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https://api.github.com/repos/langchain-ai/langchain/issues/8060
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| 1,815,365,924 |
I_kwDOIPDwls5sNEkk
| 8,060 |
DOC: Unable to Pass HuggingFace Access Token via Huggingface TextGen Inference for large language model hosted in HuggingFace Inference Endpoint in protected mode.
|
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[] | null | 7 | 2023-07-21T08:10:05 | 2023-11-14T16:07:00 | 2023-11-14T16:06:59 |
NONE
| null |
### Issue with current documentation:
**Documentation Link:** https://python.langchain.com/docs/modules/model_io/models/llms/integrations/huggingface_textgen_inference
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5sMvms
| 8,058 |
Support pydantic v2
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[] | null | 2 | 2023-07-21T07:13:37 | 2023-07-21T14:19:37 | 2023-07-21T14:19:37 |
CONTRIBUTOR
| null |
### Feature request
Please support pydantic v2 version.
### Motivation
Pydantic v2 [released](https://docs.pydantic.dev/latest/blog/pydantic-v2-final/) a few weeks ago with [a lot of features](https://docs.pydantic.dev/latest/blog/pydantic-v2/#performance) shipped.
> Looking at the pydantic-core [benchmarks](https://github.com/pydantic/pydantic-core/tree/main/tests/benchmarks) today,
> pydantic V2 is between 4x and 50x faster than pydantic V1.9.1.
> In general, pydantic V2 is about 17x faster than V1 when validating a model containing a range of common fields.
### Your contribution
Let me know if you allow contribution to this then will try to work on this.
|
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I_kwDOIPDwls5sMpXr
| 8,057 |
Issue: using Fine-tuning model with Langchain
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[] | null | 1 | 2023-07-21T06:51:49 | 2023-10-27T16:05:48 | 2023-10-27T16:05:47 |
NONE
| null |
### Issue you'd like to raise.
Hi, I am using a fine tuned model that generates user prompts to SQL queries, instead of the default model provided by langchain. The reason for doing this is because langchain does not know about all the data from the database unless you provide context to it but there is a lot of data hence why it can create incorrect SQL queries also it is unable to form complex queries for similar reasons even after you give it context.
So my question is based upon the output I am getting below. Is there a way to keep the initial question I asked the same throughout like in the action input, instead of "SOME_BRANCH_NAME" I want the entire sentence to go through to the SQLDatabaseChain like the user initially asked which is "what is the summary of last 3 issues reported by SOME_BRANCH_NAME". Basically since the Action Input is different from what the user asked, it is generating the wrong SQL query since what it should be doing is this, "SELECT summary FROM sla_tat_summary WHERE organization like '%SOME_BRANCH_NAME%' ORDER BY ReportedDate DESC LIMIT 3;" instead of what is shown below. I could just use the SQLDatabaseChain on its own which does get the exact query I want since I was able to make sure only the prompt the user gave went through, but the agent is needed since I am using it for things other than SQL generation.
user prompt: what is the summary of last 3 issues reported by SOME_BRANCH_NAME
> Entering new AgentExecutor chain...
I need to find out what the last 3 issues reported by SOME_BRANCH_NAME were.
Action: TPS Issue Tracker Database
Action Input: SOME_BRANCH_NAME
> Entering new SQLDatabaseChain chain...
SOME_BRANCH_NAME:
SELECT organization, COUNT(*) FROM sla_tat_summary WHERE severity = 'Level 2 - Critical' GROUP BY organization ORDER BY COUNT(*) DESC LIMIT 1;
### Suggestion:
In summary send user prompt as it is to Action Input.
|
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I_kwDOIPDwls5sMnzJ
| 8,056 |
Invalid prompt schema; check for mismatched or missing input parameters. {'relevant_context', 'user_query'} (type=value_error)Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 2 | 2023-07-21T06:45:54 | 2023-10-27T16:05:53 | 2023-10-27T16:05:53 |
NONE
| null |
### Issue you'd like to raise.
Getting missing input parameter error. Is there anything i am missing?
````
ValidationError Traceback (most recent call last)
Input In [82], in <cell line: 17>()
1 from langchain import LLMChain, OpenAI, PromptTemplate
2 prompt_template = f"""
3 Answer the question based on the contexts below.
4 If the question cannot be answered using the information
(...)
14 Question:{user_query}
15 Answer:"""
---> 17 prompt = PromptTemplate(
18 input_variables=["relevant_context","user_query"], template=prompt_template
19 )
20 llm = LLMChain(llm=OpenAI(), prompt=prompt)
File ~/anaconda3/lib/python3.9/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for PromptTemplate
__root__
Invalid prompt schema; check for mismatched or missing input parameters. {'relevant_context', 'user_query'} (type=value_error)`
```
```
`from langchain import LLMChain, OpenAI, PromptTemplate
prompt_template = f"""
Answer the question based on the contexts below.
If the question cannot be answered using the information
provided answer with "I don't know".
###
Contexts:
{relevant_context}
###
Question:{user_query}
Answer:"""
prompt = PromptTemplate(
input_variables=["relevant_context","user_query"], template=prompt_template
)
llm = LLMChain(llm=OpenAI(), prompt=prompt)`
```
### Suggestion:
_No response_
|
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| 1,815,238,276 |
I_kwDOIPDwls5sMlaE
| 8,055 |
generate() got multiple keyword arguments for 'stop_sequences' while generating using hf TGI
|
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[] | null | 2 | 2023-07-21T06:36:26 | 2023-07-21T15:24:11 | 2023-07-21T15:24:11 |
CONTRIBUTOR
| null |
### System Info
### I'm not sure what exactly is causing the issue. Is it langchain, TGI, or streamlit
generate() got multiple keyword arguments for 'stop_sequences' upon running the script along with streamlit
```
TypeError: generate() got multiple values for keyword argument 'stop_sequences'
Traceback:
File "/usr/local/lib/python3.8/dist-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script exec(code, module.dict)
File "/workspace/app.py", line 154, in main()
File "/workspace/app.py", line 109, in main handle_userinput(user_question)
File "/workspace/app.py", line 79, in handle_userinput response = st.session_state.conversation({'question': user_question})
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 243, in call raise e
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 237, in call self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/conversational_retrieval/base.py", line 142, in _call answer = self.combine_docs_chain.run(
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 445, in run return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 243, in call raise e
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 237, in call self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/combine_documents/base.py", line 106, in _call output, extra_return_dict = self.combine_docs(
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/combine_documents/stuff.py", line 165, in combine_docs return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/llm.py", line 252, in predict return self(kwargs, callbacks=callbacks)[self.output_key]
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 243, in call raise e
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 237, in call self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/llm.py", line 92, in _call response = self.generate([inputs], run_manager=run_manager)
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/llm.py", line 102, in generate return self.llm.generate_prompt(
File "/usr/local/lib/python3.8/dist-packages/langchain/llms/base.py", line 188, in generate_prompt return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File "/workspace/app.py", line 18, in generate super().generate(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/langchain/llms/base.py", line 281, in generate output = self._generate_helper(
File "/usr/local/lib/python3.8/dist-packages/langchain/llms/base.py", line 225, in _generate_helper raise e
File "/usr/local/lib/python3.8/dist-packages/langchain/llms/base.py", line 212, in _generate_helper self._generate(
File "/usr/local/lib/python3.8/dist-packages/langchain/llms/base.py", line 604, in _generate self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/langchain/llms/huggingface_text_gen_inference.py", line 156, in _call res = self.client.generate(
```
```
from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInference
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = HuggingFaceTextGenInference(
inference_server_url='http://127.0.0.1:8080',
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.85,
stop_sequences=['</s>'],
repetition_penalty=1.03,
stream=True
)
print(llm("What is a proctor?", callbacks=[StreamingStdOutCallbackHandler()]))
```
the above script works properly but while using it with chainlit I run into the `generate() got multiple keyword arguments for 'stop_sequences'`
### Who can help?
@hwchase17
@baskaryan
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.llms.huggingface_text_gen_inference import HuggingFaceTextGenInference
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = HuggingFaceTextGenInference(
inference_server_url='http://127.0.0.1:8080',
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.85,
stop_sequences=['</s>'],
repetition_penalty=1.03,
stream=True
)
print(llm("What is a proctor?", callbacks=[StreamingStdOutCallbackHandler()]))
```
the above script works properly but while using it with chainlit I run into the `generate() got multiple keyword arguments for 'stop_sequences'`
### Expected behavior
generate normally
|
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I_kwDOIPDwls5sMcw5
| 8,054 |
Langchain support Alibaba Cloud's Tongyi Qianwen model?
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| false | null |
[] | null | 16 | 2023-07-21T06:00:26 | 2023-12-19T00:49:48 | 2023-12-19T00:49:47 |
NONE
| null |
### Feature request
How does Alibaba Cloud's Tongyi Qianwen Big Model combine with Langchain?
langchain如何与阿里云的通义千问大模型结合起来
### Motivation
Alibaba Cloud's Tongyi Qianwen
### Your contribution
none
|
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PR_kwDOIPDwls5WEOj2
| 8,053 |
Add Retry Events
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[] | null | 4 | 2023-07-21T05:47:45 | 2023-07-27T19:39:41 | 2023-07-27T19:39:40 |
COLLABORATOR
| null |

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| 1,815,166,208 |
I_kwDOIPDwls5sMT0A
| 8,052 |
Issue: Langsmith running using local docker containers, roadmap, local model support?
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[] | null | 5 | 2023-07-21T05:13:57 | 2023-12-01T16:08:58 | 2023-12-01T16:08:57 |
CONTRIBUTOR
| null |
Hi! I have been using langmith on on local env using docker.
Is there a roadmap for continue supporting that?
is langsmith going to be totally independent non-opensource per se?
Asking this because I can't really use the public langsmith service due to NDA, and I also have some issues that i'm happy to help fixing. Like I did with my other langchain contributions.
But since langsmith is not public I guess there is no workflow for contribution or bugfix?
Current i'm using langchain with local open source models, langsmith works great but playground won't work, my guess this is hardcoded to use OpenAI or something like that.
