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| 1,908,539,026 |
PR_kwDOIPDwls5a-Mtp
| 10,927 |
Add base docker image and ci script for building and pushing
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[] | null | 1 | 2023-09-22T09:28:47 | 2023-10-02T14:07:59 | 2023-10-02T14:07:58 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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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.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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https://api.github.com/repos/langchain-ai/langchain/issues/10925
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PR_kwDOIPDwls5a97O5
| 10,925 |
20230922 fix wrong import when launch langchain server with docker-compose since langsmith-0.0.26
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[] | null | 2 | 2023-09-22T08:41:50 | 2023-11-13T06:53:08 | 2023-11-13T06:53:07 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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https://api.github.com/repos/langchain-ai/langchain/issues/10924
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I_kwDOIPDwls5xwHuI
| 10,924 |
MatchingEngine does not store metadata in embeddings
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[] | null | 3 | 2023-09-22T08:29:48 | 2023-12-30T16:05:34 | 2023-12-30T16:05:33 |
NONE
| null |
### System Info
Langchain version : 0.0.297
### Who can help?
@hwchase17
@agola11
### 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
The _add_texts_ function accepts metadata as an input parameter, even though it is not utilized in the embedding creation process.
### Expected behavior
Metadata should be added.
SImilar to what is done for PGVECTOR.
|
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PR_kwDOIPDwls5a9ltt
| 10,923 |
Support weight only quantization with intel-extension-for-transformers
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[] | null | 4 | 2023-09-22T07:39:07 | 2023-11-27T15:28:52 | 2023-11-27T15:28:52 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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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.
-->
Support weight only quantization with intel-extension-for-transformers.
[Intel® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers) is an innovative toolkit to accelerate Transformer-based models on Intel platforms, in particular effective on 4th Intel Xeon Scalable processor Sapphire Rapids (codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html)). The toolkit provides the below key features:
* Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
* Advanced software optimizations and unique compression-aware runtime.
* Optimized Transformer-based model packages.
* [NeuralChat](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of plugins and SOTA optimizations.
* [Inference](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/llm/runtime/graph) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels.
This PR is an integration of weight only quantization feature with intel-extension-for-transformers.
* Unit test is in lib/langchain/tests/integration_tests/llm/test_weight_only_quantization.py
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| 1,908,338,148 |
PR_kwDOIPDwls5a9hIw
| 10,922 |
Implement Alibaba Tongyi chat model apis.
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[] | null | 2 | 2023-09-22T07:24:55 | 2023-10-12T23:59:38 | 2023-10-12T23:59:37 |
CONTRIBUTOR
| null |
Hi there
This PR is aim to implement chat model for Alibaba Tongyi LLM model. It contains work below:
1.Implement ChatTongyi chat model in langchain.chat_models.tongyi. Note this is different with tongyi llm model to another PR https://github.com/langchain-ai/langchain/pull/10878.
For detail it implements _generate() and _stream() function in ChatTongyi.
2. Add some examples in chat/tongyi.ipynb.
3. Add integration test in chat_models/test_tongyi.py
Note async completion for the Text API is not yet supported.
Dependencies: dashscope. It will be installed manually cause it is not need by everyone.
Happy for feedback on any aspect of this PR @hwchase17 @baskaryan.
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PR_kwDOIPDwls5a9LGZ
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add model feat table
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[] | null | 1 | 2023-09-22T06:00:10 | 2023-09-22T08:10:28 | 2023-09-22T08:10:27 |
COLLABORATOR
| null | null |
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| 1,908,167,473 |
PR_kwDOIPDwls5a88jt
| 10,920 |
fix: broken link in libs/langchain README
|
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[] | null | 1 | 2023-09-22T04:36:57 | 2023-09-22T17:14:19 | 2023-09-22T17:14:19 |
CONTRIBUTOR
| null |
**Description**
Fixes broken link to `CONTRIBUTING.md` in `libs/langchain/README.md`.
Because`libs/langchain/README.md` was copied from the top level README, and because the README contains a link to `.github/CONTRIBUTING.md`, the copied README's link relative path must be updated. This commit fixes that link.
|
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| 1,908,146,930 |
I_kwDOIPDwls5xvALy
| 10,919 |
typing errors in OllamaEmbeddings
|
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[] | null | 1 | 2023-09-22T04:07:19 | 2023-12-05T22:36:01 | 2023-12-05T22:36:01 |
CONTRIBUTOR
| null |
### System Info
`LangChain==0.0.298`, Python==3.11.5
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.embeddings import OllamaEmbeddings
_ = OllamaEmbeddings(model="llama2")
```
Running `mypy==1.5.1` on this file:
```none
a.py:3:5: error: Missing named argument "mirostat" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "mirostat_eta" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "mirostat_tau" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "num_ctx" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "num_gpu" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "num_thread" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "repeat_last_n" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "repeat_penalty" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "temperature" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "stop" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "tfs_z" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "top_k" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
a.py:3:5: error: Missing named argument "top_p" for "OllamaEmbeddings" [call-arg]
_ = OllamaEmbeddings(model="llama2")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```
It can be fixed by using `= None` as the default for the `Optional` fields
### Expected behavior
`mypy` to pass, as there are no type errors present
|
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| 1,908,133,024 |
I_kwDOIPDwls5xu8yg
| 10,918 |
Issue: unreasonable default template in document retrieval task
|
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[] | null | 3 | 2023-09-22T03:45:55 | 2023-12-30T16:05:39 | 2023-12-30T16:05:38 |
NONE
| null |
### Issue you'd like to raise.
Current default prompt template named CHAT_PROMPT in `chains/question_answering/stuff_prompt.py` contains a `SystemMessage` and `HumanMessage` combination, In which `SystemMessage` just looks like this:
```python
"""Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
{context}"""
```
As far as I know, `SystemMessage` usually intends to be a series of rules which LLM should obey in conversation, such as personality, function, usage etc. It's should stay immutable through whole conversation with users, but there is a significant and continuous transformation in real world 'cause relevant documents being retrieved has been located in the end of prompt. Is there any reason to do so?
### Suggestion:
We can put prompt in the `HumanMessage` as demonstrated below
```python
system_template = """Use the following pieces of context to answer the users question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{context}\n\n{question}"),
]
CHAT_PROMPT = ChatPromptTemplate.from_messages(messages)
```
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| 1,908,088,497 |
I_kwDOIPDwls5xux6x
| 10,917 |
"Minimax" object has no field "_client"
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open
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[] | null | 5 | 2023-09-22T02:36:07 | 2024-01-07T04:02:04 | null |
NONE
| null |
### System Info
when I run this command `python3 ./libs/langchain/tests/integration_tests/llms/test_minimax.py`
It always reports this error `ValueError: "Minimax" object has no field "_client"`
After I replaced "_client" with "client", it works.
version is `langchain==0.0.297`
### 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
python3 ./libs/langchain/tests/integration_tests/llms/test_minimax.py
### Expected behavior
Don't throw erros
|
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| 1,908,042,597 |
I_kwDOIPDwls5xumtl
| 10,916 |
Crashes when trying to stream Chat Vertex AI
|
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[] | null | 2 | 2023-09-22T01:39:08 | 2023-09-22T02:53:07 | 2023-09-22T02:53:07 |
CONTRIBUTOR
| null |
### System Info
Langchain v0.0.298
Python 3.11.4
Pop!OS 22.04
### Who can help?
@hwchase17 @agol
### 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
When trying to stream `chat-bison`:
```
File "/home/hunmonk/.pyenv/versions/lwe/lib/python3.11/site-packages/langchain/chat_models/base.py", line 204, in stream
for chunk in self._stream(
File "/home/hunmonk/.pyenv/versions/lwe/lib/python3.11/site-packages/langchain/chat_models/vertexai.py", line 195, in _stream
responses = chat.send_message_streaming(question.content, **params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ChatSession' object has no attribute 'send_message_streaming'
```
As far as I can tell, there is no `send_message_streaming()` method declared *anywhere* in Langchain.
When tryign to stream `codechat-bison`:
```
File "/home/hunmonk/git/llm-workflow-engine/lwe/backends/api/request.py", line 320, in iterate_streaming_response
for chunk in self.llm.stream(messages):
File "/home/hunmonk/.pyenv/versions/lwe/lib/python3.11/site-packages/langchain/chat_models/base.py", line 215, in stream
raise e
File "/home/hunmonk/.pyenv/versions/lwe/lib/python3.11/site-packages/langchain/chat_models/base.py", line 204, in stream
for chunk in self._stream(
File "/home/hunmonk/.pyenv/versions/lwe/lib/python3.11/site-packages/langchain/chat_models/vertexai.py", line 194, in _stream
chat = self._start_chat(history, params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/hunmonk/.pyenv/versions/lwe/lib/python3.11/site-packages/langchain/chat_models/vertexai.py", line 210, in _start_chat
return self.client.start_chat(message_history=history.history, **params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: CodeChatModel.start_chat() got an unexpected keyword argument 'message_history'
```
That looks like a Pydantic validation error in the class itself?
### Expected behavior
Streaming these models should work.
|
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PR_kwDOIPDwls5a8YV1
| 10,915 |
Add retry backoff to clarifai calls
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[] | null | 1 | 2023-09-22T00:58:54 | 2023-09-22T01:03:33 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Add retry backoff when ClarifAI requests fail
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5a8X47
| 10,914 |
Wfh/allow nonparallel
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I_kwDOIPDwls5xuTSh
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Optimization by PROmpting (OPRO)
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[] | null | 6 | 2023-09-22T00:33:14 | 2023-10-11T23:10:21 | null |
COLLABORATOR
| null |
### Feature request
[LARGE LANGUAGE MODELS AS OPTIMIZERS](https://arxiv.org/pdf/2309.03409.pdf) is promising method to optimize prompts, to find the best prompts.
Not sure the LangChain is the best tool to apply this method but Hey, it is LangChain :exclamation:
### Motivation
For workflows that use a stable prompts, this method could significantly improve results.
So, there is definitely a valid use case for it.
