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PR_kwDOIPDwls5W8-25
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[OpenSearch] : Fix AOSS Initialization
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[] | null | 1 | 2023-08-01T22:08:33 | 2023-08-01T22:33:52 | 2023-08-01T22:33:51 |
CONTRIBUTOR
| null |
### Description
This PR fixes the AOSS Initialization in Opensearch.
### Maintainers
@rlancemartin, @eyurtsev, @navneet1v
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PR_kwDOIPDwls5W8-L2
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LlamaCpp, add n_gqa flag to support Llama2
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NONE
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To load and run llama 2 70b models `n_gqa` must be set to 8. For other models it can be unset (and defaults to 1). Tested with both llama 2 70b (set) and llama 2 13b (unset) and both work.
Maintainer guess @hwchase17
Fixes https://github.com/langchain-ai/langchain/issues/8486
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PR_kwDOIPDwls5W84iW
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Fixed source key name for docugami loader
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[] | null | 5 | 2023-08-01T21:38:15 | 2023-09-17T18:54:21 | 2023-08-23T18:24:55 |
CONTRIBUTOR
| null |
The Docugami loader was not returning the source metadata key. This was triggering this exception when used with retrievers, per https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/prompt_template.py#L193C1-L195C41
The fix is simple and just updates the metadata key name for the document each chunk is sourced from, from "name" to "source" as expected.
I tested by running the python notebook that has an end to end scenario in it.
Tagging DataLoader maintainers @rlancemartin @eyurtsev
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PR_kwDOIPDwls5W8y41
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Add memory to sql chain
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CONTRIBUTOR
| null |
continuation of PR #8550
@hwchase17 please see and merge. And also close the PR #8550.
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docs: update `unstructured` install instructions
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[] | null | 2 | 2023-08-01T20:47:01 | 2023-08-01T21:17:50 | 2023-08-01T21:17:50 |
CONTRIBUTOR
| null |
### Summary
Updates the `unstructured` install instructions. For `unstructured>=0.9.0`, dependencies are broken out by document type and the base `unstructured` package includes fewer dependencies. `pip install "unstructured[local-inference]"` has been replace by `pip install "unstructured[all-docs]"`, though the `local-inference` extra is still supported for the time being.
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
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[] | null | 1 | 2023-08-01T20:09:34 | 2023-08-01T21:17:06 | 2023-08-01T21:17:05 |
COLLABORATOR
| null | null |
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| 1,831,889,289 |
PR_kwDOIPDwls5W8R4I
| 8,594 |
Support for `Segment` analytics as a utility tool
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[] | null | 4 | 2023-08-01T19:25:32 | 2023-11-01T22:57:56 | 2023-11-01T22:57:56 |
NONE
| null |
Replace this comment with:
- Description: The changes will introduce a tool `Segment` for LLM based app's analytics.
- Dependencies: `segment-analytics-python`
- Tag maintainer: @hinthornw
- Twitter handle: @yashpokar
The PR is passing linting and testing before submitting. Ran `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
- [x] a test for the integration, preferably unit tests that do not rely on network access,
- [x] an example notebook showing its use.
<!--
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_kwDOIPDwls5tMAoi
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InvalidRequestError: Unrecognized request arguments supplied: function_call, functions
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[] | null | 18 | 2023-08-01T19:06:48 | 2023-11-20T17:29:31 | null |
NONE
| null |
### Issue with current documentation:
I am following the instruction from this doc:
DOC: Structure answers with OpenAI functions
-https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa
But this gives some error.
# Code
```python
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chat_models import AzureChatOpenAI
from langchain.chat_models import ChatOpenAI
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain.chains import create_qa_with_sources_chain
path_txt = r"C:\Users\a126291\OneDrive - AmerisourceBergen(ABC)\data\langchain\state_of_the_union.txt"
def get_config_dict():
import os
import yaml
with open(os.path.expanduser('~/.config/config.yaml')) as fh:
config = yaml.safe_load(fh)
# openai
keys = ["OPENAI_API_KEY","OPENAI_API_TYPE","OPENAI_API_BASE","OPENAI_API_VERSION"]
for key in keys:
os.environ[key] = config.get(key)
return config
config = get_config_dict()
#========= qa chain
loader = TextLoader(path_txt, encoding="utf-8")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
for i, text in enumerate(texts):
text.metadata["source"] = f"{i}-pl"
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", chunk_size=1)
docsearch = Chroma.from_documents(texts, embeddings)
# vectorstore = Chroma.from_documents(texts, embeddings)
# retriever = vectorstore.as_retriever()
llm = AzureChatOpenAI(**config['kw_azure_llm'],temperature=0.4)
#------- query
qa_chain = create_qa_with_sources_chain(llm)
doc_prompt = PromptTemplate(
template="Content: {page_content}\nSource: {source}",
input_variables=["page_content", "source"],
)
final_qa_chain = StuffDocumentsChain(
llm_chain=qa_chain,
document_variable_name="context",
document_prompt=doc_prompt,
)
retrieval_qa = RetrievalQA(
retriever=docsearch.as_retriever(), combine_documents_chain=final_qa_chain
)
query = "What did the president say about russia"
retrieval_qa.run(query)
```
# Error: InvalidRequestError: Unrecognized request arguments supplied: function_call, functions
```bash
---------------------------------------------------------------------------
InvalidRequestError Traceback (most recent call last)
Cell In[36], line 69
64 retrieval_qa = RetrievalQA(
65 retriever=docsearch.as_retriever(), combine_documents_chain=final_qa_chain
66 )
68 query = "What did the president say about russia"
---> 69 retrieval_qa.run(query)
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:290, in Chain.run(self, callbacks, tags, *args, **kwargs)
288 if len(args) != 1:
289 raise ValueError("`run` supports only one positional argument.")
--> 290 return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
292 if kwargs and not args:
293 return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:166, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:160, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File ~\venv\py311openai\Lib\site-packages\langchain\chains\retrieval_qa\base.py:120, in BaseRetrievalQA._call(self, inputs, run_manager)
117 question = inputs[self.input_key]
119 docs = self._get_docs(question)
--> 120 answer = self.combine_documents_chain.run(
121 input_documents=docs, question=question, callbacks=_run_manager.get_child()
122 )
124 if self.return_source_documents:
125 return {self.output_key: answer, "source_documents": docs}
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:293, in Chain.run(self, callbacks, tags, *args, **kwargs)
290 return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
292 if kwargs and not args:
--> 293 return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
295 if not kwargs and not args:
296 raise ValueError(
297 "`run` supported with either positional arguments or keyword arguments,"
298 " but none were provided."
299 )
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:166, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:160, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File ~\venv\py311openai\Lib\site-packages\langchain\chains\combine_documents\base.py:84, in BaseCombineDocumentsChain._call(self, inputs, run_manager)
82 # Other keys are assumed to be needed for LLM prediction
83 other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
---> 84 output, extra_return_dict = self.combine_docs(
85 docs, callbacks=_run_manager.get_child(), **other_keys
86 )
87 extra_return_dict[self.output_key] = output
88 return extra_return_dict
File ~\venv\py311openai\Lib\site-packages\langchain\chains\combine_documents\stuff.py:87, in StuffDocumentsChain.combine_docs(self, docs, callbacks, **kwargs)
85 inputs = self._get_inputs(docs, **kwargs)
86 # Call predict on the LLM.
---> 87 return self.llm_chain.predict(callbacks=callbacks, **inputs), {}
File ~\venv\py311openai\Lib\site-packages\langchain\chains\llm.py:252, in LLMChain.predict(self, callbacks, **kwargs)
237 def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
238 """Format prompt with kwargs and pass to LLM.
239
240 Args:
(...)
250 completion = llm.predict(adjective="funny")
251 """
--> 252 return self(kwargs, callbacks=callbacks)[self.output_key]
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:166, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File ~\venv\py311openai\Lib\site-packages\langchain\chains\base.py:160, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File ~\venv\py311openai\Lib\site-packages\langchain\chains\llm.py:92, in LLMChain._call(self, inputs, run_manager)
87 def _call(
88 self,
89 inputs: Dict[str, Any],
90 run_manager: Optional[CallbackManagerForChainRun] = None,
91 ) -> Dict[str, str]:
---> 92 response = self.generate([inputs], run_manager=run_manager)
93 return self.create_outputs(response)[0]
File ~\venv\py311openai\Lib\site-packages\langchain\chains\llm.py:102, in LLMChain.generate(self, input_list, run_manager)
100 """Generate LLM result from inputs."""
101 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
--> 102 return self.llm.generate_prompt(
103 prompts,
104 stop,
105 callbacks=run_manager.get_child() if run_manager else None,
106 **self.llm_kwargs,
107 )
File ~\venv\py311openai\Lib\site-packages\langchain\chat_models\base.py:167, in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
159 def generate_prompt(
160 self,
161 prompts: List[PromptValue],
(...)
164 **kwargs: Any,
165 ) -> LLMResult:
166 prompt_messages = [p.to_messages() for p in prompts]
--> 167 return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File ~\venv\py311openai\Lib\site-packages\langchain\chat_models\base.py:102, in BaseChatModel.generate(self, messages, stop, callbacks, tags, **kwargs)
100 except (KeyboardInterrupt, Exception) as e:
101 run_manager.on_llm_error(e)
--> 102 raise e
103 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
104 generations = [res.generations for res in results]
File ~\venv\py311openai\Lib\site-packages\langchain\chat_models\base.py:94, in BaseChatModel.generate(self, messages, stop, callbacks, tags, **kwargs)
90 new_arg_supported = inspect.signature(self._generate).parameters.get(
91 "run_manager"
92 )
93 try:
---> 94 results = [
95 self._generate(m, stop=stop, run_manager=run_manager, **kwargs)
96 if new_arg_supported
97 else self._generate(m, stop=stop)
98 for m in messages
99 ]
100 except (KeyboardInterrupt, Exception) as e:
101 run_manager.on_llm_error(e)
File ~\venv\py311openai\Lib\site-packages\langchain\chat_models\base.py:95, in <listcomp>(.0)
90 new_arg_supported = inspect.signature(self._generate).parameters.get(
91 "run_manager"
92 )
93 try:
94 results = [
---> 95 self._generate(m, stop=stop, run_manager=run_manager, **kwargs)
96 if new_arg_supported
97 else self._generate(m, stop=stop)
98 for m in messages
99 ]
100 except (KeyboardInterrupt, Exception) as e:
101 run_manager.on_llm_error(e)
File ~\venv\py311openai\Lib\site-packages\langchain\chat_models\openai.py:359, in ChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
351 message = _convert_dict_to_message(
352 {
353 "content": inner_completion,
(...)