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| 8,051 |
CallbackManager _handle_event consumes Exceptions
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[] | null | 1 | 2023-07-21T05:09:22 | 2023-10-27T16:05:59 | 2023-10-27T16:05:58 |
NONE
| null |
### Discussed in https://github.com/hwchase17/langchain/discussions/8003
<div type='discussions-op-text'>
<sup>Originally posted by **kevinmat** July 20, 2023</sup>
Hi,
I see that function consumes the Exception , Is it possible to override this functionality so that any exception from callbacks returns back the flow instead of continuing the flow
def _handle_event(
handlers: List[BaseCallbackHandler],
event_name: str,
ignore_condition_name: Optional[str],
*args: Any,
**kwargs: Any,
) -> None:
"""Generic event handler for CallbackManager."""
message_strings: Optional[List[str]] = None
for handler in handlers:
try:
if ignore_condition_name is None or not getattr(
handler, ignore_condition_name
):
getattr(handler, event_name)(*args, **kwargs)
except NotImplementedError as e:
if event_name == "on_chat_model_start":
if message_strings is None:
message_strings = [get_buffer_string(m) for m in args[1]]
_handle_event(
[handler],
"on_llm_start",
"ignore_llm",
args[0],
message_strings,
*args[2:],
**kwargs,
)
else:
logger.warning(f"Error in {event_name} callback: {e}")
except Exception as e:
logging.warning(f"Error in {event_name} callback: {e}")</div>
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PR_kwDOIPDwls5WEIIU
| 8,050 |
Update base.py to remove ValueError
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[] | null | 6 | 2023-07-21T05:08:11 | 2023-08-04T04:08:43 | 2023-08-04T04:08:43 |
NONE
| null |
- Description: This update removes the ValueError raised in the self.memory and _chain_type methods. These errors were causing issues when users attempted to use chains without intending to save. The methods now instead raise warnings, allowing chains to function as intended when not being saved.
- Issue: Not applicable. This is an improvement rather than a bug fix.
- Dependencies: No new dependencies introduced.
- Tag maintainer: @hwchase17, as this change affects the Memory functionality of LangChain.
All checks for linting and testing are passing. No new integration is added so there are no additional tests or example notebooks included.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
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| 8,049 |
Dedup methods
|
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PR_kwDOIPDwls5WD-eX
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feat: Add with_history option for chatglm
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[] | null | 1 | 2023-07-21T04:02:15 | 2023-07-21T05:38:53 | 2023-07-21T05:25:38 |
CONTRIBUTOR
| null |
In certain 0-shot scenarios, the existing stateful language model can unintentionally send/accumulate the .history.
This commit adds the "with_history" option to chatglm, allowing users to control the behavior of .history and prevent unintended accumulation.
Possible reviewers @hwchase17 @baskaryan @mlot
Refer to discussion over this thread: https://twitter.com/wey_gu/status/1681996149543276545?s=20
|
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[] | null | 1 | 2023-07-21T02:03:51 | 2023-07-21T02:26:51 | 2023-07-21T02:26:50 |
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| 8,045 |
Added streaming support to Replicate
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[] | null | 2 | 2023-07-21T00:58:24 | 2023-09-17T18:54:20 | 2023-07-21T01:59:54 |
CONTRIBUTOR
| null |
Streaming support is useful if you are doing long-running completions or need interactivity e.g. for chat... adding it to replicate, using a similar pattern to other LLMs that support streaming.
Housekeeping: I ran `make format` and `make lint`, no issues reported in the files I touched.
I did update the replicate integration test but ran into some issues, specifically:
1. The original test was failing for me due to the model argument not being specified... perhaps this test is not regularly run? I fixed it by adding a call to the lightweight hello world model which should not be burdensome for replicate infra.
2. I couldn't get the `make integration_tests` command to pass... a lot of failures in other integration tests due to missing dependencies... however I did make sure the particluar test file I updated does pass, by running `poetry run pytest tests/integration_tests/llms/test_replicate.py`
Finally, I am @tjaffri https://twitter.com/tjaffri for feature announcement tweets... or if you could please tag @docugami https://twitter.com/docugami we would really appreciate that :-)
Tagging model maintainers @hwchase17 @baskaryan
Thank for all the awesome work you folks are doing.
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PR_kwDOIPDwls5WDmed
| 8,044 |
HuggingFaceTextGenInference bug fix: Multiple values for keyword argument
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[] | null | 3 | 2023-07-21T00:53:24 | 2023-07-21T03:37:16 | 2023-07-21T02:05:08 |
CONTRIBUTOR
| null |
Fixed the bug causing: `TypeError: generate() got multiple values for keyword argument 'stop_sequences'`
```python
res = await self.async_client.generate(
prompt,
**self._default_params,
stop_sequences=stop,
**kwargs,
)
```
The above throws an error because stop_sequences is in also in the self._default_params.
@baskaryan review please?
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Issue: Tool is always called with a string parameter instead of Model despite using OPENAI_FUNCTIONS agent
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[] | null | 2 | 2023-07-20T23:54:50 | 2023-07-25T14:25:43 | 2023-07-25T14:25:43 |
NONE
| null |
### Issue you'd like to raise.
Hi,
I have defined an OPENAI_FUNCTIONS agent. I have created a tool from a function where I have defined a BaseModel as input parameter
```
class FooInputModel(BaseModel):
id: str
name: str
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")]
}
memory = ConversationBufferMemory(memory_key="memory", return_messages=True)
tool= Tool.from_function(
name= "FooGenerator",
description= "Foo the bar",
func=foo,
args_schema= FooInputModel
)
agent = initialize_agent([tool],
llm,
agent=AgentType.OPENAI_FUNCTIONS,
agent_kwargs=agent_kwargs,
memory=memory)
```
my function foo is properly called when necessary, however the input is always a string whereas I would like a "FooInputModel". How can I achieve this ? And how can I see if the agent is actually using the functions calling from OpenAI because I have doubts it's working and when I print the agent I don't see any FunctionMessage in the history.
Thanks
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5WDbUl
| 8,041 |
fix redis cache chat model
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[] | null | 2 | 2023-07-20T23:44:17 | 2023-07-21T02:00:06 | 2023-07-21T02:00:05 |
COLLABORATOR
| null |
Redis cache currently stores model outputs as strings. Chat generations have Messages which contain more information than just a string. Until Redis cache supports fully storing messages, cache should not interact with chat generations.
|
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| 1,814,931,826 |
PR_kwDOIPDwls5WDX1J
| 8,040 |
Fixing issue with MSSQL connection
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[] | null | 1 | 2023-07-20T23:17:05 | 2023-07-24T22:45:41 | 2023-07-24T22:45:41 |
CONTRIBUTOR
| null |
My team recently faced an issue while using MSSQL and passing a schema name.
We noticed that "SET search_path TO {self.schema}" is being called for us, which is not a valid ms-sql query, and is specific to postgresql dialect.
We were able to run it locally after this fix.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5sLXJD
| 8,039 |
can it be possible to take input in custom tool
|
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[] | null | 3 | 2023-07-20T23:07:26 | 2023-10-27T16:06:04 | 2023-10-27T16:06:03 |
NONE
| null |
### Issue you'd like to raise.
Hello everyone,
I am trying to make a mcq tool but that tools need input for that particular question everytime what that quiz tool is called. Any approach which i can try
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5WDUCZ
| 8,038 |
Few Shot Chat Prompt
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[] | null | 3 | 2023-07-20T23:04:05 | 2023-07-28T01:46:12 | 2023-07-28T01:46:11 |
COLLABORATOR
| null |
Proposal for a few shot chat message example selector
|
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PR_kwDOIPDwls5WDMJB
| 8,037 |
Harrison/async web
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[] | null | 1 | 2023-07-20T22:27:55 | 2023-07-28T05:02:10 | 2023-07-28T05:02:10 |
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PR_kwDOIPDwls5WDLwv
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Async HTML loader and HTML2Text transformer
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[] | null | 1 | 2023-07-20T22:25:34 | 2023-07-21T05:30:59 | 2023-07-21T05:30:59 |
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| null |
New HTML loader that asynchronously loader a list of urls.
New transformer using [HTML2Text](https://github.com/Alir3z4/html2text/) for HTML to clean, easy-to-read plain ASCII text (valid Markdown).
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Neptune graph and openCypher QA Chain
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[] | null | 2 | 2023-07-20T22:23:14 | 2023-07-21T02:28:24 | 2023-07-21T01:56:47 |
CONTRIBUTOR
| null |
## Description
This PR adds a graph class and an openCypher QA chain to work with the Amazon Neptune database.
## Dependencies
`requests` which is included in the LangChain dependencies.
## Maintainers for Review
@krlawrence
@baskaryan
### Twitter handle
pjain7
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PR_kwDOIPDwls5WDKi_
| 8,034 |
Provides access to a Document page_content formatter in the AmazonKendraRetriever
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[] | null | 7 | 2023-07-20T22:20:08 | 2023-08-05T14:54:22 | 2023-08-04T03:54:49 |
CONTRIBUTOR
| null |
- Description:
- Provides a new attribute in the AmazonKendraRetriever which processes a ResultItem and returns a string that will be used as page_content;
- The excerpt metadata should not be changed, it will be kept as was retrieved. But it is cleaned when composing the page_content;
- Refactors the AmazonKendraRetriever to improve code reusability;
- Issue: #7787
- Tag maintainer: @3coins @baskaryan
- Twitter handle: wilsonleao
**Why?**
Some use cases need to adjust the page_content by dynamically combining the ResultItem attributes depending on the context of the item.
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PR_kwDOIPDwls5WDKOi
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Update mongodb_atlas.py docstrings
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[] | null | 2 | 2023-07-20T22:18:13 | 2023-09-13T17:16:56 | 2023-07-21T00:35:07 |
CONTRIBUTOR
| null |
Hi all, I just added the "index_name" parameter to the docstrings for mongodb_atlas.py (it is missing in the [public doc page](https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch.html#langchain-vectorstores-mongodb-atlas-mongodbatlasvectorsearch).
@rlancemartin
Thanks
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PR_kwDOIPDwls5WDHCZ
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Predibase Integration
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[] | null | 1 | 2023-07-20T22:03:42 | 2023-07-21T02:01:39 | 2023-07-21T02:01:39 |
CONTRIBUTOR
| null |
This PR adds a Predibase integration as a custom LLM wrapper. I've also added docs and an example notebook for reference.