### Your contribution
Not sure I can do it. Sorry.
|
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I_kwDOIPDwls5xuQep
| 10,912 |
LocalAI embeddings shouldn't require OpenAI
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[] | null | 5 | 2023-09-22T00:17:24 | 2023-09-22T16:57:48 | 2023-09-22T16:57:47 |
CONTRIBUTOR
| null |
### System Info
macOS Ventura 13.5.2, M1
### Who can help?
@mudler
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://github.com/langchain-ai/langchain/blob/v0.0.298/libs/langchain/langchain/embeddings/localai.py#L197
### Expected behavior
Why does LocalAI embeddings require OpenAI? I think LocalAI's embeddings has no need for OpenAI, it has a whole embeddings suite: https://localai.io/features/embeddings/
I think it should be directly usable with its [`/embeddings` endpoint](https://github.com/go-skynet/LocalAI/blob/v1.25.0/api/api.go#L190)
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PR_kwDOIPDwls5a8MQX
| 10,910 |
add vertex prod features
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[] | null | 5 | 2023-09-21T23:59:06 | 2023-11-24T07:58:11 | 2023-09-22T08:44:09 |
COLLABORATOR
| null |
- chat vertex async
- vertex stream
- vertex full generation info
- vertex use server-side stopping
- model garden async
- update docs for all the above
in follow up will add
[] chat vertex full generation info
[] chat vertex retries
[] scheduled tests
|
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I_kwDOIPDwls5xuHU3
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`_identifying_params` broken in `ChatAnthropic` class
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[] | null | 4 | 2023-09-21T23:28:35 | 2023-12-23T14:15:37 | null |
CONTRIBUTOR
| null |
### System Info
v0.0.298
### Who can help?
@hwchase17 @ago
### 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
Create a `ChatAnthropic` class instance, note that both `_identifying_params` and `dict()` do not output the proper attributes, even though `_default_params` does.
I'd make a PR to fix this, but upon examining the code, it's not clear to me what is causing the bug.
```python
from langchain.chat_models import ChatAnthropic
chat = ChatAnthropic()
chat._default_params
{'max_tokens_to_sample': 256, 'model': 'claude-2'}
chat._identifying_params
{}
chat.dict()
{'_type': 'anthropic-chat'}
```
### Expected behavior
`_identifying_params`and `dict()` should output the correct params.
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add async support for llamacpp.py
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[] | null | 9 | 2023-09-21T23:21:15 | 2023-11-30T20:19:57 | 2023-09-22T18:13:52 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Added support for llamacpp being used in chains with "a" methods, like apredict, agenerate, etc. Also now it supports `langchain.callbacks.AsyncIteratorCallbackHandler`
- **Issue:** https://github.com/langchain-ai/langchain/issues/9865
- **Dependencies:** -,
- **Tag maintainer:** @baskaryan
- **Twitter handle:** -
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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| 10,907 |
Docs: Using SupabaseVectorStore with existing documents
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[] | null | 2 | 2023-09-21T23:13:09 | 2023-09-22T15:18:56 | 2023-09-22T15:18:56 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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## Description
Adds additional docs on how to use `SupabaseVectorStore` with existing data in your DB (vs inserting new documents each time).
|
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PR_kwDOIPDwls5a8Ah0
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CONTRIBUTING.md Quick Start: focus on langchain core; clarify docs and experimental are separate
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[] | null | 2 | 2023-09-21T22:55:02 | 2023-09-22T18:51:26 | 2023-09-22T17:17:09 |
CONTRIBUTOR
| null |
follow up to https://github.com/langchain-ai/langchain/pull/7959 , explaining better to focus just on langchain core
no dependencies
twitter @cjcjameson
|
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PR_kwDOIPDwls5a7P_U
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bump 299
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Bagatur/arxiv kwargs
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[] | null | 1 | 2023-09-21T19:45:39 | 2023-09-21T19:49:57 | 2023-09-21T19:49:56 |
COLLABORATOR
| null |
support all arXiv api wrapper kwargs in loader
|
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fix in the docstring
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CONTRIBUTOR
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Description: A fix in the documentation on how to use `GoogleSearchAPIWrapper`.
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finetuned models on 3.5-turbo use base tokenizer
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NONE
| null |
**Description:**
The memory summary buffer calls `get_num_tokens_from_messages` from `openai.py`, which relies on some logic using the model name to calculate the number of tokens. This logic is incomplete, in the case of models finetuned through openai's API, which carry a naming pattern along the lines of: `ft:gpt-3.5-turbo-0613:{your_org}::{snapshot_id}`. This PR alters the model-based logic in this function so we can use finetuned models with the summary buffer memory.
**Issue:**
#10900
**Tag maintainer:**
@agola11
@hwchase17
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I_kwDOIPDwls5xspN1
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Support for openai finetuned models
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[] | null | 3 | 2023-09-21T18:22:47 | 2023-12-30T16:01:49 | null |
NONE
| null |
### System Info
langchain==0.0.198
MacOS 13.5.2 Ventura
Python 3.11
### Who can help?
@agola11
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behavior:
1. Finetune a model using OpenAI's API
2. Retrieve your model tag in the form: `ft:gpt-3.5-turbo-0613:{your_org}::{snapshot_id}`
3. Instantiate a ChatOpenAI object with the finetuned model:
```
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo](ft:gpt-3.5-turbo-0613:{your_org}::{snapshot_id}")
```
4. Load an AgentExecuter from Agent, llm, and tools
5. Execute a query, eg:
```
response = executor.run(input="hello, where is my hat?")
```
The query will execute against the LLM at first, then produce an output like this:
```
WARNING:langchain.chat_models.openai:Warning: model not found. Using cl100k_base encoding.
Traceback (most recent call last):
File "/Users/wlane/projects/api-navgpt/demo/mapgpt-agent-demo/cli.py", line 59, in <module>
executor.run(input=query)
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 261, in run
return self(kwargs, callbacks=callbacks)[self.output_keys[0]]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 149, in __call__
final_outputs: Dict[str, Any] = self.prep_outputs(
^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/base.py", line 213, in prep_outputs
self.memory.save_context(inputs, outputs)
File "/opt/homebrew/lib/python3.11/site-packages/langchain/memory/summary_buffer.py", line 60, in save_context
self.prune()
File "/opt/homebrew/lib/python3.11/site-packages/langchain/memory/summary_buffer.py", line 65, in prune
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/lib/python3.11/site-packages/langchain/chat_models/openai.py", line 458, in get_num_tokens_from_messages
raise NotImplementedError(
NotImplementedError: get_num_tokens_from_messages() is not presently implemented for model gpt-3.5-turbo.See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.
```
### Expected behavior
It is expected that the Agent handles the utterance without throwing an error about being unable to map to the right tokenizer.
The issue is in `openai.py`, which aknowledges that gpt-3.5-turbo may change over time, and maps the model str to a specific snapshot: "gpt-3.5-turbo-0301". However, the logic does not consider that `model` may be some arbitrary handle containing `...gpt-3.5-turbo-...`, as is the case when you fine tune a model on OpenAI:
```
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
tiktoken_ = _import_tiktoken()
model = self.model_name
if model == "gpt-3.5-turbo":
# gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
```
When I alter the funtion like this, it works:
```
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
tiktoken_ = _import_tiktoken()
model = self.model_name
if "gpt-3.5-turbo" in model: <----------------------------
# gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
```
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lcel benefits
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docs: updated `YouTube` and `tutorial` video links
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COLLABORATOR
| null |
updated `YouTube` and `tutorial` videos with new links.
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Reordered several links by view counters
Some formatting: emphasized the names of products
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[] | null | 1 | 2023-09-21T15:48:21 | 2023-09-21T15:48:31 | null |
NONE
| null |
- **Description:** This PR improves the regex in the output parser for the `StructuredChatAgent`. Previously, it failed to match valid json if there was a space after `json` and before the new line, i.e., the follow string won't be matched by the previous regex:
````
```json
{
"action": "Tool",
"action_input": "Tool input."
}
```
````
- **Issue:** N/A,
- **Dependencies:** N/A,
- **Tag maintainer:** don't know,
- **Twitter handle:** solitarypenman
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PR_kwDOIPDwls5a59vk
| 10,895 |
sep base url and loaded url in sub link extraction
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PR_kwDOIPDwls5a59bH
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update agent docs
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PR_kwDOIPDwls5a58yK
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Fix Runnable.transform() for false-y inputs
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[] | null | 1 | 2023-09-21T15:42:47 | 2023-09-21T18:27:11 | 2023-09-21T18:27:10 |
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/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5a5z6p
| 10,892 |
Bagatur/bump 298
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PR_kwDOIPDwls5a5zEd
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recursive loader add status check
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PR_kwDOIPDwls5a5eW8
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truncate docs if num_tokens decrease a little to avoid infinite loop
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[] | null | 1 | 2023-09-21T14:35:26 | 2023-09-21T14:35:38 | null |
NONE
| null |
**Description:**
in ReduceDocumentsChain class, truncate docs if num_tokens decrease a little to avoid infinite loop.
*Issue:**
https://github.com/langchain-ai/langchain/issues/10888
**Dependencies:**
no
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PR_kwDOIPDwls5a5Uc_
| 10,889 |
added Cassandra caches to the llm_caching notebook doc
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[] | null | 2 | 2023-09-21T14:16:39 | 2023-09-21T20:31:21 | 2023-09-21T15:52:52 |
CONTRIBUTOR
| null |
This adds a section on usage of `CassandraCache` and `CassandraSemanticCache` to the doc notebook about caching LLMs, as suggested in [this comment](https://github.com/langchain-ai/langchain/pull/9772/#issuecomment-1710544100) on a previous merged PR.
I also spotted what looks like a mismatch between different executions and propose a fix (line 98).
Being the result of several runs, the cell execution numbers are scrambled somewhat, so I volunteer to refine this PR by (manually) re-numbering the cells to restore the appearance of a single, smooth running (for the sake of orderly execution :)
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I_kwDOIPDwls5xqggn
| 10,888 |
Issue: ReduceDocumentsChain may loop for many iterations
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[] | null | 2 | 2023-09-21T13:18:28 | 2023-12-28T16:04:52 | 2023-12-28T16:04:51 |
NONE
| null |
### Issue you'd like to raise.
https://github.com/langchain-ai/langchain/blob/ebe08412ad1649c33501cc80edffbb908cc0157e/libs/langchain/langchain/chains/combine_documents/reduce.py#L247
In this loop:
```
while num_tokens is not None and num_tokens > _token_max:
new_result_doc_list = _split_list_of_docs(
result_docs, length_func, _token_max, **kwargs
)
result_docs = []
for docs in new_result_doc_list:
new_doc = _collapse_docs(docs, _collapse_docs_func, **kwargs)
result_docs.append(new_doc)
num_tokens = length_func(result_docs, **kwargs)
```
If num_tokens does not decrease, or decrease very slow, the loop will run a lot of iterations, which takes too much time, as well as high LLM api cost.
Besides, the comment "Combine multiple documents recursively" is misleading. The method is iteration actually, not recursion.
### Suggestion:
I come up with one solution, if num_tokens decrease less than 10% after one iteration, truncate each doc of result_docs to 90% of original length. If you agree with my proposal, I would like to implement this and submit a pull request.