356 }
357 )
358 return ChatResult(generations=[ChatGeneration(message=message)])
--> 359 response = self.completion_with_retry(messages=message_dicts, **params)
360 return self._create_chat_result(response)
File ~\venv\py311openai\Lib\site-packages\langchain\chat_models\openai.py:307, in ChatOpenAI.completion_with_retry(self, **kwargs)
303 @retry_decorator
304 def _completion_with_retry(**kwargs: Any) -> Any:
305 return self.client.create(**kwargs)
--> 307 return _completion_with_retry(**kwargs)
File ~\venv\py311openai\Lib\site-packages\tenacity\__init__.py:289, in BaseRetrying.wraps.<locals>.wrapped_f(*args, **kw)
287 @functools.wraps(f)
288 def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any:
--> 289 return self(f, *args, **kw)
File ~\venv\py311openai\Lib\site-packages\tenacity\__init__.py:379, in Retrying.__call__(self, fn, *args, **kwargs)
377 retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
378 while True:
--> 379 do = self.iter(retry_state=retry_state)
380 if isinstance(do, DoAttempt):
381 try:
File ~\venv\py311openai\Lib\site-packages\tenacity\__init__.py:314, in BaseRetrying.iter(self, retry_state)
312 is_explicit_retry = fut.failed and isinstance(fut.exception(), TryAgain)
313 if not (is_explicit_retry or self.retry(retry_state)):
--> 314 return fut.result()
316 if self.after is not None:
317 self.after(retry_state)
File ~\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py:449, in Future.result(self, timeout)
447 raise CancelledError()
448 elif self._state == FINISHED:
--> 449 return self.__get_result()
451 self._condition.wait(timeout)
453 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
File ~\AppData\Local\Programs\Python\Python311\Lib\concurrent\futures\_base.py:401, in Future.__get_result(self)
399 if self._exception:
400 try:
--> 401 raise self._exception
402 finally:
403 # Break a reference cycle with the exception in self._exception
404 self = None
File ~\venv\py311openai\Lib\site-packages\tenacity\__init__.py:382, in Retrying.__call__(self, fn, *args, **kwargs)
380 if isinstance(do, DoAttempt):
381 try:
--> 382 result = fn(*args, **kwargs)
383 except BaseException: # noqa: B902
384 retry_state.set_exception(sys.exc_info()) # type: ignore[arg-type]
File ~\venv\py311openai\Lib\site-packages\langchain\chat_models\openai.py:305, in ChatOpenAI.completion_with_retry.<locals>._completion_with_retry(**kwargs)
303 @retry_decorator
304 def _completion_with_retry(**kwargs: Any) -> Any:
--> 305 return self.client.create(**kwargs)
File ~\venv\py311openai\Lib\site-packages\openai\api_resources\chat_completion.py:25, in ChatCompletion.create(cls, *args, **kwargs)
23 while True:
24 try:
---> 25 return super().create(*args, **kwargs)
26 except TryAgain as e:
27 if timeout is not None and time.time() > start + timeout:
File ~\venv\py311openai\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py:153, in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)
127 @classmethod
128 def create(
129 cls,
(...)
136 **params,
137 ):
138 (
139 deployment_id,
140 engine,
(...)
150 api_key, api_base, api_type, api_version, organization, **params
151 )
--> 153 response, _, api_key = requestor.request(
154 "post",
155 url,
156 params=params,
157 headers=headers,
158 stream=stream,
159 request_id=request_id,
160 request_timeout=request_timeout,
161 )
163 if stream:
164 # must be an iterator
165 assert not isinstance(response, OpenAIResponse)
File ~\venv\py311openai\Lib\site-packages\openai\api_requestor.py:298, in APIRequestor.request(self, method, url, params, headers, files, stream, request_id, request_timeout)
277 def request(
278 self,
279 method,
(...)
286 request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
287 ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]:
288 result = self.request_raw(
289 method.lower(),
290 url,
(...)
296 request_timeout=request_timeout,
297 )
--> 298 resp, got_stream = self._interpret_response(result, stream)
299 return resp, got_stream, self.api_key
File ~\venv\py311openai\Lib\site-packages\openai\api_requestor.py:700, in APIRequestor._interpret_response(self, result, stream)
692 return (
693 self._interpret_response_line(
694 line, result.status_code, result.headers, stream=True
695 )
696 for line in parse_stream(result.iter_lines())
697 ), True
698 else:
699 return (
--> 700 self._interpret_response_line(
701 result.content.decode("utf-8"),
702 result.status_code,
703 result.headers,
704 stream=False,
705 ),
706 False,
707 )
File ~\venv\py311openai\Lib\site-packages\openai\api_requestor.py:763, in APIRequestor._interpret_response_line(self, rbody, rcode, rheaders, stream)
761 stream_error = stream and "error" in resp.data
762 if stream_error or not 200 <= rcode < 300:
--> 763 raise self.handle_error_response(
764 rbody, rcode, resp.data, rheaders, stream_error=stream_error
765 )
766 return resp
InvalidRequestError: Unrecognized request arguments supplied: function_call, functions
```
### Idea or request for content:
_No response_
|
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| 1,831,836,049 |
PR_kwDOIPDwls5W8GpJ
| 8,591 |
updated to use new llm_util query
|
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[] | null | 2 | 2023-08-01T18:47:05 | 2023-08-01T21:16:27 | 2023-08-01T21:16:15 |
CONTRIBUTOR
| null |
- Description: added memgraph_graph.py which defines the MemgraphGraph class, subclassing off the existing Neo4jGraph class. This lets you query the Memgraph graph database using natural language. It leverages the Neo4j drivers and the bolt protocol.
- Dependencies: since it is a subclass off of Neo4jGraph, it is dependent on it and the GraphCypherQA Chain implementations. It is dependent on the Neo4j drivers being present. It is dependent on having a running Memgraph instance to connect to.
- Tag maintainer: @baskaryan
- Twitter handle: @villageideate
- example usage can be seen in this repo https://github.com/brettdbrewer/MemgraphGraph/
|
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| 1,831,784,534 |
I_kwDOIPDwls5tLtBW
| 8,590 |
Missing some input keys: {'input'} when using Combined memory and ConversationalRetrievalChain
|
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[] | null | 4 | 2023-08-01T18:08:07 | 2023-11-08T16:07:09 | 2023-11-08T16:07:09 |
NONE
| null |
### Issue you'd like to raise.
Hello there,
I am currently attempting to add combined memory to ConversationalRetrievalChain (CRC). However, I am unsure if combined memory is compatible with CRC. I have made a few modifications, but unfortunately, I have encountered errors preventing it from running properly. I was hoping that someone could assist me in resolving this issue. Thank you.
```
from langchain.prompts import PromptTemplate
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferWindowMemory, CombinedMemory, ConversationSummaryMemory
chat_history = []
conv_memory = ConversationBufferWindowMemory(
memory_key="chat_history_lines",
input_key="input",
output_key='answer',
k=1
)
summary_memory = ConversationSummaryMemory(llm=turbo_llm, input_key="input", output_key='answer')
# Combined
memory = CombinedMemory(memories=[conv_memory, summary_memory])
_DEFAULT_TEMPLATE = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
{context}
Summary of conversation:
{history}
Current conversation:
{chat_history_lines}
Human: {input}
AI:"""
PROMPT_= PromptTemplate(
input_variables=["history", "input", "context", "chat_history_lines"],
template=_DEFAULT_TEMPLATE
)
qa = ConversationalRetrievalChain.from_llm(
llm=turbo_llm,
chain_type="stuff",
memory=memory,
retriever=retriever,
return_source_documents=True,
return_generated_question=True,
combine_docs_chain_kwargs={'prompt': PROMPT_})
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})`
```
Error:
`ValueError: Missing some input keys: {'input'}`
when I replace "input" with "question" in _DEFAULT_TEMPLATE and PROMPT_
then the error is :
`ValueError: One output key expected, got dict_keys(['answer', 'source_documents', 'generated_question'])
`
### Suggestion:
_No response_
|
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| 1,831,751,582 |
PR_kwDOIPDwls5W702f
| 8,589 |
Implement a router for openai functions
|
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[] | null | 1 | 2023-08-01T17:43:33 | 2023-08-09T20:17:06 | 2023-08-09T20:17:05 |
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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| 1,831,737,148 |
PR_kwDOIPDwls5W7xxX
| 8,588 |
Handling potential issue in file.py
|
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CONTRIBUTOR
| null |
Potential Resource Leak: The FileCallbackHandler class opens a file in its constructor but does not handle the case where the file opening fails. If there's an exception while opening the file, the object is not closed, which may lead to resource leaks. It's recommended to handle the file opening more gracefully using a try-except block or a context manager (with statement) to ensure the file is closed correctly, even if an exception occurs during the opening process.
@baskaryan , @hwchase17
|
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PR_kwDOIPDwls5W7tal
| 8,587 |
Callback handler for Amazon SageMaker Experiments
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[] | null | 4 | 2023-08-01T17:17:25 | 2023-08-01T21:56:48 | 2023-08-01T20:47:08 |
CONTRIBUTOR
| null |
## Description
This PR implements a callback handler for SageMaker Experiments which is similar to that of mlflow.
* When creating the callback handler, it takes the experiment's run object as an argument. All the callback outputs are then logged to the run object.
* The output of each callback action (e.g., `on_llm_start`) is saved to S3 bucket as json file.
* Optionally, you can also log additional information such as the LLM hyper-parameters to the same run object.
* Once the callback object is no more needed, you will need to call the `flush_tracker()` method. This makes sure that any intermediate files are deleted.
* A separate notebook example is provided to show how the callback is used.
Thank you [Mohamad Al Jazaery](https://github.com/mohjaz), [Shinan Zhang](https://github.com/sz640) for your contribution and review!
@3coins @agola11
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PR_kwDOIPDwls5W7sxo
| 8,586 |
Airbyte based loaders
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[] | null | 12 | 2023-08-01T17:15:12 | 2023-08-09T20:22:32 | 2023-08-08T21:49:25 |
CONTRIBUTOR
| null |
This PR adds 8 new loaders:
* `AirbyteCDKLoader` This reader can wrap and run all python-based Airbyte source connectors.
* Separate loaders for the most commonly used APIs:
* `AirbyteGongLoader`
* `AirbyteHubspotLoader`
* `AirbyteSalesforceLoader`
* `AirbyteShopifyLoader`
* `AirbyteStripeLoader`
* `AirbyteTypeformLoader`
* `AirbyteZendeskSupportLoader`
## Documentation and getting started
I added the basic shape of the config to the notebooks. This increases the maintenance effort a bit, but I think it's worth it to make sure people can get started quickly with these important connectors. This is also why I linked the spec and the documentation page in the readme as these two contain all the information to configure a source correctly (e.g. it won't suggest using oauth if that's avoidable even if the connector supports it).
## Document generation
The "documents" produced by these loaders won't have a text part (instead, all the record fields are put into the metadata). If a text is required by the use case, the caller needs to do custom transformation suitable for their use case.
## Incremental sync
All loaders support incremental syncs if the underlying streams support it. By storing the `last_state` from the reader instance away and passing it in when loading, it will only load updated records.
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I_kwDOIPDwls5tKdav
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GPTCache implementation isnt working with ChatOpenAI
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[] | null | 3 | 2023-08-01T14:51:37 | 2023-12-04T16:06:03 | 2023-12-04T16:06:02 |
CONTRIBUTOR
| null |
### System Info
langchain = "^0.0.248"
### Who can help?
@hwchase17 @agola11
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import os
import hashlib
import langchain
from gptcache import Cache # type: ignore
from gptcache.manager.factory import manager_factory # type: ignore
from gptcache.processor.pre import get_prompt # type: ignore
from langchain.cache import GPTCache
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
os.environ["OPENAI_API_KEY"] = "sk-..."
llm = ChatOpenAI(temperature=0) # type: ignore
def get_hashed_name(name):
return hashlib.sha256(name.encode()).hexdigest()
def init_gptcache(cache_obj: Cache, llm: str):
hashed_llm = get_hashed_name(llm)
cache_obj.init(
pre_embedding_func=get_prompt,
data_manager=manager_factory(manager="map", data_dir=f"map_cache_{hashed_llm}"),
)
langchain.llm_cache = GPTCache(init_gptcache)
# The first time, it is not yet in cache, so it should take longer
response = llm("Tell me a joke")
print(f"Response 1: {response}")
# The first time, it is not yet in cache, so it should take longer
response = llm("Tell me a joke")
print(f"Response 2: {response}")
```
### Expected behavior
Response 1:
Why did the chicken cross the road?
To get to the other side.
Response 2:
Why did the chicken cross the road?
To get to the other side.
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PR_kwDOIPDwls5W6xoc
| 8,583 |
SQL block potential harmful commands
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[] | null | 2 | 2023-08-01T14:42:23 | 2023-09-20T17:31:01 | null |
NONE
| null |
Description:
This PR aims to give a bit more control on the commands executed by the `SQLDatabase`. At the moment, if you ask "Drop the table X in database Y" with `SQLDatabaseChain`, it will generate code to drop the table that will be run by `SQLDatabase`. Today no checks are made on the commands in `SQLDatabase`, it's free real estate.
Ideally, rights on user must be set to perform or not this kind of action on the database. In real life, lot of people use full rights. To prevent this I added a feature that could, if provided a list of restricted actions, stop the execution of the SQL query.