Please let me know if there's anything I can do to improve the PR. When it's merged, feel free to tag https://twitter.com/predibase and https://twitter.com/AbhayyM as contributors.
|
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PR_kwDOIPDwls5WDCgz
| 8,031 |
Document transformer for regex sub
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[] | null | 2 | 2023-07-20T21:41:55 | 2023-08-04T03:49:55 | 2023-08-04T03:49:55 |
COLLABORATOR
| null |
Simply transformation to modify / clean / process `Documents`.
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PR_kwDOIPDwls5WC8xY
| 8,030 |
Update Golden integration documentation
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[] | null | 2 | 2023-07-20T21:12:55 | 2023-07-20T22:53:44 | 2023-07-20T22:53:44 |
CONTRIBUTOR
| null |
fixes some typos and cleans up onboarding for golden, thank you!
@hinthornw
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I_kwDOIPDwls5sK5LH
| 8,029 |
how to deploy langchain bot using fastapi with streaming responses
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[] | null | 2 | 2023-07-20T21:02:55 | 2023-10-26T16:04:48 | 2023-10-26T16:04:48 |
NONE
| null |
### Issue you'd like to raise.
hey there,
I am facing problem for streaming responses in fastapi can i get some info that how
to use websockets to stream the response
### Suggestion:
_No response_
|
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I_kwDOIPDwls5sK3H1
| 8,028 |
Streaming support for Oobabooga API?
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[] | null | 4 | 2023-07-20T20:54:55 | 2023-11-20T16:05:42 | 2023-11-20T16:05:41 |
NONE
| null |
### Feature request
Update `TextGen` to include `streaming` support for Oobabooga.
### Motivation
Oobabooga provides a streaming API that will be very helpful. [TextGen](https://github.com/hwchase17/langchain/pull/5997) already supports the regular API.
### Your contribution
:)
|
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https://api.github.com/repos/langchain-ai/langchain/issues/8027
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https://github.com/langchain-ai/langchain/pull/8027
| 1,814,780,803 |
PR_kwDOIPDwls5WC2ye
| 8,027 |
Add embedding and vectorstore provider info as tags
|
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[] | null | 1 | 2023-07-20T20:50:05 | 2023-07-21T05:40:02 | 2023-07-21T05:40:01 |
COLLABORATOR
| null |
Example: https://smith.langchain.com/public/bcd3714d-abba-4790-81c8-9b5718535867/r
The vectorstore implementations aren't super standardized yet, so just adding an optional embeddings property to pass in.
|
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| 1,814,736,689 |
PR_kwDOIPDwls5WCs9L
| 8,026 |
Retriever that can re-phase user inputs
|
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| false | null |
[] | null | 1 | 2023-07-20T20:18:51 | 2023-08-04T04:23:59 | 2023-08-04T04:23:59 |
COLLABORATOR
| null |
Simple retriever that applies an LLM between the user input and the query pass the to retriever.
It can be used to pre-process the user input in any way.
The default prompt:
```
DEFAULT_QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an assistant tasked with taking a natural languge query from a user
and converting it into a query for a vectorstore. In this process, you strip out
information that is not relevant for the retrieval task. Here is the user query: {question} """
)
```
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PR_kwDOIPDwls5WCszU
| 8,025 |
fix: create schema description from URIs and str w/out rdflib warnings
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closed
| false | null |
[] | null | 2 | 2023-07-20T20:18:23 | 2023-07-31T09:44:48 | 2023-07-20T22:55:20 |
CONTRIBUTOR
| null |
- Description: fix to avoid rdflib warnings when concatenating URIs and strings to create the text snippet for the knowledge graph's schema. @marioscrock pointed this out in a comment related to #7165
- Issue: None, but the problem was mentioned as a comment in #7165
- Dependencies: None
- Tag maintainer: Related to memory -> @hwchase17, maybe @baskaryan as it is a fix
|
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| 1,814,693,973 |
I_kwDOIPDwls5sKghV
| 8,024 |
Agents and agent executor are 2 different concepts and shouldn't be placed in the same bucket
|
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[] | null | 1 | 2023-07-20T19:46:36 | 2023-10-26T16:04:53 | 2023-10-26T16:04:52 |
NONE
| null |
### Feature request
This probably needs more refinement but how but there are a lot of components floating around that overlaps with one another.
As per my understanding, the broader categorization should be:
Chains: Simple scenarios - steps are hardcoded
Agent Executors: Complex scenarios - steps are determined on runtime
Agent Executor internally uses an agent to plan the next step. In that sense, the agents can probably be renamed to just planners, and agent executors can then be simply called agents(?)
Both the chains and agents can use tools (or toolkits) to execute steps in their chain.
This simplifies the overall architecture and makes the components intuitive.
### Motivation
As a beginner, I'm struggling with all the components floating around, and reducing the overall components in the core architecture reduces the learning curve of devs.
### Your contribution
Yes, I can to work on the PR if this change gets approval.
|
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| 1,814,626,098 |
I_kwDOIPDwls5sKP8y
| 8,023 |
Add a str representation for the Message
|
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[] | null | 2 | 2023-07-20T18:59:28 | 2023-10-26T16:04:58 | 2023-10-26T16:04:57 |
CONTRIBUTOR
| null |
### Feature request
E.g., it would be great if
```
m = AIMessage(content="Hi!")
print(m)
```
returned something like "AI: Hi!"
### Motivation
It would make representation of message history (e.g., for debugging or serialization into a json) a little bit easier.
### Your contribution
yes, I'm happy to do it.
|
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| 1,814,604,332 |
PR_kwDOIPDwls5WCPjv
| 8,022 |
Add Metaphor new field and reformat docs
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[] | null | 2 | 2023-07-20T18:43:40 | 2023-07-20T23:15:23 | 2023-07-20T22:50:55 |
CONTRIBUTOR
| null |
This PR reformats our python notebook example and also adds a new field we have.
|
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PR_kwDOIPDwls5WCJ9R
| 8,021 |
Add vectorstore type info
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[] | null | 1 | 2023-07-20T18:32:21 | 2023-07-24T15:56:33 | 2023-07-24T15:56:33 |
COLLABORATOR
| null |
Adds info like this https://smith.langchain.com/public/1974e2f0-5613-484c-952d-efe5c720a1a1/r
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| 1,814,527,905 |
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| 8,020 |
Issue:Using memory with agents gives wrong results
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[] | null | 4 | 2023-07-20T17:59:43 | 2023-10-28T16:04:45 | 2023-10-28T16:04:44 |
NONE
| null |
### Issue you'd like to raise.
I am trying to use create_csv_agent with memory in order to make the model answer based on previous answers so this was the code I used to achieve such task, mostly from issue #5611 with a few adjustments
```
def csv_extractor(json_request: str):
'''
Useful for extracting data from a csv file.
Takes a JSON dictionary as input in the form:
{ "prompt":"<question>", "path":"<file_name>" }
Example:
{ "prompt":"Find the maximum age in xyz.csv", "path":"xyz.csv" }
Args:
request (str): The JSON dictionary input string.
Returns:
The required information from csv file.
'''
arguments_dictionary = json.loads(json_request)
question = arguments_dictionary["prompt"]
file_name = arguments_dictionary["path"]
csv_agent = create_csv_agent(llm=OpenAI(),path=path_to_file,verbose=True)
return csv_agent(question)
request_format = '{{"prompt":"<question>","path":"<file_name>"}}'
description = f'Useful for working with a csv file. Input should be JSON in the following format: {request_format}'
csv_extractor_tool = Tool(
name="csv_extractor",
func=csv_extractor,
description=description,
verbose=True,
)
```
```
tools = [csv_extractor_tool]
# Adding memory to our agent
from langchain.agents import ZeroShotAgent
from langchain.memory import ConversationBufferMemory
prefix = """Have a conversation with a human, Answer step by step and the history of the messages is critical and very important to use. The user is expected to ask you questions that you will need to use the information you had from the previous answers. For example if the user asked you about the name of the a person, he may asks you another question on that person based on the information you have so take care. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools=tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"]
)
memory = ConversationBufferWindowMemory(
memory_key='chat_history',
k=5,
return_messages=True
)
# Creating our agent
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.agents import AgentExecutor
import json
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)
```
I am querying a CSVs containing names and dates of birth using my agent, so when I send the prompt like this
```
data = {"input": {"prompt": "give me the longest name", "path": "file.csv"}}
json_data = json.dumps(data)
result = agent_chain(json_data)
```
It returns the correct answer, let this answer be "Medjedovic"
now when I ask the model to give me the date of birth of this name by asking 'What is his birth date'? It identifies correctly in the first chain observation that I want the birth date of Medjedovic which most probably mean that the name is in the memory as it should be. However, it retrieves a different and incorrect birth date in the second chain
this is the code
```
data = {"input": {"prompt": "what is his birth date?", "path": "file.csv"}}
json_data = json.dumps(data)
result = agent_chain(json_data)
```
the output is like this:
> Entering new AgentExecutor chain...
Thought: I need to look up the birth date of Medjedovic
Action: csv_extractor
Action Input: {"prompt":"what is his birth date?","path":"file.csv"}
> Entering new AgentExecutor chain...
Thought: I need to find the birth_date column in the dataframe
Action: python_repl_ast
Action Input: df['birth_date']
Observation: 0 2/10/2000
then Final Answer: Medjedovic's date of birth is 2/10/2000.
it returned a wrong birth date of a different person which is the first person in the dataset, I want it to use the name it identified in the answer of first question and the observation in the first chain of the second question to give the right answer
How this could be solved or maintained? I am using gpt-3.5-turbo model API. I have used different prefixes also and neither of them worked.
### Suggestion:
_No response_
|
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| 1,814,519,811 |
I_kwDOIPDwls5sJ2AD
| 8,019 |
Extract Final Thoughts from Agent as part of Final Answer.
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[] | null | 3 | 2023-07-20T17:53:44 | 2023-08-14T14:47:22 | 2023-08-14T14:47:21 |
NONE
| null |
### Issue you'd like to raise.
How do you extract the last thought process from an agent?