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I_kwDOIPDwls5xqUTC
| 10,887 |
langchain.chat_models import fails
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[] | null | 3 | 2023-09-21T12:58:28 | 2023-09-22T00:42:08 | 2023-09-22T00:42:07 |
NONE
| null |
### System Info
Langchain: 0.0.81
Python: 3.11.5
System: Ubuntu
Trying to install Langchain and run the basic Quick Start sample code. Importing langchain.chat_models thows a ModuleNotFoundError.
```
$ python3
Python 3.11.5 (main, Aug 25 2023, 07:43:52) [GCC 12.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from langchain.llms import OpenAI
>>> from langchain.chat_models import ChatOpenAI
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'langchain.chat_models'
```
Any suggestions?
### 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
Steps to reproduce:
1. pip install langchain[all]
2. run python3 REPL
3. try to import langchain.chat_models
### Expected behavior
chat_models import succeeds.
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| 1,906,770,938 |
I_kwDOIPDwls5xpwP6
| 10,886 |
Issue with GooglePalm class.
|
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[] | null | 2 | 2023-09-21T11:39:15 | 2023-12-28T16:04:57 | 2023-12-28T16:04:56 |
NONE
| null |
### System Info
Getting errors while using GooglePalm class. Have a look at the error below.
File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "/home/jupyter/vertexai-pocs-in-python/contact-centre/palm2-contact-centre-automation.py", line 114, in <module>
result = display_row(selected_enquery)
File "/home/jupyter/vertexai-pocs-in-python/contact-centre/palm2-contact-centre-automation.py", line 100, in display_row
return overall_chain(customer_enquery)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 243, in __call__
raise e
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 237, in __call__
self._call(inputs, run_manager=run_manager)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/sequential.py", line 105, in _call
outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 243, in __call__
raise e
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/base.py", line 237, in __call__
self._call(inputs, run_manager=run_manager)
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/llm.py", line 93, in _call
return self.create_outputs(response)[0]
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/llm.py", line 217, in create_outputs
result = [
File "/opt/conda/lib/python3.10/site-packages/langchain/chains/llm.py", line 220, in <listcomp>
self.output_key: self.output_parser.parse_result(generation),
File "/opt/conda/lib/python3.10/site-packages/langchain/schema/output_parser.py", line 69, in parse_result
return self.parse(result[0].text)
IndexError: list index out of range
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
using LLM chain
### Expected behavior
The error is not coming with other LLM classes.
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I_kwDOIPDwls5xpt1Y
| 10,885 |
Streaming output doesn't work for `CHAT_CONVERSATIONAL_REACT_DESCRIPTION` with `FinalStreamingStdOutCallbackHandler`
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[] | null | 2 | 2023-09-21T11:33:30 | 2023-12-28T16:05:02 | 2023-12-28T16:05:01 |
NONE
| null |
### System Info
langchain==0.0.285
python==3.10.12
Using VSCode Dev Container
### Who can help?
@agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
When using `FinalStreamingStdOutCallbackHandler` with `AgentType.ZERO_SHOT_REACT_DESCRIPTION`, I can successfully get streaming outputs. However, when switching to `AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION`, the output is returned all at once, and streaming does not seem to work as expected.
To Reproduce:
Use the following code setup for `AgentType.ZERO_SHOT_REACT_DESCRIPTION`:
```
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.callbacks.streaming_stdout_final_only import (
FinalStreamingStdOutCallbackHandler,
)
from langchain.llms import OpenAI
llm = OpenAI(
streaming=True, callbacks=[FinalStreamingStdOutCallbackHandler()], temperature=0
)
tools = load_tools(["wikipedia", "llm-math"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False
)
agent.run(
"It's 2023 now. How many years ago did Konrad Adenauer become Chancellor of Germany."
)
```
This streams the output as expected.
Use the following code setup for `AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION`:
```
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.memory import ConversationBufferMemory,RedisChatMessageHistory
from langchain.callbacks.streaming_stdout_final_only import (
FinalStreamingStdOutCallbackHandler,
)
from langchain.llms import OpenAI
from loguru import logger
import redis
import os
from dotenv import find_dotenv, load_dotenv
# Load environment variables from .env file
load_dotenv(find_dotenv("../app/.env"))
# Initialize Redis connection
redis_host = os.environ.get("REDIS_HOST")
redis_port = os.environ.get("REDIS_PORT")
if redis_host and redis_port:
redis_client = redis.Redis(host=redis_host, port=redis_port)
else:
logger.error("Redis host or port not set in environment variables")
# Function to get or initialize chat history
def get_or_init_chat_history(session_id: str, redis_url: str) -> RedisChatMessageHistory:
chat_history_data = redis_client.get(session_id)
if chat_history_data is None:
return RedisChatMessageHistory(session_id, url=redis_url)
else:
return RedisChatMessageHistory.deserialize(chat_history_data, url=redis_url)
# Initialize the chat history for a sample session
sample_session_id = '21112'
redis_url = f"redis://{redis_host}:{redis_port}"
chat_history = get_or_init_chat_history(sample_session_id, redis_url)
llm = OpenAI(
streaming=True, callbacks=[FinalStreamingStdOutCallbackHandler()], temperature=0
)
# Initialize the memory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True,
chat_memory=chat_history
)
tools = load_tools(["wikipedia", "llm-math"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=False, memory=memory
)
agent.run(
"It's 2023 now. How many years ago did Konrad Adenauer become Chancellor of Germany."
)
```
This returns the output all at once, without streaming.
Expected Behavior:
The output should be streamed when using `CHAT_CONVERSATIONAL_REACT_DESCRIPTION` just like it is streamed for `ZERO_SHOT_REACT_DESCRIPTION`.
### Expected behavior
The output should be streamed when using `CHAT_CONVERSATIONAL_REACT_DESCRIPTION` just like it is streamed for `ZERO_SHOT_REACT_DESCRIPTION`.
|
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| 1,906,754,123 |
I_kwDOIPDwls5xpsJL
| 10,884 |
Error get_num_tokens_from_messages() with gpt-3.5-turbo-0613
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[] | null | 2 | 2023-09-21T11:29:34 | 2023-09-21T11:49:45 | 2023-09-21T11:49:45 |
NONE
| null |
### System Info
Windows(server on linux), python, poetry
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Langchain has problems with working with model casts
```
File "C:\Users\zabre\PycharmProjects\sales-bot\bot\src\sales_bot\text_handler.py", line 445, in get_response
memory.prune()
File "C:\Users\zabre\AppData\Local\pypoetry\Cache\virtualenvs\sales-bot-f2_0xw7_-py3.11\Lib\site-packages\langchain\memory\summary_buffer.py", line 65, in prune
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zabre\AppData\Local\pypoetry\Cache\virtualenvs\sales-bot-f2_0xw7_-py3.11\Lib\site-packages\langchain\chat_models\openai.py", line 420, in get_num_tokens_from_messages
raise NotImplementedError(
NotImplementedError: get_num_tokens_from_messages() is not presently implemented for model gpt-3.5-turbo-0613.See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.
```
For example, when working with gpt-3.5-turbo-0613, he is not able to count the number of tokens, because the moment of checking the model is hard-coded
Please correct the following in def get_num_tokens_from_messages(langchain\chat_models\openai.py)":
```
if model == "gpt-3.5-turbo":
# gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
# gpt-4 may change over time.
# Returning num tokens assuming gpt-4-0314.
model = "gpt-4-0314"
```
Replase:
```
if "gpt-3.5-turbo" in model:
# gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
elif "gpt-4" in model:
# gpt-4 may change over time.
# Returning num tokens assuming gpt-4-0314.
model = "gpt-4-0314"
```
### Expected behavior
Sorry, because the project is closed, there is no way to provide sources, but the problem arises when using blind models and when calculating the number of tokens
```
memory = ConversationSummaryBufferMemory(
llm=self.analyse_llm,
ai_prefix="1",
human_prefix="2",
max_token_limit=self.max_memory_token_limit,
)
# loaded old memory, and check length with prune causes an error
memory.prune()
```
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| 1,906,662,893 |
I_kwDOIPDwls5xpV3t
| 10,883 |
ReAct agent support using Google PaLM 2
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[] | null | 3 | 2023-09-21T10:41:49 | 2023-12-28T16:05:07 | 2023-12-28T16:05:06 |
NONE
| null |
### Feature request
Hi Team,
I'm not sure if this is possible: ReAct agent support using Google PaLM 2.
Now, when using PaLM 2 as the supporting model for ReAct agent, as following code:
```python
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
# First, let's load the language model we're going to use to control the agent.
llm = ChatOpenAI(model_name='google/palm-2-chat-bison', temperature=0,
openai_api_key='...',
openai_api_base='https://...')
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
tools = load_tools(["llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
agent.run("What is the result of 1+3")
```
An error will occur：
```
OutputParserException: Parsing LLM output produced both a final answer and a parse-able action:: I need to use the calculator to answer this question
Action: Calculator
Action Input: 1+3
Observation: 4
Thought: I now know the final answer
Final Answer: 4
```
It looks like PaLM doesn't understand the prompt requirement of using the tool and waiting for the result.
Are there any solutions to this problem so far? Or is PaLM unable to implement this function?
### Motivation
We need to build a smart scheduler based on PaLM 2.
### Your contribution
If there is no ready-made solution in LangChain, I will try to see if I can use PaLM's dialogue capabilities to achieve this requirement. I also hope to get your feedback and judge whether this is feasible based on experience.
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| 1,906,629,969 |
I_kwDOIPDwls5xpN1R
| 10,882 |
Possibility to rename input keys, output keys and memory keys for all chains consistently
|
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[] | null | 1 | 2023-09-21T10:22:11 | 2023-12-28T16:05:12 | 2023-12-28T16:05:11 |
NONE
| null |
### Feature request
It should be possible to modify input keys, output keys and memory keys for all chains. Currently, this does not seem to be the case. For instance, while all these keys can be modified for the ConversationChain, not all of them can be modified for the ConversationalRetrievalChain.
**ConversationChain**
In the ConversationChain, I can freely rename the memory_key, input_key and output_key as the example below shows. This is as expected.
```
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts.prompt import PromptTemplate
# Keys can be freely adjusted
memory_key = "foo"
input_key = "bar"
output_key = "baz"
# Initialize the context with a prompt template
template = r"""The following is a friendly conversation between a human and an AI.
