Language (LCEL)
LCEL is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.
Overview: LCEL and its benefits
Interface: The standard interface for LCEL objects
How-to: Key features of LCEL
Cookbook: Example code for accomplishing common tasks
Modules
LangChain provides standard, extendable interfaces and integrations for the following modules:
Model I/O
Interface with language models
Retrieval
Interface with application-specific data
Agents
Let models choose which tools to use given high-level directives
Examples, ecosystem, and resources
Use cases
Walkthroughs and techniques for common end-to-end use cases, like:
Document question answering
Chatbots
Analyzing structured data
and much more...
Integrations
LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of integrations.
Guides
Best practices for developing with LangChain.
API reference
Head to the reference section for full documentation of all classes and methods in the LangChain and LangChain Experimental Python packages.
Developer's guide
Check out the developer's guide for guidelines on contributing and help getting your dev environment set up.
Community
Head to the Community navigator to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.
"""
questions = qa_chain.run(context)
print(questions)
```
output
```
[{'question': 'What are the main value props of the LangChain packages?', 'answer': 'The main value props of the LangChain packages are composable tools and integrations for working with language models, off-the-shelf chains for accomplishing higher-level tasks, and the ability to easily deploy chains as a REST API.'}]
```
### Expected behavior
I expect to specify the number of responses by setting the 'k' value.
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| 2,018,961,211 |
I_kwDOIPDwls54Vuc7
| 14,073 |
Dependency Issues with sentence-transformers and chromadb
|
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open
| false | null |
[] | null | 1 | 2023-11-30T15:52:09 | 2023-11-30T16:02:01 | null |
NONE
| null |
### System Info
LangChain Version: 0.0.339
ChromaDB 0.4.18
sentence-transformers 2.2.1
Python 3.12.0
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Create a requirements file with these entries:
langchain
chromadb
sentence-transformers
Run pip install -r requirements.txt
### Expected behavior
I expect the install to complete correctly.
|
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I_kwDOIPDwls54VXIy
| 14,071 |
SQLDatabase.get_table_info is not returning useful information
|
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[] | null | 5 | 2023-11-30T15:03:55 | 2023-12-03T10:30:35 | 2023-12-03T10:28:07 |
NONE
| null |
### System Info
Python 3.11.5
langchain 0.0.343
langchain-core 0.0.7
sqlalchemy 2.0.21
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
# make a little database
import sqlite3
con = sqlite3.connect("test.db")
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS test_table")
cur.execute("CREATE TABLE test_table(a, b, c, d)")
cur.execute("INSERT INTO test_table VALUES (1,2,3,4)")
cur.execute("INSERT INTO test_table VALUES (4,5,6,7)")
cur.execute("INSERT INTO test_table VALUES (8,9,10,11)")
con.commit()
# then...
from langchain.sql_database import SQLDatabase
db = SQLDatabase.from_uri("sqlite:///test.db", include_tables=["test_table"], sample_rows_in_table_info=2)
print(db.table_info)
---------------------------------------------------------------------------
OperationalError Traceback (most recent call last)
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1965, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1964 if not evt_handled:
-> 1965 self.dialect.do_execute(
1966 cursor, str_statement, effective_parameters, context
1967 )
1969 if self._has_events or self.engine._has_events:
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/default.py:921, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
920 def do_execute(self, cursor, statement, parameters, context=None):
--> 921 cursor.execute(statement, parameters)
OperationalError: near "FROM": syntax error
The above exception was the direct cause of the following exception:
OperationalError Traceback (most recent call last)
Cell In[23], line 3
1 from langchain.sql_database import SQLDatabase
2 db = SQLDatabase.from_uri("sqlite:///test.db", include_tables=["test_table"], sample_rows_in_table_info=2)
----> 3 print(db.table_info)
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/langchain/utilities/sql_database.py:286, in SQLDatabase.table_info(self)
283 @property
284 def table_info(self) -> str:
285 """Information about all tables in the database."""
--> 286 return self.get_table_info()
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/langchain/utilities/sql_database.py:334, in SQLDatabase.get_table_info(self, table_names)
332 table_info += f"\n{self._get_table_indexes(table)}\n"
333 if self._sample_rows_in_table_info:
--> 334 table_info += f"\n{self._get_sample_rows(table)}\n"
335 if has_extra_info:
336 table_info += "*/"
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/langchain/utilities/sql_database.py:357, in SQLDatabase._get_sample_rows(self, table)
354 try:
355 # get the sample rows
356 with self._engine.connect() as connection:
--> 357 sample_rows_result = connection.execute(command) # type: ignore
358 # shorten values in the sample rows
359 sample_rows = list(
360 map(lambda ls: [str(i)[:100] for i in ls], sample_rows_result)
361 )
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1412, in Connection.execute(self, statement, parameters, execution_options)
1410 raise exc.ObjectNotExecutableError(statement) from err
1411 else:
-> 1412 return meth(
1413 self,
1414 distilled_parameters,
1415 execution_options or NO_OPTIONS,
1416 )
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/sql/elements.py:516, in ClauseElement._execute_on_connection(self, connection, distilled_params, execution_options)
514 if TYPE_CHECKING:
515 assert isinstance(self, Executable)
--> 516 return connection._execute_clauseelement(
517 self, distilled_params, execution_options
518 )
519 else:
520 raise exc.ObjectNotExecutableError(self)
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1635, in Connection._execute_clauseelement(self, elem, distilled_parameters, execution_options)
1623 compiled_cache: Optional[CompiledCacheType] = execution_options.get(
1624 "compiled_cache", self.engine._compiled_cache
1625 )
1627 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache(
1628 dialect=dialect,
1629 compiled_cache=compiled_cache,
(...)
1633 linting=self.dialect.compiler_linting | compiler.WARN_LINTING,
1634 )
-> 1635 ret = self._execute_context(
1636 dialect,
1637 dialect.execution_ctx_cls._init_compiled,
1638 compiled_sql,
1639 distilled_parameters,
1640 execution_options,
1641 compiled_sql,
1642 distilled_parameters,
1643 elem,
1644 extracted_params,
1645 cache_hit=cache_hit,
1646 )
1647 if has_events:
1648 self.dispatch.after_execute(
1649 self,
1650 elem,
(...)