The final answer from the agent is too summarized for my liking. However the 'Final Thought' process is great with all the details. I am having some difficulties extracting that information event with `return_immediate_steps=True`.
Is there anyway I can get that final thought information as part of the Final Answer?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5WBwgE
| 8,018 |
docstrings `memory`
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[] | null | 2 | 2023-07-20T17:16:53 | 2023-07-24T17:08:31 | 2023-07-24T17:05:36 |
COLLABORATOR
| null |
docstrings `memory`:
- added module summary
- added missed docstrings
- updated docstrings into consistent format
-
@baskaryan
|
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PR_kwDOIPDwls5WBteR
| 8,017 |
feat: add bedrock claude chat
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[] | null | 18 | 2023-07-20T17:07:31 | 2023-09-01T22:15:15 | 2023-09-01T20:16:57 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: Add Bedrock implementation of Anthropic Claude for Chat
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: @bwmatson
|
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PR_kwDOIPDwls5WBsTu
| 8,016 |
fix: catch pydantic validation errors
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[] | null | 1 | 2023-07-20T17:03:37 | 2023-07-20T17:16:18 | 2023-07-20T17:16:18 |
CONTRIBUTOR
| null |
This PR proposes a fix to catch `pydantic` `ValidationErrors` and request a fix of the arguments to the LLM, instead of the error stopping the execution.
The change is implemented within the `_take_next_step` and `_atake_next_step` methods of the `AgentExecutor`, where the `tool.run` is now within a `try` block. If during the execution of `tool.run` a `ValidationError` is raised, an except block now catches said error and turns the observation into a request for the LLM to fix the arguments for the requested action.
This proposed fix improves the reliability of the `AgentExecutor`.
- Maintainer: @hinthornw
- Twitter handle: @aledelunap
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| 1,814,401,030 |
I_kwDOIPDwls5sJZAG
| 8,015 |
FLARE Implementation with Local Fine-Tuned Models
|
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[] | null | 2 | 2023-07-20T16:36:17 | 2024-01-16T14:33:34 | 2023-10-26T16:05:08 |
NONE
| null |
### Feature request
I am writing to request an enhancement for the FLARE chain in Langchain. I'm wondering how I can change the class to accept local (fine-tuned) models rather then use OpenAi API. Since FLARE uses a retriever, a question generator, and a response generator, it would be interesting to leverage the strength of newer models, or even custom fine-tuned ones.
### Motivation
By doing so, it would give more flexibility to experiment with different models, and not rely on OpenAi, hence making it more open-source.
### Your contribution
I would envision the future syntax to look something like this:
```
from langchain.chains import FlareChain
flare_chain = FlareChain(
question_generator_chain=question_generator,
response_chain=response_generator,
output_parser=None, # Replace with the output parser if available
retriever=retriever,
min_prob=0.2,
min_token_gap=5,
num_pad_tokens=2,
max_iter=10,
start_with_retrieval=True,
)
```
The models will be imported like so:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from langchain.chains import RetrievalQA
tokenizer = AutoTokenizer.from_pretrained(model_id)
response_generator = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("model_id_1")
question_generator = AutoModelForSeq2SeqLM.from_pretrained("model_id_1")
retriever= RetrievalQA.from_chain_type(llm=model, chain_type="stuff", retriever=docsearch.as_retriever())
```
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| 1,814,392,844 |
PR_kwDOIPDwls5WBhNE
| 8,014 |
Update Modal.com integration docs
|
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[] | null | 2 | 2023-07-20T16:31:57 | 2023-07-20T22:53:07 | 2023-07-20T22:53:07 |
CONTRIBUTOR
| null |
Hey, I'm a Modal Labs engineer and I'm making this docs update after getting a user question in [our beta Slack space](https://join.slack.com/t/modalbetatesters/shared_invite/zt-1xl9gbob8-1QDgUY7_PRPg6dQ49hqEeQ) about the Langchain integration docs.
🔗 [Modal beta-testers link to docs discussion thread](https://modalbetatesters.slack.com/archives/C031Z7DBQFL/p1689777700594819?thread_ts=1689775859.855849&cid=C031Z7DBQFL)
@baskaryan (no one is specifically listed as docs person)
|
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| 1,814,336,477 |
PR_kwDOIPDwls5WBUqI
| 8,012 |
bump 238
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[] | null | 1 | 2023-07-20T16:00:12 | 2023-07-20T16:21:16 | 2023-07-20T16:21:16 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5sIuAP
| 8,011 |
What is the difference between OpenAI Function and OpenAI Multi Functions Agent
|
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[] | null | 4 | 2023-07-20T15:07:47 | 2023-12-16T21:51:12 | 2023-10-27T16:06:13 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I was looking up the documentation as well. However, from the documentation it seems as if both types of agents are doing the same thing. Even when I looked at the backend code, both agent codes seemed almost identical. I am not so sure what I am missing in terms of my understanding. Any leads would be appreciated.
### Suggestion:
Documentation between the two types can be made more clear.
|
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| 1,814,190,855 |
PR_kwDOIPDwls5WA0VJ
| 8,010 |
Different error strings
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I_kwDOIPDwls5sIh7I
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Error in OpenAI Functions Agent
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[] | null | 5 | 2023-07-20T14:47:06 | 2023-07-20T18:25:10 | 2023-07-20T18:25:10 |
NONE
| null |
### Issue with current documentation:
Hello every one!
I am following the tutorial of the use of agents. Specifically, now I am exploring the type [OpenAIFunctions. ](https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent).
I am following the exact same tutorial and I get some error when I want to use the agent to solve some math problems or connect to my SQL Data base which are tools that the agents have available. The error that I have is the following:
```python
ValidationError: 1 validation error for AIMessage
content
none is not an allowed value (type=type_error.none.not_allowed)
```
Does any one know what is causing this error?
Best regards,
Orlando
|
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Bagatur/apify
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[] | null | 1 | 2023-07-20T14:25:39 | 2023-07-20T15:36:03 | 2023-07-20T15:36:02 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,814,122,315 |
PR_kwDOIPDwls5WAlFE
| 8,007 |
undo vectstore memory bug
|
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[] | null | 1 | 2023-07-20T14:25:18 | 2023-07-20T14:25:25 | 2023-07-20T14:25:23 |
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| 1,814,066,314 |
PR_kwDOIPDwls5WAY6e
| 8,006 |
Support multiple filters for ElasticVectorStore
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[] | null | 6 | 2023-07-20T13:59:03 | 2023-11-07T03:43:26 | 2023-11-07T03:43:26 |
NONE
| null |
This PR will
- Enable the ElasticVectorStore to support multiple filters (combine all the filters using `AND` operator)
- Put the query in a filter context ensuring that the scores of the retrieved documents are not affected by the number of filters. (For reference on the difference between query context and filter context see https://www.elastic.co/guide/en/elasticsearch/reference/current/query-filter-context.html#filter-context)
- add support for arbitrary elasticsearch query as filter using the `filter_is_es_query` toggle
@rlancemartin, @eyurtsev
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I_kwDOIPDwls5sIDvJ
| 8,004 |
Issue: Using "llama-2-70b-chat/ggml-model-q4_0.bin" with LlamaCpp()
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[] | null | 12 | 2023-07-20T13:52:10 | 2023-11-15T16:07:27 | 2023-11-15T16:07:26 |
NONE
| null |
### Issue you'd like to raise.
Hi Everyone,
I am trying to use `llama-2-70b-chat` with the LlamaCpp() as described here : https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp#metal
My system specs are
MacBook Pro, M1 Chip, 16GB, 500GB SSD
Here is my code using LlamaCpp:
```
DEFAULT_CHAT_MODEL_LLAMA = '/Volumes/Gargantua/LLAA2/llama-2-70b-chat/ggml-model-q4_0.bin'
class HelpprBaseLLAMAV2(LlamaCpp):
model_path = DEFAULT_CHAT_MODEL_LLAMA
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
verbose = True
input = {"temperature": 0.75, "top_p": 1, "max_length": 2048}
n_gpu_layers = 1
n_batch = 512
f16_kv = True
```
Whenever I call `llm = HelpprBaseLLAMAV2()` via the following:
```
(venv) tapanjain@MacBook-Pro-2 helppr % python main.py
```
I get the following error:
```
zsh: illegal hardware instruction python main.py
```
Can anyone help me what exactly is the reason here? Is langchain right now not supported for llama 2?
### Suggestion:
_No response_
|
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| 1,814,025,506 |
I_kwDOIPDwls5sH9Ui
| 8,002 |
Difference between ConversationBufferMemory and chat_history parameter
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[] | null | 4 | 2023-07-20T13:40:25 | 2023-11-20T16:05:47 | 2023-11-20T16:05:46 |
NONE
| null |
### Issue you'd like to raise.
I came to know that there are two methods to keep memory in **ConversationalRetrievalChain**
1. Method -1
Using **ConversationBufferMemory**
```
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
qa= ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever),
memory=memory
)
```
3. Method -2
Using **chat_history** parameter
```
from langchain.chains import ConversationalRetrievalChain
qa= ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever)
)
chat_history = []
result = qa({"question": question, "chat_history": chat_history})
```
What is the exact difference between the above two methods? When should use one over another and why?
### Suggestion:
_No response_
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| 1,814,007,694 |
PR_kwDOIPDwls5WAMTu
| 8,001 |
Implement async support in Qdrant local mode
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[] | null | 1 | 2023-07-20T13:31:07 | 2023-07-21T02:04:34 | 2023-07-21T02:04:33 |
CONTRIBUTOR
| null |
I've extended the support of async API to local Qdrant mode. It is faked but allows prototyping without spinning a container. The tests are improved to test the in-memory case as well.
@baskaryan @rlancemartin @eyurtsev @agola11
|
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| 1,813,933,316 |
PR_kwDOIPDwls5V_73n
| 8,000 |
Added windows specific instructions to Llama.cpp documentation.