The AI is talkative and provides lots of specific details from its context. If the
AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
{}
Human: {}
AI:""".format("{" + memory_key + "}", "{" + input_key + "}")
prompt = PromptTemplate(
input_variables=[memory_key, input_key], template=template
)
# Initialize memory to store conversation history
memory = ConversationBufferMemory(
memory_key=memory_key, input_key=input_key, output_key=output_key
)
# Initialize large language model
model_kwargs = {"engine": "gpt-35-turbo-0613", "headers": {"x-api-key": os.environ["OPENAI_API_KEY"]}}
llm = ChatOpenAI(model_kwargs=model_kwargs, temperature=0.0)
# Initialize and return conversation chain
chain = ConversationChain(
llm=llm, memory=memory, prompt=prompt, verbose=True,
input_key=input_key, output_key=output_key
)
# Example call
user_input = "What is GPT in 5 words?"
chain({input_key: user_input})[output_key]
```
**ConversationalRetrievalChain**
If I try to pass an `input_key` (or `input_keys`) argument to `ConversationalRetrievalChain.from_llm`, then I get the following error:
```
ValidationError: 1 validation error for ConversationalRetrievalChain input_key extra fields not permitted (type=value_error.extra)
```
It seems that with the current implementation, one has to use the input keys ["question", "chat_history"] (see [code here](https://github.com/langchain-ai/langchain/blob/777b33b873b50339659963f2b5aba17b69412f5a/libs/langchain/langchain/chains/conversational_retrieval/base.py#L86)).
However, the ConversationalRetrievalChain does, thankfully, accept the input argument `output_key`. I.e., the output_key can be modified but the input_keys cannot.
### Motivation
Provide consistency and ease of use across all chains.
Currently, users may face difficulty in modifying input keys, output keys and memory keys for the ConversationalRetrievalChain which could result in inconsistency across the chains. By allowing modifications to these keys for all chains, users can have a consistent and standardized approach to building chatbots.
### Your contribution
TBD
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| 1,906,492,968 |
PR_kwDOIPDwls5a3RYf
| 10,881 |
Fixing some spelling mistakes
|
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[] | null | 2 | 2023-09-21T09:11:02 | 2023-09-27T17:56:52 | 2023-09-27T17:56:51 |
CONTRIBUTOR
| null |
@baskaryan
|
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PR_kwDOIPDwls5a3I-d
| 10,880 |
fix the Loader 'BiliBiliLoader'
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[] | null | 3 | 2023-09-21T08:53:04 | 2023-10-26T12:52:59 | 2023-10-13T06:14:45 |
CONTRIBUTOR
| null |
- **Description:** fix the Loader 'BiliBiliLoader' and it's doc
- **Issue:** Due to recent modifications in Bilibili's API regulations, it is now imperative to include credentials when making a request to obtain subtitles.
Furthermore, it is important to note that not all videos will necessarily have associated subtitles.
As a result, I have revised the logic for acquiring subtitles. Presently, in the absence of credentials, the response will include the video's title and description, with subtitles only retrieved if valid credentials are provided.
And I also changed the doc,adding how to get and provide the credential.
- **Dependencies:** Nope
|
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BigQuery - document loader, load() function throws error
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[] | null | 2 | 2023-09-21T08:45:23 | 2023-10-13T15:24:30 | 2023-10-13T15:24:30 |
NONE
| null |
### System Info
BigQuery table is not able to load. Here is the code:
```python
from langchain.document_loaders import BigQueryLoader
BASE_QUERY = """
SELECT * FROM arindam-2banerjee-0525-pimy.sf_transport.bikeshare_trips
"""
loader = BigQueryLoader(BASE_QUERY)
loader.load()
```
Error message:
```
---------------------------------------------------------------------------
NotFound Traceback (most recent call last)
[<ipython-input-24-4ebe0f70b252>](https://localhost:8080/#) in <cell line: 9>()
7 loader = BigQueryLoader(BASE_QUERY)
8
----> 9 loader.load()
9 frames
[~/.local/lib/python3.10/site-packages/langchain/document_loaders/bigquery.py](https://localhost:8080/#) in load(self)
59
60 bq_client = bigquery.Client(credentials=self.credentials, project=self.project)
---> 61 query_result = bq_client.query(self.query).result()
62 docs: List[Document] = []
63
[/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/client.py](https://localhost:8080/#) in query(self, query, job_config, job_id, job_id_prefix, location, project, retry, timeout, job_retry, api_method)
3401 )
3402 elif api_method == enums.QueryApiMethod.INSERT:
-> 3403 return _job_helpers.query_jobs_insert(
3404 self,
3405 query,
[/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/_job_helpers.py](https://localhost:8080/#) in query_jobs_insert(client, query, job_config, job_id, job_id_prefix, location, project, retry, timeout, job_retry)
112 return query_job
113
--> 114 future = do_query()
115 # The future might be in a failed state now, but if it's
116 # unrecoverable, we'll find out when we ask for it's result, at which
[/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/_job_helpers.py](https://localhost:8080/#) in do_query()
89
90 try:
---> 91 query_job._begin(retry=retry, timeout=timeout)
92 except core_exceptions.Conflict as create_exc:
93 # The thought is if someone is providing their own job IDs and they get
[/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/job/query.py](https://localhost:8080/#) in _begin(self, client, retry, timeout)
1308
1309 try:
-> 1310 super(QueryJob, self)._begin(client=client, retry=retry, timeout=timeout)
1311 except exceptions.GoogleAPICallError as exc:
1312 exc.message = _EXCEPTION_FOOTER_TEMPLATE.format(
[/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/job/base.py](https://localhost:8080/#) in _begin(self, client, retry, timeout)
691 # job has an ID.
692 span_attributes = {"path": path}
--> 693 api_response = client._call_api(
694 retry,
695 span_name="BigQuery.job.begin",
[/usr/local/lib/python3.10/dist-packages/google/cloud/bigquery/client.py](https://localhost:8080/#) in _call_api(self, retry, span_name, span_attributes, job_ref, headers, **kwargs)
811 name=span_name, attributes=span_attributes, client=self, job_ref=job_ref
812 ):
--> 813 return call()
814
815 return call()
[/usr/local/lib/python3.10/dist-packages/google/api_core/retry.py](https://localhost:8080/#) in retry_wrapped_func(*args, **kwargs)
347 self._initial, self._maximum, multiplier=self._multiplier
348 )
--> 349 return retry_target(
350 target,
351 self._predicate,
[/usr/local/lib/python3.10/dist-packages/google/api_core/retry.py](https://localhost:8080/#) in retry_target(target, predicate, sleep_generator, timeout, on_error, **kwargs)
189 for sleep in sleep_generator:
190 try:
--> 191 return target()
192
193 # pylint: disable=broad-except
[/usr/local/lib/python3.10/dist-packages/google/cloud/_http/__init__.py](https://localhost:8080/#) in api_request(self, method, path, query_params, data, content_type, headers, api_base_url, api_version, expect_json, _target_object, timeout, extra_api_info)
492
493 if not 200 <= response.status_code < 300:
--> 494 raise exceptions.from_http_response(response)
495
496 if expect_json and response.content:
NotFound: 404 POST https://bigquery.googleapis.com/bigquery/v2/projects//jobs?prettyPrint=false: Request couldn't be served.
Location: None
Job ID: 77604d22-bf1b-4dca-b6f7-ab553ac6aab8
```
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The code mentioned above.
### Expected behavior
Load a BigQuery query with one document per row.
|
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PR_kwDOIPDwls5a3Eil
| 10,878 |
Implement llm stream api of Alibaba Tongyi model.
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[] | null | 1 | 2023-09-21T08:41:32 | 2023-09-22T02:50:41 | null |
CONTRIBUTOR
| null |
Hi there
This PR is aim to refactor and implement new api for Alibaba Tongyi LLM model. It contains work below:
1. Implements langchain.llms.Tonyi stream call function.
2. Remove the _call() function as it is deprecated.
3. Add more code examples in tongyi.ipynb. One case for users who need to to use different model with Tongyi.
One case for streaming response call and with callbacks.
Note async completion for the Text API is not yet supported.
Dependencies: dashscope. It will be installed manually cause it is not need by everyone.
Happy for feedback on any aspect of this PR @hwchase17 @baskaryan.
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| 10,877 |
choose to prepend an index of page_content in retriever
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[] | null | 1 | 2023-09-21T07:17:26 | 2023-09-21T07:17:35 | null |
NONE
| null |
- **Description:** Allow users to prepend each document in the result with its index number by setting the add_index parameter to True in VectorStoreRetiever(), then it's much easier for QA inline citation,
- **Issue:** #7239,
- **Dependencies:** No dependencies required for this change,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:**
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Issue: How to set the language (default is English) when using UnstructuredImageLoader with PaddleOCR
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| false | null |
[] | null | 4 | 2023-09-21T07:05:38 | 2023-12-30T16:05:44 | 2023-12-30T16:05:43 |
NONE
| null |
### Issue you'd like to raise.
I am using langchain.document_loaders.image.UnstructuredImageLoader to load data from an image with a table on it. I chose PaddleOCR by setting os.environ["TABLE_OCR"] = "paddle" . However, I didn't find a place to set the detection language to other language. Could you please let me know how to do it. I have gotten stuck on it for a while.
### Suggestion:
_No response_
|
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| 1,906,228,353 |
PR_kwDOIPDwls5a2YGo
| 10,875 |
fix experimental imports
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[] | null | 1 | 2023-09-21T06:42:31 | 2023-09-21T06:44:18 | 2023-09-21T06:44:17 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5xni8C
| 10,874 |
Python - Stop generate response functionality
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[] | null | 1 | 2023-09-21T06:14:41 | 2023-12-28T16:05:22 | 2023-12-28T16:05:21 |
NONE
| null |
### Feature request
First of all BIG thanks for the working you are doing guys!!!
It will be nice if you can add a stop generate response implementation.
### Motivation
I'm always frustrated when I need to wait until the final response returned and then ask new question.
Sometimes my chatbot sending long JSON response that takes 55sec end-to-end it will be nice if I could stop it manually.
### Your contribution
No
|
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PR_kwDOIPDwls5a2Ow9
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Add async support to multi-query retriever.
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CONTRIBUTOR
| null |
Added async support to the MultiQueryRetriever class.
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I_kwDOIPDwls5xncMw
| 10,872 |
Qdrant collection issue
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[] | null | 1 | 2023-09-21T05:52:25 | 2023-09-21T07:01:10 | 2023-09-21T07:01:10 |
NONE
| null |
### System Info
```
try:
# Skip any validation in case of forced collection recreate.
if force_recreate:
raise ValueError
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is being thrown.
collection_info = client.get_collection(collection_name=collection_name)
current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name} does not "
f"contain vector named {vector_name}. Did you mean one of the "
f"existing vectors: {', '.join(current_vector_config.keys())}? "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_vector_config = current_vector_config.get(
vector_name
) # type: ignore[assignment]
elif isinstance(current_vector_config, dict) and vector_name is None:
raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}." # noqa
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
elif (
not isinstance(current_vector_config, dict) and vector_name is not None
):
raise QdrantException(
f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size: # type: ignore[union-attr]
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vector_config.size} " # type: ignore[union-attr]
f"dimensions. Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_distance_func = (
current_vector_config.distance.name.upper() # type: ignore[union-attr]
)
if current_distance_func != distance_func:
raise QdrantException(
f"Existing Qdrant collection is configured for "
f"{current_vector_config.distance} " # type: ignore[union-attr]
f"similarity. Please set `distance_func` parameter to "
f"`{distance_func}` if you want to reuse it. If you want to "
f"recreate the collection, set `force_recreate` parameter to "
f"`True`."
