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https://api.github.com/repos/langchain-ai/langchain/issues/6964
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| 1,782,096,897 |
PR_kwDOIPDwls5UTwgR
| 6,964 |
Improve the alibaba cloud opensearch vector store documentation
|
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[] | null | 1 | 2023-06-30T08:12:18 | 2023-07-06T13:47:49 | 2023-07-06T13:47:49 |
CONTRIBUTOR
| null |
Based on user feedback, we have improved the Alibaba Cloud OpenSearch vector store documentation.
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| 6,963 |
Adding support for URL Parameters in RequestsWrapper
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[] | null | 2 | 2023-06-30T07:58:56 | 2023-10-15T16:05:43 | 2023-10-15T16:05:42 |
NONE
| null |
### Feature request
I'd love to be able to extend the request URL with parameters.
Currently I can only provide request headers. This does not cover my use case.
### Motivation
Some APIs don't authenticate via headers and instead use URL parameters to provide API keys and Tokens.
Example: [Trello API](https://developer.atlassian.com/cloud/trello/rest/api-group-actions/#api-group-actions)
View first code snippet there for the action and you'll see.
curl --request GET \
--url 'https://api.trello.com/1/actions/{id}?key=APIKey&token=APIToken'
### Your contribution
I would need some handholding to create my first contribution here due to the testing suite but I've got some code I've tested locally.
```python
class Requests(BaseModel):
"""Wrapper around requests to handle auth and async.
The main purpose of this wrapper is to handle authentication (by saving
headers) and enable easy async methods on the same base object.
"""
headers: Optional[Dict[str, str]] = None
aiosession: Optional[aiohttp.ClientSession] = None
url_params: Optional[Dict[str, str]] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
def get(self, url: str, **kwargs: Any) -> requests.Response:
"""GET the URL and return the text."""
if self.url_params:
url = url + '&'.join([f'{k}={v}' for k, v in self.url_params.items()])
return requests.get(url, headers=self.headers, **kwargs)
def post(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""POST to the URL and return the text."""
if self.url_params:
url = url + '&'.join([f'{k}={v}' for k, v in self.url_params.items()])
return requests.post(url, json=data, headers=self.headers, **kwargs)
def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""PATCH the URL and return the text."""
if self.url_params:
url = url + '&'.join([f'{k}={v}' for k, v in self.url_params.items()])
return requests.patch(url, json=data, headers=self.headers, **kwargs)
def put(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""PUT the URL and return the text."""
if self.url_params:
url = url + '&'.join([f'{k}={v}' for k, v in self.url_params.items()])
return requests.put(url, json=data, headers=self.headers, **kwargs)
def delete(self, url: str, **kwargs: Any) -> requests.Response:
"""DELETE the URL and return the text."""
if self.url_params:
url = url + '&'.join([f'{k}={v}' for k, v in self.url_params.items()])
return requests.delete(url, headers=self.headers, **kwargs)
```
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| 1,782,073,084 |
PR_kwDOIPDwls5UTrUP
| 6,962 |
release v220
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[] | null | 1 | 2023-06-30T07:55:27 | 2023-06-30T13:52:10 | 2023-06-30T13:52:09 |
COLLABORATOR
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https://api.github.com/repos/langchain-ai/langchain/issues/6961
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| 1,782,060,256 |
I_kwDOIPDwls5qOBTg
| 6,961 |
Enable CSVLoader to take list of columns as source_column (or metadata)
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[] | null | 6 | 2023-06-30T07:44:50 | 2023-12-19T00:50:23 | 2023-12-19T00:50:22 |
NONE
| null |
### Feature request
Please update the CSVLoader to take list of columns as source_column.
### Motivation
This update will be be helpful, if the documents are embedded and used with vectordbs like Qdrant. As vectordbs like Qdrant offers filtering option, if the metadata is of Document object is used as payload then it will provide a great enhancement.
### Your contribution
No
|
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| 1,782,038,070 |
PR_kwDOIPDwls5UTj1b
| 6,960 |
Bagatur/openllm ensure available
|
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| 1,782,008,769 |
I_kwDOIPDwls5qN0vB
| 6,959 |
i try to connect mysql database,but it give me a error about llm value is not a valid dict(type=type_error.dict),how to solve the problem?
|
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[] | null | 4 | 2023-06-30T07:02:07 | 2023-10-09T16:06:21 | 2023-10-09T16:06:20 |
NONE
| null |
### Issue you'd like to raise.
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from transformers import AutoModel, AutoTokenizer
model_name = ".\\langchain-models\\THUDM\\chatglm2-6b"
local_model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
local_tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
db = SQLDatabase.from_uri("mysql+pymysql://root:root@localhost/magic")
toolkit = SQLDatabaseToolkit(db=db, llm=local_model, tokenizer=local_tokenizer)
tables = toolkit.list_tables_sql_db()
print(tables)
Traceback (most recent call last):
File "D:\chat\langchain-ChatGLM\test_sql.py", line 8, in <module>
toolkit = SQLDatabaseToolkit(db=db, llm=local_model)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for SQLDatabaseToolkit
llm
value is not a valid dict (type=type_error.dict)
### Suggestion:
_No response_
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| 1,781,991,448 |
I_kwDOIPDwls5qNwgY
| 6,958 |
CharacterTextSplitter constanly generate chunks longer than given chunk_size
|
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[] | null | 4 | 2023-06-30T06:46:26 | 2023-12-13T16:08:43 | 2023-12-13T16:08:42 |
NONE
| null |
### Feature request
CharacterTextSplitter split a size of 1GB code base with warnings exceed the log buffer, like
```
Created a chunk of size 2140, which is longer than the specified 900
Created a chunk of size 1269, which is longer than the specified 900
Created a chunk of size 1955, which is longer than the specified 900
Created a chunk of size 3410, which is longer than the specified 900
Created a chunk of size 1192, which is longer than the specified 900
Created a chunk of size 1392, which is longer than the specified 900
Created a chunk of size 1540, which is longer than the specified 900
- very very long...
```
Walking through the relvent code
```
def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]:
# We now want to combine these smaller pieces into medium size
# chunks to send to the LLM.
separator_len = self._length_function(separator)
docs = []
current_doc: List[str] = []
total = 0
for d in splits:
_len = self._length_function(d)
if (
total + _len + (separator_len if len(current_doc) > 0 else 0)
> self._chunk_size
):
if total > self._chunk_size:
logger.warning(
f"Created a chunk of size {total}, "
f"which is longer than the specified {self._chunk_size}"
)
if len(current_doc) > 0:
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_len if len(current_doc) > 0 else 0)
> self._chunk_size
and total > 0
):
total -= self._length_function(current_doc[0]) + (
separator_len if len(current_doc) > 1 else 0
)
current_doc = current_doc[1:]
current_doc.append(d)
total += _len + (separator_len if len(current_doc) > 1 else 0)
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
return docs
```
I know langchain try to keep semantic integrity of code language with language specific separtor.
### Motivation
Can we just pop the last split once the `total > self._chunk_size` in `def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]:`
### Your contribution
Maybe later I'll propose a PR
|
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| 1,781,972,766 |
PR_kwDOIPDwls5UTWIv
| 6,957 |
Support for the spatial geometry datatype for databases
|
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[] | null | 5 | 2023-06-30T06:29:15 | 2023-11-15T19:06:25 | 2023-11-07T03:49:28 |
NONE
| null |
**Description**
Support for the spatial geometry datatype for databases while creating embedding in SQLDatabaseChain.
**Issue:** #5799
**Dependencies**
Added **GeoAlchemy2** library
@dev2049
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| 1,781,940,281 |
I_kwDOIPDwls5qNkA5
| 6,956 |
ValueError: `run` not supported when there is not exactly one output key. Got ['output', 'intermediate_steps']
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[] | null | 7 | 2023-06-30T05:52:27 | 2023-12-05T13:43:06 | null |
NONE
| null |
### Issue you'd like to raise.
```
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", return_intermediate_steps=True, verbose=True)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", return_intermediate_steps=True, memory=ConversationBufferMemory(memory_key="chat_history", input_key='input', output_key="output"), verbose=True)
```
https://python.langchain.com/docs/modules/agents/how_to/intermediate_steps
<img width="857" alt="image" src="https://github.com/hwchase17/langchain/assets/42615243/9ea12e4b-1775-4114-a750-0625e1cf8302">
### Suggestion:
I don't know how to solve it!
|
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| 1,781,805,088 |
I_kwDOIPDwls5qNDAg
| 6,954 |
Conversational Retrieval QA with sources cannot return source
|
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[] | null | 2 | 2023-06-30T03:17:04 | 2023-07-03T07:34:15 | 2023-07-03T07:34:15 |
NONE
| null |
### System Info
ConversationalRetrievalChain with Question Answering with sources
```python
llm = OpenAI(temperature=0)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")
chain = ConversationalRetrievalChain(
retriever=vectorstore.as_retriever(),
question_generator=question_generator,
combine_docs_chain=doc_chain,
)
```
if i set the chain_type to "refine". it will not return sources.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
just like the turtial code.
### Expected behavior
i want the 'refine' chain return sources.
|
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| 1,781,803,499 |
PR_kwDOIPDwls5USyHx
| 6,953 |
Update redis.py
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[] | null | 3 | 2023-06-30T03:14:31 | 2023-11-07T03:50:25 | 2023-11-07T03:50:24 |
NONE
| null |
Provided an option to connect to redis cloud using password,port and db along with existing parameters
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Provided an option to connect to redis cloud using password,port and db along with existing parameters
- Issue: N/A
- Dependencies: any dependencies required for this change,
- Tag maintainer: @hwchase17
- Twitter handle: @ghatiki
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- 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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| 1,781,803,067 |
I_kwDOIPDwls5qNCg7
| 6,952 |
Sentencebert/Bert/Spacy/Doc2vec embedding support
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[] | null | 8 | 2023-06-30T03:13:49 | 2023-10-06T16:06:18 | 2023-10-06T16:06:18 |
NONE
| null |
### Feature request
Is there any way to store the Sentencebert/Bert/Spacy/Doc2vec based embeddings in the vector database using langchain
```
pages= "page content"
embeddings = OpenAIEmbeddings()
persist_directory = 'db'
vectordb = Chroma.from_documents(documents=pages, embedding=embeddings, persist_directory=persist_directory)
```
shall use **Sentencebert/Bert/Spacy/Doc2vec embeddings** instead of OpenAIEmbeddings() in the above code?
if possible then what is the syntax for that?
### Motivation
For using native embedding formats likeSentencebert/Bert/Spacy/Doc2vec in langchain
### Your contribution
For using native embedding formats likeSentencebert/Bert/Spacy/Doc2vec in langchain
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I_kwDOIPDwls5qM7tx
| 6,951 |
Issue: Get Confused with ConversationalRetrievalChain + HuggingFacePipeline always gen wizard "Helpful answer: "
|
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[] | null | 2 | 2023-06-30T02:41:25 | 2023-10-06T16:06:23 | 2023-10-06T16:06:23 |
NONE
| null |
my code:
```
from langchain.llms import HuggingFacePipeline
from langchain.chains import ConversationalRetrievalChain
pipe = pipeline(
"text-generation", # task type
model=model,
tokenizer=tokenizer,
max_new_tokens=1024,
device=0, # very trick, gpu rank
)
local_llm = HuggingFacePipeline(pipeline=pipe)
qa = ConversationalRetrievalChain.from_llm(local_llm, retriever=retriever)
questions = [
"void Plan::ScheduleWork(map<Edge*, Want>::iterator want_e) {",
]
chat_history = []
for question in questions:
result = qa({"question": question, "chat_history": chat_history})
# chat_history.append((question, result["answer"]))
print("**chat_history**", chat_history)
print(f"=>**Question**: {question} \n")
print(f"=>**Answer**: {result['answer']} \n\n")
```
which generate like this:
```
**chat_history** []
=>**Question**: void Plan::ScheduleWork(map<Edge*, Want>::iterator want_e) {
=>**Answer**: void Plan::ScheduleWork(map<Edge*, Want>::iterator want_e) {
if (want_e->second == kWantToFinish) {
// This edge has already been scheduled. We can get here again if an edge
// and one of its dependencies share an order-only input, or if a node
// duplicates an out edge (see https://github.question/: void Plan::ScheduleWork(map<Edge*, Want>::iterator want_e) {
Helpful Answer: void Plan::ScheduleWork(map<Edge*, Want>::iterator want_e) {
if (want_e->second == kWantToFinish) {
// This edge has already been scheduled. We can get here again if an edge
// and one of its dependencies share an order-only input, or if a node
// duplicates an out edge (see https://github.com/ninja-build/ninja/pull/519).