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[] | null | 1 | 2023-07-20T12:53:26 | 2023-07-20T13:31:25 | 2023-07-20T13:31:25 |
CONTRIBUTOR
| null |
- Description: Added windows specific instructions on llama.cpp in the notebook file
- Issue: #6356
- Dependencies: None
- Tag maintainer: @baskaryan
|
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| 1,813,895,614 |
I_kwDOIPDwls5sHdm-
| 7,999 |
APIfy dataset loader does not provide the attribute "page_content" in the loaded documents
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[] | null | 5 | 2023-07-20T12:32:18 | 2023-10-28T16:04:50 | 2023-10-28T16:04:49 |
NONE
| null |
### System Info
Langchain Version== 0.0.237
Platform == Google Colaboratory
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Step 1: Import packages
Step 2: Use APIfy dataset loader
loader = ApifyDatasetLoader(
dataset_id="KCoOphOr1tQmmUhsS",
dataset_mapping_function=lambda item: Document(
page_content=item["description"] or "name", metadata={"id": item["id"]}
),
)
Step 3: Attempt to create a vector store index
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator().from_loaders([loader])
### Expected behavior
It should create the index from the data loader provided, but the loader does not produce the attribute "page content" when loading APIfy datasets.
<img width="919" alt="Screenshot 2023-07-19 at 12 24 02" src="https://github.com/hwchase17/langchain/assets/63857960/11aee742-c513-4cc6-863e-56c5e10a43db">
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| 1,813,748,887 |
I_kwDOIPDwls5sG5yX
| 7,998 |
custom BaseCallbackHandler in STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent not calling on_tool_end
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[] | null | 3 | 2023-07-20T11:16:38 | 2023-07-25T14:15:21 | 2023-07-25T14:15:21 |
NONE
| null |
### System Info
`langchain 0.0.235`
I wrote my own callback handler
`class ChatHandler(BaseCallbackHandler):`
which includes the function
```
def on_tool_end(self, output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) -> Any:
"""Run when tool ends running."""
if observation_prefix is not None:
print("OBSERVATION: "+str(output))
if llm_prefix is not None:
print("llm_prefix: "+str(output))
```
as inspired by the source of the file callback handler https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/file.html#FileCallbackHandler .
I have a STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent. My goal is to capture the observation, it seems not to call the function. The handler works fine for other functions.
I attach the handler like this:
```
cHandler = chatHandler.ChatHandler()
cManager = manager.BaseCallbackManager(handlers=[cHandler])
initialize_agent(tools,llm, callback_manager=cManager, ...
```
Am I using the wrong callback manager?
### Who can help?
@agola11
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import os
from langchain.agents import initialize_agent
from langchain.callbacks import manager
from langchain.chat_models import AzureChatOpenAI
from langchain.agents import AgentType
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction
from typing import Any, Dict, List, Optional
# ENVIRONMENT KEYS; OPENAI_API_KEY
import json
vals = json.load(open("../local.settings.json"))['Values']
for k in vals.keys():
os.environ[k] = vals[k]
from langchain.agents import Tool
def a(b):
print('tool used wow')
return "frogimage.png"
t = Tool(
name="Search things online", #search location by vague name
func=a,
description="Use this tool to search and find things online.",
return_direct=False
)
BASE_URL = "https://XXX.openai.azure.com"
DEPLOYMENT_NAME = "gpt-35-turbo-16k"
model = AzureChatOpenAI(
openai_api_base=BASE_URL,
openai_api_version="2023-05-15",
deployment_name=DEPLOYMENT_NAME,
openai_api_key=os.environ["OPENAI_API_KEY"],
openai_api_type="azure",
temperature=0.0
)
class TestHandler(BaseCallbackHandler):
def on_tool_end(self, output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) -> Any:
"""Run when tool ends running."""
print("on_tool_end")
def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
"""Run on agent action."""
print("on_agent_action")
cHandler = TestHandler()
cManager = manager.BaseCallbackManager(handlers=[cHandler])
agent_chain = initialize_agent([t],model, callback_manager=cManager, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent_chain("search frog images online")
```
outputs:
> > Entering new AgentExecutor chain...
> on_agent_action
> Action:
> ```
> {
> "action": "Search things online",
> "action_input": "frog images"
> }
> ```tool used wow
>
> Observation: frogimage.png
> Thought:I found an image of a frog. Here it is:
> Action:
> ```
> {
> "action": "Final Answer",
> "action_input": "frogimage.png"
> }
> ```
>
>
> > Finished chain.
> {'input': 'search frog images online', 'output': 'frogimage.png'}
### Expected behavior
The function on_tool_end is called when the agent has used a tool, printing on_tool_end, but only on_agent_action is printed.
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| 1,813,722,288 |
I_kwDOIPDwls5sGzSw
| 7,997 |
Issue: <Combining LLMChain and ConversationalRelationChain in an agent's routes>
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[] | null | 5 | 2023-07-20T11:01:40 | 2024-01-12T05:24:25 | 2023-10-26T16:05:23 |
NONE
| null |
### Issue you'd like to raise.
I've been stuck on a task that I was working on, not knowing how to exactly proceed with it. Where I need to combine different type of chain in the routes of an Agent.
So I have managed to build the following pipeline using an Agent. I receive a query and according to the context of the query it gets routed either to any of the different LLM Chains defined (answering Greetings, Goodbyes, Unrelated Questions) or it gets routed to a RetrievalQA that searches for the answer in a VectorStore when the input query is related to the context in question.
Now what I noticed, and have been trying to fix was that sometimes, despite adding memory to my agent, I can receive a follow-up question that will not be routed to the VectorStore as it would labeled as "Unrelated". Example: Supposedly the first question was : "Am I allowed to work remotely on Fridays?". This is related for exampled and the model will reply with no from the VectorStore. Now, if I follow it up with: "How about Mondays?" the model will identify the input query as unrelated as it is not tackling the subject of "Work". Ideally, I would want it to reformulate the question to: "How about working remotely on Monday?" and to route it accordingly to the VectorStore as it is tackling the subject of "work".
I made my research and found that CoversationalRetrievalChains should be part of my solution, but I do not know how to put it exactly in my architecture. The main issue is that LLM Chains expect a certain type of input ('input':x) while ConversationalRelationChain expect another ('question':x, 'chat_history':y).
I tried using LOTR (Lord of all Retrievers) but apparently it hasn't been working as well. I only have 1 retriever (1 VectorStore) and the rest are simple LLMChains. Yet even when I tried LOTR with CoversationalRetrievalChains in order to combine retrievers I am getting the following error: "NotImplementedError: Saving not supported for this chain type."
In short I want to be able to pass the same input, regardless of the route in my Agent, whether it's a LLM Chain or a CoversationalRetrievalChains. I'm a bit lost and would love some help on the subject.
### Suggestion:
_No response_
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I_kwDOIPDwls5sGsmB
| 7,996 |
I don't have access to the short-term memory using Agent with gpt-3.5-turbo
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[] | null | 2 | 2023-07-20T10:46:50 | 2023-10-26T16:05:28 | 2023-10-26T16:05:27 |
NONE
| null |
### System Info
langchain==0.0.230
MacOS
### 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
I do not know how exactly to reproduce this, but I would like to know if someone had the same error using GPT-3.5-turbo with this agent, tool, and prompts:
Agent: LLMSingleActionAgent
Tool and prompt:
```
template_hello = """You are a chatbot, answer to this
{input}
Previous conversation history:
{history}
"""
prompt_hello = PromptTemplate(input_variables=["input", "history"], template=template_hello)
hello_chain = LLMChain(
llm=ChatOpenAI(model='gpt-3.5-turbo',
prompt=prompt_hello,
verbose=True,
memory=self.memory
)
```
Memory:
```
from langchain.memory import ConversationTokenBufferMemory
self.memory = ConversationTokenBufferMemory(llm=ChatOpenAI(temperature=0,
max_tokens=500,
model='gpt-3.5-turbo'),
memory_key="history",
max_token_limit=500)
```
When I run the agent with gpt-4 it can access to the memory, if I change the agent model to gtp-3.5-turbo I have this error:
> I don't have access to the short-term memory
### Expected behavior
Answer based on the memory
|
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| 1,813,665,397 |
I_kwDOIPDwls5sGlZ1
| 7,995 |
Can't connect to Qdrant since 0.0.234
|
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| false | null |
[] | null | 7 | 2023-07-20T10:30:28 | 2023-11-23T16:07:15 | 2023-11-23T16:07:14 |
NONE
| null |
### System Info
python 3.9
langchain 0.0.234
qdrant-client 1.1.7
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`client = qdrant_client.QdrantClient(self.qdrant_url, port=6333)`
`qdrant = Qdrant(
client=client, collection_name=self.collection_name,
embeddings=embeddings,
)`
### Expected behavior
I'm getting this error since i upgrade my langchain to 0.0.234 from 0.0.233
<AioRpcError of RPC that terminated with:
status = StatusCode.UNAVAILABLE
details = "failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:6334: Failed to connect to remote host: Connection refused"
debug_error_string = "UNKNOWN:failed to connect to all addresses; last error: UNKNOWN: ipv4:127.0.0.1:6334: Failed to connect to remote host: Connection refused {grpc_status:14, created_time:"2023-07-20T18:17:20.830536+08:00"}"
I didn't change any code in my project.