)
except (UnexpectedResponse, RpcError, ValueError):
vectors_config = rest.VectorParams(
size=vector_size,
distance=rest.Distance[distance_func],
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
}
client.recreate_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
```
I want to know which idiot wrote this code.
Recreate the collection, then won't all the data be lost?
### 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
Not all exceptions should be handled by recreate collection
### Expected behavior
Not all exceptions should be handled by recreate collection
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PR_kwDOIPDwls5a18yQ
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Upstash redis integration
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CONTRIBUTOR
| null |
- **Description:** Introduced Upstash provider with following wrappers: UpstashRedisCache, UpstashRedisEntityStore, UpstashRedisChatMessageHistory, UpstashRedisStore
- **Issue:** -,
- **Dependencies:** upstash-redis python package is needed,
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @BurakY744
|
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| 1,906,045,391 |
PR_kwDOIPDwls5a1v6w
| 10,869 |
Fix chat_history message order
|
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[] | null | 1 | 2023-09-21T04:00:14 | 2023-10-23T08:53:01 | 2023-09-22T15:16:00 |
CONTRIBUTOR
| null |
Not all databases uses id as default order, so add it explicitly
sqlite uses rawid as default order in select statement: [https://www.sqlite.org/lang_createtable.html#rowid](https://www.sqlite.org/lang_createtable.html#rowid), but some other databases like postgresql not behaves like this. since this class supports multiple db engine. we should have an order.
|
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| 1,905,981,655 |
PR_kwDOIPDwls5a1iVr
| 10,868 |
implement async exec for base.py
|
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[] | null | 1 | 2023-09-21T02:38:20 | 2023-09-21T04:21:15 | 2023-09-21T04:21:15 |
CONTRIBUTOR
| null |
This is my implementation of async exec. This code is similar to the synchronous version, but it uses async and await to make it asynchronous. To use this asynchronous version of _acall, we can simply call it with the async keyword: `output = await chain.acall(inputs)`
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,905,974,168 |
I_kwDOIPDwls5xmtuY
| 10,867 |
The model appears to be missing from the _default_params method in the baidu_qianfan_endpoint.py file
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[] | null | 7 | 2023-09-21T02:30:24 | 2023-12-24T16:01:18 | null |
NONE
| null |
### System Info
langchain 0.0.295
python 3.11
os macOS 12.5
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The model appears to be missing from the _default_params method in the baidu_qianfan_endpoint.py file
`
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
normal_params = {
"stream": self.streaming,
"request_timeout": self.request_timeout,
"top_p": self.top_p,
"temperature": self.temperature,
"penalty_score": self.penalty_score,
}
return {**normal_params, **self.model_kwargs}
`
Line 183 of the chat_completion.py file in the qianfan module failed to be executed.
`
def do(
self,
model: Optional[str] = None,
endpoint: Optional[str] = None,
stream: bool = False,
retry_count: int = 1,
request_timeout: float = 60,
backoff_factor: float = 0,
**kwargs,
) -> QfResponse:
"""
if model is EB, use EB SDK to deal with the request
"""
if "messages" in kwargs and isinstance(kwargs["messages"], QfMessages):
kwargs["messages"] = kwargs["messages"]._to_list()
if not GLOBAL_CONFIG.DISABLE_EB_SDK:
if model in ["ERNIE-Bot-turbo", "ERNIE-Bot"]:
import erniebot #line 183
erniebot.ak = self._client._auth._ak
erniebot.sk = self._client._auth._sk
erniebot.access_token = self._client._auth.access_token()
# compat with eb sdk
if model == "ERNIE-Bot":
model = "ernie-bot-3.5"
return erniebot.ChatCompletion.create(
model=model.lower(), messages=kwargs["messages"], stream=stream
)
return super().do(
model,
endpoint,
stream,
retry_count,
request_timeout,
backoff_factor,
**kwargs,
)
`
but even with model，baidu will return the following error
`
File "/Users/xxx/Workspace/xxx/xxx/chatbot/venv/lib/python3.11/site-packages/erniebot/backends.py", line 113, in handle_response
raise errors.InvalidParameterError(emsg)
erniebot.errors.InvalidParameterError: the length of messages must be an odd number
`
because there are two messages in the requested messages
### Expected behavior
able to return correct results
|
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reopened
| null | null |
https://api.github.com/repos/langchain-ai/langchain/issues/10866
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| 1,905,885,000 |
PR_kwDOIPDwls5a1OB0
| 10,866 |
beef up agent docs
|
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[] | null | 1 | 2023-09-21T00:31:15 | 2023-09-21T06:09:59 | 2023-09-21T06:09:58 |
COLLABORATOR
| null | null |
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| 1,905,866,123 |
PR_kwDOIPDwls5a1KE2
| 10,865 |
update guide imports
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[] | null | 1 | 2023-09-21T00:02:30 | 2023-09-21T00:02:47 | 2023-09-21T00:02:46 |
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| 10,864 |
When `search_type="similarity_score_threshold`, retriever returns negative scores
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[] | null | 4 | 2023-09-20T23:14:16 | 2023-10-30T16:27:45 | 2023-09-21T21:07:03 |
NONE
| null |
### System Info
- OS: Ubuntu 20.04
- `langhcain==0.0.297`
- ` chromadb==0.4.12`
- Python 3.9.18
### Who can help?
@hwchase17
@agola11
### 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.document_loaders import TextLoader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = embeddings = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2', encode_kwargs={'normalize_embeddings': True})
docsearch = Chroma.from_documents(texts, embeddings)
retriever = docsearch.as_retriever(search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.2})
print(retriever.get_relevant_documents('Ketanji'))
```
The above code returns negative similarity scores for all retrieved results:
```python
lib/python3.9/site-packages/langchain/vectorstores/base.py:257: UserWarning: Relevance scores must be between 0 and 1, got
[(Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': 'state_of_the_union.txt'}), -0.18782109124725155),
(Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': 'state_of_the_union.txt'}), -0.2858684850499025),
(Document(page_content='We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n\nTogether with our allies –we are right now enforcing powerful economic sanctions. \n\nWe are cutting off Russia’s largest banks from the international financial system. \n\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless. \n\nWe are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come. \n\nTonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n\nThe U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs. \n\nWe are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.', metadata={'source': 'state_of_the_union.txt'}), -0.3382525501830016),
(Document(page_content='For that purpose we’ve mobilized American ground forces, air squadrons, and ship deployments to protect NATO countries including Poland, Romania, Latvia, Lithuania, and Estonia. \n\nAs I have made crystal clear the United States and our Allies will defend every inch of territory of NATO countries with the full force of our collective power. \n\nAnd we remain clear-eyed. The Ukrainians are fighting back with pure courage. But the next few days weeks, months, will be hard on them. \n\nPutin has unleashed violence and chaos. But while he may make gains on the battlefield – he will pay a continuing high price over the long run. \n\nAnd a proud Ukrainian people, who have known 30 years of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards. \n\nTo all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world.', metadata={'source': 'state_of_the_union.txt'}), -0.3629898842731978)]
```
### Expected behavior
Score for each `Document` should be between 0 and 1.
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PR_kwDOIPDwls5a0_ae
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cleanup recursive url session
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[] | null | 1 | 2023-09-20T22:50:53 | 2023-09-21T14:22:15 | 2023-09-21T14:22:14 |
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Adding a connector for CogniSwitch.ai
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[] | null | 23 | 2023-09-20T22:21:34 | 2023-12-11T14:04:44 | null |
NONE
| null |
- **Description:** CogniSwitch focusses on making GenAI usage more reliable. It abstracts out the complexity & decision making required for tuning processing, storage & retrieval. Using simple APIs documents / URLs can be processed into a Knowledge Graph that can then be used to answer questions.
- **Dependencies:** No dependencies. Just network calls & API key required
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @CogniSwitch
- **Documentation:** Please check `docs/extras/use_cases/cogniswitch_chain_usage.ipynb`
- **Tests:** Unit tests added within chains/ `test_cogniswitch_store` & `test_cogniswitch_answer`
PR has passed linting and testing before this submission.
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bump 297
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PR_kwDOIPDwls5a0xQV
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fix loading of sql chain
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[] | null | 1 | 2023-09-20T21:33:04 | 2023-09-20T21:37:50 | 2023-09-20T21:37:49 |
COLLABORATOR
| null |
Closing #6889
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fix databricks docs
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PR_kwDOIPDwls5a0tMZ
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redirect platform to provider
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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/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5a0oCA
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fix recursive loader
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PR_kwDOIPDwls5a0ZS6
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fix extract sublink bug
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PR_kwDOIPDwls5a0YAC
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I_kwDOIPDwls5xlUsZ
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[] | null | 7 | 2023-09-20T19:39:26 | 2023-12-25T16:02:26 | null |
NONE
| null |
### System Info
Langhchain version: 0.0.296
Python version: 3.9.7
Platform: Windows 10
### Who can help?
I need to implement a fallback for my RAG application based on Langchain conversational retrievers because my application needs to use GPT 4 only for RAG and GPT 3.5 for generic questions (eg.: ChatGPT). A way to implement this behaviour is by means of fallbacks, because infortunately create_conversational_retrieval_agent() enforces the same LLM for all the tools. So, I wrote the following code:
```
short_llm = ChatOpenAI(
deployment_id=DEPLOYMENT_NAME_GPT_3_5_4K,
engine=DEPLOYMENT_NAME_GPT_3_5_4K,
model_name=model_gpt_3_5_4k,
temperature=0,
openai_api_base=BASE_URL,
openai_api_key=API_KEY,
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()]
)
long_llm = ChatOpenAI(
deployment_id=DEPLOYMENT_NAME_GPT_4_8K,
engine=DEPLOYMENT_NAME_GPT_4_8K,
model_name=model_gpt_4_8k,
temperature=0,
openai_api_base=BASE_URL,
openai_api_key=API_KEY,
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()]
)
chat_llm = short_llm.with_fallbacks([long_llm])
```
And to create the agent:
`agent_executor = create_conversational_retrieval_agent(chat_llm, tools, verbose=True, sytem_message=system_prompt, remember_intermediate_steps=False)`
But the following error occurs:
```
C:\ProgramData\Anaconda3\lib\site-packages\langchain\agents\agent_toolkits\conversational_retrieval\openai_functions.py in create_conversational_retrieval_agent(llm, tools, remember_intermediate_steps, memory_key, system_message, verbose, max_token_limit, **kwargs)
58
59 if not isinstance(llm, ChatOpenAI):
---> 60 raise ValueError("Only supported with ChatOpenAI models.")