1654 ret,
1655 )
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1844, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1839 return self._exec_insertmany_context(
1840 dialect,
1841 context,
1842 )
1843 else:
-> 1844 return self._exec_single_context(
1845 dialect, context, statement, parameters
1846 )
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1984, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1981 result = context._setup_result_proxy()
1983 except BaseException as e:
-> 1984 self._handle_dbapi_exception(
1985 e, str_statement, effective_parameters, cursor, context
1986 )
1988 return result
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2339, in Connection._handle_dbapi_exception(self, e, statement, parameters, cursor, context, is_sub_exec)
2337 elif should_wrap:
2338 assert sqlalchemy_exception is not None
-> 2339 raise sqlalchemy_exception.with_traceback(exc_info[2]) from e
2340 else:
2341 assert exc_info[1] is not None
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1965, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1963 break
1964 if not evt_handled:
-> 1965 self.dialect.do_execute(
1966 cursor, str_statement, effective_parameters, context
1967 )
1969 if self._has_events or self.engine._has_events:
1970 self.dispatch.after_cursor_execute(
1971 self,
1972 cursor,
(...)
1976 context.executemany,
1977 )
File ~/miniconda3/envs/forScience/lib/python3.11/site-packages/sqlalchemy/engine/default.py:921, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
920 def do_execute(self, cursor, statement, parameters, context=None):
--> 921 cursor.execute(statement, parameters)
OperationalError: (sqlite3.OperationalError) near "FROM": syntax error
[SQL: SELECT
FROM test_table
LIMIT ? OFFSET ?]
[parameters: (2, 0)]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
```
### Expected behavior
From https://python.langchain.com/docs/use_cases/qa_structured/sql I expect:
```
CREATE TABLE "test_table" (
"a" INTEGER,
"b" INTEGER,
"c" INTEGER,
"d" INTEGER
)
/*
2 rows from test_table table:
a b c d
1 2 3 4
5 6 7 8
*/
```
|
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| 2,018,694,209 |
I_kwDOIPDwls54UtRB
| 14,069 |
AzureOpenAI azure_ad_token_provider Keyerror
|
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[] | null | 6 | 2023-11-30T13:39:55 | 2023-12-05T23:54:12 | 2023-12-03T16:55:27 |
NONE
| null |
### System Info
When I use below snippet of code
```
import os
from azure.identity import DefaultAzureCredential
from azure.identity import get_bearer_token_provider
from langchain.llms import AzureOpenAI
from langchain.chat_models import AzureChatOpenAI
credential = DefaultAzureCredential(interactive_browser_tenant_id=tenant_id,
interactive_browser_client_id=client_id,
client_secret=client_secret)
token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
endpoint = "https://xxxx.openai.azure.com"
client = AzureOpenAI( azure_endpoint=endpoint,
api_version="2023-05-15",
azure_deployment="example-gpt-4",
azure_ad_token_provider=token_provider)
```
I get error :
```---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[36], line 21
18 # api_version = "2023-05-15"
19 endpoint = "https://xxxx.openai.azure.com"
---> 21 client = AzureOpenAI(
22 azure_endpoint=endpoint,
23 api_version="2023-05-15",
24 azure_deployment="example-gpt-4",
25 azure_ad_token_provider=token_provider,
26 )
File ~/PycharmProjects/aicc/env/lib/python3.9/site-packages/langchain_core/load/serializable.py:97, in Serializable.__init__(self, **kwargs)
96 def __init__(self, **kwargs: Any) -> None:
---> 97 super().__init__(**kwargs)
98 self._lc_kwargs = kwargs
File ~/PycharmProjects/aicc/env/lib/python3.9/site-packages/pydantic/v1/main.py:339, in BaseModel.__init__(__pydantic_self__, **data)
333 """
334 Create a new model by parsing and validating input data from keyword arguments.
335
336 Raises ValidationError if the input data cannot be parsed to form a valid model.
337 """
338 # Uses something other than `self` the first arg to allow "self" as a settable attribute
--> 339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
341 raise validation_error
File ~/PycharmProjects/aicc/env/lib/python3.9/site-packages/pydantic/v1/main.py:1102, in validate_model(model, input_data, cls)
1100 continue
1101 try:
-> 1102 values = validator(cls_, values)
1103 except (ValueError, TypeError, AssertionError) as exc:
1104 errors.append(ErrorWrapper(exc, loc=ROOT_KEY))
File ~/PycharmProjects/aicc/env/lib/python3.9/site-packages/langchain/llms/openai.py:887, in AzureOpenAI.validate_environment(cls, values)
877 values["openai_api_base"] += (
878 "/deployments/" + values["deployment_name"]
879 )
880 values["deployment_name"] = None
881 client_params = {
882 "api_version": values["openai_api_version"],
883 "azure_endpoint": values["azure_endpoint"],
884 "azure_deployment": values["deployment_name"],
885 "api_key": values["openai_api_key"],
886 "azure_ad_token": values["azure_ad_token"],
--> 887 "azure_ad_token_provider": values["azure_ad_token_provider"],
888 "organization": values["openai_organization"],
889 "base_url": values["openai_api_base"],
890 "timeout": values["request_timeout"],
891 "max_retries": values["max_retries"],
892 "default_headers": values["default_headers"],
893 "default_query": values["default_query"],
894 "http_client": values["http_client"],
895 }
896 values["client"] = openai.AzureOpenAI(**client_params).completions
897 values["async_client"] = openai.AsyncAzureOpenAI(
898 **client_params
899 ).completions
KeyError: 'azure_ad_token_provider'
```
Ive also tried AzureChatOpenAI , and I get the same error back.
The error is not reproduced when I use openai library AzureOpenAI .
Also on openai the azure_ad_token_provider has type azure_ad_token_provider: 'AzureADTokenProvider | None' = None while in langchain it has type azure_ad_token_provider: Optional[str] = None which also makes me wonder if it would take as input a different type than string to work with.
any ideas on how to fix this? Im actually using Azure Service principal authentication, and if I use as alternative field azure_ad_token = credential.get_token(“https://cognitiveservices.azure.com/.default”).token I get token expired after 60min which does not happen with a bearer token, so It is important to me to make the token_provider work.
libraries :
pydantic 1.10.12
pydantic_core 2.10.1
openai 1.2.0
langchain 0.0.342
langchain-core 0.0.7
### Who can help?
@hwchase17 @agola11
### 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
import os
from azure.identity import DefaultAzureCredential
from azure.identity import get_bearer_token_provider
from langchain.llms import AzureOpenAI
from langchain.chat_models import AzureChatOpenAI
credential = DefaultAzureCredential(interactive_browser_tenant_id=tenant_id,
interactive_browser_client_id=client_id,
client_secret=client_secret)
token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
endpoint = "https://xxxx.openai.azure.com"
client = AzureOpenAI( azure_endpoint=endpoint,
api_version="2023-05-15",
azure_deployment="example-gpt-4",
azure_ad_token_provider=token_provider)
### Expected behavior
client = AzureOpenAI( azure_endpoint=endpoint,
api_version="2023-05-15",
azure_deployment="example-gpt-4",
azure_ad_token_provider=token_provider)
should return a Runnable instance which I can use for LLMChain
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PR_kwDOIPDwls5gx353
| 14,068 |
Add input_type override
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[] | null | 1 | 2023-11-30T13:15:07 | 2023-12-27T17:14:46 | 2023-12-04T20:10:25 |
CONTRIBUTOR
| null |
Add option to override input_type for cohere's v3 embeddings models
|
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PR_kwDOIPDwls5gxXHn
| 14,067 |
Chat model for Llamacpp runtime
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[] | null | 6 | 2023-11-30T11:57:54 | 2023-12-15T09:36:39 | null |
NONE
| null |
**Description:**
LllamaCpp runtime has been supported by langchain for some time. However, it lacked appropriate chat model to go with it. This PR adds chat model built around llamacpp llm, using some of the common facilities of chat models.