Is there any setting change?
it seems that it connect to 6334 port of Qdrant
|
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| 1,813,647,909 |
I_kwDOIPDwls5sGhIl
| 7,994 |
Get the code applied by the pandas agent
|
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[] | null | 1 | 2023-07-20T10:19:46 | 2023-07-30T06:05:42 | 2023-07-30T06:04:54 |
NONE
| null |
### System Info
I am using the below code to make an agent that decides, upon the question, whether to use semantic search or pandas agent
to answer the question about a dataset of employees
### 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
- [x] Agents / Agent Executors
- [x] Tools / Toolkits
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
semantic_retrieval_chain = RetrievalQA.from_llm(
OpenAI(temperature=0),
retriever=vectorstore.as_retriever(),
return_source_documents=True,
# memory=memory
)
agg_df = pd.read_csv(TMP_FILE_PATH, index_col=0).iloc[:, :-1]
aggregation_agent = create_pandas_dataframe_agent(
OpenAI(temperature=0),
agg_df,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
include_df_in_prompt=True,
# handle_parse_errors=True,
# suffix="Assistant also handles parsing errors.",
)
tools = [
Tool(
name = "Semantic Search",
func=semantic_retrieval_chain,
description="""useful for retrieving employees similar to a given query specifications and answering questions not involving aggregations and operations over more than one employee"""
),
Tool(
name = "Aggregation Agent",
func=aggregation_agent.run,
description="useful for answering questions involving aggregations and descriptive statistics over more than one employee"
),
]
llm=OpenAI(temperature=0)
agent_chain = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
return_intermediate_steps=True,
# memory=memory
)
```
I was able to successfully get the documents retrieved by the semantic search tool, but when I set the return_intermediate_step to True for the aggregation agent, I had the following error:
**NotImplementedError: Saving not supported for this chain type.**
### Expected behavior
I want to get the code for the pandas agent (aggregation_agent above) whenever it is invoked. I am extremely in need for this feature to be able to display the rows (if any) from the appropriate dataframe. Thanks in advance for any help.
|
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| 1,813,647,495 |
PR_kwDOIPDwls5V-8Jq
| 7,993 |
Add async Qdrant to async_agent.ipynb
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[] | null | 1 | 2023-07-20T10:19:29 | 2023-07-20T13:23:16 | 2023-07-20T13:23:15 |
CONTRIBUTOR
| null |
I added Qdrant to the async API docs. This is the only vector store that supports full async API.
@baskaryan @rlancemartin, @eyurtsev
|
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| 1,813,565,399 |
I_kwDOIPDwls5sGM_X
| 7,992 |
ChatLangChain requesting _default_relevance_score_fn' on <module 'langchain.vectorstores.faiss> on init
|
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[] | null | 6 | 2023-07-20T09:32:36 | 2023-11-05T16:05:39 | 2023-11-05T16:05:38 |
NONE
| null |
### System Info
Langchain 0.0.237.
Python 3.11
Mac OS X M1
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
make start.
INFO: Uvicorn running on http://127.0.0.1:9002 (Press CTRL+C to quit)
INFO: Started reloader process [80851] using StatReload
INFO: Started server process [80855]
INFO: Waiting for application startup.
in startup_event
AttributeError: Can't get attribute '_default_relevance_score_fn'
### Expected behavior
Load the chat.
Note: the faiss wrapper seems updated back in May to from _default_relevance_score_fn to relevance_score_fn
|
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| 1,813,521,487 |
PR_kwDOIPDwls5V-gYj
| 7,991 |
fix _generate_with_cache use RedisCache raise "KeyError('message')"
|
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[] | null | 2 | 2023-07-20T09:07:31 | 2023-08-04T03:31:15 | 2023-08-04T03:31:15 |
NONE
| null |
Description:
This is a fix for using RedisCache raise "KeyError('message')"
Use RedisCache example:
```
import redis
import langchain
from langchain.cache import RedisCache
redis_cli = redis.Redis.from_url(url='redis://:root@localhost:16379/1')
langchain.llm_cache = RedisCache(redis_=redis_cli)
```
The following exceptions will be thrown:
```
[chain/error] [1:chain:MultiRouteChain] [2.826ms] Chain run errored with error:
"KeyError('message')"
Exception in thread Thread-11:
Traceback (most recent call last):
File "/data/opt/anaconda3/lib/python3.9/threading.py", line 980, in _bootstrap_inner
self.run()
File "/data/opt/anaconda3/lib/python3.9/threading.py", line 917, in run
self._target(*self._args, **self._kwargs)
File "/path/to/app_gradio.py", line 40, in run
answer = chain(question)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 243, in __call__
raise e
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 237, in __call__
self._call(inputs, run_manager=run_manager)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/router/base.py", line 82, in _call
route = self.router_chain.route(inputs, callbacks=callbacks)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/router/base.py", line 30, in route
result = self(inputs, callbacks=callbacks)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 243, in __call__
raise e
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 237, in __call__
self._call(inputs, run_manager=run_manager)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/router/llm_router.py", line 59, in _call
self.llm_chain.predict_and_parse(callbacks=callbacks, **inputs),
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/llm.py", line 279, in predict_and_parse
result = self.predict(callbacks=callbacks, **kwargs)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/llm.py", line 252, in predict
return self(kwargs, callbacks=callbacks)[self.output_key]
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 243, in __call__
raise e
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/base.py", line 237, in __call__
self._call(inputs, run_manager=run_manager)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/llm.py", line 92, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chains/llm.py", line 102, in generate
return self.llm.generate_prompt(
File "/path/to/venv/lib/python3.9/site-packages/langchain/chat_models/base.py", line 230, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/path/to/venv/lib/python3.9/site-packages/langchain/chat_models/base.py", line 125, in generate
raise e
File "/path/to/venv/lib/python3.9/site-packages/langchain/chat_models/base.py", line 115, in generate
self._generate_with_cache(
File "/path/to/venv/lib/python3.9/site-packages/langchain/chat_models/base.py", line 272, in _generate_with_cache
return ChatResult(generations=cache_val)
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1076, in pydantic.main.validate_model
File "pydantic/fields.py", line 895, in pydantic.fields.ModelField.validate
File "pydantic/fields.py", line 928, in pydantic.fields.ModelField._validate_sequence_like
File "pydantic/fields.py", line 1094, in pydantic.fields.ModelField._validate_singleton
File "pydantic/fields.py", line 884, in pydantic.fields.ModelField.validate
File "pydantic/fields.py", line 1101, in pydantic.fields.ModelField._validate_singleton
File "pydantic/fields.py", line 1157, in pydantic.fields.ModelField._apply_validators
File "pydantic/class_validators.py", line 337, in pydantic.class_validators._generic_validator_basic.lambda13
File "pydantic/main.py", line 719, in pydantic.main.BaseModel.validate
File "/path/to/venv/lib/python3.9/site-packages/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1102, in pydantic.main.validate_model
File "/path/to/venv/lib/python3.9/site-packages/langchain/schema/output.py", line 42, in set_text
values["text"] = values["message"].content
KeyError: 'message'
```
This commit is to fix this bug.
|
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| 1,813,507,242 |
I_kwDOIPDwls5sF-yq
| 7,990 |
Does it support 文心一言?
|
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[] | null | 15 | 2023-07-20T08:59:25 | 2023-12-18T23:49:08 | 2023-12-18T23:49:07 |
NONE
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| 1,813,454,976 |
I_kwDOIPDwls5sFyCA
| 7,989 |
OutputFixingParser is not async
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[] | null | 2 | 2023-07-20T08:29:12 | 2023-10-26T16:05:33 | 2023-10-26T16:05:33 |
NONE
| null |
### System Info
LangChain Python v0.0.237
Based on this code snippet it appears that OutputFixingParser doesn't support async flows.
https://github.com/hwchase17/langchain/blob/df84e1bb64d96377f909651f696f310c43c2f2c5/langchain/output_parsers/fix.py#L46-L52
It's calling the run function and not arun
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
1. Define async callback handler
2. Make LLM return output that is unparsable (invalid JSON or 2 code blocks)
3. OutputFixingParser will fail parsing the output and throw an exception, which will call the LLM via the run function which doesn't await on coroutines. Python will give the following error:
```
RuntimeWarning: coroutine 'AsyncCallbackHandler.on_chat_model_start' was never awaited
```
### Expected behavior
1. Should work with coroutines as expected
|
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PR_kwDOIPDwls5V-RU_
| 7,988 |
Fix key errors in weaviate hybrid retriever init
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[] | null | 1 | 2023-07-20T08:27:57 | 2023-07-20T13:22:19 | 2023-07-20T13:22:18 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,813,396,450 |
I_kwDOIPDwls5sFjvi
| 7,986 |
Redis does not use the parameters passed in by Redis.as_retriever()
|
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[] | null | 2 | 2023-07-20T07:57:13 | 2023-12-09T16:42:25 | 2023-12-09T16:42:19 |
NONE
| null |
### System Info
langchain==0.0.237
python==3.8.16
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
same issue as https://github.com/hwchase17/langchain/issues/6984 but for Redis, but the proposed fix does not work
llm = OpenAI(temperature=0.1)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(llm, chain_type="stuff")
retriever = db.as_retriever(search_kwargs={"k": 10})
chain = ConversationalRetrievalChain(
retriever=retriever, #redis retriever
question_generator=question_generator,
combine_docs_chain=doc_chain,
return_source_documents=True,
)
### Expected behavior
query = "who slayed Karna in the battle?"
result = chain({"question": query})
print(len(result['source_documents'])) #ouput: 4 but should be 10
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| 1,813,386,774 |
PR_kwDOIPDwls5V-C1x
| 7,985 |
Allow user to modify the GPU and language settings when using NLP Cloud
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: allow user to modify the GPU and language settings when using NLP Cloud,
- Tag maintainer: @rlancemartin ,
- Twitter handle: @cloud_nlp
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
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| 1,813,350,333 |
PR_kwDOIPDwls5V963H
| 7,984 |
Add Verbose support (#7982)
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[] | null | 1 | 2023-07-20T07:29:02 | 2023-07-20T13:52:14 | 2023-07-20T13:52:14 |
CONTRIBUTOR
| null |
- Description: Add verbose support for the extraction_chain
- Issue: Fixes #7982
- Dependencies: NA
- Twitter handle: sheikirfanbasha
@hwchase17 and @agola11
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PR_kwDOIPDwls5V958Z
| 7,983 |
Fix incorrect field name in MLflow AI Gateway config example
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[] | null | 1 | 2023-07-20T07:26:23 | 2023-07-20T22:25:40 | 2023-07-20T13:24:59 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
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I_kwDOIPDwls5sFGSb
| 7,982 |
TypeError: create_extraction_chain() got an unexpected keyword argument 'verbose'
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[] | null | 0 | 2023-07-20T06:39:12 | 2023-07-20T13:52:15 | 2023-07-20T13:52:15 |
CONTRIBUTOR
| null |
### Feature request
Almost all the chains offered in langchain framework support Verbose option which helps the developers understand what prompt is being applied under the hood and plan their work accordingly. It immensely help while debugging. create_extraction_chain is a very helpful one and I found this is not accepting verbose attribute.