It will raise a `PermissionError` when restricted keywords are in the command for `SQLDatabase`
Key Changes:
- Add a new argument to `SQLDatabase` class named `restricted_keywords`
- Add a new method to detect restricted keywords in the SQL command
- Raise `PermissionError` if keywords are found in the sql command
- Execute sql command like before if not keyworkds were found
- Add a test to check if the raise is working while adding one restricted keyword
`make format`, `make lint` and `make test` were tested locally
I think this could help for [CVE-2023-36189](https://github.com/advisories/GHSA-7q94-qpjr-xpgm) Where the developer could block any keywords that could be generated by `SQLDatabaseChain`. It can block any string, so he could also block the usage of wildcards, name of tables, databases. I think it can be powerfull, it gives control to the developer and protect the user doing unwanted actions or protect his database.
@baskaryan
|
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PR_kwDOIPDwls5W6rzq
| 8,582 |
fix RecursiveUrlLoader
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CONTRIBUTOR
| null |
Description: the recursive url loader does not fully crawl for all urls under base url
Maintainer: @baskaryan
|
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Cannot create "n" responses
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[] | null | 4 | 2023-08-01T13:55:11 | 2023-08-02T16:36:44 | 2023-08-02T16:36:44 |
NONE
| null |
### System Info
Verified in dockerimage python:3.9
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
how to reproduce?
1. docker run -it python:3.9 bash
2. pip install langchain
3. pip install openai
4. run script
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import SystemMessage
chat_model = ChatOpenAI(n=3)
prompt = "What is the capital of France?"
message = SystemMessage(content=prompt)
responses = chat_model.predict_messages([message], n=3)
print(responses)
```
output:
content='The capital of France is Paris.' additional_kwargs={} example=False
### Expected behavior
Hello,
When running the script I expect to get 3 responses, because I set the parameter `n=3` during initialization of ChatOpenAI and in the call of predict_messages.
still the response is a single answer!
please let me know how to correctly use the parameter n or fix the current behavior!
best regards,
LW
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| 1,831,301,181 |
I_kwDOIPDwls5tJ3A9
| 8,580 |
Need support for async memory, especially for ConversationSummaryMemory and ConversationSummaryBufferMemory
|
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[] | null | 7 | 2023-08-01T13:32:50 | 2023-11-07T22:50:05 | 2023-11-07T22:49:54 |
NONE
| null |
### Feature request
`ConversationSummaryMemory` and `ConversationSummaryBufferMemory` can be used as memory within async conversational chains, but they themselves are fully blocking. In particular, `Chain.acall()` is async, but it calls `Chain.prep_outputs()`, which calls `self.memory.save_context()`. In the case of `ConversationSummaryBufferMemory`, `save_context()` calls `prune()`, which calls `SummarizerMixin.predict_new_summary()`, which creates an `LLMChain` and then calls `LLMChain.predict()`. We really need to be able to specify an async handler when we create the memory object, pass that through to the `LLMChain`, and ultimately `await LLMChain.apredict()`.
### Motivation
The blocking nature of the `LLMChain.predict()` calls means that otherwise-async chains end up blocking regularly. If you use an ASGI server like `uvicorn`, this means that lots of requests can end up waiting. You can wrap `uvicorn` in a WSGI server like `gunicorn` so that there are multiple processes available, but blocking one whole process every time the memory object needs to summarize can still block any websocket users connected to the blocked process. It's just not good.
### Your contribution
We're going to have to solve this problem for ourselves, and we'd be happy to try to contribute a solution back to the community. However, we'd be first-time contributors and we're not sure whether there are others already making similar improvements.
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| 1,831,277,961 |
I_kwDOIPDwls5tJxWJ
| 8,579 |
Issue: Agent with memory and intermediate steps
|
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NONE
| null |
### Issue you'd like to raise.
Hi everyone,
I'm having trouble working with the agent structure in Langchain.
I would like to have an agent that has memory and can return intermediate steps.
However It doesn't work, when I make an agent executor, the return_intermediate_steps=True makes it crash as the memory cannot read the output and when I use the agent directly, the return_intermediate_steps=True does nothing.
Do you know if there is a way to have an agent with memory and that can return intermediate steps at the same time ?
Thanks for your help
### Suggestion:
_No response_
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| 8,578 |
langchain_touchmed
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langchain_touchmed，增加 prompt
<!-- 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_kwDOIPDwls5tJN0J
| 8,577 |
multiple bases have instance lay-out conflict on HumanMessageChunk class on langchain 0.0.249
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### System Info
### langchain Version **0.0.249**
### Python Version **3.8**
### Other notes
Using this on notebook in Azure Synapse Studio
### Error
```
When import happening getting bellow error message
TypeError Traceback (most recent call last)
/tmp/ipykernel_17180/722847199.py in <module>
----> 1 from langchain.vectorstores import Pinecone
2 from langchain.embeddings.openai import OpenAIEmbeddings
3
4 import datetime
5 from datetime import date, timedelta
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/__init__.py in <module>
4 from typing import Optional
5
----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
7 from langchain.cache import BaseCache
8 from langchain.chains import (
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/__init__.py in <module>
1 """Interface for agents."""
----> 2 from langchain.agents.agent import (
3 Agent,
4 AgentExecutor,
5 AgentOutputParser,
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/agent.py in <module>
13 from pydantic import BaseModel, root_validator
14
---> 15 from langchain.agents.agent_iterator import AgentExecutorIterator
16 from langchain.agents.agent_types import AgentType
17 from langchain.agents.tools import InvalidTool
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/agent_iterator.py in <module>
19 )
20
---> 21 from langchain.callbacks.manager import (
22 AsyncCallbackManager,
23 AsyncCallbackManagerForChainRun,
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/callbacks/__init__.py in <module>
1 """Callback handlers that allow listening to events in LangChain."""
2
----> 3 from langchain.callbacks.aim_callback import AimCallbackHandler
4 from langchain.callbacks.argilla_callback import ArgillaCallbackHandler
5 from langchain.callbacks.arize_callback import ArizeCallbackHandler
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/callbacks/aim_callback.py in <module>
3
4 from langchain.callbacks.base import BaseCallbackHandler
----> 5 from langchain.schema import AgentAction, AgentFinish, LLMResult
6
7
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/schema/__init__.py in <module>
1 from langchain.schema.agent import AgentAction, AgentFinish
2 from langchain.schema.document import BaseDocumentTransformer, Document
----> 3 from langchain.schema.memory import BaseChatMessageHistory, BaseMemory
4 from langchain.schema.messages import (
5 AIMessage,
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/schema/memory.py in <module>
5
6 from langchain.load.serializable import Serializable
----> 7 from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
8
9
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/schema/messages.py in <module>
136
137
--> 138 class HumanMessageChunk(HumanMessage, BaseMessageChunk):
139 pass
140
~/cluster-env/clonedenv/lib/python3.8/site-packages/pydantic/main.cpython-38-x86_64-linux-gnu.so in pydantic.main.ModelMetaclass.__new__()
~/cluster-env/clonedenv/lib/python3.8/abc.py in __new__(mcls, name, bases, namespace, **kwargs)
83 """
84 def __new__(mcls, name, bases, namespace, **kwargs):
---> 85 cls = super().__new__(mcls, name, bases, namespace, **kwargs)
86 _abc_init(cls)
87 return cls
TypeError: multiple bases have instance lay-out conflict
```
### Other installed packages in the system
Package Version
----------------------------- -------------------
absl-py 0.13.0
adal 1.2.7
adlfs 0.7.7
aiohttp 3.8.5
aiosignal 1.3.1
annotated-types 0.5.0
appdirs 1.4.4
applicationinsights 0.11.10
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
astor 0.8.1
astunparse 1.6.3
async-timeout 4.0.2
attrs 21.2.0
azure-common 1.1.27
azure-core 1.16.0
azure-datalake-store 0.0.51
azure-graphrbac 0.61.1
azure-identity 1.4.1
azure-mgmt-authorization 0.61.0
azure-mgmt-containerregistry 8.0.0
azure-mgmt-core 1.3.0
azure-mgmt-keyvault 2.2.0
azure-mgmt-resource 13.0.0
azure-mgmt-storage 11.2.0
azure-storage-blob 12.8.1
azure-synapse-ml-predict 1.0.0
azureml-core 1.34.0
azureml-dataprep 2.22.2
azureml-dataprep-native 38.0.0
azureml-dataprep-rslex 1.20.2
azureml-dataset-runtime 1.34.0
azureml-mlflow 1.34.0
azureml-opendatasets 1.34.0
azureml-synapse 0.0.1
azureml-telemetry 1.34.0
backcall 0.2.0
backports.functools-lru-cache 1.6.4
backports.tempfile 1.0
backports.weakref 1.0.post1
beautifulsoup4 4.9.3
bleach 5.0.1
blinker 1.4
bokeh 2.3.2
Brotli 1.0.9
brotlipy 0.7.0
cachetools 4.2.2
certifi 2021.5.30
cffi 1.14.5
chardet 4.0.0
charset-normalizer 3.2.0
click 8.0.1
cloudpickle 1.6.0
conda-package-handling 1.7.3
configparser 5.0.2
contextlib2 0.6.0.post1
cryptography 3.4.7
cycler 0.10.0
Cython 0.29.23
cytoolz 0.11.0
dash 1.20.0
dash-core-components 1.16.0
dash-cytoscape 0.2.0
dash-html-components 1.1.3
dash-renderer 1.9.1
dash-table 4.11.3
dask 2021.6.2
databricks-cli 0.12.1
dataclasses-json 0.5.14
debugpy 1.3.0
decorator 4.4.2
defusedxml 0.7.1
dill 0.3.4
distlib 0.3.6
distro 1.7.0
dnspython 2.4.1
docker 4.4.4
dotnetcore2 2.1.23
entrypoints 0.3
et-xmlfile 1.1.0
fastjsonschema 2.16.1
filelock 3.8.0
fire 0.4.0
Flask 2.0.1
Flask-Compress 0.0.0
flatbuffers 1.12
frozenlist 1.4.0
fsspec 2021.10.0
fsspec-wrapper 0.1.6
fusepy 3.0.1
future 0.18.2
gast 0.3.3
gensim 3.8.3
geographiclib 1.52
geopy 2.1.0
gevent 21.1.2
gitdb 4.0.7
GitPython 3.1.18
google-auth 1.32.1
google-auth-oauthlib 0.4.1
google-pasta 0.2.0
greenlet 1.1.0
grpcio 1.37.1
h5py 2.10.0
html5lib 1.1
hummingbird-ml 0.4.0
idna 2.10
imagecodecs 2021.3.31
imageio 2.9.0
importlib-metadata 4.6.1
importlib-resources 5.9.0
ipykernel 6.0.1
ipython 7.23.1
ipython-genutils 0.2.0
ipywidgets 7.6.3
isodate 0.6.0
itsdangerous 2.0.1
jdcal 1.4.1
jedi 0.18.0
jeepney 0.6.0
Jinja2 3.0.1
jmespath 0.10.0
joblib 1.0.1
jsonpickle 2.0.0
jsonschema 4.15.0
jupyter-client 6.1.12
jupyter-core 4.7.1
jupyterlab-pygments 0.2.2
jupyterlab-widgets 3.0.3
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
keras2onnx 1.6.5
kiwisolver 1.3.1
koalas 1.8.0
KqlmagicCustom 0.1.114.post8
langchain 0.0.249
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matplotlib-inline 0.1.2
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mleap 0.17.0
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openapi-schema-pydantic 1.2.4