**Issue:**
Lack of chat model to use with llamacpp runtime.
**Dependencies:**
N/A
|
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I_kwDOIPDwls54T0N9
| 14,066 |
DOC: Summarization output broken
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[] | null | 0 | 2023-11-30T11:35:27 | 2023-11-30T21:19:05 | null |
NONE
| null |
### Issue with current documentation:
At end of [summarization documentation](https://python.langchain.com/docs/use_cases/summarization) the output is shown with a ValueError. Line that fail:
```python
summarize_document_chain.run(docs[0])
```
### Idea or request for content:
Looks like the docs[0] object is not what excepected.
|
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I_kwDOIPDwls54TuLp
| 14,065 |
Disable HTTP Request logging in Langchain
|
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[] | null | 2 | 2023-11-30T11:22:43 | 2023-11-30T21:19:09 | null |
NONE
| null |
### Issue you'd like to raise.
When using `langchain.chat_models` such as `ChatAnthropic` and `ChatOpenAI`, I am seeing a bunch of HTTP logs that look like the following:
`2023-11-30 16:43:09 httpx INFO: HTTP Request: POST https://api.anthropic.com/v1/complete "HTTP[/1.1] 200 OK"`
Any suggestions on how to disable them?
### Suggestion:
_No response_
|
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I_kwDOIPDwls54TXBr
| 14,064 |
How to have agent decide what param should be pass into a tool?
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[] | null | 2 | 2023-11-30T10:31:15 | 2023-11-30T11:04:44 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I've created a custom tool to query the equity daily quote, I would like to have two parameters in the tool function: stock_name and trade_date, and I would expect LLM Agent will automatically decide how many days quotes or which day's quote it should retrieve.
```
class SearchSchema(BaseModel):
stock_name: str = Field(description="should be the name of the equity.")
trade_date: str = Field(description="should be the trading date of the equity.")
class DailyQuoteSearch(BaseTool):
name = "equity daily quote search"
description = "useful for equity daily quote retrieval"
return_direct = False
args_schema: Type[SearchSchema] = SearchSchema
def _run(self, stock_name: str, trade_date: str) -> str:
output = self.query_equity_daily_quote(stock_name, trade_date)
return output
```
However, LLM doesn't seem to know how to pass in the trade date? Is it possible to have LLM think about the what trade_date should be passed? or I expect too much on agent intelligence?
```
pydantic_core._pydantic_core.ValidationError: 1 validation error for SearchSchema
trade_date
Field required [type=missing, input_value={'stock_name': 'xxxx'}, input_type=dict]
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gwxhA
| 14,063 |
Add openai v2 adapter
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CONTRIBUTOR
| null |
### Description
Starting from [openai version 1.0.0](https://github.com/openai/openai-python/tree/17ac6779958b2b74999c634c4ea4c7b74906027a#module-level-client), the camel case form of `openai.ChatCompletion` is no longer supported and has been changed to lowercase `openai.chat.completions`. In addition, the returned object only accepts attribute access instead of index access:
```python
import openai
# optional; defaults to `os.environ['OPENAI_API_KEY']`
openai.api_key = '...'
# all client options can be configured just like the `OpenAI` instantiation counterpart
openai.base_url = "https://..."
openai.default_headers = {"x-foo": "true"}
completion = openai.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.choices[0].message.content)
```
So I implemented a compatible adapter that supports both attribute access and index access:
```python
In [1]: from langchain.adapters import openai as lc_openai
...: messages = [{"role": "user", "content": "hi"}]
In [2]: result = lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [3]: result.choices[0].message
Out[3]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [4]: result["choices"][0]["message"]
Out[4]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [5]: result = await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [6]: result.choices[0].message
Out[6]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [7]: result["choices"][0]["message"]
Out[7]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [8]: for rs in lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
In [20]: async for rs in await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
...
```
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
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I_kwDOIPDwls54S_3M
| 14,062 |
Azure OpenAI, gpt-4 model returns gpt-35-turbo
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[] | null | 3 | 2023-11-30T09:38:58 | 2023-12-01T07:08:48 | 2023-12-01T07:08:47 |
NONE
| null |
### System Info
I am using gpt-4 deployed on AzureOpenAI.
I want to get the model used. From openai, I will get the model we used. But when I tried with langchain I got an older model.
Basically, I am integrating with other services. So it is essential to get the model name intact for the cost calculations.
What could be the issue with the langcahin?
It works great with openai chat completion.
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
```
import os
from openai import AzureOpenAI
client = AzureOpenAI(
azure_endpoint = "{}",
api_key="{}",
api_version="{}",
azure_deployment="gpt4",
)
response = client.chat.completions.create(
model="gpt4",
messages=[{"role": "user", "content": "tell me a joke"}]
)
response.model
```
> 'gpt-4'
```
from langchain.schema import HumanMessage
from langchain.chat_models.azure_openai import AzureChatOpenAI
llm = AzureChatOpenAI(
azure_endpoint = "{}",
openai_api_key="{}",
openai_api_version="{}",
azure_deployment="gpt4",
model_version="0613",
temperature=0
)
response = llm.generate(messages=[[HumanMessage(content="tell me a joke")]])
response.llm_output["model_name"]
```
> 'gpt-3.5-turbo'
```
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
prompt = PromptTemplate(
input_variables=["joke"],
template="Tell me a {joke}?",
)
chain = LLMChain(llm=llm, prompt=prompt)
response = chain.generate([{"joke":"joke"}])
response.llm_output["model_name"]
```
> 'gpt-3.5-turbo'
### Expected behavior
From all we should be getting gpt-4 as I have deployed only got-4 on azure openai
|
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PR_kwDOIPDwls5gvkYs
| 14,061 |
docs(ernie): add deprecated note for ErnieChatBot
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[] | null | 2 | 2023-11-30T06:33:05 | 2023-12-01T19:16:31 | 2023-12-01T19:16:31 |
CONTRIBUTOR
| null |
- **Description:** just a little change of ErnieChatBot class description, sugguesting user to use more suitable class
- **Issue:** none,
- **Dependencies:** none,
- **Tag maintainer:** @baskaryan ,
- **Twitter handle:** none
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PR_kwDOIPDwls5gvjsT
| 14,060 |
fix(vectorstores): incorrect import for mongodb atlas DriverInfo
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[] | null | 3 | 2023-11-30T06:30:05 | 2023-12-06T09:51:50 | 2023-12-03T18:22:14 |
CONTRIBUTOR
| null |
- **Description:** fix `import` issue for `mongodb atlas` vectore store integration
- **Issue:** none
- **Dependencies:** none
while trying to follow official `langchain`'s [mongodb integration guide](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas), an import error will happen.