### Motivation
For many developers who are just following the langchain official documentation and not looking at the code used under the hood, this error will sound odd. Supporting this attribute will help in keeping things consistent and improve debugging feature of this chain
### Your contribution
I can raise the PR for this

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I_kwDOIPDwls5sE35r
| 7,981 |
Agent does not use max_tokens parameter from ChatOpenAI
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[] | null | 1 | 2023-07-20T05:58:45 | 2023-08-14T14:47:00 | 2023-08-14T14:47:00 |
NONE
| null |
### Issue you'd like to raise.
How do you increase the max_tokens for the agent? I am using gpt35turbo16k. I notice that the model is only using ~4096 tokens. Is there a way to override this?
The reason I am asking is I am ingesting large text in the prompt and the final answer I am getting is so short that does not have any meeting.
Thanks!
### Suggestion:
_No response_
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I_kwDOIPDwls5sExsY
| 7,979 |
Issue: How to pass in database name parameter into Milvus
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NONE
| null |
### Issue you'd like to raise.

In Milvus, we are able to create databases and create collections within the database itself. Hence i will be able to create a collection with the same name in different databases. How can we specify the specific database and collection to use, currently the parameters only accept collection_name and not db_name like in pymilvus.
### Suggestion:
_No response_
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PR_kwDOIPDwls5V9XoR
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Update chroma notebook
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[] | null | 1 | 2023-07-20T05:31:07 | 2023-07-20T13:25:31 | 2023-07-20T13:25:31 |
CONTRIBUTOR
| null |
Fix up the Chroma notebook
- remove `.persist()` -- this is no longer in Chroma as of `0.4.0`
- update output to match `0.4.0`
- other cleanup work
|
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PR_kwDOIPDwls5V837z
| 7,976 |
docstrings for `LLMs`
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[] | null | 1 | 2023-07-20T02:42:33 | 2023-07-20T15:32:19 | 2023-07-20T13:26:17 |
COLLABORATOR
| null |
docstrings for the `llms/`:
- added missed docstrings
- update existing docstrings to consistent format (no `Wrappers`!)
@baskaryan
|
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I_kwDOIPDwls5sEMJ_
| 7,975 |
Issue: integrate local LLM (from huggingface) into load_qa_chain
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[] | null | 2 | 2023-07-20T02:37:21 | 2023-10-26T16:05:38 | 2023-10-26T16:05:38 |
NONE
| null |
### Issue you'd like to raise.
Hi, I'm trying to use some LLM model from huggingface, for example "model_name=lmsys/vicuna-13b-v1.3", in the chain `load_qa_chain`. The LLM model could be fetched through AutoModelForCausalLM.from_pretrained, e.g.
```
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map={"": 0},
)
```
What's the best way (memory efficient) to wrap the model for integration with load_qa_chain?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5V8oBP
| 7,974 |
add experimental package
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[] | null | 1 | 2023-07-20T01:13:18 | 2023-07-22T20:47:31 | 2023-07-22T20:47:31 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5V8ac8
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docstrings for `embeddings`
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[] | null | 1 | 2023-07-19T23:59:47 | 2023-07-20T15:32:29 | 2023-07-20T13:26:45 |
COLLABORATOR
| null |
Added/updated docstrings for the `embeddings`
@baskaryan
|
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PR_kwDOIPDwls5V8aGC
| 7,972 |
One Line Fix: missing text output with huggingface TGI LLM
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CONTRIBUTOR
| null |
Small bug fix. The async _call method was missing a line to return the generated text.
@baskaryan
|
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I_kwDOIPDwls5sDqyH
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Serpapi API key not working with Langchain
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[] | null | 4 | 2023-07-19T23:53:59 | 2023-08-03T17:43:30 | 2023-08-03T17:43:30 |
NONE
| null |
### Issue you'd like to raise.
I have a valid serpapi API key that I can use in the serpapi playground and with direct calls using python requests, but when I use it with Langchain I get an error saying that the key is invalid. I have my key set as environment variable, but I continue to get this error:
ValueError: Got error from SerpAPI: Invalid API key. Your API key should be here: https://serpapi.com/manage-api-key
I have even got a new API key with the same results. I have tried Pydroid3 for Android, Ubuntu in Termux for Android (64 bit), and Ubuntu in AWS ec2, with the same result.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5V8VW4
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Bump LS Version
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[] | null | 1 | 2023-07-19T23:36:02 | 2023-07-20T00:12:17 | 2023-07-20T00:12:17 |
COLLABORATOR
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| 1,812,830,748 |
PR_kwDOIPDwls5V8JXM
| 7,969 |
docstrings `experimental`
|
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[] | null | 3 | 2023-07-19T22:35:01 | 2023-07-24T22:10:48 | 2023-07-24T21:21:48 |
COLLABORATOR
| null |
- added/changed docstring for `experimental`
- added/changed docstrings for different artifacts
-
@baskaryan
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I_kwDOIPDwls5sDV_3
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Argument 'string' has incorrect type (expected str, got lxml.etree._ElementUnicodeResult)
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[] | null | 4 | 2023-07-19T22:23:56 | 2023-10-28T16:04:55 | 2023-10-28T16:04:54 |
NONE
| null |
### System Info
LangChain version is the latest one - 0.0.237
### 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
```python
def text_split(documents):
text_splitter = SpacyTextSplitter(chunk_size=1000, chunk_overlap=10, separator='\n')
texts = []
for document in documents:
texts.extend(text_splitter.split_documents(document.load()))
return texts
```
Using this simple code, I get this error:
```
Traceback (most recent call last):
...
File "/opt/project/main/genie.py", line 41, in text_split
texts.extend(text_splitter.split_documents(document.load()))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py", line 131, in split_documents
return self.create_documents(texts, metadatas=metadatas)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py", line 116, in create_documents
for chunk in self.split_text(text):
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain/text_splitter.py", line 1047, in split_text
splits = (s.text for s in self._tokenizer(text).sents)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/spacy/language.py", line 1030, in __call__
doc = self._ensure_doc(text)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/spacy/language.py", line 1121, in _ensure_doc
return self.make_doc(doc_like)
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/spacy/language.py", line 1113, in make_doc
return self.tokenizer(text)
^^^^^^^^^^^^^^^^^^^^
TypeError: Argument 'string' has incorrect type (expected str, got lxml.etree._ElementUnicodeResult)
```
I've also tried approach with alternative `split_text` method, but still getting the same error.
### Expected behavior
If I put a breakpoint at [langchain/text_splitter.py:116](https://github.com/hwchase17/langchain/blob/master/langchain/text_splitter.py#L116) and execute `text = str(text)` and resume the process, only then it works.
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| 1,812,789,717 |
I_kwDOIPDwls5sDPnV
| 7,967 |
Doc metadata can get passed into prompt unexpectedly
|
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[] | null | 1 | 2023-07-19T21:57:17 | 2023-10-25T16:05:37 | 2023-10-25T16:05:36 |
COLLABORATOR
| null |
### System Info
Mac M2 Max 32GB
### Who can help?
@rlancemartin
### 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
We pass docs into a prompt, and pass prompt to an LLM chain:
```
# Prompt
prompt = PromptTemplate.from_template(
"Human: Summarize the main themes in these retrieved docs: {docs} Assistant: "
)
# Chain
llm_chain = LLMChain(llm=llm, prompt=prompt)
# Run
question = "What are the approaches to Task Decomposition?"
docs = vectorstore.similarity_search(question)
result = llm_chain(docs)
```
The doc metadata gets formatted w/ prompt and passed to LLM.
This can cause hallucination (relative to intended doc page_content) if metadata contain irrelevant information.
It also chews up tokens potentially with redundant information.
See good example here:
https://smith.langchain.com/public/9af32e5b-2ca9-41d9-a5a6-8243b218208a/r
### Expected behavior
Only pass `page_content` from retrieved documents into the prompt.
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| 1,812,782,537 |
PR_kwDOIPDwls5V7-2h
| 7,966 |
Fix bad docs sidebar header
|
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[] | null | 1 | 2023-07-19T21:49:52 | 2023-07-21T02:06:57 | 2023-07-21T02:06:57 |
CONTRIBUTOR
| null |
Quick fix for:
<img width="283" alt="Screenshot 2023-07-19 at 2 49 44 PM" src="https://github.com/hwchase17/langchain/assets/6952323/91e4868c-b75e-413d-9f8f-d34762abf164">
CC @baskaryan
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I_kwDOIPDwls5sDNfo
| 7,965 |
Petition for docs to be dark mode by default
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[] | null | 1 | 2023-07-19T21:48:22 | 2023-08-08T20:10:36 | 2023-08-08T20:10:36 |
NONE
| null |
1. Dark mode by default would be great
2. A theme that uses black instead of dark grey (for OLED screens) would also be appreciated
I'm sure there are others here who read the new additions to docs before bed lol
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I_kwDOIPDwls5sDJuf
| 7,964 |
Issue: [Azure] Summary chain with chat 3.5 turbo - Not being parallelized
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[] | null | 2 | 2023-07-19T21:34:45 | 2023-10-25T16:05:41 | 2023-10-25T16:05:41 |
NONE
| null |
### Issue you'd like to raise.
Hi,
Not sure if someone is facting this "issue" or is something wrong I'm doing.
So far .. I read, GPT 3.5 turbo and later should be used with "chat_models" instead of "models". While testing the "summary" chaing (map_reduce). I noticed that using "model" llm it does indeed run in parallel, but using chat_model does run in sequence.
From the src in langchain .. I saw:
[langchain][chains][combine_documents] map_reduce.py
> map_results = await self.llm_chain.aapply(
# FYI - this is parallelized and so it is fast.
[{**{self.document_variable_name: d.page_content}, **kwargs} for d in docs],
callbacks=callbacks,
)
And tracing the execution down
**AzureOpenAI chat_model**: it will execute a for loop and wait for the response. Multiple API calls to the endpoint
> results.append(
self._generate_with_cache(
m,
stop=stop,
run_manager=run_managers[i] if run_managers else None,
**kwargs,
)
)
**AzureOpenAI model**: (aka completion) ... it generates a single call with all the prompts.
> response = completion_with_retry(self, prompt=_prompts, **params)
And here are my outcomes:
- Using ChatModel (azure) ... it works as expected, following the prompt and creating the expected output, but in a sequential execution.