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packaging 21.0
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partd 1.2.0
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pyparsing 2.4.7
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pyzmq 22.1.0
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requests 2.31.0
requests-oauthlib 1.3.0
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rsa 4.7.2
ruamel.yaml 0.17.4
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ruamel-yaml-conda 0.15.100
SALib 1.3.11
scikit-image 0.18.1
scikit-learn 0.23.2
scipy 1.5.3
seaborn 0.11.1
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Send2Trash 1.8.0
setuptools 49.6.0.post20210108
shap 0.39.0
six 1.16.0
skl2onnx 1.8.0
sklearn-pandas 2.2.0
slicer 0.0.7
smart-open 5.1.0
smmap 3.0.5
soupsieve 2.2.1
SQLAlchemy 1.4.20
sqlanalyticsconnectorpy 1.0.1
statsmodels 0.12.2
synapseml-cognitive 0.10.2.dev1
synapseml-core 0.10.2.dev1
synapseml-deep-learning 0.10.2.dev1
synapseml-internal 0.0.0.dev1
synapseml-lightgbm 0.10.2.dev1
synapseml-opencv 0.10.2.dev1
synapseml-vw 0.10.2.dev1
tabulate 0.8.9
tenacity 8.2.2
tensorboard 2.4.1
tensorboard-plugin-wit 1.8.0
tensorflow 2.4.1
tensorflow-estimator 2.4.0
termcolor 1.1.0
terminado 0.15.0
textblob 0.15.3
threadpoolctl 2.1.0
tifffile 2021.4.8
tiktoken 0.4.0
tinycss2 1.1.1
toolz 0.11.1
torch 1.8.1
torchvision 0.9.1
tornado 6.1
tqdm 4.65.0
traitlets 5.0.5
typed-ast 1.4.3
typing_extensions 4.5.0
typing-inspect 0.8.0
urllib3 1.26.4
virtualenv 20.14.0
wcwidth 0.2.5
webencodings 0.5.1
websocket-client 1.1.0
Werkzeug 2.0.1
wheel 0.36.2
widgetsnbextension 3.5.2
wrapt 1.12.1
xgboost 1.4.0
XlsxWriter 3.0.3
yarl 1.6.3
zipp 3.5.0
zope.event 4.5.0
zope.interface 5.4.0
``
### 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
- [X] Async
### Reproduction
Go to Azure Synapse Studio
Open a Notebook
Select Pyspark(Python) as Language
Please note your node should run python 3.8
Then put bellow
### Installed bellow to the session.
```
!pip install --upgrade pip
!pip install tqdm
!pip install pinecone-client
!pip install typing-extensions==4.5.0
!pip install langchain
!pip install openai
!pip install tiktoken
```
### Here are my imports
```
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import datetime
from datetime import date, timedelta
import time
import csv
import openai
import requests
import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit, current_timestamp
from pyspark.sql import functions as F
from pyspark.sql import Row
from pyspark.sql.utils import AnalysisException
from pyspark.sql.types import StructType, StructField, StringType, TimestampType, LongType
from pyspark.sql.window import Window
from pyspark.sql import types as T
import concurrent.futures
import pinecone
import tiktoken
```
Run the notebook.
### Expected behavior
Import should be happend without any issues.
|
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PR_kwDOIPDwls5W4-Ab
| 8,574 |
fix apparent spelling inconsistencies
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[] | null | 2 | 2023-08-01T09:59:08 | 2023-08-04T06:17:11 | 2023-08-01T16:09:10 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: fix apparent spelling inconsistencies in raising ImportError
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: @MLOpsj
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5W4imO
| 8,573 |
Add Support for Loading Documents from Huawei OBS
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[] | null | 2 | 2023-08-01T08:50:15 | 2023-08-01T16:30:39 | 2023-08-01T16:30:30 |
CONTRIBUTOR
| null |
Description:
This PR adds support for loading documents from Huawei OBS (Object Storage Service) in Langchain. OBS is a cloud-based object storage service provided by Huawei Cloud. With this enhancement, Langchain users can now easily access and load documents stored in Huawei OBS directly into the system.
Key Changes:
- Added a new document loader module specifically for Huawei OBS integration.
- Implemented the necessary logic to authenticate and connect to Huawei OBS using access credentials.
- Enabled the loading of individual documents from a specified bucket and object key in Huawei OBS.
- Provided the option to specify custom authentication information or obtain security tokens from Huawei Cloud ECS for easy access.
How to Test:
1. Ensure the required package "esdk-obs-python" is installed.
2. Configure the endpoint, access key, secret key, and bucket details for Huawei OBS in the Langchain settings.
3. Load documents from Huawei OBS using the updated document loader module.
4. Verify that documents are successfully retrieved and loaded into Langchain for further processing.
Please review this PR and let us know if any further improvements are needed. Your feedback is highly appreciated!
@rlancemartin, @eyurtsev
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I_kwDOIPDwls5tH1uT
| 8,572 |
Issue: Structure of this repo is confusing
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[] | null | 1 | 2023-08-01T08:48:57 | 2023-11-07T16:05:58 | 2023-11-07T16:05:57 |
NONE
| null |
### Issue you'd like to raise.
This repo was split up between the core package and some experimental package. While this is a good idea, it's kinda odd to have both packages in the same repo. Even further you're releasing versions for both of these packages within the same repo. So 5 days ago there was a 0.0.5 release while later on you had 0.0.249.
I've actually never seen such a structure before and it's messing up scripted automation to follow releases.
### Suggestion:
Create a new repo for `LangChain Experimental` and have those releases there.
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PR_kwDOIPDwls5W4UvU
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bump 249
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[] | null | 1 | 2023-08-01T08:12:07 | 2023-08-01T08:20:17 | 2023-08-01T08:20:16 |
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PR_kwDOIPDwls5W4Nsb
| 8,570 |
Update README.md
|
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[] | null | 1 | 2023-08-01T07:53:57 | 2023-11-27T20:17:09 | 2023-11-13T06:07:49 |
CONTRIBUTOR
| null |
- Description: updated readme.
- Tag maintainer: @baskaryan
- Twitter handle: @Levalencia
|
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I_kwDOIPDwls5tHbAJ
| 8,569 |
Enhance retrieval callback hooks to capture retrieved document IDs and embeddings
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[] | null | 3 | 2023-08-01T07:44:33 | 2023-11-01T18:45:54 | null |
NONE
| null |
### Feature request
Enhance the retrieval callback hooks to include:
- document IDs from the underlying vector store
- document embeddings used during retrieval
### Motivation
I want to build a callback handler that enables LangChain users to visualize their data in [Phoenix](https://github.com/Arize-ai/phoenix), an open-source tool that provides debugging workflows for retrieval-augmented generation. At the moment, I am only able to get retrieved document text from the callback system, not the IDs or embeddings of the retrieved documents.
### Your contribution
I am willing to implement, test, and document this feature with guidance from the LangChain team. I am also happy to provide feedback on an implementation by the LangChain team by building an example callback handler using the enhancement retrieval hook functionality.
|
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PR_kwDOIPDwls5W4G_0
| 8,568 |
improve insert performance for hologres
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CONTRIBUTOR
| null |
Hi,
I made some code changes on the Hologres vector store to improve the data insertion performance.
The code has passed the format/lint/spell check. And I have run the unit test for Hologres connecting to my own database.
There is already an integration test `test_hologres.py` that covers the content of this change.
Please check this PR, thanks!
@baskaryan
@rlancemartin, @eyurtsev
Best,
Changgeng,
Developer @ Alibaba Cloud
|
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I_kwDOIPDwls5tHRjy
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Enhance retrieval callback hooks to include information on cosine similarity scores or other retrieval metrics
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[] | null | 4 | 2023-08-01T07:18:29 | 2023-12-01T20:41:39 | null |
NONE
| null |
### Feature request
Make cosine similarity (or other retrieval metric, e.g., Euclidean distance) between query and retrieved documents available at the `retriever` callback hook.
Currently, these scores are computed during retrieval and are discarded before they become available to the retrieval callback hook:
https://github.com/langchain-ai/langchain/blob/125ae6d9dec440e04a60f63f0f4fc8411f482df8/libs/langchain/langchain/vectorstores/base.py#L500
https://github.com/langchain-ai/langchain/blob/125ae6d9dec440e04a60f63f0f4fc8411f482df8/libs/langchain/langchain/schema/retriever.py#L174
### Motivation
I want to build a callback handler that enables LangChain users to visualize their data in [Phoenix](https://github.com/Arize-ai/phoenix), an open-source tool that provides debugging workflows for retrieval-augmented generation. At the moment, it is not possible to get the similarity scores between queries and retrieved documents out of LangChain's callback system, for example, when using the `RetrievalQA` chain. Here is an [example notebook](https://github.com/Arize-ai/phoenix/blob/main/tutorials/langchain_pinecone_search_and_retrieval_tutorial.ipynb) where I sub-class `Pinecone` to get out the similarity scores:
```
class PineconeWrapper(Pinecone):
query_text_to_document_score_tuples: Dict[str, List[Tuple[Document, float]]] = {}
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
document_score_tuples = super().similarity_search_with_score(
query=query,
k=k,
filter=filter,
namespace=namespace,
)
self.query_text_to_document_score_tuples[query] = document_score_tuples
return document_score_tuples
@property
def retrieval_dataframe(self) -> pd.DataFrame:
query_texts = []
document_texts = []
retrieval_ranks = []
scores = []
for query_text, document_score_tuples in self.query_text_to_document_score_tuples.items():
for retrieval_rank, (document, score) in enumerate(document_score_tuples):
query_texts.append(query_text)
document_texts.append(document.page_content)
retrieval_ranks.append(retrieval_rank)
scores.append(score)
return pd.DataFrame.from_dict(
{
"query_text": query_texts,
"document_text": document_texts,
"retrieval_rank": retrieval_ranks,
"score": scores,
}
)
```
I would like the LangChain callback system to support this use-case.
### Your contribution
I am willing to implement, test, and document this feature with guidance from the LangChain team. I am also happy to provide feedback on an implementation by the LangChain team by building an example callback handler using the enhancement retrieval hook functionality.
|
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PR_kwDOIPDwls5W381v
| 8,566 |
fix bug in AzureML Endpoint
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[] | null | 10 | 2023-08-01T07:00:01 | 2023-12-08T06:08:34 | null |
NONE
| null |
Description:
fix bug in azureml_endpoint.py
Issue:
when using the AzureMLOnlineEndpoint class without passing the content_formatter parameter, an AttributeError will occur. similarly, with classAzureMLChatOnlineEndpoint.
Details:
1. update ContentFormatterBase to inherit from BaseModel for Pydantic validation support.
2. add validators to AzureMLOnlineEndpoint and AzureMLChatOnlineEndpoint with informative error messages for enhanced usability.
Dependencies:
no dependencies required
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| 1,830,592,050 |
I_kwDOIPDwls5tHJ4y
| 8,565 |
Issue: How to use chain_type args in LLMchain?
|
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[] | null | 2 | 2023-08-01T06:55:55 | 2023-11-08T16:07:15 | 2023-11-08T16:07:14 |
NONE
| null |
### Issue you'd like to raise.
I am currently working on the task of **article rewriting**, and both the QA chain and Summarize chain are not quite suitable for my task.
Since I need to rewrite **multiple "lengthy"** articles, I would like to know how to use the chain_type in LLMchain. Alternatively, are there any other methods to achieve segment-level rewriting? Thank you.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5tG8be
| 8,564 |
Add callback support for embeddings
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[] | null | 2 | 2023-08-01T06:13:53 | 2023-11-07T16:06:08 | 2023-11-07T16:06:07 |
NONE
| null |
### Feature request
Add embedding support to the callback system. Here is one approach I have in mind.
- [ ] Add `on_embedding_start` method on `CallbackManagerMixin` in `libs/langchain/langchain/callbacks/base.py`.
- [ ] Implement `EmbeddingManagerMixin` with `on_embedding_end` and `on_embedding_error` methods in `libs/langchain/langchain/callbacks/base.py`.
- [ ] Add embedding callback hook to `Embeddings` abstract base class in `libs/langchain/langchain/embeddings/base.py`.
- [ ] Tweak concrete embeddings implementations in `libs/langchain/langchain/embeddings` as necessary.
One minimally invasive approach would be:
- Implement concrete `embed_documents`, `embed_query`, `aembed_documents`, and `aembed_query` methods on the abstract `Embeddings` base class that contain the embeddings callback hook. Add abstract methods `_embed_documents` and `_embed_query` methods and unimplemented `_aembed_documents` and `_aembed_query` methods to the base class.
- Rename existing concrete implementations of `embed_documents`, `embed_query`, `aembed_documents`, and `aembed_query` to `_embed_documents`, `_embed_query`, `_aembed_documents`, and `_aembed_query`.