It's caused by incorrect import location:
- `from pymongo import DriverInfo` should be `from pymongo.driver_info import DriverInfo`
- reference: [pymongo's DriverInfo class](https://pymongo.readthedocs.io/en/stable/api/pymongo/driver_info.html#pymongo.driver_info.DriverInfo)
Thanks!
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PR_kwDOIPDwls5gvPUl
| 14,059 |
Create Closed Captioning Chain for .mp4 videos
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[] | null | 2 | 2023-11-30T04:59:08 | 2023-12-04T21:32:15 | null |
NONE
| null |
Description: Video imagery to text (Closed Captioning)
This pull request introduces the VideoCaptioningChain, a tool for automated video captioning. It processes audio and video to generate subtitles and closed captions, merging them into a single SRT output.
Issue: https://github.com/langchain-ai/langchain/issues/11770
Dependencies: opencv-python, ffmpeg-python, assemblyai, transformers, pillow, torch, openai
Tag maintainer:
@baskaryan
@hwchase17
Hello!
We are a group of students from the University of Toronto (@LunarECL, @TomSadan, @nicoledroi1, @A2113S) that want to make a contribution to the LangChain community! We have ran make format, make lint and make test locally before submitting the PR. To our knowledge, our changes do not introduce any new errors.
Thank you for taking the time to review our PR!
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PR_kwDOIPDwls5gvO30
| 14,058 |
fix: Running SQLDatabaseChain adds prefix "SQLQuery:\n"
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[] | null | 1 | 2023-11-30T04:56:40 | 2023-12-02T03:26:17 | 2023-12-02T03:26:16 |
CONTRIBUTOR
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- **Issue:** https://github.com/langchain-ai/langchain/issues/12077
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Don't proceed with chatting until the user provides his name and phone number
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[] | null | 5 | 2023-11-30T03:53:16 | 2023-12-10T16:35:42 | 2023-12-10T16:35:42 |
NONE
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### Issue you'd like to raise.
I want to customize a LLM (a GPT-3.5-turbo) for chatting but don't proceed with chatting until the user provides his name and his phone number and validates the count of his phone number.
Please help me I tried many methods but without any significant result
### Suggestion:
_No response_
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Harrison/mongo agent
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I_kwDOIPDwls54RFRl
| 14,054 |
Issue: I can't visit the page of popular chains
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[] | null | 1 | 2023-11-30T02:16:12 | 2023-11-30T02:22:50 | null |
NONE
| null |
### Issue you'd like to raise.
Do you have a new url?
https://python.langchain.com/docs/modules/chains/popular/
thank you
### Suggestion:
_No response_
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PR_kwDOIPDwls5guvwV
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Add unit tests for Huggingface dataset loader
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[] | null | 1 | 2023-11-30T01:59:20 | 2023-12-01T20:42:32 | 2023-12-01T20:42:32 |
CONTRIBUTOR
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- **Description:** Add unit tests for huggingface dataset loader and sample huggingface dataset for future tests. Updates dependencies for `datasets` module.
- Adds coverage for [previous pull request](https://github.com/langchain-ai/langchain/pull/13864)
- **Tag maintainer:** @hwchase17
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PR_kwDOIPDwls5gupHz
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Bagatur/lcel get started
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[] | null | 1 | 2023-11-30T01:20:56 | 2023-12-01T20:42:48 | 2023-12-01T20:42:48 |
COLLABORATOR
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5gulek
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docs[patch]: Add mermaid JS theme dependency to docusaurus
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[] | null | 1 | 2023-11-30T01:00:44 | 2023-12-01T19:06:30 | 2023-12-01T19:06:29 |
CONTRIBUTOR
| null |
- **Description:** Add mermaid JS dependency and configs to documentation. Allows inline doc diagrams in markdown.
- **Dependencies:** NPM package @docusaurus/theme-mermaid
|
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| 2,017,681,132 |
I_kwDOIPDwls54Q17s
| 14,050 |
OpenAIAssistantRunnable input validation error
|
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[] | null | 2 | 2023-11-30T01:00:23 | 2023-12-29T05:56:35 | null |
NONE
| null |
### System Info
Python 3.11 running locally in PyCharm.
### Who can help?
@hwchase17 @agola11
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
def research_tool(input, thread_id=None):
# Initialize Tavily Search
search = TavilySearchAPIWrapper()
tavily_tool = TavilySearchResults(api_wrapper=search)
salesforce_history_tool = create_pinecone_tool()
# Initialize PlayWright Web Browser
sync_browser = create_sync_playwright_browser()
toolkit = PlayWrightBrowserToolkit.from_browser(sync_browser=sync_browser)
# Initialize the Toolkit
tools = toolkit.get_tools()
tools.append(tavily_tool) # Add Tavily Search to the Toolkit
tools.append(salesforce_history_tool) # Add Salesforce History to the Toolkit
tools.extend(sf_tools) # Add Salesforce Tools to the Toolkit
agent = OpenAIAssistantRunnable.create_assistant(
name="Research Assistant",
instructions="You are a personal research assistant on company information",
tools=tools,
model="gpt-4-1106-preview",
as_agent=True,
)
agent_executor = AgentExecutor(agent=agent, tools=tools)
if thread_id:
result = agent_executor.invoke({"content": input, "thread_id": thread_id})
else:
result = agent_executor.invoke({"content": input})
output = result['output']
thread_id = result['thread_id']
return output, thread_id
```
### Expected behavior
I am looking to have my agent run using the Assistants API. Instead, I receive the following error:
```
[chain/start] [1:chain:AgentExecutor > 4:chain:OpenAIAssistantRunnable] Entering Chain run with input:
[inputs]
[chain/error] [1:chain:AgentExecutor > 4:chain:OpenAIAssistantRunnable] [315ms] Chain run errored with error:
"BadRequestError(\"Error code: 400 - {'error': {'message': '1 validation error for Request\\\\nbody -> tool_outputs -> 0 -> output\\\\n str type expected (type=type_error.str)', 'type': 'invalid_request_error', 'param': None, 'code': None}}\")"
```
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I_kwDOIPDwls54Qn6T
| 14,048 |
Callback.on_chain_start has wrong "inputs" in RefineDocumentsChain
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[] | null | 1 | 2023-11-29T23:49:27 | 2023-11-30T19:01:31 | 2023-11-30T19:01:31 |