- Using LLM Model (azure - aka completion) ... it runs in parallel, but the "summaries" are not correct, it "creates ramdon content" not related to the topic (I have set the temperature to 0 and top_p 0.9) ... but still does not "creates" summaries of provided text.
So my question/concerns are:
1. Is summarization chain expected to run in parallel mode with chatModel LLMs? If so ... can anyone provide a sample .. can't make it to work in parallel.
2. Is "completion llms" (aka normal llm model) only good for "generating content" but not for "summaries"? Using gpt 3.5 turbo?
Thanks for your help in advace.
### Suggestion:
_No response_
|
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| 1,812,764,337 |
I_kwDOIPDwls5sDJax
| 7,963 |
Embedding Seems Unsuccessful for Chroma + OpenAI
|
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[] | null | 6 | 2023-07-19T21:33:31 | 2023-08-22T09:48:08 | 2023-07-22T20:45:06 |
NONE
| null |
### System Info
openai==0.27.7
langchain==0.0.237
chromadb==0.4.2
Platform: Windows 11
Python Version: 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Within this file, I was expecting db_collection to have embeddings when it was printed. However, the output is like this:
> db_collection {'ids': ['1234_5678_1'], 'embeddings': None, 'metadatas': [{'source': 'Test0720.txt'}], 'documents': ['Nuclear power in the United States is provided by 99 commercial reactors with a net capacity of 100,350 megawatts (MW), 65 pressurized water reactors and 34 boiling water reactors.\n\nIn 2016 they produced a total of 805.3 terawatt-hours of electricity, which accounted for 19.7% of the nation's total electric energy generation.\n\nIn 2016, nuclear energy comprised nearly 60 percent of U.S. emission-free generation.']}
The value for "embeddings" is empty.
Here is the code:
```
import os
from flask import Blueprint, request, jsonify
from werkzeug.utils import secure_filename
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
chroma_bp = Blueprint('chroma_bp', __name__, url_prefix='/v1/resource')
openai_key = os.getenv('OPENAI_API_KEY')
os.environ["OPENAI_API_KEY"] = openai_key
@chroma_bp.route('/save_to_chroma', methods=['POST'])
def api_handler():
file = request.files['file']
user_id = request.form.get('user_id')
file_id = request.form.get('file_id')
try:
response = create_chroma_db_from_file(file, file_id, user_id)
return jsonify({'response': 'Chroma DB created successfully'}), 200
except Exception as e:
print(f"Exception: {e}") # Debug print statement
return jsonify({'error': str(e)}), 500
def create_chroma_db_from_file(file, file_id, user_id):
filename = secure_filename(file.filename)
file.save(filename)
# load the document and split it into chunks
loader = TextLoader(filename)
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
print(f"Number of documents: {len(docs)}")
print(f"Documents:", docs)
# # create the open-source embedding function
embeddings = OpenAIEmbeddings(openai_api_key=openai_key)
# load it into Chroma
ids = [f"{file_id}_{user_id}_{i}" for i in range(1, len(docs) + 1)]
db = Chroma.from_documents(
documents=docs, embedding=embeddings, ids=ids, persist_directory="../chromadb")
print(f"db", db)
print(f"db_collection", db._collection.get(ids=[ids[0]]))
db.persist()
# query it
query = "Nuclear power in the United States is provided by 99 commercial reactors with a net capacity of 100,350 megawatts (MW), 65 pressurized water reactors and 34 boiling water reactors. In 2016 they produced a total of 805.3 terawatt-hours of electricity, which accounted for 19.7% of the nation's total electric energy generation. In 2016, nuclear energy comprised nearly 60 percent of U.S. emission-free generation."
search_result = db.similarity_search(query)
# print results
print(search_result[0].page_content)
os.remove(filename)
return True
```
### Expected behavior
The embedding is done successfully and could be shown in logs. Thank you!
|
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PR_kwDOIPDwls5V74Pe
| 7,962 |
nb fix
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[] | null | 1 | 2023-07-19T21:22:11 | 2023-07-19T22:27:44 | 2023-07-19T22:27:43 |
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PR_kwDOIPDwls5V72Sp
| 7,961 |
Refactored `math_utils`
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[] | null | 1 | 2023-07-19T21:15:10 | 2023-07-21T03:12:50 | 2023-07-21T01:55:43 |
COLLABORATOR
| null |
`math_utils.py` is in the root code folder. This creates the `langchain.math_utils: Math Utils` group on the API Reference navigation ToC, on the same level with `Chains` and `Agents` which is not correct.
Refactoring:
- created the `utils/` folder
- moved `math_utils.py` to `utils/math.py`
- moved `utils.py` to `utils/utils.py`
- split `utils.py` into `utils.py, env.py, strings.py`
- added module description
@baskaryan
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(WIP) set up experimental
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I_kwDOIPDwls5sC52M
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AgentType.OPENAI_MULTI_FUNCTIONS with FAISS VectorStore results with "ToolException('Too many arguments to single-input" error
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| false | null |
[] | null | 1 | 2023-07-19T20:43:20 | 2023-10-25T16:05:46 | 2023-10-25T16:05:45 |
NONE
| null |
### System Info
langchain 0.0.237
python 3.10
### 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
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
i have an agent of type AgentType.OPENAI_MULTI_FUNCTIONS and AzureChatOpenAI LLM. I'm trying to use the FAISS embedding capabilities with a VectorStore,
as shown in this example https://techcommunity.microsoft.com/t5/startups-at-microsoft/build-a-chatbot-to-query-your-documentation-using-langchain-and/ba-p/3833134
combine with the instructions here on how to work with agents and vector store
https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore
while the chain seems to be calling the desired function in order to do a similiarity search
` "generations": [
[
{
"text": "",
"generation_info": {
"finish_reason": "function_call"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "",
"additional_kwargs": {
"function_call": {
"name": "tool_selection",
"arguments": "{\n \"actions\": [\n {\n \"action_name\": \"<<MY_FUNCTION_NAME>>\",\n \"action\": {}\n }\n ]\n}"
}
}
}
}
}
]
]`
the function call to qa Tool fails with the error "ToolException('Too many arguments to single-input tool <<MY_FUNCTION_NAME>>. Args: []')"
any idea what may cause this error ?
### Expected behavior
similarity search works as expected (same as here https://python.langchain.com/docs/modules/agents/how_to/agent_vectorstore ) even when using AgentType.OPENAI_MULTI_FUNCTIONS agent
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| 1,812,584,022 |
PR_kwDOIPDwls5V7UAV
| 7,956 |
Add debugging guide
|
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[] | null | 1 | 2023-07-19T19:20:34 | 2023-07-19T21:15:12 | 2023-07-19T21:15:11 |
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PR_kwDOIPDwls5V7Gli
| 7,955 |
Add datadog-langchain integration doc
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[] | null | 1 | 2023-07-19T18:46:12 | 2023-07-20T18:52:34 | 2023-07-20T13:44:58 |
CONTRIBUTOR
| null |
## Description
Added a doc about the [Datadog APM integration for LangChain](https://github.com/DataDog/dd-trace-py/pull/6137).
Note that the integration is on `ddtrace`'s end and so no code is introduced/required by this integration into the langchain library. For that reason I've refrained from adding an example notebook (although I've added setup instructions for enabling the integration in the doc) as no code is technically required to enable the integration.
Tagging @baskaryan as reviewer on this PR, thank you very much!
## Dependencies
Datadog APM users will need to have `ddtrace` installed, but the integration is on `ddtrace` end and so does not introduce any external dependencies to the LangChain project.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,812,521,192 |
PR_kwDOIPDwls5V7F3V
| 7,954 |
async support for SQLDatabaseToolkit
|
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[] | null | 4 | 2023-07-19T18:43:35 | 2023-09-20T13:45:04 | 2023-09-20T13:40:20 |
NONE
| null |
- Description: Add async support for tools within SQLDatabaseToolkit
- Issue: None that I know of.
- Dependencies: None
- Tag maintainer: @hinthornw
- Twitter handle: dot2dotseurat
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I_kwDOIPDwls5sCKSZ
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TypeError("aiohttp.client.ClientSession.request() got multiple values for keyword argument 'auth'" with arun and requests_get tool on an agent
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[] | null | 1 | 2023-07-19T18:32:02 | 2023-10-25T16:05:51 | 2023-10-25T16:05:51 |
NONE
| null |
### System Info
langchain 0.0.234
Python 3.10.9
aiohttp 3.8.4
Getting the following error when running an agent in async with agent.arun('Get time in Salta of Argentina') with a requests_get tool.
[tool/start] [1:chain:AgentExecutor > 7:tool:requests_get] Entering Tool run with input:
"http://worldtimeapi.org/api/timezone/America/Argentina/Salta"
[tool/error] [1:chain:AgentExecutor > 7:tool:requests_get] [78.06s] Tool run errored with error:
TypeError("aiohttp.client.ClientSession.request() got multiple values for keyword argument 'auth'"
The issue lies in https://github.com/hwchase17/langchain/commit/663b0933e488383e6a9bc2a04b4b1cf866a8ea94 which was done to fix issue #7542
### Who can help?
@agola11 @EricSpeidel
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an agent utilizing the requests_get tool
2. Run the agent in async using arun with a prompt making it generate a GET request using the tool
### Expected behavior
The auth should not be pass explicitly to sent to session.request in the code below. It will pass through automatically as part of kwargs.
```
@asynccontextmanager
async def _arequest(
self, method: str, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""Make an async request."""
if not self.aiosession:
async with aiohttp.ClientSession() as session:
async with session.request(
method, url, headers=self.headers, auth=self.auth, **kwargs
) as response:
yield response
else:
async with self.aiosession.request(
method, url, headers=self.headers, auth=self.auth, **kwargs
) as response:
yield response
```
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PR_kwDOIPDwls5V64yC
| 7,952 |
Add llama-v2 to local document QA
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PR_kwDOIPDwls5V61mD
| 7,951 |
docs: fix a couple of small indentation errors in the strings
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[] | null | 1 | 2023-07-19T18:04:14 | 2023-07-20T13:41:47 | 2023-07-20T13:34:02 |
CONTRIBUTOR
| null |
Fixed a few indentations I came across in the docs @baskaryan
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