// Avoid scheduling the work again.
return;
}
assert(want_e->second == kWantToStart);
want_e->second = kWantToFinish;
```
my `model` is locally fine-tuned starcode with perf merged, `retriever` is DeepLake vectorstore with some args like cos etc.
I do not get the "Helpful Answer" block, WHERE ON EARTH THIS COME FROM???
### Suggestion:
_No response_
|
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| 1,781,757,970 |
I_kwDOIPDwls5qM3gS
| 6,950 |
LLMSummarizationCheckerFromSource
|
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| false | null |
[] | null | 1 | 2023-06-30T02:19:22 | 2023-10-06T16:06:29 | 2023-10-06T16:06:28 |
NONE
| null |
### Feature request
Just curious if there is a feature to check whether the summarized output from a source document hallucinates or not by extending the checker chain.
### Motivation
LLMSummarizationCheckerChain checks facts of summaries using LLM knowledge. The motivation is to develop a feature that checks for facts from an augmented source document.
### Your contribution
Will submit a PR if this does not exist yet and if it is a nice to have feature
|
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PR_kwDOIPDwls5USfHn
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Add back in
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[] | null | 1 | 2023-06-30T01:35:22 | 2023-06-30T02:21:48 | 2023-06-30T02:21:47 |
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!
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- 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_kwDOIPDwls5USZKj
| 6,948 |
redirect vecstores
|
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closed
| false | null |
[] | null | 1 | 2023-06-30T01:00:20 | 2023-06-30T02:22:22 | 2023-06-30T02:22:21 |
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!
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- 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_kwDOIPDwls5qMjK9
| 6,947 |
Inconsistent naming of document loaders
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[] | null | 1 | 2023-06-30T00:41:17 | 2023-06-30T15:56:45 | 2023-06-30T15:56:45 |
NONE
| null |
### Issue you'd like to raise.
Genuinely wanted to know the reasonings behind the inclusion of "Mu" in the "PyMuPDFLoader". Coming from an Indian background my friend and I held 1-2 hours of discussion over what this represents and the conclusions were not so appropriate.
We landed on this discussion after we noticed the inconsistency in the naming of the single pdf loader (i.e. "PyMuPDFLoader") and the multiple pdf loader (i.e. "PyPDFDirectoryLoader").
(Being a pioneer in LLM Orchestration we admire the open-course revolution. LangChain has opened a wider scope for opensource collaborations and we thank @hwchase17 for it.)
### Suggestion:
_No response_
|
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I_kwDOIPDwls5qMh31
| 6,946 |
FewShotPromptTemplate with Chat models
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[] | null | 1 | 2023-06-30T00:35:45 | 2023-10-06T16:06:34 | 2023-10-06T16:06:33 |
NONE
| null |
### Issue with current documentation:
From the [documentation](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples), it is not clear whether ‘FewShotPromptTemplate’ can be used with Chat models: in particular, it does not appear to be compatible with OpenAI’s suggested best practices (mentioned on the same page) of having alternating “name: example_user” and “name: example_$role” JSON passed to the OpenAI API for each few-shot example message passed in the call.
### Idea or request for content:
Is it planned to have a separate “ChatFewShotPromptTemplate” class? If so, perhaps a placeholder could be added to the documentation to make this clear. If not, perhaps the documentation could be updated to make the mismatch explicit so that the reader understands the limitations of the current FewShotPromptTemplate in the context of Chat models.
|
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Change code block color scheme
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[] | null | 1 | 2023-06-30T00:07:52 | 2023-06-30T02:21:12 | 2023-06-30T02:21:12 |
CONTRIBUTOR
| null |
Adds contrast, makes code blocks more readable.
@dev2049
|
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| 1,781,634,208 |
PR_kwDOIPDwls5USN4o
| 6,944 |
Simplify eval arg names
|
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[] | null | 1 | 2023-06-29T23:33:05 | 2023-06-30T14:47:54 | 2023-06-30T14:47:53 |
COLLABORATOR
| null |
It'll be easier to switch between these if the names of predictions are consistent
|
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| 1,781,628,784 |
PR_kwDOIPDwls5USMv7
| 6,943 |
Use an Enum for search type
|
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[] | null | 3 | 2023-06-29T23:23:57 | 2023-11-07T03:50:38 | 2023-11-07T03:50:38 |
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!
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
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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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## Description
Description: Presently, vectorstores and `VectorStoreRetrievers` use strings to identify the search type that should be used. This approach has a few shortcomings:
1. Strings can be easily mistyped, and IDEs are unlikely to let you know.
2. Code completion may be unreliable.
3. IDEs likely won't allow you to easily refactor usage of string literals throughout the code.
4. Users and maintainers must read through various classes to understand what types of searches are supported by the system as a whole
5. If maintainers would like enable new search types within the framework, it may not be obvious how such system-wide changes can be made.
6. Without a clear list of supported search types, "synonyms" may be introduced, making the system inconsistent and increasing the cognitive load on the maintainers and users. For an example, see `RedisVectorStoreRetriever`, which defines a search type of "similarity_limit", which I believe is synonymous with "similarity_score_threshold".
Replacing the use of string literals with an `Enum` solves these problems. It provides stronger typing and (hopefully) a reduced risk of introducing defects into new or existing code. Users and maintainers alike can easily see what the system supports in a single location, and the system is more likely to be self-consistent.
## Tag Maintainer
@rlancemartin @eyurtsev @hwchase17
## Note
These changes should be fully backwards compatible since the `Enum` also inherits `str`.
|
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| 1,781,601,017 |
PR_kwDOIPDwls5USGyK
| 6,942 |
Load Evaluator
|
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[] | null | 1 | 2023-06-29T22:40:20 | 2023-07-06T20:59:00 | 2023-07-06T20:58:59 |
COLLABORATOR
| null |
Create a `load_evaluators()` function so you don't have to import all the individual evaluator classes
|
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| 1,781,559,211 |
PR_kwDOIPDwls5UR9pd
| 6,940 |
Added support for Amazon SageMaker Asynchronous Endpoints
|
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[] | null | 13 | 2023-06-29T21:49:57 | 2023-11-07T03:53:23 | 2023-11-07T03:53:22 |
NONE
| null |
## Description
Added support for Amazon SageMaker Asynchronous Endpoints.
## Issue
#6928
## Dependencies
boto3, uuid
## Maintainer
@hwchase17
## Twitter handle
DGallitelli95
## Code to test this
```python
from typing import Dict
from langchain import PromptTemplate
from langchain.llms.sagemaker_endpoint import LLMContentHandler, SagemakerAsyncEndpoint
from langchain.chains import LLMChain
# This ContentHandler has been tested with Falcon40B Instruct from SageMaker JumpStart
class ContentHandler(LLMContentHandler):
content_type:str = "application/json"
accepts:str = "application/json"
len_prompt:int = 0
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
self.len_prompt = len(prompt)
input_str = json.dumps({"inputs": prompt, "parameters": {"max_new_tokens": 100, "do_sample": False, "repetition_penalty": 1.1}})
return input_str.encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = output.read()
res = json.loads(response_json)
ans = res[0]['generated_text']
return ans
chain = LLMChain(
llm=SagemakerAsyncEndpoint(
input_bucket=bucket,
input_prefix=prefix,
endpoint_name=my_model.endpoint_name,
region_name=sagemaker.Session().boto_region_name,
content_handler=ContentHandler(),
),
prompt=PromptTemplate(
input_variables=["query"],
template="{query}",
),
)
print(chain.run("What is the purpose of life?"))
```
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I_kwDOIPDwls5qMGji
| 6,939 |
add `Brave` Search
|
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[] | null | 2 | 2023-06-29T21:47:51 | 2023-06-30T15:24:25 | 2023-06-30T15:24:25 |
COLLABORATOR
| null |
### Feature request
[Brave Search](https://api.search.brave.com/app/dashboard) is a new interesting search engine
It can be used in place of the `Google Search`.
### Motivation
Users who are subscribers of Brave Search can use this.
### Your contribution
I can try to implement it if somebody is interested in this integration.
|
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I_kwDOIPDwls5qL_Iw
| 6,938 |
Chroma (duckdb+parquet) DB isn't saving the parquet files for persisted DB until application is killed
|
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[] | null | 9 | 2023-06-29T21:15:14 | 2023-10-06T16:39:27 | 2023-10-06T16:39:20 |
NONE
| null |
### System Info
Chroma v0.2.36, python 3.9
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`
settings = Settings(
chroma_db_impl='duckdb+parquet',
persist_directory="db",
anonymized_telemetry=False
)
pages = self._load_single_document(file_path=file_path)
docs = text_splitter.split_documents(pages)
db = Chroma.from_documents(docs, embedding_function, client_settings=settings)
`
### Expected behavior
All files for the database should be created in the db directory. However, the parquet files are not being created when db.from_documents() is called.
However, this is not happening. All the files in the index directory are created at this time.
Later, when the application is killed (flask application, so in between requests the DB is torn down and thus should be persisited), then the parquet files show up.
|
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| 1,781,513,642 |
PR_kwDOIPDwls5URz0t
| 6,937 |
docstrings `document_loaders` 3
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COLLABORATOR
| null |
- Updated docstrings for `document_loaders`
- Mass update `"""Loader that loads` to `"""Loads`
@baskaryan - please, review
|
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I_kwDOIPDwls5qLXgE
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Missing reference to o365_toolkit module in __init__.py of agent_toolkits
|
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[] | null | 4 | 2023-06-29T19:23:25 | 2023-09-28T16:20:50 | 2023-09-28T16:20:50 |
NONE
| null |
### System Info
Langchain 0.0.218
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
### Expected behavior
The module should be found automatically.
|
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| 1,781,255,515 |
I_kwDOIPDwls5qK81b
| 6,934 |
SageMaker Endpoint - Validations
|
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[] | null | 3 | 2023-06-29T17:43:12 | 2023-11-30T16:08:26 | 2023-11-30T16:08:25 |
NONE
| null |
### System Info
The SageMaker Endpoint validation function currently throws a missing credential error if the region name is not provided in the input. However, it is crucial to perform input validation for the region name to provide the user with clear error information.
GitHub Issue Reference:
For the code reference related to this issue, please visit: [GitHub Link](https://github.com/hwchase17/langchain/blob/7dcc698ebf4ebf4eb7331d25cec279f402918629472b/langchain/llms/sagemaker_endpoint.py#L177)
### Who can help?
@3coins
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This can be reproduced by creating a SageMaker Endpoing LangChain without specifying region name. Please ensure that you have valid AWS credentials configured.