### Motivation
Embeddings are useful for LLM application monitoring and debugging. I want to build a callback handler that enables LangChain users to visualize their data in [Phoenix](https://github.com/Arize-ai/phoenix), an open-source tool that provides debugging workflows for retrieval-augmented generation. At the moment, it is not possible to get the query embeddings out of LangChain's callback system, for example, when using the `RetrievalQA` chain. Here is an [example notebook](https://github.com/Arize-ai/phoenix/blob/main/tutorials/langchain_pinecone_search_and_retrieval_tutorial.ipynb) where I sub-class `OpenAIEmbeddings` to get out the embedding data:
```
class OpenAIEmbeddingsWrapper(OpenAIEmbeddings):
"""
A wrapper around OpenAIEmbeddings that stores the query and document
embeddings.
"""
query_text_to_embedding: Dict[str, List[float]] = {}
document_text_to_embedding: Dict[str, List[float]] = {}
def embed_query(self, text: str) -> List[float]:
embedding = super().embed_query(text)
self.query_text_to_embedding[text] = embedding
return embedding
def embed_documents(self, texts: List[str], chunk_size: Optional[int] = 0) -> List[List[float]]:
embeddings = super().embed_documents(texts, chunk_size)
for text, embedding in zip(texts, embeddings):
self.document_text_to_embedding[text] = embedding
return embeddings
@property
def query_embedding_dataframe(self) -> pd.DataFrame:
return self._convert_text_to_embedding_map_to_dataframe(self.query_text_to_embedding)
@property
def document_embedding_dataframe(self) -> pd.DataFrame:
return self._convert_text_to_embedding_map_to_dataframe(self.document_text_to_embedding)
@staticmethod
def _convert_text_to_embedding_map_to_dataframe(
text_to_embedding: Dict[str, List[float]]
) -> pd.DataFrame:
texts, embeddings = map(list, zip(*text_to_embedding.items()))
embedding_arrays = [np.array(embedding) for embedding in embeddings]
return pd.DataFrame.from_dict(
{
"text": texts,
"text_vector": embedding_arrays,
}
)
```
I would like the LangChain callback system to support this use-case.
This feature has been [requested for TypeScript](https://github.com/hwchase17/langchainjs/issues/586) and has an [open PR](https://github.com/hwchase17/langchainjs/pull/1859). An additional motivation is to maintain parity with the TypeScript library.
### Your contribution
I am willing to implement, test, and document this feature with guidance from the LangChain team. I am also happy to provide feedback on an implementation by the LangChain team by building an example callback handler using the embeddings hook.
|
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I_kwDOIPDwls5tGz0O
| 8,563 |
Petals API Support
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[] | null | 2 | 2023-08-01T05:38:35 | 2023-11-28T11:01:45 | 2023-11-07T16:06:12 |
NONE
| null |
### Feature request
Currently, langchain supports petals, but I think we should also support using a petals API endpoint. https://github.com/petals-infra/chat.petals.dev
### Motivation
The idea here is that users don't need to run the base application on their system, and can just use the API directly.
I think this is useful as a developer for speed, and just testing things quickly. I also think it would be the easiest and most reliabile way for any langchain user to get access to a high quality LLM on low-spec hardware.
### Your contribution
I am happy to add this myself if I can be guided on what parts of the code need changed.
|
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PR_kwDOIPDwls5W3VnE
| 8,562 |
implement async exec for base.py
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[] | null | 2 | 2023-08-01T03:53:40 | 2023-09-21T04:00:41 | 2023-09-21T04:00:41 |
CONTRIBUTOR
| null |
Can we such a method for async? The return type is specified as `asyncio.Task[Dict[str, str]]` to represent the dictionary return type wrapped in an `asyncio.Task`. `Since _acall()` is an asynchronous method, any coroutine calls within it should be awaited. So, we use await when calling `run_manager.on_text()` to properly await the completion of the asynchronous operation.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5W3Lqx
| 8,560 |
Add enum rendering to api reference
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[] | null | 4 | 2023-08-01T02:50:39 | 2023-08-04T20:08:03 | 2023-08-04T20:08:02 |
COLLABORATOR
| null |
This is a hack to improve rendering of enums -- it's determining the type of
the class based on membership checks of its attributes. It does not feel like
the correct solution, but the correct solution seems very involved in this
case.
|
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Harrison/loader bug
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[] | null | 1 | 2023-08-01T01:17:09 | 2023-08-01T20:31:51 | 2023-08-01T20:31:50 |
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| 1,830,288,016 |
I_kwDOIPDwls5tF_qQ
| 8,558 |
Add metadata parameter(s) to SQLDatabase class
|
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[] | null | 2 | 2023-08-01T01:08:42 | 2023-11-07T16:06:18 | 2023-11-07T16:06:17 |
NONE
| null |
### Feature request
Most databases include comment metadata on both tables and columns in a table. It would be nice to be able to pass this additional context to the LLM to get a better response.
The implementation could include adding two parameters to the [SQLDatabase](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/utilities/sql_database.py) object. For example:
- `table_metadata_sql` (perhaps `table_comment_sql`) runs a query and expects a table with two columns `table_name` and `comment`
- `column_metadata_sql` (perhaps `column_comment_sql` runs a query and expects a table with three columns `table_name`, `col_name`, and `comment`
Perhaps these two params could be combined into a single `metadata_sql` which returns for column table with `table_name`, `table_comment`, `column_name`, and `column_comment`
### Motivation
The main motivation behind this is to provide the LLM additional context beyond the CREATE TABLE and sample rows. Although this will be a more costly request (more tokens), I believe in many instances will lead to better SQL being generated. I also want to encourage better documentation of database objects in the data warehouse or data lake.
### Your contribution
Happy to submit a PR for this. Please weigh in on any design decisions:
- how many parameters to use 1 or 2?
- where to include the comments in the prompt, part of the CREATE TABLE as SQL comments?
|
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PR_kwDOIPDwls5W251R
| 8,557 |
Fix combining runnable sequences
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[] | null | 1 | 2023-08-01T00:57:54 | 2023-08-01T01:17:47 | 2023-08-01T01:17:47 |
CONTRIBUTOR
| null |
Combining runnable sequences was dropping a step in the middle.
@nfcampos @baskaryan
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I_kwDOIPDwls5tF5MS
| 8,556 |
ValueError: Expected metadata value to be a str, int, or float, got [{'text': 'Git', 'url': '#git'}] which is a <class 'list'> when storing into Chroma vector stores using using element mode of UnstructuredMarkdownLoader
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[] | null | 14 | 2023-08-01T00:31:42 | 2024-01-15T08:37:43 | null |
NONE
| null |
### System Info
LangChain: 0.0.248
Python: 3.9.17
OS version: Linux 6.1.27-43.48.amzn2023.x86_64
### Who can help?
I will submit a PR for a solution to this problem
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code:
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.text_splitter import CharacterTextSplitter
def testElement():
loader = UnstructuredMarkdownLoader(
"filepath", mode="elements")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
split_docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(split_docs, embeddings)
```
Also need to have a link format in the markdown file to be load, for example:
```
- [Google Developer Documentation Style Guide](https://developers.google.com/style)
```
Error Message:
```
138 # isinstance(True, int) evaluates to True, so we need to check for bools separately
139 if not isinstance(value, (str, int, float)) or isinstance(value, bool):
--> 140 raise ValueError(
141 f"Expected metadata value to be a str, int, or float, got {value} which is a {type(value)}"
142 )
ValueError: Expected metadata value to be a str, int, or float, got [{'text': 'Git', 'url': '#git'}] which is a <class 'list'>
```
### Expected behavior
I expect to see the split documents loaded into Chroma, however, this raise error for not passing type check for metadata.
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PR_kwDOIPDwls5W2zis
| 8,555 |
Update openai_functions.py to handle KeyError: 'function_call' error
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[] | null | 3 | 2023-08-01T00:16:50 | 2023-08-07T18:30:07 | 2023-08-07T18:30:07 |
NONE
| null |
Description: Sometime when trying to custom use Output parsers for function call we get KeyError: 'function_call' when function_call key is not present in the response for a particular request. This exception handles that case.
<img width="1275" alt="Screenshot 2023-08-01 at 5 42 52 AM" src="https://github.com/langchain-ai/langchain/assets/108733252/a1031a80-9541-4fed-81e8-a3e60e288538">
Issue: N/A
Tag maintainer: @rlancemartin
Dependencies: None
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PR_kwDOIPDwls5W2yKq
| 8,554 |
Chatbots use case
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[] | null | 2 | 2023-08-01T00:10:53 | 2023-08-04T14:02:15 | 2023-08-04T14:02:14 |
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PR_kwDOIPDwls5W2wRZ
| 8,553 |
Rm RunTypeEnum
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[] | null | 1 | 2023-08-01T00:01:53 | 2023-08-01T06:32:08 | 2023-08-01T06:32:07 |
COLLABORATOR
| null |
We already support raw strings in the SDK but would like to deprecate client-side validation of run types. This removes its usage
|
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PR_kwDOIPDwls5W2u93
| 8,552 |
add example for memory
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PR_kwDOIPDwls5W2u0W
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AzureSearch Vector Store: Moving the usage of additional_fields into context of it's definition (bug fix from python error)
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[] | null | 2 | 2023-07-31T23:52:14 | 2023-08-01T00:25:58 | 2023-08-01T00:25:58 |
CONTRIBUTOR
| null |
Description: Using Azure Cognitive Search as a VectorStore. Calling the `add_texts` method throws an error if there is no metadata property specified. The `additional_fields` field is set in an `if` statement and then is used later outside the if statement. This PR just moves the declaration of `additional_fields` below and puts the usage of it in context.
Issue: https://github.com/langchain-ai/langchain/issues/8544
Tagging @rlancemartin, @eyurtsev as this is related to Vector stores.
`make format`, `make lint`, `make spellcheck`, and `make test` have been run
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PR_kwDOIPDwls5W2ruq
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(wip) add memory to sql
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[] | null | 4 | 2023-07-31T23:31:10 | 2023-10-02T23:28:53 | 2023-10-02T23:28:52 |
COLLABORATOR
| null |
@keenborder786 this adds it in a way where it is on the outermost chain (which i think is what is desired)
do you want to maybe work off this and add tests to this?
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PR_kwDOIPDwls5W2hp9
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📖 docs: Integration Cards
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[] | null | 2 | 2023-07-31T22:58:06 | 2023-08-30T16:15:22 | 2023-08-30T16:15:21 |
COLLABORATOR
| null |
- applied the `Integration Card` term to the `integration summary` pages. In the same manner, as HuggingFace uses a `Model Card` and `Dataset Card`, the `Integration Card` is used as a root to access all related info for this integration.
@baskaryan
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PR_kwDOIPDwls5W2WHJ
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docs: Update ArangoDB Colab URL
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CONTRIBUTOR
| null |
1-commit PR to update the Google Colab URL of the ArangoDB Graph QA Chain notebook
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API use case
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| false | null |
[] | null | 1 | 2023-07-31T21:55:27 | 2023-07-31T21:55:45 | 2023-07-31T21:55:45 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,830,035,282 |
I_kwDOIPDwls5tFB9S
| 8,544 |
Vector Store: Azure Cognitive Search :: add_texts throws an error if called with no metadata
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closed
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[] | null | 2 | 2023-07-31T21:13:43 | 2023-11-06T16:05:33 | 2023-11-06T16:05:33 |
CONTRIBUTOR
| null |
### System Info
Python: v3.11
Langchain: v0.0.248
### Who can help?
I'll submit a PR for it tonight, just wanted to get the Issue in before.
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [x] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Background
I'm using Azure Cognitive Search as my vector store. In `azuresearch`, if you call `addtexts` with no metadata, you get an exception
Steps to reproduce behavior:
1. create AzureSearch object
2. call `add_texts` without specifying the metadata parameter
3. You'll get an error
> UnboundLocalError: cannot access local variable 'additional_fields' where it is not associated with a value
### Expected behavior
No error occurs it adds the text to the `texts` parameter to the Vector store.