NONE
| null |
### System Info
Python 3.10, mac OS
### Who can help?
@agola11 @hwchase17
### 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
With this script
```
from langchain.chains import RefineDocumentsChain, LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
document_prompt = PromptTemplate.from_template("{page_content}")
document_variable_name = "context"
llm = OpenAI()
# The prompt here should take as an input variable the
# `document_variable_name`
prompt = PromptTemplate.from_template(
"Summarize this content: {context}"
)
initial_llm_chain = LLMChain(llm=llm, prompt=prompt)
initial_response_name = "prev_response"
# The prompt here should take as an input variable the
# `document_variable_name` as well as `initial_response_name`
prompt_refine = PromptTemplate.from_template(
"Here's your first summary: {prev_response}. "
"Now add to it based on the following context: {context}"
)
refine_llm_chain = LLMChain(llm=llm, prompt=prompt_refine)
chain = RefineDocumentsChain(
initial_llm_chain=initial_llm_chain,
refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name,
initial_response_name=initial_response_name,
)
from langchain.schema import Document
text = """Nuclear power in space is the use of nuclear power in outer space, typically either small fission systems or radioactive decay for electricity or heat. Another use is for scientific observation, as in a Mössbauer spectrometer. The most common type is a radioisotope thermoelectric generator, which has been used on many space probes and on crewed lunar missions. Small fission reactors for Earth observation satellites, such as the TOPAZ nuclear reactor, have also been flown.[1] A radioisotope heater unit is powered by radioactive decay and can keep components from becoming too cold to function, potentially over a span of decades.[2]
The United States tested the SNAP-10A nuclear reactor in space for 43 days in 1965,[3] with the next test of a nuclear reactor power system intended for space use occurring on 13 September 2012 with the Demonstration Using Flattop Fission (DUFF) test of the Kilopower reactor.[4]
After a ground-based test of the experimental 1965 Romashka reactor, which used uranium and direct thermoelectric conversion to electricity,[5] the USSR sent about 40 nuclear-electric satellites into space, mostly powered by the BES-5 reactor. The more powerful TOPAZ-II reactor produced 10 kilowatts of electricity.[3]
Examples of concepts that use nuclear power for space propulsion systems include the nuclear electric rocket (nuclear powered ion thruster(s)), the radioisotope rocket, and radioisotope electric propulsion (REP).[6] One of the more explored concepts is the nuclear thermal rocket, which was ground tested in the NERVA program. Nuclear pulse propulsion was the subject of Project Orion.[7]
"""
docs = [
Document(
page_content=split,
metadata={"source": "https://en.wikipedia.org/wiki/Nuclear_power_in_space"},
)
for split in text.split("\n\n")
]
```
When I attach a callback that prints the [inputs](https://github.com/langchain-ai/langchain/blob/00a6e8962cc778cd8f6268cefc304465598c02cf/libs/core/langchain_core/callbacks/base.py#L197) in `on_chain_start`, even if every sub-chain only uses one Document, the `inputs` is always the full Document list.
### Expected behavior
I expect the `inputs` in `on_chain_start` only includes the inputs that are used by this chain, not always the full list, otherwise it's totally meaningless to duplicate the same inputs many times.
|
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PR_kwDOIPDwls5guCiK
| 14,047 |
DROP BOX Loader Documentation Update
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CONTRIBUTOR
| null |
- **Description:** Update the document for drop box loader + made the messages more verbose when loading pdf file since people were getting confused
- **Issue:** #13952
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17,
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[core/minor] Runnables: Implement a context api
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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PR_kwDOIPDwls5gttBB
| 14,045 |
docs[patch]: Add getting started section to LCEL doc
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[] | null | 2 | 2023-11-29T21:13:44 | 2023-12-01T20:23:44 | 2023-12-01T20:23:43 |
CONTRIBUTOR
| null |
### Description:
Doc addition for LCEL introduction. Adds a more basic starter guide for using LCEL.
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PR_kwDOIPDwls5gts9O
| 14,044 |
Update Prompt Format Error
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[] | null | 1 | 2023-11-29T21:13:31 | 2023-12-01T17:06:36 | 2023-12-01T17:06:35 |
COLLABORATOR
| null |
The number of times I try to format a string (especially in lcel) is embarrassingly high. Think this may be more actionable than the default error message. Now I get nice helpful errors
```
KeyError: "Input to ChatPromptTemplate is missing variable 'input'. Expected: ['input'] Received: ['dialogue']"
```
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PR_kwDOIPDwls5gtcnl
| 14,043 |
templates[patch]: rag-google-cloud-sdp readme
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COLLABORATOR
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I_kwDOIPDwls54Pkm-
| 14,042 |
DOC: Expand documentation on how to authenticate and connect to Amazon OpenSearch Serverless
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CONTRIBUTOR
| null |
### Issue with current documentation:
The current [section on Amazon OpenSearch Serverless][1] (AOSS) vector store uses the `AWS4Auth` class to authenticate to AOSS, yet the official [OpenSearch documentation][2] suggests using the `AWS4SignerAuth` class instead. Using the latter does currently **not** work with LangChain due to how AOSS is detected when the class is instantiated (c.f. #14129 )
Further, the notebook lacks information on where to import the `AWS4Auth` class from and how to configure it with different AWS credentials (static access key/secret key, temporary credentials, etc.). It also lacks references on how to configure access policies (IAM, AOSS data access policies, etc.)
[1]: https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/vectorstores/opensearch.ipynb
[2]: https://opensearch.org/docs/latest/clients/python-low-level/#connecting-to-amazon-opensearch-serverless
### Idea or request for content:
Add installation instructions for the [requests_aws4auth][1] package and links to its [Github repo][2] in order to showcase configuration with different AWS credentials. Additionally reference [AWS documentation for AOSS][3] in order to get started [setting up permissions][4]
[1]: https://pypi.org/project/requests-aws4auth/
[2]: https://github.com/tedder/requests-aws4auth#basic-usage
[3]: https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless-getting-started.html
[4]: https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless-data-access.html
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I_kwDOIPDwls54PiTx
| 14,041 |
Missing metadata in some callback handler methods
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[] | null | 2 | 2023-11-29T20:08:28 | 2023-11-29T20:13:37 | null |
NONE
| null |
### Issue you'd like to raise.
I only see `metadata` as parameter for few callback handler methods, but I would like to have access to metadata in other methods such as `on_chain_error()` and `on_llm_error()`.
Currently, I can only see the `tags` in these methods.
I have error handling in my callback handler using `on_chain_error()` and I'd like to add information in my metadata dictionary to my exceptions (such as LLM name, model, things related to my chain, etc...).