### Expected behavior
The error message must state
```Region name is missing. Please enter valid region name. E.g: us-west-2.```
|
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| 1,781,214,707 |
I_kwDOIPDwls5qKy3z
| 6,933 |
General OpenAI Function Mapping from Pydantic, Dicts directly to function, parameters, and properties
|
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[] | null | 9 | 2023-06-29T17:07:09 | 2023-07-08T01:50:27 | 2023-07-08T01:50:27 |
NONE
| null |
### Feature request
A universal approach to add openai functions specifically for output format control to `LLMchain`. Create a class that takes in dict, json, pydantic, or even rail specs (from guardrails) to preprocess classes or dictionaries into openai functions (function, parameters, and properties) for the `llm_kwargs` in `LLMChain` class in conjunction with the openai function parsers.
### Motivation
It is hard to use the function calling feature right now if you are not working with agents (can access using `format_tool_to_openai_function`) or a specific use case, such as the Extractor or QA chains. The idea is to create a general class that takes in dict, json, pydantic, or even rail specs (from guardrails) to preprocess classes or dictionaries into openai functions for better control of the output format.
### Your contribution
I can help with this, should I put this in the `docs/modules` folder?
|
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I_kwDOIPDwls5qKynZ
| 6,932 |
Issue: How to create async calls to custom LLMs
|
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[] | null | 2 | 2023-06-29T17:06:20 | 2023-06-29T22:20:21 | 2023-06-29T22:20:21 |
NONE
| null |
### Issue you'd like to raise.
Hello!
I am currently using Langchain to interface with a [customLLM](https://python.langchain.com/docs/modules/model_io/models/llms/how_to/custom_llm) async.
To do so, I am overriding the original `LLM` class as explained in the tutorial as such:
```
class LLMInterface(LLM):
@property
def _llm_type(self) -> str:
return "Ruggero's LLM"
async def send_request(self, payload):
return await some_function_that_queries_custom_llm(payload)
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
if stop is not None:
raise ValueError("stop kwargs are not permitted.")
payload = json.dumps({"messages": [{"role": "user", "content": prompt}]})
future = self.run_in_executor(self.send_request, payload)
raw_response = future.result() # get the actual result
print(raw_response)
# Parse JSON response
response_json = json.loads(raw_response)
# Extract content
for message in response_json["messages"]:
if message["role"] == "bot":
content = message["content"]
return content
# Return empty string if no bot message found
return ""
```
Unfortunately I get:
```
159 reductor = getattr(x, "__reduce_ex__", None)
160 if reductor is not None:
--> 161 rv = reductor(4)
162 else:
163 reductor = getattr(x, "__reduce__", None)
TypeError: cannot pickle '_queue.SimpleQueue' object
```
Even if a use an executor I get the similar problem
```
def run_in_executor(self, coro_func: Any, *args: Any) -> Any:
future_result = Future()
def wrapper():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
result = loop.run_until_complete(coro_func(*args))
future_result.set_result(result)
except Exception as e:
future_result.set_exception(e)
finally:
loop.run_until_complete(loop.shutdown_asyncgens())
loop.close()
Thread(target=wrapper).start()
return future_result
```
In my code I use Langchain as such:
```
llm_chain = ConversationChain(llm=llm, memory=memory)
output = llm_chain.run(self.llm_input)
```
What is the appropriate way to interface a custom LLM that it is queried async?
i know that Langchain supports [async calls ](https://blog.langchain.dev/async-api/) but I was not able to make it work
Thank you!
### Suggestion:
providing a
```
async def _async_call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
```
method would be ideal
Any alternative work-around would be fine too
Thank you!
|
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| 1,781,186,169 |
I_kwDOIPDwls5qKr55
| 6,931 |
Issue: How to handle RouterChain when 1 or more destination chain(s) which is expecting a different input variable?
|
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[] | null | 8 | 2023-06-29T16:45:54 | 2023-08-03T08:30:01 | 2023-07-06T06:04:48 |
NONE
| null |
### Issue you'd like to raise.
Would like to ask how we should deal with multiple destination chains where the chains are expecting different input variables?
For e.g. in the tutorial for MultiPromptChain, i would like math questions to be directed to the PalChain instead of the standard LLMChain. With the initial LLMRouterChain, the router prompt uses `input` as the input_variables. however, when it has decided that the input `what is 2+2` is a math question and should be routed to PalChain, i am presented the error
```
[/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py](https://localhost:8080/#) in _validate_inputs(self, inputs)
101 missing_keys = set(self.input_keys).difference(inputs)
102 if missing_keys:
--> 103 raise ValueError(f"Missing some input keys: {missing_keys}")
104
105 def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
ValueError: Missing some input keys: {'question'}
```
manually replacing the MATH_PROMPT that PalChain uses from {question} to {input} works but I would like to know how I can specify the input variable that the destination chain is expecting when setting up the destination_chains array here:
```
chain = MultiPromptChain(router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain,
verbose=True
)
```
been at it for 2 nights so am seeking help. thanks!
### Suggestion:
_No response_
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I_kwDOIPDwls5qKiy6
| 6,930 |
PR-s wait the review forever
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[] | null | 7 | 2023-06-29T16:19:06 | 2023-11-19T22:54:48 | 2023-11-19T22:52:39 |
COLLABORATOR
| null |
### Issue you'd like to raise.
The PRs wait for review (for any feedback) very long time.
- Some sort of triage would be helpful.
- Some sort of PR closure can be helpful. If something is wrong with PR this should be explained/discussed/addressed. Not providing **any feedback** is demoralizing for contributors.
Thanks!
PS Take a look at the PR list and check PRs that passed all checks and has no answer/feedback. Hundreds of such PRs?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5UQWVC
| 6,929 |
Implement AgentExecutorIterator
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CONTRIBUTOR
| null |
- Description: Implements a `.iter()` method for the `AgentExecutor` class. This allows hooking into and intercepting intermediate agent steps.
- Issue: #6925
- Dependencies: None
- Tag maintainer: @vowelparrot @agola11
- Twitter handle: @SlapDron3 @lacicocodes
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| 1,781,073,650 |
I_kwDOIPDwls5qKQby
| 6,928 |
Support for Amazon SageMaker Asynchronous Endpoints
|
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[] | null | 4 | 2023-06-29T15:31:14 | 2023-12-25T16:02:32 | null |
NONE
| null |
### Feature request
Please add support to invoke [Amazon SageMaker Asynchronous Endpoints](https://docs.aws.amazon.com/sagemaker/latest/dg/async-inference.html).
This would require some code changes to the _call method of the SageMaker Endpoint class. Therefore, there is a need to create a new class, called SagemakerAsyncEndpoint, or introduce a logic in the SagemakerEndpoint class to check whether the endpoint is async or not.
### Motivation
They are a great way to have models running on expensive GPU instances, while keeping cost control especially during POC phase.
### Your contribution
Submitting a PR.
|
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| 1,781,057,005 |
PR_kwDOIPDwls5UQRpk
| 6,927 |
integrate JinaChat
|
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[] | null | 5 | 2023-06-29T15:23:54 | 2023-07-08T09:47:40 | 2023-07-08T06:17:05 |
CONTRIBUTOR
| null |
Integration with https://chat.jina.ai/api. It is OpenAI compatible API.
- Twitter handle: [https://twitter.com/JinaAI_](https://twitter.com/JinaAI_)
cc @hwchase17, @baskaryan
<!-- 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!
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- 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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| 1,781,037,761 |
I_kwDOIPDwls5qKHrB
| 6,926 |
ValidationError: 1 validation error for MapRerankDocumentsChain
|
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[] | null | 9 | 2023-06-29T15:15:32 | 2023-12-27T04:38:34 | null |
CONTRIBUTOR
| null |
### System Info
* Langchain-0.0.215
* Python3.8.6
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The chain type in `RetrievalQA.from_chain_type()`:
* `stuff`: work successfully
* `refine`: not work unless uses correctly naming or parameter. #6912
* `map_rerank`: not work
* `map_reduce`: not work
All the error of each type like:
code:
```python
prompt_template = """
Use the following pieces of context to answer the question, if you don't know the answer, leave it blank don't try to make up an answer.
{context}
Question: {question}
Answer in JSON representations
"""
QA_PROMPT = PromptTemplate(
template=prompt_template,
input_variables=['context', 'question']
)
chain_type_kwargs = {
'prompt': QA_PROMPT,
'verbose': True
}
docs = PyMuPDFLoader('file.pdf').load()
splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap
)
docs = splitter.split_documents(document)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(
documents=docs,
embedding= embeddings
)
qa_cahin = RetrievalQA.from_chain_type(
llm=OpenAI(temperature=0.2),
chain_type='map_rerank',
retriever=db.as_retriever(),
chain_type_kwargs=chain_type_kwargs
)
```
result:
```
ValidationError Traceback (most recent call last)
[c:\Users\JunXiang\AppData\Local\Programs\Python\Python38\lib\site-packages\langchain\chains\retrieval_qa\base.py](file:///C:/Users/JunXiang/AppData/Local/Programs/Python/Python38/lib/site-packages/langchain/chains/retrieval_qa/base.py) in from_chain_type(cls, llm, chain_type, chain_type_kwargs, **kwargs)
89 """Load chain from chain type."""
90 _chain_type_kwargs = chain_type_kwargs or {}
---> 91 combine_documents_chain = load_qa_chain(
92 llm, chain_type=chain_type, **_chain_type_kwargs
93 )
[c:\Users\JunXiang\AppData\Local\Programs\Python\Python38\lib\site-packages\langchain\chains\question_answering\__init__.py](file:///C:/Users/JunXiang/AppData/Local/Programs/Python/Python38/lib/site-packages/langchain/chains/question_answering/__init__.py) in load_qa_chain(llm, chain_type, verbose, callback_manager, **kwargs)
236 f"Should be one of {loader_mapping.keys()}"
...
[c:\Users\JunXiang\AppData\Local\Programs\Python\Python38\lib\site-packages\pydantic\main.cp38-win_amd64.pyd](file:///C:/Users/JunXiang/AppData/Local/Programs/Python/Python38/lib/site-packages/pydantic/main.cp38-win_amd64.pyd) in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for MapRerankDocumentsChain
__root__
Output parser of llm_chain should be a RegexParser, got None (type=value_error)
```
### Expected behavior
Does not crash, when i try to run it.
|
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| 1,781,033,687 |
I_kwDOIPDwls5qKGrX
| 6,925 |
Iterator version of AgentExecutor
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[] | null | 2 | 2023-06-29T15:13:26 | 2023-07-25T16:33:49 | 2023-07-25T16:33:49 |
CONTRIBUTOR
| null |
### Feature request
A colleague and I would like to implement an iterator version of the AgentExecutor. That is, we'd like to expose each intermediate step of the plan/action/observation loop. This should look something like:
```python3
inputs = ... # complex question that requires tool usage, multi-step
for step in agent_executor.iter(inputs=inputs):
do_stuff_with(step)
```
### Motivation
This would be useful for a few applications. By hooking into agent steps we could:
- Route different thoughts/actions/observations conditionally to:
- different frontend components
- different postprocessing/analysis logic
- other agents
We could also "pause" agent execution and intervene.
### Your contribution
Yes, we have a PR ~ready.
|
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I_kwDOIPDwls5qJynw
| 6,924 |
Support BingNewsApiWrapper on utilities
|
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[] | null | 2 | 2023-06-29T14:28:14 | 2023-08-31T07:22:13 | 2023-08-31T07:22:12 |
CONTRIBUTOR
| null |
### Feature request
Hello, I'd like to ask about supporting [BingNewsAPI](https://www.microsoft.com/en-us/bing/apis/bing-news-search-api), which the apis and response format is different from bing web search api, that I cannot utilize existing [BingSearchAPIWrapper](https://python.langchain.com/docs/modules/agents/tools/integrations/bing_search) result or run method.
I know that you provide separating host url in existing BingSearchWrapper, but as the response format is different, there is an parsing error on calling result method.