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PR_kwDOIPDwls5W2B8P
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Bagatur/runnable with fallbacks
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[] | null | 1 | 2023-07-31T21:02:44 | 2023-08-04T21:06:06 | 2023-08-04T21:06:05 |
COLLABORATOR
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| 1,830,021,223 |
I_kwDOIPDwls5tE-hn
| 8,542 |
Error: 'OpenAICallbackHandler' object has no attribute 'on_retry'`
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[] | null | 3 | 2023-07-31T21:01:43 | 2023-08-14T23:45:18 | 2023-08-14T23:45:18 |
NONE
| null |
### System Info
**LangChain:** 0.0.248
**Python:** 3.10.10
**OS version:** Linux 5.10.178-162.673.amzn2.x86_64
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
**Code:**
```
try:
with get_openai_callback() as cb:
llm_chain = LLMChain(llm=llm, prompt=prompt_main)
all_text = str(template) + str(prompt) + str(usescases) + str(transcript)
threshold = (llm.get_num_tokens(text=all_text) + 800)
dataframe_copy.loc[index, "Total Tokens"] = threshold
if int(threshold) <= 4000:
chatgpt_output = llm_chain.run({"prompt":prompt, "use_cases_dictionary":usescases, "transcript":transcript})
chatgpt_output = text_post_processing(chatgpt_output)
dataframe_copy.loc[index, "ChatGPT Output"] = chatgpt_output
dataframe_copy.loc[index, "Cost (USD)"] = cb.total_cost
else:
dataframe_copy.loc[index, "ChatGPT Output"] = " "
dataframe_copy.loc[index, "Cost (USD)"] = " "
except Exception as e:
dataframe_copy.loc[index, "ChatGPT Output"] = " "
dataframe_copy.loc[index, "Cost (USD)"] = " "
continue
```
**Error Message:**
`Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIError: Bad gateway. {"error":{"code":502,"message":"Bad gateway.","param":null,"type":"cf_bad_gateway"}} 502 {'error': {'code': 502, 'message': 'Bad gateway.', 'param': None, 'type': 'cf_bad_gateway'}} {'Date': 'Mon, 31 Jul 2023 20:24:53 GMT', 'Content-Type': 'application/json', 'Content-Length': '84', 'Connection': 'keep-alive', 'X-Frame-Options': 'SAMEORIGIN', 'Referrer-Policy': 'same-origin', 'Cache-Control': 'private, max-age=0, no-store, no-cache, must-revalidate, post-check=0, pre-check=0', 'Expires': 'Thu, 01 Jan 1970 00:00:01 GMT', 'Server': 'cloudflare', 'CF-RAY': '7ef889a50eaca7f3-SYD', 'alt-svc': 'h3=":443"; ma=86400'}.
Error in OpenAICallbackHandler.on_retry callback: 'OpenAICallbackHandler' object has no attribute 'on_retry'`
### Expected behavior
I went through the callback [documentation ](https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html) and yes the "on_retry" method wasn't included over there. So I guess the team needs to modify the core code for OpenAICallbackHandler because it's calling "on_retry" for some reason.
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| 1,829,945,862 |
PR_kwDOIPDwls5W1xNB
| 8,541 |
rfc: fallbacks on model
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[] | null | 1 | 2023-07-31T20:11:23 | 2023-07-31T23:13:46 | 2023-07-31T23:13:46 |
COLLABORATOR
| null |
https://dev.smith.langchain.com/public/dadc238d-0737-4211-a1da-1e4aa363548b/r
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PR_kwDOIPDwls5W1tVi
| 8,540 |
Update reduce_openapi_spec in spec.py to handle NULL value for 'docs'
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[] | null | 3 | 2023-07-31T20:00:39 | 2023-11-10T22:41:42 | 2023-11-10T22:41:42 |
NONE
| null |
@hinthornw
**Description**
When iterating through an openapi spec, if the value of `docs` is NULL within a get/post/patch or delete endpoint, then the code throws an error - docs not have attribute "get".
**Example**
An openapi compliant json spec where this is material fix - https://github.com/SpaceTradersAPI/api-docs/blob/main/reference/SpaceTraders.json
Code blob to load the file and check
```
from langchain.tools import OpenAPISpec
from langchain.agents.agent_toolkits.openapi.spec import reduce_openapi_spec
raw_spacetraders_spec = OpenAPISpec.from_file('Path/to/SpaceTraders.json') # load referenced JSON openapi spec
converted_spacetraders_spec = json.loads(raw_spacetraders_spec.json())
reduced_spacetraders_spec = reduce_openapi_spec(converted_spacetraders_spec)
# The above line throws an Attribute error because a null type doesn't have attribute 'get'
```
**Suggested Fix**
We can additionally check for docs being NULL as a part of the if statement. Thus, only if an endpoint is get/post/patch/delete AND the route is not null, do we store the tuple
<!-- 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
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- Memory: @hwchase17
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- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
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PR_kwDOIPDwls5W1k37
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Fix AgentExecutor tracing in LangSmith
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[] | null | 4 | 2023-07-31T19:38:07 | 2023-08-01T08:24:57 | 2023-08-01T08:24:57 |
CONTRIBUTOR
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Description: this PR fixes an issue when AgentExecutor run is not properly traced and uploaded to LangSmith.
Before: AgentExecutor hangs indefinitely in Pending mode, output & tokens & latency are not tracked to the system
After: everything works as expected
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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
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PR_kwDOIPDwls5W1i0r
| 8,538 |
fix web loader
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[] | null | 1 | 2023-07-31T19:31:34 | 2023-07-31T19:47:34 | 2023-07-31T19:47:33 |
COLLABORATOR
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PR_kwDOIPDwls5W1gpl
| 8,537 |
updates some spelling mistakes
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[] | null | 2 | 2023-07-31T19:23:41 | 2023-08-01T00:15:30 | 2023-08-01T00:15:29 |
CONTRIBUTOR
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Just updating some spelling / grammar issues in the documentation. No code changes.
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PR_kwDOIPDwls5W1fT9
| 8,536 |
Added Memory Parameter to SQL-Chain (Langchain Experimental)
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[] | null | 3 | 2023-07-31T19:18:55 | 2023-08-01T07:48:57 | 2023-08-01T07:48:53 |
CONTRIBUTOR
| null |
- Description: SQL Database Chain and Agent does not allow to pass in memory, therefore, added this feature with additional tests.
I had created a PR #7546 for this but chain has moved to langchain_experimental lib therefore created a new PR.
- Issue: #6918
- Tag maintainer: @hwchase17
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I_kwDOIPDwls5tEShJ
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DOC: Missing documentation langchain.utilities.SQLDatabase
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[] | null | 1 | 2023-07-31T19:01:18 | 2023-11-06T16:05:38 | 2023-11-06T16:05:38 |
NONE
| null |
### Issue with current documentation:
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.sql_database.SQLDatabase.html
### Idea or request for content:
Documentation is missing, although i can import this class in the lateste version.
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PR_kwDOIPDwls5W1Y4S
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Rely less on positional arg order in subclasses of vector store when calling async methods
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[] | null | 1 | 2023-07-31T18:56:32 | 2023-07-31T19:13:13 | 2023-07-31T19:13:12 |
COLLABORATOR
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<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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Maintainer responsibilities:
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PR_kwDOIPDwls5W1Y1m
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fix error in chroma docker instructions
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[] | null | 1 | 2023-07-31T18:56:23 | 2023-07-31T23:34:26 | 2023-07-31T23:32:53 |
CONTRIBUTOR
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This makes the Chroma instructions for Docker work!
https://python.langchain.com/docs/integrations/vectorstores/chroma#basic-example-using-the-docker-container
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PR_kwDOIPDwls5W1WB-
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conversational retrieval chain in lcel
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PR_kwDOIPDwls5W1PHV
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Updated docs on Vertex AI going GA
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[] | null | 2 | 2023-07-31T18:23:24 | 2023-08-01T00:15:04 | 2023-08-01T00:15:04 |
CONTRIBUTOR
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#8074
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PR_kwDOIPDwls5W1OMc
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doc(prompts): Follow up on broken Prompt Sublink pages
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[] | null | 1 | 2023-07-31T18:21:30 | 2023-07-31T23:46:14 | 2023-07-31T23:46:13 |
CONTRIBUTOR
| null |
- Description: Follow up of #8478
- Issue: #8477
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: [@BharatR123](twitter.com/BharatR123)
The links were still broken after #8478 and sadly the issue was not caught with either the Vercel app build and `make docs_linkcheck`
<!-- 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.
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2. an example notebook showing its use.
Maintainer responsibilities:
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PR_kwDOIPDwls5W1KP7
| 8,529 |
Added formatting system message option to get_buffer_string
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[] | null | 5 | 2023-07-31T18:08:48 | 2023-11-29T01:51:02 | 2023-11-29T01:51:02 |
CONTRIBUTOR
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#8427
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PR_kwDOIPDwls5W1GSl
| 8,528 |
fix issue #8357 by making json backtick regex greedy
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[] | null | 3 | 2023-07-31T17:57:23 | 2023-08-03T09:04:04 | 2023-07-31T23:36:57 |
CONTRIBUTOR
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- Description: Markdown code blocks in json response should not break the parser
- Issue: #8357
@baskaryan @hinthornw
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Cannot import name 'HumanMessage' from 'langchain.schema'
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[] | null | 8 | 2023-07-31T17:47:47 | 2023-08-01T16:35:32 | 2023-08-01T16:35:07 |
NONE
| null |
### System Info
LangChain ==0.0.248
Platform Windows 10
Python == 3.10.9
Whenever I use from langchain.schema import HumanMessage I get error,
**ImportError: cannot import name 'HumanMessage' from 'langchain.schema'**
I have tried updating llama-index but still getting same error
@agola11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="key")
from langchain.schema import HumanMessage
### Expected behavior
It should work
|
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| 1,829,703,530 |
I_kwDOIPDwls5tDw9q
| 8,526 |
how to Stream Search tool responses
|
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[] | null | 2 | 2023-07-31T17:26:05 | 2023-11-16T16:06:41 | 2023-11-16T16:06:40 |
NONE
| null |
### Issue you'd like to raise.
search = SerpAPIWrapper()
tools = define_tools(llm_chain,search)
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
# return_direct = True
),
]
prompt = CustomPromptTemplate(
template=template,
tools=tools,
input_variables=["input", "intermediate_steps", "chat_history"]
)
output_parser = CustomOutputParser()
llm = ChatOpenAI(temperature=0, model_name = "gpt-4-0613", streaming = True, callbacks = [MyCustomHandler(new_payload=new_payload)])
llm_chain = LLMChain(llm=llm, prompt=prompt)
agent = LLMSingleActionAgent(llm_chain=llm_chain, output_parser=output_parser, stop=["\nObservation:"], allowed_tools=tools)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent,
tools=tools,
verbose=True, memory = memory, early_stopping_method='generate')
### Suggestion:
_No response_
|
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| 1,829,681,157 |
PR_kwDOIPDwls5W03bN
| 8,525 |
Add serialisable support for Replicate
|
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[] | null | 1 | 2023-07-31T17:09:33 | 2023-08-11T18:35:22 | 2023-08-11T18:35:22 |
CONTRIBUTOR
| null | null |
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| 1,829,657,483 |
I_kwDOIPDwls5tDluL
| 8,524 |
Issue: ImportError: cannot import name 'SQLDatabaseChain' from 'langchain'
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[] | null | 8 | 2023-07-31T16:53:56 | 2023-09-22T01:01:29 | 2023-09-22T01:01:28 |
NONE
| null |
### Issue you'd like to raise.
I am just trying to from langchain import LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain
Traceback (most recent call last):
File "/home/huangj/01_LangChain/LangChainCHSample/05_05_SQL_Chain.py", line 1, in <module>
from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
ImportError: cannot import name 'SQLDatabaseChain' from 'langchain' (/home/huangj/01_LangChain/langchain_env/lib/python3.8/site-packages/langchain/__init__.py)
### Suggestion:
_No response_
|
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| 1,829,649,522 |
PR_kwDOIPDwls5W0wck
| 8,523 |
Update prompts to only return SQL command
|
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[] | null | 4 | 2023-07-31T16:48:56 | 2023-08-23T08:27:16 | 2023-08-15T12:41:48 |
NONE
| null |
v0.0.248 included new documentation for the sql integration (#8442). I had been struggling already to get sql integration working right, so as soon as I saw that I followed it. Using the `create_sql_query_chain` function resulted in the following error (it's marked as a warning, but sqlite still doesn't do anything with it:
> Warning: You can only execute one statement at a time.