I can put a list of tags, but I'd much prefer to use a dictionary to get certain keys and have my exceptions instantiated properly.
### Suggestion:
Please make both tags and metadata available for all callback handler methods.
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PR_kwDOIPDwls5gtLMn
| 14,040 |
Add Hugging Face chat wrapper
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[] | null | 7 | 2023-11-29T19:24:35 | 2023-12-21T17:29:05 | 2023-12-21T17:29:04 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
## Issue
There previously has been no easy way to make use of models hosted on Hugging Face (via Inference API or Inference Endpoints) in combination with LangChains ChatModel abstraction.
## Description
This PR introduces a new chat_model integration that creates a wrapper around the `BaseChatModel` class that interfaces between LangChain's and the hosted LLM by leveraging [Hugging Face's Chat Templates](https://huggingface.co/docs/transformers/chat_templating).
## To do
- [x] Add wrapper class around`BaseChatModel` to interface with HF LLM integrations
- [x] Create a notebook `docs/integrations/chat` that demonstrates its use
- [x] Add unit test
## Tag maintainer
@hwchase17
## Twitter handle
@andrewrreed
|
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PR_kwDOIPDwls5gtHmx
| 14,039 |
Imdb feature and imdm toolkit
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[] | null | 1 | 2023-11-29T19:16:01 | 2023-11-30T03:12:00 | null |
NONE
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I_kwDOIPDwls54PCtx
| 14,038 |
Add Optional Parameter for Unique IDs in FAISS.add_documents Function
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[] | null | 1 | 2023-11-29T18:48:20 | 2023-11-29T18:48:40 | null |
NONE
| null |
### Feature request
I notice the other functions, such as add_texts and add_embeddings allow you to pass a unique list of IDs that can get paired with your embedding. There is no such parameter for the add_documents function. This means when you delete a document and add an updated version of it using add_documents, its unique ID won't be added to the vector store.
### Motivation
Inability to add unique ID to document after calling the add_documents function from FAISS
### Your contribution
Not sure as far as I know. If I can, let me know what you need from me.
|
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PR_kwDOIPDwls5gszc5
| 14,037 |
langchain[patch]: Release 0.0.343
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I_kwDOIPDwls54O0ie
| 14,036 |
Issue: support for Azure Open AI latest GPT models like, GPT 4 turpo in the get_openai_callback()
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NONE
| null |
### Issue you'd like to raise.
Would be great to add the support for Azure GPT latest models in the get_openai_callback() --> https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/callbacks/openai_info.py
https://github.com/langchain-ai/langchain/issues/12994
### Suggestion:
Please add the Azure GPT models (latest) https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/callbacks/openai_info.py
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template pyproject updates
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ConversationChain to Agent
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[] | null | 1 | 2023-11-29T17:07:22 | 2023-11-29T17:15:03 | null |
NONE
| null |
### Issue you'd like to raise.
Is it possible to convert a Conversation Chain into an Agent?
### Suggestion:
_No response_
|
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| 2,017,018,704 |
I_kwDOIPDwls54OUNQ
| 14,033 |
Optimal Integration of the ConversationalRetrievalChain (Open source llama-2) into gradio.
|
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[] | null | 1 | 2023-11-29T16:50:09 | 2023-11-29T16:57:01 | null |
NONE
| null |
### Issue you'd like to raise.
I want to integrate ConversationalRetrievalChain with history into Gradio app.
For now I have the following approach:
```
# Memory buffer
memory = ConversationBufferWindowMemory(k=2,memory_key="chat_history", return_messages=True)
# LLM chain
chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
retriever=vector_store.as_retriever(
search_kwargs={"k": 2}),
memory=memory)
with gr.Blocks() as demo:
gr.Markdown("# SageMaker Docs Chat 🤗")
gr.Markdown("### Ask me question about Amazon SageMaker!")
chatbot = gr.Chatbot(label="Chat history")
message = gr.Textbox(label="Ask me a question!")
clear = gr.Button("Clear")
def user(user_message, chat_history):
return gr.update(value="", interactive=False), chat_history + [[user_message, None]]
def bot(chat_history):
user_message = chat_history[-1][0]
llm_response = qa({"question": user_message})
bot_message = llm_response["answer"]
chat_history[-1][1] = ""
for character in bot_message:
chat_history[-1][1] += character
time.sleep(0.005)
yield chat_history
response = message.submit(user, [message, chatbot], [message, chatbot], queue=False).then(
bot, chatbot, chatbot
)
response.then(lambda: gr.update(interactive=True), None, [message], queue=False)
demo.queue()
demo.launch()
```
which works fine for the simple question answer without history. I tried to implement something similar to this guide (https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/Llama2_Gradio.ipynb), but failed to do so. Do you have any solution for this use-case or any specific guide?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5gsNnf
| 14,032 |
feat: Add a feature to serialize and deserialize memory types to and from JSON
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[] | null | 4 | 2023-11-29T16:35:20 | 2024-01-07T19:19:58 | null |
NONE
| null |
Description: Add a feature to serialize and deserialize the memory types into JSON format,
Issue: #11275,
Dependencies: No new dependencies,
Tag maintainer: @baskaryan, @eyurtsev, @hwchase17
Co-Authors: @D3nam, @avaove, @malharpandya
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| 2,016,963,828 |
I_kwDOIPDwls54OGz0
| 14,031 |
Finished chain without an answer but full context have results
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[] | null | 2 | 2023-11-29T16:24:00 | 2023-12-09T00:56:03 | null |
NONE
| null |
### System Info
langchain==0.0.330, python 3.9
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I created an agent as follows:
```
def batch_embedding(node_label:str, text_node_properties:list):
vector_index = Neo4jVector.from_existing_graph(
OpenAIEmbeddings(),
url=URL_DB_GRAPH,
username=USERNAME,
password=PASSWORD,
index_name= node_label,
node_label= node_label,
text_node_properties=text_node_properties,
embedding_node_property='embedding',
)
return vector_index
```
model_name= "gpt-4"
llm= ChatOpenAI(temperature=0, model_name=model_name)
vector_index = batch_embedding("Alarms", ["solution", "description", "type"])
vector_qa = RetrievalQA.from_chain_type(
llm=llm, chain_type="stuff", retriever=vector_index.as_retriever())
```
cypher_chain = GraphCypherQAChain.from_llm(
cypher_llm = llm,
qa_llm = ChatOpenAI(temperature=0), graph=graph, verbose=True, cypher_prompt=CYPHER_GENERATION_PROMPT
)
```
tools = [
Tool(
name="Alarms",
func=vector_qa.run,
description=prompt_per_alarms,
),
Tool(
name="Graph",
func=cypher_chain.run,
description=prompt_per_Graph,
),
]
mrkl = initialize_agent(
tools, ChatOpenAI(temperature=0, model_name=model_name), agent=AgentType.OPENAI_FUNCTIONS, verbose=True, memory=memory
)
message = request.form['message']
response = mrkl.run(message)
When I receive an answer from cypher_chain.run I can see that I've a full context with an output but the finished chain says "I'm sorry, but I don't have the information.." (see the image attached)
.