### Motivation
I cannot utilize existing [BingSearchAPIWrapper](https://python.langchain.com/docs/modules/agents/tools/integrations/bing_search) s result or run method on [BingNewsAPI](https://www.microsoft.com/en-us/bing/apis/bing-news-search-api)
### Your contribution
If you allow me, I'd like to create pr for BingNewsSearchAPIWrapper utilities.
|
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| 6,923 |
Structured Agent Search Not Working
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[] | null | 5 | 2023-06-29T14:25:29 | 2023-11-27T08:25:46 | null |
NONE
| null |
### System Info
Trying to get an agent to describe the contents of a particular URL for me, however, **my agent does not execute past first step**. Attached image to show what my code looks like and where it freezes as it runs.
<img width="683" alt="freezing_agent" src="https://github.com/hwchase17/langchain/assets/97087128/b3888b6e-f016-479e-ac1a-3036b7f16d97">
Thanks <3 !
(also posted as discussion, was not sure where to add)
### Who can help?
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from config import SERPAPI_API_KEY, OPENAI_API_KEY
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit
from langchain.tools.playwright.utils import create_async_playwright_browser
import asyncio
async_browser = create_async_playwright_browser()
browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)
tools = browser_toolkit.get_tools()
gpt_model = "gpt-4"
llm = ChatOpenAI(
temperature=0,
model_name=gpt_model,
openai_api_key=OPENAI_API_KEY
)
agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
async def main():
response = await agent_chain.arun(input="Browse to https://www.theverge.com/2023/3/14/23638033/openai-gpt-4-chatgpt-multimodal-deep-learning and describe it to me.")
return response
result = asyncio.run(main())
print(result)
### Expected behavior
Enter chain, navigate to browser, read content in browser, return description.
Based on: https://python.langchain.com/docs/modules/agents/agent_types/structured_chat.html
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DOC:
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[] | null | 4 | 2023-06-29T13:42:48 | 2023-09-28T18:26:50 | 2023-09-28T18:26:50 |
NONE
| null |
### Issue with current documentation:
Hey,
Love your product, and just wanted to give some constructive feedback. Your new layout and organization strategy on "https://python.langchain.com/docs/get_started/introduction.html" is significantly harder to navigate
### Idea or request for content:
_No response_
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[] | null | 3 | 2023-06-29T13:15:39 | 2023-09-28T17:27:55 | 2023-09-28T17:27:37 |
NONE
| null |
### System Info
Langchain 0.0.200
Python 3.11.4
Windows 10
### 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
1. Use with `load_qa_chain`
Error in console:
```
Error in on_chain_start callback: 'name'
```
Log file content is missing "Entering new chain"
### Expected behavior
1. No error in console
2. "Entering new chain" is present in log file
Fix: access dict with fallback like StdOutCallbackHandler does:
```python
class_name = serialized.get("name", "")
```
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| 1,780,791,122 |
I_kwDOIPDwls5qJLdS
| 6,919 |
FileCallbackHandler should open file in UTF-8 encoding
|
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[] | null | 4 | 2023-06-29T13:10:04 | 2023-09-28T17:29:58 | 2023-09-28T17:29:42 |
NONE
| null |
### System Info
Langchain 0.0.200
Python 3.11.4
Windows 10
### 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
Use the handler with a prompt containing characters that aren't represented on the platform charset (e.g. cp1252)
Error appears in console:
```
[manager.py:207] Error in on_text callback: 'charmap' codec can't encode character '\u2010' in position 776: character maps to <undefined>
```
Target logfile only has "entering new chain" and "finished chain" lines.
### Expected behavior
1. No error
2. Log file has usual output
Fix: in constructor: ```self.file = cast(TextIO, open(file_path, "a", encoding="utf-8"))```
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I_kwDOIPDwls5qJIyo
| 6,918 |
How to add memory to SQLDatabaseChain?
|
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[] | null | 30 | 2023-06-29T13:02:50 | 2024-01-11T16:59:50 | null |
NONE
| null |
### Issue you'd like to raise.
I want to create a chain to make query against my database. Also I want to add memory to this chain.
Example of dialogue I want to see:
Query: Who is an owner of website with domain domain.com?
Answer: Boba Bobovich
Query: Tell me his email Answer:
Boba Bobovich's email is [email protected]
I have this code:
```
import os
from langchain import OpenAI, SQLDatabase, SQLDatabaseChain, PromptTemplate
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
db = SQLDatabase.from_uri(os.getenv("DB_URI"))
llm = OpenAI(temperature=0, verbose=True)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True, memory=memory)
db_chain.run("Who is owner of the website with domain https://damon.name")
db_chain.run("Tell me his email")
print(memory.load_memory_variables({}))
```
It gives:
```
> Entering new chain...
Who is owner of the website with domain https://damon.name
SQLQuery:SELECT first_name, last_name FROM owners JOIN websites ON owners.id = websites.owner_id WHERE domain = 'https://damon.name' LIMIT 5;
SQLResult: [('Geo', 'Mertz')]
Answer:Geo Mertz is the owner of the website with domain https://damon.name.
> Finished chain.
> Entering new chain...
Tell me his email
SQLQuery:SELECT email FROM owners WHERE first_name = 'Westley' AND last_name = 'Waters'
SQLResult: [('[email protected]',)]
Answer:Westley Waters' email is [email protected].
> Finished chain.
{'history': "Human: Who is owner of the website with domain https://damon.name\nAI: Geo Mertz is the owner of the website with domain https://damon.name.\nHuman: Tell me his email\nAI: Westley Waters' email is [email protected]."}
```
Well, it saves context to memory but chain doesn't use it to give a proper answer (wrong email). How to fix it?
Also I don't want to use an agent because I want to manage to do this with a simple chain first. Tell me if it's impossible with simple chain.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5qI_Y4
| 6,917 |
Issue: User based chat history
|
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[] | null | 3 | 2023-06-29T12:37:35 | 2023-11-13T16:08:20 | 2023-11-13T16:08:19 |
NONE
| null |
### User based chat history
How I do implementation of User based chat history management and thread management. Plaese give any suggestion regarding this.
### Suggestion:
_No response_
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I_kwDOIPDwls5qIp5T
| 6,915 |
make - ModuleNotFoundError: No module named 'scripts'
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[] | null | 5 | 2023-06-29T11:40:35 | 2023-11-25T16:08:49 | 2023-11-25T16:08:48 |
NONE
| null |
### System Info
OS: `Ubuntu 22.04`
Langchain Version: `0.0.219`
Poetry Version: `1.5.1`
Python Version: `3.10.6`
### 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
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I would like to contribute to the project, I followed instructions for [contributing](https://github.com/hwchase17/langchain/blob/8502117f62fc4caa53d504ccc9d4e6a512006e7f/.github/CONTRIBUTING.md) and have installed poetry.
After setting up the virtual env, the command: `poetry install -E all` runs successfully :heavy_check_mark:
However when trying to do a `make test` I get the following error:
```
Traceback (most recent call last):
File "[redacted]/langchain/.venv/bin/make", line 5, in <module>
from scripts.proto import main
ModuleNotFoundError: No module named 'scripts'
```
I have the virtual env activated and the command `make` is using the one installed in the virtual env. (see above error)
I'm assuming I'm missing a dependency but it's not obvious to determine, can you help please?
### Expected behavior
`make` should run properly
|
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PR_kwDOIPDwls5UO0nT
| 6,914 |
Fix run_inline to run hooks in the same context
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CONTRIBUTOR
| null |
Since https://github.com/hwchase17/langchain/commit/e1b801be36d2d492eea071dcb594e966414b31cf, run_inline flags on callbacks (introduced in [PR](https://github.com/hwchase17/langchain/pull/6424)) are broken.
Because higher-level on_llm_start uses asyncio.gather, it's creates a task for given coroutines, and changes the hooks' context. Hence, it's not run inline and context is lost. Added test shows that if ran without changes in the manager.py.
This approach changes the interface of `_ahandle_event` to be synchronous method that returns an awaitable (I think it doesn't change much), which is a result of one `asyncio.wait` statement.
This ensures that `run_inline` hooks will be run before async hooks, but it doesn't ensure that relative order of inline hooks is preserved.
I'm not sure if that's a good approach, but posting it anyway so we can discuss the direction.
|
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PR_kwDOIPDwls5UOzM0
| 6,913 |
Add JSON Lines support to JSONLoader
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CONTRIBUTOR
| null |
**Description**:
The JSON Lines format is used by some services such as OpenAI and HuggingFace. It's also a convenient alternative to CSV.
This PR adds JSON Lines support to `JSONLoader` and also updates related tests.
**Tag maintainer**: @rlancemartin, @eyurtsev.
PS I was not able to build docs locally so didn't update related section.
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PR_kwDOIPDwls5UOtQr
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Fix different naming of params for chain type
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[] | null | 1 | 2023-06-29T11:07:29 | 2023-09-04T08:11:47 | 2023-07-06T14:00:57 |
CONTRIBUTOR
| null |
# Changed and Fixed what?
Renaming the parameters in different chain type.
To enable smooth and error-free invocation of `RetrievalQA.from_chain_type` functions with different chain types, and to make the usage more intuitive.
# Problem description
`RetrievalQA.from_chain_type` can execute successfully with `stuff` chain type.
However, it cannot use with `map_reduce`, `refine`.
If we use `RetrievalQA.from_chain_type()` with `map_reduce` or `refine` chain type, we will get `ValidationError`:
## Code
```python
prompt_template = """
Use the following pieces of context to answer the question, if you don't know the answer, leave it blank don't try to make up an answer.
{context}
Question: {question}
Answer in JSON representations
"""
QA_PROMPT = PromptTemplate(
template=prompt_template,
input_variables=['context', 'question']
)
chain_type_kwargs = {'prompt': QA_PROMPT, 'verbose': True}
qa_cahin = RetrievalQA.from_chain_type(
llm=OpenAI(temperature=0.2),
chain_type='refine',
retriever=db.as_retriever(),
chain_type_kwargs=chain_type_kwargs
)
```
## Result
```
ValidationError: 1 validation error for RefineDocumentsChain
prompt
extra fields not permitted (type=value_error.extra)
```
---
# Source of error
Check the [source code](https://github.com/hwchase17/langchain/blob/d85f57ef9cbbbd5e512e064fb81c531b28c6591c/langchain/chains/question_answering/__init__.py#L146)
The different types have **different naming of parameter** as following:
* stuff
```python
def _load_stuff_chain(
llm: BaseLanguageModel,
prompt: Optional[BasePromptTemplate] = None,
document_variable_name: str = "context",
verbose: Optional[bool] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> StuffDocumentsChain:
```
* map_reduce
```python
def _load_map_reduce_chain(
llm: BaseLanguageModel,
question_prompt: Optional[BasePromptTemplate] = None,
combine_prompt: Optional[BasePromptTemplate] = None,
combine_document_variable_name: str = "summaries",
map_reduce_document_variable_name: str = "context",
collapse_prompt: Optional[BasePromptTemplate] = None,
reduce_llm: Optional[BaseLanguageModel] = None,
collapse_llm: Optional[BaseLanguageModel] = None,
verbose: Optional[bool] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> MapReduceDocumentsChain:
```
* refine
```python
def _load_refine_chain(
llm: BaseLanguageModel,
question_prompt: Optional[BasePromptTemplate] = None,
refine_prompt: Optional[BasePromptTemplate] = None,
document_variable_name: str = "context_str",
initial_response_name: str = "existing_answer",
refine_llm: Optional[BaseLanguageModel] = None,
verbose: Optional[bool] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> RefineDocumentsChain:
```
---
If want to use different chaint type, we should:
```python
prompt_template = """
Use the following pieces of context to answer the question, if you don't know the answer, leave it blank don't try to make up an answer.