61 if remember_intermediate_steps:
62 memory: BaseMemory = AgentTokenBufferMemory(
ValueError: Only supported with ChatOpenAI models.
```
In fact, chat_llm has the following type: langchain.schema.runnable.base.RunnableWithFallbacks. It apparently does not inherit ChatOpenAI .
Is there any workaround? My requirement is to use long LLM for RAG/retriever tools and a short one for normal conversation with ChatGPT 3.5
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
short_llm = ChatOpenAI(
deployment_id=DEPLOYMENT_NAME_GPT_3_5_4K,
engine=DEPLOYMENT_NAME_GPT_3_5_4K,
model_name=model_gpt_3_5_4k,
temperature=0,
openai_api_base=BASE_URL,
openai_api_key=API_KEY,
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()]
)
long_llm = ChatOpenAI(
deployment_id=DEPLOYMENT_NAME_GPT_4_8K,
engine=DEPLOYMENT_NAME_GPT_4_8K,
model_name=model_gpt_4_8k,
temperature=0,
openai_api_base=BASE_URL,
openai_api_key=API_KEY,
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()]
)
chat_llm = short_llm.with_fallbacks([long_llm])
```
And to create the agent:
`agent_executor = create_conversational_retrieval_agent(chat_llm, tools, verbose=True, sytem_message=system_prompt, remember_intermediate_steps=False)`
### Expected behavior
Conversational retrieval agents should be able to work with fallbacks.
|
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PR_kwDOIPDwls5azqJm
| 10,851 |
bump exp 19
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[] | null | 1 | 2023-09-20T17:17:45 | 2023-09-20T17:17:55 | 2023-09-20T17:17:52 |
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| 1,905,420,396 |
PR_kwDOIPDwls5azqIt
| 10,850 |
add lcel how to index
|
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[] | null | 1 | 2023-09-20T17:17:43 | 2023-09-20T17:19:44 | 2023-09-20T17:19:43 |
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I_kwDOIPDwls5xkdfv
| 10,849 |
Issue: running integration tests
|
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[] | null | 1 | 2023-09-20T16:50:05 | 2023-09-21T11:13:05 | 2023-09-21T11:13:05 |
NONE
| null |
### integration tests.
I have tried to follow the readme in langchain/tests to run the integration tests but got these errors :
`ModuleNotFoundError: No module named 'qdrant_client'`
Using poetry with python version = 3.10
I already tried the `poetry install` and` poetry install --with test_integration` but does't helped. The readme seems don't have much information around this and couldn't find any document that what is guideline to run the integration tests.
The unit tests are running fine.
Is it maybe something wrong with my env or I might missing a step before running integration tests?
### Suggestion:
_No response_
|
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ValueError: The following `model_kwargs` are not used by the model while running Microsoft/Phi-1.5 using HuggingFacePipeline
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[] | null | 3 | 2023-09-20T16:49:29 | 2023-12-27T16:04:18 | 2023-12-27T16:04:17 |
NONE
| null |
### System Info
Python 3.9.18
langchain 0.0.292
transformers 4.33.2
torch 2.0.1
os macOs Ventura 13.5.2
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from transformers import pipeline
from langchain import PromptTemplate
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
from langchain.chains import LLMChain
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5",
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
"microsoft/phi-1_5", trust_remote_code=True, use_cache=True, return_attention_mask=False)
question = "Who won the FIFA World Cup in the year 1994? "
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
# llm_pipeline = HuggingFacePipeline(pipeline=generate)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
llm_pipeline = HuggingFacePipeline(pipeline=pipe)
generator_chain = LLMChain(llm=llm_pipeline, prompt=prompt)
generator_outputs = generator_chain.run(question)
print(generator_outputs)
```
ValueError: The following `model_kwargs` are not used by the model: ['attention_mask'] (note: typos in the generate arguments will also show up in this list)
### Expected behavior
Return the response from the model
|
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PR_kwDOIPDwls5azgMK
| 10,847 |
improvement: Remove custom SingleStoreDBRetriever
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[] | null | 5 | 2023-09-20T16:43:18 | 2023-10-17T10:33:54 | 2023-10-12T11:55:38 |
CONTRIBUTOR
| null |
The SingleStoreDB vector store does not need custom logic for the retriever, so the logic from the base class should be used.
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Improvements to llm/deepinfra
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[] | null | 8 | 2023-09-20T15:57:20 | 2023-10-24T16:54:24 | 2023-10-24T16:54:24 |
CONTRIBUTOR
| null |
- replace `requests` package with `langchain.requests`
- add `_acall` support
- add `_stream` and `_astream`
- freshen up the documentation a bit
- update vendor doc
|
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PR_kwDOIPDwls5azNzl
| 10,845 |
doc: update the openai_function_agent document
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CONTRIBUTOR
| null |
- **Description:** This pull request updates the openai_function_agent document. The update enhances the document in three significant ways:
- Firstly, it eliminates imports that lead to import errors such as `langchain_experimental`.
- Secondly, it initializes the agent by directly calling the `OpenAIFunctionsAgent`, instead of using the less intuitive `initialize_agent`.
- Lastly, it substitutes the tools with those that don't require additional external services, simplifying the process for users to reproduce the notebook.
- **Tag maintainer:** @baskaryan
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I_kwDOIPDwls5xkGKF
| 10,844 |
Installing from source (pip install -e .) fails with multiple top-level packags found
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[] | null | 3 | 2023-09-20T15:51:04 | 2023-11-07T16:42:53 | 2023-09-20T16:08:29 |
CONTRIBUTOR
| null |
### System Info
Linux, Python 3.11.5
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. `python -m venv .venv`
2. `. .venv/bin/activate`
3. `pip install -e .`
### Expected behavior
To install package in a local virtualenv.
What happens:
```
% pip install -e .
Obtaining file:///home/iskren/src/deepinfra/libs/langchain
Installing build dependencies ... done
Checking if build backend supports build_editable ... done
Getting requirements to build editable ... error
error: subprocess-exited-with-error
× Getting requirements to build editable did not run successfully.
│ exit code: 1
╰─> [14 lines of output]
error: Multiple top-level packages discovered in a flat-layout: ['libs', 'langchain'].
To avoid accidental inclusion of unwanted files or directories,
setuptools will not proceed with this build.
If you are trying to create a single distribution with multiple packages
on purpose, you should not rely on automatic discovery.
Instead, consider the following options:
1. set up custom discovery (`find` directive with `include` or `exclude`)
2. use a `src-layout`
3. explicitly set `py_modules` or `packages` with a list of names
To find more information, look for "package discovery" on setuptools docs.
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build editable did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
```
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PR_kwDOIPDwls5azIzV
| 10,843 |
Update base.py
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[] | null | 1 | 2023-09-20T15:40:15 | 2023-09-27T18:15:58 | 2023-09-27T18:15:58 |
CONTRIBUTOR
| null |
Fixing a typo in the example code in the docstring...
You have to start somewhere though right?
|
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bump 296
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[] | null | 1 | 2023-09-20T15:25:00 | 2023-09-20T15:31:16 | 2023-09-20T15:31:15 |
COLLABORATOR
| null | null |
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Directory loader does not extract text between <strong> </strong> tags in HTML
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[] | null | 3 | 2023-09-20T14:50:31 | 2023-11-23T12:36:32 | null |
CONTRIBUTOR
| null |
### System Info
LANGCHAIN - 0.0.0286
OS - Windows
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce, take a HTML document that has text between STRONG tags of HTML tags and extract using directoryloader
### Expected behavior
It should extract all the text from HTML
|
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| 1,905,102,656 |
I_kwDOIPDwls5xjY9A
| 10,840 |
Confused behavior of CharacterTextSplitter with regex
|
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open
| false | null |
[] | null | 5 | 2023-09-20T14:16:53 | 2024-01-02T05:42:03 | null |
NONE
| null |
### System Info
langchain=0.0.294
python=3.10
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
pattern = "\.|,|\n\n"
text_splitter = CharacterTextSplitter(separator=pattern, is_separator_regex=True,
chunk_size=100, chunk_overlap=0,
keep_separator=False)
docs = text_splitter.split_documents(documents)
for doc in docs:
print(doc.page_content)
```
the output would be
```
'Madam Speaker\\.|,|\n\n Madam Vice President\\.|,|\n\n our First Lady and Second Gentleman'
'Members of Congress and the Cabinet\\.|,|\n\n Justices of the Supreme Court\\.|,|\n\n My fellow Americans'
'\\.|,|\n\nLast year COVID-19 kept us apart\\.|,|\n\n This year we are finally together again\\.|,|'
```
which includes my regex expression. I even set keep_seperator=False.
with keep_seperator=True, the seperator become the leading character, which is still stange.
```
'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman'
'. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.'
'Last year COVID-19 kept us apart. This year we are finally together again.'
```
### Expected behavior
```
'Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.'
'Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.'
'Last year COVID-19 kept us apart. This year we are finally together again.'
```
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| 1,905,089,020 |
PR_kwDOIPDwls5ayiBA
| 10,839 |
remove get_schema method from Neo4jGraph class
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[] | null | 6 | 2023-09-20T14:09:56 | 2023-09-21T07:55:44 | 2023-09-21T07:55:43 |
CONTRIBUTOR
| null |
- **Description:** Remove get_schema method from Neo4jGraph. A property should not be named **get**_schema, and We can just use `self.schema` to get access the schema.
- **Tag maintainer:** @baskaryan (I am sorry but the relevant maintainer are not showed in default pull request message)
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| 1,904,926,280 |
PR_kwDOIPDwls5ax-4g
| 10,837 |
Fix PyMUPDFLoader kwargs
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closed
| false | null |
[] | null | 1 | 2023-09-20T12:49:02 | 2023-10-05T12:56:30 | 2023-10-05T12:56:30 |
CONTRIBUTOR
| null |
- **Description:** Fix the `PyMuPDFLoader` to accept `loader_kwargs` from the document loader's `loader_kwargs` option. This provides more flexibility in formatting the output documents.
- **Issue:** The `loader_kwargs` is not passed into the `load` method from the document loader, which limits configuration options.