I noticed that this issue come back when I have a full context with an array of data.
### Expected behavior
Finished chain contains the full context and write the answer.
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PR_kwDOIPDwls5gsFes
| 14,030 |
Update typo in map.ipynb
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[] | null | 2 | 2023-11-29T16:18:51 | 2023-11-29T17:14:30 | 2023-11-29T17:14:29 |
CONTRIBUTOR
| null |
fix the typo in docs, using "with" instead of "when"
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PR_kwDOIPDwls5gr-dC
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fix #12899
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[] | null | 2 | 2023-11-29T16:02:47 | 2023-11-29T21:03:54 | 2023-11-29T20:57:07 |
CONTRIBUTOR
| null |
- **Description:** use post field validation for `CohereRerank`
- **Issue:** #12899 and #13058
- **Dependencies:**
- **Tag maintainer:** @baskaryan
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| 2,016,875,537 |
PR_kwDOIPDwls5gr0nx
| 14,028 |
Fix .env file path in integration_test README.md
|
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[] | null | 1 | 2023-11-29T15:40:18 | 2023-11-30T03:14:28 | 2023-11-30T03:14:28 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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### Description
Hello,
The [integration_test README](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests) was indicating incorrect paths for the `.env.example` and `.env` files.
`tests/.env.example` ->`tests/integration_tests/.env.example`
While it’s a minor error, it could **potentially lead to confusion** for the document’s readers, so I’ve made the necessary corrections.
Thank you! ☺️
### Related Issue
- https://github.com/langchain-ai/langchain/pull/2806
|
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I_kwDOIPDwls54Nkz6
| 14,027 |
Issue: Request: Need Help with CustomAgentExecutor for Accessing Dynamically Created Prompts
|
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[] | null | 3 | 2023-11-29T15:19:25 | 2023-12-01T07:29:06 | null |
NONE
| null |
### Issue you'd like to raise.
I am currently piecing together some of the tutorials on the langchain documentation page to use a CustomOutputParser and a CustomPrompt Template similar to the ZeroShotReact Template.
While parsing the actions, I have a scenario where, the model returns Action as None & Action Input as None. In that case, I would like access to the dynamically created prompt with in the CustomOutputParser to call another LLM and return the action as call to another LLM to complete the action.
**Current Approach:**
```python
# Set up a prompt template
class CustomPromptTemplatePirate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
class CustomOutputParserPirate(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
# Set up the base template
template = """Complete the objective as best you can. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
These were previous tasks you completed:
Begin!
Question: {input}
{agent_scratchpad}"""
search = SerpAPIWrapper()
tools= [Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
)]
prompt = CustomPromptTemplatePirate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
)
output_parser = CustomOutputParserPirate()
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many people live in canada as of 2023?")
```
**Challenges Faced:**
I am unable to figure out how to access the callback or any other similar approach to use the complete Prompt/User Input with in the context of the CustomOutputParser
**Desired Outcome:**
```python
def parse(self, llm_output: str, prompt:start) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
.....
if not 'Action' & not 'Action Input' :
call AgentAction with another llm fine_tuned defined as tool with the user question as input ex: Write a summary on Canada. I need a way to access the user question here.
```
I would greatly appreciate any advice, documentation, or examples that could assist me in accomplishing this task. Thank you very much for your time and support.
### Suggestion:
_No response_
|
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| 2,016,585,972 |
PR_kwDOIPDwls5gq0r3
| 14,025 |
Update text_splitter.py
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[] | null | 3 | 2023-11-29T13:23:21 | 2023-12-05T10:33:11 | 2023-12-01T19:57:50 |
CONTRIBUTOR
| null |
Description: Fixes a minor bug to make HTMLHeaderTextSplitter work.
Issue: https://github.com/langchain-ai/langchain/issues/14024
Dependencies: none,
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Fixes a minor bug to make HTMLHeaderTextSplitter work.,
- **Issue:** [the issue # it fixes (if applicable),](https://github.com/langchain-ai/langchain/issues/14024)
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @GovindaTotla
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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I_kwDOIPDwls54MoBq
| 14,024 |
HTMLHeaderTextSplitter throws TypeError: cannot parse from 'PosixPath'
|
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[] | null | 1 | 2023-11-29T13:17:28 | 2023-11-29T13:22:01 | null |
CONTRIBUTOR
| null |
### System Info
Python 3.10
Langchain 0.0.311
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
url = "https://plato.stanford.edu/entries/goedel/"
headers_to_split_on = [
("h1", "Header 1"),
("h2", "Header 2"),
("h3", "Header 3"),
("h4", "Header 4"),
]
html_splitter = HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
# for local file use html_splitter.split_text_from_file(<path_to_file>)
html_header_splits = html_splitter.split_text_from_url(url)
chunk_size = 500
chunk_overlap = 30
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
# Split
splits = text_splitter.split_documents(html_header_splits)
splits[80:85]
```
[Reference](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/HTML_header_metadata#2-pipelined-to-another-splitter-with-html-loaded-from-a-web-url)
The bug seems to be in [etree](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/text_splitter.py#L586). A simple fix is perhaps like below:
```python
from lxml import etree
from pathlib import Path
path = Path(".../langchain/document_transformers/xsl/html_chunks_with_headers.xslt")
# etree.parse(path) Throws
etree.parse(str(path))
```
### Expected behavior
The code in the reproducer should work.
|
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| 14,022 |
Recursive query when connecting to postgres db
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[] | null | 1 | 2023-11-29T10:55:01 | 2023-11-29T11:09:53 | null |
NONE
| null |
> langchain-experimental : 0.0.42
> langchain : 0.0.340
> gpt4all : 2.0.2
> PostgreSQL : 15.5
I am trying to query my postgres db using the following code:
```
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_experimental.sql import SQLDatabaseChain
from langchain.memory import ConversationBufferMemory
from langchain import SQLDatabase
from langchain.llms import GPT4All
from langchain.prompts import PromptTemplate
from langchain.globals import set_verbose
import os
username = "postgres"
password = "password"
host = "127.0.0.1" # internal IP
port = "5432"
mydatabase = "reporting_db"
pg_uri = f'postgresql://{username}:{password}@{host}:{port}/{mydatabase}'
my_db = SQLDatabase.from_uri(pg_uri)
_DEFAULT_TEMPLATE = '''Given an input question, first create a syntactically correct {dialect} query to run,
then look at the results of the query and return the answer.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
If someone asks for the book written, they really mean the work table.