{context_str}
Question: {question}
Answer in JSON representations
"""
QA_PROMPT = PromptTemplate(
template=prompt_template,
input_variables=['context_str', 'question']
)
chain_type_kwargs = {'question_prompt': QA_PROMPT, 'verbose': True}
qa_cahin = RetrievalQA.from_chain_type(
llm=OpenAI(temperature=0.2),
chain_type='refine',
retriever=db.as_retriever(),
chain_type_kwargs=chain_type_kwargs
)
```
There're a confusing in the different naming of parameters.
* `context` vs `context_str`
* `prompt` vs `question_prompt`
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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Maintainer responsibilities:
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| 1,780,551,489 |
PR_kwDOIPDwls5UOjNJ
| 6,911 |
Timeout for base llms
|
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[] | null | 3 | 2023-06-29T10:35:20 | 2023-11-07T03:53:33 | 2023-11-07T03:53:33 |
NONE
| null |
Description: Adding timeout as an option to the BaseLLM class.
|
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PR_kwDOIPDwls5UOR6V
| 6,909 |
Fix the different naming of params for chain type
|
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[] | null | 1 | 2023-06-29T09:40:20 | 2023-09-04T08:11:47 | 2023-06-29T11:09:15 |
CONTRIBUTOR
| null |
# Description
Renaming the different naming of parameters for chain type.
Fixed the `ValidationError: 1 validation error for RefineDocumentsChain`
# The promblem
`RetrievalQA.from_chain_type` can execute successfully with 'stuff' chain type.
However, it cannot use with `map_reduce`, `refine`.
## Code
```python
prompt_template = """
Use the following pieces of context to answer the question, if you don't know the answer, leave it blank don't try to make up an answer.
{context}
Question: {question}
Answer in JSON representations
"""
QA_PROMPT = PromptTemplate(
template=prompt_template,
input_variables=['context', 'question']
)
chain_type_kwargs = {'prompt': QA_PROMPT, 'verbose': True}
qa_cahin = RetrievalQA.from_chain_type(
llm=OpenAI(temperature=0.2),
chain_type='refine',
retriever=db.as_retriever(),
chain_type_kwargs=chain_type_kwargs
)
```
## Result
```
ValidationError: 1 validation error for RefineDocumentsChain
prompt
extra fields not permitted (type=value_error.extra)
```
---
I've tried to find the [source code](https://github.com/hwchase17/langchain/blob/d85f57ef9cbbbd5e512e064fb81c531b28c6591c/langchain/chains/question_answering/__init__.py#L146)
I see the different types have **different naming of parameter** as following:
* stuff
```python
def _load_stuff_chain(
llm: BaseLanguageModel,
prompt: Optional[BasePromptTemplate] = None,
document_variable_name: str = "context",
verbose: Optional[bool] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> StuffDocumentsChain:
```
* map_reduce
```python
def _load_map_reduce_chain(
llm: BaseLanguageModel,
question_prompt: Optional[BasePromptTemplate] = None,
combine_prompt: Optional[BasePromptTemplate] = None,
combine_document_variable_name: str = "summaries",
map_reduce_document_variable_name: str = "context",
collapse_prompt: Optional[BasePromptTemplate] = None,
reduce_llm: Optional[BaseLanguageModel] = None,
collapse_llm: Optional[BaseLanguageModel] = None,
verbose: Optional[bool] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> MapReduceDocumentsChain:
```
* refine
```python
def _load_refine_chain(
llm: BaseLanguageModel,
question_prompt: Optional[BasePromptTemplate] = None,
refine_prompt: Optional[BasePromptTemplate] = None,
document_variable_name: str = "context_str",
initial_response_name: str = "existing_answer",
refine_llm: Optional[BaseLanguageModel] = None,
verbose: Optional[bool] = None,
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> RefineDocumentsChain:
```
---
If I use the following code, it can work:
```python
prompt_template = """
Use the following pieces of context to answer the question, if you don't know the answer, leave it blank don't try to make up an answer.
{context_str}
Question: {question}
Answer in JSON representations
"""
QA_PROMPT = PromptTemplate(
template=prompt_template,
input_variables=['context_str', 'question']
)
chain_type_kwargs = {'question_prompt': QA_PROMPT, 'verbose': True}
qa_cahin = RetrievalQA.from_chain_type(
llm=OpenAI(temperature=0.2),
chain_type='refine',
retriever=db.as_retriever(),
chain_type_kwargs=chain_type_kwargs
)
```
Maybe, the error comes from the different naming of parameters ?
* `context` vs `context_str`
* `prompt` vs `question_prompt`
|
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PR_kwDOIPDwls5UOR4S
| 6,908 |
Add password to PyPDR loader and parser
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[] | null | 2 | 2023-06-29T09:40:14 | 2023-06-30T00:35:52 | 2023-06-30T00:35:51 |
CONTRIBUTOR
| null |
Add password to PyPDR loader and parser
|
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I_kwDOIPDwls5qH8Oc
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Stream Mode does not recognize openai.error
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[] | null | 1 | 2023-06-29T09:36:28 | 2023-10-06T16:06:39 | 2023-10-06T16:06:38 |
CONTRIBUTOR
| null |
### System Info
langchain-0.0.219
python-3.9
macos-13.4.1 (22F82)
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
1. use async style request in stream mode
```python
ChatOpenAI(streaming=True).agenerate([...])
```
2. Got an error if openai sdk raises `openai.error.APIError`
3. `acompletion_with_retry` not effective because the exception was thrown by `"openai/api_requestor.py", line 763, in _interpret_response_line` which is after “request”
in: tenacity/_asyncio.py
### Expected behavior
Catch exception, and do retry.
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"ParserError: Error tokenizing data. C error: Expected 2 fields in line 4, saw 4"
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[] | null | 3 | 2023-06-29T09:29:43 | 2023-11-03T16:07:17 | 2023-11-03T16:07:16 |
NONE
| null |
### System Info
Using Google Colab, Python 3.10.12. Installing latest version of langchain as of today (v0.0.219, I believe).
### Who can help?
@hwchase17
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When running the following function to create a chain on a collection of text files , I get the following error:
```
def get_company_qa_chain(company_text_dir):
loader = DirectoryLoader(company_text_dir, glob="*.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size = 1000,
chunk_overlap = 100,
length_function = len,
)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings() # default model="text-embedding-ada-002"
docsearch = Chroma.from_documents(docs, embeddings)
model_name = "gpt-3.5-turbo"
temperature=0
chat = ChatOpenAI(model_name = model_name, temperature=temperature)
template="""You are a helpful utility for answering questions. Communicate in English only."""
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = """Use the following context to answer the question that follows. If you don't know the answer,
say you don't know, don't try to make up an answer:
{context}
Question: {question}
"""
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template,
input_variables=["context", "question"])
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain_type_kwargs = {"prompt": chat_prompt}
qa = RetrievalQA.from_chain_type(llm=chat, chain_type="stuff",
retriever=docsearch.as_retriever(),
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs)
return qa
```
```
/usr/local/lib/python3.10/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()
ParserError: Error tokenizing data. C error: Expected 2 fields in line 4, saw 4
```
I have isolated the error to a particular text file, which has been produced by pymupdf. There are no clearly strange characters in the file, and everything has been cleaned using the following function before writing to text file:
```
def clean_str(text):
text = unicodedata.normalize('NFKC', text)
text = text.replace("{", "").replace("}", "").replace("[", "").replace("]", "")
return text
```
### Expected behavior
I would expect the code to handle this error and skip the problematic file. I would be also totally happy with a way of cleaning the input files to ensure this doesn't happen again. It was quite a bit of work to hunt down the problem file manually.
|
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PR_kwDOIPDwls5UOJ7k
| 6,905 |
Create repo1
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[] | null | 1 | 2023-06-29T09:14:40 | 2023-06-29T19:57:06 | 2023-06-29T19:57:06 |
NONE
| 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!
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- 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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| 1,780,426,625 |
PR_kwDOIPDwls5UOIFs
| 6,904 |
chore: NebulaGraph prompt optmization
|
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[] | null | 2 | 2023-06-29T09:08:30 | 2023-06-30T00:04:35 | 2023-06-29T19:57:40 |
CONTRIBUTOR
| null |
Was preparing for a demo project of NebulaGraphQAChain to find out the prompt needed to be optimized a little bit.
Please @hwchase17 kindly help review.
Thanks!
|
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PR_kwDOIPDwls5UNx4W
| 6,903 |
Extend the QA use cases by DataChad example
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[] | null | 1 | 2023-06-29T07:57:26 | 2023-07-03T07:25:01 | 2023-06-29T19:59:13 |
NONE
| null |
- Description: Add an use case example to question_answering section
- Issue: tN/A
- Dependencies: N/A
- Tag maintainer: @hwchase17
- Twitter handle: @thegreatgustby
@hwchase17 we had spoken in twitter dms (12th May) to include the project in the use-cases section.
I forgot to do it since, so wanted to do it now. Hope it's still accepted.
Thanks!
|
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| 1,780,310,048 |
PR_kwDOIPDwls5UNu9X
| 6,902 |
Add API Header for Amazon API Gateway Authentication
|
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[] | null | 1 | 2023-06-29T07:46:45 | 2023-06-29T19:58:08 | 2023-06-29T19:58:07 |
CONTRIBUTOR
| null |
Add API Headers support for Amazon API Gateway to enable Authentication using DynamoDB.
<!-- 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!
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- 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_kwDOIPDwls5UNplh
| 6,901 |
move octo notebook
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I_kwDOIPDwls5qHDVR
| 6,900 |
load_prompt Unable to set encoding for JSON files
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[] | null | 2 | 2023-06-29T06:48:33 | 2023-12-06T14:51:42 | 2023-10-05T16:08:17 |
NONE
| null |
### System Info
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=load_prompt("coolapk_prompt.json"))
config = json.load(f)
UnicodeDecodeError: 'gbk' codec can't decode byte 0xaf in position 108: illegal multibyte sequence
### Who can help?
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=load_prompt("coolapk_prompt.json"))
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=load_prompt("coolapk_prompt.json"))
### Expected behavior
add set encoding for JSON files
|
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PR_kwDOIPDwls5UNYXL
| 6,899 |
bump version to 219
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| 1,780,203,422 |
I_kwDOIPDwls5qG7-e
| 6,898 |
The ReDOS Attack for the regex in langchain
|
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[] | null | 1 | 2023-06-29T06:18:22 | 2023-10-05T16:06:45 | 2023-10-05T16:06:44 |
NONE
| null |
### System Info
python 3.9
langchain 0.0.199
### Who can help?
@hwchase17 @agola11 @eyu
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
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- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The regex such as https://github.com/hwchase17/langchain/blob/6370808d41eda0d056375015fda9284e9f01280c/langchain/agents/mrkl/output_parser.py#L18
will raise a ReDOS when the model output something below
`'Action' + '\n'*n + ':' + '\t'*n + '\t0Input:' + '\t'*n + '\nAction' + '\t'*n + 'Input' + '\t'*n + '\t0Input:ActionInput:'`
full code:
```
import re
n=780
llm_output='Action' + '\n'*n + ':' + '\t'*n + '\t0Input:' + '\t'*n + '\nAction' + '\t'*n + 'Input' + '\t'*n + '\t0Input:ActionInput:'
regex = (
r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
)
match = re.search(regex, llm_output, re.DOTALL)
action = match.group(1).strip()
print(action)
action_input = match.group(2)
```
To slow the regex
### Expected behavior
```
Protection
There are several things you can do to protect yourself from ReDoS attacks.
1.Look at safer alternative libraries such as Facebook’s [pyre2](https://pypi.org/project/pyre2/), which is a python wrapper around Google’s C++ Regex Library, [re2](https://github.com/google/re2/).