- **Dependencies:** None
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| 1,904,823,416 |
I_kwDOIPDwls5xiUx4
| 10,836 |
below code is not able to show grpahs at frontend , rather it returns code ,
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[] | null | 2 | 2023-09-20T11:52:08 | 2023-12-27T16:04:28 | 2023-12-27T16:04:27 |
NONE
| null |
### Discussed in https://github.com/langchain-ai/langchain/discussions/10835
<div type='discussions-op-text'>
<sup>Originally posted by **astro-siddhesh** September 20, 2023</sup>
import pandas as pd
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain.agents.agent_types import AgentType
df = pd.read_csv('/Users/siddheshphapale/Desktop/project/sqlcsv.csv')
llm = ChatOpenAI(openai_api_key= "my key" , temperature=0 ,max_tokens= 500 , verbose= False)
agent = create_pandas_dataframe_agent(llm, df, agent_type=AgentType.OPENAI_FUNCTIONS)
from langsmith import Client
client = Client()
def send_feedback(run_id, score):
client.create_feedback(run_id, "user_score", score=score)
st.set_page_config(page_title='🦜🔗 Ask the CSV App')
st.title('🦜🔗 Ask the CSV App')
query_text = st.text_input('Enter your question:', placeholder = 'Who was in cabin C128?')
# Form input and query
result = None
with st.form('myform', clear_on_submit=True):
submitted = st.form_submit_button('Submit')
if submitted:
with st.spinner('Calculating...'):
response = agent({"input": query_text}, include_run_info=True)
result = response["output"]
run_id = response["__run"].run_id
if result is not None:
st.info(result)
col_blank, col_text, col1, col2 = st.columns([10, 2,1,1])
with col_text:
st.text("Feedback:")
with col1:
st.button("👍", on_click=send_feedback, args=(run_id, 1))
with col2:
st.button("👎", on_click=send_feedback, args=(run_id, 0))
<img width="600" alt="Screenshot 2023-09-20 at 5 21 38 PM" src="https://github.com/langchain-ai/langchain/assets/118797304/536155f7-fb12-4fa2-8dc8-80aea70e7389">
</div>
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| 1,904,777,227 |
I_kwDOIPDwls5xiJgL
| 10,834 |
how to use LongContextReorder with RetrievalQAChain
|
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[] | null | 9 | 2023-09-20T11:22:57 | 2023-12-22T03:31:21 | null |
NONE
| null |
### Issue you'd like to raise.
in docs，has a simple demo to use LongContextReorder to reorder docs.
but ,if i want use it with RetrievalQAChain ,how can i use it. thx
### Suggestion:
_No response_
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| 1,904,764,017 |
I_kwDOIPDwls5xiGRx
| 10,833 |
LLMSingleActionAgent mixes up tools
|
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[] | null | 5 | 2023-09-20T11:14:36 | 2023-10-18T15:11:47 | 2023-10-18T15:11:47 |
NONE
| null |
### System Info
My systeminfo:
langchain 0.0.295
Python 3.11
### Who can help?
_No response_
### Information
- [X] 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
That is my code where I set up my agent with custom tools, each tool has a name, description and access to a specific vector store with a collection name.
```python
def process_prompt(user_prompt: str, session_id="allgemein", debug=False):
openai.api_key = config.openai_api_key
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k", max_tokens=4096)
service_context = ServiceContext.from_defaults(llm=llm)
used_tools = []
for tool in tools_to_use:
vector_store = get_vector_store(tool.get("collection"))
index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context)
filters = None
vector_store_info = VectorStoreInfo(
content_info=tool.get("description"),
metadata_info=[]
)
retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info)
used_tools.append(
Tool(
name=tool.get("name"),
description=tool.get("description"),
return_direct=tool.get("return_direct"),
func=lambda q: str(retriever.retrieve(q)),
)
)
message_history = PostgresChatMessageHistory(
connection_string=f"postgresql://postgres:{config.postgres}, session_id=session_id, table_name="chat_history",
)
memory = ConversationBufferMemory(
memory_key="chat_history", chat_memory=message_history
)
prompt = PromptTemplate(input_variables=["input", "chat_history"], template=summary_prompt_template)
summary_chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=True,
memory=memory, # <--- this is the only change
)
used_tools.append(
Tool(
name="Summary Tool",
description="useful for retrieving memory summaries",
func=summary_chain.run,
)
)
prompt = CustomPromptTemplate(template=get_prompt_template(), tools=used_tools,
input_variables=["input", "intermediate_steps", "chat_history"])
output_parser = CustomOutputParser()
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in used_tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"], #
allowed_tools=tool_names,
handle_parsing_errors=True,
)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=used_tools, verbose=debug,
memory=memory, return_intermediate_steps=False)
response = agent_executor.run(user_prompt)
```
Here is my code for my custom prompt template and output parser:
```python
class CustomPromptTemplate(BaseChatPromptTemplate):
template: str
tools: List[Tool]
def format_messages(self, **kwargs) -> list[HumanMessage]:
# 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
# print(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])
formatted = self.template.format(**kwargs)
return [HumanMessage(content=formatted)]
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
# print(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]},
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 match:
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)
else:
# raise ValueError(f"Could not parse output: {llm_output}")
return AgentFinish(return_values={"output": llm_output.split("Thought:")[-1]}, log=llm_output)
```
### Expected behavior
The agent should use exactly the tool what is defined in the action, Instead he always writes:
Example:
```
action: Notion Tool
Input Action: [some input]
Thought: I use the Notion Tool to retrieve some information about the input.
....
```
But when I print the agent_scratchpad it shows that he retrieved information from an other Tool instead of that what he uses.
|
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| 1,904,680,537 |
I_kwDOIPDwls5xhx5Z
| 10,832 |
PyPDF2 used in Google Drive loader has vulnerability issues
|
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[] | null | 4 | 2023-09-20T10:26:26 | 2023-12-27T16:04:33 | 2023-12-27T16:04:32 |
NONE
| null |
### System Info
The GoogleDrive loader currently uses the PyPDF2 library instead of the PyPDF.
Since PyPDF2 was already merged to the original one, it shouldn't be used anymore.
[https://data.safetycli.com/v/59234/f17/?utm_source=pyupio&utm_medium=redirect&utm_campaign=pyup_rd&utm_id=081](url)
This is the vulnerability issue that gets picked up, note that it is fixed in PyPDF
Also I tried to change the loader using `file_loader_cls` but it just doesn't work
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
No functionality issue but https://data.safetycli.com/v/59234/f17/?utm_source=pyupio&utm_medium=redirect&utm_campaign=pyup_rd&utm_id=0817&utm_content=data showcases the bug
### Expected behavior
This error should not pop up
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I_kwDOIPDwls5xhudZ
| 10,831 |
Issue: use QianfanChatEndpoint and RetrievalQA Error
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[] | null | 5 | 2023-09-20T10:18:23 | 2023-12-25T16:02:46 | null |
NONE
| null |
### Issue you'd like to raise.
`
llm = QianfanChatEndpoint(
model="ChatGLM2-6B-32K",
qianfan_ak="xxx",
qianfan_sk="xxx"
)
embedding = ErnieEmbeddings(
qianfan_ak="xxx",
qianfan_sk="xxx"
)
db = Chroma.from_documents(texts, embedding)
retriever = db.as_retriever()
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
qa.run("请问事项的名称是什么？")
`
an error occurred in the above code.
`
File "/Users/xxx/Workspace/xxx/smart_gov_nav/chatbot/venv/lib/python3.11/site-packages/langchain/chat_models/baidu_qianfan_endpoint.py", line 63, in convert_message_to_dict
raise TypeError(f"Got unknown type {message}")
TypeError: Got unknown type content="Use the following pieces of context to answer the users question. \nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n----------------\n办事大厅名称\n " additional_kwargs={}
`
Because the type of message in the convert_message_to_dict argument is SystemMessage. That's why this error occurred. How to use it correctly?
### Suggestion:
_No response_
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PR_kwDOIPDwls5axGGY
| 10,830 |
Refactor(ernie): support function_call, reimplment ernie_bot with erniebot SDK.
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[] | null | 4 | 2023-09-20T10:17:15 | 2023-10-13T11:03:32 | 2023-10-13T11:03:21 |
CONTRIBUTOR
| null |
- **Description:**
* Introducing the [ERNIE-Bot SDK](https://github.com/PaddlePaddle/ERNIE-Bot-SDK) to refactor the current implementation of Ernie in Langchain
* Rename ernie.py -> ernie_bot. py, because the usage of `xx-bot` models is only for chat completion generation.
* Support Function Calling capability in chat_models/ernie_bot.py
* Add ipynb and test
- **Issue:** no
- **Dependencies:** no
- **Tag maintainer:** @baskaryan
- **Twitter handle:** no
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I_kwDOIPDwls5xhptB
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Simirality search not working for Conversational Retrieval Agents
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[] | null | 3 | 2023-09-20T10:06:42 | 2023-12-27T16:04:43 | 2023-12-27T16:04:42 |
NONE
| null |
### System Info
Hello! I'm using Conversational Retrieval Agents in a Nodejs environment with Faiss vectorStore. The problem is that the agent does not search for simurality. Vectors are normally well created and saved in a folder by Faiss.
It should be noted that the chatbot communicates well but does not take vectors into account.