Question: {input}'''
PROMPT = PromptTemplate(
input_variables=["input", "table_info", "dialect"],
template=_DEFAULT_TEMPLATE
)
path = "/var/lib/postgresql/data/llama-2-7b.Q2_K.gguf"
callbacks = [StreamingStdOutCallbackHandler()]
llm = GPT4All(model = path,
callbacks=callbacks,
n_threads=8,
max_tokens=81920,
verbose=True
)
set_verbose(True)
db_chain = SQLDatabaseChain.from_llm(llm = llm,
db = my_db,
prompt = PROMPT,
use_query_checker=True,
verbose = True
)
question = 'Count the rows on table Access'
answer = db_chain(question)
print(answer)```
but I am getting the following error:
```ERROR: sqlalchemy.exc.ProgrammingError: (psycopg2.errors.SyntaxError) syntax error at or near "```"
LINE 1: ```sql
^
[SQL: ```sql
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (
SELECT * FROM (]
```
### Suggestion:
_No response_
|
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| 2,016,308,490 |
I_kwDOIPDwls54Lm0K
| 14,021 |
Unable to Integrate ConversationChain with Tools like duckduckgo or serp-api
|
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| false | null |
[] | null | 3 | 2023-11-29T10:47:49 | 2023-11-29T11:17:59 | null |
NONE
| null |
### Issue you'd like to raise.
Hi, I'm looking for some implementation that's utilizing a ConversationChain and giving it access to Internet. I want to integrate duckduckgo or bing-api or serpapi into my ConversationChain.
Would appreciate any help! Thanks
### Suggestion:
_No response_
|
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| 2,016,305,395 |
I_kwDOIPDwls54LmDz
| 14,020 |
Doctran translate documents
|
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| false | null |
[] | null | 2 | 2023-11-29T10:46:04 | 2023-12-04T11:18:55 | null |
NONE
| null |
### System Info
IPython : 8.15.0
ipykernel : 6.25.0
ipywidgets : 8.0.4
jupyter_client : 7.4.9
jupyter_core : 5.5.0
jupyter_server : 1.23.4
jupyterlab : 3.5.3
nbclient : 0.8.0
nbconvert : 7.10.0
nbformat : 5.9.2
notebook : 6.5.4
qtconsole : 5.4.2
traitlets : 5.7.1
Python 3.11.6
Langchain '0.0.340'
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
TypeError: object Document can't be used in 'await' expression
translated_document = await qa_translator.atransform_documents(documents)
from official documentation notebook :
https://github.com/langchain-ai/langchain/blob/1cd9d5f3328e144cbe5d6ef52a22029d4fdf0cce/docs/docs/integrations/document_transformers/doctran_translate_document.ipynb
### Expected behavior
The problem might be arise due to specific python version or asyncio .
|
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| 2,016,297,886 |
I_kwDOIPDwls54LkOe
| 14,019 |
Ability to use custom tools and openai build in functions on OpenAIAssistantRunnable
|
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| false | null |
[] | null | 1 | 2023-11-29T10:41:57 | 2023-11-29T10:42:16 | null |
NONE
| null |
### Feature request
It would interesting to have the option tu run a OpenAIAssistantRunnable that has access to custom tools and OpenAI build in tools like code_intrepreter, vision and retrieval.
### Motivation
It would increase the developers capabilty of creating even more powerful agents.
### Your contribution
n/a
|
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I_kwDOIPDwls54LZsZ
| 14,018 |
Issue: openai.NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}
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[] | null | 6 | 2023-11-29T10:18:06 | 2023-12-31T16:33:50 | null |
NONE
| null |
### Issue you'd like to raise.
I use the below code to load data and splitted it and embedded it and finally pushing it into vector store. during that process,
I'm getting **openai.NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}**. After this method failed, I also tried with AzurecosmosDBVectorsearch vector store it also failed and returned the same error. Kindly help on this.
```
from langchain.embeddings.azure_openai import AzureOpenAIEmbeddings
from langchain.vectorstores.azure_cosmos_db import AzureCosmosDBVectorSearch
from langchain.vectorstores.chroma import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
# Load PDF
loaders = [
PyPDFLoader("ai.pdf")
]
docs = []
for loader in loaders:
docs.extend(loader.load())
# Define the Text Splitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1500,
chunk_overlap=150
)
# Create a split of the document using the text splitter
res_splits = text_splitter.split_documents(docs)
embedding = AzureOpenAIEmbeddings(
openai_api_version="1699-02-30",
openai_api_key="xxxxxxxxxxxxxxxxxxxxxxxxx",
# model_name="gpt-35-turbo",
azure_endpoint="https://ggggggggggggggggggggggg.openai.azure.com/")
persist_directory = 'docs/chroma/'
# Create the vector store
vectordb = Chroma.from_documents(
documents=res_splits,
embedding=embedding,
persist_directory=persist_directory
)
print(vectordb._collection.count())
```
### Suggestion:
_No response_
|
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I_kwDOIPDwls54KyEg
| 14,017 |
when I run the demo from the cookbook，I get error Error code: 404 - {'error': {'message': 'The model `text-davinci-003` does not exist1', 'type': 'invalid_request_error', 'param': None, 'code': 'model_not_found'}}
|
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open
| false | null |
[] | null | 1 | 2023-11-29T08:45:42 | 2023-11-29T08:58:32 | null |
NONE
| null |
### System Info
Python 3.10.10 [email protected]
langchain-core@ 0.0.7
### 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
md_file = docs[0].page_content
headers_to_split_on = [
("###", "Section"),
]
markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
md_header_splits = markdown_splitter.split_text(md_file)
chunk_size = 500
chunk_overlap = 0
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
all_splits = text_splitter.split_documents(md_header_splits)
vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
metadata_field_info = [
AttributeInfo(
name="Section",
description="Part of the document that the text comes from",
type="string or list[string]",
),
]
document_content_description = "Major sections of the document"
# Define self query retriever
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
qa_chain = RetrievalQA.from_chain_type(llm, retriever=retriever)
qa_chain.run("衰老有哪些因素？")
### Expected behavior
how can I fix the problem
|
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https://github.com/langchain-ai/langchain/pull/14016
| 2,016,049,187 |
PR_kwDOIPDwls5go-1T
| 14,016 |
Improve HyDe with custom prompts and ability to supply the run_manager
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| false | null |
[] | null | 1 | 2023-11-29T08:17:41 | 2023-11-29T17:40:54 | 2023-11-29T17:40:53 |
CONTRIBUTOR
| null |
- **Description:** The class allows to only select between a few predefined prompts from the paper. That is not ideal, since other use cases might need a custom prompt. The changes made allow for this. To be able to monitor those, I also added functionality to supply a custom run_manager.
- **Issue:** no issue, but a new feature,
- **Dependencies:** none,
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** @yvesloy
|
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