I figured out this is because the prompt is still telling the LLM to try executing the sql to get an answer. In my case (on a local LLM and using OpenAI). So using the code:
```python
chain = create_sql_query_chain(llm, db, prompt=pipeline_prompt, k=10)
response = chain.invoke({"question": "How many records are in the service_logs table?"})
print(f"==={response}===") # Using === to show it's all one string
```
I got:
> ===SELECT COUNT(*) FROM "service_logs";
>
> SQLResult:
> Answer: 3===
I was able to fix this by removing the instruction to execute the query from the response, as well as the template that shows the query, answer, etc.
Note: I am only able to test this on sqlite. I don't have access to the other databases.
Tag: @hwchase17 @baskaryan
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| 1,829,461,763 |
PR_kwDOIPDwls5W0GOv
| 8,521 |
Fix bug in SPARQL intent selection
|
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[] | null | 6 | 2023-07-31T15:24:04 | 2023-09-07T20:37:10 | 2023-09-05T21:37:02 |
CONTRIBUTOR
| null |
- Description: Fix bug in SPARQL intent selection
- Issue: After the change in #7758 the intent is always set to "UPDATE". Indeed, if the answer to the prompt contains only "SELECT" the `find("SELECT")` operation returns a higher value w.r.t. `-1` returned by `find("UPDATE")`.
- Dependencies: None,
- Tag maintainer: @baskaryan @aditya-29
- Twitter handle: @mario_scrock
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PR_kwDOIPDwls5Wzoha
| 8,519 |
🐛 Docs Fixes [2 one-liners, examples broken]
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[] | null | 2 | 2023-07-31T14:35:50 | 2023-08-02T20:39:41 | 2023-08-02T20:39:41 |
CONTRIBUTOR
| null |
## Description:
1)Map reduce example in docs is missing an important import statement. Figured other people would benefit from being able to copy 🍝 the code.
2)RefineDocumentsChain example also broken.
## Issue:
None
## Dependencies:
None. One liner.
## Tag maintainer:
@baskaryan
## Twitter handle:
I mean, it's a one line fix lol. But @will_thompson_k is my twitter handle.
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bump 248
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Add OpenAI Whisper Audio Loader
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| null |
- Description: Introducing an ability to load a transcription document of audio file using OpenAI Whisper
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5WzNRH
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Bugfix for async requests made through plugins failing making calls through agent.
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[] | null | 3 | 2023-07-31T13:32:11 | 2023-12-21T09:27:27 | null |
NONE
| null |
This auth parameter being passed to the method results in a duplicate keyword argument since auth doesn't exist on the parameters.
This is an issue fix for https://github.com/langchain-ai/langchain/issues/7953
It was introduced as part of this change.
https://github.com/langchain-ai/langchain/commit/663b0933e488383e6a9bc2a04b4b1cf866a8ea94
@agola11 @baskaryan
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If you're adding a new integration, please include:
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2. an example notebook showing its use.
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Bugfix for async requests made through plugins failing making calls through agent.
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NONE
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This auth parameter being passed to the method results in a duplicate keyword argument since auth doesn't exist on the parameters.
This is an issue fix for #7953
It was introduced as part of this change.
https://github.com/langchain-ai/langchain/commit/663b0933e488383e6a9bc2a04b4b1cf866a8ea94
@agola11 @baskaryan
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Replace this comment with:
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- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
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MMR Support for Matching Engine
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[] | null | 1 | 2023-07-31T13:08:29 | 2023-11-06T16:05:43 | 2023-11-06T16:05:42 |
NONE
| null |
### Feature request
MMR Support for Vertex AI Matching Engine
### Motivation
The results of Matching Engine are not optimal
### Your contribution
MMR Support for Vertex AI Matching Engine
|
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SQL use case docs
|
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[] | null | 5 | 2023-07-31T13:01:49 | 2023-08-08T10:30:19 | 2023-08-08T10:30:19 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5tBIMl
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Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
|
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[] | null | 4 | 2023-07-31T11:47:12 | 2023-11-07T16:06:23 | 2023-11-07T16:06:22 |
NONE
| null |
### Issue you'd like to raise.
when i want to use openai, i install it with command"pip3 install openai", but i really wanti to use chatglm, when i run "pip3 install chartglm ", it does not work",please help to answer the question
### Suggestion:
_No response_
|
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I_kwDOIPDwls5tAR1w
| 8,511 |
Add memory to an Agent using RedisChatMessageHistory with sentinels throwing an error
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[] | null | 3 | 2023-07-31T09:34:40 | 2023-11-08T16:07:19 | 2023-11-08T16:07:19 |
NONE
| null |
### System Info
I am trying to use `RedisChatMessageHistory` within an agent, but I'm encountering this error:
My redis url I am using looks like that:
`redis_url = "redis+sentinel://:password@host:port/service_name/db"`
### Who can help?
@hwchase17
### 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
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
message_history = RedisChatMessageHistory(url=redis_url, ttl=600, session_id="test_id")
message_history.add_user_message("hi!")
message_history.add_ai_message("whats up?")
memory = ConversationBufferWindowMemory(memory_key="memory", chat_memory=message_history, return_messages=True, k=15)
```
### Expected behavior
See on Redis the messages I added in my code.
|
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I_kwDOIPDwls5tARso
| 8,510 |
Issue: output_parsers to work with lists of custom objects?
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[] | null | 2 | 2023-07-31T09:34:19 | 2023-08-01T14:42:01 | 2023-08-01T14:42:00 |
NONE
| null |
### Issue you'd like to raise.
So It's possible to parse "complex" objects with
```python
response_schemas = [
ResponseSchema(name="date", description="The date of the event"),
ResponseSchema(name="place", description="The place where the event will happen"),
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
```
and
```python
class Pair_of_numbers(BaseModel):
L: int = Field(description="A number")
R: int = Field(description="A number that ends in 2, 3 or 4")
@validator("R")
def question_ends_with_question_mark(cls, field):
if int(str(field)[-1]) not in [2, 3, 4]:
raise ValueError("No bro :(")
return field
parser = PydanticOutputParser(pydantic_object=Pair_of_numbers)
```
But I can't seem to find information on how to parse a list of custom objects/dicts? There is the [list parser](https://python.langchain.com/docs/modules/model_io/output_parsers/comma_separated), but this is just for simple strings. It's possible there is a way to achieve this wrapping a pydantic `BaseModel` in a python `List`, but so far I've had no luck 😫
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5WxZ3T
| 8,509 |
add 3 property types in metadata for notiondb loader
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[] | null | 2 | 2023-07-31T08:33:31 | 2023-09-28T00:38:05 | 2023-09-28T00:38:05 |
CONTRIBUTOR
| null |
### Description:
NotionDB supports a number of common property types. I have found three common types that are not included in notiondb loader. When programs loaded them with notiondb, which will cause some metadata information not to be passed to langchain. Therefore, I added three common types:
- date
- created_time
- last_edit_time.
### Issue:
no
### Dependencies:
No dependencies added :)
### Tag maintainer:
@rlancemartin, @eyurtsev
### Twitter handle:
@BJTUTC
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PR_kwDOIPDwls5WxZoH
| 8,508 |
Add OpenAI Whisper Audio Loader
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| false | null |
[] | null | 1 | 2023-07-31T08:32:47 | 2023-07-31T08:33:11 | 2023-07-31T08:33:11 |
CONTRIBUTOR
| null |
Introducing an ability to load an audi transcription document via OpenAI Whisper.
<!-- 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_kwDOIPDwls5s_abV
| 8,506 |
DOC: why remove concepts.md from the latest document page
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[] | null | 1 | 2023-07-31T07:18:44 | 2023-11-06T16:05:58 | 2023-11-06T16:05:57 |
NONE
| null |
### Issue with current documentation:
I did not find this page on https://python.langchain.com/docs/get_started/introduction.html
I'm just wondering, what's the motive ？
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5s_Uv3
| 8,505 |
langchain/document_loaders/web_base.py
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[] | null | 2 | 2023-07-31T07:02:32 | 2023-11-06T16:06:03 | 2023-11-06T16:06:02 |
NONE
| null |
### System Info
langchain/document_loaders/web_base.py > works for me only when i change:
```
return await response.text()
```
with:
```
body = await response.read()
return body.decode('utf-8', errors='ignore')
```
otherwise:
der Code produziert leider einen Fehler:
/home/codespace/.py
thon/current/bin/python3 /workspaces/b3rn_zero_ai/notebooks/ignite_vectorstore.py
Fetching pages: 13%|###8 | 33/256 [00:03<00:19, 11.18it/s]Traceback (most recent call last):
File "/workspaces/b3rn_zero_ai/notebooks/ignite_vectorstore.py", line 68, in <module>
documents = loader.load()
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/document_loaders/sitemap.py", line 142, in load
results = self.scrape_all([el["loc"].strip() for el in els if "loc" in el])
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/document_loaders/web_base.py", line 168, in scrape_all
results = asyncio.run(self.fetch_all(urls))
File "/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py", line 35, in run
return loop.run_until_complete(task)
File "/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py", line 90, in run_until_complete
return f.result()
File "/home/codespace/.python/current/lib/python3.10/asyncio/futures.py", line 201, in result
raise self._exception.with_traceback(self._exception_tb)
File "/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py", line 232, in __step
result = coro.send(None)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/document_loaders/web_base.py", line 148, in fetch_all
return await tqdm_asyncio.gather(
File "/home/codespace/.python/current/lib/python3.10/site-packages/tqdm/asyncio.py", line 79, in gather
res = [await f for f in cls.as_completed(ifs, loop=loop, timeout=timeout,
File "/home/codespace/.python/current/lib/python3.10/site-packages/tqdm/asyncio.py", line 79, in <listcomp>
res = [await f for f in cls.as_completed(ifs, loop=loop, timeout=timeout,
File "/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py", line 571, in _wait_for_one
return f.result() # May raise f.exception().
File "/home/codespace/.python/current/lib/python3.10/asyncio/futures.py", line 201, in result
raise self._exception.with_traceback(self._exception_tb)
File "/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py", line 234, in __step
result = coro.throw(exc)
File "/home/codespace/.python/current/lib/python3.10/site-packages/tqdm/asyncio.py", line 76, in wrap_awaitable
return i, await f
File "/home/codespace/.python/current/lib/python3.10/asyncio/futures.py", line 285, in __await__
yield self # This tells Task to wait for completion.
File "/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py", line 304, in __wakeup
future.result()
File "/home/codespace/.python/current/lib/python3.10/asyncio/futures.py", line 201, in result
raise self._exception.with_traceback(self._exception_tb)
File "/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py", line 232, in __step
result = coro.send(None)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/document_loaders/web_base.py", line 136, in _fetch_with_rate_limit
return await self._fetch(url)
File "/home/codespace/.python/current/lib/python3.10/site-packages/langchain/document_loaders/web_base.py", line 120, in _fetch
return await response.text()
File "/home/codespace/.python/current/lib/python3.10/site-packages/aiohttp/client_reqrep.py", line 1086, in text
return self._body.decode( # type: ignore[no-any-return,union-attr]
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 11: invalid start byte
Fetching pages: 15%|####4 | 38/256 [00:04<00:23, 9.25it/s]
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I tried to make embedding from a website in "french" language.