Here are the snippets of my code :
**creation and export of the chain**
`const llm = new ChatOpenAI({
temperature: 0,
maxTokens: 300,
modelName: "gpt-4"
});
const memory = new OpenAIAgentTokenBufferMemory({
llm: llm,
memoryKey: "chat_history",
returnMessages: true,
inputKey:"input",
outputKey:"output",
});
const makeChain = async (vectorstore,basePrompt) => {
const retriever = vectorstore.asRetriever();
const tool = createRetrieverTool(retriever, {
name: "search_state_of_union",
description: "Searches and returns documents regarding the state-of-the-union.",
});
const tools = [tool]
const chain = await initializeAgentExecutorWithOptions(tools,llm,{
verbose: true,
agentType: "openai-functions",
memory:memory,
returnIntermediateSteps: true,
agentArgs: {
prefix:basePrompt +` `+`Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.`,
},
});
return chain;
};
`
**Using of the chain**
` const vectorStorePath = process.env.VECTOR_STORE_PATH || path.join(__dirname, '..', 'vectorStore');
const directory = path.join(vectorStorePath, chatbot.name);
console.log(directory);
const vectorStore=await FaissStore.load(
directory,
new OpenAIEmbeddings()
);
/**const resultTwo = await vectorStore.similaritySearch("nestor", 1);
console.log(resultTwo); */
// Sanitize the message
const sanitizedMessage = message.trim().replaceAll('\n', ' ')
//create chain
const chain = await makeChain(vectorStore, chatbot.basePrompt);
//Ask a question using chat history
const response = await chain.call({
input: sanitizedMessage,
chat_history: chatHistory || []
});
`
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Create and export the chain**
`const llm = new ChatOpenAI({
temperature: 0,
maxTokens: 300,
modelName: "gpt-4"
});
const memory = new OpenAIAgentTokenBufferMemory({
llm: llm,
memoryKey: "chat_history",
returnMessages: true,
inputKey:"input",
outputKey:"output",
});
const makeChain = async (vectorstore,basePrompt) => {
const retriever = vectorstore.asRetriever();
const tool = createRetrieverTool(retriever, {
name: "search_state_of_union",
description: "Searches and returns documents regarding the state-of-the-union.",
});
const tools = [tool]
const chain = await initializeAgentExecutorWithOptions(tools,llm,{
verbose: true,
agentType: "openai-functions",
memory:memory,
returnIntermediateSteps: true,
agentArgs: {
prefix:basePrompt +` `+`Do your best to answer the questions. Feel free to use any tools available to look up relevant information, only if necessary.`,
},
});
return chain;
};
`
**Using of the chain**
` const vectorStorePath = process.env.VECTOR_STORE_PATH || path.join(__dirname, '..', 'vectorStore');
const directory = path.join(vectorStorePath, chatbot.name);
console.log(directory);
const vectorStore=await FaissStore.load(
directory,
new OpenAIEmbeddings()
);
/**const resultTwo = await vectorStore.similaritySearch("nestor", 1);
console.log(resultTwo); */
// Sanitize the message
const sanitizedMessage = message.trim().replaceAll('\n', ' ')
//create chain
const chain = await makeChain(vectorStore, chatbot.basePrompt);
//Ask a question using chat history
const response = await chain.call({
input: sanitizedMessage,
chat_history: chatHistory || []
});
`
### Expected behavior
Help me find a solution
|
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PR_kwDOIPDwls5aw2zt
| 10,827 |
Title：fixed a bug that was causing the streaming transfer to not work…
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[] | null | 2 | 2023-09-20T09:36:28 | 2023-12-02T02:57:51 | 2023-12-02T02:57:51 |
CONTRIBUTOR
| null |
… properly
Fixed a bug that was causing the streaming transfer to not work properly.
- **Description:
1、The on_llm_new_token method in the streaming callback can now be called properly in streaming transfer mode.
2、In streaming transfer mode, LLM can now correctly output the complete response instead of just the first token.
- **Tag maintainer: @wangxuqi
- **Twitter handle: @kGX7XJjuYxzX9Km
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PR_kwDOIPDwls5awotS
| 10,826 |
Add ConversationBufferMemory to ConversationalRetrievalChain
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[] | null | 1 | 2023-09-20T09:01:00 | 2023-09-20T09:37:18 | 2023-09-20T09:37:12 |
NONE
| null |
This should help to add memory buffer directly when create ConversationalRetrievalChain from_llm
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5awd0a
| 10,825 |
Use shallow copy on runnable locals
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[] | null | 1 | 2023-09-20T08:37:55 | 2023-09-20T15:13:08 | 2023-09-20T15:13:07 |
COLLABORATOR
| null |
- deep copy prevents storing complex objects in locals
<!-- 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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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5awd0T
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Criteria Parser Improvements
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PR_kwDOIPDwls5awRzG
| 10,823 |
fix: impl missing embeddings method
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[] | null | 2 | 2023-09-20T08:10:02 | 2023-10-19T06:51:29 | 2023-10-19T06:51:29 |
CONTRIBUTOR
| null |
FAISS does not implement embeddings method and use embed_query to embedding texts which is wrong for some embedding models.
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Providing `-1` to `max_tokens` while creating an OpenAI LLM using the `gpt-3.5-turbo-instruct` is failing
|
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[] | null | 2 | 2023-09-20T07:58:46 | 2023-09-20T08:10:49 | 2023-09-20T08:10:48 |
NONE
| null |
### System Info
LangChain version: tested with both versions 0.0.294 and 0.0.295.
Python Version: 3.10.10 running on Linux x86_64 (Ubuntu 22.04).
Tested with OpenAI library, both versions 0.27.8 and 0.28.0.
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the following snippet:
```python
from langchain import OpenAI
llm = OpenAI(
model="gpt-3.5-turbo-instruct",
verbose=True,
max_tokens=-1,
)
llm("I am ")
```
The following error is returned (just the relevant stack trace):
```
File .venv/lib/python3.10/site-packages/langchain/llms/base.py:831, in BaseLLM.__call__(self, prompt, stop, callbacks, tags, metadata, **kwargs)
824 if not isinstance(prompt, str):
825 raise ValueError(
826 "Argument `prompt` is expected to be a string. Instead found "
827 f"{type(prompt)}. If you want to run the LLM on multiple prompts, use "
828 "`generate` instead."
829 )
830 return (
--> 831 self.generate(
832 [prompt],
833 stop=stop,
834 callbacks=callbacks,
835 tags=tags,
836 metadata=metadata,
837 **kwargs,
838 )
839 .generations[0][0]
840 .text
841 )
File .venv/lib/python3.10/site-packages/langchain/llms/base.py:627, in BaseLLM.generate(self, prompts, stop, callbacks, tags, metadata, **kwargs)
618 raise ValueError(
619 "Asked to cache, but no cache found at `langchain.cache`."
620 )
621 run_managers = [
622 callback_manager.on_llm_start(
623 dumpd(self), [prompt], invocation_params=params, options=options
624 )[0]
625 for callback_manager, prompt in zip(callback_managers, prompts)
626 ]
--> 627 output = self._generate_helper(
628 prompts, stop, run_managers, bool(new_arg_supported), **kwargs
629 )
630 return output
631 if len(missing_prompts) > 0:
File .venv/lib/python3.10/site-packages/langchain/llms/base.py:529, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
527 for run_manager in run_managers:
528 run_manager.on_llm_error(e)
--> 529 raise e
530 flattened_outputs = output.flatten()
531 for manager, flattened_output in zip(run_managers, flattened_outputs):
File .venv/lib/python3.10/site-packages/langchain/llms/base.py:516, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
506 def _generate_helper(
507 self,
508 prompts: List[str],
(...)
512 **kwargs: Any,
513 ) -> LLMResult:
514 try:
515 output = (
--> 516 self._generate(
517 prompts,
518 stop=stop,
519 # TODO: support multiple run managers
520 run_manager=run_managers[0] if run_managers else None,
521 **kwargs,
522 )
523 if new_arg_supported
524 else self._generate(prompts, stop=stop)
525 )
526 except BaseException as e:
527 for run_manager in run_managers:
File .venv/lib/python3.10/site-packages/langchain/llms/openai.py:357, in BaseOpenAI._generate(self, prompts, stop, run_manager, **kwargs)
355 params = self._invocation_params
356 params = {**params, **kwargs}
--> 357 sub_prompts = self.get_sub_prompts(params, prompts, stop)
358 choices = []
359 token_usage: Dict[str, int] = {}
File .venv/lib/python3.10/site-packages/langchain/llms/openai.py:459, in BaseOpenAI.get_sub_prompts(self, params, prompts, stop)
455 if len(prompts) != 1:
456 raise ValueError(
457 "max_tokens set to -1 not supported for multiple inputs."
458 )
--> 459 params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
460 sub_prompts = [
461 prompts[i : i + self.batch_size]
462 for i in range(0, len(prompts), self.batch_size)
463 ]
464 return sub_prompts
File .venv/lib/python3.10/site-packages/langchain/llms/openai.py:616, in BaseOpenAI.max_tokens_for_prompt(self, prompt)
602 """Calculate the maximum number of tokens possible to generate for a prompt.
603
604 Args:
(...)
613 max_tokens = openai.max_token_for_prompt("Tell me a joke.")
614 """
615 num_tokens = self.get_num_tokens(prompt)
--> 616 return self.max_context_size - num_tokens
File .venv/lib/python3.10/site-packages/langchain/llms/openai.py:599, in BaseOpenAI.max_context_size(self)
596 @property
597 def max_context_size(self) -> int:
598 """Get max context size for this model."""
--> 599 return self.modelname_to_contextsize(self.model_name)
File .venv/lib/python3.10/site-packages/langchain/llms/openai.py:589, in BaseOpenAI.modelname_to_contextsize(modelname)
586 context_size = model_token_mapping.get(modelname, None)
588 if context_size is None:
--> 589 raise ValueError(
590 f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
591 "Known models are: " + ", ".join(model_token_mapping.keys())
592 )
594 return context_size
ValueError: Unknown model: gpt-3.5-turbo-instruct. Please provide a valid OpenAI model name.Known models are: gpt-4, gpt-4-0314, gpt-4-0613, gpt-4-32k, gpt-4-32k-0314, gpt-4-32k-0613, gpt-3.5-turbo, gpt-3.5-turbo-0301, gpt-3.5-turbo-0613, gpt-3.5-turbo-16k, gpt-3.5-turbo-16k-0613, text-ada-001, ada, text-babbage-001, babbage, text-curie-001, curie, davinci, text-davinci-003, text-davinci-002, code-davinci-002, code-davinci-001, code-cushman-002, code-cushman-001
```
Originally got this through a `LLMChain`, but tracked it down to the LLM itself.
### Expected behavior
Should be processing the input correctly. Can confirm this works with other models like `text-davinci-003`.
|
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https://github.com/langchain-ai/langchain/pull/10821
| 1,904,330,397 |
PR_kwDOIPDwls5av9ZZ
| 10,821 |
Fixed some Gramm.. errors
|
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[] | null | 1 | 2023-09-20T07:20:37 | 2023-09-21T09:04:56 | 2023-09-21T09:04:55 |
CONTRIBUTOR
| null |
Fixed some Gramm.. errors
@baskaryan
|
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| 1,904,284,433 |
I_kwDOIPDwls5xgRMR
| 10,820 |
MongoDBAtlasVectorSearch: Make It Work with Existing MongoDB Collection (rather than having to create a new one)
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[] | null | 1 | 2023-09-20T06:52:26 | 2023-10-05T21:58:37 | null |
NONE
| null |
### Feature request
When using MongoDBAtlasVectorSearch, the user is limited to only first populate a MongoDB collection of documents using either add_document or add_text methods. Those methods create documents on the given collection using a fixed structure. What if the user already has a collection on MongoDB with custom structure (still containing the field 'embedding' which is used to measure similarity)? This is not currently supported and the user would have to implement a wrapper around MongoDBAtlasVectorSearch to achieve that.
For instance the following code won't work (docs will be empty):
```
from langchain.vectorstores import MongoDBAtlasVectorSearch
from langchain.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
index_name = "index_name_from_MongoDB"
docsearch = MongoDBAtlasVectorSearch(embeddings, collection=collection, index_name=index_name)
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
print(docs[0].page_content)
```
This is because the collection is not populated using any of the original methods of MongoDBAtlasVectorSearch, e.g. add_document or add_text, hence the structure of the collection might be different (containing additional fields and missing the 'text' field for example).
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