2.Always double check all regex you add to your application and never blindly trust regex patterns you find online.
3.Utilize SAST and fuzzing tools to test your own code, and check out [Ochrona](https://ochrona.dev/) to make sure your dependencies are not vulnerable to ReDoS attacks.
4.If possible, limit the length of your input to avoid longer than necessary strings.
```
from https://medium.com/ochrona/python-dos-prevention-the-redos-attack-7267a8fa2d5c
Also you can check your regex online https://devina.io/redos-checker.
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PR_kwDOIPDwls5UNRG0
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Harrison/octo ml
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PR_kwDOIPDwls5UMtMT
| 6,896 |
chore(openllm): make sure to download the model if not exists
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[] | null | 7 | 2023-06-29T02:16:19 | 2023-06-30T08:41:25 | 2023-06-30T07:54:48 |
CONTRIBUTOR
| null |
OpenLLM Runner now include a ensure_available argument to separate the process
of automatic setup of the given model. This was introduced to allow better UX
with openllm.
I updated the OpenLLM accordingly, since we are just loading the model into
memory in "local" mode, `ensure_available` should set to `True`
cc @dev2049
Signed-off-by: Aaron <[email protected]>
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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PR_kwDOIPDwls5UMrrw
| 6,895 |
Re-add Support for SQLAlchemy <1.4
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CONTRIBUTOR
| null |
Support for SQLAlchemy 1.3 was removed in version 0.0.203 by change #6086. Re-adding support.
- Description: Imports SQLAlchemy Row at class creation time instead of at init to support SQLAlchemy <1.4. This is the only breaking change and was introduced in version 0.0.203 #6086.
A similar change was merged before: https://github.com/hwchase17/langchain/pull/4647
- Dependencies: Reduces SQLAlchemy dependency to > 1.3
- Tag maintainer: @rlancemartin, @eyurtsev, @hwchase17, @wangxuqi
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PR_kwDOIPDwls5UMjW5
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Add Input Mapper in run_on_dataset
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[] | null | 1 | 2023-06-29T01:02:08 | 2023-06-29T23:53:50 | 2023-06-29T23:53:49 |
CONTRIBUTOR
| null |
If you create a dataset from runs and run the same chain or llm on it later, it usually works great.
If you have an agent dataset and want to run a different agent on it, or have more complex schema, it's hard for us to automatically map these values every time. This PR lets you pass in an input_mapper function that converts the example inputs to whatever format your model expects
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PR_kwDOIPDwls5UMUfT
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Add pg_hnsw vectorstore integration
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CONTRIBUTOR
| null |
Hi @rlancemartin, @eyurtsev!
- Description: Adding HNSW extension support for Postgres. Similar to pgvector vectorstore, with 3 differences
1. it uses HNSW extension for exact and ANN searches,
2. Vectors are of type array of real
3. Only supports L2
- Dependencies: [HNSW](https://github.com/knizhnik/hnsw) extension for Postgres
- Example:
```python
db = HNSWVectoreStore.from_documents(
embedding=embeddings,
documents=docs,
collection_name=collection_name,
connection_string=connection_string
)
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
```
The example notebook is in the PR too.
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PR_kwDOIPDwls5UMTDU
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Update Zapier Jupyter notebook to include brief OAuth example
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[] | null | 1 | 2023-06-28T23:01:34 | 2023-06-29T01:06:23 | 2023-06-29T01:06:22 |
CONTRIBUTOR
| null |
Description: Adds a brief example of using an OAuth access token with the Zapier wrapper. Also links to the Zapier documentation to learn more about OAuth flows.
Tag maintainer: @dev2049
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PR_kwDOIPDwls5UMNVu
| 6,891 |
Use `getLogger` instead of `basicConfig` in `multi_query.py`
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[] | null | 6 | 2023-06-28T22:22:51 | 2023-07-03T21:13:32 | 2023-06-29T01:06:10 |
CONTRIBUTOR
| null |
Remove `logging.basicConfig`, which turns on logging. Use `getLogger` instead.
cc @rlancemartin
|
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PR_kwDOIPDwls5UMH4x
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docstrings `document_loaders` 2
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COLLABORATOR
| null |
updated docstring for the `document_loaders`
Maintainer responsibilities:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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I_kwDOIPDwls5qFZqV
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SQLDatabaseChain cannot be loaded
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[] | null | 3 | 2023-06-28T21:29:40 | 2023-12-27T16:06:48 | 2023-12-27T16:06:47 |
CONTRIBUTOR
| null |
### System Info
langchain==0.0.217
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The following code fails:
```python
def create_sqlite_db_file(db_dir):
# Connect to SQLite database (or create it if it doesn't exist)
conn = sqlite3.connect(db_dir)
# Create a cursor
c = conn.cursor()
# Create a dummy table
c.execute('''
CREATE TABLE IF NOT EXISTS employees(
id INTEGER PRIMARY KEY,
name TEXT,
salary REAL,
department TEXT,
position TEXT,
hireDate TEXT);
''')
# Insert dummy data into the table
c.execute('''
INSERT INTO employees (name, salary, department, position, hireDate)
VALUES ('John Doe', 80000, 'IT', 'Engineer', '2023-06-26');
''')
# Commit the transaction
conn.commit()
# Close the connection
conn.close()
def test_log_and_load_sql_database_chain(tmp_path):
# Create the SQLDatabaseChain
db_file_path = tmp_path / "my_database.db"
sqlite_uri = f"sqlite:///{db_file_path}"
llm = OpenAI(temperature=0)
create_sqlite_db_file(db_file_path)
db = SQLDatabase.from_uri(sqlite_uri)
db_chain = SQLDatabaseChain.from_llm(llm, db)
db_chain.save('/path/to/test_chain.yaml')
from langchain.chains import load_chain
loaded_chain = load_chain('/path/to/test_chain.yaml', database=db)
```
Error:
```
def load_llm_from_config(config: dict) -> BaseLLM:
"""Load LLM from Config Dict."""
> if "_type" not in config:
E TypeError: argument of type 'NoneType' is not iterable
```
### Expected behavior
No error should occur.
|
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PR_kwDOIPDwls5UL-Es
| 6,888 |
Accept any single input
|
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[] | null | 1 | 2023-06-28T21:12:05 | 2023-06-29T17:29:18 | 2023-06-29T17:29:17 |
CONTRIBUTOR
| null |
If I upload a dataset with a single input and output column, we should be able to let the chain prepare the input without having to maintain a strict dataset format.
|
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PR_kwDOIPDwls5UL7xR
| 6,887 |
Update `langchain.chains.create_extraction_chain_pydantic` to parse results successfully
|
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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!
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- 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
-->
- Description:
- The current code uses `PydanticSchema.schema()` and `_get_extraction_function` at the same time. As a result, a response from OpenAI has two nested `info`, and `PydanticAttrOutputFunctionsParser` fails to parse it. This PR will use the pydantic class given as an arg instead.
- Issue: no related issue yet
- Dependencies: no dependency change
- Tag maintainer: @dev2049
- Twitter handle: @shotarok28
|
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PR_kwDOIPDwls5UL7LI
| 6,886 |
Harrison/marqo
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[] | null | 1 | 2023-06-28T21:00:28 | 2023-07-06T14:01:21 | 2023-07-06T14:01:21 |
COLLABORATOR
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MultiQueryReceiver does not work
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[] | null | 0 | 2023-06-28T20:24:26 | 2023-06-28T20:37:18 | 2023-06-28T20:37:18 |
NONE
| null |
### System Info
langchain-0.0.218
Python 3.10.10
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Cannot import MultiQueryRetriever, because when i run it, it says:
Traceback (most recent call last):
File "C:\langchain\JETPACK-FLOW-MULTIQUERY.py", line 14, in <module>
from langchain.retrievers.multi_query import MultiQueryRetriever
File "C:\Users\roman\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\retrievers\__init__.py", line 21, in <module>
from langchain.retrievers.self_query.base import SelfQueryRetriever
File "C:\Users\roman\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\retrievers\self_query\base.py", line 9, in <module>
from langchain.chains.query_constructor.base import load_query_constructor_chain
File "C:\Users\roman\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\query_constructor\base.py", line 14, in <module>
from langchain.chains.query_constructor.parser import get_parser
File "C:\Users\roman\AppData\Local\Programs\Python\Python310\lib\site-packages\langchain\chains\query_constructor\parser.py", line 9, in <module>
raise ValueError(
ValueError: Lark should be at least version 1.1.5, got 0.12.0
There was same error reported in this bug report:
https://github.com/hwchase17/langchain/discussions/6068
Their solution was simply to install langchain version 0.0.201, but for me it does not help, because there is no MultiQueryReceiver in this version.
I am using: langchain-0.0.218
### Expected behavior
Does not crash, when i try to run it.
|
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I_kwDOIPDwls5qFBCI
| 6,884 |
Include pruning on VectorStoreRetrieverMemory
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[] | null | 0 | 2023-06-28T20:23:47 | 2023-06-28T20:49:16 | 2023-06-28T20:49:16 |
NONE
| null |
### Feature request
Several memory storage options have a "buffer" type which includes a pruning function that allows users to specify a max_tokens_limit which stops token overflow issues. I'm requesting this be included with the VectorStoreRetrieverMemory as well.
### Motivation
Running into token overflow issues is annoying and a pruning option is a simple way to get around this when using vector store based memory.
### Your contribution
I'm happy to attempt a solution on this given I'm able to use my company computer this weekend. Otherwise I would appreciate any support people are able to give.
|
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I_kwDOIPDwls5qEzG8
| 6,883 |
Incompatibility with latest Anthropic Client
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[] | null | 5 | 2023-06-28T19:50:00 | 2023-10-17T16:06:25 | 2023-10-17T16:06:24 |
NONE
| null |
### System Info
Massive client changes as of this pr:
https://github.com/anthropics/anthropic-sdk-python/commit/8d1d6af6527a3ef80b74dc3a466166ab7df057df
Installing the latest anthropic client will cause langchain Anthropic LLM utiltiies to fail.
Looks like api_url no longer exists (they've moved to base_url).
```
???
E pydantic.error_wrappers.ValidationError: 1 validation error for ChatAnthropic
E __root__
E __init__() got an unexpected keyword argument 'api_url' (type=type_error)
```
### Who can help?
@hwchase17
### 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
1. Install latest version of anthropic.
2. Attempt to run Anthropic LLM and it will produce a runtime failure about api_url which no longer exists in the Anthropic client initializer.
### Expected behavior
Should work with latest release of Anthropic client.
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Document loader for Cube Semantic Layer
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CONTRIBUTOR
| null |
### Description
This pull request introduces the "Cube Semantic Layer" document loader, which demonstrates the retrieval of Cube's data model metadata in a format suitable for passing to LLMs as embeddings. This enhancement aims to provide contextual information and improve the understanding of data.
Twitter handle:
@the_cube_dev
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CONTRIBUTOR
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Replace this comment with:
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Added some additional logs to better be able to troubleshoot and understand the performance of the call to PBI vs the rest of the work.
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Time To First Token
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[] | null | 1 | 2023-06-28T16:55:19 | 2023-08-11T00:18:35 | 2023-08-11T00:18:35 |
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Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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If you're adding a new integration, please include:
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
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I_kwDOIPDwls5qDmCx
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Allow skipping "condense_question_prompt" when using ConversationalRetrievalChain
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NONE
| null |
### Feature request
Add a parameter to ConversationalRetrievalChain to skip the condense question prompt procedure.
### Motivation
Currently, when using ConversationalRetrievalChain (with the from_llm() function), we have to run the input through a LLMChain with a default "condense_question_prompt" which condenses the chat history and the input to make a standalone question out of it.