### Expected behavior
we need a solution when : UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb5 in position 11: invalid start byte
|
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| 1,828,472,740 |
I_kwDOIPDwls5s_Eek
| 8,503 |
How to use langchain to create my own databricks dolly chat robot
|
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[] | null | 0 | 2023-07-31T06:08:19 | 2023-07-31T06:14:31 | 2023-07-31T06:14:31 |
NONE
| null |
I have a operation manual pdf file about a website and i want to use langchain let dolly can responed the question about the website
below is my code:
```
from langchain.embeddings import HuggingFaceEmbeddings
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import HuggingFacePipeline
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain.prompts import PromptTemplate
import torch
hf_embed = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
from google.colab import drive
drive.mount('/content/gdrive', force_remount=True)
root_dir = "/content/gdrive/My Drive/"
reader = PdfReader('/content/gdrive/My Drive/data/operation Manual.pdf')
raw_text = ''
for i, page in enumerate(reader.pages):
text = page.extract_text()
if text:
raw_text += text
text_splitter = CharacterTextSplitter(
separator = "\n",
chunk_size = 1000,
chunk_overlap = 200,
length_function = len,
)
texts = text_splitter.split_text(raw_text)
docsearch = FAISS.from_texts(texts, hf_embed)
model_name = "databricks/dolly-v2-3b"
instruct_pipeline = pipeline(model=model_name, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto",
return_full_text=True, max_new_tokens=256, top_p=0.95, top_k=50)
hf_pipe = HuggingFacePipeline(pipeline=instruct_pipeline)
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
query = "I forgot my login password."
docs = docsearch.similarity_search(query)
chain = load_qa_chain(llm = hf_pipe, chain_type="stuff", prompt=PROMPT)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)
```
I am refer to the article in the databricks website [https://www.databricks.com/resources/demos/tutorials/data-science-and-ai/build-your-chat-bot-with-dolly?itm_data=demo_center]
the output is not the answer that in the operation Manual and it have to spend much time (about 2.5 hours) to output answer. where did i do wrong?
|
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PR_kwDOIPDwls5WwdoV
| 8,502 |
Async Recursive URL loader
|
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CONTRIBUTOR
| null |
Description: This PR improves the function of recursive_url_loader, such as limiting the depth of the access, and customizable extractors(from the raw webpage to the text of the Document object), so that users can use other tools to extract the webpage. This PR also includes the document and test for the new loader.
Old PR closed due to project structure change. #7756
Because socket requests are not allowed, the old unit test was removed.
Issue: N/A
Dependencies: asyncio, aiohttp
Tag maintainer: @rlancemartin
Twitter handle: @ Zend_Nihility
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| 1,828,319,217 |
PR_kwDOIPDwls5WwLwL
| 8,500 |
Fixed typo: papaer -> paper
|
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[] | null | 2 | 2023-07-31T03:15:18 | 2023-07-31T04:08:12 | 2023-07-31T04:08:12 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5s-MY_
| 8,499 |
API Key Leakage in the PromptLayerChatOpenAI Model
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[] | null | 1 | 2023-07-31T02:14:44 | 2023-11-02T08:48:45 | 2023-11-02T08:48:37 |
NONE
| null |
### System Info
**Langchain**:0.0.247
**Python**:3.10.5
**System**: macOS 13.4 arm64
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
There is the minimal produce demo
```python
from langchain.chat_models import PromptLayerChatOpenAI
from dotenv import load_dotenv
load_dotenv()
import promptlayer
from langchain.schema import HumanMessage
promptlayer.api_key = "xxxxxxxxxxx"
llm = PromptLayerChatOpenAI(model="gpt-3.5-turbo", verbose=True, pl_tags=["test"])
print(llm([HumanMessage(content="I am a cat and I want")]))
```
I found it on the [**PromptLayer**](https://promptlayer.com/)
<img width="978" alt="image" src="https://github.com/langchain-ai/langchain/assets/3949397/770f6c3f-ec8a-42df-967d-8e207b3208d4">
### Expected behavior
Why is the API key carried in this PromptLayer? This is insecure, it should be removed. Then, I discovered the root of the problem
The paras are generated from this function (**_generate** and **_agenerate**).
And I got it inherit from **ChatOpenAI** model function __create_message_dicts_ and __client_params_, then I found the api_key
https://github.com/langchain-ai/langchain/blob/08f5e6b8012f5eda2609103f33676199a3781a15/libs/langchain/langchain/chat_models/openai.py#L487
but the api_key is necessary for ChatOpenAI model to get the result.
so it should be changed with this two line
https://github.com/langchain-ai/langchain/blob/08f5e6b8012f5eda2609103f33676199a3781a15/libs/langchain/langchain/chat_models/promptlayer_openai.py#L59
https://github.com/langchain-ai/langchain/blob/08f5e6b8012f5eda2609103f33676199a3781a15/libs/langchain/langchain/chat_models/promptlayer_openai.py#L98
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| 8,498 |
Loading documents from a YouTube url doesn't work.
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[] | null | 2 | 2023-07-30T20:47:14 | 2023-07-31T06:48:09 | 2023-07-31T06:48:09 |
CONTRIBUTOR
| null |
### System Info
LangChain 0.0.247, Python 3.11, Linux.
### Who can help?
@rlancemartin
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Described [here](https://python.langchain.com/docs/integrations/document_loaders/youtube_audio).
```python
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import OpenAIWhisperParser
from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
# Two Karpathy lecture videos
urls = ["https://youtu.be/kCc8FmEb1nY", "https://youtu.be/VMj-3S1tku0"]
# Directory to save audio files
save_dir = "~/Downloads/YouTube"
# Transcribe the videos to text
loader = GenericLoader(YoutubeAudioLoader(urls, save_dir), OpenAIWhisperParser())
docs = loader.load()
print(docs)
```
I get an empty list.
```log
[youtube] Extracting URL: https://youtu.be/kCc8FmEb1nY
[youtube] kCc8FmEb1nY: Downloading webpage
[youtube] kCc8FmEb1nY: Downloading ios player API JSON
[youtube] kCc8FmEb1nY: Downloading android player API JSON
[youtube] kCc8FmEb1nY: Downloading m3u8 information
[info] kCc8FmEb1nY: Downloading 1 format(s): 140
[download] Destination: /home/dm/Downloads/YouTube/Let's build GPT： from scratch, in code, spelled out..m4a
[download] 100% of 107.73MiB in 00:00:11 at 9.19MiB/s
[FixupM4a] Correcting container of "/home/dm/Downloads/YouTube/Let's build GPT： from scratch, in code, spelled out..m4a"
[ExtractAudio] Not converting audio /home/dm/Downloads/YouTube/Let's build GPT： from scratch, in code, spelled out..m4a; file is already in target format m4a
[youtube] Extracting URL: https://youtu.be/VMj-3S1tku0
[youtube] VMj-3S1tku0: Downloading webpage
[youtube] VMj-3S1tku0: Downloading ios player API JSON
[youtube] VMj-3S1tku0: Downloading android player API JSON
[youtube] VMj-3S1tku0: Downloading m3u8 information
[info] VMj-3S1tku0: Downloading 1 format(s): 140
[download] Destination: /home/dm/Downloads/YouTube/The spelled-out intro to neural networks and backpropagation： building micrograd.m4a
[download] 100% of 135.08MiB in 00:00:13 at 9.65MiB/s
[FixupM4a] Correcting container of "/home/dm/Downloads/YouTube/The spelled-out intro to neural networks and backpropagation： building micrograd.m4a"
[ExtractAudio] Not converting audio /home/dm/Downloads/YouTube/The spelled-out intro to neural networks and backpropagation： building micrograd.m4a; file is already in target format m4a
[]
```
### Expected behavior
A non-empty list of documents is expected.
|
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| 1,828,079,937 |
PR_kwDOIPDwls5WvYau
| 8,497 |
Add StreamlitChatMessageHistory
|
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[] | null | 10 | 2023-07-30T20:35:07 | 2023-08-03T07:45:16 | 2023-08-01T21:28:15 |
CONTRIBUTOR
| null |
Add a StreamlitChatMessageHistory class that stores chat messages in [Streamlit's Session State](https://docs.streamlit.io/library/api-reference/session-state).
Note: The integration test uses a currently-experimental Streamlit testing framework to simulate the execution of a Streamlit app. Marking this PR as draft until I confirm with the Streamlit team that we're comfortable supporting it.
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PR_kwDOIPDwls5WvX-b
| 8,496 |
router runnable
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[] | null | 1 | 2023-07-30T20:24:54 | 2023-07-31T18:07:11 | 2023-07-31T18:07:10 |
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| 1,828,038,702 |
I_kwDOIPDwls5s9agu
| 8,494 |
RuntimeError: asyncio.run() cannot be called from a running event loop
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[] | null | 6 | 2023-07-30T19:10:58 | 2023-12-13T16:07:53 | 2023-12-13T16:07:52 |
NONE
| null |
### System Info
python 3.11
### Who can help?
@rlancemartin
File langchain/document_loaders/async_html.py:136--> results = asyncio.run(self.fetch_all(self.web_paths))
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.retrievers.web_research import WebResearchRetriever
import pinecone
import os
from langchain.vectorstores import Pinecone
from langchain.utilities import GoogleSearchAPIWrapper
from common_util.llms import LLM_FACT, EMBEDDINGS
from dotenv import load_dotenv
from common_util.namespaceEnum import PineconeNamespaceEnum
load_dotenv()
index_name = "langchain-demo"
pinecone.init(api_key=os.getenv("PINECONE_API_KEY"), environment=os.getenv("PINECONE_ENV"))
# Vectorstore
vectorstore = Pinecone.from_existing_index(index_name, EMBEDDINGS, namespace=PineconeNamespaceEnum.WEB_SEARCH.value)
# LLM
llm = LLM_FACT
# Search
os.environ["GOOGLE_CSE_ID"] = os.getenv("GOOGLE_CSE_ID")
os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
search = GoogleSearchAPIWrapper()
web_research_retriever = WebResearchRetriever.from_llm(
vectorstore=vectorstore,
llm=llm,
search=search,
)
from langchain.chains import RetrievalQAWithSourcesChain
user_input = "How do LLM Powered Autonomous Agents work?"
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=web_research_retriever)
result = qa_chain({"question": user_input})
result
### Expected behavior
should search the web async
|
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| 8,493 |
Agent stopped due to iteration limit or time limit
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[] | null | 6 | 2023-07-30T17:37:50 | 2023-11-01T05:28:25 | null |
NONE
| null |
### Issue you'd like to raise.
I am getting "Agent stopped due to iteration limit or time limit" as the error , As my max_iteration is also 15 then too i am getting this error.
I need some particular output from the model.
Following is my code
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent,
tools=tools,
verbose=True, memory = memory, max_iterations = 15)
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5WvBbd
| 8,492 |
Added Proper Documentation for `faiss-gpu` Installation
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[] | null | 1 | 2023-07-30T15:04:27 | 2023-07-30T20:24:30 | 2023-07-30T20:24:30 |
CONTRIBUTOR
| null |
### Description
In the LangChain Documentation and Comments, I've Noticed that `pip install faiss` was mentioned, instead of `pip install faiss-gpu`, since installing `pip install faiss` results in an error. I've gone ahead and updated the Documentation, and `faiss.ipynb`. This Change will ensure ease of use for the end user, trying to install `faiss-gpu`.
### Issue:
Documentation / Comments Related.
### Dependencies:
No Dependencies we're changed only updated the files with the wrong reference.
### Tag maintainer:
@rlancemartin, @eyurtsev (Thank You for your contributions :smile: )
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| 1,827,955,183 |
PR_kwDOIPDwls5Wu_HK
| 8,490 |
bump experimental to 008
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PR_kwDOIPDwls5Wu6di
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Re-add __add__ functionality for messages (revert #8245)
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[] | null | 1 | 2023-07-30T13:07:04 | 2023-08-02T14:51:46 | 2023-08-02T14:51:44 |
COLLABORATOR
| null |
This PR reverts #8245, so `__add__` is defined on base messages.
Resolves issue: https://github.com/langchain-ai/langchain/issues/8472
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PR_kwDOIPDwls5WuxCT
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Add new run types for Runnables
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[] | null | 1 | 2023-07-30T10:27:39 | 2023-08-01T11:56:41 | 2023-08-01T11:56:40 |
COLLABORATOR
| null |
- allow overriding run_type in on_chain_start
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