This could be a good idea, but in my test cases (in French) it performs very poorly when switching between topics. It mixes things up and hallucinates a new question which is nowhere near the original question.
The only solution I can think of is to just allow the actual chat history to be sent in the prompt but we can't skip the condense question prompt part. For now, a workaround could be to provide a custom condense prompt asking the LLM to do nothing but that would call the LLM twice for no reason...
### Your contribution
Since I am not sure why this isn't already an option, I'd assume it's for a good reason so I'm asking here before thinking of implementing anything.
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delete api_key requirements
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NONE
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- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
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I_kwDOIPDwls5qDazK
| 6,877 |
In memory vector store as python object based on purely numpy
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[] | null | 1 | 2023-06-28T16:17:22 | 2023-10-05T16:06:50 | 2023-10-05T16:06:49 |
CONTRIBUTOR
| null |
### Feature request
Can someone please create and integrate an in memory vector store based on purely numpy?
I don't like that ChromaDB downloads to much deps when I just want something really simple.
### Motivation
I saw this https://github.com/jdagdelen/hyperDB and it works so this should be in langchain
### Your contribution
I dont have the time rn but I want to publish this idea
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Added filter and delete all option to delete function in Pinecone integration, updated base VectorStore's delete function
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CONTRIBUTOR
| null |
### Description:
Updated the delete function in the Pinecone integration to allow for deletion of vectors by specifying a filter condition, and to delete all vectors in a namespace.
Made the ids parameter optional in the delete function in the base VectorStore class and allowed for additional keyword arguments.
Updated the delete function in several classes (Redis, Chroma, Supabase, Deeplake, Elastic, Weaviate, and Cassandra) to match the changes made in the base VectorStore class. This involved making the ids parameter optional and allowing for additional keyword arguments.
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I_kwDOIPDwls5qDB_Q
| 6,875 |
I think the SitemapLoader should be as robust with default options as the UnstructuredURLLoader, which has an option continue_on_failure set to true.
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[] | null | 1 | 2023-06-28T15:16:30 | 2023-10-05T16:06:55 | 2023-10-05T16:06:54 |
NONE
| null |
### System Info
0.0.218
OSX
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create or find a sitemap with url(s) that run into an infinite redirect loop.
2. Run `SitemapLoader` with that.
3. Observe it crashing
### Expected behavior
The loader to continue on such errors, or have a config flag to say so, like `UnstructuredURLLoader`'s `continue_on_failure`.
|
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PR_kwDOIPDwls5UJwfF
| 6,874 |
Throw error if evaluation key not present
|
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[] | null | 1 | 2023-06-28T15:00:57 | 2023-06-29T17:30:41 | 2023-06-29T17:30:40 |
CONTRIBUTOR
| null | null |
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| 1,779,065,593 |
PR_kwDOIPDwls5UJdTr
| 6,873 |
Accept Chain
|
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[] | null | 1 | 2023-06-28T14:24:28 | 2023-07-12T06:49:44 | 2023-07-12T06:49:44 |
CONTRIBUTOR
| null |
Relax the requirement that you use a chain factory instead of a chain directly. It wasn't foolproof anyway since sub-chains could have memory as well. Log warning if the chain has memory. Retain ability to pass in factory though
We could alternatively call `clear_memory()` every time we make a prediction but I'm unsure of the side effects of that for certain implementations?
|
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| 1,779,010,819 |
I_kwDOIPDwls5qCY0D
| 6,872 |
Issue: Unable to store user specific chat history in redis. Using ConversationalRetrievalChain along with ConversationBufferMemory
|
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[] | null | 3 | 2023-06-28T13:57:42 | 2023-12-13T09:13:59 | 2023-07-20T05:04:27 |
NONE
| null |
### Issue you'd like to raise.
embeddings = OpenAIEmbeddings(model='text-embedding-ada-002',deployment='XXXXX',chunk_size=1)
db = Chroma.from_documents(texts, embeddings)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True,chat_memory=ChatMessageHistory(messages=[]))
model = ConversationalRetrievalChain.from_llm(llm=AzureOpenAI(model_name='gpt-35-turbo',deployment_name='XXXXX',temperature=0.7,openai_api_base=os.environ['OPENAI_API_BASE'],openai_api_key=os.environ['OPENAI_API_KEY']), retriever=db.as_retriever(), memory=memory)
I'm using ConversationalRetrievalChain to query from embeddings and also using memory to store chat history.
I've written a Flask api to fetch result from model.
I'm trying to store memory of each user in redis store, so that there wont be any mix and match in chat history.
For that I'm trying with below code, but I'm unable to store memory in redis.
`r = redis.Redis(host='redis-1XXX.c3XXX.ap-south-1-1.ec2.cloud.redislabs.com', port=17506, db=0, password='XXXX')`
`memory = {}`
`memory_key = f"memory_{employee_code}"`
`messages = r.get(memory_key)`
`messages = json.loads(messages)`
`memory[memory_key] = ConversationBufferMemory(memory_key="chat_history", return_messages=True, chat_memory=ChatMessageHistory(messages=messages))`
`model = ConversationalRetrievalChain.from_llm(llm=AzureOpenAI(model_name='gpt-35-turbo',deployment_name='XXXXXX',temperature=0.7,openai_api_base=os.environ['OPENAI_API_BASE'],openai_api_key=os.environ['OPENAI_API_KEY']), retriever=db.as_retriever(), memory=memory[memory_key])`
`res=model.run(query)`
`messages.append({query:res})`
`r.set(memory_key,json.dumps(messages))`
This is not working. Can anyone help me on this?
Thanks in advance.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5UJOVn
| 6,871 |
Support named vectors in Qdrant
|
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CONTRIBUTOR
| null |
# Description
This PR makes it possible to use named vectors from Qdrant in Langchain. That was requested multiple times, as people want to reuse externally created collections in Langchain. It doesn't change anything for the existing applications. The changes were covered with some integration tests and included in the docs.
## Example
```python
Qdrant.from_documents(
docs,
embeddings,
location=":memory:",
collection_name="my_documents",
vector_name="custom_vector",
)
```
### Issue: #2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
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SQLDatabaseChain - Questions/error
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NONE
| null |
### System Info
While trying to run example from https://python.langchain.com/docs/modules/chains/popular/sqlite I get the following error:
````
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[42], line 1
----> 1 db_chain.run("What is the expected assisted goals by Erling Haaland")
File ~/.local/lib/python3.10/site-packages/langchain/chains/base.py:273, in Chain.run(self, callbacks, tags, *args, **kwargs)
271 if len(args) != 1:
272 raise ValueError("`run` supports only one positional argument.")
--> 273 return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
275 if kwargs and not args:
276 return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
File ~/.local/lib/python3.10/site-packages/langchain/chains/base.py:149, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
147 except (KeyboardInterrupt, Exception) as e:
148 run_manager.on_chain_error(e)
--> 149 raise e
150 run_manager.on_chain_end(outputs)
151 final_outputs: Dict[str, Any] = self.prep_outputs(
152 inputs, outputs, return_only_outputs
153 )
File ~/.local/lib/python3.10/site-packages/langchain/chains/base.py:143, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
137 run_manager = callback_manager.on_chain_start(
138 dumpd(self),
139 inputs,
140 )
141 try:
142 outputs = (
--> 143 self._call(inputs, run_manager=run_manager)
144 if new_arg_supported
145 else self._call(inputs)
146 )
147 except (KeyboardInterrupt, Exception) as e:
148 run_manager.on_chain_error(e)
File ~/.local/lib/python3.10/site-packages/langchain/chains/sql_database/base.py:105, in SQLDatabaseChain._call(self, inputs, run_manager)
103 # If not present, then defaults to None which is all tables.
104 table_names_to_use = inputs.get("table_names_to_use")
--> 105 table_info = self.database.get_table_info(table_names=table_names_to_use)
106 llm_inputs = {
107 "input": input_text,
108 "top_k": str(self.top_k),
(...)
111 "stop": ["\nSQLResult:"],
112 }
113 intermediate_steps: List = []
File ~/.local/lib/python3.10/site-packages/langchain/sql_database.py:289, in SQLDatabase.get_table_info(self, table_names)
287 table_info += f"\n{self._get_table_indexes(table)}\n"
288 if self._sample_rows_in_table_info:
--> 289 table_info += f"\n{self._get_sample_rows(table)}\n"
290 if has_extra_info:
291 table_info += "*/"
File ~/.local/lib/python3.10/site-packages/langchain/sql_database.py:311, in SQLDatabase._get_sample_rows(self, table)
308 try:
309 # get the sample rows
310 with self._engine.connect() as connection:
--> 311 sample_rows_result = connection.execute(command) # type: ignore
312 # shorten values in the sample rows
313 sample_rows = list(
314 map(lambda ls: [str(i)[:100] for i in ls], sample_rows_result)
315 )
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1413, in Connection.execute(self, statement, parameters, execution_options)
1411 raise exc.ObjectNotExecutableError(statement) from err
1412 else:
-> 1413 return meth(
1414 self,
1415 distilled_parameters,
1416 execution_options or NO_OPTIONS,
1417 )
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/sql/elements.py:483, in ClauseElement._execute_on_connection(self, connection, distilled_params, execution_options)
481 if TYPE_CHECKING:
482 assert isinstance(self, Executable)
--> 483 return connection._execute_clauseelement(
484 self, distilled_params, execution_options
485 )
486 else:
487 raise exc.ObjectNotExecutableError(self)
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/engine/base.py:1629, in Connection._execute_clauseelement(self, elem, distilled_parameters, execution_options)
1621 schema_translate_map = execution_options.get(
1622 "schema_translate_map", None
1623 )
1625 compiled_cache: Optional[CompiledCacheType] = execution_options.get(
1626 "compiled_cache", self.engine._compiled_cache
1627 )
-> 1629 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache(
1630 dialect=dialect,
1631 compiled_cache=compiled_cache,
1632 column_keys=keys,
1633 for_executemany=for_executemany,
1634 schema_translate_map=schema_translate_map,
1635 linting=self.dialect.compiler_linting | compiler.WARN_LINTING,
1636 )
1637 ret = self._execute_context(
1638 dialect,
1639 dialect.execution_ctx_cls._init_compiled,
(...)
1647 cache_hit=cache_hit,
1648 )
1649 if has_events:
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/sql/elements.py:684, in ClauseElement._compile_w_cache(self, dialect, compiled_cache, column_keys, for_executemany, schema_translate_map, **kw)
682 else:
683 extracted_params = None
--> 684 compiled_sql = self._compiler(
685 dialect,
686 cache_key=elem_cache_key,
687 column_keys=column_keys,
688 for_executemany=for_executemany,
689 schema_translate_map=schema_translate_map,
690 **kw,
691 )
693 if not dialect._supports_statement_cache:
694 cache_hit = dialect.NO_DIALECT_SUPPORT
File /opt/conda/lib/python3.10/site-packages/sqlalchemy/sql/elements.py:288, in CompilerElement._compiler(self, dialect, **kw)
286 if TYPE_CHECKING:
287 assert isinstance(self, ClauseElement)
--> 288 return dialect.statement_compiler(dialect, self, **kw)
File ~/.local/lib/python3.10/site-packages/pybigquery/sqlalchemy_bigquery.py:137, in BigQueryCompiler.__init__(self, dialect, statement, column_keys, inline, **kwargs)
135 if isinstance(statement, Column):
136 kwargs['compile_kwargs'] = util.immutabledict({'include_table': False})
--> 137 super(BigQueryCompiler, self).__init__(dialect, statement, column_keys, inline, **kwargs)
TypeError: SQLCompiler.__init__() got multiple values for argument 'cache_key'
````
I use a VertexAI model (MODEL_TEXT_BISON_001) as the LLM.
Some essential library versions:
langchain == 0.0.206
SQLAlchemy == 2.0.11
ipython == 8.12.1
python == 3.10.10
google-cloud-bigquery == 3.10.0
google-cloud-bigquery-storage == 2.16.2
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`db` = SQLDatabase.from_uri(f"bigquery://{project_id}/{dataset}")
toolkit = SQLDatabaseToolkit(llm=llm, db=db)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run("What is the sport that generates highest revenue")`
### Expected behavior
When running the db_chain, I expect to get an answer from the bigquery database
|
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| 1,778,919,115 |
I_kwDOIPDwls5qCCbL
| 6,869 |
how to track token usage for OpenAIEmbeddings? it seems that get_openai_callback always return 0.
|
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[] | null | 3 | 2023-06-28T13:14:02 | 2023-10-06T16:06:44 | 2023-10-06T16:06:43 |
NONE
| null |
### Issue you'd like to raise.
how to track token usage for OpenAIEmbeddings? it seems that get_openai_callback always return 0.
### Suggestion:
_No response_
|
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| 1,778,835,710 |
I_kwDOIPDwls5qBuD-
| 6,868 |
Word2vec/Glove/FastText embedding support
|
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[] | null | 2 | 2023-06-28T12:26:22 | 2023-12-25T16:01:36 | null |
NONE
| null |
### Feature request
Is there any way to store the word2vec/glove/fasttext based embeddings in the vector database using langchain
```
pages= "page content"
embeddings = OpenAIEmbeddings()
persist_directory = 'db'
vectordb = Chroma.from_documents(documents=pages, embedding=embeddings, persist_directory=persist_directory)
```
shall use **word2vec/glove/fasttext** embeddings instead of **OpenAIEmbeddings()** in the above code?
if possible then what is the syntax for that?
### Motivation
For using native embedding formats
### Your contribution
For using native embedding formats like word2vec/glove/fasttext in langchain
|
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| 1,778,781,194 |
PR_kwDOIPDwls5UIfVs
| 6,867 |
add with score option for max marginal relevance
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[] | null | 1 | 2023-06-28T11:50:30 | 2023-06-29T05:00:35 | 2023-06-29T05:00:34 |
CONTRIBUTOR
| null |
### Adding the functionality to return the scores with retrieved documents when using the max marginal relevance
- Description: Add the method `max_marginal_relevance_search_with_score_by_vector` to the FAISS wrapper. Functionality operates the same as `similarity_search_with_score_by_vector` except for using the max marginal relevance retrieval framework like is used in the `max_marginal_relevance_search_by_vector` method.
- Dependencies: None
- Tag maintainer: @rlancemartin @eyurtsev
- Twitter handle: @RianDolphin
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| 1,778,756,225 |
I_kwDOIPDwls5qBaqB
| 6,866 |
PGVector is Lacking Basic Features
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[] | null | 6 | 2023-06-28T11:32:05 | 2023-10-16T16:06:54 | 2023-10-16T16:06:53 |
NONE
| null |
### Feature request
PGVector lacks Upsert and deletion capabilities.
I have to custom create this functionality.
### Motivation
I want to use PGVector because it's easy to implement and I don't require to deal with DB vector providers.
### Your contribution
If you deem this useful I will try propose a pull request.
|
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PR_kwDOIPDwls5UIMRB
| 6,865 |
added example notebook - use custom functions with openai agent
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[] | null | 1 | 2023-06-28T10:48:46 | 2023-06-29T05:07:34 | 2023-06-29T05:07:33 |
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),
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PR_kwDOIPDwls5UIAcr
| 6,864 |
Chroma: add vector search with scores
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[] | null | 1 | 2023-06-28T10:14:00 | 2023-09-29T13:25:41 | 2023-07-06T14:01:56 |
CONTRIBUTOR
| null |
- Description: Adding to Chroma integration the option to run a similarity search by a vector with relevance scores. Fixing two minor typos.
- Issue: The "lambda_mult" typo is related to #4861
- Maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5UHyAp
| 6,863 |
Increase flexibility of ElasticVectorSearch
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CONTRIBUTOR
| null |
Hey @rlancemartin, @eyurtsev ,
I did some minimal changes to the `ElasticVectorSearch` client so that it plays better with existing ES indices.
Main changes are as follows:
1. You can pass the dense vector field name into `_default_script_query`
2. You can pass a custom script query implementation and the respective parameters to `similarity_search_with_score`
3. You can pass functions for building page content and metadata for the resulting `Document`
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- 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!
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4. an example notebook showing its use.
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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_kwDOIPDwls5UHkCu
| 6,862 |
Add `call_actor_task` to the Apify integration
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[] | null | 1 | 2023-06-28T08:58:02 | 2023-06-29T05:13:48 | 2023-06-29T05:13:47 |
CONTRIBUTOR
| null |
A user has been testing the Apify integration inside langchain and he was not able to run saved Actor tasks.
This PR adds support for calling saved Actor tasks on the Apify platform to the existing integration. The structure of very similar to the one of calling Actors.
|
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I_kwDOIPDwls5qASXu
| 6,861 |
sqldatabasechain result incomplete
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[] | null | 8 | 2023-06-28T08:26:32 | 2023-12-08T16:06:35 | 2023-12-08T16:06:34 |
NONE
| null |
### Issue you'd like to raise.
db_chain = SQLDatabaseChain(llm=llm, database=db, prompt=PROMPT, verbose=True, return_intermediate_steps=True, top_k=3)
result = db_chain("Make a list of those taking the exam")
result['result'] is incomplete，why ？token ?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5UHWEf
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feat: ctransformers support async chain
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[] | null | 5 | 2023-06-28T08:16:18 | 2023-07-10T08:23:49 | 2023-07-10T08:23:41 |
CONTRIBUTOR
| null |
- Description: Adding async method for CTransformers
- Issue: I've found impossible without this code to run Websockets inside a FastAPI micro service and a CTransformers model.
- Tag maintainer: Not necessary yet, I don't like to mention directly
- Twitter handle: @_semoal
-
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: I've found impossible without this code to run Websockets inside a FastAPI micro service and a CTransformers model.
- 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!
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:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
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- 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_kwDOIPDwls5UG48_
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Provided default values for tags and inheritable_tags args in BaseRun…
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[] | null | 1 | 2023-06-28T06:35:39 | 2023-06-30T05:01:09 | 2023-06-30T05:01:08 |
CONTRIBUTOR
| null |
…Manager
To avoid the error message:
TypeError: BaseRunManager.__init__() missing 2 required keyword-only arguments: 'tags' and 'inheritable_tags'
when running AsyncCallbackManagerForChainRun (from langchain.callbacks.manager import AsyncCallbackManagerForChainRun), provided default values for tags and inheritable_tages of empty lists in manager.py BaseRunManager.
- Description: In manager.py, `BaseRunManager`, default values were provided for the `__init__` args `tags` and `inheritable_tags`. They default to empty lists (`[]`).
- Issue: When trying to use Nvidia NeMo Guardrails with LangChain, the following exception was raised:
```
[autoreload of chatbot failed: Traceback (most recent call last):
File "C:\Users\Siraj\anaconda3\envs\redacre_test_1\lib\site-packages\IPython\extensions\autoreload.py", line 276, in check
superreload(m, reload, self.old_objects)
File "C:\Users\Siraj\anaconda3\envs\redacre_test_1\lib\site-packages\IPython\extensions\autoreload.py", line 475, in superreload
module = reload(module)
File "C:\Users\Siraj\anaconda3\envs\redacre_test_1\lib\importlib\__init__.py", line 169, in reload
_bootstrap._exec(spec, module)
File "<frozen importlib._bootstrap>", line 619, in _exec
File "<frozen importlib._bootstrap_external>", line 883, in exec_module
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "C:\Users\Siraj\Documents\Personal\Work\Aurelio\20230530 Red Acre\automationai\backend\chatbot\automationai\chatbot.py", line 9, in <module>
from nemoguardrails import LLMRails, RailsConfig
File "C:\Users\Siraj\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\__init__.py", line 17, in <module>
from .rails import LLMRails, RailsConfig
File "C:\Users\Siraj\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\rails\__init__.py", line 17, in <module>
from .llm.llmrails import LLMRails
File "C:\Users\Siraj\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\rails\llm\llmrails.py", line 26, in <module>
from nemoguardrails.actions.llm.generation import LLMGenerationActions
TypeError: BaseRunManager.__init__() missing 2 required keyword-only arguments: 'tags' and 'inheritable_tags'
]
Traceback (most recent call last):
File ~\anaconda3\envs\redacre_test_1\lib\site-packages\spyder_kernels\py3compat.py:356 in compat_exec
exec(code, globals, locals)
File c:\users\siraj\documents\personal\work\aurelio\20230530 red acre\automationai\backend\chatbot\automationai\sra_test_1.py:8
from chatbot import Chatbot
File ~\Documents\Personal\Work\Aurelio\20230530 Red Acre\automationai\backend\chatbot\automationai\chatbot.py:9
from nemoguardrails import LLMRails, RailsConfig
File ~\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\__init__.py:17
from .rails import LLMRails, RailsConfig
File ~\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\rails\__init__.py:17
from .llm.llmrails import LLMRails
File ~\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\rails\llm\llmrails.py:26
from nemoguardrails.actions.llm.generation import LLMGenerationActions
File ~\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\actions\llm\generation.py:46
from nemoguardrails.logging.callbacks import logging_callbacks
File ~\anaconda3\envs\redacre_test_1\lib\site-packages\nemoguardrails\logging\callbacks.py:237
logging_callback_manager_for_chain = AsyncCallbackManagerForChainRun(
TypeError: BaseRunManager.__init__() missing 2 required keyword-only arguments: 'tags' and 'inheritable_tags'
```
I was using the NeMo Guardrails `LLMRails` module to create an app objects:
`self.app = LLMRails(config, verbose =True)`
The I tried to use:
`bot_message = self.app.generate_async(prompt=self.sys_msg, messages=history)`
The LLMRails` class makes use of `LLMGenerationActions`, which comes from:
`from nemoguardrails.actions.llm.generation import LLMGenerationActions`
In the generations.py module we have:
`from nemoguardrails.logging.callbacks import logging_callbacks`
and in callbacks.py, we have
`from langchain.callbacks.manager import AsyncCallbackManagerForChainRun`
and the code (Let's call this 'Code A'):
```
logging_callback_manager_for_chain = AsyncCallbackManagerForChainRun(
run_id=uuid.uuid4(),
parent_run_id=None,
handlers=handlers,
inheritable_handlers=handlers,
)
```
In the LangChain manager.py module, the class `AsyncCallbackManagerForChainRun` inherits the `AsyncRunManager` class, which in turn inherits the `BaseRunManager` class, which previously had (let's call this 'Code B'):
```
def __init__(
self,
*,
run_id: UUID,
handlers: List[BaseCallbackHandler],
inheritable_handlers: List[BaseCallbackHandler],
parent_run_id: Optional[UUID] = None,
tags: List[str],
inheritable_tags: List[str],
)
```
As you can see, Code A fails to provide the tags and inheritable_tags arguments. My guess was that these can be optional arguments, but perhaps I'm wrong. So, I changed Code B to:
```
def __init__(
self,
*,
run_id: UUID,
handlers: List[BaseCallbackHandler],
inheritable_handlers: List[BaseCallbackHandler],
parent_run_id: Optional[UUID] = None,
tags: List[str]=[],
inheritable_tags: List[str]=[],
)
```
- Dependencies: The fix doesn't require any dependencies. Although to recreate the issue you probably need to install Nvidia NeMo Guardrails.
- Tag maintainer: @agola11 @vowelparrot
- Twitter handle: n/a.
|
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"merged_at": "2023-06-30T05:01:08"
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|
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